From 70aaa31f756efdbfe14ac92fbd95b40e6ab779cc Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 15 Mar 2022 13:55:24 +0200 Subject: [PATCH 01/42] Segmentation example - 1st commit --- .../segmentation/Fuse_segmentation.py | 584 ++++++++++++++++++ fuse_examples/segmentation/create_dataset.py | 169 +++++ .../segmentation/data_source_segmentation.py | 111 ++++ fuse_examples/segmentation/runner_seg.py | 482 +++++++++++++++ .../segmentation/seg_input_processor.py | 178 ++++++ fuse_examples/segmentation/unet.py | 113 ++++ 6 files changed, 1637 insertions(+) create mode 100644 fuse_examples/segmentation/Fuse_segmentation.py create mode 100644 fuse_examples/segmentation/create_dataset.py create mode 100644 fuse_examples/segmentation/data_source_segmentation.py create mode 100644 fuse_examples/segmentation/runner_seg.py create mode 100644 fuse_examples/segmentation/seg_input_processor.py create mode 100644 fuse_examples/segmentation/unet.py diff --git a/fuse_examples/segmentation/Fuse_segmentation.py b/fuse_examples/segmentation/Fuse_segmentation.py new file mode 100644 index 000000000..9734a0ebf --- /dev/null +++ b/fuse_examples/segmentation/Fuse_segmentation.py @@ -0,0 +1,584 @@ +import logging +import random +from pathlib import Path +from glob import glob +import matplotlib.pylab as plt +import os +import numpy as np +import pandas as pd +from skimage.io import imread + +import torch +from torch.utils.data.dataset import Dataset +from torch.utils.data import DataLoader + +torch.__version__ + +import sys +sys.path.append('Pytorch-UNet/') +sys.path.append('Pytorch-UNet/unet/') + +from unet import UNet + +# parameters +SZ = 512 +# TRAIN = f'siim/data_bin/data{SZ}/train/' +# TEST = f'siim/data_bin/data{SZ}/test/' +# MASKS = f'siim/data_bin/data{SZ}/masks/' +TRAIN = f'siim/data{SZ}/train/' +TEST = f'siim/data{SZ}/test/' +MASKS = f'siim/data{SZ}/masks/' + + +def perform_softmax(output): + if isinstance(output, torch.Tensor): # validation + logits = output + else: # train + logits = output.logits + cls_preds = F.softmax(logits, dim=1) + return logits, cls_preds + + +def mask_size(fn): + sz = [] + for f in fn: + im = imread(f) + sz.append(np.array(im>0).sum()) + # if im.sum() > 0: + # plt.figure() + # plt.imshow(im) + # plt.show() + return sz + + +from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault +from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper +from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch +from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt +from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.utils.utils_logger import fuse_logger_start + +# imports for training +from fuse.models.model_wrapper import FuseModelWrapper +from fuse.losses.loss_default import FuseLossDefault +from fuse.losses.segmentation.loss_dice import BinaryDiceLoss, DiceBCELoss +from fuse.losses.segmentation.loss_dice import FuseDiceLoss + +# imports for validation/inference/performance +from fuse.metrics.classification.metric_accuracy import FuseMetricAccuracy +from fuse.metrics.classification.metric_roc_curve import FuseMetricROCCurve +from fuse.metrics.classification.metric_auc import FuseMetricAUC +from fuse.analyzer.analyzer_default import FuseAnalyzerDefault +from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel +from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap +from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame + +import torch.nn.functional as F +import torch.optim as optim +from fuse.managers.manager_default import FuseManagerDefault +from fuse.utils.utils_gpu import FuseUtilsGPU + +from data_source_segmentation import FuseDataSourceSeg +from seg_input_processor import SegInputProcessor + +# TODO: Path to save model +ROOT = '' +# TODO: path to store the data (?? what data? after download?) +ROOT_DATA = ROOT +# TODO: Name of the experiment +EXPERIMENT = 'unet_seg_results' +# TODO: Path to cache data +CACHE_PATH = '' +# TODO: Name of the cached data folder +EXPERIMENT_CACHE = 'exp_cache' + +PATHS = {'data_dir': [TRAIN, MASKS, TEST], + 'model_dir': os.path.join(ROOT, EXPERIMENT, 'model_dir'), + 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message. + 'cache_dir': os.path.join(CACHE_PATH, EXPERIMENT_CACHE+'_cache_dir'), + 'inference_dir': os.path.join(ROOT, EXPERIMENT, 'infer_dir'), + 'analyze_dir': os.path.join(ROOT, EXPERIMENT, 'analyze_dir')} + +# # augmentations from skin-fuse-code + +# TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ +# [ +# ('data.input.input_0',), +# aug_op_affine, +# {'rotate': Uniform(-180.0, 180.0), 'translate': (RandInt(-50, 50), RandInt(-50, 50)), +# 'flip': (RandBool(0.3), RandBool(0.3)), 'scale': Uniform(0.9, 1.1)}, +# {'apply': RandBool(0.9)} +# ], +# [ +# ('data.input.input_0',), +# aug_op_color, +# {'add': Uniform(-0.06, 0.06), 'mul': Uniform(0.95, 1.05), 'gamma': Uniform(0.9, 1.1), +# 'contrast': Uniform(0.85, 1.15)}, +# {'apply': RandBool(0.7)} +# ], +# [ +# ('data.input.input_0',), +# aug_op_gaussian, +# {'std': 0.03}, +# {'apply': RandBool(0.7)} +# ], +# ] + +########################################## +# Train Common Params +########################################## +# ============ +# Data +# ============ +TRAIN_COMMON_PARAMS = {} +TRAIN_COMMON_PARAMS['data.batch_size'] = 32 +TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 +TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 +TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ + # TODO: define the augmentation pipeline here + # Fuse TIP: Use as a reference the simple augmentation pipeline written in Fuse.data.augmentor.augmentor_toolbox.aug_image_default_pipeline + [ + ('data.input.input_0','data.gt.gt_global'), + aug_op_affine_group, + {'rotate': Uniform(-20.0, 20.0), # Uniform(-20.0, 20.0), + 'flip': (RandBool(0.0), RandBool(0.5)), # (RandBool(1.0), RandBool(0.5)), + 'scale': Uniform(0.9, 1.1), + 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, + {'apply': RandBool(0.9)} + ], + [ + ('data.input.input_0','data.gt.gt_global'), + aug_op_elastic_transform, + {}, + {'apply': RandBool(0.7)} + ], + [ + ('data.input.input_0',), + aug_op_color, + { + 'add': Uniform(-0.06, 0.06), + 'mul': Uniform(0.95, 1.05), + 'gamma': Uniform(0.9, 1.1), + 'contrast': Uniform(0.85, 1.15) + }, + {'apply': RandBool(0.7)} + ], + [ + ('data.input.input_0',), + aug_op_gaussian, + {'std': 0.05}, + {'apply': RandBool(0.7)} + ], +] +# =============== +# Manager - Train1 +# =============== +TRAIN_COMMON_PARAMS['manager.train_params'] = { + 'num_epochs': 200, + + 'virtual_batch_size': 1, # number of batches in one virtual batch + 'start_saving_epochs': 10, # first epoch to start saving checkpoints from + 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint +} +TRAIN_COMMON_PARAMS['manager.best_epoch_source'] = { + 'source': 'losses.total_loss', # can be any key from 'epoch_results' (either metrics or losses result) + 'optimization': 'min', # can be either min/max + 'on_equal_values': 'better', ## ?? why is it important?? + # can be either better/worse - whether to consider best epoch when values are equal +} +TRAIN_COMMON_PARAMS['manager.learning_rate'] = 1e-1 +TRAIN_COMMON_PARAMS['manager.weight_decay'] = 1e-4 # 0.001 +TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint +## Give a default checkpoint name? load a default checkpoint? + +# allocate gpus +NUM_GPUS = 4 +if NUM_GPUS == 0: + TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' + +TRAIN_COMMON_PARAMS['partition_file'] = 'train_val_split.pickle' +TRAIN_COMMON_PARAMS['manager.train'] = False # if not None, will try to load the checkpoint +# ================================================================== +def vis_batch(sample, num_rows=3): + img_names = sample['data']['descriptor'][0] + mask_names = sample['data']['descriptor'][1] + + img = sample['data']['input']['input_0'] + mask = sample['data']['gt']['gt_global'] + + n = img.shape[0] + num_col = n // num_rows + 1 + fig, ax = plt.subplots(num_rows, num_col, figsize=(14, 3*num_rows)) + ax = ax.ravel() + for i in range(n): + im = img[i].squeeze() + msk = mask[i].squeeze() + + if im.shape[0] == 3: + im = im.permute((1,2,0)) # im is a tensor + + ax[i].imshow(im,cmap='bone') + ax[i].imshow(msk,alpha=0.5,cmap='Reds') + # ax[i, 1].imshow(msk) + + +def main(paths: dict, train_common_params: dict, train=True, infer=True): + + # uncomment if you want to use specific gpus instead of automatically looking for free ones + force_gpus = None # [0] + FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + + train_common_params['manager.train'] = train # if not None, will try to load the checkpoint + train_common_params['manager.infer'] = infer + + train_path = paths['data_dir'][0] + mask_path = paths['data_dir'][1] + test_path =paths['data_dir'][2] + + train_fn = glob(train_path + '/*') + train_fn.sort() + + masks_fn = glob(mask_path + '/*') + masks_fn.sort() + + m_size = mask_size(masks_fn) + size_inx = np.argsort(m_size) + + # train_fn = np.array(train_fn)[size_inx[-200:]] + # masks_fn = np.array(masks_fn)[size_inx[-200:]] + + fuse_logger_start(output_path=paths['model_dir'], console_verbose_level=logging.INFO) + + # fn = list(zip(train_fn, masks_fn)) + + # # split to train-validation - + # VAL_SPLIT = 0.2 # frac of validation set + # n_train = int(len(fn) * (1-VAL_SPLIT)) + + # # random shuffle the file-list + # # random.shuffle(fn) + # train_fn = fn[:n_train] + # train_size = m_size[:n_train] + # val_fn = fn[n_train:] + + # size_inx = np.argsort(train_size) + # train_fn = np.array(train_fn)[size_inx[-3000:]].tolist() + # train_fn = [tuple(tr) for tr in train_fn] + + # # filter only train samples with positive mask + # train_fn = np.array(train_fn)[np.array(train_size) > 0].tolist() + # train_fn = [tuple(tr) for tr in train_fn] + + train_data_source = FuseDataSourceSeg(image_source=train_path, + mask_source=mask_path, + partition_file=train_common_params['partition_file'], + train=True) + # train_data_source = FuseDataSourceSeg(train_fn) + print(train_data_source.summary()) + + ## Create data processors: + input_processors = { + 'input_0': SegInputProcessor(name='image') + } + gt_processors = { + 'gt_global': SegInputProcessor(name='mask') + } + + ## Create data augmentation (optional) + augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + + # Create visualizer (optional) + visualiser = FuseVisualizerDefault(image_name='data.input.input_0', + mask_name='data.gt.gt_global', + pred_name='model.logits.classification') + + train_dataset = FuseDatasetDefault(cache_dest=None, + data_source=train_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + augmentor=augmentor, + visualizer=visualiser) + train_dataset.create() + + # debug_size = [] + # for data in train_dataset: + # img = data['data']['input']['input_0'].numpy().squeeze() + # mask = data['data']['gt']['gt_global'].numpy().squeeze() + # # if mask.sum() > 0: + # debug_size.append(mask.sum()) + + # ================================================================== + # Validation dataset + valid_data_source = FuseDataSourceSeg(image_source=train_path, + mask_source=mask_path, + partition_file=train_common_params['partition_file'], + train=False) + print(valid_data_source.summary()) + # valid_data_source = FuseDataSourceSeg(val_fn) + # valid_data_source.summary() + + valid_dataset = FuseDatasetDefault(cache_dest=None, + data_source=valid_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + visualizer=visualiser) + valid_dataset.create() + + ## Create sampler + # sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + # balanced_class_name='data.gt.gt_global.tensor', + # num_balanced_classes=2, + # batch_size=train_common_params['data.batch_size']) + + + ## Create dataloader + train_dataloader = DataLoader(dataset=train_dataset, + shuffle=True, + drop_last=False, + batch_size=train_common_params['data.batch_size'], + collate_fn=train_dataset.collate_fn, + num_workers=train_common_params['data.train_num_workers']) + # batch_sampler=sampler, collate_fn=train_dataset.collate_fn, + # num_workers=train_common_params['data.train_num_workers']) + + ## Create dataloader + validation_dataloader = DataLoader(dataset=valid_dataset, + shuffle=False, + drop_last=False, + batch_size=train_common_params['data.batch_size'], + collate_fn=train_dataset.collate_fn, + num_workers=train_common_params['data.validation_num_workers']) + + if False: + # train_dataset.visualize(10) + + inx = 10 #2405 + data = train_dataset.get(inx) + img = data['data']['input']['input_0'].numpy().squeeze() + mask = data['data']['gt']['gt_global'].numpy().squeeze() + + data = train_dataset.getitem_without_augmentation(inx) + img_aug = data['data']['input']['input_0'].numpy().squeeze() + mask_aug = data['data']['gt']['gt_global'].numpy().squeeze() + + if img.shape[0] == 3: + img = img.transpose((1,2,0)) + img_aug = img_aug.transpose((1,2,0)) + + fig, axs = plt.subplots(1,2, figsize=(14,7)) + axs[0].imshow(img, plt.cm.bone) + axs[0].imshow(1-mask, 'hot', alpha=0.4) + axs[1].imshow(img_aug, plt.cm.bone) + axs[1].imshow(1-mask_aug, 'hot', alpha=0.4) + # axs[1].imshow(mask, interpolation=None) + plt.show() + + print('Num of positive pixels - ', mask.sum()) + + i = 0 + for batch in train_dataloader: + vis_batch(batch) + i += 1 + if i > 10: + break + plt.show() + # ================================================================== + + # # Training graph + torch_model = UNet(n_channels=1, n_classes=1, bilinear=False) + net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True) + + import ipdb; ipdb.set_trace(context=7) # BREAKPOINT + model = FuseModelWrapper(model=torch_model, + model_inputs=['data.input.input_0'], + post_forward_processing_function=perform_softmax, + model_outputs=['logits.classification', 'output.classification'] + ) + + # # take one batch: + # batch = next(iter(train_dataloader)) + # img = batch['data']['input']['input_0'] + # img.shape + + # pred_mask = torch_model(img) + # pred_mask.shape + + # ==================================================================================== + # Loss + # ==================================================================================== + # dice_loss = BinaryDiceLoss() + dice_loss = DiceBCELoss() + # losses = { + # 'dice_loss': FuseDiceLoss(pred_name='model.logits.classification', + # target_name='data.gt.gt_global') + # } + losses = { + 'cls_loss': FuseLossDefault(pred_name='model.logits.classification', + target_name='data.gt.gt_global', + callable=dice_loss, + weight=1.0) + } + + model = model.cuda() + # create optimizer + # optimizer = optim.AdamW(model.parameters(), + # lr=train_common_params['manager.learning_rate'], + # weight_decay=train_common_params['manager.weight_decay']) + optimizer = optim.SGD(model.parameters(), + lr=train_common_params['manager.learning_rate'], + momentum=0.9, + weight_decay=train_common_params['manager.weight_decay']) + + scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5) + + # train from scratch + if train_common_params['manager.train']: + manager = FuseManagerDefault(output_model_dir=paths['model_dir'], + force_reset=paths['force_reset_model_dir']) + else: + manager = FuseManagerDefault() + + # ===================================================================================== + # Callbacks + # ===================================================================================== + callbacks = [ + # default callbacks + FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file + FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + ] + + # Providing the objects required for the training process. + manager.set_objects(net=model, + optimizer=optimizer, + losses=losses, + lr_scheduler=scheduler, + callbacks=callbacks, + best_epoch_source=train_common_params['manager.best_epoch_source'], + train_params=train_common_params['manager.train_params'], + output_model_dir=paths['model_dir']) + + + # Start training + if train_common_params['manager.train']: + manager.train(train_dataloader=train_dataloader, + validation_dataloader=validation_dataloader) + + # plot the training process: + csv_file = os.path.join(paths['model_dir'], 'metrics.csv') + metrics = pd.read_csv(csv_file) + metrics.drop(index=metrics.index[0], axis=0, inplace=True) # remove the 1st validation run + + epochs = metrics[metrics['mode'] == 'validation']['epoch'] + loss_key = 'losses.' + list(losses.keys())[0] + val_loss = metrics[metrics['mode'] == 'validation'][loss_key] + train_loss = metrics[metrics['mode'] == 'train'][loss_key] + + plt.figure() + plt.plot(epochs, val_loss, '.-', label='validation') + plt.plot(epochs, train_loss, '.-', label='train') + plt.legend() + plt.title('train and validation loss') + plt.xlabel('Epochs') + plt.ylabel('loss') + plt.savefig(os.path.join(paths['model_dir'], 'train_progress.png')) + plt.close() + + ################################################################################ + # Inference + ################################################################################ + + if train_common_params['manager.infer']: + ###################################### + # Inference Common Params + ###################################### + INFER_COMMON_PARAMS = {} + INFER_COMMON_PARAMS['infer_filename'] = os.path.join(PATHS['inference_dir'], 'validation_set_infer.gz') + INFER_COMMON_PARAMS['checkpoint'] = 'best' #'best' # Fuse TIP: possible values are 'best', 'last' or epoch_index. + output_columns = ['model.logits.classification', 'data.gt.gt_global'] + infer_common_params = INFER_COMMON_PARAMS + + manager.load_checkpoint(infer_common_params['checkpoint'], + model_dir=paths['model_dir']) + print('Skip training ...') + + manager.infer(data_loader=validation_dataloader, + input_model_dir=paths['model_dir'], + output_columns=output_columns, + output_file_name=infer_common_params['infer_filename']) #, + # num_workers=0) + + # visualize the predictions + infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) + descriptors_list = infer_processor.get_samples_descriptors() + out_name = 'model.logits.classification' + gt_name = 'data.gt.gt_global' + for desc in descriptors_list[:10]: + data = infer_processor(desc) + pred = np.squeeze(data[out_name]) + gt = np.squeeze(data[gt_name]) + _, ax = plt.subplots(1,2) + ax[0].imshow(pred) + ax[0].set_title('prediction') + ax[1].imshow(gt) + ax[1].set_title('gt') + plt.show() + + ###################################### + # Analyze Common Params + ###################################### + ANALYZE_COMMON_PARAMS = {} + ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] + ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['analyze_dir'], 'all_metrics') + analyze_common_params = ANALYZE_COMMON_PARAMS + + # metrics + metrics = { + # 'accuracy': FuseMetricAccuracy(pred_name='model.logits.classification', target_name='data.gt.gt_global'), + # 'roc': FuseMetricROCCurve(pred_name='model.logits.classification', target_name='data.gt.gt_global', output_filename='roc_curve.png'), + # 'auc': FuseMetricAUC(pred_name='model.logits.classification', target_name='data.gt.gt_global') + 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', + target_name='data.gt.gt_global', + output_filename='roc_curve.png'), + 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', + target_name='data.gt.gt_global', + hard_threshold=True, threshold=0.5) + } + + # manager.visualize(visualizer=visualiser, + # data_loader=validation_dataloader, device='cpu') + # descriptors=, + # display_func=, + # infer_processor=None) + + # create analyzer + analyzer = FuseAnalyzerDefault() + + # run + # FIXME: simplify analyze interface for this case + analyzer.analyze(gt_processors=gt_processors, + data_pickle_filename=analyze_common_params['infer_filename'], + metrics=metrics, + print_results=True, + output_filename=analyze_common_params['output_filename'], + num_workers=0) + + +if __name__ == '__main__': + import argparse + + my_parser = argparse.ArgumentParser() + my_parser.add_argument('--train', action='store_true') + my_parser.add_argument('--infer', action='store_true') + args = my_parser.parse_args() + + print(vars(args)) + params = vars(args) + main(PATHS, TRAIN_COMMON_PARAMS, train=params['train'], infer=params['infer']) diff --git a/fuse_examples/segmentation/create_dataset.py b/fuse_examples/segmentation/create_dataset.py new file mode 100644 index 000000000..2a0ae95dd --- /dev/null +++ b/fuse_examples/segmentation/create_dataset.py @@ -0,0 +1,169 @@ +# from fastai.vision import * +import pydicom +from pathlib import Path +import pandas as pd +from tqdm import tqdm as progress_bar +import PIL +import numpy as np +import matplotlib.pylab as plt +# from mask_functions import * +# Here out RLE encoding is little bit different + +import sys +sys.path.append('JustImage/') +sys.path.append('JustImage/CV') +from JustImage.CV.Binarization import bin_mosaic1, bin_mosaic3 + +main_out_path = 'data' +dataset_path = 'siim' + +""" +download dataset from - +https://www.kaggle.com/seesee/siim-train-test + +The path to the extracted data should be updated in the variable. +The output images will be stored at . +""" + +def create_mosaic(im, DEBUG=False): + # im = im.astype(np.uint8) + im = np.asarray(im) + org_min = np.min(im) + org_max = np.max(im) + + _, im_after = bin_mosaic1(im, tiles=(8, 8), locality_size=8) + + s = (im_after - np.min(im_after)) / (np.max(im_after) - np.min(im_after)) + out_im = s * (org_max - org_min) + org_min + out_im = out_im.astype(np.uint8) + + _, im_after = bin_mosaic1(im, tiles=(16, 16), locality_size=8) + + s = (im_after - np.min(im_after)) / (np.max(im_after) - np.min(im_after)) + out_im2 = s * (org_max - org_min) + org_min + out_im2 = out_im2.astype(np.uint8) + + rgb = np.stack([im, out_im, out_im2], axis=-1) + + if DEBUG: + fig, ax = plt.subplots(2, 3, figsize=(15, 8)) + ax[0, 0].imshow(im, cmap=plt.cm.bone) + ax[0, 1].imshow(out_im, cmap=plt.cm.bone) + ax[0, 2].imshow(out_im2, cmap=plt.cm.bone) + + ax[1, 0].hist(im.ravel()*255, np.arange(0,255)); + ax[1, 1].hist(out_im.ravel(), np.arange(np.min(out_im), np.max(out_im))); + ax[1, 2].hist(out_im2.ravel(), np.arange(np.min(out_im2), np.max(out_im2))); + + plt.figure() + plt.imshow(rgb) + plt.show() + + return PIL.Image.fromarray(rgb) + +def rle2mask(rles, width, height): + """ + + rle encoding if images + input: rles(list of rle), width and height of image + returns: mask of shape (width,height) + """ + + mask= np.zeros(width* height) + for rle in rles: + array = np.asarray([int(x) for x in rle.split()]) + starts = array[0::2] + lengths = array[1::2] + + current_position = 0 + for index, start in enumerate(starts): + current_position += start + mask[current_position:current_position+lengths[index]] = 255 + current_position += lengths[index] + + return mask.reshape(width, height).T + +def filter_files(files, include=[], exclude=[]): + for incl in include: + files = [f for f in files if incl in f.name] + for excl in exclude: + files = [f for f in files if excl not in f.name] + return sorted(files) + +def ls(x, recursive=False, include=[], exclude=[]): + if not recursive: + out = list(x.iterdir()) + else: + out = [o for o in x.glob('**/*')] + out = filter_files(out, include=include, exclude=exclude) + return out + +Path.ls = ls + +class InOutPath(): + def __init__(self, input_path:Path, output_path:Path): + if isinstance(input_path, str): input_path = Path(input_path) + if isinstance(output_path, str): output_path = Path(output_path) + self.inp = input_path + self.out = output_path + self.mkoutdir() + + def mkoutdir(self): + self.out.mkdir(exist_ok=True, parents=True) + + def __repr__(self): + return '\n'.join([f'{i}: {o}' for i, o in self.__dict__.items()]) + '\n' + +def dcm2png(SZ, dataset): + path = InOutPath(Path(dataset_path + f'/dicom-images-{dataset}'), Path(main_out_path + f'/data{SZ}/{dataset}')) + files = path.inp.ls(recursive=True, include=['.dcm']) + for f in progress_bar(files): + dcm = pydicom.read_file(str(f)).pixel_array + # PIL.Image.fromarray(dcm).resize((SZ,SZ)).save(path.out/f'{f.stem}.png') + + # make a rgb like image with mosaic images: + im = PIL.Image.fromarray(dcm).resize((SZ,SZ)) + im = create_mosaic(im) + im.save(path.out/f'{f.stem}.png') + +def masks2png(SZ): + path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) + for i in progress_bar(list(set(rle_df.ImageId.values))): + I = rle_df.ImageId == i + name = rle_df.loc[I, 'ImageId'] + enc = rle_df.loc[I, ' EncodedPixels'] + if sum(I) == 1: + enc = enc.values[0] + name = name.values[0] + if enc == '-1': # ' -1': + m = np.zeros((1024, 1024)).astype(np.uint8) + else: + m = rle2mask([enc], 1024, 1024).astype(np.uint8) + PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name}.png') + else: + # m = np.array([rle2mask(e, 1024, 1024).astype(np.uint8) for e in enc.values]) + m = rle2mask(enc.values, 1024, 1024).astype(np.uint8) + # m = m.sum(0).astype(np.uint8).T + PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name.values[0]}.png') + +rle_df = pd.read_csv(dataset_path + '/train-rle.csv') + +size_list = [128] # [64, 128, 256, 512, 1024] +for SZ in progress_bar(size_list): + print(f'Converting data for train{SZ}') + dcm2png(SZ, 'train') + print(f'Converting data for test{SZ}') + dcm2png(SZ, 'test') + print(f'Generating masks for size {SZ}') + masks2png(SZ) + +for SZ in progress_bar(size_list): + # Missing masks set to 0 + print('Generating missing masks as zeros') + train_images = [o.name for o in Path(main_out_path + f'/data{SZ}/train').ls(include=['.png'])] + train_masks = [o.name for o in Path(main_out_path + f'/data{SZ}/masks').ls(include=['.png'])] + missing_masks = set(train_images) - set(train_masks) + path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) + for name in progress_bar(missing_masks): + m = np.zeros((1024, 1024)).astype(np.uint8).T + PIL.Image.fromarray(m).resize((SZ,SZ)).save(main_out_path + f'/data{SZ}/masks/{name}') diff --git a/fuse_examples/segmentation/data_source_segmentation.py b/fuse_examples/segmentation/data_source_segmentation.py new file mode 100644 index 000000000..1ce555bad --- /dev/null +++ b/fuse_examples/segmentation/data_source_segmentation.py @@ -0,0 +1,111 @@ +import pandas as pd +from glob import glob +import random +import pickle +from typing import Sequence, Hashable, Union, Optional, List, Dict +from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.utils.utils_misc import autodetect_input_source + + +class FuseDataSourceSeg(FuseDataSourceBase): + def __init__(self, + image_source: str, + mask_source: Optional[str] = None, + partition_file: Optional[str] = None, + train: bool = True, + val_split: float = 0.2, + override_partition: bool = True, + data_shuffle: bool = True + ): + """ + Create DataSource + :param input_source: path to images + :param partition_file: Optional, name of a pickle file when no validation set is available + If train = True, train/val indices are dumped into the file, + If train = False, train/val indices are loaded + :param train: specifies if we are in training phase + :param val_split: validation proportion in case of splitting + :param override_partition: specifies if the given partition file is filled with new train/val splits + """ + + + # Extract entities + # ---------------- + if partition_file is not None: + if train: + if override_partition: + train_fn = glob(image_source + '/*') + train_fn.sort() + + masks_fn = glob(mask_source + '/*') + masks_fn.sort() + + fn = list(zip(train_fn, masks_fn)) + + if len(fn) == 0: + raise Exception('Error detecting input source in FuseDataSourceDefault') + + if data_shuffle: + # random shuffle the file-list + random.shuffle(fn) + + # split to train-validation - + n_train = int(len(fn) * (1-val_split)) + + train_fn = fn[:n_train] + val_fn = fn[n_train:] + splits = {'train': train_fn, 'val': val_fn} + + with open(partition_file, "wb") as pickle_out: + pickle.dump(splits, pickle_out) + sample_descs = train_fn + else: + # read from a previous train/test split to evaluate on the same partition + with open(partition_file, "rb") as splits: + repartition = pickle.load(splits) + sample_descs = repartition['train'] + else: + with open(partition_file, "rb") as splits: + repartition = pickle.load(splits) + sample_descs = repartition['val'] + else: + # TODO - this option is not clear - if the partition file is not give? do we train + # with all the data? or just dont save the partition? (than we will not be able + # to re-run the experiment ... + for sample_id in input_df.iloc[:, 0]: + sample_descs.append(sample_id) + + self.samples = sample_descs + + self.input_source = [image_source, mask_source] + + # prev version + # self.samples = input_source + + # @staticmethod + # def filter_by_conditions(samples: pd.DataFrame, conditions: Optional[List[Dict[str, List]]]): + # """ + # Returns a vector of the samples that passed the conditions + # :param samples: dataframe to check. expected to have at least sample_desc column. + # :param conditions: list of dictionaries. each dictionary has column name as keys and possible values as the values. + # for each dict in the list: + # the keys are applied with AND between them. + # the dict conditions are applied with OR between them. + # :return: boolean vector with the filtered samples + # """ + # to_keep = samples.sample_desc.isna() # start with all false + # for condition_list in conditions: + # condition_to_keep = samples.sample_desc.notna() # start with all true + # for column, values in condition_list.items(): + # condition_to_keep = condition_to_keep & samples[column].isin(values) # all conditions in list must be met + # to_keep = to_keep | condition_to_keep # add this condition samples to_keep + # return to_keep + + def get_samples_description(self): + return self.samples + # return list(self.samples_df['sample_desc']) + + def summary(self) -> str: + summary_str = '' + summary_str += 'FuseDataSourceSeg - %d samples\n' % len(self.samples) + return summary_str diff --git a/fuse_examples/segmentation/runner_seg.py b/fuse_examples/segmentation/runner_seg.py new file mode 100644 index 000000000..6e9e73c2f --- /dev/null +++ b/fuse_examples/segmentation/runner_seg.py @@ -0,0 +1,482 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +""" + +(C) Copyright 2021 IBM Corp. +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + http://www.apache.org/licenses/LICENSE-2.0 +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +Created on June 30, 2021 + +""" +import os +import logging +from glob import glob +import random +import numpy as np +import matplotlib.pylab as plt +from pathlib import Path + +import torch +from torch.utils.data import DataLoader +import torch.optim as optim +import torch.nn.functional as F + +from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool +from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt +from fuse.utils.utils_gpu import FuseUtilsGPU +from fuse.utils.utils_logger import fuse_logger_start +from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault +from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.models.model_wrapper import FuseModelWrapper +from fuse.losses.segmentation.loss_dice import DiceBCELoss +from fuse.losses.segmentation.loss_dice import FuseDiceLoss +from fuse.losses.loss_default import FuseLossDefault +from fuse.managers.manager_default import FuseManagerDefault +from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame +from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel +from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap +from fuse.analyzer.analyzer_default import FuseAnalyzerDefault + +from data_source_segmentation import FuseDataSourceSeg +from seg_input_processor import SegInputProcessor + +from unet import UNet + + +def perform_softmax(output): + if isinstance(output, torch.Tensor): # validation + logits = output + else: # train + logits = output.logits + cls_preds = F.softmax(logits, dim=1) + return logits, cls_preds + + +SZ = 512 +TRAIN = f'../../data/siim/data{SZ}/train/' +TEST = f'../../data/siim/data{SZ}/test/' +MASKS = f'../../data/siim/data{SZ}/masks/' + +# TODO: Path to save model +ROOT = '../results/' +# TODO: path to store the data +ROOT_DATA = ROOT +# TODO: Name of the experiment +EXPERIMENT = 'unet_seg_results' +# TODO: Path to cache data +CACHE_PATH = '../results/' +# TODO: Name of the cached data folder +EXPERIMENT_CACHE = 'exp_cache' + +PATHS = {'data_dir': [TRAIN, MASKS, TEST], + 'model_dir': os.path.join(ROOT, EXPERIMENT, 'model_dir'), + 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message. + 'cache_dir': os.path.join(CACHE_PATH, EXPERIMENT_CACHE+'_cache_dir'), + 'inference_dir': os.path.join(ROOT, EXPERIMENT, 'infer_dir'), + 'analyze_dir': os.path.join(ROOT, EXPERIMENT, 'analyze_dir')} + +########################################## +# Train Common Params +########################################## +# ============ +# Data +# ============ +TRAIN_COMMON_PARAMS = {} +TRAIN_COMMON_PARAMS['data.batch_size'] = 8 +TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 +TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 +TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ + [ + ('data.input.input_0','data.gt.gt_global'), + aug_op_affine_group, + {'rotate': Uniform(-20.0, 20.0), + 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left + 'scale': Uniform(0.9, 1.1), + 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, + {'apply': RandBool(0.9)} + ], + [ + ('data.input.input_0','data.gt.gt_global'), + aug_op_elastic_transform, + {}, + {'apply': RandBool(0.7)} + ], + [ + ('data.input.input_0',), + aug_op_color, + { + 'add': Uniform(-0.06, 0.06), + 'mul': Uniform(0.95, 1.05), + 'gamma': Uniform(0.9, 1.1), + 'contrast': Uniform(0.85, 1.15) + }, + {'apply': RandBool(0.7)} + ], + [ + ('data.input.input_0',), + aug_op_gaussian, + {'std': 0.05}, + {'apply': RandBool(0.7)} + ], +] + +# =============== +# Manager - Train1 +# =============== +TRAIN_COMMON_PARAMS['manager.train_params'] = { + 'num_epochs': 20, + 'virtual_batch_size': 1, # number of batches in one virtual batch + 'start_saving_epochs': 10, # first epoch to start saving checkpoints from + 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint +} +TRAIN_COMMON_PARAMS['manager.best_epoch_source'] = { + 'source': 'losses.total_loss', # can be any key from 'epoch_results' (either metrics or losses result) + 'optimization': 'min', # can be either min/max +} +TRAIN_COMMON_PARAMS['manager.learning_rate'] = 1e-1 +TRAIN_COMMON_PARAMS['manager.weight_decay'] = 1e-4 +TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint +TRAIN_COMMON_PARAMS['partition_file'] = 'train_val_split.pickle' + +################################# +# Train Template +################################# +def run_train(paths: dict, train_common_params: dict): + # ============================================================================== + # Logger + # ============================================================================== + fuse_logger_start(output_path=paths['model_dir'], console_verbose_level=logging.INFO) + lgr = logging.getLogger('Fuse') + + # Download data + # TODO - function to download + arrange the data + + lgr.info('\nFuse Train', {'attrs': ['bold', 'underline']}) + + lgr.info(f'model_dir={paths["model_dir"]}', {'color': 'magenta'}) + lgr.info(f'cache_dir={paths["cache_dir"]}', {'color': 'magenta'}) + + train_path = paths['data_dir'][0] + mask_path = paths['data_dir'][1] + + #### Train Data + lgr.info(f'Train Data:', {'attrs': 'bold'}) + + train_data_source = FuseDataSourceSeg(image_source=train_path, + mask_source=mask_path, + partition_file=train_common_params['partition_file'], + train=True) + print(train_data_source.summary()) + + ## Create data processors: + input_processors = { + 'input_0': SegInputProcessor(name='image') + } + gt_processors = { + 'gt_global': SegInputProcessor(name='mask') + } + + ## Create data augmentation (optional) + augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + + # Create visualizer (optional) + visualiser = FuseVisualizerDefault(image_name='data.input.input_0', + mask_name='data.gt.gt_global', + pred_name='model.logits.classification') + + train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + data_source=train_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + augmentor=augmentor, + visualizer=visualiser) + + lgr.info(f'- Load and cache data:') + train_dataset.create() + lgr.info(f'- Load and cache data: Done') + + ## Create dataloader + train_dataloader = DataLoader(dataset=train_dataset, + shuffle=True, + drop_last=False, + batch_size=train_common_params['data.batch_size'], + collate_fn=train_dataset.collate_fn, + num_workers=train_common_params['data.train_num_workers']) + lgr.info(f'Train Data: Done', {'attrs': 'bold'}) + # ================================================================== + # Validation dataset + lgr.info(f'Validation Data:', {'attrs': 'bold'}) + + valid_data_source = FuseDataSourceSeg(image_source=train_path, + mask_source=mask_path, + partition_file=train_common_params['partition_file'], + train=False) + print(valid_data_source.summary()) + + valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + data_source=valid_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + visualizer=visualiser) + + lgr.info(f'- Load and cache data:') + valid_dataset.create() + lgr.info(f'- Load and cache data: Done') + + ## Create dataloader + validation_dataloader = DataLoader(dataset=valid_dataset, + shuffle=False, + drop_last=False, + batch_size=train_common_params['data.batch_size'], + collate_fn=valid_dataset.collate_fn, + num_workers=train_common_params['data.validation_num_workers']) + + lgr.info(f'Validation Data: Done', {'attrs': 'bold'}) + # ================================================================== + # # Training graph + lgr.info('Model:', {'attrs': 'bold'}) + torch_model = UNet(n_channels=1, n_classes=1, bilinear=False) + + model = FuseModelWrapper(model=torch_model, + model_inputs=['data.input.input_0'], + post_forward_processing_function=perform_softmax, + model_outputs=['logits.classification', 'output.classification'] + ) + + lgr.info('Model: Done', {'attrs': 'bold'}) + # ==================================================================================== + # Loss + # ==================================================================================== + losses = { + 'dice_loss': DiceBCELoss(pred_name='model.logits.classification', + target_name='data.gt.gt_global') + } + + model = model.cuda() + optimizer = optim.SGD(model.parameters(), + lr=train_common_params['manager.learning_rate'], + momentum=0.9, + weight_decay=train_common_params['manager.weight_decay']) + + scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5) + + # train from scratch + manager = FuseManagerDefault(output_model_dir=paths['model_dir'], + force_reset=paths['force_reset_model_dir']) + + # ===================================================================================== + # Callbacks + # ===================================================================================== + callbacks = [ + # default callbacks + FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file + FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + ] + + # Providing the objects required for the training process. + manager.set_objects(net=model, + optimizer=optimizer, + losses=losses, + lr_scheduler=scheduler, + callbacks=callbacks, + best_epoch_source=train_common_params['manager.best_epoch_source'], + train_params=train_common_params['manager.train_params'], + output_model_dir=paths['model_dir']) + + manager.train(train_dataloader=train_dataloader, + validation_dataloader=validation_dataloader) + lgr.info('Train: Done', {'attrs': 'bold'}) + + +###################################### +# Inference Common Params +###################################### +INFER_COMMON_PARAMS = {} +INFER_COMMON_PARAMS['infer_filename'] = os.path.join(PATHS['inference_dir'], 'validation_set_infer.gz') +INFER_COMMON_PARAMS['checkpoint'] = 'last' # Fuse TIP: possible values are 'best', 'last' or epoch_index. +INFER_COMMON_PARAMS['data.train_num_workers'] = TRAIN_COMMON_PARAMS['data.train_num_workers'] +INFER_COMMON_PARAMS['partition_file'] = TRAIN_COMMON_PARAMS['partition_file'] +INFER_COMMON_PARAMS['data.batch_size'] = TRAIN_COMMON_PARAMS['data.batch_size'] + +###################################### +# Inference Template +###################################### +def run_infer(paths: dict, infer_common_params: dict): + #### Logger + fuse_logger_start(output_path=paths['inference_dir'], console_verbose_level=logging.INFO) + lgr = logging.getLogger('Fuse') + lgr.info('Fuse Inference', {'attrs': ['bold', 'underline']}) + lgr.info(f'infer_filename={os.path.join(paths["inference_dir"], infer_common_params["infer_filename"])}', {'color': 'magenta'}) + + train_path = paths['data_dir'][0] + mask_path = paths['data_dir'][1] + # ================================================================== + # Validation dataset + lgr.info(f'Test Data:', {'attrs': 'bold'}) + + infer_data_source = FuseDataSourceSeg(image_source=train_path, + mask_source=mask_path, + partition_file=infer_common_params['partition_file'], + train=False) + print(infer_data_source.summary()) + + ## Create data processors: + input_processors = { + 'input_0': SegInputProcessor(name='image') + } + gt_processors = { + 'gt_global': SegInputProcessor(name='mask') + } + + # Create visualizer (optional) + visualiser = FuseVisualizerDefault(image_name='data.input.input_0', + mask_name='data.gt.gt_global', + pred_name='model.logits.classification') + + infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + data_source=infer_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + visualizer=visualiser) + + lgr.info(f'- Load and cache data:') + infer_dataset.create() + lgr.info(f'- Load and cache data: Done') + + ## Create dataloader + infer_dataloader = DataLoader(dataset=infer_dataset, + shuffle=False, + drop_last=False, + batch_size=infer_common_params['data.batch_size'], + collate_fn=infer_dataset.collate_fn, + num_workers=infer_common_params['data.train_num_workers']) + + lgr.info(f'Test Data: Done', {'attrs': 'bold'}) + + #### Manager for inference + manager = FuseManagerDefault() + # extract just the global classification per sample and save to a file + output_columns = ['model.logits.classification', 'data.gt.gt_global'] + manager.infer(data_loader=infer_dataloader, + input_model_dir=paths['model_dir'], + checkpoint=infer_common_params['checkpoint'], + output_columns=output_columns, + output_file_name=infer_common_params["infer_filename"]) + + # visualize the predictions + infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) + descriptors_list = infer_processor.get_samples_descriptors() + out_name = 'model.logits.classification' + gt_name = 'data.gt.gt_global' + for desc in descriptors_list[:10]: + data = infer_processor(desc) + pred = np.squeeze(data[out_name]) + gt = np.squeeze(data[gt_name]) + _, ax = plt.subplots(1,2) + ax[0].imshow(pred) + ax[0].set_title('prediction') + ax[1].imshow(gt) + ax[1].set_title('gt') + fn = os.path.join(paths["inference_dir"], Path(desc[0]).name) + plt.savefig(fn) + +###################################### +# Analyze Common Params +###################################### +ANALYZE_COMMON_PARAMS = {} +ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] +ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['analyze_dir'], 'all_metrics.txt') +ANALYZE_COMMON_PARAMS['num_workers'] = 4 +ANALYZE_COMMON_PARAMS['batch_size'] = 8 + +###################################### +# Analyze Template +###################################### +def run_analyze(paths: dict, analyze_common_params: dict): + fuse_logger_start(output_path=None, console_verbose_level=logging.INFO) + lgr = logging.getLogger('Fuse') + lgr.info('Fuse Analyze', {'attrs': ['bold', 'underline']}) + + gt_processors = { + 'gt_global': SegInputProcessor(name='mask') + } + + # metrics + metrics = { + 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', + target_name='data.gt.gt_global'), + 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', + target_name='data.gt.gt_global', + hard_threshold=True, threshold=0.5) + } + + # create analyzer + analyzer = FuseAnalyzerDefault() + + # run + analyzer.analyze(gt_processors=gt_processors, + data_pickle_filename=analyze_common_params['infer_filename'], + metrics=metrics, + print_results=True, + output_filename=analyze_common_params['output_filename'], + num_workers=0) + + +###################################### +# Run +###################################### +if __name__ == "__main__": + # allocate gpus + NUM_GPUS = 1 + if NUM_GPUS == 0: + TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' + # uncomment if you want to use specific gpus instead of automatically looking for free ones + force_gpus = None # [0] + FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + + RUNNING_MODES = ['train', 'infer', 'analyze'] # Options: 'train', 'infer', 'analyze' + RUNNING_MODES = ['analyze'] # Options: 'train', 'infer', 'analyze' + + # train + if 'train' in RUNNING_MODES: + run_train(paths=PATHS, train_common_params=TRAIN_COMMON_PARAMS) + + # infer + if 'infer' in RUNNING_MODES: + run_infer(paths=PATHS, infer_common_params=INFER_COMMON_PARAMS) + + # analyze + if 'analyze' in RUNNING_MODES: + run_analyze(paths=PATHS, analyze_common_params=ANALYZE_COMMON_PARAMS) + diff --git a/fuse_examples/segmentation/seg_input_processor.py b/fuse_examples/segmentation/seg_input_processor.py new file mode 100644 index 000000000..129207e10 --- /dev/null +++ b/fuse_examples/segmentation/seg_input_processor.py @@ -0,0 +1,178 @@ + +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import numpy as np +from skimage.io import imread +import torch + +from typing import Optional, Tuple + +from fuse.data.processor.processor_base import FuseProcessorBase + + +class SegInputProcessor(FuseProcessorBase): + def __init__(self, + input_data: str = None, + name: str = 'image', # can be 'image' or 'mask' + normalized_target_range: Tuple = (-1, 1), + resize_to: Optional[Tuple] = (299, 299), + padding: Optional[Tuple] = (0, 0), + ): + # """ + # Create Input processor + # :param input_data: path to images + # :param normalized_target_range: range for image normalization + # :param resize_to: Optional, new size of input images, keeping proportions + # :param padding: Optional, padding size + # """ + + # self.input_data = input_data + # self.normalized_target_range = normalized_target_range + # self.resize_to = np.subtract(resize_to, (2*padding[0], 2*padding[1])) + # self.padding = padding + self.name = name + if self.name == 'image': + self.im_inx = 0 + elif self.name == 'mask': + self.im_inx = 1 + else: + print('Wrong input!!') + + def __call__(self, + image_fn, + *args, **kwargs): + + try: + image_fn = image_fn[self.im_inx] + image = imread(image_fn) + + # ====================================================================== + # TODO - change type to float if input image and to int it mask image + if self.name == 'image': + image = image.astype('float32') + image = image / 255.0 + else: + image = image > 0 + image = image.astype('float32') + # ===================================================================== + + # img_path = self.input_data + str(inner_image_desc) + '.jpg' + + # # read image + # inner_image = skimage.io.imread(img_path) + + # # convert to numpy + # inner_image = np.asarray(inner_image) + + # # normalize + # inner_image = normalize_to_range(inner_image, range=self.normalized_target_range) + + # # resize + # inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] + + # if self.resize_to is not None: + # if inner_image_height > self.resize_to[0]: + # h_ratio = self.resize_to[0] / inner_image_height + # else: + # h_ratio = 1 + # if inner_image_width > self.resize_to[1]: + # w_ratio = self.resize_to[1] / inner_image_width + # else: + # w_ratio = 1 + + # resize_ratio = min(h_ratio, w_ratio) + # if resize_ratio != 1: + # inner_image = skimage.transform.resize(inner_image, + # output_shape=(int(inner_image_height * resize_ratio), + # int(inner_image_width * resize_ratio)), + # mode='reflect', + # anti_aliasing=True + # ) + + # # padding + # if self.padding is not None: + # # "Pad" around inner image + # inner_image = inner_image.astype('float32') + + # inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] + # inner_image[0:inner_image_height, 0] = 0 + # inner_image[0:inner_image_height, inner_image_width-1] = 0 + # inner_image[0, 0:inner_image_width] = 0 + # inner_image[inner_image_height-1, 0:inner_image_width] = 0 + + # if self.normalized_target_range is None: + # pad_value = 0 + # else: + # pad_value = self.normalized_target_range[0] + + # image = pad_image(inner_image, outer_height=self.resize_to[0] + 2*self.padding[0], outer_width=self.resize_to[1] + 2*self.padding[1], pad_value=pad_value) + + # else: + # image = inner_image + + # convert image from shape (H x W x C) to shape (C x H x W) with C=3 + if len(image.shape) > 2: + image = np.moveaxis(image, -1, 0) + else: + image = np.expand_dims(image, 0) + + # numpy to tensor + sample = torch.from_numpy(image) + + except: + return None + + return sample + + +def normalize_to_range(input_image: np.ndarray, range: Tuple = (-1.0, 1.0)): + """ + Scales tensor to range + @param input_image: image of shape (H x W x C) + @param range: bounds for normalization + @return: normalized image + """ + max_val = input_image.max() + min_val = input_image.min() + if min_val == max_val == 0: + return input_image + input_image = input_image - min_val + input_image = input_image / (max_val - min_val) + input_image = input_image * (range[1] - range[0]) + input_image = input_image + range[0] + return input_image + + +def pad_image(image: np.ndarray, outer_height: int, outer_width: int, pad_value: Tuple): + """ + Pastes input image in the middle of a larger one + @param image: image of shape (H x W x C) + @param outer_height: final outer height + @param outer_width: final outer width + @param pad_value: value for padding around inner image + @return: padded image + """ + inner_height, inner_width = image.shape[0], image.shape[1] + h_offset = int((outer_height - inner_height) / 2.0) + w_offset = int((outer_width - inner_width) / 2.0) + outer_image = np.ones((outer_height, outer_width, 3), dtype=image.dtype) * pad_value + outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width, :] = image + + return outer_image diff --git a/fuse_examples/segmentation/unet.py b/fuse_examples/segmentation/unet.py new file mode 100644 index 000000000..225de96a8 --- /dev/null +++ b/fuse_examples/segmentation/unet.py @@ -0,0 +1,113 @@ + +""" Full assembly of the parts to form the complete network """ + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2""" + + def __init__(self, in_channels, out_channels, mid_channels=None): + super().__init__() + if not mid_channels: + mid_channels = out_channels + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(mid_channels), + nn.ReLU(inplace=True), + nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), + DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) + else: + self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2) + self.conv = DoubleConv(in_channels, out_channels) + + + def forward(self, x1, x2): + x1 = self.up(x1) + # input is CHW + diffY = x2.size()[2] - x1.size()[2] + diffX = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, + diffY // 2, diffY - diffY // 2]) + # if you have padding issues, see + # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a + # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(OutConv, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + self.sig = nn.Sigmoid() + + def forward(self, x): + return self.sig(self.conv(x)) + + +class UNet(nn.Module): + def __init__(self, n_channels, n_classes, bilinear=True): + super(UNet, self).__init__() + self.n_channels = n_channels + self.n_classes = n_classes + self.bilinear = bilinear + + self.inc = DoubleConv(n_channels, 64) + self.down1 = Down(64, 128) + self.down2 = Down(128, 256) + self.down3 = Down(256, 512) + factor = 2 if bilinear else 1 + self.down4 = Down(512, 1024 // factor) + self.up1 = Up(1024, 512 // factor, bilinear) + self.up2 = Up(512, 256 // factor, bilinear) + self.up3 = Up(256, 128 // factor, bilinear) + self.up4 = Up(128, 64, bilinear) + self.outc = OutConv(64, n_classes) + + def forward(self, x): + x1 = self.inc(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + x = self.up1(x5, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + logits = self.outc(x) + return logits From c8674b06910fa4a2ff43691aee8d57037d9c6fb5 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 15 Mar 2022 20:34:23 +0200 Subject: [PATCH 02/42] fix elastic augmentation and loss bce+dice; update for new eval package (not completed) --- fuse/data/augmentor/augmentor_toolbox.py | 67 ++++++++++++++++----- fuse/losses/segmentation/loss_dice.py | 76 +++++++++++++++++++++++- fuse_examples/segmentation/runner_seg.py | 58 +++++++++++------- 3 files changed, 161 insertions(+), 40 deletions(-) diff --git a/fuse/data/augmentor/augmentor_toolbox.py b/fuse/data/augmentor/augmentor_toolbox.py index 0333344c2..047cfbf75 100644 --- a/fuse/data/augmentor/augmentor_toolbox.py +++ b/fuse/data/augmentor/augmentor_toolbox.py @@ -27,12 +27,16 @@ from scipy.ndimage.filters import gaussian_filter from scipy.ndimage.interpolation import map_coordinates from torch import Tensor +import elasticdeform as ed from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerGaussianPatch as Gaussian from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform +import PIL +import torch.nn.functional as F + ######## Affine augmentation def aug_op_affine(aug_input: Tensor, rotate: float = 0.0, translate: Tuple[float, float] = (0.0, 0.0), @@ -63,7 +67,12 @@ def aug_op_affine(aug_input: Tensor, rotate: float = 0.0, translate: Tuple[float for channel in channels: aug_channel_tensor = aug_input[channel].numpy() aug_channel_tensor = Image.fromarray(aug_channel_tensor) - aug_channel_tensor = TTF.affine(aug_channel_tensor, angle=rotate, scale=scale, translate=translate, shear=shear) + aug_channel_tensor = TTF.affine(aug_channel_tensor, + angle=rotate, + scale=scale, + resample=PIL.Image.BILINEAR, + translate=translate, + shear=shear) if flip[0]: aug_channel_tensor = TTF.vflip(aug_channel_tensor) if flip[1]: @@ -265,23 +274,13 @@ def aug_op_elastic_transform(aug_input: Tensor, alpha: float = 1, sigma: float = :param channels: which channels to apply the augmentation :return distorted image """ - random_state = numpy.random.RandomState(None) - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input.numpy() - for channel in channels: - aug_channel_tensor = aug_input[channel].numpy() - shape = aug_channel_tensor.shape - dx1 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - dx2 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha + # convert back to torch tensor + aug_input = [numpy.array(t) for t in aug_input] + aug_input_d = ed.deform_random_grid(aug_input, sigma=7, points=3, axis=[(1, 2), (1,2)]) - x1, x2 = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1])) - indices = numpy.reshape(x2 + dx2, (-1, 1)), numpy.reshape(x1 + dx1, (-1, 1)) + aug_output = [torch.from_numpy(t) for t in aug_input_d] - distored_image = map_coordinates(aug_channel_tensor, indices, order=1, mode='reflect') - distored_image = distored_image.reshape(aug_channel_tensor.shape) - aug_tensor[channel] = distored_image - return torch.from_numpy(aug_tensor) + return aug_output ######### Default / Example augmentation pipline for a 2D image @@ -452,3 +451,39 @@ def aug_op_batch_mix_up(aug_input: Tuple[Tensor, Tensor], factor: float) -> Tupl img = img * (1.0 - factor) + factor * img_mix_up labels = labels * (1.0 - factor) + factor * labels_mix_up return img, labels + + +def aug_op_random_crop_and_resize(aug_input: Tensor, + out_size, + crop_size: float = 1.0, # or optional - Tuple[float, float] + x_off: float = 1.0, + y_off: float = 1.0, + z_off: float = 1.0) -> Tensor: + """ + random crop a (3d) tensor and resize it to a given size + :param crop_size: float <= 1.0 - the fraction to crop from the original tensor for each dim + :param x_off: float <= 1.0 - the x-offset to take + :param y_off: + :param z_off: + :param out_size: the size of the output tensor + :return: the output tensor + """ + in_shape = aug_input.shape + + if len(aug_input.shape) == 4: + ch, z, y, x = in_shape + + x_width = int(crop_size * x) + x_off = int(x_off * (x - x_width)) + + y_width = int(crop_size * y) + y_off = int(y_off * (y - y_width)) + + z_width = int(crop_size * z) + z_off = int(z_off * (z - z_width)) + + aug_tensor = aug_input[:, z_off:z_off+z_width, y_off:y_off+y_width, x_off:x_off+x_width] + + aug_tensor = F.interpolate(aug_tensor, out_size) + + return aug_tensor diff --git a/fuse/losses/segmentation/loss_dice.py b/fuse/losses/segmentation/loss_dice.py index 177d0379c..c0a68c93f 100644 --- a/fuse/losses/segmentation/loss_dice.py +++ b/fuse/losses/segmentation/loss_dice.py @@ -65,7 +65,7 @@ def __call__(self, predict, target): predict = predict.contiguous().view(predict.shape[0], -1) target = target.contiguous().view(target.shape[0], -1) - if target.dtype == torch.int64: + if target.dtype == torch.int64 or target.dtype == torch.int32: target = target.type(torch.float32).to(target.device) num = 2*torch.sum(torch.mul(predict, target), dim=1) + self.eps den = torch.sum(predict.pow(self.p) + target.pow(self.p), dim=1) + self.eps @@ -82,6 +82,80 @@ def __call__(self, predict, target): raise Exception('Unexpected reduction {}'.format(self.reduction)) +class DiceBCELoss(FuseLossBase): + + def __init__(self, + pred_name, + target_name, + filter_func: Optional[Callable]=None, + class_weights=None, + bce_weight: float=1.0, + power: int=1, + eps: float=1., + reduction: str='mean'): + ''' + Compute a weighted sum of dice-loss and cross entropy loss. + + :param pred_name: batch_dict key for predicted output (e.g., class probabilities after softmax). + Expected Tensor shape = [batch, num_classes, height, width] + :param target_name: batch_dict key for target (e.g., ground truth label). Expected Tensor shape = [batch, height, width] + :param filter_func: function that filters batch_dict/ The function gets ans input batch_dict and returns filtered batch_dict + :param class_weights: An array of shape [num_classes,] + :param bce_weight: weight to attach to the bce loss, default : 1.0 + :param power: Denominator value: \sum{x^p} + \sum{y^p}, default: 1 + :param eps: A float number to smooth loss, and avoid NaN error, default: 1 + :param reduction: Reduction method to apply, return mean over batch if 'mean', + return sum if 'sum', return a tensor of shape [N,] if 'none' + + Returns: Loss tensor according to arg reduction + Raise: Exception if unexpected reduction + ''' + + super().__init__(pred_name, target_name, 1.0) + self.class_weights = class_weights + self.bce_weight = bce_weight + self.filter_func = filter_func + self.dice = BinaryDiceLoss(power, eps, reduction) + + def __call__(self, batch_dict): + + if self.filter_func is not None: + batch_dict = self.filter_func(batch_dict) + predict = FuseUtilsHierarchicalDict.get(batch_dict, self.pred_name).float() + target = FuseUtilsHierarchicalDict.get(batch_dict, self.target_name).long() + + target = target.type(torch.float32).to(target.device) + + total_loss = 0 + n_classes = predict.shape[1] + + # Convert target to one hot encoding + if n_classes > 1 and target.shape[1] != n_classes: + target = make_one_hot(target, n_classes) + + assert predict.shape == target.shape, 'predict & target shape do not match' + + # import ipdb; ipdb.set_trace(context=7) # BREAKPOINT + total_class_weights = sum(self.class_weights) if self.class_weights is not None else n_classes + for cls_index in range(n_classes): + dice_loss = self.dice(predict[:, cls_index, :, :], target[:, cls_index, :, :]) + if self.bce_weight > 0.0: + bce_loss = F.binary_cross_entropy(predict[:, cls_index, :, :].view(-1), + target[:, cls_index, :, :].view(-1), + reduction='mean') + dice_loss += self.bce_weight * bce_loss + + if self.class_weights is not None: + assert self.class_weights.shape[0] == n_classes, \ + 'Expect weight shape [{}], got[{}]'.format(n_classes, self.class_weights.shape[0]) + dice_loss *= self.class_weights[cls_index] + + total_loss += dice_loss + total_loss /= total_class_weights + + return self.weight*total_loss + + class FuseDiceLoss(FuseLossBase): def __init__(self, pred_name, diff --git a/fuse_examples/segmentation/runner_seg.py b/fuse_examples/segmentation/runner_seg.py index 6e9e73c2f..a948d8a32 100644 --- a/fuse_examples/segmentation/runner_seg.py +++ b/fuse_examples/segmentation/runner_seg.py @@ -39,6 +39,7 @@ import numpy as np import matplotlib.pylab as plt from pathlib import Path +from collections import OrderedDict import torch from torch.utils.data import DataLoader @@ -63,9 +64,11 @@ from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame -from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel -from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap -from fuse.analyzer.analyzer_default import FuseAnalyzerDefault +# from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel +# from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap +# from fuse.analyzer.analyzer_default import FuseAnalyzerDefault +from fuse.eval.evaluator import EvaluatorDefault +from fuse.eval.metrics.segmentation.metrics_segmentation_common import MetricDice, MetricIouJaccard, MetricOverlap, Metric2DHausdorff, MetricPixelAccuracy from data_source_segmentation import FuseDataSourceSeg from seg_input_processor import SegInputProcessor @@ -154,7 +157,7 @@ def perform_softmax(output): # Manager - Train1 # =============== TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 20, + 'num_epochs': 2, 'virtual_batch_size': 1, # number of batches in one virtual batch 'start_saving_epochs': 10, # first epoch to start saving checkpoints from 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint @@ -428,29 +431,38 @@ def run_analyze(paths: dict, analyze_common_params: dict): lgr = logging.getLogger('Fuse') lgr.info('Fuse Analyze', {'attrs': ['bold', 'underline']}) - gt_processors = { - 'gt_global': SegInputProcessor(name='mask') - } + # gt_processors = { + # 'gt_global': SegInputProcessor(name='mask') + # } # metrics - metrics = { - 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', - target_name='data.gt.gt_global'), - 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', - target_name='data.gt.gt_global', - hard_threshold=True, threshold=0.5) - } + # { + # 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', + # target_name='data.gt.gt_global'), + # 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', + # target_name='data.gt.gt_global', + # hard_threshold=True, threshold=0.5) + # } + + metrics = OrderedDict([ + ("dice", MetricDice(pred='model.logits.classification', + target='data.gt.gt_global')), + ]) # create analyzer - analyzer = FuseAnalyzerDefault() - - # run - analyzer.analyze(gt_processors=gt_processors, - data_pickle_filename=analyze_common_params['infer_filename'], - metrics=metrics, - print_results=True, - output_filename=analyze_common_params['output_filename'], - num_workers=0) + evaluator = EvaluatorDefault() + + results = evaluator.eval(ids=None, + data=analyze_common_params['infer_filename'], + metrics=metrics) + +# # run +# analyzer.analyze(gt_processors=gt_processors, +# data_pickle_filename=analyze_common_params['infer_filename'], +# metrics=metrics, +# print_results=True, +# output_filename=analyze_common_params['output_filename'], +# num_workers=0) ###################################### From 98ebeeecf69a20dc23ee4dd79722ba96a7fe2857 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sun, 20 Mar 2022 16:53:20 +0200 Subject: [PATCH 03/42] Change to eval package --- fuse_examples/segmentation/runner_seg.py | 42 ++++++++++-------------- 1 file changed, 18 insertions(+), 24 deletions(-) diff --git a/fuse_examples/segmentation/runner_seg.py b/fuse_examples/segmentation/runner_seg.py index a948d8a32..5a216fd5b 100644 --- a/fuse_examples/segmentation/runner_seg.py +++ b/fuse_examples/segmentation/runner_seg.py @@ -37,6 +37,7 @@ from glob import glob import random import numpy as np +import pandas as pd import matplotlib.pylab as plt from pathlib import Path from collections import OrderedDict @@ -157,7 +158,7 @@ def perform_softmax(output): # Manager - Train1 # =============== TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 2, + 'num_epochs': 20, 'virtual_batch_size': 1, # number of batches in one virtual batch 'start_saving_epochs': 10, # first epoch to start saving checkpoints from 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint @@ -431,39 +432,32 @@ def run_analyze(paths: dict, analyze_common_params: dict): lgr = logging.getLogger('Fuse') lgr.info('Fuse Analyze', {'attrs': ['bold', 'underline']}) - # gt_processors = { - # 'gt_global': SegInputProcessor(name='mask') - # } - - # metrics - # { - # 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', - # target_name='data.gt.gt_global'), - # 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', - # target_name='data.gt.gt_global', - # hard_threshold=True, threshold=0.5) - # } + # define iterator + def data_iter(): + data = pd.read_pickle(analyze_common_params['infer_filename']) + n_samples = data.shape[0] + threshold = 0.5 + for inx in range(n_samples): + row = data.loc[inx] + sample_dict = {} + sample_dict["id"] = row['id'] + sample_dict["pred.array"] = row['model.logits.classification'] > threshold + sample_dict["label.array"] = row['data.gt.gt_global'] + yield sample_dict metrics = OrderedDict([ - ("dice", MetricDice(pred='model.logits.classification', - target='data.gt.gt_global')), + ("dice", MetricDice(pred='pred.array', + target='label.array')), ]) # create analyzer evaluator = EvaluatorDefault() results = evaluator.eval(ids=None, - data=analyze_common_params['infer_filename'], + data=data_iter(), + batch_size=1, metrics=metrics) -# # run -# analyzer.analyze(gt_processors=gt_processors, -# data_pickle_filename=analyze_common_params['infer_filename'], -# metrics=metrics, -# print_results=True, -# output_filename=analyze_common_params['output_filename'], -# num_workers=0) - ###################################### # Run From 26c1ffe871d71481d0bf33f4c127150e44e7d0d7 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sat, 26 Mar 2022 23:51:34 +0300 Subject: [PATCH 04/42] move example to a new folder --- fuse/losses/segmentation/loss_dice.py | 1 - fuse_examples/segmentation/{ => siim}/create_dataset.py | 0 .../segmentation/{ => siim}/data_source_segmentation.py | 0 fuse_examples/segmentation/{ => siim}/runner_seg.py | 0 fuse_examples/segmentation/{ => siim}/seg_input_processor.py | 0 fuse_examples/segmentation/{ => siim}/unet.py | 0 6 files changed, 1 deletion(-) rename fuse_examples/segmentation/{ => siim}/create_dataset.py (100%) rename fuse_examples/segmentation/{ => siim}/data_source_segmentation.py (100%) rename fuse_examples/segmentation/{ => siim}/runner_seg.py (100%) rename fuse_examples/segmentation/{ => siim}/seg_input_processor.py (100%) rename fuse_examples/segmentation/{ => siim}/unet.py (100%) diff --git a/fuse/losses/segmentation/loss_dice.py b/fuse/losses/segmentation/loss_dice.py index c0a68c93f..0df34de6a 100644 --- a/fuse/losses/segmentation/loss_dice.py +++ b/fuse/losses/segmentation/loss_dice.py @@ -135,7 +135,6 @@ def __call__(self, batch_dict): assert predict.shape == target.shape, 'predict & target shape do not match' - # import ipdb; ipdb.set_trace(context=7) # BREAKPOINT total_class_weights = sum(self.class_weights) if self.class_weights is not None else n_classes for cls_index in range(n_classes): dice_loss = self.dice(predict[:, cls_index, :, :], target[:, cls_index, :, :]) diff --git a/fuse_examples/segmentation/create_dataset.py b/fuse_examples/segmentation/siim/create_dataset.py similarity index 100% rename from fuse_examples/segmentation/create_dataset.py rename to fuse_examples/segmentation/siim/create_dataset.py diff --git a/fuse_examples/segmentation/data_source_segmentation.py b/fuse_examples/segmentation/siim/data_source_segmentation.py similarity index 100% rename from fuse_examples/segmentation/data_source_segmentation.py rename to fuse_examples/segmentation/siim/data_source_segmentation.py diff --git a/fuse_examples/segmentation/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py similarity index 100% rename from fuse_examples/segmentation/runner_seg.py rename to fuse_examples/segmentation/siim/runner_seg.py diff --git a/fuse_examples/segmentation/seg_input_processor.py b/fuse_examples/segmentation/siim/seg_input_processor.py similarity index 100% rename from fuse_examples/segmentation/seg_input_processor.py rename to fuse_examples/segmentation/siim/seg_input_processor.py diff --git a/fuse_examples/segmentation/unet.py b/fuse_examples/segmentation/siim/unet.py similarity index 100% rename from fuse_examples/segmentation/unet.py rename to fuse_examples/segmentation/siim/unet.py From 59afc5515db0b5d1d35ca4630f3e60aa5508e0dc Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sat, 26 Mar 2022 23:53:41 +0300 Subject: [PATCH 05/42] Update the create_data script + add some comment regarding the origin of the unet code --- .../segmentation/siim/create_dataset.py | 108 ++++++------------ fuse_examples/segmentation/siim/unet.py | 10 +- 2 files changed, 43 insertions(+), 75 deletions(-) diff --git a/fuse_examples/segmentation/siim/create_dataset.py b/fuse_examples/segmentation/siim/create_dataset.py index 2a0ae95dd..f784ed22d 100644 --- a/fuse_examples/segmentation/siim/create_dataset.py +++ b/fuse_examples/segmentation/siim/create_dataset.py @@ -1,4 +1,3 @@ -# from fastai.vision import * import pydicom from pathlib import Path import pandas as pd @@ -6,16 +5,7 @@ import PIL import numpy as np import matplotlib.pylab as plt -# from mask_functions import * -# Here out RLE encoding is little bit different -import sys -sys.path.append('JustImage/') -sys.path.append('JustImage/CV') -from JustImage.CV.Binarization import bin_mosaic1, bin_mosaic3 - -main_out_path = 'data' -dataset_path = 'siim' """ download dataset from - @@ -23,43 +13,15 @@ The path to the extracted data should be updated in the variable. The output images will be stored at . +the output size is defined by (the output is created with a folder for each size) """ +########################################## +# Params +########################################## +main_out_path = '../siim_data' +dataset_path = '../siim/' +out_size_list = [256, 512] -def create_mosaic(im, DEBUG=False): - # im = im.astype(np.uint8) - im = np.asarray(im) - org_min = np.min(im) - org_max = np.max(im) - - _, im_after = bin_mosaic1(im, tiles=(8, 8), locality_size=8) - - s = (im_after - np.min(im_after)) / (np.max(im_after) - np.min(im_after)) - out_im = s * (org_max - org_min) + org_min - out_im = out_im.astype(np.uint8) - - _, im_after = bin_mosaic1(im, tiles=(16, 16), locality_size=8) - - s = (im_after - np.min(im_after)) / (np.max(im_after) - np.min(im_after)) - out_im2 = s * (org_max - org_min) + org_min - out_im2 = out_im2.astype(np.uint8) - - rgb = np.stack([im, out_im, out_im2], axis=-1) - - if DEBUG: - fig, ax = plt.subplots(2, 3, figsize=(15, 8)) - ax[0, 0].imshow(im, cmap=plt.cm.bone) - ax[0, 1].imshow(out_im, cmap=plt.cm.bone) - ax[0, 2].imshow(out_im2, cmap=plt.cm.bone) - - ax[1, 0].hist(im.ravel()*255, np.arange(0,255)); - ax[1, 1].hist(out_im.ravel(), np.arange(np.min(out_im), np.max(out_im))); - ax[1, 2].hist(out_im2.ravel(), np.arange(np.min(out_im2), np.max(out_im2))); - - plt.figure() - plt.imshow(rgb) - plt.show() - - return PIL.Image.fromarray(rgb) def rle2mask(rles, width, height): """ @@ -83,6 +45,7 @@ def rle2mask(rles, width, height): return mask.reshape(width, height).T + def filter_files(files, include=[], exclude=[]): for incl in include: files = [f for f in files if incl in f.name] @@ -90,6 +53,7 @@ def filter_files(files, include=[], exclude=[]): files = [f for f in files if excl not in f.name] return sorted(files) + def ls(x, recursive=False, include=[], exclude=[]): if not recursive: out = list(x.iterdir()) @@ -98,8 +62,10 @@ def ls(x, recursive=False, include=[], exclude=[]): out = filter_files(out, include=include, exclude=exclude) return out + Path.ls = ls + class InOutPath(): def __init__(self, input_path:Path, output_path:Path): if isinstance(input_path, str): input_path = Path(input_path) @@ -113,19 +79,17 @@ def mkoutdir(self): def __repr__(self): return '\n'.join([f'{i}: {o}' for i, o in self.__dict__.items()]) + '\n' + def dcm2png(SZ, dataset): path = InOutPath(Path(dataset_path + f'/dicom-images-{dataset}'), Path(main_out_path + f'/data{SZ}/{dataset}')) files = path.inp.ls(recursive=True, include=['.dcm']) for f in progress_bar(files): dcm = pydicom.read_file(str(f)).pixel_array - # PIL.Image.fromarray(dcm).resize((SZ,SZ)).save(path.out/f'{f.stem}.png') - - # make a rgb like image with mosaic images: im = PIL.Image.fromarray(dcm).resize((SZ,SZ)) - im = create_mosaic(im) im.save(path.out/f'{f.stem}.png') + def masks2png(SZ): path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) for i in progress_bar(list(set(rle_df.ImageId.values))): @@ -141,29 +105,29 @@ def masks2png(SZ): m = rle2mask([enc], 1024, 1024).astype(np.uint8) PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name}.png') else: - # m = np.array([rle2mask(e, 1024, 1024).astype(np.uint8) for e in enc.values]) m = rle2mask(enc.values, 1024, 1024).astype(np.uint8) - # m = m.sum(0).astype(np.uint8).T PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name.values[0]}.png') -rle_df = pd.read_csv(dataset_path + '/train-rle.csv') - -size_list = [128] # [64, 128, 256, 512, 1024] -for SZ in progress_bar(size_list): - print(f'Converting data for train{SZ}') - dcm2png(SZ, 'train') - print(f'Converting data for test{SZ}') - dcm2png(SZ, 'test') - print(f'Generating masks for size {SZ}') - masks2png(SZ) - -for SZ in progress_bar(size_list): - # Missing masks set to 0 - print('Generating missing masks as zeros') - train_images = [o.name for o in Path(main_out_path + f'/data{SZ}/train').ls(include=['.png'])] - train_masks = [o.name for o in Path(main_out_path + f'/data{SZ}/masks').ls(include=['.png'])] - missing_masks = set(train_images) - set(train_masks) - path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) - for name in progress_bar(missing_masks): - m = np.zeros((1024, 1024)).astype(np.uint8).T - PIL.Image.fromarray(m).resize((SZ,SZ)).save(main_out_path + f'/data{SZ}/masks/{name}') + + +if __name__ == '__main__': + rle_df = pd.read_csv(dataset_path + '/train-rle.csv') + + for SZ in progress_bar(out_size_list): + print(f'Converting data for train{SZ}') + dcm2png(SZ, 'train') + print(f'Converting data for test{SZ}') + dcm2png(SZ, 'test') + print(f'Generating masks for size {SZ}') + masks2png(SZ) + + for SZ in progress_bar(out_size_list): + # Missing masks set to 0 + print('Generating missing masks as zeros') + train_images = [o.name for o in Path(main_out_path + f'/data{SZ}/train').ls(include=['.png'])] + train_masks = [o.name for o in Path(main_out_path + f'/data{SZ}/masks').ls(include=['.png'])] + missing_masks = set(train_images) - set(train_masks) + path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) + for name in progress_bar(missing_masks): + m = np.zeros((1024, 1024)).astype(np.uint8).T + PIL.Image.fromarray(m).resize((SZ,SZ)).save(main_out_path + f'/data{SZ}/masks/{name}') diff --git a/fuse_examples/segmentation/siim/unet.py b/fuse_examples/segmentation/siim/unet.py index 225de96a8..3b41ba7f5 100644 --- a/fuse_examples/segmentation/siim/unet.py +++ b/fuse_examples/segmentation/siim/unet.py @@ -1,11 +1,15 @@ - -""" Full assembly of the parts to form the complete network """ - import torch import torch.nn as nn import torch.nn.functional as F +""" +implementation of Unet based on - +U-Net: Convolutional Networks for Biomedical Image Segmentation +https://arxiv.org/abs/1505.04597 +Code from - https://github.com/milesial/Pytorch-UNet +""" + class DoubleConv(nn.Module): """(convolution => [BN] => ReLU) * 2""" From 310726e6049e84f3dfd30390b204071d2455ac3b Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sun, 27 Mar 2022 00:01:05 +0300 Subject: [PATCH 06/42] remove old script and update main script according to PR comments (not complete) --- .../segmentation/Fuse_segmentation.py | 584 ------------------ fuse_examples/segmentation/siim/runner_seg.py | 41 +- 2 files changed, 20 insertions(+), 605 deletions(-) delete mode 100644 fuse_examples/segmentation/Fuse_segmentation.py diff --git a/fuse_examples/segmentation/Fuse_segmentation.py b/fuse_examples/segmentation/Fuse_segmentation.py deleted file mode 100644 index 9734a0ebf..000000000 --- a/fuse_examples/segmentation/Fuse_segmentation.py +++ /dev/null @@ -1,584 +0,0 @@ -import logging -import random -from pathlib import Path -from glob import glob -import matplotlib.pylab as plt -import os -import numpy as np -import pandas as pd -from skimage.io import imread - -import torch -from torch.utils.data.dataset import Dataset -from torch.utils.data import DataLoader - -torch.__version__ - -import sys -sys.path.append('Pytorch-UNet/') -sys.path.append('Pytorch-UNet/unet/') - -from unet import UNet - -# parameters -SZ = 512 -# TRAIN = f'siim/data_bin/data{SZ}/train/' -# TEST = f'siim/data_bin/data{SZ}/test/' -# MASKS = f'siim/data_bin/data{SZ}/masks/' -TRAIN = f'siim/data{SZ}/train/' -TEST = f'siim/data{SZ}/test/' -MASKS = f'siim/data{SZ}/masks/' - - -def perform_softmax(output): - if isinstance(output, torch.Tensor): # validation - logits = output - else: # train - logits = output.logits - cls_preds = F.softmax(logits, dim=1) - return logits, cls_preds - - -def mask_size(fn): - sz = [] - for f in fn: - im = imread(f) - sz.append(np.array(im>0).sum()) - # if im.sum() > 0: - # plt.figure() - # plt.imshow(im) - # plt.show() - return sz - - -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.utils.utils_logger import fuse_logger_start - -# imports for training -from fuse.models.model_wrapper import FuseModelWrapper -from fuse.losses.loss_default import FuseLossDefault -from fuse.losses.segmentation.loss_dice import BinaryDiceLoss, DiceBCELoss -from fuse.losses.segmentation.loss_dice import FuseDiceLoss - -# imports for validation/inference/performance -from fuse.metrics.classification.metric_accuracy import FuseMetricAccuracy -from fuse.metrics.classification.metric_roc_curve import FuseMetricROCCurve -from fuse.metrics.classification.metric_auc import FuseMetricAUC -from fuse.analyzer.analyzer_default import FuseAnalyzerDefault -from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel -from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame - -import torch.nn.functional as F -import torch.optim as optim -from fuse.managers.manager_default import FuseManagerDefault -from fuse.utils.utils_gpu import FuseUtilsGPU - -from data_source_segmentation import FuseDataSourceSeg -from seg_input_processor import SegInputProcessor - -# TODO: Path to save model -ROOT = '' -# TODO: path to store the data (?? what data? after download?) -ROOT_DATA = ROOT -# TODO: Name of the experiment -EXPERIMENT = 'unet_seg_results' -# TODO: Path to cache data -CACHE_PATH = '' -# TODO: Name of the cached data folder -EXPERIMENT_CACHE = 'exp_cache' - -PATHS = {'data_dir': [TRAIN, MASKS, TEST], - 'model_dir': os.path.join(ROOT, EXPERIMENT, 'model_dir'), - 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message. - 'cache_dir': os.path.join(CACHE_PATH, EXPERIMENT_CACHE+'_cache_dir'), - 'inference_dir': os.path.join(ROOT, EXPERIMENT, 'infer_dir'), - 'analyze_dir': os.path.join(ROOT, EXPERIMENT, 'analyze_dir')} - -# # augmentations from skin-fuse-code - -# TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ -# [ -# ('data.input.input_0',), -# aug_op_affine, -# {'rotate': Uniform(-180.0, 180.0), 'translate': (RandInt(-50, 50), RandInt(-50, 50)), -# 'flip': (RandBool(0.3), RandBool(0.3)), 'scale': Uniform(0.9, 1.1)}, -# {'apply': RandBool(0.9)} -# ], -# [ -# ('data.input.input_0',), -# aug_op_color, -# {'add': Uniform(-0.06, 0.06), 'mul': Uniform(0.95, 1.05), 'gamma': Uniform(0.9, 1.1), -# 'contrast': Uniform(0.85, 1.15)}, -# {'apply': RandBool(0.7)} -# ], -# [ -# ('data.input.input_0',), -# aug_op_gaussian, -# {'std': 0.03}, -# {'apply': RandBool(0.7)} -# ], -# ] - -########################################## -# Train Common Params -########################################## -# ============ -# Data -# ============ -TRAIN_COMMON_PARAMS = {} -TRAIN_COMMON_PARAMS['data.batch_size'] = 32 -TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 -TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 -TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ - # TODO: define the augmentation pipeline here - # Fuse TIP: Use as a reference the simple augmentation pipeline written in Fuse.data.augmentor.augmentor_toolbox.aug_image_default_pipeline - [ - ('data.input.input_0','data.gt.gt_global'), - aug_op_affine_group, - {'rotate': Uniform(-20.0, 20.0), # Uniform(-20.0, 20.0), - 'flip': (RandBool(0.0), RandBool(0.5)), # (RandBool(1.0), RandBool(0.5)), - 'scale': Uniform(0.9, 1.1), - 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, - {'apply': RandBool(0.9)} - ], - [ - ('data.input.input_0','data.gt.gt_global'), - aug_op_elastic_transform, - {}, - {'apply': RandBool(0.7)} - ], - [ - ('data.input.input_0',), - aug_op_color, - { - 'add': Uniform(-0.06, 0.06), - 'mul': Uniform(0.95, 1.05), - 'gamma': Uniform(0.9, 1.1), - 'contrast': Uniform(0.85, 1.15) - }, - {'apply': RandBool(0.7)} - ], - [ - ('data.input.input_0',), - aug_op_gaussian, - {'std': 0.05}, - {'apply': RandBool(0.7)} - ], -] -# =============== -# Manager - Train1 -# =============== -TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 200, - - 'virtual_batch_size': 1, # number of batches in one virtual batch - 'start_saving_epochs': 10, # first epoch to start saving checkpoints from - 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint -} -TRAIN_COMMON_PARAMS['manager.best_epoch_source'] = { - 'source': 'losses.total_loss', # can be any key from 'epoch_results' (either metrics or losses result) - 'optimization': 'min', # can be either min/max - 'on_equal_values': 'better', ## ?? why is it important?? - # can be either better/worse - whether to consider best epoch when values are equal -} -TRAIN_COMMON_PARAMS['manager.learning_rate'] = 1e-1 -TRAIN_COMMON_PARAMS['manager.weight_decay'] = 1e-4 # 0.001 -TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint -## Give a default checkpoint name? load a default checkpoint? - -# allocate gpus -NUM_GPUS = 4 -if NUM_GPUS == 0: - TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' - -TRAIN_COMMON_PARAMS['partition_file'] = 'train_val_split.pickle' -TRAIN_COMMON_PARAMS['manager.train'] = False # if not None, will try to load the checkpoint -# ================================================================== -def vis_batch(sample, num_rows=3): - img_names = sample['data']['descriptor'][0] - mask_names = sample['data']['descriptor'][1] - - img = sample['data']['input']['input_0'] - mask = sample['data']['gt']['gt_global'] - - n = img.shape[0] - num_col = n // num_rows + 1 - fig, ax = plt.subplots(num_rows, num_col, figsize=(14, 3*num_rows)) - ax = ax.ravel() - for i in range(n): - im = img[i].squeeze() - msk = mask[i].squeeze() - - if im.shape[0] == 3: - im = im.permute((1,2,0)) # im is a tensor - - ax[i].imshow(im,cmap='bone') - ax[i].imshow(msk,alpha=0.5,cmap='Reds') - # ax[i, 1].imshow(msk) - - -def main(paths: dict, train_common_params: dict, train=True, infer=True): - - # uncomment if you want to use specific gpus instead of automatically looking for free ones - force_gpus = None # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) - - train_common_params['manager.train'] = train # if not None, will try to load the checkpoint - train_common_params['manager.infer'] = infer - - train_path = paths['data_dir'][0] - mask_path = paths['data_dir'][1] - test_path =paths['data_dir'][2] - - train_fn = glob(train_path + '/*') - train_fn.sort() - - masks_fn = glob(mask_path + '/*') - masks_fn.sort() - - m_size = mask_size(masks_fn) - size_inx = np.argsort(m_size) - - # train_fn = np.array(train_fn)[size_inx[-200:]] - # masks_fn = np.array(masks_fn)[size_inx[-200:]] - - fuse_logger_start(output_path=paths['model_dir'], console_verbose_level=logging.INFO) - - # fn = list(zip(train_fn, masks_fn)) - - # # split to train-validation - - # VAL_SPLIT = 0.2 # frac of validation set - # n_train = int(len(fn) * (1-VAL_SPLIT)) - - # # random shuffle the file-list - # # random.shuffle(fn) - # train_fn = fn[:n_train] - # train_size = m_size[:n_train] - # val_fn = fn[n_train:] - - # size_inx = np.argsort(train_size) - # train_fn = np.array(train_fn)[size_inx[-3000:]].tolist() - # train_fn = [tuple(tr) for tr in train_fn] - - # # filter only train samples with positive mask - # train_fn = np.array(train_fn)[np.array(train_size) > 0].tolist() - # train_fn = [tuple(tr) for tr in train_fn] - - train_data_source = FuseDataSourceSeg(image_source=train_path, - mask_source=mask_path, - partition_file=train_common_params['partition_file'], - train=True) - # train_data_source = FuseDataSourceSeg(train_fn) - print(train_data_source.summary()) - - ## Create data processors: - input_processors = { - 'input_0': SegInputProcessor(name='image') - } - gt_processors = { - 'gt_global': SegInputProcessor(name='mask') - } - - ## Create data augmentation (optional) - augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) - - # Create visualizer (optional) - visualiser = FuseVisualizerDefault(image_name='data.input.input_0', - mask_name='data.gt.gt_global', - pred_name='model.logits.classification') - - train_dataset = FuseDatasetDefault(cache_dest=None, - data_source=train_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - augmentor=augmentor, - visualizer=visualiser) - train_dataset.create() - - # debug_size = [] - # for data in train_dataset: - # img = data['data']['input']['input_0'].numpy().squeeze() - # mask = data['data']['gt']['gt_global'].numpy().squeeze() - # # if mask.sum() > 0: - # debug_size.append(mask.sum()) - - # ================================================================== - # Validation dataset - valid_data_source = FuseDataSourceSeg(image_source=train_path, - mask_source=mask_path, - partition_file=train_common_params['partition_file'], - train=False) - print(valid_data_source.summary()) - # valid_data_source = FuseDataSourceSeg(val_fn) - # valid_data_source.summary() - - valid_dataset = FuseDatasetDefault(cache_dest=None, - data_source=valid_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - visualizer=visualiser) - valid_dataset.create() - - ## Create sampler - # sampler = FuseSamplerBalancedBatch(dataset=train_dataset, - # balanced_class_name='data.gt.gt_global.tensor', - # num_balanced_classes=2, - # batch_size=train_common_params['data.batch_size']) - - - ## Create dataloader - train_dataloader = DataLoader(dataset=train_dataset, - shuffle=True, - drop_last=False, - batch_size=train_common_params['data.batch_size'], - collate_fn=train_dataset.collate_fn, - num_workers=train_common_params['data.train_num_workers']) - # batch_sampler=sampler, collate_fn=train_dataset.collate_fn, - # num_workers=train_common_params['data.train_num_workers']) - - ## Create dataloader - validation_dataloader = DataLoader(dataset=valid_dataset, - shuffle=False, - drop_last=False, - batch_size=train_common_params['data.batch_size'], - collate_fn=train_dataset.collate_fn, - num_workers=train_common_params['data.validation_num_workers']) - - if False: - # train_dataset.visualize(10) - - inx = 10 #2405 - data = train_dataset.get(inx) - img = data['data']['input']['input_0'].numpy().squeeze() - mask = data['data']['gt']['gt_global'].numpy().squeeze() - - data = train_dataset.getitem_without_augmentation(inx) - img_aug = data['data']['input']['input_0'].numpy().squeeze() - mask_aug = data['data']['gt']['gt_global'].numpy().squeeze() - - if img.shape[0] == 3: - img = img.transpose((1,2,0)) - img_aug = img_aug.transpose((1,2,0)) - - fig, axs = plt.subplots(1,2, figsize=(14,7)) - axs[0].imshow(img, plt.cm.bone) - axs[0].imshow(1-mask, 'hot', alpha=0.4) - axs[1].imshow(img_aug, plt.cm.bone) - axs[1].imshow(1-mask_aug, 'hot', alpha=0.4) - # axs[1].imshow(mask, interpolation=None) - plt.show() - - print('Num of positive pixels - ', mask.sum()) - - i = 0 - for batch in train_dataloader: - vis_batch(batch) - i += 1 - if i > 10: - break - plt.show() - # ================================================================== - - # # Training graph - torch_model = UNet(n_channels=1, n_classes=1, bilinear=False) - net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True) - - import ipdb; ipdb.set_trace(context=7) # BREAKPOINT - model = FuseModelWrapper(model=torch_model, - model_inputs=['data.input.input_0'], - post_forward_processing_function=perform_softmax, - model_outputs=['logits.classification', 'output.classification'] - ) - - # # take one batch: - # batch = next(iter(train_dataloader)) - # img = batch['data']['input']['input_0'] - # img.shape - - # pred_mask = torch_model(img) - # pred_mask.shape - - # ==================================================================================== - # Loss - # ==================================================================================== - # dice_loss = BinaryDiceLoss() - dice_loss = DiceBCELoss() - # losses = { - # 'dice_loss': FuseDiceLoss(pred_name='model.logits.classification', - # target_name='data.gt.gt_global') - # } - losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.classification', - target_name='data.gt.gt_global', - callable=dice_loss, - weight=1.0) - } - - model = model.cuda() - # create optimizer - # optimizer = optim.AdamW(model.parameters(), - # lr=train_common_params['manager.learning_rate'], - # weight_decay=train_common_params['manager.weight_decay']) - optimizer = optim.SGD(model.parameters(), - lr=train_common_params['manager.learning_rate'], - momentum=0.9, - weight_decay=train_common_params['manager.weight_decay']) - - scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5) - - # train from scratch - if train_common_params['manager.train']: - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], - force_reset=paths['force_reset_model_dir']) - else: - manager = FuseManagerDefault() - - # ===================================================================================== - # Callbacks - # ===================================================================================== - callbacks = [ - # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler - ] - - # Providing the objects required for the training process. - manager.set_objects(net=model, - optimizer=optimizer, - losses=losses, - lr_scheduler=scheduler, - callbacks=callbacks, - best_epoch_source=train_common_params['manager.best_epoch_source'], - train_params=train_common_params['manager.train_params'], - output_model_dir=paths['model_dir']) - - - # Start training - if train_common_params['manager.train']: - manager.train(train_dataloader=train_dataloader, - validation_dataloader=validation_dataloader) - - # plot the training process: - csv_file = os.path.join(paths['model_dir'], 'metrics.csv') - metrics = pd.read_csv(csv_file) - metrics.drop(index=metrics.index[0], axis=0, inplace=True) # remove the 1st validation run - - epochs = metrics[metrics['mode'] == 'validation']['epoch'] - loss_key = 'losses.' + list(losses.keys())[0] - val_loss = metrics[metrics['mode'] == 'validation'][loss_key] - train_loss = metrics[metrics['mode'] == 'train'][loss_key] - - plt.figure() - plt.plot(epochs, val_loss, '.-', label='validation') - plt.plot(epochs, train_loss, '.-', label='train') - plt.legend() - plt.title('train and validation loss') - plt.xlabel('Epochs') - plt.ylabel('loss') - plt.savefig(os.path.join(paths['model_dir'], 'train_progress.png')) - plt.close() - - ################################################################################ - # Inference - ################################################################################ - - if train_common_params['manager.infer']: - ###################################### - # Inference Common Params - ###################################### - INFER_COMMON_PARAMS = {} - INFER_COMMON_PARAMS['infer_filename'] = os.path.join(PATHS['inference_dir'], 'validation_set_infer.gz') - INFER_COMMON_PARAMS['checkpoint'] = 'best' #'best' # Fuse TIP: possible values are 'best', 'last' or epoch_index. - output_columns = ['model.logits.classification', 'data.gt.gt_global'] - infer_common_params = INFER_COMMON_PARAMS - - manager.load_checkpoint(infer_common_params['checkpoint'], - model_dir=paths['model_dir']) - print('Skip training ...') - - manager.infer(data_loader=validation_dataloader, - input_model_dir=paths['model_dir'], - output_columns=output_columns, - output_file_name=infer_common_params['infer_filename']) #, - # num_workers=0) - - # visualize the predictions - infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) - descriptors_list = infer_processor.get_samples_descriptors() - out_name = 'model.logits.classification' - gt_name = 'data.gt.gt_global' - for desc in descriptors_list[:10]: - data = infer_processor(desc) - pred = np.squeeze(data[out_name]) - gt = np.squeeze(data[gt_name]) - _, ax = plt.subplots(1,2) - ax[0].imshow(pred) - ax[0].set_title('prediction') - ax[1].imshow(gt) - ax[1].set_title('gt') - plt.show() - - ###################################### - # Analyze Common Params - ###################################### - ANALYZE_COMMON_PARAMS = {} - ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] - ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['analyze_dir'], 'all_metrics') - analyze_common_params = ANALYZE_COMMON_PARAMS - - # metrics - metrics = { - # 'accuracy': FuseMetricAccuracy(pred_name='model.logits.classification', target_name='data.gt.gt_global'), - # 'roc': FuseMetricROCCurve(pred_name='model.logits.classification', target_name='data.gt.gt_global', output_filename='roc_curve.png'), - # 'auc': FuseMetricAUC(pred_name='model.logits.classification', target_name='data.gt.gt_global') - 'auc': FuseMetricAUCPerPixel(pred_name='model.logits.classification', - target_name='data.gt.gt_global', - output_filename='roc_curve.png'), - 'seg': FuseMetricScoreMap(pred_name='model.logits.classification', - target_name='data.gt.gt_global', - hard_threshold=True, threshold=0.5) - } - - # manager.visualize(visualizer=visualiser, - # data_loader=validation_dataloader, device='cpu') - # descriptors=, - # display_func=, - # infer_processor=None) - - # create analyzer - analyzer = FuseAnalyzerDefault() - - # run - # FIXME: simplify analyze interface for this case - analyzer.analyze(gt_processors=gt_processors, - data_pickle_filename=analyze_common_params['infer_filename'], - metrics=metrics, - print_results=True, - output_filename=analyze_common_params['output_filename'], - num_workers=0) - - -if __name__ == '__main__': - import argparse - - my_parser = argparse.ArgumentParser() - my_parser.add_argument('--train', action='store_true') - my_parser.add_argument('--infer', action='store_true') - args = my_parser.parse_args() - - print(vars(args)) - params = vars(args) - main(PATHS, TRAIN_COMMON_PARAMS, train=params['train'], infer=params['infer']) diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index 5a216fd5b..83915a3f2 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -65,11 +65,9 @@ from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame -# from fuse.metrics.metric_auc_per_pixel import FuseMetricAUCPerPixel -# from fuse.metrics.segmentation.metric_score_map import FuseMetricScoreMap -# from fuse.analyzer.analyzer_default import FuseAnalyzerDefault from fuse.eval.evaluator import EvaluatorDefault from fuse.eval.metrics.segmentation.metrics_segmentation_common import MetricDice, MetricIouJaccard, MetricOverlap, Metric2DHausdorff, MetricPixelAccuracy +from fuse.utils.utils_debug import FuseUtilsDebug from data_source_segmentation import FuseDataSourceSeg from seg_input_processor import SegInputProcessor @@ -77,19 +75,19 @@ from unet import UNet -def perform_softmax(output): - if isinstance(output, torch.Tensor): # validation - logits = output - else: # train - logits = output.logits - cls_preds = F.softmax(logits, dim=1) - return logits, cls_preds - +########################################## +# Debug modes +########################################## +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug +debug = FuseUtilsDebug(mode) +########################################## +# Output and data Paths +########################################## SZ = 512 -TRAIN = f'../../data/siim/data{SZ}/train/' -TEST = f'../../data/siim/data{SZ}/test/' -MASKS = f'../../data/siim/data{SZ}/masks/' +TRAIN = f'../siim_data/data{SZ}/train/' +TEST = f'../siim_data/data{SZ}/test/' +MASKS = f'../siim_data/data{SZ}/masks/' # TODO: Path to save model ROOT = '../results/' @@ -158,7 +156,7 @@ def perform_softmax(output): # Manager - Train1 # =============== TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 20, + 'num_epochs': 50, 'virtual_batch_size': 1, # number of batches in one virtual batch 'start_saving_epochs': 10, # first epoch to start saving checkpoints from 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint @@ -273,8 +271,7 @@ def run_train(paths: dict, train_common_params: dict): model = FuseModelWrapper(model=torch_model, model_inputs=['data.input.input_0'], - post_forward_processing_function=perform_softmax, - model_outputs=['logits.classification', 'output.classification'] + model_outputs=['logits.classification'] ) lgr.info('Model: Done', {'attrs': 'bold'}) @@ -436,7 +433,7 @@ def run_analyze(paths: dict, analyze_common_params: dict): def data_iter(): data = pd.read_pickle(analyze_common_params['infer_filename']) n_samples = data.shape[0] - threshold = 0.5 + threshold = 1e-7 #0.5 for inx in range(n_samples): row = data.loc[inx] sample_dict = {} @@ -446,8 +443,10 @@ def data_iter(): yield sample_dict metrics = OrderedDict([ - ("dice", MetricDice(pred='pred.array', - target='label.array')), + ("dice", MetricDice(pred='pred.array', target='label.array')), + ("IOU", MetricIouJaccard(pred='pred.array', target='label.array')), + ("Overlap", MetricOverlap(pred='pred.array', target='label.array')), + ("PixelAcc", MetricPixelAccuracy(pred='pred.array', target='label.array')), ]) # create analyzer @@ -472,7 +471,7 @@ def data_iter(): FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'analyze'] # Options: 'train', 'infer', 'analyze' - RUNNING_MODES = ['analyze'] # Options: 'train', 'infer', 'analyze' + # RUNNING_MODES = ['analyze'] # Options: 'train', 'infer', 'analyze' # train if 'train' in RUNNING_MODES: From 76f834d44a44ec566862116f4bf9a93417bbbea9 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sun, 27 Mar 2022 00:12:21 +0300 Subject: [PATCH 07/42] change names to eval* --- fuse_examples/segmentation/siim/runner_seg.py | 23 ++----------------- 1 file changed, 2 insertions(+), 21 deletions(-) diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index 83915a3f2..eaa8ccdb4 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -15,22 +15,6 @@ Created on June 30, 2021 -""" - -""" - -(C) Copyright 2021 IBM Corp. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -Created on June 30, 2021 - """ import os import logging @@ -105,7 +89,7 @@ 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message. 'cache_dir': os.path.join(CACHE_PATH, EXPERIMENT_CACHE+'_cache_dir'), 'inference_dir': os.path.join(ROOT, EXPERIMENT, 'infer_dir'), - 'analyze_dir': os.path.join(ROOT, EXPERIMENT, 'analyze_dir')} + 'eval_dir': os.path.join(ROOT, EXPERIMENT, 'eval_dir')} ########################################## # Train Common Params @@ -180,9 +164,6 @@ def run_train(paths: dict, train_common_params: dict): fuse_logger_start(output_path=paths['model_dir'], console_verbose_level=logging.INFO) lgr = logging.getLogger('Fuse') - # Download data - # TODO - function to download + arrange the data - lgr.info('\nFuse Train', {'attrs': ['bold', 'underline']}) lgr.info(f'model_dir={paths["model_dir"]}', {'color': 'magenta'}) @@ -417,7 +398,7 @@ def run_infer(paths: dict, infer_common_params: dict): ###################################### ANALYZE_COMMON_PARAMS = {} ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] -ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['analyze_dir'], 'all_metrics.txt') +ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['eval_dir'], 'all_metrics.txt') ANALYZE_COMMON_PARAMS['num_workers'] = 4 ANALYZE_COMMON_PARAMS['batch_size'] = 8 From 13aa1da52c475ae9f1a149058dbe83d45c637012 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sun, 27 Mar 2022 20:14:47 +0300 Subject: [PATCH 08/42] Changes following the comments on the PR --- fuse/data/augmentor/augmentor_toolbox.py | 39 ++++-- fuse/losses/segmentation/loss_dice.py | 13 +- fuse_examples/segmentation/siim/README.md | 1 + .../siim/data_source_segmentation.py | 25 ---- fuse_examples/segmentation/siim/runner_seg.py | 51 ++++---- .../segmentation/siim/seg_input_processor.py | 112 ++---------------- requirements.txt | 1 + 7 files changed, 72 insertions(+), 170 deletions(-) create mode 100644 fuse_examples/segmentation/siim/README.md diff --git a/fuse/data/augmentor/augmentor_toolbox.py b/fuse/data/augmentor/augmentor_toolbox.py index 047cfbf75..5aa15c350 100644 --- a/fuse/data/augmentor/augmentor_toolbox.py +++ b/fuse/data/augmentor/augmentor_toolbox.py @@ -18,7 +18,7 @@ """ from copy import deepcopy -from typing import Tuple, Any, List, Iterable, Optional +from typing import Tuple, Any, List, Iterable, Optional, Union import numpy import torch @@ -264,19 +264,23 @@ def aug_op_gaussian(aug_input: Tensor, mean: float = 0.0, std: float = 0.03, cha return aug_tensor -def aug_op_elastic_transform(aug_input: Tensor, alpha: float = 1, sigma: float = 50, channels: Optional[List[int]] = None): +def aug_op_elastic_transform(aug_input: Tuple[Tensor], + sigma: float = 50, + num_points: int = 3): """Elastic deformation of images as described in [Simard2003]_. .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", - :param aug_input: input tensor of shape (C,Y,X) - :param alpha: global pixel shifting (correlated to the article) + :param aug_input: list of tensors of shape (C,Y,X) :param sigma: Gaussian filter parameter - :param channels: which channels to apply the augmentation + :param num_points: define the resolution of the deformation gris + see https://github.com/gvtulder/elasticdeform for more info. :return distorted image """ # convert back to torch tensor aug_input = [numpy.array(t) for t in aug_input] - aug_input_d = ed.deform_random_grid(aug_input, sigma=7, points=3, axis=[(1, 2), (1,2)]) + # for a (ch X Rows X cols) image - deform the 2 last axis + axis = [(1,2) for _ in range(len(aug_input))] + aug_input_d = ed.deform_random_grid(aug_input, sigma=sigma, points=num_points, axis=axis) aug_output = [torch.from_numpy(t) for t in aug_input_d] @@ -454,7 +458,7 @@ def aug_op_batch_mix_up(aug_input: Tuple[Tensor, Tensor], factor: float) -> Tupl def aug_op_random_crop_and_resize(aug_input: Tensor, - out_size, + out_size: Union[int, Tuple[int, int], Tuple[int, int, int]], crop_size: float = 1.0, # or optional - Tuple[float, float] x_off: float = 1.0, y_off: float = 1.0, @@ -463,8 +467,8 @@ def aug_op_random_crop_and_resize(aug_input: Tensor, random crop a (3d) tensor and resize it to a given size :param crop_size: float <= 1.0 - the fraction to crop from the original tensor for each dim :param x_off: float <= 1.0 - the x-offset to take - :param y_off: - :param z_off: + :param y_off: float <= 1.0 - the y-offset to take + :param z_off: float <= 1.0 - the z-offset to take :param out_size: the size of the output tensor :return: the output tensor """ @@ -486,4 +490,21 @@ def aug_op_random_crop_and_resize(aug_input: Tensor, aug_tensor = F.interpolate(aug_tensor, out_size) + elif len(aug_input.shape) == 3: + ch, y, x = in_shape + + x_width = int(crop_size * x) + x_off = int(x_off * (x - x_width)) + + y_width = int(crop_size * y) + y_off = int(y_off * (y - y_width)) + + aug_tensor = aug_input[:, y_off:y_off+y_width, x_off:x_off+x_width] + + aug_tensor = F.interpolate(aug_tensor, out_size) + + # else: + + + return aug_tensor diff --git a/fuse/losses/segmentation/loss_dice.py b/fuse/losses/segmentation/loss_dice.py index 0df34de6a..beff59af5 100644 --- a/fuse/losses/segmentation/loss_dice.py +++ b/fuse/losses/segmentation/loss_dice.py @@ -66,7 +66,7 @@ def __call__(self, predict, target): target = target.contiguous().view(target.shape[0], -1) if target.dtype == torch.int64 or target.dtype == torch.int32: - target = target.type(torch.float32).to(target.device) + target = target.type(torch.float32) num = 2*torch.sum(torch.mul(predict, target), dim=1) + self.eps den = torch.sum(predict.pow(self.p) + target.pow(self.p), dim=1) + self.eps loss = 1 - num / den @@ -85,8 +85,8 @@ def __call__(self, predict, target): class DiceBCELoss(FuseLossBase): def __init__(self, - pred_name, - target_name, + pred_name: str = None, + target_name: str = None, filter_func: Optional[Callable]=None, class_weights=None, bce_weight: float=1.0, @@ -124,7 +124,7 @@ def __call__(self, batch_dict): predict = FuseUtilsHierarchicalDict.get(batch_dict, self.pred_name).float() target = FuseUtilsHierarchicalDict.get(batch_dict, self.target_name).long() - target = target.type(torch.float32).to(target.device) + target = target.type(torch.float32) total_loss = 0 n_classes = predict.shape[1] @@ -157,8 +157,9 @@ def __call__(self, batch_dict): class FuseDiceLoss(FuseLossBase): - def __init__(self, pred_name, - target_name, + def __init__(self, + pred_name: str = None, + target_name: str = None, filter_func: Optional[Callable] = None, class_weights=None, ignore_cls_index_list=None, diff --git a/fuse_examples/segmentation/siim/README.md b/fuse_examples/segmentation/siim/README.md new file mode 100644 index 000000000..7f5522da6 --- /dev/null +++ b/fuse_examples/segmentation/siim/README.md @@ -0,0 +1 @@ +# SIIM-ACR Pneumothorax Segmentation with Fute diff --git a/fuse_examples/segmentation/siim/data_source_segmentation.py b/fuse_examples/segmentation/siim/data_source_segmentation.py index 1ce555bad..48f9e4e01 100644 --- a/fuse_examples/segmentation/siim/data_source_segmentation.py +++ b/fuse_examples/segmentation/siim/data_source_segmentation.py @@ -69,9 +69,6 @@ def __init__(self, repartition = pickle.load(splits) sample_descs = repartition['val'] else: - # TODO - this option is not clear - if the partition file is not give? do we train - # with all the data? or just dont save the partition? (than we will not be able - # to re-run the experiment ... for sample_id in input_df.iloc[:, 0]: sample_descs.append(sample_id) @@ -79,31 +76,9 @@ def __init__(self, self.input_source = [image_source, mask_source] - # prev version - # self.samples = input_source - - # @staticmethod - # def filter_by_conditions(samples: pd.DataFrame, conditions: Optional[List[Dict[str, List]]]): - # """ - # Returns a vector of the samples that passed the conditions - # :param samples: dataframe to check. expected to have at least sample_desc column. - # :param conditions: list of dictionaries. each dictionary has column name as keys and possible values as the values. - # for each dict in the list: - # the keys are applied with AND between them. - # the dict conditions are applied with OR between them. - # :return: boolean vector with the filtered samples - # """ - # to_keep = samples.sample_desc.isna() # start with all false - # for condition_list in conditions: - # condition_to_keep = samples.sample_desc.notna() # start with all true - # for column, values in condition_list.items(): - # condition_to_keep = condition_to_keep & samples[column].isin(values) # all conditions in list must be met - # to_keep = to_keep | condition_to_keep # add this condition samples to_keep - # return to_keep def get_samples_description(self): return self.samples - # return list(self.samples_df['sample_desc']) def summary(self) -> str: summary_str = '' diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index eaa8ccdb4..fa8e33565 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -114,7 +114,8 @@ [ ('data.input.input_0','data.gt.gt_global'), aug_op_elastic_transform, - {}, + {'sigma': 7, + 'num_points': 3}, {'apply': RandBool(0.7)} ], [ @@ -195,7 +196,7 @@ def run_train(paths: dict, train_common_params: dict): # Create visualizer (optional) visualiser = FuseVisualizerDefault(image_name='data.input.input_0', mask_name='data.gt.gt_global', - pred_name='model.logits.classification') + pred_name='model.logits.segmentation') train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], data_source=train_data_source, @@ -252,7 +253,7 @@ def run_train(paths: dict, train_common_params: dict): model = FuseModelWrapper(model=torch_model, model_inputs=['data.input.input_0'], - model_outputs=['logits.classification'] + model_outputs=['logits.segmentation'] ) lgr.info('Model: Done', {'attrs': 'bold'}) @@ -260,11 +261,11 @@ def run_train(paths: dict, train_common_params: dict): # Loss # ==================================================================================== losses = { - 'dice_loss': DiceBCELoss(pred_name='model.logits.classification', + 'dice_loss': DiceBCELoss(pred_name='model.logits.segmentation', target_name='data.gt.gt_global') } - model = model.cuda() + # model = model.cuda() optimizer = optim.SGD(model.parameters(), lr=train_common_params['manager.learning_rate'], momentum=0.9, @@ -344,7 +345,7 @@ def run_infer(paths: dict, infer_common_params: dict): # Create visualizer (optional) visualiser = FuseVisualizerDefault(image_name='data.input.input_0', mask_name='data.gt.gt_global', - pred_name='model.logits.classification') + pred_name='model.logits.segmentation') infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], data_source=infer_data_source, @@ -368,8 +369,8 @@ def run_infer(paths: dict, infer_common_params: dict): #### Manager for inference manager = FuseManagerDefault() - # extract just the global classification per sample and save to a file - output_columns = ['model.logits.classification', 'data.gt.gt_global'] + # extract just the global segmentation per sample and save to a file + output_columns = ['model.logits.segmentation', 'data.gt.gt_global'] manager.infer(data_loader=infer_dataloader, input_model_dir=paths['model_dir'], checkpoint=infer_common_params['checkpoint'], @@ -379,7 +380,7 @@ def run_infer(paths: dict, infer_common_params: dict): # visualize the predictions infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) descriptors_list = infer_processor.get_samples_descriptors() - out_name = 'model.logits.classification' + out_name = 'model.logits.segmentation' gt_name = 'data.gt.gt_global' for desc in descriptors_list[:10]: data = infer_processor(desc) @@ -394,32 +395,32 @@ def run_infer(paths: dict, infer_common_params: dict): plt.savefig(fn) ###################################### -# Analyze Common Params +# Evaluation Common Params ###################################### -ANALYZE_COMMON_PARAMS = {} -ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] -ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['eval_dir'], 'all_metrics.txt') -ANALYZE_COMMON_PARAMS['num_workers'] = 4 -ANALYZE_COMMON_PARAMS['batch_size'] = 8 +EVAL_COMMON_PARAMS = {} +EVAL_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] +EVAL_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['eval_dir'], 'all_metrics.txt') +EVAL_COMMON_PARAMS['num_workers'] = 4 +EVAL_COMMON_PARAMS['batch_size'] = 8 ###################################### # Analyze Template ###################################### -def run_analyze(paths: dict, analyze_common_params: dict): +def run_eval(paths: dict, eval_common_params: dict): fuse_logger_start(output_path=None, console_verbose_level=logging.INFO) lgr = logging.getLogger('Fuse') - lgr.info('Fuse Analyze', {'attrs': ['bold', 'underline']}) + lgr.info('Fuse eval', {'attrs': ['bold', 'underline']}) # define iterator def data_iter(): - data = pd.read_pickle(analyze_common_params['infer_filename']) + data = pd.read_pickle(eval_common_params['infer_filename']) n_samples = data.shape[0] threshold = 1e-7 #0.5 for inx in range(n_samples): row = data.loc[inx] sample_dict = {} sample_dict["id"] = row['id'] - sample_dict["pred.array"] = row['model.logits.classification'] > threshold + sample_dict["pred.array"] = row['model.logits.segmentation'] > threshold sample_dict["label.array"] = row['data.gt.gt_global'] yield sample_dict @@ -430,7 +431,7 @@ def data_iter(): ("PixelAcc", MetricPixelAccuracy(pred='pred.array', target='label.array')), ]) - # create analyzer + # create evaluator evaluator = EvaluatorDefault() results = evaluator.eval(ids=None, @@ -451,8 +452,8 @@ def data_iter(): force_gpus = None # [0] FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) - RUNNING_MODES = ['train', 'infer', 'analyze'] # Options: 'train', 'infer', 'analyze' - # RUNNING_MODES = ['analyze'] # Options: 'train', 'infer', 'analyze' + RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' + # RUNNING_MODES = ['eval'] # Options: 'train', 'infer', 'eval' # train if 'train' in RUNNING_MODES: @@ -462,7 +463,7 @@ def data_iter(): if 'infer' in RUNNING_MODES: run_infer(paths=PATHS, infer_common_params=INFER_COMMON_PARAMS) - # analyze - if 'analyze' in RUNNING_MODES: - run_analyze(paths=PATHS, analyze_common_params=ANALYZE_COMMON_PARAMS) + # eval + if 'eval' in RUNNING_MODES: + run_eval(paths=PATHS, eval_common_params=EVAL_COMMON_PARAMS) diff --git a/fuse_examples/segmentation/siim/seg_input_processor.py b/fuse_examples/segmentation/siim/seg_input_processor.py index 129207e10..cc33e235a 100644 --- a/fuse_examples/segmentation/siim/seg_input_processor.py +++ b/fuse_examples/segmentation/siim/seg_input_processor.py @@ -35,18 +35,13 @@ def __init__(self, resize_to: Optional[Tuple] = (299, 299), padding: Optional[Tuple] = (0, 0), ): - # """ - # Create Input processor - # :param input_data: path to images - # :param normalized_target_range: range for image normalization - # :param resize_to: Optional, new size of input images, keeping proportions - # :param padding: Optional, padding size - # """ - - # self.input_data = input_data - # self.normalized_target_range = normalized_target_range - # self.resize_to = np.subtract(resize_to, (2*padding[0], 2*padding[1])) - # self.padding = padding + """ + Create Input processor + :param input_data: path to images + :param normalized_target_range: range for image normalization + :param resize_to: Optional, new size of input images, keeping proportions + :param padding: Optional, padding size + """ self.name = name if self.name == 'image': self.im_inx = 0 @@ -63,69 +58,12 @@ def __call__(self, image_fn = image_fn[self.im_inx] image = imread(image_fn) - # ====================================================================== - # TODO - change type to float if input image and to int it mask image if self.name == 'image': image = image.astype('float32') image = image / 255.0 else: image = image > 0 image = image.astype('float32') - # ===================================================================== - - # img_path = self.input_data + str(inner_image_desc) + '.jpg' - - # # read image - # inner_image = skimage.io.imread(img_path) - - # # convert to numpy - # inner_image = np.asarray(inner_image) - - # # normalize - # inner_image = normalize_to_range(inner_image, range=self.normalized_target_range) - - # # resize - # inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - - # if self.resize_to is not None: - # if inner_image_height > self.resize_to[0]: - # h_ratio = self.resize_to[0] / inner_image_height - # else: - # h_ratio = 1 - # if inner_image_width > self.resize_to[1]: - # w_ratio = self.resize_to[1] / inner_image_width - # else: - # w_ratio = 1 - - # resize_ratio = min(h_ratio, w_ratio) - # if resize_ratio != 1: - # inner_image = skimage.transform.resize(inner_image, - # output_shape=(int(inner_image_height * resize_ratio), - # int(inner_image_width * resize_ratio)), - # mode='reflect', - # anti_aliasing=True - # ) - - # # padding - # if self.padding is not None: - # # "Pad" around inner image - # inner_image = inner_image.astype('float32') - - # inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - # inner_image[0:inner_image_height, 0] = 0 - # inner_image[0:inner_image_height, inner_image_width-1] = 0 - # inner_image[0, 0:inner_image_width] = 0 - # inner_image[inner_image_height-1, 0:inner_image_width] = 0 - - # if self.normalized_target_range is None: - # pad_value = 0 - # else: - # pad_value = self.normalized_target_range[0] - - # image = pad_image(inner_image, outer_height=self.resize_to[0] + 2*self.padding[0], outer_width=self.resize_to[1] + 2*self.padding[1], pad_value=pad_value) - - # else: - # image = inner_image # convert image from shape (H x W x C) to shape (C x H x W) with C=3 if len(image.shape) > 2: @@ -140,39 +78,3 @@ def __call__(self, return None return sample - - -def normalize_to_range(input_image: np.ndarray, range: Tuple = (-1.0, 1.0)): - """ - Scales tensor to range - @param input_image: image of shape (H x W x C) - @param range: bounds for normalization - @return: normalized image - """ - max_val = input_image.max() - min_val = input_image.min() - if min_val == max_val == 0: - return input_image - input_image = input_image - min_val - input_image = input_image / (max_val - min_val) - input_image = input_image * (range[1] - range[0]) - input_image = input_image + range[0] - return input_image - - -def pad_image(image: np.ndarray, outer_height: int, outer_width: int, pad_value: Tuple): - """ - Pastes input image in the middle of a larger one - @param image: image of shape (H x W x C) - @param outer_height: final outer height - @param outer_width: final outer width - @param pad_value: value for padding around inner image - @return: padded image - """ - inner_height, inner_width = image.shape[0], image.shape[1] - h_offset = int((outer_height - inner_height) / 2.0) - w_offset = int((outer_width - inner_width) / 2.0) - outer_image = np.ones((outer_height, outer_width, 3), dtype=image.dtype) * pad_value - outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width, :] = image - - return outer_image diff --git a/requirements.txt b/requirements.txt index eb1241323..58820bcdd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,3 +23,4 @@ nibabel pycocotools>=2.0.1 xmlrunner paramiko +elasticdeform From 4ad76f2f9721c9b8d12bb1d4e680c68b5c39a9ad Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Mon, 4 Apr 2022 16:11:17 +0300 Subject: [PATCH 09/42] remove commented code --- fuse_examples/segmentation/siim/runner_seg.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index fa8e33565..418125ecc 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -265,7 +265,6 @@ def run_train(paths: dict, train_common_params: dict): target_name='data.gt.gt_global') } - # model = model.cuda() optimizer = optim.SGD(model.parameters(), lr=train_common_params['manager.learning_rate'], momentum=0.9, @@ -453,7 +452,6 @@ def data_iter(): FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' - # RUNNING_MODES = ['eval'] # Options: 'train', 'infer', 'eval' # train if 'train' in RUNNING_MODES: From 5a4a6cf60d34cfffdefa6c64c387d076614161fd Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Sun, 10 Apr 2022 13:27:40 +0300 Subject: [PATCH 10/42] change input desc to file names and processor to compute mask images from rle encoding --- .../siim/data_source_segmentation.py | 76 +++++++++++++------ fuse_examples/segmentation/siim/runner_seg.py | 73 +++++++++++------- .../segmentation/siim/seg_input_processor.py | 73 ++++++++++++++---- 3 files changed, 156 insertions(+), 66 deletions(-) diff --git a/fuse_examples/segmentation/siim/data_source_segmentation.py b/fuse_examples/segmentation/siim/data_source_segmentation.py index 48f9e4e01..0554559b0 100644 --- a/fuse_examples/segmentation/siim/data_source_segmentation.py +++ b/fuse_examples/segmentation/siim/data_source_segmentation.py @@ -3,16 +3,34 @@ import random import pickle from typing import Sequence, Hashable, Union, Optional, List, Dict +from pathlib import Path + from fuse.data.data_source.data_source_base import FuseDataSourceBase from fuse.utils.utils_misc import autodetect_input_source +def filter_files(files, include=[], exclude=[]): + for incl in include: + files = [f for f in files if incl in f.name] + for excl in exclude: + files = [f for f in files if excl not in f.name] + return sorted(files) + + +def ls(x, recursive=False, include=[], exclude=[]): + if not recursive: + out = list(x.iterdir()) + else: + out = [o for o in x.glob('**/*')] + out = filter_files(out, include=include, exclude=exclude) + return out + + class FuseDataSourceSeg(FuseDataSourceBase): def __init__(self, - image_source: str, - mask_source: Optional[str] = None, + phase: str, # can be ['train', 'validation'] + data_folder: Optional[str] = None, partition_file: Optional[str] = None, - train: bool = True, val_split: float = 0.2, override_partition: bool = True, data_shuffle: bool = True @@ -28,54 +46,68 @@ def __init__(self, :param override_partition: specifies if the given partition file is filled with new train/val splits """ - # Extract entities # ---------------- if partition_file is not None: - if train: + if phase == 'train': if override_partition: - train_fn = glob(image_source + '/*') - train_fn.sort() - masks_fn = glob(mask_source + '/*') - masks_fn.sort() + # rle_df = pd.read_csv(data_source) + + Path.ls = ls + files = Path(data_folder).ls(recursive=True, include=['.dcm']) - fn = list(zip(train_fn, masks_fn)) + sample_descs = [str(fn) for fn in files] + # sample_descs = [] + # for fn in files: + # I = rle_df.ImageId == fn.stem + # desc = {'name': fn.stem, + # 'dcm': str(fn), + # 'rle_encoding': rle_df.loc[I, ' EncodedPixels'].values} + # sample_descs.append(desc) - if len(fn) == 0: + if len(sample_descs) == 0: raise Exception('Error detecting input source in FuseDataSourceDefault') if data_shuffle: # random shuffle the file-list - random.shuffle(fn) + random.shuffle(sample_descs) # split to train-validation - - n_train = int(len(fn) * (1-val_split)) + n_train = int(len(sample_descs) * (1-val_split)) - train_fn = fn[:n_train] - val_fn = fn[n_train:] - splits = {'train': train_fn, 'val': val_fn} + train_samples = sample_descs[:n_train] + val_samples = sample_descs[n_train:] + splits = {'train': train_samples, 'val': val_samples} with open(partition_file, "wb") as pickle_out: pickle.dump(splits, pickle_out) - sample_descs = train_fn + sample_descs = train_samples else: # read from a previous train/test split to evaluate on the same partition with open(partition_file, "rb") as splits: repartition = pickle.load(splits) sample_descs = repartition['train'] - else: + elif phase == 'validation': with open(partition_file, "rb") as splits: repartition = pickle.load(splits) sample_descs = repartition['val'] else: - for sample_id in input_df.iloc[:, 0]: - sample_descs.append(sample_id) + rle_df = pd.read_csv(data_source) - self.samples = sample_descs + Path.ls = ls + files = Path(data_folder).ls(recursive=True, include=['.dcm']) - self.input_source = [image_source, mask_source] + sample_descs = [str(fn) for fn in files] + # sample_descs = [] + # for fn in files: + # I = rle_df.ImageId == fn.stem + # desc = {'name': rle_df.loc[I, 'ImageId'].values[0], + # 'dcm': fn, + # 'rle_encoding': rle_df.loc[I, ' EncodedPixels'].values} + # sample_descs.append(desc) + self.samples = sample_descs def get_samples_description(self): return self.samples diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index 418125ecc..42c9f73ac 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -62,21 +62,20 @@ ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug +mode = 'debug' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug debug = FuseUtilsDebug(mode) ########################################## # Output and data Paths ########################################## -SZ = 512 -TRAIN = f'../siim_data/data{SZ}/train/' -TEST = f'../siim_data/data{SZ}/test/' -MASKS = f'../siim_data/data{SZ}/masks/' -# TODO: Path to save model +# # TODO: path to save model ROOT = '../results/' -# TODO: path to store the data -ROOT_DATA = ROOT + +# TODO: path for siim data +# Download instructions can be found in README +DATA_ROOT = '../siim/' + # TODO: Name of the experiment EXPERIMENT = 'unet_seg_results' # TODO: Path to cache data @@ -84,7 +83,10 @@ # TODO: Name of the cached data folder EXPERIMENT_CACHE = 'exp_cache' -PATHS = {'data_dir': [TRAIN, MASKS, TEST], +PATHS = {#'data_dir': [TRAIN, MASKS, TEST], + 'train_rle_file': os.path.join(DATA_ROOT, 'train-rle.csv'), + 'train_folder': os.path.join(DATA_ROOT, 'dicom-images-train'), + 'test_folder': os.path.join(DATA_ROOT, 'dicom-images-test'), 'model_dir': os.path.join(ROOT, EXPERIMENT, 'model_dir'), 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message. 'cache_dir': os.path.join(CACHE_PATH, EXPERIMENT_CACHE+'_cache_dir'), @@ -98,6 +100,7 @@ # Data # ============ TRAIN_COMMON_PARAMS = {} +TRAIN_COMMON_PARAMS['data.image_size'] = 512 TRAIN_COMMON_PARAMS['data.batch_size'] = 8 TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 @@ -170,24 +173,30 @@ def run_train(paths: dict, train_common_params: dict): lgr.info(f'model_dir={paths["model_dir"]}', {'color': 'magenta'}) lgr.info(f'cache_dir={paths["cache_dir"]}', {'color': 'magenta'}) - train_path = paths['data_dir'][0] - mask_path = paths['data_dir'][1] + # train_path = paths['data_dir'][0] + # mask_path = paths['data_dir'][1] #### Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) - train_data_source = FuseDataSourceSeg(image_source=train_path, - mask_source=mask_path, - partition_file=train_common_params['partition_file'], - train=True) + train_data_source = FuseDataSourceSeg(phase='train', + data_folder=paths['train_folder'], + partition_file=train_common_params['partition_file']) + + # train_data_source = FuseDataSourceSeg(image_source=train_path, + # mask_source=mask_path, + # train=True) print(train_data_source.summary()) ## Create data processors: input_processors = { - 'input_0': SegInputProcessor(name='image') + 'input_0': SegInputProcessor(name='image', + size=train_common_params['data.image_size']) } gt_processors = { - 'gt_global': SegInputProcessor(name='mask') + 'gt_global': SegInputProcessor(name='mask', + data_csv=paths['train_rle_file'], + size=train_common_params['data.image_size']) } ## Create data augmentation (optional) @@ -221,10 +230,13 @@ def run_train(paths: dict, train_common_params: dict): # Validation dataset lgr.info(f'Validation Data:', {'attrs': 'bold'}) - valid_data_source = FuseDataSourceSeg(image_source=train_path, - mask_source=mask_path, - partition_file=train_common_params['partition_file'], - train=False) + # valid_data_source = FuseDataSourceSeg(image_source=train_path, + # mask_source=mask_path, + # partition_file=train_common_params['partition_file'], + # train=False) + valid_data_source = FuseDataSourceSeg(phase='validation', + data_folder=paths['train_folder'], + partition_file=train_common_params['partition_file']) print(valid_data_source.summary()) valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], @@ -309,6 +321,7 @@ def run_train(paths: dict, train_common_params: dict): INFER_COMMON_PARAMS['checkpoint'] = 'last' # Fuse TIP: possible values are 'best', 'last' or epoch_index. INFER_COMMON_PARAMS['data.train_num_workers'] = TRAIN_COMMON_PARAMS['data.train_num_workers'] INFER_COMMON_PARAMS['partition_file'] = TRAIN_COMMON_PARAMS['partition_file'] +INFER_COMMON_PARAMS['data.image_size'] = TRAIN_COMMON_PARAMS['data.image_size'] INFER_COMMON_PARAMS['data.batch_size'] = TRAIN_COMMON_PARAMS['data.batch_size'] ###################################### @@ -327,18 +340,20 @@ def run_infer(paths: dict, infer_common_params: dict): # Validation dataset lgr.info(f'Test Data:', {'attrs': 'bold'}) - infer_data_source = FuseDataSourceSeg(image_source=train_path, - mask_source=mask_path, - partition_file=infer_common_params['partition_file'], - train=False) - print(infer_data_source.summary()) + train_data_source = FuseDataSourceSeg(phase='validation', + data_folder=paths['train_folder'], + partition_file=infer_common_params['partition_file']) + print(train_data_source.summary()) ## Create data processors: input_processors = { - 'input_0': SegInputProcessor(name='image') + 'input_0': SegInputProcessor(name='image', + size=infer_common_params['data.image_size']) } gt_processors = { - 'gt_global': SegInputProcessor(name='mask') + 'gt_global': SegInputProcessor(name='mask', + data_csv=paths['train_rle_file'], + size=infer_common_params['data.image_size']) } # Create visualizer (optional) @@ -444,7 +459,7 @@ def data_iter(): ###################################### if __name__ == "__main__": # allocate gpus - NUM_GPUS = 1 + NUM_GPUS = 0 if NUM_GPUS == 0: TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones diff --git a/fuse_examples/segmentation/siim/seg_input_processor.py b/fuse_examples/segmentation/siim/seg_input_processor.py index cc33e235a..f275fd0a6 100644 --- a/fuse_examples/segmentation/siim/seg_input_processor.py +++ b/fuse_examples/segmentation/siim/seg_input_processor.py @@ -19,21 +19,47 @@ """ import numpy as np +import pandas as pd from skimage.io import imread import torch +from pathlib import Path +import PIL +import pydicom from typing import Optional, Tuple from fuse.data.processor.processor_base import FuseProcessorBase +def rle2mask(rles, width, height): + """ + + rle encoding if images + input: rles(list of rle), width and height of image + returns: mask of shape (width,height) + """ + + mask= np.zeros(width* height) + for rle in rles: + array = np.asarray([int(x) for x in rle.split()]) + starts = array[0::2] + lengths = array[1::2] + + current_position = 0 + for index, start in enumerate(starts): + current_position += start + mask[current_position:current_position+lengths[index]] = 255 + current_position += lengths[index] + + return mask.reshape(width, height).T + + class SegInputProcessor(FuseProcessorBase): def __init__(self, - input_data: str = None, name: str = 'image', # can be 'image' or 'mask' - normalized_target_range: Tuple = (-1, 1), - resize_to: Optional[Tuple] = (299, 299), - padding: Optional[Tuple] = (0, 0), + data_csv: str = None, + size: int = 512, + normalization: float = 255.0, ): """ Create Input processor @@ -43,26 +69,43 @@ def __init__(self, :param padding: Optional, padding size """ self.name = name - if self.name == 'image': - self.im_inx = 0 - elif self.name == 'mask': - self.im_inx = 1 - else: - print('Wrong input!!') + assert self.name == 'image' or self.name == 'mask', "Error: name can be image or mask only." + + if data_csv: + self.df = pd.read_csv(data_csv) + + self.size = (size, size) + self.norm = normalization def __call__(self, - image_fn, + desc, *args, **kwargs): try: - image_fn = image_fn[self.im_inx] - image = imread(image_fn) if self.name == 'image': + dcm = pydicom.read_file(desc).pixel_array + image = np.asarray(PIL.Image.fromarray(dcm).resize(self.size)) + image = image.astype('float32') image = image / 255.0 - else: - image = image > 0 + + else: # create mask + I = self.df.ImageId == Path(desc).stem + enc = self.df.loc[I, ' EncodedPixels'] + if sum(I) == 0: + im = np.zeros((1024, 1024)).astype(np.uint8) + elif sum(I) == 1: + enc = enc.values[0] + if enc == '-1': + im = np.zeros((1024, 1024)).astype(np.uint8) + else: + im = rle2mask([enc], 1024, 1024).astype(np.uint8) + else: + im = rle2mask(enc.values, 1024, 1024).astype(np.uint8) + + im = np.asarray(PIL.Image.fromarray(im).resize(self.size)) + image = im > 0 image = image.astype('float32') # convert image from shape (H x W x C) to shape (C x H x W) with C=3 From e1e601dc01e9f5e5948f33a3e45e2acea0293017 Mon Sep 17 00:00:00 2001 From: moshiko Date: Wed, 13 Apr 2022 16:20:35 +0300 Subject: [PATCH 11/42] factor out end to end examples to seprate package --- README.md | 20 +++++---- VERSION.txt | 1 + .../classification/MG_CMMD/README.md | 0 .../fuse_examples}/classification/__init__.py | 0 .../classification/bright/README.md | 0 .../classification/bright/eval/__init__.py | 0 .../validation_baseline_task1_predictions.csv | 0 .../validation_baseline_task2_predictions.csv | 0 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{fuse_examples => examples/fuse_examples}/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb (100%) create mode 100644 examples/requirements.txt create mode 100644 examples/setup.py diff --git a/README.md b/README.md index e418110d0..b6daa7bdc 100644 --- a/README.md +++ b/README.md @@ -51,12 +51,16 @@ $ pip install -e . ``` This mode, allows to edit the source code and easily contribute back to the open-source project. +In this mode you can also install and run our end to end examples using: +```bash +$ pip install -e examples +``` An alternative, is to simply install using PyPI ```bash $ pip install fuse-med-ml ``` - FuseMedML supports Python 3.6 or later and PyTorch 1.5 or later. A full list of dependencies can be found in [**requirements.txt**](https://github.com/IBM/fuse-med-ml/tree/master/requirements.txt). + FuseMedML supports Python 3.7 or later and PyTorch 1.5 or later. A full list of dependencies can be found in [**requirements.txt**](https://github.com/IBM/fuse-med-ml/tree/master/requirements.txt). # Ready to get started? @@ -69,13 +73,13 @@ $ pip install fuse-med-ml ## Examples * classification - * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/mnist/) - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/) - * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge. - * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML - * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017) - * [**Prostate Gleason Classifiaction**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38) - * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903) - * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/fuse_examples/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508) + * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/mnist/) - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/) + * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge. + * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML + * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017) + * [**Prostate Gleason Classifiaction**](https://github.com/IBM/fuse-med-ml/tree/master/example/fuse_examples/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38) + * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903) + * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508) ## Walkthrough template * [**Walkthrough Template**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/templates/walkthrough_template.py) - includes several TODO notes, marking the minimal scope of code required to get your pipeline up and running. The template also includes useful explanations and tips. diff --git a/VERSION.txt b/VERSION.txt new file mode 100644 index 000000000..341cf11fa --- /dev/null +++ b/VERSION.txt @@ -0,0 +1 @@ +0.2.0 \ No newline at end of file diff --git a/fuse_examples/classification/MG_CMMD/README.md b/examples/fuse_examples/classification/MG_CMMD/README.md similarity index 100% rename from fuse_examples/classification/MG_CMMD/README.md rename to examples/fuse_examples/classification/MG_CMMD/README.md diff --git a/fuse_examples/classification/__init__.py b/examples/fuse_examples/classification/__init__.py similarity index 100% rename from fuse_examples/classification/__init__.py rename to examples/fuse_examples/classification/__init__.py diff --git a/fuse_examples/classification/bright/README.md b/examples/fuse_examples/classification/bright/README.md similarity index 100% rename from fuse_examples/classification/bright/README.md rename to examples/fuse_examples/classification/bright/README.md diff 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examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/results.md diff --git a/fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png b/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png similarity index 100% rename from fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png rename to examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png diff --git a/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv b/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv similarity index 100% rename from fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv rename to examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv diff --git a/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md b/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md similarity index 100% rename from fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md rename to examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md diff --git a/fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png b/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png similarity index 100% rename from fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png rename to examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png diff --git a/fuse_examples/classification/knight/eval/eval.py b/examples/fuse_examples/classification/knight/eval/eval.py similarity index 100% rename from fuse_examples/classification/knight/eval/eval.py rename to examples/fuse_examples/classification/knight/eval/eval.py diff --git a/fuse_examples/classification/knight/eval/example/example_targets.csv b/examples/fuse_examples/classification/knight/eval/example/example_targets.csv similarity index 100% rename from fuse_examples/classification/knight/eval/example/example_targets.csv rename to examples/fuse_examples/classification/knight/eval/example/example_targets.csv diff --git a/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv b/examples/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv similarity index 100% rename from fuse_examples/classification/knight/eval/example/example_task1_predictions.csv rename to examples/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv diff --git a/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv b/examples/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv similarity index 100% rename from fuse_examples/classification/knight/eval/example/example_task2_predictions.csv rename to examples/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv diff --git a/fuse_examples/classification/knight/eval/example/results/results.csv b/examples/fuse_examples/classification/knight/eval/example/results/results.csv similarity index 100% rename from fuse_examples/classification/knight/eval/example/results/results.csv rename to examples/fuse_examples/classification/knight/eval/example/results/results.csv diff --git a/fuse_examples/classification/knight/eval/example/results/results.md b/examples/fuse_examples/classification/knight/eval/example/results/results.md similarity index 100% rename from fuse_examples/classification/knight/eval/example/results/results.md rename to examples/fuse_examples/classification/knight/eval/example/results/results.md diff --git a/fuse_examples/classification/knight/eval/example/results/task1_roc.png b/examples/fuse_examples/classification/knight/eval/example/results/task1_roc.png similarity index 100% rename from fuse_examples/classification/knight/eval/example/results/task1_roc.png rename to examples/fuse_examples/classification/knight/eval/example/results/task1_roc.png diff --git a/fuse_examples/classification/knight/eval/example/results/task2_roc.png b/examples/fuse_examples/classification/knight/eval/example/results/task2_roc.png similarity index 100% rename from fuse_examples/classification/knight/eval/example/results/task2_roc.png rename to examples/fuse_examples/classification/knight/eval/example/results/task2_roc.png diff --git a/fuse_examples/classification/knight/make_predictions_file.py b/examples/fuse_examples/classification/knight/make_predictions_file.py similarity index 100% rename from fuse_examples/classification/knight/make_predictions_file.py rename to examples/fuse_examples/classification/knight/make_predictions_file.py diff --git a/fuse_examples/classification/knight/make_targets_file.py b/examples/fuse_examples/classification/knight/make_targets_file.py similarity index 100% rename from fuse_examples/classification/knight/make_targets_file.py rename to examples/fuse_examples/classification/knight/make_targets_file.py diff --git a/fuse_examples/classification/mnist/__init__.py b/examples/fuse_examples/classification/mnist/__init__.py similarity index 100% rename from fuse_examples/classification/mnist/__init__.py rename to examples/fuse_examples/classification/mnist/__init__.py diff --git a/fuse_examples/classification/mnist/lenet.py b/examples/fuse_examples/classification/mnist/lenet.py similarity index 100% rename from fuse_examples/classification/mnist/lenet.py rename to examples/fuse_examples/classification/mnist/lenet.py diff --git a/fuse_examples/classification/mnist/runner.py b/examples/fuse_examples/classification/mnist/runner.py similarity index 100% rename from fuse_examples/classification/mnist/runner.py rename to examples/fuse_examples/classification/mnist/runner.py diff --git a/fuse_examples/classification/prostate_x/README.md b/examples/fuse_examples/classification/prostate_x/README.md similarity index 100% rename from fuse_examples/classification/prostate_x/README.md rename to examples/fuse_examples/classification/prostate_x/README.md diff --git a/fuse_examples/classification/prostate_x/__init__.py b/examples/fuse_examples/classification/prostate_x/__init__.py similarity index 100% rename from fuse_examples/classification/prostate_x/__init__.py rename to examples/fuse_examples/classification/prostate_x/__init__.py diff --git a/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py b/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py similarity index 100% rename from fuse_examples/classification/prostate_x/backbone_3d_multichannel.py rename to examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py diff --git a/fuse_examples/classification/prostate_x/data_utils.py b/examples/fuse_examples/classification/prostate_x/data_utils.py similarity index 100% rename from fuse_examples/classification/prostate_x/data_utils.py rename to examples/fuse_examples/classification/prostate_x/data_utils.py diff --git a/fuse_examples/classification/prostate_x/dataset.py b/examples/fuse_examples/classification/prostate_x/dataset.py similarity index 100% rename from fuse_examples/classification/prostate_x/dataset.py rename to examples/fuse_examples/classification/prostate_x/dataset.py diff --git a/fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle b/examples/fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle similarity index 100% rename from fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle rename to examples/fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle diff --git a/fuse_examples/classification/prostate_x/patient_data_source.py b/examples/fuse_examples/classification/prostate_x/patient_data_source.py similarity index 100% rename from fuse_examples/classification/prostate_x/patient_data_source.py rename to examples/fuse_examples/classification/prostate_x/patient_data_source.py diff --git a/fuse_examples/classification/prostate_x/post_processor.py b/examples/fuse_examples/classification/prostate_x/post_processor.py similarity index 100% rename from fuse_examples/classification/prostate_x/post_processor.py rename to examples/fuse_examples/classification/prostate_x/post_processor.py diff --git a/fuse_examples/classification/prostate_x/processor.py b/examples/fuse_examples/classification/prostate_x/processor.py similarity index 100% rename from fuse_examples/classification/prostate_x/processor.py rename to examples/fuse_examples/classification/prostate_x/processor.py diff --git a/fuse_examples/classification/prostate_x/run_train_3dpatch.py b/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py similarity index 100% rename from fuse_examples/classification/prostate_x/run_train_3dpatch.py rename to examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py diff --git a/fuse_examples/classification/prostate_x/tasks.py b/examples/fuse_examples/classification/prostate_x/tasks.py similarity index 100% rename from fuse_examples/classification/prostate_x/tasks.py rename to examples/fuse_examples/classification/prostate_x/tasks.py diff --git a/fuse_examples/classification/skin_lesion/README.md b/examples/fuse_examples/classification/skin_lesion/README.md similarity index 100% rename from fuse_examples/classification/skin_lesion/README.md rename to examples/fuse_examples/classification/skin_lesion/README.md diff --git a/fuse_examples/classification/skin_lesion/__init__.py b/examples/fuse_examples/classification/skin_lesion/__init__.py similarity index 100% rename from fuse_examples/classification/skin_lesion/__init__.py rename to examples/fuse_examples/classification/skin_lesion/__init__.py diff --git a/fuse_examples/classification/skin_lesion/data_source.py b/examples/fuse_examples/classification/skin_lesion/data_source.py similarity index 100% rename from fuse_examples/classification/skin_lesion/data_source.py rename to examples/fuse_examples/classification/skin_lesion/data_source.py diff --git a/fuse_examples/classification/skin_lesion/download.py b/examples/fuse_examples/classification/skin_lesion/download.py similarity index 100% rename from fuse_examples/classification/skin_lesion/download.py rename to examples/fuse_examples/classification/skin_lesion/download.py diff --git a/fuse_examples/classification/skin_lesion/ground_truth_processor.py b/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py similarity index 100% rename from fuse_examples/classification/skin_lesion/ground_truth_processor.py rename to examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py diff --git a/fuse_examples/classification/skin_lesion/input_processor.py b/examples/fuse_examples/classification/skin_lesion/input_processor.py similarity index 100% rename from fuse_examples/classification/skin_lesion/input_processor.py rename to examples/fuse_examples/classification/skin_lesion/input_processor.py diff --git a/fuse_examples/classification/skin_lesion/runner.py b/examples/fuse_examples/classification/skin_lesion/runner.py similarity index 100% rename from fuse_examples/classification/skin_lesion/runner.py rename to examples/fuse_examples/classification/skin_lesion/runner.py diff --git a/fuse_examples/tests/__init__.py b/examples/fuse_examples/tests/__init__.py similarity index 100% rename from fuse_examples/tests/__init__.py rename to examples/fuse_examples/tests/__init__.py diff --git a/fuse_examples/tests/colab_tests.ipynb b/examples/fuse_examples/tests/colab_tests.ipynb similarity index 100% rename from fuse_examples/tests/colab_tests.ipynb rename to examples/fuse_examples/tests/colab_tests.ipynb diff --git a/fuse_examples/tests/test_classification_bright,py b/examples/fuse_examples/tests/test_classification_bright,py similarity index 100% rename from fuse_examples/tests/test_classification_bright,py rename to examples/fuse_examples/tests/test_classification_bright,py diff --git a/fuse_examples/tests/test_classification_cmmd.py b/examples/fuse_examples/tests/test_classification_cmmd.py similarity index 100% rename from fuse_examples/tests/test_classification_cmmd.py rename to examples/fuse_examples/tests/test_classification_cmmd.py diff --git a/fuse_examples/tests/test_classification_knight.py b/examples/fuse_examples/tests/test_classification_knight.py similarity index 100% rename from fuse_examples/tests/test_classification_knight.py rename to examples/fuse_examples/tests/test_classification_knight.py diff --git a/fuse_examples/tests/test_classification_mnist.py b/examples/fuse_examples/tests/test_classification_mnist.py similarity index 100% rename from fuse_examples/tests/test_classification_mnist.py rename to examples/fuse_examples/tests/test_classification_mnist.py diff --git a/fuse_examples/tests/test_classification_prostatex.py b/examples/fuse_examples/tests/test_classification_prostatex.py similarity index 100% rename from fuse_examples/tests/test_classification_prostatex.py rename to examples/fuse_examples/tests/test_classification_prostatex.py diff --git a/fuse_examples/tests/test_classification_skin_lesion.py b/examples/fuse_examples/tests/test_classification_skin_lesion.py similarity index 100% rename from fuse_examples/tests/test_classification_skin_lesion.py rename to examples/fuse_examples/tests/test_classification_skin_lesion.py diff --git a/fuse_examples/tutorials/hello_world/hello_world.ipynb b/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb similarity index 100% rename from fuse_examples/tutorials/hello_world/hello_world.ipynb rename to examples/fuse_examples/tutorials/hello_world/hello_world.ipynb diff --git a/fuse_examples/tutorials/hello_world/hello_world_utils.py b/examples/fuse_examples/tutorials/hello_world/hello_world_utils.py similarity index 100% rename from fuse_examples/tutorials/hello_world/hello_world_utils.py rename to examples/fuse_examples/tutorials/hello_world/hello_world_utils.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/arch.png b/examples/fuse_examples/tutorials/multimodality_image_clinical/arch.png similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/arch.png rename to examples/fuse_examples/tutorials/multimodality_image_clinical/arch.png diff --git a/fuse_examples/tutorials/multimodality_image_clinical/data_source.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/data_source.py rename to examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/dataset.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/dataset.py rename to examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/download.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/download.py similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/download.py rename to examples/fuse_examples/tutorials/multimodality_image_clinical/download.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png b/examples/fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png rename to examples/fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png diff --git a/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py rename to examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/input_processor.py rename to examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py diff --git a/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb b/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb similarity index 100% rename from fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb rename to examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb diff --git a/examples/requirements.txt b/examples/requirements.txt new file mode 100644 index 000000000..6df543d81 --- /dev/null +++ b/examples/requirements.txt @@ -0,0 +1,2 @@ +# All requirements +# python>=3.7 diff --git a/examples/setup.py b/examples/setup.py new file mode 100644 index 000000000..5737aafca --- /dev/null +++ b/examples/setup.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python +import os +import pathlib +from setuptools import setup, find_packages + +# The directory containing this file +HERE = pathlib.Path(__file__).parent + +# The text of the README file +with open(os.path.join(HERE, "README.md"), "r", encoding="utf-8") as fh: + long_description = fh.read() + +# list of requirements +requirements = [] +with open(os.path.join(HERE, 'requirements.txt'), 'r') as fh: + for line in fh: + if not line.startswith('#'): + requirements.append(line.strip()) + +# version +version_file = open(os.path.join(HERE, '../VERSION.txt')) +version = version_file.read().strip() + +setup(name='fuse-med-ml-examples', + version=version, + description='Open-source PyTorch based framework designed to facilitate deep learning R&D in medical imaging', + long_description=long_description, + long_description_content_type="text/markdown", + url='https://github.com/IBM/fuse-med-ml/', + author='IBM Research - Machine Learning for Healthcare and Life Sciences', + author_email='moshiko.raboh@ibm.com', + packages=find_packages(), + license='Apache License 2.0', + install_requires=requirements + ) diff --git a/requirements.txt b/requirements.txt index 5dd10ed9b..0cbc54248 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,3 +25,4 @@ pycocotools>=2.0.1 xmlrunner paramiko tables + diff --git a/run_all_unit_tests.py b/run_all_unit_tests.py index 9b952006f..1cfc27eaf 100644 --- a/run_all_unit_tests.py +++ b/run_all_unit_tests.py @@ -15,6 +15,9 @@ def mehikon(a,b): import xmlrunner if __name__ == '__main__': + mode = None + if len(sys.argv) > 1: + mode = sys.argv[1] # options "examples", "core" or None for both "core" and "examples" os.environ['DISPLAY'] = '' #disable display in unit tests is_jenkins_job = 'WORKSPACE' in os.environ and len(os.environ['WORKSPACE'])>2 @@ -23,16 +26,22 @@ def mehikon(a,b): output = f"{search_base}/test-reports/" print('will generate unit tests output xml at :',output) - # with open(f'{search_base}/packages.txt','r') as f: - # sub_sections = [x.split('#')[-1].strip()+'/fuse/' for x in f.readlines() if len(x)>4] - # print('found sub_sections = ', sub_sections) - sub_sections = ["fuse/tests", "fuse_examples/tests", "fuse/eval", "fuse/utils"] + sub_sections_core = [("fuse/tests", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base)] + sub_sections_examples = [("examples/fuse_examples", os.path.join(search_base, "examples"))] + if mode is None: + sub_sections = sub_sections_core + sub_sections_examples + elif mode == "core": + sub_sections = sub_sections_core + elif mode == "examples": + sub_sections = sub_sections_examples + else: + raise Exception(f"Error: unexpected mode {mode}") suite = None - for curr_subsection in sub_sections: + for curr_subsection, top_dir in sub_sections: curr_subsuite = unittest.TestLoader().discover( - f'{search_base}/{curr_subsection}', 'test*.py', top_level_dir=search_base + f'{search_base}/{curr_subsection}', 'test*.py', top_level_dir=top_dir ) if suite is None: suite = curr_subsuite diff --git a/run_all_unit_tests.sh b/run_all_unit_tests.sh index bc7ea5741..c79261e38 100755 --- a/run_all_unit_tests.sh +++ b/run_all_unit_tests.sh @@ -16,10 +16,17 @@ lockfailed() create_env() { force_cuda_version=$1 env_path=$2 + mode=$3 + + requirements=$(cat requirements.txt) + if [ $mode = "examples" ]; then + requirements+=$(cat examples/requirements.txt) + fi PYTHON_VER=3.7 - ENV_NAME="fuse_$PYTHON_VER_"$(sha256sum requirements.txt | awk '{print $1;}') + ENV_NAME="fuse_$PYTHON_VER-$(echo -n $requirements | sha256sum | awk '{print $1;}')" echo $ENV_NAME + # env full name if [ $env_path = "no" ]; then env="-n $ENV_NAME" @@ -56,9 +63,15 @@ create_env() { fi # install local repository (fuse-med-ml) - echo "Installing requirements" + echo "Installing core requirements" conda run $env --no-capture-output --live-stream pip install -r requirements.txt - echo "Installing requirements - Done" + echo "Installing core requirements - Done" + + if [ $mode = "examples" ]; then + echo "Installing examples requirements" + conda run $env --no-capture-output --live-stream pip install -r examples/requirements.txt + echo "Installing examples requirements - Done" + fi fi ) 873>$lock_filename @@ -82,9 +95,18 @@ else env_path="no" fi -echo "Force cuda version: $force_cuda_version" -create_env $force_cuda_version $env_path +echo "Create core env" +create_env $force_cuda_version $env_path "core" +echo "Create core env - Done" + +echo "Running core unittests in $ENV_TO_USE" +conda run $env --no-capture-output --live-stream python ./run_all_unit_tests.py core +echo "Running core unittests - Done" + +echo "Create examples env" +create_env $force_cuda_version $env_path "examples" +echo "Create examples env - Done" -echo "Runng unittests in $ENV_TO_USE" -conda run $env --no-capture-output --live-stream python ./run_all_unit_tests.py -echo "Running unittests - Done" +echo "Running examples unittests in $ENV_TO_USE" +conda run $env --no-capture-output --live-stream python ./run_all_unit_tests.py examples +echo "Running examples unittests - Done" diff --git a/setup.py b/setup.py index c05e87755..cffc36a55 100644 --- a/setup.py +++ b/setup.py @@ -3,6 +3,8 @@ import pathlib from setuptools import setup, find_packages +import sys + # The directory containing this file HERE = pathlib.Path(__file__).parent @@ -17,15 +19,19 @@ if not line.startswith('#'): requirements.append(line.strip()) +# version +version_file = open(os.path.join(HERE, 'VERSION.txt')) +version = version_file.read().strip() + setup(name='fuse-med-ml', - version='0.1.12', + version=version, description='Open-source PyTorch based framework designed to facilitate deep learning R&D in medical imaging', long_description=long_description, long_description_content_type="text/markdown", url='https://github.com/IBM/fuse-med-ml/', - author='IBM Research Haifa Labs - Machine Learning for Healthcare and Life Sciences', + author='IBM Research - Machine Learning for Healthcare and Life Sciences', author_email='moshiko.raboh@ibm.com', - packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]), + packages=find_packages(), license='Apache License 2.0', install_requires=requirements ) From 75ce22b04bc1d6ccf22d90692fb3ad05e8da9b42 Mon Sep 17 00:00:00 2001 From: moshiko Date: Wed, 13 Apr 2022 16:41:06 +0300 Subject: [PATCH 12/42] add examples to PYTHONPATH --- run_all_unit_tests.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/run_all_unit_tests.sh b/run_all_unit_tests.sh index c79261e38..91f7c031d 100755 --- a/run_all_unit_tests.sh +++ b/run_all_unit_tests.sh @@ -105,6 +105,7 @@ echo "Running core unittests - Done" echo "Create examples env" create_env $force_cuda_version $env_path "examples" +PYTHONPATH=$PYTHONPATH:./examples echo "Create examples env - Done" echo "Running examples unittests in $ENV_TO_USE" From c53c96ca69807722a56251f33af1b7de38aac1cf Mon Sep 17 00:00:00 2001 From: moshiko Date: Wed, 13 Apr 2022 17:56:53 +0300 Subject: [PATCH 13/42] run unittests in examples --- examples/setup.py | 2 +- run_all_unit_tests.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/setup.py b/examples/setup.py index 5737aafca..a10454602 100644 --- a/examples/setup.py +++ b/examples/setup.py @@ -7,7 +7,7 @@ HERE = pathlib.Path(__file__).parent # The text of the README file -with open(os.path.join(HERE, "README.md"), "r", encoding="utf-8") as fh: +with open(os.path.join(HERE, "../README.md"), "r", encoding="utf-8") as fh: long_description = fh.read() # list of requirements diff --git a/run_all_unit_tests.py b/run_all_unit_tests.py index 1cfc27eaf..40214b5f6 100644 --- a/run_all_unit_tests.py +++ b/run_all_unit_tests.py @@ -27,7 +27,7 @@ def mehikon(a,b): print('will generate unit tests output xml at :',output) sub_sections_core = [("fuse/tests", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base)] - sub_sections_examples = [("examples/fuse_examples", os.path.join(search_base, "examples"))] + sub_sections_examples = [("examples/fuse_examples/tests", os.path.join(search_base, "examples"))] if mode is None: sub_sections = sub_sections_core + sub_sections_examples elif mode == "core": From 35515332fbbc29002ebf74489b40864121d2aba4 Mon Sep 17 00:00:00 2001 From: moshiko Date: Thu, 14 Apr 2022 13:34:55 +0300 Subject: [PATCH 14/42] remove fuse1 data package --- fuse/data/__init__.py | 0 fuse/data/augmentor/__init__.py | 0 fuse/data/augmentor/augmentor_base.py | 65 -- .../augmentor_batch_level_callback.py | 40 - fuse/data/augmentor/augmentor_default.py | 107 --- fuse/data/augmentor/augmentor_toolbox.py | 455 ----------- fuse/data/cache/__init__.py | 0 fuse/data/cache/cache_base.py | 105 --- fuse/data/cache/cache_files.py | 228 ------ fuse/data/cache/cache_memory.py | 104 --- fuse/data/cache/cache_null.py | 85 -- fuse/data/data_source/__init__.py | 0 fuse/data/data_source/data_source_base.py | 40 - fuse/data/data_source/data_source_default.py | 120 --- fuse/data/data_source/data_source_folds.py | 106 --- .../data/data_source/data_source_from_list.py | 40 - fuse/data/data_source/data_source_toolbox.py | 118 --- fuse/data/dataset/__init__.py | 0 fuse/data/dataset/dataset_base.py | 130 --- fuse/data/dataset/dataset_dataframe.py | 75 -- fuse/data/dataset/dataset_default.py | 756 ------------------ fuse/data/dataset/dataset_generator.py | 561 ------------- fuse/data/dataset/dataset_wrapper.py | 70 -- fuse/data/processor/__init__.py | 0 fuse/data/processor/processor_base.py | 30 - fuse/data/processor/processor_csv.py | 88 -- fuse/data/processor/processor_dataframe.py | 128 --- fuse/data/processor/processor_dicom_mri.py | 647 --------------- fuse/data/processor/processor_rand.py | 37 - .../processor/processors_image_toolbox.py | 141 ---- fuse/data/sampler/__init__.py | 0 fuse/data/sampler/sampler_balanced_batch.py | 212 ----- fuse/data/utils/export.py | 69 -- fuse/data/visualizer/__init__.py | 0 fuse/data/visualizer/visualizer_base.py | 45 -- fuse/data/visualizer/visualizer_default.py | 236 ------ fuse/data/visualizer/visualizer_default_3d.py | 276 ------- .../visualizer/visualizer_image_analysis.py | 112 --- fuse/tests/data/__init__.py | 0 fuse/tests/data/test_data_source_toolbox.py | 102 --- fuse/tests/data/test_processor_dataframe.py | 94 --- fuse/tests/data/test_sampler.py | 130 --- 42 files changed, 5552 deletions(-) delete mode 100644 fuse/data/__init__.py delete mode 100644 fuse/data/augmentor/__init__.py delete mode 100644 fuse/data/augmentor/augmentor_base.py delete mode 100644 fuse/data/augmentor/augmentor_batch_level_callback.py delete mode 100644 fuse/data/augmentor/augmentor_default.py delete mode 100644 fuse/data/augmentor/augmentor_toolbox.py delete mode 100644 fuse/data/cache/__init__.py delete mode 100644 fuse/data/cache/cache_base.py delete mode 100644 fuse/data/cache/cache_files.py delete mode 100644 fuse/data/cache/cache_memory.py delete mode 100644 fuse/data/cache/cache_null.py delete mode 100644 fuse/data/data_source/__init__.py delete mode 100644 fuse/data/data_source/data_source_base.py delete mode 100644 fuse/data/data_source/data_source_default.py delete mode 100644 fuse/data/data_source/data_source_folds.py delete mode 100644 fuse/data/data_source/data_source_from_list.py delete mode 100644 fuse/data/data_source/data_source_toolbox.py delete mode 100644 fuse/data/dataset/__init__.py delete mode 100644 fuse/data/dataset/dataset_base.py delete mode 100644 fuse/data/dataset/dataset_dataframe.py delete mode 100644 fuse/data/dataset/dataset_default.py delete mode 100644 fuse/data/dataset/dataset_generator.py delete mode 100644 fuse/data/dataset/dataset_wrapper.py delete mode 100644 fuse/data/processor/__init__.py delete mode 100644 fuse/data/processor/processor_base.py delete mode 100644 fuse/data/processor/processor_csv.py delete mode 100644 fuse/data/processor/processor_dataframe.py delete mode 100755 fuse/data/processor/processor_dicom_mri.py delete mode 100644 fuse/data/processor/processor_rand.py delete mode 100644 fuse/data/processor/processors_image_toolbox.py delete mode 100644 fuse/data/sampler/__init__.py delete mode 100644 fuse/data/sampler/sampler_balanced_batch.py delete mode 100644 fuse/data/utils/export.py delete mode 100644 fuse/data/visualizer/__init__.py delete mode 100644 fuse/data/visualizer/visualizer_base.py delete mode 100644 fuse/data/visualizer/visualizer_default.py delete mode 100644 fuse/data/visualizer/visualizer_default_3d.py delete mode 100644 fuse/data/visualizer/visualizer_image_analysis.py delete mode 100644 fuse/tests/data/__init__.py delete mode 100644 fuse/tests/data/test_data_source_toolbox.py delete mode 100644 fuse/tests/data/test_processor_dataframe.py delete mode 100644 fuse/tests/data/test_sampler.py diff --git a/fuse/data/__init__.py b/fuse/data/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/augmentor/__init__.py b/fuse/data/augmentor/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/augmentor/augmentor_base.py b/fuse/data/augmentor/augmentor_base.py deleted file mode 100644 index 041fd07eb..000000000 --- a/fuse/data/augmentor/augmentor_base.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Augmentor Base class -""" -from abc import ABC, abstractmethod -from typing import Any - - -class FuseAugmentorBase(ABC): - """ - Base class for augmentor. - Given an augmenatation pipline description, expected to sample random parameters first and then apply them. - """ - - @abstractmethod - def get_random_augmentation_desc(self) -> Any: - """ - Sample random parameters for augmentation - :return: - """ - raise NotImplementedError - - @abstractmethod - def apply_augmentation(self, sample: Any, augmentation_desc: Any) -> Any: - """ - Apply the augmenation according to the given parameters. Must be deterministic. - :param sample: the original sample as generated by the dataset - :param augmentation_desc: augmentation parameters. Output of get_random_augmentation_desc() - :return: augmented sample - """ - raise NotImplementedError - - @abstractmethod - def summary(self) -> str: - """ - String summary of the object - """ - raise NotImplementedError - - def __call__(self, sample: Any): - """ - generate random and apply the augmentation at once. - :param sample: - :return: - """ - augmentation_desc = self.get_random_augmentation_desc() - return self.apply_augmentation(sample, augmentation_desc) diff --git a/fuse/data/augmentor/augmentor_batch_level_callback.py b/fuse/data/augmentor/augmentor_batch_level_callback.py deleted file mode 100644 index a48b899db..000000000 --- a/fuse/data/augmentor/augmentor_batch_level_callback.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Dict, List, Sequence - -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.managers.callbacks.callback_base import FuseCallback - - -class FuseAugmentorBatchCallback(FuseCallback): - """ - Simple class which gets augmentation pipeline and apply augmentation on a batch level batch dict - """ - def __init__(self, aug_pipeline: List, modes: Sequence[str] = ('train',)): - """ - :param aug_pipeline: See FuseAugmentorDefault - :param modes: modees to apply the augmentation: 'train', 'validation' and/or 'infer' - """ - self._augmentor = FuseAugmentorDefault(aug_pipeline) - self._modes = modes - - def on_data_fetch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: - if mode in self._modes: - self._augmentor(batch_dict) \ No newline at end of file diff --git a/fuse/data/augmentor/augmentor_default.py b/fuse/data/augmentor/augmentor_default.py deleted file mode 100644 index 002ef9058..000000000 --- a/fuse/data/augmentor/augmentor_default.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Augmentor Default class -""" -from typing import Any, Iterable - -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state, convert_state_to_str -from fuse.utils.rand.param_sampler import draw_samples_recursively - - -class FuseAugmentorDefault(FuseAugmentorBase): - """ - Default generic implementation for Fuse augmentor. Aimed to be used by most experiments. - """ - - def __init__(self, augmentation_pipeline: Iterable[Any] = ()): - """ - :param augmentation_pipeline: list of augmentation operation description, - Each operation description expected to be a tuple of 4 elements: - Element 0 - the sample keys affected by this operation - Element 1 - callback to a function performing the operation. This function expected to support input parameter 'aug_ingput' - Element 2 - dictionary including the input parameters for the callback function. See AugmentorSamplerDefault - to learn how to use random numbers - Element 3 - general parameters: TBD - - Example: - See in aug_image_default_pipeline() - """ - # log object input state - log_object_input_state(self, locals()) - - self.augmentation_pipeline = augmentation_pipeline - - def get_random_augmentation_desc(self) -> Any: - """ - See description in super class. - """ - return draw_samples_recursively(self.augmentation_pipeline) - - def apply_augmentation(self, sample: Any, augmentation_desc: Any) -> Any: - """ - See description in super class. - """ - aug_sample = sample - for op_desc in augmentation_desc: - # decode augmentation description - sample_keys = op_desc[0] - augment_function = op_desc[1] - augment_function_parameters = op_desc[2] - general_parameters: dict = op_desc[3] - - # If apply sampled as False skip - by default it will always be True - apply = general_parameters.get('apply', True) - if not apply: - continue - - # Extract augmentation input - if sample_keys is None: - aug_input = aug_sample - elif len(sample_keys) == 1: - aug_input = FuseUtilsHierarchicalDict.get(aug_sample, sample_keys[0]) - else: - aug_input = tuple((FuseUtilsHierarchicalDict.get(aug_sample, key) for key in sample_keys)) - augment_function_parameters = augment_function_parameters.copy() - augment_function_parameters['aug_input'] = aug_input - - # apply augmentation - aug_result = augment_function(**augment_function_parameters) - - # modify the sample accordingly - if sample_keys is None: - aug_sample = aug_result - elif len(sample_keys) == 1: - FuseUtilsHierarchicalDict.set(aug_sample, sample_keys[0], aug_result) - else: - for index, key in enumerate(sample_keys): - FuseUtilsHierarchicalDict.set(aug_sample, key, aug_result[index]) - - return aug_sample - - def summary(self) -> str: - """ - String summary of the object - """ - return \ - f'Class = {self, __class__}\n' \ - f'Pipeline = {convert_state_to_str(self.augmentation_pipeline)}' diff --git a/fuse/data/augmentor/augmentor_toolbox.py b/fuse/data/augmentor/augmentor_toolbox.py deleted file mode 100644 index b24e2a2db..000000000 --- a/fuse/data/augmentor/augmentor_toolbox.py +++ /dev/null @@ -1,455 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from copy import deepcopy -from typing import Tuple, Any, List, Iterable, Optional - -import numpy -import torch -import torchvision.transforms.functional as TTF -from PIL import Image -from scipy.ndimage.filters import gaussian_filter -from scipy.ndimage.interpolation import map_coordinates -from torch import Tensor - -from fuse.utils.rand.param_sampler import Gaussian, RandBool, RandInt, Uniform - - -######## Affine augmentation -def aug_op_affine(aug_input: Tensor, rotate: float = 0.0, translate: Tuple[float, float] = (0.0, 0.0), - scale: Tuple[float, float] = 1.0, flip: Tuple[bool, bool] = (False, False), shear: float = 0.0, - channels: Optional[List[int]] = None) -> Tensor: - """ - Affine augmentation - :param aug_input: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] - :param rotate: angle [0.0 - 360.0] - :param translate: translation per spatial axis (number of pixels). The sign used as the direction. - :param scale: scale factor - :param flip: flip per spatial axis flip[0] for vertical flip and flip[1] for horizontal flip - :param shear: shear factor - :param channels: apply the augmentation on the specified channels. Set to None to apply to all channels. - :return: the augmented image - """ - # Support for 2D inputs - implicit single channel - if len(aug_input.shape) == 2: - aug_input = aug_input.unsqueeze(dim=0) - remember_to_squeeze = True - else: - remember_to_squeeze = False - - # convert to PIL (required by affine augmentation function) - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input - for channel in channels: - aug_channel_tensor = aug_input[channel].numpy() - aug_channel_tensor = Image.fromarray(aug_channel_tensor) - aug_channel_tensor = TTF.affine(aug_channel_tensor, angle=rotate, scale=scale, translate=translate, shear=shear) - if flip[0]: - aug_channel_tensor = TTF.vflip(aug_channel_tensor) - if flip[1]: - aug_channel_tensor = TTF.hflip(aug_channel_tensor) - - # convert back to torch tensor - aug_channel_tensor = numpy.array(aug_channel_tensor) - aug_channel_tensor = torch.from_numpy(aug_channel_tensor) - - # set the augmented channel - aug_tensor[channel] = aug_channel_tensor - - # squeeze back to 2-dim if needed - if remember_to_squeeze: - aug_tensor = aug_tensor.squeeze(dim=0) - - return aug_tensor - - -def aug_op_affine_group(aug_input: Tuple[Tensor], **kwargs) -> Tuple[Tensor]: - """ - Applies same augmentation on multiple tensors. For example, augmenting both input image and its corresponding - segmentation mask in the same way. This method wraps 'aug_op_affine'. - :param aug_input: tuple of tensors - :param kwargs: augmentation params, same kwargs as 'aug_op_affine' - see docstring there - :return: tuple of tensors, all augmented the same way - """ - return tuple((aug_op_affine(element, **kwargs) for element in aug_input)) - - -def aug_op_crop_and_resize(aug_input: Tensor, - scale: Tuple[float, float], - channels: Optional[List[int]] = None) -> Tensor: - """ - Alternative to rescaling: center crop and resize back to the original dimensions. if scale is bigger than 1.0. the image first padded. - :param aug_input: The tensor to augment - :param scale: tuple of positive floats - :param channels: apply augmentation on the specified channels or None for all of them - :return: the augmented tensor - """ - if len(aug_input.shape) == 2: - aug_input = aug_input.unsqueeze(dim=0) - remember_to_squeeze = True - else: - remember_to_squeeze = False - - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input - for channel in channels: - aug_channel_tensor = aug_input[channel] - - if scale[0] != 1.0 or scale[1] != 1.0: - cropped_shape = (int(aug_channel_tensor.shape[0] * scale[0]), int(aug_channel_tensor.shape[1] * scale[1])) - padding = [[0, 0], [0, 0]] - for dim in range(2): - if scale[dim] > 1.0: - padding[dim][0] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) // 2 - padding[dim][1] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) - padding[dim][0] - aug_channel_tensor_pad = TTF.pad(aug_channel_tensor.unsqueeze(0), (padding[1][0], padding[0][0], padding[1][1], padding[0][1])) - aug_channel_tensor_cropped = TTF.center_crop(aug_channel_tensor_pad, cropped_shape) - aug_channel_tensor = TTF.resize(aug_channel_tensor_cropped, aug_channel_tensor.shape).squeeze(0) - # set the augmented channel - aug_tensor[channel] = aug_channel_tensor - - # squeeze back to 2-dim if needed - if remember_to_squeeze: - aug_tensor = aug_tensor.squeeze(dim=0) - - return aug_tensor - - -######## Color augmentation -def aug_op_clip(aug_input: Tensor, clip: Tuple[float, float] = (-1.0, 1.0)) -> Tensor: - """ - Clip pixel values - :param aug_input: the tensor to clip - :param clip: values for clipping from both sides - :return: Clipped tensor - """ - aug_tensor = aug_input - if clip is not None: - aug_tensor = torch.clamp(aug_tensor, clip[0], clip[1], out=aug_tensor) - return aug_tensor - - -def aug_op_add_col(aug_input: Tensor, add: float) -> Tensor: - """ - Adding a values to all pixels - :param aug_input: the tensor to augment - :param add: the value to add to each pixel - :return: the augmented tensor - """ - aug_tensor = aug_input + add - aug_tensor = aug_op_clip(aug_tensor, clip=(0, 1)) - return aug_tensor - - -def aug_op_mul_col(aug_input: Tensor, mul: float) -> Tensor: - """ - multiply each pixel - :param aug_input: the tensor to augment - :param mul: the multiplication factor - :return: the augmented tensor - """ - input_tensor = aug_input * mul - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_gamma(aug_input: Tensor, gain: float, gamma: float) -> Tensor: - """ - Gamma augmentation - :param aug_input: the tensor to augment - :param gain: gain factor - :param gamma: gamma factor - :return: None - """ - input_tensor = (aug_input ** gamma) * gain - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_contrast(aug_input: Tensor, factor: float) -> Tensor: - """ - Adjust contrast (notice - calculated across the entire input tensor, even if it's 3d) - :param aug_input:the tensor to augment - :param factor: contrast factor. 1.0 is neutral - :return: the augmented tensor - """ - calculated_mean = aug_input.mean() - input_tensor = ((aug_input - calculated_mean) * factor) + calculated_mean - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_color(aug_input: Tensor, add: Optional[float] = None, mul: Optional[float] = None, - gamma: Optional[float] = None, contrast: Optional[float] = None, channels: Optional[List[int]] = None): - """ - Color augmentaion: including addition, multiplication, gamma and contrast adjusting - :param aug_input: the tensor to augment - :param add: value to add to each pixel - :param mul: multiplication factor - :param gamma: gamma factor - :param contrast: contrast factor - :param channels: Apply clipping just over the specified channels. If set to None will apply on all channels. - :return: - """ - aug_tensor = aug_input - if channels is None: - if add is not None: - aug_tensor = aug_op_add_col(aug_tensor, add) - if mul is not None: - aug_tensor = aug_op_mul_col(aug_tensor, mul) - if gamma is not None: - aug_tensor = aug_op_gamma(aug_tensor, 1.0, gamma) - if contrast is not None: - aug_tensor = aug_op_contrast(aug_tensor, contrast) - else: - if add is not None: - aug_tensor[channels] = aug_op_add_col(aug_tensor[channels], add) - if mul is not None: - aug_tensor[channels] = aug_op_mul_col(aug_tensor[channels], mul) - if gamma is not None: - aug_tensor[channels] = aug_op_gamma(aug_tensor[channels], 1.0, gamma) - if contrast is not None: - aug_tensor[channels] = aug_op_contrast(aug_tensor[channels], contrast) - - return aug_tensor - - -######## Gaussian noise -def aug_op_gaussian(aug_input: Tensor, mean: float = 0.0, std: float = 0.03, channels: Optional[List[int]] = None) -> Tensor: - """ - Add gaussian noise - :param aug_input: the tensor to augment - :param mean: mean gaussian distribution - :param std: std gaussian distribution - :param channels: Apply just over the specified channels. If set to None will apply on all channels. - :return: the augmented tensor - """ - aug_tensor = aug_input - dtype = aug_tensor.dtype - - if channels is None: - rand_patch = Gaussian(aug_tensor.shape, mean, std).sample() - aug_tensor = aug_tensor + rand_patch - else: - rand_patch = Gaussian(aug_tensor[channels].shape, mean, std).sample() - aug_tensor[channels] = aug_tensor[channels] + rand_patch - - aug_tensor = aug_tensor.to(dtype=dtype) - return aug_tensor - - -def aug_op_elastic_transform(aug_input: Tensor, alpha: float = 1, sigma: float = 50, channels: Optional[List[int]] = None): - """Elastic deformation of images as described in [Simard2003]_. - .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for - Convolutional Neural Networks applied to Visual Document Analysis", - :param aug_input: input tensor of shape (C,Y,X) - :param alpha: global pixel shifting (correlated to the article) - :param sigma: Gaussian filter parameter - :param channels: which channels to apply the augmentation - :return distorted image - """ - random_state = numpy.random.RandomState(None) - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input.numpy() - for channel in channels: - aug_channel_tensor = aug_input[channel].numpy() - shape = aug_channel_tensor.shape - dx1 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - dx2 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - - x1, x2 = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1])) - indices = numpy.reshape(x2 + dx2, (-1, 1)), numpy.reshape(x1 + dx1, (-1, 1)) - - distored_image = map_coordinates(aug_channel_tensor, indices, order=1, mode='reflect') - distored_image = distored_image.reshape(aug_channel_tensor.shape) - aug_tensor[channel] = distored_image - return torch.from_numpy(aug_tensor) - - -######### Default / Example augmentation pipline for a 2D image -def aug_image_default_pipeline(input_pointer: str) -> List[Any]: - """ - Return default image augmentation pipeline. optimised for breast project (GMP model). - In case paramter tunning is required - copy and change the values - :param input_pointer: global dict pointer to the image - :return: the default pipeline - """ - return [ - [ - (input_pointer,), - aug_op_affine, - {'rotate': Uniform(-30.0, 30.0), 'translate': (RandInt(-10, 10), RandInt(-10, 10)), - 'flip': (RandBool(0.3), RandBool(0.3)), 'scale': Uniform(0.9, 1.1)}, - {'apply': RandBool(0.5)} - ], - [ - (input_pointer,), - aug_op_color, - {'add': Uniform(-0.06, 0.06), 'mul': Uniform(0.95, 1.05), 'gamma': Uniform(0.9, 1.1), - 'contrast': Uniform(0.85, 1.15)}, - {'apply': RandBool(0.5)} - ], - [ - (input_pointer,), - aug_op_gaussian, - {'std': 0.03}, - {'apply': RandBool(0.5)} - ], - ] - - -# general utilities -def aug_pipeline_step_replicate(step: List, key: str, values: Iterable) -> List[List]: - """ - Replicate a step, but set different value for each replication for the specified key - :param step: The step to replicate - :param key: the key to override (withing te augmentation dunction input) - :param values: Iterable specify the value for each replication - :return: - """ - list_of_steps = [] - for value in values: - step_copy = deepcopy(step) - step_copy[2][key] = value - list_of_steps.append(step_copy) - - return list_of_steps - - -def aug_op_rescale_pixel_values(aug_input: Tensor, target_range: Tuple[float, float] = (-1.0, 1.0)) -> Tensor: - """ - Scales pixel values to specific range. - :param aug_input: input tensor - :param target_range: target range, (min, max) - :return: rescaled tensor - """ - max_val = aug_input.max() - min_val = aug_input.min() - if min_val == max_val == 0: - return aug_input - aug_input = aug_input - min_val - aug_input = aug_input / (max_val - min_val) - aug_input = aug_input * (target_range[1] - target_range[0]) - aug_input = aug_input + target_range[0] - return aug_input - - -def squeeze_3d_to_2d(aug_input: Tensor, axis_squeeze: str) -> Tensor: - ''' - squeeze selected axis of volume image into channel dimension, in - order to fit the 2D augmentation functions - :param aug_input: input of shape: (channel, z, y, x) - :return: - ''' - # aug_input shape is [channels, z, y, x] - if axis_squeeze == 'y': - aug_input = aug_input.permute((0, 2, 1, 3)) - # aug_input shape is [channels, y, z, x] - elif axis_squeeze == 'x': - aug_input = aug_input.permute((0, 3, 2, 1)) - # aug_input shape is [channels, x, y, z] - else: - assert axis_squeeze == 'z', "axis squeeze must be a string of either x, y, or z" - return aug_input.reshape((aug_input.shape[0] * aug_input.shape[1],) + aug_input.shape[2:]) - - -def unsqueeze_2d_to_3d(aug_input: Tensor, channels: int, axis_squeeze: str) -> Tensor: - ''' - unsqueeze selected axis to original shape, and add the batch dimension - :param aug_input: - :return: - ''' - aug_input = aug_input - aug_input = aug_input.reshape((channels, aug_input.shape[0] // channels) + aug_input.shape[1:]) - if axis_squeeze == 'y': - aug_input = aug_input.permute((0, 2, 1, 3)) - # aug_input shape is [channels, z, y, x] - elif axis_squeeze == 'x': - aug_input = aug_input.permute((0, 3, 2, 1)) - # aug_input shape is [channels, z, y, x] - else: - assert axis_squeeze == 'z', "axis squeeze must be a string of either x, y, or z" - return aug_input - - -def rotation_in_3d(aug_input: Tensor, z_rot: float = 0.0, y_rot: float = 0.0, x_rot: float = 0): - """ - rotates an input tensor around an axis, when for example z_rot is chosen, - the rotation is in the x-y plane. - Note: rotation angles are in relation to the original axis (not the rotated one) - rotation angles should be given in degrees - :param aug_input:image input should be in shape [channel, z, y, x] - :param z_rot: angle to rotate x-y plane clockwise - :param y_rot: angle to rotate x-z plane clockwise - :param x_rot: angle to rotate z-y plane clockwise - :return: - """ - assert len(aug_input.shape) == 4 # will only work for 3d - channels = aug_input.shape[0] - if z_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='z') - rot_squeeze = aug_op_affine(squeez_img, rotate=z_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'z') - if x_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='x') - rot_squeeze = aug_op_affine(squeez_img, rotate=x_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'x') - if y_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='y') - rot_squeeze = aug_op_affine(squeez_img, rotate=y_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'y') - - return aug_input - - -def aug_cut_out(aug_input: Tensor, fill: float = None, size: int = 16) -> Tensor: - """ - removing small patch of the image. https://arxiv.org/abs/1708.04552 - :param aug_input: the tensor to augment - :param fill: value to fill the patch - :param size: size of patch - :return: the augmented tensor - """ - fill = aug_input.mean(-1).mean(-1) if fill is None else fill - sx = torch.randint(0, aug_input.shape[1] - size, (1,)) - sy = torch.randint(0, aug_input.shape[2] - size, (1,)) - aug_input[:, sx:sx + size, sy:sy + size] = fill[:, None, None] - - return aug_input - - -def aug_op_batch_mix_up(aug_input: Tuple[Tensor, Tensor], factor: float) -> Tuple[Tensor, Tensor]: - """ - mixup augmentation on a batch level - :param aug_input: batch level input to augment. tuple of image and one hot vector of targets - :param factor: background factor - :return: the augmented batch - """ - img = aug_input[0] - labels = aug_input[1] - perm = numpy.arange(img.shape[0]) - numpy.random.shuffle(perm) - img_mix_up = img[perm] - labels_mix_up = labels[perm] - img = img * (1.0 - factor) + factor * img_mix_up - labels = labels * (1.0 - factor) + factor * labels_mix_up - return img, labels diff --git a/fuse/data/cache/__init__.py b/fuse/data/cache/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/cache/cache_base.py b/fuse/data/cache/cache_base.py deleted file mode 100644 index d7e55b85a..000000000 --- a/fuse/data/cache/cache_base.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Base class for caching -""" -from abc import ABC, abstractmethod -from multiprocessing import Manager -from typing import Hashable, Any, List - - -class FuseCacheBase(ABC): - - @abstractmethod - def __contains__(self, key: Hashable) -> bool: - """ - return true if key is already in cache - :param key: any kind of hashable key - :return: boolean. True if exist. - """ - raise NotImplementedError - - @abstractmethod - def __getitem__(self, key: Hashable) -> Any: - """ - Get an item from cache. Will raise an error if key does not exist - :param key: any kind of hashable key - :return: the item - """ - raise NotImplementedError - - @abstractmethod - def __delitem__(self, key: Hashable) -> None: - """ - Delete key. Will raise an error if key does not exist - :param key: any kind of hashable key - :return: None - """ - raise NotImplementedError - - @abstractmethod - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - Set key. Will override previous value if already exist. - :param key: any kind of hashable key - :param value: any kind of value to sture - :return: None - """ - raise NotImplementedError - - @abstractmethod - def save(self) -> None: - """ - Save data to cache - :return: None - """ - raise NotImplementedError - - @abstractmethod - def exist(self) -> bool: - """ - return True if cache exist and contains the samples - """ - raise NotImplementedError - - @abstractmethod - def reset(self) -> None: - """ - Reset cache and delete all data - :return: None - """ - raise NotImplementedError - - @abstractmethod - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - Get all keys currently cached - :param include_none: include or filter 'none samples' which represents no samples or bad samples - :return: List of keys - """ - raise NotImplementedError - - def start_caching(self, manager: Manager) -> None: - """ - start caching - the caching will be done in save(). - :param manager: multiprocessing manager to create shared data structures - :return: None - """ - raise NotImplementedError diff --git a/fuse/data/cache/cache_files.py b/fuse/data/cache/cache_files.py deleted file mode 100644 index 3c5d5b79c..000000000 --- a/fuse/data/cache/cache_files.py +++ /dev/null @@ -1,228 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Cache to file per sample -""" -import gzip -import logging -import os -import pickle -import traceback -from multiprocessing import Manager -import multiprocessing -from typing import Hashable, Any, List -import torch -torch.multiprocessing.set_sharing_strategy('file_system') - -from fuse.data.cache.cache_base import FuseCacheBase -from fuse.utils.file_io.atomic_file import AtomicFileWriter -from fuse.utils.file_io.file_io import create_dir, remove_dir_content - - -class FuseCacheFiles(FuseCacheBase): - def __init__(self, cache_file_dir: str, reset_cache: bool, single_file: bool=False): - """ - :param cache_file_dir: path to cache dir - :param reset_cache: reset previous cache if exist or continue - """ - super().__init__() - - self._cache_file_dir = cache_file_dir - self._save_cache_index = 100 - - # create dir if not already exist - create_dir(cache_file_dir) - - # pointer to cache index - self._cache_file_name = os.path.join(self._cache_file_dir, 'cache_index.pkl') - self._cache_prop_file_name = os.path.join(self._cache_file_dir, 'cache_properties.pkl') - - # reset or load from disk - if reset_cache or not os.path.exists(self._cache_file_name): - self.reset() - self.single_file = single_file - # save initial properties - with AtomicFileWriter(filename=self._cache_prop_file_name) as cache_prop_file: - pickle.dump({'single_file': self.single_file}, cache_prop_file) - else: - # get last modified time of the index - self._cache_index_mtime = os.path.getmtime(self._cache_file_name) - - # load current cache - try: - with open(self._cache_file_name, 'rb') as cache_index_file: - self._cache_index = pickle.load(cache_index_file) - except: - # backward compatibility - used to be saved in gz format - with gzip.open(self._cache_file_name, 'rb') as cache_index_file: - self._cache_index = pickle.load(cache_index_file) - self._cache_list = list(self._cache_index.keys()) - self._cache_size = len(self._cache_list) - - # load mode for backward compatibility - try: - with open(self._cache_prop_file_name, 'rb') as cache_prop_file: - cache_prop = pickle.load(cache_prop_file) - self.single_file = cache_prop['single_file'] - except: - self.single_file = False - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return key in self._cache_index - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - if self.single_file: - return self._cache_index.get(key, None) - - value_file_name = self._cache_index.get(key, None) - if value_file_name is None: - return None - value_file_name = os.path.join(self._cache_file_dir, value_file_name) - - # make sure file not exist - if os.path.exists(value_file_name): - # store the file - with gzip.open(value_file_name, 'rb') as value_file: - value = pickle.load(value_file) - else: - raise Exception(f'cache file {value_file_name} not found') - - return value - - def __delitem__(self, key: Hashable) -> None: - """ - Not supported - """ - raise NotImplementedError - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - if self._cache_lock is None: - index = self._cache_size - self._cache_list.append(key) - self._cache_size = index + 1 - else: - with self._cache_lock: - index = self._cache_size.value - self._cache_list.append(key) - self._cache_size.value = index + 1 - - # if value is none, just update cache index - if value is None: - self._cache_index[key] = None - return - if self.single_file: - self._cache_index[key] = value - else: - value_file_name = str(index).zfill(10) + '.pkl.gz' - value_abs_file_name = os.path.join(self._cache_file_dir, value_file_name) - self._cache_index[key] = value_file_name - - # make sure file not exist - if os.path.exists(value_abs_file_name): - logging.getLogger('Fuse').warning(f'cache file {value_abs_file_name} unexpectedly exist, overriding it.') - - # store the file - with AtomicFileWriter(value_abs_file_name) as value_file: - pickle.dump(value, value_file) - - # store the cache index - just for a case of crashing - if index % self._save_cache_index == 0: - try: - with AtomicFileWriter(filename=self._cache_file_name) as cache_index_file: - pickle.dump(dict(self._cache_index), cache_index_file) - except: - # do not trow error- just print warning - lgr = logging.getLogger('Fuse') - track = traceback.format_exc() - lgr.warning(track) - - def save(self) -> None: - """ - Save cache index file - """ - # disable caching - self._cache_enable = False - - with AtomicFileWriter(filename=self._cache_file_name) as cache_index_file: - pickle.dump(dict(self._cache_index), cache_index_file) - - # move back to simple data structures - self._cache_index = dict(self._cache_index) - self._cache_list = list(self._cache_list) - self._cache_size = len(self._cache_list) - self._cache_lock = None - - def exist(self) -> bool: - """ - See base class - """ - return bool(self._cache_index) - - def reset(self) -> None: - """ - See base class - """ - # make sure the dir content is empty - remove_dir_content(self._cache_file_dir) - - # create empty data structures - self._cache_enable = False - self._cache_index = {} - self._cache_list = [] - self._cache_size = 0 - self._cache_index_mtime = -1 - self._cache_lock = None - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - if include_none: - return list(self._cache_index.keys()) - else: - return [key for key, value in self._cache_index.items() if value is not None] - - def start_caching(self, manager: Manager): - """ - See base class - """ - self._cache_enable = True - # if manager is None assume that the it's not multiprocessing caching - if manager is not None: - # create dictionary and adds it one by one to workaround multiprocessing limitation - cache_index = manager.dict() - for k, v in self._cache_index.items(): - cache_index[k] = v - self._cache_index = cache_index - self._cache_list = manager.list(self._cache_list) - self._cache_size = manager.Value("i", len(self._cache_list)) - self._cache_lock = manager.Lock() diff --git a/fuse/data/cache/cache_memory.py b/fuse/data/cache/cache_memory.py deleted file mode 100644 index 994e17540..000000000 --- a/fuse/data/cache/cache_memory.py +++ /dev/null @@ -1,104 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Cache to Memory -""" -from multiprocessing import Manager -from typing import Hashable, Any, List - -from fuse.data.cache.cache_base import FuseCacheBase - - -class FuseCacheMemory(FuseCacheBase): - """ - Cache to Memory - """ - - def __init__(self): - super().__init__() - - self.reset() - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return key in self._cache_dict - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - return self._cache_dict.get(key, None) - - def __delitem__(self, key: Hashable) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - item = self._cache_dict.pop(key, None) - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - self._cache_dict[key] = value - - def save(self) -> None: - """ - Not saving, moving back to simple data structures - """ - self._cache_enable = False - self._cache_dict = dict(self._cache_dict) - - def exist(self) -> bool: - """ - See base class - """ - return len(self._cache_dict) > 0 - - def reset(self) -> None: - """ - See base class - """ - self._cache_dict = {} - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - if include_none: - return list(self._cache_dict.keys()) - else: - return [key for key, value in self._cache_dict.items() if value is not None] - - def start_caching(self, manager: Manager) -> None: - """ - Moving to multiprocessing data structures - """ - self._cache_enable = True - # if manager is None assume that the it's not multiprocessing caching - if manager is not None: - self._cache_dict = manager.dict(self._cache_dict) diff --git a/fuse/data/cache/cache_null.py b/fuse/data/cache/cache_null.py deleted file mode 100644 index 96e92935a..000000000 --- a/fuse/data/cache/cache_null.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Dummy cache implementation, doing nothing -""" -from multiprocessing import Manager -from typing import Hashable, Any, List - -from fuse.data.cache.cache_base import FuseCacheBase - - -class FuseCacheNull(FuseCacheBase): - def __init__(self): - super().__init__() - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return False - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - return None - - def __delitem__(self, key: Hashable) -> None: - """ - See base clas - """ - pass - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - pass - - def save(self) -> None: - """ - See base class - """ - pass - - def exist(self) -> bool: - """ - See base class - """ - return True - - def reset(self) -> None: - """ - See base class - """ - pass - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - return [] - - def start_caching(self, manager: Manager): - """ - See base class - """ - pass diff --git a/fuse/data/data_source/__init__.py b/fuse/data/data_source/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/data_source/data_source_base.py b/fuse/data/data_source/data_source_base.py deleted file mode 100644 index 647a4fef8..000000000 --- a/fuse/data/data_source/data_source_base.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Data source base -""" -from abc import ABC, abstractmethod - - -class FuseDataSourceBase(ABC): - - @abstractmethod - def get_samples_description(self): - """ - :return: list of samples description - """ - raise NotImplementedError - - @abstractmethod - def summary(self) -> str: - """ - String summary of the object - """ - raise NotImplementedError diff --git a/fuse/data/data_source/data_source_default.py b/fuse/data/data_source/data_source_default.py deleted file mode 100644 index a61a70aa9..000000000 --- a/fuse/data/data_source/data_source_default.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging - -import pandas as pd -from typing import Sequence, Hashable, Union, Optional, List, Dict - -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.utils.misc.misc import autodetect_input_source - - -class FuseDataSourceDefault(FuseDataSourceBase): - """ - DataSource for the following aut-detectable types: - - 1. DataFrame (instance or path to pickled object) - 2. Python list of sample descriptors - 3. Text file (needs to end with '.txt' or '.text' extension) - - """ - - def __init__(self, input_source: Union[str, pd.DataFrame, Sequence[Hashable]] = None, - folds: Optional[Union[int, Sequence[int]]] = None, conditions: Optional[List[Dict[str, List]]] = None) -> None: - """ - :param input_source: auto-detectable input source - :param folds: if input is a DataFrame having a 'fold' column, filter by this fold(s) - :param conditions: conditions to apply on data source. - the conditions are column names that are expected to be in input_source data frame. - - Structure: - * List of 'Filter Queries' with logical OR between them. - * Each Filter Query is a dictionary of data source column and a list of possible values, with logical AND between the keys. - - Example - selecting only negative or positive biopsy samples: - [{'biopsy' : ['positive', 'negative']}] - Example - selecting negative or positive biopsy biopsy samples that are of type 'tumor': - [{'biopsy': ['positive', 'negative'], 'type': ['tumor']}] - Example - selecting negative/positive biopsy samples that are of type 'calcification' AND marked as BIRAD 0 or 5: - [{'biopsy': ['positive', 'negative'], 'type': ['calcification'], 'birad': ['BIRAD0', 'BIRAD5']}] - Example - selecting samples that are either positive biopsy OR marked as BIRAD 0: - [{'biopsy': ['positive']}, {'birad': ['BIRAD0']}] - - """ - self.samples_df = autodetect_input_source(input_source) - - if conditions is not None: - before = len(self.samples_df) - to_keep = self.filter_by_conditions(self.samples_df, conditions) - self.samples_df = self.samples_df[to_keep].copy() - logging.getLogger('Fuse').info(f"Remove {before - len(self.samples_df)} records that did not meet conditions") - - if self.samples_df is None: - raise Exception('Error detecting input source in FuseDataSourceDefault') - - if isinstance(folds, int): - self.folds = [folds] - else: - self.folds = folds - - if self.folds is not None: - assert 'fold' in self.samples_df, f'Data cannot be filtered by folds {folds} as folds are specified in the collected data' - self.samples_df = self.samples_df[self.samples_df['fold'].isin(self.folds)] - - @staticmethod - def filter_by_conditions(samples: pd.DataFrame, conditions: Optional[List[Dict[str, List]]]): - """ - Returns a vector of the samples that passed the conditions - :param samples: dataframe to check. expected to have at least sample_desc column. - :param conditions: list of dictionaries. each dictionary has column name as keys and possible values as the values. - for each dict in the list: - the keys are applied with AND between them. - the dict conditions are applied with OR between them. - :return: boolean vector with the filtered samples - """ - to_keep = samples.sample_desc.isna() # start with all false - for condition_list in conditions: - condition_to_keep = samples.sample_desc.notna() # start with all true - for column, values in condition_list.items(): - condition_to_keep = condition_to_keep & samples[column].isin(values) # all conditions in list must be met - to_keep = to_keep | condition_to_keep # add this condition samples to_keep - return to_keep - - def get_samples_description(self): - return list(self.samples_df['sample_desc']) - - def summary(self) -> str: - summary_str = '' - summary_str += 'FuseDataSourceDefault - %d samples\n' % len(self.samples_df) - return summary_str - - -if __name__ == '__main__': - my_df = pd.DataFrame({'sample_desc': range(11, 16), - 'A': range(1, 6), - 'B': range(10, 0, -2), - 'C': range(10, 5, -1)}) - print(my_df) - clist = [{'A': [2, 3, 4], 'B': [8, 2]}, {'C': [8, 7]}] - to_keep = FuseDataSourceDefault.filter_by_conditions(my_df, clist) - print(my_df[to_keep]) - - to_keep = FuseDataSourceDefault.filter_by_conditions(my_df, [{}]) - print(my_df[to_keep]) diff --git a/fuse/data/data_source/data_source_folds.py b/fuse/data/data_source/data_source_folds.py deleted file mode 100644 index 56dba4157..000000000 --- a/fuse/data/data_source/data_source_folds.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on January 06, 2022 - -""" - -import pandas as pd -import os -import numpy as np -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from typing import Optional, Tuple -from fuse.data.data_source.data_source_toolbox import FuseDataSourceToolbox - - -class FuseDataSourceFolds(FuseDataSourceBase): - def __init__(self, - input_source: str, - input_df : pd.DataFrame, - phase: str, - no_mixture_id: str, - balance_keys: np.ndarray, - reset_partition_file: bool, - folds: Tuple[int], - num_folds : int =5, - partition_file_name: str = None - ): - - """ - Create DataSource which is divided to num_folds folds, supports either a path to a csv or data frame as input source. - The function creates a partition file which saves the fold partition - :param input_source: path to dataframe containing the samples ( optional ) - :param input_df: dataframe containing the samples ( optional ) - :param no_mixture_id: The key column for which no mixture between folds should be forced - :param balance_keys: keys for which balancing is forced - :param reset_partition_file: boolean flag which indicate if we want to reset the partition file - :param folds indicates which folds we want to retrieve from the fold partition - :param num_folds: number of folds to divide the data - :param partition_file_name:name of a csv file for the fold partition - If train = True, train/val indices are dumped into the file, - If train = False, train/val indices are loaded - :param phase: specifies if we are in train/validation/test/all phase - """ - self.nfolds = num_folds - self.key_columns = balance_keys - if reset_partition_file is True and phase not in ['train','all']: - raise Exception("Sorry, it is possible to reset partition file only in train / all phase") - if reset_partition_file is True or not os.path.isfile(partition_file_name): - # Load csv file - # ---------------------- - - if input_source is not None : - input_df = pd.read_csv(input_source) - self.folds_df = FuseDataSourceToolbox.balanced_division(df = input_df , - no_mixture_id = no_mixture_id, - key_columns = self.key_columns , - nfolds = self.nfolds , - print_flag=True ) - # Extract entities - # ---------------- - else: - self.folds_df = pd.read_csv(partition_file_name) - - sample_descs = [] - for fold in folds: - sample_descs += self.folds_df[self.folds_df['fold'] == fold]['file'].to_list() - - self.samples = sample_descs - - self.input_source = input_source - - def get_samples_description(self): - """ - Returns a list of samples ids. - :return: list[str] - """ - return self.samples - - def summary(self) -> str: - """ - Returns a data summary. - :return: str - """ - summary_str = '' - summary_str += 'Class = '+type(self).__name__+'\n' - - if isinstance(self.input_source, str): - summary_str += 'Input source filename = %s\n' % self.input_source - - summary_str += FuseDataSourceToolbox.print_folds_stat(db = self.folds_df , - nfolds = self.nfolds , - key_columns = self.key_columns ) - - return summary_str diff --git a/fuse/data/data_source/data_source_from_list.py b/fuse/data/data_source/data_source_from_list.py deleted file mode 100644 index 11408000f..000000000 --- a/fuse/data/data_source/data_source_from_list.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Sequence, Hashable - -from fuse.data.data_source.data_source_base import FuseDataSourceBase - - -class FuseDataSourceFromList(FuseDataSourceBase): - """ - Simple DataSource that can be initialized with a Python list (or other sequence). - Does nothing but passing the list to Dataset. - """ - - def __init__(self, list_of_samples: Sequence[Hashable] = []) -> None: - self.list_of_samples = list_of_samples - - def get_samples_description(self): - return self.list_of_samples - - def summary(self) -> str: - summary_str = '' - summary_str += 'FuseDataSourceFromList - %d samples\n' % len(self.list_of_samples) - return summary_str diff --git a/fuse/data/data_source/data_source_toolbox.py b/fuse/data/data_source/data_source_toolbox.py deleted file mode 100644 index df1ccfa54..000000000 --- a/fuse/data/data_source/data_source_toolbox.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Optional -from sklearn.utils import shuffle -import pandas as pd -import numpy as np -from collections import defaultdict -import pickle -import os - - -class FuseDataSourceToolbox(): - - @staticmethod - def print_folds_stat(db: pd.DataFrame, nfolds: int, key_columns: np.ndarray): - """ - Print fold statistics - :param db: dataframe which contains the fold patition - :param nfolds: Number of folds to divide the data - :param key_columns: keys for which balancing is forced - """ - result ='' - for f in range(nfolds): - for key in key_columns: - result += '----------fold' + str(f) +'\n' - result += 'key: ' + key +'\n' - result += db[db['fold'] == f][key].value_counts().to_string()+'\n' - return result - @staticmethod - def balanced_division(df : pd.DataFrame, no_mixture_id : str, key_columns: np.ndarray, nfolds : int, seed : int=1357, - excluded_samples: np.ndarray=[], print_flag : bool =False, debug_mode : bool=False) -> pd.DataFrame: - """ - Partition the data into folds while using no_mixture_id for which no mixture between folds should be forced. - and using key_columns as the keys for which balancing is forced. - The functions creates ID level labeling which is the cross-section of all possible mixture of key columns for that id - it creates the folds so each fold will have about same proportions of ID level labeling while each ID will appear only in one fold - For exmaple - patient with ID 1234 has 2 images , each image has a binary classification (benign / malignant) . - it can be that both of his images are benign or both are malignant or one is benign and the other is malignant. - For example - :param df: dataframe containing all samples including id and key_columns - :param no_mixture_id: The key column for which no mixture between folds should be forced - :param key_columns: keys for which balancing is forced - :param nfolds: number of folds to divide the data - :param seed: random seed used for creating folds - :param excluded_samples: sampled id which we do not want to include in the folds - :param print_flag: boolean flag which indicates if to print fold statistics - """ - id_level_labels = [] - record_labels = [] - for field in key_columns: - values = df[field].unique() - for value in values: - value2 = str.replace(str(value), '+', '') - # creates a binary label for each record and label - record_key = 'is' + value2 - df[record_key] = df[field] == value - # creates a binary label for each id and label ( is anyone with this id has his label) - id_level_key = 'sample_id_' + field + '_' + value2 - df[id_level_key] = df.groupby([no_mixture_id])[record_key].transform(sum) > 0 - id_level_labels.append(id_level_key) - record_labels.append(record_key) - - # drop duplicate id records - samples_col = [no_mixture_id] + [col for col in id_level_labels] - df_samples = df[samples_col].drop_duplicates() - - # generates a new label for each id based on sample_id value, using id's which are not in excluded_samples - excluded_samples_df = df_samples[no_mixture_id].isin(excluded_samples) - included_samples_df = df_samples[id_level_labels][~excluded_samples_df] - df_samples['y_class'] = [str(t) for t in included_samples_df.values] - y_values = list(df_samples['y_class'].unique()) - - # initialize folds to empty list of ids - db_samples = {} - for f in range(nfolds): - db_samples['data_fold' + str(f)] = [] - - # creates a dictionary with key=fold , and values = ID which is in the fold - # the partition goes as following : for each id level labels we shuffle the ID's and split equally ( as possible) to nfolds - for y_value in y_values: - patients_w_value = list(df_samples[no_mixture_id][df_samples['y_class'] == y_value]) - patients_w_value_shuffled = shuffle(patients_w_value, random_state=seed) - splitted_array = np.array_split(patients_w_value_shuffled, nfolds) - for f in range(nfolds): - db_samples['data_fold' + str(f)] = db_samples['data_fold' + str(f)] + list(splitted_array[f]) - - # creates a dictionary of dataframes, each dataframes holds all records for the fold - # each ID appears only in one fold - db = {} - for f in range(nfolds): - fold_df = df[df[no_mixture_id].isin(db_samples['data_fold' + str(f)])].copy() - fold_df['fold'] = f - db['data_fold' + str(f)] = fold_df - folds = pd.concat(db, ignore_index=True) - if print_flag is True: - FuseDataSourceToolbox.print_folds_stat(folds, nfolds, key_columns) - # remove labels used for creating the partition to folds - if not debug_mode : - folds.drop(id_level_labels+record_labels, axis=1, inplace=True) - return folds - diff --git a/fuse/data/dataset/__init__.py b/fuse/data/dataset/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/dataset/dataset_base.py b/fuse/data/dataset/dataset_base.py deleted file mode 100644 index 0a9cda4d2..000000000 --- a/fuse/data/dataset/dataset_base.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Fuse Dataset Base -""" -import pickle -from abc import abstractmethod -from enum import Enum -from typing import Any, List, Optional - -from torch.utils.data.dataset import Dataset - - -class FuseDatasetBase(Dataset): - """ - Abstract base class for Fuse dataset. - All subclasses should overwrite the following abstract methods inherited from torch.utils.data.Dataset - `__getitem__`, supporting fetching a data sample for a given key. - `__len__`, which is expected to return the size of the dataset - And the ones listed below - """ - - class SaveMode(Enum): - # store just the info required for inference - INFERENCE = 1, - # store all the info - TRAINING = 2 - - def __init__(self): - super().__init__() - - @abstractmethod - def create(self, **kwargs) -> None: - """ - Used to enable the instance - Typically will load caching, etc - :param kwargs: different parameters per subclass - :return: None - """ - raise NotImplementedError - - @abstractmethod - def get(self, index: Optional[int], key: Optional[str], use_cache: bool = False) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item or None for all - :param key: string representing the exact information required, use None for all. - :param use_cache: if true, will try to reload the sample from caching mechanism in case exist. - :return: the required info of a single sample of a list of samples - """ - raise NotImplementedError - - @abstractmethod - def collate_fn(self, samples: List[Any]) -> Any: - """ - collate list of samples into batch - :param samples: list of samples - :return: batch - """ - raise NotImplementedError - - # misc - @abstractmethod - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - String summary of the object - :param statistic_keys: Optional. list of keys to output statistics about. - """ - raise NotImplementedError - - # save and load datasets - @abstractmethod - def get_instance_to_save(self, mode: SaveMode) -> 'FuseDatasetBase': - """ - Create lite instance version of dataset with just the info required to recreate it - :param mode: see SaveMode for available modes - :return: the instance to save - """ - raise NotImplementedError - - @staticmethod - def save(dataset: 'FuseDatasetBase', mode: SaveMode, filename: str) -> None: - """ - Static method save dataset to the disc (see SaveMode for available modes) - :param dataset: the dataset to save - :param mode: required mode to save - :param filename: file name to use - :return: None - """ - # get instance version to save - dataset_to_save = dataset.get_instance_to_save(mode) - - # save this instance - with open(filename, 'wb') as pickle_file: - pickle.dump(dataset_to_save, pickle_file) - - @staticmethod - def load(filename: str, **kwargs) -> 'FuseDatasetBase': - """ - load dataset - :param filename: path to saved dataset - :param kwargs: arguments of create() function - :return: the dataset object - """ - # load saved instance - with open(filename, 'rb') as pickle_file: - dataset = pickle.load(pickle_file) - - # recreate dataset - dataset.create(**kwargs) - - return dataset diff --git a/fuse/data/dataset/dataset_dataframe.py b/fuse/data/dataset/dataset_dataframe.py deleted file mode 100644 index 1c202911c..000000000 --- a/fuse/data/dataset/dataset_dataframe.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Optional, List, Dict, Union - -import torch -import pandas as pd - -from fuse.data.data_source.data_source_from_list import FuseDataSourceFromList -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame - - -class FuseDatasetDataframe(FuseDatasetDefault): - """ - Simple dataset, based on FuseDatasetDefault, that converts dataframe into dataset. - """ - def __init__(self, - data: Optional[pd.DataFrame] = None, - data_pickle_filename: Optional[str] = None, - sample_desc_column: Optional[str] = 'descriptor', - columns_to_extract: Optional[List[str]] = None, - rename_columns: Optional[Dict[str, str]] = None, - columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None, - **kwargs): - """ - :param data: input DataFrame - :param data_pickle_filename: path to a pickled DataFrame (possible gzipped) - :param sample_desc_column: name of the sample descriptor column within the pickle file, - if set to None.will simply use dataframe index as descriptors - :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted - :param rename_columns: rename columns from dataframe, when None (default) column names are kept - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - :param kwargs: additional DatasetDefault arguments. See DatasetDefault - - """ - # create processor - processor = FuseProcessorDataFrame(data=data, - data_pickle_filename=data_pickle_filename, - sample_desc_column=sample_desc_column, - columns_to_extract=columns_to_extract, - rename_columns=rename_columns, - columns_to_tensor=columns_to_tensor) - - # extract descriptor list and create datasource - descriptors_list = processor.get_samples_descriptors() - - data_source = FuseDataSourceFromList(descriptors_list) - - super().__init__( - data_source=data_source, - gt_processors=None, - input_processors=None, - processors=processor, - **kwargs - ) diff --git a/fuse/data/dataset/dataset_default.py b/fuse/data/dataset/dataset_default.py deleted file mode 100644 index a425d5e8d..000000000 --- a/fuse/data/dataset/dataset_default.py +++ /dev/null @@ -1,756 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -import os -from multiprocessing import Manager -from multiprocessing.pool import Pool, ThreadPool -from typing import Any, Dict, Optional, Hashable, List, Union, Tuple, Callable - -import numpy as np -import torch -from pandas import DataFrame -from torch import Tensor -from tqdm import tqdm, trange - -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase -from fuse.data.cache.cache_base import FuseCacheBase -from fuse.data.cache.cache_files import FuseCacheFiles -from fuse.data.cache.cache_memory import FuseCacheMemory -from fuse.data.cache.cache_null import FuseCacheNull -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_debug import FuseUtilsDebug -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -from fuse.utils.misc.misc import get_pretty_dataframe, Misc - - -class FuseDatasetDefault(FuseDatasetBase): - """ - Fuse Dataset Default - Default generic implementation aimed to be used in most of the scenarios. - """ - - #### CONSTRUCTOR - def __init__(self, data_source: FuseDataSourceBase, - input_processors: Optional[Dict[str, FuseProcessorBase]], gt_processors: Optional[Dict[str, FuseProcessorBase]], processors: Union[FuseProcessorBase, Dict[str, FuseProcessorBase]] = None, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[FuseAugmentorBase] = None, - visualizer: Optional[FuseVisualizerBase] = None, post_processing_func=None, - statistic_keys: Optional[List[str]] = None, - filter_keys: Optional[List[str]] = None, - data_key_prefix: Optional[str] = 'data'): - """ - :param data_source: objects provides the list of object description - :param input_processors:dictionary of all the input data processors - :param gt_processors: dictionary of all the ground truth data processors - :param processors: Use in case the ground truth and input are coupled. Could be either a single processor or dictionary of processors. - If used, input_processors and gt_processors must be set to None. - :param cache_dest: Optional, path to save caching. - When cache_dest = 'memory', data is cached to Memory. - Else, if it's a string, data is saved to files under cache_desc dir - :param augmentor: Optional, object that perform the augmentation - :param visualizer: Optional, object that visualize the data - :param post_processing_func: callback that allows to dynamically modify the data. - Called as last step (after augmentation) - :param statistic_keys: Optional. list of statistic keys to output in default self.summary() implementation - :param filter_keys: Optional. list of keys to remove from the sample dictionary when getting an item - :param data_key_prefix: every key added to sample_dict by the dataset will be prepended with this prefix to get unique name. - """ - # log object input state - log_object_input_state(self, locals()) - - super().__init__() - - # store input params - self.cache_dest = cache_dest - self.data_source = data_source - if processors is None: - self.processors = {'input': input_processors, 'gt': gt_processors} - else: - if input_processors is not None: - msg = f'Either processors or input_processors should be set to None' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - if gt_processors is not None: - msg = f'Either processors or gt_processors should be set to None' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - self.processors = processors - - self.augmentor = augmentor - self.visualizer = visualizer - self.post_processing_func = post_processing_func - self.statistic_keys = statistic_keys or [] - self.filter_keys = filter_keys or [] - self.data_key_prefix = data_key_prefix - # initial values - # map sample running index to sample description (mush be hashable) - self.samples_description = [] - - # create dummy cache for now - the cache will be created and loaded in create() - self.cache: FuseCacheBase = FuseCacheNull() - # create dummy cache self.cache_fields used to store specific fields of the sample - used to optimize the running time of dataset.get( - # key=, use_cache=True) - self.cache_fields: FuseCacheBase = FuseCacheNull() - - # debug modes - read configuration - self.sample_stages_debug = FuseUtilsDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseUtilsDebug().get_setting('dataset_user') != 'default' - - def create(self, cache_all: bool = True, reset_cache: bool = False, - num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[FuseDataSourceBase] = None, - pool_type: str = 'process') -> None: - """ - Create the data set, including loading sample descriptions and caching - :param cache_all: if True will try to cache all - :param reset_cache: if False and cache_all is True, will use load caching instead of re creating it. - :param num_workers: number of workers used for caching - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :param override_datasource: might be used to change the data source - :param pool_type: multiprocess pooling type, can be either 'thread' (for ThreadPool) or 'process' (for 'Pool', default). - :return: None - """ - # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - assert pool_type in ['thread', 'process'], f'Invalid pool_type: {pool_type}. Multiprocessing pooling type can be either "thread" or "process"' - self.pool_type = pool_type - - # override data source if required - if override_datasource is not None: - self.data_source = override_datasource - - # extract list of sample description - self.samples_description = self.data_source.get_samples_description() - - # debug - override number of samples - dataset_override_num_samples = FuseUtilsDebug().get_setting('dataset_override_num_samples') - if dataset_override_num_samples != 'default': - self.samples_description = self.samples_description[:dataset_override_num_samples] - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) - - # cache object - if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: FuseCacheBase = FuseCacheMemory() - elif isinstance(self.cache_dest, str): - self.cache: FuseCacheBase = FuseCacheFiles(self.cache_dest, reset_cache) - - # cache samples if required - if not isinstance(self.cache, FuseCacheNull) and cache_all: - self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) - - # update descriptors - all_descriptors = set(self.samples_description) - cached_descriptors = set(self.cache.get_all_keys()) - self.samples_description = sorted(list(all_descriptors & cached_descriptors)) - - self.sample_descriptor_to_index = {v: k for k, v in enumerate(self.samples_description)} - - #### ITERATE AND GET DATA - def __len__(self): - return len(self.samples_description) - - def getitem_without_augmentation(self, index: int) -> Any: - """ - Get the original item, just before applying the augmentation. - The returned value will be stored in cache - :param index: the index of the item - :return: the original sample - """ - sample_description = self.samples_description[index] - sample = self.getitem_without_augmentation_static(self.processors, sample_description, data_key_prefix=self.data_key_prefix) - # make sure sample was loaded correctly - if sample is None: - msg = f'Failed to load data sample_desc={sample_description}, skipping is only possible when caching is enabled' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - return sample - - @staticmethod - def getitem_without_augmentation_static(processors: Union[Dict[str, FuseProcessorBase], FuseProcessorBase], descr: Hashable, data_key_prefix: Optional[str]) -> Any: - """ - Get the original item, just before applying the augmentation. - The returned value will be stored in cache - Static version - :param processors: the processors required to generate the sample - :param descr: sample descriptor - :return: the original sample as a dict, using the processors to retrieve its data. - e.g., - single processor - ----------------- - {'data.descriptor': image id string, - 'data.input': tensor of image - } - multi processors - ---------------- - {'data.descriptor':image id string, - 'data.input.image': tensor of image, - 'data.gt,gt_global': tensor of global gt - } - - """ - lgr = logging.getLogger('Fuse') - sample_data = {} - if data_key_prefix is not None: - sample = {data_key_prefix : sample_data} - else: - sample = sample_data - - # extract the sample description to be used by the processors - sample_data['descriptor'] = descr - # process data - if isinstance(processors, FuseProcessorBase): # handle a case of single processor - try: - processor = processors - value = processor(descr) - - if value is None: - lgr.error(f'processor failed to load data sample_desc={descr}, got None, skipping sample') - return None - elif isinstance(value, dict): - value = value.copy() - - sample_data.update(value) - except: - lgr.error(f'processor failed to load data sample_desc={descr}') - raise - else: # otherwise, dictionary that includes multiple processors - sample_data['input'] = {} - all_keys = FuseUtilsHierarchicalDict.get_all_keys(processors) - for key in all_keys: - try: - processor = FuseUtilsHierarchicalDict.get(processors, key) - value = processor(descr) - - if value is None: - lgr.error(f'processor {key} failed to load data sample_desc={descr}, got None, skipping sample') - return None - elif isinstance(value, dict): - value = value.copy() - - FuseUtilsHierarchicalDict.set(sample_data, key, value) - except: - lgr.error(f'processor {key} failed to load data sample_desc={descr}') - raise - - return sample - - def get_from_cache(self, index: Optional[int], key: str): - """ - Get input, ground truth or metadata of a sample. - First try to read from cache. Fallback to run the processor if not in cache. - - :param index: the index of the item, if None will return all items - :param key: string representing the exact information required - :return: the required info - """ - - if index is None: - # return all samples - values = [] - for index in trange(len(self)): - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - values.append(self.cache_fields[desc_field]) - else: - # if not found get the all sample and then extract the specified field - values.append(FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key)) - return values - else: - # return single sample - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - return self.cache_fields[desc_field] - else: - # if not found get the all sample and then extract the specified field - return FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key) - - def get(self, index: Optional[Union[int, Hashable]], key: Optional[str] = None, use_cache: bool = False) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item, if None will return all items - If not an int or None, will assume that index is sample descriptor - - :param key: string representing the exact information required. If None, will return all samples - :param use_cache: if true, will try to reload the sample from caching mechanism - :return: the required info - """ - if index is not None and not isinstance(index, int): - # get sample giving sample descriptor - # assume index is sample description - index = self.samples_description.index(index) - - # if key not specified return the all sample - if key is None: - if index is None: - return [self.getitem(index, apply_augmentation=False) for index in trange(len(self))] - else: - return self.getitem(index) - - # if use cache - if use_cache: - return self.get_from_cache(index, key) - - ## otherwise run the processor - if isinstance(self.processors, FuseProcessorBase): # single processor case - processor = self.processors - inner_key = key[len('data.'):] - else: # dictionary including multiple processors - all_processor_keys = FuseUtilsHierarchicalDict.get_all_keys(self.processors) - required_processor_key = None - inner_key = None - for processor_key in all_processor_keys: - if key.startswith(f'data.{processor_key}'): - required_processor_key = processor_key - inner_key = key[len(f'data.{processor_key}.'):] - break - - if required_processor_key is None: - raise Exception(f'processor not found for key {key}') - - processor = FuseUtilsHierarchicalDict.get(self.processors, required_processor_key) - - if index is None: - try: - value = processor.get_all(self.samples_description) - except: - value = [processor(sample_description) for sample_description in self.samples_description] - if inner_key != '': - value = [FuseUtilsHierarchicalDict.get(v, inner_key) for v in value] - else: - # get the sample description to be used by the processors - sample_description = self.samples_description[index] - value = processor(sample_description) - if inner_key != '': - value = FuseUtilsHierarchicalDict.get(value, inner_key) - - return value - - def __getitem__(self, index: int) -> Any: - """ - Get sample, read it from cache if possible, apply augmentation and post processing - :param index: sample index - :return: the required sample after augmentation - """ - sample_stages_debug = self.sample_stages_debug - return self.getitem(index, sample_stages_debug=sample_stages_debug) - - def getitem(self, index: int, apply_augmentation: bool = True, apply_post_processing: bool = True, sample_stages_debug: bool = False) -> Any: - """ - Get sample, read it from cache if possible - :param index: sample index - :param apply_augmentation: if true, will apply augmentation - :param apply_post_processing: If true, will apply post processing - :param sample_stages_debug: True will log the sample dict after each stage - :return: the required sample after augmentation - """ - - # either load from cache or generate and store in cache - sample_desc = self.samples_description[index] - - if sample_desc in self.cache: - sample = self.cache[sample_desc] - else: - sample = self.getitem_without_augmentation(index) - - # filter some of the keys if required - if self.filter_keys is not None: - for key in self.filter_keys: - try: - FuseUtilsHierarchicalDict.pop(sample, key) - except KeyError: - pass - - # debug mode - print original sample before augmentation and before post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - original sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - # one time print - self.sample_stages_debug = False - - # apply augmentation if enabled - if self.augmentor is not None and apply_augmentation: - sample = self.augmentor(sample) - - # debug mode - print sample after augmentation - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - augmented sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - # apply post processing - if self.post_processing_func is not None and apply_post_processing: - self.post_processing_func(sample) - - # debug mode - print sample after post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - post processed sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - return sample - - #### BATCHING - def collate_fn(self, samples: List[Dict], avoid_stack_keys: Tuple = tuple()) -> Dict: - """ - collate list of samples into batch_dict - :param samples: list of samples - :param avoid_stack_keys: list of keys to just collect to a list and avoid stack operation - :return: batch_dict - """ - batch_dict = {} - keys = FuseUtilsHierarchicalDict.get_all_keys(samples[0]) - for key in keys: - try: - collected_value = [FuseUtilsHierarchicalDict.get(sample, key) for sample in samples if sample is not None] - if key in avoid_stack_keys: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - elif isinstance(collected_value[0], Tensor): - FuseUtilsHierarchicalDict.set(batch_dict, key, torch.stack(collected_value)) - elif isinstance(collected_value[0], np.ndarray): - FuseUtilsHierarchicalDict.set(batch_dict, key, np.stack(collected_value)) - else: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - except: - logging.getLogger('Fuse').error(f'Failed to collect key {key}') - raise - - return batch_dict - - #### CACHING - def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None) -> None: - """ - Cache all data - :param num_workers: num of workers used to cache the samples - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :return: None - """ - lgr = logging.getLogger('Fuse') - - # check if cache is required - all_descriptors = set(self.samples_description) - cached_descriptors = set(self.cache.get_all_keys(include_none=True)) - descriptors_to_cache = all_descriptors - cached_descriptors - - if len(descriptors_to_cache) != 0: - # multi process cache - lgr.info(f'FuseDatasetDefault: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') - with Manager() as manager: - # change cache mode - to caching (writing) - self.cache.start_caching(manager) - - # multi process cache - if num_workers > 0: - the_pool = ThreadPool if self.pool_type == 'thread' else Pool - pool = the_pool(processes=num_workers, initializer=worker_init_func, initargs=worker_init_args) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample, - iterable=[(self.processors, desc, self.cache, self.data_key_prefix) for desc in descriptors_to_cache]), - total=len(descriptors_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - else: - for desc in tqdm(descriptors_to_cache): - self._cache_sample((self.processors, desc, self.cache, self.data_key_prefix)) - - # save and move back to read mode - self.cache.save() - lgr.info('FuseDatasetDefault: caching done') - else: - lgr.info(f'FuseDatasetDefault: all {len(all_descriptors)} samples are already cached') - - def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_workers: int = 8, cache_dest: Optional[str] = None) -> None: - """ - Cache specific fields (keys in batch_dict) - Used to optimize the running time of of dataset.get(key=, use_cache=True) - :param fields: list of keys in batch_dict - :param reset_cache: If True will reset cache first - :param num_workers: num workers used for caching - :param cache_dest: path to cache dir - :return: None - """ - lgr = logging.getLogger('Fuse') - - # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - if cache_dest is None: - cache_dest = os.path.join(self.cache_dest, 'fields') - - # create cache field object upon request - if isinstance(self.cache_fields, FuseCacheNull): - # cache object - if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: FuseCacheBase = FuseCacheMemory() - elif isinstance(cache_dest, str): - self.cache_fields: FuseCacheBase = FuseCacheFiles(cache_dest, reset_cache, single_file=True) - - # get list of desc to cache - desc_list = self.samples_description - desc_field_list = set([(desc, field) for desc in desc_list for field in fields]) - cached_desc_field = set(self.cache_fields.get_all_keys(include_none=True)) - desc_field_to_cache = desc_field_list - cached_desc_field - desc_to_cache = set([desc_field[0] for desc_field in desc_field_to_cache]) - - # multi thread caching - if len(desc_to_cache) != 0: - lgr.info(f'FuseDatasetDefault: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') - if num_workers > 0: - with Manager() as manager: - self.cache_fields.start_caching(manager) - pool = Pool(processes=num_workers) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample_fields, - iterable=[(desc, fields) for desc in desc_to_cache]), - total=len(desc_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - self.cache_fields.save() - else: - self.cache_fields.start_caching(None) - for desc in tqdm(desc_to_cache): - self._cache_sample_fields((desc, fields)) - self.cache_fields.save() - else: - lgr.info('FuseDatasetDefault: all samples fields are already cached') - - def _cache_sample_fields(self, args): - # decode args - desc, fields = args - index = self.samples_description.index(desc) - sample = self.getitem(index, apply_augmentation=False) - for field in fields: - # create field desc and save it in cache - desc_field = (desc, field) - if desc_field not in self.cache_fields: - value = FuseUtilsHierarchicalDict.get(sample, field) - self.cache_fields[desc_field] = value - - @staticmethod - def _cache_sample(args: Tuple) -> None: - """ - Store in cache single sample - :param args: tuple of processors, sample descriptor and cache object - :return: None - """ - processors, desc, cache, data_key_prefix = args - sample = FuseDatasetDefault.getitem_without_augmentation_static(processors, desc, data_key_prefix=data_key_prefix) - cache[desc] = sample - - #### Filtering - def filter(self, key: str, values: List[Any]) -> None: - """ - Filter sample if batch_dict[key] in values - :param key: key in batch_dict - :param values: list of values to filter - :return: None - """ - lgr = logging.getLogger('Fuse') - lgr.info(f'DatasetDefault: filtering key {key}, values {values}') - new_samples_desc = [] - for index, desc in tqdm(enumerate(self.samples_description), total=len(self.samples_description)): - value = self.get(index, key, use_cache=True) - if value not in values: - new_samples_desc.append(desc) - - self.samples_description = new_samples_desc - - #### VISUALIZE - def visualize(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True, **kwargs): - """ - visualize sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample , only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index, **kwargs) - - self.visualizer.visualize(batch_dict, block) - - def visualize_augmentation(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize augmentation of a sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample, only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index, apply_augmentation=False) - batch_dict_aug = self.getitem(index) - - self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) - - # save and load dataset - def get_instance_to_save(self, mode: FuseDatasetBase.SaveMode) -> FuseDatasetBase: - """ - See base class - """ - - # prepare data to save - dataset = FuseDatasetDefault(data_source=None, - input_processors={}, - gt_processors={}, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func, - statistic_keys=self.statistic_keys, - visualizer=self.visualizer) - if mode == FuseDatasetBase.SaveMode.INFERENCE and isinstance(self.processors, dict) and 'input' in self.processors: - dataset.processors = {'input': self.processors['input']} # for inference we can save only input processors if available - else: - dataset.processors = self.processors - - return dataset - - # misc - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - Returns a data summary. - Should be called after create() - :param statistic_keys: Optional. list of keys to output statistics about. - When None (default), self.statistic_keys are output. - :return: str - """ - statistic_keys_to_use = statistic_keys if statistic_keys is not None else self.statistic_keys - - sum = \ - f'Class = {self.__class__}\n' - sum += \ - f'Processors:\n' \ - f'------------------------\n' \ - f'{self.processors}\n' - sum += \ - f'Cache destination:\n' \ - f'------------------\n' \ - f'{self.cache_dest}\n' - sum += \ - f'Augmentor:\n' \ - f'----------\n' \ - f'{self.augmentor.summary() if self.augmentor is not None else None}\n' - sum += \ - f'Data source:\n' \ - f'------------\n' \ - f'{self.data_source.summary() if self.data_source is not None else None}\n' - sum += \ - f'Sample keys:\n' \ - f'------------\n' \ - f'{FuseUtilsHierarchicalDict.get_all_keys(self.getitem(0)) if self.data_source is not None else None}\n' - sum += \ - f'Basic Data Statistic:\n' + \ - f'-------------------\n' + \ - self.basic_data_summary(statistic_keys_to_use) - return sum - - def basic_data_summary(self, statistic_keys: List[str] = []) -> str: - """ - Provide string including basic stat that can be retrieved fast - :return: string stat - """ - # collect data that can be retrieved fast - collected_data = self.collect_basic_data(statistic_keys) - - # basic statistic - sum = '' - all_keys = FuseUtilsHierarchicalDict.get_all_keys(collected_data) - for processor_name in all_keys: - df = DataFrame(data=FuseUtilsHierarchicalDict.get(collected_data, processor_name), columns=[processor_name]) - stat_df = DataFrame() - stat_df['Value'] = df[processor_name].value_counts().index - stat_df['Count'] = df[processor_name].value_counts().values - stat_df['Percent'] = df[processor_name].value_counts(normalize=True).values * 100 - sum += \ - f'\n{processor_name} Statistics:\n' + \ - f'{get_pretty_dataframe(stat_df)}' - return sum - - def collect_basic_data(self, statistic_keys: List[str]) -> dict: - """ - Collect data that can be retrieved by get_all() or included in statistic_keys - :param statistic_keys: list of keys to collect data about - :return: hierarchical dict including the collect data - """ - sample_data = {} - if self.data_key_prefix: - samples = {self.data_key_prefix: sample_data} - else: - samples = sample_data - - # in case of multi processors, collect data of the ones implementing get_all() method - if not isinstance(self.processors, FuseProcessorBase): - all_keys = FuseUtilsHierarchicalDict.get_all_keys(self.processors) - for key in all_keys: - processor = FuseUtilsHierarchicalDict.get(self.processors, key) - try: - values_list = processor.get_all(self.samples_description) - if isinstance(values_list[0], dict): - for inner_key in FuseUtilsHierarchicalDict.get_all_keys(values_list[0]): - value_to_set = [int(FuseUtilsHierarchicalDict.get(value, inner_key)) for value in values_list] - FuseUtilsHierarchicalDict.set(sample_data, f'{key}.{inner_key}', value_to_set) - else: - # FIXME: maybe we will need to filter here according to value type one day - value_to_set = [int(value) for value in values_list] - FuseUtilsHierarchicalDict.set(sample_data, key, value_to_set) - except: - # do nothing - pass - - for key in statistic_keys: - values = self.get(index=None, key=key, use_cache=True) - # convert to int - maybe we will need to support additional types one day - value_to_set = [int(value) for value in values] - FuseUtilsHierarchicalDict.set(sample_data, key, value_to_set) - return samples diff --git a/fuse/data/dataset/dataset_generator.py b/fuse/data/dataset/dataset_generator.py deleted file mode 100644 index a4f4f18a1..000000000 --- a/fuse/data/dataset/dataset_generator.py +++ /dev/null @@ -1,561 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -import os -from multiprocessing import Manager -from multiprocessing.pool import Pool, ThreadPool -from typing import Any, Dict, Optional, Hashable, List, Union, Tuple, Callable - -import numpy as np -import torch -from pandas import DataFrame -from torch import Tensor -from tqdm import tqdm, trange - -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase -from fuse.data.cache.cache_base import FuseCacheBase -from fuse.data.cache.cache_files import FuseCacheFiles -from fuse.data.cache.cache_memory import FuseCacheMemory -from fuse.data.cache.cache_null import FuseCacheNull -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_debug import FuseUtilsDebug -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -from fuse.utils.misc.misc import get_pretty_dataframe, Misc - - -class FuseDatasetGenerator(FuseDatasetBase): - """ - Fuse Dataset Generator - Used when it's more convient to generate sevral samples at once - """ - - #### CONSTRUCTOR - def __init__(self, data_source: FuseDataSourceBase, processor: FuseProcessorBase, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[FuseAugmentorBase] = None, - visualizer: Optional[FuseVisualizerBase] = None, post_processing_func=None, - statistic_keys: Optional[List[str]] = None, - filter_keys: Optional[List[str]] = None): - """ - :param data_source: objects provides the list of object description - :param processor: data generator - :param cache_dest: Optional, path to save caching - :param augmentor: Optional, object that perform the augmentation - :param visualizer: Optional, object that visualize the data - :param post_processing_func: callback that allows to dynamically modify the data. - Called as last step (after augmentation) - :param statistic_keys: Optional. list of statistic keys to output in default self.summary() implementation - :param filter_keys: Optional. list of keys to remove from the sample dictionary when getting an item - """ - # log object input state - log_object_input_state(self, locals()) - - super().__init__() - - # store input params - self.cache_dest = cache_dest - self.augmentor = augmentor - self.visualizer = visualizer - self.processor = processor - self.data_source = data_source - self.post_processing_func = post_processing_func - self.statistic_keys = statistic_keys or [] - self.filter_keys = filter_keys or [] - # initial values - # map sample running index to sample description (mush be hashable) - self.subsets_description = [] - - # create default cache for now - the cache will be created and loaded in create() - self.cache: FuseCacheBase = FuseCacheMemory() - # create dummy cache - # self.cache_fields is used to store specific fields of the sample - - # used to optimize the running time of dataset.get(key=, use_cache=True) - self.cache_fields: FuseCacheBase = FuseCacheNull() - - # debug modes - read configuration - self.sample_stages_debug = FuseUtilsDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseUtilsDebug().get_setting('dataset_user') != 'default' - - def create(self, reset_cache: bool = False, - num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[FuseDataSourceBase] = None, override_cache_dest: Optional[str] = None, - pool_type: str = 'process') -> None: - - """ - Create the data set, including loading sample descriptions and caching - :param reset_cache: if False and cache_all is True, will use load caching instead of re creating it. - :param num_workers: number of workers used for caching - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :param override_datasource: might be used to change the data source - :param override_cache_dest: might be user to change the cache destination - :param pool_type: multiprocess pooling type, can be either 'thread' (for ThreadPool) or 'process' (for 'Pool', default). - :return: None - """ - # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - assert pool_type in ['thread', 'process'], f'Invalid pool_type: {pool_type}. Multiprocessing pooling type can be either "thread" or "process"' - self.pool_type = pool_type - - # override data source if required - if override_datasource is not None: - self.data_source = override_datasource - # override destination cache if required - if override_cache_dest is not None: - self.cache_dest = override_cache_dest - # extract list of sample description - self.subsets_description = self.data_source.get_samples_description() - - # debug - override number of samples - dataset_override_num_samples = FuseUtilsDebug().get_setting('dataset_override_num_samples') - if dataset_override_num_samples != 'default': - self.subsets_description = self.subsets_description[:dataset_override_num_samples] - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) - - # cache object - if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: FuseCacheBase = FuseCacheMemory() - elif isinstance(self.cache_dest, str): - self.cache: FuseCacheBase = FuseCacheFiles(self.cache_dest, reset_cache) - - # cache samples if required - if not isinstance(self.cache, FuseCacheNull): - self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) - - # update descriptors - all_descriptors = self.subsets_description - cached_descriptors = self.cache.get_all_keys() - self.samples_description = sorted([desc for desc in cached_descriptors if desc[0] in all_descriptors]) - - self.sample_descriptor_to_index = {v: k for k, v in enumerate(self.samples_description)} - #### ITERATE AND GET DATA - def __len__(self): - return len(self.samples_description) - - def get(self, index: Optional[Union[int, Hashable]], key: Optional[str] = None, use_cache: bool = True) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item, if None will return all items. - If not an int or None, will assume that imdex is sample descriptor - :param key: string representing the exact information required. If None, will return all sample - :param use_cache: if true, will try to reload the sample from caching mechanism - :return: the required info - """ - if index is not None and not isinstance(index, int): - # get sample giving sample descriptor - # assume index is sample description - index = self.samples_description.index(index) - - # if key not specified return the all sample - if key is None: - assert index != -1, 'get all samples is not supported when key = None' - return self.getitem(index) - - assert use_cache == True, f'{type(self)} support only use_cache=True' - - if index is None: - # return all samples - values = [] - for index in trange(len(self)): - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - values.append(self.cache_fields[desc_field]) - else: - # if not found get the all sample and then extract the specified field - values.append(FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key)) - return values - else: - # return single sample - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - return self.cache_fields[desc_field] - else: - # if not found get the all sample and then extract the specified field - return FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key) - - def __getitem__(self, index: int) -> Any: - """ - Get sample, read it from cache if possible, apply augmentation and post processing - :param index: sample index - :return: the required sample after augmentation - """ - sample_stages_debug = self.sample_stages_debug - return self.getitem(index, sample_stages_debug=sample_stages_debug) - - def getitem(self, index: int, apply_augmentation: bool = True, apply_post_processing: bool = True, sample_stages_debug: bool = False) -> Any: - """ - Get sample, read it from cache if possible - :param index: sample index - :param apply_augmentation: if true, will apply augmentation - :param apply_post_processing: If true, will apply post processing - :param sample_stages_debug: True will log the sample dict after each stage - :return: the required sample after augmentation - """ - - # load from cache - sample_desc = self.samples_description[index] - sample = self.cache[sample_desc] - - # filter some of the keys if required - if self.filter_keys is not None: - for key in self.filter_keys: - try: - FuseUtilsHierarchicalDict.pop(sample, key) - except KeyError: - pass - - # debug mode - print original sample before augmentation and before post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - original sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - # one time print - self.sample_stages_debug = False - - # apply augmentation if enabled - if self.augmentor is not None and apply_augmentation: - sample = self.augmentor(sample) - - # debug mode - print sample after augmentation - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - augmented sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - # apply post processing - if self.post_processing_func is not None and apply_post_processing: - self.post_processing_func(sample) - - # debug mode - print sample after post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - post processed sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - return sample - - #### BATCHING - def collate_fn(self, samples: List[Dict], avoid_stack_keys: Tuple = tuple()) -> Dict: - """ - collate list of samples into batch_dict - :param samples: list of samples - :param avoid_stack_keys: list of keys to just collect to a list and avoid stack operation - :return: batch_dict - """ - batch_dict = {} - keys = FuseUtilsHierarchicalDict.get_all_keys(samples[0]) - for key in keys: - try: - collected_value = [FuseUtilsHierarchicalDict.get(sample, key) for sample in samples if sample is not None] - if key in avoid_stack_keys: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - elif isinstance(collected_value[0], Tensor): - FuseUtilsHierarchicalDict.set(batch_dict, key, torch.stack(collected_value)) - elif isinstance(collected_value[0], np.ndarray): - FuseUtilsHierarchicalDict.set(batch_dict, key, np.stack(collected_value)) - else: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - except: - logging.getLogger('Fuse').error(f'Failed to collect key {key}') - raise - - return batch_dict - - #### CACHING - def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None) -> None: - """ - Cache all data - :param num_workers: num of workers used to cache the samples - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :return: None - """ - lgr = logging.getLogger('Fuse') - - # check if cache is required - all_descriptors = set([(subset_desc, 0) for subset_desc in self.subsets_description]) - cached_descriptors = set(self.cache.get_all_keys(include_none=True)) - descriptors_to_cache = all_descriptors - cached_descriptors - - if len(descriptors_to_cache) != 0: - # multi process cache - lgr.info(f'FuseDatasetGenerator: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') - with Manager() as manager: - # change cache mode - to caching (writing) - self.cache.start_caching(manager) - - # multi process cache - if num_workers > 0: - the_pool = ThreadPool if self.pool_type == 'thread' else Pool - pool = the_pool(processes=num_workers, initializer=worker_init_func, initargs=worker_init_args) - for _ in tqdm(pool.imap_unordered(func=self._cache_subset, - iterable=[(self.processor, subset_desc[0], self.cache) for subset_desc in descriptors_to_cache]), - total=len(descriptors_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - else: - for subset_desc in tqdm(descriptors_to_cache): - self._cache_subset((self.processor, subset_desc[0], self.cache)) - - # save and move back to read mode - self.cache.save() - lgr.info('FuseDatasetGenerator: caching done') - else: - lgr.info('FuseDatasetGenerator: all samples are already cached') - - @staticmethod - def _cache_subset(args: Tuple) -> None: - """ - Store in cache single sample - :param args: tuple of processor and subset descriptor - :return: None - """ - processor, subset_desc, cache = args - samples = processor(subset_desc) - if not isinstance(samples, List): - samples = [samples] - if samples: - for sample_index, sample_data in enumerate(samples): - - assert isinstance(sample_data, dict), f'expecting sample_data to be dictionary, got {type(sample_data)}' - sample_data = sample_data.copy() - - sample = {'data': sample_data} - sample_data['descriptor'] = (subset_desc, sample_index) - cache[sample_data['descriptor']] = sample - else: - # no samples extracted mark it as an invalid descriptor - cache[(subset_desc, 0)] = None - - def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_workers: int = 8, cache_dest: Optional[str] = None) -> None: - """ - Cache specific fields (keys in batch_dict) - Used to optimize the running time of of dataset.get(key=, use_cache=True) - :param fields: list of keys in batch_dict - :param reset_cache: If True will reset cache first - :param num_workers: num workers used for caching - :param cache_dest: path to cache dir - :return: None - """ - lgr = logging.getLogger('Fuse') - - # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - if cache_dest is None: - cache_dest = os.path.join(self.cache_dest, 'fields') - - # create cache field object upon request - if isinstance(self.cache_fields, FuseCacheNull): - # cache object - if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: FuseCacheBase = FuseCacheMemory() - elif isinstance(cache_dest, str): - self.cache_fields: FuseCacheBase = FuseCacheFiles(cache_dest, reset_cache, single_file=True) - - # get list of desc to cache - desc_list = self.samples_description - desc_field_list = set([(desc, field) for desc in desc_list for field in fields]) - cached_desc_field = set(self.cache_fields.get_all_keys(include_none=True)) - desc_field_to_cache = desc_field_list - cached_desc_field - desc_to_cache = set([desc_field[0] for desc_field in desc_field_to_cache]) - - # multi thread caching - if len(desc_to_cache) != 0: - lgr.info(f'FuseDatasetGenerator: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') - if num_workers > 0: - with Manager() as manager: - self.cache_fields.start_caching(manager) - pool = Pool(processes=num_workers) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample_fields, - iterable=[(desc, fields) for desc in desc_to_cache]), - total=len(desc_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - self.cache_fields.save() - else: - self.cache_fields.start_caching(None) - for desc in tqdm(desc_to_cache): - self._cache_sample_fields((desc, fields)) - self.cache_fields.save() - else: - lgr.info('FuseDatasetGenerator: all samples fields are already cached') - - def _cache_sample_fields(self, args): - # decode args - desc, fields = args - index = self.samples_description.index(desc) - sample = self.getitem(index, apply_augmentation=False) - for field in fields: - # create field desc and save it in cache - desc_field = (desc, field) - if desc_field not in self.cache_fields: - value = FuseUtilsHierarchicalDict.get(sample, field) - self.cache_fields[desc_field] = value - - #### Filtering - def filter(self, key: str, values: List[Any]) -> None: - """ - Filter sample if batch_dict[key] in values - :param key: key in batch_dict - :param values: list of values to filter - :return: None - """ - lgr = logging.getLogger('Fuse') - lgr.info(f'DatasetGenerator: filtering key {key}, values {values}') - new_samples_desc = [] - for index, desc in tqdm(enumerate(self.samples_description), total=len(self.samples_description)): - value = self.get(index, key, use_cache=True) - if value not in values: - new_samples_desc.append(desc) - - self.samples_description = new_samples_desc - - - - #### VISUALISE - def visualize(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample , only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index) - - self.visualizer.visualize(batch_dict, block) - - def visualize_augmentation(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize augmentation of a sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample, only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - batch_dict = self.getitem(index, apply_augmentation=False) - batch_dict_aug = self.getitem(index) - - self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) - - # save and load dataset - def get_instance_to_save(self, mode: FuseDatasetBase.SaveMode) -> FuseDatasetBase: - """ - See base class - """ - - # prepare data to save - if mode == FuseDatasetBase.SaveMode.INFERENCE: - dataset = FuseDatasetGenerator(data_source=None, - processor=self.processor, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func - ) - elif mode == FuseDatasetBase.SaveMode.TRAINING: - dataset = FuseDatasetGenerator(data_source=self.data_source, - processor=self.processor, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func, - visualizer=self.visualizer) - else: - raise Exception(f'Unexpected SaveMode {mode}') - - return dataset - - # misc - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - Returns a data summary. - Should be called after create() - :param statistic_keys: Optional. list of keys to output statistics about. - When None (default), self.statistic_keys are output. - :return: str - """ - statistic_keys_to_use = statistic_keys if statistic_keys is not None else self.statistic_keys - sum = \ - f'Class = {self.__class__}\n' - sum += \ - f'Processor:\n' \ - f'-----------------\n' \ - f'{self.processor}\n' - sum += \ - f'Cache destination:\n' \ - f'------------------\n' \ - f'{self.cache_dest}\n' - sum += \ - f'Augmentor:\n' \ - f'----------\n' \ - f'{self.augmentor.summary() if self.augmentor is not None else None}\n' - sum += \ - f'Sample keys:\n' \ - f'------------\n' \ - f'{FuseUtilsHierarchicalDict.get_all_keys(self.getitem(0)) if self.data_source is not None else None}\n' - if len(statistic_keys_to_use) > 0: - for key in statistic_keys_to_use: - values = self.get(index=None, key=key, use_cache=True) - # convert to int - maybe we will need to supporty additional types one day - values = [int(value) for value in values] - df = DataFrame(data=values, - columns=[key]) - stat_df = DataFrame() - stat_df['Value'] = df[key].value_counts().index - stat_df['Count'] = df[key].value_counts().values - stat_df['Percent'] = df[key].value_counts(normalize=True).values * 100 - sum += \ - f'\n{key} Statistics:\n' + \ - f'{get_pretty_dataframe(stat_df)}' - return sum diff --git a/fuse/data/dataset/dataset_wrapper.py b/fuse/data/dataset/dataset_wrapper.py deleted file mode 100644 index d832d768d..000000000 --- a/fuse/data/dataset/dataset_wrapper.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Union, Sequence, Dict, Tuple - -from torch.utils.data import Dataset - -from fuse.data.data_source.data_source_from_list import FuseDataSourceFromList -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.processor.processor_base import FuseProcessorBase - - -# Dataset processor -class DatasetProcessor(FuseProcessorBase): - """ - Processor that extract data from pytorch dataset and convert each sample to dictionary - """ - - def __init__(self, dataset: Dataset, mapping: Sequence[str]): - """ - :param dataset: the pytorch dataset to convert - :param mapping: dictionary key for each element returned by the pytorch dataset - """ - # store input arguments - self.mapping = mapping - self.dataset = dataset - - def __call__(self, desc: Tuple[str, int], *args, **kwargs): - index = desc[1] - sample = self.dataset[index] - sample = {self.mapping[i]: val for i, val in enumerate(sample)} - - return sample - - -class FuseDatasetWrapper(FuseDatasetDefault): - """ - Fuse Dataset Wrapper - wraps pytorch dataset. - Each sample will be converted to dictionary according to mapping. - And this dataset inherits all FuseDatasetDefault features - """ - - #### CONSTRUCTOR - def __init__(self, name: str, dataset: Dataset, mapping: Union[Sequence, Dict[str, str]], **kwargs): - """ - :param name: name of the data extracted from dataset, typically: 'train', 'validation;, 'test' - :param dataset: the dataset to extract the data from - :param mapping: including name for each returned object from dataset - :param kwargs: optinal, additional argumentes to provide to FuseDatasetDefault - """ - data_source = FuseDataSourceFromList([(name, i) for i in range(len(dataset))]) - processor = DatasetProcessor(dataset, mapping) - super().__init__(data_source=data_source, input_processors=None, gt_processors=None,processors=processor, **kwargs) diff --git a/fuse/data/processor/__init__.py b/fuse/data/processor/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/processor/processor_base.py b/fuse/data/processor/processor_base.py deleted file mode 100644 index 9fced4140..000000000 --- a/fuse/data/processor/processor_base.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Processors Base class -""" -from abc import ABC, abstractmethod -from typing import Hashable - - -class FuseProcessorBase(ABC): - @abstractmethod - def __call__(self, sample_desc: Hashable): - raise NotImplementedError diff --git a/fuse/data/processor/processor_csv.py b/fuse/data/processor/processor_csv.py deleted file mode 100644 index 08daee0b7..000000000 --- a/fuse/data/processor/processor_csv.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import ast -import pandas as pd - -from fuse.data.processor.processor_base import FuseProcessorBase -import logging -from typing import Hashable, List, Optional, Dict, Union -from torch import Tensor -import torch - -class FuseProcessorCSV(FuseProcessorBase): - """ - Processor reading data from csv file. - Covert each row to a dictionary - """ - - def __init__(self, csv_filename: str, sample_desc_column: str='descriptor', columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None): - """ - Processor reading data from csv file. - :param csv_filename: path to the csv file - :param sample_desc_column: name of the sample descriptor column within the csv file - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - """ - self.sample_desc_column = sample_desc_column - self.csv_filename = csv_filename - # read csv - self.data = pd.read_csv(csv_filename) - self.columns_to_tensor = columns_to_tensor - - def __call__(self, sample_desc: Hashable): - """ - See base class - """ - # locate the required item - items = self.data.loc[self.data[self.sample_desc_column] == str(sample_desc)] - # convert to dictionary - assumes there is only one item with the requested descriptor - sample_data = items.to_dict('records')[0] - for key in sample_data.keys(): - if 'output' in key and isinstance(sample_data[key], str): - tuple_data = sample_data[key] - if tuple_data.startswith('[') and tuple_data.endswith(']'): - sample_data[key] = ast.literal_eval(tuple_data.replace(" ", ",")) - # convert to tensor - if self.columns_to_tensor is not None: - if isinstance(self.columns_to_tensor, list): - for col in self.columns_to_tensor: - self.convert_to_tensor(sample_data, col) - elif isinstance(self.columns_to_tensor, dict): - for col, tensor_dtype in self.columns_to_tensor.items(): - self.convert_to_tensor(sample_data, col, tensor_dtype) - return sample_data - - @staticmethod - def convert_to_tensor(sample: dict, key: str, tensor_dtype: Optional[str] = None) -> None: - """ - Convert value to tensor, use tensor_dtype to specify non-default type/ - :param sample: sample dictionary - :param key: key of item in sample dict to convert - :param tensor_dtype: Optional, None for default,. - """ - if key not in sample: - lgr = logging.getLogger('Fuse') - lgr.error(f'Column {key} does not exit in dataframe, it is ignored and not converted to {tensor_dtype}') - elif isinstance(sample[key], Tensor): - sample[key] = sample[key] - else: - sample[key] = torch.tensor(sample[key], dtype=tensor_dtype) diff --git a/fuse/data/processor/processor_dataframe.py b/fuse/data/processor/processor_dataframe.py deleted file mode 100644 index 0a930533b..000000000 --- a/fuse/data/processor/processor_dataframe.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Hashable, List, Optional, Dict, Union -import logging -import torch -import pandas as pd -from torch import Tensor - -from fuse.data.processor.processor_base import FuseProcessorBase - - -class FuseProcessorDataFrame(FuseProcessorBase): - """ - Processor reading data from pickle file / dataframe object. - Covert each row to a dictionary - """ - - def __init__(self, - data: Optional[pd.DataFrame] = None, - data_pickle_filename: Optional[str] = None, - sample_desc_column: Optional[str] = 'descriptor', - columns_to_extract: Optional[List[str]] = None, - rename_columns: Optional[Dict[str, str]] = None, - columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None): - """ - :param data: input DataFrame - :param data_pickle_filename: path to a pickled DataFrame (possible gzipped) - :param sample_desc_column: name of the sample descriptor column within the pickle file, - if set to None.will simply use dataframe index as descriptors - :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted - :param rename_columns: rename columns from dataframe, when None (default) column names are kept - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - """ - # verify input - lgr = logging.getLogger('Fuse') - if data is None and data_pickle_filename is None: - msg = "Error in FuseProcessorDataFrame - need to provide either in-memory DataFrame or a path to pickled DataFrame." - lgr.error(msg) - raise Exception(msg) - elif data is not None and data_pickle_filename is not None: - msg = "Error in FuseProcessorDataFrame - need to provide either 'data' or 'data_pickle_filename' args, bot not both." - lgr.error(msg) - raise Exception(msg) - - # read dataframe - if data is not None: - self.data = data - self.pickle_filename = 'in-memory' - elif data_pickle_filename is not None: - self.data = pd.read_pickle(data_pickle_filename) - self.pickle_filename = data_pickle_filename - - # store input arguments - self.sample_desc_column = sample_desc_column - self.columns_to_extract = columns_to_extract - self.columns_to_tensor = columns_to_tensor - - # extract only specified columns (in case not specified, extract all) - if self.columns_to_extract is not None: - self.data = self.data[self.columns_to_extract] - - # rename columns - if rename_columns is not None: - self.data.rename(rename_columns, axis=1, inplace=True) - - # convert to dictionary: {index -> {column -> value}} - self.data = self.data.set_index(self.sample_desc_column) - self.data = self.data.to_dict(orient='index') - - def __call__(self, sample_desc: Hashable): - """ - See base class - """ - # locate the required item - sample_data = self.data[sample_desc].copy() - - # convert to tensor - if self.columns_to_tensor is not None: - if isinstance(self.columns_to_tensor, list): - for col in self.columns_to_tensor: - self.convert_to_tensor(sample_data, col) - elif isinstance(self.columns_to_tensor, dict): - for col, tensor_dtype in self.columns_to_tensor.items(): - self.convert_to_tensor(sample_data, col, tensor_dtype) - - return sample_data - - def get_samples_descriptors(self) -> List[Hashable]: - """ - :return: list of descriptors dataframe index values - """ - return list(self.data.keys()) - - @staticmethod - def convert_to_tensor(sample: dict, key: str, tensor_dtype: Optional[str] = None) -> None: - """ - Convert value to tensor, use tensor_dtype to specify non-default type/ - :param sample: sample dictionary - :param key: key of item in sample dict to convert - :param tensor_dtype: Optional, None for default,. - """ - if key not in sample: - lgr = logging.getLogger('Fuse') - lgr.error(f'Column {key} does not exit in dataframe, it is ignored and not converted to {tensor_dtype}') - elif isinstance(sample[key], Tensor): - sample[key] = sample[key] - else: - sample[key] = torch.tensor(sample[key], dtype=tensor_dtype) diff --git a/fuse/data/processor/processor_dicom_mri.py b/fuse/data/processor/processor_dicom_mri.py deleted file mode 100755 index 7e9e6df40..000000000 --- a/fuse/data/processor/processor_dicom_mri.py +++ /dev/null @@ -1,647 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" -import os, glob -import numpy as np -import SimpleITK as sitk -import pydicom -from scipy.ndimage.morphology import binary_dilation -import logging -import h5py -from typing import Tuple -import pandas as pd -from fuse.data.processor.processor_base import FuseProcessorBase - - -# ======================================================================== -# sequences to be read, and the sequence name -SEQ_DICT = \ - { - 't2_tse_tra': 'T2', - 't2_tse_tra_Grappa3': 'T2', - 't2_tse_tra_320_p2': 'T2', - - 'ep2d-advdiff-3Scan-high bvalue 100': 'b', - 'ep2d-advdiff-3Scan-high bvalue 500': 'b', - 'ep2d-advdiff-3Scan-high bvalue 1400': 'b', - 'ep2d_diff_tra2x2_Noise0_FS_DYNDISTCALC_BVAL': 'b', - - 'ep2d_diff_tra_DYNDIST': 'b_mix', - 'ep2d_diff_tra_DYNDIST_MIX': 'b_mix', - 'diffusie-3Scan-4bval_fs': 'b_mix', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen': 'b_mix', - 'diff tra b 50 500 800 WIP511b alle spoelen': 'b_mix', - - 'ep2d_diff_tra_DYNDIST_MIX_ADC': 'ADC', - 'diffusie-3Scan-4bval_fs_ADC': 'ADC', - 'ep2d-advdiff-MDDW-12dir_spair_511b_ADC': 'ADC', - 'ep2d-advdiff-3Scan-4bval_spair_511b_ADC': 'ADC', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen_ADC': 'ADC', - 'diff tra b 50 500 800 WIP511b alle spoelen_ADC': 'ADC', - 'ADC_S3_1': 'ADC', - 'ep2d_diff_tra_DYNDIST_ADC': 'ADC', - - } - -# patients with special fix -EXP_PATIENTS = ['ProstateX-0191', 'ProstateX-0148', 'ProstateX-0180'] - -SEQ_TO_USE = ['T2', 'b', 'b_mix', 'ADC', 'ktrans'] -SUB_SEQ_TO_USE = ['T2', 'b400', 'b800', 'ADC', 'ktrans'] -SER_INX_TO_USE = {} -SER_INX_TO_USE['all'] = {'T2': -1, 'b': [0, 2], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0148'] = {'T2': 1, 'b': [1, 2], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0191'] = {'T2': -1, 'b': [0, 0], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0180'] = {'T2': -1, 'b': [1, 2], 'ADC': 0, 'ktrans': 0} - -# sequences with special fix -B_SER_FIX = ['diffusie-3Scan-4bval_fs', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen', - 'diff tra b 50 500 800 WIP511b alle spoelen'] - -class FuseDicomMRIProcessor(FuseProcessorBase): - def __init__(self,verbose: bool=True,reference_inx: int=0,seq_dict:dict=SEQ_DICT, - seq_to_use:list=SEQ_TO_USE,subseq_to_use:list=SUB_SEQ_TO_USE, - ser_inx_to_use:dict=SER_INX_TO_USE,exp_patients:dict=EXP_PATIENTS, - use_order_indicator: bool=False): - ''' - FuseDicomMRIProcessor is MRI volume processor - :param verbose: if print verbose - :param reference_inx: index for the sequence that is selected as reference from SEQ_TO_USE (0 for T2) - :param seq_dict: dictionary in which varies series descriptions are grouped - together based on dict key. - :param seq_to_use: The sequences to use are selected - :param subseq_to_use: - :param ser_inx_to_use: The series index to use - :param exp_patients: patients with missing series that are treated in a special inx - default params are for prostate_x dataset - ''' - - self._verbose = verbose - self._reference_inx = reference_inx - self._seq_dict = seq_dict - self._seq_to_use = seq_to_use - self._subseq_to_use = subseq_to_use - self._ser_inx_to_use = ser_inx_to_use - self._exp_patients = exp_patients - self._use_order_indicator = use_order_indicator - - - - - - def __call__(self, - sample_desc, - *args, **kwargs): - """ - sample_desc contains: - :param images_path: path to directory in which dicom data is located - :param ktrans_data_path: path to directory of Ktrans seq (prostate x) - :param patient_id: patient indicator - :return: 4D tensor of MRI volumes, reference volume - """ - - imgs_path, ktrans_data_path, patient_id = sample_desc - - self._imgs_path = imgs_path - self._ktrans_data_path = ktrans_data_path - self._patient_id = patient_id - - - # ======================================================================== - # extract stk vol list per sequence - vols_dict, seq_info = self.extract_vol_per_seq() - - # ======================================================================== - # list of sitk volumes (z,x,y) per sequence - # order of volumes as defined in SER_INX_TO_USE - # if missing volume, replaces with volume of zeros - vol_list = self.extract_list_of_rel_vol(vols_dict, seq_info) - vol_ref = vol_list[self._reference_inx] - # ======================================================================== - # vol_4D is multichannel volume (z,x,y,chan(sequence)) - vol_4D = self.preprocess_and_stack_seq(vol_list, reference_inx=self._reference_inx) - - return vol_4D,vol_ref - - # ======================================================================== - - def extract_stk_vol(self,img_path:str, img_list:list=[str], reverse_order:bool=False, is_path:bool=True)->list: - """ - extract_stk_vol loads dicoms into sitk vol - :param img_path: path to dicoms - load all dicoms from this path - :param img_list: list of dicoms to load - :param reverse_order: sometimes reverse dicoms orders is needed - (for b series in which more than one sequence is provided inside the img_path) - :param is_path: if True loads all dicoms from img_path - :return: list of stk vols - """ - - stk_vols = [] - - try: - # load from HDF5 - if img_path[-4::] in 'hdf5': - with h5py.File(img_path, 'r') as hf: - _array = np.array(hf['array']) - _spacing = hf.attrs['spacing'] - _origin = hf.attrs['origin'] - _world_matrix = np.array(hf.attrs['world_matrix'])[:3, :3] - _world_matrix_unit = _world_matrix / np.linalg.norm(_world_matrix, axis=0) - _world_matrix_unit_flat = _world_matrix_unit.flatten() - - - # volume 2 sitk - vol = sitk.GetImageFromArray(_array) - vol.SetOrigin([_origin[i] for i in [1, 2, 0]]) - vol.SetDirection(_world_matrix_unit_flat) - vol.SetSpacing([_spacing[i] for i in [1, 2, 0]]) - stk_vols.append(vol) - return stk_vols - - elif is_path: - vol = sitk.ReadImage(img_path) - stk_vols.append(vol) - return stk_vols - - else: - series_reader = sitk.ImageSeriesReader() - - if img_list == []: - img_list = [series_reader.GetGDCMSeriesFileNames(img_path)] - - for n, imgs_names in enumerate(img_list): - if img_path not in img_list[0][0]: - imgs_names = [os.path.join(img_path, n) for n in imgs_names] - dicom_names = imgs_names[::-1] if reverse_order else imgs_names - series_reader.SetFileNames(dicom_names) - imgs = series_reader.Execute() - stk_vols.append(imgs) - - return stk_vols - - except Exception as e: - print(e) - - - - - - - - # ======================================================================== - - def sort_dicom_by_dicom_field(self,dcm_files: list, dicom_field: tuple =(0x19, 0x100c))->list: - """ - sort_dicom_by_dicom_field sorts the dcm_files based on dicom_field - For some MRI sequences different kinds of MRI series are mixed together (as in bWI) case - This function creates a dict={dicom_field_type:list of relevant dicoms}, - than concats all to a list of the different series types - - :param dcm_files: list of all dicoms , mixed - :param dicom_field: dicom field to sort based on - :return: sorted_names_list, list of sorted dicom series - """ - - dcm_values = {} - dcm_patient_z = {} - dcm_instance = {} - for index,dcm in enumerate(dcm_files): - dcm_ds = pydicom.dcmread(dcm) - patient_z = int(dcm_ds.ImagePositionPatient[2]) - instance_num = int(dcm_ds.InstanceNumber) - try: - val = int(dcm_ds[dicom_field].value) - if val not in dcm_values: - dcm_values[val] = [] - dcm_patient_z[val] = [] - dcm_instance[val] = [] - dcm_values[val].append(os.path.split(dcm)[-1]) - dcm_patient_z[val].append(patient_z) - dcm_instance[val].append(instance_num) - except: - #sort by - if index==0: - patient_z_ = [] - for dcm_ in dcm_files: - dcm_ds_ = pydicom.dcmread(dcm_) - patient_z_.append(dcm_ds_.ImagePositionPatient[2]) - val = int(np.floor((instance_num-1)/len(np.unique(patient_z_)))) - if val not in dcm_values: - dcm_values[val] = [] - dcm_patient_z[val] =[] - dcm_instance[val] = [] - dcm_values[val].append(os.path.split(dcm)[-1]) - dcm_patient_z[val].append(patient_z) - dcm_instance[val].append(instance_num) - - sorted_keys = np.sort(list(dcm_values.keys())) - sorted_names_list = [dcm_values[key] for key in sorted_keys] - dcm_patient_z_list = [dcm_patient_z[key] for key in sorted_keys] - dcm_instance_list = [dcm_instance[key] for key in sorted_keys] - - if self._use_order_indicator: - # sort from low patient z to high patient z - sorted_names_list2 = [list(np.array(list_of_names)[np.argsort(list_of_z)]) for list_of_names,list_of_z in zip(sorted_names_list,dcm_patient_z_list)] - else: - # sort by instance number - sorted_names_list2 = [list(np.array(list_of_names)[np.argsort(list_of_z)]) for list_of_names,list_of_z in zip(sorted_names_list,dcm_instance_list)] - - return sorted_names_list2 - - - # ======================================================================== - - def extract_vol_per_seq(self)-> dict: - """ - extract_vol_per_seq arranges sequences in sitk volumes dict - dict{seq_description: list of sitk} - :return: - vols_dict, dict{seq_description: list of sitk} - sequences_dict,dict{seq_description: list of series descriptions} - """ - - ktrans_path = os.path.join(self._ktrans_data_path, self._patient_id) - - if self._verbose: - print('Patient ID: %s' % (self._patient_id)) - - # ------------------------ - # images dict and sequences description dict - - vols_dict = {k: [] for k in self._seq_to_use} - sequences_dict = {k: [] for k in self._seq_to_use} - sequences_num_dict = {k: [] for k in self._seq_to_use} - - for img_path in os.listdir(self._imgs_path): - try: - full_path = os.path.join(self._imgs_path, img_path) - dcm_files = glob.glob(os.path.join(full_path, '*.dcm')) - series_desc = pydicom.dcmread(dcm_files[0]).SeriesDescription - try: - series_num = int(pydicom.dcmread(dcm_files[0]).AcquisitionNumber) - except: - series_num = int(pydicom.dcmread(dcm_files[0]).SeriesNumber) - - - #------------------------ - # print series description - series_desc_general = self._seq_dict[series_desc] \ - if series_desc in self._seq_dict else 'UNKNOWN' - if self._verbose: - print('\t- Series description:',' %s (%s)' % (series_desc, series_desc_general)) - - - - #------------------------ - # ignore UNKNOWN series - if series_desc not in self._seq_dict or \ - self._seq_dict[series_desc] not in self._seq_to_use: - continue - - #------------------------ - # b-series - sorting images by b-value - - if self._seq_dict[series_desc] == 'b_mix': - dcm_ds = pydicom.dcmread(dcm_files[0]) - if 'DiffusionBValue' in dcm_ds: - dicom_field = (0x0018,0x9087)#'DiffusionBValue' - else: - dicom_field = (0x19, 0x100c) - - if self._use_order_indicator: - reverse_order = False - else: - #default - reverse_order = True - - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files, dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=reverse_order, is_path=False) - - # ------------------------ - # MASK - elif self._seq_dict[series_desc] == 'MASK': - dicom_field = (0x0020, 0x0011)#series number - - if self._use_order_indicator: - reverse_order = False - else: - # default - reverse_order = True - - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files, dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=reverse_order, - is_path=False) - - #------------------------ - # DCE - sorting images by time phases - elif 'DCE' in self._seq_dict[series_desc]: - dcm_ds = pydicom.dcmread(dcm_files[0]) - if 'TemporalPositionIdentifier' in dcm_ds: - dicom_field = (0x0020, 0x0100) #Temporal Position Identifier - elif 'TemporalPositionIndex' in dcm_ds: - dicom_field = (0x0020, 0x9128) - else: - dicom_field = (0x0020, 0x0012)#Acqusition Number - - if self._use_order_indicator: - reverse_order = False - else: - #default - reverse_order = False - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files,dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=False, is_path=False) - - - #------------------------ - # general case - else: - # images are sorted based instance number - stk_vols = self.extract_stk_vol(full_path, img_list=[], reverse_order=False, is_path=False) - - #------------------------ - # volume dictionary - - if self._seq_dict[series_desc] == 'b_mix': - vols_dict['b'] += stk_vols - sequences_dict['b'] += [series_desc] - sequences_num_dict['b']+=[series_num] - else: - vols_dict[self._seq_dict[series_desc]] += stk_vols - sequences_dict[self._seq_dict[series_desc]] += [series_desc] - sequences_num_dict[self._seq_dict[series_desc]] += [series_num] - - except Exception as e: - print(e) - - #------------------------ - # Read ktrans image - try: - - if glob.glob(os.path.join(ktrans_path, '*.mhd')): - mhd_path = glob.glob(os.path.join(ktrans_path, '*.mhd'))[0] - print('\t- Reading: %s (%s) (%s)' % (os.path.split(mhd_path)[-1], 'Ktrans', 'ktrans')) - stk_vols = self.extract_stk_vol(mhd_path, img_list=[], reverse_order=False, is_path=True) - vols_dict['ktrans'] = stk_vols - sequences_dict['ktrans'] = [ktrans_path] - - - except Exception as e: - print(e) - - if 'b_mix' in vols_dict.keys(): - vols_dict.pop('b_mix') - sequences_dict.pop('b_mix') - - # handle multiphase DCE in different series - if ('DCE_mix_ph1' in vols_dict.keys()) | ('DCE_mix_ph2' in vols_dict.keys()) | ('DCE_mix_ph3' in vols_dict.keys()): - if (len(vols_dict['DCE_mix_ph1'])>0) | (len(vols_dict['DCE_mix_ph2'])>0) | (len(vols_dict['DCE_mix_ph3'])>0): - keys_list = [tmp for tmp in list(vols_dict.keys()) if 'DCE_mix_' in tmp] - for key in keys_list: - stk_vols = vols_dict[key] - series_desc = sequences_dict[key] - vols_dict['DCE_mix'] += stk_vols - sequences_dict['DCE_mix'] += [series_desc] - vols_dict.pop(key) - sequences_dict.pop(key) - - if ('DCE_mix_ph' in vols_dict.keys()): - if (len(vols_dict['DCE_mix_ph'])>0): - keys_list = [tmp for tmp in list(sequences_num_dict.keys()) if 'DCE_mix_' in tmp] - for key in keys_list: - stk_vols = vols_dict[key] - if (len(stk_vols)>0): - inx_sorted = np.argsort(sequences_num_dict[key]) - for ser_num_inx in inx_sorted: - vols_dict['DCE_mix'] += [stk_vols[int(ser_num_inx)]] - sequences_dict['DCE_mix'] += [series_desc] - vols_dict.pop(key) - sequences_dict.pop(key) - return vols_dict, sequences_dict - - # ======================================================================== - def extract_list_of_rel_vol(self,vols_dict:dict,seq_info:dict)->list: - """ - extract_list_of_rel_vol extract the volume per seq based on SER_INX_TO_USE - and put in one list - :param vols_dict: dict of sitk vols per seq - :param seq_info: dict of seq description per seq - :return: - """ - - def get_zeros_vol(vol): - - if vol.GetNumberOfComponentsPerPixel() > 1: - ref_zeros_vol = sitk.VectorIndexSelectionCast(vol, 0) - else: - ref_zeros_vol = vol - zeros_vol = np.zeros_like(sitk.GetArrayFromImage(ref_zeros_vol)) - zeros_vol = sitk.GetImageFromArray(zeros_vol) - zeros_vol.CopyInformation(ref_zeros_vol) - return zeros_vol - - def stack_rel_vol_in_list(vols,series_inx_to_use,seq): - vols_list = [] - for s, v0 in vols.items(): - vol_inx_to_use = series_inx_to_use['all'][s] - - if self._patient_id in self._exp_patients: - vol_inx_to_use = series_inx_to_use[self._patient_id][s] - - if isinstance(vol_inx_to_use,list): - for inx in vol_inx_to_use: - if len(v0)==0: - vols_list.append(get_zeros_vol(vols_list[0])) - elif len(v0)0.3] = 1 - vol_array[:,:,:,mask_ch_inx] = bool_mask - - vol_final = sitk.GetImageFromArray(vol_array, isVector=True) - vol_final.CopyInformation(vol_backup) - vol_final = sitk.Image(vol_final) - - return vol_final - - # ======================================================================== - def apply_rescaling(self,img:np.array, thres:tuple=(1.0, 99.0), method:str='noclip'): - """ - apply_rescaling rescale each channal using method - :param img: - :param thres: - :param method: - :return: - """ - eps = 0.000001 - - def rescale_single_channel_image(img): - # Deal with negative values first - min_value = np.min(img) - if min_value < 0: - img -= min_value - if method == 'clip': - val_l, val_h = np.percentile(img, thres) - img2 = img - img2[img < val_l] = val_l - img2[img > val_h] = val_h - img2 = (img2.astype(np.float32) - val_l) / (val_h - val_l + eps) - elif method == 'mean': - img2 = img / max(np.mean(img), 1) - elif method == 'median': - img2 = img / max(np.median(img), 1) - elif method == 'noclip': - val_l, val_h = np.percentile(img, thres) - img2 = img - img2 = (img2.astype(np.float32) - val_l) / (val_h - val_l + eps) - else: - img2 = img - return img2 - - # fix outlier image values - img[np.isnan(img)] = 0 - # Process each channel independently - if len(img.shape) == 4: - for i in range(img.shape[-1]): - img[..., i] = rescale_single_channel_image(img[..., i]) - else: - img = rescale_single_channel_image(img) - - return img - - # ======================================================================== - def create_resample(self,vol_ref:sitk.sitkFloat32, interpolation: str, size:Tuple[int,int,int], spacing: Tuple[float,float,float]): - """ - create_resample create resample operator - :param vol_ref: sitk vol to use as a ref - :param interpolation:['linear','nn','bspline'] - :param size: in pixels () - :param spacing: in mm () - :return: resample sitk operator - """ - - if interpolation == 'linear': - interpolator = sitk.sitkLinear - elif interpolation == 'nn': - interpolator = sitk.sitkNearestNeighbor - elif interpolation == 'bspline': - interpolator = sitk.sitkBSpline - - resample = sitk.ResampleImageFilter() - resample.SetReferenceImage(vol_ref) - resample.SetOutputSpacing(spacing) - resample.SetInterpolator(interpolator) - resample.SetSize(size) - return resample - - - diff --git a/fuse/data/processor/processor_rand.py b/fuse/data/processor/processor_rand.py deleted file mode 100644 index 53533bc28..000000000 --- a/fuse/data/processor/processor_rand.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Processor generating random ground truth - useful for testing and sanity check -""" -from typing import Hashable, Tuple - -import torch - -from fuse.data.processor.processor_base import FuseProcessorBase - - -class FuseProcessorRandInt(FuseProcessorBase): - def __init__(self, min: int = 0, max: int = 1, shape: Tuple = (1,)): - self.min = min - self.max = max - self.shape = shape - - def __call__(self, sample_desc: Hashable): - return {'tensor': torch.randint(self.min, self.max + 1, self.shape)} diff --git a/fuse/data/processor/processors_image_toolbox.py b/fuse/data/processor/processors_image_toolbox.py deleted file mode 100644 index b05045bb6..000000000 --- a/fuse/data/processor/processors_image_toolbox.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Tuple -import pydicom -import numpy as np -import skimage -import skimage.transform as transform - -class FuseProcessorsImageToolBox: - """ - Common utils for image processors - """ - - @staticmethod - def read_dicom_image_to_numpy(img_path: str) -> np.ndarray : - """ - read a dicom file given a file path - :param img_path: file path - :return: numpy object of the dicom image - """ - # read image - dcm = pydicom.dcmread(img_path) - inner_image = dcm.pixel_array - # convert to numpy - inner_image = np.asarray(inner_image) - return inner_image - - @staticmethod - def resize_image(inner_image: np.ndarray, resize_to: Tuple[int,int]) -> np.ndarray : - """ - resize image to the required resolution - :param inner_image: image of shape [H, W, C] - :param resize_to: required resolution [height, width] - :return: resized image - """ - inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - if inner_image_height > resize_to[0]: - h_ratio = resize_to[0] / inner_image_height - else: - h_ratio = 1 - if inner_image_width > resize_to[1]: - w_ratio = resize_to[1] / inner_image_width - else: - w_ratio = 1 - - resize_ratio = min(h_ratio, w_ratio) - if resize_ratio != 1: - inner_image = skimage.transform.resize(inner_image, - output_shape=(int(inner_image_height * resize_ratio), - int(inner_image_width * resize_ratio)), - mode='reflect', - anti_aliasing=True - ) - return inner_image - - @staticmethod - def pad_image(inner_image: np.ndarray, padding: Tuple[float, float], resize_to: Tuple[int, int], - normalized_target_range: Tuple[float, float], number_of_channels: int) -> np.ndarray : - """ - pads image to requested size , - pads both side equally by the same input padding size (left = right = padding[1] , up = down= padding[0] ) , - padding default value is zero or minimum value in normalized target range - :param inner_image: image of shape [H, W, C] - :param padding: required padding [x,y] - :param resize_to: original requested resolution - :param normalized_target_range: requested normalized image pixels range - :param number_of_channels: number of color channels in the image - :return: padded image - """ - inner_image = inner_image.astype('float32') - # "Pad" around inner image - inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - inner_image[0:inner_image_height, 0] = 0 - inner_image[0:inner_image_height, inner_image_width - 1] = 0 - inner_image[0, 0:inner_image_width] = 0 - inner_image[inner_image_height - 1, 0:inner_image_width] = 0 - - if normalized_target_range is None: - pad_value = 0 - else: - pad_value = normalized_target_range[0] - - image = FuseProcessorsImageToolBox.pad_inner_image(inner_image, outer_height=resize_to[0] + 2 * padding[0], - outer_width=resize_to[1] + 2 * padding[1], pad_value=pad_value, number_of_channels=number_of_channels) - return image - - @staticmethod - def normalize_to_range(input_image: np.ndarray, range: Tuple[float, float] = (0, 1.0)) -> np.ndarray : - """ - Scales tensor to range - :param input_image: image of shape [H, W, C] - :param range: bounds for normalization - :return: normalized image - """ - max_val = input_image.max() - min_val = input_image.min() - if min_val == max_val == 0: - return input_image - input_image = input_image - min_val - input_image = input_image / (max_val - min_val) - input_image = input_image * (range[1] - range[0]) - input_image = input_image + range[0] - return input_image - - def pad_inner_image(image: np.ndarray, outer_height: int, outer_width: int, pad_value: float, number_of_channels: int) -> np.ndarray : - """ - Pastes input image in the middle of a larger one - :param image: image of shape [H, W, C] - :param outer_height: final outer height - :param outer_width: final outer width - :param pad_value: value for padding around inner image - :number_of_channels final number of channels in the image - :return: padded image - """ - inner_height, inner_width = image.shape[0], image.shape[1] - h_offset = int((outer_height - inner_height) / 2.0) - w_offset = int((outer_width - inner_width) / 2.0) - if number_of_channels > 1 : - outer_image = np.ones((outer_height, outer_width, number_of_channels), dtype=image.dtype) * pad_value - outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width, :] = image - elif number_of_channels == 1 : - outer_image = np.ones((outer_height, outer_width), dtype=image.dtype) * pad_value - outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width] = image - return outer_image diff --git a/fuse/data/sampler/__init__.py b/fuse/data/sampler/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/sampler/sampler_balanced_batch.py b/fuse/data/sampler/sampler_balanced_batch.py deleted file mode 100644 index 81df89591..000000000 --- a/fuse/data/sampler/sampler_balanced_batch.py +++ /dev/null @@ -1,212 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Torch batch sampler - balancing per batch -""" -import logging -import math -from typing import Any, List, Optional - -import numpy as np -from torch.utils.data.sampler import Sampler - -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.utils.utils_debug import FuseUtilsDebug -from fuse.utils.utils_logger import log_object_input_state - - -class FuseSamplerBalancedBatch(Sampler): - """ - Torch batch sampler - balancing per batch - """ - - def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balanced_classes: int, batch_size: int, - balanced_class_weights: Optional[List[int]] = None, balanced_class_probs: Optional[List[float]] = None, - num_batches: Optional[int] = None, use_dataset_cache: bool = False) -> None: - """ - :param dataset: dataset used to extract the balanced class from each sample - :param balanced_class_name: the name of balanced class to extract from dataset - :param num_balanced_classes: number of classes to balance between - :param batch_size: batch_size. - - If balanced_class_weights=Nobe, Must be divided by num_balanced_classes - - Otherwise must be equal to sum of balanced_class_weights - :param balanced_class_weights: Optional, integer per balanced class, - specifying the number of samples from each class to include in each batch. - If not specified and equal number of samples from each class will be used. - :param balanced_class_probs: Optional, probability per class. Random sampling approach will be performed. - such that an epoch will go over all the data at least once. - :param num_batches: Optional, Set number of batches. If not set. The number of batches will automatically set. - - :param use_dataset_cache: to retrieve the balanced class from dataset try to use caching. - Should be set to True if reading it from cache is faster than running the single processor - """ - # log object - log_object_input_state(self, locals()) - - super().__init__(None) - - # store input - self.dataset = dataset - self.balanced_class_name = balanced_class_name - self.num_balanced_classes = num_balanced_classes - self.batch_size = batch_size - self.balanced_class_weights = balanced_class_weights - self.balanced_class_probs = balanced_class_probs - self.num_batches = num_batches - self.use_dataset_cache = use_dataset_cache - - # validate input - if balanced_class_weights is not None and balanced_class_probs is not None: - raise Exception('Set either balanced_class_weights or balanced_class_probs, not both.') - elif balanced_class_weights is None and balanced_class_probs is None: - if batch_size % num_balanced_classes != 0: - raise Exception(f'batch_size ({batch_size}) % num_balanced_classes ({num_balanced_classes}) must be 0') - elif balanced_class_weights is not None: - if len(balanced_class_weights) != num_balanced_classes: - raise Exception( - f'Expecting balance_class_weights ({balanced_class_weights}) to have a weight per balanced class ({num_balanced_classes})') - if sum(balanced_class_weights) != batch_size: - raise Exception(f'balanced_class_weights {balanced_class_weights} expected to sum up to batch_size {batch_size}') - else: - # noinspection PyTypeChecker - if len(balanced_class_probs) != num_balanced_classes: - raise Exception( - f'Expecting balance_class_probs ({balanced_class_probs}) to have a probability per balanced class ({num_balanced_classes})') - if not math.isclose(sum(balanced_class_probs), 1.0): - raise Exception(f'balanced_class_probs {balanced_class_probs} expected to sum up to 1.0') - - # if weights not specified, set weights to equally balance per batch - if self.balanced_class_weights is None and self.balanced_class_probs is None: - self.balanced_class_weights = [self.batch_size // self.num_balanced_classes] * self.num_balanced_classes - - lgr = logging.getLogger('Fuse') - lgr.debug(f'FuseSamplerBalancedBatch: balancing per batch - balanced_class_name {self.balanced_class_name}, ' - f'batch_size={batch_size}, weights={self.balanced_class_weights}, probs={self.balanced_class_probs}') - - # get balanced classes per each sample - self.balanced_classes = dataset.get(None, self.balanced_class_name, use_cache=use_dataset_cache) - self.balanced_classes = np.array(self.balanced_classes) - self.balanced_class_indices = [np.where(self.balanced_classes == cls_i)[0] for cls_i in range(self.num_balanced_classes)] - self.balanced_class_sizes = [len(self.balanced_class_indices[cls_i]) for cls_i in range(self.num_balanced_classes)] - lgr.debug('FuseSamplerBalancedBatch: samples per each balanced class {}'.format(self.balanced_class_sizes)) - - # debug - simple batch - batch_mode = FuseUtilsDebug().get_setting('sampler_batch_mode') - if batch_mode == 'simple': - num_avail_bcls = sum( - bcls_num_samples != 0 - for bcls_num_samples in self.balanced_class_sizes - ) - - self.balanced_class_weights = None - self.balanced_class_probs = [1.0/num_avail_bcls if bcls_num_samples != 0 else 0.0 for bcls_num_samples in self.balanced_class_sizes] - lgr.info('FuseSamplerBalancedBatch: debug mode - override to random sample') - - # calc batch index to balanced class mapping according to weights - if self.balanced_class_weights is not None: - self.batch_index_to_class = [] - for balanced_cls in range(self.num_balanced_classes): - self.batch_index_to_class.extend([balanced_cls] * self.balanced_class_weights[balanced_cls]) - else: - # probabilistic method - will be randomly select per epoch - self.batch_index_to_class = None - - # make sure that size != 0 for all balanced classes - for cls_size in enumerate(self.balanced_class_sizes): - if ( - ( - self.balanced_class_weights is not None - and self.balanced_class_weights != 0 - ) - or ( - self.balanced_class_probs is not None - and self.balanced_class_probs != 0.0 - ) - ) and cls_size == 0: - msg = f'Every balanced class must include at least one sample (num of samples per balanced class{self.balanced_class_sizes})' - raise Exception(msg) - - # Shuffle balanced class indices - for indices in self.balanced_class_indices: - np.random.shuffle(indices) - - # Calculate num batches. Number of batches to iterate over all data at least once - # Calculate only if not directly specified by the user - if self.num_batches is None: - if self.balanced_class_weights is not None: - balanced_class_weighted_sizes = [self.balanced_class_sizes[cls_i] // self.balanced_class_weights[cls_i] if self.balanced_class_weights[cls_i] != 0 else 0 for cls_i in - range(self.num_balanced_classes)] - else: - # approximate size! - balanced_class_weighted_sizes = [ - self.balanced_class_sizes[cls_i] // (self.balanced_class_probs[cls_i] * self.batch_size) if self.balanced_class_probs[ - cls_i] != 0.0 else 0 for - cls_i in range(self.num_balanced_classes)] - bigger_balanced_class_weighted_size = max(balanced_class_weighted_sizes) - self.num_batches = int(bigger_balanced_class_weighted_size) + 1 - lgr.debug(f'FuseSamplerBalancedBatch: num_batches = {self.num_batches}') - - # pointers per class - self.cls_pointers = [0] * self.num_balanced_classes - self.sample_pointer = 0 - - def __iter__(self) -> np.ndarray: - for _ in range(self.num_batches): - yield self._make_batch() - - def __len__(self) -> int: - return self.num_batches - - def _get_sample(self, balanced_class: int) -> Any: - """ - sample index given balanced class value - :param balanced_class: integer representing balanced class value - :return: sample index - """ - if self.balanced_class_indices[balanced_class].shape[0] == 0: - msg = f'There are no samples in balanced class {balanced_class}' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - - sample_idx = self.balanced_class_indices[balanced_class][self.cls_pointers[balanced_class]] - - self.cls_pointers[balanced_class] += 1 - if self.cls_pointers[balanced_class] == self.balanced_class_sizes[balanced_class]: - self.cls_pointers[balanced_class] = 0 - np.random.shuffle(self.balanced_class_indices[balanced_class]) - - return sample_idx - - def _make_batch(self) -> list: - """ - :return: list of indices to collate batch - """ - if self.batch_index_to_class is not None: - batch_index_to_class = self.batch_index_to_class - else: - # calc one according to probabilities - batch_index_to_class = np.random.choice(np.arange(self.num_balanced_classes), self.batch_size, p=self.balanced_class_probs) - batch_sample_indices = [] - for batch_index in range(self.batch_size): - balanced_class = batch_index_to_class[batch_index] - batch_sample_indices.append(self._get_sample(balanced_class)) - - np.random.shuffle(batch_sample_indices) - return batch_sample_indices diff --git a/fuse/data/utils/export.py b/fuse/data/utils/export.py deleted file mode 100644 index 76b9b4287..000000000 --- a/fuse/data/utils/export.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" -from typing import Optional, Sequence -import pandas as pd - -from fuse.data.dataset.dataset_base import FuseDatasetBase - -from fuse.utils.file_io.file_io import save_dataframe -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict - -class DatasetExport: - """ - Export data - """ - - @staticmethod - def export_to_dataframe(dataset: FuseDatasetBase, keys: Sequence[str], output_filename: Optional[str] = None, sample_id_key: str = "data.descriptor", **dataset_get_kwargs) -> pd.DataFrame: - """ - extract from dataset the specified and keys and create a dataframe. - If output_filename will be specified, the dataframe will also be saved in a file. - :param dataset: the dataset to extract the values from - :param keys: keys to extract from sample_dict - :param output_filename: Optional, if set, will save the dataframe into a file. - The file type will be inferred from filename, see fuse.utils.file_io.file_io.save_dataframe for more details - :param dataset_get_kwargs: additional parameters to dataset.get(), might be used to optimize the running time - """ - # add sample_id to keys list - if keys is not None: - all_keys = [] - all_keys += list(keys) - if sample_id_key not in keys: - all_keys.append(sample_id_key) - else: - all_keys = None - - # read all the data - data = dataset.get(None, **dataset_get_kwargs) - - - # store in dataframe - df = pd.DataFrame() - - for key in all_keys: - values = [FuseUtilsHierarchicalDict.get(sample_dict, key) for sample_dict in data] - df[key] = values - - # set sample_id as index - df = df.set_index(sample_id_key) - - if output_filename is not None: - save_dataframe(df, output_filename) - - return df diff --git a/fuse/data/visualizer/__init__.py b/fuse/data/visualizer/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/data/visualizer/visualizer_base.py b/fuse/data/visualizer/visualizer_base.py deleted file mode 100644 index 272536826..000000000 --- a/fuse/data/visualizer/visualizer_base.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class FuseVisualizerBase(ABC): - - @abstractmethod - def visualize(self, sample: Any, block: bool = True) -> None: - """ - visualize sample - :param sample: sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - raise NotImplementedError - - @abstractmethod - def visualize_aug(self, orig_sample: Any, aug_sample: Any, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: original sample - :param aug_sample: augmented sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - raise NotImplementedError diff --git a/fuse/data/visualizer/visualizer_default.py b/fuse/data/visualizer/visualizer_default.py deleted file mode 100644 index 3cf3db94f..000000000 --- a/fuse/data/visualizer/visualizer_default.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -from typing import Optional, Iterable, Any, Tuple - -import matplotlib.pyplot as plt - -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -import fuse.utils.imaging.image_processing as ImageProcessing -import torch - - -class FuseVisualizerDefault(FuseVisualizerBase): - """ - Visualizer for data including single 2D image with optional mask - """ - - def __init__(self, image_name: str, mask_name: Optional[str] = None, - label_name: Optional[str] = None, metadata_names: Iterable[str] = tuple(), - pred_name: Optional[str] = None, - gray_scale: bool = True): - """ - :param image_name: hierarchical key name of the image in batch_dict - :param mask_name: hierarchical key name of the mask (gt map) in batch_dict. - Optional, won't be displayed if not specified. - :param label_name: hierarchical key name of the to a global label in batch_dict. - Optional, won't be displayed if not specified. - :param metadata_names: list of hierarchical key name of the metadata - will be printed for every sample - :param pred_name: hierarchical key name of the prediction in batch_dict. - Optional, won't be displayed if not specified. - :param gray_scale: If True, each channel will be displayed as gray scale image. Otherwise, assuming 3 channels and RGB image either normalize to [0-1] or to [0-255] - """ - # log object input state - log_object_input_state(self, locals()) - - # store input parameters - self.image_pointer = image_name - self.mask_name = mask_name - self.label_name = label_name - self.metadata_pointers = metadata_names - self.pred_name = pred_name - self.matching_function = ImageProcessing.match_img_to_input - self._gray_scale = gray_scale - - def extract_data(self, sample: dict) -> Tuple[Any, Any, Any, Any, Any]: - """ - extract required data to visualize from sample - :param sample: global dict of a sample - :return: image, mask, label, metadata - """ - - # image - image = FuseUtilsHierarchicalDict.get(sample, self.image_pointer) - - # mask - if self.mask_name is not None: - mask = FuseUtilsHierarchicalDict.get(sample, self.mask_name) - else: - mask = None - - # label - if self.label_name is not None: - label = FuseUtilsHierarchicalDict.get(sample, self.label_name) - else: - label = '' - - # mask - if self.pred_name is not None: - pred_mask = FuseUtilsHierarchicalDict.get(sample, self.pred_name) - else: - pred_mask = None - - # metadata - metadata = {metadata_ptr: FuseUtilsHierarchicalDict.get(sample, metadata_ptr) for metadata_ptr in - self.metadata_pointers} - - return image, mask, label, metadata, pred_mask - - def visualize(self, sample: dict, block: bool = True) -> None: - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image, mask, label, metadata, pred_mask = self.extract_data(sample) - - if mask is not None: - mask = self.matching_function(mask, image) - - if pred_mask is not None: - pred_mask = self.matching_function(pred_mask, image) - - # visualize - if self._gray_scale: - num_channels = image.shape[0] - - if pred_mask is not None: - fig, ax = plt.subplots(num_channels, pred_mask.shape[0]+1, squeeze=False) - else: - fig, ax = plt.subplots(num_channels, 1, squeeze=False) - - for channel_idx in range(num_channels): - ax[channel_idx, 0].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(label))) - - ax[channel_idx, 0].imshow(image[channel_idx].squeeze(), cmap='gray') - if mask is not None: - ax[channel_idx, 0].imshow(mask[channel_idx], alpha=0.3) - - if pred_mask is not None: - for c_id in range(pred_mask.shape[0]): - max_prob = pred_mask[c_id].max() - ax[channel_idx, c_id+1].title.set_text('image (ch %d) (max prob %s)' % (channel_idx, str(max_prob))) - - ax[channel_idx, c_id+1].imshow(image[channel_idx].squeeze(), cmap='gray') - ax[channel_idx, c_id+1].imshow(pred_mask[c_id], alpha=0.3) - else: - if pred_mask is not None: - fig, ax = plt.subplots(1, pred_mask.shape[0]+1, squeeze=False) - else: - fig, ax = plt.subplots(1, 1, squeeze=False) - - ax[0, 0].title.set_text('image (lbl %s)' % (str(label))) - - image = image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - image = torch.clip(image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 0].imshow(image) - if mask is not None: - ax[0, 0].imshow(mask, alpha=0.3) - - if pred_mask is not None: - for c_id in range(pred_mask.shape[0]): - max_prob = pred_mask[c_id].max() - ax[0, c_id+1].title.set_text('image(max prob %s)' % (str(max_prob))) - ax[0, c_id+1].imshow(pred_mask[c_id], cmap='gray') - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - lgr.info(metadata) - lgr.info('image label = ' + str(label)) - lgr.info('------------------------------------------') - - try: - mng = plt.get_current_fig_manager() - mng.resize(*mng.window.maxsize()) - except: - pass - - fig.tight_layout() - plt.show(block=block) - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: batch_dict to extract the original sample from - :param aug_sample: batch_dict to extract the augmented sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - orig_image, orig_mask, orig_label, orig_metadata, pred_mask = self.extract_data(orig_sample) - aug_image, aug_mask, aug_label, aug_metadata, pred_mask = self.extract_data(aug_sample) - - # visualize - if self._gray_scale: - num_channels = orig_image.shape[0] - - fig, ax = plt.subplots(num_channels, 2, squeeze=False) - for channel_idx in range(num_channels): - # orig - ax[channel_idx, 0].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(orig_label))) - ax[channel_idx, 0].imshow(orig_image[channel_idx].squeeze(), cmap='gray') - if (orig_mask is not None) and (None not in orig_mask): - ax[channel_idx, 0].imshow(orig_mask, alpha=0.3) - - # augmented - ax[channel_idx, 1].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(aug_label))) - ax[channel_idx, 1].imshow(aug_image[channel_idx].squeeze(), cmap='gray') - if (aug_mask is not None) and (None not in aug_mask): - ax[channel_idx, 1].imshow(aug_mask, alpha=0.3) - else: - fig, ax = plt.subplots(1, 2, squeeze=False) - # orig - ax[0, 0].title.set_text('image (lbl %s)' % (str(orig_label))) - orig_image = orig_image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - orig_image = torch.clip(orig_image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 0].imshow(orig_image) - if (orig_mask is not None) and (None not in orig_mask): - ax[0, 0].imshow(orig_mask, alpha=0.3) - - # augmented - ax[0, 1].title.set_text('image (lbl %s)' % (str(aug_label))) - aug_image = aug_image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - aug_image = torch.clip(aug_image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 1].imshow(aug_image) - if (aug_mask is not None) and (None not in aug_mask): - ax[1].imshow(aug_mask, alpha=0.3) - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - lgr.info("original") - lgr.info(orig_metadata) - lgr.info('image label = ' + str(orig_label)) - lgr.info("augmented") - lgr.info(aug_metadata) - lgr.info('image label = ' + str(aug_label)) - lgr.info('------------------------------------------') - - try: - mng = plt.get_current_fig_manager() - mng.resize(*mng.window.maxsize()) - except: - pass - - fig.tight_layout() - plt.show(block=block) diff --git a/fuse/data/visualizer/visualizer_default_3d.py b/fuse/data/visualizer/visualizer_default_3d.py deleted file mode 100644 index cf6d9a450..000000000 --- a/fuse/data/visualizer/visualizer_default_3d.py +++ /dev/null @@ -1,276 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -from typing import Optional, Iterable, Any, Tuple - -import matplotlib.pyplot as plt -from skimage.color import gray2rgb -from skimage.segmentation import mark_boundaries - -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state - - -class Fuse3DVisualizerDefault(FuseVisualizerBase): - """ - Visualiser for data including 3D volume with optional local annotations - """ - - def __init__(self, image_name: str, mask_name: Optional[str] = None, - label_name: Optional[str] = None, metadata_pointers: Iterable[str] = tuple(), - ): - """ - :param image_name: pointer to an image in batch_dict, image will be in shape (B,C,VOL). - :param mask_name: optional, pointer mask (gt map) in batch_dict. If mask location is not part of the batch dict - - override the extract_data method - :param label_name: pointer to a global label in batch_dict - :param metadata_pointers: list of pointers to metadata - will be printed for every sample - - """ - # log object input state - log_object_input_state(self, locals()) - - # store input parameters - self.image_name = image_name - self.mask_name = mask_name - self.label_name = label_name - self.metadata_pointers = metadata_pointers - - def extract_data(self, sample: dict) -> Tuple[Any, Any, Any, Any]: - """ - extract required data to visualize from sample - :param sample: global dict of a sample - :return: image, mask, label, metadata - """ - - # image - image = FuseUtilsHierarchicalDict.get(sample, self.image_name) - assert len(image.shape) == 4 - image = image.numpy() - - # mask - if self.mask_name is not None: - if not isinstance(self.mask_name, list): - self.mask_name = [self.mask_name] - masks = [FuseUtilsHierarchicalDict.get(sample, mask_name).numpy() for mask_name in self.mask_name] - else: - masks = None - - # label - if self.label_name is not None: - label = FuseUtilsHierarchicalDict.get(sample, self.label_name) - else: - label = '' - - # metadata - metadata = {metadata_ptr: FuseUtilsHierarchicalDict.get(sample, metadata_ptr) for metadata_ptr in - self.metadata_pointers} - - return image, masks, label, metadata - - def visualize(self, sample: dict, block: bool = True) -> None: - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image, masks, label, metadata = self.extract_data(sample) - # visualize - chan = 0 - chan_image = image[chan, ...] - - def key_event(e: Any, position_list: Any): # using left/right key to move between slices - def on_press(e: Any): # use mouse click in order to toggle mask/no mask - 'toggle the visible state of the two images' - if e.button: - vis_image = plt_img.get_visible() - vis_mask = plt_mask.get_visible() - plt_img.set_visible(not vis_image) - plt_mask.set_visible(not vis_mask) - plt.draw() - - if e.key == "right": - position_list[0] += 1 - elif e.key == "left": - position_list[0] -= 1 - elif e.key == "up": - position_list[1] += 1 - elif e.key == "down": - position_list[1] -= 1 - else: - return - position_list[0] = position_list[0] % image.shape[1] - position_list[1] = position_list[1] % image.shape[0] - chan_image = image[position_list[1]] - - ax.cla() - slice_image = gray2rgb(chan_image[position_list[0]]) - if masks is not None: - slice_image_with_mask = slice_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(masks): - slice_image_with_mask = mark_boundaries(slice_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt.title(f'Slice {position_list[0]} channel {position_list[1]}') - plt_img = ax.imshow(slice_image) - plt_img.set_visible(False) - if (mask is not None) and (None not in mask): - plt_mask = ax.imshow(slice_image_with_mask) - plt_mask.set_visible(True) - fig.canvas.mpl_connect('button_press_event', on_press) - fig.canvas.draw() - - fig = plt.figure() - position_list = [0, 0] - plt.title(f'Slice {position_list[0]} channel {position_list[1]}') - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - ax = fig.add_subplot(111) - slice_image = gray2rgb(chan_image[0]) - if masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(masks): - slice_image = mark_boundaries(slice_image, mask[position_list[0]].astype(int), color=colors[index % len(colors)]) - ax.imshow(slice_image) - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - if metadata is not None: - if isinstance(metadata, dict): - lgr.info(FuseUtilsHierarchicalDict.to_string(metadata), {'color': 'magenta'}) - else: - lgr.info(metadata) - - if label is not None and label != '': - lgr.info('image label = ' + str(label), {'color': 'magenta'}) - lgr.info('------------------------------------------') - - plt.show() - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: batch_dict to extract the original sample from - :param aug_sample: batch_dict to extract the augmented sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - # extract data - orig_image, orig_masks, orig_label, orig_metadata = self.extract_data(orig_sample) - aug_image, aug_masks, aug_label, aug_metadata = self.extract_data(aug_sample) - - # visualize - def key_event(e: Any, position_list: Any): # using left/right key to move between slices - def on_press(e: Any): # use mouse click in order to toggle mask/no mask - 'toggle the visible state of the two images' - if e.button: - # Toggle image with no augmentations - vis_image = plt_img.get_visible() - vis_mask = plt_mask.get_visible() - plt_img.set_visible(not vis_image) - plt_mask.set_visible(not vis_mask) - # Toggle image with augmentations - vis_aug_image = plt_aug_img.get_visible() - vis_aug_mask = plt_aug_mask.get_visible() - plt_aug_img.set_visible(not vis_aug_image) - plt_aug_mask.set_visible(not vis_aug_mask) - - plt.draw() - - if e.key == "right": - position_list[0] += 1 - elif e.key == "left": - position_list[0] -= 1 - elif e.key == "up": - position_list[1] += 1 - elif e.key == "down": - position_list[1] -= 1 - else: - return - position_list[0] = position_list[0] % orig_image.shape[1] - position_list[1] = position_list[1] % orig_image.shape[0] - chan_image = orig_image[position_list[1]] - chan_aug_image = aug_image[position_list[1]] - # clearing subplots - axs[0].cla() - axs[1].cla() - # creating image without augmentations and with toggling mask - slice_image = gray2rgb(chan_image[position_list[0]]) - plt_img = axs[0].imshow(slice_image) - plt_img.set_visible(False) - - if orig_masks is not None: - slice_image_with_mask = slice_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(orig_masks): - slice_image_with_mask = mark_boundaries(slice_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt_mask = axs[0].imshow(slice_image_with_mask) - plt_mask.set_visible(True) - - # creating image with augmentations and with toggling mask - slice_aug_image = gray2rgb(chan_aug_image[position_list[0]]) - plt_aug_img = axs[1].imshow(slice_aug_image) - plt_aug_img.set_visible(False) - if aug_masks is not None: - slice_aug_image_with_mask = slice_aug_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(aug_masks): - slice_aug_image_with_mask = mark_boundaries(slice_aug_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt_aug_mask = axs[1].imshow(slice_aug_image_with_mask) - plt_aug_mask.set_visible(True) - # drawing - axs[0].title.set_text(f"Original - Slice {position_list[0]} channel {position_list[1]}") - axs[1].title.set_text(f"Augmented - Slice {position_list[0]} channel {position_list[1]}") - fig.canvas.mpl_connect('button_press_event', on_press) - fig.canvas.draw() - - fig, axs = plt.subplots(ncols=2) - position_list = [0, 0] - chan_image = orig_image[position_list[1]] - chan_aug_image = aug_image[position_list[1]] - - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - slice_image = gray2rgb(chan_image[position_list[0]]) - if orig_masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(orig_masks): - slice_image = mark_boundaries(slice_image, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - slice_aug_image = gray2rgb(chan_aug_image[0]) - if aug_masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(aug_masks): - slice_aug_image = mark_boundaries(slice_aug_image, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - axs[0].title.set_text(f"Original - Slice {position_list[0]} channel {position_list[1]}") - axs[1].title.set_text(f"Augmented - Slice {position_list[0]} channel {position_list[1]}") - axs[0].imshow(slice_image) - axs[1].imshow(slice_aug_image) - plt.show() diff --git a/fuse/data/visualizer/visualizer_image_analysis.py b/fuse/data/visualizer/visualizer_image_analysis.py deleted file mode 100644 index 2110b61d6..000000000 --- a/fuse/data/visualizer/visualizer_image_analysis.py +++ /dev/null @@ -1,112 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Any - -import matplotlib.pyplot as plt -import numpy as np - -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict - - -class FuseVisualizerImageAnalysis(FuseVisualizerBase): - """ - Class for producing analysis of an image - """ - - def __init__(self, image_name: str): - """ - :param image_name: pointer to an image in batch_dict - - """ - self.image_name = image_name - - def visualize(self, sample: Any, block: bool = True): - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image = FuseUtilsHierarchicalDict.get(sample, self.image_name) - image = image.numpy() - num_channels = image.shape[0] - for i in range(num_channels): - channel_image = image[i, ...] - if len(channel_image.shape) == 3: - self.visualize_3dimage(channel_image, title="Image and its Histogram of channel:" + str(i), block=block) - else: - assert len(channel_image.shape) == 2 - self.visualise_2dimage(channel_image, title="Image and its Histogram of channel:" + str(i), block=block) - - def visualize_3dimage(self, image: np.array, title: str = "Image and its Histogram", bins=256, block: bool = True) -> None: - def key_event(e, curr_pos): - if e.key == "right": - curr_pos[0] = curr_pos[0] + 1 - elif e.key == "left": - curr_pos[0] = curr_pos[0] - 1 - else: - return - curr_pos[0] = curr_pos[0] % image.shape[0] - - axs[0].cla() - axs[1].cla() - axs[0].imshow(image[curr_pos[0]]) - axs[1].hist(image[curr_pos[0]].ravel(), bins=bins, fc='k', ec='k') - fig.canvas.draw() - plt.suptitle(title + " at slice:" + str(curr_pos[0])) - - fig, axs = plt.subplots(2) - position_list = [0] - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - axs[0].imshow(image[0]) - axs[1].hist(image.ravel(), bins=bins, fc='k', ec='k') # calculating histogram - plt.suptitle(title + " at slice:" + str(position_list[0])) - plt.show() - - def visualise_2dimage(self, image: np.array, title: str = "Image and its Histogram", block: bool = True) -> None: - """ - visualize sample - :param image: image in the form of np.array - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - fig = plt.figure() - fig.add_subplot(221) - plt.title('image') - plt.imshow(image) - - fig.add_subplot(222) - plt.title('histogram') - plt.hist(image.ravel(), bins=256, fc='k', ec='k') # calculating histogram - - plt.suptitle(title) - plt.show(block=block) - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: original sample - :param aug_sample: augmented sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - raise NotImplementedError diff --git a/fuse/tests/data/__init__.py b/fuse/tests/data/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/fuse/tests/data/test_data_source_toolbox.py b/fuse/tests/data/test_data_source_toolbox.py deleted file mode 100644 index 5c0ba8dca..000000000 --- a/fuse/tests/data/test_data_source_toolbox.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import pandas as pd -import numpy as np -import torch -import os -from scipy import stats - -from fuse.data.data_source.data_source_toolbox import FuseDataSourceToolbox -import pathlib - - -class FuseDataSourceToolBoxTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - pass - - def test_balanced_division(self): - # FIXME: removed after adding the missing file - return - input_df = pd.read_csv(os.path.join(pathlib.Path(__file__).parent.resolve(),'file_for_test.csv')) - - # configure input for fold partition - unique_ID = 'ID1' - label = 'label1' - folds = 5 - - partition_df = FuseDataSourceToolbox.balanced_division(df = input_df , - no_mixture_id = unique_ID, - key_columns =[label] , - nfolds = folds , - print_flag=False, - debug_mode = True) - # set expected values for the partition - - # expeted id_level values and unique records - id_level_value_counter= {'[ True False]': 465, '[False True]': 1280, '[ True True]': 30} - - # label balance value - population_mean = input_df[label].mean() - - # observed label balance in folds - means = [partition_df[partition_df['fold'] == i][label].mean() for i in range(folds)] - - # get number of unique ID in each folds - folds_size = [len(partition_df[partition_df['fold'] == i][unique_ID].unique()) for i in range(folds)] - - # number of records in each fold - records_size = [len(partition_df[partition_df['fold'] == i]) for i in range(folds)] - - # confidence level for confidence intervals - confidence_level = 0.95 - CI_label = stats.t.interval(confidence_level, len(means)-1, loc=np.mean(means), scale=stats.sem(means)) - - # min and max fold size in terms of expected unique ID in each - min_fold_size = np.sum([id_level_value_counter[value]/folds for value in id_level_value_counter]) - max_fold_size = min_fold_size + len(id_level_value_counter) - - # check if expected conditions hold - - # checks if the sum of all unique id in all folds is like in original file - self.assertTrue(np.sum(folds_size) == len(input_df[unique_ID].unique())) - - # checks if number or records in the folds in like in original file - self.assertTrue(np.sum(records_size) == len(input_df)) - - # checks if label balancing is distributed around the original balance - self.assertTrue(CI_label[0] <= population_mean <= CI_label[1]) - - # checks if in each fold number of unique ID is in the expected range - for size in folds_size : - self.assertTrue(min_fold_size <= size <= max_fold_size) - - - - - - def tearDown(self): - pass - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/fuse/tests/data/test_processor_dataframe.py b/fuse/tests/data/test_processor_dataframe.py deleted file mode 100644 index 8aeb1c51f..000000000 --- a/fuse/tests/data/test_processor_dataframe.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import pandas as pd -import torch - -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame - - -class FuseProcessorDataFrameTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - pass - - def test_all_columns(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc') - - self.assertDictEqual(proc('one'), {'int_val': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': 6, 'string_val':'val3'}) - - def test_rename(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', rename_columns={'int_val': 'new_name'}) - - self.assertDictEqual(proc('one'), {'new_name': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'new_name': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'new_name': 6, 'string_val':'val3'}) - - def test_specific_columns(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', rename_columns={'int_val': 'new_name'}, - columns_to_extract=['string_val', 'desc']) - - self.assertDictEqual(proc('one'), {'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'string_val':'val3'}) - - def test_tensors(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', columns_to_tensor=['int_val', 'invalid_column']) - - self.assertDictEqual(proc('one'), {'int_val': torch.tensor(4, dtype=torch.int64), 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': torch.tensor(5, dtype=torch.int64), 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': torch.tensor(6, dtype=torch.int64), 'string_val':'val3'}) - - def test_tensors_with_types(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "float_val": [4.1, 5.3, 6.5], - "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', - columns_to_tensor={'int_val': torch.int8, 'float_val': torch.float64}) - - self.assertDictEqual(proc('one'), {'int_val': torch.tensor(4, dtype=torch.int8), - 'float_val': torch.tensor(4.1, dtype=torch.float64), 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': torch.tensor(5, dtype=torch.int8), - 'float_val': torch.tensor(5.3, dtype=torch.float64), 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': torch.tensor(6, dtype=torch.int8), - 'float_val': torch.tensor(6.5, dtype=torch.float64), 'string_val':'val3'}) - - - def test_tensors_non_existing(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', columns_to_tensor=['invalid_column']) - - self.assertDictEqual(proc('one'), {'int_val': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': 6, 'string_val':'val3'}) - - - def tearDown(self): - pass - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/fuse/tests/data/test_sampler.py b/fuse/tests/data/test_sampler.py deleted file mode 100644 index 4d75f8133..000000000 --- a/fuse/tests/data/test_sampler.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import numpy as np -import torchvision -from torch.utils.data.dataloader import DataLoader -from torchvision import transforms - -from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict - - -class FuseSamplerBalancedTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ]) - # Create dataset - self.torch_dataset = torchvision.datasets.MNIST('/tmp/mnist_test', download=True, train=True, transform=transform) - # wrapping torch dataset - self.dataset = FuseDatasetWrapper(name='test', dataset=self.torch_dataset, mapping=('image', 'label')) - self.dataset.create(reset_cache=True) - pass - - def test_balanced_dataset(self): - num_classes = 10 - batch_size = 5 - probs = 1.0 / num_classes - num_samples = 60000 - - print(self.dataset.summary(statistic_keys=['data.label'])) - sampler = FuseSamplerBalancedBatch(dataset=self.dataset, - balanced_class_name='data.label', - num_balanced_classes=num_classes, - batch_size=batch_size, - # balanced_class_weights=[1,1,1,1,1,1,1,1,1,1]) # relevant when batch size is % num classes - balanced_class_probs=[probs] * num_classes) - - labels = np.zeros(num_classes) - - # Create dataloader - dataloader = DataLoader(dataset=self.dataset, batch_sampler=sampler, num_workers=0) - iter1 = iter(dataloader) - for _ in range(len(dataloader)): - batch_dict = next(iter1) - labels_in_batch = FuseUtilsHierarchicalDict.get(batch_dict, 'data.label') - for label in labels_in_batch: - labels[label] += 1 - - # final balance - print(labels) - for idx in range(num_classes): - sampled = labels[idx] / num_samples - print(f'Class {idx}: {sampled * 100}% of data') - self.assertAlmostEqual(sampled, probs, delta=probs * 0.5, msg=f'Unbalanced class {idx}, expected 0.1+-0.05 and got {sampled}') - - def test_unbalanced_dataset(self): - num_classes = 10 - batch_size = 5 - probs = 1.0 / num_classes - - # wrapping torch dataset - unbalanced_dataset = self.dataset - - samples_to_save = [] - stats = [1000, 200, 200, 200, 300, 500, 700, 800, 900, 1000] - chosen = np.zeros(10) - for idx in range(60000): - label = unbalanced_dataset.get(idx, 'data.label') - if stats[label] > chosen[label]: - samples_to_save.append(("test", idx)) - chosen[label] += 1 - - unbalanced_dataset.samples_description = samples_to_save - - sampler = FuseSamplerBalancedBatch(dataset=unbalanced_dataset, - balanced_class_name='data.label', - num_balanced_classes=num_classes, - batch_size=batch_size, - # balanced_class_weights=[1,1,1,1,1,1,1,1,1,1]) # relevant when batch size is % num classes - balanced_class_probs=[probs] * num_classes) - - labels = np.zeros(num_classes) - - # Create dataloader - dataloader = DataLoader(dataset=unbalanced_dataset, batch_sampler=sampler, num_workers=0) - iter1 = iter(dataloader) - num_items = 0 - for _ in range(len(dataloader)): - batch_dict = next(iter1) - labels_in_batch = FuseUtilsHierarchicalDict.get(batch_dict, 'data.label') - for label in labels_in_batch: - labels[label] += 1 - num_items += 1 - - # final balance - print(labels) - for idx in range(num_classes): - sampled = labels[idx] / num_items - print(f'Class {idx}: {sampled * 100}% of data') - self.assertAlmostEqual(sampled, probs, delta=probs * 0.5, msg=f'Unbalanced class {idx}, expected 0.1(+-0.05) and got {sampled}') - - def tearDown(self): - pass - - -if __name__ == '__main__': - unittest.main() From 97eac6b2f3f86c2b7e841c8339e16742de65c303 Mon Sep 17 00:00:00 2001 From: moshiko Date: Thu, 14 Apr 2022 14:19:15 +0300 Subject: [PATCH 15/42] remove dataset from manager --- fuse/managers/manager_default.py | 140 ++++--------------------------- 1 file changed, 15 insertions(+), 125 deletions(-) diff --git a/fuse/managers/manager_default.py b/fuse/managers/manager_default.py index 5a3690fb6..7626548e7 100644 --- a/fuse/managers/manager_default.py +++ b/fuse/managers/manager_default.py @@ -30,10 +30,6 @@ from tqdm import trange, tqdm from typing import Dict, Any, List, Iterator, Optional, Union, Sequence, Hashable, Callable -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase from fuse.losses.loss_base import FuseLossBase from fuse.managers.callbacks.callback_base import FuseCallback from fuse.managers.callbacks.callback_debug import FuseCallbackDebug @@ -148,11 +144,10 @@ def set_objects(self, self.logger.info(f'Manager - debug mode - append debug callback', {'color': 'red'}) pass - def _save_objects(self, validation_dataloader: DataLoader) -> None: + def _save_objects(self) -> None: """ Saves objects using torch.save (net, losses, metrics, best_epoch_source, optimizer, lr_scheduler, callbacks). Each parameter is saved into a separate file (called losses.pth, metrics.pth, etc) under self.output_model_dir. - :param validation_dataloader: dataloader to extract dataset definitions from (saved on inference_dataset.pth) """ def _torch_save(parameter_to_save: Any, parameter_name: str) -> None: @@ -174,12 +169,7 @@ def _torch_save(parameter_to_save: Any, parameter_name: str) -> None: _torch_save(self.state.best_epoch_source, 'best_epoch_source') _torch_save(self.state.train_params, 'train_params') - # also save validation_dataset in inference mode - if validation_dataloader is not None: - FuseDatasetBase.save(validation_dataloader.dataset, mode=FuseDatasetBase.SaveMode.INFERENCE, - filename=os.path.join(self.state.output_model_dir, "inference_dataset.pth")) - pass - + def load_objects(self, input_model_dir: Union[str, Sequence[str]], list_of_object_names: List[str] = None, mode: str = 'infer') -> Dict[str, Any]: """ Loads objects from torch saved pth files under input_model_dir. @@ -345,7 +335,7 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader {'color': 'red', 'attrs': 'bold'}) # save model and parameters for future use (e.g., infer or resume_from_weights) - self._save_objects(validation_dataloader) + self._save_objects() # save datasets summary into file and logger self._handle_dataset_summaries(train_dataloader, validation_dataloader) @@ -413,85 +403,14 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader pass - def visualize(self, visualizer: FuseVisualizerBase, data_loader: Optional[DataLoader] = None, infer_processor: Optional[FuseProcessorBase] = None, - descriptors: Optional[List[Hashable]] = None, device: str = 'cuda', display_func: Optional[Callable] = None): - - """ - Visualize data including the input and the output. - Expected Sequence: - 1. Using a loaded model to extract the output: - manager = FuseManagerDefault() - - manager.load_objects(, mode='infer') # this method can load either a single model or an ensemble - manager.load_checkpoint(checkpoint=, mode='infer') - manager.visualize(visualizer=visualizer, - data_loader=dataloader, - descriptors=, - display_func=, - infer_processor=None) - - 2. using inference processor - manager = FuseManagerDefault() - manager.visualize(visualizer=visualizer, - data_loader=dataloader, - descriptors=, - display_func=, - infer_processor=infer_processor) - - :param visualizer: The visualizer, getting a batch_dict as an input and doing it's magic - :param data_loader: data loader as used for validation / training / inference - :param infer_processor: Optional, if specified this function will not run the model and instead extract the output from infer processor - :param descriptors: Optional. List of sample descriptors, if None will go over the entire dataset. Might be also list of dataset indices. - :param device: options: 'cuda', 'cpu', 'cuda:0', ... (default 'cuda') - :param display_func: Function getting the batch dict as an input and returns boolean specifying if to visualize this sample or not. - :return: None - """ - dataset: FuseDatasetBase = data_loader.dataset - if infer_processor is None: - if not hasattr(self, 'net') or self.state.net is None: - self.logger.error(f"Cannot visualize without either net or infer_processor") - raise Exception(f"Cannot visualize without either net or infer_processor") - - # prepare net - self.state.net = self.state.net.to(device) - if self.state.device != 'cpu': - self.state.net = nn.DataParallel(self.state.net) - - if descriptors is None: - descriptors = range(len(dataset)) - for desc in tqdm(descriptors): - # extract sample - batch_dict = dataset.get(desc) - if infer_processor is None: - # apply model in case infer processor is not specified - # convert dimensions to batch - batch_dict = dataset.collate_fn([batch_dict]) - # run model - batch_dict['model'] = self.state.net(batch_dict) - # convert dimensions back to single sample - FuseUtilsHierarchicalDict.apply_on_all(batch_dict, Misc.squeeze_obj) - else: - # get the sample descriptor of the sample - sample_descriptor = FuseUtilsHierarchicalDict.get(batch_dict, 'data.descriptor') - # get the infer data - infer_data = infer_processor(sample_descriptor) - # add infer data to batch_dict - for key in infer_data: - FuseUtilsHierarchicalDict.set(batch_dict, key, infer_data[key]) - - if display_func is None or display_func(batch_dict): - visualizer.visualize(batch_dict) - def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, checkpoint: Optional[Union[str, int, Sequence[Union[str, int]]]] = None, - data_source: Optional[FuseDataSourceBase] = None, data_loader: Optional[DataLoader] = None, - num_workers: Optional[int] = 4, batch_size: Optional[int] = 2, + data_loader: Optional[DataLoader] = None, output_columns: List[str] = None, output_file_name: str = None, strict: bool = True, append_default_inference_callback: bool = True, checkpoint_index: int = 0) -> pd.DataFrame: """ - Inference of net on data. Either the data_source or data_loader should be defined. - When data_source is defined, validation_dataset is loaded from the original model_dir and is used to create a dataloader. + Inference of net on data. Returns the inference Results as dict: { 'descriptor': [id_1, id_2, ...], @@ -524,10 +443,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, (either checkpoint_best_epoch.pth, checkpoint_last_epoch.pth or checkpoint_{checkpoint}_epoch.pth) when None, no checkpoint is loaded (assumes that the weights were already loaded. in ensemble mode, can provide either one checkpoint for all models or a sequence of separate checkpoints for each. - :param data_source: data source to use :param data_loader: data loader to use - :param num_workers: number of processes for Dataloader, effective only if 'data_loader' param is None - :param batch_size: batch size for Dataloader, effective only if 'data_loader' param is None :param output_columns: output columns to return. When None (default) all columns are returned. When not None, FuseInferResultsCallback callback is created. @@ -538,14 +454,6 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, :return: infer results in a DataFrame """ - # debug - num workers - override_num_workers = FuseUtilsDebug().get_setting('manager_override_num_dataloader_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - if data_loader is not None: - data_loader.num_workers = override_num_workers - self.logger.info(f'Manager - debug mode - override dataloader num_workers to {override_num_workers}', {'color': 'red'}) - if input_model_dir is not None: # user provided model dir(s), and Manager has no 'net' attribute - need to load modules if not hasattr(self.state, 'net'): @@ -570,30 +478,6 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, if append_default_inference_callback: self.callbacks.append(FuseInferResultsCallback(output_file=output_file_name, output_columns=output_columns)) - # either optional_datasource or optional_dataloader - if data_loader is not None and data_source is not None: - self.logger.error('Cannot have both data_loader and data_source defined') - raise Exception('Cannot have both data_loader and data_source defined') - if data_loader is None and data_source is None: - self.logger.error('Either data_loader or data_source should be defined') - raise Exception('Either data_loader or data_source should be defined') - - if data_loader is None: - # need to create a data loader - # first check that we have the model dir to get these data from - if input_model_dir is None: - self.logger.error('Missing parameter input_model_dir! Cannot load data_set from previous model.') - raise Exception('Missing parameter input_model_dir! Cannot load data_set from previous model.') - - if isinstance(input_model_dir, (tuple, list)): - data_set_filename = os.path.join(input_model_dir[0], "inference_dataset.pth") - else: - data_set_filename = os.path.join(input_model_dir, "inference_dataset.pth") - self.logger.info(f"Loading data source definitions from {data_set_filename}", {'color': 'yellow'}) - infer_dataset = FuseDatasetBase.load(filename=data_set_filename, override_datasource=data_source) - data_loader = DataLoader(dataset=infer_dataset, shuffle=False, drop_last=False, batch_sampler=None, - batch_size=batch_size, num_workers=num_workers, collate_fn=infer_dataset.collate_fn) - # prepare net self.state.net = self.state.net.to(self.state.device) if self.state.device != 'cpu': @@ -806,12 +690,12 @@ def update_scheduler(self, train_results: Dict, validation_results: Dict) -> Non :param train_results: hierarchical dict train epoch results. contains the keys: losses, metrics. - losses is a dict where values are the commputed mean loss for each loss. + losses is a dict where values are the computed mean loss for each loss. and an additional key 'total_loss' which is the mean total loss of the epoch. metrics is a dict where values are the computed metrics. :param validation_results: hierarchical validation epoch results dict. contains the keys: losses, metrics. - losses is a dict where values are the commputed mean loss for each loss. + losses is a dict where values are the computed mean loss for each loss. and an additional key 'total_loss' which is the mean total loss of the epoch. metrics is a dict where values are the computed metrics. Note, if validation was not done on the epoch, this parameter can be None @@ -1037,7 +921,10 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat :param validation_dataloader: validation data (can be None) """ # train dataset summary - dataset_summary = train_dataloader.dataset.summary() + if hasattr(train_dataloader.dataset, "summary"): + dataset_summary = train_dataloader.dataset.summary() + else: + dataset_summary = "" train_dataset_summary_file = os.path.join(self.state.output_model_dir, 'train_dataset_summary.txt') with open(train_dataset_summary_file, 'w') as sum_file: @@ -1047,7 +934,10 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat # validation dataset summary, if exists if validation_dataloader is not None: - dataset_summary = validation_dataloader.dataset.summary() + if hasattr(validation_dataloader.dataset, "summary"): + dataset_summary = validation_dataloader.dataset.summary() + else: + dataset_summary = "" validation_dataset_summary_file = os.path.join(self.state.output_model_dir, 'validation_dataset_summary.txt') with open(validation_dataset_summary_file, 'w') as sum_file: sum_file.write(dataset_summary) From dddac6d888ae64ff038851b764af048277b0793a Mon Sep 17 00:00:00 2001 From: moshiko Date: Thu, 14 Apr 2022 19:18:05 +0300 Subject: [PATCH 16/42] convert mnist to fuse2 style --- .../fuse_examples/classification/mnist/runner.py | 15 ++++++++------- fuse/managers/callbacks/callback_infer_results.py | 13 ++++++------- fuse/managers/manager_default.py | 3 --- fuse/utils/multiprocessing/run_multiprocessed.py | 6 +++--- fuse/utils/ndict.py | 4 +++- requirements.txt | 1 + 6 files changed, 21 insertions(+), 21 deletions(-) diff --git a/examples/fuse_examples/classification/mnist/runner.py b/examples/fuse_examples/classification/mnist/runner.py index 80b85e265..ff13b64a6 100644 --- a/examples/fuse_examples/classification/mnist/runner.py +++ b/examples/fuse_examples/classification/mnist/runner.py @@ -31,8 +31,9 @@ from torchvision import transforms from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch +from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict +from fuse.data.utils.samplers import BatchSamplerDefault +from fuse.data.utils.collates import CollateDefault from fuse.losses.loss_default import FuseLossDefault from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback @@ -137,10 +138,10 @@ def run_train(paths: dict, train_params: dict): torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform) # wrapping torch dataset # FIXME: support also using torch dataset directly - train_dataset = FuseDatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label')) + train_dataset = DatasetWrapSeqToDict(name='train', dataset=torch_train_dataset, sample_keys=("data.image", "data.label")) train_dataset.create() lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = BatchSamplerDefault(dataset=train_dataset, balanced_class_name='data.label', num_balanced_classes=10, batch_size=train_params['data.batch_size'], @@ -156,7 +157,7 @@ def run_train(paths: dict, train_params: dict): # Create dataset torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset - validation_dataset = FuseDatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) + validation_dataset = DatasetWrapSeqToDict(name='validation', dataset=torch_validation_dataset, sample_keys=("data.image", "data.label")) validation_dataset.create() # dataloader @@ -272,10 +273,10 @@ def run_infer(paths: dict, infer_common_params: dict): # Create dataset torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset - validation_dataset = FuseDatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) + validation_dataset = DatasetWrapSeqToDict(name='validation', dataset=torch_validation_dataset, sample_keys=("data.image", "data.label")) validation_dataset.create() # dataloader - validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2) + validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=CollateDefault(), batch_size=2, num_workers=2) ## Manager for inference manager = FuseManagerDefault() diff --git a/fuse/managers/callbacks/callback_infer_results.py b/fuse/managers/callbacks/callback_infer_results.py index ea372d9d5..a8f05f83a 100644 --- a/fuse/managers/callbacks/callback_infer_results.py +++ b/fuse/managers/callbacks/callback_infer_results.py @@ -52,7 +52,7 @@ def __init__(self, output_file: Optional[str] = None, output_columns: Optional[L pass def reset(self): - self.aggregated_dict = {'descriptor': [], 'output': {}} + self.aggregated_dict = {'id': [], 'output': {}} self.infer_results_df = pd.DataFrame() def on_epoch_begin(self, mode: str, epoch: int) -> None: @@ -82,8 +82,7 @@ def on_epoch_end(self, mode: str, epoch: int, epoch_results: Dict = None) -> Non # prepare dataframe from the results infer_results_df = pd.DataFrame() - infer_results_df['descriptor'] = self.aggregated_dict['descriptor'] - infer_results_df['id'] = self.aggregated_dict['descriptor'] # for future support - evaluation package + infer_results_df['id'] = self.aggregated_dict['id'] for output in FuseUtilsHierarchicalDict.get_all_keys(self.aggregated_dict['output']): infer_results_df[output] = list( @@ -109,10 +108,10 @@ def on_batch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: return # for infer we need the descriptor and the output predictions - descriptors = batch_dict['data'].get('descriptor', None) - if isinstance(descriptors, Tensor): - descriptors = list(descriptors.detach().cpu().numpy()) - self.aggregated_dict['descriptor'].extend(descriptors) + sample_ids = batch_dict['data'].get('sample_id', None) + if isinstance(sample_ids, Tensor): + sample_ids = list(sample_ids.detach().cpu().numpy()) + self.aggregated_dict['id'].extend(sample_ids) if self.output_columns is not None and len(self.output_columns) > 0: output_cols = self.output_columns diff --git a/fuse/managers/manager_default.py b/fuse/managers/manager_default.py index 7626548e7..4b1b9322f 100644 --- a/fuse/managers/manager_default.py +++ b/fuse/managers/manager_default.py @@ -957,9 +957,6 @@ def _extend_results_dict(mode: str, current_dict: Dict, aggregated_dict: Dict) - if mode == 'infer': return {} else: - # handle the case where batch dict is empty (the end of the last virtual mini batch) - if current_dict == {}: - return aggregated_dict # for train and validation we need the loss values cur_keys = FuseUtilsHierarchicalDict.get_all_keys(current_dict) # aggregate just keys that start with losses diff --git a/fuse/utils/multiprocessing/run_multiprocessed.py b/fuse/utils/multiprocessing/run_multiprocessed.py index 2c0bdfbad..1aef554c4 100644 --- a/fuse/utils/multiprocessing/run_multiprocessed.py +++ b/fuse/utils/multiprocessing/run_multiprocessed.py @@ -90,7 +90,7 @@ def some_worker(args): return ans args_list: a list in which each element is the input to func workers: number of processes to use. Use 0 for no spawning of processes (helpful when debugging) - copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accesible to worker_func. + copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accessible to worker_func. calling get_from_global_storage(...) will allow access to it from within any worker_func This allows to create a significant speedup in certain cases, and the main idea is that it allows to drastically reduce the amount of data that gets (automatically) pickled by python's multiprocessing library. @@ -134,12 +134,12 @@ def _run_multiprocessed_as_iterator_impl(worker_func, args_list, workers=0, verb worker_func: a worker function, must accept only a single positional argument and no optional args. For example: def some_worker(args): - speed, height, banana = args + speed: height, banana = args ... return ans args_list: a list in which each element is the input to func workers: number of processes to use. Use 0 for no spawning of processes (helpful when debugging) - copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accesible to worker_func. + copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accessible to worker_func. calling get_from_global_storage(...) will allow access to it from within any worker_func This allows to create a significant speedup in certain cases, and the main idea is that it allows to drastically reduce the amount of data that gets (automatically) pickled by python's multiprocessing library. diff --git a/fuse/utils/ndict.py b/fuse/utils/ndict.py index 98eb1b971..4523afdc2 100644 --- a/fuse/utils/ndict.py +++ b/fuse/utils/ndict.py @@ -71,7 +71,9 @@ def __init__(self, d: Union[dict, tuple, types.GeneratorType, NDict, None]=None) self._stored = {} elif isinstance(d, NDict): self._stored = d._stored - else: + else: + if not isinstance(d, dict): + d = dict(d) for k,d in d.items(): self[k] = d diff --git a/requirements.txt b/requirements.txt index 0cbc54248..40a55cb74 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,4 +25,5 @@ pycocotools>=2.0.1 xmlrunner paramiko tables +psutil From 83936d00af4c4a310c88afe3f4b40e9389cbe74e Mon Sep 17 00:00:00 2001 From: moshiko Date: Sun, 17 Apr 2022 09:52:46 +0300 Subject: [PATCH 17/42] create dl package --- .../classification/cmmd/runner.py | 16 +-- .../duke_breast_cancer/run_train_3dpatch.py | 12 +- .../knight/baseline/fuse_baseline.py | 14 +- .../knight/make_predictions_file.py | 2 +- .../classification/mnist/runner.py | 12 +- .../prostate_x/backbone_3d_multichannel.py | 2 +- .../prostate_x/run_train_3dpatch.py | 12 +- .../classification/skin_lesion/runner.py | 18 +-- .../tutorials/hello_world/hello_world.ipynb | 8 +- .../multimodality_image_clinical.ipynb | 14 +- .../augmentor_batch_level_callback.py | 2 +- fuse/{losses => dl}/__init__.py | 0 .../classification => dl/losses}/__init__.py | 0 .../losses/classification}/__init__.py | 0 .../loss_segmentation_cross_entropy.py | 2 +- fuse/{ => dl}/losses/loss_base.py | 0 fuse/{ => dl}/losses/loss_default.py | 2 +- fuse/{ => dl}/losses/loss_warm_up.py | 0 .../losses/segmentation}/__init__.py | 0 .../{ => dl}/losses/segmentation/loss_dice.py | 2 +- .../losses/segmentation/loss_focalLoss.py | 2 +- .../callbacks => dl/managers}/__init__.py | 0 .../managers/callbacks}/__init__.py | 0 .../managers/callbacks/callback_base.py | 2 +- .../managers/callbacks/callback_debug.py | 2 +- .../callbacks/callback_infer_results.py | 2 +- .../callbacks/callback_metric_statistics.py | 2 +- .../callbacks/callback_tensorboard.py | 4 +- .../callbacks/callback_time_statistics.py | 4 +- fuse/{ => dl}/managers/manager_default.py | 12 +- fuse/{ => dl}/managers/manager_state.py | 2 +- .../backbones => dl/models}/__init__.py | 0 .../heads => dl/models/backbones}/__init__.py | 0 .../backbones/backbone_inception_resnet_v2.py | 0 .../{ => dl}/models/backbones/backbone_mlp.py | 0 .../models/backbones/backbone_resnet.py | 0 .../models/backbones/backbone_resnet_3d.py | 0 .../models/heads}/__init__.py | 0 fuse/{ => dl}/models/heads/common.py | 0 .../models/heads/head_1d_classifier.py | 0 .../models/heads/head_3D_classifier.py | 2 +- .../models/heads/head_dense_segmentation.py | 2 +- .../heads/head_global_pooling_classifier.py | 2 +- fuse/{ => dl}/models/model_default.py | 6 +- fuse/{ => dl}/models/model_ensemble.py | 0 fuse/{ => dl}/models/model_multistream.py | 6 +- fuse/{ => dl}/models/model_siamese.py | 6 +- fuse/{ => dl}/models/model_wrapper.py | 4 +- fuse/{templates => dl/optimizers}/__init__.py | 0 fuse/{ => dl}/optimizers/opt_closure_cb.py | 4 +- fuse/{ => dl}/optimizers/opt_sam.py | 4 +- fuse/{tests => dl/templates}/__init__.py | 0 .../templates/walkthrough_template.py | 10 +- fuse/{tests/data => dl/tests}/__init__.py | 0 fuse/{ => dl}/tests/mananger/__init__.py | 0 fuse/{ => dl}/tests/mananger/test_manager.py | 2 +- fuse/tests/data/test_data_source_toolbox.py | 102 -------------- fuse/tests/data/test_processor_dataframe.py | 94 ------------- fuse/tests/data/test_sampler.py | 130 ------------------ run_all_unit_tests.py | 2 +- 60 files changed, 100 insertions(+), 426 deletions(-) rename fuse/{losses => dl}/__init__.py (100%) rename fuse/{losses/classification => dl/losses}/__init__.py (100%) rename fuse/{losses/segmentation => dl/losses/classification}/__init__.py (100%) rename fuse/{ => dl}/losses/classification/loss_segmentation_cross_entropy.py (99%) rename fuse/{ => dl}/losses/loss_base.py (100%) rename fuse/{ => dl}/losses/loss_default.py (98%) rename fuse/{ => dl}/losses/loss_warm_up.py (100%) rename fuse/{managers => dl/losses/segmentation}/__init__.py (100%) rename fuse/{ => dl}/losses/segmentation/loss_dice.py (99%) rename fuse/{ => dl}/losses/segmentation/loss_focalLoss.py (99%) rename fuse/{managers/callbacks => dl/managers}/__init__.py (100%) rename fuse/{models => dl/managers/callbacks}/__init__.py (100%) rename fuse/{ => dl}/managers/callbacks/callback_base.py (98%) rename fuse/{ => dl}/managers/callbacks/callback_debug.py (99%) rename fuse/{ => dl}/managers/callbacks/callback_infer_results.py (98%) rename fuse/{ => dl}/managers/callbacks/callback_metric_statistics.py (98%) rename fuse/{ => dl}/managers/callbacks/callback_tensorboard.py (97%) rename fuse/{ => dl}/managers/callbacks/callback_time_statistics.py (97%) rename fuse/{ => dl}/managers/manager_default.py (99%) rename fuse/{ => dl}/managers/manager_state.py (97%) rename fuse/{models/backbones => dl/models}/__init__.py (100%) rename fuse/{models/heads => dl/models/backbones}/__init__.py (100%) rename fuse/{ => dl}/models/backbones/backbone_inception_resnet_v2.py (100%) rename fuse/{ => dl}/models/backbones/backbone_mlp.py (100%) rename fuse/{ => dl}/models/backbones/backbone_resnet.py (100%) rename fuse/{ => dl}/models/backbones/backbone_resnet_3d.py (100%) rename fuse/{optimizers => dl/models/heads}/__init__.py (100%) rename fuse/{ => dl}/models/heads/common.py (100%) rename fuse/{ => dl}/models/heads/head_1d_classifier.py (100%) rename fuse/{ => dl}/models/heads/head_3D_classifier.py (99%) rename fuse/{ => dl}/models/heads/head_dense_segmentation.py (98%) rename fuse/{ => dl}/models/heads/head_global_pooling_classifier.py (98%) rename fuse/{ => dl}/models/model_default.py (94%) rename fuse/{ => dl}/models/model_ensemble.py (100%) rename fuse/{ => dl}/models/model_multistream.py (96%) rename fuse/{ => dl}/models/model_siamese.py (92%) rename fuse/{ => dl}/models/model_wrapper.py (96%) rename fuse/{templates => dl/optimizers}/__init__.py (100%) rename fuse/{ => dl}/optimizers/opt_closure_cb.py (92%) rename fuse/{ => dl}/optimizers/opt_sam.py (97%) rename fuse/{tests => dl/templates}/__init__.py (100%) rename fuse/{ => dl}/templates/walkthrough_template.py (98%) rename fuse/{tests/data => dl/tests}/__init__.py (100%) rename fuse/{ => dl}/tests/mananger/__init__.py (100%) rename fuse/{ => dl}/tests/mananger/test_manager.py (99%) delete mode 100644 fuse/tests/data/test_data_source_toolbox.py delete mode 100644 fuse/tests/data/test_processor_dataframe.py delete mode 100644 fuse/tests/data/test_sampler.py diff --git a/examples/fuse_examples/classification/cmmd/runner.py b/examples/fuse_examples/classification/cmmd/runner.py index 3d9b0bb5a..778b917ab 100644 --- a/examples/fuse_examples/classification/cmmd/runner.py +++ b/examples/fuse_examples/classification/cmmd/runner.py @@ -32,20 +32,20 @@ from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.models.model_default import FuseModelDefault -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.model_default import FuseModelDefault +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier -from fuse.losses.loss_default import FuseLossDefault +from fuse.dl.losses.loss_default import FuseLossDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricROCCurve -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse_examples.classification.cmmd.dataset import CMMD_2021_dataset -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 from fuse.eval.evaluator import EvaluatorDefault diff --git a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py b/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py index b92f76e72..3b565af03 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py +++ b/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py @@ -23,16 +23,16 @@ from fuse.eval.metrics.classification.metrics_classification_common import MetricROCCurve, MetricAUCROC from fuse.eval.evaluator import EvaluatorDefault from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.losses.loss_default import FuseLossDefault -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.utils_logger import fuse_logger_start -from fuse.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient diff --git a/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py b/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py index 25d81d67e..7c3588f6d 100644 --- a/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py +++ b/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py @@ -7,18 +7,18 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')) from baseline.dataset import knight_dataset import pandas as pd -from fuse.models.model_default import FuseModelDefault -from fuse.models.backbones.backbone_resnet_3d import FuseBackboneResnet3D -from fuse.models.heads.head_3D_classifier import FuseHead3dClassifier -from fuse.losses.loss_default import FuseLossDefault +from fuse.dl.models.model_default import FuseModelDefault +from fuse.dl.models.backbones.backbone_resnet_3d import FuseBackboneResnet3D +from fuse.dl.models.heads.head_3D_classifier import FuseHead3dClassifier +from fuse.dl.losses.loss_default import FuseLossDefault import torch.nn.functional as F import torch.nn as nn from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricConfusion from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds import torch.optim as optim -from fuse.managers.manager_default import FuseManagerDefault -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.rand.seed import Seed import logging diff --git a/examples/fuse_examples/classification/knight/make_predictions_file.py b/examples/fuse_examples/classification/knight/make_predictions_file.py index 0e9042bda..559c14f5c 100644 --- a/examples/fuse_examples/classification/knight/make_predictions_file.py +++ b/examples/fuse_examples/classification/knight/make_predictions_file.py @@ -27,7 +27,7 @@ from fuse.utils.utils_logger import fuse_logger_start from fuse.utils.file_io.file_io import save_dataframe -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse_examples.classification.knight.eval.eval import TASK1_CLASS_NAMES, TASK2_CLASS_NAMES from baseline.dataset import knight_dataset diff --git a/examples/fuse_examples/classification/mnist/runner.py b/examples/fuse_examples/classification/mnist/runner.py index 80b85e265..a4bbd2e88 100644 --- a/examples/fuse_examples/classification/mnist/runner.py +++ b/examples/fuse_examples/classification/mnist/runner.py @@ -33,13 +33,13 @@ from fuse.eval.evaluator import EvaluatorDefault from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.losses.loss_default import FuseLossDefault -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve -from fuse.models.model_wrapper import FuseModelWrapper +from fuse.dl.models.model_wrapper import FuseModelWrapper from fuse.utils.utils_debug import FuseUtilsDebug import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.utils_logger import fuse_logger_start diff --git a/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py b/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py index 144d47187..dbe6b3960 100644 --- a/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py +++ b/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py @@ -20,7 +20,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import numpy as np -from fuse.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier # 3x3 convolution diff --git a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py b/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py index ea91ec5f8..bfc8e56ac 100644 --- a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py +++ b/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py @@ -24,11 +24,11 @@ from fuse.data.dataset.dataset_base import FuseDatasetBase from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.losses.loss_default import FuseLossDefault -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.utils_logger import fuse_logger_start @@ -38,7 +38,7 @@ from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient from fuse_examples.classification.prostate_x.tasks import FuseProstateXTask -from fuse.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier ########################################## diff --git a/examples/fuse_examples/classification/skin_lesion/runner.py b/examples/fuse_examples/classification/skin_lesion/runner.py index 5f7fb9c87..d773dde68 100644 --- a/examples/fuse_examples/classification/skin_lesion/runner.py +++ b/examples/fuse_examples/classification/skin_lesion/runner.py @@ -39,20 +39,20 @@ from fuse.data.augmentor.augmentor_toolbox import aug_op_affine, aug_op_color, aug_op_gaussian from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.models.model_default import FuseModelDefault -from fuse.models.backbones.backbone_resnet import FuseBackboneResnet -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.model_default import FuseModelDefault +from fuse.dl.models.backbones.backbone_resnet import FuseBackboneResnet +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.losses.loss_default import FuseLossDefault +from fuse.dl.losses.loss_default import FuseLossDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricROCCurve from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse_examples.classification.skin_lesion.data_source import FuseSkinDataSource diff --git a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb b/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb index 6d8214b49..9253dc1bd 100644 --- a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb @@ -85,12 +85,12 @@ "from fuse.eval.evaluator import EvaluatorDefault\n", "from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper\n", "from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch\n", - "from fuse.losses.loss_default import FuseLossDefault\n", - "from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", - "from fuse.managers.manager_default import FuseManagerDefault\n", + "from fuse.dl.losses.loss_default import FuseLossDefault\n", + "from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", + "from fuse.dl.managers.manager_default import FuseManagerDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", - "from fuse.models.model_wrapper import FuseModelWrapper\n", + "from fuse.dl.models.model_wrapper import FuseModelWrapper\n", "from fuse_examples.tutorials.hello_world.hello_world_utils import LeNet, perform_softmax" ] }, diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb b/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb index 0067e6ee8..199f0156a 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb @@ -538,10 +538,10 @@ "outputs": [], "source": [ "\n", - "from fuse.models.model_default import FuseModelDefault\n", - "from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier\n", - "from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2\n", - "from fuse.models.backbones.backbone_resnet import FuseBackboneResnet\n", + "from fuse.dl.models.model_default import FuseModelDefault\n", + "from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier\n", + "from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2\n", + "from fuse.dl.models.backbones.backbone_resnet import FuseBackboneResnet\n", "\n", "model = FuseModelDefault(\n", " conv_inputs=(('data.input.image', 3),),\n", @@ -568,7 +568,7 @@ "source": [ "from collections import OrderedDict\n", "import torch.nn.functional as F\n", - "from fuse.losses.loss_default import FuseLossDefault\n", + "from fuse.dl.losses.loss_default import FuseLossDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricConfusion\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "# ====================================================================================\n", @@ -912,8 +912,8 @@ ], "source": [ "import torch.optim as optim\n", - "from fuse.managers.manager_default import FuseManagerDefault\n", - "from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", + "from fuse.dl.managers.manager_default import FuseManagerDefault\n", + "from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", "\n", "# create optimizer\n", "optimizer = optim.Adam(model.parameters(), lr=1e-5,\n", diff --git a/fuse/data/augmentor/augmentor_batch_level_callback.py b/fuse/data/augmentor/augmentor_batch_level_callback.py index a48b899db..dc757faf8 100644 --- a/fuse/data/augmentor/augmentor_batch_level_callback.py +++ b/fuse/data/augmentor/augmentor_batch_level_callback.py @@ -20,7 +20,7 @@ from typing import Dict, List, Sequence from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import FuseCallback class FuseAugmentorBatchCallback(FuseCallback): diff --git a/fuse/losses/__init__.py b/fuse/dl/__init__.py similarity index 100% rename from fuse/losses/__init__.py rename to fuse/dl/__init__.py diff --git a/fuse/losses/classification/__init__.py b/fuse/dl/losses/__init__.py similarity index 100% rename from fuse/losses/classification/__init__.py rename to fuse/dl/losses/__init__.py diff --git a/fuse/losses/segmentation/__init__.py b/fuse/dl/losses/classification/__init__.py similarity index 100% rename from fuse/losses/segmentation/__init__.py rename to fuse/dl/losses/classification/__init__.py diff --git a/fuse/losses/classification/loss_segmentation_cross_entropy.py b/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py similarity index 99% rename from fuse/losses/classification/loss_segmentation_cross_entropy.py rename to fuse/dl/losses/classification/loss_segmentation_cross_entropy.py index 6ec1944b3..63bd79eed 100644 --- a/fuse/losses/classification/loss_segmentation_cross_entropy.py +++ b/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py @@ -22,7 +22,7 @@ import numpy as np import torch -from fuse.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import FuseLossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/losses/loss_base.py b/fuse/dl/losses/loss_base.py similarity index 100% rename from fuse/losses/loss_base.py rename to fuse/dl/losses/loss_base.py diff --git a/fuse/losses/loss_default.py b/fuse/dl/losses/loss_default.py similarity index 98% rename from fuse/losses/loss_default.py rename to fuse/dl/losses/loss_default.py index dc18e2ed9..8e6769433 100644 --- a/fuse/losses/loss_default.py +++ b/fuse/dl/losses/loss_default.py @@ -21,7 +21,7 @@ import torch -from fuse.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import FuseLossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/losses/loss_warm_up.py b/fuse/dl/losses/loss_warm_up.py similarity index 100% rename from fuse/losses/loss_warm_up.py rename to fuse/dl/losses/loss_warm_up.py diff --git a/fuse/managers/__init__.py b/fuse/dl/losses/segmentation/__init__.py similarity index 100% rename from fuse/managers/__init__.py rename to fuse/dl/losses/segmentation/__init__.py diff --git a/fuse/losses/segmentation/loss_dice.py b/fuse/dl/losses/segmentation/loss_dice.py similarity index 99% rename from fuse/losses/segmentation/loss_dice.py rename to fuse/dl/losses/segmentation/loss_dice.py index 177d0379c..944e39bde 100644 --- a/fuse/losses/segmentation/loss_dice.py +++ b/fuse/dl/losses/segmentation/loss_dice.py @@ -21,7 +21,7 @@ import torch.nn as nn import torch.nn.functional as F import numpy as np -from fuse.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import FuseLossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from typing import Callable, Dict, Optional diff --git a/fuse/losses/segmentation/loss_focalLoss.py b/fuse/dl/losses/segmentation/loss_focalLoss.py similarity index 99% rename from fuse/losses/segmentation/loss_focalLoss.py rename to fuse/dl/losses/segmentation/loss_focalLoss.py index c1333402b..fa9cd29f3 100644 --- a/fuse/losses/segmentation/loss_focalLoss.py +++ b/fuse/dl/losses/segmentation/loss_focalLoss.py @@ -22,7 +22,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from fuse.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import FuseLossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import numpy as np diff --git a/fuse/managers/callbacks/__init__.py b/fuse/dl/managers/__init__.py similarity index 100% rename from fuse/managers/callbacks/__init__.py rename to fuse/dl/managers/__init__.py diff --git a/fuse/models/__init__.py b/fuse/dl/managers/callbacks/__init__.py similarity index 100% rename from fuse/models/__init__.py rename to fuse/dl/managers/callbacks/__init__.py diff --git a/fuse/managers/callbacks/callback_base.py b/fuse/dl/managers/callbacks/callback_base.py similarity index 98% rename from fuse/managers/callbacks/callback_base.py rename to fuse/dl/managers/callbacks/callback_base.py index 970cb984c..513bbd800 100644 --- a/fuse/managers/callbacks/callback_base.py +++ b/fuse/dl/managers/callbacks/callback_base.py @@ -19,7 +19,7 @@ from typing import Dict -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.managers.manager_state import FuseManagerState class FuseCallback(object): diff --git a/fuse/managers/callbacks/callback_debug.py b/fuse/dl/managers/callbacks/callback_debug.py similarity index 99% rename from fuse/managers/callbacks/callback_debug.py rename to fuse/dl/managers/callbacks/callback_debug.py index 3da8bebf9..c156ff435 100644 --- a/fuse/managers/callbacks/callback_debug.py +++ b/fuse/dl/managers/callbacks/callback_debug.py @@ -23,7 +23,7 @@ import torch.nn as nn -from fuse.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import FuseCallback from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.misc.misc import Misc diff --git a/fuse/managers/callbacks/callback_infer_results.py b/fuse/dl/managers/callbacks/callback_infer_results.py similarity index 98% rename from fuse/managers/callbacks/callback_infer_results.py rename to fuse/dl/managers/callbacks/callback_infer_results.py index ea372d9d5..83ca919fa 100644 --- a/fuse/managers/callbacks/callback_infer_results.py +++ b/fuse/dl/managers/callbacks/callback_infer_results.py @@ -26,7 +26,7 @@ import torch from torch import Tensor -from fuse.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import FuseCallback from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/managers/callbacks/callback_metric_statistics.py b/fuse/dl/managers/callbacks/callback_metric_statistics.py similarity index 98% rename from fuse/managers/callbacks/callback_metric_statistics.py rename to fuse/dl/managers/callbacks/callback_metric_statistics.py index d5830b8ce..2e695a6dd 100644 --- a/fuse/managers/callbacks/callback_metric_statistics.py +++ b/fuse/dl/managers/callbacks/callback_metric_statistics.py @@ -24,7 +24,7 @@ import pandas as pd -from fuse.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import FuseCallback from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/managers/callbacks/callback_tensorboard.py b/fuse/dl/managers/callbacks/callback_tensorboard.py similarity index 97% rename from fuse/managers/callbacks/callback_tensorboard.py rename to fuse/dl/managers/callbacks/callback_tensorboard.py index f8e643299..bc364134c 100644 --- a/fuse/managers/callbacks/callback_tensorboard.py +++ b/fuse/dl/managers/callbacks/callback_tensorboard.py @@ -19,8 +19,8 @@ import os from typing import Dict -from fuse.managers.callbacks.callback_base import FuseCallback -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.manager_state import FuseManagerState from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import torch diff --git a/fuse/managers/callbacks/callback_time_statistics.py b/fuse/dl/managers/callbacks/callback_time_statistics.py similarity index 97% rename from fuse/managers/callbacks/callback_time_statistics.py rename to fuse/dl/managers/callbacks/callback_time_statistics.py index a55c9cef2..e8e205a1a 100644 --- a/fuse/managers/callbacks/callback_time_statistics.py +++ b/fuse/dl/managers/callbacks/callback_time_statistics.py @@ -23,8 +23,8 @@ import torch.nn as nn -from fuse.managers.callbacks.callback_base import FuseCallback -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.manager_state import FuseManagerState from fuse.utils.misc.misc import get_time_delta, time_display diff --git a/fuse/managers/manager_default.py b/fuse/dl/managers/manager_default.py similarity index 99% rename from fuse/managers/manager_default.py rename to fuse/dl/managers/manager_default.py index 5a3690fb6..dd248a2cd 100644 --- a/fuse/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -34,13 +34,13 @@ from fuse.data.dataset.dataset_base import FuseDatasetBase from fuse.data.processor.processor_base import FuseProcessorBase from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.losses.loss_base import FuseLossBase -from fuse.managers.callbacks.callback_base import FuseCallback -from fuse.managers.callbacks.callback_debug import FuseCallbackDebug -from fuse.managers.callbacks.callback_infer_results import FuseInferResultsCallback -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_debug import FuseCallbackDebug +from fuse.dl.managers.callbacks.callback_infer_results import FuseInferResultsCallback +from fuse.dl.managers.manager_state import FuseManagerState from fuse.eval import MetricBase -from fuse.models.model_ensemble import FuseModelEnsemble +from fuse.dl.models.model_ensemble import FuseModelEnsemble from fuse.utils.dl.checkpoint import FuseCheckpoint from fuse.utils.utils_debug import FuseUtilsDebug from fuse.utils.file_io.file_io import create_or_reset_dir diff --git a/fuse/managers/manager_state.py b/fuse/dl/managers/manager_state.py similarity index 97% rename from fuse/managers/manager_state.py rename to fuse/dl/managers/manager_state.py index 3540db2d6..5a9e4eaee 100644 --- a/fuse/managers/manager_state.py +++ b/fuse/dl/managers/manager_state.py @@ -25,7 +25,7 @@ from torch.optim.optimizer import Optimizer from torch.utils.data.dataloader import DataLoader -from fuse.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import FuseLossBase from fuse.eval import MetricBase diff --git a/fuse/models/backbones/__init__.py b/fuse/dl/models/__init__.py similarity index 100% rename from fuse/models/backbones/__init__.py rename to fuse/dl/models/__init__.py diff --git a/fuse/models/heads/__init__.py b/fuse/dl/models/backbones/__init__.py similarity index 100% rename from fuse/models/heads/__init__.py rename to fuse/dl/models/backbones/__init__.py diff --git a/fuse/models/backbones/backbone_inception_resnet_v2.py b/fuse/dl/models/backbones/backbone_inception_resnet_v2.py similarity index 100% rename from fuse/models/backbones/backbone_inception_resnet_v2.py rename to fuse/dl/models/backbones/backbone_inception_resnet_v2.py diff --git a/fuse/models/backbones/backbone_mlp.py b/fuse/dl/models/backbones/backbone_mlp.py similarity index 100% rename from fuse/models/backbones/backbone_mlp.py rename to fuse/dl/models/backbones/backbone_mlp.py diff --git a/fuse/models/backbones/backbone_resnet.py b/fuse/dl/models/backbones/backbone_resnet.py similarity index 100% rename from fuse/models/backbones/backbone_resnet.py rename to fuse/dl/models/backbones/backbone_resnet.py diff --git a/fuse/models/backbones/backbone_resnet_3d.py b/fuse/dl/models/backbones/backbone_resnet_3d.py similarity index 100% rename from fuse/models/backbones/backbone_resnet_3d.py rename to fuse/dl/models/backbones/backbone_resnet_3d.py diff --git a/fuse/optimizers/__init__.py b/fuse/dl/models/heads/__init__.py similarity index 100% rename from fuse/optimizers/__init__.py rename to fuse/dl/models/heads/__init__.py diff --git a/fuse/models/heads/common.py b/fuse/dl/models/heads/common.py similarity index 100% rename from fuse/models/heads/common.py rename to fuse/dl/models/heads/common.py diff --git a/fuse/models/heads/head_1d_classifier.py b/fuse/dl/models/heads/head_1d_classifier.py similarity index 100% rename from fuse/models/heads/head_1d_classifier.py rename to fuse/dl/models/heads/head_1d_classifier.py diff --git a/fuse/models/heads/head_3D_classifier.py b/fuse/dl/models/heads/head_3D_classifier.py similarity index 99% rename from fuse/models/heads/head_3D_classifier.py rename to fuse/dl/models/heads/head_3D_classifier.py index 793d4a03d..bc597f4fe 100644 --- a/fuse/models/heads/head_3D_classifier.py +++ b/fuse/dl/models/heads/head_3D_classifier.py @@ -24,7 +24,7 @@ import torch.nn.functional as F from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.models.heads.common import ClassifierMLP +from fuse.dl.models.heads.common import ClassifierMLP class FuseHead3dClassifier(nn.Module): diff --git a/fuse/models/heads/head_dense_segmentation.py b/fuse/dl/models/heads/head_dense_segmentation.py similarity index 98% rename from fuse/models/heads/head_dense_segmentation.py rename to fuse/dl/models/heads/head_dense_segmentation.py index 01b38e157..cc5d50f29 100644 --- a/fuse/models/heads/head_dense_segmentation.py +++ b/fuse/dl/models/heads/head_dense_segmentation.py @@ -23,7 +23,7 @@ import torch.nn as nn import torch.nn.functional as F -from fuse.models.heads.common import ClassifierFCN +from fuse.dl.models.heads.common import ClassifierFCN from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/models/heads/head_global_pooling_classifier.py b/fuse/dl/models/heads/head_global_pooling_classifier.py similarity index 98% rename from fuse/models/heads/head_global_pooling_classifier.py rename to fuse/dl/models/heads/head_global_pooling_classifier.py index a294814f5..1f9acbae9 100644 --- a/fuse/models/heads/head_global_pooling_classifier.py +++ b/fuse/dl/models/heads/head_global_pooling_classifier.py @@ -23,7 +23,7 @@ import torch.nn as nn import torch.nn.functional as F -from fuse.models.heads.common import ClassifierFCN, ClassifierMLP +from fuse.dl.models.heads.common import ClassifierFCN, ClassifierMLP from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/models/model_default.py b/fuse/dl/models/model_default.py similarity index 94% rename from fuse/models/model_default.py rename to fuse/dl/models/model_default.py index 2721402ff..7f2d4b436 100644 --- a/fuse/models/model_default.py +++ b/fuse/dl/models/model_default.py @@ -21,8 +21,8 @@ import torch -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict @@ -76,7 +76,7 @@ def forward(self, if __name__ == '__main__': - from fuse.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation + from fuse.dl.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation import torch import os diff --git a/fuse/models/model_ensemble.py b/fuse/dl/models/model_ensemble.py similarity index 100% rename from fuse/models/model_ensemble.py rename to fuse/dl/models/model_ensemble.py diff --git a/fuse/models/model_multistream.py b/fuse/dl/models/model_multistream.py similarity index 96% rename from fuse/models/model_multistream.py rename to fuse/dl/models/model_multistream.py index bf66cf53b..be7a13a7c 100644 --- a/fuse/models/model_multistream.py +++ b/fuse/dl/models/model_multistream.py @@ -21,8 +21,8 @@ import torch -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict @@ -96,7 +96,7 @@ def forward(self, if __name__ == '__main__': - from fuse.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation + from fuse.dl.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation backbone_0 = FuseBackboneInceptionResnetV2(logical_units_num=8) backbone_1 = FuseBackboneInceptionResnetV2(logical_units_num=8) diff --git a/fuse/models/model_siamese.py b/fuse/dl/models/model_siamese.py similarity index 92% rename from fuse/models/model_siamese.py rename to fuse/dl/models/model_siamese.py index 6509c1ddb..54b4fdd04 100644 --- a/fuse/models/model_siamese.py +++ b/fuse/dl/models/model_siamese.py @@ -21,9 +21,9 @@ import torch -from fuse.models.model_default import FuseModelDefault -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.model_default import FuseModelDefault +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/models/model_wrapper.py b/fuse/dl/models/model_wrapper.py similarity index 96% rename from fuse/models/model_wrapper.py rename to fuse/dl/models/model_wrapper.py index 70064b93a..6a04e3e52 100644 --- a/fuse/models/model_wrapper.py +++ b/fuse/dl/models/model_wrapper.py @@ -21,8 +21,8 @@ import torch -from fuse.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict diff --git a/fuse/templates/__init__.py b/fuse/dl/optimizers/__init__.py similarity index 100% rename from fuse/templates/__init__.py rename to fuse/dl/optimizers/__init__.py diff --git a/fuse/optimizers/opt_closure_cb.py b/fuse/dl/optimizers/opt_closure_cb.py similarity index 92% rename from fuse/optimizers/opt_closure_cb.py rename to fuse/dl/optimizers/opt_closure_cb.py index a15069429..4877b493f 100644 --- a/fuse/optimizers/opt_closure_cb.py +++ b/fuse/dl/optimizers/opt_closure_cb.py @@ -18,8 +18,8 @@ """ from typing import Dict -from fuse.managers.callbacks.callback_base import FuseCallback -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.manager_state import FuseManagerState class FuseCallbackOptClosure(FuseCallback): """ diff --git a/fuse/optimizers/opt_sam.py b/fuse/dl/optimizers/opt_sam.py similarity index 97% rename from fuse/optimizers/opt_sam.py rename to fuse/dl/optimizers/opt_sam.py index a740d09c8..15d740490 100644 --- a/fuse/optimizers/opt_sam.py +++ b/fuse/dl/optimizers/opt_sam.py @@ -31,8 +31,8 @@ import torch from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.managers.callbacks.callback_base import FuseCallback -from fuse.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.manager_state import FuseManagerState class SAM(torch.optim.Optimizer): diff --git a/fuse/tests/__init__.py b/fuse/dl/templates/__init__.py similarity index 100% rename from fuse/tests/__init__.py rename to fuse/dl/templates/__init__.py diff --git a/fuse/templates/walkthrough_template.py b/fuse/dl/templates/walkthrough_template.py similarity index 98% rename from fuse/templates/walkthrough_template.py rename to fuse/dl/templates/walkthrough_template.py index c057f2a6b..d6afa91f4 100644 --- a/fuse/templates/walkthrough_template.py +++ b/fuse/dl/templates/walkthrough_template.py @@ -36,12 +36,12 @@ from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.models.model_default import FuseModelDefault +from fuse.dl.models.model_default import FuseModelDefault -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse.analyzer.analyzer_default import FuseAnalyzerDefault diff --git a/fuse/tests/data/__init__.py b/fuse/dl/tests/__init__.py similarity index 100% rename from fuse/tests/data/__init__.py rename to fuse/dl/tests/__init__.py diff --git a/fuse/tests/mananger/__init__.py b/fuse/dl/tests/mananger/__init__.py similarity index 100% rename from fuse/tests/mananger/__init__.py rename to fuse/dl/tests/mananger/__init__.py diff --git a/fuse/tests/mananger/test_manager.py b/fuse/dl/tests/mananger/test_manager.py similarity index 99% rename from fuse/tests/mananger/test_manager.py rename to fuse/dl/tests/mananger/test_manager.py index 657f4b187..712cf6a1d 100644 --- a/fuse/tests/mananger/test_manager.py +++ b/fuse/dl/tests/mananger/test_manager.py @@ -25,7 +25,7 @@ import logging from fuse.utils.utils_logger import fuse_logger_start -from fuse.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.manager_default import FuseManagerDefault from fuse.utils.file_io.file_io import create_or_reset_dir diff --git a/fuse/tests/data/test_data_source_toolbox.py b/fuse/tests/data/test_data_source_toolbox.py deleted file mode 100644 index 5c0ba8dca..000000000 --- a/fuse/tests/data/test_data_source_toolbox.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import pandas as pd -import numpy as np -import torch -import os -from scipy import stats - -from fuse.data.data_source.data_source_toolbox import FuseDataSourceToolbox -import pathlib - - -class FuseDataSourceToolBoxTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - pass - - def test_balanced_division(self): - # FIXME: removed after adding the missing file - return - input_df = pd.read_csv(os.path.join(pathlib.Path(__file__).parent.resolve(),'file_for_test.csv')) - - # configure input for fold partition - unique_ID = 'ID1' - label = 'label1' - folds = 5 - - partition_df = FuseDataSourceToolbox.balanced_division(df = input_df , - no_mixture_id = unique_ID, - key_columns =[label] , - nfolds = folds , - print_flag=False, - debug_mode = True) - # set expected values for the partition - - # expeted id_level values and unique records - id_level_value_counter= {'[ True False]': 465, '[False True]': 1280, '[ True True]': 30} - - # label balance value - population_mean = input_df[label].mean() - - # observed label balance in folds - means = [partition_df[partition_df['fold'] == i][label].mean() for i in range(folds)] - - # get number of unique ID in each folds - folds_size = [len(partition_df[partition_df['fold'] == i][unique_ID].unique()) for i in range(folds)] - - # number of records in each fold - records_size = [len(partition_df[partition_df['fold'] == i]) for i in range(folds)] - - # confidence level for confidence intervals - confidence_level = 0.95 - CI_label = stats.t.interval(confidence_level, len(means)-1, loc=np.mean(means), scale=stats.sem(means)) - - # min and max fold size in terms of expected unique ID in each - min_fold_size = np.sum([id_level_value_counter[value]/folds for value in id_level_value_counter]) - max_fold_size = min_fold_size + len(id_level_value_counter) - - # check if expected conditions hold - - # checks if the sum of all unique id in all folds is like in original file - self.assertTrue(np.sum(folds_size) == len(input_df[unique_ID].unique())) - - # checks if number or records in the folds in like in original file - self.assertTrue(np.sum(records_size) == len(input_df)) - - # checks if label balancing is distributed around the original balance - self.assertTrue(CI_label[0] <= population_mean <= CI_label[1]) - - # checks if in each fold number of unique ID is in the expected range - for size in folds_size : - self.assertTrue(min_fold_size <= size <= max_fold_size) - - - - - - def tearDown(self): - pass - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/fuse/tests/data/test_processor_dataframe.py b/fuse/tests/data/test_processor_dataframe.py deleted file mode 100644 index 8aeb1c51f..000000000 --- a/fuse/tests/data/test_processor_dataframe.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import pandas as pd -import torch - -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame - - -class FuseProcessorDataFrameTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - pass - - def test_all_columns(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc') - - self.assertDictEqual(proc('one'), {'int_val': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': 6, 'string_val':'val3'}) - - def test_rename(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', rename_columns={'int_val': 'new_name'}) - - self.assertDictEqual(proc('one'), {'new_name': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'new_name': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'new_name': 6, 'string_val':'val3'}) - - def test_specific_columns(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', rename_columns={'int_val': 'new_name'}, - columns_to_extract=['string_val', 'desc']) - - self.assertDictEqual(proc('one'), {'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'string_val':'val3'}) - - def test_tensors(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', columns_to_tensor=['int_val', 'invalid_column']) - - self.assertDictEqual(proc('one'), {'int_val': torch.tensor(4, dtype=torch.int64), 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': torch.tensor(5, dtype=torch.int64), 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': torch.tensor(6, dtype=torch.int64), 'string_val':'val3'}) - - def test_tensors_with_types(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "float_val": [4.1, 5.3, 6.5], - "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', - columns_to_tensor={'int_val': torch.int8, 'float_val': torch.float64}) - - self.assertDictEqual(proc('one'), {'int_val': torch.tensor(4, dtype=torch.int8), - 'float_val': torch.tensor(4.1, dtype=torch.float64), 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': torch.tensor(5, dtype=torch.int8), - 'float_val': torch.tensor(5.3, dtype=torch.float64), 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': torch.tensor(6, dtype=torch.int8), - 'float_val': torch.tensor(6.5, dtype=torch.float64), 'string_val':'val3'}) - - - def test_tensors_non_existing(self): - df = pd.DataFrame({"desc": ['one', 'two', 'three'], "int_val": [4, 5, 6], "string_val": ['val1', 'val2', 'val3']}) - proc = FuseProcessorDataFrame(data=df, sample_desc_column='desc', columns_to_tensor=['invalid_column']) - - self.assertDictEqual(proc('one'), {'int_val': 4, 'string_val':'val1'}) - self.assertDictEqual(proc('two'), {'int_val': 5, 'string_val':'val2'}) - self.assertDictEqual(proc('three'), {'int_val': 6, 'string_val':'val3'}) - - - def tearDown(self): - pass - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/fuse/tests/data/test_sampler.py b/fuse/tests/data/test_sampler.py deleted file mode 100644 index 4d75f8133..000000000 --- a/fuse/tests/data/test_sampler.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import unittest -import numpy as np -import torchvision -from torch.utils.data.dataloader import DataLoader -from torchvision import transforms - -from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict - - -class FuseSamplerBalancedTestCase(unittest.TestCase): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def setUp(self): - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ]) - # Create dataset - self.torch_dataset = torchvision.datasets.MNIST('/tmp/mnist_test', download=True, train=True, transform=transform) - # wrapping torch dataset - self.dataset = FuseDatasetWrapper(name='test', dataset=self.torch_dataset, mapping=('image', 'label')) - self.dataset.create(reset_cache=True) - pass - - def test_balanced_dataset(self): - num_classes = 10 - batch_size = 5 - probs = 1.0 / num_classes - num_samples = 60000 - - print(self.dataset.summary(statistic_keys=['data.label'])) - sampler = FuseSamplerBalancedBatch(dataset=self.dataset, - balanced_class_name='data.label', - num_balanced_classes=num_classes, - batch_size=batch_size, - # balanced_class_weights=[1,1,1,1,1,1,1,1,1,1]) # relevant when batch size is % num classes - balanced_class_probs=[probs] * num_classes) - - labels = np.zeros(num_classes) - - # Create dataloader - dataloader = DataLoader(dataset=self.dataset, batch_sampler=sampler, num_workers=0) - iter1 = iter(dataloader) - for _ in range(len(dataloader)): - batch_dict = next(iter1) - labels_in_batch = FuseUtilsHierarchicalDict.get(batch_dict, 'data.label') - for label in labels_in_batch: - labels[label] += 1 - - # final balance - print(labels) - for idx in range(num_classes): - sampled = labels[idx] / num_samples - print(f'Class {idx}: {sampled * 100}% of data') - self.assertAlmostEqual(sampled, probs, delta=probs * 0.5, msg=f'Unbalanced class {idx}, expected 0.1+-0.05 and got {sampled}') - - def test_unbalanced_dataset(self): - num_classes = 10 - batch_size = 5 - probs = 1.0 / num_classes - - # wrapping torch dataset - unbalanced_dataset = self.dataset - - samples_to_save = [] - stats = [1000, 200, 200, 200, 300, 500, 700, 800, 900, 1000] - chosen = np.zeros(10) - for idx in range(60000): - label = unbalanced_dataset.get(idx, 'data.label') - if stats[label] > chosen[label]: - samples_to_save.append(("test", idx)) - chosen[label] += 1 - - unbalanced_dataset.samples_description = samples_to_save - - sampler = FuseSamplerBalancedBatch(dataset=unbalanced_dataset, - balanced_class_name='data.label', - num_balanced_classes=num_classes, - batch_size=batch_size, - # balanced_class_weights=[1,1,1,1,1,1,1,1,1,1]) # relevant when batch size is % num classes - balanced_class_probs=[probs] * num_classes) - - labels = np.zeros(num_classes) - - # Create dataloader - dataloader = DataLoader(dataset=unbalanced_dataset, batch_sampler=sampler, num_workers=0) - iter1 = iter(dataloader) - num_items = 0 - for _ in range(len(dataloader)): - batch_dict = next(iter1) - labels_in_batch = FuseUtilsHierarchicalDict.get(batch_dict, 'data.label') - for label in labels_in_batch: - labels[label] += 1 - num_items += 1 - - # final balance - print(labels) - for idx in range(num_classes): - sampled = labels[idx] / num_items - print(f'Class {idx}: {sampled * 100}% of data') - self.assertAlmostEqual(sampled, probs, delta=probs * 0.5, msg=f'Unbalanced class {idx}, expected 0.1(+-0.05) and got {sampled}') - - def tearDown(self): - pass - - -if __name__ == '__main__': - unittest.main() diff --git a/run_all_unit_tests.py b/run_all_unit_tests.py index 40214b5f6..18edf1c39 100644 --- a/run_all_unit_tests.py +++ b/run_all_unit_tests.py @@ -26,7 +26,7 @@ def mehikon(a,b): output = f"{search_base}/test-reports/" print('will generate unit tests output xml at :',output) - sub_sections_core = [("fuse/tests", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base)] + sub_sections_core = [("fuse/dl", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base)] sub_sections_examples = [("examples/fuse_examples/tests", os.path.join(search_base, "examples"))] if mode is None: sub_sections = sub_sections_core + sub_sections_examples From 69a3b09a41fd6a1d8a6bc16ed6f79e7d5644aa99 Mon Sep 17 00:00:00 2001 From: moshiko Date: Sun, 17 Apr 2022 12:19:20 +0300 Subject: [PATCH 18/42] remove Fuse prefix --- .../classification/cmmd/dataset.py | 36 ++-- .../cmmd/ground_truth_processor.py | 4 +- .../classification/cmmd/input_processor.py | 14 +- .../classification/cmmd/runner.py | 44 ++--- .../duke_breast_cancer/dataset.py | 24 +-- .../duke_breast_cancer/processor.py | 18 +- .../duke_breast_cancer/run_train_3dpatch.py | 52 ++--- .../duke_breast_cancer/tasks.py | 12 +- .../knight/baseline/clinical_processor.py | 6 +- .../classification/knight/baseline/dataset.py | 24 +-- .../knight/baseline/fuse_baseline.py | 32 +-- .../knight/baseline/input_processor.py | 4 +- .../knight/make_predictions_file.py | 4 +- .../classification/mnist/runner.py | 48 ++--- .../prostate_x/backbone_3d_multichannel.py | 8 +- .../classification/prostate_x/data_utils.py | 10 +- .../classification/prostate_x/dataset.py | 28 +-- .../prostate_x/patient_data_source.py | 12 +- .../classification/prostate_x/processor.py | 18 +- .../prostate_x/run_train_3dpatch.py | 50 ++--- .../classification/prostate_x/tasks.py | 8 +- .../classification/skin_lesion/data_source.py | 6 +- .../skin_lesion/ground_truth_processor.py | 4 +- .../skin_lesion/input_processor.py | 4 +- .../classification/skin_lesion/runner.py | 94 ++++----- .../tests/test_classification_mnist.py | 4 +- .../tests/test_classification_prostatex.py | 4 +- .../tests/test_classification_skin_lesion.py | 4 +- .../tutorials/hello_world/hello_world.ipynb | 40 ++-- .../data_source.py | 6 +- .../multimodality_image_clinical/dataset.py | 38 ++-- .../ground_truth_processor.py | 4 +- .../input_processor.py | 4 +- .../multimodality_image_clinical.ipynb | 84 ++++---- fuse/data/augmentor/augmentor_base.py | 2 +- .../augmentor_batch_level_callback.py | 10 +- fuse/data/augmentor/augmentor_default.py | 4 +- fuse/data/cache/cache_base.py | 2 +- fuse/data/cache/cache_files.py | 4 +- fuse/data/cache/cache_memory.py | 4 +- fuse/data/cache/cache_null.py | 4 +- fuse/data/data_source/data_source_base.py | 2 +- fuse/data/data_source/data_source_default.py | 12 +- fuse/data/data_source/data_source_folds.py | 10 +- .../data/data_source/data_source_from_list.py | 6 +- fuse/data/data_source/data_source_toolbox.py | 4 +- fuse/data/dataset/dataset_base.py | 8 +- fuse/data/dataset/dataset_dataframe.py | 14 +- fuse/data/dataset/dataset_default.py | 84 ++++---- fuse/data/dataset/dataset_generator.py | 76 ++++---- fuse/data/dataset/dataset_wrapper.py | 16 +- fuse/data/processor/processor_base.py | 2 +- fuse/data/processor/processor_csv.py | 4 +- fuse/data/processor/processor_dataframe.py | 8 +- fuse/data/processor/processor_dicom_mri.py | 6 +- fuse/data/processor/processor_rand.py | 4 +- .../processor/processors_image_toolbox.py | 4 +- fuse/data/sampler/sampler_balanced_batch.py | 18 +- fuse/data/utils/export.py | 4 +- fuse/data/visualizer/visualizer_base.py | 2 +- fuse/data/visualizer/visualizer_default.py | 4 +- fuse/data/visualizer/visualizer_default_3d.py | 4 +- .../visualizer/visualizer_image_analysis.py | 4 +- .../loss_segmentation_cross_entropy.py | 4 +- fuse/dl/losses/loss_base.py | 10 +- fuse/dl/losses/loss_default.py | 30 +-- fuse/dl/losses/loss_warm_up.py | 4 +- fuse/dl/losses/segmentation/loss_dice.py | 4 +- fuse/dl/losses/segmentation/loss_focalLoss.py | 4 +- fuse/dl/managers/callbacks/callback_base.py | 8 +- fuse/dl/managers/callbacks/callback_debug.py | 4 +- .../callbacks/callback_infer_results.py | 4 +- .../callbacks/callback_metric_statistics.py | 4 +- .../callbacks/callback_tensorboard.py | 8 +- .../callbacks/callback_time_statistics.py | 8 +- fuse/dl/managers/manager_default.py | 82 ++++---- fuse/dl/managers/manager_state.py | 8 +- .../backbones/backbone_inception_resnet_v2.py | 2 +- fuse/dl/models/backbones/backbone_mlp.py | 2 +- fuse/dl/models/backbones/backbone_resnet.py | 2 +- .../dl/models/backbones/backbone_resnet_3d.py | 2 +- fuse/dl/models/heads/head_1d_classifier.py | 2 +- fuse/dl/models/heads/head_3D_classifier.py | 2 +- .../models/heads/head_dense_segmentation.py | 2 +- .../heads/head_global_pooling_classifier.py | 2 +- fuse/dl/models/model_default.py | 16 +- fuse/dl/models/model_ensemble.py | 2 +- fuse/dl/models/model_multistream.py | 34 ++-- fuse/dl/models/model_siamese.py | 12 +- fuse/dl/models/model_wrapper.py | 8 +- fuse/dl/optimizers/opt_closure_cb.py | 8 +- fuse/dl/optimizers/opt_sam.py | 12 +- fuse/dl/templates/walkthrough_template.py | 183 +++++++----------- fuse/dl/tests/mananger/test_manager.py | 6 +- fuse/doc/high_level_example.md | 80 ++++---- fuse/doc/user_guide.md | 28 +-- fuse/utils/dl/checkpoint.py | 2 +- fuse/utils/gpu.py | 12 +- fuse/utils/imaging/align/utils_align_base.py | 2 +- fuse/utils/imaging/align/utils_align_ecc.py | 4 +- fuse/utils/imaging/image_processing.py | 6 +- fuse/utils/utils_debug.py | 2 +- fuse/utils/utils_logger.py | 4 +- 103 files changed, 814 insertions(+), 883 deletions(-) diff --git a/examples/fuse_examples/classification/cmmd/dataset.py b/examples/fuse_examples/classification/cmmd/dataset.py index 89153a599..d34ddd470 100644 --- a/examples/fuse_examples/classification/cmmd/dataset.py +++ b/examples/fuse_examples/classification/cmmd/dataset.py @@ -7,22 +7,22 @@ from pathlib import Path -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.visualizer.visualizer_default import VisualizerDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import aug_op_color, aug_op_gaussian, aug_op_affine -from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.data.dataset.dataset_default import DatasetDefault from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool -from fuse_examples.classification.cmmd.input_processor import FuseMGInputProcessor -from fuse_examples.classification.cmmd.ground_truth_processor import FuseMGGroundTruthProcessor -from fuse.data.data_source.data_source_folds import FuseDataSourceFolds +from fuse_examples.classification.cmmd.input_processor import MGInputProcessor +from fuse_examples.classification.cmmd.ground_truth_processor import MGGroundTruthProcessor +from fuse.data.data_source.data_source_folds import DataSourceFolds from typing import Tuple def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache', reset_cache: bool = False, - post_cache_processing_func: Optional[Callable] = None) -> Tuple[FuseDatasetDefault, FuseDatasetDefault]: + post_cache_processing_func: Optional[Callable] = None) -> Tuple[DatasetDefault, DatasetDefault]: """ Creates Fuse Dataset object for training, validation and test :param data_dir: dataset root path @@ -30,7 +30,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache :param cache_dir: Optional, name of the cache folder :param reset_cache: Optional,specifies if we want to clear the cache first :param post_cache_processing_func: Optional, function run post cache processing - :return: training, validation and test FuseDatasetDefault objects + :return: training, validation and test DatasetDefault objects """ augmentation_pipeline = [ [ @@ -60,7 +60,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache input_source_gt = merge_clinical_data_with_dicom_tags(data_dir, data_misc_dir, target) partition_file_path = os.path.join(data_misc_dir, 'data_fold_new.csv') - train_data_source = FuseDataSourceFolds(input_source=input_source_gt, + train_data_source = DataSourceFolds(input_source=input_source_gt, input_df=None, phase='train', no_mixture_id='ID1', @@ -73,21 +73,21 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache # Create data processors: input_processors = { - 'image': FuseMGInputProcessor(input_data=data_dir) + 'image': MGInputProcessor(input_data=data_dir) } gt_processors = { - 'classification': FuseMGGroundTruthProcessor(input_data=input_source_gt) + 'classification': MGGroundTruthProcessor(input_data=input_source_gt) } # Create data augmentation (optional) - augmentor = FuseAugmentorDefault( + augmentor = AugmentorDefault( augmentation_pipeline=augmentation_pipeline) # Create visualizer (optional) - visualiser = FuseVisualizerDefault(image_name='data.input.image', label_name='data.gt.classification') + visualiser = VisualizerDefault(image_name='data.input.image', label_name='data.gt.classification') # Create train dataset - train_dataset = FuseDatasetDefault(cache_dest=cache_dir, + train_dataset = DatasetDefault(cache_dest=cache_dir, data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -100,7 +100,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache lgr.info(f'- Load and cache data: Done') # Create validation data source - validation_data_source = FuseDataSourceFolds(input_source=input_source_gt, + validation_data_source = DataSourceFolds(input_source=input_source_gt, input_df=None, phase='validation', no_mixture_id='ID1', @@ -111,7 +111,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache partition_file_name=partition_file_path) ## Create dataset - validation_dataset = FuseDatasetDefault(cache_dest=cache_dir, + validation_dataset = DatasetDefault(cache_dest=cache_dir, data_source=validation_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -120,7 +120,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache visualizer=visualiser) validation_dataset.create( pool_type='thread') # use ThreadPool to create this dataset, to avoid cv2 problems in multithreading - test_data_source = FuseDataSourceFolds(input_source=input_source_gt, + test_data_source = DataSourceFolds(input_source=input_source_gt, input_df=None, phase='test', no_mixture_id='ID1', @@ -129,7 +129,7 @@ def CMMD_2021_dataset(data_dir: str, data_misc_dir: str ,cache_dir: str = 'cache folds=[4], num_folds=5, partition_file_name=partition_file_path) - test_dataset = FuseDatasetDefault(cache_dest=cache_dir, + test_dataset = DatasetDefault(cache_dest=cache_dir, data_source=test_data_source, input_processors=input_processors, gt_processors=gt_processors, diff --git a/examples/fuse_examples/classification/cmmd/ground_truth_processor.py b/examples/fuse_examples/classification/cmmd/ground_truth_processor.py index 888822ef0..9749e3f28 100644 --- a/examples/fuse_examples/classification/cmmd/ground_truth_processor.py +++ b/examples/fuse_examples/classification/cmmd/ground_truth_processor.py @@ -23,10 +23,10 @@ import pandas as pd import numpy as np -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseMGGroundTruthProcessor(FuseProcessorBase): +class MGGroundTruthProcessor(ProcessorBase): def __init__(self, input_data: str): diff --git a/examples/fuse_examples/classification/cmmd/input_processor.py b/examples/fuse_examples/classification/cmmd/input_processor.py index 1e9562ecc..684366f85 100644 --- a/examples/fuse_examples/classification/cmmd/input_processor.py +++ b/examples/fuse_examples/classification/cmmd/input_processor.py @@ -22,11 +22,11 @@ import torch from typing import Optional, Tuple -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.processor.processors_image_toolbox import FuseProcessorsImageToolBox +from fuse.data.processor.processor_base import ProcessorBase +from fuse.data.processor.processors_image_toolbox import ProcessorsImageToolBox -class FuseMGInputProcessor(FuseProcessorBase): +class MGInputProcessor(ProcessorBase): """ This processor expects configuration parameters to process an image and path to a mammography dicom file it then reads it (just the image), @@ -60,17 +60,17 @@ def __call__(self, *args, **kwargs): image_full_path = os.path.join(self.input_data, inner_image_desc) - inner_image = FuseProcessorsImageToolBox.read_dicom_image_to_numpy(image_full_path) + inner_image = ProcessorsImageToolBox.read_dicom_image_to_numpy(image_full_path) inner_image = standardize_breast_image(inner_image, self.normalized_target_range) # resize if self.resize_to is not None: - inner_image = FuseProcessorsImageToolBox.resize_image(inner_image, self.resize_to) + inner_image = ProcessorsImageToolBox.resize_image(inner_image, self.resize_to) # padding if self.padding is not None: - image = FuseProcessorsImageToolBox.pad_image(inner_image, self.padding, self.resize_to, self.normalized_target_range, 1) + image = ProcessorsImageToolBox.pad_image(inner_image, self.padding, self.resize_to, self.normalized_target_range, 1) else: image = inner_image @@ -175,6 +175,6 @@ def standardize_breast_image(inner_image : np.ndarray, normalized_target_range: aabb = find_breast_aabb(inner_image) inner_image = inner_image[aabb[0]: aabb[2], aabb[1]: aabb[3]].copy() # normalize - inner_image = FuseProcessorsImageToolBox.normalize_to_range(inner_image, range=normalized_target_range) + inner_image = ProcessorsImageToolBox.normalize_to_range(inner_image, range=normalized_target_range) return inner_image \ No newline at end of file diff --git a/examples/fuse_examples/classification/cmmd/runner.py b/examples/fuse_examples/classification/cmmd/runner.py index 778b917ab..fad731187 100644 --- a/examples/fuse_examples/classification/cmmd/runner.py +++ b/examples/fuse_examples/classification/cmmd/runner.py @@ -19,7 +19,7 @@ import os from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.utils.utils_debug import FuseDebug from fuse.utils.gpu import choose_and_enable_multiple_gpus import logging @@ -30,30 +30,30 @@ from fuse.utils.utils_logger import fuse_logger_start -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch -from fuse.dl.models.model_default import FuseModelDefault -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.model_default import ModelDefault +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier -from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.losses.loss_default import LossDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricROCCurve -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault from fuse_examples.classification.cmmd.dataset import CMMD_2021_dataset -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 from fuse.eval.evaluator import EvaluatorDefault ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug -debug = FuseUtilsDebug(mode) +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseDebug +debug = FuseDebug(mode) ########################################## @@ -122,7 +122,7 @@ def run_train(paths: dict, train_common_params: dict, reset_cache: bool): ## Create sampler lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.gt.classification', num_balanced_classes=2, batch_size=train_common_params['data.batch_size']) @@ -158,11 +158,11 @@ def run_train(paths: dict, train_common_params: dict, reset_cache: bool): # ============================================================================== lgr.info('Model:', {'attrs': 'bold'}) - model = FuseModelDefault( + model = ModelDefault( conv_inputs=(('data.input.image', 1),), - backbone=FuseBackboneInceptionResnetV2(input_channels_num=1), + backbone=BackboneInceptionResnetV2(input_channels_num=1), heads=[ - FuseHeadGlobalPoolingClassifier(head_name='head_0', + HeadGlobalPoolingClassifier(head_name='head_0', dropout_rate=0.5, conv_inputs=[('model.backbone_features', 384)], layers_description=(256,), @@ -178,7 +178,7 @@ def run_train(paths: dict, train_common_params: dict, reset_cache: bool): # Loss # ==================================================================================== losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.head_0', target_name='data.gt.classification', + 'cls_loss': LossDefault(pred='model.logits.head_0', target='data.gt.classification', callable=F.cross_entropy, weight=1.0) } @@ -196,9 +196,9 @@ def run_train(paths: dict, train_common_params: dict, reset_cache: bool): # ===================================================================================== callbacks = [ # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] # ===================================================================================== @@ -215,7 +215,7 @@ def run_train(paths: dict, train_common_params: dict, reset_cache: bool): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -260,7 +260,7 @@ def run_infer(paths: dict, infer_common_params: dict): lgr.info(f'Test Data: Done', {'attrs': 'bold'}) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # extract just the global classification per sample and save to a file output_columns = ['model.output.head_0','data.gt.classification'] manager.infer(data_loader=infer_dataloader, diff --git a/examples/fuse_examples/classification/duke_breast_cancer/dataset.py b/examples/fuse_examples/classification/duke_breast_cancer/dataset.py index 9f25c1da4..0edca55db 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/dataset.py +++ b/examples/fuse_examples/classification/duke_breast_cancer/dataset.py @@ -1,20 +1,20 @@ import pandas as pd from functools import partial -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import unsqueeze_2d_to_3d, aug_op_affine, squeeze_3d_to_2d, \ rotation_in_3d -from fuse.data.dataset.dataset_generator import FuseDatasetGenerator +from fuse.data.dataset.dataset_generator import DatasetGenerator from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool from fuse.data.visualizer.visualizer_default_3d import Fuse3DVisualizerDefault -from fuse.data.processor.processor_dicom_mri import FuseDicomMRIProcessor +from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor -from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient +from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient from fuse_examples.classification.duke_breast_cancer.post_processor import post_processing -from fuse_examples.classification.duke_breast_cancer.processor import FusePatchProcessor +from fuse_examples.classification.duke_breast_cancer.processor import PatchProcessor def process_mri_series(metadata_path: str): @@ -70,7 +70,7 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): lgr.info(f'database_revision={DATABASE_REVISION}', {'color': 'magenta'}) # create data source - train_data_source = FuseProstateXDataSourcePatient(paths['data_dir'], 'train', + train_data_source = ProstateXDataSourcePatient(paths['data_dir'], 'train', db_ver=train_common_params['partition_version'], db_name=train_common_params['db_name'], fold_no=train_common_params['fold_no']) @@ -85,7 +85,7 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): ######################################################################################### seq_dict, SER_INX_TO_USE, exp_patients,seq_to_use,subseq_to_use = \ process_mri_series(paths['metadata_path']) - mri_vol_processor = FuseDicomMRIProcessor(seq_dict=seq_dict, + mri_vol_processor = DicomMRIProcessor(seq_dict=seq_dict, seq_to_use=seq_to_use, subseq_to_use=subseq_to_use, ser_inx_to_use=SER_INX_TO_USE, @@ -93,7 +93,7 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): reference_inx=0, use_order_indicator=False) - generate_processor = FusePatchProcessor( + generate_processor = PatchProcessor( vol_processor=mri_vol_processor, path_to_db=paths['data_dir'], data_path=paths['data_path'], @@ -144,11 +144,11 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): {} ], ] - augmentor = FuseAugmentorDefault(augmentation_pipeline=aug_pipeline) + augmentor = AugmentorDefault(augmentation_pipeline=aug_pipeline) visualizer = Fuse3DVisualizerDefault(image_name='data.input', label_name='data.isLargeTumorSize') # Create dataset - train_dataset = FuseDatasetGenerator(cache_dest=paths['cache_dir'], + train_dataset = DatasetGenerator(cache_dest=paths['cache_dir'], data_source=train_data_source, processor=generate_processor, post_processing_func=train_post_processor, @@ -167,7 +167,7 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): lgr.info(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - validation_data_source = FuseProstateXDataSourcePatient(paths['data_dir'], 'validation', + validation_data_source = ProstateXDataSourcePatient(paths['data_dir'], 'validation', db_ver=DATABASE_REVISION, db_name=train_common_params['db_name'], fold_no=train_common_params['fold_no']) @@ -176,7 +176,7 @@ def duke_breast_cancer_dataset(paths,train_common_params,lgr): validation_post_processor = partial(post_processing, label=train_common_params['classification_task']) ## Create dataset - validation_dataset = FuseDatasetGenerator(cache_dest=paths['cache_dir'], + validation_dataset = DatasetGenerator(cache_dest=paths['cache_dir'], data_source=validation_data_source, processor=generate_processor, post_processing_func=validation_post_processor, diff --git a/examples/fuse_examples/classification/duke_breast_cancer/processor.py b/examples/fuse_examples/classification/duke_breast_cancer/processor.py index 7b9f9e331..a8f5fcef7 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/processor.py +++ b/examples/fuse_examples/classification/duke_breast_cancer/processor.py @@ -20,13 +20,13 @@ import logging import cv2 from scipy.ndimage.morphology import binary_dilation -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.processor.processor_dicom_mri import FuseDicomMRIProcessor +from fuse.data.processor.processor_base import ProcessorBase +from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor -from fuse_examples.classification.prostate_x.data_utils import FuseProstateXUtilsData +from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData -class FusePatchProcessor(FuseProcessorBase): +class PatchProcessor(ProcessorBase): """ This processor crops the lesion volume from within 4D MRI volume base on lesion location as appears in the database. @@ -40,7 +40,7 @@ class FusePatchProcessor(FuseProcessorBase): 'ClinSig': row['ClinSig']: Clinical significant ( 0 for benign and 3+3 lesions, 1 for rest) """ def __init__(self, - vol_processor: FuseDicomMRIProcessor = FuseDicomMRIProcessor(), + vol_processor: DicomMRIProcessor = DicomMRIProcessor(), path_to_db: str = None, data_path: str = None, ktrans_data_path: str = None, @@ -272,8 +272,8 @@ def __call__(self, # ======================================================================== # get db - lesions - db_full = FuseProstateXUtilsData.get_dataset(self.path_to_db,'other',self.db_ver,self.db_name,self.fold_no) - db = FuseProstateXUtilsData.get_lesions_prostate_x(db_full) + db_full = ProstateXUtilsData.get_dataset(self.path_to_db,'other',self.db_ver,self.db_name,self.fold_no) + db = ProstateXUtilsData.get_lesions_prostate_x(db_full) # ======================================================================== # get patient @@ -374,7 +374,7 @@ def __call__(self, root_data = '/gpfs/haifa/projects/m/msieve2/Platform/BigMedilytics/Data/Duke-Breast-Cancer-MRI/manifest-1607053360376/' seq_dict,SER_INX_TO_USE,exp_patients,_,_ = process_mri_series(root_data+'/metadata.csv') - mri_vol_processor = FuseDicomMRIProcessor(seq_dict=seq_dict, + mri_vol_processor = DicomMRIProcessor(seq_dict=seq_dict, seq_to_use=['DCE_mix_ph1', 'DCE_mix_ph2', 'DCE_mix_ph3', @@ -388,7 +388,7 @@ def __call__(self, reference_inx=0, use_order_indicator=False) - a = FusePatchProcessor(vol_processor=mri_vol_processor, + a = PatchProcessor(vol_processor=mri_vol_processor, path_to_db=path_to_db, data_path=root_data + 'Duke-Breast-Cancer-MRI', ktrans_data_path='', diff --git a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py b/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py index 3b565af03..ddfd8921d 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py +++ b/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py @@ -15,32 +15,32 @@ import logging import os import pathlib -from fuse.data.dataset.dataset_base import FuseDatasetBase +from fuse.data.dataset.dataset_base import DatasetBase import torch.nn.functional as F import torch.optim as optim from torch.utils.data.dataloader import DataLoader from fuse.eval.metrics.classification.metrics_classification_common import MetricROCCurve, MetricAUCROC from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.dl.losses.loss_default import FuseLossDefault -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault - -import fuse.utils.gpu as FuseUtilsGPU +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.dl.losses.loss_default import LossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault + +import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start -from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse.dl.models.heads.head_1d_classifier import Head1dClassifier from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet -from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient +from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient from fuse_examples.classification.duke_breast_cancer.dataset import duke_breast_cancer_dataset -from fuse_examples.classification.duke_breast_cancer.tasks import FuseTask +from fuse_examples.classification.duke_breast_cancer.tasks import Task ########################################## @@ -127,7 +127,7 @@ # supported tasks are: 'Staging Tumor Size','Histology Type','is High Tumor Grade Total','PCR' TRAIN_COMMON_PARAMS['classification_task'] = 'Staging Tumor Size' -TRAIN_COMMON_PARAMS['task'] = FuseTask(TRAIN_COMMON_PARAMS['classification_task'], 0) +TRAIN_COMMON_PARAMS['task'] = Task(TRAIN_COMMON_PARAMS['classification_task'], 0) TRAIN_COMMON_PARAMS['class_num'] = TRAIN_COMMON_PARAMS['task'].num_classes() # backbone parameters @@ -155,7 +155,7 @@ def train_template(paths: dict, train_common_params: dict): ## Create dataloader lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.ground_truth', num_balanced_classes=train_common_params['class_num'], batch_size=train_common_params['data.batch_size'], @@ -189,9 +189,9 @@ def train_template(paths: dict, train_common_params: dict): conv_inputs=(('data.input', 1),), backbone= ResNet(ch_num=TRAIN_COMMON_PARAMS['backbone_model_dict']['input_channels_num']), # since backbone resnet contains pooling and fc, the feature output is 1D, - # hence we use FuseHead1dClassifier as classification head + # hence we use Head1dClassifier as classification head heads=[ - FuseHead1dClassifier(head_name='isLargeTumorSize', + Head1dClassifier(head_name='isLargeTumorSize', conv_inputs=[('model.backbone_features', train_common_params['num_backbone_features'])], post_concat_inputs = train_common_params['post_concat_inputs'], post_concat_model = train_common_params['post_concat_model'], @@ -210,8 +210,8 @@ def train_template(paths: dict, train_common_params: dict): lgr.info('Losses: CrossEntropy', {'attrs': 'bold'}) losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.isLargeTumorSize', - target_name='data.ground_truth', + 'cls_loss': LossDefault(pred='model.logits.isLargeTumorSize', + target='data.ground_truth', callable=F.cross_entropy, weight=1.0), } @@ -232,10 +232,10 @@ def train_template(paths: dict, train_common_params: dict): # Callbacks # ===================================================================================== callbacks = [ - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics for tensorboard in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] @@ -253,7 +253,7 @@ def train_template(paths: dict, train_common_params: dict): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -301,7 +301,7 @@ def infer_template(paths: dict, infer_common_params: dict): #### create dataloader ## Create data source: - infer_data_source = FuseProstateXDataSourcePatient(paths['data_dir'],'validation', + infer_data_source = ProstateXDataSourcePatient(paths['data_dir'],'validation', db_ver=infer_common_params['partition_version'], db_name = infer_common_params['db_name'], fold_no=infer_common_params['fold_no']) @@ -310,7 +310,7 @@ def infer_template(paths: dict, infer_common_params: dict): lgr.info(f'db_name={infer_common_params["db_name"]}', {'color': 'magenta'}) ### load dataset data_set_filename = os.path.join(paths["model_dir"], "inference_dataset.pth") - dataset = FuseDatasetBase.load(filename=data_set_filename, override_datasource=infer_data_source, override_cache_dest=paths["cache_dir"], num_workers=0) + dataset = DatasetBase.load(filename=data_set_filename, override_datasource=infer_data_source, override_cache_dest=paths["cache_dir"], num_workers=0) dataloader = DataLoader(dataset=dataset, shuffle=False, drop_last=False, @@ -319,7 +319,7 @@ def infer_template(paths: dict, infer_common_params: dict): collate_fn=dataset.collate_fn) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # extract just the global classification per sample and save to a file output_columns = ['model.output.isLargeTumorSize','data.ground_truth'] manager.infer(data_loader=dataloader, @@ -370,7 +370,7 @@ def eval_template(paths: dict, eval_common_params: dict): TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones force_gpus = [1] # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' diff --git a/examples/fuse_examples/classification/duke_breast_cancer/tasks.py b/examples/fuse_examples/classification/duke_breast_cancer/tasks.py index 0cd067140..fc3437deb 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/tasks.py +++ b/examples/fuse_examples/classification/duke_breast_cancer/tasks.py @@ -16,7 +16,7 @@ from typing import List -class FuseTask(): +class Task(): tasks = {} def __init__(self, task_name: str, version: int): self._task_name, self._task_version, self._task_mapping, self._task_class_names = \ @@ -58,13 +58,13 @@ def get_task(cls, task_name: str, version: int): tumor_size_VER_0 = [['HIGH'], ['LOW']], tumor_grade_VER_0 = [['HIGH'], ['LOW']], histotype_VER_0 = [['HIGH'], ['LOW']], -FuseTask.register('ispCR', 0, pcr_SCORE_VER_0, ['HIGH','LOW']) -FuseTask.register('Histology Type', 0, histotype_VER_0, ['HIGH','LOW']) -FuseTask.register('is High Tumor Grade Total', 0, tumor_grade_VER_0, ['HIGH','LOW']) -FuseTask.register('Staging Tumor Size', 0, tumor_size_VER_0, ['HIGH','LOW']) +Task.register('ispCR', 0, pcr_SCORE_VER_0, ['HIGH','LOW']) +Task.register('Histology Type', 0, histotype_VER_0, ['HIGH','LOW']) +Task.register('is High Tumor Grade Total', 0, tumor_grade_VER_0, ['HIGH','LOW']) +Task.register('Staging Tumor Size', 0, tumor_size_VER_0, ['HIGH','LOW']) if __name__ == '__main__': - mp_task = FuseTask('gleason_score', 0) + mp_task = Task('gleason_score', 0) print(mp_task.name()) print(mp_task.class_names()) print(len(mp_task.class_names())) diff --git a/examples/fuse_examples/classification/knight/baseline/clinical_processor.py b/examples/fuse_examples/classification/knight/baseline/clinical_processor.py index 628fcf672..119cb0ade 100644 --- a/examples/fuse_examples/classification/knight/baseline/clinical_processor.py +++ b/examples/fuse_examples/classification/knight/baseline/clinical_processor.py @@ -20,13 +20,13 @@ import ast import pandas as pd -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase import logging from typing import Hashable, List, Optional, Dict, Union from torch import Tensor import torch -class KiCClinicalProcessor(FuseProcessorBase): +class KiCClinicalProcessor(ProcessorBase): """ Processor reading KiC clinical data. """ @@ -116,7 +116,7 @@ def convert_to_tensor(sample: dict, key: str, tensor_dtype: Optional[str] = None else: sample[key] = torch.tensor(sample[key], dtype=tensor_dtype) -class KiCGTProcessor(FuseProcessorBase): +class KiCGTProcessor(ProcessorBase): """ Processor reading KiC ground truth data. """ diff --git a/examples/fuse_examples/classification/knight/baseline/dataset.py b/examples/fuse_examples/classification/knight/baseline/dataset.py index d07da1c2e..f6863c081 100644 --- a/examples/fuse_examples/classification/knight/baseline/dataset.py +++ b/examples/fuse_examples/classification/knight/baseline/dataset.py @@ -2,15 +2,15 @@ import os from fuse.data.visualizer.visualizer_default_3d import Fuse3DVisualizerDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import aug_op_affine, aug_op_gaussian -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch +from fuse.data.dataset.dataset_default import DatasetDefault +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool from torch.utils.data.dataloader import DataLoader from .input_processor import KiTSBasicInputProcessor -from fuse.data.data_source.data_source_default import FuseDataSourceDefault +from fuse.data.data_source.data_source_default import DataSourceDefault from fuse.data.augmentor.augmentor_toolbox import rotation_in_3d, squeeze_3d_to_2d, unsqueeze_2d_to_3d from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict @@ -105,7 +105,7 @@ def knight_dataset(data_dir: str = 'data', cache_dir: str = 'cache', split: dict ] if 'train' in split: - train_data_source = FuseDataSourceDefault(list(split['train'])) + train_data_source = DataSourceDefault(list(split['train'])) image_dir = os.path.join(data_dir, 'knight', 'data') json_filepath = os.path.join(image_dir, 'knight.json') gt_processors = { @@ -136,14 +136,14 @@ def knight_dataset(data_dir: str = 'data', cache_dir: str = 'cache', split: dict post_processing_func=prepare_clinical # Create data augmentation (optional) - augmentor = FuseAugmentorDefault( + augmentor = AugmentorDefault( augmentation_pipeline=augmentation_pipeline) # Create visualizer (optional) visualizer = Fuse3DVisualizerDefault(image_name = 'data.input.image', label_name=target_name) # Create dataset if 'train' in split: - train_dataset = FuseDatasetDefault(cache_dest=cache_dir, + train_dataset = DatasetDefault(cache_dest=cache_dir, data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -158,7 +158,7 @@ def knight_dataset(data_dir: str = 'data', cache_dir: str = 'cache', split: dict ## Create sampler print(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name=target_name, num_balanced_classes=num_classes, batch_size=batch_size, @@ -180,11 +180,11 @@ def knight_dataset(data_dir: str = 'data', cache_dir: str = 'cache', split: dict print(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - validation_data_source = FuseDataSourceDefault(list(split['val'])) + validation_data_source = DataSourceDefault(list(split['val'])) ## Create dataset - validation_dataset = FuseDatasetDefault(cache_dest=cache_dir, + validation_dataset = DatasetDefault(cache_dest=cache_dir, data_source=validation_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -211,10 +211,10 @@ def knight_dataset(data_dir: str = 'data', cache_dir: str = 'cache', split: dict print(f'Test Data:', {'attrs': 'bold'}) ## Create data source - test_data_source = FuseDataSourceDefault(list(split['test'])) + test_data_source = DataSourceDefault(list(split['test'])) ## Create dataset - test_dataset = FuseDatasetDefault(cache_dest=cache_dir, + test_dataset = DatasetDefault(cache_dest=cache_dir, data_source=test_data_source, input_processors=input_processors, gt_processors=gt_processors, diff --git a/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py b/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py index 7c3588f6d..fcb811171 100644 --- a/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py +++ b/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py @@ -7,19 +7,19 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')) from baseline.dataset import knight_dataset import pandas as pd -from fuse.dl.models.model_default import FuseModelDefault -from fuse.dl.models.backbones.backbone_resnet_3d import FuseBackboneResnet3D -from fuse.dl.models.heads.head_3D_classifier import FuseHead3dClassifier -from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.models.model_default import ModelDefault +from fuse.dl.models.backbones.backbone_resnet_3d import BackboneResnet3D +from fuse.dl.models.heads.head_3D_classifier import Head3dClassifier +from fuse.dl.losses.loss_default import LossDefault import torch.nn.functional as F import torch.nn as nn from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricConfusion from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds import torch.optim as optim -from fuse.dl.managers.manager_default import FuseManagerDefault -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -import fuse.utils.gpu as FuseUtilsGPU +from fuse.dl.managers.manager_default import ManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +import fuse.utils.gpu as GPU from fuse.utils.rand.seed import Seed import logging import time @@ -88,7 +88,7 @@ def main(): rand_gen = Seed.set_seed(1234, deterministic_mode=True) # select gpus - FuseUtilsGPU.choose_and_enable_multiple_gpus(len(force_gpus), force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(len(force_gpus), force_gpus=force_gpus) ## FuseMedML dataset preparation ############################################################################## @@ -121,7 +121,7 @@ def main(): ############################################################################## if use_data['imaging']: - backbone = FuseBackboneResnet3D(in_channels=1) + backbone = BackboneResnet3D(in_channels=1) conv_inputs = [('model.backbone_features', 512)] else: backbone = nn.Identity() @@ -131,11 +131,11 @@ def main(): else: append_features = None - model = FuseModelDefault( + model = ModelDefault( conv_inputs=(('data.input.image', 1),), backbone=backbone, heads=[ - FuseHead3dClassifier(head_name='head_0', + Head3dClassifier(head_name='head_0', conv_inputs=conv_inputs, dropout_rate=imaging_dropout, num_classes=num_classes, @@ -150,7 +150,7 @@ def main(): # Loss definition: ############################################################################## losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.head_0', target_name=target_name, + 'cls_loss': LossDefault(pred='model.logits.head_0', target=target_name, callable=F.cross_entropy, weight=1.0) } @@ -183,11 +183,11 @@ def main(): # set tensorboard callback callbacks = { - FuseTensorboardCallback(model_dir=model_dir), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=model_dir + "/metrics.csv"), # save statistics a csv file + TensorboardCallback(model_dir=model_dir), # save statistics for tensorboard + MetricStatisticsCallback(output_path=model_dir + "/metrics.csv"), # save statistics a csv file } - manager = FuseManagerDefault(output_model_dir=model_dir, force_reset=True) + manager = ManagerDefault(output_model_dir=model_dir, force_reset=True) manager.set_objects(net=model, optimizer=optimizer, losses=losses, diff --git a/examples/fuse_examples/classification/knight/baseline/input_processor.py b/examples/fuse_examples/classification/knight/baseline/input_processor.py index 1558abca8..f693cfe5a 100644 --- a/examples/fuse_examples/classification/knight/baseline/input_processor.py +++ b/examples/fuse_examples/classification/knight/baseline/input_processor.py @@ -27,11 +27,11 @@ import traceback from typing import Optional, Tuple -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase import SimpleITK as sitk -class KiTSBasicInputProcessor(FuseProcessorBase): +class KiTSBasicInputProcessor(ProcessorBase): def __init__(self, input_data: str, normalized_target_range: Tuple = (0, 1), diff --git a/examples/fuse_examples/classification/knight/make_predictions_file.py b/examples/fuse_examples/classification/knight/make_predictions_file.py index 559c14f5c..21a09ecfc 100644 --- a/examples/fuse_examples/classification/knight/make_predictions_file.py +++ b/examples/fuse_examples/classification/knight/make_predictions_file.py @@ -27,7 +27,7 @@ from fuse.utils.utils_logger import fuse_logger_start from fuse.utils.file_io.file_io import save_dataframe -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.manager_default import ManagerDefault from fuse_examples.classification.knight.eval.eval import TASK1_CLASS_NAMES, TASK2_CLASS_NAMES from baseline.dataset import knight_dataset @@ -76,7 +76,7 @@ def make_predictions_file(model_dir: str, dl = validation_dl # Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() predictions_df = manager.infer(data_loader=dl, input_model_dir=model_dir, checkpoint=checkpoint, diff --git a/examples/fuse_examples/classification/mnist/runner.py b/examples/fuse_examples/classification/mnist/runner.py index a4bbd2e88..97dd5ec77 100644 --- a/examples/fuse_examples/classification/mnist/runner.py +++ b/examples/fuse_examples/classification/mnist/runner.py @@ -31,17 +31,17 @@ from torchvision import transforms from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.dl.losses.loss_default import FuseLossDefault -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.data.dataset.dataset_wrapper import DatasetWrapper +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.dl.losses.loss_default import LossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve -from fuse.dl.models.model_wrapper import FuseModelWrapper -from fuse.utils.utils_debug import FuseUtilsDebug -import fuse.utils.gpu as FuseUtilsGPU +from fuse.dl.models.model_wrapper import ModelWrapper +from fuse.utils.utils_debug import FuseDebug +import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start from fuse_examples.classification.mnist import lenet ########################################################################################################### @@ -50,8 +50,8 @@ ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug -debug = FuseUtilsDebug(mode) +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseDebug +debug = FuseDebug(mode) ########################################## # Output Paths @@ -137,10 +137,10 @@ def run_train(paths: dict, train_params: dict): torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform) # wrapping torch dataset # FIXME: support also using torch dataset directly - train_dataset = FuseDatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label')) + train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label')) train_dataset.create() lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.label', num_balanced_classes=10, batch_size=train_params['data.batch_size'], @@ -156,7 +156,7 @@ def run_train(paths: dict, train_params: dict): # Create dataset torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset - validation_dataset = FuseDatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) + validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) validation_dataset.create() # dataloader @@ -179,7 +179,7 @@ def run_train(paths: dict, train_params: dict): elif train_params['model'] == 'lenet': torch_model = lenet.LeNet() - model = FuseModelWrapper(model=torch_model, + model = ModelWrapper(model=torch_model, model_inputs=['data.image'], post_forward_processing_function=perform_softmax, model_outputs=['logits.classification', 'output.classification'] @@ -191,7 +191,7 @@ def run_train(paths: dict, train_params: dict): # Loss # ==================================================================================== losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.classification', target_name='data.label', callable=F.cross_entropy, weight=1.0), + 'cls_loss': LossDefault(pred='model.logits.classification', target='data.label', callable=F.cross_entropy, weight=1.0), } # ==================================================================================== @@ -207,9 +207,9 @@ def run_train(paths: dict, train_params: dict): # ===================================================================================== callbacks = [ # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics a csv file - FuseTimeStatisticsCallback(num_epochs=train_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics a csv file + TimeStatisticsCallback(num_epochs=train_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] # ===================================================================================== @@ -224,7 +224,7 @@ def run_train(paths: dict, train_params: dict): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -272,13 +272,13 @@ def run_infer(paths: dict, infer_common_params: dict): # Create dataset torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset - validation_dataset = FuseDatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) + validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) validation_dataset.create() # dataloader validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2) ## Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() output_columns = ['model.output.classification', 'data.label'] manager.infer(data_loader=validation_dataloader, input_model_dir=paths['model_dir'], @@ -335,7 +335,7 @@ def run_eval(paths: dict, eval_common_params: dict): TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones force_gpus = None # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' # train diff --git a/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py b/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py index dbe6b3960..49b3d59a2 100644 --- a/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py +++ b/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py @@ -20,7 +20,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import numpy as np -from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse.dl.models.heads.head_1d_classifier import Head1dClassifier # 3x3 convolution @@ -178,7 +178,7 @@ class Fuse_model_3d_multichannel(torch.nn.Module): def __init__(self, conv_inputs: Tuple[Tuple[str, int], ...] = (('data.input', 1),), backbone: ResNet = ResNet(), - heads: Sequence[torch.nn.Module] = (FuseHead1dClassifier(),), + heads: Sequence[torch.nn.Module] = (Head1dClassifier(),), ch_num = None, ) -> None: """ @@ -227,7 +227,7 @@ def forward(self, conv_inputs=(('data.input', 1),), backbone= ResNet(), heads=[ - FuseHead1dClassifier(head_name='ClinSig', + Head1dClassifier(head_name='ClinSig', conv_inputs=[('model.backbone_features', num_features)], post_concat_inputs=None, dropout_rate=0.25, @@ -264,7 +264,7 @@ def forward(self, res = {} - # manager = FuseManagerDefault() + # manager = ManagerDefault() # manager.set_objects(net=model) # checkpoint = '/gpfs/haifa/projects/m/msieve_dev3/usr/Tal/fus_sessions/multiclass_MG/malignant_multi_class_sentara_baptist_froedtert/exp_12_pretrain_normal-mal-benign_head/model_2/checkpoint_80_epoch.pth' # aa = manager.load_checkpoint(checkpoint) diff --git a/examples/fuse_examples/classification/prostate_x/data_utils.py b/examples/fuse_examples/classification/prostate_x/data_utils.py index 94d1a3160..95434e30c 100644 --- a/examples/fuse_examples/classification/prostate_x/data_utils.py +++ b/examples/fuse_examples/classification/prostate_x/data_utils.py @@ -16,7 +16,7 @@ import pandas as pd import os -class FuseProstateXUtilsData: +class ProstateXUtilsData: @staticmethod def get_dataset(path_to_db: str,set_type: str, db_ver: int,db_name: str,fold_no: int): db_name = os.path.join(path_to_db,f'dataset_{db_name}_folds_ver{db_ver}_seed1.pickle') @@ -57,8 +57,8 @@ def get_lesions_prostate_x(data: pd.DataFrame): if __name__ == "__main__": path_to_db = '/gpfs/haifa/projects/m/msieve_dev3/usr/Tal/my_research/virtual_biopsy/prostate/experiments/V1/' - # data = FuseCAPVUtilsData.get_dataset(path_to_db=path_to_db,set_type='train', db_ver=18042021,db_name='tcia',fold_no=0) - # data_lesion = FuseCAPVUtilsData.get_lesions(data) + # data = CAPVUtilsData.get_dataset(path_to_db=path_to_db,set_type='train', db_ver=18042021,db_name='tcia',fold_no=0) + # data_lesion = CAPVUtilsData.get_lesions(data) - data = FuseProstateXUtilsData.get_dataset(path_to_db=path_to_db, set_type='train', db_ver=29042021, db_name='prostate_x',fold_no=0) - data_lesion = FuseProstateXUtilsData.get_lesions_prostate_x(data) \ No newline at end of file + data = ProstateXUtilsData.get_dataset(path_to_db=path_to_db, set_type='train', db_ver=29042021, db_name='prostate_x',fold_no=0) + data_lesion = ProstateXUtilsData.get_lesions_prostate_x(data) \ No newline at end of file diff --git a/examples/fuse_examples/classification/prostate_x/dataset.py b/examples/fuse_examples/classification/prostate_x/dataset.py index de9bb2e15..4fcb9a798 100644 --- a/examples/fuse_examples/classification/prostate_x/dataset.py +++ b/examples/fuse_examples/classification/prostate_x/dataset.py @@ -1,19 +1,19 @@ from functools import partial from multiprocessing import Manager -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import unsqueeze_2d_to_3d, aug_op_color, aug_op_affine, squeeze_3d_to_2d, \ rotation_in_3d -from fuse.data.dataset.dataset_generator import FuseDatasetGenerator +from fuse.data.dataset.dataset_generator import DatasetGenerator -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool, Choice -from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient -from fuse_examples.classification.prostate_x.processor import FuseProstateXPatchProcessor +from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.classification.prostate_x.processor import ProstateXPatchProcessor from fuse_examples.classification.prostate_x.post_processor import post_processing -# from fuse_examples.classification.prostate_x.processor_dicom_mri import FuseDicomMRIProcessor -from fuse.data.processor.processor_dicom_mri import FuseDicomMRIProcessor +# from fuse_examples.classification.prostate_x.processor_dicom_mri import DicomMRIProcessor +from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor def process_mri_series(): @@ -73,7 +73,7 @@ def prostate_x_dataset(paths,train_common_params,lgr): lgr.info(f'database_revision={DATABASE_REVISION}', {'color': 'magenta'}) # create data source - train_data_source = FuseProstateXDataSourcePatient(paths['data_dir'], 'train', + train_data_source = ProstateXDataSourcePatient(paths['data_dir'], 'train', db_ver=train_common_params['db_version'], db_name=train_common_params['db_name'], fold_no=train_common_params['fold_no']) @@ -89,8 +89,8 @@ def prostate_x_dataset(paths,train_common_params,lgr): seq_to_use_dict, SER_INX_TO_USE, \ exp_patients, seq_to_use, subseq_to_use = process_mri_series() - generate_processor = FuseProstateXPatchProcessor( - vol_processor=FuseDicomMRIProcessor(reference_inx=0, + generate_processor = ProstateXPatchProcessor( + vol_processor=DicomMRIProcessor(reference_inx=0, seq_dict=seq_to_use_dict, seq_to_use=seq_to_use, subseq_to_use=subseq_to_use, @@ -155,10 +155,10 @@ def prostate_x_dataset(paths,train_common_params,lgr): {} ], ] - augmentor = FuseAugmentorDefault(augmentation_pipeline=aug_pipeline) + augmentor = AugmentorDefault(augmentation_pipeline=aug_pipeline) # Create dataset - train_dataset = FuseDatasetGenerator(cache_dest=paths['cache_dir'], + train_dataset = DatasetGenerator(cache_dest=paths['cache_dir'], data_source=train_data_source, processor=generate_processor, post_processing_func=train_post_processor, @@ -177,7 +177,7 @@ def prostate_x_dataset(paths,train_common_params,lgr): lgr.info(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - validation_data_source = FuseProstateXDataSourcePatient(paths['data_dir'], 'validation', + validation_data_source = ProstateXDataSourcePatient(paths['data_dir'], 'validation', db_ver=DATABASE_REVISION, db_name=train_common_params['db_name'], fold_no=train_common_params['fold_no']) @@ -186,7 +186,7 @@ def prostate_x_dataset(paths,train_common_params,lgr): validation_post_processor = partial(post_processing) ## Create dataset - validation_dataset = FuseDatasetGenerator(cache_dest=paths['cache_dir'], + validation_dataset = DatasetGenerator(cache_dest=paths['cache_dir'], data_source=validation_data_source, processor=generate_processor, post_processing_func=validation_post_processor, diff --git a/examples/fuse_examples/classification/prostate_x/patient_data_source.py b/examples/fuse_examples/classification/prostate_x/patient_data_source.py index 15f8bc0fa..94af5a6e9 100644 --- a/examples/fuse_examples/classification/prostate_x/patient_data_source.py +++ b/examples/fuse_examples/classification/prostate_x/patient_data_source.py @@ -14,10 +14,10 @@ from typing import List, Tuple -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse_examples.classification.prostate_x.data_utils import FuseProstateXUtilsData +from fuse.data.data_source.data_source_base import DataSourceBase +from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData -class FuseProstateXDataSourcePatient(FuseDataSourceBase): +class ProstateXDataSourcePatient(DataSourceBase): def __init__(self, db_path: str, set_type: str, @@ -65,9 +65,9 @@ def generate_patient_list(self) -> List[Tuple]: Go Over all patients and create a tuple list of (db_ver, set_type, patient_id [,'gt']) :return: list of patient descriptors ''' - data = FuseProstateXUtilsData.get_dataset(self.db_path,self.set_type, self.db_ver,self.db_name,self.fold_no) + data = ProstateXUtilsData.get_dataset(self.db_path,self.set_type, self.db_ver,self.db_name,self.fold_no) if (self.db_name=='prostate_x') | (self.db_name=='ISPY2')| (self.db_name=='DUKE'): - data_lesions = FuseProstateXUtilsData.get_lesions_prostate_x(data) + data_lesions = ProstateXUtilsData.get_lesions_prostate_x(data) patients = list(data_lesions['Patient ID'].unique()) @@ -75,4 +75,4 @@ def generate_patient_list(self) -> List[Tuple]: if __name__ == "__main__": path_to_db = '/gpfs/haifa/projects/m/msieve_dev3/usr/Tal/my_research/virtual_biopsy/prostate/experiments/V1/' - train_data_source = FuseProstateXDataSourcePatient(path_to_db,'train',db_name='tcia', db_ver='18042021',fold_no=0, include_gt=False) \ No newline at end of file + train_data_source = ProstateXDataSourcePatient(path_to_db,'train',db_name='tcia', db_ver='18042021',fold_no=0, include_gt=False) \ No newline at end of file diff --git a/examples/fuse_examples/classification/prostate_x/processor.py b/examples/fuse_examples/classification/prostate_x/processor.py index 6027975a2..e3b9f40ef 100644 --- a/examples/fuse_examples/classification/prostate_x/processor.py +++ b/examples/fuse_examples/classification/prostate_x/processor.py @@ -20,14 +20,14 @@ import logging from scipy.ndimage.morphology import binary_dilation -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -from fuse_examples.classification.prostate_x.data_utils import FuseProstateXUtilsData -# from fuse_examples.classification.prostate_x.processor_dicom_mri import FuseDicomMRIProcessor -from fuse.data.processor.processor_dicom_mri import FuseDicomMRIProcessor +from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData +# from fuse_examples.classification.prostate_x.processor_dicom_mri import DicomMRIProcessor +from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor -class FuseProstateXPatchProcessor(FuseProcessorBase): +class ProstateXPatchProcessor(ProcessorBase): """ This processor crops the lesion volume from within 4D MRI volume base on lesion location as appears in the database. @@ -41,7 +41,7 @@ class FuseProstateXPatchProcessor(FuseProcessorBase): 'ClinSig': row['ClinSig']: Clinical significant ( 0 for benign and 3+3 lesions, 1 for rest) """ def __init__(self, - vol_processor: FuseDicomMRIProcessor = FuseDicomMRIProcessor(), + vol_processor: DicomMRIProcessor = DicomMRIProcessor(), path_to_db: str = None, data_path: str = None, ktrans_data_path: str = None, @@ -186,8 +186,8 @@ def __call__(self, # ======================================================================== # get db - lesions - db_full = FuseProstateXUtilsData.get_dataset(self.path_to_db,'other',self.db_ver,self.db_name,self.fold_no) - db = FuseProstateXUtilsData.get_lesions_prostate_x(db_full) + db_full = ProstateXUtilsData.get_dataset(self.path_to_db,'other',self.db_ver,self.db_name,self.fold_no) + db = ProstateXUtilsData.get_lesions_prostate_x(db_full) # ======================================================================== # get patient @@ -270,7 +270,7 @@ def __call__(self, Ktrain_data_path = path_to_dataset + '/ProstateXKtrains-train-fixed/' sample = ('29062021', 'train', 'ProstateX-0148', 'pred') - a = FuseProstateXPatchProcessor(vol_processor=FuseDicomMRIProcessor(reference_inx=0),path_to_db = path_to_db, + a = ProstateXPatchProcessor(vol_processor=DicomMRIProcessor(reference_inx=0),path_to_db = path_to_db, data_path=prostate_data_path,ktrans_data_path=Ktrain_data_path, db_name=dataset,fold_no=1,lsn_shape=(13, 74, 74)) samples = a.__call__(sample) diff --git a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py b/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py index bfc8e56ac..2acac491c 100644 --- a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py +++ b/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py @@ -15,30 +15,30 @@ import logging import os import pathlib -from fuse.data.dataset.dataset_base import FuseDatasetBase +from fuse.data.dataset.dataset_base import DatasetBase import torch.nn.functional as F import torch.optim as optim from torch.utils.data.dataloader import DataLoader from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricROCCurve from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_base import FuseDatasetBase +from fuse.data.dataset.dataset_base import DatasetBase -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.dl.losses.loss_default import FuseLossDefault -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.dl.losses.loss_default import LossDefault +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start from fuse_examples.classification.prostate_x.dataset import prostate_x_dataset from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet -from fuse_examples.classification.prostate_x.patient_data_source import FuseProstateXDataSourcePatient -from fuse_examples.classification.prostate_x.tasks import FuseProstateXTask -from fuse.dl.models.heads.head_1d_classifier import FuseHead1dClassifier +from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.classification.prostate_x.tasks import ProstateXTask +from fuse.dl.models.heads.head_1d_classifier import Head1dClassifier ########################################## @@ -111,7 +111,7 @@ TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint TRAIN_COMMON_PARAMS['num_backbone_features'] = 512 -TRAIN_COMMON_PARAMS['task'] = FuseProstateXTask('ClinSig', 0) +TRAIN_COMMON_PARAMS['task'] = ProstateXTask('ClinSig', 0) TRAIN_COMMON_PARAMS['class_num'] = TRAIN_COMMON_PARAMS['task'].num_classes() # backbone parameters @@ -139,7 +139,7 @@ def train_template(paths: dict, train_common_params: dict): ## Create dataloader lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.ground_truth', num_balanced_classes=train_common_params['task'].num_classes(), batch_size=train_common_params['data.batch_size'], @@ -173,7 +173,7 @@ def train_template(paths: dict, train_common_params: dict): conv_inputs=(('data.input', 1),), backbone= ResNet(ch_num=TRAIN_COMMON_PARAMS['backbone_model_dict']['input_channels_num']), heads=[ - FuseHead1dClassifier(head_name='ClinSig', + Head1dClassifier(head_name='ClinSig', conv_inputs=[('model.backbone_features', train_common_params['num_backbone_features'])], post_concat_inputs=None, dropout_rate=0.25, @@ -191,8 +191,8 @@ def train_template(paths: dict, train_common_params: dict): lgr.info('Losses: CrossEntropy', {'attrs': 'bold'}) losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.ClinSig', - target_name='data.ground_truth', + 'cls_loss': LossDefault(pred='model.logits.ClinSig', + target='data.ground_truth', callable=F.cross_entropy, weight=1.0), } @@ -213,10 +213,10 @@ def train_template(paths: dict, train_common_params: dict): # Callbacks # ===================================================================================== callbacks = [ - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics for tensorboard in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] @@ -234,7 +234,7 @@ def train_template(paths: dict, train_common_params: dict): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -282,14 +282,14 @@ def infer_template(paths: dict, infer_common_params: dict): lgr.info(f'db_name={infer_common_params["db_name"]}', {'color': 'magenta'}) ## Create data source: - infer_data_source = FuseProstateXDataSourcePatient(paths['data_dir'],'validation', + infer_data_source = ProstateXDataSourcePatient(paths['data_dir'],'validation', db_ver=infer_common_params['db_version'], db_name = infer_common_params['db_name'], fold_no=infer_common_params['fold_no']) ### load dataset data_set_filename = os.path.join(paths["model_dir"], "inference_dataset.pth") - dataset = FuseDatasetBase.load(filename=data_set_filename, override_datasource=infer_data_source, override_cache_dest=paths["cache_dir"], num_workers=0) + dataset = DatasetBase.load(filename=data_set_filename, override_datasource=infer_data_source, override_cache_dest=paths["cache_dir"], num_workers=0) dataloader = DataLoader(dataset=dataset, shuffle=False, drop_last=False, @@ -297,7 +297,7 @@ def infer_template(paths: dict, infer_common_params: dict): num_workers=5, collate_fn=dataset.collate_fn) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # extract just the global classification per sample and save to a file output_columns = ['model.output.ClinSig','data.ground_truth'] manager.infer(data_loader=dataloader, @@ -348,7 +348,7 @@ def eval_template(paths: dict, eval_common_params: dict): TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones force_gpus = None # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train','infer', 'eval'] # Options: 'train', 'infer', 'eval' diff --git a/examples/fuse_examples/classification/prostate_x/tasks.py b/examples/fuse_examples/classification/prostate_x/tasks.py index ab3b3585a..9a22308fa 100644 --- a/examples/fuse_examples/classification/prostate_x/tasks.py +++ b/examples/fuse_examples/classification/prostate_x/tasks.py @@ -16,7 +16,7 @@ from typing import List -class FuseProstateXTask(): +class ProstateXTask(): tasks = {} def __init__(self, task_name: str, version: int): self._task_name, self._task_version, self._task_mapping, self._task_class_names = \ @@ -58,10 +58,10 @@ def get_task(cls, task_name: str, version: int): CLINSIG_VER_0 = [['HIGH'], ['LOW']], -FuseProstateXTask.register('gleason_score', 0, GLEASON_SCORE_VER_0, ['HIGH','LOW','BENIGN']) -FuseProstateXTask.register('ClinSig', 0, CLINSIG_VER_0, ['HIGH','LOW']) +ProstateXTask.register('gleason_score', 0, GLEASON_SCORE_VER_0, ['HIGH','LOW','BENIGN']) +ProstateXTask.register('ClinSig', 0, CLINSIG_VER_0, ['HIGH','LOW']) if __name__ == '__main__': - mp_task = FuseProstateXTask('gleason_score', 0) + mp_task = ProstateXTask('gleason_score', 0) print(mp_task.name()) print(mp_task.class_names()) print(len(mp_task.class_names())) diff --git a/examples/fuse_examples/classification/skin_lesion/data_source.py b/examples/fuse_examples/classification/skin_lesion/data_source.py index 378a52878..298714adf 100644 --- a/examples/fuse_examples/classification/skin_lesion/data_source.py +++ b/examples/fuse_examples/classification/skin_lesion/data_source.py @@ -23,10 +23,10 @@ import numpy as np import pickle -from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.data.data_source.data_source_base import DataSourceBase -class FuseSkinDataSource(FuseDataSourceBase): +class SkinDataSource(DataSourceBase): def __init__(self, input_source: str, partition_file: Optional[str] = None, @@ -103,7 +103,7 @@ def summary(self) -> str: :return: str """ summary_str = '' - summary_str += 'Class = FuseSkinDataSource\n' + summary_str += 'Class = SkinDataSource\n' if isinstance(self.input_source, str): summary_str += 'Input source filename = %s\n' % self.input_source diff --git a/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py b/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py index abd213fd6..534e3ed6f 100644 --- a/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py +++ b/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py @@ -23,10 +23,10 @@ import pandas as pd import numpy as np -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseSkinGroundTruthProcessor(FuseProcessorBase): +class SkinGroundTruthProcessor(ProcessorBase): def __init__(self, input_data: str, train: Optional[bool] = True, diff --git a/examples/fuse_examples/classification/skin_lesion/input_processor.py b/examples/fuse_examples/classification/skin_lesion/input_processor.py index bf055910d..239308797 100644 --- a/examples/fuse_examples/classification/skin_lesion/input_processor.py +++ b/examples/fuse_examples/classification/skin_lesion/input_processor.py @@ -26,10 +26,10 @@ import traceback from typing import Optional, Tuple -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseSkinInputProcessor(FuseProcessorBase): +class SkinInputProcessor(ProcessorBase): def __init__(self, input_data: str, normalized_target_range: Tuple = (0, 1), diff --git a/examples/fuse_examples/classification/skin_lesion/runner.py b/examples/fuse_examples/classification/skin_lesion/runner.py index d773dde68..54a9bdf18 100644 --- a/examples/fuse_examples/classification/skin_lesion/runner.py +++ b/examples/fuse_examples/classification/skin_lesion/runner.py @@ -20,9 +20,9 @@ from typing import OrderedDict from fuse.eval.evaluator import EvaluatorDefault -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.utils.utils_debug import FuseDebug -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU import logging @@ -33,39 +33,39 @@ from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool from fuse.utils.utils_logger import fuse_logger_start -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.data.visualizer.visualizer_default import VisualizerDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import aug_op_affine, aug_op_color, aug_op_gaussian -from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.data.dataset.dataset_default import DatasetDefault -from fuse.dl.models.model_default import FuseModelDefault -from fuse.dl.models.backbones.backbone_resnet import FuseBackboneResnet -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 +from fuse.dl.models.model_default import ModelDefault +from fuse.dl.models.backbones.backbone_resnet import BackboneResnet +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 -from fuse.dl.losses.loss_default import FuseLossDefault +from fuse.dl.losses.loss_default import LossDefault from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricROCCurve from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault -from fuse_examples.classification.skin_lesion.data_source import FuseSkinDataSource -from fuse_examples.classification.skin_lesion.input_processor import FuseSkinInputProcessor -from fuse_examples.classification.skin_lesion.ground_truth_processor import FuseSkinGroundTruthProcessor +from fuse_examples.classification.skin_lesion.data_source import SkinDataSource +from fuse_examples.classification.skin_lesion.input_processor import SkinInputProcessor +from fuse_examples.classification.skin_lesion.ground_truth_processor import SkinGroundTruthProcessor from fuse_examples.classification.skin_lesion.download import download_and_extract_isic ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug -debug = FuseUtilsDebug(mode) +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseDebug +debug = FuseDebug(mode) ########################################## # Output Paths @@ -188,29 +188,29 @@ def run_train(paths: dict, train_common_params: dict): partition_file = {'2016': "train_val_split.pickle", '2017': None} # as no validation set is available in 2016 dataset, it needs to be created - train_data_source = FuseSkinDataSource(input_source_gt[train_common_params['data.year']], + train_data_source = SkinDataSource(input_source_gt[train_common_params['data.year']], partition_file=partition_file[train_common_params['data.year']], train=True, override_partition=True) ## Create data processors: input_processors = { - 'input_0': FuseSkinInputProcessor(input_data=training_data[train_common_params['data.year']]) + 'input_0': SkinInputProcessor(input_data=training_data[train_common_params['data.year']]) } gt_processors = { - 'gt_global': FuseSkinGroundTruthProcessor(input_data=input_source_gt[train_common_params['data.year']], + 'gt_global': SkinGroundTruthProcessor(input_data=input_source_gt[train_common_params['data.year']], year=train_common_params['data.year']) } # Create data augmentation (optional) - augmentor = FuseAugmentorDefault( + augmentor = AugmentorDefault( augmentation_pipeline=train_common_params['data.augmentation_pipeline']) # Create visualizer (optional) - visualizer = FuseVisualizerDefault(image_name='data.input.input_0', label_name='data.gt.gt_global.tensor') + visualizer = VisualizerDefault(image_name='data.input.input_0', label_name='data.gt.gt_global.tensor') # Create dataset - train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + train_dataset = DatasetDefault(cache_dest=paths['cache_dir'], data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -223,7 +223,7 @@ def run_train(paths: dict, train_common_params: dict): ## Create sampler lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.gt.gt_global.tensor', num_balanced_classes=2, batch_size=train_common_params['data.batch_size']) @@ -241,20 +241,20 @@ def run_train(paths: dict, train_common_params: dict): lgr.info(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - validation_data_source = FuseSkinDataSource(input_source_gt_val[train_common_params['data.year']], + validation_data_source = SkinDataSource(input_source_gt_val[train_common_params['data.year']], partition_file=partition_file[train_common_params['data.year']], train=False) input_processors = { - 'input_0': FuseSkinInputProcessor(input_data=validation_data[train_common_params['data.year']]) + 'input_0': SkinInputProcessor(input_data=validation_data[train_common_params['data.year']]) } gt_processors = { - 'gt_global': FuseSkinGroundTruthProcessor(input_data=input_source_gt_val[train_common_params['data.year']], + 'gt_global': SkinGroundTruthProcessor(input_data=input_source_gt_val[train_common_params['data.year']], year=train_common_params['data.year']) } ## Create dataset - validation_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + validation_dataset = DatasetDefault(cache_dest=paths['cache_dir'], data_source=validation_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -280,12 +280,12 @@ def run_train(paths: dict, train_common_params: dict): # ============================================================================== lgr.info('Model:', {'attrs': 'bold'}) - model = FuseModelDefault( + model = ModelDefault( conv_inputs=(('data.input.input_0', 1),), - backbone={'Resnet18': FuseBackboneResnet(pretrained=True, in_channels=3, name='resnet18'), - 'InceptionResnetV2': FuseBackboneInceptionResnetV2(input_channels_num=3, logical_units_num=43)}['InceptionResnetV2'], + backbone={'Resnet18': BackboneResnet(pretrained=True, in_channels=3, name='resnet18'), + 'InceptionResnetV2': BackboneInceptionResnetV2(input_channels_num=3, logical_units_num=43)}['InceptionResnetV2'], heads=[ - FuseHeadGlobalPoolingClassifier(head_name='head_0', + HeadGlobalPoolingClassifier(head_name='head_0', dropout_rate=0.5, conv_inputs=[('model.backbone_features', 1536)], num_classes=2, @@ -299,7 +299,7 @@ def run_train(paths: dict, train_common_params: dict): # Loss # ==================================================================================== losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.head_0', target_name='data.gt.gt_global.tensor', + 'cls_loss': LossDefault(pred='model.logits.head_0', target='data.gt.gt_global.tensor', callable=F.cross_entropy, weight=1.0) } @@ -318,9 +318,9 @@ def run_train(paths: dict, train_common_params: dict): # ===================================================================================== callbacks = [ # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] # ===================================================================================== @@ -338,7 +338,7 @@ def run_train(paths: dict, train_common_params: dict): 'CosineAnnealing': optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=1)}['ReduceLROnPlateau'] # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -384,22 +384,22 @@ def run_infer(paths: dict, infer_common_params: dict): infer_data = {'2016': os.path.join(paths['data_dir'], 'data/ISIC2016_Test_Data/'), '2017': os.path.join(paths['data_dir'], 'data/ISIC2017_Test_Data/')} - infer_data_source = FuseSkinDataSource(input_source[infer_common_params['data.year']]) + infer_data_source = SkinDataSource(input_source[infer_common_params['data.year']]) # Create data processors input_processors_infer = { - 'input_0': FuseSkinInputProcessor(input_data=infer_data[infer_common_params['data.year']]) + 'input_0': SkinInputProcessor(input_data=infer_data[infer_common_params['data.year']]) } gt_processors_infer = { - 'gt_global': FuseSkinGroundTruthProcessor(input_data=input_source[infer_common_params['data.year']], + 'gt_global': SkinGroundTruthProcessor(input_data=input_source[infer_common_params['data.year']], train=False, year=infer_common_params['data.year']) } # Create visualizer (optional) - visualizer = FuseVisualizerDefault(image_name='data.input.input_0', label_name='data.gt.gt_global.tensor') + visualizer = VisualizerDefault(image_name='data.input.input_0', label_name='data.gt.gt_global.tensor') # Create dataset - infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + infer_dataset = DatasetDefault(cache_dest=paths['cache_dir'], data_source=infer_data_source, input_processors=input_processors_infer, gt_processors=gt_processors_infer, @@ -416,7 +416,7 @@ def run_infer(paths: dict, infer_common_params: dict): lgr.info(f'Test Data: Done', {'attrs': 'bold'}) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # extract just the global classification per sample and save to a file output_columns = ['model.output.head_0', 'data.gt.gt_global.tensor'] manager.infer(data_loader=infer_dataloader, @@ -479,7 +479,7 @@ def run_eval(paths: dict, eval_common_params: dict): TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones force_gpus = None # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' diff --git a/examples/fuse_examples/tests/test_classification_mnist.py b/examples/fuse_examples/tests/test_classification_mnist.py index b7617dae3..b9aba8c93 100644 --- a/examples/fuse_examples/tests/test_classification_mnist.py +++ b/examples/fuse_examples/tests/test_classification_mnist.py @@ -22,7 +22,7 @@ import unittest import os -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU from fuse_examples.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ EVAL_COMMON_PARAMS @@ -47,7 +47,7 @@ def setUp(self): self.analyze_common_params = EVAL_COMMON_PARAMS def test_template(self): - num_gpus_allocated = FuseUtilsGPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) + num_gpus_allocated = GPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) if num_gpus_allocated == 0: self.train_common_params['manager.train_params']['device'] = 'cpu' run_train(self.paths, self.train_common_params) diff --git a/examples/fuse_examples/tests/test_classification_prostatex.py b/examples/fuse_examples/tests/test_classification_prostatex.py index aca225b5f..32228731e 100644 --- a/examples/fuse_examples/tests/test_classification_prostatex.py +++ b/examples/fuse_examples/tests/test_classification_prostatex.py @@ -23,7 +23,7 @@ import os import pathlib -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU from fuse_examples.classification.prostate_x.run_train_3dpatch import TRAIN_COMMON_PARAMS, train_template, infer_template, eval_template, INFER_COMMON_PARAMS, \ EVAL_COMMON_PARAMS @@ -58,7 +58,7 @@ def setUp(self): # 1. Get path as an env variable # 2. modify the result value check def test_template(self): - num_gpus_allocated = FuseUtilsGPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) + num_gpus_allocated = GPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) if num_gpus_allocated == 0: self.train_common_params['manager.train_params']['device'] = 'cpu' train_template(self.paths, self.train_common_params) diff --git a/examples/fuse_examples/tests/test_classification_skin_lesion.py b/examples/fuse_examples/tests/test_classification_skin_lesion.py index 483352db8..81a2c76c5 100644 --- a/examples/fuse_examples/tests/test_classification_skin_lesion.py +++ b/examples/fuse_examples/tests/test_classification_skin_lesion.py @@ -27,7 +27,7 @@ from fuse_examples.classification.skin_lesion.runner import TRAIN_COMMON_PARAMS, \ INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer -import fuse.utils.gpu as FuseUtilsGPU +import fuse.utils.gpu as GPU @unittest.skipIf(True, "Long test") class ClassificationSkinLesionTestCase(unittest.TestCase): @@ -61,7 +61,7 @@ def setUp(self): self.analyze_common_params['data.year'] = self.train_common_params['data.year'] def test_runner(self): - num_gpus_allocated = FuseUtilsGPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) + num_gpus_allocated = GPU.choose_and_enable_multiple_gpus(1, use_cpu_if_fail=True) if num_gpus_allocated == 0: self.train_common_params['manager.train_params']['device'] = 'cpu' diff --git a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb b/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb index 9253dc1bd..5bab30f5a 100644 --- a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb @@ -83,14 +83,14 @@ "from torchvision import transforms\n", "\n", "from fuse.eval.evaluator import EvaluatorDefault\n", - "from fuse.data.dataset.dataset_wrapper import FuseDatasetWrapper\n", - "from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch\n", - "from fuse.dl.losses.loss_default import FuseLossDefault\n", - "from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", - "from fuse.dl.managers.manager_default import FuseManagerDefault\n", + "from fuse.data.dataset.dataset_wrapper import DatasetWrapper\n", + "from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch\n", + "from fuse.dl.losses.loss_default import LossDefault\n", + "from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback\n", + "from fuse.dl.managers.manager_default import ManagerDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", - "from fuse.dl.models.model_wrapper import FuseModelWrapper\n", + "from fuse.dl.models.model_wrapper import ModelWrapper\n", "from fuse_examples.tutorials.hello_world.hello_world_utils import LeNet, perform_softmax" ] }, @@ -180,10 +180,10 @@ "source": [ "##### **Data**\n", "Downloading the MNIST dataset and building dataloaders (torch.utils.data.DataLoader) for both train and validation using Fuse components:\n", - "1. Wrapper - **FuseDatasetWrapper**:\n", + "1. Wrapper - **DatasetWrapper**:\n", "\n", " Wraps PyTorch dataset such that each sample is being converted to dictionary according to the provided mapping.\n", - "2. Sampler - **FuseSamplerBalancedBatch**:\n", + "2. Sampler - **SamplerBalancedBatch**:\n", "\n", " Implementing 'torch.utils.data.sampler'.\n", " \n", @@ -205,10 +205,10 @@ "torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform)\n", "\n", "# wrapping torch dataset\n", - "train_dataset = FuseDatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label'))\n", + "train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label'))\n", "train_dataset.create()\n", "\n", - "sampler = FuseSamplerBalancedBatch(dataset=train_dataset,\n", + "sampler = SamplerBalancedBatch(dataset=train_dataset,\n", " balanced_class_name='data.label',\n", " num_balanced_classes=10,\n", " batch_size=train_params['data.batch_size'],\n", @@ -221,7 +221,7 @@ "# Create dataset\n", "torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform)\n", "# wrapping torch dataset\n", - "validation_dataset = FuseDatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label'))\n", + "validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label'))\n", "validation_dataset.create()\n", "\n", "# dataloader\n", @@ -247,7 +247,7 @@ "source": [ "torch_model = LeNet()\n", "\n", - "model = FuseModelWrapper(model=torch_model,\n", + "model = ModelWrapper(model=torch_model,\n", " model_inputs=['data.image'],\n", " post_forward_processing_function=perform_softmax,\n", " model_outputs=['logits.classification', 'output.classification']\n", @@ -259,7 +259,7 @@ "metadata": {}, "source": [ "##### **Loss function**\n", - "Dictionary of loss elements. each element is a sub-class of FuseLossBase.\n", + "Dictionary of loss elements. each element is a sub-class of LossBase.\n", "\n", "The total loss will be the weighted sum of all the elements.\n", "\n", @@ -273,7 +273,7 @@ "outputs": [], "source": [ "losses = {\n", - " 'cls_loss': FuseLossDefault(pred_name='model.logits.classification', target_name='data.label', callable=F.cross_entropy, weight=1.0),\n", + " 'cls_loss': LossDefault(pred='model.logits.classification', target='data.label', callable=F.cross_entropy, weight=1.0),\n", "}" ] }, @@ -282,7 +282,7 @@ "metadata": {}, "source": [ "##### **Metrics**\n", - "Dictionary of metric elements. Each element is a sub-class of FuseMetricBase.\n", + "Dictionary of metric elements. Each element is a sub-class of MetricBase.\n", "\n", "The metrics will be calculated per epoch for both the validation and train.\n", "\n", @@ -306,7 +306,7 @@ "metadata": {}, "source": [ "##### **Callbacks**\n", - "Callbacks are sub-classes of FuseCallbackBase.\n", + "Callbacks are sub-classes of CallbackBase.\n", "\n", "A callback is an object that can preform actions at various stages of training.\n", "\n", @@ -320,7 +320,7 @@ "outputs": [], "source": [ "callbacks = [\n", - " FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard\n", + " TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard\n", "]" ] }, @@ -331,7 +331,7 @@ "##### **Train**\n", "Building Fuse's manager and supplying it PyTorch's optimizer and scheduler.\n", "\n", - "Possible workflows are listed in the FuseMangerDefault's documentation.\n", + "Possible workflows are listed in the MangerDefault's documentation.\n", "\n", "Note that the manger is using the training paremeter that we've set above." ] @@ -349,7 +349,7 @@ "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)\n", "\n", "# train from scratch\n", - "manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir'])\n", + "manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir'])\n", "\n", "# Providing the objects required for the training process.\n", "manager.set_objects(net=model,\n", @@ -408,7 +408,7 @@ "validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2)\n", "\n", "## Manager for inference\n", - "manager = FuseManagerDefault()\n", + "manager = ManagerDefault()\n", "output_columns = ['model.output.classification', 'data.label']\n", "manager.infer(data_loader=validation_dataloader,\n", " input_model_dir=paths['model_dir'],\n", diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py index e8bffa27d..86666dbc5 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py @@ -24,10 +24,10 @@ import numpy as np import pickle -from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.data.data_source.data_source_base import DataSourceBase -class FuseSkinDataSource(FuseDataSourceBase): +class SkinDataSource(DataSourceBase): def __init__(self, input_source: str, size: Optional[int] = None, @@ -103,7 +103,7 @@ def summary(self) -> str: :return: str """ summary_str = '' - summary_str += 'Class = FuseSkinDataSource\n' + summary_str += 'Class = SkinDataSource\n' if isinstance(self.input_source, str): summary_str += 'Input source filename = %s\n' % self.input_source diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py index eb853d3ff..57f757285 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py @@ -3,21 +3,21 @@ import sys from typing import Callable, Optional -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +from fuse.data.visualizer.visualizer_default import VisualizerDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault from fuse.data.augmentor.augmentor_toolbox import aug_op_affine, aug_op_color, aug_op_gaussian -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.data.processor.processor_csv import FuseProcessorCSV +from fuse.data.dataset.dataset_default import DatasetDefault +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.data.processor.processor_csv import ProcessorCSV from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool from fuse_examples.tutorials.multimodality_image_clinical.download import download_and_extract_isic from torch.utils.data.dataloader import DataLoader sys.path.append(".") -from .input_processor import FuseSkinInputProcessor -from .ground_truth_processor import FuseSkinGroundTruthProcessor -from .data_source import FuseSkinDataSource +from .input_processor import SkinInputProcessor +from .ground_truth_processor import SkinGroundTruthProcessor +from .data_source import SkinDataSource def isic_2019_dataset(data_dir: str = 'data', size: int = None, reset_cache: bool = False, post_cache_processing_func: Optional[Callable] = None): #data_dir = "data" @@ -45,7 +45,7 @@ def isic_2019_dataset(data_dir: str = 'data', size: int = None, reset_cache: boo ], ] path = os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_GroundTruth.csv') - train_data_source = FuseSkinDataSource(path, + train_data_source = SkinDataSource(path, partition_file=os.path.join(data_dir, 'ISIC2019/partition.pickle'), train=True, size=size, @@ -53,24 +53,24 @@ def isic_2019_dataset(data_dir: str = 'data', size: int = None, reset_cache: boo input_processors = { - 'image': FuseSkinInputProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Input')), - # 'clinical': FuseSkinClinicalProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Metadata.csv')) - 'clinical': FuseProcessorCSV(csv_filename=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Metadata.csv'), sample_desc_column="image") + 'image': SkinInputProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Input')), + # 'clinical': SkinClinicalProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Metadata.csv')) + 'clinical': ProcessorCSV(csv_filename=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_Metadata.csv'), sample_desc_column="image") } gt_processors = { - 'gt_global': FuseSkinGroundTruthProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_GroundTruth.csv')) + 'gt_global': SkinGroundTruthProcessor(input_data=os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_GroundTruth.csv')) } # Create data augmentation (optional) - augmentor = FuseAugmentorDefault( + augmentor = AugmentorDefault( augmentation_pipeline=augmentation_pipeline) # Create visualizer (optional) - visualizer = FuseVisualizerDefault(image_name='data.input.image', label_name='data.gt.gt_global.tensor', metadata_names=["data.input.clinical"], gray_scale=False) + visualizer = VisualizerDefault(image_name='data.input.image', label_name='data.gt.gt_global.tensor', metadata_names=["data.input.clinical"], gray_scale=False) # Create dataset - train_dataset = FuseDatasetDefault(cache_dest=cache_dir, + train_dataset = DatasetDefault(cache_dest=cache_dir, data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -85,7 +85,7 @@ def isic_2019_dataset(data_dir: str = 'data', size: int = None, reset_cache: boo ## Create sampler print(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.gt.gt_global.tensor', num_balanced_classes=8, batch_size=8, @@ -104,14 +104,14 @@ def isic_2019_dataset(data_dir: str = 'data', size: int = None, reset_cache: boo print(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - validation_data_source = FuseSkinDataSource(os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_GroundTruth.csv'), + validation_data_source = SkinDataSource(os.path.join(data_dir, 'ISIC2019/ISIC_2019_Training_GroundTruth.csv'), size=size, partition_file=os.path.join(data_dir, 'ISIC2019/partition.pickle'), train=False) ## Create dataset - validation_dataset = FuseDatasetDefault(cache_dest=cache_dir, + validation_dataset = DatasetDefault(cache_dest=cache_dir, data_source=validation_data_source, input_processors=input_processors, gt_processors=gt_processors, diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py index 77a4fd1a2..eae89203f 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py @@ -23,10 +23,10 @@ import pandas as pd import numpy as np -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseSkinGroundTruthProcessor(FuseProcessorBase): +class SkinGroundTruthProcessor(ProcessorBase): def __init__(self, input_data: str, train: Optional[bool] = True, diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py b/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py index d03ee5070..1e6f318ce 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py @@ -27,10 +27,10 @@ import traceback from typing import Optional, Tuple -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseSkinInputProcessor(FuseProcessorBase): +class SkinInputProcessor(ProcessorBase): def __init__(self, input_data: str, normalized_target_range: Tuple = (0, 1), diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb b/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb index 199f0156a..015adbcf5 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb +++ b/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb @@ -63,10 +63,10 @@ "\n", "**Example of the decoupling approach:**\n", "```python\n", - "FuseMetricAUC(pred_name='model.output.classification', target_name='data.gt.classification') \n", + "MetricAUC(pred_name='model.output.classification', target_name='data.gt.classification') \n", "```\n", "\n", - "`FuseMetricAUC` will read the required tensors to compute AUC from `batch_dict`. The relevant dictionary keys are `pred_name` and `target_name`. \n", + "`MetricAUC` will read the required tensors to compute AUC from `batch_dict`. The relevant dictionary keys are `pred_name` and `target_name`. \n", "\n", "This approach allows writing a generic metric which is completely independent of the model and data extractor. \n", "\n", @@ -280,19 +280,19 @@ "Create data source:\n", "\n", "```python\n", - "train_data_source = FuseSkinDataSource(...)\n", + "train_data_source = SkinDataSource(...)\n", "```\n", "\n", "Create processors:\n", "\n", "```python\n", "input_processors = {\n", - " 'image': FuseSkinInputProcessor(...),\n", - " 'clinical': FuseProcessorCSV(...)\n", + " 'image': SkinInputProcessor(...),\n", + " 'clinical': ProcessorCSV(...)\n", "}\n", "\n", "gt_processors = {\n", - " 'gt_global': FuseSkinGroundTruthProcessor(...)\n", + " 'gt_global': SkinGroundTruthProcessor(...)\n", "}\n", "```\n", "\n", @@ -319,13 +319,13 @@ " ],\n", "]\n", "\n", - "augmentor = FuseAugmentorDefault(augmentation_pipeline=augmentation_pipeline)\n", + "augmentor = AugmentorDefault(augmentation_pipeline=augmentation_pipeline)\n", "```\n", "\n", "create pytorch dataset:\n", "\n", "```python\n", - "train_dataset = FuseDatasetDefault(cache_dest=cache_dir,\n", + "train_dataset = DatasetDefault(cache_dest=cache_dir,\n", " data_source=train_data_source,\n", " input_processors=input_processors,\n", " gt_processors=gt_processors,\n", @@ -338,7 +338,7 @@ "\n", "Create pytorch dataloader:\n", "```python\n", - "sampler = FuseSamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.gt.gt_global.tensor', ...)\n", + "sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='data.gt.gt_global.tensor', ...)\n", "\n", "train_dataloader = DataLoader(dataset=train_dataset,\n", " batch_sampler=sampler, collate_fn=train_dataset.collate_fn, ...)\n", @@ -401,20 +401,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Class = \n", + "Class = \n", "Processors:\n", "------------------------\n", - "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", + "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", "Cache destination:\n", "------------------\n", "cache\n", "Augmentor:\n", "----------\n", - "Class = (, )\n", + "Class = (, )\n", "Pipeline = [[('data.input.image',), '', {'rotate': 'RandUniform [-180.0 - 180.0] ', 'translate': ('RandInt [-50 - 50] ', 'RandInt [-50 - 50] '), 'flip': ('RandBool p=0.3] ', 'RandBool p=0.3] '), 'scale': 'RandUniform [0.9 - 1.1] '}, {'apply': 'RandBool p=0.9] '}], [('data.input.image',), '', {'add': 'RandUniform [-0.06 - 0.06] ', 'mul': 'RandUniform [0.95 - 1.05] ', 'gamma': 'RandUniform [0.9 - 1.1] ', 'contrast': 'RandUniform [0.85 - 1.15] '}, {'apply': 'RandBool p=0.7] '}], [('data.input.image',), '', {'std': '0.03'}, {'apply': 'RandBool p=0.7] '}]]\n", "Data source:\n", "------------\n", - "Class = FuseSkinDataSource\n", + "Class = SkinDataSource\n", "Input source filename = data/ISIC2019/ISIC_2019_Training_GroundTruth.csv\n", "Number of samples = 280\n", "\n", @@ -538,17 +538,17 @@ "outputs": [], "source": [ "\n", - "from fuse.dl.models.model_default import FuseModelDefault\n", - "from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier\n", - "from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2\n", - "from fuse.dl.models.backbones.backbone_resnet import FuseBackboneResnet\n", + "from fuse.dl.models.model_default import ModelDefault\n", + "from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier\n", + "from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2\n", + "from fuse.dl.models.backbones.backbone_resnet import BackboneResnet\n", "\n", - "model = FuseModelDefault(\n", + "model = ModelDefault(\n", " conv_inputs=(('data.input.image', 3),),\n", - " backbone=FuseBackboneInceptionResnetV2(input_channels_num=3, pretrained_weights_url=None),\n", - " # backbone=FuseBackboneResnet(in_channels=3),\n", + " backbone=BackboneInceptionResnetV2(input_channels_num=3, pretrained_weights_url=None),\n", + " # backbone=BackboneResnet(in_channels=3),\n", " heads=[\n", - " FuseHeadGlobalPoolingClassifier(head_name='head_0',\n", + " HeadGlobalPoolingClassifier(head_name='head_0',\n", " dropout_rate=0.5,\n", " conv_inputs=[('model.backbone_features', 384)],\n", " layers_description=(256,),\n", @@ -568,14 +568,14 @@ "source": [ "from collections import OrderedDict\n", "import torch.nn.functional as F\n", - "from fuse.dl.losses.loss_default import FuseLossDefault\n", + "from fuse.dl.losses.loss_default import LossDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricAccuracy, MetricConfusion\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "# ====================================================================================\n", "# Loss\n", "# ====================================================================================\n", "losses = {\n", - " 'cls_loss': FuseLossDefault(pred_name='model.logits.head_0', target_name='data.gt.gt_global.tensor',\n", + " 'cls_loss': LossDefault(pred='model.logits.head_0', target='data.gt.gt_global.tensor',\n", " callable=F.cross_entropy, weight=1.0)\n", "}\n", "\n", @@ -614,20 +614,20 @@ "\u001b[33mKey lr_sch_target not found in config parameter, setting value to default (train.losses.total_loss)\u001b[0m\n", "\u001b[1m\u001b[31mTotal number of parameters in model:7,818,600, trainable parameters:7,818,600\u001b[0m\n", "Train Dataset Summary:\n", - "Class = \n", + "Class = \n", "Processors:\n", "------------------------\n", - "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", + "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", "Cache destination:\n", "------------------\n", "cache\n", "Augmentor:\n", "----------\n", - "Class = (, )\n", + "Class = (, )\n", "Pipeline = [[('data.input.image',), '', {'rotate': 'RandUniform [-180.0 - 180.0] ', 'translate': ('RandInt [-50 - 50] ', 'RandInt [-50 - 50] '), 'flip': ('RandBool p=0.3] ', 'RandBool p=0.3] '), 'scale': 'RandUniform [0.9 - 1.1] '}, {'apply': 'RandBool p=0.9] '}], [('data.input.image',), '', {'add': 'RandUniform [-0.06 - 0.06] ', 'mul': 'RandUniform [0.95 - 1.05] ', 'gamma': 'RandUniform [0.9 - 1.1] ', 'contrast': 'RandUniform [0.85 - 1.15] '}, {'apply': 'RandBool p=0.7] '}], [('data.input.image',), '', {'std': '0.03'}, {'apply': 'RandBool p=0.7] '}]]\n", "Data source:\n", "------------\n", - "Class = FuseSkinDataSource\n", + "Class = SkinDataSource\n", "Input source filename = data/ISIC2019/ISIC_2019_Training_GroundTruth.csv\n", "Number of samples = 280\n", "\n", @@ -638,10 +638,10 @@ "-------------------\n", "\n", "Validation Dataset Summary:\n", - "Class = \n", + "Class = \n", "Processors:\n", "------------------------\n", - "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", + "{'input': {'image': , 'clinical': }, 'gt': {'gt_global': }}\n", "Cache destination:\n", "------------------\n", "cache\n", @@ -650,7 +650,7 @@ "None\n", "Data source:\n", "------------\n", - "Class = FuseSkinDataSource\n", + "Class = SkinDataSource\n", "Input source filename = data/ISIC2019/ISIC_2019_Training_GroundTruth.csv\n", "Number of samples = 120\n", "\n", @@ -912,8 +912,8 @@ ], "source": [ "import torch.optim as optim\n", - "from fuse.dl.managers.manager_default import FuseManagerDefault\n", - "from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback\n", + "from fuse.dl.managers.manager_default import ManagerDefault\n", + "from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback\n", "\n", "# create optimizer\n", "optimizer = optim.Adam(model.parameters(), lr=1e-5,\n", @@ -924,10 +924,10 @@ "\n", "#set\n", "callbacks = {\n", - " FuseTensorboardCallback(model_dir='model_dir')\n", + " TensorboardCallback(model_dir='model_dir')\n", "}\n", "# train from scratch\n", - "manager = FuseManagerDefault(output_model_dir=\"model_dir\", force_reset=True)\n", + "manager = ManagerDefault(output_model_dir=\"model_dir\", force_reset=True)\n", "# Providing the objects required for the training process.\n", "manager.set_objects(net=model,\n", " optimizer=optimizer,\n", @@ -1035,11 +1035,11 @@ "train_dl, valid_dl = isic_2019_dataset(size=size, reset_cache=False, post_cache_processing_func=post_cache_processing_clinical_encoding)\n", "\n", "### Define model\n", - "model = FuseModelDefault(\n", + "model = ModelDefault(\n", " conv_inputs=(('data.input.image', 3),),\n", - " backbone=FuseBackboneInceptionResnetV2(input_channels_num=3, pretrained_weights_url=None),\n", + " backbone=BackboneInceptionResnetV2(input_channels_num=3, pretrained_weights_url=None),\n", " heads=[\n", - " FuseHeadGlobalPoolingClassifier(head_name='head_0',\n", + " HeadGlobalPoolingClassifier(head_name='head_0',\n", " dropout_rate=0.5,\n", " conv_inputs=[('model.backbone_features', 384)],\n", " tabular_data_inputs=[(\"data.input.clinical.all\", 11)],\n", @@ -1053,7 +1053,7 @@ "\n", "\n", "### Strart a training process\n", - "manager = FuseManagerDefault(output_model_dir=\"model_dir_late_fuse\", force_reset=True)\n", + "manager = ManagerDefault(output_model_dir=\"model_dir_late_fuse\", force_reset=True)\n", "# Providing the objects required for the training process.\n", "manager.set_objects(net=model,\n", " optimizer=optimizer,\n", @@ -1099,11 +1099,11 @@ "train_dl, valid_dl = isic_2019_dataset(size=size, reset_cache=False, post_cache_processing_func=post_cache_processing_clinical_pad_to_image)\n", "\n", "### Define model\n", - "model = FuseModelDefault(\n", + "model = ModelDefault(\n", " conv_inputs=(('data.input.image', 14),),\n", - " backbone=FuseBackboneInceptionResnetV2(input_channels_num=14, pretrained_weights_url=None),\n", + " backbone=BackboneInceptionResnetV2(input_channels_num=14, pretrained_weights_url=None),\n", " heads=[\n", - " FuseHeadGlobalPoolingClassifier(head_name='head_0',\n", + " HeadGlobalPoolingClassifier(head_name='head_0',\n", " dropout_rate=0.5,\n", " conv_inputs=[('model.backbone_features', 384)],\n", " layers_description=(256,),\n", @@ -1115,7 +1115,7 @@ "\n", "\n", "### Strart a training process\n", - "manager = FuseManagerDefault(output_model_dir=\"model_dir_early_fuse\", force_reset=True)\n", + "manager = ManagerDefault(output_model_dir=\"model_dir_early_fuse\", force_reset=True)\n", "# Providing the objects required for the training process.\n", "manager.set_objects(net=model,\n", " optimizer=optimizer,\n", diff --git a/fuse/data/augmentor/augmentor_base.py b/fuse/data/augmentor/augmentor_base.py index 041fd07eb..fe5d1f08d 100644 --- a/fuse/data/augmentor/augmentor_base.py +++ b/fuse/data/augmentor/augmentor_base.py @@ -24,7 +24,7 @@ from typing import Any -class FuseAugmentorBase(ABC): +class AugmentorBase(ABC): """ Base class for augmentor. Given an augmenatation pipline description, expected to sample random parameters first and then apply them. diff --git a/fuse/data/augmentor/augmentor_batch_level_callback.py b/fuse/data/augmentor/augmentor_batch_level_callback.py index dc757faf8..aa906c2b1 100644 --- a/fuse/data/augmentor/augmentor_batch_level_callback.py +++ b/fuse/data/augmentor/augmentor_batch_level_callback.py @@ -19,20 +19,20 @@ from typing import Dict, List, Sequence -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.data.augmentor.augmentor_default import AugmentorDefault +from fuse.dl.managers.callbacks.callback_base import Callback -class FuseAugmentorBatchCallback(FuseCallback): +class AugmentorBatchCallback(Callback): """ Simple class which gets augmentation pipeline and apply augmentation on a batch level batch dict """ def __init__(self, aug_pipeline: List, modes: Sequence[str] = ('train',)): """ - :param aug_pipeline: See FuseAugmentorDefault + :param aug_pipeline: See AugmentorDefault :param modes: modees to apply the augmentation: 'train', 'validation' and/or 'infer' """ - self._augmentor = FuseAugmentorDefault(aug_pipeline) + self._augmentor = AugmentorDefault(aug_pipeline) self._modes = modes def on_data_fetch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: diff --git a/fuse/data/augmentor/augmentor_default.py b/fuse/data/augmentor/augmentor_default.py index 002ef9058..0e7fb20dc 100644 --- a/fuse/data/augmentor/augmentor_default.py +++ b/fuse/data/augmentor/augmentor_default.py @@ -22,13 +22,13 @@ """ from typing import Any, Iterable -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase +from fuse.data.augmentor.augmentor_base import AugmentorBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state, convert_state_to_str from fuse.utils.rand.param_sampler import draw_samples_recursively -class FuseAugmentorDefault(FuseAugmentorBase): +class AugmentorDefault(AugmentorBase): """ Default generic implementation for Fuse augmentor. Aimed to be used by most experiments. """ diff --git a/fuse/data/cache/cache_base.py b/fuse/data/cache/cache_base.py index d7e55b85a..dd5763f01 100644 --- a/fuse/data/cache/cache_base.py +++ b/fuse/data/cache/cache_base.py @@ -25,7 +25,7 @@ from typing import Hashable, Any, List -class FuseCacheBase(ABC): +class CacheBase(ABC): @abstractmethod def __contains__(self, key: Hashable) -> bool: diff --git a/fuse/data/cache/cache_files.py b/fuse/data/cache/cache_files.py index 3c5d5b79c..c7dbe6985 100644 --- a/fuse/data/cache/cache_files.py +++ b/fuse/data/cache/cache_files.py @@ -31,12 +31,12 @@ import torch torch.multiprocessing.set_sharing_strategy('file_system') -from fuse.data.cache.cache_base import FuseCacheBase +from fuse.data.cache.cache_base import CacheBase from fuse.utils.file_io.atomic_file import AtomicFileWriter from fuse.utils.file_io.file_io import create_dir, remove_dir_content -class FuseCacheFiles(FuseCacheBase): +class CacheFiles(CacheBase): def __init__(self, cache_file_dir: str, reset_cache: bool, single_file: bool=False): """ :param cache_file_dir: path to cache dir diff --git a/fuse/data/cache/cache_memory.py b/fuse/data/cache/cache_memory.py index 994e17540..ee8ff000d 100644 --- a/fuse/data/cache/cache_memory.py +++ b/fuse/data/cache/cache_memory.py @@ -23,10 +23,10 @@ from multiprocessing import Manager from typing import Hashable, Any, List -from fuse.data.cache.cache_base import FuseCacheBase +from fuse.data.cache.cache_base import CacheBase -class FuseCacheMemory(FuseCacheBase): +class CacheMemory(CacheBase): """ Cache to Memory """ diff --git a/fuse/data/cache/cache_null.py b/fuse/data/cache/cache_null.py index 96e92935a..92073cefa 100644 --- a/fuse/data/cache/cache_null.py +++ b/fuse/data/cache/cache_null.py @@ -23,10 +23,10 @@ from multiprocessing import Manager from typing import Hashable, Any, List -from fuse.data.cache.cache_base import FuseCacheBase +from fuse.data.cache.cache_base import CacheBase -class FuseCacheNull(FuseCacheBase): +class CacheNull(CacheBase): def __init__(self): super().__init__() diff --git a/fuse/data/data_source/data_source_base.py b/fuse/data/data_source/data_source_base.py index 647a4fef8..6119fc5e6 100644 --- a/fuse/data/data_source/data_source_base.py +++ b/fuse/data/data_source/data_source_base.py @@ -23,7 +23,7 @@ from abc import ABC, abstractmethod -class FuseDataSourceBase(ABC): +class DataSourceBase(ABC): @abstractmethod def get_samples_description(self): diff --git a/fuse/data/data_source/data_source_default.py b/fuse/data/data_source/data_source_default.py index a61a70aa9..fd923968c 100644 --- a/fuse/data/data_source/data_source_default.py +++ b/fuse/data/data_source/data_source_default.py @@ -22,11 +22,11 @@ import pandas as pd from typing import Sequence, Hashable, Union, Optional, List, Dict -from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.data.data_source.data_source_base import DataSourceBase from fuse.utils.misc.misc import autodetect_input_source -class FuseDataSourceDefault(FuseDataSourceBase): +class DataSourceDefault(DataSourceBase): """ DataSource for the following aut-detectable types: @@ -67,7 +67,7 @@ def __init__(self, input_source: Union[str, pd.DataFrame, Sequence[Hashable]] = logging.getLogger('Fuse').info(f"Remove {before - len(self.samples_df)} records that did not meet conditions") if self.samples_df is None: - raise Exception('Error detecting input source in FuseDataSourceDefault') + raise Exception('Error detecting input source in DataSourceDefault') if isinstance(folds, int): self.folds = [folds] @@ -102,7 +102,7 @@ def get_samples_description(self): def summary(self) -> str: summary_str = '' - summary_str += 'FuseDataSourceDefault - %d samples\n' % len(self.samples_df) + summary_str += 'DataSourceDefault - %d samples\n' % len(self.samples_df) return summary_str @@ -113,8 +113,8 @@ def summary(self) -> str: 'C': range(10, 5, -1)}) print(my_df) clist = [{'A': [2, 3, 4], 'B': [8, 2]}, {'C': [8, 7]}] - to_keep = FuseDataSourceDefault.filter_by_conditions(my_df, clist) + to_keep = DataSourceDefault.filter_by_conditions(my_df, clist) print(my_df[to_keep]) - to_keep = FuseDataSourceDefault.filter_by_conditions(my_df, [{}]) + to_keep = DataSourceDefault.filter_by_conditions(my_df, [{}]) print(my_df[to_keep]) diff --git a/fuse/data/data_source/data_source_folds.py b/fuse/data/data_source/data_source_folds.py index 56dba4157..68a54e3ea 100644 --- a/fuse/data/data_source/data_source_folds.py +++ b/fuse/data/data_source/data_source_folds.py @@ -20,12 +20,12 @@ import pandas as pd import os import numpy as np -from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.data.data_source.data_source_base import DataSourceBase from typing import Optional, Tuple -from fuse.data.data_source.data_source_toolbox import FuseDataSourceToolbox +from fuse.data.data_source.data_source_toolbox import DataSourceToolbox -class FuseDataSourceFolds(FuseDataSourceBase): +class DataSourceFolds(DataSourceBase): def __init__(self, input_source: str, input_df : pd.DataFrame, @@ -63,7 +63,7 @@ def __init__(self, if input_source is not None : input_df = pd.read_csv(input_source) - self.folds_df = FuseDataSourceToolbox.balanced_division(df = input_df , + self.folds_df = DataSourceToolbox.balanced_division(df = input_df , no_mixture_id = no_mixture_id, key_columns = self.key_columns , nfolds = self.nfolds , @@ -99,7 +99,7 @@ def summary(self) -> str: if isinstance(self.input_source, str): summary_str += 'Input source filename = %s\n' % self.input_source - summary_str += FuseDataSourceToolbox.print_folds_stat(db = self.folds_df , + summary_str += DataSourceToolbox.print_folds_stat(db = self.folds_df , nfolds = self.nfolds , key_columns = self.key_columns ) diff --git a/fuse/data/data_source/data_source_from_list.py b/fuse/data/data_source/data_source_from_list.py index 11408000f..9cd1e340d 100644 --- a/fuse/data/data_source/data_source_from_list.py +++ b/fuse/data/data_source/data_source_from_list.py @@ -19,10 +19,10 @@ from typing import Sequence, Hashable -from fuse.data.data_source.data_source_base import FuseDataSourceBase +from fuse.data.data_source.data_source_base import DataSourceBase -class FuseDataSourceFromList(FuseDataSourceBase): +class DataSourceFromList(DataSourceBase): """ Simple DataSource that can be initialized with a Python list (or other sequence). Does nothing but passing the list to Dataset. @@ -36,5 +36,5 @@ def get_samples_description(self): def summary(self) -> str: summary_str = '' - summary_str += 'FuseDataSourceFromList - %d samples\n' % len(self.list_of_samples) + summary_str += 'DataSourceFromList - %d samples\n' % len(self.list_of_samples) return summary_str diff --git a/fuse/data/data_source/data_source_toolbox.py b/fuse/data/data_source/data_source_toolbox.py index df1ccfa54..527bd12fd 100644 --- a/fuse/data/data_source/data_source_toolbox.py +++ b/fuse/data/data_source/data_source_toolbox.py @@ -26,7 +26,7 @@ import os -class FuseDataSourceToolbox(): +class DataSourceToolbox(): @staticmethod def print_folds_stat(db: pd.DataFrame, nfolds: int, key_columns: np.ndarray): @@ -110,7 +110,7 @@ def balanced_division(df : pd.DataFrame, no_mixture_id : str, key_columns: np.nd db['data_fold' + str(f)] = fold_df folds = pd.concat(db, ignore_index=True) if print_flag is True: - FuseDataSourceToolbox.print_folds_stat(folds, nfolds, key_columns) + DataSourceToolbox.print_folds_stat(folds, nfolds, key_columns) # remove labels used for creating the partition to folds if not debug_mode : folds.drop(id_level_labels+record_labels, axis=1, inplace=True) diff --git a/fuse/data/dataset/dataset_base.py b/fuse/data/dataset/dataset_base.py index 0a9cda4d2..b24adc9a6 100644 --- a/fuse/data/dataset/dataset_base.py +++ b/fuse/data/dataset/dataset_base.py @@ -28,7 +28,7 @@ from torch.utils.data.dataset import Dataset -class FuseDatasetBase(Dataset): +class DatasetBase(Dataset): """ Abstract base class for Fuse dataset. All subclasses should overwrite the following abstract methods inherited from torch.utils.data.Dataset @@ -88,7 +88,7 @@ def summary(self, statistic_keys: Optional[List[str]] = None) -> str: # save and load datasets @abstractmethod - def get_instance_to_save(self, mode: SaveMode) -> 'FuseDatasetBase': + def get_instance_to_save(self, mode: SaveMode) -> 'DatasetBase': """ Create lite instance version of dataset with just the info required to recreate it :param mode: see SaveMode for available modes @@ -97,7 +97,7 @@ def get_instance_to_save(self, mode: SaveMode) -> 'FuseDatasetBase': raise NotImplementedError @staticmethod - def save(dataset: 'FuseDatasetBase', mode: SaveMode, filename: str) -> None: + def save(dataset: 'DatasetBase', mode: SaveMode, filename: str) -> None: """ Static method save dataset to the disc (see SaveMode for available modes) :param dataset: the dataset to save @@ -113,7 +113,7 @@ def save(dataset: 'FuseDatasetBase', mode: SaveMode, filename: str) -> None: pickle.dump(dataset_to_save, pickle_file) @staticmethod - def load(filename: str, **kwargs) -> 'FuseDatasetBase': + def load(filename: str, **kwargs) -> 'DatasetBase': """ load dataset :param filename: path to saved dataset diff --git a/fuse/data/dataset/dataset_dataframe.py b/fuse/data/dataset/dataset_dataframe.py index 1c202911c..252369faf 100644 --- a/fuse/data/dataset/dataset_dataframe.py +++ b/fuse/data/dataset/dataset_dataframe.py @@ -22,14 +22,14 @@ import torch import pandas as pd -from fuse.data.data_source.data_source_from_list import FuseDataSourceFromList -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame +from fuse.data.data_source.data_source_from_list import DataSourceFromList +from fuse.data.dataset.dataset_default import DatasetDefault +from fuse.data.processor.processor_dataframe import ProcessorDataFrame -class FuseDatasetDataframe(FuseDatasetDefault): +class DatasetDataframe(DatasetDefault): """ - Simple dataset, based on FuseDatasetDefault, that converts dataframe into dataset. + Simple dataset, based on DatasetDefault, that converts dataframe into dataset. """ def __init__(self, data: Optional[pd.DataFrame] = None, @@ -54,7 +54,7 @@ def __init__(self, """ # create processor - processor = FuseProcessorDataFrame(data=data, + processor = ProcessorDataFrame(data=data, data_pickle_filename=data_pickle_filename, sample_desc_column=sample_desc_column, columns_to_extract=columns_to_extract, @@ -64,7 +64,7 @@ def __init__(self, # extract descriptor list and create datasource descriptors_list = processor.get_samples_descriptors() - data_source = FuseDataSourceFromList(descriptors_list) + data_source = DataSourceFromList(descriptors_list) super().__init__( data_source=data_source, diff --git a/fuse/data/dataset/dataset_default.py b/fuse/data/dataset/dataset_default.py index a425d5e8d..84c42de40 100644 --- a/fuse/data/dataset/dataset_default.py +++ b/fuse/data/dataset/dataset_default.py @@ -29,32 +29,32 @@ from torch import Tensor from tqdm import tqdm, trange -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase -from fuse.data.cache.cache_base import FuseCacheBase -from fuse.data.cache.cache_files import FuseCacheFiles -from fuse.data.cache.cache_memory import FuseCacheMemory -from fuse.data.cache.cache_null import FuseCacheNull -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.data.augmentor.augmentor_base import AugmentorBase +from fuse.data.cache.cache_base import CacheBase +from fuse.data.cache.cache_files import CacheFiles +from fuse.data.cache.cache_memory import CacheMemory +from fuse.data.cache.cache_null import CacheNull +from fuse.data.data_source.data_source_base import DataSourceBase +from fuse.data.dataset.dataset_base import DatasetBase +from fuse.data.processor.processor_base import ProcessorBase +from fuse.data.visualizer.visualizer_base import VisualizerBase +from fuse.utils.utils_debug import FuseDebug from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state from fuse.utils.misc.misc import get_pretty_dataframe, Misc -class FuseDatasetDefault(FuseDatasetBase): +class DatasetDefault(DatasetBase): """ Fuse Dataset Default Default generic implementation aimed to be used in most of the scenarios. """ #### CONSTRUCTOR - def __init__(self, data_source: FuseDataSourceBase, - input_processors: Optional[Dict[str, FuseProcessorBase]], gt_processors: Optional[Dict[str, FuseProcessorBase]], processors: Union[FuseProcessorBase, Dict[str, FuseProcessorBase]] = None, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[FuseAugmentorBase] = None, - visualizer: Optional[FuseVisualizerBase] = None, post_processing_func=None, + def __init__(self, data_source: DataSourceBase, + input_processors: Optional[Dict[str, ProcessorBase]], gt_processors: Optional[Dict[str, ProcessorBase]], processors: Union[ProcessorBase, Dict[str, ProcessorBase]] = None, + cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[AugmentorBase] = None, + visualizer: Optional[VisualizerBase] = None, post_processing_func=None, statistic_keys: Optional[List[str]] = None, filter_keys: Optional[List[str]] = None, data_key_prefix: Optional[str] = 'data'): @@ -107,18 +107,18 @@ def __init__(self, data_source: FuseDataSourceBase, self.samples_description = [] # create dummy cache for now - the cache will be created and loaded in create() - self.cache: FuseCacheBase = FuseCacheNull() + self.cache: CacheBase = CacheNull() # create dummy cache self.cache_fields used to store specific fields of the sample - used to optimize the running time of dataset.get( # key=, use_cache=True) - self.cache_fields: FuseCacheBase = FuseCacheNull() + self.cache_fields: CacheBase = CacheNull() # debug modes - read configuration - self.sample_stages_debug = FuseUtilsDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseUtilsDebug().get_setting('dataset_user') != 'default' + self.sample_stages_debug = FuseDebug().get_setting('dataset_sample_stages_info') != 'default' + self.sample_user_debug = FuseDebug().get_setting('dataset_user') != 'default' def create(self, cache_all: bool = True, reset_cache: bool = False, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[FuseDataSourceBase] = None, + override_datasource: Optional[DataSourceBase] = None, pool_type: str = 'process') -> None: """ Create the data set, including loading sample descriptions and caching @@ -132,7 +132,7 @@ def create(self, cache_all: bool = True, reset_cache: bool = False, :return: None """ # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') + override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') if override_num_workers != 'default': num_workers = override_num_workers logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) @@ -148,19 +148,19 @@ def create(self, cache_all: bool = True, reset_cache: bool = False, self.samples_description = self.data_source.get_samples_description() # debug - override number of samples - dataset_override_num_samples = FuseUtilsDebug().get_setting('dataset_override_num_samples') + dataset_override_num_samples = FuseDebug().get_setting('dataset_override_num_samples') if dataset_override_num_samples != 'default': self.samples_description = self.samples_description[:dataset_override_num_samples] logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) # cache object if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: FuseCacheBase = FuseCacheMemory() + self.cache: CacheBase = CacheMemory() elif isinstance(self.cache_dest, str): - self.cache: FuseCacheBase = FuseCacheFiles(self.cache_dest, reset_cache) + self.cache: CacheBase = CacheFiles(self.cache_dest, reset_cache) # cache samples if required - if not isinstance(self.cache, FuseCacheNull) and cache_all: + if not isinstance(self.cache, CacheNull) and cache_all: self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) # update descriptors @@ -191,7 +191,7 @@ def getitem_without_augmentation(self, index: int) -> Any: return sample @staticmethod - def getitem_without_augmentation_static(processors: Union[Dict[str, FuseProcessorBase], FuseProcessorBase], descr: Hashable, data_key_prefix: Optional[str]) -> Any: + def getitem_without_augmentation_static(processors: Union[Dict[str, ProcessorBase], ProcessorBase], descr: Hashable, data_key_prefix: Optional[str]) -> Any: """ Get the original item, just before applying the augmentation. The returned value will be stored in cache @@ -223,7 +223,7 @@ def getitem_without_augmentation_static(processors: Union[Dict[str, FuseProcesso # extract the sample description to be used by the processors sample_data['descriptor'] = descr # process data - if isinstance(processors, FuseProcessorBase): # handle a case of single processor + if isinstance(processors, ProcessorBase): # handle a case of single processor try: processor = processors value = processor(descr) @@ -319,7 +319,7 @@ def get(self, index: Optional[Union[int, Hashable]], key: Optional[str] = None, return self.get_from_cache(index, key) ## otherwise run the processor - if isinstance(self.processors, FuseProcessorBase): # single processor case + if isinstance(self.processors, ProcessorBase): # single processor case processor = self.processors inner_key = key[len('data.'):] else: # dictionary including multiple processors @@ -466,7 +466,7 @@ def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = if len(descriptors_to_cache) != 0: # multi process cache - lgr.info(f'FuseDatasetDefault: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') + lgr.info(f'DatasetDefault: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') with Manager() as manager: # change cache mode - to caching (writing) self.cache.start_caching(manager) @@ -487,9 +487,9 @@ def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = # save and move back to read mode self.cache.save() - lgr.info('FuseDatasetDefault: caching done') + lgr.info('DatasetDefault: caching done') else: - lgr.info(f'FuseDatasetDefault: all {len(all_descriptors)} samples are already cached') + lgr.info(f'DatasetDefault: all {len(all_descriptors)} samples are already cached') def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_workers: int = 8, cache_dest: Optional[str] = None) -> None: """ @@ -504,7 +504,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ lgr = logging.getLogger('Fuse') # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') + override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') if override_num_workers != 'default': num_workers = override_num_workers lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) @@ -513,12 +513,12 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ cache_dest = os.path.join(self.cache_dest, 'fields') # create cache field object upon request - if isinstance(self.cache_fields, FuseCacheNull): + if isinstance(self.cache_fields, CacheNull): # cache object if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: FuseCacheBase = FuseCacheMemory() + self.cache_fields: CacheBase = CacheMemory() elif isinstance(cache_dest, str): - self.cache_fields: FuseCacheBase = FuseCacheFiles(cache_dest, reset_cache, single_file=True) + self.cache_fields: CacheBase = CacheFiles(cache_dest, reset_cache, single_file=True) # get list of desc to cache desc_list = self.samples_description @@ -529,7 +529,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ # multi thread caching if len(desc_to_cache) != 0: - lgr.info(f'FuseDatasetDefault: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') + lgr.info(f'DatasetDefault: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') if num_workers > 0: with Manager() as manager: self.cache_fields.start_caching(manager) @@ -547,7 +547,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ self._cache_sample_fields((desc, fields)) self.cache_fields.save() else: - lgr.info('FuseDatasetDefault: all samples fields are already cached') + lgr.info('DatasetDefault: all samples fields are already cached') def _cache_sample_fields(self, args): # decode args @@ -569,7 +569,7 @@ def _cache_sample(args: Tuple) -> None: :return: None """ processors, desc, cache, data_key_prefix = args - sample = FuseDatasetDefault.getitem_without_augmentation_static(processors, desc, data_key_prefix=data_key_prefix) + sample = DatasetDefault.getitem_without_augmentation_static(processors, desc, data_key_prefix=data_key_prefix) cache[desc] = sample #### Filtering @@ -636,20 +636,20 @@ def visualize_augmentation(self, index: Optional[int] = None, descriptor: Option self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) # save and load dataset - def get_instance_to_save(self, mode: FuseDatasetBase.SaveMode) -> FuseDatasetBase: + def get_instance_to_save(self, mode: DatasetBase.SaveMode) -> DatasetBase: """ See base class """ # prepare data to save - dataset = FuseDatasetDefault(data_source=None, + dataset = DatasetDefault(data_source=None, input_processors={}, gt_processors={}, augmentor=self.augmentor, post_processing_func=self.post_processing_func, statistic_keys=self.statistic_keys, visualizer=self.visualizer) - if mode == FuseDatasetBase.SaveMode.INFERENCE and isinstance(self.processors, dict) and 'input' in self.processors: + if mode == DatasetBase.SaveMode.INFERENCE and isinstance(self.processors, dict) and 'input' in self.processors: dataset.processors = {'input': self.processors['input']} # for inference we can save only input processors if available else: dataset.processors = self.processors @@ -730,7 +730,7 @@ def collect_basic_data(self, statistic_keys: List[str]) -> dict: samples = sample_data # in case of multi processors, collect data of the ones implementing get_all() method - if not isinstance(self.processors, FuseProcessorBase): + if not isinstance(self.processors, ProcessorBase): all_keys = FuseUtilsHierarchicalDict.get_all_keys(self.processors) for key in all_keys: processor = FuseUtilsHierarchicalDict.get(self.processors, key) diff --git a/fuse/data/dataset/dataset_generator.py b/fuse/data/dataset/dataset_generator.py index a4f4f18a1..1cda462b3 100644 --- a/fuse/data/dataset/dataset_generator.py +++ b/fuse/data/dataset/dataset_generator.py @@ -29,31 +29,31 @@ from torch import Tensor from tqdm import tqdm, trange -from fuse.data.augmentor.augmentor_base import FuseAugmentorBase -from fuse.data.cache.cache_base import FuseCacheBase -from fuse.data.cache.cache_files import FuseCacheFiles -from fuse.data.cache.cache_memory import FuseCacheMemory -from fuse.data.cache.cache_null import FuseCacheNull -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.data.augmentor.augmentor_base import AugmentorBase +from fuse.data.cache.cache_base import CacheBase +from fuse.data.cache.cache_files import CacheFiles +from fuse.data.cache.cache_memory import CacheMemory +from fuse.data.cache.cache_null import CacheNull +from fuse.data.data_source.data_source_base import DataSourceBase +from fuse.data.dataset.dataset_base import DatasetBase +from fuse.data.processor.processor_base import ProcessorBase +from fuse.data.visualizer.visualizer_base import VisualizerBase +from fuse.utils.utils_debug import FuseDebug from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state from fuse.utils.misc.misc import get_pretty_dataframe, Misc -class FuseDatasetGenerator(FuseDatasetBase): +class DatasetGenerator(DatasetBase): """ Fuse Dataset Generator Used when it's more convient to generate sevral samples at once """ #### CONSTRUCTOR - def __init__(self, data_source: FuseDataSourceBase, processor: FuseProcessorBase, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[FuseAugmentorBase] = None, - visualizer: Optional[FuseVisualizerBase] = None, post_processing_func=None, + def __init__(self, data_source: DataSourceBase, processor: ProcessorBase, + cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[AugmentorBase] = None, + visualizer: Optional[VisualizerBase] = None, post_processing_func=None, statistic_keys: Optional[List[str]] = None, filter_keys: Optional[List[str]] = None): """ @@ -86,19 +86,19 @@ def __init__(self, data_source: FuseDataSourceBase, processor: FuseProcessorBase self.subsets_description = [] # create default cache for now - the cache will be created and loaded in create() - self.cache: FuseCacheBase = FuseCacheMemory() + self.cache: CacheBase = CacheMemory() # create dummy cache # self.cache_fields is used to store specific fields of the sample - # used to optimize the running time of dataset.get(key=, use_cache=True) - self.cache_fields: FuseCacheBase = FuseCacheNull() + self.cache_fields: CacheBase = CacheNull() # debug modes - read configuration - self.sample_stages_debug = FuseUtilsDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseUtilsDebug().get_setting('dataset_user') != 'default' + self.sample_stages_debug = FuseDebug().get_setting('dataset_sample_stages_info') != 'default' + self.sample_user_debug = FuseDebug().get_setting('dataset_user') != 'default' def create(self, reset_cache: bool = False, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[FuseDataSourceBase] = None, override_cache_dest: Optional[str] = None, + override_datasource: Optional[DataSourceBase] = None, override_cache_dest: Optional[str] = None, pool_type: str = 'process') -> None: """ @@ -113,7 +113,7 @@ def create(self, reset_cache: bool = False, :return: None """ # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') + override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') if override_num_workers != 'default': num_workers = override_num_workers logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) @@ -131,19 +131,19 @@ def create(self, reset_cache: bool = False, self.subsets_description = self.data_source.get_samples_description() # debug - override number of samples - dataset_override_num_samples = FuseUtilsDebug().get_setting('dataset_override_num_samples') + dataset_override_num_samples = FuseDebug().get_setting('dataset_override_num_samples') if dataset_override_num_samples != 'default': self.subsets_description = self.subsets_description[:dataset_override_num_samples] logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) # cache object if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: FuseCacheBase = FuseCacheMemory() + self.cache: CacheBase = CacheMemory() elif isinstance(self.cache_dest, str): - self.cache: FuseCacheBase = FuseCacheFiles(self.cache_dest, reset_cache) + self.cache: CacheBase = CacheFiles(self.cache_dest, reset_cache) # cache samples if required - if not isinstance(self.cache, FuseCacheNull): + if not isinstance(self.cache, CacheNull): self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) # update descriptors @@ -309,7 +309,7 @@ def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = if len(descriptors_to_cache) != 0: # multi process cache - lgr.info(f'FuseDatasetGenerator: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') + lgr.info(f'DatasetGenerator: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') with Manager() as manager: # change cache mode - to caching (writing) self.cache.start_caching(manager) @@ -330,9 +330,9 @@ def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = # save and move back to read mode self.cache.save() - lgr.info('FuseDatasetGenerator: caching done') + lgr.info('DatasetGenerator: caching done') else: - lgr.info('FuseDatasetGenerator: all samples are already cached') + lgr.info('DatasetGenerator: all samples are already cached') @staticmethod def _cache_subset(args: Tuple) -> None: @@ -371,7 +371,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ lgr = logging.getLogger('Fuse') # debug - override num workers - override_num_workers = FuseUtilsDebug().get_setting('dataset_override_num_workers') + override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') if override_num_workers != 'default': num_workers = override_num_workers lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) @@ -380,12 +380,12 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ cache_dest = os.path.join(self.cache_dest, 'fields') # create cache field object upon request - if isinstance(self.cache_fields, FuseCacheNull): + if isinstance(self.cache_fields, CacheNull): # cache object if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: FuseCacheBase = FuseCacheMemory() + self.cache_fields: CacheBase = CacheMemory() elif isinstance(cache_dest, str): - self.cache_fields: FuseCacheBase = FuseCacheFiles(cache_dest, reset_cache, single_file=True) + self.cache_fields: CacheBase = CacheFiles(cache_dest, reset_cache, single_file=True) # get list of desc to cache desc_list = self.samples_description @@ -396,7 +396,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ # multi thread caching if len(desc_to_cache) != 0: - lgr.info(f'FuseDatasetGenerator: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') + lgr.info(f'DatasetGenerator: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') if num_workers > 0: with Manager() as manager: self.cache_fields.start_caching(manager) @@ -414,7 +414,7 @@ def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_ self._cache_sample_fields((desc, fields)) self.cache_fields.save() else: - lgr.info('FuseDatasetGenerator: all samples fields are already cached') + lgr.info('DatasetGenerator: all samples fields are already cached') def _cache_sample_fields(self, args): # decode args @@ -493,20 +493,20 @@ def visualize_augmentation(self, index: Optional[int] = None, descriptor: Option self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) # save and load dataset - def get_instance_to_save(self, mode: FuseDatasetBase.SaveMode) -> FuseDatasetBase: + def get_instance_to_save(self, mode: DatasetBase.SaveMode) -> DatasetBase: """ See base class """ # prepare data to save - if mode == FuseDatasetBase.SaveMode.INFERENCE: - dataset = FuseDatasetGenerator(data_source=None, + if mode == DatasetBase.SaveMode.INFERENCE: + dataset = DatasetGenerator(data_source=None, processor=self.processor, augmentor=self.augmentor, post_processing_func=self.post_processing_func ) - elif mode == FuseDatasetBase.SaveMode.TRAINING: - dataset = FuseDatasetGenerator(data_source=self.data_source, + elif mode == DatasetBase.SaveMode.TRAINING: + dataset = DatasetGenerator(data_source=self.data_source, processor=self.processor, augmentor=self.augmentor, post_processing_func=self.post_processing_func, diff --git a/fuse/data/dataset/dataset_wrapper.py b/fuse/data/dataset/dataset_wrapper.py index d832d768d..d0dd71390 100644 --- a/fuse/data/dataset/dataset_wrapper.py +++ b/fuse/data/dataset/dataset_wrapper.py @@ -21,13 +21,13 @@ from torch.utils.data import Dataset -from fuse.data.data_source.data_source_from_list import FuseDataSourceFromList -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.data_source.data_source_from_list import DataSourceFromList +from fuse.data.dataset.dataset_default import DatasetDefault +from fuse.data.processor.processor_base import ProcessorBase # Dataset processor -class DatasetProcessor(FuseProcessorBase): +class DatasetProcessor(ProcessorBase): """ Processor that extract data from pytorch dataset and convert each sample to dictionary """ @@ -49,12 +49,12 @@ def __call__(self, desc: Tuple[str, int], *args, **kwargs): return sample -class FuseDatasetWrapper(FuseDatasetDefault): +class DatasetWrapper(DatasetDefault): """ Fuse Dataset Wrapper wraps pytorch dataset. Each sample will be converted to dictionary according to mapping. - And this dataset inherits all FuseDatasetDefault features + And this dataset inherits all DatasetDefault features """ #### CONSTRUCTOR @@ -63,8 +63,8 @@ def __init__(self, name: str, dataset: Dataset, mapping: Union[Sequence, Dict[st :param name: name of the data extracted from dataset, typically: 'train', 'validation;, 'test' :param dataset: the dataset to extract the data from :param mapping: including name for each returned object from dataset - :param kwargs: optinal, additional argumentes to provide to FuseDatasetDefault + :param kwargs: optinal, additional argumentes to provide to DatasetDefault """ - data_source = FuseDataSourceFromList([(name, i) for i in range(len(dataset))]) + data_source = DataSourceFromList([(name, i) for i in range(len(dataset))]) processor = DatasetProcessor(dataset, mapping) super().__init__(data_source=data_source, input_processors=None, gt_processors=None,processors=processor, **kwargs) diff --git a/fuse/data/processor/processor_base.py b/fuse/data/processor/processor_base.py index 9fced4140..dba316a4c 100644 --- a/fuse/data/processor/processor_base.py +++ b/fuse/data/processor/processor_base.py @@ -24,7 +24,7 @@ from typing import Hashable -class FuseProcessorBase(ABC): +class ProcessorBase(ABC): @abstractmethod def __call__(self, sample_desc: Hashable): raise NotImplementedError diff --git a/fuse/data/processor/processor_csv.py b/fuse/data/processor/processor_csv.py index 08daee0b7..a47909620 100644 --- a/fuse/data/processor/processor_csv.py +++ b/fuse/data/processor/processor_csv.py @@ -20,13 +20,13 @@ import ast import pandas as pd -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase import logging from typing import Hashable, List, Optional, Dict, Union from torch import Tensor import torch -class FuseProcessorCSV(FuseProcessorBase): +class ProcessorCSV(ProcessorBase): """ Processor reading data from csv file. Covert each row to a dictionary diff --git a/fuse/data/processor/processor_dataframe.py b/fuse/data/processor/processor_dataframe.py index 0a930533b..d8aef40c5 100644 --- a/fuse/data/processor/processor_dataframe.py +++ b/fuse/data/processor/processor_dataframe.py @@ -23,10 +23,10 @@ import pandas as pd from torch import Tensor -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseProcessorDataFrame(FuseProcessorBase): +class ProcessorDataFrame(ProcessorBase): """ Processor reading data from pickle file / dataframe object. Covert each row to a dictionary @@ -54,11 +54,11 @@ def __init__(self, # verify input lgr = logging.getLogger('Fuse') if data is None and data_pickle_filename is None: - msg = "Error in FuseProcessorDataFrame - need to provide either in-memory DataFrame or a path to pickled DataFrame." + msg = "Error in ProcessorDataFrame - need to provide either in-memory DataFrame or a path to pickled DataFrame." lgr.error(msg) raise Exception(msg) elif data is not None and data_pickle_filename is not None: - msg = "Error in FuseProcessorDataFrame - need to provide either 'data' or 'data_pickle_filename' args, bot not both." + msg = "Error in ProcessorDataFrame - need to provide either 'data' or 'data_pickle_filename' args, bot not both." lgr.error(msg) raise Exception(msg) diff --git a/fuse/data/processor/processor_dicom_mri.py b/fuse/data/processor/processor_dicom_mri.py index 7e9e6df40..aac1204fb 100755 --- a/fuse/data/processor/processor_dicom_mri.py +++ b/fuse/data/processor/processor_dicom_mri.py @@ -25,7 +25,7 @@ import h5py from typing import Tuple import pandas as pd -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase # ======================================================================== @@ -74,13 +74,13 @@ 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen', 'diff tra b 50 500 800 WIP511b alle spoelen'] -class FuseDicomMRIProcessor(FuseProcessorBase): +class DicomMRIProcessor(ProcessorBase): def __init__(self,verbose: bool=True,reference_inx: int=0,seq_dict:dict=SEQ_DICT, seq_to_use:list=SEQ_TO_USE,subseq_to_use:list=SUB_SEQ_TO_USE, ser_inx_to_use:dict=SER_INX_TO_USE,exp_patients:dict=EXP_PATIENTS, use_order_indicator: bool=False): ''' - FuseDicomMRIProcessor is MRI volume processor + DicomMRIProcessor is MRI volume processor :param verbose: if print verbose :param reference_inx: index for the sequence that is selected as reference from SEQ_TO_USE (0 for T2) :param seq_dict: dictionary in which varies series descriptions are grouped diff --git a/fuse/data/processor/processor_rand.py b/fuse/data/processor/processor_rand.py index 53533bc28..9fbf43c6d 100644 --- a/fuse/data/processor/processor_rand.py +++ b/fuse/data/processor/processor_rand.py @@ -24,10 +24,10 @@ import torch -from fuse.data.processor.processor_base import FuseProcessorBase +from fuse.data.processor.processor_base import ProcessorBase -class FuseProcessorRandInt(FuseProcessorBase): +class ProcessorRandInt(ProcessorBase): def __init__(self, min: int = 0, max: int = 1, shape: Tuple = (1,)): self.min = min self.max = max diff --git a/fuse/data/processor/processors_image_toolbox.py b/fuse/data/processor/processors_image_toolbox.py index b05045bb6..432e443c6 100644 --- a/fuse/data/processor/processors_image_toolbox.py +++ b/fuse/data/processor/processors_image_toolbox.py @@ -23,7 +23,7 @@ import skimage import skimage.transform as transform -class FuseProcessorsImageToolBox: +class ProcessorsImageToolBox: """ Common utils for image processors """ @@ -97,7 +97,7 @@ def pad_image(inner_image: np.ndarray, padding: Tuple[float, float], resize_to: else: pad_value = normalized_target_range[0] - image = FuseProcessorsImageToolBox.pad_inner_image(inner_image, outer_height=resize_to[0] + 2 * padding[0], + image = ProcessorsImageToolBox.pad_inner_image(inner_image, outer_height=resize_to[0] + 2 * padding[0], outer_width=resize_to[1] + 2 * padding[1], pad_value=pad_value, number_of_channels=number_of_channels) return image diff --git a/fuse/data/sampler/sampler_balanced_batch.py b/fuse/data/sampler/sampler_balanced_batch.py index 81df89591..5047accb0 100644 --- a/fuse/data/sampler/sampler_balanced_batch.py +++ b/fuse/data/sampler/sampler_balanced_batch.py @@ -27,17 +27,17 @@ import numpy as np from torch.utils.data.sampler import Sampler -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.data.dataset.dataset_base import DatasetBase +from fuse.utils.utils_debug import FuseDebug from fuse.utils.utils_logger import log_object_input_state -class FuseSamplerBalancedBatch(Sampler): +class SamplerBalancedBatch(Sampler): """ Torch batch sampler - balancing per batch """ - def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balanced_classes: int, batch_size: int, + def __init__(self, dataset: DatasetBase, balanced_class_name: str, num_balanced_classes: int, batch_size: int, balanced_class_weights: Optional[List[int]] = None, balanced_class_probs: Optional[List[float]] = None, num_batches: Optional[int] = None, use_dataset_cache: bool = False) -> None: """ @@ -97,7 +97,7 @@ def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balan self.balanced_class_weights = [self.batch_size // self.num_balanced_classes] * self.num_balanced_classes lgr = logging.getLogger('Fuse') - lgr.debug(f'FuseSamplerBalancedBatch: balancing per batch - balanced_class_name {self.balanced_class_name}, ' + lgr.debug(f'SamplerBalancedBatch: balancing per batch - balanced_class_name {self.balanced_class_name}, ' f'batch_size={batch_size}, weights={self.balanced_class_weights}, probs={self.balanced_class_probs}') # get balanced classes per each sample @@ -105,10 +105,10 @@ def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balan self.balanced_classes = np.array(self.balanced_classes) self.balanced_class_indices = [np.where(self.balanced_classes == cls_i)[0] for cls_i in range(self.num_balanced_classes)] self.balanced_class_sizes = [len(self.balanced_class_indices[cls_i]) for cls_i in range(self.num_balanced_classes)] - lgr.debug('FuseSamplerBalancedBatch: samples per each balanced class {}'.format(self.balanced_class_sizes)) + lgr.debug('SamplerBalancedBatch: samples per each balanced class {}'.format(self.balanced_class_sizes)) # debug - simple batch - batch_mode = FuseUtilsDebug().get_setting('sampler_batch_mode') + batch_mode = FuseDebug().get_setting('sampler_batch_mode') if batch_mode == 'simple': num_avail_bcls = sum( bcls_num_samples != 0 @@ -117,7 +117,7 @@ def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balan self.balanced_class_weights = None self.balanced_class_probs = [1.0/num_avail_bcls if bcls_num_samples != 0 else 0.0 for bcls_num_samples in self.balanced_class_sizes] - lgr.info('FuseSamplerBalancedBatch: debug mode - override to random sample') + lgr.info('SamplerBalancedBatch: debug mode - override to random sample') # calc batch index to balanced class mapping according to weights if self.balanced_class_weights is not None: @@ -161,7 +161,7 @@ def __init__(self, dataset: FuseDatasetBase, balanced_class_name: str, num_balan cls_i in range(self.num_balanced_classes)] bigger_balanced_class_weighted_size = max(balanced_class_weighted_sizes) self.num_batches = int(bigger_balanced_class_weighted_size) + 1 - lgr.debug(f'FuseSamplerBalancedBatch: num_batches = {self.num_batches}') + lgr.debug(f'SamplerBalancedBatch: num_batches = {self.num_batches}') # pointers per class self.cls_pointers = [0] * self.num_balanced_classes diff --git a/fuse/data/utils/export.py b/fuse/data/utils/export.py index 76b9b4287..9ce6b8e2e 100644 --- a/fuse/data/utils/export.py +++ b/fuse/data/utils/export.py @@ -19,7 +19,7 @@ from typing import Optional, Sequence import pandas as pd -from fuse.data.dataset.dataset_base import FuseDatasetBase +from fuse.data.dataset.dataset_base import DatasetBase from fuse.utils.file_io.file_io import save_dataframe from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict @@ -30,7 +30,7 @@ class DatasetExport: """ @staticmethod - def export_to_dataframe(dataset: FuseDatasetBase, keys: Sequence[str], output_filename: Optional[str] = None, sample_id_key: str = "data.descriptor", **dataset_get_kwargs) -> pd.DataFrame: + def export_to_dataframe(dataset: DatasetBase, keys: Sequence[str], output_filename: Optional[str] = None, sample_id_key: str = "data.descriptor", **dataset_get_kwargs) -> pd.DataFrame: """ extract from dataset the specified and keys and create a dataframe. If output_filename will be specified, the dataframe will also be saved in a file. diff --git a/fuse/data/visualizer/visualizer_base.py b/fuse/data/visualizer/visualizer_base.py index 272536826..a42b7eec4 100644 --- a/fuse/data/visualizer/visualizer_base.py +++ b/fuse/data/visualizer/visualizer_base.py @@ -21,7 +21,7 @@ from typing import Any -class FuseVisualizerBase(ABC): +class VisualizerBase(ABC): @abstractmethod def visualize(self, sample: Any, block: bool = True) -> None: diff --git a/fuse/data/visualizer/visualizer_default.py b/fuse/data/visualizer/visualizer_default.py index 3cf3db94f..b3d7f6eef 100644 --- a/fuse/data/visualizer/visualizer_default.py +++ b/fuse/data/visualizer/visualizer_default.py @@ -22,14 +22,14 @@ import matplotlib.pyplot as plt -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase +from fuse.data.visualizer.visualizer_base import VisualizerBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state import fuse.utils.imaging.image_processing as ImageProcessing import torch -class FuseVisualizerDefault(FuseVisualizerBase): +class VisualizerDefault(VisualizerBase): """ Visualizer for data including single 2D image with optional mask """ diff --git a/fuse/data/visualizer/visualizer_default_3d.py b/fuse/data/visualizer/visualizer_default_3d.py index cf6d9a450..07603dff7 100644 --- a/fuse/data/visualizer/visualizer_default_3d.py +++ b/fuse/data/visualizer/visualizer_default_3d.py @@ -24,12 +24,12 @@ from skimage.color import gray2rgb from skimage.segmentation import mark_boundaries -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase +from fuse.data.visualizer.visualizer_base import VisualizerBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state -class Fuse3DVisualizerDefault(FuseVisualizerBase): +class Fuse3DVisualizerDefault(VisualizerBase): """ Visualiser for data including 3D volume with optional local annotations """ diff --git a/fuse/data/visualizer/visualizer_image_analysis.py b/fuse/data/visualizer/visualizer_image_analysis.py index 2110b61d6..d2fbdf63b 100644 --- a/fuse/data/visualizer/visualizer_image_analysis.py +++ b/fuse/data/visualizer/visualizer_image_analysis.py @@ -22,11 +22,11 @@ import matplotlib.pyplot as plt import numpy as np -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase +from fuse.data.visualizer.visualizer_base import VisualizerBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseVisualizerImageAnalysis(FuseVisualizerBase): +class VisualizerImageAnalysis(VisualizerBase): """ Class for producing analysis of an image """ diff --git a/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py b/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py index 63bd79eed..ee7a26f6d 100644 --- a/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py +++ b/fuse/dl/losses/classification/loss_segmentation_cross_entropy.py @@ -22,11 +22,11 @@ import numpy as np import torch -from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import LossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseLossSegmentationCrossEntropy(FuseLossBase): +class LossSegmentationCrossEntropy(LossBase): def __init__(self, pred_name: str = None, target_name: str = None, diff --git a/fuse/dl/losses/loss_base.py b/fuse/dl/losses/loss_base.py index 2eea9a7c7..3952ca9be 100644 --- a/fuse/dl/losses/loss_base.py +++ b/fuse/dl/losses/loss_base.py @@ -20,17 +20,17 @@ import torch -class FuseLossBase(torch.nn.Module): +class LossBase(torch.nn.Module): """ Base class for Fuse loss functions """ def __init__(self, - pred_name: str = None, - target_name: str = None, + pred: str = None, + target: str = None, weight: float = 1.0, ) -> None: super().__init__() - self.pred_name = pred_name - self.target_name = target_name + self.pred_name = pred + self.target_name = target self.weight = weight diff --git a/fuse/dl/losses/loss_default.py b/fuse/dl/losses/loss_default.py index 8e6769433..068fcef2a 100644 --- a/fuse/dl/losses/loss_default.py +++ b/fuse/dl/losses/loss_default.py @@ -21,18 +21,18 @@ import torch -from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import LossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseLossDefault(FuseLossBase): +class LossDefault(LossBase): """ Default Fuse loss function """ def __init__(self, - pred_name: str = None, - target_name: str = None, + pred: str = None, + target: str = None, batch_kwargs_name: str = None, callable: Callable = None, sample_weight_name: Optional[str] = None, @@ -54,8 +54,8 @@ def __init__(self, :param kwargs: kwargs for PyTorch loss function """ super().__init__() - self.pred_name = pred_name - self.target_name = target_name + self.pred_name = pred + self.target_name = target self.batch_kwargs_name = batch_kwargs_name self.callable = callable self.sample_weight_name = sample_weight_name @@ -82,21 +82,3 @@ def __call__(self, batch_dict: Dict) -> torch.Tensor: loss_obj = torch.mean(weighted_loss) return loss_obj - - -if __name__ == '__main__': - import torch - - batch_dict = {'pred': torch.randn(3, 5, requires_grad=True), - 'gt': torch.empty(3, dtype=torch.long).random_(5), - 'batch_loss_kwargs': {'reduction': 'mean', 'ignore_index': 0}} - - loss = FuseLossDefault(pred_name='pred', - target_name='gt', - batch_kwargs_name='batch_loss_kwargs', - callable=torch.nn.functional.cross_entropy, - weight=1.0, - reduction='sum') - - res = loss(batch_dict) - print('Loss output = ' + str(res)) diff --git a/fuse/dl/losses/loss_warm_up.py b/fuse/dl/losses/loss_warm_up.py index 3a9da5192..fd03960fa 100644 --- a/fuse/dl/losses/loss_warm_up.py +++ b/fuse/dl/losses/loss_warm_up.py @@ -1,9 +1,7 @@ from typing import Dict import torch -from fuse.metrics.metric_base import FuseMetricBase - -class FuseLossWarmUp(torch.nn.Module): +class LossWarmUp(torch.nn.Module): def __init__(self, loss: torch.nn.Module, nof_iterations: int): super().__init__() self._loss = loss diff --git a/fuse/dl/losses/segmentation/loss_dice.py b/fuse/dl/losses/segmentation/loss_dice.py index 944e39bde..86cdea4ab 100644 --- a/fuse/dl/losses/segmentation/loss_dice.py +++ b/fuse/dl/losses/segmentation/loss_dice.py @@ -21,7 +21,7 @@ import torch.nn as nn import torch.nn.functional as F import numpy as np -from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import LossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from typing import Callable, Dict, Optional @@ -82,7 +82,7 @@ def __call__(self, predict, target): raise Exception('Unexpected reduction {}'.format(self.reduction)) -class FuseDiceLoss(FuseLossBase): +class DiceLoss(LossBase): def __init__(self, pred_name, target_name, diff --git a/fuse/dl/losses/segmentation/loss_focalLoss.py b/fuse/dl/losses/segmentation/loss_focalLoss.py index fa9cd29f3..c89e3aff3 100644 --- a/fuse/dl/losses/segmentation/loss_focalLoss.py +++ b/fuse/dl/losses/segmentation/loss_focalLoss.py @@ -22,7 +22,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import LossBase from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import numpy as np @@ -70,7 +70,7 @@ def forward(self, inputs, targets): return F_loss.mean() -class FuseFocalLoss(FuseLossBase): +class FocalLoss(LossBase): def __init__(self, pred_name: str = None, diff --git a/fuse/dl/managers/callbacks/callback_base.py b/fuse/dl/managers/callbacks/callback_base.py index 513bbd800..bbf6db055 100644 --- a/fuse/dl/managers/callbacks/callback_base.py +++ b/fuse/dl/managers/callbacks/callback_base.py @@ -19,10 +19,10 @@ from typing import Dict -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.dl.managers.manager_state import ManagerState -class FuseCallback(object): +class Callback(object): """ Abstract base class used to build new callbacks. Callbacks are called at various stages during training and infer. @@ -141,12 +141,12 @@ def on_batch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: """ pass - def on_train_begin(self, state: FuseManagerState) -> None: + def on_train_begin(self, state: ManagerState) -> None: """ Called at the beginning of the train procedure, after initialization of all variables and model. :param state: manager state object. - Contains the state of the manager. For details, see FuseManagerState. + Contains the state of the manager. For details, see ManagerState. """ pass diff --git a/fuse/dl/managers/callbacks/callback_debug.py b/fuse/dl/managers/callbacks/callback_debug.py index c156ff435..6188b8644 100644 --- a/fuse/dl/managers/callbacks/callback_debug.py +++ b/fuse/dl/managers/callbacks/callback_debug.py @@ -23,12 +23,12 @@ import torch.nn as nn -from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import Callback from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.misc.misc import Misc -class FuseCallbackDebug(FuseCallback): +class CallbackDebug(Callback): """ Callback used to log the information about each stage: begin/end time and fuse main structure: batch_dict, virtual_batch_results and epoch_results diff --git a/fuse/dl/managers/callbacks/callback_infer_results.py b/fuse/dl/managers/callbacks/callback_infer_results.py index 83ca919fa..d115196a1 100644 --- a/fuse/dl/managers/callbacks/callback_infer_results.py +++ b/fuse/dl/managers/callbacks/callback_infer_results.py @@ -26,12 +26,12 @@ import torch from torch import Tensor -from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import Callback from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseInferResultsCallback(FuseCallback): +class InferResultsCallback(Callback): """ Responsible of writing the data of inference results into a CSV file. Collects the output data (corresponding to the output_columns) at the end of handle_batch into an aggregated dict, diff --git a/fuse/dl/managers/callbacks/callback_metric_statistics.py b/fuse/dl/managers/callbacks/callback_metric_statistics.py index 2e695a6dd..6927b7a1a 100644 --- a/fuse/dl/managers/callbacks/callback_metric_statistics.py +++ b/fuse/dl/managers/callbacks/callback_metric_statistics.py @@ -24,12 +24,12 @@ import pandas as pd -from fuse.dl.managers.callbacks.callback_base import FuseCallback +from fuse.dl.managers.callbacks.callback_base import Callback from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseMetricStatisticsCallback(FuseCallback): +class MetricStatisticsCallback(Callback): """ Responsible of writing the metric results into a CSV file under output_path The columns are: mode, epoch, metric_name, metric_value diff --git a/fuse/dl/managers/callbacks/callback_tensorboard.py b/fuse/dl/managers/callbacks/callback_tensorboard.py index bc364134c..9c2bf4e58 100644 --- a/fuse/dl/managers/callbacks/callback_tensorboard.py +++ b/fuse/dl/managers/callbacks/callback_tensorboard.py @@ -19,15 +19,15 @@ import os from typing import Dict -from fuse.dl.managers.callbacks.callback_base import FuseCallback -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import Callback +from fuse.dl.managers.manager_state import ManagerState from fuse.utils.file_io.file_io import create_dir from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict import torch import numpy as np -class FuseTensorboardCallback(FuseCallback): +class TensorboardCallback(Callback): """ Responsible for writing the data of both training and validation to tensorborad loggers under model_dir. """ @@ -73,7 +73,7 @@ def on_step_end(self, step: int, train_results: Dict = None, validation_results: return - def on_train_begin(self, state: FuseManagerState) -> None: + def on_train_begin(self, state: ManagerState) -> None: """ Called at the beginning of the train procedure. diff --git a/fuse/dl/managers/callbacks/callback_time_statistics.py b/fuse/dl/managers/callbacks/callback_time_statistics.py index e8e205a1a..47902296a 100644 --- a/fuse/dl/managers/callbacks/callback_time_statistics.py +++ b/fuse/dl/managers/callbacks/callback_time_statistics.py @@ -23,12 +23,12 @@ import torch.nn as nn -from fuse.dl.managers.callbacks.callback_base import FuseCallback -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import Callback +from fuse.dl.managers.manager_state import ManagerState from fuse.utils.misc.misc import get_time_delta, time_display -class FuseTimeStatisticsCallback(FuseCallback): +class TimeStatisticsCallback(Callback): """ Counts time of procedures. """ @@ -131,7 +131,7 @@ def on_batch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: logging.getLogger('Fuse').debug(f"Time for {mode} batch {batch}: {get_time_delta(self.batch_begin_time)}") pass - def on_train_begin(self, state: FuseManagerState) -> None: + def on_train_begin(self, state: ManagerState) -> None: # update number of epochs from the manager's state: self.num_epochs = state.num_epochs self.train_begin_time = time.time() diff --git a/fuse/dl/managers/manager_default.py b/fuse/dl/managers/manager_default.py index dd248a2cd..8c1f4e827 100644 --- a/fuse/dl/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -30,19 +30,19 @@ from tqdm import trange, tqdm from typing import Dict, Any, List, Iterator, Optional, Union, Sequence, Hashable, Callable -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.data.dataset.dataset_base import FuseDatasetBase -from fuse.data.processor.processor_base import FuseProcessorBase -from fuse.data.visualizer.visualizer_base import FuseVisualizerBase -from fuse.dl.losses.loss_base import FuseLossBase -from fuse.dl.managers.callbacks.callback_base import FuseCallback -from fuse.dl.managers.callbacks.callback_debug import FuseCallbackDebug -from fuse.dl.managers.callbacks.callback_infer_results import FuseInferResultsCallback -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.data.data_source.data_source_base import DataSourceBase +from fuse.data.dataset.dataset_base import DatasetBase +from fuse.data.processor.processor_base import ProcessorBase +from fuse.data.visualizer.visualizer_base import VisualizerBase +from fuse.dl.losses.loss_base import LossBase +from fuse.dl.managers.callbacks.callback_base import Callback +from fuse.dl.managers.callbacks.callback_debug import CallbackDebug +from fuse.dl.managers.callbacks.callback_infer_results import InferResultsCallback +from fuse.dl.managers.manager_state import ManagerState from fuse.eval import MetricBase -from fuse.dl.models.model_ensemble import FuseModelEnsemble -from fuse.utils.dl.checkpoint import FuseCheckpoint -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.dl.models.model_ensemble import ModelEnsemble +from fuse.utils.dl.checkpoint import Checkpoint +from fuse.utils.utils_debug import FuseDebug from fuse.utils.file_io.file_io import create_or_reset_dir import fuse.utils.gpu as gpu from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict @@ -50,7 +50,7 @@ from fuse.utils.misc.misc import Misc, get_pretty_dataframe -class FuseManagerDefault: +class ManagerDefault: """ Default implementation of manager. Supports Train and Infer functionality. @@ -58,17 +58,17 @@ class FuseManagerDefault: Possible Work flows for using the manager are (see function documentations for parameters description): For train: - FuseManagerDefault() -> manager.set_objects() -> manager.train() + ManagerDefault() -> manager.set_objects() -> manager.train() For Resume training: - FuseManagerDefault() -> manager.load_objects() -> manager.load_checkpoint() -> manager.train() + ManagerDefault() -> manager.load_objects() -> manager.load_checkpoint() -> manager.train() For Train using existing model: - FuseManagerDefault() -> manager.set_objects() [-> manager.load_objects()] [-> manager.load_checkpoint()] -> manager.train() + ManagerDefault() -> manager.set_objects() [-> manager.load_objects()] [-> manager.load_checkpoint()] -> manager.train() For Infer: - FuseManagerDefault() -> manager.infer() + ManagerDefault() -> manager.infer() or - - FuseManagerDefault() -> manager.load_objects() -> manager.load_checkpoint() -> manager.infer() + ManagerDefault() -> manager.load_objects() -> manager.load_checkpoint() -> manager.infer() For Infer given model: - FuseManagerDefault() -> manager.set_objects() -> manager.load_checkpoint() -> manager.infer() + ManagerDefault() -> manager.set_objects() -> manager.load_checkpoint() -> manager.infer() """ def __init__(self, output_model_dir: str = None, force_reset: bool = False): @@ -83,7 +83,7 @@ def __init__(self, output_model_dir: str = None, force_reset: bool = False): log_object_input_state(self, locals()) self.logger = logging.getLogger('Fuse') - self.state = FuseManagerState() + self.state = ManagerState() self.state.output_model_dir = output_model_dir self.state.current_epoch = 0 @@ -91,15 +91,15 @@ def __init__(self, output_model_dir: str = None, force_reset: bool = False): # prepare model_dir create_or_reset_dir(output_model_dir, ignore_files=['logs', 'source_files'], force_reset=force_reset) - self.callbacks: List[FuseCallback] = list() # callback can be empty + self.callbacks: List[Callback] = list() # callback can be empty pass def set_objects(self, net: nn.Module = None, # ensemble_nets: Sequence[nn.Module] = None, metrics: Dict[str, MetricBase] = None, - losses: Dict[str, FuseLossBase] = None, - callbacks: List[FuseCallback] = None, + losses: Dict[str, LossBase] = None, + callbacks: List[Callback] = None, optimizer: Optimizer = None, lr_scheduler: Any = None, best_epoch_source: Union[List[Dict[str, str]], Dict[str, str]] = None, @@ -143,8 +143,8 @@ def set_objects(self, if output_model_dir is not None: self.state.output_model_dir = output_model_dir # debug mode - append debug callback - if FuseUtilsDebug().get_setting('manager_stages') != 'default': - self.callbacks.append(FuseCallbackDebug()) + if FuseDebug().get_setting('manager_stages') != 'default': + self.callbacks.append(CallbackDebug()) self.logger.info(f'Manager - debug mode - append debug callback', {'color': 'red'}) pass @@ -176,7 +176,7 @@ def _torch_save(parameter_to_save: Any, parameter_name: str) -> None: # also save validation_dataset in inference mode if validation_dataloader is not None: - FuseDatasetBase.save(validation_dataloader.dataset, mode=FuseDatasetBase.SaveMode.INFERENCE, + DatasetBase.save(validation_dataloader.dataset, mode=DatasetBase.SaveMode.INFERENCE, filename=os.path.join(self.state.output_model_dir, "inference_dataset.pth")) pass @@ -217,7 +217,7 @@ def load_if_exists(object_name, force=False): for model_idx, model_dir in enumerate(input_model_dir): self.logger.info("Loading ensemble model %d from: %s" % (model_idx, model_dir)) - self.state.net = FuseModelEnsemble(input_model_dir) + self.state.net = ModelEnsemble(input_model_dir) input_model_dir = input_model_dir[0] else: # load single module @@ -293,7 +293,7 @@ def should_load(object_name): raise Exception(msg) str_vals = 'all' if values_to_resume is None else str(values_to_resume) self.logger.info(f'Loading checkpoint file: {checkpoint_file}. values_to_resume {str_vals}', {'color': 'yellow'}) - checkpoint_objs.append(FuseCheckpoint.load_from_file(checkpoint_file)) + checkpoint_objs.append(Checkpoint.load_from_file(checkpoint_file)) if should_load('net'): net_state_dict_list = [checkpoint.net_state_dict for checkpoint in checkpoint_objs] @@ -327,7 +327,7 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader self._verify_all_objects_initialized(mode='train') # debug - num workers - override_num_workers = FuseUtilsDebug().get_setting('manager_override_num_dataloader_workers') + override_num_workers = FuseDebug().get_setting('manager_override_num_dataloader_workers') if override_num_workers != 'default': train_dataloader.num_workers = override_num_workers validation_dataloader.num_workers = override_num_workers @@ -380,7 +380,7 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader state_dict = self.state.net.module.state_dict() else: state_dict = self.state.net.state_dict() - epoch_checkpoint = FuseCheckpoint(state_dict, self.state.current_epoch, self.get_current_learning_rate()) + epoch_checkpoint = Checkpoint(state_dict, self.state.current_epoch, self.get_current_learning_rate()) # if this is the best epoch yet for i in range(self.state.num_models_to_save): @@ -413,14 +413,14 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader pass - def visualize(self, visualizer: FuseVisualizerBase, data_loader: Optional[DataLoader] = None, infer_processor: Optional[FuseProcessorBase] = None, + def visualize(self, visualizer: VisualizerBase, data_loader: Optional[DataLoader] = None, infer_processor: Optional[ProcessorBase] = None, descriptors: Optional[List[Hashable]] = None, device: str = 'cuda', display_func: Optional[Callable] = None): """ Visualize data including the input and the output. Expected Sequence: 1. Using a loaded model to extract the output: - manager = FuseManagerDefault() + manager = ManagerDefault() manager.load_objects(, mode='infer') # this method can load either a single model or an ensemble manager.load_checkpoint(checkpoint=, mode='infer') @@ -431,7 +431,7 @@ def visualize(self, visualizer: FuseVisualizerBase, data_loader: Optional[DataLo infer_processor=None) 2. using inference processor - manager = FuseManagerDefault() + manager = ManagerDefault() manager.visualize(visualizer=visualizer, data_loader=dataloader, descriptors=, @@ -446,7 +446,7 @@ def visualize(self, visualizer: FuseVisualizerBase, data_loader: Optional[DataLo :param display_func: Function getting the batch dict as an input and returns boolean specifying if to visualize this sample or not. :return: None """ - dataset: FuseDatasetBase = data_loader.dataset + dataset: DatasetBase = data_loader.dataset if infer_processor is None: if not hasattr(self, 'net') or self.state.net is None: self.logger.error(f"Cannot visualize without either net or infer_processor") @@ -484,7 +484,7 @@ def visualize(self, visualizer: FuseVisualizerBase, data_loader: Optional[DataLo def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, checkpoint: Optional[Union[str, int, Sequence[Union[str, int]]]] = None, - data_source: Optional[FuseDataSourceBase] = None, data_loader: Optional[DataLoader] = None, + data_source: Optional[DataSourceBase] = None, data_loader: Optional[DataLoader] = None, num_workers: Optional[int] = 4, batch_size: Optional[int] = 2, output_columns: List[str] = None, output_file_name: str = None, strict: bool = True, append_default_inference_callback: bool = True, @@ -530,7 +530,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, :param batch_size: batch size for Dataloader, effective only if 'data_loader' param is None :param output_columns: output columns to return. When None (default) all columns are returned. - When not None, FuseInferResultsCallback callback is created. + When not None, InferResultsCallback callback is created. :param output_file_name: output file path. when None (default) results are not saved to file. :param strict: strict state dict loading when loading checkpoint weights. default is True. :param append_default_inference_callback: if True, appends Fuse's default results collector callback @@ -539,7 +539,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, """ # debug - num workers - override_num_workers = FuseUtilsDebug().get_setting('manager_override_num_dataloader_workers') + override_num_workers = FuseDebug().get_setting('manager_override_num_dataloader_workers') if override_num_workers != 'default': num_workers = override_num_workers if data_loader is not None: @@ -568,7 +568,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, #TODO I don't like this flag - maybe think about a way to get rid of it? # append inference callback if append_default_inference_callback: - self.callbacks.append(FuseInferResultsCallback(output_file=output_file_name, output_columns=output_columns)) + self.callbacks.append(InferResultsCallback(output_file=output_file_name, output_columns=output_columns)) # either optional_datasource or optional_dataloader if data_loader is not None and data_source is not None: @@ -590,7 +590,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, else: data_set_filename = os.path.join(input_model_dir, "inference_dataset.pth") self.logger.info(f"Loading data source definitions from {data_set_filename}", {'color': 'yellow'}) - infer_dataset = FuseDatasetBase.load(filename=data_set_filename, override_datasource=data_source) + infer_dataset = DatasetBase.load(filename=data_set_filename, override_datasource=data_source) data_loader = DataLoader(dataset=infer_dataset, shuffle=False, drop_last=False, batch_sampler=None, batch_size=batch_size, num_workers=num_workers, collate_fn=infer_dataset.collate_fn) @@ -604,7 +604,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, # if infer CB is in the callback list, then return its result for callback in self.callbacks: - if isinstance(callback, FuseInferResultsCallback): + if isinstance(callback, InferResultsCallback): return callback.get_infer_results() def handle_epoch(self, mode: str, epoch: int, data_loader: DataLoader) -> Dict: @@ -855,7 +855,7 @@ def verify_value(self_object, parameter): if mode == 'train': self.state.num_epochs: int = full_config['num_epochs'] # debug - num epochs - override_num_epochs = FuseUtilsDebug().get_setting('manager_override_num_epochs') + override_num_epochs = FuseDebug().get_setting('manager_override_num_epochs') if override_num_epochs != 'default': self.state.num_epochs = override_num_epochs self.logger.info(f'Manager - debug mode - override num_epochs to {self.state.num_epochs}', {'color': 'red'}) diff --git a/fuse/dl/managers/manager_state.py b/fuse/dl/managers/manager_state.py index 5a9e4eaee..a98da9535 100644 --- a/fuse/dl/managers/manager_state.py +++ b/fuse/dl/managers/manager_state.py @@ -25,13 +25,13 @@ from torch.optim.optimizer import Optimizer from torch.utils.data.dataloader import DataLoader -from fuse.dl.losses.loss_base import FuseLossBase +from fuse.dl.losses.loss_base import LossBase from fuse.eval import MetricBase -class FuseManagerState: +class ManagerState: """ - FuseManagerState contains the current state of the manager. + ManagerState contains the current state of the manager. """ def __init__(self) -> None: @@ -40,7 +40,7 @@ def __init__(self) -> None: self.net: nn.Module self.metrics: Dict[str, MetricBase] = {} - self.losses: Dict[str, FuseLossBase] = {} + self.losses: Dict[str, LossBase] = {} self.optimizer: Optimizer self.lr_scheduler: Any self.train_params: Dict = {} diff --git a/fuse/dl/models/backbones/backbone_inception_resnet_v2.py b/fuse/dl/models/backbones/backbone_inception_resnet_v2.py index 2d011879b..c6c4ec6bf 100644 --- a/fuse/dl/models/backbones/backbone_inception_resnet_v2.py +++ b/fuse/dl/models/backbones/backbone_inception_resnet_v2.py @@ -243,7 +243,7 @@ def forward(self, x): return out # results in 2080 channels -class FuseBackboneInceptionResnetV2(nn.Module): +class BackboneInceptionResnetV2(nn.Module): def __init__(self, logical_units_num: int = 14, intra_block_cut_level: int = 384, diff --git a/fuse/dl/models/backbones/backbone_mlp.py b/fuse/dl/models/backbones/backbone_mlp.py index 3c65712d9..046def1aa 100644 --- a/fuse/dl/models/backbones/backbone_mlp.py +++ b/fuse/dl/models/backbones/backbone_mlp.py @@ -24,7 +24,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseMultilayerPerceptronBackbone(torch.nn.Module): +class MultilayerPerceptronBackbone(torch.nn.Module): def __init__(self, layers: List[int] = (64, 192, 320, 320, 1088, 384), diff --git a/fuse/dl/models/backbones/backbone_resnet.py b/fuse/dl/models/backbones/backbone_resnet.py index 20e024f78..74c66fe01 100644 --- a/fuse/dl/models/backbones/backbone_resnet.py +++ b/fuse/dl/models/backbones/backbone_resnet.py @@ -28,7 +28,7 @@ from torch import Tensor -class FuseBackboneResnet(ResNet): +class BackboneResnet(ResNet): """ 2D ResNet backbone """ diff --git a/fuse/dl/models/backbones/backbone_resnet_3d.py b/fuse/dl/models/backbones/backbone_resnet_3d.py index 7ec50a8e1..8a8539b7a 100644 --- a/fuse/dl/models/backbones/backbone_resnet_3d.py +++ b/fuse/dl/models/backbones/backbone_resnet_3d.py @@ -25,7 +25,7 @@ from torchvision.models.video.resnet import VideoResNet, BasicBlock, Conv3DSimple, BasicStem, model_urls -class FuseBackboneResnet3D(VideoResNet): +class BackboneResnet3D(VideoResNet): """ 3D model classifier (ResNet architecture" """ diff --git a/fuse/dl/models/heads/head_1d_classifier.py b/fuse/dl/models/heads/head_1d_classifier.py index 9211e9867..ca43709b2 100644 --- a/fuse/dl/models/heads/head_1d_classifier.py +++ b/fuse/dl/models/heads/head_1d_classifier.py @@ -84,7 +84,7 @@ def forward(self, x): x = self.classifier(x) return x -class FuseHead1dClassifier(nn.Module): +class Head1dClassifier(nn.Module): def __init__(self, head_name: str = 'head_0', conv_inputs: Sequence[Tuple[str, int]] = (('model.backbone_features', 193),), diff --git a/fuse/dl/models/heads/head_3D_classifier.py b/fuse/dl/models/heads/head_3D_classifier.py index bc597f4fe..aba1925f6 100644 --- a/fuse/dl/models/heads/head_3D_classifier.py +++ b/fuse/dl/models/heads/head_3D_classifier.py @@ -27,7 +27,7 @@ from fuse.dl.models.heads.common import ClassifierMLP -class FuseHead3dClassifier(nn.Module): +class Head3dClassifier(nn.Module): """ Model that capture slice feature including the 3D context given the local feature about a slice. """ diff --git a/fuse/dl/models/heads/head_dense_segmentation.py b/fuse/dl/models/heads/head_dense_segmentation.py index cc5d50f29..fae2fe763 100644 --- a/fuse/dl/models/heads/head_dense_segmentation.py +++ b/fuse/dl/models/heads/head_dense_segmentation.py @@ -27,7 +27,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseHeadDenseSegmentation(nn.Module): +class HeadDenseSegmentation(nn.Module): def __init__(self, head_name: str = 'head_0', conv_inputs: Sequence[Tuple[str, int]] = (('model.backbone_features', 384),), diff --git a/fuse/dl/models/heads/head_global_pooling_classifier.py b/fuse/dl/models/heads/head_global_pooling_classifier.py index 1f9acbae9..96001a88d 100644 --- a/fuse/dl/models/heads/head_global_pooling_classifier.py +++ b/fuse/dl/models/heads/head_global_pooling_classifier.py @@ -27,7 +27,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseHeadGlobalPoolingClassifier(nn.Module): +class HeadGlobalPoolingClassifier(nn.Module): def __init__(self, head_name: str = 'head_0', conv_inputs: Sequence[Tuple[str, int]] = (('model.backbone_features', 384),), diff --git a/fuse/dl/models/model_default.py b/fuse/dl/models/model_default.py index 7f2d4b436..cebd4a9ec 100644 --- a/fuse/dl/models/model_default.py +++ b/fuse/dl/models/model_default.py @@ -21,12 +21,12 @@ import torch -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseModelDefault(torch.nn.Module): +class ModelDefault(torch.nn.Module): """ Default Fuse model - convolutional neural network with multiple heads """ @@ -76,7 +76,7 @@ def forward(self, if __name__ == '__main__': - from fuse.dl.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation + from fuse.dl.models.heads.head_dense_segmentation import HeadDenseSegmentation import torch import os @@ -85,16 +85,16 @@ def forward(self, DEVICE = 'cpu' # 'cuda' DATAPARALLEL = False # True - model = FuseModelDefault( + model = ModelDefault( conv_inputs=(('data.input.input_0.tensor', 1),), - backbone=FuseBackboneInceptionResnetV2(), + backbone=BackboneInceptionResnetV2(), heads=[ - FuseHeadGlobalPoolingClassifier(head_name='head_0', + HeadGlobalPoolingClassifier(head_name='head_0', conv_inputs=[('model.backbone_features', 384)], post_concat_inputs=None, num_classes=2), - FuseHeadDenseSegmentation(head_name='head_1', + HeadDenseSegmentation(head_name='head_1', conv_inputs=[('model.backbone_features', 384)], num_classes=2) ] diff --git a/fuse/dl/models/model_ensemble.py b/fuse/dl/models/model_ensemble.py index 3708908ec..db0b1ec62 100644 --- a/fuse/dl/models/model_ensemble.py +++ b/fuse/dl/models/model_ensemble.py @@ -24,7 +24,7 @@ from typing import Sequence, Dict, List -class FuseModelEnsemble(torch.nn.Module): +class ModelEnsemble(torch.nn.Module): """ Ensemble Module - runs several sub-modules sequentially. In addition to producing a dictionary with predictions of each model in the ensemble, diff --git a/fuse/dl/models/model_multistream.py b/fuse/dl/models/model_multistream.py index be7a13a7c..1df51cf1a 100644 --- a/fuse/dl/models/model_multistream.py +++ b/fuse/dl/models/model_multistream.py @@ -21,21 +21,21 @@ import torch -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseModelMultistream(torch.nn.Module): +class ModelMultistream(torch.nn.Module): """ Multi-stream Fuse model - convolutional neural network with multiple processing streams and multiple heads """ def __init__(self, conv_inputs: Tuple[str, int] = ('data.input.input_0.tensor', 1), - backbone_streams: Sequence[torch.nn.Module] = (FuseBackboneInceptionResnetV2(logical_units_num=12), - FuseBackboneInceptionResnetV2(logical_units_num=12)), - heads: Sequence[torch.nn.Module] = (FuseHeadGlobalPoolingClassifier(),), + backbone_streams: Sequence[torch.nn.Module] = (BackboneInceptionResnetV2(logical_units_num=12), + BackboneInceptionResnetV2(logical_units_num=12)), + heads: Sequence[torch.nn.Module] = (HeadGlobalPoolingClassifier(),), split_logic: Optional[Callable] = None, join_logic: Optional[Callable] = None, ) -> None: @@ -76,7 +76,7 @@ def forward(self, elif callable(self.split_logic): stream_outputs = self.split_logic(batch_dict, self.backbone_streams) else: - raise Exception('Error in FuseModelMultistream - bad split logic provided') + raise Exception('Error in ModelMultistream - bad split logic provided') # Combining feature maps from multiple streams # -------------------------------------------- @@ -86,7 +86,7 @@ def forward(self, elif callable(self.join_logic): backbone_features = self.join_logic(batch_dict, stream_outputs) else: - raise Exception('Error in FuseModelMultistream - bad join logic provided') + raise Exception('Error in ModelMultistream - bad join logic provided') FuseUtilsHierarchicalDict.set(batch_dict, 'model.backbone_features', backbone_features) for head in self.heads: @@ -96,36 +96,36 @@ def forward(self, if __name__ == '__main__': - from fuse.dl.models.heads.head_dense_segmentation import FuseHeadDenseSegmentation + from fuse.dl.models.heads.head_dense_segmentation import HeadDenseSegmentation - backbone_0 = FuseBackboneInceptionResnetV2(logical_units_num=8) - backbone_1 = FuseBackboneInceptionResnetV2(logical_units_num=8) + backbone_0 = BackboneInceptionResnetV2(logical_units_num=8) + backbone_1 = BackboneInceptionResnetV2(logical_units_num=8) - non_shared_model = FuseModelMultistream( + non_shared_model = ModelMultistream( conv_inputs=('data.input.input_0.tensor', 2), backbone_streams=[backbone_0, backbone_1], heads=[ - FuseHeadGlobalPoolingClassifier(head_name='head_0', + HeadGlobalPoolingClassifier(head_name='head_0', conv_inputs=[('model.backbone_features', 640)], post_concat_inputs=None, num_classes=2), - FuseHeadDenseSegmentation(head_name='head_1', + HeadDenseSegmentation(head_name='head_1', conv_inputs=[('model.backbone_features', 640)], num_classes=2) ] ) - shared_model = FuseModelMultistream( + shared_model = ModelMultistream( conv_inputs=('data.input.input_0.tensor', 2), backbone_streams=[backbone_0, backbone_0], heads=[ - FuseHeadGlobalPoolingClassifier(head_name='head_0', + HeadGlobalPoolingClassifier(head_name='head_0', conv_inputs=[('model.backbone_features', 640)], post_concat_inputs=None, num_classes=2), - FuseHeadDenseSegmentation(head_name='head_1', + HeadDenseSegmentation(head_name='head_1', conv_inputs=[('model.backbone_features', 640)], num_classes=2) ] diff --git a/fuse/dl/models/model_siamese.py b/fuse/dl/models/model_siamese.py index 54b4fdd04..a4b936966 100644 --- a/fuse/dl/models/model_siamese.py +++ b/fuse/dl/models/model_siamese.py @@ -21,13 +21,13 @@ import torch -from fuse.dl.models.model_default import FuseModelDefault -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.model_default import ModelDefault +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseModelSiamese(FuseModelDefault): +class ModelSiamese(ModelDefault): """ Fuse Siamese model - 2 branches of the same convolutional neural network with multiple heads """ @@ -35,8 +35,8 @@ class FuseModelSiamese(FuseModelDefault): def __init__(self, conv_inputs_0: Tuple[Tuple[str, int], ...] = (('data.input.input_0.tensor', 1),), conv_inputs_1: Tuple[Tuple[str, int], ...] = (('data.input.input_1.tensor', 1),), - backbone: torch.nn.Module = FuseBackboneInceptionResnetV2(), - heads: Sequence[torch.nn.Module] = (FuseHeadGlobalPoolingClassifier(),) + backbone: torch.nn.Module = BackboneInceptionResnetV2(), + heads: Sequence[torch.nn.Module] = (HeadGlobalPoolingClassifier(),) ) -> None: """ Fuse Siamese model - two branches with same convolutional neural network with multiple heads diff --git a/fuse/dl/models/model_wrapper.py b/fuse/dl/models/model_wrapper.py index 6a04e3e52..16d908b45 100644 --- a/fuse/dl/models/model_wrapper.py +++ b/fuse/dl/models/model_wrapper.py @@ -21,12 +21,12 @@ import torch -from fuse.dl.models.backbones.backbone_inception_resnet_v2 import FuseBackboneInceptionResnetV2 -from fuse.dl.models.heads.head_global_pooling_classifier import FuseHeadGlobalPoolingClassifier +from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 +from fuse.dl.models.heads.head_global_pooling_classifier import HeadGlobalPoolingClassifier from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class FuseModelWrapper(torch.nn.Module): +class ModelWrapper(torch.nn.Module): """ Fuse model wrapper for wrapping torch modules and passing through Fuse """ @@ -113,7 +113,7 @@ def load_state_dict(self, *args, **kwargs): def convert_googlenet_outputs(output): return output.logits - model = FuseModelWrapper( + model = ModelWrapper( model_inputs=['data.input.input_0.tensor'], model=googlenet_model, post_forward_processing_function=convert_googlenet_outputs, diff --git a/fuse/dl/optimizers/opt_closure_cb.py b/fuse/dl/optimizers/opt_closure_cb.py index 4877b493f..cb1542894 100644 --- a/fuse/dl/optimizers/opt_closure_cb.py +++ b/fuse/dl/optimizers/opt_closure_cb.py @@ -18,14 +18,14 @@ """ from typing import Dict -from fuse.dl.managers.callbacks.callback_base import FuseCallback -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import Callback +from fuse.dl.managers.manager_state import ManagerState -class FuseCallbackOptClosure(FuseCallback): +class CallbackOptClosure(Callback): """ Use this callback if an optimizer requires closure argument """ - def on_train_begin(self, state: FuseManagerState): + def on_train_begin(self, state: ManagerState): self.virtual_batch = [] self.state = state self.state.opt_closure = self.opt_closure diff --git a/fuse/dl/optimizers/opt_sam.py b/fuse/dl/optimizers/opt_sam.py index 15d740490..18cd4a22d 100644 --- a/fuse/dl/optimizers/opt_sam.py +++ b/fuse/dl/optimizers/opt_sam.py @@ -31,22 +31,22 @@ import torch from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.dl.managers.callbacks.callback_base import FuseCallback -from fuse.dl.managers.manager_state import FuseManagerState +from fuse.dl.managers.callbacks.callback_base import Callback +from fuse.dl.managers.manager_state import ManagerState class SAM(torch.optim.Optimizer): """ SAM optimizer - see https://github.com/davda54/sam/blob/main/sam.py To use in FuseMedML: - Create the optimizer and add FuseCallbackSamOpt() to the list of callbacks:. + Create the optimizer and add CallbackSamOpt() to the list of callbacks:. Examples: base_optimizer = torch.optim.SGD optimizer = SAM(model.parameters(), base_optimizer, lr=lr, momentum=momentum, weight_decay=weight_decay) - callbacks.append(FuseCallbackSamOpt()) + callbacks.append(CallbackSamOpt()) """ def __init__(self, params, base_optimizer, rho=0.05, **kwargs): @@ -99,12 +99,12 @@ def _grad_norm(self): ) -class FuseCallbackSamOpt(FuseCallback): +class CallbackSamOpt(Callback): """ Use this callback for SAM optimizer """ - def on_train_begin(self, state: FuseManagerState): + def on_train_begin(self, state: ManagerState): self.state = state def on_batch_end(self, mode: str, batch: int, batch_dict: Dict = None): diff --git a/fuse/dl/templates/walkthrough_template.py b/fuse/dl/templates/walkthrough_template.py index d6afa91f4..b2f8bd3c8 100644 --- a/fuse/dl/templates/walkthrough_template.py +++ b/fuse/dl/templates/walkthrough_template.py @@ -19,8 +19,8 @@ import os -from fuse.utils.utils_debug import FuseUtilsDebug -import fuse.utils.gpu as FuseUtilsGPU +from fuse.utils.utils_debug import FuseDebug +import fuse.utils.gpu as GPU os.environ['skip_broker'] = '1' @@ -31,19 +31,19 @@ from fuse.utils.utils_logger import fuse_logger_start -from fuse.data.sampler.sampler_balanced_batch import FuseSamplerBalancedBatch -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.data.visualizer.visualizer_default import VisualizerDefault +from fuse.data.augmentor.augmentor_default import AugmentorDefault +from fuse.data.dataset.dataset_default import DatasetDefault -from fuse.dl.models.model_default import FuseModelDefault +from fuse.dl.models.model_default import ModelDefault -from fuse.dl.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.dl.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.dl.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import StatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +from fuse.dl.managers.manager_default import ManagerDefault -from fuse.analyzer.analyzer_default import FuseAnalyzerDefault +from fuse.eval.evaluator import EvaluatorDefault ########################################################################################################### # Fuse @@ -83,8 +83,8 @@ ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug -debug = FuseUtilsDebug(mode) +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseDebug +debug = FuseDebug(mode) ########################################## # Output Paths @@ -150,70 +150,70 @@ def train_template(paths: dict, train_common_params: dict): # Build dataloaders (torch.utils.data.DataLoader) for both train and validation. # Using Fuse generic components: # - # (1) Data Source - FuseDataSourceBase - + # (1) Data Source - DataSourceBase - # Class providing list of sample descriptors # - # (2) Processor - FuseProcessorBase - + # (2) Processor - ProcessorBase - # Group of classes extracting sample data given the sample descriptor. # We divide the processor to two groups (1) 'input' (2) 'gt'. # Both groups will be used for training, but only the 'input' group will be used for 'inference' # The input processors data will be aggregated in batch_dict['data.input..*'] # The gt processors data will be aggregated in batch_dict['data.gt..*'] # Available Processor classes are: - # FuseProcessorCSV - Reads CSV file, call() returns a sample as dict - # FuseProcessorDataFrame - Reads either data frame or pickled data frame, call() returns a sample as dict - # FuseProcessorDataFrameWithGT - Reads either data frame or pickled data frame, call() returns a gt tensor value as dict - # FuseProcessorRand - Processor generating random ground truth - useful for testing and sanity check + # ProcessorCSV - Reads CSV file, call() returns a sample as dict + # ProcessorDataFrame - Reads either data frame or pickled data frame, call() returns a sample as dict + # ProcessorDataFrameWithGT - Reads either data frame or pickled data frame, call() returns a gt tensor value as dict + # ProcessorRand - Processor generating random ground truth - useful for testing and sanity check # - # (3) Dataset - FuseDatasetBase - + # (3) Dataset - DatasetBase - # Extended pytorch Dataset class - providing additional functionality such as caching, filtering, visualizing. # Available Dataset classes are: - # FuseDatasetDefault - generic default implementation - # FuseDatasetGenerator - to be used when generating simple samples at once (e.g., patches of a single image) - # FuseDatasetWrapper - wraps Pytorch's dataset, converts each sample into a dict. + # DatasetDefault - generic default implementation + # DatasetGenerator - to be used when generating simple samples at once (e.g., patches of a single image) + # DatasetWrapper - wraps Pytorch's dataset, converts each sample into a dict. # - # (4) Augmentor - FuseAugmentorBase - + # (4) Augmentor - AugmentorBase - # Optional class applying the augmentation - # See FuseAugmentorDefault for default generic implementation of augmentor. It is aimed to be used by most experiments. + # See AugmentorDefault for default generic implementation of augmentor. It is aimed to be used by most experiments. # See fuse.data.augmentor.augmentor_toolbox.py for implemented augmentation functions to be used in the pipeline. # - # (5) Visualizer - FuseVisualizerBase - + # (5) Visualizer - VisualizerBase - # Optional class visualizing the data before and after augmentations # Available visualizers: - # FuseVisualizerDefault - Visualizer for data including single 2D image with optional mask + # VisualizerDefault - Visualizer for data including single 2D image with optional mask # Fuse3DVisualizerDefault - Visualizer for data including 3D volume with optional mask - # FuseVisualizerImageAnalysis - Visualizer for producing analysis of an image + # VisualizerImageAnalysis - Visualizer for producing analysis of an image # # (6) Sampler - implementing 'torch.utils.data.sampler' - # Class retrieving list of samples to use for each batch # Available Sampler; - # FuseSamplerBalancedBatch - balances data per batch. Supports balancing of classes by weights/probabilities. + # SamplerBalancedBatch - balances data per batch. Supports balancing of classes by weights/probabilities. # ============================================================================== #### Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) ## Create data source: - # TODO: Create instance of FuseDataSourceBase - # Reference: FuseMGDataSource + # TODO: Create instance of DataSourceBase + # Reference: MGDataSource train_data_source = None ## Create data processors: - # TODO - Create instances of FuseProcessorBase and add to the dictionaries below - # Reference: FuseProcessorDataFrame, FuseProcessorCSV, DatasetProcessor (for pytorch data) + # TODO - Create instances of ProcessorBase and add to the dictionaries below + # Reference: ProcessorDataFrame, ProcessorCSV, DatasetProcessor (for pytorch data) input_processors = {} gt_processors = {} ## Create data augmentation (optional) - augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + augmentor = AugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) # Create visualizer (optional) # TODO - Either use the default visualizer or an alternative one - visualiser = FuseVisualizerDefault(image_name='TODO', label_name='TODO') + visualiser = VisualizerDefault(image_name='TODO', label_name='TODO') # Create dataset - # Fuse TIP: If it's more convenient to generate few samples at once, use FuseDatasetGenerator - train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + # Fuse TIP: If it's more convenient to generate few samples at once, use DatasetGenerator + train_dataset = DatasetDefault(cache_dest=paths['cache_dir'], data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -237,7 +237,7 @@ def train_template(paths: dict, train_common_params: dict): # 2. You don't have to equally balance between the classes. # Use balanced_class_weights to specify the number of required samples in a batch per each class lgr.info(f'- Create sampler:') - sampler = FuseSamplerBalancedBatch(dataset=train_dataset, + sampler = SamplerBalancedBatch(dataset=train_dataset, balanced_class_name='TODO', num_balanced_classes='TODO', batch_size=train_common_params['data.batch_size'], @@ -257,13 +257,13 @@ def train_template(paths: dict, train_common_params: dict): lgr.info(f'Validation Data:', {'attrs': 'bold'}) ## Create data source - # TODO: Create instance of FuseDataSourceBase - # Reference: FuseDataSourceDefault + # TODO: Create instance of DataSourceBase + # Reference: DataSourceDefault validation_data_source = 'TODO' ## Create dataset - # Fuse TIP: If it's more convenient to generate few samples at once, use FuseDatasetGenerator - validation_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + # Fuse TIP: If it's more convenient to generate few samples at once, use DatasetGenerator + validation_dataset = DatasetDefault(cache_dest=paths['cache_dir'], data_source=validation_data_source, input_processors=input_processors, gt_processors=gt_processors, @@ -287,75 +287,69 @@ def train_template(paths: dict, train_common_params: dict): # =================================================================================================================== # Model # Build a model (torch.nn.Module) using generic Fuse componnets: - # 1. FuseModelDefault - generic component supporting single backbone with multiple heads - # 2. FuseBackbone - simple backbone model - # 3. FuseHead* - generic head implementations + # 1. ModelDefault - generic component supporting single backbone with multiple heads + # 2. Backbone - simple backbone model + # 3. Head* - generic head implementations # The model outputs will be aggregated in batch_dict['model.*'] # Each head output will be aggregated in batch_dict['model..*'] # # Additional implemented models: - # * FuseModelEnsemble - runs several sub-modules sequentially - # * FuseModelMultistream - convolutional neural network with multiple processing streams and multiple heads + # * ModelEnsemble - runs several sub-modules sequentially + # * ModelMultistream - convolutional neural network with multiple processing streams and multiple heads # * # =================================================================================================================== lgr.info('Model:', {'attrs': 'bold'}) # TODO - define / create a model - model = FuseModelDefault( + model = ModelDefault( conv_inputs=(('data.input.input_0.tensor', 1),), - backbone='TODO', # Reference: FuseBackboneInceptionResnetV2 - heads=['TODO'] # References: FuseHeadGlobalPoolingClassifier, FuseHeadDenseSegmentation + backbone='TODO', # Reference: BackboneInceptionResnetV2 + heads=['TODO'] # References: HeadGlobalPoolingClassifier, HeadDenseSegmentation ) lgr.info('Model: Done', {'attrs': 'bold'}) # ========================================================================================================================================== # Loss - # Dictionary of loss elements each element is a sub-class of FuseLossBase + # Dictionary of loss elements each element is a sub-class of LossBase # The total loss will be the weighted sum of all the elements. # Each element output loss will be aggregated in batch_dict['losses.'] # The average batch loss per epoch will be included in epoch_result['losses.'], # and the total loss in epoch_result['losses.total_loss'] # The 'best_epoch_source', used to save the best model could be based on one of this losses. # Available Losses: - # FuseLossDefault - wraps a PyTorch loss function with a Fuse api. - # FuseLossSegmentationCrossEntropy - calculates cross entropy loss per location ("dense") of a class activation map ("segmentation") + # LossDefault - wraps a PyTorch loss function with a Fuse api. + # LossSegmentationCrossEntropy - calculates cross entropy loss per location ("dense") of a class activation map ("segmentation") # # ========================================================================================================================================== losses = { - # TODO add losses here (instances of FuseLossBase) + # TODO add losses here (instances of LossBase) } # ========================================================================================================= # Metrics - # Dictionary of metric elements. Each element is a sub-class of FuseMetricBase + # Dictionary of metric elements. Each element is a sub-class of MetricBase # The metrics will be calculated per epoch for both the validation and train. # The results will be included in epoch_result['metrics.'] # The 'best_epoch_source', used to save the best model could be based on one of this metrics. # Available Metrics: - # FuseMetricAccuracy - basic accuracy - # FuseMetricAUC - Area under receiver operating characteristic curve - # FuseMetricBoundingBoxes - Bounding boxes metric - # FuseMetricConfidenceInterval - Wrapper Metric to compute the confidence interval of another metric - # FuseMetricConfusionMatrix - Confusion matrix - # FuseMetricPartialAUC - Partial area under receiver operating characteristic curve - # FuseMetricScoreMap - Segmentation metric + # See fuse/eva;/README.md for more details # # ========================================================================================================= metrics = { - # TODO add metrics here (instances of FuseMetricBase) + # TODO add metrics here (instances of MetricBase) } # ========================================================================================================== # Callbacks - # Callbacks are sub-classes of FuseCallbackBase. + # Callbacks are sub-classes of CallbackBase. # A callback is an object that can perform actions at various stages of training, # In each stage it allows to manipulate either the data, batch_dict or epoch_results. # ========================================================================================================== callbacks = [ # Fuse TIPs: add additional callbacks here # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statstics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statisticsin a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + TensorboardCallback(model_dir=paths['model_dir']), # save statstics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statisticsin a csv file + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] # ===================================================================================== @@ -372,7 +366,7 @@ def train_template(paths: dict, train_common_params: dict): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # Providing the objects required for the training process. manager.set_objects(net=model, optimizer=optimizer, @@ -414,13 +408,13 @@ def infer_template(paths: dict, infer_common_params: dict): lgr.info(f'infer_filename={infer_common_params["infer_filename"]}', {'color': 'magenta'}) #### create infer datasource - # TODO: Create instance of FuseDataSourceBase - # Reference: FuseDataSourceDefault + # TODO: Create instance of DataSourceBase + # Reference: DataSourceDefault infer_data_source = 'TODO' lgr.info(f'experiment={infer_common_params["experiment_filename"]}', {'color': 'magenta'}) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # TODO - define the keys out of batch_dict that will be saved to a file output_columns = ['TODO'] manager.infer(data_source=infer_data_source, @@ -430,45 +424,6 @@ def infer_template(paths: dict, infer_common_params: dict): output_file_name=infer_common_params['infer_filename']) -###################################### -# Analyze Common Params -###################################### -ANALYZE_COMMON_PARAMS = {} -ANALYZE_COMMON_PARAMS['infer_filename'] = INFER_COMMON_PARAMS['infer_filename'] -ANALYZE_COMMON_PARAMS['output_filename'] = os.path.join(PATHS['analyze_dir'], 'all_metrics') -ANALYZE_COMMON_PARAMS['num_workers'] = 4 -ANALYZE_COMMON_PARAMS['batch_size'] = 2 - - -###################################### -# Analyze Template -###################################### -def analyze_template(paths: dict, analyze_common_params: dict): - fuse_logger_start(output_path=None, console_verbose_level=logging.INFO) - lgr = logging.getLogger('Fuse') - lgr.info('Fuse Analyze', {'attrs': ['bold', 'underline']}) - - # TODO - include all the gt processors required for analyzing - gt_processors = { - 'TODO', - } - - # metrics - # Fuse TIP: use metric FuseMetricConfidenceInterval for computing confidence interval for metrics - metrics = { - # TODO : add required metrics - } - - # create analyzer - analyzer = FuseAnalyzerDefault() - - # run - analyzer.analyze(gt_processors=gt_processors, - data_pickle_filename=analyze_common_params['infer_filename'], - metrics=metrics, - output_filename=analyze_common_params['output_filename'], - num_workers=analyze_common_params['num_workers'], - batch_size=analyze_common_params['num_workers']) ###################################### @@ -479,7 +434,7 @@ def analyze_template(paths: dict, analyze_common_params: dict): NUM_GPUS = 1 # uncomment if you want to use specific gpus instead of automatically looking for free ones force_gpus = None # [0] - FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) + GPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train'] # Options: 'train', 'infer', 'analyze' @@ -491,6 +446,4 @@ def analyze_template(paths: dict, analyze_common_params: dict): if 'infer' in RUNNING_MODES: infer_template(paths=PATHS, infer_common_params=INFER_COMMON_PARAMS) - # analyze - if 'analyze' in RUNNING_MODES: - analyze_template(analyze_common_params=ANALYZE_COMMON_PARAMS) + \ No newline at end of file diff --git a/fuse/dl/tests/mananger/test_manager.py b/fuse/dl/tests/mananger/test_manager.py index 712cf6a1d..e15244e07 100644 --- a/fuse/dl/tests/mananger/test_manager.py +++ b/fuse/dl/tests/mananger/test_manager.py @@ -25,11 +25,11 @@ import logging from fuse.utils.utils_logger import fuse_logger_start -from fuse.dl.managers.manager_default import FuseManagerDefault +from fuse.dl.managers.manager_default import ManagerDefault from fuse.utils.file_io.file_io import create_or_reset_dir -class FuseManagerTestCase(unittest.TestCase): +class ManagerTestCase(unittest.TestCase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -38,7 +38,7 @@ def setUp(self): create_or_reset_dir(self.tempdir, force_reset=True) fuse_logger_start(output_path=self.tempdir, console_verbose_level=logging.INFO) - self.manager = FuseManagerDefault(self.tempdir, force_reset=True) + self.manager = ManagerDefault(self.tempdir, force_reset=True) self.train_dict = {'metric_1': 100, 'metric_2': 80, 'metric_3': 75} self.validation_dict = {'metric_1': 90, 'metric_2': 70, 'metric_3': 60} self.manager.state.current_epoch = 7 diff --git a/fuse/doc/high_level_example.md b/fuse/doc/high_level_example.md index cf7383167..31983044e 100644 --- a/fuse/doc/high_level_example.md +++ b/fuse/doc/high_level_example.md @@ -7,7 +7,7 @@ An example for a binary classifier for mammography (MG) images. The example also Create a simple object that returns a list of sample descriptors: The implementation is specific to MG project and reads sample descriptors and the fold from a file. ```python -train_data_source = FuseMGDataSource(input_source='/path/to/experiment_file.pkl', folds=[1, 2, 3, 4]) +train_data_source = MGDataSource(input_source='/path/to/experiment_file.pkl', folds=[1, 2, 3, 4]) ``` ### processors @@ -17,16 +17,16 @@ This project include two input processor and two ground processor. A sample will # Model input extractors # ======================== input_processors = { - 'image': FuseMGInputProcessor(**image_processing_args), - 'clinical_data': FuseMGClinicalDataProcessor(features=['age', 'bmi'], normalize=True) + 'image': MGInputProcessor(**image_processing_args), + 'clinical_data': MGClinicalDataProcessor(features=['age', 'bmi'], normalize=True) } # Ground truth extractors # ======================= ground_truth_processors = { - 'classification': FuseMGGroundTruthProcessorGlobalLabelTask(task=Task(MG_Biopsy_Neg_or_Normal(), + 'classification': MGGroundTruthProcessorGlobalLabelTask(task=Task(MG_Biopsy_Neg_or_Normal(), MG_Biopsy_Pos()), - 'segmentation': FuseMGGroundTruthProcessorSegmentation(contours_desc=[{'biopsy': ['positive']}]) + 'segmentation': MGGroundTruthProcessorSegmentation(contours_desc=[{'biopsy': ['positive']}]) } ``` @@ -37,13 +37,13 @@ Given those processors the format of the sample would be: { "input": { - "image": FuseMGInputProcessor(...)(sample_descr), - "clinical_data": FuseMGClinicalDataProcessor(...)(sample_descr) + "image": MGInputProcessor(...)(sample_descr), + "clinical_data": MGClinicalDataProcessor(...)(sample_descr) } "gt": { - "classification": FuseMGGroundTruthProcessorGlobalLabelTask(...)(sample_descr), - "segmentation": FuseMGGroundTruthProcessorSegmentation(...)(sample_descr) + "classification": MGGroundTruthProcessorGlobalLabelTask(...)(sample_descr), + "segmentation": MGGroundTruthProcessorSegmentation(...)(sample_descr) } } } @@ -52,15 +52,15 @@ Given those processors the format of the sample would be: ## Train dataset & dataloader ```python -augmentor = FuseAugmentor(...) -train_dataset = FuseDatasetDefault(cache_dest='/path/to/cache_dir', +augmentor = Augmentor(...) +train_dataset = DatasetDefault(cache_dest='/path/to/cache_dir', data_source=train_data_source, input_processors=input_processors, gt_processors=gt_processors, augmentor=augmentor) train_dataloader = DataLoader(dataset=train_dataset, - batch_sampler=FuseSamplerBalancedBatch(balanced_class_name='data.gt.classification'), + batch_sampler=SamplerBalancedBatch(balanced_class_name='data.gt.classification'), num_workers=4) ``` @@ -70,16 +70,16 @@ In this example we will define a model with heads: classification and auxiliary ```python # Multi-headed model, with clinical data appended to classification head # ====================================================================== -model = FuseModelDefault( +model = ModelDefault( conv_inputs=(('data.input.image', 1),), - backbone=FuseBackboneInceptionResnetV2(), + backbone=BackboneInceptionResnetV2(), heads=[ - FuseHeadGlobalPoolingClassifier(head_name='classifier', + HeadGlobalPoolingClassifier(head_name='classifier', conv_inputs=[('model.backbone_features', 384)], post_concat_inputs=[('data.input.clinical_data')], num_classes=2), - FuseHeadDenseSegmentation(head_name='segmentation', + HeadDenseSegmentation(head_name='segmentation', conv_inputs=[('model.backbone_features', 384)], num_classes=2) ] @@ -89,17 +89,16 @@ model = FuseModelDefault( # Losses ```python losses = { - 'cls_loss': FuseLossDefault(pred_name='model.logits.classifier', target_name='data.gt.classification', callable=F.cross_entropy, weight=1.0), - 'seg_loss': FuseLossSegmentationCrossEntropy(pred_name='model.logits.segmentation', target_name='data.gt.segmentation', weight=2.0), + 'cls_loss': LossDefault(pred='model.logits.classifier', target='data.gt.classification', callable=F.cross_entropy, weight=1.0), + 'seg_loss': LossSegmentationCrossEntropy(pred_name='model.logits.segmentation', target_name='data.gt.segmentation', weight=2.0), } ``` # Metrics ```python metrics = { - 'auc': FuseMetricAUC(pred_name='model.output.classifier', target_name='data.gt.classification'), - 'accuracy': FuseMetricAccuracy(pred_name='model.output.classifier', target_name='data.gt.classification'), - 'iou': FuseMetricIOU(pred_name='model.output.segmentation', target_name='data.gt.segmentation') + 'auc': MetricAUCROC(pred_name='model.output.classifier', target_name='data.gt.classification'), + 'iou': MetricIOU(pred_name='model.output.segmentation', target_name='data.gt.segmentation') } best_epoch_source = { @@ -116,9 +115,9 @@ Start a training process # Train model - using a Manager instance # ====================================== callbacks = [ - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard ] -manager = FuseManagerDefault(output_model_dir='/path/to/model_dir') +manager = ManagerDefault(output_model_dir='/path/to/model_dir') manager.set_objects(net=model, optimizer=Adam(model.parameters(), lr=1e-4, weight_decay=0.001), losses=losses, @@ -139,10 +138,10 @@ Output predictions and labels to a file ```python # Inference # ========= -manager = FuseManagerDefault(output_model_dir='/path/to/inference/output') +manager = ManagerDefault(output_model_dir='/path/to/inference/output') # extract only class scores and save to a file -manager.infer(data_source=FuseMGDataSource(input_source='/path/to/experiment_file.pkl', folds=[0]), +manager.infer(data_loader=infer_data_loder, input_model_dir='/path/to/model_dir', checkpoint='best', output_columns=['model.output.classifier', 'model.gt.classification'], @@ -152,22 +151,21 @@ manager.infer(data_source=FuseMGDataSource(input_source='/path/to/experiment_fil ## Analyze Read the inference file and evaluate given a collection of metrics. ```python -# Analyzer -# ========= -# create analyzer -analyzer = FuseAnalyzerDefault() - -# define metrics -metrics = { - 'auc': FuseMetricAUC(pred_name='model.output.classifier', target_name='data.gt.classification'), - 'accuracy': FuseMetricAccuracy(pred_name='model.output.classifier', target_name='data.gt.classification'), -} - -# run -analyzer.analyze(gt_processors={}, # No need: labels are already included in inference in this case - data_pickle_filename='/path/to/infer_file', - metrics=metrics, - output_filename='/path/to/analyze_file') + metrics = OrderedDict([ + ('operation_point', MetricApplyThresholds(pred='model.output.classifier')), # will apply argmax + ('accuracy', MetricAccuracy(pred='results:metrics.operation_point.cls_pred', target='data.label')), + ('roc', MetricROCCurve(pred='model.output.classifier', target='model.gt.classification', class_names=class_names, output_filename=os.path.join(paths['inference_dir'], 'roc_curve.png'))), + ('auc', MetricAUCROC(pred='model.output.classifier', target='data.label', class_names=class_names)), + ]) + + # create evaluator + evaluator = EvaluatorDefault() + + # run + results = evaluator.eval(ids=None, + data=os.path.join(paths["inference_dir"], eval_common_params["infer_filename"]), + metrics=metrics, + output_dir=paths['eval_dir']) ``` diff --git a/fuse/doc/user_guide.md b/fuse/doc/user_guide.md index ee09347ff..2b02807f0 100644 --- a/fuse/doc/user_guide.md +++ b/fuse/doc/user_guide.md @@ -12,10 +12,10 @@ will return `batch_dict[‘model’][‘output’][‘classification’]` **Example of the decoupling approach:** ```python -FuseMetricAUC(pred_name='model.output.classifier_head', target_name='data.gt.gt_global.tensor') +FusMetricAUCROC(pred='model.output.classifier_head', target='data.gt.gt_global.tensor') ``` -`FuseMetricAUC` will read the required tensors to compute AUC from `batch_dict`. The relevant dictionary keys are `pred_name` and `target_name`. This approach allows writing a generic metric which is completely independent of the model and data extractor. In addition, it allows to easily re-use this object in a plug & play manner without adding extra code. Such an approach also allows us to use it several times in case we have multiple heads/tasks. +`FetricAUCROC` will read the required tensors to compute AUC from `batch_dict`. The relevant dictionary keys are `pred_name` and `target_name`. This approach allows writing a generic metric which is completely independent of the model and data extractor. In addition, it allows to easily re-use this object in a plug & play manner without adding extra code. Such an approach also allows us to use it several times in case we have multiple heads/tasks. FuseMedML includes pre-implemented versions of the abstract classes which can be used in (almost) any context. Nevertheless, if needed, they can be replaced by the user without affecting other components. @@ -24,16 +24,16 @@ Below is a list of the main abstract classes and their purpose: ## Data | Module | Purpose | Implementation Examples |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -| `FuseDataSourceBase` | A simple object that generates a cohort of samples unique identifiers (sample descriptors). This class is usually project-specific and expected to be implemented by the user. However, simple generic implementations are included in FuseMedML. | `FuseDataSourceDefault` reads a table in a DataFrame format, including two columns: (1) sample descriptors (2) fold. It outputs a list of sample descriptors for the required folds. An example of a sample descriptor would be a path to a DICOM file that uniquely identifies a sample. -| `FuseProcessorBase` | The processor extract and pre-process a single sample or part of a sample given a sample descriptor. A processor is usually project-specific and commonly will be implemented per project. However, common implementations are provided such as a processor that reads DICOM file for MRI | Given a path to DICOM file on disk (sample descriptor) - load image data, resize, normalize pixel values, crop, convert to PyTorch Tensor. -| `FuseCacheBase` | Stores pre-processed sample for quick retrieval. | Disk cache or in-memory cache options are built-in in `FuseDatasetDefault` and `FuseDatasetGenerator` -| `FuseAugmentorBase` | Runs a pipeline of random augmentations| An object that able to apply 2D / 3D affine augmentations, color perturbations, etc. See `FuseAugmentorDefault`. -| `FuseDatasetBase` | Implementation of PyTorch dataset, including additional utilities. Unlike PyTorch dataset, FuseMedML Dataset returns a dictionary naming each element in the dataset. For example, 'image' and 'label'. However, Pytorch datasets can be easily used by wrapping them with `FuseDatasetWrapper`| `FuseDatasetDefault` is a generic dataset implementation that supports caching, augmentation, data_source, processor, etc. -| `FuseVisualizerBase` | Debug tool, visualizes network input before/after augmentations| `FuseVisualizerDefault` is a 2D image visualizer +| `DataSourceBase` | A simple object that generates a cohort of samples unique identifiers (sample descriptors). This class is usually project-specific and expected to be implemented by the user. However, simple generic implementations are included in FuseMedML. | `DataSourceDefault` reads a table in a DataFrame format, including two columns: (1) sample descriptors (2) fold. It outputs a list of sample descriptors for the required folds. An example of a sample descriptor would be a path to a DICOM file that uniquely identifies a sample. +| `ProcessorBase` | The processor extract and pre-process a single sample or part of a sample given a sample descriptor. A processor is usually project-specific and commonly will be implemented per project. However, common implementations are provided such as a processor that reads DICOM file for MRI | Given a path to DICOM file on disk (sample descriptor) - load image data, resize, normalize pixel values, crop, convert to PyTorch Tensor. +| `CacheBase` | Stores pre-processed sample for quick retrieval. | Disk cache or in-memory cache options are built-in in `DatasetDefault` and `DatasetGenerator` +| `AugmentorBase` | Runs a pipeline of random augmentations| An object that able to apply 2D / 3D affine augmentations, color perturbations, etc. See `AugmentorDefault`. +| `DatasetBase` | Implementation of PyTorch dataset, including additional utilities. Unlike PyTorch dataset, FuseMedML Dataset returns a dictionary naming each element in the dataset. For example, 'image' and 'label'. However, Pytorch datasets can be easily used by wrapping them with `DatasetWrapper`| `DatasetDefault` is a generic dataset implementation that supports caching, augmentation, data_source, processor, etc. +| `VisualizerBase` | Debug tool, visualizes network input before/after augmentations| `VisualizerDefault` is a 2D image visualizer ## Model FuseMedML includes three types of model objects. -* Model - an object that includes the entire model end to end. FuseMedML Model is PyTorch model that gets as input `batch_dict`, adds the model outputs to a dictionary `batch_dict[‘model’]` and returns `batch_dict[‘model’]`. PyTorch model can be easily converted to FuseMedML style model using a wrapper `FuseModelWrapper`. +* Model - an object that includes the entire model end to end. FuseMedML Model is PyTorch model that gets as input `batch_dict`, adds the model outputs to a dictionary `batch_dict[‘model’]` and returns `batch_dict[‘model’]`. PyTorch model can be easily converted to FuseMedML style model using a wrapper `ModelWrapper`. * Backbone - an object that extracts spatial features from an image. Backbone is a PyTorch model which gets as input tensor/ sequence of tensors and returns tensor/sequence of tensors. * Head - an object that maps features to prediction and usually includes pooling layers and dense / conv 1x1 layers. Head gets as an input `batch_dict` and returns `batch_dict`. @@ -42,22 +42,22 @@ All those types inherit directly from `torch.nn.Module`. ## Losses | Module | Purpose | Implementation Examples |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -| `FuseLossBase` | compute the entire or part of the loss | `FuseLossDefault` apply a given callable such as `torch.nn.functional.cross_entropy` to compute the loss +| `LossBase` | compute the entire or part of the loss | `LossDefault` apply a given callable such as `torch.nn.functional.cross_entropy` to compute the loss ## Metrics | Module | Purpose | Implementation Examples |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -| `FuseMetricBase` | compute metric value / values. Collect the relevant data to computing the metric from`batch_dict`, and at the end of an epoch compute the metric. Can return a single value or a dictionary that contains several values. | `FuseMetricAUC` is a subclass that computes the AUC. +| `MetricBase` | compute metric value / values. Collect the relevant data to computing the metric from`batch_dict`, and at the end of an epoch compute the metric. Can return a single value or a dictionary that contains several values. | `NetricAUCROC` is a subclass that computes the AUC. ## Manager -The manager `FuseManagerDefault` responsibility is to use all the components provided by the user and to train a model accordingly. +The manager `ManagerDefault` responsibility is to use all the components provided by the user and to train a model accordingly. -To keep the flexibility, the manager supports callbacks that can affect its state and dictionaries: `batch_dict` and `epoch_results` dynamically. See `FuseCallback`. An example of a pre-implemented callback is `FuseTensorboardCallback` which is responsible for writing the data of both training and validation to tensorborad loggers under model_dir. +To keep the flexibility, the manager supports callbacks that can affect its state and dictionaries: `batch_dict` and `epoch_results` dynamically. See `Callback`. An example of a pre-implemented callback is `TensorboardCallback` which is responsible for writing the data of both training and validation to tensorborad loggers under model_dir. It’s also possible to modify the manager behavior by overriding functions such as `handle_batch()` or alternatively implement a new manager. The manager provides also a function called `infer` that restore from `model_dir` (manger train procedure stores the information in this directory) the required objects and runs inference on the required sample descriptors. ## Analyzer -`FuseAnalyzerDefault` responsibility is to evaluate a trained model. +`AnalyzerDefault` responsibility is to evaluate a trained model. Analyzer gets an inference file, generated by `manager.infer()`. The inference file is expected to include sample descriptors and their predictions. The inference file might also include the ground truth targets and metadata about each of the samples. If not, a processor or a dataset should be provided to extract the target given a sample descriptor. diff --git a/fuse/utils/dl/checkpoint.py b/fuse/utils/dl/checkpoint.py index d3a4445fc..9b4442640 100644 --- a/fuse/utils/dl/checkpoint.py +++ b/fuse/utils/dl/checkpoint.py @@ -23,7 +23,7 @@ import torch.nn as nn -class FuseCheckpoint(): +class Checkpoint(): def __init__(self, net: Union[nn.Module, dict], epoch_idx: int, learning_rate: float): if isinstance(net, nn.Module): self.net_state_dict = net.state_dict() diff --git a/fuse/utils/gpu.py b/fuse/utils/gpu.py index c8a380a69..e5e0c9246 100644 --- a/fuse/utils/gpu.py +++ b/fuse/utils/gpu.py @@ -25,7 +25,7 @@ import torch -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.utils.utils_debug import FuseDebug def get_available_gpu_ids() -> List[int]: @@ -53,7 +53,7 @@ def choose_and_enable_multiple_gpus(num_gpus_needed: int, force_gpus: Optional[L """ # debug - num gpus try: - override_num_gpus = FuseUtilsDebug().get_setting('manager_override_num_gpus') + override_num_gpus = FuseDebug().get_setting('manager_override_num_gpus') if override_num_gpus != 'default': num_gpus_needed = min(override_num_gpus, num_gpus_needed) logging.getLogger('Fuse').info(f'Manager - debug mode - override num_gpus to {num_gpus_needed}', {'color': 'red'}) @@ -67,13 +67,13 @@ def choose_and_enable_multiple_gpus(num_gpus_needed: int, force_gpus: Optional[L available_gpu_ids = force_gpus if available_gpu_ids is None: - raise Exception('FuseUtilsGPU: could not auto-detect available GPUs') + raise Exception('could not auto-detect available GPUs') elif len(available_gpu_ids) < num_gpus_needed: - raise Exception('FuseUtilsGPU: not enough GPUs available, requested %d GPUs but only IDs %s are available!' % ( + raise Exception('not enough GPUs available, requested %d GPUs but only IDs %s are available!' % ( num_gpus_needed, str(available_gpu_ids))) else: selected_gpu_ids = sorted(available_gpu_ids, reverse=True)[:num_gpus_needed] - logging.getLogger('Fuse').info('FuseUtilsGPU: selecting GPUs %s' % str(selected_gpu_ids)) + logging.getLogger('Fuse').info('selecting GPUs %s' % str(selected_gpu_ids)) set_cuda_visible_devices(selected_gpu_ids) torch.backends.cudnn.benchmark = False # to prevent gpu illegal instruction exceptions @@ -139,7 +139,7 @@ def run_nvidia_smi() -> str: nvidia_smi_output, stderr = process.communicate() status = process.poll() if status != 0: - print("FuseUtilsGPU: Failed to run nvidia-smi") + print("Failed to run nvidia-smi") return None nvidia_smi_output = str(nvidia_smi_output) return nvidia_smi_output diff --git a/fuse/utils/imaging/align/utils_align_base.py b/fuse/utils/imaging/align/utils_align_base.py index 23c72b010..85f892c62 100644 --- a/fuse/utils/imaging/align/utils_align_base.py +++ b/fuse/utils/imaging/align/utils_align_base.py @@ -20,7 +20,7 @@ from abc import ABC -class FuseAlignMapBase(ABC): +class AlignMapBase(ABC): def __init__(self): """ AlignMap settings, e.g. number of iterations for an iterative algorithm. diff --git a/fuse/utils/imaging/align/utils_align_ecc.py b/fuse/utils/imaging/align/utils_align_ecc.py index 9a6ef187b..789110176 100644 --- a/fuse/utils/imaging/align/utils_align_ecc.py +++ b/fuse/utils/imaging/align/utils_align_ecc.py @@ -17,12 +17,12 @@ """ -from fuse.utils.align.utils_align_base import FuseAlignMapBase +from fuse.utils.align.utils_align_base import AlignMapBase import numpy as np import cv2 -class FuseAlignMapECC(FuseAlignMapBase): +class AlignMapECC(AlignMapBase): def __init__(self, transformation_type='homography', num_iterations=600, termination_eps=1e-4): transformation_type = transformation_type.lower() assert transformation_type in ['homography', 'affine'] diff --git a/fuse/utils/imaging/image_processing.py b/fuse/utils/imaging/image_processing.py index 8d8448f2d..9b69381ec 100644 --- a/fuse/utils/imaging/image_processing.py +++ b/fuse/utils/imaging/image_processing.py @@ -102,17 +102,17 @@ def align_ecc(img1: np.ndarray, img2: np.ndarray, num_iterations: int = 400, ter :param transformation: type of transformation to perform. If None (default), cv2.MOTION_AFFINE is performed. :return transformed img2 - The implementation was moved to a separate class; see FuseAlignMapECC. This function serves for backward + The implementation was moved to a separate class; see AlignMapECC. This function serves for backward compatibility """ try: import cv2 - from fuse.utils.imaging.align.utils_align_ecc import FuseAlignMapECC + from fuse.utils.imaging.align.utils_align_ecc import AlignMapECC transformation = transformation or cv2.MOTION_AFFINE - aligner = FuseAlignMapECC(transformation_type=transformation, + aligner = AlignMapECC(transformation_type=transformation, num_iterations=num_iterations, termination_eps=termination_eps) diff --git a/fuse/utils/utils_debug.py b/fuse/utils/utils_debug.py index 6060643c8..7879c424e 100644 --- a/fuse/utils/utils_debug.py +++ b/fuse/utils/utils_debug.py @@ -22,7 +22,7 @@ from fuse.utils.misc.misc import Singleton -class FuseUtilsDebug(metaclass=Singleton): +class FuseDebug(metaclass=Singleton): """ Debug settings. See __init__() for available modes """ diff --git a/fuse/utils/utils_logger.py b/fuse/utils/utils_logger.py index ceaca6022..12f7b35e6 100644 --- a/fuse/utils/utils_logger.py +++ b/fuse/utils/utils_logger.py @@ -49,7 +49,7 @@ def release(self): self._locks[current_process_id].release() -class FuseConsoleFormatter(logging.Formatter): +class ConsoleFormatter(logging.Formatter): """Logging Formatter to add colors per verbose level and file, line number in case of warnning/error""" grey = "\x1b[38;21m" @@ -113,7 +113,7 @@ def fuse_logger_start(output_path: Optional[str] = None, console_verbose_level: # console console_handler = ProcessSafeHandler(stream=sys.stdout) console_handler.setLevel(console_verbose_level) - console_formatter = FuseConsoleFormatter() + console_formatter = ConsoleFormatter() console_handler.setFormatter(console_formatter) lgr.addHandler(console_handler) lgr.propagate = False From 5771c855814269da57fd3d52fd1c2f6d35cdec50 Mon Sep 17 00:00:00 2001 From: "moshiko.raboh#ibm.com" Date: Sun, 17 Apr 2022 13:35:32 +0300 Subject: [PATCH 19/42] reorg examples --- .gitignore | 10 ++--- README.md | 16 ++++---- .../{classification => imaging}/__init__.py | 0 .../classification/MG_CMMD/README.md | 0 .../classification}/__init__.py | 0 .../classification/bright/README.md | 18 ++++----- .../classification/bright}/eval/__init__.py | 0 .../validation_baseline_task1_predictions.csv | 0 .../validation_baseline_task2_predictions.csv | 0 .../baseline/validation_results/results.csv | 0 .../baseline/validation_results/results.md | 0 .../classification/bright/eval/eval.py | 4 +- .../bright/eval/example/example_targets.csv | 0 .../example/example_task1_predictions.csv | 0 .../example/example_task2_predictions.csv | 0 .../bright/eval/example/results/results.csv | 0 .../bright/eval/example/results/results.md | 0 .../bright/eval/validation_targets.csv | 0 .../classification/cmmd/dataset.py | 4 +- .../cmmd/ground_truth_processor.py | 0 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.../image_clinical}/input_processor.py | 0 .../multimodality_image_clinical.ipynb | 13 +++---- .../fuse_examples/tests/colab_tests.ipynb | 4 +- .../tests/test_classification_bright,py | 8 ++-- .../tests/test_classification_cmmd.py | 2 +- .../tests/test_classification_knight.py | 14 +++---- .../tests/test_classification_mnist.py | 2 +- .../tests/test_classification_prostatex.py | 2 +- .../tests/test_classification_skin_lesion.py | 2 +- 94 files changed, 84 insertions(+), 121 deletions(-) rename examples/fuse_examples/{classification => imaging}/__init__.py (100%) rename examples/fuse_examples/{ => imaging}/classification/MG_CMMD/README.md (100%) rename examples/fuse_examples/{classification/bright/eval => imaging/classification}/__init__.py (100%) rename examples/fuse_examples/{ => imaging}/classification/bright/README.md (83%) rename examples/fuse_examples/{classification/knight => imaging/classification/bright}/eval/__init__.py (100%) rename examples/fuse_examples/{ => 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imaging}/classification/skin_lesion/data_source.py (100%) rename examples/fuse_examples/{ => imaging}/classification/skin_lesion/download.py (100%) rename examples/fuse_examples/{ => imaging}/classification/skin_lesion/ground_truth_processor.py (100%) rename examples/fuse_examples/{ => imaging}/classification/skin_lesion/input_processor.py (100%) rename examples/fuse_examples/{ => imaging}/classification/skin_lesion/runner.py (98%) rename examples/fuse_examples/{tutorials => imaging}/hello_world/hello_world.ipynb (99%) rename examples/fuse_examples/{tutorials => imaging}/hello_world/hello_world_utils.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/arch.png (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/data_source.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/dataset.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/download.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/fusemedml-release-plans.png (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/ground_truth_processor.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/input_processor.py (100%) rename examples/fuse_examples/{tutorials/multimodality_image_clinical => multimodality/image_clinical}/multimodality_image_clinical.ipynb (99%) diff --git a/.gitignore b/.gitignore index f4b14b2bf..2b1913bcd 100755 --- a/.gitignore +++ b/.gitignore @@ -29,10 +29,10 @@ lib64 **/*.log Result __pycache__ -fuse_examples/classification/knight/baseline/*.csv -fuse_examples/classification/knight/baseline/clinical_data/* -fuse_examples/classification/knight/baseline/model_dir +fuse_examples/imaging/classification/knight/baseline/*.csv +fuse_examples/imaging/classification/knight/baseline/clinical_data/* +fuse_examples/imaging/classification/knight/baseline/model_dir .gitignore.save -fuse_examples/classification/mnist/examples -fuse_examples/tutorials/hello_world/examples/ +fuse_examples/imaging/classification/mnist/examples +fuse_examples/imaging/hello_world/examples/ .vscode/ diff --git a/README.md b/README.md index b6daa7bdc..6428dce4e 100644 --- a/README.md +++ b/README.md @@ -67,19 +67,19 @@ $ pip install fuse-med-ml ## FuseMedML from the ground up [**User Guide**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/doc/user_guide.md) - including detailed explanation about FuseMedML modules, structure, concept, and more. -[**Hello World**](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/hello_world/hello_world.ipynb) - Introductory hands-on notebook on the well-known MNIST dataset. +[**Hello World**](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/hello_world/hello_world.ipynb) - Introductory hands-on notebook on the well-known MNIST dataset. [**High Level Code Example**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/doc/high_level_example.md) - example of binary classifier for mammography with an auxiliary segmentation loss and clinical data ## Examples * classification - * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/mnist/) - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/) - * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge. - * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML - * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017) - * [**Prostate Gleason Classifiaction**](https://github.com/IBM/fuse-med-ml/tree/master/example/fuse_examples/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38) - * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903) - * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508) + * [**MNIST**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/mnist/) - a simple example, including training, inference and evaluation over [MNIST dataset](http://yann.lecun.com/exdb/mnist/) + * [**KNIGHT Challenge**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/knight) - preoperative prediction of risk class for patients with renal masses identified in clinical Computed Tomography (CT) imaging of the kidneys. Including data pre-processing, baseline implementation and evaluation pipeline for the challenge. + * [**Multimodality tutorial**](https://github.com/IBM/fuse-med-ml/blob/master/examples/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb) - demonstration of two popular simple methods integrating imaging and clinical data (tabular) using FuseMedML + * [**Skin Lesion**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/skin_lesion/) - skin lesion classification , including training, inference and evaluation over the public dataset introduced in [ISIC challenge](https://challenge.isic-archive.com/landing/2017) + * [**Prostate Gleason Classification**](https://github.com/IBM/fuse-med-ml/tree/master/example/fuse_examples/imaging/classification/prostate_x/) - lesions classification of Gleason score in prostate over the public dataset introduced in [SPIE-AAPM-NCI PROSTATEx challenge](https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges#23691656d4622c5ad5884bdb876d6d441994da38) + * [**Lesion Stage Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/duke_breast_cancer/) - lesions classification of Tumor Stage (Size) in breast MRI over the public dataset introduced in [Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903) + * [**Breast Cancer Lesion Classification**](https://github.com/IBM/fuse-med-ml/tree/master/examples/fuse_examples/imaging/classification/MG_CMMD) - lesions classification of tumor ( benign, malignant) in breast mammography over the public dataset introduced in [The Chinese Mammography Database (CMMD)](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230508) ## Walkthrough template * [**Walkthrough Template**](https://github.com/IBM/fuse-med-ml/tree/master/fuse/templates/walkthrough_template.py) - includes several TODO notes, marking the minimal scope of code required to get your pipeline up and running. The template also includes useful explanations and tips. diff --git a/examples/fuse_examples/classification/__init__.py b/examples/fuse_examples/imaging/__init__.py similarity index 100% rename from examples/fuse_examples/classification/__init__.py rename to examples/fuse_examples/imaging/__init__.py diff --git a/examples/fuse_examples/classification/MG_CMMD/README.md b/examples/fuse_examples/imaging/classification/MG_CMMD/README.md similarity index 100% rename from examples/fuse_examples/classification/MG_CMMD/README.md rename to examples/fuse_examples/imaging/classification/MG_CMMD/README.md diff --git a/examples/fuse_examples/classification/bright/eval/__init__.py b/examples/fuse_examples/imaging/classification/__init__.py similarity index 100% rename from examples/fuse_examples/classification/bright/eval/__init__.py rename to examples/fuse_examples/imaging/classification/__init__.py diff --git a/examples/fuse_examples/classification/bright/README.md b/examples/fuse_examples/imaging/classification/bright/README.md similarity index 83% rename from examples/fuse_examples/classification/bright/README.md rename to examples/fuse_examples/imaging/classification/bright/README.md index 5805a2290..66103dc28 100644 --- a/examples/fuse_examples/classification/bright/README.md +++ b/examples/fuse_examples/imaging/classification/bright/README.md @@ -42,16 +42,16 @@ The participants should submit a .csv file per task containing a row with a fina **Task 1 Prediction File:** \[image_name,predicted_label,Noncancerous-score,Precancerous-score,Cancerous-score\] -See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/example/example_task1_predictions.csv) +See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/example/example_task1_predictions.csv) **Task 2 Prediction File:** \[image_name,predicted_label,PB-score,UDH-score,FEA-score,ADH-score,DCIS-score,IC-score\] -See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/example/example_task2_predictions.csv) +See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/example/example_task2_predictions.csv) Where “image_name" represents the sample (e.g. BRACS_264) and all scores represent the probability of a patient to belong to a class. -The evaluation script together with a dummy prediction files can be found in `fuse-med-ml/fuse_examples/classification/bright/eval` +The evaluation script together with a dummy prediction files can be found in `fuse-med-ml/fuse_examples/imaging/classification/bright/eval` More details can be found in [challenge website](https://research.ibm.com/haifa/Workshops/BRIGHT) @@ -59,12 +59,12 @@ More details can be found in [challenge website](https://research.ibm.com/haifa/ To run the evaluation script: ``` -cd fuse-med-ml/fuse_examples/classification/knight/eval +cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval python eval.py ``` To evaluate the dummy example predictions and targets ``` -cd fuse-med-ml/fuse_examples/classification/knight/eval +cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results ``` @@ -72,13 +72,13 @@ python eval.py example/example_targets.csv example/example_task1_predictions.csv As an additional example, we also include the validation prediction files and validation target file of the challenge baseline implementation: -See [validation baseline prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv) +See [validation baseline prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv) -See [validation baseline prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv) +See [validation baseline prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv) -See [validation targets file](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/bright/eval/validation_targets.csv) +See [validation targets file](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/bright/eval/validation_targets.csv) @@ -86,6 +86,6 @@ See [validation targets file](https://github.com/IBM/fuse-med-ml/blob/master/fus To evaluate the baseline predictions over the validation set: ``` -cd fuse-med-ml/fuse_examples/classification/bright/eval +cd fuse-med-ml/fuse_examples/imaging.classification/bright/eval python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results ``` diff --git a/examples/fuse_examples/classification/knight/eval/__init__.py b/examples/fuse_examples/imaging/classification/bright/eval/__init__.py similarity index 100% rename from examples/fuse_examples/classification/knight/eval/__init__.py rename to examples/fuse_examples/imaging/classification/bright/eval/__init__.py diff --git a/examples/fuse_examples/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv b/examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv rename to examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv diff --git a/examples/fuse_examples/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv b/examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv rename to examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv diff --git a/examples/fuse_examples/classification/bright/eval/baseline/validation_results/results.csv b/examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_results/results.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/baseline/validation_results/results.csv rename to examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_results/results.csv diff --git a/examples/fuse_examples/classification/bright/eval/baseline/validation_results/results.md b/examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_results/results.md similarity index 100% rename from examples/fuse_examples/classification/bright/eval/baseline/validation_results/results.md rename to examples/fuse_examples/imaging/classification/bright/eval/baseline/validation_results/results.md diff --git a/examples/fuse_examples/classification/bright/eval/eval.py b/examples/fuse_examples/imaging/classification/bright/eval/eval.py similarity index 97% rename from examples/fuse_examples/classification/bright/eval/eval.py rename to examples/fuse_examples/imaging/classification/bright/eval/eval.py index 60e2b8de7..f431a769c 100644 --- a/examples/fuse_examples/classification/bright/eval/eval.py +++ b/examples/fuse_examples/imaging/classification/bright/eval/eval.py @@ -207,8 +207,8 @@ def eval(task1_prediction_filename: str, task2_prediction_filename: str, target_ """ Run evaluation: Usage: python eval.py - Run dummy example (set the working dir to fuse-med-ml/fuse_examples/classification/bright/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results - Run baseline (set the working dir to fuse-med-ml/fuse_examples/classification/bright/eval): python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results + Run dummy example (set the working dir to fuse-med-ml/fuse_examples/imaging/classification/bright/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results + Run baseline (set the working dir to fuse-med-ml/fuse_examples/imaging/classification/bright/eval): python eval.py validation_targets.csv baseline/validation_baseline_task1_predictions.csv baseline/validation_baseline_task2_predictions.csv baseline/validation_results """ if len(sys.argv) == 1: diff --git a/examples/fuse_examples/classification/bright/eval/example/example_targets.csv b/examples/fuse_examples/imaging/classification/bright/eval/example/example_targets.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/example/example_targets.csv rename to examples/fuse_examples/imaging/classification/bright/eval/example/example_targets.csv diff --git a/examples/fuse_examples/classification/bright/eval/example/example_task1_predictions.csv b/examples/fuse_examples/imaging/classification/bright/eval/example/example_task1_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/example/example_task1_predictions.csv rename to examples/fuse_examples/imaging/classification/bright/eval/example/example_task1_predictions.csv diff --git a/examples/fuse_examples/classification/bright/eval/example/example_task2_predictions.csv b/examples/fuse_examples/imaging/classification/bright/eval/example/example_task2_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/example/example_task2_predictions.csv rename to examples/fuse_examples/imaging/classification/bright/eval/example/example_task2_predictions.csv diff --git a/examples/fuse_examples/classification/bright/eval/example/results/results.csv b/examples/fuse_examples/imaging/classification/bright/eval/example/results/results.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/example/results/results.csv rename to examples/fuse_examples/imaging/classification/bright/eval/example/results/results.csv diff --git a/examples/fuse_examples/classification/bright/eval/example/results/results.md b/examples/fuse_examples/imaging/classification/bright/eval/example/results/results.md similarity index 100% rename from examples/fuse_examples/classification/bright/eval/example/results/results.md rename to examples/fuse_examples/imaging/classification/bright/eval/example/results/results.md diff --git a/examples/fuse_examples/classification/bright/eval/validation_targets.csv b/examples/fuse_examples/imaging/classification/bright/eval/validation_targets.csv similarity index 100% rename from examples/fuse_examples/classification/bright/eval/validation_targets.csv rename to examples/fuse_examples/imaging/classification/bright/eval/validation_targets.csv diff --git a/examples/fuse_examples/classification/cmmd/dataset.py b/examples/fuse_examples/imaging/classification/cmmd/dataset.py similarity index 97% rename from examples/fuse_examples/classification/cmmd/dataset.py rename to examples/fuse_examples/imaging/classification/cmmd/dataset.py index d34ddd470..d382db3aa 100644 --- a/examples/fuse_examples/classification/cmmd/dataset.py +++ b/examples/fuse_examples/imaging/classification/cmmd/dataset.py @@ -14,8 +14,8 @@ from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool -from fuse_examples.classification.cmmd.input_processor import MGInputProcessor -from fuse_examples.classification.cmmd.ground_truth_processor import MGGroundTruthProcessor +from fuse_examples.imaging.classification.cmmd.input_processor import MGInputProcessor +from fuse_examples.imaging.classification.cmmd.ground_truth_processor import MGGroundTruthProcessor from fuse.data.data_source.data_source_folds import DataSourceFolds from typing import Tuple diff --git a/examples/fuse_examples/classification/cmmd/ground_truth_processor.py b/examples/fuse_examples/imaging/classification/cmmd/ground_truth_processor.py similarity index 100% rename from examples/fuse_examples/classification/cmmd/ground_truth_processor.py rename to examples/fuse_examples/imaging/classification/cmmd/ground_truth_processor.py diff --git a/examples/fuse_examples/classification/cmmd/input_processor.py b/examples/fuse_examples/imaging/classification/cmmd/input_processor.py similarity index 100% rename from examples/fuse_examples/classification/cmmd/input_processor.py rename to examples/fuse_examples/imaging/classification/cmmd/input_processor.py diff --git a/examples/fuse_examples/classification/cmmd/runner.py b/examples/fuse_examples/imaging/classification/cmmd/runner.py similarity index 99% rename from examples/fuse_examples/classification/cmmd/runner.py rename to examples/fuse_examples/imaging/classification/cmmd/runner.py index fad731187..d09556bd7 100644 --- a/examples/fuse_examples/classification/cmmd/runner.py +++ b/examples/fuse_examples/imaging/classification/cmmd/runner.py @@ -44,7 +44,7 @@ from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback from fuse.dl.managers.manager_default import ManagerDefault -from fuse_examples.classification.cmmd.dataset import CMMD_2021_dataset +from fuse_examples.imaging.classification.cmmd.dataset import CMMD_2021_dataset from fuse.dl.models.backbones.backbone_inception_resnet_v2 import BackboneInceptionResnetV2 diff --git a/examples/fuse_examples/classification/duke_breast_cancer/README.md b/examples/fuse_examples/imaging/classification/duke_breast_cancer/README.md similarity index 100% rename from examples/fuse_examples/classification/duke_breast_cancer/README.md rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/README.md diff --git a/examples/fuse_examples/classification/duke_breast_cancer/dataset.py b/examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset.py similarity index 96% rename from examples/fuse_examples/classification/duke_breast_cancer/dataset.py rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset.py index 0edca55db..cd858403d 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/dataset.py +++ b/examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset.py @@ -10,11 +10,11 @@ from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor -from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.imaging.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient -from fuse_examples.classification.duke_breast_cancer.post_processor import post_processing -from fuse_examples.classification.duke_breast_cancer.processor import PatchProcessor +from fuse_examples.imaging.classification.duke_breast_cancer.post_processor import post_processing +from fuse_examples.imaging.classification.duke_breast_cancer.processor import PatchProcessor def process_mri_series(metadata_path: str): diff --git a/examples/fuse_examples/classification/duke_breast_cancer/dataset_DUKE_folds_ver10012022Recurrence_seed1.pickle b/examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset_DUKE_folds_ver10012022Recurrence_seed1.pickle similarity index 100% rename from examples/fuse_examples/classification/duke_breast_cancer/dataset_DUKE_folds_ver10012022Recurrence_seed1.pickle rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset_DUKE_folds_ver10012022Recurrence_seed1.pickle diff --git a/examples/fuse_examples/classification/duke_breast_cancer/dataset_DUKE_folds_ver11102021TumorSize_seed1.pickle b/examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset_DUKE_folds_ver11102021TumorSize_seed1.pickle similarity index 100% rename from examples/fuse_examples/classification/duke_breast_cancer/dataset_DUKE_folds_ver11102021TumorSize_seed1.pickle rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/dataset_DUKE_folds_ver11102021TumorSize_seed1.pickle diff --git a/examples/fuse_examples/classification/duke_breast_cancer/post_processor.py b/examples/fuse_examples/imaging/classification/duke_breast_cancer/post_processor.py similarity index 100% rename from examples/fuse_examples/classification/duke_breast_cancer/post_processor.py rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/post_processor.py diff --git a/examples/fuse_examples/classification/duke_breast_cancer/processor.py b/examples/fuse_examples/imaging/classification/duke_breast_cancer/processor.py similarity index 89% rename from examples/fuse_examples/classification/duke_breast_cancer/processor.py rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/processor.py index a8f5fcef7..dea754456 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/processor.py +++ b/examples/fuse_examples/imaging/classification/duke_breast_cancer/processor.py @@ -23,7 +23,7 @@ from fuse.data.processor.processor_base import ProcessorBase from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor -from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData +from fuse_examples.imaging.classification.prostate_x.data_utils import ProstateXUtilsData class PatchProcessor(ProcessorBase): @@ -365,37 +365,3 @@ def __call__(self, return samples - - -if __name__ == "__main__": - from fuse_examples.classification.duke_breast_cancer.dataset import process_mri_series - - path_to_db = '.' - root_data = '/gpfs/haifa/projects/m/msieve2/Platform/BigMedilytics/Data/Duke-Breast-Cancer-MRI/manifest-1607053360376/' - - seq_dict,SER_INX_TO_USE,exp_patients,_,_ = process_mri_series(root_data+'/metadata.csv') - mri_vol_processor = DicomMRIProcessor(seq_dict=seq_dict, - seq_to_use=['DCE_mix_ph1', - 'DCE_mix_ph2', - 'DCE_mix_ph3', - 'DCE_mix_ph4', - 'DCE_mix', - 'DCE_mix_ph', - 'MASK'], - subseq_to_use=['DCE_mix_ph2', 'MASK'], - ser_inx_to_use=SER_INX_TO_USE, - exp_patients=exp_patients, - reference_inx=0, - use_order_indicator=False) - - a = PatchProcessor(vol_processor=mri_vol_processor, - path_to_db=path_to_db, - data_path=root_data + 'Duke-Breast-Cancer-MRI', - ktrans_data_path='', - db_name='DUKE',db_version='11102021TumorSize', - fold_no=0, lsn_shape=(9, 100, 100), lsn_spacing=(1, 0.5, 0.5)) - - - - sample = 'Breast_MRI_900' - samples = a.__call__(sample) \ No newline at end of file diff --git a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py b/examples/fuse_examples/imaging/classification/duke_breast_cancer/run_train_3dpatch.py similarity index 97% rename from examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/run_train_3dpatch.py index ddfd8921d..174b259ab 100644 --- a/examples/fuse_examples/classification/duke_breast_cancer/run_train_3dpatch.py +++ b/examples/fuse_examples/imaging/classification/duke_breast_cancer/run_train_3dpatch.py @@ -34,13 +34,13 @@ from fuse.dl.models.heads.head_1d_classifier import Head1dClassifier -from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet -from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.imaging.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet +from fuse_examples.imaging.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient -from fuse_examples.classification.duke_breast_cancer.dataset import duke_breast_cancer_dataset -from fuse_examples.classification.duke_breast_cancer.tasks import Task +from fuse_examples.imaging.classification.duke_breast_cancer.dataset import duke_breast_cancer_dataset +from fuse_examples.imaging.classification.duke_breast_cancer.tasks import Task ########################################## diff --git a/examples/fuse_examples/classification/duke_breast_cancer/tasks.py b/examples/fuse_examples/imaging/classification/duke_breast_cancer/tasks.py similarity index 100% rename from examples/fuse_examples/classification/duke_breast_cancer/tasks.py rename to examples/fuse_examples/imaging/classification/duke_breast_cancer/tasks.py diff --git a/examples/fuse_examples/classification/knight/README.md b/examples/fuse_examples/imaging/classification/knight/README.md similarity index 92% rename from examples/fuse_examples/classification/knight/README.md rename to examples/fuse_examples/imaging/classification/knight/README.md index e42755425..39c27d52a 100644 --- a/examples/fuse_examples/classification/knight/README.md +++ b/examples/fuse_examples/imaging/classification/knight/README.md @@ -47,16 +47,16 @@ The participants should submit a .csv file per task containing a row with class **Task 1 Prediction File:** \[case_id,NoAT-score,CanAT-score\] -See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv) +See [example prediction file for task 1](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/knight/eval/example/example_task1_predictions.csv) **Task 2 Prediction File:** \[case_id,B-score,LR-score,IR-score,HR-score,VHR-score\] -See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv) +See [example prediction file for task 2](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/classification/knight/eval/example/example_task2_predictions.csv) Here, "case_id" represents the sample (e.g. 00000) and all scores represent the probability of a patient to belong to a class. -The evaluation script together with a dummy prediction file can be found in `fuse-med-ml/fuse_examples/classification/knight/eval` +The evaluation script together with a dummy prediction file can be found in `fuse-med-ml/fuse_examples/imagin/classification/knight/eval` More details can be found in [challenge website](https://research.ibm.com/haifa/Workshops/KNIGHT) @@ -65,7 +65,7 @@ More details can be found in [challenge website](https://research.ibm.com/haifa/ To run the evaluation script use the following command: ``` -cd fuse-med-ml/fuse_examples/classification/knight/eval +cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval python eval.py ``` @@ -73,7 +73,7 @@ If you only want to evaluate Task 1, you may pass an empty string in place of `` As an example, this command will evaluate the dummy example predictions and targets: ``` -cd fuse-med-ml/fuse_examples/classification/knight/eval +cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results ``` @@ -136,7 +136,7 @@ Here are some of the things we knowingly avoided for the sake of simplicity: 3. We didn't resample the images with respect to their spacing, but only resized to a common voxel size. Addressing the trade-off between input patch size (limited by the GPU memory) and the amount of contextual information that it contains (controlled by a possible resampling procedure) can be important. You may want to resample the volumes to a common spacing, and you may want (or be forced to, due to GPU memory constraints), to train on smaller cropped patches, with some logic which "prefers" foreground patches. ### **Make targets file for evaluation** -'fuse-med-ml/fuse_examples/classification/knight/make_targets_file.py' is a script that makes a targets file for the evaluation script. +'fuse-med-ml/fuse_examples/imaging/classification/knight/make_targets_file.py' is a script that makes a targets file for the evaluation script. Targets file is a csv file that holds just the labels for both tasks. This files is one of the inputs of the evaluation script. @@ -144,13 +144,13 @@ The script extracts the labels from the PyTorch dataset included in baseline imp The baseline implementation is using specific train/validation split, You can either use the same train/validation split or set a different split. -The script including additional details and documentation can be found in: 'fuse-med-ml/fuse_examples/classification/knight/make_targets_file.py' +The script including additional details and documentation can be found in: 'fuse-med-ml/fuse_examples/imaging/classification/knight/make_targets_file.py' ### **Make predictions file for evaluation** -'fuse-med-ml/fuse_examples/classification/knight/make_predictions_file.py' is a script that automatically makes predictions files for any model trained using FuseMedML. +'fuse-med-ml/fuse_examples/imaging/classification/knight/make_predictions_file.py' is a script that automatically makes predictions files for any model trained using FuseMedML. Predictions file is a csv file that include prediction score per class and should adhere a format specified in evaluation section. -The script including additional details and documentation can be found in: 'fuse-med-ml/fuse_examples/classification/knight/make_predictions_file.py' +The script including additional details and documentation can be found in: 'fuse-med-ml/fuse_examples/imaging/classification/knight/make_predictions_file.py' diff --git a/examples/fuse_examples/classification/knight/baseline/clinical_processor.py b/examples/fuse_examples/imaging/classification/knight/baseline/clinical_processor.py similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/clinical_processor.py rename to examples/fuse_examples/imaging/classification/knight/baseline/clinical_processor.py diff --git a/examples/fuse_examples/classification/knight/baseline/dataset.py b/examples/fuse_examples/imaging/classification/knight/baseline/dataset.py similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/dataset.py rename to examples/fuse_examples/imaging/classification/knight/baseline/dataset.py diff --git a/examples/fuse_examples/classification/knight/baseline/fuse_baseline.py b/examples/fuse_examples/imaging/classification/knight/baseline/fuse_baseline.py similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/fuse_baseline.py rename to examples/fuse_examples/imaging/classification/knight/baseline/fuse_baseline.py diff --git a/examples/fuse_examples/classification/knight/baseline/input_processor.py b/examples/fuse_examples/imaging/classification/knight/baseline/input_processor.py similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/input_processor.py rename to examples/fuse_examples/imaging/classification/knight/baseline/input_processor.py diff --git a/examples/fuse_examples/classification/knight/baseline/splits_final.pkl b/examples/fuse_examples/imaging/classification/knight/baseline/splits_final.pkl similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/splits_final.pkl rename to examples/fuse_examples/imaging/classification/knight/baseline/splits_final.pkl diff --git a/examples/fuse_examples/classification/knight/baseline/utils.py b/examples/fuse_examples/imaging/classification/knight/baseline/utils.py similarity index 100% rename from examples/fuse_examples/classification/knight/baseline/utils.py rename to examples/fuse_examples/imaging/classification/knight/baseline/utils.py diff --git a/examples/fuse_examples/classification/mnist/__init__.py b/examples/fuse_examples/imaging/classification/knight/eval/__init__.py similarity index 100% rename from examples/fuse_examples/classification/mnist/__init__.py rename to examples/fuse_examples/imaging/classification/knight/eval/__init__.py diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_baseline_task1_predictions.csv b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_baseline_task1_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_baseline_task1_predictions.csv rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_baseline_task1_predictions.csv diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_baseline_task2_predictions.csv b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_baseline_task2_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_baseline_task2_predictions.csv rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_baseline_task2_predictions.csv diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/results.csv b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/results.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/results.csv rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/results.csv diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/results.md b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/results.md similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/results.md rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/results.md diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/task1_roc.png similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task1/task1_roc.png rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task1/task1_roc.png diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/results.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.csv rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/results.csv diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/results.md similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/results.md rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/results.md diff --git a/examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png b/examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/task2_roc.png similarity index 100% rename from examples/fuse_examples/classification/knight/eval/baseline/validation_results_task2/task2_roc.png rename to examples/fuse_examples/imaging/classification/knight/eval/baseline/validation_results_task2/task2_roc.png diff --git a/examples/fuse_examples/classification/knight/eval/eval.py b/examples/fuse_examples/imaging/classification/knight/eval/eval.py similarity index 98% rename from examples/fuse_examples/classification/knight/eval/eval.py rename to examples/fuse_examples/imaging/classification/knight/eval/eval.py index 12a22a019..81b962054 100644 --- a/examples/fuse_examples/classification/knight/eval/eval.py +++ b/examples/fuse_examples/imaging/classification/knight/eval/eval.py @@ -236,7 +236,7 @@ def eval(task1_prediction_filename: str, task2_prediction_filename: str, target_ Run evaluation: Usage: python eval.py See details in function eval() - Run dummy example (set the working dir to fuse-med-ml/fuse_examples/classification/knight/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results + Run dummy example (set the working dir to fuse-med-ml/fuse_examples/imaging/classification/knight/eval): python eval.py example/example_targets.csv example/example_task1_predictions.csv example/example_task2_predictions.csv example/results """ if len(sys.argv) == 1: dir_path = pathlib.Path(__file__).parent.resolve() diff --git a/examples/fuse_examples/classification/knight/eval/example/example_targets.csv b/examples/fuse_examples/imaging/classification/knight/eval/example/example_targets.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/example_targets.csv rename to examples/fuse_examples/imaging/classification/knight/eval/example/example_targets.csv diff --git a/examples/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv b/examples/fuse_examples/imaging/classification/knight/eval/example/example_task1_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/example_task1_predictions.csv rename to examples/fuse_examples/imaging/classification/knight/eval/example/example_task1_predictions.csv diff --git a/examples/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv b/examples/fuse_examples/imaging/classification/knight/eval/example/example_task2_predictions.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/example_task2_predictions.csv rename to examples/fuse_examples/imaging/classification/knight/eval/example/example_task2_predictions.csv diff --git a/examples/fuse_examples/classification/knight/eval/example/results/results.csv b/examples/fuse_examples/imaging/classification/knight/eval/example/results/results.csv similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/results/results.csv rename to examples/fuse_examples/imaging/classification/knight/eval/example/results/results.csv diff --git a/examples/fuse_examples/classification/knight/eval/example/results/results.md b/examples/fuse_examples/imaging/classification/knight/eval/example/results/results.md similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/results/results.md rename to examples/fuse_examples/imaging/classification/knight/eval/example/results/results.md diff --git a/examples/fuse_examples/classification/knight/eval/example/results/task1_roc.png b/examples/fuse_examples/imaging/classification/knight/eval/example/results/task1_roc.png similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/results/task1_roc.png rename to examples/fuse_examples/imaging/classification/knight/eval/example/results/task1_roc.png diff --git a/examples/fuse_examples/classification/knight/eval/example/results/task2_roc.png b/examples/fuse_examples/imaging/classification/knight/eval/example/results/task2_roc.png similarity index 100% rename from examples/fuse_examples/classification/knight/eval/example/results/task2_roc.png rename to examples/fuse_examples/imaging/classification/knight/eval/example/results/task2_roc.png diff --git a/examples/fuse_examples/classification/knight/make_predictions_file.py b/examples/fuse_examples/imaging/classification/knight/make_predictions_file.py similarity index 98% rename from examples/fuse_examples/classification/knight/make_predictions_file.py rename to examples/fuse_examples/imaging/classification/knight/make_predictions_file.py index 21a09ecfc..985d9691e 100644 --- a/examples/fuse_examples/classification/knight/make_predictions_file.py +++ b/examples/fuse_examples/imaging/classification/knight/make_predictions_file.py @@ -29,7 +29,7 @@ from fuse.utils.file_io.file_io import save_dataframe from fuse.dl.managers.manager_default import ManagerDefault -from fuse_examples.classification.knight.eval.eval import TASK1_CLASS_NAMES, TASK2_CLASS_NAMES +from fuse_examples.imaging.classification.knight.eval.eval import TASK1_CLASS_NAMES, TASK2_CLASS_NAMES from baseline.dataset import knight_dataset def make_predictions_file(model_dir: str, diff --git a/examples/fuse_examples/classification/knight/make_targets_file.py b/examples/fuse_examples/imaging/classification/knight/make_targets_file.py similarity index 100% rename from examples/fuse_examples/classification/knight/make_targets_file.py rename to examples/fuse_examples/imaging/classification/knight/make_targets_file.py diff --git a/examples/fuse_examples/classification/prostate_x/__init__.py b/examples/fuse_examples/imaging/classification/mnist/__init__.py similarity index 100% rename from examples/fuse_examples/classification/prostate_x/__init__.py rename to examples/fuse_examples/imaging/classification/mnist/__init__.py diff --git a/examples/fuse_examples/classification/mnist/lenet.py b/examples/fuse_examples/imaging/classification/mnist/lenet.py similarity index 100% rename from examples/fuse_examples/classification/mnist/lenet.py rename to examples/fuse_examples/imaging/classification/mnist/lenet.py diff --git a/examples/fuse_examples/classification/mnist/runner.py b/examples/fuse_examples/imaging/classification/mnist/runner.py similarity index 99% rename from examples/fuse_examples/classification/mnist/runner.py rename to examples/fuse_examples/imaging/classification/mnist/runner.py index 97dd5ec77..d5ce99fd1 100644 --- a/examples/fuse_examples/classification/mnist/runner.py +++ b/examples/fuse_examples/imaging/classification/mnist/runner.py @@ -43,7 +43,7 @@ from fuse.utils.utils_debug import FuseDebug import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start -from fuse_examples.classification.mnist import lenet +from fuse_examples.imaging.classification.mnist import lenet ########################################################################################################### # Fuse ########################################################################################################### diff --git a/examples/fuse_examples/classification/prostate_x/README.md b/examples/fuse_examples/imaging/classification/prostate_x/README.md similarity index 100% rename from examples/fuse_examples/classification/prostate_x/README.md rename to examples/fuse_examples/imaging/classification/prostate_x/README.md diff --git a/examples/fuse_examples/classification/skin_lesion/__init__.py b/examples/fuse_examples/imaging/classification/prostate_x/__init__.py similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/__init__.py rename to examples/fuse_examples/imaging/classification/prostate_x/__init__.py diff --git a/examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py b/examples/fuse_examples/imaging/classification/prostate_x/backbone_3d_multichannel.py similarity index 100% rename from examples/fuse_examples/classification/prostate_x/backbone_3d_multichannel.py rename to examples/fuse_examples/imaging/classification/prostate_x/backbone_3d_multichannel.py diff --git a/examples/fuse_examples/classification/prostate_x/data_utils.py b/examples/fuse_examples/imaging/classification/prostate_x/data_utils.py similarity index 100% rename from examples/fuse_examples/classification/prostate_x/data_utils.py rename to examples/fuse_examples/imaging/classification/prostate_x/data_utils.py diff --git a/examples/fuse_examples/classification/prostate_x/dataset.py b/examples/fuse_examples/imaging/classification/prostate_x/dataset.py similarity index 95% rename from examples/fuse_examples/classification/prostate_x/dataset.py rename to examples/fuse_examples/imaging/classification/prostate_x/dataset.py index 4fcb9a798..121860458 100644 --- a/examples/fuse_examples/classification/prostate_x/dataset.py +++ b/examples/fuse_examples/imaging/classification/prostate_x/dataset.py @@ -9,10 +9,9 @@ import fuse.utils.gpu as GPU from fuse.utils.rand.param_sampler import Uniform, RandInt, RandBool, Choice -from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient -from fuse_examples.classification.prostate_x.processor import ProstateXPatchProcessor -from fuse_examples.classification.prostate_x.post_processor import post_processing -# from fuse_examples.classification.prostate_x.processor_dicom_mri import DicomMRIProcessor +from fuse_examples.imaging.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.imaging.classification.prostate_x.processor import ProstateXPatchProcessor +from fuse_examples.imaging.classification.prostate_x.post_processor import post_processing from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor diff --git a/examples/fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle b/examples/fuse_examples/imaging/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle similarity index 100% rename from examples/fuse_examples/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle rename to examples/fuse_examples/imaging/classification/prostate_x/dataset_prostate_x_folds_ver29062021_seed1.pickle diff --git a/examples/fuse_examples/classification/prostate_x/patient_data_source.py b/examples/fuse_examples/imaging/classification/prostate_x/patient_data_source.py similarity index 96% rename from examples/fuse_examples/classification/prostate_x/patient_data_source.py rename to examples/fuse_examples/imaging/classification/prostate_x/patient_data_source.py index 94af5a6e9..15b4f2ca0 100644 --- a/examples/fuse_examples/classification/prostate_x/patient_data_source.py +++ b/examples/fuse_examples/imaging/classification/prostate_x/patient_data_source.py @@ -15,7 +15,7 @@ from typing import List, Tuple from fuse.data.data_source.data_source_base import DataSourceBase -from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData +from fuse_examples.imaging.classification.prostate_x.data_utils import ProstateXUtilsData class ProstateXDataSourcePatient(DataSourceBase): def __init__(self, diff --git a/examples/fuse_examples/classification/prostate_x/post_processor.py b/examples/fuse_examples/imaging/classification/prostate_x/post_processor.py similarity index 100% rename from examples/fuse_examples/classification/prostate_x/post_processor.py rename to examples/fuse_examples/imaging/classification/prostate_x/post_processor.py diff --git a/examples/fuse_examples/classification/prostate_x/processor.py b/examples/fuse_examples/imaging/classification/prostate_x/processor.py similarity index 98% rename from examples/fuse_examples/classification/prostate_x/processor.py rename to examples/fuse_examples/imaging/classification/prostate_x/processor.py index e3b9f40ef..b8aeb161a 100644 --- a/examples/fuse_examples/classification/prostate_x/processor.py +++ b/examples/fuse_examples/imaging/classification/prostate_x/processor.py @@ -22,8 +22,7 @@ from fuse.data.processor.processor_base import ProcessorBase -from fuse_examples.classification.prostate_x.data_utils import ProstateXUtilsData -# from fuse_examples.classification.prostate_x.processor_dicom_mri import DicomMRIProcessor +from fuse_examples.imaging.classification.prostate_x.data_utils import ProstateXUtilsData from fuse.data.processor.processor_dicom_mri import DicomMRIProcessor diff --git a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py b/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py similarity index 97% rename from examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py rename to examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py index 2acac491c..b348ff415 100644 --- a/examples/fuse_examples/classification/prostate_x/run_train_3dpatch.py +++ b/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py @@ -34,10 +34,10 @@ from fuse.utils.utils_logger import fuse_logger_start -from fuse_examples.classification.prostate_x.dataset import prostate_x_dataset -from fuse_examples.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet -from fuse_examples.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient -from fuse_examples.classification.prostate_x.tasks import ProstateXTask +from fuse_examples.imaging.classification.prostate_x.dataset import prostate_x_dataset +from fuse_examples.imaging.classification.prostate_x.backbone_3d_multichannel import Fuse_model_3d_multichannel,ResNet +from fuse_examples.imaging.classification.prostate_x.patient_data_source import ProstateXDataSourcePatient +from fuse_examples.imaging.classification.prostate_x.tasks import ProstateXTask from fuse.dl.models.heads.head_1d_classifier import Head1dClassifier diff --git a/examples/fuse_examples/classification/prostate_x/tasks.py b/examples/fuse_examples/imaging/classification/prostate_x/tasks.py similarity index 100% rename from examples/fuse_examples/classification/prostate_x/tasks.py rename to examples/fuse_examples/imaging/classification/prostate_x/tasks.py diff --git a/examples/fuse_examples/classification/skin_lesion/README.md b/examples/fuse_examples/imaging/classification/skin_lesion/README.md similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/README.md rename to examples/fuse_examples/imaging/classification/skin_lesion/README.md diff --git a/examples/fuse_examples/imaging/classification/skin_lesion/__init__.py b/examples/fuse_examples/imaging/classification/skin_lesion/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/fuse_examples/classification/skin_lesion/data_source.py b/examples/fuse_examples/imaging/classification/skin_lesion/data_source.py similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/data_source.py rename to examples/fuse_examples/imaging/classification/skin_lesion/data_source.py diff --git a/examples/fuse_examples/classification/skin_lesion/download.py b/examples/fuse_examples/imaging/classification/skin_lesion/download.py similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/download.py rename to examples/fuse_examples/imaging/classification/skin_lesion/download.py diff --git a/examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py b/examples/fuse_examples/imaging/classification/skin_lesion/ground_truth_processor.py similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/ground_truth_processor.py rename to examples/fuse_examples/imaging/classification/skin_lesion/ground_truth_processor.py diff --git a/examples/fuse_examples/classification/skin_lesion/input_processor.py b/examples/fuse_examples/imaging/classification/skin_lesion/input_processor.py similarity index 100% rename from examples/fuse_examples/classification/skin_lesion/input_processor.py rename to examples/fuse_examples/imaging/classification/skin_lesion/input_processor.py diff --git a/examples/fuse_examples/classification/skin_lesion/runner.py b/examples/fuse_examples/imaging/classification/skin_lesion/runner.py similarity index 98% rename from examples/fuse_examples/classification/skin_lesion/runner.py rename to examples/fuse_examples/imaging/classification/skin_lesion/runner.py index 54a9bdf18..c4bb4bbc7 100644 --- a/examples/fuse_examples/classification/skin_lesion/runner.py +++ b/examples/fuse_examples/imaging/classification/skin_lesion/runner.py @@ -55,10 +55,10 @@ from fuse.dl.managers.manager_default import ManagerDefault -from fuse_examples.classification.skin_lesion.data_source import SkinDataSource -from fuse_examples.classification.skin_lesion.input_processor import SkinInputProcessor -from fuse_examples.classification.skin_lesion.ground_truth_processor import SkinGroundTruthProcessor -from fuse_examples.classification.skin_lesion.download import download_and_extract_isic +from fuse_examples.imaging.classification.skin_lesion.data_source import SkinDataSource +from fuse_examples.imaging.classification.skin_lesion.input_processor import SkinInputProcessor +from fuse_examples.imaging.classification.skin_lesion.ground_truth_processor import SkinGroundTruthProcessor +from fuse_examples.imaging.classification.skin_lesion.download import download_and_extract_isic ########################################## diff --git a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb similarity index 99% rename from examples/fuse_examples/tutorials/hello_world/hello_world.ipynb rename to examples/fuse_examples/imaging/hello_world/hello_world.ipynb index 5bab30f5a..e1235345e 100644 --- a/examples/fuse_examples/tutorials/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -24,7 +24,7 @@ "\n", "By the end of the session we hope you'll be familiar with basic Fuse's workflow and acknowledge it's potential.\n", "\n", - "Open and run this notebook in [Google Colab](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/hello_world/hello_world.ipynb)\n", + "Open and run this notebook in [Google Colab](https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/imaging/hello_world/hello_world.ipynb)\n", "\n", "ENJOY" ] diff --git a/examples/fuse_examples/tutorials/hello_world/hello_world_utils.py b/examples/fuse_examples/imaging/hello_world/hello_world_utils.py similarity index 100% rename from examples/fuse_examples/tutorials/hello_world/hello_world_utils.py rename to examples/fuse_examples/imaging/hello_world/hello_world_utils.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/arch.png b/examples/fuse_examples/multimodality/image_clinical/arch.png similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/arch.png rename to examples/fuse_examples/multimodality/image_clinical/arch.png diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py b/examples/fuse_examples/multimodality/image_clinical/data_source.py similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/data_source.py rename to examples/fuse_examples/multimodality/image_clinical/data_source.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py b/examples/fuse_examples/multimodality/image_clinical/dataset.py similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/dataset.py rename to examples/fuse_examples/multimodality/image_clinical/dataset.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/download.py b/examples/fuse_examples/multimodality/image_clinical/download.py similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/download.py rename to examples/fuse_examples/multimodality/image_clinical/download.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png b/examples/fuse_examples/multimodality/image_clinical/fusemedml-release-plans.png similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/fusemedml-release-plans.png rename to examples/fuse_examples/multimodality/image_clinical/fusemedml-release-plans.png diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py b/examples/fuse_examples/multimodality/image_clinical/ground_truth_processor.py similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/ground_truth_processor.py rename to examples/fuse_examples/multimodality/image_clinical/ground_truth_processor.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py b/examples/fuse_examples/multimodality/image_clinical/input_processor.py similarity index 100% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/input_processor.py rename to examples/fuse_examples/multimodality/image_clinical/input_processor.py diff --git a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb b/examples/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb similarity index 99% rename from examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb rename to examples/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb index 015adbcf5..6f7228fa9 100644 --- a/examples/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb +++ b/examples/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb @@ -12,7 +12,7 @@ "\n", "Open and run this notebook in Google Colab (instructions can be found in 'Installation Details - Google Colab' section):\n", "\n", - "https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/tutorials/multimodality_image_clinical/multimodality_image_clinical.ipynb\n", + "https://colab.research.google.com/github/IBM/fuse-med-ml/blob/master/fuse_examples/multimodality/image_clinical/multimodality_image_clinical.ipynb\n", "\n", "## Session take-away\n", "* Introduction to FuseMedML framework\n", @@ -203,8 +203,7 @@ "source": [ "!git clone https://github.com/IBM/fuse-med-ml.git\n", "%cd fuse-med-ml\n", - "!pip install -e .\n", - "%cd fuse_examples/tutorials/multimodality_image_clinical" + "!pip install -e .\n" ] }, { @@ -221,7 +220,7 @@ "outputs": [], "source": [ "import os\n", - "%cd fuse-med-ml/fuse_examples/tutorials/multimodality_image_clinical\n", + "%cd fuse-med-ml/fuse_examples/multimodality/image_clinical\n", "!export PYTHONPATH=$PYTHONPATH:$(pwd)\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", "from fuse.utils.utils_logger import fuse_logger_start\n", @@ -262,7 +261,7 @@ "outputs": [], "source": [ "# explain about FuseMedML data pipeline\n", - "from fuse_examples.tutorials.multimodality_image_clinical.dataset import isic_2019_dataset\n", + "from fuse_examples.multimodality.image_clinical.dataset import isic_2019_dataset\n", "\n", "train_dl, valid_dl = isic_2019_dataset(size=size, reset_cache=True, post_cache_processing_func=None)\n" ] @@ -271,7 +270,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The original code can be found [here](https://github.com/IBM/fuse-med-ml/blob/multimodality_tutorial/fuse_examples/tutorials/multimodality_image_clinical/dataset.py).\n", + "The original code can be found [here](https://github.com/IBM/fuse-med-ml/blob/master/fuse_examples/multimodality/image_cliical/dataset.py).\n", "\n", "
\n", "\n", @@ -1006,7 +1005,7 @@ "source": [ "from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict\n", "import torch\n", - "from fuse_examples.tutorials.multimodality_image_clinical.dataset import ANATOM_SITE_INDEX, SEX_INDEX\n", + "from fuse_examples.multimodality.image_clinical.dataset import ANATOM_SITE_INDEX, SEX_INDEX\n", "\n", "### Generate Data\n", "def post_cache_processing_clinical_encoding(sample_dict: dict) -> dict:\n", diff --git a/examples/fuse_examples/tests/colab_tests.ipynb b/examples/fuse_examples/tests/colab_tests.ipynb index 55a96e3df..26b2bdfdc 100644 --- a/examples/fuse_examples/tests/colab_tests.ipynb +++ b/examples/fuse_examples/tests/colab_tests.ipynb @@ -120,7 +120,7 @@ "metadata": {}, "outputs": [], "source": [ - "%cd fuse-med-ml/fuse_examples/classification/knight/eval\n", + "%cd fuse-med-ml/fuse_examples/imaging/classification/knight/eval\n", "!python eval.py example_targets.csv example_task1_predictions.csv example_task2_predictions.csv ./example_output_dir\n", "from IPython.display import Markdown, display\n", "%cd example_output_dir\n", @@ -134,7 +134,7 @@ "metadata": {}, "outputs": [], "source": [ - "%cd fuse-med-ml/fuse_examples/classification/bright/eval\n", + "%cd fuse-med-ml/fuse_examples/imaging/classification/bright/eval\n", "!python eval.py example_targets.csv example_task1_predictions.csv example_task2_predictions.csv ./example_output_dir\n", "from IPython.display import Markdown, display\n", "%cd example_output_dir\n", diff --git a/examples/fuse_examples/tests/test_classification_bright,py b/examples/fuse_examples/tests/test_classification_bright,py index 410b11560..f5ab1fd5a 100644 --- a/examples/fuse_examples/tests/test_classification_bright,py +++ b/examples/fuse_examples/tests/test_classification_bright,py @@ -23,7 +23,7 @@ import tempfile import unittest import os -from fuse_examples.classification.bright.eval.eval import eval +from fuse_examples.imaging.classification.bright.eval.eval import eval class BrightTestCase(unittest.TestCase): @@ -33,9 +33,9 @@ class BrightTestCase(unittest.TestCase): def test_eval(self): dir_path = pathlib.Path(__file__).parent.resolve() - target_filename = os.path.join(dir_path, "../classification/bright/eval/validation_targets.csv") - task1_prediction_filename = os.path.join(dir_path, "../classification/bright/eval/baseline/validation_baseline_task1_predictions.csv") - task2_prediction_filename = os.path.join(dir_path, "../classification/bright/eval/baseline/validation_baseline_task2_predictions.csv") + target_filename = os.path.join(dir_path, "../imaging/classification/bright/eval/validation_targets.csv") + task1_prediction_filename = os.path.join(dir_path, "../imaging/classification/bright/eval/baseline/validation_baseline_task1_predictions.csv") + task2_prediction_filename = os.path.join(dir_path, "../imaging/classification/bright/eval/baseline/validation_baseline_task2_predictions.csv") eval(target_filename=target_filename, task1_prediction_filename=task1_prediction_filename, task2_prediction_filename=task2_prediction_filename, output_dir=self.root) def tearDown(self): diff --git a/examples/fuse_examples/tests/test_classification_cmmd.py b/examples/fuse_examples/tests/test_classification_cmmd.py index cd767e829..abc5cfa15 100644 --- a/examples/fuse_examples/tests/test_classification_cmmd.py +++ b/examples/fuse_examples/tests/test_classification_cmmd.py @@ -16,7 +16,7 @@ Created on June 30, 2021 """ -from fuse_examples.classification.cmmd.runner import TRAIN_COMMON_PARAMS, \ +from fuse_examples.imaging.classification.cmmd.runner import TRAIN_COMMON_PARAMS, \ INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer import unittest diff --git a/examples/fuse_examples/tests/test_classification_knight.py b/examples/fuse_examples/tests/test_classification_knight.py index f70f3ed79..35eb09cc8 100644 --- a/examples/fuse_examples/tests/test_classification_knight.py +++ b/examples/fuse_examples/tests/test_classification_knight.py @@ -25,9 +25,9 @@ from fuse.utils.file_io.file_io import create_dir import wget -from fuse_examples.classification.knight.eval.eval import eval -from fuse_examples.classification.knight.make_targets_file import make_targets_file -import fuse_examples.classification.knight.baseline.fuse_baseline as baseline +from fuse_examples.imaging.classification.knight.eval.eval import eval +from fuse_examples.imaging.classification.knight.make_targets_file import make_targets_file +import fuse_examples.imaging.classification.knight.baseline.fuse_baseline as baseline class KnightTestTestCase(unittest.TestCase): @@ -36,16 +36,16 @@ def setUp(self): def test_eval(self): dir_path = pathlib.Path(__file__).parent.resolve() - target_filename = os.path.join(dir_path, "../classification/knight/eval/example/example_targets.csv") - task1_prediction_filename = os.path.join(dir_path, "../classification/knight/eval/example/example_task1_predictions.csv") - task2_prediction_filename = os.path.join(dir_path, "../classification/knight/eval/example/example_task2_predictions.csv") + target_filename = os.path.join(dir_path, "../imaging/classification/knight/eval/example/example_targets.csv") + task1_prediction_filename = os.path.join(dir_path, "../imaging/classification/knight/eval/example/example_task1_predictions.csv") + task2_prediction_filename = os.path.join(dir_path, "../imaging/classification/knight/eval/example/example_task2_predictions.csv") eval(target_filename=target_filename, task1_prediction_filename=task1_prediction_filename, task2_prediction_filename=task2_prediction_filename, output_dir=self.root) def test_make_targets(self): dir_path = pathlib.Path(__file__).parent.resolve() data_path = os.path.join(self.root, "data") cache_path = os.path.join(self.root, "cache") - split = os.path.join(dir_path, "../classification/knight/baseline/splits_final.pkl") + split = os.path.join(dir_path, "../imaging/classification/knight/baseline/splits_final.pkl") output_filename = os.path.join(self.root, "output/validation_targets.csv") create_dir(os.path.join(data_path, "knight", "data")) diff --git a/examples/fuse_examples/tests/test_classification_mnist.py b/examples/fuse_examples/tests/test_classification_mnist.py index b9aba8c93..5c92cd063 100644 --- a/examples/fuse_examples/tests/test_classification_mnist.py +++ b/examples/fuse_examples/tests/test_classification_mnist.py @@ -23,7 +23,7 @@ import os import fuse.utils.gpu as GPU -from fuse_examples.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ +from fuse_examples.imaging.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ EVAL_COMMON_PARAMS diff --git a/examples/fuse_examples/tests/test_classification_prostatex.py b/examples/fuse_examples/tests/test_classification_prostatex.py index 32228731e..0f0a031fb 100644 --- a/examples/fuse_examples/tests/test_classification_prostatex.py +++ b/examples/fuse_examples/tests/test_classification_prostatex.py @@ -24,7 +24,7 @@ import pathlib import fuse.utils.gpu as GPU -from fuse_examples.classification.prostate_x.run_train_3dpatch import TRAIN_COMMON_PARAMS, train_template, infer_template, eval_template, INFER_COMMON_PARAMS, \ +from fuse_examples.imaging.classification.prostate_x.run_train_3dpatch import TRAIN_COMMON_PARAMS, train_template, infer_template, eval_template, INFER_COMMON_PARAMS, \ EVAL_COMMON_PARAMS diff --git a/examples/fuse_examples/tests/test_classification_skin_lesion.py b/examples/fuse_examples/tests/test_classification_skin_lesion.py index 81a2c76c5..fe238564b 100644 --- a/examples/fuse_examples/tests/test_classification_skin_lesion.py +++ b/examples/fuse_examples/tests/test_classification_skin_lesion.py @@ -24,7 +24,7 @@ import shutil from fuse.utils.utils_logger import fuse_logger_end -from fuse_examples.classification.skin_lesion.runner import TRAIN_COMMON_PARAMS, \ +from fuse_examples.imaging.classification.skin_lesion.runner import TRAIN_COMMON_PARAMS, \ INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer import fuse.utils.gpu as GPU From 3e3a476deaecab02a60c7784c1659e341b973bd2 Mon Sep 17 00:00:00 2001 From: "moshiko.raboh#ibm.com" Date: Sun, 17 Apr 2022 21:52:51 +0300 Subject: [PATCH 20/42] add fuse data package --- fuse/data/__init__.py | 19 + fuse/data/datasets/__init__.py | 1 + fuse/data/datasets/caching/__init__.py | 0 .../caching/object_caching_handlers.py | 59 +++ fuse/data/datasets/caching/samples_cacher.py | 357 ++++++++++++++++++ fuse/data/datasets/caching/tests/__init__.py | 0 .../caching/tests/test_sample_caching.py | 168 +++++++++ fuse/data/datasets/dataset_base.py | 46 +++ fuse/data/datasets/dataset_default.py | 304 +++++++++++++++ .../data/datasets/dataset_wrap_seq_to_dict.py | 97 +++++ fuse/data/datasets/sample_caching_audit.py | 96 +++++ fuse/data/datasets/tests/__init__.py | 0 .../datasets/tests/test_dataset_default.py | 264 +++++++++++++ .../test_dataset_default_audit_feature.py | 250 ++++++++++++ .../tests/test_dataset_wrap_seq_to_dict.py | 90 +++++ fuse/data/key_types.py | 47 +++ fuse/data/key_types_for_testing.py | 24 ++ fuse/data/ops/__init__.py | 1 + fuse/data/ops/caching_tools.py | 137 +++++++ fuse/data/ops/op_base.py | 128 +++++++ fuse/data/ops/ops_aug_common.py | 164 ++++++++ fuse/data/ops/ops_cast.py | 167 ++++++++ fuse/data/ops/ops_common.py | 357 ++++++++++++++++++ fuse/data/ops/ops_common_for_testing.py | 7 + fuse/data/ops/ops_read.py | 101 +++++ fuse/data/ops/ops_visprobe.py | 186 +++++++++ fuse/data/ops/tests/__init__.py | 0 fuse/data/ops/tests/test_op_base.py | 43 +++ fuse/data/ops/tests/test_op_visprobe.py | 284 ++++++++++++++ fuse/data/ops/tests/test_ops_aug_common.py | 125 ++++++ fuse/data/ops/tests/test_ops_cast.py | 97 +++++ fuse/data/ops/tests/test_ops_common.py | 208 ++++++++++ fuse/data/ops/tests/test_ops_read.py | 76 ++++ fuse/data/patterns.py | 56 +++ fuse/data/pipelines/__init__.py | 0 fuse/data/pipelines/pipeline_default.py | 130 +++++++ fuse/data/pipelines/tests/__init__.py | 0 .../pipelines/tests/test_pipeline_default.py | 117 ++++++ fuse/data/tests/__init__.py | 0 fuse/data/tests/test_version.py | 38 ++ fuse/data/utils/__init__.py | 0 fuse/data/utils/collates.py | 129 +++++++ fuse/data/utils/sample.py | 102 +++++ fuse/data/utils/samplers.py | 208 ++++++++++ fuse/data/utils/tests/__init__.py | 0 fuse/data/utils/tests/test_collates.py | 101 +++++ fuse/data/utils/tests/test_dataset_export.py | 69 ++++ fuse/data/utils/tests/test_samplers.py | 163 ++++++++ 48 files changed, 5016 insertions(+) create mode 100644 fuse/data/__init__.py create mode 100644 fuse/data/datasets/__init__.py create mode 100644 fuse/data/datasets/caching/__init__.py create mode 100644 fuse/data/datasets/caching/object_caching_handlers.py create mode 100644 fuse/data/datasets/caching/samples_cacher.py create mode 100644 fuse/data/datasets/caching/tests/__init__.py create mode 100644 fuse/data/datasets/caching/tests/test_sample_caching.py create mode 100644 fuse/data/datasets/dataset_base.py create mode 100644 fuse/data/datasets/dataset_default.py create mode 100644 fuse/data/datasets/dataset_wrap_seq_to_dict.py create mode 100644 fuse/data/datasets/sample_caching_audit.py create mode 100644 fuse/data/datasets/tests/__init__.py create mode 100644 fuse/data/datasets/tests/test_dataset_default.py create mode 100644 fuse/data/datasets/tests/test_dataset_default_audit_feature.py create mode 100644 fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py create mode 100644 fuse/data/key_types.py create mode 100644 fuse/data/key_types_for_testing.py create mode 100644 fuse/data/ops/__init__.py create mode 100644 fuse/data/ops/caching_tools.py create mode 100644 fuse/data/ops/op_base.py create mode 100644 fuse/data/ops/ops_aug_common.py create mode 100644 fuse/data/ops/ops_cast.py create mode 100644 fuse/data/ops/ops_common.py create mode 100644 fuse/data/ops/ops_common_for_testing.py create mode 100644 fuse/data/ops/ops_read.py create mode 100644 fuse/data/ops/ops_visprobe.py create mode 100644 fuse/data/ops/tests/__init__.py create mode 100644 fuse/data/ops/tests/test_op_base.py create mode 100644 fuse/data/ops/tests/test_op_visprobe.py create mode 100644 fuse/data/ops/tests/test_ops_aug_common.py create mode 100644 fuse/data/ops/tests/test_ops_cast.py create mode 100644 fuse/data/ops/tests/test_ops_common.py create mode 100644 fuse/data/ops/tests/test_ops_read.py create mode 100644 fuse/data/patterns.py create mode 100644 fuse/data/pipelines/__init__.py create mode 100644 fuse/data/pipelines/pipeline_default.py create mode 100644 fuse/data/pipelines/tests/__init__.py create mode 100644 fuse/data/pipelines/tests/test_pipeline_default.py create mode 100644 fuse/data/tests/__init__.py create mode 100644 fuse/data/tests/test_version.py create mode 100644 fuse/data/utils/__init__.py create mode 100644 fuse/data/utils/collates.py create mode 100644 fuse/data/utils/sample.py create mode 100644 fuse/data/utils/samplers.py create mode 100644 fuse/data/utils/tests/__init__.py create mode 100644 fuse/data/utils/tests/test_collates.py create mode 100644 fuse/data/utils/tests/test_dataset_export.py create mode 100644 fuse/data/utils/tests/test_samplers.py diff --git a/fuse/data/__init__.py b/fuse/data/__init__.py new file mode 100644 index 000000000..c20ed02e2 --- /dev/null +++ b/fuse/data/__init__.py @@ -0,0 +1,19 @@ +import os +import pathlib + +# version +with open(os.path.join(pathlib.Path(__file__).parent, "..", "..", "VERSION.txt")) as version_file: + __version__ = version_file.read().strip() + +# import shortcuts +from fuse.data.utils.sample import get_sample_id, set_sample_id, get_sample_id_key +from fuse.data.utils.sample import create_initial_sample, get_initial_sample_id, get_initial_sample_id_key, get_specific_sample_from_potentially_morphed +from fuse.data.ops.op_base import OpBase #DataTypeForTesting, +from fuse.data.ops.ops_common import OpApplyPatterns, OpLambda, OpFunc, OpRepeat, OpKeepKeypaths +from fuse.data.ops.ops_aug_common import OpRandApply, OpSample, OpSampleAndRepeat +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.ops.ops_cast import OpToTensor, OpToNumpy +from fuse.data.utils.collates import CollateDefault +from fuse.data.utils.export import ExportDataset +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.datasets.dataset_default import DatasetBase, DatasetDefault diff --git a/fuse/data/datasets/__init__.py b/fuse/data/datasets/__init__.py new file mode 100644 index 000000000..5066236a8 --- /dev/null +++ b/fuse/data/datasets/__init__.py @@ -0,0 +1 @@ +from .dataset_default import DatasetDefault \ No newline at end of file diff --git a/fuse/data/datasets/caching/__init__.py b/fuse/data/datasets/caching/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/datasets/caching/object_caching_handlers.py b/fuse/data/datasets/caching/object_caching_handlers.py new file mode 100644 index 000000000..1393e6394 --- /dev/null +++ b/fuse/data/datasets/caching/object_caching_handlers.py @@ -0,0 +1,59 @@ +from typing import List, Dict +import numpy as np +from fuse.utils.ndict import NDict +import torch +#TODO: support custom _object_requires_hdf5_single +# maybe even more flexible (knowing key name etc., patterns, explicit name, regular expr.) + +#TODO: should we require OrderedDict?? and for the internal dicts as well ?? +#TODO: maybe it's better to flatten the dictionaries first + +def _object_requires_hdf5_recurse(curr: NDict, str_base='') -> List[str]: + ''' + Iterates on keys and checks + ''' + keys = curr.keypaths() + ans = [] + for k in keys: + data = curr[k] + if _object_requires_hdf5_single(data): + ans.append(k) + return ans + +# def _PREV__object_requires_hdf5_recurse(curr: NDict, str_base='') -> List[str]: +# """ +# Recurses (only into dicts!) and returns a list of keys that require storing into HDF5 +# (which allows reading only sub-parts) + +# :return: a list of keys as strings, e.g. ['data.cc.img', 'data.mlo.img'] +# """ +# #print('str_base=', str_base) +# if _object_requires_hdf5_single(curr): +# return str_base + +# if isinstance(curr, dict): +# ans = [] +# for k,d in curr.items(): +# curr_ans = _object_requires_hdf5_recurse( +# d, str_base+'.'+k if str_base!='' else k, +# ) +# if curr_ans is None: +# pass +# elif isinstance(curr_ans, list): +# ans.extend(curr_ans) +# else: +# ans.append(curr_ans) +# return ans + +# return None + + +def _object_requires_hdf5_single(obj, minimal_ndarray_size=100): + ans = isinstance(obj, np.ndarray) and (obj.size>minimal_ndarray_size) + + if isinstance(obj, torch.Tensor): + raise Exception("You need to cast to tensor in the dynamic pipeline as it takes a lot of time pickling torch.Tensor") + + #if ans: + # print(f'found hfd5 requiring object! shape={obj.shape}, size={obj.size}') + return ans \ No newline at end of file diff --git a/fuse/data/datasets/caching/samples_cacher.py b/fuse/data/datasets/caching/samples_cacher.py new file mode 100644 index 000000000..b775dbce6 --- /dev/null +++ b/fuse/data/datasets/caching/samples_cacher.py @@ -0,0 +1,357 @@ +from typing import Hashable, List, Optional, Sequence, Union, Callable, Dict, Callable, Any, Tuple + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.utils.sample import set_initial_sample_id +import numpy as np +from collections import OrderedDict +from fuse.data.datasets.caching.object_caching_handlers import _object_requires_hdf5_recurse +from fuse.utils.ndict import NDict +import os +import psutil +from fuse.utils.file_io.file_io import load_hdf5, save_hdf5_safe, load_pickle, save_pickle_safe +from fuse.data import get_sample_id, create_initial_sample, get_specific_sample_from_potentially_morphed +import hashlib +from fuse.utils.file_io import delete_directory_tree +from glob import glob +from fuse.utils.multiprocessing.run_multiprocessed import run_multiprocessed, get_from_global_storage +from collections import OrderedDict +from fuse.data.datasets.sample_caching_audit import SampleCachingAudit +from fuse.data.utils.sample import get_initial_sample_id, set_initial_sample_id +from warnings import warn + +class SamplesCacher: + def __init__(self, + unique_name: str, + pipeline: PipelineDefault, + cache_dirs: Union[str,List[str]], + custom_write_dir_callable: Optional[Callable] = None, + custom_read_dirs_callable: Optional[Callable] = None, + restart_cache:bool=False, + workers:int = 0, + **audit_kwargs:dict, + ) -> None: + """ + Supports caching samples, used by datasets implementations. + :param unique_name: a unique name for this cache. + cache dir will be [cache dir]/[unique_name] + :param cache_dirs: a path in which the cache will be created, + you may provide a list of paths, which will be tried in order, moving the next when available space is exausted. + :param parameter: + :param custom_write_dir_callable: optional callable with the signature foo(cache_dirs:List[str]) -> str + which returns the write directory to use. + :param custom_read_dirs_callable: optional callable with the signature foo() -> List[str] + which returns a list of directories to attempt to read from. Attempts will be in the provided order. + :param restart_cache: if set to True, will DELETE all of the content of the defined cache dirs. + Should be used every time that any of the OPs participating in the "static cache" part changed in any way + (for example, code change) + :param workers: number of multiprocessing workers used when building the cache. Default value is 0 (no multiprocessing) + :param **audit_kwargs: optional custom kwargs to pass to SampleCachingAudit instance. + auditing cached samples (usually periodically) is very important, in order to avoid "stale" cached samples. + To disable pass audit_first_sample=False, audit_rate=None, + Note that it's not recommended to completely disable it, and at the very least you should use audit_first_sample=True, audit_rate=None + which only tests the first loaded sample for staleness. + To learn more read SampleCachingAudit doc + """ + if not isinstance(cache_dirs, list): + cache_dirs = [cache_dirs] + self._cache_dirs = [os.path.join(x, unique_name) for x in cache_dirs] + + self._unique_name = unique_name + + if custom_write_dir_callable is None: + self._write_dir_logic = _get_available_write_location + else: + self._write_dir_logic = custom_write_dir_callable + + if custom_read_dirs_callable is None: + self._read_dirs_logic = lambda : self._cache_dirs + else: + self._read_dirs_logic = custom_read_dirs_callable + + self._pipeline = pipeline + self._pipeline_desc_text = str(pipeline) + self._pipeline_desc_hash = 'hash_'+hashlib.md5(self._pipeline_desc_text.encode('utf-8')).hexdigest() + + self._restart_cache = restart_cache + if self._restart_cache: + self.delete_cache() + + self._audit_kwargs = audit_kwargs + self._audit = SampleCachingAudit(**self._audit_kwargs) + + self._workers = workers + if self._workers < 2: + warn('Multi processing is not active in SamplesCacher. Seting "workers" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with "workers=0".') + + self._verify_no_other_pipelines_cache() + + + def _verify_no_other_pipelines_cache(self)->None: + dirs_to_check = self._get_read_dirs() + [self._get_write_dir()] + for d in dirs_to_check: + search_pat = os.path.realpath(os.path.join(d, '..', 'hash_*')) + found_sub_dirs = glob(search_pat) + for found_dir in found_sub_dirs: + if not os.path.isdir(found_dir): + continue + if os.path.basename(found_dir) != self._pipeline_desc_hash: + raise Exception(f'Found samples cache for pipeline hash {os.path.basename(found_dir)} which is different from the current loaded pipeline hash {self._pipeline_desc_hash} !!\n' + 'This is not allowed, you may only use a single pipeline per uniquely named cache.\n' + 'You can use "restart_cache=True" to rebuild the cache or delete the different cache manually.\n' + ) + + + def delete_cache(self) -> None: + ''' + Will delete this specific named cache from all read and write dirs + ''' + dirs_to_delete = self._get_read_dirs() + [self._get_write_dir()] + dirs_to_delete = list(set(dirs_to_delete)) + dirs_to_delete = [os.path.realpath(os.path.join(x, '..')) for x in dirs_to_delete] #one dir above the pipeline hash dir + print('Due to "delete_cache" call, about to delete the following dirs:') + + for del_dir in dirs_to_delete: + print(del_dir) + print('---- list end ----') + print('deleting ... ') + for del_dir in dirs_to_delete: + print(f'deleting {del_dir} ...') + all_found = glob(os.path.join(del_dir, 'hash_*')) + for found in all_found: + if not os.path.isdir(found): + continue + delete_directory_tree(found) + + + def _get_write_dir(self): + ans = self._write_dir_logic(self._cache_dirs) + ans = os.path.join(ans, self._pipeline_desc_hash) + return ans + + def _get_read_dirs(self): + ans = self._read_dirs_logic() + ans = [os.path.join(x, self._pipeline_desc_hash) for x in ans] + return ans + + def cache_samples(self, orig_sample_ids:List[Any]) -> List[Tuple[str,Union[None,List[str]],str]]: + ''' + Go over all of orig_sample_ids, and cache resulting samples + + returns information that helps to map from original sample id to the resulting sample id + (an op might return None, discarding a sample, or optional generate different one or more samples from an original single sample_id) + #TODO: have a single doc location that explains this concept and can be pointed to from any related location + + ''' + #TODO: remember that it means that we need proper extraction of args (pos or kwargs...) + #possibly by extracting info from __call__ signature or process() if we modify from call to it + + #TODO: + + sample_ids_text = '@'.join([str(x) for x in sorted(orig_sample_ids)]) + samples_ids_hash = hashlib.md5(sample_ids_text.encode('utf-8')).hexdigest() + + hash_filename = 'samples_ids_hash@'+samples_ids_hash+'.pkl.gz' + + read_dirs = self._get_read_dirs() + for curr_read_dir in read_dirs: + fullpath_filename = os.path.join(curr_read_dir, 'full_sets_info', hash_filename) + if os.path.isfile(fullpath_filename): + print(f'entire samples set {hash_filename} already cached. Found {fullpath_filename}') + return load_pickle(fullpath_filename) + + orig_sid_to_final = OrderedDict() + for_global_storage = {'samples_cacher_instance': self} + all_ans = run_multiprocessed( + SamplesCacher._cache_worker, + orig_sample_ids, + workers=self._workers, + copy_to_global_storage=for_global_storage, + verbose=1, + ) + + for initial_sample_id, output_sample_ids in zip(orig_sample_ids, all_ans): + orig_sid_to_final[initial_sample_id] = output_sample_ids + + write_dir = self._get_write_dir() + set_info_dir = os.path.join(write_dir, 'full_sets_info') + os.makedirs(set_info_dir, exist_ok=True) + fullpath_filename = os.path.join(set_info_dir, hash_filename) + save_pickle_safe(orig_sid_to_final, fullpath_filename, compress=True) + + return orig_sid_to_final + + @staticmethod + def get_final_sample_id_hash(sample_id): + ''' + sample_id is the final sample_id that came out of the pipeline + + note: our pipeline supports Ops returning None, thus, discarding a sample (in that case, it will not have any final sample_id), + additionally, the pipeline may return *multiple* samples, each with their own sample_id + + ''' + curr_sample_id_str = str(sample_id) #TODO repr or str ? + output_sample_hash = hashlib.md5(curr_sample_id_str.encode('utf-8')).hexdigest() + ans = f'out_sample_id@{output_sample_hash}' + return ans + + @staticmethod + def get_orig_sample_id_hash(orig_sample_id): + ''' + orig_sample_id is the original sample_id that was provided, regardless if it turned out to become None, the same sample_id, or different sample_id(s) + ''' + orig_sample_id_str = str(orig_sample_id) + if orig_sample_id_str.startswith('<') and orig_sample_id_str.endswith('>'): #and '0x' in orig_sample_id_str + #<__main__.SomeClass at 0x7fc3e6645e20> + raise Exception(f'You must implement a proper __str__ for orig_sample_id. String representations like <__main__.SomeClass at 0x7fc3e6645e20> are not descriptibe enough and also not persistent between runs. Got: {orig_sample_id_str}') + ans = hashlib.md5(orig_sample_id_str.encode('utf-8')).hexdigest() + ans = 'out_info_for_orig_sample@' + ans + return ans + + def get_orig_sample_id_from_final_sample_id(self, orig_sample_id): + pass + + + def load_sample(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + ''' + :param sample_id: the sample_id of the sample to load + :param keys: optionally, provide a subset of the keys to load in this sample. + This is useful for speeding up loading. + ''' + + sample_from_cache = self._load_sample_from_cache(sample_id, keys) + audit_required = self._audit.update() + + if audit_required: + initial_sample_id = get_initial_sample_id(sample_from_cache) + fresh_sample = self._load_sample_using_pipeline(initial_sample_id, keys) + fresh_sample = get_specific_sample_from_potentially_morphed(fresh_sample, sample_id) + + self._audit.audit(sample_from_cache, fresh_sample) + + return sample_from_cache + + + def _load_sample_using_pipeline(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + sample_dict = create_initial_sample(sample_id) + result_sample = self._pipeline(sample_dict) + return result_sample + + + def _load_sample_from_cache(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + """ + TODO: add comments + """ + read_dirs = self._get_read_dirs() + sample_hash = SamplesCacher.get_final_sample_id_hash(sample_id) + + for curr_read_dir in read_dirs: + extension_less = os.path.join(curr_read_dir, sample_hash) + if os.path.isfile(extension_less+'.pkl.gz'): + loaded_sample = load_pickle(extension_less+'.pkl.gz') + if os.path.isfile(extension_less+'.hdf5'): + loaded_sample_hdf5_part = load_hdf5(extension_less+'.hdf5') + loaded_sample = NDict.combine(loaded_sample, loaded_sample_hdf5_part) + return loaded_sample + + raise Exception(f'Expected to find a cached sample for sample_id={sample_id} but could not find any!') + + @staticmethod + def _cache_worker(orig_sample_id:Any): + cacher = get_from_global_storage('samples_cacher_instance') + ans = cacher._cache(orig_sample_id) + return ans + + + def _cache(self, orig_sample_id:Any): + ''' + :param orig_sample_id: the original sample id, which was provided as the input to the pipeline + :param sample: the result of the pipeline - can be None if it was dropped, a dictionary in the typical standard case, + and a list of dictionaries in case the sample was split into multiple samples (ops are allowed to do that during the static part of the processing) + ''' + + write_dir = self._get_write_dir() + os.makedirs(write_dir, exist_ok=True) + read_dirs = self._get_read_dirs() + + was_processed_hash = SamplesCacher.get_orig_sample_id_hash(orig_sample_id) + was_processed_fn = was_processed_hash+'.pkl' + + # checking in all read directories if information related to this sample(s) was already cached + for curr_read_dir in read_dirs: + fn = os.path.join(curr_read_dir, was_processed_fn) + if os.path.isfile(fn): + ans = load_pickle(fn) + return ans + + result_sample = self._load_sample_using_pipeline(orig_sample_id) + + if isinstance(result_sample, dict): + result_sample = [result_sample] + + if isinstance(result_sample, list): + if 0 == len(result_sample): + result_sample = None + for s in result_sample: + set_initial_sample_id(s, orig_sample_id) + + if not isinstance(result_sample, (list, dict, type(None))): + raise Exception(f'Unsupported sample type, got {type(result_sample)}. Supported types are dict, list-of-dicts and None.') + + if result_sample is not None: + output_info = [] + for curr_sample in result_sample: + curr_sample_id = get_sample_id(curr_sample) + output_info.append(curr_sample_id) + output_sample_hash = SamplesCacher.get_final_sample_id_hash(curr_sample_id) + + requiring_hdf5_keys = _object_requires_hdf5_recurse(curr_sample) + if len(requiring_hdf5_keys)>0: + requiring_hdf5_dict = NDict.get_multi(curr_sample, requiring_hdf5_keys) + requiring_hdf5_dict = requiring_hdf5_dict.flatten() + + hdf5_filename = os.path.join(write_dir, output_sample_hash+'.hdf5') + save_hdf5_safe(hdf5_filename, **requiring_hdf5_dict) + + #remove all hdf5 entries from the sample_dict that will be pickled + for k in requiring_hdf5_dict: + _ = curr_sample.pop(k) + + save_pickle_safe(curr_sample, os.path.join(write_dir, output_sample_hash+'.pkl.gz'), compress=True) + else: + output_info = None + #requiring_hdf5_keys = None + + save_pickle_safe(output_info, os.path.join(write_dir, was_processed_fn)) + return output_info + + + +def _get_available_write_location(cache_dirs:List[str], max_allowed_used_space=0.95): + ''' + :param cache_dirs: write directories. Directories are checked in order that they are provided. + :param max_allowed_used_space: set to a value between 0.0 to 1.0. + a value of 0.95 means that once the available space is greater or equal to 95% of the the disk capacity, + it will be considered full, and the next directory will be attempted. + ''' + + for curr_loc in cache_dirs: + if max_allowed_used_space is None: + return curr_loc + os.makedirs(curr_loc, exist_ok=True) + drive_stats = psutil.disk_usage(curr_loc) + actual_usage_part = drive_stats.percent/100.0 + if actual_usage_part < max_allowed_used_space: + return curr_loc + + raise Exception('Could not find any location to write.\n' + f'write_cache_locations={cache_dirs}\n' + f'max_allowed_used_space={max_allowed_used_space}' + ) + + + + + + + + diff --git a/fuse/data/datasets/caching/tests/__init__.py b/fuse/data/datasets/caching/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/datasets/caching/tests/test_sample_caching.py b/fuse/data/datasets/caching/tests/test_sample_caching.py new file mode 100644 index 000000000..6762248fd --- /dev/null +++ b/fuse/data/datasets/caching/tests/test_sample_caching.py @@ -0,0 +1,168 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest + +from fuse.utils.rand.seed import Seed +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional, Dict +from fuse.data.datasets.caching.samples_cacher import SamplesCacher + +from fuse.utils.ndict import NDict + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + else: + raise Exception(f'unfamiliar sample_id: {sid}') + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestSampleCaching(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + + def test_cache_samples(self): + orig_sample_ids = ['case_1', 'case_2', 'case_3', 'case_4'] + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + pipeline_desc = [ + (OpFakeLoad(), {}), + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + + cacher = SamplesCacher('unittests_cache', pl, cache_dirs, restart_cache=True) + + cacher.cache_samples(orig_sample_ids) + + sample = cacher.load_sample('case_1') + sample = cacher.load_sample('case_2') + sample = cacher.load_sample('case_4_subcase_1') + sample = cacher.load_sample('case_4_subcase_2') + #sample = cacher.load_sample('case_3') #isn't supposed to work + #sample = cacher.load_sample('case_4') #isn't supposed to work + + banana=123 + + def test_same_uniquely_named_cache_and_multiple_pipeline_hashes(self): + orig_sample_ids = ['case_1', 'case_2', 'case_3', 'case_4'] + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_c'), + os.path.join(tmpdir, 'cache_d'), + ] + + pipeline_desc = [ + (OpFakeLoad(), {}), + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + cacher = SamplesCacher('unittests_cache', pl, cache_dirs, restart_cache=True) + + cacher.cache_samples(orig_sample_ids) + + ### now, we modify the pipeline and we DO NOT set restart_cache, to verify an exception is thrown + pipeline_desc = [ + (OpFakeLoad(), {}), + (OpFakeLoad(), {}), ###just doubled it to change the pipeline hash + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + self.assertRaises(Exception, SamplesCacher, 'unittests_cache', pl, cache_dirs, restart_cache=False) + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = NDict(dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + )) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = NDict(dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + )) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/dataset_base.py b/fuse/data/datasets/dataset_base.py new file mode 100644 index 000000000..c4e02b245 --- /dev/null +++ b/fuse/data/datasets/dataset_base.py @@ -0,0 +1,46 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from abc import abstractmethod +from typing import Dict, Hashable, List, Optional, Sequence, Union + +import torch + +class DatasetBase(torch.nn.Module): + @abstractmethod + def create(self, **kwargs) -> None: + """ + Make the dataset operational: might include data caching, reloading and more. + """ + raise NotImplementedError + + @abstractmethod + def summary(self) -> str: + """ + Get string including summary of the dataset + """ + raise NotImplementedError + + @abstractmethod + def get_multi(self, items: Optional[Sequence[Union[int, Hashable]]] = None, *args) -> List[Dict]: + """ + Get multiple items, optionally just some of the keys + :param items: specify the list of sequence to read or None for all + """ + raise NotImplementedError \ No newline at end of file diff --git a/fuse/data/datasets/dataset_default.py b/fuse/data/datasets/dataset_default.py new file mode 100644 index 000000000..d163dab27 --- /dev/null +++ b/fuse/data/datasets/dataset_default.py @@ -0,0 +1,304 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import Any, Dict, Hashable, List, Optional, Sequence, Union + +from warnings import warn +from fuse.data.datasets.dataset_base import DatasetBase +from fuse.data.ops.ops_common import OpCollectMarker +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.utils.ndict import NDict +from fuse.utils.multiprocessing.run_multiprocessed import run_multiprocessed, get_from_global_storage +from fuse.data import get_sample_id, create_initial_sample, get_specific_sample_from_potentially_morphed +import copy +from collections import OrderedDict +import numpy as np +from fuse.data import OpToTensor, OpRepeat + + +class DatasetDefault(DatasetBase): + def __init__(self, + sample_ids: Sequence[Hashable], + static_pipeline: Optional[PipelineDefault] = None, + dynamic_pipeline: Optional[PipelineDefault] = None, + cacher:Optional[SamplesCacher] = None, + allow_uncached_sample_morphing:bool = False, + ): + """ + :param sample_ids: list of sample_ids included in dataset. + :param static_pipeline: static_pipeline, the output of this pipeline will be automatically cached. + :param dynamic_pipeline: dynamic_pipeline. applied sequentially after the static_pipeline, but not automatically cached. + changing it will NOT trigger recaching of the static_pipeline part. + :param cacher: optional SamplesCacher instance which will be used for caching samples to speed up samples loading + :param allow_uncached_sample_morphing: when enabled, allows an Op, to return None, or to return multiple samples (in a list) + + """ + super().__init__() + + # store arguments + self._static_pipeline = static_pipeline + self._dynamic_pipeline = dynamic_pipeline + self._cacher = cacher + self._orig_sample_ids = sample_ids + self._allow_uncached_sample_morphing = allow_uncached_sample_morphing + + #verify unique names for dynamic pipelines + if self._dynamic_pipeline is not None and self._static_pipeline is not None: + if self._static_pipeline.get_name() == self._dynamic_pipeline.get_name(): + raise Exception(f'Detected identical name for static pipeline and dynamic pipeline ({self._static_pipeline.get_name(self._static_pipeline.get_name())}).\nThis is not allowed, please initiate the pipelines with different names.') + + if self._static_pipeline is None: + self._static_pipeline = PipelineDefault("dummy_static_pipeline", ops_and_kwargs=[]) + if self._dynamic_pipeline is None: + self._dynamic_pipeline = PipelineDefault("dummy_dynamic_pipeline", ops_and_kwargs=[]) + + if self._dynamic_pipeline is not None: + assert isinstance(self._dynamic_pipeline, PipelineDefault), f'dynamic_pipeline may be None or a PipelineDefault instance. Instead got {type(self._dynamic_pipeline)}' + + if self._static_pipeline is not None: + assert isinstance(self._static_pipeline, PipelineDefault), f'static_pipeline may be None or a PipelineDefault instance. Instead got {type(self._static_pipeline)}' + + if self._allow_uncached_sample_morphing: + warn("allow_uncached_sample_morphing is enabled! It is a significantly slower mode and should be used ONLY for debugging") + + self._created = False + + + def create(self, num_workers:int = 0) -> None: + """ + Create the data set, including caching + :param num_workers: number of workers. used only when caching is disabled and allow_uncached_sample_morphing is enabled + set num_workers=0 to disable multiprocessing (more convenient for debugging) + Setting num_workers for caching is done in cacher constructor. + :return: None + """ + + self._output_sample_ids_info = None + if self._cacher is not None: + self._output_sample_ids_info = self._cacher.cache_samples(self._orig_sample_ids) + elif self._allow_uncached_sample_morphing: + _output_sample_ids_info_list = run_multiprocessed(DatasetDefault._process_orig_sample_id, + [(sid, self._static_pipeline, False) for sid in self._orig_sample_ids], + workers=num_workers) + + self._output_sample_ids_info = OrderedDict() + self._final_sid_to_orig_sid = {} + for sample_in_out_info in _output_sample_ids_info_list: + orig_sid, out_sids = sample_in_out_info[0], sample_in_out_info[1] + self._output_sample_ids_info[orig_sid] = out_sids + if out_sids is not None: + assert isinstance(out_sids, list) + for final_sid in out_sids: + self._final_sid_to_orig_sid[final_sid] = orig_sid + + if self._output_sample_ids_info is not None: #sample morphing is allowed + self._final_sample_ids = [] + for orig_sid,out_sids in self._output_sample_ids_info.items(): + if out_sids is None: + continue + self._final_sample_ids.extend(out_sids) + else: + self._final_sample_ids = copy.deepcopy(self._orig_sample_ids) + + self._created = True + + def get_all_sample_ids(self): + if not self._created: + raise Exception('you must first call create()') + + return copy.deepcopy(self._final_sample_ids) + + + def __getitem__(self, item: Union[int, Hashable]) -> dict: + """ + Get sample, read from cache if possible + :param item: either int representing sample index or sample_id + :return: sample_dict + """ + return self.getitem(item) + + def getitem(self, item: Union[int, Hashable], collect_marker_name: Optional[str] = None, keys: Optional[Sequence[str]] = None) -> dict: + """ + Get sample, read from cache if possible + :param item: either int representing sample index or sample_id + :param collect_marker_name: Optional, specify name of collect marker op to optimize the running time + :param keys: Optional, return just the specified keys or everything available if set to None + :return: sample_dict + """ + if not self._created: + raise Exception('you must first call create()') + + # get sample id + if isinstance(item, (int, np.integer)): + sample_id = self._final_sample_ids[item] + else: + sample_id = item + + # get collect marker info + collect_marker_info = self._get_collect_marker_info(collect_marker_name) + + # read sample + if self._cacher is not None: + sample = self._cacher.load_sample(sample_id, collect_marker_info["static_keys_deps"]) + + if self._cacher is None: + if not self._allow_uncached_sample_morphing: + sample = create_initial_sample(sample_id) + sample = self._static_pipeline(sample) + if not isinstance(sample, dict): + raise Exception(f'By default when caching is disabled sample morphing is not allowed, and the output of the static pipeline is expected to be a dict. Instead got {type(sample)}. You can use "allow_uncached_sample_morphing=True" to allow this, but be aware it is slow and should be used only for debugging') + else: + orig_sid = self._final_sid_to_orig_sid[sample_id] + sample = create_initial_sample(orig_sid) + sample = self._static_pipeline(sample) + + assert sample is not None + sample = get_specific_sample_from_potentially_morphed(sample, sample_id) + + sample = self._dynamic_pipeline(sample, until_op_id=collect_marker_info['op_id']) + + if not isinstance(sample, dict): + raise Exception(f'The final output of dataset static (+optional dynamic) pipelines is expected to be a dict. Instead got {type(sample)}') + + # get just required keys + if keys is not None: + sample = NDict.get_multi(sample, keys) + + return sample + + + + def _get_multi_multiprocess_func(self, args): + sid, kwargs = args + return self.getitem(sid, **kwargs) + + @staticmethod + def _getitem_multiprocess(item: Union[Hashable, int, np.integer]): + """ + getitem method used to optimize the running time in a multiprocess mode + """ + dataset = get_from_global_storage("dataset_default_get_multi_dataset") + kwargs = get_from_global_storage("dataset_default_get_multi_kwargs") + return dataset.getitem(item, **kwargs) + + + def get_multi(self, items: Optional[Sequence[Union[int, Hashable]]] = None, workers: int = 10, verbose: int = 1, **kwargs) -> List[Dict]: + """ + See super class + :param workers: number of processes to read the data. set to 0 for a single process. + """ + if items is None: + sample_ids = self._final_sample_ids + else: + sample_ids = items + + for_global_storage = {"dataset_default_get_multi_dataset": self, "dataset_default_get_multi_kwargs": kwargs} + + list_sample_dict = run_multiprocessed( + worker_func=self._getitem_multiprocess, + copy_to_global_storage=for_global_storage, + args_list=sample_ids, workers=workers, verbose=verbose) + return list_sample_dict + + def __len__(self): + if not self._created: + raise Exception('you must first call create()') + + return len(self._final_sample_ids) + + # internal methods + + @staticmethod + def _process_orig_sample_id(args): + ''' + Process, without caching, single sample + ''' + orig_sample_id, pipeline, return_sample_dict = args + sample = create_initial_sample(orig_sample_id) + + sample = pipeline(sample) + + output_sample_ids = None + + if sample is not None: + output_sample_ids = [] + if not isinstance(sample, list): + sample = [sample] + for curr_sample in sample: + output_sample_ids.append(get_sample_id(curr_sample)) + + if not return_sample_dict: + return orig_sample_id, output_sample_ids + + return orig_sample_id, output_sample_ids, sample + + def _get_collect_marker_info(self, collect_marker_name: str): + """ + Find the required collect marker (OpCollectMarker in the dynamic pipeline). + See OpCollectMarker for more details + :param collect_marker_name: name to identify the required collect marker + :return: a dictionary with the required info - including: name, op_id and static_keys_deps. + if collect_marker_name is None will return default instruct to run the entire dynamic pipeline + """ + # default values for case collect marker info is not used + if collect_marker_name is None: + return { + "name": None, + "op_id": None, + "static_keys_deps": None + } + + # find the required collect markers and extract the info + collect_marker_info = None + for (op, _), op_id in reversed(zip(self._dynamic_pipeline.ops_and_kwargs, self._dynamic_pipeline._op_ids)): + if isinstance(op, OpCollectMarker): + collect_marker_info_cur = op.get_info() + if collect_marker_info_cur['name'] == collect_marker_name: + if collect_marker_info is None: + collect_marker_info = collect_marker_info_cur + collect_marker_info['op_id'] = op_id + # continue to make sure this is the only one + else: + # throw an error if found more than one collect marker + raise Exception(f"Error: two collect markers with name {collect_marker_info} found in dynamic pipeline") + if collect_marker_info is None: + raise Exception(f"Error: didn't find collect marker with name {collect_marker_info} in dynamic pipeline.") + + return collect_marker_info + + def summary(self) -> str: + sum = "" + sum += f"Type: {type(self).__name__}\n" + sum += f"Num samples: {len(self._final_sample_ids)}\n" + # TODO + # sum += f"Cacher: {self._cacher.summary()}" + # sum += f"Pipeline static: {self._static_pipeline.summary()}" + # sum += f"Pipeline dynamic: {self._dynamic_pipeline.summary()}" + + return sum + + + + + + + + + diff --git a/fuse/data/datasets/dataset_wrap_seq_to_dict.py b/fuse/data/datasets/dataset_wrap_seq_to_dict.py new file mode 100644 index 000000000..10110b215 --- /dev/null +++ b/fuse/data/datasets/dataset_wrap_seq_to_dict.py @@ -0,0 +1,97 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import List, Optional, Union, Sequence +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.utils.sample import get_sample_id + +from torch.utils.data import Dataset + +from fuse.data.ops.op_base import OpBase +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.utils.ndict import NDict + +# Dataset processor +class OpFuse(OpBase): + """ + Op that extract data from pytorch dataset that returning sequence of values and adds those values to sample_dict + """ + + def __init__(self, dataset: Dataset, sample_keys: Sequence[str]): + """ + :param dataset: the pytorch dataset to convert. The dataset[i] expected to return sequence of values or a single value + :param sample_keys: sequence keys - naming each value returned by dataset[i] + """ + # store input arguments + super().__init__() + self._sample_keys = sample_keys + self._dataset = dataset + + def __call__(self, sample_dict: NDict, op_id: Optional[str]) -> Union[None, dict, List[dict]]: + """ + See super class + """ + # extact dataset index + name, dataset_index = get_sample_id(sample_dict) + + # extract values + sample_values = self._dataset[dataset_index] + if not isinstance(sample_values, Sequence): + sample_values = [sample_values] + assert len(self._sample_keys) == len(sample_values), f"Error: expecting dataset[i] to return {len(self._sample_keys)} to match sample keys" + + # add values to sample_dict + for key, elem in zip(self._sample_keys, sample_values): + sample_dict[key] = elem + return sample_dict + + +class DatasetWrapSeqToDict(DatasetDefault): + """ + Fuse Dataset Wrapper + wraps pytorch sequence dataset (pytorch dataset in which each sample, dataset[i] is a sequence of values). + Each value extracted from pytorch sequence dataset will be added to sample_dict. + Plus this dataset inherits all DatasetDefault features + + Example: + torch_seq_dataset = torchvision.datasets.MNIST(path, download=True, train=True) + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='train', dataset=torch_seq_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + + # get sample + sample = train_dataset[index] # sample is a dict with keys: 'data.sample_id', 'data.image' and 'data.label' + """ + + def __init__(self, name: str, dataset: Dataset, sample_keys: Union[Sequence[str], str], cache_dir: Optional[str] = None, **kwargs): + """ + :param name: name of the data extracted from dataset, typically: 'train', 'validation;, 'test' + :param dataset: the dataset to extract the data from + :param sample_keys: sequence keys - naming each value returned by dataset[i] + :param cache_dir: Optional - provied a path in case caching is required to help optimize the running time + :param kwargs: optional, additional arguments to provide to DatasetDefault + """ + sample_ids =[(name, i) for i in range(len(dataset))] + static_pipeline = PipelineDefault(name="staticp", ops_and_kwargs=[(OpFuse(dataset, sample_keys), {})]) + if cache_dir is not None: + cacher = SamplesCacher('dataset_test_cache', static_pipeline, cache_dir, restart_cache=True) + else: + cacher = None + super().__init__(sample_ids=sample_ids, static_pipeline=static_pipeline, cacher=cacher, **kwargs) diff --git a/fuse/data/datasets/sample_caching_audit.py b/fuse/data/datasets/sample_caching_audit.py new file mode 100644 index 000000000..06b104995 --- /dev/null +++ b/fuse/data/datasets/sample_caching_audit.py @@ -0,0 +1,96 @@ +from typing import Optional +from time import time +from deepdiff import DeepDiff +from fuse.data import get_sample_id + +''' +By auditing the samples, "stale" caches can be found, which is very important to detect. +A stale cache of a sample is a cached sample which contains different information then the same sample as it is being freshly created. +There are several reasons that it can happen, for example, a change in some code dependency in some operation in the sample processing pipeline. +Note - setting a too high audit frequency will slow your training. +audit example usage: +# a minimalistic approach, testing only the first sample. Almost no slow down of entire train session, but not periodic audit so higher chance to miss a stale cached sample. +SampleCachingAudit(audit_first_sample=True,audit_rate=None) +) + +#another audit usage example - in this case the first sample will be audited, and also one sample every 20 minutes +SampleCachingAudit(audit_first_sample=True, audit_rate=20, audit_units='minutes') +) +''' + +class SampleCachingAudit: + def __init__(self, + audit_first_sample:bool = True, + audit_rate:Optional[int] = 30, + audit_units:str = 'minutes', + **audit_diff_kwargs:Optional[dict], + ): + ''' + :param audit_rate: how frequently, a sample will be both loaded from cache AND loaded fully without using cache. + Pass 0 or None to disable. + The purpose of this is to detect cases in which the cached samples no longer match the sample loading sequence of Ops, + and a cache reset is required. + Will be ignored if no cacher is provided. + :param audit_units: the units in which audit_rate will be used. Supported options are ['minutes', 'samples'] + Will be ignored if no cacher is provided. + :param **audit_diff_kwargs: optionally, pass custom kwargs to DeepDiff comparison. + This is useful if, for example, you want small epsilon differences to be ignored. + In such case, you can provide math_epsilon=1e-9 to avoid throwing exception for small differences + ''' + + _audit_unit_options = ['minutes', 'samples', None] + if audit_units not in _audit_unit_options: + raise Exception(f'audit_units must be one of {_audit_unit_options}') + self._audit_rate = audit_rate + self._audit_first_sample = audit_first_sample + self._audited_so_far = 0 + if self._audit_rate == 0: + self._audit_rate = None + self._audit_units = audit_units + self._audit_units_passed_since_last_audit = 0.0 + if self._audit_units == 'minutes': + self._prev_time = time() + self._audit_diff_kwargs = audit_diff_kwargs + + def update(self) -> bool: + ''' + Updates internal state related to the audit features (comparison of a sample loaded from cache with a fully loaded/processed sample) + returns whether an audit should occur now or not. + ''' + if (self._audit_first_sample) and (self._audited_so_far==0): + return True + if (self._audit_rate is not None): + #progress audit units passed so far + if self._audit_units == 'minutes': + self._audit_units_passed_since_last_audit += (time()-self._prev_time)/60.0 + self._prev_time = time() + elif self._audit_units == 'samples': + self._audit_units_passed_since_last_audit += 1 + else: + assert False + + #check if we need an audit now + if self._audit_units_passed_since_last_audit >= self._audit_rate: + #reset it + if self._audit_units == 'minutes': + self._audit_units_passed_since_last_audit %= self._audit_rate + else: + self._audit_units_passed_since_last_audit = 0.0 + return True + return False + + def audit(self, cached_sample, fresh_sample): + diff = DeepDiff(cached_sample, fresh_sample, **self._audit_diff_kwargs) + self._audited_so_far += 1 + if len(diff)>0: + raise Exception(f'Error! During AUDIT found a mismatch between cached_sample and loaded sample.\n' + 'Please reset your cache.\n' + 'Note - this can happen if a change in your (static) pipeline Ops is not expressed in the calculated hash function.\n' + 'There are several reasons that can cause this, for example, you are calling, from within your op external code.\n' + 'This is perfectly fine to do, just make sure you reset your cache after such change.\n' + 'Gladly, the Audit feature caught this stale cache state! :)\n' + f'sample id in which this staleness was caught: {get_sample_id(fresh_sample)}\n' + 'NOTE: if small changes between the saved cached and the live-loaded/processed sample are ok for your use case, you can set a tolerance epsilon like this: audit_diff_kwargs={"math_epsilon":1e-9}' + ) + + \ No newline at end of file diff --git a/fuse/data/datasets/tests/__init__.py b/fuse/data/datasets/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/datasets/tests/test_dataset_default.py b/fuse/data/datasets/tests/test_dataset_default.py new file mode 100644 index 000000000..e855d61cc --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_default.py @@ -0,0 +1,264 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest + +from fuse.utils.rand.seed import Seed +#from fuse.utils.file_io.file_io import SAFE_save_hdf5, load_hdf5 +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.utils.ndict import NDict + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + # elif 'case_4_subcase_1' == sid: + # sample_dict = NDict.combine(sample_dict, _generate_sample_1(41)) + # elif 'case_4_subcase_2' == sid: + # sample_dict = NDict.combine(sample_dict, _generate_sample_2(42)) + else: + raise Exception(f'unfamiliar sample_id: {sid}') + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + +class OpPrintContents(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + print(f'sid={sid}') + for k in sample_dict.keypaths(): + print(k) + print('-------------------------\n') + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestDatasetDefault(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_cache_samples_with_sample_morphing(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2','case_3','case_4'] + ################ cached + sample morphing + cacher = SamplesCacher('dataset_test_cache', static_pl, cache_dirs, restart_cache=True) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + ############### not cached + sample morphing + + ds_not_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=None, + allow_uncached_sample_morphing=True, + ) + ds_not_cached.create(num_workers=0) + not_cached_final_sample_ids = ds_not_cached.get_all_sample_ids() + + self.assertEqual( + sorted(cached_final_sample_ids), + sorted(not_cached_final_sample_ids), + ) + + sample_from_cached = ds_cached[3] + sample_from_not_cached = ds_not_cached[3] + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + sample_from_not_cached['data']['cc']['img'].sum() + ) + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + 49948.825007353706 + ) + banana=123 + + def test_cache_samples_no_sample_morphing(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2'] + ################ cached + no sample morphing + cacher = SamplesCacher('dataset_test_cache', static_pl, cache_dirs, restart_cache=True) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + ############### not cached + no sample morphing + + ds_not_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=None, + ###allow_uncached_sample_morphing=False, + ) + ds_not_cached.create(num_workers=0) + not_cached_final_sample_ids = ds_not_cached.get_all_sample_ids() + + self.assertEqual( + sorted(cached_final_sample_ids), + sorted(not_cached_final_sample_ids), + ) + + sample_from_cached = ds_cached[1] + sample_from_not_cached = ds_not_cached[1] + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + sample_from_not_cached['data']['cc']['img'].sum() + ) + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + 50012.88698394645 + ) + banana=123 + + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + ) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + ) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/tests/test_dataset_default_audit_feature.py b/fuse/data/datasets/tests/test_dataset_default_audit_feature.py new file mode 100644 index 000000000..80b82acd0 --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_default_audit_feature.py @@ -0,0 +1,250 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest +from fuse.utils.rand.seed import Seed +from fuse.utils.ndict import NDict + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.datasets.dataset_default import DatasetDefault + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + else: + raise Exception(f'unfamiliar sample_id: {sid}') + sample_dict = ForMonkeyPatching.identity_transform(sample_dict) + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + +class ForMonkeyPatching: + @staticmethod + def identity_transform(sample_dict): + ''' + returns the sample as is. The purpose of this is to be monkey-patched in the audit test. + When it will be modified, the cached samples will become stale, + as this code is called from within an op, and therefore does not participate in the hash generation. + ''' + return sample_dict + +class OpPrintContents(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + print(f'sid={sid}') + for k in sample_dict.keypaths(): + print(k) + print('-------------------------\n') + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestDatasetDefault(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_audit(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2'] + ################ cached + no sample morphing + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_rate=1, + audit_units='samples') + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + print('a...') + sample_from_cached = ds_cached[0] + print('b...') + sample_from_cached = ds_cached[0] + + def small_change(sample_dict): + sample_dict['data']['cc']['img'][10,100,100] += 0.001 + return sample_dict + + ForMonkeyPatching.identity_transform = small_change + + print('c...') + self.assertRaises(Exception, ds_cached, 0) + #sample_from_cached = ds_cached[0] + + ForMonkeyPatching.identity_transform = lambda x:x #return it to previous state + + + ########### do it again, and now test the audit_first_sample + + #recreating cacher to change audit parameters + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_first_sample=True, + audit_rate=None,) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + + ForMonkeyPatching.identity_transform = small_change + + #the first one is expected to raise an exception + self.assertRaises(Exception, ds_cached, 0) + + ForMonkeyPatching.identity_transform = lambda x:x #return it to previous state + + ############################## testing audit_first_sample a bit more + ############################## this time we do the monkey patching only AFTER the first sample was audited (and the staleness will be missed) + + #recreating cacher to change audit params + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_first_sample=True, + audit_rate=None, + ) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + + #there is no problem yet, should work well + sample_from_cached = ds_cached[0] + + #we now monkey patch it, creating a mismatch between the hash and the static pipeline logic + ForMonkeyPatching.identity_transform = small_change + #it won't be caught as it didn't happen in the first sample, and we've set audit_rate to None + sample_from_cached = ds_cached[0] + sample_from_cached = ds_cached[0] + + banana=123 + + + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + ) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + ) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py b/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py new file mode 100644 index 000000000..6064871da --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py @@ -0,0 +1,90 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import os +import unittest + +import random + +import torchvision +from torchvision import transforms +from fuse.utils.rand.seed import Seed +from fuse.utils.ndict import NDict + +import tempfile +from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict + +class TestDatasetWrapSeqToDict(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_dataset_wrap_seq_to_dict(self): + Seed.set_seed(1234) + path = tempfile.gettempdir() + + # Create dataset + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + + torch_train_dataset = torchvision.datasets.MNIST(path, download=True, train=True, transform=transform) + # wrapping torch dataset + train_dataset = DatasetWrapSeqToDict(name='train', dataset=torch_train_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + + # get value + index = random.randint(0, len(train_dataset)) + sample = train_dataset[index] + item = torch_train_dataset[index] + + self.assertTrue(isinstance(sample, dict)) + self.assertTrue('data.image' in sample) + self.assertTrue('data.label' in sample) + self.assertTrue((sample['data.image'] == item[0]).all()) + self.assertEqual(sample['data.label'], item[1]) + + + def test_dataset_cache(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=None) + print(f"torch dataset size = {len(torch_dataset)}") + + + # wrapping torch dataset + tmpdir = tempfile.gettempdir() + cache_dir = os.path.join(tmpdir, 'cache_dir') + + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label'), cache_dir=cache_dir) + dataset.create() + + + def tearDown(self): + pass + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/key_types.py b/fuse/data/key_types.py new file mode 100644 index 000000000..878c0b36f --- /dev/null +++ b/fuse/data/key_types.py @@ -0,0 +1,47 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import * +from fuse.data.patterns import Patterns + +class DataTypeBasic(Enum): + UNKNOWN = -1 #TODO: change to Unknown? + +class TypeDetectorBase(ABC): + @abstractmethod + def get_type(self, sample_dict:Dict, key:str): + ''' + Returns the type of key + The most common implementation can be seen in TypeDetectorPatternsBased. + ''' + raise NotImplementedError + + @abstractmethod + def verify_type(self, sample_dict:Dict, key:str, types: Sequence[Enum]): + ''' + Raises exception if key is not one of the types found in types + ''' + raise NotImplementedError + +class TypeDetectorPatternsBased(TypeDetectorBase): + def __init__(self, patterns_dict:Dict[str,Enum]): + ''' + type detection based on the key (NDict "style" - for example 'data.cc.img') + get_type ignores the sample_dict completely. + TODO: provide usage example + ''' + self._patterns_dict = patterns_dict + self._patterns = Patterns(self._patterns_dict, DataTypeBasic.UNKNOWN) + + def get_type(self, sample_dict:Dict, key:str): + return self._patterns.get_value(key) + + def verify_type(self, sample_dict:Dict, key:str, types: Sequence[Enum]): + self._patterns.verify_value_in(key, types) + + + + + + + + diff --git a/fuse/data/key_types_for_testing.py b/fuse/data/key_types_for_testing.py new file mode 100644 index 000000000..c74f4021e --- /dev/null +++ b/fuse/data/key_types_for_testing.py @@ -0,0 +1,24 @@ +from enum import Enum +from fuse.data.key_types import DataTypeBasic, TypeDetectorPatternsBased +from typing import * + +class DataTypeForTesting(Enum): + """ + Possible data types stored in sample_dict. + Using Patterns - the type will be inferred from the key name + """ + # Default options for types + IMAGE_FOR_TESTING = 0, # Image + SEG_FOR_TESTING = 1, # Segmentation Map + BBOX_FOR_TESTING = 2, # Bounding Box + CTR_FOR_TESTING = 3, # Contour + +PATTERNS_DICT_FOR_TESTING = { + r".*img_for_testing$": DataTypeForTesting.IMAGE_FOR_TESTING, + r".*seg_for_testing$": DataTypeForTesting.SEG_FOR_TESTING, + r".*bbox_for_testing$": DataTypeForTesting.BBOX_FOR_TESTING, + r".*ctr_for_testing$": DataTypeForTesting.CTR_FOR_TESTING, + r".*$": DataTypeBasic.UNKNOWN, + } + +type_detector_for_testing = TypeDetectorPatternsBased(PATTERNS_DICT_FOR_TESTING) diff --git a/fuse/data/ops/__init__.py b/fuse/data/ops/__init__.py new file mode 100644 index 000000000..28791caa7 --- /dev/null +++ b/fuse/data/ops/__init__.py @@ -0,0 +1 @@ +from fuse.data.ops.caching_tools import get_function_call_str diff --git a/fuse/data/ops/caching_tools.py b/fuse/data/ops/caching_tools.py new file mode 100644 index 000000000..a6ac1ea7a --- /dev/null +++ b/fuse/data/ops/caching_tools.py @@ -0,0 +1,137 @@ +import inspect +from typing import Callable, Any, Type, Optional, Sequence +from inspect import stack +import warnings + +def get_function_call_str(func, *_args, **_kwargs) -> str: + ''' + Converts a function and its kwargs into a hash value which can be used for caching. + NOTE: + 1. This is far from being bulletproof, the op might call another function which is not covered and is changed, + which will make the caching processing be unaware. + 2. This is a mechanism that helps to spot SOME of such issues, NOT ALL + 3. Only a specific subset of arg types contribute to the caching, mainly simple native python types. + see 'value_to_string' for more details. + For example, if an arg is an entire numpy array, it will not contribute to the total hash. + The reason is that it will make the cache calculation too slow, and might + ''' + + kwargs = convert_func_call_into_kwargs_only(func, *_args, **_kwargs) + + args_flat_str = func.__name__+'@' + args_flat_str += '@'.join(['{}@{}'.format(str(k), value_to_string(kwargs[k])) for k in sorted(kwargs.keys())]) + args_flat_str += '@' + str(inspect.getmodule(func)) #adding full (including scope) name of the function, for the case of multiple functions with the same name + args_flat_str += '@'+inspect.getsource(func) #considering the source code (first level of it...) + + return args_flat_str + +def value_to_string(val:Any, warn_on_types:Optional[Sequence]=None) -> str: + ''' + Used by default in several caching related hash builders. + Ignores <...> string as they usually change between different runs + (for example, due to pointing to a specific memory address) + ''' + if warn_on_types is not None: + if isinstance(val, tuple(list(warn_on_types))): + warnings.warn(f'type {type(val)} is possibly participating in hashing, this is usually not optimal performance wise.') + ans = str(val) + if ans.startswith('<'): + return '' + return str(val) + +def convert_func_call_into_kwargs_only(func:Callable, *args, **kwargs) -> dict: + ''' + considers positional and kwargs (including their default values !) + and converts into ONLY kwargs + ''' + signature = inspect.signature(func) + + my_kwargs = { + k: v.default + for k, v in signature.parameters.items() + if v.default is not inspect.Parameter.empty + } + + #convert positional args into kwargs + #uses the fact that zip stops on the smallest length ( so only as much as len(args)) + for curr_pos_arg, pos_arg_name in zip(args, inspect.getfullargspec(func).args): + my_kwargs[pos_arg_name] = curr_pos_arg + + my_kwargs.update(kwargs) + + return my_kwargs + +def get_callers_string_description( + max_look_up:int, + expected_class: Type, + expected_function_name: str, + value_to_string_func: Callable = value_to_string, + ): + ''' + iterates on the callstack, and accumulates a string representation of the callers args. + Used in OpBase to "record" the __init__ args, to be used in the string representation of an Op, + which is used for building a hash value for samples caching in SamplesCacher + + example call: + + class A: + def __init__(self): + text = get_callers_string_description(4, A, + + class B(A): + def __init__(self, blah, blah2): + super().__init__() + #... some logic + + + + :param max_look_up: how many stack frames to look up + :param expected_class: what class is the method expected to be, + stack frames in a different class will be skipped. + pass None for not requiring any class + :param expected_function_name: what is the name of the function to allow, + stack frames in a different function name will be skipped, + pass None for not requiring anything + :param value_to_string_func: allows to provide a custom function for converting values to strings + :param + ''' + + str_desc = '' + try: + curr_stack = stack() + curr_locals = None + #note: frame 0 is this function, frame 1 is whoever called this (and wanted to know about its callers), + #so both frames 0+1 are skipped. + for i in range(2, min(len(curr_stack),max_look_up+2)): + curr_locals = curr_stack[i].frame.f_locals + if expected_class is not None: + if 'self' not in curr_locals: + continue + if not isinstance(curr_locals['self'], expected_class): + continue + + if expected_function_name is not None: + if expected_function_name != str(curr_stack[i].function): + continue + + curr_str = '.'.join([ + str(curr_locals['self'].__module__), #module is probably not needed as class already contains it + str(curr_locals['self'].__class__), + str(curr_stack[i].function), + ]) + + curr_str += inspect.getsource(curr_stack[i].frame) + for k,d in curr_stack[i].frame.f_locals.items(): + if 'self' == k: + continue + if k.startswith('__'): + continue + curr_str += '@'+str(k)+'@'+value_to_string_func(d) + + str_desc += curr_str + + finally: + del curr_locals + del curr_stack + + return str_desc \ No newline at end of file diff --git a/fuse/data/ops/op_base.py b/fuse/data/ops/op_base.py new file mode 100644 index 000000000..b34e62a2b --- /dev/null +++ b/fuse/data/ops/op_base.py @@ -0,0 +1,128 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import Dict, Union, List, Sequence, Any, Optional, Callable +from abc import ABC, abstractmethod +from enum import Enum +from collections import OrderedDict +from fuse.data.patterns import Patterns +from fuse.data.ops import get_function_call_str +from inspect import stack +from fuse.data.ops.caching_tools import get_callers_string_description, value_to_string +from fuse.utils.ndict import NDict + +class OpBase(ABC): + """ + Operator Base Class + Operators are the building blocks of the sample processing pipeline. + Each operator gets as an input the sample_dict as created be the previous operators + and can either add/delete/modify fields in sample_dict. + """ + + _MISSING_SUPER_INIT_ERR_MSG = 'Did you forget to call super().__init__() ? Also, make sure you call it BEFORE setting any attribute.' + + def __init__(self, value_to_string_func: Callable = value_to_string): + ''' + :param value_to_string_func: when init is called, a string representation of the caller(s) init args are recorded. + This is used in __str__ which is used later for hashing in caching related tools (for example, SamplesCacher) + value_to_string_func allows to provide a custom function that converts a value to string. + This is useful if, for example, a custom behavior is desired for an object like numpy array or DataFrame. + The expected signature is: foo(val:Any) -> str + ''' + + #the following is used to extract callers args, for __init__ calls up the stack of classes inheirting from OpBase + #this way it can happen in the base class and then anyone creating new Ops will typically only need to add + #super().__init__ in their __init__ implementation + self._stored_init_str_representation = get_callers_string_description( + max_look_up=4, + expected_class=OpBase, + expected_function_name='__init__', + value_to_string_func = value_to_string_func + ) + + def __setattr__(self, name, value): + ''' + Verifies that super().__init__() is called before setting any attribute + ''' + storage_name = '_stored_init_str_representation' + if name != storage_name and not hasattr(self, storage_name): + raise Exception(OpBase._MISSING_SUPER_INIT_ERR_MSG) + super().__setattr__(name, value) + + @abstractmethod + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + call function that apply the operation + :param sample_dict: the generated dictionary generated so far (generated be the previous ops in the pipeline) + The first op will typically get just the sample_id stored in sample_dict['data']['sample_id'] + :param op_id: unique identifier for an operation. + Might be used to support reverse operation as sample_dict key in case information should be stored in sample_dict. + In such a case use sample_dict[op_id] = info_to_store + :param kwargs: additional arguments defined per operation + :return: Typically modified sample_dict. + There are two special cases supported only if the operation is in static pipeline: + * return None - ignore the sample and do not raise an error + * return list of sample_dict - a case splitted to few samples. for example image splitted to patches. + """ + raise NotImplementedError + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + reverse operation + If a reverse operation is not necessary (for example operator that reads an image), + just implement a reverse method that does nothing. + + If reverse operation is necessary but not required by the project, + keep the base implementation which will throw an NotImplementedError in case the reverse operation will be called. + + To support reverse operation, store the parameters which necessary to apply the reverse operation + such as key to the transformed value and the argument to the transform operation in sample_dict[op_id]. + Those values can be extracted back during the reverse operation. + + :param sample_dict: the dictionary as modified by the previous steps (reversed direction) + :param op_id: See op_id in __call__ function + :param key_to_reverse: the required value to reverse + :param key_to_follow: run the reverse according to the operation applied on this value + :return: modified sample_dict + """ + raise NotImplemented + + def __str__(self) -> str: + ''' + A string representation of this operation, which will be used for hashing. + It includes recorded (string) data describing the args that were used in __init__() + you can override/extend it in the rare cases that it's needed + + example: + + class OpSomethingNew(OpBase): + def __init__(self): + super().__init__() + def __str__(self): + ans = super().__str__(self) + ans += 'whatever you want to add" + + ''' + + if not hasattr(self, '_stored_init_str_representation'): + raise Exception(OpBase._MISSING_SUPER_INIT_ERR_MSG) + call_repr = get_function_call_str(self.__call__, ) + + return f'init_{self._stored_init_str_representation}@call_{call_repr}' + + diff --git a/fuse/data/ops/ops_aug_common.py b/fuse/data/ops/ops_aug_common.py new file mode 100644 index 000000000..3d175a7e6 --- /dev/null +++ b/fuse/data/ops/ops_aug_common.py @@ -0,0 +1,164 @@ +from typing import List, Optional, Sequence, Union + + +from fuse.utils.rand.param_sampler import RandBool, draw_samples_recursively + +from fuse.data.ops.op_base import OpBase +from fuse.data.ops.ops_common import OpRepeat + +from fuse.utils.ndict import NDict + +class OpRandApply(OpBase): + def __init__(self, op: OpBase, probability: float): + """ + Randomly apply the op (according to the given probability) + :param op: op + """ + super().__init__() + self._op = op + self._param_sampler = RandBool(probability=probability) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + apply = self._param_sampler.sample() + sample_dict[op_id] = apply + if apply: + sample_dict = self._op(sample_dict, f"{op_id}.apply", **kwargs) + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + apply = sample_dict[op_id] + if apply: + sample_dict = self._op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}.apply") + + return sample_dict + +class OpSample(OpBase): + """ + recursively searches for ParamSamplerBase instances in kwargs, and replaces the drawn values inplace before calling to op.__call__() + + For example: + from fuse.utils.rand.param_sampler import Uniform + pipeline_desc = [ + #... + OpSample(OpRotateImage()), {'rotate_angle': Uniform(0.0,360.0)} + #... + ] + + OpSample will draw from the Uniform distribution, and will (e.g.) pass rotate_angle=129.43 to OpRotateImage call. + + """ + + def __init__(self, op: OpBase): + """ + :param op: op + """ + super().__init__() + self._op = op + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + sampled_kwargs = draw_samples_recursively(kwargs) + return self._op(sample_dict, op_id, **sampled_kwargs) + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + return self._op.reverse(sample_dict, key_to_reverse, key_to_follow, op_id) + +class OpSampleAndRepeat(OpSample): + """ + First sample kwargs and then repeat op with the exact same sampled arguments. + This is the equivalent of using OpSample around an OpRepeat. + + Typical usage pattern: + pipeline_desc = [ + (OpSampleAndRepeat( + [op to run], + [a list of dicts describing what to repeat] ), + [a dictionary describing values that should be the same in all repeated invocations, may include sampling operations like Uniform, RandBool, etc.] ), + ] + + Example use case: + randomly choose a rotation angle, and then use the same randomly selected rotation angle + for both an image and its respective ground truth segmentation map + + from fuse.utils.rand.param_sampler import Uniform + pipeline_desc = [ + #... + (OpSampleAndRepeat(OpRotateImage(), + [dict(key='data.input.img'), dict(key='data.gt.seg')] ), + dict(angle=Uniform(0.0,360.0)) #this will be drawn only once and the same value will be passed on both OpRotateImage invocation + ), + #... + ] + + #note: this is a convinience op, and it is the equivalent of composing OpSample and OpRepeat yourself. + The previous example is effectively the same as: + + pipeline_desc = [ + #... + OpSample(OpRepeat(OpRotateImage( + [dict(key='data.input.img'), dict(key='data.gt.seg')]), + dict(angle=Uniform(0.0,360.0))) + ), + #... + ] + + note: see OpRepeatAndSample if you are searching for the opposite flow - drawing a different value per repeat invocation + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + :param op: the operation to repeat with the same sampled arguments + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__(OpRepeat(op, kwargs_per_step_to_add)) + +class OpRepeatAndSample(OpRepeat): + """ + Repeats an op multiple times, each time with different kwargs, and draws random values from distribution SEPARATELY per invocation. + + An example usage scenario, let's say that you train a model which is expected get as input two images: + 'data.input.adult_img' which is an image of an adult, and + 'data.input.child_img' which is an image of a child + + the model task is to predict if this child is a child of this adult (a binary classification task). + + The model is expected to work on images that are rotated to any angle, and there's no reason to suspect correlation between the rotation of the two images, + so you would like to use rotation augmentation separately for the two images. + + In this case you could do: + + pipeline_desc = [ + #... + (OpRepeatAndSample(OpRotateImage(), + [dict(key='data.input.adult_img'), dict(key='data.input.child_img')]), + dict(dict(angle=Uniform(0.0,360.0)) ### this will be drawn separately per OpRotateImage invocation + ) + #... + ] + + + note: see also OpSampleAndRepeat if you are looking for the opposite flow, drawing the same value and using it for all repeat invocations + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + :param op: the operation to repeat + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__(OpSample(op), kwargs_per_step_to_add) + + + diff --git a/fuse/data/ops/ops_cast.py b/fuse/data/ops/ops_cast.py new file mode 100644 index 000000000..d3ee3d99c --- /dev/null +++ b/fuse/data/ops/ops_cast.py @@ -0,0 +1,167 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from abc import abstractmethod +from os import stat +from typing import Any, List, Optional, Sequence, Union +import numpy as np + +from fuse.data import OpBase +import torch +from torch import Tensor +from fuse.utils.ndict import NDict + +class Cast: + """ + Cast methods + """ + @staticmethod + def to_tensor(value: Any, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> Tensor: + """ + Convert many types to tensor + """ + if isinstance(value, torch.Tensor) and dtype is None and device is None: + pass # do nothing + elif isinstance(value, (torch.Tensor)): + value = value.to(dtype=dtype, device=device) + elif isinstance(value, (np.ndarray, int, float, list)): + value = torch.tensor(value, dtype=dtype, device=device) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_numpy(value: Any, dtype: Optional[np.dtype] = None) -> np.ndarray: + """ + Convert many types to numpy + """ + if isinstance(value, np.ndarray) and dtype is None: + pass # do nothing + elif isinstance(value, (torch.Tensor, int, float, list, np.ndarray)): + value = np.array(value, dtype=dtype) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_int(value: Any) -> np.ndarray: + """ + Convert many types to int + """ + if isinstance(value, int): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray, float)): + value = int(value) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_float(value: Any) -> np.ndarray: + """ + Convert many types to float + """ + + if isinstance(value, float): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray, int)): + value = float(value) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_list(value: Any) -> np.ndarray: + """ + Convert many types to list + """ + + if isinstance(value, list): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray)): + value = value.tolist() + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + def to(value: Any, type_name: str) -> Any: + """ + Convert any type to type specified in type_name + """ + + if type_name == "ndarray": + return Cast.to_numpy(value) + if type_name == "Tensor": + return Cast.to_tensor(value) + if type_name == "float": + return Cast.to_float(value) + if type_name == "list": + return Cast.to_list(value) + + +class OpCast(OpBase): + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: Union[str, Sequence[str]], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + :param key: single key or list of keys from sample_dict to convert + """ + if isinstance(key, str): + keys = [key] + else: + keys = key + + for key_name in keys: + value = sample_dict[key_name] + sample_dict[f"{op_id}_{key_name}"] = type(value).__name__ + value = self._cast(value, **kwargs) + sample_dict[key_name] = value + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + type_name = sample_dict[f"{op_id}_{key_to_follow}"] + value = sample_dict[key_to_reverse] + value = Cast.to(value, type_name) + sample_dict[key_to_reverse] = value + + return sample_dict + + @abstractmethod + def _cast(self): + raise NotImplementedError + +class OpToTensor(OpCast): + """ + Convert many types to tensor + """ + def _cast(self, value: Any, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> Tensor: + return Cast.to_tensor(value, dtype, device) + + +class OpToNumpy(OpCast): + """ + Convert many types to numpy + """ + def _cast(self, value: Any, dtype: Optional[np.dtype] = None) -> np.ndarray: + return Cast.to_numpy(value) \ No newline at end of file diff --git a/fuse/data/ops/ops_common.py b/fuse/data/ops/ops_common.py new file mode 100644 index 000000000..802794861 --- /dev/null +++ b/fuse/data/ops/ops_common.py @@ -0,0 +1,357 @@ +from typing import Callable, Dict, List, Optional, OrderedDict, Sequence, Tuple, Union +from fuse.data.key_types import TypeDetectorBase +import copy +from enum import Enum +from fuse.data.key_types import TypeDetectorBase +from .op_base import OpBase, Patterns #DataType, +from fuse.utils.ndict import NDict + + +class OpRepeat(OpBase): + """ + Repeat an op multiple times + + Typically used to apply the same operation on a list of keys in sample_dict + Example: + " + + repeat_for = + + #... + (OpRepeat(OpCropToMinimalBBox(), + [dict(key='data.cc.image'), dict(key='data.mlo.image'),dict(key='data.mlo.seg', margin=100)] #per provided dict a new OpCropToMinimalBBox invocation will be triggered + )), + dict(margin=12)), #this value will be passed to all OpCropToMinimalBBox invocations + #... + ] + + note - the values in provided in the list of dicts will *override* any kwargs + In the example above, margin=12 will be used for both 'data.cc.image' and 'data.mlo.image', + but a value of margin=100 will be used for 'data.mlo.seg' + + " + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + See example above + :param op: the operation to repeat + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__() + self._op = op + self._kwargs_per_step_to_add = kwargs_per_step_to_add + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + + for step_index, step_kwargs_to_add in enumerate(self._kwargs_per_step_to_add): + step_kwargs = copy.copy(kwargs) + step_kwargs.update(step_kwargs_to_add) + full_step_id = f"{op_id}_{step_index}" + sample_dict[full_step_id+'_debug_info.op_name'] = self._op.__class__.__name__ + sample_dict = self._op(sample_dict, full_step_id, **step_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + for step_index in reversed(range(len(self._kwargs_per_step_to_add))): + sample_dict = self._op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{step_index}") + + return sample_dict + +class OpLambda(OpBase): + """ + Apply simple lambda function / function to transform single value from sample_dict (or the all dictionary) + Optionally add reverse method if required. + Example: + OpLambda(func=lambda x: torch.tensor(x)) + """ + def __init__(self, + func: Callable, + func_reverse: Optional[Callable] = None, + **kwargs): + super().__init__(**kwargs) + self._func = func + self._func_reverse = func_reverse + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, **kwargs) -> Union[None, dict, List[dict]]: + """ + More details in super class + :param key: apply lambda func on sample_dict[key]. If none the input and output of the lambda function are the entire sample_dict + """ + sample_dict[op_id] = key + if key is not None: + value = sample_dict[key] + value = self._func(value, **kwargs) + sample_dict[key] = value + else: + sample_dict = self._func(sample_dict) + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + key = sample_dict[op_id] + if key is not None: + if key == key_to_follow: + value = sample_dict[key_to_reverse] + value = self._func_reverse(value) + sample_dict[key_to_reverse] = value + else: + sample_dict = self._func_reverse(sample_dict) + + return sample_dict + +class OpFunc(OpBase): + ''' + Helps to wrap an existing simple python function without writing boilerplate code. + + The wrapped function format is: + + def foo(*, *kwargs) -> Tuple: + pass + + + Example: + + def add_seperator(text:str, sep=' '): + return sep.join(text) + + OpAddSeperator = OpFunc(add_seperator) + + usage in pipeline: + + pipeline = [ + (OpAddSeperator, dict(inputs={'data.text_input':'text'}, outputs='data.text_input'), # + ] + + + ''' + def __init__(self, func: Callable, **kwargs): + """ + :param func: a callable to call in __call__() + :param inputs: benedictionary that map between the key_name of a value stored in sample_dict the the input argument name in func + :param outputs: sequence of key_names to store each return value of func. + """ + super().__init__(**kwargs) + self._func = func + + def __call__(self, sample_dict: NDict, op_id: Optional[str], inputs: Dict[str, str], outputs: Union[Sequence[str], str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + # extract inputs from sample dict + kwargs_from_sample_dict = {} + for input_key_name, func_arg_name in inputs.items(): + value = sample_dict[input_key_name] + kwargs_from_sample_dict[func_arg_name] = value + + # all kwargs + all_kwargs = copy.copy(kwargs) + all_kwargs.update(kwargs_from_sample_dict) + func_outputs = self._func(**all_kwargs) + + # add to sample_dict + if isinstance(outputs, str): + sample_dict[outputs] = func_outputs + elif isinstance(outputs, Sequence): + assert len(func_outputs) == len(outputs), f"expecting that function {self._func} will output {len(outputs)} values" + for output_name, output_value in zip(outputs, func_outputs): + sample_dict[output_name] = output_value + else: + raise Exception(f"expecting outputs to be either str or sequence of str. got {type(self._outputs).__name__}") + + + return sample_dict + +class OpApplyPatterns(OpBase): + """ + Select and apply an operation according to key name. + Instead of specifying every relevant key, the op will be applied for every key that matched a specified pattern + Example: + patterns_dict = OrderedDict([(r"^.*.cc.img$|^.*.cc.seg$", (op_affine, dict(rotate=Uniform(-90.0, 90.0))), + (r"^.*.mlo.img$|^.*.mlo.seg$", (op_affine, dict(rotate=Uniform(-45.0, 54.0)))]) + op_apply_pat = OpApplyPatterns(patterns_dict) + """ + def __init__(self, patterns_dict: Optional[OrderedDict] = None): + """ + :param patterns_dict: map a regex pattern to a pair of op and arguments (will be added/override the arguments provided in __call__() function). + For given value in a sample dict, it will look for the first match in the order dict and will apply the op on this specific key. + The ops specified in patterns_dict, must implement a __call__ method with an argument called key. + """ + super().__init__() + self._patterns_dict = Patterns(patterns_dict, (None, None)) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + + for key in sample_dict.keypaths(): + op, op_kwargs_to_add = self._patterns_dict.get_value(key) + if op is None: + continue + + op_kwargs = copy.copy(kwargs) + op_kwargs.update(op_kwargs_to_add) + sample_dict = op(sample_dict, f"{op_id}_{key}", key=key, **op_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + op, _ = self._patterns_dict.get_value(key_to_follow) + if op is None: + return + + sample_dict = op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{key_to_follow}") + + return sample_dict + +class OpApplyTypes(OpBase): + """ + Select and apply an operation according value type (inferred from key name). See OpBase for more information about how it is inferred. + Instead of specifying every relevant key, the op will be applied for every key that matched a specified pattern + Example: + types_dict = { DataType.Image: (op_affine_image, dict()), + DataType.Seg: (op_affine_image, dict()), + BBox: (op_affine_bbox, dict())} + + op_apply_type = OpApplyTypes(types_dict) + """ + def __init__(self, + type_to_op_dict: Dict[Enum, Tuple[OpBase, dict]], + type_detector: TypeDetectorBase): + """ + :param type_to_op_dict: map a type (See enum DataType) to a pair of op and correspending arguments (will be added/override the arguments provided in __call__() function) + """ + super().__init__() + self._type_to_op_dict = type_to_op_dict + self._type_detector = type_detector + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + all_keys = sample_dict.keypaths() + for key in all_keys: + key_type = self._type_detector.get_type(sample_dict, key) + + op, op_kwargs_to_add = self._type_to_op_dict.get(key_type, (None, None)) + if op is None: + continue + + op_kwargs = copy.copy(kwargs) + op_kwargs.update(op_kwargs_to_add) + if 'key' in op_kwargs: + raise Exception('OpApplyTypes::"key" is already found in kwargs. Are you calling OpApplyTypes from within OpApplyTypes? it is not supported.') + sample_dict = op(sample_dict, f"{op_id}_{key}", key, **op_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + key_type = self._type_detector.get_type(sample_dict, key_to_follow) + op, _ = self._type_to_op_dict.get(key_type, (None, None)) + if op is None: + return + + sample_dict = op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{key_to_follow}") + + return sample_dict + +class OpCollectMarker(OpBase): + """ + Use this op within the dynamic pipeline to optimizer the reading time for components such as sampler, export and stats that don't need to read the entire sample. + OpCollectMarker will specify the last op to call to get all the required information from sample. + In addition, to avoid from reading the entire sample including images, OpCollectMarker can also specify the list of keys required for the relevant part of the dynamic pipeline. + + Examples: + 1. + The static pipeline generates a sample including an image ('data.image') and a label ('data.label'). + The training set sampler configured to balance a batch according to 'data.label' + To optimize the reading time of the sampler: + Add at the beginning of the dynamic pipeline - + OpCollectMarker(name="sampler", static_keys_deps=["data.label"]) + 2. + The static pipeline generate an image ('data.image') and a metadata ('data.metadata'). + The dynamic pipeline includes few operations reading 'data.metadata' and that set a value used to balance the class (op_do and op_convert). + To optimize the reading time of the sampler: + Move op_do and op_convert to the beginning of the pipeline. + Add just after them the following op: + OpCollectMarker(name="sampler", static_kets_deps=["data.metadata"]) + + In both cases the sampler can now read subset of the sample using: dataset.get_multi(collect_marker_name="sampler", ..) + """ + def __init__(self, name: str, static_key_deps: Sequence[str]): + super().__init__() + self._name = name + self._static_keys_deps = static_key_deps + + def get_info(self) -> dict: + """ + Returns collect marker info including name and static_keys_deps + """ + return { + "name": self._name, + "static_keys_deps": self._static_keys_deps + } + + def __call__(self, sample_dict: dict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + pass + + def reverse(self, sample_dict: dict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + pass + + +class OpKeepKeypaths(OpBase): + ''' + Use this op to keep only the defined keypaths in the sample + A case where this is useful is if you want to limit the amount of data that gets transfered by multiprocessing by DataLoader workers. + You can keep only what you want to enter the collate. + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], keep_keypaths:List[str]) -> Union[None, dict, List[dict]]: + prev_sample_dict = sample_dict + sample_dict = NDict() + for k in keep_keypaths: + sample_dict[k] = prev_sample_dict[k] + return sample_dict + + diff --git a/fuse/data/ops/ops_common_for_testing.py b/fuse/data/ops/ops_common_for_testing.py new file mode 100644 index 000000000..b6c19fdea --- /dev/null +++ b/fuse/data/ops/ops_common_for_testing.py @@ -0,0 +1,7 @@ +from fuse.data.ops.ops_common import OpApplyTypes +from fuse.data.key_types_for_testing import type_detector_for_testing +from functools import partial + +OpApplyTypesImaging = partial(OpApplyTypes, + type_detector = type_detector_for_testing, +) \ No newline at end of file diff --git a/fuse/data/ops/ops_read.py b/fuse/data/ops/ops_read.py new file mode 100644 index 000000000..5555e7f0f --- /dev/null +++ b/fuse/data/ops/ops_read.py @@ -0,0 +1,101 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import Hashable, List, Optional, Dict, Union +import pandas as pd + +from fuse.data import OpBase +from fuse.utils.ndict import NDict + +class OpReadDataframe(OpBase): + """ + Op reading data from pickle file / dataframe object. + Each row will be added as a value to sample dict + """ + + def __init__(self, + data: Optional[pd.DataFrame] = None, + data_filename: Optional[str] = None, + columns_to_extract: Optional[List[str]] = None, + rename_columns: Optional[Dict[str, str]] = None, + key_name: str = 'data.sample_id', + key_column: str = 'sample_id'): + """ + :param data: input DataFrame + :param data_filename: path to a pickled DataFrame (possible zipped) + :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted + :param rename_columns: rename columns from dataframe, when None (default) column names are kept + :param key_name: name of value in sample_dict which will be used as the key/index + :param key_column: name of the column which use as key/index + """ + super().__init__() + + # store input + self._data_filename = data_filename + self._columns_to_extract = columns_to_extract + self._rename_columns = rename_columns + self._key_name = key_name + self._key_column = key_column + df = data + + # verify input + if data is None and data_filename is None: + msg = "Error: need to provide either in-memory DataFrame or a path to file." + raise Exception(msg) + elif data is not None and data_filename is not None: + msg = "Error: need to provide either 'data' or 'data_filename' args, bot not both." + raise Exception(msg) + + # read dataframe + if self._data_filename is not None: + df = pd.read_pickle(self._data_filename) + + # extract only specified columns (in case not specified, extract all) + if self._columns_to_extract is not None: + df = df[self._columns_to_extract] + + # rename columns + if self._rename_columns is not None: + df.rename(self._rename_columns, axis=1, inplace=True) + + # convert to dictionary: {index -> {column -> value}} + df = df.set_index(self._key_column) + self._data = df.to_dict(orient='index') + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See base class + """ + key = sample_dict[self._key_name] + # locate the required item + sample_data = self._data[key].copy() + + # add values tp sample_dict + for name, value in sample_data.items(): + sample_dict[f"data.{name}"] = value + + return sample_dict + + def get_all_keys(self) -> List[Hashable]: + """ + :return: list of dataframe index values + """ + return list(self.data.keys()) + + \ No newline at end of file diff --git a/fuse/data/ops/ops_visprobe.py b/fuse/data/ops/ops_visprobe.py new file mode 100644 index 000000000..fa6b05b08 --- /dev/null +++ b/fuse/data/ops/ops_visprobe.py @@ -0,0 +1,186 @@ +from typing import Callable, Dict, List, Optional, OrderedDict, Sequence, Tuple, Union +import copy +import enum + +from fuse.utils.ndict import NDict +from fuse.data.visualizer.visualizer_base import VisualizerBase +from .op_base import OpBase +from fuse.data.key_types import TypeDetectorBase + +class VisFlag(enum.IntFlag): + COLLECT = 1 #save current state for future comparison + SHOW_CURRENT = 2 #show current state + SHOW_COLLECTED = 4 #show comparison of all previuosly collected states + CLEAR = 8 #clear all collected states until this point in the pipeline + ONLINE = 16 #show operations will prompt the user with the releveant plot + OFFLINE = 32 #show operations will write to disk (using the caching mechanism) the relevant info (state or states for comparison) + FORWARD = 64 #visualization operation will be activated on forward pipeline execution flow + REVERSE = 128 #visualization operation will be activated on reverse pipeline execution flow + SHOW_ALL_COLLECTED = 256 #show comparison of all previuosly collected states + +class VisProbe(OpBase): + """ + Handle visualization, saves, shows and compares the sample with respect to the current state inside a pipeline + In most cases VisProbe can be used regardless of the domain, and the domain specific code will be implemented + as a Visualizer inheriting from VisualizerBase. In some cases there might be need to also inherit from VisProbe. + + Important notes: + - running in a cached environment is dangerous and is prohibited + - this Operation is not thread safe ans so multithreading is also discouraged + + " + """ + + def __init__(self,flags: VisFlag, + keys: Union[List, dict] , + type_detector: TypeDetectorBase, + id_filter: Union[None, List] = None, + visualizer: VisualizerBase = None, + cache_path: str = "~/"): + """ + :param flags: operation flags (or possible concatentation of flags using IntFlag), details: + COLLECT - save current state for future comparison + SHOW_CURRENT - show current state + SHOW_COllected - show comparison of all previuosly collected states + CLEAR - clear all collected states until this point in the pipeline + ONLINE - show operations will prompt the user with the releveant plot + OFFLINE - show operations will write to disk (using the caching mechanism) the relevant info (state or states for comparison) + FORWARD - visualization operation will be activated on forward pipeline execution flow + REVERSE - visualization operation will be activated on reverse pipeline execution flow + :param keys: for which sample keys to handle visualization, also can be grouped in a dictionary + :param id_filter: for which sample id's to be activated, if None, active for all samples + :param visualizer: the actual visualization handler, depands on domain and use case, should implement Visualizer Base + :param cache_path: root dir to save the visualization outputs in offline mode + + few issues to be aware of, detailed in github issues regarding static cached pipeline and multiprocessing + note - if both forward and reverse are on, then by default, on forward we do collect and on reverse we do show_collected to + compare reverse operations + for each domain we inherit for VisProbe like ImagingVisProbe,... +""" + super().__init__() + self._id_filter = id_filter + self._keys = keys + self._flags = flags + self._cacher = None + self._collected_prefix = "data.$vis" + self._cache_path = cache_path + self._visualizer = visualizer + self._type_detector = type_detector + + def _extract_collected(self, sample_dict: NDict): + res = [] + if not self._collected_prefix in sample_dict: + return res + else: + for vdata in sample_dict[self._collected_prefix]: + res.append(vdata) + return res + + def _extract_data(self, sample_dict: NDict, keys, op_id): + if type(keys) is list: + # infer keys groups + keys.sort() + first_type = self._type_detector.get_type(sample_dict, keys[0]) + num_of_groups = len([self._type_detector.get_type(sample_dict, k) for k in keys if self._type_detector.get_type(sample_dict, k) == first_type]) + keys_per_group = len(keys) // num_of_groups + keys = {f"group{i}": keys[i:i + keys_per_group] for i in range(0, len(keys), keys_per_group)} + + res = NDict() + for group_id, group_keys in keys.items(): + for key in group_keys: + prekey = f'groups.{group_id}.{key.replace(".", "_")}' + res[f'{prekey}.value'] = sample_dict[key] + res[f'{prekey}.type'] = self._type_detector.get_type(sample_dict, key) + res['$step_id'] = op_id + return res + + + def _save(self, vis_data: Union[List, dict]): + # use caching to save all relevant vis_data + print("saving vis_data", vis_data) + + def _handle_flags(self, flow, sample_dict: NDict, op_id: Optional[str]): + """ + See super class + """ + # sample was filtered out by its id + if self._id_filter and self.get_idx(sample_dict) not in self._id_filter: + return None + if flow not in self._flags: + return None + + # grouped key dictionary with the following structure: + #vis_data = {"cc_group": + # { + # "key1": { + # "value": ndarray, + # "type": DataType.Image, + # "op_id": "test1"} + # "key2": { + # "value": ndarray, + # "type": DataType.BBox, + # "op_id": "test1"} + # }, + # "mlo_goup": + # { + # "key3": { + # "value": ndarray, + # "type": DataType.Image, + # "op_id": "test1"} + # "key4": { + # "value": ndarray, + # "type": DataType.BBox, + # "op_id": "test1"} + # }, + # } + vis_data = self._extract_data(sample_dict, self._keys, op_id) + both_fr = (VisFlag.REVERSE | VisFlag.FORWARD) in self._flags + dir_forward = flow == VisFlag.FORWARD + dir_reverse = flow == VisFlag.REVERSE + any_show_collected = VisFlag.SHOW_ALL_COLLECTED|VisFlag.SHOW_COLLECTED + + if VisFlag.COLLECT in self._flags or (dir_forward and both_fr): + if not self._collected_prefix in sample_dict: + sample_dict[self._collected_prefix] = [] + sample_dict[self._collected_prefix].append(vis_data) + + + if VisFlag.SHOW_CURRENT in self._flags: + if VisFlag.ONLINE in self._flags: + self._visualizer.show(vis_data) + if VisFlag.OFFLINE in self._flags: + self._save(vis_data) + + if (VisFlag.SHOW_ALL_COLLECTED in self._flags or VisFlag.SHOW_COLLECTED in self._flags) and ( + (both_fr and dir_reverse) or not both_fr): + vis_data = self._extract_collected(sample_dict) + [vis_data] + if both_fr: + if VisFlag.SHOW_COLLECTED in self._flags: + vis_data = vis_data[-2:] + if VisFlag.ONLINE in self._flags: + self._visualizer.show(vis_data) + if VisFlag.OFFLINE in self._flags: + self.save(vis_data) + + if VisFlag.CLEAR in self._flags: + sample_dict[self._collected_prefix] = [] + + if VisFlag.SHOW_COLLECTED in self._flags and both_fr and dir_reverse: + sample_dict[self._collected_prefix].pop() + + return sample_dict + + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + res = self._handle_flags(VisFlag.FORWARD, sample_dict, op_id) + return res + + def reverse(self, sample_dict: NDict, op_id: Optional[str], key_to_reverse: str, key_to_follow: str) -> dict: + """ + See super class + """ + res = self._handle_flags(VisFlag.REVERSE, sample_dict, op_id) + if res is None: + res = sample_dict + return res + diff --git a/fuse/data/ops/tests/__init__.py b/fuse/data/ops/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/ops/tests/test_op_base.py b/fuse/data/ops/tests/test_op_base.py new file mode 100644 index 000000000..5592c34b3 --- /dev/null +++ b/fuse/data/ops/tests/test_op_base.py @@ -0,0 +1,43 @@ +import unittest + +from typing import Union, List +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data.key_types import DataTypeBasic +from fuse.data import create_initial_sample +from fuse.data.key_types_for_testing import DataTypeForTesting, type_detector_for_testing + + + +class TestOpBase(unittest.TestCase): + def test_for_type_detector(self): + td = type_detector_for_testing + sample = create_initial_sample('dummy') + + self.assertEqual(td.get_type(sample, "data.cc.img_for_testing"), DataTypeForTesting.IMAGE_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.cc_img_for_testing"), DataTypeForTesting.IMAGE_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.img_seg_for_testing"), DataTypeForTesting.SEG_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.imgseg_for_testing"), DataTypeForTesting.SEG_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data"), DataTypeBasic.UNKNOWN) + self.assertEqual(td.get_type(sample, "bbox_for_testing"), DataTypeForTesting.BBOX_FOR_TESTING) + self.assertEqual(td.get_type(sample, "a.bbox_for_testing"), DataTypeForTesting.BBOX_FOR_TESTING) + + def test_op_base(self): + class OpImp(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + sample_dict["data.cc.seg_for_testing"] = 5 + return sample_dict + + op = OpImp() + sample_dict = {} + sample_dict = op(sample_dict, "id") + self.assertTrue("data.cc.seg_for_testing" in sample_dict) + self.assertTrue(sample_dict["data.cc.seg_for_testing"] == 5) + self.assertTrue(type_detector_for_testing.get_type(sample_dict, "data.cc.seg_for_testing")== DataTypeForTesting.SEG_FOR_TESTING) + type_detector_for_testing.verify_type(sample_dict, "data.cc.seg_for_testing", [DataTypeForTesting.SEG_FOR_TESTING]) + self.assertRaises(ValueError, type_detector_for_testing.verify_type, sample_dict, "data.cc.seg_for_testing", [DataTypeForTesting.IMAGE_FOR_TESTING]) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_op_visprobe.py b/fuse/data/ops/tests/test_op_visprobe.py new file mode 100644 index 000000000..d771bfab8 --- /dev/null +++ b/fuse/data/ops/tests/test_op_visprobe.py @@ -0,0 +1,284 @@ +import unittest + +from typing import Any, Union, List +import copy +from functools import partial + +from fuse.utils.ndict import NDict + +from fuse.data.ops.ops_visprobe import VisFlag, VisProbe +from fuse.data.visualizer.visualizer_base import VisualizerBase +from fuse.data.ops.op_base import OpBase +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.key_types_for_testing import type_detector_for_testing + + +class OpSetForTest(OpBase): + def __init__(self): + super().__init__() + def __call__(self, sample_dict: NDict, op_id: str, key: str, val: Any) -> Union[None, dict, List[dict]]: + # store information for reverse operation + sample_dict[f"{op_id}.key"] = key + if key in sample_dict: + prev_val = sample_dict[key] + sample_dict[f"{op_id}.prev_val"] = prev_val + + # set + sample_dict[key] = val + return sample_dict + + def reverse(self, sample_dict: NDict, op_id: str, key_to_reverse: str, key_to_follow: str) -> dict: + key = sample_dict[f"{op_id}.key"] + if key == key_to_follow: + if f"{op_id}.prev_val" in sample_dict: + prev_val = sample_dict[f"{op_id}.prev_val"] + sample_dict[key_to_reverse] = prev_val + else: + if key_to_reverse in sample_dict: + sample_dict.pop(key_to_reverse) + return sample_dict + +class DebugVisualizer(VisualizerBase): + acc = [] + def __init__(self) -> None: + super().__init__() + + def _show(self, vis_data): + if issubclass(type(vis_data), dict): + DebugVisualizer.acc.append([vis_data]) + else: + DebugVisualizer.acc.append(vis_data) + +testing_img_key = "img_for_testing" +testing_seg_key = "seg_for_testing" +g1_testing_image_key = "data.test_pipeline." + testing_img_key +g1_testing_seg_key = "data.test_pipeline." + testing_seg_key +g2_testing_image_key = "data.test_pipeline2." + testing_img_key +g2_testing_seg_key = "data.test_pipeline2." + testing_seg_key + +VProbe = partial(VisProbe, + keys= [g1_testing_image_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + +class TestVisProbe(unittest.TestCase): + + def test_basic_show(self): + """ + Test standard backward and forward pipeline + """ + global g1_testing_image_key + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 6}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + g1_testing_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{g1_testing_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{g1_testing_key}.value"], 6) + DebugVisualizer.acc.clear() + + + def test_multi_label(self): + """ + Test standard backward and forward pipeline + """ + + VMProbe = partial(VisProbe, + keys= [g1_testing_image_key, g1_testing_seg_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4, + testing_seg_key: 4}, + "test_pipeline2": + {testing_img_key: 4, + testing_seg_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + test_image_key = g1_testing_image_key.replace('.','_') + test_seg_key = g1_testing_seg_key.replace('.','_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_seg_key}.value"], 4) + self.assertFalse('group1' in DebugVisualizer.acc[0][0]['groups']) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_seg_key}.value"], 4) + self.assertFalse('group1' in DebugVisualizer.acc[1][0]) + DebugVisualizer.acc.clear() + + def test_multi_groups(self): + """ + Test standard backward and forward pipeline + """ + + VMProbe = partial(VisProbe, + keys= [g1_testing_image_key, g2_testing_image_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4, + testing_seg_key: 4}, + "test_pipeline2": + {testing_img_key: 4, + testing_seg_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + test_image_key_g1 = g1_testing_image_key.replace('.', '_') + test_image_key_g2 = g2_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f'groups.group0.{test_image_key_g1}.value'], 5) + self.assertEqual(DebugVisualizer.acc[0][0][f'groups.group1.{test_image_key_g2}.value'], 4) + self.assertEqual(DebugVisualizer.acc[1][0][f'groups.group0.{test_image_key_g1}.value'], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f'groups.group1.{test_image_key_g2}.value'], 4) + DebugVisualizer.acc.clear() + + def test_collected_show(self): + """ + Test basic collected compare + """ + forward_flags = VisFlag.FORWARD | VisFlag.ONLINE + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=forward_flags | VisFlag.COLLECT), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=forward_flags | VisFlag.SHOW_COLLECTED), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 6}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 1) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 6) + DebugVisualizer.acc.clear() + + def test_reverse_compare(self): + """ + Test compare of collected forward with reverse of same op + """ + revfor_flags = VisFlag.FORWARD | VisFlag.ONLINE | VisFlag.REVERSE | VisFlag.SHOW_COLLECTED + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + sample_dict = pipe.reverse(sample_dict, g1_testing_image_key, g1_testing_image_key) + self.assertEqual(len(DebugVisualizer.acc), 1) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(sample_dict[g1_testing_image_key], 4) + + DebugVisualizer.acc.clear() + + def test_multiple_reverse(self): + + """ + Test compare of multiple collected forward with reverse of same op + """ + revfor_flags = VisFlag.FORWARD | VisFlag.ONLINE | VisFlag.REVERSE | VisFlag.SHOW_COLLECTED + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + sample_dict = pipe.reverse(sample_dict, g1_testing_image_key, g1_testing_image_key) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[1][1][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(sample_dict[g1_testing_image_key], 4) + + DebugVisualizer.acc.clear() + + + def tearDown(self) -> None: + return super().tearDown() + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_ops_aug_common.py b/fuse/data/ops/tests/test_ops_aug_common.py new file mode 100644 index 000000000..317580ab8 --- /dev/null +++ b/fuse/data/ops/tests/test_ops_aug_common.py @@ -0,0 +1,125 @@ +import unittest + +from typing import Optional, Union, List +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data import create_initial_sample +from fuse.data import OpRepeat +from fuse.data.ops.ops_aug_common import OpRandApply, OpSample, OpSampleAndRepeat, OpRepeatAndSample +from fuse.utils.rand.param_sampler import Choice, RandBool, RandInt, Uniform +from fuse.utils import Seed + +class OpArgsForTest(OpBase): + def __init__(self): + super().__init__() + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + return {"op_id": op_id, "kwargs": kwargs} + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return {"op_id": op_id} + + +class OpBasicSetter(OpBase): + ''' + A basic op for testing, which sets sample_dict[key] to set_key_to_val + ''' + + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key, set_key_to_val, **kwargs) -> Union[None, dict, List[dict]]: + sample_dict[key] = set_key_to_val + return sample_dict + +class TestOpsAugCommon(unittest.TestCase): + + def test_op_sample(self): + Seed.set_seed(0) + a = {"a": 5, "b": [3, RandInt(1, 5), 9], "c": {"d": 3, "f": [1, 2, RandBool(0.5), {"h": RandInt(10, 15)}]}, "e": {"g": Choice([6, 7, 8])}} + op = OpSample(OpArgsForTest()) + result = op({}, "op_id", **a) + b = result["kwargs"] + call_op_id = result["op_id"] + # make sure the same op_id passed to internal op + self.assertEqual(call_op_id, "op_id") + + # make srgs sampled correctly + self.assertEqual(a["a"], a["a"]) + self.assertEqual(b["b"][0], a["b"][0]) + self.assertEqual(b["b"][2], a["b"][2]) + self.assertEqual(b["c"]["d"], a["c"]["d"]) + self.assertEqual(b["c"]["f"][1], a["c"]["f"][1]) + self.assertIn(b["b"][1], [1, 2, 3, 4, 5]) + self.assertIn(b["c"]["f"][2], [True, False]) + self.assertIn(b["c"]["f"][3]["h"], [10, 11, 12, 13, 14, 15]) + self.assertIn(b["e"]["g"], [6, 7, 8]) + + # make sure the same op_id passed also in reverse + result = op.reverse({}, "", "", "op_id") + reversed_op_id = result["op_id"] + self.assertEqual(reversed_op_id, "op_id") + + def test_op_sample_and_repeat(self): + Seed.set_seed(1337) + sample_1 = create_initial_sample(0) + op = OpSampleAndRepeat(OpBasicSetter(), [dict(key='data.input.img'), dict(key='data.gt.seg')]) + sample_1 = op(sample_1, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + Seed.set_seed(1337) + sample_2 = create_initial_sample(0) + op = OpSample(OpRepeat(OpBasicSetter(), + [dict(key='data.input.img'), dict(key='data.gt.seg')])) + sample_2 = op(sample_2, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + self.assertEqual(sample_1['data.input.img'], sample_1['data.gt.seg']) + self.assertEqual(sample_1['data.input.img'], sample_2['data.input.img']) + + + def test_op_repeat_and_sample(self): + Seed.set_seed(1337) + sample_1 = create_initial_sample(0) + op = OpRepeatAndSample(OpBasicSetter(), [dict(key='data.input.img'), dict(key='data.gt.seg')]) + sample_1 = op(sample_1, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + Seed.set_seed(1337) + sample_2 = create_initial_sample(0) + op = OpRepeat( + OpSample(OpBasicSetter(), ), + [dict(key='data.input.img'), dict(key='data.gt.seg')] + ) + sample_2 = op(sample_2, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + self.assertEqual(sample_1['data.input.img'], sample_2['data.input.img']) + self.assertEqual(sample_1['data.gt.seg'], sample_2['data.gt.seg']) + + def test_op_rand_apply(self): + """ + Test OpRandApply + """ + Seed.set_seed(0) + op = OpRandApply(OpArgsForTest(), 0.5) + + def sample(op): + return "kwargs" in op({}, "op_id", a=5) + + # test range + self.assertIn(sample(op), [True, False]) + + # test generate more than a single number + Seed.set_seed(0) + values = [sample(op) for _ in range(4)] + self.assertIn(True, values) + self.assertIn(False, values) + + # test probs + Seed.set_seed(0) + op = OpRandApply(OpArgsForTest(), 0.99) + count = 0 + for _ in range(1000): + if sample(op) == True: + count += 1 + self.assertGreaterEqual(count, 980) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/data/ops/tests/test_ops_cast.py b/fuse/data/ops/tests/test_ops_cast.py new file mode 100644 index 000000000..8c49518fe --- /dev/null +++ b/fuse/data/ops/tests/test_ops_cast.py @@ -0,0 +1,97 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +import unittest + +from typing import List +from fuse.utils.ndict import NDict +import pandas as pd +import torch +import numpy as np +from fuse.data.ops.ops_cast import OpToNumpy, OpToTensor + + + +class TestOpsCast(unittest.TestCase): + + + def test_op_to_tensor(self): + """ + Test OpToTensor __call__ and reverse + """ + op = OpToTensor() + sample = NDict({ + "sample_id": 7, + "values" : { + "val_np": np.array([7, 8, 9]), + "val_torch": torch.tensor([1,2,3]), + "val_int": 3, + "val_float": 3.5, + "str": "hi!" + } + }) + + sample = op(sample, "_.test_id", key="values.val_np") + self.assertIsInstance(sample["values.val_np"], torch.Tensor) + self.assertTrue((sample["values.val_np"] == torch.tensor([7,8,9])).all()) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op(sample, "_.test_id", key=["values.val_torch", "values.val_float"]) + self.assertIsInstance(sample["values.val_torch"], torch.Tensor) + self.assertIsInstance(sample["values.val_float"], torch.Tensor) + self.assertTrue((sample["values.val_torch"] == torch.tensor([1,2,3])).all()) + self.assertEqual(sample["values.val_float"], torch.tensor(3.5)) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op.reverse(sample, key_to_follow="values.val_np", key_to_reverse="values.val_np", op_id="_.test_id") + self.assertIsInstance(sample["values.val_np"], np.ndarray) + + def test_op_to_numpy(self): + """ + Test OpToNumpy __call__ and reverse + """ + op = OpToNumpy() + sample = NDict({ + "sample_id": 7, + "values" : { + "val_np": np.array([7, 8, 9]), + "val_torch": torch.tensor([1,2,3]), + "val_int": 3, + "val_float": 3.5, + "str": "hi!" + } + }) + + sample = op(sample, "_.test_id", key="values.val_torch") + self.assertIsInstance(sample["values.val_torch"], np.ndarray) + self.assertTrue((sample["values.val_torch"] == np.array([1,2,3])).all()) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op(sample, "_.test_id", key=["values.val_np", "values.val_float"]) + self.assertIsInstance(sample["values.val_np"], np.ndarray) + self.assertIsInstance(sample["values.val_float"], np.ndarray) + self.assertTrue((sample["values.val_np"] == np.array([7,8,9])).all()) + self.assertEqual(sample["values.val_float"], np.array(3.5)) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op.reverse(sample, key_to_follow="values.val_torch", key_to_reverse="values.val_torch", op_id="_.test_id") + self.assertIsInstance(sample["values.val_torch"], torch.Tensor) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_ops_common.py b/fuse/data/ops/tests/test_ops_common.py new file mode 100644 index 000000000..7a2f07372 --- /dev/null +++ b/fuse/data/ops/tests/test_ops_common.py @@ -0,0 +1,208 @@ +import unittest + +from typing import Optional, OrderedDict, Union, List + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data.key_types_for_testing import DataTypeForTesting + +from fuse.data.ops.ops_common import OpApplyPatterns, OpFunc, OpLambda, OpRepeat +from fuse.data.ops.ops_common_for_testing import OpApplyTypesImaging + +class OpIncrForTest(OpBase): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], incr_value: int, key_in: str, key_out: str) -> Union[None, dict, List[dict]]: + # save for reverse + sample_dict[op_id] = {'key_out': key_out, 'incr_value': incr_value} + # apply + value = sample_dict[key_in] + sample_dict[key_out] = value + incr_value + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + # not really reverse, but help the test + orig_args = sample_dict[op_id] + + if orig_args['key_out'] != key_to_follow: + return sample_dict + + value = sample_dict[key_to_reverse] + sample_dict[key_to_reverse] = value - orig_args['incr_value'] + + return sample_dict + +class TestOpsCommon(unittest.TestCase): + + def test_op_repeat(self): + """ + Test OpRepeat __call__() and reverse() + """ + op_base=OpIncrForTest() + kwargs_per_step_to_add = [dict(key_in='data.val.a', key_out='data.val.b'), dict(key_in='data.val.b', key_out='data.val.c'), dict(key_in='data.val.b', key_out='data.val.d'), dict(key_in='data.val.d', key_out='data.val.d')] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", incr_value=3) + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 14) + + op_repeat.reverse(sample_dict, key_to_follow='data.val.d', key_to_reverse='data.val.d', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 8) + + sample_dict['data.val.e'] = 48 + op_repeat.reverse(sample_dict, key_to_follow='data.val.d', key_to_reverse='data.val.e', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 8) + self.assertEqual(sample_dict['data.val.e'], 42) + + def test_op_lambda(self): + """ + Test OpLambda __call__() and reverse() + """ + op_base=OpLambda(func=lambda x: x + 3) + kwargs_per_step_to_add = [dict(), dict(), dict()] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a') + self.assertEqual(sample_dict['data.val.a'], 14) + + op_base=OpLambda(func=lambda x: x + 3, func_reverse=lambda x: x - 3) + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a') + self.assertEqual(sample_dict['data.val.a'], 14) + + op_repeat.reverse(sample_dict, key_to_follow='data.val.a', key_to_reverse='data.val.a', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + + sample_dict['data.val.b'] = 51 + op_repeat.reverse(sample_dict, key_to_follow='data.val.a', key_to_reverse='data.val.b', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 42) + + + def test_op_lambda_with_kwargs(self): + """ + Test OpLambda __call__() with kwargs + """ + op_base=OpLambda(func=lambda x, y: x + y) + kwargs_per_step_to_add = [dict(), dict(), dict()] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict() + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a', y=5) + self.assertEqual(sample_dict['data.val.a'], 20) + + def test_op_func(self): + """ + Test OpFunc __call__() + """ + + def func_single_output(a, b, c): + return a+b+c + def func_multi_output(a, b, c): + return a+b, a+c + + single_output_op = OpFunc(func=func_single_output) + sample_dict = NDict({}) + sample_dict["data.first"] = 5 + sample_dict["data.second"] = 9 + sample_dict = single_output_op(sample_dict, "_.test_func", c=2, inputs={"data.first": "a", "data.second": "b"}, outputs="data.out") + self.assertEqual(sample_dict['data.out'], 16) + + multi_output_op = OpFunc(func=func_multi_output) + sample_dict = NDict({}) + sample_dict["data.first"] = 5 + sample_dict["data.second"] = 9 + sample_dict = multi_output_op(sample_dict, "_.test_func", c=2, inputs={"data.first": "a", "data.second": "b"}, outputs=["data.out", "data.more"]) + self.assertEqual(sample_dict['data.out'], 14) + self.assertEqual(sample_dict['data.more'], 7) + + + def test_op_apply_patterns(self): + """ + Test OpRApplyPatterns __call__() and reverse() + """ + + op_add_1 = OpLambda(func=lambda x: x + 1, func_reverse=lambda x: x-1) + op_mul_2 = OpLambda(func=lambda x: x*2, func_reverse=lambda x: x//2) + op_mul_4 = OpLambda(func=lambda x: x*4, func_reverse=lambda x: x//4) + + sample_dict = NDict({}) + sample_dict["data.val.img_for_testing"] = 3 + sample_dict["data.test.img_for_testing"] = 3 + sample_dict["data.test.seg_for_testing"] = 3 + sample_dict["data.test.bbox_for_testing"] = 3 + sample_dict["data.test.meta"] = 3 + + patterns_dict = OrderedDict([(r"^data.val.img_for_testing$", (op_add_1, dict())), + (r"^.*img_for_testing$|^.*seg_for_testing$", (op_mul_2, dict())), + (r"^data.[^.]*.bbox_for_testing", (op_mul_4, dict()))]) + op_apply_pat = OpApplyPatterns(patterns_dict) + + sample_dict = op_apply_pat(sample_dict, "_.test_apply_pat") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.img_for_testing'], 6) + self.assertEqual(sample_dict['data.test.seg_for_testing'], 6) + self.assertEqual(sample_dict['data.test.bbox_for_testing'], 12) + self.assertEqual(sample_dict['data.test.meta'], 3) + + sample_dict["model.seg_for_testing"] = 3 + op_apply_pat.reverse(sample_dict, key_to_follow="data.val.img_for_testing", key_to_reverse="model.seg_for_testing", op_id="_.test_apply_pat") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['model.seg_for_testing'], 2) + + + + + def test_op_apply_types(self): + """ + Test OpApplyTypes __call__() and reverse() + """ + + op_add_1 = OpLambda(func=lambda x: x + 1, func_reverse=lambda x: x-1) + op_mul_2 = OpLambda(func=lambda x: x*2, func_reverse=lambda x: x//2) + op_mul_4 = OpLambda(func=lambda x: x*4, func_reverse=lambda x: x//4) + + sample_dict = NDict({}) + sample_dict["data.val.img_for_testing"] = 3 + sample_dict["data.test.img_for_testing"] = 3 + sample_dict["data.test.seg_for_testing"] = 3 + sample_dict["data.test.bbox_for_testing"] = 3 + sample_dict["data.test.meta"] = 3 + + types_dict = {DataTypeForTesting.IMAGE_FOR_TESTING: (op_add_1, dict()), + DataTypeForTesting.SEG_FOR_TESTING: (op_mul_2, dict()), + DataTypeForTesting.BBOX_FOR_TESTING: (op_mul_4, dict())} + + op_apply_type = OpApplyTypesImaging(types_dict) + + sample_dict = op_apply_type(sample_dict, "_.test_apply_type") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.seg_for_testing'], 6) + self.assertEqual(sample_dict['data.test.bbox_for_testing'], 12) + self.assertEqual(sample_dict['data.test.meta'], 3) + + sample_dict["model.a_seg_for_testing"] = 3 + op_apply_type.reverse(sample_dict, key_to_follow="data.val.img_for_testing", key_to_reverse="model.a_seg_for_testing", op_id="_.test_apply_type") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['model.a_seg_for_testing'], 2) + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/data/ops/tests/test_ops_read.py b/fuse/data/ops/tests/test_ops_read.py new file mode 100644 index 000000000..b78f73b0a --- /dev/null +++ b/fuse/data/ops/tests/test_ops_read.py @@ -0,0 +1,76 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest + +import pandas as pd +from fuse.utils.ndict import NDict +from fuse.data.ops.ops_read import OpReadDataframe + + + +class TestOpsRead(unittest.TestCase): + + def test_op_read_dataframe(self): + """ + Test OpReadDataframe + """ + data = { + "sample_id": ["a", "b", "c", "d"], + "value1": [10, 7, 3, 9], + "value2": ["5", "4", "3", "2"] + } + df = pd.DataFrame(data) + op = OpReadDataframe(data=df) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertEqual(sample_dict["data.value1"], 3) + self.assertEqual(sample_dict["data.value2"], "3") + + + op = OpReadDataframe(data=df, columns_to_extract=["sample_id", "value2"]) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertFalse("data.value1" in sample_dict) + self.assertEqual(sample_dict["data.value2"], "3") + + op = OpReadDataframe(data=df, columns_to_extract=["sample_id", "value2"], rename_columns={"value2": "value3"}) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertFalse("data.value1" in sample_dict) + self.assertFalse("data.value2" in sample_dict) + self.assertEqual(sample_dict["data.value3"], "3") + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/patterns.py b/fuse/data/patterns.py new file mode 100644 index 000000000..273b62ba4 --- /dev/null +++ b/fuse/data/patterns.py @@ -0,0 +1,56 @@ +from collections import OrderedDict +from typing import * +import re + +class Patterns: + """ + Utility to match a string to a pattern. + Typically used to infer data type from key in sample_dict + """ + def __init__(self, patterns_dict: OrderedDict, default_value: Any = None): + """ + :param patterns_dict: ordered dictionary, the key is a regex expression. + The value of the first matched key will be returned. + Example: + patterns = { + r".*img$": DataType.IMAGE, + r".*seg$": DataType.SEG, + r".*bbox$": DataType.BBOX, + r".*$": DataType.UNKNOWN + } + pp = Patterns(patterns) + print(pp.get_type("data.cc.img")) -> DataType.IMAGE + print(pp.get_type("data.cc_img")) -> DataType.IMAGE + print(pp.get_type("data.img_seg")) -> DataType.SEG + print(pp.get_type("data.imgseg")) -> DataType.SEG + print(pp.get_type("data")) -> DataType.UNKNOWN + print(pp.get_type("bbox")) -> DataType.BBox + print(pp.get_type("a.bbox")) -> DataType.BBOX + + :param default_value: value to return in case there is not match + """ + self._patterns = patterns_dict + self._default_value = default_value + + def get_value(self, key: str) -> Any: + """ + :param key: string to match + :return: the first value from patterns with pattern that match to key + """ + for pattern in self._patterns: + if re.match(pattern, key) is not None: + return self._patterns[pattern] + + return self._default_value + + def verify_value_in(self, key: str, values: Sequence[Any]) -> None: + """ + Raise an exception of the matched value not in values + :param key: string to match + :param values: list of supported values + :return: None + """ + val_type = self.get_value(key) + if val_type not in values: + raise ValueError( + f"key {key} mapped to unsupported type {val_type}.\n List of supported types {values} \n Patterns {self._patterns}") diff --git a/fuse/data/pipelines/__init__.py b/fuse/data/pipelines/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/pipelines/pipeline_default.py b/fuse/data/pipelines/pipeline_default.py new file mode 100644 index 000000000..39c49eec6 --- /dev/null +++ b/fuse/data/pipelines/pipeline_default.py @@ -0,0 +1,130 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import List, Tuple, Union, Optional +from fuse.data.ops.op_base import OpBase +from fuse.utils.misc.context import DummyContext +from fuse.utils.ndict import NDict +from fuse.utils.cpu_profiling.timer import Timer + +class PipelineDefault(OpBase): + """ + Pipeline default implementation + Pipeline to run sequence of ops with a dictionary passing information between the ops. + See OpBase for more information + """ + + def __init__(self, name: str, ops_and_kwargs: List[Tuple[OpBase, dict]], op_ids: Optional[List[str]] = None, verbose: bool=False): + """ + :param name: pipeline name + :param ops_and_args: List of tuples. Each tuple include op and dictionary includes op specific arguments. + :param op_ids: Optional, set op_id - unique name for every op. If not set, an index will be used + :param verbose: set to True for debug messages such as the running time of each operation + """ + super().__init__() + self._name = name + self._ops_and_kwargs = ops_and_kwargs + if op_ids is None: + self._op_ids = [str(index) for index in range(len(self._ops_and_kwargs))] + else: + assert len(self._ops_and_kwargs) == len(op_ids), "Expecting op_id for every op" + assert len(set(op_ids)) == len(op_ids), "Expecting unique op id for every op." + self._op_ids = op_ids + self._verbose = verbose + + def get_name(self) -> str: + return self._name + + def __str__(self) -> str: + text = [] + for (op, op_kwargs) in zip(self._op_ids, self._ops_and_kwargs): + text.append(str(op)+'@'+str(op_kwargs)+'@') + + return ''.join(text) #this is faster than accumulate_str+=new_str + + def __call__(self, sample_dict: NDict, op_id: Optional[str] = None, until_op_id: Optional[str] = None) -> Union[None, dict, List[dict]]: + """ + See super class + plus + :param until_op_id: optional - stop after the specified op_id - might be used for optimization + """ + # set op_id if not specified + if op_id is None: + op_id = self._name + + samples_to_process = [sample_dict] + for sub_op_id, (op, op_kwargs) in zip(self._op_ids, self._ops_and_kwargs): + if self._verbose: + context = Timer(f"Pipeline {self._name}: op {type(op).__name__}, op_id {sub_op_id}", self._verbose) + else: + context = DummyContext() + with context: + try: + samples_to_process_next = [] + + for sample in samples_to_process: + + try: + sample = op(sample, f"{op_id}.{sub_op_id}", **op_kwargs) + except: + #error messages are cryptic without this. For example, you can get "TypeError: __call__() got an unexpected keyword argument 'key_out_input'" , without any reference to the relevant op! + print(f'error in op={op}') + raise + + # three options for return value: + # None - ignore the sample + # List of dicts - split sample + # dict - modified sample + if sample is None: + return None + elif isinstance(sample, list): + samples_to_process_next += sample + elif isinstance(sample, dict): + samples_to_process_next.append(sample) + else: + raise Exception( + f"unexpected sample type returned by {type(op)}: {type(sample)}") + except Exception as e: + raise Exception(f"Error: op {type(op).__name__}, op_id {sub_op_id} failed ") from e + + # continue to process with next op + samples_to_process = samples_to_process_next + + # if required - stop after the specified op id + if until_op_id is not None and sub_op_id == until_op_id: + break + + # if single sample - return it, otherwise return list of samples. + if len(samples_to_process) == 1: + return samples_to_process[0] + else: + return samples_to_process + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str] = None) -> dict: + """ + See super class + """ + # set op_id if not specified + if op_id is None: + op_id = self._name + + for sub_op_id, (op, _) in zip(reversed(self._op_ids), reversed(self._ops_and_kwargs)): + sample_dict = op.reverse( + sample_dict, f"{op_id}.{sub_op_id}", key_to_reverse, key_to_follow) + + return sample_dict diff --git a/fuse/data/pipelines/tests/__init__.py b/fuse/data/pipelines/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/pipelines/tests/test_pipeline_default.py b/fuse/data/pipelines/tests/test_pipeline_default.py new file mode 100644 index 000000000..06b87da30 --- /dev/null +++ b/fuse/data/pipelines/tests/test_pipeline_default.py @@ -0,0 +1,117 @@ +import unittest + +from fuse.utils.ndict import NDict +from typing import Any, Union, List +import copy +from unittest.case import expectedFailure + +from fuse.data.ops.op_base import OpBase +from fuse.data.pipelines.pipeline_default import PipelineDefault + + +class OpSetForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, key: str, val: Any) -> Union[None, dict, List[dict]]: + # store information for reverse operation + sample_dict[f"{op_id}.key"] = key + if key in sample_dict: + prev_val = sample_dict[key] + sample_dict[f"{op_id}.prev_val"] = prev_val + + # set + sample_dict[key] = val + return sample_dict + + def reverse(self, sample_dict: NDict, op_id: str, key_to_reverse: str, key_to_follow: str) -> dict: + key = sample_dict[f"{op_id}.key"] + if key == key_to_follow: + if f"{op_id}.prev_val" in sample_dict: + prev_val = sample_dict[f"{op_id}.prev_val"] + sample_dict[key_to_reverse] = prev_val + else: + if key_to_reverse in sample_dict: + sample_dict.pop(key_to_reverse) + return sample_dict + + +class OpNoneForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + return None + + +class OpSplitForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + sample_id = sample_dict['data.sample_id'] + samples = [] + split_num = 10 + for index in range(split_num): + sample = copy.deepcopy(sample_dict) + sample['data.sample_id'] = (sample_id, index) + samples.append(sample) + + return samples + + +class TestPipelineDefault(unittest.TestCase): + + def test_pipeline(self): + """ + Test standard backward and forward pipeline + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpSetForTest(), dict(key="data.test_pipeline", val=6)), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({}) + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict["data.test_pipeline"], 6) + self.assertEqual(sample_dict["data.test_pipeline_2"], 7) + + sample_dict = pipe.reverse(sample_dict, 'data.test_pipeline', 'data.test_pipeline') + self.assertEqual("data.test_pipeline" in sample_dict, False) + self.assertEqual(sample_dict["data.test_pipeline_2"], 7) + + sample_dict = pipe.reverse(sample_dict, 'data.test_pipeline_2', 'data.test_pipeline_2') + self.assertEqual("data.test_pipeline" in sample_dict, False) + self.assertEqual("data.test_pipeline_2" in sample_dict, False) + + def test_none(self): + """ + Test pipeline with an op returning None + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpNoneForTest(), dict()), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + sample_dict = NDict({}) + sample_dict = pipe(sample_dict) + self.assertIsNone(sample_dict) + + def test_split(self): + """ + Test pipeline with an op splitting samples to multiple samples + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpSplitForTest(), dict()), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + sample_dict = NDict({'data': {'sample_id': 0}}) + sample_dict = pipe(sample_dict) + self.assertTrue(isinstance(sample_dict, list)) + self.assertEqual(len(sample_dict), 10) + expected_samples = [(0, i) for i in range(10)] + samples = [sample['data.sample_id'] for sample in sample_dict] + self.assertListEqual(expected_samples, samples) + + def tearDown(self) -> None: + return super().tearDown() + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/tests/__init__.py b/fuse/data/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/tests/test_version.py b/fuse/data/tests/test_version.py new file mode 100644 index 000000000..f46838a27 --- /dev/null +++ b/fuse/data/tests/test_version.py @@ -0,0 +1,38 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest + +import fuse.data +import pkg_resources # part of setuptools + + +class TestVersion(unittest.TestCase): + def test_version(self): + """ + Make sure data version equal to the installed version + """ + pass + # FIXME: uncomment when fixed in jenkins + # version = pkg_resources.require("fuse-med-ml-data")[0].version + # self.assertEqual(fuse.data.__version__, version) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/utils/__init__.py b/fuse/data/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/utils/collates.py b/fuse/data/utils/collates.py new file mode 100644 index 000000000..ff6c0685d --- /dev/null +++ b/fuse/data/utils/collates.py @@ -0,0 +1,129 @@ +import collections +from typing import Any, Callable, Dict, List, Sequence, Tuple + +import numpy as np +import torch +from torch.utils.data._utils.collate import default_collate +import torch.nn.functional as F + +from fuse.utils import NDict +from fuse.utils.data.collate import CollateToBatchList +from fuse.data import get_sample_id, get_sample_id_key + +class CollateDefault(CollateToBatchList): + """ + Default collate_fn method to be used when creating a DataLoader. + Will collate each value with PyTorch default collate. + Special collates per key can be specified in special_handlers_keys + sample_id key will be collected to a list. + Few options to special handlers implemented in this class as static methods + """ + def __init__(self, skip_keys: Sequence[str]=tuple(), raise_error_key_missing: bool = True, special_handlers_keys: Dict[str, Callable] = None): + """ + :param skip_keys: do not collect the listed keys + :param special_handlers_keys: per key specify a callable which gets as an input list of values and convert it to a batch. + The rest of the keys will be converted to batch using PyTorch default collate_fn() + :param raise_error_key_missing: if False, will not raise an error if there are keys that do not exist in some of the samples. Instead will set those values to None. + """ + super().__init__(skip_keys, raise_error_key_missing) + self._special_handlers_keys = {} + if special_handlers_keys is not None: + self._special_handlers_keys.update(special_handlers_keys) + self._special_handlers_keys[get_sample_id_key()] = CollateDefault.just_collect_to_list + + def __call__(self, samples: List[Dict]) -> Dict: + """ + collate list of samples into batch_dict + :param samples: list of samples + :return: batch_dict + """ + batch_dict = NDict() + + # collect all keys + keys = self._collect_all_keys(samples) + + # collect values + for key in keys: + + # skip keys + if key in self._skip_keys: + continue + + try: + # collect values into a list + collected_values, has_error = self._collect_values_to_list(samples, key) + + # batch values + self._batch_dispatch(batch_dict, samples, key, has_error, collected_values) + except: + print(f'Error: Failed to collect key {key}') + raise + + return batch_dict + + def _batch_dispatch(self, batch_dict: dict, samples: List[dict], key: str, has_error: bool, collected_values: list) -> None: + """ + dispatch a key into collate function and save it into batch_dict + :param batch_dict: batch dictionary to update + :param samples: list of samples + :param key: key to collate + :param has_error: True, if the key is missing in one of the samples + :param collected values: the values collected from samples + :return: nothing - the new batch will be added to batch_dict + """ + if has_error: + # do nothing when error occurs + batch_dict[key] = collected_values + elif key in self._special_handlers_keys: + # use special handler if specified + batch_dict[key] = self._special_handlers_keys[key](collected_values) + elif isinstance(collected_values[0], (torch.Tensor, np.ndarray, float, int, str, bytes, collections.abc.Sequence)): + # batch with default PyTorch implementation + batch_dict[key] = default_collate(collected_values) + else: + batch_dict[key] = collected_values + + + @staticmethod + def just_collect_to_list(values: List[Any]): + """ + special handler doing nothing - will just keep the collected list + """ + return values + + @staticmethod + def pad_all_tensors_to_same_size(values: List[torch.Tensor], pad_val: float=0.0): + """ + pad tensors and create a batch - the shape will be the max size per dim + values: list of tensor - all should have the same number of dimensions + pad_val: constant value for padding + :return: torch.stack of padded tensors + """ + + # verify all are tensor and that they have the same dim size + assert isinstance(values[0], torch.Tensor), f"Expecting just tensors, got {type(values[0])}" + num_dims = len(values[0].shape) + for value in values: + assert isinstance(value, torch.Tensor), f"Expecting just tensors, got {type(value)}" + assert len(value.shape) == num_dims, f"Expecting all tensors to have the same dim size, got {len(value.shape)} and {num_dims}" + + # get max per dim + max_per_dim = np.amax(np.stack([value.shape for value in values]), axis=0) + + # pad + def _pad_size(value, dim): + assert max_per_dim[dim] >= value.shape[dim] + return [0, max_per_dim[dim]-value.shape[dim]] + + padded_values = [] + + for value in values: + padding = [] + # F.pad padding description is expected to be provided in REVERSE order (see torch.nn.functional.pad doc) + for dim in reversed(range(num_dims)): + padding += _pad_size(value,dim) + padded_value = F.pad(value, padding, mode='constant', value=pad_val) + padded_values.append(padded_value) + + + return default_collate(padded_values) diff --git a/fuse/data/utils/sample.py b/fuse/data/utils/sample.py new file mode 100644 index 000000000..0f58de7c9 --- /dev/null +++ b/fuse/data/utils/sample.py @@ -0,0 +1,102 @@ +from typing import Dict, Hashable +from fuse.utils.ndict import NDict + +''' +helper utilities for creating empty samples, and setting and getting sample_id within samples + +A sample is a NDict, which is a special "flavor" of a dictionry, allowing accessing elements within it using x['a.b.c.d'] instead of x['a']['b']['c']['d'], +which is very useful as it allows defining a nested element, or a nested sub-dict using a single string. + +The bare minimum that a sample is required to contain are: + +'initial_sample_id' - this is an arbitrary (Hashable) identifier. Usually a string, but doesn't have to be. + It represnts the initial sample_id that was provided before a pipeline was used to process the sample, and potentially use "sample morphing". + "sample morphing" means that a sample might change during the pipeline execution. + 1. Discard - one type of morphing is that a sample is being discarded. Example use case is discarding an MRI volume because it has too little segmentation info that interests a certain research design. + 2. Split - another type of morphing is that a sample can be split into multiple samples. + For example, the initial_sample_id represents an entire CT volume, which results in multiple samples, each having the same initial_sample_id, but a different sample_id, + each representing a slice within the CT volume which contains enough segmentation information + +'sample_id' - the sample id, uniquely identifying it. It must be Hashable. Again, usually a string, but doesn't have to be. + +''' + +def create_initial_sample(initial_sample_id:Hashable, sample_id=None): + ''' + creates an empty sample dict and sets both sample_id and initial_sample_id + :param sample_id: + :param initial_sample_id: optional. If not provided, sample_id will be used for it as well + ''' + ans = NDict() + + if sample_id is None: + sample_id = initial_sample_id + + set_initial_sample_id(ans, initial_sample_id) + set_sample_id(ans, sample_id) + + return ans + + +##### sample_id + +def get_sample_id_key() -> str: + ''' + return sample id key + ''' + return 'data.sample_id' + +def get_sample_id(sample:Dict) -> Hashable: + ''' + extracts sample_id from the sample dict + ''' + if get_sample_id_key() not in sample: + raise Exception + return sample[get_sample_id_key()] + + +def set_sample_id(sample:Dict, sample_id:Hashable): + ''' + sets sample_id in an existing sample dict + ''' + sample[get_sample_id_key()] = sample_id + + +#### dealing with initial sample id - this is related to morphing, and describes the original provided sample_id, prior to the morphing effect + +def get_initial_sample_id_key() -> str: + ''' + return initial sample id key + ''' + return 'data.initial_sample_id' + +def set_initial_sample_id(sample:Dict, initial_sample_id:Hashable): + ''' + sets initial_sample_id in an existing sample dict + ''' + sample[get_initial_sample_id_key()] = initial_sample_id + +def get_initial_sample_id(sample:Dict) -> Hashable: + ''' + extracts initial_sample_id from the sample dict + ''' + if get_initial_sample_id_key() not in sample: + raise Exception + return sample[get_initial_sample_id_key()] + + +#### + +def get_specific_sample_from_potentially_morphed(sample, sample_id): + if isinstance(sample, dict): + assert get_sample_id(sample) == sample_id + return sample + elif isinstance(sample, list): + for curr_sample in sample: + if get_sample_id(curr_sample) == sample_id: + return curr_sample + raise Exception(f'Could not find requested sample_id={sample_id}') + else: + raise Exception('Expected the sample to be either a dict or a list of dicts. None does not make sense in this context.') + + assert False #should never reach here \ No newline at end of file diff --git a/fuse/data/utils/samplers.py b/fuse/data/utils/samplers.py new file mode 100644 index 000000000..8bda89e8c --- /dev/null +++ b/fuse/data/utils/samplers.py @@ -0,0 +1,208 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import math +from typing import Any, List, Optional, Union + +import numpy as np +from torch.utils.data.sampler import Sampler + +from fuse.data.datasets.dataset_base import DatasetBase + +class BatchSamplerDefault(Sampler): + """ + Torch batch sampler - balancing per batch + """ + + def __init__(self, + dataset: DatasetBase, + balanced_class_name: str, + num_balanced_classes: int, + batch_size: Optional[int] = None, + mode: str = "exact", + balanced_class_weights: Union[List[int], List[float], None] = None, + num_batches: Optional[int] = None, + **dataset_get_multi_kwargs) -> None: + """ + :param dataset: dataset used to extract the balanced class from each sample + :param balanced_class_name: the name of balanced class to extract from dataset + :param num_balanced_classes: number of classes to balance between + :param batch_size: batch_size. + - In "exact" mode + If balanced_class_weights is None, must be set and divided by num_balanced_classes. Otherwise keep None. + - In "approx" mode + Must be set + :param mode: either 'exact' or 'approx'. if 'exact each element in balanced_class_weights will specify the exact number of samples from this class. + if 'approx' - each element will specify the a probability that a sample will be from this class + :param balanced_class_weights: Optional, integer/float per balanced class, Expected length is num_balanced_classes. + In mode 'exact' expecting list of integers that sums up to batch dict. + In mode 'approx' expecting list of floats that sums up to ~1 + If not specified and equal number of samples from each class will be used. + :param num_batches: optional - if set will force num_batches, otherwise num_batches will be automatically to go over each sample at least once (exactly or approximately). + :param dataset_get_multi_kwargs: extra parameters for dataset.get_multi() to optimize the running time. + """ + super().__init__(None) + + # store input + self._mode = mode + self._dataset = dataset + self._balanced_class_name = balanced_class_name + self._num_balanced_classes = num_balanced_classes + self._batch_size = batch_size + self._balanced_class_weights = balanced_class_weights + self._num_batches = num_batches + self._dataset_get_multi_kwargs = dataset_get_multi_kwargs + # modify relevant keys + if self._balanced_class_name not in self._dataset_get_multi_kwargs: + self._dataset_get_multi_kwargs["keys"] = [self._balanced_class_name] + + # validate input + # modes + if self._mode not in ['exact', 'approx']: + raise Exception("Error, expected sampler mode to be either 'exact' or 'approx', got {mode}") + + # weights + if self._mode == 'exact': + if self._balanced_class_weights is not None: + for weight in self._balanced_class_weights: + if not isinstance(weight, int): + raise Exception(f"Error: in mode 'exact', expecting only integers in balanced_class_weights, got {type(weight)}") + if self._batch_size is not None: + if self._batch_size != sum(self._balanced_class_weights): + raise Exception(f"Error: in mode 'exact', expecting balanced_class_weights {self._balanced_class_weights} to sum up to batch size {self._batch_size}. Consider setting batch_size to None to automatically compute the batch size.") + else: + self._batch_size = sum(self._balanced_class_weights) + elif self._batch_size is None: + raise Exception("Error: In 'approx' mode, either batch_size or balanced_class_weights") + + if self._mode == "approx": + if self._batch_size is None: + raise Exception(f"Error: in mode 'approx', batch size must be set.") + if balanced_class_weights is not None: + for weight in balanced_class_weights: + if not isinstance(weight, float): + raise Exception(f"Error: in mode 'exact', expecting only floats in balanced_class_weights, got {type(weight)}") + if not math.isclose(sum(self._balanced_class_weights), 1.0): + raise Exception(f"Error: in mode 'exact', expecting balanced_class_weight to sum up to almost one, got {balanced_class_weights}") + + if balanced_class_weights is not None: + if len(balanced_class_weights) != num_balanced_classes: + raise Exception( + f'Expecting balance_class_weights ({balanced_class_weights}) to have a weight per balanced class ({num_balanced_classes})') + + # if weights not specified, set weights to equally balance per batch + if self._balanced_class_weights is None: + if self._mode == "exact": + self._balanced_class_weights = [self._batch_size // self._num_balanced_classes] * self._num_balanced_classes + elif self._mode == "approx": + self._balanced_class_weights = [1 / self._num_balanced_classes] * self._num_balanced_classes + + # get balanced classes per each sample + collected_data = dataset.get_multi(None, **self._dataset_get_multi_kwargs) + self._balanced_classes = self._extract_balanced_classes(collected_data) + + # split samples to groups + self._balanced_class_indices = [np.where(self._balanced_classes == cls_i)[0] for cls_i in range(self._num_balanced_classes)] + self._balanced_class_sizes = [len(self._balanced_class_indices[cls_i]) for cls_i in range(self._num_balanced_classes)] + + # make sure that size != 0 for all balanced classes + for cls_ind, cls_size in enumerate(self._balanced_class_sizes): + if self._balanced_class_weights[cls_ind] != 0.0 and cls_size == 0: + msg = f'Every balanced class must include at least one sample (num of samples per balanced class{self._balanced_class_sizes} and weights are {self._balanced_class_weights})' + raise Exception(msg) + + # calc batch index to balanced class mapping according to weights + if self._mode == 'exact': + self._batch_index_to_class = [] + for balanced_cls in range(self._num_balanced_classes): + self._batch_index_to_class.extend([balanced_cls] * self._balanced_class_weights[balanced_cls]) + else: + # probabilistic method - will be randomly select per epoch + self._batch_index_to_class = None + + + # Shuffle balanced class indices + for indices in self._balanced_class_indices: + np.random.shuffle(indices) + + # Calculate num batches. Number of batches to iterate over all data at least once (exactly or approximately) + # Calculate only if not directly specified by the user + if self._num_batches is None: + if self._mode == 'exact': + samples_per_batch = self._balanced_class_weights + else: # mode is approx + # approximate size! + samples_per_batch = [val * self._batch_size for val in self._balanced_class_weights] + balanced_class_weighted_sizes = \ + [math.ceil(self._balanced_class_sizes[cls_i] / samples_per_batch[cls_i]) if self._balanced_class_weights[cls_i] != 0 else 0 for cls_i in range(self._num_balanced_classes)] + bigger_balanced_class_weighted_size = max(balanced_class_weighted_sizes) + self._num_batches = int(bigger_balanced_class_weighted_size) + + # pointers per class + self._cls_pointers = [0] * self._num_balanced_classes + self._sample_pointer = 0 + + def __iter__(self) -> np.ndarray: + for batch_idx in range(self._num_batches): + yield self._make_batch() + + def __len__(self) -> int: + return self._num_batches + + def _get_sample(self, balanced_class: int) -> Any: + """ + sample index given balanced class value + :param balanced_class: integer representing balanced class value + :return: sample index + """ + sample_idx = self._balanced_class_indices[balanced_class][self._cls_pointers[balanced_class]] + + self._cls_pointers[balanced_class] += 1 + if self._cls_pointers[balanced_class] == self._balanced_class_sizes[balanced_class]: + self._cls_pointers[balanced_class] = 0 + np.random.shuffle(self._balanced_class_indices[balanced_class]) + + return sample_idx + + def _make_batch(self) -> list: + """ + :return: list of indices to collate batch + """ + if self._mode == 'exact': + batch_index_to_class = self._batch_index_to_class + else: # mode == approx + # calc one according to probabilities + batch_index_to_class = np.random.choice(np.arange(self._num_balanced_classes), self._batch_size, p=self._balanced_class_weights) + batch_sample_indices = [] + for batch_index in range(self._batch_size): + balanced_class = batch_index_to_class[batch_index] + batch_sample_indices.append(self._get_sample(balanced_class)) + + np.random.shuffle(batch_sample_indices) + return batch_sample_indices + + def _extract_balanced_classes(self, collected_data: List[dict]) -> np.ndarray: + """ + Extracting balanced class values from collected data. + If - special extra logic is required. Either override this method or the logic in Op and append to dataset pipeline + """ + assert len(collected_data) > 0, "Error: sampling failed, dataset size is 0" + balanced_classes = [sample[self._balanced_class_name] for sample in collected_data] + return np.array(balanced_classes) + diff --git a/fuse/data/utils/tests/__init__.py b/fuse/data/utils/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/utils/tests/test_collates.py b/fuse/data/utils/tests/test_collates.py new file mode 100644 index 000000000..8bac6a6b8 --- /dev/null +++ b/fuse/data/utils/tests/test_collates.py @@ -0,0 +1,101 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import List, Optional, Union +import unittest + +import pandas as pds +import numpy as np +import torch +from torch.utils.data.dataloader import DataLoader + +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.utils.collates import CollateDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.ops.op_base import OpBase +from fuse.data import get_sample_id + +class OpCustomCollateDefTest(OpBase): + def __call__(self, sample_dict: dict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + if get_sample_id(sample_dict) == "a": + sample_dict["data.partial"] = 1 + return sample_dict + +class TestCollate(unittest.TestCase): + def test_collate_default(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "nps": [np.array(4), np.array(2), np.array(5), np.array(1), np.array(4)], + "torch": [torch.tensor(7), torch.tensor(4), torch.tensor(9), torch.tensor(2), torch.tensor(4)], + "not_important": [12] * 5 + } + df = pds.DataFrame(data) + + # create simple pipeline + op_df = OpReadDataframe(df) + op_partial = OpCustomCollateDefTest() + pipeline = PipelineDefault("test", [(op_df, {}), (op_partial, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + # Use the collate function + dl = DataLoader(dataset, 3, collate_fn=CollateDefault(skip_keys=["data.not_important"], raise_error_key_missing=False)) + batch = next(iter(dl)) + + # verify + self.assertTrue("data.sample_id" in batch) + self.assertListEqual(batch["data.sample_id"], ["a", "b", "c"]) + self.assertTrue((batch["data.values"] == torch.tensor([7, 4, 9])).all()) + self.assertTrue( "data.nps" in batch) + self.assertTrue((batch["data.nps"] == torch.stack([torch.tensor(4), torch.tensor(2), torch.tensor(5)])).all()) + self.assertTrue("data.torch" in batch) + self.assertTrue((batch["data.torch"] == torch.stack([torch.tensor(7), torch.tensor(4), torch.tensor(9)])).all()) + self.assertTrue("data.partial" in batch) + self.assertListEqual(batch["data.partial"], [1, None, None]) + self.assertFalse("data.not_important" in batch) + + + def test_pad_all_tensors_to_same_size(self): + a = torch.zeros((1, 1, 3)) + b = torch.ones((1, 2, 1)) + values = CollateDefault.pad_all_tensors_to_same_size([a, b]) + + self.assertTrue((np.array(values.shape[1:]) == np.maximum(a.shape, b.shape)).all()) + self.assertTrue((values[1][:, :, :1] == b).all()) + self.assertTrue(values[1].sum() == b.sum()) + + def test_pad_all_tensors_to_same_size_bs_1(self): + a = torch.ones((1, 2, 1)) + values = CollateDefault.pad_all_tensors_to_same_size([a]) + self.assertTrue((values[0] == a).all()) + + def test_pad_all_tensors_to_same_size_bs_3(self): + a = torch.ones((1, 2, 3)) + b = torch.ones((3, 2, 1)) + c = torch.ones((1, 3, 2)) + values = CollateDefault.pad_all_tensors_to_same_size([a, b, c]) + self.assertListEqual(list(values.shape), [3, 3, 3, 3]) + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/utils/tests/test_dataset_export.py b/fuse/data/utils/tests/test_dataset_export.py new file mode 100644 index 000000000..f7bd83084 --- /dev/null +++ b/fuse/data/utils/tests/test_dataset_export.py @@ -0,0 +1,69 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest + +from tempfile import mkstemp +import pandas as pds + + +from fuse.utils.file_io.file_io import read_dataframe +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.utils.export import ExportDataset +from fuse.data.pipelines.pipeline_default import PipelineDefault + + + + +class TestDatasetExport(unittest.TestCase): + def test_export_to_dataframe(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "not_important": [12] * 5 + } + df = pds.DataFrame(data) + + # create simple pipeline + op = OpReadDataframe(df) + pipeline = PipelineDefault("test", [(op, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + df = df.set_index("sample_id") + + # export dataset - only get + export_df = ExportDataset.export_to_dataframe(dataset, ["data.values"]) + for sid in data["sample_id"]: + self.assertEqual(export_df.loc[sid]["data.values"], df.loc[sid]["values"]) + + # export dataset - including save + _, filename = mkstemp(suffix=".gz") + _ = ExportDataset.export_to_dataframe(dataset, ["data.values"], output_filename=filename) + export_df = read_dataframe(filename) + for sid in data["sample_id"]: + self.assertEqual(export_df.loc[sid]["data.values"], df.loc[sid]["values"]) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/utils/tests/test_samplers.py b/fuse/data/utils/tests/test_samplers.py new file mode 100644 index 000000000..bd56196cb --- /dev/null +++ b/fuse/data/utils/tests/test_samplers.py @@ -0,0 +1,163 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest +import pandas as pds +import numpy as np +from tqdm.std import tqdm +import torchvision +from torchvision import transforms +from torch.utils.data.dataloader import DataLoader + +from fuse.utils import Seed + +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict +from fuse.data.utils.collates import CollateDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.utils.samplers import BatchSamplerDefault + +class TestSamplers(unittest.TestCase): + def setUp(self): + pass + + def test_balanced_dataset(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=transform) + print(f"torch dataset size = {len(torch_dataset)}") + + num_classes = 10 + num_samples = len(torch_dataset) + + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label')) + dataset.create() + print(dataset.summary()) + batch_sampler = BatchSamplerDefault(dataset=dataset, + balanced_class_name='data.label', + num_balanced_classes=num_classes, + batch_size=32, + mode="approx", + balanced_class_weights=[1 / num_classes] * num_classes, + workers=10) + + labels = np.zeros(num_classes) + + # Create dataloader + dataloader = DataLoader(dataset=dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler, shuffle=False, drop_last=False) + iter1 = iter(dataloader) + for _ in tqdm(range(len(dataloader))): + batch_dict = next(iter1) + labels_in_batch = batch_dict['data.label'] + for label in labels_in_batch: + labels[label] += 1 + + # final balance + print(labels) + for idx in range(num_classes): + sampled = labels[idx] / num_samples + print(f'Class {idx}: {sampled * 100}% of data') + self.assertAlmostEqual(sampled, 1 / num_classes, delta=1 / num_classes * 0.5, msg=f'Unbalanced class {idx}, expected 0.1+-0.05 and got {sampled}') + + def test_not_equalbalance_dataset(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=transform) + print(f"torch dataset size = {len(torch_dataset)}") + + num_classes = 10 + probs = 1 / num_classes + + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label')) + dataset.create() + + balanced_class_weights=[1]*5 +[3]*5 + batch_size = 20 + batch_sampler = BatchSamplerDefault(dataset=dataset, + balanced_class_name='data.label', + num_balanced_classes=num_classes, + batch_size=batch_size, + mode="exact", + balanced_class_weights=balanced_class_weights) + + # Create dataloader + labels = np.zeros(num_classes) + dataloader = DataLoader(dataset=dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler, shuffle=False, drop_last=False) + iter1 = iter(dataloader) + num_items = 0 + for _ in tqdm(range(len(dataloader))): + batch_dict = next(iter1) + labels_in_batch = batch_dict['data.label'] + for label in labels_in_batch: + labels[label] += 1 + num_items += 1 + + # final balance + print(labels) + for idx in range(num_classes): + sampled = labels[idx] / num_items + print(f'Class {idx}: {sampled * 100}% of data') + self.assertEqual(sampled, balanced_class_weights[idx] / batch_size) + + def test_sampler_default(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "class": [0, 1, 2, 0, 0], + } + df = pds.DataFrame(data) + + # create simple pipeline + op_df = OpReadDataframe(df) + pipeline = PipelineDefault("test", [(op_df, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + # create sampler + batch_sampler = BatchSamplerDefault(dataset, batch_size=3, balanced_class_name="data.class", num_balanced_classes=3, workers=0) + + # Use the collate function + dl = DataLoader(dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler) + batch = next(iter(dl)) + + # verify + self.assertEqual(len(batch_sampler), 3) + self.assertIn(0, batch["data.class"]) + self.assertIn(1, batch["data.class"]) + self.assertIn(2, batch["data.class"]) + + +if __name__ == '__main__': + unittest.main() From 9b511cc619ecf59397aeeee84ace4a151dfe90ea Mon Sep 17 00:00:00 2001 From: "moshiko.raboh" Date: Sun, 17 Apr 2022 22:09:49 +0300 Subject: [PATCH 21/42] adjust mnist runner --- .../imaging/classification/mnist/runner.py | 57 ++++++------------- .../tests/test_classification_mnist.py | 7 +-- fuse/data/datasets/caching/samples_cacher.py | 18 ++++++ .../data/datasets/dataset_wrap_seq_to_dict.py | 4 +- fuse/data/utils/collates.py | 21 ++++++- fuse/utils/data/collate.py | 7 ++- 6 files changed, 64 insertions(+), 50 deletions(-) diff --git a/examples/fuse_examples/imaging/classification/mnist/runner.py b/examples/fuse_examples/imaging/classification/mnist/runner.py index 48af48798..12f2f42a4 100644 --- a/examples/fuse_examples/imaging/classification/mnist/runner.py +++ b/examples/fuse_examples/imaging/classification/mnist/runner.py @@ -20,29 +20,34 @@ import logging import os from typing import OrderedDict -from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds import torch import torch.nn.functional as F import torch.optim as optim -import torchvision import torchvision.models as models from torch.utils.data.dataloader import DataLoader from torchvision import transforms from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_wrapper import DatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds +from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve + +from fuse.data.utils.samplers import BatchSamplerDefault +from fuse.data.utils.collates import CollateDefault + from fuse.dl.losses.loss_default import LossDefault from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback from fuse.dl.managers.manager_default import ManagerDefault -from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve from fuse.dl.models.model_wrapper import ModelWrapper + from fuse.utils.utils_debug import FuseDebug import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start + +from fuseimg.datasets.mnist import MNIST + from fuse_examples.imaging.classification.mnist import lenet ########################################################################################################### # Fuse @@ -129,25 +134,9 @@ def run_train(paths: dict, train_params: dict): # Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ]) - # Create dataset - torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform) - # wrapping torch dataset - # FIXME: support also using torch dataset directly -<<<<<<< HEAD:examples/fuse_examples/classification/mnist/runner.py - train_dataset = DatasetWrapSeqToDict(name='train', dataset=torch_train_dataset, sample_keys=("data.image", "data.label")) - train_dataset.create() + train_dataset = MNIST.dataset(paths["cache_dir"], train=True) lgr.info(f'- Create sampler:') sampler = BatchSamplerDefault(dataset=train_dataset, -======= - train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label')) - train_dataset.create() - lgr.info(f'- Create sampler:') - sampler = SamplerBalancedBatch(dataset=train_dataset, ->>>>>>> 3e3a476deaecab02a60c7784c1659e341b973bd2:examples/fuse_examples/imaging/classification/mnist/runner.py balanced_class_name='data.label', num_balanced_classes=10, batch_size=train_params['data.batch_size'], @@ -155,23 +144,16 @@ def run_train(paths: dict, train_params: dict): lgr.info(f'- Create sampler: Done') # Create dataloader - train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, num_workers=train_params['data.train_num_workers']) + train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, collate_fn=CollateDefault(), num_workers=train_params['data.train_num_workers']) lgr.info(f'Train Data: Done', {'attrs': 'bold'}) ## Validation data lgr.info(f'Validation Data:', {'attrs': 'bold'}) - # Create dataset - torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset -<<<<<<< HEAD:examples/fuse_examples/classification/mnist/runner.py - validation_dataset = DatasetWrapSeqToDict(name='validation', dataset=torch_validation_dataset, sample_keys=("data.image", "data.label")) -======= - validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) ->>>>>>> 3e3a476deaecab02a60c7784c1659e341b973bd2:examples/fuse_examples/imaging/classification/mnist/runner.py - validation_dataset.create() - + validation_dataset = MNIST.dataset(paths["cache_dir"], train=False) + # dataloader - validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], + validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], collate_fn=CollateDefault(), num_workers=train_params['data.validation_num_workers']) lgr.info(f'Validation Data: Done', {'attrs': 'bold'}) @@ -281,14 +263,7 @@ def run_infer(paths: dict, infer_common_params: dict): transforms.Normalize((0.1307,), (0.3081,)) ]) # Create dataset - torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) - # wrapping torch dataset -<<<<<<< HEAD:examples/fuse_examples/classification/mnist/runner.py - validation_dataset = DatasetWrapSeqToDict(name='validation', dataset=torch_validation_dataset, sample_keys=("data.image", "data.label")) -======= - validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) ->>>>>>> 3e3a476deaecab02a60c7784c1659e341b973bd2:examples/fuse_examples/imaging/classification/mnist/runner.py - validation_dataset.create() + validation_dataset = MNIST.dataset(paths["cache_dir"], train=False) # dataloader validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=CollateDefault(), batch_size=2, num_workers=2) diff --git a/examples/fuse_examples/tests/test_classification_mnist.py b/examples/fuse_examples/tests/test_classification_mnist.py index 09b94d6bc..11359e8a5 100644 --- a/examples/fuse_examples/tests/test_classification_mnist.py +++ b/examples/fuse_examples/tests/test_classification_mnist.py @@ -24,11 +24,10 @@ import fuse.utils.gpu as GPU -# FIXME: data_package -#from fuse_examples.imaging.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ -# EVAL_COMMON_PARAMS +from fuse_examples.imaging.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ + EVAL_COMMON_PARAMS + -@unittest.skip("FIXME: data_package") class ClassificationMnistTestCase(unittest.TestCase): def setUp(self): diff --git a/fuse/data/datasets/caching/samples_cacher.py b/fuse/data/datasets/caching/samples_cacher.py index b775dbce6..a10a4ad58 100644 --- a/fuse/data/datasets/caching/samples_cacher.py +++ b/fuse/data/datasets/caching/samples_cacher.py @@ -1,3 +1,21 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" from typing import Hashable, List, Optional, Sequence, Union, Callable, Dict, Callable, Any, Tuple from fuse.data.pipelines.pipeline_default import PipelineDefault diff --git a/fuse/data/datasets/dataset_wrap_seq_to_dict.py b/fuse/data/datasets/dataset_wrap_seq_to_dict.py index 10110b215..ce16134c2 100644 --- a/fuse/data/datasets/dataset_wrap_seq_to_dict.py +++ b/fuse/data/datasets/dataset_wrap_seq_to_dict.py @@ -29,7 +29,7 @@ from fuse.utils.ndict import NDict # Dataset processor -class OpFuse(OpBase): +class OpReadDataset(OpBase): """ Op that extract data from pytorch dataset that returning sequence of values and adds those values to sample_dict """ @@ -89,7 +89,7 @@ def __init__(self, name: str, dataset: Dataset, sample_keys: Union[Sequence[str] :param kwargs: optional, additional arguments to provide to DatasetDefault """ sample_ids =[(name, i) for i in range(len(dataset))] - static_pipeline = PipelineDefault(name="staticp", ops_and_kwargs=[(OpFuse(dataset, sample_keys), {})]) + static_pipeline = PipelineDefault(name="staticp", ops_and_kwargs=[(OpReadDataset(dataset, sample_keys), {})]) if cache_dir is not None: cacher = SamplesCacher('dataset_test_cache', static_pipeline, cache_dir, restart_cache=True) else: diff --git a/fuse/data/utils/collates.py b/fuse/data/utils/collates.py index ff6c0685d..8e885e7e8 100644 --- a/fuse/data/utils/collates.py +++ b/fuse/data/utils/collates.py @@ -1,4 +1,21 @@ -import collections +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" from typing import Any, Callable, Dict, List, Sequence, Tuple import numpy as np @@ -77,7 +94,7 @@ def _batch_dispatch(self, batch_dict: dict, samples: List[dict], key: str, has_e elif key in self._special_handlers_keys: # use special handler if specified batch_dict[key] = self._special_handlers_keys[key](collected_values) - elif isinstance(collected_values[0], (torch.Tensor, np.ndarray, float, int, str, bytes, collections.abc.Sequence)): + elif isinstance(collected_values[0], (torch.Tensor, np.ndarray, float, int, str, bytes)): # batch with default PyTorch implementation batch_dict[key] = default_collate(collected_values) else: diff --git a/fuse/utils/data/collate.py b/fuse/utils/data/collate.py index 0e32b5d14..c3cd2f1e9 100644 --- a/fuse/utils/data/collate.py +++ b/fuse/utils/data/collate.py @@ -16,6 +16,7 @@ Created on June 30, 2021 """ +import logging from typing import Any, Callable, Dict, List, Sequence, Tuple from fuse.utils import NDict @@ -124,7 +125,11 @@ def uncollate(batch: Dict) -> List[Dict]: sample = NDict() for key in keys: if isinstance(batch[key], (np.ndarray, torch.Tensor, list, tuple)): - sample[key] = batch[key][sample_index] + try: + sample[key] = batch[key][sample_index] + except IndexError: + logging.error(f"Error - IndexError - key={key}, batch_size={batch_size}, len={batch[key]}") + raise else: sample[key] = batch[key] # broadcast single value for all batch From 532b347a036b0b44c04bda608a864dcbcf91b504 Mon Sep 17 00:00:00 2001 From: "moshiko.raboh" Date: Mon, 18 Apr 2022 12:48:33 +0300 Subject: [PATCH 22/42] imaging extension --- .../imaging/align => fuseimg}/__init__.py | 0 fuseimg/data/__init__.py | 0 fuseimg/data/ops/__init__.py | 0 fuseimg/data/ops/aug/color.py | 135 ++++ fuseimg/data/ops/aug/geometry.py | 221 +++++++ fuseimg/data/ops/color.py | 134 ++++ fuseimg/data/ops/debug_ops.py | 81 +++ fuseimg/data/ops/image_loader.py | 36 + fuseimg/data/ops/ops_common_imaging.py | 7 + fuseimg/data/ops/shape_ops.py | 88 +++ fuseimg/data/ops/tests/__init__.py | 0 fuseimg/data/ops/tests/test_ops.py | 80 +++ .../data/ops/tests/test_pipeline_caching.py | 46 ++ fuseimg/datasets/__init__.py | 0 fuseimg/datasets/kits21.py | 236 +++++++ fuseimg/datasets/kits21_example.ipynb | 625 ++++++++++++++++++ fuseimg/datasets/mnist.py | 54 ++ fuseimg/datasets/tests/__init__.py | 0 fuseimg/datasets/tests/test_datasets.py | 72 ++ fuseimg/utils/__init__.py | 0 fuseimg/utils/align/__init__.py | 0 .../utils}/align/utils_align_base.py | 0 .../utils}/align/utils_align_ecc.py | 0 .../utils}/image_processing.py | 0 fuseimg/utils/typing/key_types_imaging.py | 23 + fuseimg/utils/typing/typed_element.py | 36 + 26 files changed, 1874 insertions(+) rename {fuse/utils/imaging/align => fuseimg}/__init__.py (100%) create mode 100644 fuseimg/data/__init__.py create mode 100644 fuseimg/data/ops/__init__.py create mode 100644 fuseimg/data/ops/aug/color.py create mode 100644 fuseimg/data/ops/aug/geometry.py create mode 100644 fuseimg/data/ops/color.py create mode 100644 fuseimg/data/ops/debug_ops.py create mode 100644 fuseimg/data/ops/image_loader.py create mode 100644 fuseimg/data/ops/ops_common_imaging.py create mode 100755 fuseimg/data/ops/shape_ops.py create mode 100644 fuseimg/data/ops/tests/__init__.py create mode 100644 fuseimg/data/ops/tests/test_ops.py create mode 100644 fuseimg/data/ops/tests/test_pipeline_caching.py create mode 100644 fuseimg/datasets/__init__.py create mode 100644 fuseimg/datasets/kits21.py create mode 100644 fuseimg/datasets/kits21_example.ipynb create mode 100644 fuseimg/datasets/mnist.py create mode 100644 fuseimg/datasets/tests/__init__.py create mode 100644 fuseimg/datasets/tests/test_datasets.py create mode 100644 fuseimg/utils/__init__.py create mode 100644 fuseimg/utils/align/__init__.py rename {fuse/utils/imaging => fuseimg/utils}/align/utils_align_base.py (100%) rename {fuse/utils/imaging => fuseimg/utils}/align/utils_align_ecc.py (100%) rename {fuse/utils/imaging => fuseimg/utils}/image_processing.py (100%) create mode 100644 fuseimg/utils/typing/key_types_imaging.py create mode 100644 fuseimg/utils/typing/typed_element.py diff --git a/fuse/utils/imaging/align/__init__.py b/fuseimg/__init__.py similarity index 100% rename from fuse/utils/imaging/align/__init__.py rename to fuseimg/__init__.py diff --git a/fuseimg/data/__init__.py b/fuseimg/data/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/__init__.py b/fuseimg/data/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/aug/color.py b/fuseimg/data/ops/aug/color.py new file mode 100644 index 000000000..94fb95f25 --- /dev/null +++ b/fuseimg/data/ops/aug/color.py @@ -0,0 +1,135 @@ +from typing import List, Optional +from fuse.data.ops.op_base import OpBase +from fuse.utils.ndict import NDict +from fuse.utils.rand.param_sampler import Gaussian +from fuseimg.data.ops.color import OpClip +from torch import Tensor +import torch + + +class OpAugColor(OpBase): + """ + Color augmentation for gray scale images of any dimensions, including addition, multiplication, gamma and contrast adjusting + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this op expects torch tensor of range [0, 1]. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, add: Optional[float] = None, mul: Optional[float] = None, + gamma: Optional[float] = None, contrast: Optional[float] = None, channels: Optional[List[int]] = None): + """ + :param key: key to a image stored in sample_dict: torch tensor of range [0, 1] representing an image to , + :param add: value to add to each pixel + :param mul: multiplication factor + :param gamma: gamma factor + :param contrast: contrast factor + :param channels: Apply clipping just over the specified channels. If set to None will apply on all channels. + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugColor expects torch Tensor, got {type(aug_input)}" + assert aug_input.min() >= 0.0 and aug_input.max() <= 1.0 , f"Error: OpAugColor expects tensor in range [0.0-1.0]. got [{aug_input.min()}-{aug_input.max()}]" + + aug_tensor = aug_input + if channels is None: + if add is not None: + aug_tensor = self.aug_op_add_col(aug_tensor, add) + if mul is not None: + aug_tensor = self.aug_op_mul_col(aug_tensor, mul) + if gamma is not None: + aug_tensor = self.aug_op_gamma(aug_tensor, 1.0, gamma) + if contrast is not None: + aug_tensor = self.aug_op_contrast(aug_tensor, contrast) + else: + if add is not None: + aug_tensor[channels] = self.aug_op_add_col(aug_tensor[channels], add) + if mul is not None: + aug_tensor[channels] = self.aug_op_mul_col(aug_tensor[channels], mul) + if gamma is not None: + aug_tensor[channels] = self.aug_op_gamma(aug_tensor[channels], 1.0, gamma) + if contrast is not None: + aug_tensor[channels] = self.aug_op_contrast(aug_tensor[channels], contrast) + + sample_dict[key] = aug_tensor + return sample_dict + + @staticmethod + def aug_op_add_col(aug_input: Tensor, add: float) -> Tensor: + """ + Adding a values to all pixels + :param aug_input: the tensor to augment + :param add: the value to add to each pixel + :return: the augmented tensor + """ + aug_tensor = aug_input + add + aug_tensor = OpClip.clip(aug_tensor, clip=(0.0, 1.0)) + return aug_tensor + + @staticmethod + def aug_op_mul_col(aug_input: Tensor, mul: float) -> Tensor: + """ + multiply each pixel + :param aug_input: the tensor to augment + :param mul: the multiplication factor + :return: the augmented tensor + """ + input_tensor = aug_input * mul + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + @staticmethod + def aug_op_gamma(aug_input: Tensor, gain: float, gamma: float) -> Tensor: + """ + Gamma augmentation + :param aug_input: the tensor to augment + :param gain: gain factor + :param gamma: gamma factor + :return: None + """ + input_tensor = (aug_input ** gamma) * gain + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + @staticmethod + def aug_op_contrast(aug_input: Tensor, factor: float) -> Tensor: + """ + Adjust contrast (notice - calculated across the entire input tensor, even if it's 3d) + :param aug_input:the tensor to augment + :param factor: contrast factor. 1.0 is neutral + :return: the augmented tensor + """ + calculated_mean = aug_input.mean() + input_tensor = ((aug_input - calculated_mean) * factor) + calculated_mean + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + +class OpAugGaussian(OpBase): + """ + Add gaussian noise to numpy array or torch tensor of any dimensions + """ + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, mean: float = 0.0, std: float = 0.03, channels: Optional[List[int]] = None) -> Tensor: + """ + :param key: key to a tensor or numpy array stored in sample_dict: any dimension and any range + :param mean: mean gaussian distribution + :param std: std gaussian distribution + :param channels: Apply just over the specified channels. If set to None will apply on all channels. + """ + aug_input = sample_dict[key] + + aug_tensor = aug_input + if channels is None: + rand_patch = Gaussian(aug_tensor.shape, mean, std).sample() + aug_tensor = aug_tensor + rand_patch + else: + rand_patch = Gaussian(aug_tensor[channels].shape, mean, std).sample() + aug_tensor[channels] = aug_tensor[channels] + rand_patch + + sample_dict[key] = aug_tensor + return sample_dict + diff --git a/fuseimg/data/ops/aug/geometry.py b/fuseimg/data/ops/aug/geometry.py new file mode 100644 index 000000000..49092b4ff --- /dev/null +++ b/fuseimg/data/ops/aug/geometry.py @@ -0,0 +1,221 @@ +from typing import List, Optional, Tuple, Union + +from torch import Tensor +from PIL import Image + +import numpy +import torch +import torchvision.transforms.functional as TTF + +from fuse.utils.ndict import NDict + +from fuse.data import OpBase + +class OpAugAffine2D(OpBase): + """ + 2D affine transformation + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this op expects torch tensor with either 2 or 3 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, rotate: float = 0.0, translate: Tuple[float, float] = (0.0, 0.0), + scale: Tuple[float, float] = 1.0, flip: Tuple[bool, bool] = (False, False), shear: float = 0.0, + channels: Optional[List[int]] = None) -> Union[None, dict, List[dict]]: + """ + :param key: key to a tensor stored in sample_dict: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] + :param rotate: angle [-360.0 - 360.0] + :param translate: translation per spatial axis (number of pixels). The sign used as the direction. + :param scale: scale factor + :param flip: flip per spatial axis flip[0] for vertical flip and flip[1] for horizontal flip + :param shear: shear factor + :param channels: apply the augmentation on the specified channels. Set to None to apply to all channels. + :return: the augmented image + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugAffine2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) in [2, 3], f"Error: OpAugAffine2D expects tensor with 2 or 3 dimensions. got {aug_input.shape}" + + # Support for 2D inputs - implicit single channel + if len(aug_input.shape) == 2: + aug_input = aug_input.unsqueeze(dim=0) + remember_to_squeeze = True + else: + remember_to_squeeze = False + + # convert to PIL (required by affine augmentation function) + if channels is None: + channels = list(range(aug_input.shape[0])) + aug_tensor = aug_input + for channel in channels: + aug_channel_tensor = aug_input[channel].numpy() + aug_channel_tensor = Image.fromarray(aug_channel_tensor) + aug_channel_tensor = TTF.affine(aug_channel_tensor, angle=rotate, scale=scale, translate=translate, shear=shear) + if flip[0]: + aug_channel_tensor = TTF.vflip(aug_channel_tensor) + if flip[1]: + aug_channel_tensor = TTF.hflip(aug_channel_tensor) + + # convert back to torch tensor + aug_channel_tensor = numpy.array(aug_channel_tensor) + aug_channel_tensor = torch.from_numpy(aug_channel_tensor) + + # set the augmented channel + aug_tensor[channel] = aug_channel_tensor + + # squeeze back to 2-dim if needed + if remember_to_squeeze: + aug_tensor = aug_tensor.squeeze(dim=0) + + sample_dict[key] = aug_tensor + return sample_dict + + +class OpAugCropAndResize2D(OpBase): + """ + Alternative to rescaling in OpAugAffine2D: center crop and resize back to the original dimensions. if scale is bigger than 1.0. the image first padded. + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with either 2 or 3 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + scale: Tuple[float, float], + channels: Optional[List[int]] = None) -> Union[None, dict, List[dict]]: + """ + :param key: key to a tensor stored in sample_dict: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] + :param scale: tuple of positive floats + :param channels: apply augmentation on the specified channels or None for all of them + :return: the augmented tensor + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugCropAndResize2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) in [2, 3], f"Error: OpAugCropAndResize2D expects tensor with 2 or 3 dimensions. got {aug_input.shape}" + + if len(aug_input.shape) == 2: + aug_input = aug_input.unsqueeze(dim=0) + remember_to_squeeze = True + else: + remember_to_squeeze = False + + if channels is None: + channels = list(range(aug_input.shape[0])) + aug_tensor = aug_input + for channel in channels: + aug_channel_tensor = aug_input[channel] + + if scale[0] != 1.0 or scale[1] != 1.0: + cropped_shape = (int(aug_channel_tensor.shape[0] * scale[0]), int(aug_channel_tensor.shape[1] * scale[1])) + padding = [[0, 0], [0, 0]] + for dim in range(2): + if scale[dim] > 1.0: + padding[dim][0] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) // 2 + padding[dim][1] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) - padding[dim][0] + aug_channel_tensor_pad = TTF.pad(aug_channel_tensor.unsqueeze(0), (padding[1][0], padding[0][0], padding[1][1], padding[0][1])) + aug_channel_tensor_cropped = TTF.center_crop(aug_channel_tensor_pad, cropped_shape) + aug_channel_tensor = TTF.resize(aug_channel_tensor_cropped, aug_channel_tensor.shape).squeeze(0) + # set the augmented channel + aug_tensor[channel] = aug_channel_tensor + + # squeeze back to 2-dim if needed + if remember_to_squeeze: + aug_tensor = aug_tensor.squeeze(dim=0) + + sample_dict[key] = aug_tensor + return sample_dict + + +class OpAugSqueeze3Dto2D(OpBase): + """ + Squeeze selected axis of volume image into channel dimension, in order to fit the 2D augmentation functions + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with 4 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, axis_squeeze: int) -> NDict: + """ + :param key: key to a tensor stored in sample_dict: 3D tensor representing an image to augment, shape [num_channels, spatial axis 1, spatial axis 2, spatial axis 3] + :param axis_squeeze: the axis (1, 2 or 3) to squeeze into channel dimension - typically z axis + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugSqueeze3Dto2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) == 4, f"Error: OpAugSqueeze3Dto2D expects tensor with 4 dimensions. got {aug_input.shape}" + + # aug_input shape is [channels, axis_1, axis_2, axis_3] + if axis_squeeze == 1: + pass + elif axis_squeeze == 2: + aug_input = aug_input.permute((0, 2, 1, 3)) + # aug_input shape is [channels, axis_2, axis_1, axis_3] + elif axis_squeeze == 3: + aug_input = aug_input.permute((0, 3, 1, 2)) + # aug_input shape is [channels, axis_3, axis_1, axis_2] + else: + raise Exception(f"Error: axis squeeze must be 1, 2, or 3, got {axis_squeeze}") + + aug_output = aug_input.reshape((aug_input.shape[0] * aug_input.shape[1],) + aug_input.shape[2:]) + + sample_dict[key] = aug_output + return sample_dict + +class OpAugUnsqueeze3DFrom2D(OpBase): + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with 2 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + """ + Unsqueeze selected axis of volume image from channel dimension, restore the original shape squeezed by OpAugSqueeze3Dto2D + """ + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, axis_squeeze: int, channels: int) -> NDict: + """ + :param key: key to a tensor stored in sample_dict and squeezed by OpAugSqueeze3Dto2D + :param axis_squeeze: axis squeeze as specified in OpAugSqueeze3Dto2D + :param channels: number of channels in the original tensor (before OpAugSqueeze3Dto2D) + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugUnsqueeze3DFrom2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) == 3, f"Error: OpAugUnsqueeze3DFrom2D expects tensor with 3 dimensions. got {aug_input.shape}" + + + aug_output = aug_input.reshape((channels, aug_input.shape[0] // channels) + aug_input.shape[1:]) + + if axis_squeeze == 1: + pass + elif axis_squeeze == 2: + # aug_output shape is [channels, axis_2, axis_1, axis_3] + aug_output = aug_output.permute((0, 2, 1, 3)) + # aug_input shape is [channels, axis 1, axis 2, axis 3] + elif axis_squeeze == 3: + # aug_output shape is [channels, axis_3, axis_1, axis_2] + aug_output = aug_output.permute((0, 2, 3, 1)) + # aug_input shape is [channels, axis 1, axis 2, axis 3] + else: + raise Exception(f"Error: axis squeeze must be 1, 2, or 3, got {axis_squeeze}") + + sample_dict[key] = aug_output + return sample_dict diff --git a/fuseimg/data/ops/color.py b/fuseimg/data/ops/color.py new file mode 100644 index 000000000..b16f46048 --- /dev/null +++ b/fuseimg/data/ops/color.py @@ -0,0 +1,134 @@ +from typing import Optional, Tuple, Union +import numpy as np +import torch + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase + +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging + + + +class OpClip(OpBase): + """ + Clip values - support both torh tensor and numpy array + """ + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + clip = (0.0, 1.0), + ): + """ + Clip values + :param key: key to an image in sample_dict: either torh tensor or numpy array and any dimension + :param clip: values for clipping from both sides + """ + + img = sample_dict[key] + + processed_img = self.clip(img, clip) + + sample_dict[key] = processed_img + return sample_dict + + @staticmethod + def clip(img: Union[np.ndarray, torch.Tensor], clip: Tuple[float, float] = (0.0, 1.0)) -> Union[np.ndarray, torch.Tensor]: + if isinstance(img, np.ndarray): + processed_img = np.clip(img, clip[0], clip[1]) + elif isinstance(img, torch.Tensor): + processed_img = torch.clamp(img, clip[0], clip[1], out=img) + else: + raise Exception(f"Error: unexpected type {type(img)}") + return processed_img + +op_clip_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpClip(), {}) }) + +class OpNormalizeAgainstSelfImpl(OpBase): + ''' + normalizes a tensor into [0.0, 1.0] using its own statistics (NOT against a dataset) + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + ): + img = sample_dict[key] + img -= img.min() + img /= img.max() + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + +op_normalize_against_self_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpNormalizeAgainstSelfImpl(), {}) }) + + +class OpToIntImageSpace(OpBase): + ''' + normalizes a tensor into [0, 255] int gray-scale using its own statistics (NOT against a dataset) + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + ): + img = sample_dict[key] + img -= img.min() + img /= img.max() + img *=255.0 + img = img.astype(np.uint8).copy() + # img = img.transpose((1, 2, 0)) + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_to_int_image_space_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpToIntImageSpace(), {}) }) + +class OpToRange(OpBase): + ''' + linearly project from a range to a different range + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + from_range: Tuple[float, float], + to_range: Tuple[float, float], + ): + + from_range_start = from_range[0] + from_range_end = from_range[1] + to_range_start = to_range[0] + to_range_end = to_range[1] + + img = sample_dict[key] + + # shift to start at 0 + img -= from_range_start + + #scale to be in desired range + img *= (to_range_end-to_range_start) / (from_range_end-from_range_start) + + #shift to start in desired start val + img += to_range_start + + sample_dict[key] = img + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_to_range_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpToRange(), {}) }) + + + + + \ No newline at end of file diff --git a/fuseimg/data/ops/debug_ops.py b/fuseimg/data/ops/debug_ops.py new file mode 100644 index 000000000..2f6195789 --- /dev/null +++ b/fuseimg/data/ops/debug_ops.py @@ -0,0 +1,81 @@ +import cv2 +from typing import Optional + +from fuse.data.ops.op_base import OpBase +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging +from fuse.utils.ndict import NDict + +#import SimpleITK as sitk + +def no_op(input_tensor): + return input_tensor + +def draw_grid_3d_op(input_tensor, start_slice=0, end_slice=None, line_color=255, thickness=10, type_=cv2.LINE_4, pxstep=50): + ''' + Draws a grid pattern. + #todo: it is possible to change this function to support both 2d and 3d + + :param input_tensor: a numpy array, either HW format for grayscale or HWC + if HWC and C >4 then assumed to be a 3d grayscale + + :param line_color: + :param thickness: + :param type_: + :param pxstep: + :return: + ''' + + #grid = sitk.GridSource(outputPixelType=sitk.sitkUInt16, size=input_tensor.shape, sigma=(0.5, 0.5,0.5), gridSpacing=(100.0, 100.0, 100.0), gridOffset=(0.0, 0.0, 0.0), spacing=(0.2, 0.2, 0.2)) + #grid = sitk.GetArrayFromImage(grid) + + if end_slice is None: + end_slice = input_tensor.shape[2]-1 + + for s in range(start_slice, end_slice+1): + x = pxstep + y = pxstep + while x < input_tensor.shape[1]: + cv2.line(input_tensor[...,s], (x, 0), (x, input_tensor.shape[0]), color=line_color, lineType=type_, + thickness=thickness) + x += pxstep + + while y < input_tensor.shape[0]: + cv2.line(input_tensor[...,s], (0, y), (input_tensor.shape[1], y), color=line_color, lineType=type_, + thickness=thickness) + y += pxstep + + return input_tensor + +# Define function to draw a grid +def draw_grid(im, grid_size): + # Draw grid lines + # im = Image.fromarray(im) + # im = im.astype(np.float32) + for i in range(0, im.shape[1], grid_size): + cv2.line(im, (i, 0), (i, im.shape[0]), color=(255,)) + for j in range(0, im.shape[0], grid_size): + cv2.line(im, (0, j), (im.shape[1], j), color=(255,)) + return im + + +class OpDrawGrid(OpBase): + ''' + draws a 2d grid on the input tensor for debugging + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, grid_size + ): + img = sample_dict[key] + draw_grid(img, grid_size=grid_size) + + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_draw_grid_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpDrawGrid(), {}) }) + \ No newline at end of file diff --git a/fuseimg/data/ops/image_loader.py b/fuseimg/data/ops/image_loader.py new file mode 100644 index 000000000..d5cdce8fc --- /dev/null +++ b/fuseimg/data/ops/image_loader.py @@ -0,0 +1,36 @@ +import os +from fuse.data.ops.op_base import OpBase +from typing import Optional +import numpy as np +from fuse.data.ops.ops_common import OpApplyTypes +import nibabel as nib +from fuse.utils.ndict import NDict + +class OpLoadImage(OpBase): + ''' + Loads a medical image, currently only nii is supported + ''' + def __init__(self, dir_path: str, **kwargs): + super().__init__(**kwargs) + self._dir_path = dir_path + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key_in:str, key_out: str, format:str="infer"): + ''' + :param key_in: the key name in sample_dict that holds the filename + :param key_out: + ''' + img_filename = os.path.join(self._dir_path, sample_dict[key_in]) + img_filename_suffix = img_filename.split(".")[-1] + if (format == "infer" and img_filename_suffix in ["nii"]) or \ + (format in ["nii", "nib"]): + img = nib.load(img_filename) + img_np = img.get_fdata() + else: + raise Exception(f"OpLoadImage: case format {format} and {img_filename_suffix} is not supported") + + sample_dict[key_out] = img_np + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict diff --git a/fuseimg/data/ops/ops_common_imaging.py b/fuseimg/data/ops/ops_common_imaging.py new file mode 100644 index 000000000..1763f6691 --- /dev/null +++ b/fuseimg/data/ops/ops_common_imaging.py @@ -0,0 +1,7 @@ +from fuse.data.ops.ops_common import OpApplyTypes +from fuseimg.utils.typing.key_types_imaging import type_detector_imaging +from functools import partial + +OpApplyTypesImaging = partial(OpApplyTypes, + type_detector = type_detector_imaging, +) \ No newline at end of file diff --git a/fuseimg/data/ops/shape_ops.py b/fuseimg/data/ops/shape_ops.py new file mode 100755 index 000000000..58fc0832e --- /dev/null +++ b/fuseimg/data/ops/shape_ops.py @@ -0,0 +1,88 @@ + +from typing import Optional +import numpy as np +from torch import Tensor + + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase + +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging +import torch + +def sanity_check_HWC(input_tensor): + if 3!=input_tensor.ndim: + raise Exception(f'expected 3 dim tensor, instead got {input_tensor.shape}') + assert input_tensor.shape[2] NDict: + ''' + :param key: key to torch tensor of shape [H, W, C] + ''' + input_tensor: Tensor = sample_dict[key] + + sanity_check_HWC(input_tensor) + input_tensor = input_tensor.permute(dims = (2, 0, 1)) + sanity_check_CHW(input_tensor) + + sample_dict[key] = input_tensor + return sample_dict + +class OpCHWToHWC(OpBase): + """ + CHW (channel, height, width) to HWC (height, width, channel) + """ + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str) -> NDict: + ''' + :param key: key to torch tensor of shape [C, H, W] + ''' + input_tensor: Tensor = sample_dict[key] + + sanity_check_CHW(input_tensor) + input_tensor = input_tensor.permute(dims = (1, 2, 0)) + sanity_check_HWC(input_tensor) + + sample_dict[key] = input_tensor + return sample_dict + +class OpSelectSlice(OpBase): + ''' + select one slice from the input tensor, + from the first dimmention of a >2 dimensional input + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + slice_idx: int + ): + ''' + :param slice_idx: the index of the selected slice from the 1st dimmention of an input tensor + ''' + + img = sample_dict[key] + if len(img.shape) < 3: + return sample_dict + + img = img[slice_idx] + sample_dict[key] = img + return sample_dict + +op_select_slice_img_and_seg = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpSelectSlice(), {}), + DataTypeImaging.SEG : (OpSelectSlice(), {}) }) + diff --git a/fuseimg/data/ops/tests/__init__.py b/fuseimg/data/ops/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/tests/test_ops.py b/fuseimg/data/ops/tests/test_ops.py new file mode 100644 index 000000000..63732eb1a --- /dev/null +++ b/fuseimg/data/ops/tests/test_ops.py @@ -0,0 +1,80 @@ +import unittest + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuseimg.data.ops.color import OpClip, OpToRange + +from fuse.utils.ndict import NDict + +import numpy as np + + +class TestOps(unittest.TestCase): + + def test_basic_1(self): + """ + Test basic imaging ops + """ + + sample = NDict() + sample["data.input.img"] = np.array([5, 0.5, -5, 3]) + + pipeline = PipelineDefault('test_pipeline', [ + #(op_normalize_against_self, {} ), + (OpClip(), dict(key="data.input.img", clip=(-0.5, 3.0))), + (OpToRange(), dict(key="data.input.img", from_range=(-0.5, 3.0), to_range=(-3.5, 3.5))), + ]) + + sample = pipeline(sample) + + self.assertLessEqual(sample['data.input.img'].max(), 3.5) + self.assertGreaterEqual(sample['data.input.img'].min(), -3.5) + self.assertEqual(sample['data.input.img'][-1], 3.5) + + # FIXME: visualizer + # def test_basic_show(self): + # """ + # Test standard backward and forward pipeline + # """ + + # sample = TestOps.create_sample_1(views=1) + # visual = Imaging2dVisualizer() + # VProbe = partial(VisProbe, + # keys= ["data.viewpoint1.img", "data.viewpoint1.seg" ], + # type_detector=type_detector_imaging, + # visualizer = visual, cache_path="~/") + # show_flags = VisFlag.COLLECT | VisFlag.FORWARD | VisFlag.ONLINE + + # image_downsample_factor = 0.5 + # pipeline = PipelineDefault('test_pipeline', [ + # (OpRepeat(OpLoadImage(), [ + # dict(key_in = 'data.viewpoint1.img_filename', key_out='data.viewpoint1.img'), + # dict(key_in = 'data.viewpoint1.seg_filename', key_out='data.viewpoint1.seg')]), {}), + # (op_select_slice, {"slice_idx": 50}), + # (op_to_int_image_space, {} ), + # (op_draw_grid, {"grid_size": 50}), + # (VProbe(flags=VisFlag.SHOW_ALL_COLLECTED | VisFlag.FORWARD|VisFlag.REVERSE|VisFlag.ONLINE), {}), + # (OpSample(OpAffineTransform2D(do_image_reverse=True)), { + # 'auto_center' : True, + # 'output_safety_size_rel': 2.0, #this is only the buffer + # 'final_scale': image_downsample_factor, + # 'rotate': Uniform(-180.0,360.0), #double range (was middle of range originaly) #-6.0,12.0], #['dist@uniform',-90.0,180.0], #uniform(-90.0, 180.0), + # 'resampling_api': 'cv', + # 'zoom': Uniform(1.0,0.5), #uniform(1.0, 0.1), 1.0, + # 'translate_rel_pre' : 0.0, #['dist@uniform',0.0,0.05], #uniform(0.0,0.05), + # #'interp' : 'linear', #1 is linear, 0 is nearest - notice - nearest may have a problem in opencv resampling_api + # 'interp': 'linear', #Choice(['linear','nearest']), + # 'flip_lr': RandBool(0.5)}), + # (OpCropNonEmptyAABB(), {}), + # (VProbe( flags=VisFlag.COLLECT | VisFlag.FORWARD | VisFlag.ONLINE), {}), + # # (OpSample(op_gamma), dict(gamma=Uniform(0.8,1.2), gain=Uniform(0.9,1.1), clip=(0,1))), + # ]) + + # sample = pipeline(sample) + # rev = pipeline.reverse(sample, key_to_follow='data.viewpoint1.img', key_to_reverse='data.viewpoint1.img') + + + + +if __name__ == '__main__': + unittest.main() + \ No newline at end of file diff --git a/fuseimg/data/ops/tests/test_pipeline_caching.py b/fuseimg/data/ops/tests/test_pipeline_caching.py new file mode 100644 index 000000000..fd56c426e --- /dev/null +++ b/fuseimg/data/ops/tests/test_pipeline_caching.py @@ -0,0 +1,46 @@ +import unittest +import os +import tempfile + + +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher + +from fuseimg.datasets.kits21 import KITS21 + +class TestPipelineCaching(unittest.TestCase): + + def test_basic_1(self): + """ + Test basic imaging ops + """ + tmpdir = tempfile.mkdtemp() + kits_dir = os.path.join(tmpdir, "kits21") + cases = [100,150,200] + KITS21.download(kits_dir, cases) + + static_pipeline = KITS21.static_pipeline(kits_dir) + dynamic_pipeline = KITS21.dynamic_pipeline() + + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + cacher = SamplesCacher('fuseimg_ops_testing_cache', + static_pipeline, + cache_dirs) + + sample_ids = [f'case_{_:05}' for _ in cases] + ds = DatasetDefault(sample_ids, + static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, + ) + + ds.create() + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuseimg/datasets/__init__.py b/fuseimg/datasets/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/datasets/kits21.py b/fuseimg/datasets/kits21.py new file mode 100644 index 000000000..bb1d47e6e --- /dev/null +++ b/fuseimg/datasets/kits21.py @@ -0,0 +1,236 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from functools import partial +import os +from typing import Hashable, List, Optional, Sequence, Tuple, Union + +import numpy as np +import torch +from tqdm import tqdm +import skimage +import skimage.transform + + +from fuse.utils import NDict +from fuse.utils.rand.param_sampler import RandBool, RandInt, Uniform +import wget + +from fuse.data import DatasetDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data import PipelineDefault, OpSampleAndRepeat, OpToTensor, OpRepeat +from fuse.data.ops.op_base import OpBase +from fuse.data.ops.ops_aug_common import OpSample +from fuse.data.ops.ops_common import OpLambda + +from fuse.data.utils.sample import get_sample_id + +from fuseimg.data.ops.aug.color import OpAugColor +from fuseimg.data.ops.aug.geometry import OpAugAffine2D +from fuseimg.data.ops.image_loader import OpLoadImage +from fuseimg.data.ops.color import OpClip, OpToRange + +class OpKits21SampleIDDecode(OpBase): + ''' + decodes sample id into image and segmentation filename + ''' + + def __call__(self, sample_dict: NDict, op_id: Optional[str]) -> NDict: + ''' + + ''' + sid = get_sample_id(sample_dict) + + img_filename_key = 'data.input.img_path' + sample_dict[img_filename_key] = os.path.join(sid, 'imaging.nii.gz') + + seg_filename_key = 'data.gt.seg_path' + sample_dict[seg_filename_key] = os.path.join(sid, 'aggregated_MAJ_seg.nii.gz') + + return sample_dict + +def my_resize(input_tensor: torch.Tensor, resize_to: Tuple[int, int, int]) -> torch.Tensor: + """ + Custom resize operation for the CT image + """ + + inner_image_height = input_tensor.shape[0] + inner_image_width = input_tensor.shape[1] + inner_image_depth = input_tensor.shape[2] + h_ratio = resize_to[0] / inner_image_height + w_ratio = resize_to[1] / inner_image_width + if h_ratio>=1 and w_ratio>=1: + resize_ratio_xy = min(h_ratio, w_ratio) + elif h_ratio<1 and w_ratio<1: + resize_ratio_xy = max(h_ratio, w_ratio) + else: + resize_ratio_xy = 1 + #resize_ratio_z = self.resize_to[2] / inner_image_depth + if resize_ratio_xy != 1 or inner_image_depth != resize_to[2]: + input_tensor = skimage.transform.resize(input_tensor, + output_shape=(int(inner_image_height * resize_ratio_xy), + int(inner_image_width * resize_ratio_xy), + int(resize_to[2])), + mode='reflect', + anti_aliasing=True + ) + return input_tensor + +class KITS21: + """ + 2021 Kidney and Kidney Tumor Segmentation Challenge Dataset + KITS21 data pipeline impelemtation. See https://github.com/neheller/kits21 + Currently including only the image and segmentation map + """ + # bump whenever the static pipeline modified + KITS21_DATASET_VER = 0 + + @staticmethod + def download(path: str, cases:Optional[Union[int,List[int]]]=None) -> None: + ''' + :param cases: pass None (default) to download all 300 cases. OR + pass a list of integers with cases num in the range [0,299]. OR + pass a single int to download a single case + ''' + if cases is None: + cases = list(range(300)) + elif isinstance(cases, int): + cases = [cases] + elif not isinstance(cases, list): + raise Exception('Unsupported args! please provide None, int or list of ints') + + dl_dir = path + + for i in tqdm(cases, total=len(cases)): + destination_dir = os.path.join(dl_dir,f'case_{i:05d}') + os.makedirs(destination_dir, exist_ok=True) + + # imaging + destination_file = os.path.join(destination_dir, 'imaging.nii.gz') + src = f'https://kits19.sfo2.digitaloceanspaces.com/master_{i:05d}.nii.gz' + if not os.path.exists(destination_file): + wget.download(src, destination_file) + else: + print(f"imaging.nii.gz number {i} was found") + + # segmentation + seg_file = 'aggregated_MAJ_seg.nii.gz' + destination_file = os.path.join(destination_dir, seg_file) + src = f'https://github.com/neheller/kits21/raw/master/kits21/data/case_{i:05d}/aggregated_MAJ_seg.nii.gz' + if not os.path.exists(destination_file): + wget.download(src, destination_file) + else: + print(f"{seg_file} number {i} was found") + + + @staticmethod + def sample_ids(): + """ + get all the sample ids in trainset + sample_id is case_{id:05d} (for example case_00001 or case_00100) + """ + return [f"case_{case_id:05d}" for case_id in range(300)] + + @staticmethod + def static_pipeline(data_path: str) -> PipelineDefault: + """ + Get suggested static pipeline (which will be cached), typically loading the data plus design choices that we won't experiment with. + :param data_path: path to original kits21 data (can be downloaded by KITS21.download()) + """ + static_pipeline = PipelineDefault("static", [ + # decoding sample ID + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + + # loading data + (OpLoadImage(data_path), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_path), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), + + + # fixed image normalization + (OpClip(), dict(key="data.input.img", clip=(-500, 500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + + # transposing so the depth channel will be first + (OpLambda(lambda x: np.moveaxis(x, -1, 0)), dict(key="data.input.img")), # convert image from shape [H, W, D] to shape [D, H, W] + ]) + return static_pipeline + + @staticmethod + def dynamic_pipeline(): + """ + Get suggested dynamic pipeline. including pre-processing that might be modified and augmentation operations. + """ + repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] + + dynamic_pipeline = PipelineDefault("dynamic", [ + + # resize image to (110, 256, 256) + (OpRepeat(OpLambda(func=partial(my_resize, resize_to=(110, 256, 256))), kwargs_per_step_to_add=repeat_for), dict()), + + # Numpy to tensor + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), + + # affine transformation per slice but with the same arguments + (OpSampleAndRepeat(OpAugAffine2D(), kwargs_per_step_to_add=repeat_for), dict( + rotate=Uniform(-180.0,180.0), + scale=Uniform(0.8, 1.2), + flip=(RandBool(0.5), RandBool(0.5)), + translate=(RandInt(-15, 15), RandInt(-15, 15)) + )), + + # color augmentation - check if it is useful in CT images + (OpSample(OpAugColor()), dict( + key="data.input.img", + gamma=Uniform(0.8,1.2), + contrast=Uniform(0.9,1.1), + add=Uniform(-0.01, 0.01) + )), + + # add channel dimension -> [C=1, D, H, W] + (OpLambda(lambda x: x.unsqueeze(dim=0)), dict(key="data.input.img")), + ]) + return dynamic_pipeline + + @staticmethod + def dataset(data_path: str, cache_dir: str, reset_cache: bool = False, num_workers:int = 10, sample_ids: Optional[Sequence[Hashable]] = None) -> DatasetDefault: + """ + Get cached dataset + :param data_path: path to store the original data + :param cache_dir: path to store the cache + :param reset_cache: set to True tp reset the cache + :param num_workers: number of processes used for caching + :param sample_ids: dataset including the specified sample_ids or None for all the samples. sample_id is case_{id:05d} (for example case_00001 or case_00100). + """ + + if sample_ids is None: + sample_ids = KITS21.sample_ids() + + static_pipeline = KITS21.static_pipeline(data_path) + dynamic_pipeline = KITS21.dynamic_pipeline() + + cacher = SamplesCacher(f'kits21_cache_ver{KITS21.KITS21_DATASET_VER}', + static_pipeline, + [cache_dir], restart_cache=reset_cache, workers=num_workers) + + my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, + ) + my_dataset.create() + return my_dataset diff --git a/fuseimg/datasets/kits21_example.ipynb b/fuseimg/datasets/kits21_example.ipynb new file mode 100644 index 000000000..b28e88c9c --- /dev/null +++ b/fuseimg/datasets/kits21_example.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7e240708", + "metadata": {}, + "source": [ + "# Data Package\n", + "Extremely flexible pipeline allowing data loading, processing, and augmentation suitable for machine learning experiments. Supports caching to avoid redundant calculations and to speed up research iteration times significantly. The data package comes with a rich collection of pre-implemented operations and utilities that facilitates data processing. \n", + "\n", + "## Terminology\n", + "\n", + "**sample_dict** - Represents a single sample and contains all relevant information about the sample.\n", + "\n", + "No specific structure of this dictionary is required, but a useful pattern is to split it into sections (keys that define a \"namespace\" ): such as \"data\", \"model\", etc.\n", + "NDict (fuse/utils/ndict.py) class is used instead of python standard dictionary in order to allow easy \".\" seperated access. For example:\n", + "`sample_dict[“data.input.img”]` is the equivallent of `sample_dict[\"data\"][\"input\"][\"img\"]`\n", + "\n", + "Another recommended convention is to include suffix specifying the type of the value (\"img\", \"seg\", \"bbox\")\n", + "\n", + "\n", + "**sample_id** - a unique identifier of a sample. Each sample in the dataset must have an id that uniquely identifies it.\n", + "Examples of sample ids:\n", + "* path to the image file\n", + "* Tuple of (provider_id, patient_id, image_id)\n", + "* Running index\n", + "\n", + "The unique identifier will be stored in sample_dict[\"data.sample_id\"]\n", + "\n", + "## Op(erator)\n", + "\n", + "Operators are the building blocks of the sample processing pipeline. Each operator gets as input the *sample_dict* as created by the previous operators and can either add/delete/modify fields in sample_dict. The operator interface is specified in OpBase class. \n", + "A pipeline is built as a sequence of operators, which do everything - loading a new sample, preprocessing, augmentation, and more.\n", + "\n", + "## Pipeline\n", + "\n", + "A sequence of operators loading, pre-processing, and augmenting a sample. We split the pipeline into two parts - static and dynamic, which allow us to control the part out of the entire pipeline that will be cached. To learn more see *Adding a dynamic part*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "df330722", + "metadata": {}, + "outputs": [], + "source": [ + "from fuse.data.pipelines.pipeline_default import PipelineDefault\n", + "from fuse.data.datasets.dataset_default import DatasetDefault\n", + "from fuse.data.ops.op_base import OpBase\n", + "from fuse.data.ops.ops_aug_common import OpSample\n", + "from fuse.data.datasets.caching.samples_cacher import SamplesCacher\n", + "from fuse.data.ops.ops_common import OpLambda\n", + "from fuse.data.utils.samplers import BatchSamplerDefault\n", + "from fuse.data import PipelineDefault, OpSampleAndRepeat, OpToTensor, OpRepeat\n", + "from fuse.utils.rand.param_sampler import RandBool, RandInt, Uniform\n", + "import torch\n", + "import numpy as np\n", + "from functools import partial\n", + "from tempfile import mkdtemp\n", + "\n", + "import os\n", + "from fuse.data.ops.ops_cast import OpToTensor\n", + "from fuse.utils.ndict import NDict\n", + "from fuseimg.data.ops.image_loader import OpLoadImage \n", + "from fuseimg.data.ops.color import OpClip, OpToRange\n", + "from fuseimg.data.ops.aug.color import OpAugColor\n", + "from fuseimg.data.ops.aug.geometry import OpAugAffine2D\n", + "\n", + "from fuseimg.datasets.kits21 import OpKits21SampleIDDecode, KITS21" + ] + }, + { + "cell_type": "markdown", + "id": "e79a0b1a", + "metadata": {}, + "source": [ + "## Basic example - a static pipeline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9d12c6d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 1075.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imaging.nii.gz number 0 was found\n", + "aggregated_MAJ_seg.nii.gz number 0 was found\n", + "imaging.nii.gz number 1 was found\n", + "aggregated_MAJ_seg.nii.gz number 1 was found\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "num_samples = 2\n", + "data_dir = os.path.join(mkdtemp(prefix=\"kits21_data\"))\n", + "KITS21.download(data_dir, cases=list(range(num_samples)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "532e7c3c", + "metadata": {}, + "outputs": [], + "source": [ + "static_pipeline = PipelineDefault(\"static\", [\n", + " # decoding sample ID\n", + " (OpKits21SampleIDDecode(), dict()), # will save image and seg path to \"data.input.img_path\", \"data.gt.seg_path\" \n", + "\n", + " # loading data\n", + " (OpLoadImage(data_dir), dict(key_in=\"data.input.img_path\", key_out=\"data.input.img\", format=\"nib\")),\n", + " (OpLoadImage(data_dir), dict(key_in=\"data.gt.seg_path\", key_out=\"data.gt.seg\", format=\"nib\")),\n", + "\n", + "\n", + " # fixed image normalization\n", + " (OpClip(), dict(key=\"data.input.img\", clip=(-500, 500))),\n", + " (OpToRange(), dict(key=\"data.input.img\", from_range=(-500, 500), to_range=(0, 1))),\n", + "])\n", + "sample_ids=[f\"case_{id:05d}\" for id in range(num_samples)]\n", + "my_dataset = DatasetDefault(sample_ids=sample_ids,\n", + " static_pipeline=static_pipeline, \n", + ")\n", + "my_dataset.create()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3309180", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'min = 0.0 | max = 1.0'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(f\"min = {np.min(my_dataset[0]['data.input.img'])} | max = {np.max(my_dataset[0]['data.input.img'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c904655c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(611, 512, 512)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_dataset[0][\"data.input.img\"].shape" + ] + }, + { + "cell_type": "markdown", + "id": "22514dcb", + "metadata": {}, + "source": [ + "A basic example, including static pipeline only that loading and pre-processing an image and a corresponding segmentation map. \n", + "A pipeline is created from a list of tuples. Each tuple includes an op and op arguments. The required arguments for an op specified in its \\_\\_call\\_\\_() method.\n", + "In this example \"sample_id\" is a running index. OpKits21SampleIDDecode() is a custom op converting the index to image path and segmentation path which then loaded by OpImageLoad(). Finally, OpClip() and OpToRange() pre-process the image.\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "11b0c6c9", + "metadata": {}, + "source": [ + "## Caching\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d3340ee1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/user/il018850/code/fuse-med-ml-2/data/fuse/data/datasets/caching/samples_cacher.py:84: UserWarning: Multi processing is not active in SamplesCacher. Seting \"workers\" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with \"workers=0\".\n", + " warn('Multi processing is not active in SamplesCacher. Seting \"workers\" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with \"workers=0\".')\n", + " 0%| | 0/2 [00:00 DatasetDefault: + """ + Get mnist dataset - each sample includes: 'data.image', 'data.label' and 'data.sample_id' + :param cache_dir: optional - destination to cache mnist + :param train: If True, creates dataset from ``train-images-idx3-ubyte``, + otherwise from ``t10k-images-idx3-ubyte``. + """ + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_train_dataset = torchvision.datasets.MNIST(cache_dir, download=True, train=train, transform=transform) + # wrapping torch dataset + train_dataset = DatasetWrapSeqToDict(name=f'mnist-{train}', dataset=torch_train_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + return train_dataset \ No newline at end of file diff --git a/fuseimg/datasets/tests/__init__.py b/fuseimg/datasets/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/datasets/tests/test_datasets.py b/fuseimg/datasets/tests/test_datasets.py new file mode 100644 index 000000000..ffbb4e16f --- /dev/null +++ b/fuseimg/datasets/tests/test_datasets.py @@ -0,0 +1,72 @@ +import os +import pathlib +import shutil +from tempfile import gettempdir, mkdtemp +import unittest +from fuse.data.utils.sample import get_sample_id +from fuse.utils.file_io.file_io import create_dir + +from fuseimg.datasets.kits21 import KITS21 +from tqdm import trange +from testbook import testbook + +notebook_path = os.path.join(pathlib.Path(__file__).parent.resolve(), "../kits21_example.ipynb") + +class TestDatasets(unittest.TestCase): + + def setUp(self) -> None: + super().setUp() + self.kits21_cache_dir = mkdtemp(prefix="kits21_cache") + self.kits21_data_dir = mkdtemp(prefix="kits21_data") + def test_kits32(self): + KITS21.download(self.kits21_data_dir, cases=list(range(10))) + + create_dir(self.kits21_cache_dir) + dataset = KITS21.dataset(data_path=self.kits21_data_dir, cache_dir=self.kits21_cache_dir, reset_cache=True, sample_ids=[f"case_{id:05d}" for id in range(10)]) + self.assertEqual(len(dataset), 10) + for sample_index in trange(10): + sample = dataset[sample_index] + self.assertEqual(get_sample_id(sample), f"case_{sample_index:05d}") + + @testbook(notebook_path, execute=range(0,4)) + def test_basic(tb, self): + tb.execute_cell([4,5]) + + tb.inject( + """ + assert(np.max(my_dataset[0]['data.input.img'])>=0 and np.max(my_dataset[0]['data.input.img'])<=1) + """ + ) + + @testbook(notebook_path, execute=range(0,4)) + def test_caching(tb, self): + tb.execute_cell([9]) + + tb.execute_cell([16,17]) + tb.inject( + """ + assert(isinstance(my_dataset[0]["data.gt.seg"], torch.Tensor)) + """ + ) + + @testbook(notebook_path, execute=range(0,4)) + def test_custom(tb, self): + tb.execute_cell([25]) + + tb.inject( + """ + assert(my_dataset[0]["data.gt.seg"].shape[1:] == (4, 256, 256)) + """ + ) + + + def tearDown(self) -> None: + shutil.rmtree(self.kits21_cache_dir) + shutil.rmtree(self.kits21_data_dir) + + super().tearDown() + + + +if __name__ == '__main__': + unittest.main() diff --git a/fuseimg/utils/__init__.py b/fuseimg/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/utils/align/__init__.py b/fuseimg/utils/align/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/utils/imaging/align/utils_align_base.py b/fuseimg/utils/align/utils_align_base.py similarity index 100% rename from fuse/utils/imaging/align/utils_align_base.py rename to fuseimg/utils/align/utils_align_base.py diff --git a/fuse/utils/imaging/align/utils_align_ecc.py b/fuseimg/utils/align/utils_align_ecc.py similarity index 100% rename from fuse/utils/imaging/align/utils_align_ecc.py rename to fuseimg/utils/align/utils_align_ecc.py diff --git a/fuse/utils/imaging/image_processing.py b/fuseimg/utils/image_processing.py similarity index 100% rename from fuse/utils/imaging/image_processing.py rename to fuseimg/utils/image_processing.py diff --git a/fuseimg/utils/typing/key_types_imaging.py b/fuseimg/utils/typing/key_types_imaging.py new file mode 100644 index 000000000..928d4f565 --- /dev/null +++ b/fuseimg/utils/typing/key_types_imaging.py @@ -0,0 +1,23 @@ +from enum import Enum +from fuse.data.key_types import DataTypeBasic, TypeDetectorPatternsBased +from typing import * + +class DataTypeImaging(Enum): + """ + Possible data types stored in sample_dict. + Using Patterns - the type will be inferred from the key name + """ + IMAGE = "image" # Image + SEG = "seg" # Segmentation Map + BBOX = "bboxes" # Bounding Box + CTR = "contours" # Contour + +PATTERNS_DICT_IMAGING = { + r".*img$": DataTypeImaging.IMAGE, + r".*seg$": DataTypeImaging.SEG, + r".*bbox$": DataTypeImaging.BBOX, + r".*ctr$": DataTypeImaging.CTR, + r".*$": DataTypeBasic.UNKNOWN +} + +type_detector_imaging = TypeDetectorPatternsBased(PATTERNS_DICT_IMAGING) diff --git a/fuseimg/utils/typing/typed_element.py b/fuseimg/utils/typing/typed_element.py new file mode 100644 index 000000000..6a92992c3 --- /dev/null +++ b/fuseimg/utils/typing/typed_element.py @@ -0,0 +1,36 @@ +import numpy as np +from fuse.data.key_types import DataTypeBasic +from fuse.data.patterns import Patterns +from fuse.utils.ndict import NDict + +class TypedElement: + ''' + encapsulates a single item view with all its overlayed data + ''' + def __init__(self, image=None, seg=None, contours=None, bboxes=None, labels=None, metadata=None) -> None: + assert isinstance(image, (np.ndarray, type(None))) + assert isinstance(seg, (np.ndarray, type(None))) + #assert isinstance(contours, (np.ndarray, type(None))) + #assert isinstance(bboxes, (np.ndarray, type(None))) + #assert isinstance(labels, (np.ndarray, type(None))) + + self.image = image + self.seg = seg + self.contours = contours + self.bboxes = bboxes + self.labels = labels + self.metadata = metadata + +def typedElementFromSample(sample_dict, key_pattern, td): + patterns = Patterns({key_pattern: True}, False) + all_keys = [k for k in sample_dict.get_all_keys() if patterns.get_value(k)] + + content = {td.get_type(sample_dict, k).value: sample_dict[k] for k in all_keys if td.get_type(sample_dict, k) != DataTypeBasic.UNKNOWN} + keymap = {td.get_type(sample_dict, k): k for k in all_keys if td.get_type(sample_dict, k) != DataTypeBasic.UNKNOWN} + elem = TypedElement(**content) + return elem, keymap + +def typedElementToSample(sample_dict, typed_element, keymap): + for k,v in keymap.items(): + sample_dict[v] = typed_element.__getattribute__(k.value) + return sample_dict \ No newline at end of file From 92d78d13c93e1221f05d0b38162ec8b96eafb65d Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 18 Apr 2022 15:37:49 +0300 Subject: [PATCH 23/42] Move changes from master's branch to mnist_fuse2_style's branch --- .../imaging/hello_world/__init__.py | 0 .../imaging/hello_world/hello_world.ipynb | 31 ++++++++++++++----- .../tests/test_notebook_hello_world.py | 26 ++++++++++++++++ fuse/dl/managers/manager_default.py | 10 ++++++ 4 files changed, 60 insertions(+), 7 deletions(-) create mode 100644 examples/fuse_examples/imaging/hello_world/__init__.py create mode 100644 examples/fuse_examples/tests/test_notebook_hello_world.py diff --git a/examples/fuse_examples/imaging/hello_world/__init__.py b/examples/fuse_examples/imaging/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index e1235345e..42d73e921 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -47,9 +47,13 @@ "metadata": {}, "outputs": [], "source": [ - "!git clone https://github.com/IBM/fuse-med-ml.git\n", - "%cd fuse-med-ml\n", - "!pip install -e ." + "install_fuse = False # change to 'True' to clone and install fuse-med-ml.\n", + "\n", + "if install_fuse:\n", + " !git clone https://github.com/IBM/fuse-med-ml.git\n", + " %cd fuse-med-ml\n", + " !pip install -e .\n", + " !pip install -e examples" ] }, { @@ -108,7 +112,7 @@ "metadata": {}, "outputs": [], "source": [ - "ROOT = 'examples' # TODO: fill path here\n", + "ROOT = 'examples'\n", "PATHS = {'model_dir': os.path.join(ROOT, 'mnist/model_dir'),\n", " 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message.\n", " 'cache_dir': os.path.join(ROOT, 'mnist/cache_dir'),\n", @@ -146,7 +150,6 @@ "\n", "### Manager ###\n", "TRAIN_COMMON_PARAMS['manager.train_params'] = {\n", - " 'device': 'cuda', \n", " 'num_epochs': 5,\n", " 'virtual_batch_size': 1, # number of batches in one virtual batch\n", " 'start_saving_epochs': 10, # first epoch to start saving checkpoints from\n", @@ -162,7 +165,6 @@ "TRAIN_COMMON_PARAMS['manager.weight_decay'] = 0.001\n", "TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint\n", "\n", - "TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu'\n", "\n", "train_params = TRAIN_COMMON_PARAMS" ] @@ -361,6 +363,8 @@ " callbacks=callbacks,\n", " train_params=train_params['manager.train_params'])\n", "\n", + "# manager.set_device('cpu') # uncomment to use cpu\n", + "\n", "# Start training\n", "manager.train(train_dataloader=train_dataloader, validation_dataloader=validation_dataloader)" ] @@ -409,6 +413,7 @@ "\n", "## Manager for inference\n", "manager = ManagerDefault()\n", + "# manager.set_device('cpu') # uncomment to use cpu\n", "output_columns = ['model.output.classification', 'data.label']\n", "manager.infer(data_loader=validation_dataloader,\n", " input_model_dir=paths['model_dir'],\n", @@ -489,7 +494,19 @@ "results = evaluator.eval(ids=None,\n", " data=os.path.join(paths[\"inference_dir\"], eval_common_params[\"infer_filename\"]),\n", " metrics=metrics,\n", - " output_dir=paths['eval_dir'])" + " output_dir=paths['eval_dir'])\n", + "\n", + "# For testing purposes\n", + "test_result_acc = results['metrics.accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Done!\")" ] } ], diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py new file mode 100644 index 000000000..ca4ac8fec --- /dev/null +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -0,0 +1,26 @@ +import os +import unittest +from testbook import testbook +import fuse.utils.gpu as FuseUtilsGPU + +class NotebookHelloWorldTestCase(unittest.TestCase): + + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + def test_notebook(self): + NUM_OF_CELLS = 36 + notebook_path = "fuse_examples/tutorials/hello_world/hello_world.ipynb" + + # Execute the whole notebook and save it as an object + with testbook(notebook_path, execute=True, timeout=600) as tb: + + # Sanity check + test_result_acc = tb.ref("test_result_acc") + assert(test_result_acc > 0.95) + + # Check that all the notebook's cell executed + last_cell_output = tb.cell_output_text(NUM_OF_CELLS - 1) + assert(last_cell_output == 'Done!') + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/dl/managers/manager_default.py b/fuse/dl/managers/manager_default.py index 08d3a5090..c23c2d1eb 100644 --- a/fuse/dl/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -945,6 +945,16 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat self.logger.info(dataset_summary) pass + def set_device(self, device: str): + """ + set the manger's device to a given one. + :param device: device to set + """ + train_params = {'device' : device} + self.set_objects(train_params=train_params) + + pass + def _extend_results_dict(mode: str, current_dict: Dict, aggregated_dict: Dict) -> Dict: """ From 0b8b57332094bb818ec67d5df3a2caeedd85491a Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 18 Apr 2022 15:59:34 +0300 Subject: [PATCH 24/42] Fixed import path --- examples/fuse_examples/imaging/hello_world/hello_world.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index 42d73e921..ff0480a69 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -95,7 +95,7 @@ "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "from fuse.dl.models.model_wrapper import ModelWrapper\n", - "from fuse_examples.tutorials.hello_world.hello_world_utils import LeNet, perform_softmax" + "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax" ] }, { From fb723868a8ce0303a8d6afdc5714d53d275937b0 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 19 Apr 2022 19:26:55 +0300 Subject: [PATCH 25/42] remove the create-data script and move all its functionality to input_processor --- .../segmentation/siim/create_dataset.py | 133 ------------------ fuse_examples/segmentation/siim/runner_seg.py | 33 ++--- 2 files changed, 10 insertions(+), 156 deletions(-) delete mode 100644 fuse_examples/segmentation/siim/create_dataset.py diff --git a/fuse_examples/segmentation/siim/create_dataset.py b/fuse_examples/segmentation/siim/create_dataset.py deleted file mode 100644 index f784ed22d..000000000 --- a/fuse_examples/segmentation/siim/create_dataset.py +++ /dev/null @@ -1,133 +0,0 @@ -import pydicom -from pathlib import Path -import pandas as pd -from tqdm import tqdm as progress_bar -import PIL -import numpy as np -import matplotlib.pylab as plt - - -""" -download dataset from - -https://www.kaggle.com/seesee/siim-train-test - -The path to the extracted data should be updated in the variable. -The output images will be stored at . -the output size is defined by (the output is created with a folder for each size) -""" -########################################## -# Params -########################################## -main_out_path = '../siim_data' -dataset_path = '../siim/' -out_size_list = [256, 512] - - -def rle2mask(rles, width, height): - """ - - rle encoding if images - input: rles(list of rle), width and height of image - returns: mask of shape (width,height) - """ - - mask= np.zeros(width* height) - for rle in rles: - array = np.asarray([int(x) for x in rle.split()]) - starts = array[0::2] - lengths = array[1::2] - - current_position = 0 - for index, start in enumerate(starts): - current_position += start - mask[current_position:current_position+lengths[index]] = 255 - current_position += lengths[index] - - return mask.reshape(width, height).T - - -def filter_files(files, include=[], exclude=[]): - for incl in include: - files = [f for f in files if incl in f.name] - for excl in exclude: - files = [f for f in files if excl not in f.name] - return sorted(files) - - -def ls(x, recursive=False, include=[], exclude=[]): - if not recursive: - out = list(x.iterdir()) - else: - out = [o for o in x.glob('**/*')] - out = filter_files(out, include=include, exclude=exclude) - return out - - -Path.ls = ls - - -class InOutPath(): - def __init__(self, input_path:Path, output_path:Path): - if isinstance(input_path, str): input_path = Path(input_path) - if isinstance(output_path, str): output_path = Path(output_path) - self.inp = input_path - self.out = output_path - self.mkoutdir() - - def mkoutdir(self): - self.out.mkdir(exist_ok=True, parents=True) - - def __repr__(self): - return '\n'.join([f'{i}: {o}' for i, o in self.__dict__.items()]) + '\n' - - -def dcm2png(SZ, dataset): - path = InOutPath(Path(dataset_path + f'/dicom-images-{dataset}'), Path(main_out_path + f'/data{SZ}/{dataset}')) - files = path.inp.ls(recursive=True, include=['.dcm']) - for f in progress_bar(files): - dcm = pydicom.read_file(str(f)).pixel_array - im = PIL.Image.fromarray(dcm).resize((SZ,SZ)) - im.save(path.out/f'{f.stem}.png') - - -def masks2png(SZ): - path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) - for i in progress_bar(list(set(rle_df.ImageId.values))): - I = rle_df.ImageId == i - name = rle_df.loc[I, 'ImageId'] - enc = rle_df.loc[I, ' EncodedPixels'] - if sum(I) == 1: - enc = enc.values[0] - name = name.values[0] - if enc == '-1': # ' -1': - m = np.zeros((1024, 1024)).astype(np.uint8) - else: - m = rle2mask([enc], 1024, 1024).astype(np.uint8) - PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name}.png') - else: - m = rle2mask(enc.values, 1024, 1024).astype(np.uint8) - PIL.Image.fromarray(m).resize((SZ,SZ)).save(f'{path.out}/{name.values[0]}.png') - - - -if __name__ == '__main__': - rle_df = pd.read_csv(dataset_path + '/train-rle.csv') - - for SZ in progress_bar(out_size_list): - print(f'Converting data for train{SZ}') - dcm2png(SZ, 'train') - print(f'Converting data for test{SZ}') - dcm2png(SZ, 'test') - print(f'Generating masks for size {SZ}') - masks2png(SZ) - - for SZ in progress_bar(out_size_list): - # Missing masks set to 0 - print('Generating missing masks as zeros') - train_images = [o.name for o in Path(main_out_path + f'/data{SZ}/train').ls(include=['.png'])] - train_masks = [o.name for o in Path(main_out_path + f'/data{SZ}/masks').ls(include=['.png'])] - missing_masks = set(train_images) - set(train_masks) - path = InOutPath(Path('data'), Path(main_out_path + f'/data{SZ}/masks')) - for name in progress_bar(missing_masks): - m = np.zeros((1024, 1024)).astype(np.uint8).T - PIL.Image.fromarray(m).resize((SZ,SZ)).save(main_out_path + f'/data{SZ}/masks/{name}') diff --git a/fuse_examples/segmentation/siim/runner_seg.py b/fuse_examples/segmentation/siim/runner_seg.py index 42c9f73ac..38417a44d 100644 --- a/fuse_examples/segmentation/siim/runner_seg.py +++ b/fuse_examples/segmentation/siim/runner_seg.py @@ -62,7 +62,7 @@ ########################################## # Debug modes ########################################## -mode = 'debug' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug debug = FuseUtilsDebug(mode) ########################################## @@ -153,7 +153,7 @@ 'source': 'losses.total_loss', # can be any key from 'epoch_results' (either metrics or losses result) 'optimization': 'min', # can be either min/max } -TRAIN_COMMON_PARAMS['manager.learning_rate'] = 1e-1 +TRAIN_COMMON_PARAMS['manager.learning_rate'] = 1e-2 TRAIN_COMMON_PARAMS['manager.weight_decay'] = 1e-4 TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint TRAIN_COMMON_PARAMS['partition_file'] = 'train_val_split.pickle' @@ -173,19 +173,12 @@ def run_train(paths: dict, train_common_params: dict): lgr.info(f'model_dir={paths["model_dir"]}', {'color': 'magenta'}) lgr.info(f'cache_dir={paths["cache_dir"]}', {'color': 'magenta'}) - # train_path = paths['data_dir'][0] - # mask_path = paths['data_dir'][1] - #### Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) train_data_source = FuseDataSourceSeg(phase='train', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) - - # train_data_source = FuseDataSourceSeg(image_source=train_path, - # mask_source=mask_path, - # train=True) print(train_data_source.summary()) ## Create data processors: @@ -230,20 +223,16 @@ def run_train(paths: dict, train_common_params: dict): # Validation dataset lgr.info(f'Validation Data:', {'attrs': 'bold'}) - # valid_data_source = FuseDataSourceSeg(image_source=train_path, - # mask_source=mask_path, - # partition_file=train_common_params['partition_file'], - # train=False) valid_data_source = FuseDataSourceSeg(phase='validation', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) print(valid_data_source.summary()) valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - data_source=valid_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - visualizer=visualiser) + data_source=valid_data_source, + input_processors=input_processors, + gt_processors=gt_processors, + visualizer=visualiser) lgr.info(f'- Load and cache data:') valid_dataset.create() @@ -293,7 +282,7 @@ def run_train(paths: dict, train_common_params: dict): # ===================================================================================== callbacks = [ # default callbacks - FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + # FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] @@ -334,16 +323,14 @@ def run_infer(paths: dict, infer_common_params: dict): lgr.info('Fuse Inference', {'attrs': ['bold', 'underline']}) lgr.info(f'infer_filename={os.path.join(paths["inference_dir"], infer_common_params["infer_filename"])}', {'color': 'magenta'}) - train_path = paths['data_dir'][0] - mask_path = paths['data_dir'][1] # ================================================================== # Validation dataset lgr.info(f'Test Data:', {'attrs': 'bold'}) - train_data_source = FuseDataSourceSeg(phase='validation', + infer_data_source = FuseDataSourceSeg(phase='validation', data_folder=paths['train_folder'], partition_file=infer_common_params['partition_file']) - print(train_data_source.summary()) + print(infer_data_source.summary()) ## Create data processors: input_processors = { @@ -459,7 +446,7 @@ def data_iter(): ###################################### if __name__ == "__main__": # allocate gpus - NUM_GPUS = 0 + NUM_GPUS = 1 if NUM_GPUS == 0: TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones From bab4bf7728a730d4b1ccef4f9a800762449f189b Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 25 Apr 2022 12:13:41 +0300 Subject: [PATCH 26/42] Updated the notebook (mnist example) to fuse2 --- .../imaging/hello_world/hello_world.ipynb | 48 +++++++------------ .../tests/test_notebook_hello_world.py | 4 +- 2 files changed, 18 insertions(+), 34 deletions(-) diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index ff0480a69..397ec77b6 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -87,15 +87,18 @@ "from torchvision import transforms\n", "\n", "from fuse.eval.evaluator import EvaluatorDefault\n", - "from fuse.data.dataset.dataset_wrapper import DatasetWrapper\n", - "from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch\n", "from fuse.dl.losses.loss_default import LossDefault\n", "from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback\n", "from fuse.dl.managers.manager_default import ManagerDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "from fuse.dl.models.model_wrapper import ModelWrapper\n", - "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax" + "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax\n", + "from fuse.data.utils.samplers import BatchSamplerDefault\n", + "from fuse.data.utils.collates import CollateDefault\n", + "\n", + "\n", + "from fuseimg.datasets.mnist import MNIST" ] }, { @@ -181,15 +184,7 @@ "metadata": {}, "source": [ "##### **Data**\n", - "Downloading the MNIST dataset and building dataloaders (torch.utils.data.DataLoader) for both train and validation using Fuse components:\n", - "1. Wrapper - **DatasetWrapper**:\n", - "\n", - " Wraps PyTorch dataset such that each sample is being converted to dictionary according to the provided mapping.\n", - "2. Sampler - **SamplerBalancedBatch**:\n", - "\n", - " Implementing 'torch.utils.data.sampler'.\n", - " \n", - " The sampler creates a balanced batch comprised of an equal number of samples per label." + "Downloading the MNIST dataset and building dataloaders (torch.utils.data.DataLoader) for both train and validation using Fuse components.\n" ] }, { @@ -198,36 +193,25 @@ "metadata": {}, "outputs": [], "source": [ - "transform = transforms.Compose([\n", - " transforms.ToTensor(),\n", - " transforms.Normalize((0.1307,), (0.3081,))\n", - "])\n", - "\n", - "# Create dataset\n", - "torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform)\n", - "\n", - "# wrapping torch dataset\n", - "train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label'))\n", - "train_dataset.create()\n", + "## Train Data\n", + "train_dataset = MNIST.dataset(paths[\"cache_dir\"], train=True)\n", "\n", - "sampler = SamplerBalancedBatch(dataset=train_dataset,\n", + "# Create Sampler\n", + "sampler = BatchSamplerDefault(dataset=train_dataset,\n", " balanced_class_name='data.label',\n", " num_balanced_classes=10,\n", " batch_size=train_params['data.batch_size'],\n", " balanced_class_weights=None)\n", "\n", "# Create dataloader\n", - "train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, num_workers=train_params['data.train_num_workers'])\n", + "train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, collate_fn=CollateDefault(), num_workers=train_params['data.train_num_workers'])\n", "\n", "## Validation data\n", "# Create dataset\n", - "torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform)\n", - "# wrapping torch dataset\n", - "validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label'))\n", - "validation_dataset.create()\n", + "validation_dataset = MNIST.dataset(paths[\"cache_dir\"], train=False)\n", "\n", "# dataloader\n", - "validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'],\n", + "validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], collate_fn=CollateDefault(),\n", " num_workers=train_params['data.validation_num_workers'])" ] }, @@ -409,7 +393,7 @@ "metadata": {}, "outputs": [], "source": [ - "validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2)\n", + "validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=CollateDefault(), batch_size=2, num_workers=2)\n", "\n", "## Manager for inference\n", "manager = ManagerDefault()\n", @@ -529,7 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.11" + "version": "3.7.13" }, "orig_nbformat": 4 }, diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index ca4ac8fec..680a90167 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,10 +5,10 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + # @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 - notebook_path = "fuse_examples/tutorials/hello_world/hello_world.ipynb" + notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" # Execute the whole notebook and save it as an object with testbook(notebook_path, execute=True, timeout=600) as tb: From d4a5c02d34a4ddffe3b21918713ad702f1cf4609 Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 25 Apr 2022 13:58:22 +0300 Subject: [PATCH 27/42] Skip test - temp --- examples/fuse_examples/tests/test_notebook_hello_world.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 680a90167..5dab500e2 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - # @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" @@ -17,7 +17,7 @@ def test_notebook(self): test_result_acc = tb.ref("test_result_acc") assert(test_result_acc > 0.95) - # Check that all the notebook's cell executed + # Check that all the notebook's cell were executed last_cell_output = tb.cell_output_text(NUM_OF_CELLS - 1) assert(last_cell_output == 'Done!') From 68df39cf5726fdf0ff84375b9670b5d6b5f20ed1 Mon Sep 17 00:00:00 2001 From: Sagi Polaczek <56922146+SagiPolaczek@users.noreply.github.com> Date: Mon, 25 Apr 2022 14:22:45 +0300 Subject: [PATCH 28/42] Update test_notebook_hello_world.py --- examples/fuse_examples/tests/test_notebook_hello_world.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 5dab500e2..1de475ed5 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. +# @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" @@ -23,4 +23,4 @@ def test_notebook(self): if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() From ae69698e02334f90494ce169c87690d9309ee750 Mon Sep 17 00:00:00 2001 From: Moshiko Raboh <86309179+mosheraboh@users.noreply.github.com> Date: Thu, 28 Apr 2022 12:38:41 +0300 Subject: [PATCH 29/42] Data package (#61) * remove fuse1 data package * remove dataset from manager * convert mnist to fuse2 style * add fuse data package * adjust mnist runner * imaging extension Co-authored-by: moshiko Co-authored-by: Alex Golts --- .../imaging/classification/mnist/runner.py | 46 +- .../prostate_x/run_train_3dpatch.py | 4 +- .../tests/test_classification_cmmd.py | 5 +- .../tests/test_classification_knight.py | 9 +- .../tests/test_classification_mnist.py | 3 +- .../tests/test_classification_prostatex.py | 5 +- .../tests/test_classification_skin_lesion.py | 5 +- fuse/data/__init__.py | 19 + fuse/data/augmentor/augmentor_base.py | 65 -- .../augmentor_batch_level_callback.py | 40 - fuse/data/augmentor/augmentor_default.py | 107 --- fuse/data/augmentor/augmentor_toolbox.py | 455 ----------- fuse/data/cache/cache_base.py | 105 --- fuse/data/cache/cache_files.py | 228 ------ fuse/data/cache/cache_memory.py | 104 --- fuse/data/cache/cache_null.py | 85 -- fuse/data/data_source/data_source_default.py | 120 --- fuse/data/data_source/data_source_folds.py | 106 --- .../data/data_source/data_source_from_list.py | 40 - fuse/data/data_source/data_source_toolbox.py | 118 --- fuse/data/dataset/dataset_base.py | 130 --- fuse/data/dataset/dataset_dataframe.py | 75 -- fuse/data/dataset/dataset_default.py | 756 ------------------ fuse/data/dataset/dataset_generator.py | 561 ------------- fuse/data/dataset/dataset_wrapper.py | 70 -- fuse/data/datasets/__init__.py | 1 + .../caching}/__init__.py | 0 .../caching/object_caching_handlers.py | 59 ++ fuse/data/datasets/caching/samples_cacher.py | 375 +++++++++ .../caching/tests}/__init__.py | 0 .../caching/tests/test_sample_caching.py | 168 ++++ fuse/data/datasets/dataset_base.py | 46 ++ fuse/data/datasets/dataset_default.py | 304 +++++++ .../data/datasets/dataset_wrap_seq_to_dict.py | 97 +++ fuse/data/datasets/sample_caching_audit.py | 96 +++ .../tests}/__init__.py | 0 .../datasets/tests/test_dataset_default.py | 264 ++++++ .../test_dataset_default_audit_feature.py | 250 ++++++ .../tests/test_dataset_wrap_seq_to_dict.py | 90 +++ fuse/data/key_types.py | 47 ++ fuse/data/key_types_for_testing.py | 24 + fuse/data/ops/__init__.py | 1 + fuse/data/ops/caching_tools.py | 137 ++++ fuse/data/ops/op_base.py | 128 +++ fuse/data/ops/ops_aug_common.py | 164 ++++ fuse/data/ops/ops_cast.py | 167 ++++ fuse/data/ops/ops_common.py | 357 +++++++++ fuse/data/ops/ops_common_for_testing.py | 7 + fuse/data/ops/ops_read.py | 101 +++ fuse/data/ops/ops_visprobe.py | 186 +++++ fuse/data/{dataset => ops/tests}/__init__.py | 0 fuse/data/ops/tests/test_op_base.py | 43 + fuse/data/ops/tests/test_op_visprobe.py | 284 +++++++ fuse/data/ops/tests/test_ops_aug_common.py | 125 +++ fuse/data/ops/tests/test_ops_cast.py | 97 +++ fuse/data/ops/tests/test_ops_common.py | 208 +++++ fuse/data/ops/tests/test_ops_read.py | 76 ++ fuse/data/patterns.py | 56 ++ .../data/{processor => pipelines}/__init__.py | 0 fuse/data/pipelines/pipeline_default.py | 130 +++ .../{sampler => pipelines/tests}/__init__.py | 0 .../pipelines/tests/test_pipeline_default.py | 117 +++ fuse/data/processor/processor_base.py | 30 - fuse/data/processor/processor_csv.py | 88 -- fuse/data/processor/processor_dataframe.py | 128 --- fuse/data/processor/processor_dicom_mri.py | 647 --------------- fuse/data/processor/processor_rand.py | 37 - .../processor/processors_image_toolbox.py | 141 ---- fuse/data/sampler/sampler_balanced_batch.py | 212 ----- fuse/data/{visualizer => tests}/__init__.py | 0 .../test_version.py} | 28 +- .../imaging/align => data/utils}/__init__.py | 0 fuse/data/utils/collates.py | 146 ++++ fuse/data/utils/export.py | 15 +- fuse/data/utils/sample.py | 102 +++ fuse/data/utils/samplers.py | 208 +++++ fuse/data/utils/tests/__init__.py | 0 fuse/data/utils/tests/test_collates.py | 101 +++ fuse/data/utils/tests/test_dataset_export.py | 69 ++ fuse/data/utils/tests/test_samplers.py | 163 ++++ fuse/data/visualizer/visualizer_base.py | 80 +- fuse/data/visualizer/visualizer_default.py | 236 ------ fuse/data/visualizer/visualizer_default_3d.py | 276 ------- .../visualizer/visualizer_image_analysis.py | 112 --- .../callbacks/callback_infer_results.py | 13 +- fuse/dl/managers/manager_default.py | 147 +--- fuse/utils/data/collate.py | 7 +- .../multiprocessing/run_multiprocessed.py | 6 +- fuse/utils/ndict.py | 4 +- fuseimg/__init__.py | 0 fuseimg/data/__init__.py | 0 fuseimg/data/ops/__init__.py | 0 fuseimg/data/ops/aug/color.py | 135 ++++ fuseimg/data/ops/aug/geometry.py | 221 +++++ fuseimg/data/ops/color.py | 134 ++++ fuseimg/data/ops/debug_ops.py | 81 ++ fuseimg/data/ops/image_loader.py | 36 + fuseimg/data/ops/ops_common_imaging.py | 7 + fuseimg/data/ops/shape_ops.py | 88 ++ fuseimg/data/ops/tests/__init__.py | 0 fuseimg/data/ops/tests/test_ops.py | 80 ++ .../data/ops/tests/test_pipeline_caching.py | 46 ++ fuseimg/datasets/__init__.py | 0 fuseimg/datasets/kits21.py | 236 ++++++ fuseimg/datasets/kits21_example.ipynb | 625 +++++++++++++++ fuseimg/datasets/mnist.py | 54 ++ fuseimg/datasets/tests/__init__.py | 0 fuseimg/datasets/tests/test_datasets.py | 72 ++ fuseimg/utils/__init__.py | 0 fuseimg/utils/align/__init__.py | 0 .../utils}/align/utils_align_base.py | 0 .../utils}/align/utils_align_ecc.py | 0 .../utils}/image_processing.py | 0 fuseimg/utils/typing/key_types_imaging.py | 23 + fuseimg/utils/typing/typed_element.py | 36 + requirements.txt | 4 +- run_all_unit_tests.py | 2 +- 117 files changed, 7026 insertions(+), 5316 deletions(-) delete mode 100644 fuse/data/augmentor/augmentor_base.py delete mode 100644 fuse/data/augmentor/augmentor_batch_level_callback.py delete mode 100644 fuse/data/augmentor/augmentor_default.py delete mode 100644 fuse/data/augmentor/augmentor_toolbox.py delete mode 100644 fuse/data/cache/cache_base.py delete mode 100644 fuse/data/cache/cache_files.py delete mode 100644 fuse/data/cache/cache_memory.py delete mode 100644 fuse/data/cache/cache_null.py delete mode 100644 fuse/data/data_source/data_source_default.py delete mode 100644 fuse/data/data_source/data_source_folds.py delete mode 100644 fuse/data/data_source/data_source_from_list.py delete mode 100644 fuse/data/data_source/data_source_toolbox.py delete mode 100644 fuse/data/dataset/dataset_base.py delete mode 100644 fuse/data/dataset/dataset_dataframe.py delete mode 100644 fuse/data/dataset/dataset_default.py delete mode 100644 fuse/data/dataset/dataset_generator.py delete mode 100644 fuse/data/dataset/dataset_wrapper.py create mode 100644 fuse/data/datasets/__init__.py rename fuse/data/{augmentor => datasets/caching}/__init__.py (100%) create mode 100644 fuse/data/datasets/caching/object_caching_handlers.py create mode 100644 fuse/data/datasets/caching/samples_cacher.py rename fuse/data/{cache => datasets/caching/tests}/__init__.py (100%) create mode 100644 fuse/data/datasets/caching/tests/test_sample_caching.py create mode 100644 fuse/data/datasets/dataset_base.py create mode 100644 fuse/data/datasets/dataset_default.py create mode 100644 fuse/data/datasets/dataset_wrap_seq_to_dict.py create mode 100644 fuse/data/datasets/sample_caching_audit.py rename fuse/data/{data_source => datasets/tests}/__init__.py (100%) create mode 100644 fuse/data/datasets/tests/test_dataset_default.py create mode 100644 fuse/data/datasets/tests/test_dataset_default_audit_feature.py create mode 100644 fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py create mode 100644 fuse/data/key_types.py create mode 100644 fuse/data/key_types_for_testing.py create mode 100644 fuse/data/ops/__init__.py create mode 100644 fuse/data/ops/caching_tools.py create mode 100644 fuse/data/ops/op_base.py create mode 100644 fuse/data/ops/ops_aug_common.py create mode 100644 fuse/data/ops/ops_cast.py create mode 100644 fuse/data/ops/ops_common.py create mode 100644 fuse/data/ops/ops_common_for_testing.py create mode 100644 fuse/data/ops/ops_read.py create mode 100644 fuse/data/ops/ops_visprobe.py rename fuse/data/{dataset => ops/tests}/__init__.py (100%) create mode 100644 fuse/data/ops/tests/test_op_base.py create mode 100644 fuse/data/ops/tests/test_op_visprobe.py create mode 100644 fuse/data/ops/tests/test_ops_aug_common.py create mode 100644 fuse/data/ops/tests/test_ops_cast.py create mode 100644 fuse/data/ops/tests/test_ops_common.py create mode 100644 fuse/data/ops/tests/test_ops_read.py create mode 100644 fuse/data/patterns.py rename fuse/data/{processor => pipelines}/__init__.py (100%) create mode 100644 fuse/data/pipelines/pipeline_default.py rename fuse/data/{sampler => pipelines/tests}/__init__.py (100%) create mode 100644 fuse/data/pipelines/tests/test_pipeline_default.py delete mode 100644 fuse/data/processor/processor_base.py delete mode 100644 fuse/data/processor/processor_csv.py delete mode 100644 fuse/data/processor/processor_dataframe.py delete mode 100755 fuse/data/processor/processor_dicom_mri.py delete mode 100644 fuse/data/processor/processor_rand.py delete mode 100644 fuse/data/processor/processors_image_toolbox.py delete mode 100644 fuse/data/sampler/sampler_balanced_batch.py rename fuse/data/{visualizer => tests}/__init__.py (100%) rename fuse/data/{data_source/data_source_base.py => tests/test_version.py} (58%) rename fuse/{utils/imaging/align => data/utils}/__init__.py (100%) create mode 100644 fuse/data/utils/collates.py create mode 100644 fuse/data/utils/sample.py create mode 100644 fuse/data/utils/samplers.py create mode 100644 fuse/data/utils/tests/__init__.py create mode 100644 fuse/data/utils/tests/test_collates.py create mode 100644 fuse/data/utils/tests/test_dataset_export.py create mode 100644 fuse/data/utils/tests/test_samplers.py delete mode 100644 fuse/data/visualizer/visualizer_default.py delete mode 100644 fuse/data/visualizer/visualizer_default_3d.py delete mode 100644 fuse/data/visualizer/visualizer_image_analysis.py create mode 100644 fuseimg/__init__.py create mode 100644 fuseimg/data/__init__.py create mode 100644 fuseimg/data/ops/__init__.py create mode 100644 fuseimg/data/ops/aug/color.py create mode 100644 fuseimg/data/ops/aug/geometry.py create mode 100644 fuseimg/data/ops/color.py create mode 100644 fuseimg/data/ops/debug_ops.py create mode 100644 fuseimg/data/ops/image_loader.py create mode 100644 fuseimg/data/ops/ops_common_imaging.py create mode 100755 fuseimg/data/ops/shape_ops.py create mode 100644 fuseimg/data/ops/tests/__init__.py create mode 100644 fuseimg/data/ops/tests/test_ops.py create mode 100644 fuseimg/data/ops/tests/test_pipeline_caching.py create mode 100644 fuseimg/datasets/__init__.py create mode 100644 fuseimg/datasets/kits21.py create mode 100644 fuseimg/datasets/kits21_example.ipynb create mode 100644 fuseimg/datasets/mnist.py create mode 100644 fuseimg/datasets/tests/__init__.py create mode 100644 fuseimg/datasets/tests/test_datasets.py create mode 100644 fuseimg/utils/__init__.py create mode 100644 fuseimg/utils/align/__init__.py rename {fuse/utils/imaging => fuseimg/utils}/align/utils_align_base.py (100%) rename {fuse/utils/imaging => fuseimg/utils}/align/utils_align_ecc.py (100%) rename {fuse/utils/imaging => fuseimg/utils}/image_processing.py (100%) create mode 100644 fuseimg/utils/typing/key_types_imaging.py create mode 100644 fuseimg/utils/typing/typed_element.py diff --git a/examples/fuse_examples/imaging/classification/mnist/runner.py b/examples/fuse_examples/imaging/classification/mnist/runner.py index d5ce99fd1..12f2f42a4 100644 --- a/examples/fuse_examples/imaging/classification/mnist/runner.py +++ b/examples/fuse_examples/imaging/classification/mnist/runner.py @@ -20,29 +20,34 @@ import logging import os from typing import OrderedDict -from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds import torch import torch.nn.functional as F import torch.optim as optim -import torchvision import torchvision.models as models from torch.utils.data.dataloader import DataLoader from torchvision import transforms from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_wrapper import DatasetWrapper -from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch +from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds +from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve + +from fuse.data.utils.samplers import BatchSamplerDefault +from fuse.data.utils.collates import CollateDefault + from fuse.dl.losses.loss_default import LossDefault from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback from fuse.dl.managers.manager_default import ManagerDefault -from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve from fuse.dl.models.model_wrapper import ModelWrapper + from fuse.utils.utils_debug import FuseDebug import fuse.utils.gpu as GPU from fuse.utils.utils_logger import fuse_logger_start + +from fuseimg.datasets.mnist import MNIST + from fuse_examples.imaging.classification.mnist import lenet ########################################################################################################### # Fuse @@ -129,18 +134,9 @@ def run_train(paths: dict, train_params: dict): # Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize((0.1307,), (0.3081,)) - ]) - # Create dataset - torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform) - # wrapping torch dataset - # FIXME: support also using torch dataset directly - train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label')) - train_dataset.create() + train_dataset = MNIST.dataset(paths["cache_dir"], train=True) lgr.info(f'- Create sampler:') - sampler = SamplerBalancedBatch(dataset=train_dataset, + sampler = BatchSamplerDefault(dataset=train_dataset, balanced_class_name='data.label', num_balanced_classes=10, batch_size=train_params['data.batch_size'], @@ -148,19 +144,16 @@ def run_train(paths: dict, train_params: dict): lgr.info(f'- Create sampler: Done') # Create dataloader - train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, num_workers=train_params['data.train_num_workers']) + train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, collate_fn=CollateDefault(), num_workers=train_params['data.train_num_workers']) lgr.info(f'Train Data: Done', {'attrs': 'bold'}) ## Validation data lgr.info(f'Validation Data:', {'attrs': 'bold'}) - # Create dataset - torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) # wrapping torch dataset - validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) - validation_dataset.create() - + validation_dataset = MNIST.dataset(paths["cache_dir"], train=False) + # dataloader - validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], + validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], collate_fn=CollateDefault(), num_workers=train_params['data.validation_num_workers']) lgr.info(f'Validation Data: Done', {'attrs': 'bold'}) @@ -270,12 +263,9 @@ def run_infer(paths: dict, infer_common_params: dict): transforms.Normalize((0.1307,), (0.3081,)) ]) # Create dataset - torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform) - # wrapping torch dataset - validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label')) - validation_dataset.create() + validation_dataset = MNIST.dataset(paths["cache_dir"], train=False) # dataloader - validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2) + validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=CollateDefault(), batch_size=2, num_workers=2) ## Manager for inference manager = ManagerDefault() diff --git a/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py b/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py index b348ff415..3ea3b0593 100644 --- a/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py +++ b/examples/fuse_examples/imaging/classification/prostate_x/run_train_3dpatch.py @@ -15,15 +15,15 @@ import logging import os import pathlib -from fuse.data.dataset.dataset_base import DatasetBase import torch.nn.functional as F import torch.optim as optim from torch.utils.data.dataloader import DataLoader from fuse.eval.metrics.classification.metrics_classification_common import MetricAUCROC, MetricROCCurve from fuse.eval.evaluator import EvaluatorDefault -from fuse.data.dataset.dataset_base import DatasetBase +from fuse.data.dataset.dataset_base import DatasetBase from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch + from fuse.dl.losses.loss_default import LossDefault from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback diff --git a/examples/fuse_examples/tests/test_classification_cmmd.py b/examples/fuse_examples/tests/test_classification_cmmd.py index abc5cfa15..fd14e497c 100644 --- a/examples/fuse_examples/tests/test_classification_cmmd.py +++ b/examples/fuse_examples/tests/test_classification_cmmd.py @@ -16,8 +16,9 @@ Created on June 30, 2021 """ -from fuse_examples.imaging.classification.cmmd.runner import TRAIN_COMMON_PARAMS, \ - INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer +# FIXME: data_package +#from fuse_examples.imaging.classification.cmmd.runner import TRAIN_COMMON_PARAMS, \ +# INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer import unittest import os diff --git a/examples/fuse_examples/tests/test_classification_knight.py b/examples/fuse_examples/tests/test_classification_knight.py index 35eb09cc8..e008d719e 100644 --- a/examples/fuse_examples/tests/test_classification_knight.py +++ b/examples/fuse_examples/tests/test_classification_knight.py @@ -25,10 +25,13 @@ from fuse.utils.file_io.file_io import create_dir import wget -from fuse_examples.imaging.classification.knight.eval.eval import eval -from fuse_examples.imaging.classification.knight.make_targets_file import make_targets_file -import fuse_examples.imaging.classification.knight.baseline.fuse_baseline as baseline +# FIXME: data_package +#from fuse_examples.imaging.classification.knight.eval.eval import eval +#from fuse_examples.imaging.classification.knight.make_targets_file import make_targets_file +#import fuse_examples.imaging.classification.knight.baseline.fuse_baseline as baseline + +@unittest.skip("FIXME: data_package") class KnightTestTestCase(unittest.TestCase): def setUp(self): diff --git a/examples/fuse_examples/tests/test_classification_mnist.py b/examples/fuse_examples/tests/test_classification_mnist.py index 5c92cd063..11359e8a5 100644 --- a/examples/fuse_examples/tests/test_classification_mnist.py +++ b/examples/fuse_examples/tests/test_classification_mnist.py @@ -23,8 +23,9 @@ import os import fuse.utils.gpu as GPU + from fuse_examples.imaging.classification.mnist.runner import TRAIN_COMMON_PARAMS, run_train, run_infer, run_eval, INFER_COMMON_PARAMS, \ - EVAL_COMMON_PARAMS + EVAL_COMMON_PARAMS class ClassificationMnistTestCase(unittest.TestCase): diff --git a/examples/fuse_examples/tests/test_classification_prostatex.py b/examples/fuse_examples/tests/test_classification_prostatex.py index 0f0a031fb..c39acc8d8 100644 --- a/examples/fuse_examples/tests/test_classification_prostatex.py +++ b/examples/fuse_examples/tests/test_classification_prostatex.py @@ -24,8 +24,9 @@ import pathlib import fuse.utils.gpu as GPU -from fuse_examples.imaging.classification.prostate_x.run_train_3dpatch import TRAIN_COMMON_PARAMS, train_template, infer_template, eval_template, INFER_COMMON_PARAMS, \ - EVAL_COMMON_PARAMS +# FIXME: data_package +#from fuse_examples.imaging.classification.prostate_x.run_train_3dpatch import TRAIN_COMMON_PARAMS, train_template, infer_template, eval_template, INFER_COMMON_PARAMS, \ +# EVAL_COMMON_PARAMS class ClassificationProstateXTestCase(unittest.TestCase): diff --git a/examples/fuse_examples/tests/test_classification_skin_lesion.py b/examples/fuse_examples/tests/test_classification_skin_lesion.py index fe238564b..108f7daa2 100644 --- a/examples/fuse_examples/tests/test_classification_skin_lesion.py +++ b/examples/fuse_examples/tests/test_classification_skin_lesion.py @@ -24,8 +24,9 @@ import shutil from fuse.utils.utils_logger import fuse_logger_end -from fuse_examples.imaging.classification.skin_lesion.runner import TRAIN_COMMON_PARAMS, \ - INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer +# FIXME: data_package +#from fuse_examples.imaging.classification.skin_lesion.runner import TRAIN_COMMON_PARAMS, \ +# INFER_COMMON_PARAMS, EVAL_COMMON_PARAMS, run_train, run_eval, run_infer import fuse.utils.gpu as GPU diff --git a/fuse/data/__init__.py b/fuse/data/__init__.py index e69de29bb..c20ed02e2 100644 --- a/fuse/data/__init__.py +++ b/fuse/data/__init__.py @@ -0,0 +1,19 @@ +import os +import pathlib + +# version +with open(os.path.join(pathlib.Path(__file__).parent, "..", "..", "VERSION.txt")) as version_file: + __version__ = version_file.read().strip() + +# import shortcuts +from fuse.data.utils.sample import get_sample_id, set_sample_id, get_sample_id_key +from fuse.data.utils.sample import create_initial_sample, get_initial_sample_id, get_initial_sample_id_key, get_specific_sample_from_potentially_morphed +from fuse.data.ops.op_base import OpBase #DataTypeForTesting, +from fuse.data.ops.ops_common import OpApplyPatterns, OpLambda, OpFunc, OpRepeat, OpKeepKeypaths +from fuse.data.ops.ops_aug_common import OpRandApply, OpSample, OpSampleAndRepeat +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.ops.ops_cast import OpToTensor, OpToNumpy +from fuse.data.utils.collates import CollateDefault +from fuse.data.utils.export import ExportDataset +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.datasets.dataset_default import DatasetBase, DatasetDefault diff --git a/fuse/data/augmentor/augmentor_base.py b/fuse/data/augmentor/augmentor_base.py deleted file mode 100644 index fe5d1f08d..000000000 --- a/fuse/data/augmentor/augmentor_base.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Augmentor Base class -""" -from abc import ABC, abstractmethod -from typing import Any - - -class AugmentorBase(ABC): - """ - Base class for augmentor. - Given an augmenatation pipline description, expected to sample random parameters first and then apply them. - """ - - @abstractmethod - def get_random_augmentation_desc(self) -> Any: - """ - Sample random parameters for augmentation - :return: - """ - raise NotImplementedError - - @abstractmethod - def apply_augmentation(self, sample: Any, augmentation_desc: Any) -> Any: - """ - Apply the augmenation according to the given parameters. Must be deterministic. - :param sample: the original sample as generated by the dataset - :param augmentation_desc: augmentation parameters. Output of get_random_augmentation_desc() - :return: augmented sample - """ - raise NotImplementedError - - @abstractmethod - def summary(self) -> str: - """ - String summary of the object - """ - raise NotImplementedError - - def __call__(self, sample: Any): - """ - generate random and apply the augmentation at once. - :param sample: - :return: - """ - augmentation_desc = self.get_random_augmentation_desc() - return self.apply_augmentation(sample, augmentation_desc) diff --git a/fuse/data/augmentor/augmentor_batch_level_callback.py b/fuse/data/augmentor/augmentor_batch_level_callback.py deleted file mode 100644 index aa906c2b1..000000000 --- a/fuse/data/augmentor/augmentor_batch_level_callback.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Dict, List, Sequence - -from fuse.data.augmentor.augmentor_default import AugmentorDefault -from fuse.dl.managers.callbacks.callback_base import Callback - - -class AugmentorBatchCallback(Callback): - """ - Simple class which gets augmentation pipeline and apply augmentation on a batch level batch dict - """ - def __init__(self, aug_pipeline: List, modes: Sequence[str] = ('train',)): - """ - :param aug_pipeline: See AugmentorDefault - :param modes: modees to apply the augmentation: 'train', 'validation' and/or 'infer' - """ - self._augmentor = AugmentorDefault(aug_pipeline) - self._modes = modes - - def on_data_fetch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: - if mode in self._modes: - self._augmentor(batch_dict) \ No newline at end of file diff --git a/fuse/data/augmentor/augmentor_default.py b/fuse/data/augmentor/augmentor_default.py deleted file mode 100644 index 0e7fb20dc..000000000 --- a/fuse/data/augmentor/augmentor_default.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Augmentor Default class -""" -from typing import Any, Iterable - -from fuse.data.augmentor.augmentor_base import AugmentorBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state, convert_state_to_str -from fuse.utils.rand.param_sampler import draw_samples_recursively - - -class AugmentorDefault(AugmentorBase): - """ - Default generic implementation for Fuse augmentor. Aimed to be used by most experiments. - """ - - def __init__(self, augmentation_pipeline: Iterable[Any] = ()): - """ - :param augmentation_pipeline: list of augmentation operation description, - Each operation description expected to be a tuple of 4 elements: - Element 0 - the sample keys affected by this operation - Element 1 - callback to a function performing the operation. This function expected to support input parameter 'aug_ingput' - Element 2 - dictionary including the input parameters for the callback function. See AugmentorSamplerDefault - to learn how to use random numbers - Element 3 - general parameters: TBD - - Example: - See in aug_image_default_pipeline() - """ - # log object input state - log_object_input_state(self, locals()) - - self.augmentation_pipeline = augmentation_pipeline - - def get_random_augmentation_desc(self) -> Any: - """ - See description in super class. - """ - return draw_samples_recursively(self.augmentation_pipeline) - - def apply_augmentation(self, sample: Any, augmentation_desc: Any) -> Any: - """ - See description in super class. - """ - aug_sample = sample - for op_desc in augmentation_desc: - # decode augmentation description - sample_keys = op_desc[0] - augment_function = op_desc[1] - augment_function_parameters = op_desc[2] - general_parameters: dict = op_desc[3] - - # If apply sampled as False skip - by default it will always be True - apply = general_parameters.get('apply', True) - if not apply: - continue - - # Extract augmentation input - if sample_keys is None: - aug_input = aug_sample - elif len(sample_keys) == 1: - aug_input = FuseUtilsHierarchicalDict.get(aug_sample, sample_keys[0]) - else: - aug_input = tuple((FuseUtilsHierarchicalDict.get(aug_sample, key) for key in sample_keys)) - augment_function_parameters = augment_function_parameters.copy() - augment_function_parameters['aug_input'] = aug_input - - # apply augmentation - aug_result = augment_function(**augment_function_parameters) - - # modify the sample accordingly - if sample_keys is None: - aug_sample = aug_result - elif len(sample_keys) == 1: - FuseUtilsHierarchicalDict.set(aug_sample, sample_keys[0], aug_result) - else: - for index, key in enumerate(sample_keys): - FuseUtilsHierarchicalDict.set(aug_sample, key, aug_result[index]) - - return aug_sample - - def summary(self) -> str: - """ - String summary of the object - """ - return \ - f'Class = {self, __class__}\n' \ - f'Pipeline = {convert_state_to_str(self.augmentation_pipeline)}' diff --git a/fuse/data/augmentor/augmentor_toolbox.py b/fuse/data/augmentor/augmentor_toolbox.py deleted file mode 100644 index b24e2a2db..000000000 --- a/fuse/data/augmentor/augmentor_toolbox.py +++ /dev/null @@ -1,455 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from copy import deepcopy -from typing import Tuple, Any, List, Iterable, Optional - -import numpy -import torch -import torchvision.transforms.functional as TTF -from PIL import Image -from scipy.ndimage.filters import gaussian_filter -from scipy.ndimage.interpolation import map_coordinates -from torch import Tensor - -from fuse.utils.rand.param_sampler import Gaussian, RandBool, RandInt, Uniform - - -######## Affine augmentation -def aug_op_affine(aug_input: Tensor, rotate: float = 0.0, translate: Tuple[float, float] = (0.0, 0.0), - scale: Tuple[float, float] = 1.0, flip: Tuple[bool, bool] = (False, False), shear: float = 0.0, - channels: Optional[List[int]] = None) -> Tensor: - """ - Affine augmentation - :param aug_input: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] - :param rotate: angle [0.0 - 360.0] - :param translate: translation per spatial axis (number of pixels). The sign used as the direction. - :param scale: scale factor - :param flip: flip per spatial axis flip[0] for vertical flip and flip[1] for horizontal flip - :param shear: shear factor - :param channels: apply the augmentation on the specified channels. Set to None to apply to all channels. - :return: the augmented image - """ - # Support for 2D inputs - implicit single channel - if len(aug_input.shape) == 2: - aug_input = aug_input.unsqueeze(dim=0) - remember_to_squeeze = True - else: - remember_to_squeeze = False - - # convert to PIL (required by affine augmentation function) - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input - for channel in channels: - aug_channel_tensor = aug_input[channel].numpy() - aug_channel_tensor = Image.fromarray(aug_channel_tensor) - aug_channel_tensor = TTF.affine(aug_channel_tensor, angle=rotate, scale=scale, translate=translate, shear=shear) - if flip[0]: - aug_channel_tensor = TTF.vflip(aug_channel_tensor) - if flip[1]: - aug_channel_tensor = TTF.hflip(aug_channel_tensor) - - # convert back to torch tensor - aug_channel_tensor = numpy.array(aug_channel_tensor) - aug_channel_tensor = torch.from_numpy(aug_channel_tensor) - - # set the augmented channel - aug_tensor[channel] = aug_channel_tensor - - # squeeze back to 2-dim if needed - if remember_to_squeeze: - aug_tensor = aug_tensor.squeeze(dim=0) - - return aug_tensor - - -def aug_op_affine_group(aug_input: Tuple[Tensor], **kwargs) -> Tuple[Tensor]: - """ - Applies same augmentation on multiple tensors. For example, augmenting both input image and its corresponding - segmentation mask in the same way. This method wraps 'aug_op_affine'. - :param aug_input: tuple of tensors - :param kwargs: augmentation params, same kwargs as 'aug_op_affine' - see docstring there - :return: tuple of tensors, all augmented the same way - """ - return tuple((aug_op_affine(element, **kwargs) for element in aug_input)) - - -def aug_op_crop_and_resize(aug_input: Tensor, - scale: Tuple[float, float], - channels: Optional[List[int]] = None) -> Tensor: - """ - Alternative to rescaling: center crop and resize back to the original dimensions. if scale is bigger than 1.0. the image first padded. - :param aug_input: The tensor to augment - :param scale: tuple of positive floats - :param channels: apply augmentation on the specified channels or None for all of them - :return: the augmented tensor - """ - if len(aug_input.shape) == 2: - aug_input = aug_input.unsqueeze(dim=0) - remember_to_squeeze = True - else: - remember_to_squeeze = False - - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input - for channel in channels: - aug_channel_tensor = aug_input[channel] - - if scale[0] != 1.0 or scale[1] != 1.0: - cropped_shape = (int(aug_channel_tensor.shape[0] * scale[0]), int(aug_channel_tensor.shape[1] * scale[1])) - padding = [[0, 0], [0, 0]] - for dim in range(2): - if scale[dim] > 1.0: - padding[dim][0] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) // 2 - padding[dim][1] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) - padding[dim][0] - aug_channel_tensor_pad = TTF.pad(aug_channel_tensor.unsqueeze(0), (padding[1][0], padding[0][0], padding[1][1], padding[0][1])) - aug_channel_tensor_cropped = TTF.center_crop(aug_channel_tensor_pad, cropped_shape) - aug_channel_tensor = TTF.resize(aug_channel_tensor_cropped, aug_channel_tensor.shape).squeeze(0) - # set the augmented channel - aug_tensor[channel] = aug_channel_tensor - - # squeeze back to 2-dim if needed - if remember_to_squeeze: - aug_tensor = aug_tensor.squeeze(dim=0) - - return aug_tensor - - -######## Color augmentation -def aug_op_clip(aug_input: Tensor, clip: Tuple[float, float] = (-1.0, 1.0)) -> Tensor: - """ - Clip pixel values - :param aug_input: the tensor to clip - :param clip: values for clipping from both sides - :return: Clipped tensor - """ - aug_tensor = aug_input - if clip is not None: - aug_tensor = torch.clamp(aug_tensor, clip[0], clip[1], out=aug_tensor) - return aug_tensor - - -def aug_op_add_col(aug_input: Tensor, add: float) -> Tensor: - """ - Adding a values to all pixels - :param aug_input: the tensor to augment - :param add: the value to add to each pixel - :return: the augmented tensor - """ - aug_tensor = aug_input + add - aug_tensor = aug_op_clip(aug_tensor, clip=(0, 1)) - return aug_tensor - - -def aug_op_mul_col(aug_input: Tensor, mul: float) -> Tensor: - """ - multiply each pixel - :param aug_input: the tensor to augment - :param mul: the multiplication factor - :return: the augmented tensor - """ - input_tensor = aug_input * mul - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_gamma(aug_input: Tensor, gain: float, gamma: float) -> Tensor: - """ - Gamma augmentation - :param aug_input: the tensor to augment - :param gain: gain factor - :param gamma: gamma factor - :return: None - """ - input_tensor = (aug_input ** gamma) * gain - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_contrast(aug_input: Tensor, factor: float) -> Tensor: - """ - Adjust contrast (notice - calculated across the entire input tensor, even if it's 3d) - :param aug_input:the tensor to augment - :param factor: contrast factor. 1.0 is neutral - :return: the augmented tensor - """ - calculated_mean = aug_input.mean() - input_tensor = ((aug_input - calculated_mean) * factor) + calculated_mean - input_tensor = aug_op_clip(input_tensor, clip=(0, 1)) - return input_tensor - - -def aug_op_color(aug_input: Tensor, add: Optional[float] = None, mul: Optional[float] = None, - gamma: Optional[float] = None, contrast: Optional[float] = None, channels: Optional[List[int]] = None): - """ - Color augmentaion: including addition, multiplication, gamma and contrast adjusting - :param aug_input: the tensor to augment - :param add: value to add to each pixel - :param mul: multiplication factor - :param gamma: gamma factor - :param contrast: contrast factor - :param channels: Apply clipping just over the specified channels. If set to None will apply on all channels. - :return: - """ - aug_tensor = aug_input - if channels is None: - if add is not None: - aug_tensor = aug_op_add_col(aug_tensor, add) - if mul is not None: - aug_tensor = aug_op_mul_col(aug_tensor, mul) - if gamma is not None: - aug_tensor = aug_op_gamma(aug_tensor, 1.0, gamma) - if contrast is not None: - aug_tensor = aug_op_contrast(aug_tensor, contrast) - else: - if add is not None: - aug_tensor[channels] = aug_op_add_col(aug_tensor[channels], add) - if mul is not None: - aug_tensor[channels] = aug_op_mul_col(aug_tensor[channels], mul) - if gamma is not None: - aug_tensor[channels] = aug_op_gamma(aug_tensor[channels], 1.0, gamma) - if contrast is not None: - aug_tensor[channels] = aug_op_contrast(aug_tensor[channels], contrast) - - return aug_tensor - - -######## Gaussian noise -def aug_op_gaussian(aug_input: Tensor, mean: float = 0.0, std: float = 0.03, channels: Optional[List[int]] = None) -> Tensor: - """ - Add gaussian noise - :param aug_input: the tensor to augment - :param mean: mean gaussian distribution - :param std: std gaussian distribution - :param channels: Apply just over the specified channels. If set to None will apply on all channels. - :return: the augmented tensor - """ - aug_tensor = aug_input - dtype = aug_tensor.dtype - - if channels is None: - rand_patch = Gaussian(aug_tensor.shape, mean, std).sample() - aug_tensor = aug_tensor + rand_patch - else: - rand_patch = Gaussian(aug_tensor[channels].shape, mean, std).sample() - aug_tensor[channels] = aug_tensor[channels] + rand_patch - - aug_tensor = aug_tensor.to(dtype=dtype) - return aug_tensor - - -def aug_op_elastic_transform(aug_input: Tensor, alpha: float = 1, sigma: float = 50, channels: Optional[List[int]] = None): - """Elastic deformation of images as described in [Simard2003]_. - .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for - Convolutional Neural Networks applied to Visual Document Analysis", - :param aug_input: input tensor of shape (C,Y,X) - :param alpha: global pixel shifting (correlated to the article) - :param sigma: Gaussian filter parameter - :param channels: which channels to apply the augmentation - :return distorted image - """ - random_state = numpy.random.RandomState(None) - if channels is None: - channels = list(range(aug_input.shape[0])) - aug_tensor = aug_input.numpy() - for channel in channels: - aug_channel_tensor = aug_input[channel].numpy() - shape = aug_channel_tensor.shape - dx1 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - dx2 = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha - - x1, x2 = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1])) - indices = numpy.reshape(x2 + dx2, (-1, 1)), numpy.reshape(x1 + dx1, (-1, 1)) - - distored_image = map_coordinates(aug_channel_tensor, indices, order=1, mode='reflect') - distored_image = distored_image.reshape(aug_channel_tensor.shape) - aug_tensor[channel] = distored_image - return torch.from_numpy(aug_tensor) - - -######### Default / Example augmentation pipline for a 2D image -def aug_image_default_pipeline(input_pointer: str) -> List[Any]: - """ - Return default image augmentation pipeline. optimised for breast project (GMP model). - In case paramter tunning is required - copy and change the values - :param input_pointer: global dict pointer to the image - :return: the default pipeline - """ - return [ - [ - (input_pointer,), - aug_op_affine, - {'rotate': Uniform(-30.0, 30.0), 'translate': (RandInt(-10, 10), RandInt(-10, 10)), - 'flip': (RandBool(0.3), RandBool(0.3)), 'scale': Uniform(0.9, 1.1)}, - {'apply': RandBool(0.5)} - ], - [ - (input_pointer,), - aug_op_color, - {'add': Uniform(-0.06, 0.06), 'mul': Uniform(0.95, 1.05), 'gamma': Uniform(0.9, 1.1), - 'contrast': Uniform(0.85, 1.15)}, - {'apply': RandBool(0.5)} - ], - [ - (input_pointer,), - aug_op_gaussian, - {'std': 0.03}, - {'apply': RandBool(0.5)} - ], - ] - - -# general utilities -def aug_pipeline_step_replicate(step: List, key: str, values: Iterable) -> List[List]: - """ - Replicate a step, but set different value for each replication for the specified key - :param step: The step to replicate - :param key: the key to override (withing te augmentation dunction input) - :param values: Iterable specify the value for each replication - :return: - """ - list_of_steps = [] - for value in values: - step_copy = deepcopy(step) - step_copy[2][key] = value - list_of_steps.append(step_copy) - - return list_of_steps - - -def aug_op_rescale_pixel_values(aug_input: Tensor, target_range: Tuple[float, float] = (-1.0, 1.0)) -> Tensor: - """ - Scales pixel values to specific range. - :param aug_input: input tensor - :param target_range: target range, (min, max) - :return: rescaled tensor - """ - max_val = aug_input.max() - min_val = aug_input.min() - if min_val == max_val == 0: - return aug_input - aug_input = aug_input - min_val - aug_input = aug_input / (max_val - min_val) - aug_input = aug_input * (target_range[1] - target_range[0]) - aug_input = aug_input + target_range[0] - return aug_input - - -def squeeze_3d_to_2d(aug_input: Tensor, axis_squeeze: str) -> Tensor: - ''' - squeeze selected axis of volume image into channel dimension, in - order to fit the 2D augmentation functions - :param aug_input: input of shape: (channel, z, y, x) - :return: - ''' - # aug_input shape is [channels, z, y, x] - if axis_squeeze == 'y': - aug_input = aug_input.permute((0, 2, 1, 3)) - # aug_input shape is [channels, y, z, x] - elif axis_squeeze == 'x': - aug_input = aug_input.permute((0, 3, 2, 1)) - # aug_input shape is [channels, x, y, z] - else: - assert axis_squeeze == 'z', "axis squeeze must be a string of either x, y, or z" - return aug_input.reshape((aug_input.shape[0] * aug_input.shape[1],) + aug_input.shape[2:]) - - -def unsqueeze_2d_to_3d(aug_input: Tensor, channels: int, axis_squeeze: str) -> Tensor: - ''' - unsqueeze selected axis to original shape, and add the batch dimension - :param aug_input: - :return: - ''' - aug_input = aug_input - aug_input = aug_input.reshape((channels, aug_input.shape[0] // channels) + aug_input.shape[1:]) - if axis_squeeze == 'y': - aug_input = aug_input.permute((0, 2, 1, 3)) - # aug_input shape is [channels, z, y, x] - elif axis_squeeze == 'x': - aug_input = aug_input.permute((0, 3, 2, 1)) - # aug_input shape is [channels, z, y, x] - else: - assert axis_squeeze == 'z', "axis squeeze must be a string of either x, y, or z" - return aug_input - - -def rotation_in_3d(aug_input: Tensor, z_rot: float = 0.0, y_rot: float = 0.0, x_rot: float = 0): - """ - rotates an input tensor around an axis, when for example z_rot is chosen, - the rotation is in the x-y plane. - Note: rotation angles are in relation to the original axis (not the rotated one) - rotation angles should be given in degrees - :param aug_input:image input should be in shape [channel, z, y, x] - :param z_rot: angle to rotate x-y plane clockwise - :param y_rot: angle to rotate x-z plane clockwise - :param x_rot: angle to rotate z-y plane clockwise - :return: - """ - assert len(aug_input.shape) == 4 # will only work for 3d - channels = aug_input.shape[0] - if z_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='z') - rot_squeeze = aug_op_affine(squeez_img, rotate=z_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'z') - if x_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='x') - rot_squeeze = aug_op_affine(squeez_img, rotate=x_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'x') - if y_rot != 0: - squeez_img = squeeze_3d_to_2d(aug_input, axis_squeeze='y') - rot_squeeze = aug_op_affine(squeez_img, rotate=y_rot) - aug_input = unsqueeze_2d_to_3d(rot_squeeze, channels, 'y') - - return aug_input - - -def aug_cut_out(aug_input: Tensor, fill: float = None, size: int = 16) -> Tensor: - """ - removing small patch of the image. https://arxiv.org/abs/1708.04552 - :param aug_input: the tensor to augment - :param fill: value to fill the patch - :param size: size of patch - :return: the augmented tensor - """ - fill = aug_input.mean(-1).mean(-1) if fill is None else fill - sx = torch.randint(0, aug_input.shape[1] - size, (1,)) - sy = torch.randint(0, aug_input.shape[2] - size, (1,)) - aug_input[:, sx:sx + size, sy:sy + size] = fill[:, None, None] - - return aug_input - - -def aug_op_batch_mix_up(aug_input: Tuple[Tensor, Tensor], factor: float) -> Tuple[Tensor, Tensor]: - """ - mixup augmentation on a batch level - :param aug_input: batch level input to augment. tuple of image and one hot vector of targets - :param factor: background factor - :return: the augmented batch - """ - img = aug_input[0] - labels = aug_input[1] - perm = numpy.arange(img.shape[0]) - numpy.random.shuffle(perm) - img_mix_up = img[perm] - labels_mix_up = labels[perm] - img = img * (1.0 - factor) + factor * img_mix_up - labels = labels * (1.0 - factor) + factor * labels_mix_up - return img, labels diff --git a/fuse/data/cache/cache_base.py b/fuse/data/cache/cache_base.py deleted file mode 100644 index dd5763f01..000000000 --- a/fuse/data/cache/cache_base.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Base class for caching -""" -from abc import ABC, abstractmethod -from multiprocessing import Manager -from typing import Hashable, Any, List - - -class CacheBase(ABC): - - @abstractmethod - def __contains__(self, key: Hashable) -> bool: - """ - return true if key is already in cache - :param key: any kind of hashable key - :return: boolean. True if exist. - """ - raise NotImplementedError - - @abstractmethod - def __getitem__(self, key: Hashable) -> Any: - """ - Get an item from cache. Will raise an error if key does not exist - :param key: any kind of hashable key - :return: the item - """ - raise NotImplementedError - - @abstractmethod - def __delitem__(self, key: Hashable) -> None: - """ - Delete key. Will raise an error if key does not exist - :param key: any kind of hashable key - :return: None - """ - raise NotImplementedError - - @abstractmethod - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - Set key. Will override previous value if already exist. - :param key: any kind of hashable key - :param value: any kind of value to sture - :return: None - """ - raise NotImplementedError - - @abstractmethod - def save(self) -> None: - """ - Save data to cache - :return: None - """ - raise NotImplementedError - - @abstractmethod - def exist(self) -> bool: - """ - return True if cache exist and contains the samples - """ - raise NotImplementedError - - @abstractmethod - def reset(self) -> None: - """ - Reset cache and delete all data - :return: None - """ - raise NotImplementedError - - @abstractmethod - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - Get all keys currently cached - :param include_none: include or filter 'none samples' which represents no samples or bad samples - :return: List of keys - """ - raise NotImplementedError - - def start_caching(self, manager: Manager) -> None: - """ - start caching - the caching will be done in save(). - :param manager: multiprocessing manager to create shared data structures - :return: None - """ - raise NotImplementedError diff --git a/fuse/data/cache/cache_files.py b/fuse/data/cache/cache_files.py deleted file mode 100644 index c7dbe6985..000000000 --- a/fuse/data/cache/cache_files.py +++ /dev/null @@ -1,228 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Cache to file per sample -""" -import gzip -import logging -import os -import pickle -import traceback -from multiprocessing import Manager -import multiprocessing -from typing import Hashable, Any, List -import torch -torch.multiprocessing.set_sharing_strategy('file_system') - -from fuse.data.cache.cache_base import CacheBase -from fuse.utils.file_io.atomic_file import AtomicFileWriter -from fuse.utils.file_io.file_io import create_dir, remove_dir_content - - -class CacheFiles(CacheBase): - def __init__(self, cache_file_dir: str, reset_cache: bool, single_file: bool=False): - """ - :param cache_file_dir: path to cache dir - :param reset_cache: reset previous cache if exist or continue - """ - super().__init__() - - self._cache_file_dir = cache_file_dir - self._save_cache_index = 100 - - # create dir if not already exist - create_dir(cache_file_dir) - - # pointer to cache index - self._cache_file_name = os.path.join(self._cache_file_dir, 'cache_index.pkl') - self._cache_prop_file_name = os.path.join(self._cache_file_dir, 'cache_properties.pkl') - - # reset or load from disk - if reset_cache or not os.path.exists(self._cache_file_name): - self.reset() - self.single_file = single_file - # save initial properties - with AtomicFileWriter(filename=self._cache_prop_file_name) as cache_prop_file: - pickle.dump({'single_file': self.single_file}, cache_prop_file) - else: - # get last modified time of the index - self._cache_index_mtime = os.path.getmtime(self._cache_file_name) - - # load current cache - try: - with open(self._cache_file_name, 'rb') as cache_index_file: - self._cache_index = pickle.load(cache_index_file) - except: - # backward compatibility - used to be saved in gz format - with gzip.open(self._cache_file_name, 'rb') as cache_index_file: - self._cache_index = pickle.load(cache_index_file) - self._cache_list = list(self._cache_index.keys()) - self._cache_size = len(self._cache_list) - - # load mode for backward compatibility - try: - with open(self._cache_prop_file_name, 'rb') as cache_prop_file: - cache_prop = pickle.load(cache_prop_file) - self.single_file = cache_prop['single_file'] - except: - self.single_file = False - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return key in self._cache_index - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - if self.single_file: - return self._cache_index.get(key, None) - - value_file_name = self._cache_index.get(key, None) - if value_file_name is None: - return None - value_file_name = os.path.join(self._cache_file_dir, value_file_name) - - # make sure file not exist - if os.path.exists(value_file_name): - # store the file - with gzip.open(value_file_name, 'rb') as value_file: - value = pickle.load(value_file) - else: - raise Exception(f'cache file {value_file_name} not found') - - return value - - def __delitem__(self, key: Hashable) -> None: - """ - Not supported - """ - raise NotImplementedError - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - if self._cache_lock is None: - index = self._cache_size - self._cache_list.append(key) - self._cache_size = index + 1 - else: - with self._cache_lock: - index = self._cache_size.value - self._cache_list.append(key) - self._cache_size.value = index + 1 - - # if value is none, just update cache index - if value is None: - self._cache_index[key] = None - return - if self.single_file: - self._cache_index[key] = value - else: - value_file_name = str(index).zfill(10) + '.pkl.gz' - value_abs_file_name = os.path.join(self._cache_file_dir, value_file_name) - self._cache_index[key] = value_file_name - - # make sure file not exist - if os.path.exists(value_abs_file_name): - logging.getLogger('Fuse').warning(f'cache file {value_abs_file_name} unexpectedly exist, overriding it.') - - # store the file - with AtomicFileWriter(value_abs_file_name) as value_file: - pickle.dump(value, value_file) - - # store the cache index - just for a case of crashing - if index % self._save_cache_index == 0: - try: - with AtomicFileWriter(filename=self._cache_file_name) as cache_index_file: - pickle.dump(dict(self._cache_index), cache_index_file) - except: - # do not trow error- just print warning - lgr = logging.getLogger('Fuse') - track = traceback.format_exc() - lgr.warning(track) - - def save(self) -> None: - """ - Save cache index file - """ - # disable caching - self._cache_enable = False - - with AtomicFileWriter(filename=self._cache_file_name) as cache_index_file: - pickle.dump(dict(self._cache_index), cache_index_file) - - # move back to simple data structures - self._cache_index = dict(self._cache_index) - self._cache_list = list(self._cache_list) - self._cache_size = len(self._cache_list) - self._cache_lock = None - - def exist(self) -> bool: - """ - See base class - """ - return bool(self._cache_index) - - def reset(self) -> None: - """ - See base class - """ - # make sure the dir content is empty - remove_dir_content(self._cache_file_dir) - - # create empty data structures - self._cache_enable = False - self._cache_index = {} - self._cache_list = [] - self._cache_size = 0 - self._cache_index_mtime = -1 - self._cache_lock = None - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - if include_none: - return list(self._cache_index.keys()) - else: - return [key for key, value in self._cache_index.items() if value is not None] - - def start_caching(self, manager: Manager): - """ - See base class - """ - self._cache_enable = True - # if manager is None assume that the it's not multiprocessing caching - if manager is not None: - # create dictionary and adds it one by one to workaround multiprocessing limitation - cache_index = manager.dict() - for k, v in self._cache_index.items(): - cache_index[k] = v - self._cache_index = cache_index - self._cache_list = manager.list(self._cache_list) - self._cache_size = manager.Value("i", len(self._cache_list)) - self._cache_lock = manager.Lock() diff --git a/fuse/data/cache/cache_memory.py b/fuse/data/cache/cache_memory.py deleted file mode 100644 index ee8ff000d..000000000 --- a/fuse/data/cache/cache_memory.py +++ /dev/null @@ -1,104 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Cache to Memory -""" -from multiprocessing import Manager -from typing import Hashable, Any, List - -from fuse.data.cache.cache_base import CacheBase - - -class CacheMemory(CacheBase): - """ - Cache to Memory - """ - - def __init__(self): - super().__init__() - - self.reset() - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return key in self._cache_dict - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - return self._cache_dict.get(key, None) - - def __delitem__(self, key: Hashable) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - item = self._cache_dict.pop(key, None) - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - if not self._cache_enable: - raise Exception('First start caching using function start_caching()') - - self._cache_dict[key] = value - - def save(self) -> None: - """ - Not saving, moving back to simple data structures - """ - self._cache_enable = False - self._cache_dict = dict(self._cache_dict) - - def exist(self) -> bool: - """ - See base class - """ - return len(self._cache_dict) > 0 - - def reset(self) -> None: - """ - See base class - """ - self._cache_dict = {} - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - if include_none: - return list(self._cache_dict.keys()) - else: - return [key for key, value in self._cache_dict.items() if value is not None] - - def start_caching(self, manager: Manager) -> None: - """ - Moving to multiprocessing data structures - """ - self._cache_enable = True - # if manager is None assume that the it's not multiprocessing caching - if manager is not None: - self._cache_dict = manager.dict(self._cache_dict) diff --git a/fuse/data/cache/cache_null.py b/fuse/data/cache/cache_null.py deleted file mode 100644 index 92073cefa..000000000 --- a/fuse/data/cache/cache_null.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Dummy cache implementation, doing nothing -""" -from multiprocessing import Manager -from typing import Hashable, Any, List - -from fuse.data.cache.cache_base import CacheBase - - -class CacheNull(CacheBase): - def __init__(self): - super().__init__() - - def __contains__(self, key: Hashable) -> bool: - """ - See base class - """ - return False - - def __getitem__(self, key: Hashable) -> Any: - """ - See base class - """ - return None - - def __delitem__(self, key: Hashable) -> None: - """ - See base clas - """ - pass - - def __setitem__(self, key: Hashable, value: Any) -> None: - """ - See base class - """ - pass - - def save(self) -> None: - """ - See base class - """ - pass - - def exist(self) -> bool: - """ - See base class - """ - return True - - def reset(self) -> None: - """ - See base class - """ - pass - - def get_all_keys(self, include_none: bool = False) -> List[Hashable]: - """ - See base class - """ - return [] - - def start_caching(self, manager: Manager): - """ - See base class - """ - pass diff --git a/fuse/data/data_source/data_source_default.py b/fuse/data/data_source/data_source_default.py deleted file mode 100644 index fd923968c..000000000 --- a/fuse/data/data_source/data_source_default.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging - -import pandas as pd -from typing import Sequence, Hashable, Union, Optional, List, Dict - -from fuse.data.data_source.data_source_base import DataSourceBase -from fuse.utils.misc.misc import autodetect_input_source - - -class DataSourceDefault(DataSourceBase): - """ - DataSource for the following aut-detectable types: - - 1. DataFrame (instance or path to pickled object) - 2. Python list of sample descriptors - 3. Text file (needs to end with '.txt' or '.text' extension) - - """ - - def __init__(self, input_source: Union[str, pd.DataFrame, Sequence[Hashable]] = None, - folds: Optional[Union[int, Sequence[int]]] = None, conditions: Optional[List[Dict[str, List]]] = None) -> None: - """ - :param input_source: auto-detectable input source - :param folds: if input is a DataFrame having a 'fold' column, filter by this fold(s) - :param conditions: conditions to apply on data source. - the conditions are column names that are expected to be in input_source data frame. - - Structure: - * List of 'Filter Queries' with logical OR between them. - * Each Filter Query is a dictionary of data source column and a list of possible values, with logical AND between the keys. - - Example - selecting only negative or positive biopsy samples: - [{'biopsy' : ['positive', 'negative']}] - Example - selecting negative or positive biopsy biopsy samples that are of type 'tumor': - [{'biopsy': ['positive', 'negative'], 'type': ['tumor']}] - Example - selecting negative/positive biopsy samples that are of type 'calcification' AND marked as BIRAD 0 or 5: - [{'biopsy': ['positive', 'negative'], 'type': ['calcification'], 'birad': ['BIRAD0', 'BIRAD5']}] - Example - selecting samples that are either positive biopsy OR marked as BIRAD 0: - [{'biopsy': ['positive']}, {'birad': ['BIRAD0']}] - - """ - self.samples_df = autodetect_input_source(input_source) - - if conditions is not None: - before = len(self.samples_df) - to_keep = self.filter_by_conditions(self.samples_df, conditions) - self.samples_df = self.samples_df[to_keep].copy() - logging.getLogger('Fuse').info(f"Remove {before - len(self.samples_df)} records that did not meet conditions") - - if self.samples_df is None: - raise Exception('Error detecting input source in DataSourceDefault') - - if isinstance(folds, int): - self.folds = [folds] - else: - self.folds = folds - - if self.folds is not None: - assert 'fold' in self.samples_df, f'Data cannot be filtered by folds {folds} as folds are specified in the collected data' - self.samples_df = self.samples_df[self.samples_df['fold'].isin(self.folds)] - - @staticmethod - def filter_by_conditions(samples: pd.DataFrame, conditions: Optional[List[Dict[str, List]]]): - """ - Returns a vector of the samples that passed the conditions - :param samples: dataframe to check. expected to have at least sample_desc column. - :param conditions: list of dictionaries. each dictionary has column name as keys and possible values as the values. - for each dict in the list: - the keys are applied with AND between them. - the dict conditions are applied with OR between them. - :return: boolean vector with the filtered samples - """ - to_keep = samples.sample_desc.isna() # start with all false - for condition_list in conditions: - condition_to_keep = samples.sample_desc.notna() # start with all true - for column, values in condition_list.items(): - condition_to_keep = condition_to_keep & samples[column].isin(values) # all conditions in list must be met - to_keep = to_keep | condition_to_keep # add this condition samples to_keep - return to_keep - - def get_samples_description(self): - return list(self.samples_df['sample_desc']) - - def summary(self) -> str: - summary_str = '' - summary_str += 'DataSourceDefault - %d samples\n' % len(self.samples_df) - return summary_str - - -if __name__ == '__main__': - my_df = pd.DataFrame({'sample_desc': range(11, 16), - 'A': range(1, 6), - 'B': range(10, 0, -2), - 'C': range(10, 5, -1)}) - print(my_df) - clist = [{'A': [2, 3, 4], 'B': [8, 2]}, {'C': [8, 7]}] - to_keep = DataSourceDefault.filter_by_conditions(my_df, clist) - print(my_df[to_keep]) - - to_keep = DataSourceDefault.filter_by_conditions(my_df, [{}]) - print(my_df[to_keep]) diff --git a/fuse/data/data_source/data_source_folds.py b/fuse/data/data_source/data_source_folds.py deleted file mode 100644 index 68a54e3ea..000000000 --- a/fuse/data/data_source/data_source_folds.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on January 06, 2022 - -""" - -import pandas as pd -import os -import numpy as np -from fuse.data.data_source.data_source_base import DataSourceBase -from typing import Optional, Tuple -from fuse.data.data_source.data_source_toolbox import DataSourceToolbox - - -class DataSourceFolds(DataSourceBase): - def __init__(self, - input_source: str, - input_df : pd.DataFrame, - phase: str, - no_mixture_id: str, - balance_keys: np.ndarray, - reset_partition_file: bool, - folds: Tuple[int], - num_folds : int =5, - partition_file_name: str = None - ): - - """ - Create DataSource which is divided to num_folds folds, supports either a path to a csv or data frame as input source. - The function creates a partition file which saves the fold partition - :param input_source: path to dataframe containing the samples ( optional ) - :param input_df: dataframe containing the samples ( optional ) - :param no_mixture_id: The key column for which no mixture between folds should be forced - :param balance_keys: keys for which balancing is forced - :param reset_partition_file: boolean flag which indicate if we want to reset the partition file - :param folds indicates which folds we want to retrieve from the fold partition - :param num_folds: number of folds to divide the data - :param partition_file_name:name of a csv file for the fold partition - If train = True, train/val indices are dumped into the file, - If train = False, train/val indices are loaded - :param phase: specifies if we are in train/validation/test/all phase - """ - self.nfolds = num_folds - self.key_columns = balance_keys - if reset_partition_file is True and phase not in ['train','all']: - raise Exception("Sorry, it is possible to reset partition file only in train / all phase") - if reset_partition_file is True or not os.path.isfile(partition_file_name): - # Load csv file - # ---------------------- - - if input_source is not None : - input_df = pd.read_csv(input_source) - self.folds_df = DataSourceToolbox.balanced_division(df = input_df , - no_mixture_id = no_mixture_id, - key_columns = self.key_columns , - nfolds = self.nfolds , - print_flag=True ) - # Extract entities - # ---------------- - else: - self.folds_df = pd.read_csv(partition_file_name) - - sample_descs = [] - for fold in folds: - sample_descs += self.folds_df[self.folds_df['fold'] == fold]['file'].to_list() - - self.samples = sample_descs - - self.input_source = input_source - - def get_samples_description(self): - """ - Returns a list of samples ids. - :return: list[str] - """ - return self.samples - - def summary(self) -> str: - """ - Returns a data summary. - :return: str - """ - summary_str = '' - summary_str += 'Class = '+type(self).__name__+'\n' - - if isinstance(self.input_source, str): - summary_str += 'Input source filename = %s\n' % self.input_source - - summary_str += DataSourceToolbox.print_folds_stat(db = self.folds_df , - nfolds = self.nfolds , - key_columns = self.key_columns ) - - return summary_str diff --git a/fuse/data/data_source/data_source_from_list.py b/fuse/data/data_source/data_source_from_list.py deleted file mode 100644 index 9cd1e340d..000000000 --- a/fuse/data/data_source/data_source_from_list.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Sequence, Hashable - -from fuse.data.data_source.data_source_base import DataSourceBase - - -class DataSourceFromList(DataSourceBase): - """ - Simple DataSource that can be initialized with a Python list (or other sequence). - Does nothing but passing the list to Dataset. - """ - - def __init__(self, list_of_samples: Sequence[Hashable] = []) -> None: - self.list_of_samples = list_of_samples - - def get_samples_description(self): - return self.list_of_samples - - def summary(self) -> str: - summary_str = '' - summary_str += 'DataSourceFromList - %d samples\n' % len(self.list_of_samples) - return summary_str diff --git a/fuse/data/data_source/data_source_toolbox.py b/fuse/data/data_source/data_source_toolbox.py deleted file mode 100644 index 527bd12fd..000000000 --- a/fuse/data/data_source/data_source_toolbox.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Optional -from sklearn.utils import shuffle -import pandas as pd -import numpy as np -from collections import defaultdict -import pickle -import os - - -class DataSourceToolbox(): - - @staticmethod - def print_folds_stat(db: pd.DataFrame, nfolds: int, key_columns: np.ndarray): - """ - Print fold statistics - :param db: dataframe which contains the fold patition - :param nfolds: Number of folds to divide the data - :param key_columns: keys for which balancing is forced - """ - result ='' - for f in range(nfolds): - for key in key_columns: - result += '----------fold' + str(f) +'\n' - result += 'key: ' + key +'\n' - result += db[db['fold'] == f][key].value_counts().to_string()+'\n' - return result - @staticmethod - def balanced_division(df : pd.DataFrame, no_mixture_id : str, key_columns: np.ndarray, nfolds : int, seed : int=1357, - excluded_samples: np.ndarray=[], print_flag : bool =False, debug_mode : bool=False) -> pd.DataFrame: - """ - Partition the data into folds while using no_mixture_id for which no mixture between folds should be forced. - and using key_columns as the keys for which balancing is forced. - The functions creates ID level labeling which is the cross-section of all possible mixture of key columns for that id - it creates the folds so each fold will have about same proportions of ID level labeling while each ID will appear only in one fold - For exmaple - patient with ID 1234 has 2 images , each image has a binary classification (benign / malignant) . - it can be that both of his images are benign or both are malignant or one is benign and the other is malignant. - For example - :param df: dataframe containing all samples including id and key_columns - :param no_mixture_id: The key column for which no mixture between folds should be forced - :param key_columns: keys for which balancing is forced - :param nfolds: number of folds to divide the data - :param seed: random seed used for creating folds - :param excluded_samples: sampled id which we do not want to include in the folds - :param print_flag: boolean flag which indicates if to print fold statistics - """ - id_level_labels = [] - record_labels = [] - for field in key_columns: - values = df[field].unique() - for value in values: - value2 = str.replace(str(value), '+', '') - # creates a binary label for each record and label - record_key = 'is' + value2 - df[record_key] = df[field] == value - # creates a binary label for each id and label ( is anyone with this id has his label) - id_level_key = 'sample_id_' + field + '_' + value2 - df[id_level_key] = df.groupby([no_mixture_id])[record_key].transform(sum) > 0 - id_level_labels.append(id_level_key) - record_labels.append(record_key) - - # drop duplicate id records - samples_col = [no_mixture_id] + [col for col in id_level_labels] - df_samples = df[samples_col].drop_duplicates() - - # generates a new label for each id based on sample_id value, using id's which are not in excluded_samples - excluded_samples_df = df_samples[no_mixture_id].isin(excluded_samples) - included_samples_df = df_samples[id_level_labels][~excluded_samples_df] - df_samples['y_class'] = [str(t) for t in included_samples_df.values] - y_values = list(df_samples['y_class'].unique()) - - # initialize folds to empty list of ids - db_samples = {} - for f in range(nfolds): - db_samples['data_fold' + str(f)] = [] - - # creates a dictionary with key=fold , and values = ID which is in the fold - # the partition goes as following : for each id level labels we shuffle the ID's and split equally ( as possible) to nfolds - for y_value in y_values: - patients_w_value = list(df_samples[no_mixture_id][df_samples['y_class'] == y_value]) - patients_w_value_shuffled = shuffle(patients_w_value, random_state=seed) - splitted_array = np.array_split(patients_w_value_shuffled, nfolds) - for f in range(nfolds): - db_samples['data_fold' + str(f)] = db_samples['data_fold' + str(f)] + list(splitted_array[f]) - - # creates a dictionary of dataframes, each dataframes holds all records for the fold - # each ID appears only in one fold - db = {} - for f in range(nfolds): - fold_df = df[df[no_mixture_id].isin(db_samples['data_fold' + str(f)])].copy() - fold_df['fold'] = f - db['data_fold' + str(f)] = fold_df - folds = pd.concat(db, ignore_index=True) - if print_flag is True: - DataSourceToolbox.print_folds_stat(folds, nfolds, key_columns) - # remove labels used for creating the partition to folds - if not debug_mode : - folds.drop(id_level_labels+record_labels, axis=1, inplace=True) - return folds - diff --git a/fuse/data/dataset/dataset_base.py b/fuse/data/dataset/dataset_base.py deleted file mode 100644 index b24adc9a6..000000000 --- a/fuse/data/dataset/dataset_base.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Fuse Dataset Base -""" -import pickle -from abc import abstractmethod -from enum import Enum -from typing import Any, List, Optional - -from torch.utils.data.dataset import Dataset - - -class DatasetBase(Dataset): - """ - Abstract base class for Fuse dataset. - All subclasses should overwrite the following abstract methods inherited from torch.utils.data.Dataset - `__getitem__`, supporting fetching a data sample for a given key. - `__len__`, which is expected to return the size of the dataset - And the ones listed below - """ - - class SaveMode(Enum): - # store just the info required for inference - INFERENCE = 1, - # store all the info - TRAINING = 2 - - def __init__(self): - super().__init__() - - @abstractmethod - def create(self, **kwargs) -> None: - """ - Used to enable the instance - Typically will load caching, etc - :param kwargs: different parameters per subclass - :return: None - """ - raise NotImplementedError - - @abstractmethod - def get(self, index: Optional[int], key: Optional[str], use_cache: bool = False) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item or None for all - :param key: string representing the exact information required, use None for all. - :param use_cache: if true, will try to reload the sample from caching mechanism in case exist. - :return: the required info of a single sample of a list of samples - """ - raise NotImplementedError - - @abstractmethod - def collate_fn(self, samples: List[Any]) -> Any: - """ - collate list of samples into batch - :param samples: list of samples - :return: batch - """ - raise NotImplementedError - - # misc - @abstractmethod - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - String summary of the object - :param statistic_keys: Optional. list of keys to output statistics about. - """ - raise NotImplementedError - - # save and load datasets - @abstractmethod - def get_instance_to_save(self, mode: SaveMode) -> 'DatasetBase': - """ - Create lite instance version of dataset with just the info required to recreate it - :param mode: see SaveMode for available modes - :return: the instance to save - """ - raise NotImplementedError - - @staticmethod - def save(dataset: 'DatasetBase', mode: SaveMode, filename: str) -> None: - """ - Static method save dataset to the disc (see SaveMode for available modes) - :param dataset: the dataset to save - :param mode: required mode to save - :param filename: file name to use - :return: None - """ - # get instance version to save - dataset_to_save = dataset.get_instance_to_save(mode) - - # save this instance - with open(filename, 'wb') as pickle_file: - pickle.dump(dataset_to_save, pickle_file) - - @staticmethod - def load(filename: str, **kwargs) -> 'DatasetBase': - """ - load dataset - :param filename: path to saved dataset - :param kwargs: arguments of create() function - :return: the dataset object - """ - # load saved instance - with open(filename, 'rb') as pickle_file: - dataset = pickle.load(pickle_file) - - # recreate dataset - dataset.create(**kwargs) - - return dataset diff --git a/fuse/data/dataset/dataset_dataframe.py b/fuse/data/dataset/dataset_dataframe.py deleted file mode 100644 index 252369faf..000000000 --- a/fuse/data/dataset/dataset_dataframe.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Optional, List, Dict, Union - -import torch -import pandas as pd - -from fuse.data.data_source.data_source_from_list import DataSourceFromList -from fuse.data.dataset.dataset_default import DatasetDefault -from fuse.data.processor.processor_dataframe import ProcessorDataFrame - - -class DatasetDataframe(DatasetDefault): - """ - Simple dataset, based on DatasetDefault, that converts dataframe into dataset. - """ - def __init__(self, - data: Optional[pd.DataFrame] = None, - data_pickle_filename: Optional[str] = None, - sample_desc_column: Optional[str] = 'descriptor', - columns_to_extract: Optional[List[str]] = None, - rename_columns: Optional[Dict[str, str]] = None, - columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None, - **kwargs): - """ - :param data: input DataFrame - :param data_pickle_filename: path to a pickled DataFrame (possible gzipped) - :param sample_desc_column: name of the sample descriptor column within the pickle file, - if set to None.will simply use dataframe index as descriptors - :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted - :param rename_columns: rename columns from dataframe, when None (default) column names are kept - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - :param kwargs: additional DatasetDefault arguments. See DatasetDefault - - """ - # create processor - processor = ProcessorDataFrame(data=data, - data_pickle_filename=data_pickle_filename, - sample_desc_column=sample_desc_column, - columns_to_extract=columns_to_extract, - rename_columns=rename_columns, - columns_to_tensor=columns_to_tensor) - - # extract descriptor list and create datasource - descriptors_list = processor.get_samples_descriptors() - - data_source = DataSourceFromList(descriptors_list) - - super().__init__( - data_source=data_source, - gt_processors=None, - input_processors=None, - processors=processor, - **kwargs - ) diff --git a/fuse/data/dataset/dataset_default.py b/fuse/data/dataset/dataset_default.py deleted file mode 100644 index 84c42de40..000000000 --- a/fuse/data/dataset/dataset_default.py +++ /dev/null @@ -1,756 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -import os -from multiprocessing import Manager -from multiprocessing.pool import Pool, ThreadPool -from typing import Any, Dict, Optional, Hashable, List, Union, Tuple, Callable - -import numpy as np -import torch -from pandas import DataFrame -from torch import Tensor -from tqdm import tqdm, trange - -from fuse.data.augmentor.augmentor_base import AugmentorBase -from fuse.data.cache.cache_base import CacheBase -from fuse.data.cache.cache_files import CacheFiles -from fuse.data.cache.cache_memory import CacheMemory -from fuse.data.cache.cache_null import CacheNull -from fuse.data.data_source.data_source_base import DataSourceBase -from fuse.data.dataset.dataset_base import DatasetBase -from fuse.data.processor.processor_base import ProcessorBase -from fuse.data.visualizer.visualizer_base import VisualizerBase -from fuse.utils.utils_debug import FuseDebug -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -from fuse.utils.misc.misc import get_pretty_dataframe, Misc - - -class DatasetDefault(DatasetBase): - """ - Fuse Dataset Default - Default generic implementation aimed to be used in most of the scenarios. - """ - - #### CONSTRUCTOR - def __init__(self, data_source: DataSourceBase, - input_processors: Optional[Dict[str, ProcessorBase]], gt_processors: Optional[Dict[str, ProcessorBase]], processors: Union[ProcessorBase, Dict[str, ProcessorBase]] = None, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[AugmentorBase] = None, - visualizer: Optional[VisualizerBase] = None, post_processing_func=None, - statistic_keys: Optional[List[str]] = None, - filter_keys: Optional[List[str]] = None, - data_key_prefix: Optional[str] = 'data'): - """ - :param data_source: objects provides the list of object description - :param input_processors:dictionary of all the input data processors - :param gt_processors: dictionary of all the ground truth data processors - :param processors: Use in case the ground truth and input are coupled. Could be either a single processor or dictionary of processors. - If used, input_processors and gt_processors must be set to None. - :param cache_dest: Optional, path to save caching. - When cache_dest = 'memory', data is cached to Memory. - Else, if it's a string, data is saved to files under cache_desc dir - :param augmentor: Optional, object that perform the augmentation - :param visualizer: Optional, object that visualize the data - :param post_processing_func: callback that allows to dynamically modify the data. - Called as last step (after augmentation) - :param statistic_keys: Optional. list of statistic keys to output in default self.summary() implementation - :param filter_keys: Optional. list of keys to remove from the sample dictionary when getting an item - :param data_key_prefix: every key added to sample_dict by the dataset will be prepended with this prefix to get unique name. - """ - # log object input state - log_object_input_state(self, locals()) - - super().__init__() - - # store input params - self.cache_dest = cache_dest - self.data_source = data_source - if processors is None: - self.processors = {'input': input_processors, 'gt': gt_processors} - else: - if input_processors is not None: - msg = f'Either processors or input_processors should be set to None' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - if gt_processors is not None: - msg = f'Either processors or gt_processors should be set to None' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - self.processors = processors - - self.augmentor = augmentor - self.visualizer = visualizer - self.post_processing_func = post_processing_func - self.statistic_keys = statistic_keys or [] - self.filter_keys = filter_keys or [] - self.data_key_prefix = data_key_prefix - # initial values - # map sample running index to sample description (mush be hashable) - self.samples_description = [] - - # create dummy cache for now - the cache will be created and loaded in create() - self.cache: CacheBase = CacheNull() - # create dummy cache self.cache_fields used to store specific fields of the sample - used to optimize the running time of dataset.get( - # key=, use_cache=True) - self.cache_fields: CacheBase = CacheNull() - - # debug modes - read configuration - self.sample_stages_debug = FuseDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseDebug().get_setting('dataset_user') != 'default' - - def create(self, cache_all: bool = True, reset_cache: bool = False, - num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[DataSourceBase] = None, - pool_type: str = 'process') -> None: - """ - Create the data set, including loading sample descriptions and caching - :param cache_all: if True will try to cache all - :param reset_cache: if False and cache_all is True, will use load caching instead of re creating it. - :param num_workers: number of workers used for caching - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :param override_datasource: might be used to change the data source - :param pool_type: multiprocess pooling type, can be either 'thread' (for ThreadPool) or 'process' (for 'Pool', default). - :return: None - """ - # debug - override num workers - override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - assert pool_type in ['thread', 'process'], f'Invalid pool_type: {pool_type}. Multiprocessing pooling type can be either "thread" or "process"' - self.pool_type = pool_type - - # override data source if required - if override_datasource is not None: - self.data_source = override_datasource - - # extract list of sample description - self.samples_description = self.data_source.get_samples_description() - - # debug - override number of samples - dataset_override_num_samples = FuseDebug().get_setting('dataset_override_num_samples') - if dataset_override_num_samples != 'default': - self.samples_description = self.samples_description[:dataset_override_num_samples] - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) - - # cache object - if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: CacheBase = CacheMemory() - elif isinstance(self.cache_dest, str): - self.cache: CacheBase = CacheFiles(self.cache_dest, reset_cache) - - # cache samples if required - if not isinstance(self.cache, CacheNull) and cache_all: - self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) - - # update descriptors - all_descriptors = set(self.samples_description) - cached_descriptors = set(self.cache.get_all_keys()) - self.samples_description = sorted(list(all_descriptors & cached_descriptors)) - - self.sample_descriptor_to_index = {v: k for k, v in enumerate(self.samples_description)} - - #### ITERATE AND GET DATA - def __len__(self): - return len(self.samples_description) - - def getitem_without_augmentation(self, index: int) -> Any: - """ - Get the original item, just before applying the augmentation. - The returned value will be stored in cache - :param index: the index of the item - :return: the original sample - """ - sample_description = self.samples_description[index] - sample = self.getitem_without_augmentation_static(self.processors, sample_description, data_key_prefix=self.data_key_prefix) - # make sure sample was loaded correctly - if sample is None: - msg = f'Failed to load data sample_desc={sample_description}, skipping is only possible when caching is enabled' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - return sample - - @staticmethod - def getitem_without_augmentation_static(processors: Union[Dict[str, ProcessorBase], ProcessorBase], descr: Hashable, data_key_prefix: Optional[str]) -> Any: - """ - Get the original item, just before applying the augmentation. - The returned value will be stored in cache - Static version - :param processors: the processors required to generate the sample - :param descr: sample descriptor - :return: the original sample as a dict, using the processors to retrieve its data. - e.g., - single processor - ----------------- - {'data.descriptor': image id string, - 'data.input': tensor of image - } - multi processors - ---------------- - {'data.descriptor':image id string, - 'data.input.image': tensor of image, - 'data.gt,gt_global': tensor of global gt - } - - """ - lgr = logging.getLogger('Fuse') - sample_data = {} - if data_key_prefix is not None: - sample = {data_key_prefix : sample_data} - else: - sample = sample_data - - # extract the sample description to be used by the processors - sample_data['descriptor'] = descr - # process data - if isinstance(processors, ProcessorBase): # handle a case of single processor - try: - processor = processors - value = processor(descr) - - if value is None: - lgr.error(f'processor failed to load data sample_desc={descr}, got None, skipping sample') - return None - elif isinstance(value, dict): - value = value.copy() - - sample_data.update(value) - except: - lgr.error(f'processor failed to load data sample_desc={descr}') - raise - else: # otherwise, dictionary that includes multiple processors - sample_data['input'] = {} - all_keys = FuseUtilsHierarchicalDict.get_all_keys(processors) - for key in all_keys: - try: - processor = FuseUtilsHierarchicalDict.get(processors, key) - value = processor(descr) - - if value is None: - lgr.error(f'processor {key} failed to load data sample_desc={descr}, got None, skipping sample') - return None - elif isinstance(value, dict): - value = value.copy() - - FuseUtilsHierarchicalDict.set(sample_data, key, value) - except: - lgr.error(f'processor {key} failed to load data sample_desc={descr}') - raise - - return sample - - def get_from_cache(self, index: Optional[int], key: str): - """ - Get input, ground truth or metadata of a sample. - First try to read from cache. Fallback to run the processor if not in cache. - - :param index: the index of the item, if None will return all items - :param key: string representing the exact information required - :return: the required info - """ - - if index is None: - # return all samples - values = [] - for index in trange(len(self)): - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - values.append(self.cache_fields[desc_field]) - else: - # if not found get the all sample and then extract the specified field - values.append(FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key)) - return values - else: - # return single sample - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - return self.cache_fields[desc_field] - else: - # if not found get the all sample and then extract the specified field - return FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key) - - def get(self, index: Optional[Union[int, Hashable]], key: Optional[str] = None, use_cache: bool = False) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item, if None will return all items - If not an int or None, will assume that index is sample descriptor - - :param key: string representing the exact information required. If None, will return all samples - :param use_cache: if true, will try to reload the sample from caching mechanism - :return: the required info - """ - if index is not None and not isinstance(index, int): - # get sample giving sample descriptor - # assume index is sample description - index = self.samples_description.index(index) - - # if key not specified return the all sample - if key is None: - if index is None: - return [self.getitem(index, apply_augmentation=False) for index in trange(len(self))] - else: - return self.getitem(index) - - # if use cache - if use_cache: - return self.get_from_cache(index, key) - - ## otherwise run the processor - if isinstance(self.processors, ProcessorBase): # single processor case - processor = self.processors - inner_key = key[len('data.'):] - else: # dictionary including multiple processors - all_processor_keys = FuseUtilsHierarchicalDict.get_all_keys(self.processors) - required_processor_key = None - inner_key = None - for processor_key in all_processor_keys: - if key.startswith(f'data.{processor_key}'): - required_processor_key = processor_key - inner_key = key[len(f'data.{processor_key}.'):] - break - - if required_processor_key is None: - raise Exception(f'processor not found for key {key}') - - processor = FuseUtilsHierarchicalDict.get(self.processors, required_processor_key) - - if index is None: - try: - value = processor.get_all(self.samples_description) - except: - value = [processor(sample_description) for sample_description in self.samples_description] - if inner_key != '': - value = [FuseUtilsHierarchicalDict.get(v, inner_key) for v in value] - else: - # get the sample description to be used by the processors - sample_description = self.samples_description[index] - value = processor(sample_description) - if inner_key != '': - value = FuseUtilsHierarchicalDict.get(value, inner_key) - - return value - - def __getitem__(self, index: int) -> Any: - """ - Get sample, read it from cache if possible, apply augmentation and post processing - :param index: sample index - :return: the required sample after augmentation - """ - sample_stages_debug = self.sample_stages_debug - return self.getitem(index, sample_stages_debug=sample_stages_debug) - - def getitem(self, index: int, apply_augmentation: bool = True, apply_post_processing: bool = True, sample_stages_debug: bool = False) -> Any: - """ - Get sample, read it from cache if possible - :param index: sample index - :param apply_augmentation: if true, will apply augmentation - :param apply_post_processing: If true, will apply post processing - :param sample_stages_debug: True will log the sample dict after each stage - :return: the required sample after augmentation - """ - - # either load from cache or generate and store in cache - sample_desc = self.samples_description[index] - - if sample_desc in self.cache: - sample = self.cache[sample_desc] - else: - sample = self.getitem_without_augmentation(index) - - # filter some of the keys if required - if self.filter_keys is not None: - for key in self.filter_keys: - try: - FuseUtilsHierarchicalDict.pop(sample, key) - except KeyError: - pass - - # debug mode - print original sample before augmentation and before post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - original sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - # one time print - self.sample_stages_debug = False - - # apply augmentation if enabled - if self.augmentor is not None and apply_augmentation: - sample = self.augmentor(sample) - - # debug mode - print sample after augmentation - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - augmented sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - # apply post processing - if self.post_processing_func is not None and apply_post_processing: - self.post_processing_func(sample) - - # debug mode - print sample after post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - post processed sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - return sample - - #### BATCHING - def collate_fn(self, samples: List[Dict], avoid_stack_keys: Tuple = tuple()) -> Dict: - """ - collate list of samples into batch_dict - :param samples: list of samples - :param avoid_stack_keys: list of keys to just collect to a list and avoid stack operation - :return: batch_dict - """ - batch_dict = {} - keys = FuseUtilsHierarchicalDict.get_all_keys(samples[0]) - for key in keys: - try: - collected_value = [FuseUtilsHierarchicalDict.get(sample, key) for sample in samples if sample is not None] - if key in avoid_stack_keys: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - elif isinstance(collected_value[0], Tensor): - FuseUtilsHierarchicalDict.set(batch_dict, key, torch.stack(collected_value)) - elif isinstance(collected_value[0], np.ndarray): - FuseUtilsHierarchicalDict.set(batch_dict, key, np.stack(collected_value)) - else: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - except: - logging.getLogger('Fuse').error(f'Failed to collect key {key}') - raise - - return batch_dict - - #### CACHING - def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None) -> None: - """ - Cache all data - :param num_workers: num of workers used to cache the samples - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :return: None - """ - lgr = logging.getLogger('Fuse') - - # check if cache is required - all_descriptors = set(self.samples_description) - cached_descriptors = set(self.cache.get_all_keys(include_none=True)) - descriptors_to_cache = all_descriptors - cached_descriptors - - if len(descriptors_to_cache) != 0: - # multi process cache - lgr.info(f'DatasetDefault: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') - with Manager() as manager: - # change cache mode - to caching (writing) - self.cache.start_caching(manager) - - # multi process cache - if num_workers > 0: - the_pool = ThreadPool if self.pool_type == 'thread' else Pool - pool = the_pool(processes=num_workers, initializer=worker_init_func, initargs=worker_init_args) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample, - iterable=[(self.processors, desc, self.cache, self.data_key_prefix) for desc in descriptors_to_cache]), - total=len(descriptors_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - else: - for desc in tqdm(descriptors_to_cache): - self._cache_sample((self.processors, desc, self.cache, self.data_key_prefix)) - - # save and move back to read mode - self.cache.save() - lgr.info('DatasetDefault: caching done') - else: - lgr.info(f'DatasetDefault: all {len(all_descriptors)} samples are already cached') - - def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_workers: int = 8, cache_dest: Optional[str] = None) -> None: - """ - Cache specific fields (keys in batch_dict) - Used to optimize the running time of of dataset.get(key=, use_cache=True) - :param fields: list of keys in batch_dict - :param reset_cache: If True will reset cache first - :param num_workers: num workers used for caching - :param cache_dest: path to cache dir - :return: None - """ - lgr = logging.getLogger('Fuse') - - # debug - override num workers - override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - if cache_dest is None: - cache_dest = os.path.join(self.cache_dest, 'fields') - - # create cache field object upon request - if isinstance(self.cache_fields, CacheNull): - # cache object - if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: CacheBase = CacheMemory() - elif isinstance(cache_dest, str): - self.cache_fields: CacheBase = CacheFiles(cache_dest, reset_cache, single_file=True) - - # get list of desc to cache - desc_list = self.samples_description - desc_field_list = set([(desc, field) for desc in desc_list for field in fields]) - cached_desc_field = set(self.cache_fields.get_all_keys(include_none=True)) - desc_field_to_cache = desc_field_list - cached_desc_field - desc_to_cache = set([desc_field[0] for desc_field in desc_field_to_cache]) - - # multi thread caching - if len(desc_to_cache) != 0: - lgr.info(f'DatasetDefault: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') - if num_workers > 0: - with Manager() as manager: - self.cache_fields.start_caching(manager) - pool = Pool(processes=num_workers) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample_fields, - iterable=[(desc, fields) for desc in desc_to_cache]), - total=len(desc_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - self.cache_fields.save() - else: - self.cache_fields.start_caching(None) - for desc in tqdm(desc_to_cache): - self._cache_sample_fields((desc, fields)) - self.cache_fields.save() - else: - lgr.info('DatasetDefault: all samples fields are already cached') - - def _cache_sample_fields(self, args): - # decode args - desc, fields = args - index = self.samples_description.index(desc) - sample = self.getitem(index, apply_augmentation=False) - for field in fields: - # create field desc and save it in cache - desc_field = (desc, field) - if desc_field not in self.cache_fields: - value = FuseUtilsHierarchicalDict.get(sample, field) - self.cache_fields[desc_field] = value - - @staticmethod - def _cache_sample(args: Tuple) -> None: - """ - Store in cache single sample - :param args: tuple of processors, sample descriptor and cache object - :return: None - """ - processors, desc, cache, data_key_prefix = args - sample = DatasetDefault.getitem_without_augmentation_static(processors, desc, data_key_prefix=data_key_prefix) - cache[desc] = sample - - #### Filtering - def filter(self, key: str, values: List[Any]) -> None: - """ - Filter sample if batch_dict[key] in values - :param key: key in batch_dict - :param values: list of values to filter - :return: None - """ - lgr = logging.getLogger('Fuse') - lgr.info(f'DatasetDefault: filtering key {key}, values {values}') - new_samples_desc = [] - for index, desc in tqdm(enumerate(self.samples_description), total=len(self.samples_description)): - value = self.get(index, key, use_cache=True) - if value not in values: - new_samples_desc.append(desc) - - self.samples_description = new_samples_desc - - #### VISUALIZE - def visualize(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True, **kwargs): - """ - visualize sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample , only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index, **kwargs) - - self.visualizer.visualize(batch_dict, block) - - def visualize_augmentation(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize augmentation of a sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample, only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index, apply_augmentation=False) - batch_dict_aug = self.getitem(index) - - self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) - - # save and load dataset - def get_instance_to_save(self, mode: DatasetBase.SaveMode) -> DatasetBase: - """ - See base class - """ - - # prepare data to save - dataset = DatasetDefault(data_source=None, - input_processors={}, - gt_processors={}, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func, - statistic_keys=self.statistic_keys, - visualizer=self.visualizer) - if mode == DatasetBase.SaveMode.INFERENCE and isinstance(self.processors, dict) and 'input' in self.processors: - dataset.processors = {'input': self.processors['input']} # for inference we can save only input processors if available - else: - dataset.processors = self.processors - - return dataset - - # misc - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - Returns a data summary. - Should be called after create() - :param statistic_keys: Optional. list of keys to output statistics about. - When None (default), self.statistic_keys are output. - :return: str - """ - statistic_keys_to_use = statistic_keys if statistic_keys is not None else self.statistic_keys - - sum = \ - f'Class = {self.__class__}\n' - sum += \ - f'Processors:\n' \ - f'------------------------\n' \ - f'{self.processors}\n' - sum += \ - f'Cache destination:\n' \ - f'------------------\n' \ - f'{self.cache_dest}\n' - sum += \ - f'Augmentor:\n' \ - f'----------\n' \ - f'{self.augmentor.summary() if self.augmentor is not None else None}\n' - sum += \ - f'Data source:\n' \ - f'------------\n' \ - f'{self.data_source.summary() if self.data_source is not None else None}\n' - sum += \ - f'Sample keys:\n' \ - f'------------\n' \ - f'{FuseUtilsHierarchicalDict.get_all_keys(self.getitem(0)) if self.data_source is not None else None}\n' - sum += \ - f'Basic Data Statistic:\n' + \ - f'-------------------\n' + \ - self.basic_data_summary(statistic_keys_to_use) - return sum - - def basic_data_summary(self, statistic_keys: List[str] = []) -> str: - """ - Provide string including basic stat that can be retrieved fast - :return: string stat - """ - # collect data that can be retrieved fast - collected_data = self.collect_basic_data(statistic_keys) - - # basic statistic - sum = '' - all_keys = FuseUtilsHierarchicalDict.get_all_keys(collected_data) - for processor_name in all_keys: - df = DataFrame(data=FuseUtilsHierarchicalDict.get(collected_data, processor_name), columns=[processor_name]) - stat_df = DataFrame() - stat_df['Value'] = df[processor_name].value_counts().index - stat_df['Count'] = df[processor_name].value_counts().values - stat_df['Percent'] = df[processor_name].value_counts(normalize=True).values * 100 - sum += \ - f'\n{processor_name} Statistics:\n' + \ - f'{get_pretty_dataframe(stat_df)}' - return sum - - def collect_basic_data(self, statistic_keys: List[str]) -> dict: - """ - Collect data that can be retrieved by get_all() or included in statistic_keys - :param statistic_keys: list of keys to collect data about - :return: hierarchical dict including the collect data - """ - sample_data = {} - if self.data_key_prefix: - samples = {self.data_key_prefix: sample_data} - else: - samples = sample_data - - # in case of multi processors, collect data of the ones implementing get_all() method - if not isinstance(self.processors, ProcessorBase): - all_keys = FuseUtilsHierarchicalDict.get_all_keys(self.processors) - for key in all_keys: - processor = FuseUtilsHierarchicalDict.get(self.processors, key) - try: - values_list = processor.get_all(self.samples_description) - if isinstance(values_list[0], dict): - for inner_key in FuseUtilsHierarchicalDict.get_all_keys(values_list[0]): - value_to_set = [int(FuseUtilsHierarchicalDict.get(value, inner_key)) for value in values_list] - FuseUtilsHierarchicalDict.set(sample_data, f'{key}.{inner_key}', value_to_set) - else: - # FIXME: maybe we will need to filter here according to value type one day - value_to_set = [int(value) for value in values_list] - FuseUtilsHierarchicalDict.set(sample_data, key, value_to_set) - except: - # do nothing - pass - - for key in statistic_keys: - values = self.get(index=None, key=key, use_cache=True) - # convert to int - maybe we will need to support additional types one day - value_to_set = [int(value) for value in values] - FuseUtilsHierarchicalDict.set(sample_data, key, value_to_set) - return samples diff --git a/fuse/data/dataset/dataset_generator.py b/fuse/data/dataset/dataset_generator.py deleted file mode 100644 index 1cda462b3..000000000 --- a/fuse/data/dataset/dataset_generator.py +++ /dev/null @@ -1,561 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -import os -from multiprocessing import Manager -from multiprocessing.pool import Pool, ThreadPool -from typing import Any, Dict, Optional, Hashable, List, Union, Tuple, Callable - -import numpy as np -import torch -from pandas import DataFrame -from torch import Tensor -from tqdm import tqdm, trange - -from fuse.data.augmentor.augmentor_base import AugmentorBase -from fuse.data.cache.cache_base import CacheBase -from fuse.data.cache.cache_files import CacheFiles -from fuse.data.cache.cache_memory import CacheMemory -from fuse.data.cache.cache_null import CacheNull -from fuse.data.data_source.data_source_base import DataSourceBase -from fuse.data.dataset.dataset_base import DatasetBase -from fuse.data.processor.processor_base import ProcessorBase -from fuse.data.visualizer.visualizer_base import VisualizerBase -from fuse.utils.utils_debug import FuseDebug -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -from fuse.utils.misc.misc import get_pretty_dataframe, Misc - - -class DatasetGenerator(DatasetBase): - """ - Fuse Dataset Generator - Used when it's more convient to generate sevral samples at once - """ - - #### CONSTRUCTOR - def __init__(self, data_source: DataSourceBase, processor: ProcessorBase, - cache_dest: Optional[Union[str, int]] = None, augmentor: Optional[AugmentorBase] = None, - visualizer: Optional[VisualizerBase] = None, post_processing_func=None, - statistic_keys: Optional[List[str]] = None, - filter_keys: Optional[List[str]] = None): - """ - :param data_source: objects provides the list of object description - :param processor: data generator - :param cache_dest: Optional, path to save caching - :param augmentor: Optional, object that perform the augmentation - :param visualizer: Optional, object that visualize the data - :param post_processing_func: callback that allows to dynamically modify the data. - Called as last step (after augmentation) - :param statistic_keys: Optional. list of statistic keys to output in default self.summary() implementation - :param filter_keys: Optional. list of keys to remove from the sample dictionary when getting an item - """ - # log object input state - log_object_input_state(self, locals()) - - super().__init__() - - # store input params - self.cache_dest = cache_dest - self.augmentor = augmentor - self.visualizer = visualizer - self.processor = processor - self.data_source = data_source - self.post_processing_func = post_processing_func - self.statistic_keys = statistic_keys or [] - self.filter_keys = filter_keys or [] - # initial values - # map sample running index to sample description (mush be hashable) - self.subsets_description = [] - - # create default cache for now - the cache will be created and loaded in create() - self.cache: CacheBase = CacheMemory() - # create dummy cache - # self.cache_fields is used to store specific fields of the sample - - # used to optimize the running time of dataset.get(key=, use_cache=True) - self.cache_fields: CacheBase = CacheNull() - - # debug modes - read configuration - self.sample_stages_debug = FuseDebug().get_setting('dataset_sample_stages_info') != 'default' - self.sample_user_debug = FuseDebug().get_setting('dataset_user') != 'default' - - def create(self, reset_cache: bool = False, - num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None, - override_datasource: Optional[DataSourceBase] = None, override_cache_dest: Optional[str] = None, - pool_type: str = 'process') -> None: - - """ - Create the data set, including loading sample descriptions and caching - :param reset_cache: if False and cache_all is True, will use load caching instead of re creating it. - :param num_workers: number of workers used for caching - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :param override_datasource: might be used to change the data source - :param override_cache_dest: might be user to change the cache destination - :param pool_type: multiprocess pooling type, can be either 'thread' (for ThreadPool) or 'process' (for 'Pool', default). - :return: None - """ - # debug - override num workers - override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - assert pool_type in ['thread', 'process'], f'Invalid pool_type: {pool_type}. Multiprocessing pooling type can be either "thread" or "process"' - self.pool_type = pool_type - - # override data source if required - if override_datasource is not None: - self.data_source = override_datasource - # override destination cache if required - if override_cache_dest is not None: - self.cache_dest = override_cache_dest - # extract list of sample description - self.subsets_description = self.data_source.get_samples_description() - - # debug - override number of samples - dataset_override_num_samples = FuseDebug().get_setting('dataset_override_num_samples') - if dataset_override_num_samples != 'default': - self.subsets_description = self.subsets_description[:dataset_override_num_samples] - logging.getLogger('Fuse').info(f'Dataset - debug mode - override num samples to {dataset_override_num_samples}', {'color': 'red'}) - - # cache object - if isinstance(self.cache_dest, str) and self.cache_dest == 'memory': - self.cache: CacheBase = CacheMemory() - elif isinstance(self.cache_dest, str): - self.cache: CacheBase = CacheFiles(self.cache_dest, reset_cache) - - # cache samples if required - if not isinstance(self.cache, CacheNull): - self.cache_all_samples(num_workers=num_workers, worker_init_func=worker_init_func, worker_init_args=worker_init_args) - - # update descriptors - all_descriptors = self.subsets_description - cached_descriptors = self.cache.get_all_keys() - self.samples_description = sorted([desc for desc in cached_descriptors if desc[0] in all_descriptors]) - - self.sample_descriptor_to_index = {v: k for k, v in enumerate(self.samples_description)} - #### ITERATE AND GET DATA - def __len__(self): - return len(self.samples_description) - - def get(self, index: Optional[Union[int, Hashable]], key: Optional[str] = None, use_cache: bool = True) -> Any: - """ - Get input, ground truth or metadata of a sample. - - :param index: the index of the item, if None will return all items. - If not an int or None, will assume that imdex is sample descriptor - :param key: string representing the exact information required. If None, will return all sample - :param use_cache: if true, will try to reload the sample from caching mechanism - :return: the required info - """ - if index is not None and not isinstance(index, int): - # get sample giving sample descriptor - # assume index is sample description - index = self.samples_description.index(index) - - # if key not specified return the all sample - if key is None: - assert index != -1, 'get all samples is not supported when key = None' - return self.getitem(index) - - assert use_cache == True, f'{type(self)} support only use_cache=True' - - if index is None: - # return all samples - values = [] - for index in trange(len(self)): - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - values.append(self.cache_fields[desc_field]) - else: - # if not found get the all sample and then extract the specified field - values.append(FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key)) - return values - else: - # return single sample - # first look for the specific file inside the cache - desc_field = (self.samples_description[index], key) - if desc_field in self.cache_fields: - return self.cache_fields[desc_field] - else: - # if not found get the all sample and then extract the specified field - return FuseUtilsHierarchicalDict.get(self.getitem(index, apply_augmentation=False), key) - - def __getitem__(self, index: int) -> Any: - """ - Get sample, read it from cache if possible, apply augmentation and post processing - :param index: sample index - :return: the required sample after augmentation - """ - sample_stages_debug = self.sample_stages_debug - return self.getitem(index, sample_stages_debug=sample_stages_debug) - - def getitem(self, index: int, apply_augmentation: bool = True, apply_post_processing: bool = True, sample_stages_debug: bool = False) -> Any: - """ - Get sample, read it from cache if possible - :param index: sample index - :param apply_augmentation: if true, will apply augmentation - :param apply_post_processing: If true, will apply post processing - :param sample_stages_debug: True will log the sample dict after each stage - :return: the required sample after augmentation - """ - - # load from cache - sample_desc = self.samples_description[index] - sample = self.cache[sample_desc] - - # filter some of the keys if required - if self.filter_keys is not None: - for key in self.filter_keys: - try: - FuseUtilsHierarchicalDict.pop(sample, key) - except KeyError: - pass - - # debug mode - print original sample before augmentation and before post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - original sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - # one time print - self.sample_stages_debug = False - - # apply augmentation if enabled - if self.augmentor is not None and apply_augmentation: - sample = self.augmentor(sample) - - # debug mode - print sample after augmentation - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - augmented sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - # apply post processing - if self.post_processing_func is not None and apply_post_processing: - self.post_processing_func(sample) - - # debug mode - print sample after post processing - if sample_stages_debug: - lgr = logging.getLogger('Fuse') - sample_str = Misc.batch_dict_to_string(sample) - lgr.info(f'Dataset - post processed sample:', {'color': 'green', 'attrs': 'bold'}) - lgr.info(f'{sample_str}', {'color': 'green'}) - - return sample - - #### BATCHING - def collate_fn(self, samples: List[Dict], avoid_stack_keys: Tuple = tuple()) -> Dict: - """ - collate list of samples into batch_dict - :param samples: list of samples - :param avoid_stack_keys: list of keys to just collect to a list and avoid stack operation - :return: batch_dict - """ - batch_dict = {} - keys = FuseUtilsHierarchicalDict.get_all_keys(samples[0]) - for key in keys: - try: - collected_value = [FuseUtilsHierarchicalDict.get(sample, key) for sample in samples if sample is not None] - if key in avoid_stack_keys: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - elif isinstance(collected_value[0], Tensor): - FuseUtilsHierarchicalDict.set(batch_dict, key, torch.stack(collected_value)) - elif isinstance(collected_value[0], np.ndarray): - FuseUtilsHierarchicalDict.set(batch_dict, key, np.stack(collected_value)) - else: - FuseUtilsHierarchicalDict.set(batch_dict, key, collected_value) - except: - logging.getLogger('Fuse').error(f'Failed to collect key {key}') - raise - - return batch_dict - - #### CACHING - def cache_all_samples(self, num_workers: int = 16, worker_init_func: Callable = None, worker_init_args: Any = None) -> None: - """ - Cache all data - :param num_workers: num of workers used to cache the samples - :param worker_init_func: process initialization function (multi processing mode) - :param worker_init_args: worker init function arguments - :return: None - """ - lgr = logging.getLogger('Fuse') - - # check if cache is required - all_descriptors = set([(subset_desc, 0) for subset_desc in self.subsets_description]) - cached_descriptors = set(self.cache.get_all_keys(include_none=True)) - descriptors_to_cache = all_descriptors - cached_descriptors - - if len(descriptors_to_cache) != 0: - # multi process cache - lgr.info(f'DatasetGenerator: caching {len(descriptors_to_cache)} out of {len(all_descriptors)}') - with Manager() as manager: - # change cache mode - to caching (writing) - self.cache.start_caching(manager) - - # multi process cache - if num_workers > 0: - the_pool = ThreadPool if self.pool_type == 'thread' else Pool - pool = the_pool(processes=num_workers, initializer=worker_init_func, initargs=worker_init_args) - for _ in tqdm(pool.imap_unordered(func=self._cache_subset, - iterable=[(self.processor, subset_desc[0], self.cache) for subset_desc in descriptors_to_cache]), - total=len(descriptors_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - else: - for subset_desc in tqdm(descriptors_to_cache): - self._cache_subset((self.processor, subset_desc[0], self.cache)) - - # save and move back to read mode - self.cache.save() - lgr.info('DatasetGenerator: caching done') - else: - lgr.info('DatasetGenerator: all samples are already cached') - - @staticmethod - def _cache_subset(args: Tuple) -> None: - """ - Store in cache single sample - :param args: tuple of processor and subset descriptor - :return: None - """ - processor, subset_desc, cache = args - samples = processor(subset_desc) - if not isinstance(samples, List): - samples = [samples] - if samples: - for sample_index, sample_data in enumerate(samples): - - assert isinstance(sample_data, dict), f'expecting sample_data to be dictionary, got {type(sample_data)}' - sample_data = sample_data.copy() - - sample = {'data': sample_data} - sample_data['descriptor'] = (subset_desc, sample_index) - cache[sample_data['descriptor']] = sample - else: - # no samples extracted mark it as an invalid descriptor - cache[(subset_desc, 0)] = None - - def cache_sample_fields(self, fields: List[str], reset_cache: bool = False, num_workers: int = 8, cache_dest: Optional[str] = None) -> None: - """ - Cache specific fields (keys in batch_dict) - Used to optimize the running time of of dataset.get(key=, use_cache=True) - :param fields: list of keys in batch_dict - :param reset_cache: If True will reset cache first - :param num_workers: num workers used for caching - :param cache_dest: path to cache dir - :return: None - """ - lgr = logging.getLogger('Fuse') - - # debug - override num workers - override_num_workers = FuseDebug().get_setting('dataset_override_num_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - lgr.info(f'Dataset - debug mode - override num workers to {override_num_workers}', {'color': 'red'}) - - if cache_dest is None: - cache_dest = os.path.join(self.cache_dest, 'fields') - - # create cache field object upon request - if isinstance(self.cache_fields, CacheNull): - # cache object - if isinstance(cache_dest, str) and cache_dest == 'memory': - self.cache_fields: CacheBase = CacheMemory() - elif isinstance(cache_dest, str): - self.cache_fields: CacheBase = CacheFiles(cache_dest, reset_cache, single_file=True) - - # get list of desc to cache - desc_list = self.samples_description - desc_field_list = set([(desc, field) for desc in desc_list for field in fields]) - cached_desc_field = set(self.cache_fields.get_all_keys(include_none=True)) - desc_field_to_cache = desc_field_list - cached_desc_field - desc_to_cache = set([desc_field[0] for desc_field in desc_field_to_cache]) - - # multi thread caching - if len(desc_to_cache) != 0: - lgr.info(f'DatasetGenerator: samples fields - caching {len(desc_to_cache)} out of {len(desc_list)}') - if num_workers > 0: - with Manager() as manager: - self.cache_fields.start_caching(manager) - pool = Pool(processes=num_workers) - for _ in tqdm(pool.imap_unordered(func=self._cache_sample_fields, - iterable=[(desc, fields) for desc in desc_to_cache]), - total=len(desc_to_cache), smoothing=0.1): - pass - pool.close() - pool.join() - self.cache_fields.save() - else: - self.cache_fields.start_caching(None) - for desc in tqdm(desc_to_cache): - self._cache_sample_fields((desc, fields)) - self.cache_fields.save() - else: - lgr.info('DatasetGenerator: all samples fields are already cached') - - def _cache_sample_fields(self, args): - # decode args - desc, fields = args - index = self.samples_description.index(desc) - sample = self.getitem(index, apply_augmentation=False) - for field in fields: - # create field desc and save it in cache - desc_field = (desc, field) - if desc_field not in self.cache_fields: - value = FuseUtilsHierarchicalDict.get(sample, field) - self.cache_fields[desc_field] = value - - #### Filtering - def filter(self, key: str, values: List[Any]) -> None: - """ - Filter sample if batch_dict[key] in values - :param key: key in batch_dict - :param values: list of values to filter - :return: None - """ - lgr = logging.getLogger('Fuse') - lgr.info(f'DatasetGenerator: filtering key {key}, values {values}') - new_samples_desc = [] - for index, desc in tqdm(enumerate(self.samples_description), total=len(self.samples_description)): - value = self.get(index, key, use_cache=True) - if value not in values: - new_samples_desc.append(desc) - - self.samples_description = new_samples_desc - - - - #### VISUALISE - def visualize(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample , only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - - batch_dict = self.getitem(index) - - self.visualizer.visualize(batch_dict, block) - - def visualize_augmentation(self, index: Optional[int] = None, descriptor: Optional[Hashable] = None, block: bool = True): - """ - visualize augmentation of a sample - :param index: sample index, only one of index/descriptor can be provided - :param descriptor: descriptor of a sample, only one of index/descriptor can be provided - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - assert (index is not None) ^ (descriptor is not None), "visualize method must get one and one only of an index or a descriptor" - - lgr = logging.getLogger('Fuse') - if descriptor is not None: - index = self.sample_descriptor_to_index[descriptor] - if self.visualizer is None: - lgr.warning('Cannot visualize - visualizer was not provided') - return - batch_dict = self.getitem(index, apply_augmentation=False) - batch_dict_aug = self.getitem(index) - - self.visualizer.visualize_aug(batch_dict, batch_dict_aug, block) - - # save and load dataset - def get_instance_to_save(self, mode: DatasetBase.SaveMode) -> DatasetBase: - """ - See base class - """ - - # prepare data to save - if mode == DatasetBase.SaveMode.INFERENCE: - dataset = DatasetGenerator(data_source=None, - processor=self.processor, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func - ) - elif mode == DatasetBase.SaveMode.TRAINING: - dataset = DatasetGenerator(data_source=self.data_source, - processor=self.processor, - augmentor=self.augmentor, - post_processing_func=self.post_processing_func, - visualizer=self.visualizer) - else: - raise Exception(f'Unexpected SaveMode {mode}') - - return dataset - - # misc - def summary(self, statistic_keys: Optional[List[str]] = None) -> str: - """ - Returns a data summary. - Should be called after create() - :param statistic_keys: Optional. list of keys to output statistics about. - When None (default), self.statistic_keys are output. - :return: str - """ - statistic_keys_to_use = statistic_keys if statistic_keys is not None else self.statistic_keys - sum = \ - f'Class = {self.__class__}\n' - sum += \ - f'Processor:\n' \ - f'-----------------\n' \ - f'{self.processor}\n' - sum += \ - f'Cache destination:\n' \ - f'------------------\n' \ - f'{self.cache_dest}\n' - sum += \ - f'Augmentor:\n' \ - f'----------\n' \ - f'{self.augmentor.summary() if self.augmentor is not None else None}\n' - sum += \ - f'Sample keys:\n' \ - f'------------\n' \ - f'{FuseUtilsHierarchicalDict.get_all_keys(self.getitem(0)) if self.data_source is not None else None}\n' - if len(statistic_keys_to_use) > 0: - for key in statistic_keys_to_use: - values = self.get(index=None, key=key, use_cache=True) - # convert to int - maybe we will need to supporty additional types one day - values = [int(value) for value in values] - df = DataFrame(data=values, - columns=[key]) - stat_df = DataFrame() - stat_df['Value'] = df[key].value_counts().index - stat_df['Count'] = df[key].value_counts().values - stat_df['Percent'] = df[key].value_counts(normalize=True).values * 100 - sum += \ - f'\n{key} Statistics:\n' + \ - f'{get_pretty_dataframe(stat_df)}' - return sum diff --git a/fuse/data/dataset/dataset_wrapper.py b/fuse/data/dataset/dataset_wrapper.py deleted file mode 100644 index d0dd71390..000000000 --- a/fuse/data/dataset/dataset_wrapper.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Union, Sequence, Dict, Tuple - -from torch.utils.data import Dataset - -from fuse.data.data_source.data_source_from_list import DataSourceFromList -from fuse.data.dataset.dataset_default import DatasetDefault -from fuse.data.processor.processor_base import ProcessorBase - - -# Dataset processor -class DatasetProcessor(ProcessorBase): - """ - Processor that extract data from pytorch dataset and convert each sample to dictionary - """ - - def __init__(self, dataset: Dataset, mapping: Sequence[str]): - """ - :param dataset: the pytorch dataset to convert - :param mapping: dictionary key for each element returned by the pytorch dataset - """ - # store input arguments - self.mapping = mapping - self.dataset = dataset - - def __call__(self, desc: Tuple[str, int], *args, **kwargs): - index = desc[1] - sample = self.dataset[index] - sample = {self.mapping[i]: val for i, val in enumerate(sample)} - - return sample - - -class DatasetWrapper(DatasetDefault): - """ - Fuse Dataset Wrapper - wraps pytorch dataset. - Each sample will be converted to dictionary according to mapping. - And this dataset inherits all DatasetDefault features - """ - - #### CONSTRUCTOR - def __init__(self, name: str, dataset: Dataset, mapping: Union[Sequence, Dict[str, str]], **kwargs): - """ - :param name: name of the data extracted from dataset, typically: 'train', 'validation;, 'test' - :param dataset: the dataset to extract the data from - :param mapping: including name for each returned object from dataset - :param kwargs: optinal, additional argumentes to provide to DatasetDefault - """ - data_source = DataSourceFromList([(name, i) for i in range(len(dataset))]) - processor = DatasetProcessor(dataset, mapping) - super().__init__(data_source=data_source, input_processors=None, gt_processors=None,processors=processor, **kwargs) diff --git a/fuse/data/datasets/__init__.py b/fuse/data/datasets/__init__.py new file mode 100644 index 000000000..5066236a8 --- /dev/null +++ b/fuse/data/datasets/__init__.py @@ -0,0 +1 @@ +from .dataset_default import DatasetDefault \ No newline at end of file diff --git a/fuse/data/augmentor/__init__.py b/fuse/data/datasets/caching/__init__.py similarity index 100% rename from fuse/data/augmentor/__init__.py rename to fuse/data/datasets/caching/__init__.py diff --git a/fuse/data/datasets/caching/object_caching_handlers.py b/fuse/data/datasets/caching/object_caching_handlers.py new file mode 100644 index 000000000..1393e6394 --- /dev/null +++ b/fuse/data/datasets/caching/object_caching_handlers.py @@ -0,0 +1,59 @@ +from typing import List, Dict +import numpy as np +from fuse.utils.ndict import NDict +import torch +#TODO: support custom _object_requires_hdf5_single +# maybe even more flexible (knowing key name etc., patterns, explicit name, regular expr.) + +#TODO: should we require OrderedDict?? and for the internal dicts as well ?? +#TODO: maybe it's better to flatten the dictionaries first + +def _object_requires_hdf5_recurse(curr: NDict, str_base='') -> List[str]: + ''' + Iterates on keys and checks + ''' + keys = curr.keypaths() + ans = [] + for k in keys: + data = curr[k] + if _object_requires_hdf5_single(data): + ans.append(k) + return ans + +# def _PREV__object_requires_hdf5_recurse(curr: NDict, str_base='') -> List[str]: +# """ +# Recurses (only into dicts!) and returns a list of keys that require storing into HDF5 +# (which allows reading only sub-parts) + +# :return: a list of keys as strings, e.g. ['data.cc.img', 'data.mlo.img'] +# """ +# #print('str_base=', str_base) +# if _object_requires_hdf5_single(curr): +# return str_base + +# if isinstance(curr, dict): +# ans = [] +# for k,d in curr.items(): +# curr_ans = _object_requires_hdf5_recurse( +# d, str_base+'.'+k if str_base!='' else k, +# ) +# if curr_ans is None: +# pass +# elif isinstance(curr_ans, list): +# ans.extend(curr_ans) +# else: +# ans.append(curr_ans) +# return ans + +# return None + + +def _object_requires_hdf5_single(obj, minimal_ndarray_size=100): + ans = isinstance(obj, np.ndarray) and (obj.size>minimal_ndarray_size) + + if isinstance(obj, torch.Tensor): + raise Exception("You need to cast to tensor in the dynamic pipeline as it takes a lot of time pickling torch.Tensor") + + #if ans: + # print(f'found hfd5 requiring object! shape={obj.shape}, size={obj.size}') + return ans \ No newline at end of file diff --git a/fuse/data/datasets/caching/samples_cacher.py b/fuse/data/datasets/caching/samples_cacher.py new file mode 100644 index 000000000..a10a4ad58 --- /dev/null +++ b/fuse/data/datasets/caching/samples_cacher.py @@ -0,0 +1,375 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import Hashable, List, Optional, Sequence, Union, Callable, Dict, Callable, Any, Tuple + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.utils.sample import set_initial_sample_id +import numpy as np +from collections import OrderedDict +from fuse.data.datasets.caching.object_caching_handlers import _object_requires_hdf5_recurse +from fuse.utils.ndict import NDict +import os +import psutil +from fuse.utils.file_io.file_io import load_hdf5, save_hdf5_safe, load_pickle, save_pickle_safe +from fuse.data import get_sample_id, create_initial_sample, get_specific_sample_from_potentially_morphed +import hashlib +from fuse.utils.file_io import delete_directory_tree +from glob import glob +from fuse.utils.multiprocessing.run_multiprocessed import run_multiprocessed, get_from_global_storage +from collections import OrderedDict +from fuse.data.datasets.sample_caching_audit import SampleCachingAudit +from fuse.data.utils.sample import get_initial_sample_id, set_initial_sample_id +from warnings import warn + +class SamplesCacher: + def __init__(self, + unique_name: str, + pipeline: PipelineDefault, + cache_dirs: Union[str,List[str]], + custom_write_dir_callable: Optional[Callable] = None, + custom_read_dirs_callable: Optional[Callable] = None, + restart_cache:bool=False, + workers:int = 0, + **audit_kwargs:dict, + ) -> None: + """ + Supports caching samples, used by datasets implementations. + :param unique_name: a unique name for this cache. + cache dir will be [cache dir]/[unique_name] + :param cache_dirs: a path in which the cache will be created, + you may provide a list of paths, which will be tried in order, moving the next when available space is exausted. + :param parameter: + :param custom_write_dir_callable: optional callable with the signature foo(cache_dirs:List[str]) -> str + which returns the write directory to use. + :param custom_read_dirs_callable: optional callable with the signature foo() -> List[str] + which returns a list of directories to attempt to read from. Attempts will be in the provided order. + :param restart_cache: if set to True, will DELETE all of the content of the defined cache dirs. + Should be used every time that any of the OPs participating in the "static cache" part changed in any way + (for example, code change) + :param workers: number of multiprocessing workers used when building the cache. Default value is 0 (no multiprocessing) + :param **audit_kwargs: optional custom kwargs to pass to SampleCachingAudit instance. + auditing cached samples (usually periodically) is very important, in order to avoid "stale" cached samples. + To disable pass audit_first_sample=False, audit_rate=None, + Note that it's not recommended to completely disable it, and at the very least you should use audit_first_sample=True, audit_rate=None + which only tests the first loaded sample for staleness. + To learn more read SampleCachingAudit doc + """ + if not isinstance(cache_dirs, list): + cache_dirs = [cache_dirs] + self._cache_dirs = [os.path.join(x, unique_name) for x in cache_dirs] + + self._unique_name = unique_name + + if custom_write_dir_callable is None: + self._write_dir_logic = _get_available_write_location + else: + self._write_dir_logic = custom_write_dir_callable + + if custom_read_dirs_callable is None: + self._read_dirs_logic = lambda : self._cache_dirs + else: + self._read_dirs_logic = custom_read_dirs_callable + + self._pipeline = pipeline + self._pipeline_desc_text = str(pipeline) + self._pipeline_desc_hash = 'hash_'+hashlib.md5(self._pipeline_desc_text.encode('utf-8')).hexdigest() + + self._restart_cache = restart_cache + if self._restart_cache: + self.delete_cache() + + self._audit_kwargs = audit_kwargs + self._audit = SampleCachingAudit(**self._audit_kwargs) + + self._workers = workers + if self._workers < 2: + warn('Multi processing is not active in SamplesCacher. Seting "workers" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with "workers=0".') + + self._verify_no_other_pipelines_cache() + + + def _verify_no_other_pipelines_cache(self)->None: + dirs_to_check = self._get_read_dirs() + [self._get_write_dir()] + for d in dirs_to_check: + search_pat = os.path.realpath(os.path.join(d, '..', 'hash_*')) + found_sub_dirs = glob(search_pat) + for found_dir in found_sub_dirs: + if not os.path.isdir(found_dir): + continue + if os.path.basename(found_dir) != self._pipeline_desc_hash: + raise Exception(f'Found samples cache for pipeline hash {os.path.basename(found_dir)} which is different from the current loaded pipeline hash {self._pipeline_desc_hash} !!\n' + 'This is not allowed, you may only use a single pipeline per uniquely named cache.\n' + 'You can use "restart_cache=True" to rebuild the cache or delete the different cache manually.\n' + ) + + + def delete_cache(self) -> None: + ''' + Will delete this specific named cache from all read and write dirs + ''' + dirs_to_delete = self._get_read_dirs() + [self._get_write_dir()] + dirs_to_delete = list(set(dirs_to_delete)) + dirs_to_delete = [os.path.realpath(os.path.join(x, '..')) for x in dirs_to_delete] #one dir above the pipeline hash dir + print('Due to "delete_cache" call, about to delete the following dirs:') + + for del_dir in dirs_to_delete: + print(del_dir) + print('---- list end ----') + print('deleting ... ') + for del_dir in dirs_to_delete: + print(f'deleting {del_dir} ...') + all_found = glob(os.path.join(del_dir, 'hash_*')) + for found in all_found: + if not os.path.isdir(found): + continue + delete_directory_tree(found) + + + def _get_write_dir(self): + ans = self._write_dir_logic(self._cache_dirs) + ans = os.path.join(ans, self._pipeline_desc_hash) + return ans + + def _get_read_dirs(self): + ans = self._read_dirs_logic() + ans = [os.path.join(x, self._pipeline_desc_hash) for x in ans] + return ans + + def cache_samples(self, orig_sample_ids:List[Any]) -> List[Tuple[str,Union[None,List[str]],str]]: + ''' + Go over all of orig_sample_ids, and cache resulting samples + + returns information that helps to map from original sample id to the resulting sample id + (an op might return None, discarding a sample, or optional generate different one or more samples from an original single sample_id) + #TODO: have a single doc location that explains this concept and can be pointed to from any related location + + ''' + #TODO: remember that it means that we need proper extraction of args (pos or kwargs...) + #possibly by extracting info from __call__ signature or process() if we modify from call to it + + #TODO: + + sample_ids_text = '@'.join([str(x) for x in sorted(orig_sample_ids)]) + samples_ids_hash = hashlib.md5(sample_ids_text.encode('utf-8')).hexdigest() + + hash_filename = 'samples_ids_hash@'+samples_ids_hash+'.pkl.gz' + + read_dirs = self._get_read_dirs() + for curr_read_dir in read_dirs: + fullpath_filename = os.path.join(curr_read_dir, 'full_sets_info', hash_filename) + if os.path.isfile(fullpath_filename): + print(f'entire samples set {hash_filename} already cached. Found {fullpath_filename}') + return load_pickle(fullpath_filename) + + orig_sid_to_final = OrderedDict() + for_global_storage = {'samples_cacher_instance': self} + all_ans = run_multiprocessed( + SamplesCacher._cache_worker, + orig_sample_ids, + workers=self._workers, + copy_to_global_storage=for_global_storage, + verbose=1, + ) + + for initial_sample_id, output_sample_ids in zip(orig_sample_ids, all_ans): + orig_sid_to_final[initial_sample_id] = output_sample_ids + + write_dir = self._get_write_dir() + set_info_dir = os.path.join(write_dir, 'full_sets_info') + os.makedirs(set_info_dir, exist_ok=True) + fullpath_filename = os.path.join(set_info_dir, hash_filename) + save_pickle_safe(orig_sid_to_final, fullpath_filename, compress=True) + + return orig_sid_to_final + + @staticmethod + def get_final_sample_id_hash(sample_id): + ''' + sample_id is the final sample_id that came out of the pipeline + + note: our pipeline supports Ops returning None, thus, discarding a sample (in that case, it will not have any final sample_id), + additionally, the pipeline may return *multiple* samples, each with their own sample_id + + ''' + curr_sample_id_str = str(sample_id) #TODO repr or str ? + output_sample_hash = hashlib.md5(curr_sample_id_str.encode('utf-8')).hexdigest() + ans = f'out_sample_id@{output_sample_hash}' + return ans + + @staticmethod + def get_orig_sample_id_hash(orig_sample_id): + ''' + orig_sample_id is the original sample_id that was provided, regardless if it turned out to become None, the same sample_id, or different sample_id(s) + ''' + orig_sample_id_str = str(orig_sample_id) + if orig_sample_id_str.startswith('<') and orig_sample_id_str.endswith('>'): #and '0x' in orig_sample_id_str + #<__main__.SomeClass at 0x7fc3e6645e20> + raise Exception(f'You must implement a proper __str__ for orig_sample_id. String representations like <__main__.SomeClass at 0x7fc3e6645e20> are not descriptibe enough and also not persistent between runs. Got: {orig_sample_id_str}') + ans = hashlib.md5(orig_sample_id_str.encode('utf-8')).hexdigest() + ans = 'out_info_for_orig_sample@' + ans + return ans + + def get_orig_sample_id_from_final_sample_id(self, orig_sample_id): + pass + + + def load_sample(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + ''' + :param sample_id: the sample_id of the sample to load + :param keys: optionally, provide a subset of the keys to load in this sample. + This is useful for speeding up loading. + ''' + + sample_from_cache = self._load_sample_from_cache(sample_id, keys) + audit_required = self._audit.update() + + if audit_required: + initial_sample_id = get_initial_sample_id(sample_from_cache) + fresh_sample = self._load_sample_using_pipeline(initial_sample_id, keys) + fresh_sample = get_specific_sample_from_potentially_morphed(fresh_sample, sample_id) + + self._audit.audit(sample_from_cache, fresh_sample) + + return sample_from_cache + + + def _load_sample_using_pipeline(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + sample_dict = create_initial_sample(sample_id) + result_sample = self._pipeline(sample_dict) + return result_sample + + + def _load_sample_from_cache(self, sample_id: Hashable, keys: Optional[Sequence[str]] = None): + """ + TODO: add comments + """ + read_dirs = self._get_read_dirs() + sample_hash = SamplesCacher.get_final_sample_id_hash(sample_id) + + for curr_read_dir in read_dirs: + extension_less = os.path.join(curr_read_dir, sample_hash) + if os.path.isfile(extension_less+'.pkl.gz'): + loaded_sample = load_pickle(extension_less+'.pkl.gz') + if os.path.isfile(extension_less+'.hdf5'): + loaded_sample_hdf5_part = load_hdf5(extension_less+'.hdf5') + loaded_sample = NDict.combine(loaded_sample, loaded_sample_hdf5_part) + return loaded_sample + + raise Exception(f'Expected to find a cached sample for sample_id={sample_id} but could not find any!') + + @staticmethod + def _cache_worker(orig_sample_id:Any): + cacher = get_from_global_storage('samples_cacher_instance') + ans = cacher._cache(orig_sample_id) + return ans + + + def _cache(self, orig_sample_id:Any): + ''' + :param orig_sample_id: the original sample id, which was provided as the input to the pipeline + :param sample: the result of the pipeline - can be None if it was dropped, a dictionary in the typical standard case, + and a list of dictionaries in case the sample was split into multiple samples (ops are allowed to do that during the static part of the processing) + ''' + + write_dir = self._get_write_dir() + os.makedirs(write_dir, exist_ok=True) + read_dirs = self._get_read_dirs() + + was_processed_hash = SamplesCacher.get_orig_sample_id_hash(orig_sample_id) + was_processed_fn = was_processed_hash+'.pkl' + + # checking in all read directories if information related to this sample(s) was already cached + for curr_read_dir in read_dirs: + fn = os.path.join(curr_read_dir, was_processed_fn) + if os.path.isfile(fn): + ans = load_pickle(fn) + return ans + + result_sample = self._load_sample_using_pipeline(orig_sample_id) + + if isinstance(result_sample, dict): + result_sample = [result_sample] + + if isinstance(result_sample, list): + if 0 == len(result_sample): + result_sample = None + for s in result_sample: + set_initial_sample_id(s, orig_sample_id) + + if not isinstance(result_sample, (list, dict, type(None))): + raise Exception(f'Unsupported sample type, got {type(result_sample)}. Supported types are dict, list-of-dicts and None.') + + if result_sample is not None: + output_info = [] + for curr_sample in result_sample: + curr_sample_id = get_sample_id(curr_sample) + output_info.append(curr_sample_id) + output_sample_hash = SamplesCacher.get_final_sample_id_hash(curr_sample_id) + + requiring_hdf5_keys = _object_requires_hdf5_recurse(curr_sample) + if len(requiring_hdf5_keys)>0: + requiring_hdf5_dict = NDict.get_multi(curr_sample, requiring_hdf5_keys) + requiring_hdf5_dict = requiring_hdf5_dict.flatten() + + hdf5_filename = os.path.join(write_dir, output_sample_hash+'.hdf5') + save_hdf5_safe(hdf5_filename, **requiring_hdf5_dict) + + #remove all hdf5 entries from the sample_dict that will be pickled + for k in requiring_hdf5_dict: + _ = curr_sample.pop(k) + + save_pickle_safe(curr_sample, os.path.join(write_dir, output_sample_hash+'.pkl.gz'), compress=True) + else: + output_info = None + #requiring_hdf5_keys = None + + save_pickle_safe(output_info, os.path.join(write_dir, was_processed_fn)) + return output_info + + + +def _get_available_write_location(cache_dirs:List[str], max_allowed_used_space=0.95): + ''' + :param cache_dirs: write directories. Directories are checked in order that they are provided. + :param max_allowed_used_space: set to a value between 0.0 to 1.0. + a value of 0.95 means that once the available space is greater or equal to 95% of the the disk capacity, + it will be considered full, and the next directory will be attempted. + ''' + + for curr_loc in cache_dirs: + if max_allowed_used_space is None: + return curr_loc + os.makedirs(curr_loc, exist_ok=True) + drive_stats = psutil.disk_usage(curr_loc) + actual_usage_part = drive_stats.percent/100.0 + if actual_usage_part < max_allowed_used_space: + return curr_loc + + raise Exception('Could not find any location to write.\n' + f'write_cache_locations={cache_dirs}\n' + f'max_allowed_used_space={max_allowed_used_space}' + ) + + + + + + + + diff --git a/fuse/data/cache/__init__.py b/fuse/data/datasets/caching/tests/__init__.py similarity index 100% rename from fuse/data/cache/__init__.py rename to fuse/data/datasets/caching/tests/__init__.py diff --git a/fuse/data/datasets/caching/tests/test_sample_caching.py b/fuse/data/datasets/caching/tests/test_sample_caching.py new file mode 100644 index 000000000..6762248fd --- /dev/null +++ b/fuse/data/datasets/caching/tests/test_sample_caching.py @@ -0,0 +1,168 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest + +from fuse.utils.rand.seed import Seed +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional, Dict +from fuse.data.datasets.caching.samples_cacher import SamplesCacher + +from fuse.utils.ndict import NDict + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + else: + raise Exception(f'unfamiliar sample_id: {sid}') + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestSampleCaching(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + + def test_cache_samples(self): + orig_sample_ids = ['case_1', 'case_2', 'case_3', 'case_4'] + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + pipeline_desc = [ + (OpFakeLoad(), {}), + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + + cacher = SamplesCacher('unittests_cache', pl, cache_dirs, restart_cache=True) + + cacher.cache_samples(orig_sample_ids) + + sample = cacher.load_sample('case_1') + sample = cacher.load_sample('case_2') + sample = cacher.load_sample('case_4_subcase_1') + sample = cacher.load_sample('case_4_subcase_2') + #sample = cacher.load_sample('case_3') #isn't supposed to work + #sample = cacher.load_sample('case_4') #isn't supposed to work + + banana=123 + + def test_same_uniquely_named_cache_and_multiple_pipeline_hashes(self): + orig_sample_ids = ['case_1', 'case_2', 'case_3', 'case_4'] + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_c'), + os.path.join(tmpdir, 'cache_d'), + ] + + pipeline_desc = [ + (OpFakeLoad(), {}), + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + cacher = SamplesCacher('unittests_cache', pl, cache_dirs, restart_cache=True) + + cacher.cache_samples(orig_sample_ids) + + ### now, we modify the pipeline and we DO NOT set restart_cache, to verify an exception is thrown + pipeline_desc = [ + (OpFakeLoad(), {}), + (OpFakeLoad(), {}), ###just doubled it to change the pipeline hash + ] + pl = PipelineDefault('example_pipeline', pipeline_desc) + self.assertRaises(Exception, SamplesCacher, 'unittests_cache', pl, cache_dirs, restart_cache=False) + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = NDict(dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + )) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = NDict(dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + )) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/dataset_base.py b/fuse/data/datasets/dataset_base.py new file mode 100644 index 000000000..c4e02b245 --- /dev/null +++ b/fuse/data/datasets/dataset_base.py @@ -0,0 +1,46 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from abc import abstractmethod +from typing import Dict, Hashable, List, Optional, Sequence, Union + +import torch + +class DatasetBase(torch.nn.Module): + @abstractmethod + def create(self, **kwargs) -> None: + """ + Make the dataset operational: might include data caching, reloading and more. + """ + raise NotImplementedError + + @abstractmethod + def summary(self) -> str: + """ + Get string including summary of the dataset + """ + raise NotImplementedError + + @abstractmethod + def get_multi(self, items: Optional[Sequence[Union[int, Hashable]]] = None, *args) -> List[Dict]: + """ + Get multiple items, optionally just some of the keys + :param items: specify the list of sequence to read or None for all + """ + raise NotImplementedError \ No newline at end of file diff --git a/fuse/data/datasets/dataset_default.py b/fuse/data/datasets/dataset_default.py new file mode 100644 index 000000000..d163dab27 --- /dev/null +++ b/fuse/data/datasets/dataset_default.py @@ -0,0 +1,304 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import Any, Dict, Hashable, List, Optional, Sequence, Union + +from warnings import warn +from fuse.data.datasets.dataset_base import DatasetBase +from fuse.data.ops.ops_common import OpCollectMarker +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.utils.ndict import NDict +from fuse.utils.multiprocessing.run_multiprocessed import run_multiprocessed, get_from_global_storage +from fuse.data import get_sample_id, create_initial_sample, get_specific_sample_from_potentially_morphed +import copy +from collections import OrderedDict +import numpy as np +from fuse.data import OpToTensor, OpRepeat + + +class DatasetDefault(DatasetBase): + def __init__(self, + sample_ids: Sequence[Hashable], + static_pipeline: Optional[PipelineDefault] = None, + dynamic_pipeline: Optional[PipelineDefault] = None, + cacher:Optional[SamplesCacher] = None, + allow_uncached_sample_morphing:bool = False, + ): + """ + :param sample_ids: list of sample_ids included in dataset. + :param static_pipeline: static_pipeline, the output of this pipeline will be automatically cached. + :param dynamic_pipeline: dynamic_pipeline. applied sequentially after the static_pipeline, but not automatically cached. + changing it will NOT trigger recaching of the static_pipeline part. + :param cacher: optional SamplesCacher instance which will be used for caching samples to speed up samples loading + :param allow_uncached_sample_morphing: when enabled, allows an Op, to return None, or to return multiple samples (in a list) + + """ + super().__init__() + + # store arguments + self._static_pipeline = static_pipeline + self._dynamic_pipeline = dynamic_pipeline + self._cacher = cacher + self._orig_sample_ids = sample_ids + self._allow_uncached_sample_morphing = allow_uncached_sample_morphing + + #verify unique names for dynamic pipelines + if self._dynamic_pipeline is not None and self._static_pipeline is not None: + if self._static_pipeline.get_name() == self._dynamic_pipeline.get_name(): + raise Exception(f'Detected identical name for static pipeline and dynamic pipeline ({self._static_pipeline.get_name(self._static_pipeline.get_name())}).\nThis is not allowed, please initiate the pipelines with different names.') + + if self._static_pipeline is None: + self._static_pipeline = PipelineDefault("dummy_static_pipeline", ops_and_kwargs=[]) + if self._dynamic_pipeline is None: + self._dynamic_pipeline = PipelineDefault("dummy_dynamic_pipeline", ops_and_kwargs=[]) + + if self._dynamic_pipeline is not None: + assert isinstance(self._dynamic_pipeline, PipelineDefault), f'dynamic_pipeline may be None or a PipelineDefault instance. Instead got {type(self._dynamic_pipeline)}' + + if self._static_pipeline is not None: + assert isinstance(self._static_pipeline, PipelineDefault), f'static_pipeline may be None or a PipelineDefault instance. Instead got {type(self._static_pipeline)}' + + if self._allow_uncached_sample_morphing: + warn("allow_uncached_sample_morphing is enabled! It is a significantly slower mode and should be used ONLY for debugging") + + self._created = False + + + def create(self, num_workers:int = 0) -> None: + """ + Create the data set, including caching + :param num_workers: number of workers. used only when caching is disabled and allow_uncached_sample_morphing is enabled + set num_workers=0 to disable multiprocessing (more convenient for debugging) + Setting num_workers for caching is done in cacher constructor. + :return: None + """ + + self._output_sample_ids_info = None + if self._cacher is not None: + self._output_sample_ids_info = self._cacher.cache_samples(self._orig_sample_ids) + elif self._allow_uncached_sample_morphing: + _output_sample_ids_info_list = run_multiprocessed(DatasetDefault._process_orig_sample_id, + [(sid, self._static_pipeline, False) for sid in self._orig_sample_ids], + workers=num_workers) + + self._output_sample_ids_info = OrderedDict() + self._final_sid_to_orig_sid = {} + for sample_in_out_info in _output_sample_ids_info_list: + orig_sid, out_sids = sample_in_out_info[0], sample_in_out_info[1] + self._output_sample_ids_info[orig_sid] = out_sids + if out_sids is not None: + assert isinstance(out_sids, list) + for final_sid in out_sids: + self._final_sid_to_orig_sid[final_sid] = orig_sid + + if self._output_sample_ids_info is not None: #sample morphing is allowed + self._final_sample_ids = [] + for orig_sid,out_sids in self._output_sample_ids_info.items(): + if out_sids is None: + continue + self._final_sample_ids.extend(out_sids) + else: + self._final_sample_ids = copy.deepcopy(self._orig_sample_ids) + + self._created = True + + def get_all_sample_ids(self): + if not self._created: + raise Exception('you must first call create()') + + return copy.deepcopy(self._final_sample_ids) + + + def __getitem__(self, item: Union[int, Hashable]) -> dict: + """ + Get sample, read from cache if possible + :param item: either int representing sample index or sample_id + :return: sample_dict + """ + return self.getitem(item) + + def getitem(self, item: Union[int, Hashable], collect_marker_name: Optional[str] = None, keys: Optional[Sequence[str]] = None) -> dict: + """ + Get sample, read from cache if possible + :param item: either int representing sample index or sample_id + :param collect_marker_name: Optional, specify name of collect marker op to optimize the running time + :param keys: Optional, return just the specified keys or everything available if set to None + :return: sample_dict + """ + if not self._created: + raise Exception('you must first call create()') + + # get sample id + if isinstance(item, (int, np.integer)): + sample_id = self._final_sample_ids[item] + else: + sample_id = item + + # get collect marker info + collect_marker_info = self._get_collect_marker_info(collect_marker_name) + + # read sample + if self._cacher is not None: + sample = self._cacher.load_sample(sample_id, collect_marker_info["static_keys_deps"]) + + if self._cacher is None: + if not self._allow_uncached_sample_morphing: + sample = create_initial_sample(sample_id) + sample = self._static_pipeline(sample) + if not isinstance(sample, dict): + raise Exception(f'By default when caching is disabled sample morphing is not allowed, and the output of the static pipeline is expected to be a dict. Instead got {type(sample)}. You can use "allow_uncached_sample_morphing=True" to allow this, but be aware it is slow and should be used only for debugging') + else: + orig_sid = self._final_sid_to_orig_sid[sample_id] + sample = create_initial_sample(orig_sid) + sample = self._static_pipeline(sample) + + assert sample is not None + sample = get_specific_sample_from_potentially_morphed(sample, sample_id) + + sample = self._dynamic_pipeline(sample, until_op_id=collect_marker_info['op_id']) + + if not isinstance(sample, dict): + raise Exception(f'The final output of dataset static (+optional dynamic) pipelines is expected to be a dict. Instead got {type(sample)}') + + # get just required keys + if keys is not None: + sample = NDict.get_multi(sample, keys) + + return sample + + + + def _get_multi_multiprocess_func(self, args): + sid, kwargs = args + return self.getitem(sid, **kwargs) + + @staticmethod + def _getitem_multiprocess(item: Union[Hashable, int, np.integer]): + """ + getitem method used to optimize the running time in a multiprocess mode + """ + dataset = get_from_global_storage("dataset_default_get_multi_dataset") + kwargs = get_from_global_storage("dataset_default_get_multi_kwargs") + return dataset.getitem(item, **kwargs) + + + def get_multi(self, items: Optional[Sequence[Union[int, Hashable]]] = None, workers: int = 10, verbose: int = 1, **kwargs) -> List[Dict]: + """ + See super class + :param workers: number of processes to read the data. set to 0 for a single process. + """ + if items is None: + sample_ids = self._final_sample_ids + else: + sample_ids = items + + for_global_storage = {"dataset_default_get_multi_dataset": self, "dataset_default_get_multi_kwargs": kwargs} + + list_sample_dict = run_multiprocessed( + worker_func=self._getitem_multiprocess, + copy_to_global_storage=for_global_storage, + args_list=sample_ids, workers=workers, verbose=verbose) + return list_sample_dict + + def __len__(self): + if not self._created: + raise Exception('you must first call create()') + + return len(self._final_sample_ids) + + # internal methods + + @staticmethod + def _process_orig_sample_id(args): + ''' + Process, without caching, single sample + ''' + orig_sample_id, pipeline, return_sample_dict = args + sample = create_initial_sample(orig_sample_id) + + sample = pipeline(sample) + + output_sample_ids = None + + if sample is not None: + output_sample_ids = [] + if not isinstance(sample, list): + sample = [sample] + for curr_sample in sample: + output_sample_ids.append(get_sample_id(curr_sample)) + + if not return_sample_dict: + return orig_sample_id, output_sample_ids + + return orig_sample_id, output_sample_ids, sample + + def _get_collect_marker_info(self, collect_marker_name: str): + """ + Find the required collect marker (OpCollectMarker in the dynamic pipeline). + See OpCollectMarker for more details + :param collect_marker_name: name to identify the required collect marker + :return: a dictionary with the required info - including: name, op_id and static_keys_deps. + if collect_marker_name is None will return default instruct to run the entire dynamic pipeline + """ + # default values for case collect marker info is not used + if collect_marker_name is None: + return { + "name": None, + "op_id": None, + "static_keys_deps": None + } + + # find the required collect markers and extract the info + collect_marker_info = None + for (op, _), op_id in reversed(zip(self._dynamic_pipeline.ops_and_kwargs, self._dynamic_pipeline._op_ids)): + if isinstance(op, OpCollectMarker): + collect_marker_info_cur = op.get_info() + if collect_marker_info_cur['name'] == collect_marker_name: + if collect_marker_info is None: + collect_marker_info = collect_marker_info_cur + collect_marker_info['op_id'] = op_id + # continue to make sure this is the only one + else: + # throw an error if found more than one collect marker + raise Exception(f"Error: two collect markers with name {collect_marker_info} found in dynamic pipeline") + if collect_marker_info is None: + raise Exception(f"Error: didn't find collect marker with name {collect_marker_info} in dynamic pipeline.") + + return collect_marker_info + + def summary(self) -> str: + sum = "" + sum += f"Type: {type(self).__name__}\n" + sum += f"Num samples: {len(self._final_sample_ids)}\n" + # TODO + # sum += f"Cacher: {self._cacher.summary()}" + # sum += f"Pipeline static: {self._static_pipeline.summary()}" + # sum += f"Pipeline dynamic: {self._dynamic_pipeline.summary()}" + + return sum + + + + + + + + + diff --git a/fuse/data/datasets/dataset_wrap_seq_to_dict.py b/fuse/data/datasets/dataset_wrap_seq_to_dict.py new file mode 100644 index 000000000..ce16134c2 --- /dev/null +++ b/fuse/data/datasets/dataset_wrap_seq_to_dict.py @@ -0,0 +1,97 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import List, Optional, Union, Sequence +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.utils.sample import get_sample_id + +from torch.utils.data import Dataset + +from fuse.data.ops.op_base import OpBase +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.utils.ndict import NDict + +# Dataset processor +class OpReadDataset(OpBase): + """ + Op that extract data from pytorch dataset that returning sequence of values and adds those values to sample_dict + """ + + def __init__(self, dataset: Dataset, sample_keys: Sequence[str]): + """ + :param dataset: the pytorch dataset to convert. The dataset[i] expected to return sequence of values or a single value + :param sample_keys: sequence keys - naming each value returned by dataset[i] + """ + # store input arguments + super().__init__() + self._sample_keys = sample_keys + self._dataset = dataset + + def __call__(self, sample_dict: NDict, op_id: Optional[str]) -> Union[None, dict, List[dict]]: + """ + See super class + """ + # extact dataset index + name, dataset_index = get_sample_id(sample_dict) + + # extract values + sample_values = self._dataset[dataset_index] + if not isinstance(sample_values, Sequence): + sample_values = [sample_values] + assert len(self._sample_keys) == len(sample_values), f"Error: expecting dataset[i] to return {len(self._sample_keys)} to match sample keys" + + # add values to sample_dict + for key, elem in zip(self._sample_keys, sample_values): + sample_dict[key] = elem + return sample_dict + + +class DatasetWrapSeqToDict(DatasetDefault): + """ + Fuse Dataset Wrapper + wraps pytorch sequence dataset (pytorch dataset in which each sample, dataset[i] is a sequence of values). + Each value extracted from pytorch sequence dataset will be added to sample_dict. + Plus this dataset inherits all DatasetDefault features + + Example: + torch_seq_dataset = torchvision.datasets.MNIST(path, download=True, train=True) + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='train', dataset=torch_seq_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + + # get sample + sample = train_dataset[index] # sample is a dict with keys: 'data.sample_id', 'data.image' and 'data.label' + """ + + def __init__(self, name: str, dataset: Dataset, sample_keys: Union[Sequence[str], str], cache_dir: Optional[str] = None, **kwargs): + """ + :param name: name of the data extracted from dataset, typically: 'train', 'validation;, 'test' + :param dataset: the dataset to extract the data from + :param sample_keys: sequence keys - naming each value returned by dataset[i] + :param cache_dir: Optional - provied a path in case caching is required to help optimize the running time + :param kwargs: optional, additional arguments to provide to DatasetDefault + """ + sample_ids =[(name, i) for i in range(len(dataset))] + static_pipeline = PipelineDefault(name="staticp", ops_and_kwargs=[(OpReadDataset(dataset, sample_keys), {})]) + if cache_dir is not None: + cacher = SamplesCacher('dataset_test_cache', static_pipeline, cache_dir, restart_cache=True) + else: + cacher = None + super().__init__(sample_ids=sample_ids, static_pipeline=static_pipeline, cacher=cacher, **kwargs) diff --git a/fuse/data/datasets/sample_caching_audit.py b/fuse/data/datasets/sample_caching_audit.py new file mode 100644 index 000000000..06b104995 --- /dev/null +++ b/fuse/data/datasets/sample_caching_audit.py @@ -0,0 +1,96 @@ +from typing import Optional +from time import time +from deepdiff import DeepDiff +from fuse.data import get_sample_id + +''' +By auditing the samples, "stale" caches can be found, which is very important to detect. +A stale cache of a sample is a cached sample which contains different information then the same sample as it is being freshly created. +There are several reasons that it can happen, for example, a change in some code dependency in some operation in the sample processing pipeline. +Note - setting a too high audit frequency will slow your training. +audit example usage: +# a minimalistic approach, testing only the first sample. Almost no slow down of entire train session, but not periodic audit so higher chance to miss a stale cached sample. +SampleCachingAudit(audit_first_sample=True,audit_rate=None) +) + +#another audit usage example - in this case the first sample will be audited, and also one sample every 20 minutes +SampleCachingAudit(audit_first_sample=True, audit_rate=20, audit_units='minutes') +) +''' + +class SampleCachingAudit: + def __init__(self, + audit_first_sample:bool = True, + audit_rate:Optional[int] = 30, + audit_units:str = 'minutes', + **audit_diff_kwargs:Optional[dict], + ): + ''' + :param audit_rate: how frequently, a sample will be both loaded from cache AND loaded fully without using cache. + Pass 0 or None to disable. + The purpose of this is to detect cases in which the cached samples no longer match the sample loading sequence of Ops, + and a cache reset is required. + Will be ignored if no cacher is provided. + :param audit_units: the units in which audit_rate will be used. Supported options are ['minutes', 'samples'] + Will be ignored if no cacher is provided. + :param **audit_diff_kwargs: optionally, pass custom kwargs to DeepDiff comparison. + This is useful if, for example, you want small epsilon differences to be ignored. + In such case, you can provide math_epsilon=1e-9 to avoid throwing exception for small differences + ''' + + _audit_unit_options = ['minutes', 'samples', None] + if audit_units not in _audit_unit_options: + raise Exception(f'audit_units must be one of {_audit_unit_options}') + self._audit_rate = audit_rate + self._audit_first_sample = audit_first_sample + self._audited_so_far = 0 + if self._audit_rate == 0: + self._audit_rate = None + self._audit_units = audit_units + self._audit_units_passed_since_last_audit = 0.0 + if self._audit_units == 'minutes': + self._prev_time = time() + self._audit_diff_kwargs = audit_diff_kwargs + + def update(self) -> bool: + ''' + Updates internal state related to the audit features (comparison of a sample loaded from cache with a fully loaded/processed sample) + returns whether an audit should occur now or not. + ''' + if (self._audit_first_sample) and (self._audited_so_far==0): + return True + if (self._audit_rate is not None): + #progress audit units passed so far + if self._audit_units == 'minutes': + self._audit_units_passed_since_last_audit += (time()-self._prev_time)/60.0 + self._prev_time = time() + elif self._audit_units == 'samples': + self._audit_units_passed_since_last_audit += 1 + else: + assert False + + #check if we need an audit now + if self._audit_units_passed_since_last_audit >= self._audit_rate: + #reset it + if self._audit_units == 'minutes': + self._audit_units_passed_since_last_audit %= self._audit_rate + else: + self._audit_units_passed_since_last_audit = 0.0 + return True + return False + + def audit(self, cached_sample, fresh_sample): + diff = DeepDiff(cached_sample, fresh_sample, **self._audit_diff_kwargs) + self._audited_so_far += 1 + if len(diff)>0: + raise Exception(f'Error! During AUDIT found a mismatch between cached_sample and loaded sample.\n' + 'Please reset your cache.\n' + 'Note - this can happen if a change in your (static) pipeline Ops is not expressed in the calculated hash function.\n' + 'There are several reasons that can cause this, for example, you are calling, from within your op external code.\n' + 'This is perfectly fine to do, just make sure you reset your cache after such change.\n' + 'Gladly, the Audit feature caught this stale cache state! :)\n' + f'sample id in which this staleness was caught: {get_sample_id(fresh_sample)}\n' + 'NOTE: if small changes between the saved cached and the live-loaded/processed sample are ok for your use case, you can set a tolerance epsilon like this: audit_diff_kwargs={"math_epsilon":1e-9}' + ) + + \ No newline at end of file diff --git a/fuse/data/data_source/__init__.py b/fuse/data/datasets/tests/__init__.py similarity index 100% rename from fuse/data/data_source/__init__.py rename to fuse/data/datasets/tests/__init__.py diff --git a/fuse/data/datasets/tests/test_dataset_default.py b/fuse/data/datasets/tests/test_dataset_default.py new file mode 100644 index 000000000..e855d61cc --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_default.py @@ -0,0 +1,264 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest + +from fuse.utils.rand.seed import Seed +#from fuse.utils.file_io.file_io import SAFE_save_hdf5, load_hdf5 +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.utils.ndict import NDict + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + # elif 'case_4_subcase_1' == sid: + # sample_dict = NDict.combine(sample_dict, _generate_sample_1(41)) + # elif 'case_4_subcase_2' == sid: + # sample_dict = NDict.combine(sample_dict, _generate_sample_2(42)) + else: + raise Exception(f'unfamiliar sample_id: {sid}') + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + +class OpPrintContents(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + print(f'sid={sid}') + for k in sample_dict.keypaths(): + print(k) + print('-------------------------\n') + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestDatasetDefault(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_cache_samples_with_sample_morphing(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2','case_3','case_4'] + ################ cached + sample morphing + cacher = SamplesCacher('dataset_test_cache', static_pl, cache_dirs, restart_cache=True) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + ############### not cached + sample morphing + + ds_not_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=None, + allow_uncached_sample_morphing=True, + ) + ds_not_cached.create(num_workers=0) + not_cached_final_sample_ids = ds_not_cached.get_all_sample_ids() + + self.assertEqual( + sorted(cached_final_sample_ids), + sorted(not_cached_final_sample_ids), + ) + + sample_from_cached = ds_cached[3] + sample_from_not_cached = ds_not_cached[3] + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + sample_from_not_cached['data']['cc']['img'].sum() + ) + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + 49948.825007353706 + ) + banana=123 + + def test_cache_samples_no_sample_morphing(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2'] + ################ cached + no sample morphing + cacher = SamplesCacher('dataset_test_cache', static_pl, cache_dirs, restart_cache=True) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + ############### not cached + no sample morphing + + ds_not_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=None, + ###allow_uncached_sample_morphing=False, + ) + ds_not_cached.create(num_workers=0) + not_cached_final_sample_ids = ds_not_cached.get_all_sample_ids() + + self.assertEqual( + sorted(cached_final_sample_ids), + sorted(not_cached_final_sample_ids), + ) + + sample_from_cached = ds_cached[1] + sample_from_not_cached = ds_not_cached[1] + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + sample_from_not_cached['data']['cc']['img'].sum() + ) + + self.assertEqual( + sample_from_cached['data']['cc']['img'].sum(), + 50012.88698394645 + ) + banana=123 + + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + ) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + ) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/tests/test_dataset_default_audit_feature.py b/fuse/data/datasets/tests/test_dataset_default_audit_feature.py new file mode 100644 index 000000000..80b82acd0 --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_default_audit_feature.py @@ -0,0 +1,250 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import unittest +from fuse.utils.rand.seed import Seed +from fuse.utils.ndict import NDict + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data import get_sample_id, create_initial_sample +import numpy as np +import tempfile +import os +from fuse.data.ops.op_base import OpBase +from typing import List, Union, Optional +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.datasets.dataset_default import DatasetDefault + +class OpFakeLoad(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + if 'case_1' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_1()) + elif 'case_2' == sid: + sample_dict = NDict.combine(sample_dict, _generate_sample_2()) + elif 'case_3' == sid: + return None + elif 'case_4' == sid: + sample_1 = create_initial_sample('case_4', 'case_4_subcase_1') + sample_1 = NDict.combine(sample_1, _generate_sample_1(41)) + + sample_2 = create_initial_sample('case_4', 'case_4_subcase_2') + sample_2 = NDict.combine(sample_2, _generate_sample_2(42)) + + return [sample_1, sample_2] + else: + raise Exception(f'unfamiliar sample_id: {sid}') + sample_dict = ForMonkeyPatching.identity_transform(sample_dict) + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + +class ForMonkeyPatching: + @staticmethod + def identity_transform(sample_dict): + ''' + returns the sample as is. The purpose of this is to be monkey-patched in the audit test. + When it will be modified, the cached samples will become stale, + as this code is called from within an op, and therefore does not participate in the hash generation. + ''' + return sample_dict + +class OpPrintContents(OpBase): + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + sid = get_sample_id(sample_dict) + print(f'sid={sid}') + for k in sample_dict.keypaths(): + print(k) + print('-------------------------\n') + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + def __repr__(self): + return __class__.__name__ + + +class TestDatasetDefault(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_audit(self): + tmpdir = tempfile.gettempdir() + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + static_pipeline_desc = [ + (OpFakeLoad(), {}), + ] + + dynamic_pipeline_desc = [ + (OpPrintContents(), {}), + ] + + static_pl = PipelineDefault('static_pipeline', static_pipeline_desc, ) + dynamic_pl = PipelineDefault('dynamic_pipeline', dynamic_pipeline_desc, ) + + orig_sample_ids = ['case_1','case_2'] + ################ cached + no sample morphing + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_rate=1, + audit_units='samples') + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + cached_final_sample_ids = ds_cached.get_all_sample_ids() + + print('a...') + sample_from_cached = ds_cached[0] + print('b...') + sample_from_cached = ds_cached[0] + + def small_change(sample_dict): + sample_dict['data']['cc']['img'][10,100,100] += 0.001 + return sample_dict + + ForMonkeyPatching.identity_transform = small_change + + print('c...') + self.assertRaises(Exception, ds_cached, 0) + #sample_from_cached = ds_cached[0] + + ForMonkeyPatching.identity_transform = lambda x:x #return it to previous state + + + ########### do it again, and now test the audit_first_sample + + #recreating cacher to change audit parameters + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_first_sample=True, + audit_rate=None,) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + + ForMonkeyPatching.identity_transform = small_change + + #the first one is expected to raise an exception + self.assertRaises(Exception, ds_cached, 0) + + ForMonkeyPatching.identity_transform = lambda x:x #return it to previous state + + ############################## testing audit_first_sample a bit more + ############################## this time we do the monkey patching only AFTER the first sample was audited (and the staleness will be missed) + + #recreating cacher to change audit params + cacher = SamplesCacher('dataset_default_audit_test_cache', static_pl, cache_dirs, restart_cache=True, + audit_first_sample=True, + audit_rate=None, + ) + + ds_cached = DatasetDefault(orig_sample_ids, + static_pl, + dynamic_pipeline=dynamic_pl, + cacher=cacher, + ) + + ds_cached.create(num_workers=0) + + #there is no problem yet, should work well + sample_from_cached = ds_cached[0] + + #we now monkey patch it, creating a mismatch between the hash and the static pipeline logic + ForMonkeyPatching.identity_transform = small_change + #it won't be caught as it didn't happen in the first sample, and we've set audit_rate to None + sample_from_cached = ds_cached[0] + sample_from_cached = ds_cached[0] + + banana=123 + + + + def tearDown(self): + pass + +def _generate_sample_1(seed=1337): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(30,200,200)), + dicom_tags = [10,13,40,'banana'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [100,130,400,'banana123'], + ), + gt_labels_style_1 = [1,3,100,12], + gt_labels_style_2 = np.array([3,4,10,12]), + clinical_info_input = np.random.rand(1000), + ) + ) + return sample + +def _generate_sample_2(seed=1234): + Seed.set_seed(seed) + sample = dict( + data = dict( + cc = dict( + img = np.random.rand(10,100,100), + seg = np.random.randint(0,16, size=(10,100,10)), + dicom_tags = [20,23,60,'bananaphone'], + ), + mlo = dict( + img = np.random.rand(30,200,200), + seg = np.random.randint(0,16, size=(40,100,164)), + dicom_tags = [12,13,40,'porcupine123'], + ), + gt_labels_style_1 = [5,2,13,16], + gt_labels_style_2 = np.array([8,14,11,1]), + clinical_info_input = np.random.rand(90), + ) + ) + return sample + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py b/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py new file mode 100644 index 000000000..6064871da --- /dev/null +++ b/fuse/data/datasets/tests/test_dataset_wrap_seq_to_dict.py @@ -0,0 +1,90 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import os +import unittest + +import random + +import torchvision +from torchvision import transforms +from fuse.utils.rand.seed import Seed +from fuse.utils.ndict import NDict + +import tempfile +from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict + +class TestDatasetWrapSeqToDict(unittest.TestCase): + """ + Test sample caching + """ + + def setUp(self): + pass + + def test_dataset_wrap_seq_to_dict(self): + Seed.set_seed(1234) + path = tempfile.gettempdir() + + # Create dataset + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + + torch_train_dataset = torchvision.datasets.MNIST(path, download=True, train=True, transform=transform) + # wrapping torch dataset + train_dataset = DatasetWrapSeqToDict(name='train', dataset=torch_train_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + + # get value + index = random.randint(0, len(train_dataset)) + sample = train_dataset[index] + item = torch_train_dataset[index] + + self.assertTrue(isinstance(sample, dict)) + self.assertTrue('data.image' in sample) + self.assertTrue('data.label' in sample) + self.assertTrue((sample['data.image'] == item[0]).all()) + self.assertEqual(sample['data.label'], item[1]) + + + def test_dataset_cache(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=None) + print(f"torch dataset size = {len(torch_dataset)}") + + + # wrapping torch dataset + tmpdir = tempfile.gettempdir() + cache_dir = os.path.join(tmpdir, 'cache_dir') + + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label'), cache_dir=cache_dir) + dataset.create() + + + def tearDown(self): + pass + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/key_types.py b/fuse/data/key_types.py new file mode 100644 index 000000000..878c0b36f --- /dev/null +++ b/fuse/data/key_types.py @@ -0,0 +1,47 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import * +from fuse.data.patterns import Patterns + +class DataTypeBasic(Enum): + UNKNOWN = -1 #TODO: change to Unknown? + +class TypeDetectorBase(ABC): + @abstractmethod + def get_type(self, sample_dict:Dict, key:str): + ''' + Returns the type of key + The most common implementation can be seen in TypeDetectorPatternsBased. + ''' + raise NotImplementedError + + @abstractmethod + def verify_type(self, sample_dict:Dict, key:str, types: Sequence[Enum]): + ''' + Raises exception if key is not one of the types found in types + ''' + raise NotImplementedError + +class TypeDetectorPatternsBased(TypeDetectorBase): + def __init__(self, patterns_dict:Dict[str,Enum]): + ''' + type detection based on the key (NDict "style" - for example 'data.cc.img') + get_type ignores the sample_dict completely. + TODO: provide usage example + ''' + self._patterns_dict = patterns_dict + self._patterns = Patterns(self._patterns_dict, DataTypeBasic.UNKNOWN) + + def get_type(self, sample_dict:Dict, key:str): + return self._patterns.get_value(key) + + def verify_type(self, sample_dict:Dict, key:str, types: Sequence[Enum]): + self._patterns.verify_value_in(key, types) + + + + + + + + diff --git a/fuse/data/key_types_for_testing.py b/fuse/data/key_types_for_testing.py new file mode 100644 index 000000000..c74f4021e --- /dev/null +++ b/fuse/data/key_types_for_testing.py @@ -0,0 +1,24 @@ +from enum import Enum +from fuse.data.key_types import DataTypeBasic, TypeDetectorPatternsBased +from typing import * + +class DataTypeForTesting(Enum): + """ + Possible data types stored in sample_dict. + Using Patterns - the type will be inferred from the key name + """ + # Default options for types + IMAGE_FOR_TESTING = 0, # Image + SEG_FOR_TESTING = 1, # Segmentation Map + BBOX_FOR_TESTING = 2, # Bounding Box + CTR_FOR_TESTING = 3, # Contour + +PATTERNS_DICT_FOR_TESTING = { + r".*img_for_testing$": DataTypeForTesting.IMAGE_FOR_TESTING, + r".*seg_for_testing$": DataTypeForTesting.SEG_FOR_TESTING, + r".*bbox_for_testing$": DataTypeForTesting.BBOX_FOR_TESTING, + r".*ctr_for_testing$": DataTypeForTesting.CTR_FOR_TESTING, + r".*$": DataTypeBasic.UNKNOWN, + } + +type_detector_for_testing = TypeDetectorPatternsBased(PATTERNS_DICT_FOR_TESTING) diff --git a/fuse/data/ops/__init__.py b/fuse/data/ops/__init__.py new file mode 100644 index 000000000..28791caa7 --- /dev/null +++ b/fuse/data/ops/__init__.py @@ -0,0 +1 @@ +from fuse.data.ops.caching_tools import get_function_call_str diff --git a/fuse/data/ops/caching_tools.py b/fuse/data/ops/caching_tools.py new file mode 100644 index 000000000..a6ac1ea7a --- /dev/null +++ b/fuse/data/ops/caching_tools.py @@ -0,0 +1,137 @@ +import inspect +from typing import Callable, Any, Type, Optional, Sequence +from inspect import stack +import warnings + +def get_function_call_str(func, *_args, **_kwargs) -> str: + ''' + Converts a function and its kwargs into a hash value which can be used for caching. + NOTE: + 1. This is far from being bulletproof, the op might call another function which is not covered and is changed, + which will make the caching processing be unaware. + 2. This is a mechanism that helps to spot SOME of such issues, NOT ALL + 3. Only a specific subset of arg types contribute to the caching, mainly simple native python types. + see 'value_to_string' for more details. + For example, if an arg is an entire numpy array, it will not contribute to the total hash. + The reason is that it will make the cache calculation too slow, and might + ''' + + kwargs = convert_func_call_into_kwargs_only(func, *_args, **_kwargs) + + args_flat_str = func.__name__+'@' + args_flat_str += '@'.join(['{}@{}'.format(str(k), value_to_string(kwargs[k])) for k in sorted(kwargs.keys())]) + args_flat_str += '@' + str(inspect.getmodule(func)) #adding full (including scope) name of the function, for the case of multiple functions with the same name + args_flat_str += '@'+inspect.getsource(func) #considering the source code (first level of it...) + + return args_flat_str + +def value_to_string(val:Any, warn_on_types:Optional[Sequence]=None) -> str: + ''' + Used by default in several caching related hash builders. + Ignores <...> string as they usually change between different runs + (for example, due to pointing to a specific memory address) + ''' + if warn_on_types is not None: + if isinstance(val, tuple(list(warn_on_types))): + warnings.warn(f'type {type(val)} is possibly participating in hashing, this is usually not optimal performance wise.') + ans = str(val) + if ans.startswith('<'): + return '' + return str(val) + +def convert_func_call_into_kwargs_only(func:Callable, *args, **kwargs) -> dict: + ''' + considers positional and kwargs (including their default values !) + and converts into ONLY kwargs + ''' + signature = inspect.signature(func) + + my_kwargs = { + k: v.default + for k, v in signature.parameters.items() + if v.default is not inspect.Parameter.empty + } + + #convert positional args into kwargs + #uses the fact that zip stops on the smallest length ( so only as much as len(args)) + for curr_pos_arg, pos_arg_name in zip(args, inspect.getfullargspec(func).args): + my_kwargs[pos_arg_name] = curr_pos_arg + + my_kwargs.update(kwargs) + + return my_kwargs + +def get_callers_string_description( + max_look_up:int, + expected_class: Type, + expected_function_name: str, + value_to_string_func: Callable = value_to_string, + ): + ''' + iterates on the callstack, and accumulates a string representation of the callers args. + Used in OpBase to "record" the __init__ args, to be used in the string representation of an Op, + which is used for building a hash value for samples caching in SamplesCacher + + example call: + + class A: + def __init__(self): + text = get_callers_string_description(4, A, + + class B(A): + def __init__(self, blah, blah2): + super().__init__() + #... some logic + + + + :param max_look_up: how many stack frames to look up + :param expected_class: what class is the method expected to be, + stack frames in a different class will be skipped. + pass None for not requiring any class + :param expected_function_name: what is the name of the function to allow, + stack frames in a different function name will be skipped, + pass None for not requiring anything + :param value_to_string_func: allows to provide a custom function for converting values to strings + :param + ''' + + str_desc = '' + try: + curr_stack = stack() + curr_locals = None + #note: frame 0 is this function, frame 1 is whoever called this (and wanted to know about its callers), + #so both frames 0+1 are skipped. + for i in range(2, min(len(curr_stack),max_look_up+2)): + curr_locals = curr_stack[i].frame.f_locals + if expected_class is not None: + if 'self' not in curr_locals: + continue + if not isinstance(curr_locals['self'], expected_class): + continue + + if expected_function_name is not None: + if expected_function_name != str(curr_stack[i].function): + continue + + curr_str = '.'.join([ + str(curr_locals['self'].__module__), #module is probably not needed as class already contains it + str(curr_locals['self'].__class__), + str(curr_stack[i].function), + ]) + + curr_str += inspect.getsource(curr_stack[i].frame) + for k,d in curr_stack[i].frame.f_locals.items(): + if 'self' == k: + continue + if k.startswith('__'): + continue + curr_str += '@'+str(k)+'@'+value_to_string_func(d) + + str_desc += curr_str + + finally: + del curr_locals + del curr_stack + + return str_desc \ No newline at end of file diff --git a/fuse/data/ops/op_base.py b/fuse/data/ops/op_base.py new file mode 100644 index 000000000..b34e62a2b --- /dev/null +++ b/fuse/data/ops/op_base.py @@ -0,0 +1,128 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import Dict, Union, List, Sequence, Any, Optional, Callable +from abc import ABC, abstractmethod +from enum import Enum +from collections import OrderedDict +from fuse.data.patterns import Patterns +from fuse.data.ops import get_function_call_str +from inspect import stack +from fuse.data.ops.caching_tools import get_callers_string_description, value_to_string +from fuse.utils.ndict import NDict + +class OpBase(ABC): + """ + Operator Base Class + Operators are the building blocks of the sample processing pipeline. + Each operator gets as an input the sample_dict as created be the previous operators + and can either add/delete/modify fields in sample_dict. + """ + + _MISSING_SUPER_INIT_ERR_MSG = 'Did you forget to call super().__init__() ? Also, make sure you call it BEFORE setting any attribute.' + + def __init__(self, value_to_string_func: Callable = value_to_string): + ''' + :param value_to_string_func: when init is called, a string representation of the caller(s) init args are recorded. + This is used in __str__ which is used later for hashing in caching related tools (for example, SamplesCacher) + value_to_string_func allows to provide a custom function that converts a value to string. + This is useful if, for example, a custom behavior is desired for an object like numpy array or DataFrame. + The expected signature is: foo(val:Any) -> str + ''' + + #the following is used to extract callers args, for __init__ calls up the stack of classes inheirting from OpBase + #this way it can happen in the base class and then anyone creating new Ops will typically only need to add + #super().__init__ in their __init__ implementation + self._stored_init_str_representation = get_callers_string_description( + max_look_up=4, + expected_class=OpBase, + expected_function_name='__init__', + value_to_string_func = value_to_string_func + ) + + def __setattr__(self, name, value): + ''' + Verifies that super().__init__() is called before setting any attribute + ''' + storage_name = '_stored_init_str_representation' + if name != storage_name and not hasattr(self, storage_name): + raise Exception(OpBase._MISSING_SUPER_INIT_ERR_MSG) + super().__setattr__(name, value) + + @abstractmethod + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + call function that apply the operation + :param sample_dict: the generated dictionary generated so far (generated be the previous ops in the pipeline) + The first op will typically get just the sample_id stored in sample_dict['data']['sample_id'] + :param op_id: unique identifier for an operation. + Might be used to support reverse operation as sample_dict key in case information should be stored in sample_dict. + In such a case use sample_dict[op_id] = info_to_store + :param kwargs: additional arguments defined per operation + :return: Typically modified sample_dict. + There are two special cases supported only if the operation is in static pipeline: + * return None - ignore the sample and do not raise an error + * return list of sample_dict - a case splitted to few samples. for example image splitted to patches. + """ + raise NotImplementedError + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + reverse operation + If a reverse operation is not necessary (for example operator that reads an image), + just implement a reverse method that does nothing. + + If reverse operation is necessary but not required by the project, + keep the base implementation which will throw an NotImplementedError in case the reverse operation will be called. + + To support reverse operation, store the parameters which necessary to apply the reverse operation + such as key to the transformed value and the argument to the transform operation in sample_dict[op_id]. + Those values can be extracted back during the reverse operation. + + :param sample_dict: the dictionary as modified by the previous steps (reversed direction) + :param op_id: See op_id in __call__ function + :param key_to_reverse: the required value to reverse + :param key_to_follow: run the reverse according to the operation applied on this value + :return: modified sample_dict + """ + raise NotImplemented + + def __str__(self) -> str: + ''' + A string representation of this operation, which will be used for hashing. + It includes recorded (string) data describing the args that were used in __init__() + you can override/extend it in the rare cases that it's needed + + example: + + class OpSomethingNew(OpBase): + def __init__(self): + super().__init__() + def __str__(self): + ans = super().__str__(self) + ans += 'whatever you want to add" + + ''' + + if not hasattr(self, '_stored_init_str_representation'): + raise Exception(OpBase._MISSING_SUPER_INIT_ERR_MSG) + call_repr = get_function_call_str(self.__call__, ) + + return f'init_{self._stored_init_str_representation}@call_{call_repr}' + + diff --git a/fuse/data/ops/ops_aug_common.py b/fuse/data/ops/ops_aug_common.py new file mode 100644 index 000000000..3d175a7e6 --- /dev/null +++ b/fuse/data/ops/ops_aug_common.py @@ -0,0 +1,164 @@ +from typing import List, Optional, Sequence, Union + + +from fuse.utils.rand.param_sampler import RandBool, draw_samples_recursively + +from fuse.data.ops.op_base import OpBase +from fuse.data.ops.ops_common import OpRepeat + +from fuse.utils.ndict import NDict + +class OpRandApply(OpBase): + def __init__(self, op: OpBase, probability: float): + """ + Randomly apply the op (according to the given probability) + :param op: op + """ + super().__init__() + self._op = op + self._param_sampler = RandBool(probability=probability) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + apply = self._param_sampler.sample() + sample_dict[op_id] = apply + if apply: + sample_dict = self._op(sample_dict, f"{op_id}.apply", **kwargs) + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + apply = sample_dict[op_id] + if apply: + sample_dict = self._op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}.apply") + + return sample_dict + +class OpSample(OpBase): + """ + recursively searches for ParamSamplerBase instances in kwargs, and replaces the drawn values inplace before calling to op.__call__() + + For example: + from fuse.utils.rand.param_sampler import Uniform + pipeline_desc = [ + #... + OpSample(OpRotateImage()), {'rotate_angle': Uniform(0.0,360.0)} + #... + ] + + OpSample will draw from the Uniform distribution, and will (e.g.) pass rotate_angle=129.43 to OpRotateImage call. + + """ + + def __init__(self, op: OpBase): + """ + :param op: op + """ + super().__init__() + self._op = op + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + sampled_kwargs = draw_samples_recursively(kwargs) + return self._op(sample_dict, op_id, **sampled_kwargs) + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + return self._op.reverse(sample_dict, key_to_reverse, key_to_follow, op_id) + +class OpSampleAndRepeat(OpSample): + """ + First sample kwargs and then repeat op with the exact same sampled arguments. + This is the equivalent of using OpSample around an OpRepeat. + + Typical usage pattern: + pipeline_desc = [ + (OpSampleAndRepeat( + [op to run], + [a list of dicts describing what to repeat] ), + [a dictionary describing values that should be the same in all repeated invocations, may include sampling operations like Uniform, RandBool, etc.] ), + ] + + Example use case: + randomly choose a rotation angle, and then use the same randomly selected rotation angle + for both an image and its respective ground truth segmentation map + + from fuse.utils.rand.param_sampler import Uniform + pipeline_desc = [ + #... + (OpSampleAndRepeat(OpRotateImage(), + [dict(key='data.input.img'), dict(key='data.gt.seg')] ), + dict(angle=Uniform(0.0,360.0)) #this will be drawn only once and the same value will be passed on both OpRotateImage invocation + ), + #... + ] + + #note: this is a convinience op, and it is the equivalent of composing OpSample and OpRepeat yourself. + The previous example is effectively the same as: + + pipeline_desc = [ + #... + OpSample(OpRepeat(OpRotateImage( + [dict(key='data.input.img'), dict(key='data.gt.seg')]), + dict(angle=Uniform(0.0,360.0))) + ), + #... + ] + + note: see OpRepeatAndSample if you are searching for the opposite flow - drawing a different value per repeat invocation + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + :param op: the operation to repeat with the same sampled arguments + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__(OpRepeat(op, kwargs_per_step_to_add)) + +class OpRepeatAndSample(OpRepeat): + """ + Repeats an op multiple times, each time with different kwargs, and draws random values from distribution SEPARATELY per invocation. + + An example usage scenario, let's say that you train a model which is expected get as input two images: + 'data.input.adult_img' which is an image of an adult, and + 'data.input.child_img' which is an image of a child + + the model task is to predict if this child is a child of this adult (a binary classification task). + + The model is expected to work on images that are rotated to any angle, and there's no reason to suspect correlation between the rotation of the two images, + so you would like to use rotation augmentation separately for the two images. + + In this case you could do: + + pipeline_desc = [ + #... + (OpRepeatAndSample(OpRotateImage(), + [dict(key='data.input.adult_img'), dict(key='data.input.child_img')]), + dict(dict(angle=Uniform(0.0,360.0)) ### this will be drawn separately per OpRotateImage invocation + ) + #... + ] + + + note: see also OpSampleAndRepeat if you are looking for the opposite flow, drawing the same value and using it for all repeat invocations + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + :param op: the operation to repeat + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__(OpSample(op), kwargs_per_step_to_add) + + + diff --git a/fuse/data/ops/ops_cast.py b/fuse/data/ops/ops_cast.py new file mode 100644 index 000000000..d3ee3d99c --- /dev/null +++ b/fuse/data/ops/ops_cast.py @@ -0,0 +1,167 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from abc import abstractmethod +from os import stat +from typing import Any, List, Optional, Sequence, Union +import numpy as np + +from fuse.data import OpBase +import torch +from torch import Tensor +from fuse.utils.ndict import NDict + +class Cast: + """ + Cast methods + """ + @staticmethod + def to_tensor(value: Any, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> Tensor: + """ + Convert many types to tensor + """ + if isinstance(value, torch.Tensor) and dtype is None and device is None: + pass # do nothing + elif isinstance(value, (torch.Tensor)): + value = value.to(dtype=dtype, device=device) + elif isinstance(value, (np.ndarray, int, float, list)): + value = torch.tensor(value, dtype=dtype, device=device) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_numpy(value: Any, dtype: Optional[np.dtype] = None) -> np.ndarray: + """ + Convert many types to numpy + """ + if isinstance(value, np.ndarray) and dtype is None: + pass # do nothing + elif isinstance(value, (torch.Tensor, int, float, list, np.ndarray)): + value = np.array(value, dtype=dtype) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_int(value: Any) -> np.ndarray: + """ + Convert many types to int + """ + if isinstance(value, int): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray, float)): + value = int(value) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_float(value: Any) -> np.ndarray: + """ + Convert many types to float + """ + + if isinstance(value, float): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray, int)): + value = float(value) + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + @staticmethod + def to_list(value: Any) -> np.ndarray: + """ + Convert many types to list + """ + + if isinstance(value, list): + pass # do nothing + elif isinstance(value, (torch.Tensor, np.ndarray)): + value = value.tolist() + else: + raise Exception(f"Unsupported type {type(value)} - add here support for this type") + + return value + + def to(value: Any, type_name: str) -> Any: + """ + Convert any type to type specified in type_name + """ + + if type_name == "ndarray": + return Cast.to_numpy(value) + if type_name == "Tensor": + return Cast.to_tensor(value) + if type_name == "float": + return Cast.to_float(value) + if type_name == "list": + return Cast.to_list(value) + + +class OpCast(OpBase): + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: Union[str, Sequence[str]], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + :param key: single key or list of keys from sample_dict to convert + """ + if isinstance(key, str): + keys = [key] + else: + keys = key + + for key_name in keys: + value = sample_dict[key_name] + sample_dict[f"{op_id}_{key_name}"] = type(value).__name__ + value = self._cast(value, **kwargs) + sample_dict[key_name] = value + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + type_name = sample_dict[f"{op_id}_{key_to_follow}"] + value = sample_dict[key_to_reverse] + value = Cast.to(value, type_name) + sample_dict[key_to_reverse] = value + + return sample_dict + + @abstractmethod + def _cast(self): + raise NotImplementedError + +class OpToTensor(OpCast): + """ + Convert many types to tensor + """ + def _cast(self, value: Any, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None) -> Tensor: + return Cast.to_tensor(value, dtype, device) + + +class OpToNumpy(OpCast): + """ + Convert many types to numpy + """ + def _cast(self, value: Any, dtype: Optional[np.dtype] = None) -> np.ndarray: + return Cast.to_numpy(value) \ No newline at end of file diff --git a/fuse/data/ops/ops_common.py b/fuse/data/ops/ops_common.py new file mode 100644 index 000000000..802794861 --- /dev/null +++ b/fuse/data/ops/ops_common.py @@ -0,0 +1,357 @@ +from typing import Callable, Dict, List, Optional, OrderedDict, Sequence, Tuple, Union +from fuse.data.key_types import TypeDetectorBase +import copy +from enum import Enum +from fuse.data.key_types import TypeDetectorBase +from .op_base import OpBase, Patterns #DataType, +from fuse.utils.ndict import NDict + + +class OpRepeat(OpBase): + """ + Repeat an op multiple times + + Typically used to apply the same operation on a list of keys in sample_dict + Example: + " + + repeat_for = + + #... + (OpRepeat(OpCropToMinimalBBox(), + [dict(key='data.cc.image'), dict(key='data.mlo.image'),dict(key='data.mlo.seg', margin=100)] #per provided dict a new OpCropToMinimalBBox invocation will be triggered + )), + dict(margin=12)), #this value will be passed to all OpCropToMinimalBBox invocations + #... + ] + + note - the values in provided in the list of dicts will *override* any kwargs + In the example above, margin=12 will be used for both 'data.cc.image' and 'data.mlo.image', + but a value of margin=100 will be used for 'data.mlo.seg' + + " + """ + def __init__(self, + op: OpBase, + kwargs_per_step_to_add: Sequence[dict]): + """ + See example above + :param op: the operation to repeat + :param kwargs_per_step_to_add: sequence of arguments (kwargs format) specific for a single repetition. those arguments will be added/overide the kwargs provided in __call__() function. + """ + super().__init__() + self._op = op + self._kwargs_per_step_to_add = kwargs_per_step_to_add + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + + for step_index, step_kwargs_to_add in enumerate(self._kwargs_per_step_to_add): + step_kwargs = copy.copy(kwargs) + step_kwargs.update(step_kwargs_to_add) + full_step_id = f"{op_id}_{step_index}" + sample_dict[full_step_id+'_debug_info.op_name'] = self._op.__class__.__name__ + sample_dict = self._op(sample_dict, full_step_id, **step_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + for step_index in reversed(range(len(self._kwargs_per_step_to_add))): + sample_dict = self._op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{step_index}") + + return sample_dict + +class OpLambda(OpBase): + """ + Apply simple lambda function / function to transform single value from sample_dict (or the all dictionary) + Optionally add reverse method if required. + Example: + OpLambda(func=lambda x: torch.tensor(x)) + """ + def __init__(self, + func: Callable, + func_reverse: Optional[Callable] = None, + **kwargs): + super().__init__(**kwargs) + self._func = func + self._func_reverse = func_reverse + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, **kwargs) -> Union[None, dict, List[dict]]: + """ + More details in super class + :param key: apply lambda func on sample_dict[key]. If none the input and output of the lambda function are the entire sample_dict + """ + sample_dict[op_id] = key + if key is not None: + value = sample_dict[key] + value = self._func(value, **kwargs) + sample_dict[key] = value + else: + sample_dict = self._func(sample_dict) + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + key = sample_dict[op_id] + if key is not None: + if key == key_to_follow: + value = sample_dict[key_to_reverse] + value = self._func_reverse(value) + sample_dict[key_to_reverse] = value + else: + sample_dict = self._func_reverse(sample_dict) + + return sample_dict + +class OpFunc(OpBase): + ''' + Helps to wrap an existing simple python function without writing boilerplate code. + + The wrapped function format is: + + def foo(*, *kwargs) -> Tuple: + pass + + + Example: + + def add_seperator(text:str, sep=' '): + return sep.join(text) + + OpAddSeperator = OpFunc(add_seperator) + + usage in pipeline: + + pipeline = [ + (OpAddSeperator, dict(inputs={'data.text_input':'text'}, outputs='data.text_input'), # + ] + + + ''' + def __init__(self, func: Callable, **kwargs): + """ + :param func: a callable to call in __call__() + :param inputs: benedictionary that map between the key_name of a value stored in sample_dict the the input argument name in func + :param outputs: sequence of key_names to store each return value of func. + """ + super().__init__(**kwargs) + self._func = func + + def __call__(self, sample_dict: NDict, op_id: Optional[str], inputs: Dict[str, str], outputs: Union[Sequence[str], str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + # extract inputs from sample dict + kwargs_from_sample_dict = {} + for input_key_name, func_arg_name in inputs.items(): + value = sample_dict[input_key_name] + kwargs_from_sample_dict[func_arg_name] = value + + # all kwargs + all_kwargs = copy.copy(kwargs) + all_kwargs.update(kwargs_from_sample_dict) + func_outputs = self._func(**all_kwargs) + + # add to sample_dict + if isinstance(outputs, str): + sample_dict[outputs] = func_outputs + elif isinstance(outputs, Sequence): + assert len(func_outputs) == len(outputs), f"expecting that function {self._func} will output {len(outputs)} values" + for output_name, output_value in zip(outputs, func_outputs): + sample_dict[output_name] = output_value + else: + raise Exception(f"expecting outputs to be either str or sequence of str. got {type(self._outputs).__name__}") + + + return sample_dict + +class OpApplyPatterns(OpBase): + """ + Select and apply an operation according to key name. + Instead of specifying every relevant key, the op will be applied for every key that matched a specified pattern + Example: + patterns_dict = OrderedDict([(r"^.*.cc.img$|^.*.cc.seg$", (op_affine, dict(rotate=Uniform(-90.0, 90.0))), + (r"^.*.mlo.img$|^.*.mlo.seg$", (op_affine, dict(rotate=Uniform(-45.0, 54.0)))]) + op_apply_pat = OpApplyPatterns(patterns_dict) + """ + def __init__(self, patterns_dict: Optional[OrderedDict] = None): + """ + :param patterns_dict: map a regex pattern to a pair of op and arguments (will be added/override the arguments provided in __call__() function). + For given value in a sample dict, it will look for the first match in the order dict and will apply the op on this specific key. + The ops specified in patterns_dict, must implement a __call__ method with an argument called key. + """ + super().__init__() + self._patterns_dict = Patterns(patterns_dict, (None, None)) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + + for key in sample_dict.keypaths(): + op, op_kwargs_to_add = self._patterns_dict.get_value(key) + if op is None: + continue + + op_kwargs = copy.copy(kwargs) + op_kwargs.update(op_kwargs_to_add) + sample_dict = op(sample_dict, f"{op_id}_{key}", key=key, **op_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + op, _ = self._patterns_dict.get_value(key_to_follow) + if op is None: + return + + sample_dict = op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{key_to_follow}") + + return sample_dict + +class OpApplyTypes(OpBase): + """ + Select and apply an operation according value type (inferred from key name). See OpBase for more information about how it is inferred. + Instead of specifying every relevant key, the op will be applied for every key that matched a specified pattern + Example: + types_dict = { DataType.Image: (op_affine_image, dict()), + DataType.Seg: (op_affine_image, dict()), + BBox: (op_affine_bbox, dict())} + + op_apply_type = OpApplyTypes(types_dict) + """ + def __init__(self, + type_to_op_dict: Dict[Enum, Tuple[OpBase, dict]], + type_detector: TypeDetectorBase): + """ + :param type_to_op_dict: map a type (See enum DataType) to a pair of op and correspending arguments (will be added/override the arguments provided in __call__() function) + """ + super().__init__() + self._type_to_op_dict = type_to_op_dict + self._type_detector = type_detector + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See super class + """ + all_keys = sample_dict.keypaths() + for key in all_keys: + key_type = self._type_detector.get_type(sample_dict, key) + + op, op_kwargs_to_add = self._type_to_op_dict.get(key_type, (None, None)) + if op is None: + continue + + op_kwargs = copy.copy(kwargs) + op_kwargs.update(op_kwargs_to_add) + if 'key' in op_kwargs: + raise Exception('OpApplyTypes::"key" is already found in kwargs. Are you calling OpApplyTypes from within OpApplyTypes? it is not supported.') + sample_dict = op(sample_dict, f"{op_id}_{key}", key, **op_kwargs) + + assert not isinstance(sample_dict, list), f"splitting samples within {type(self).__name__} operation is not supported" + + if sample_dict is None: + return None + elif not isinstance(sample_dict, dict): + raise Exception(f"unexpected sample_dict type {type(sample_dict)}") + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + """ + See super class + """ + key_type = self._type_detector.get_type(sample_dict, key_to_follow) + op, _ = self._type_to_op_dict.get(key_type, (None, None)) + if op is None: + return + + sample_dict = op.reverse(sample_dict, key_to_reverse, key_to_follow, f"{op_id}_{key_to_follow}") + + return sample_dict + +class OpCollectMarker(OpBase): + """ + Use this op within the dynamic pipeline to optimizer the reading time for components such as sampler, export and stats that don't need to read the entire sample. + OpCollectMarker will specify the last op to call to get all the required information from sample. + In addition, to avoid from reading the entire sample including images, OpCollectMarker can also specify the list of keys required for the relevant part of the dynamic pipeline. + + Examples: + 1. + The static pipeline generates a sample including an image ('data.image') and a label ('data.label'). + The training set sampler configured to balance a batch according to 'data.label' + To optimize the reading time of the sampler: + Add at the beginning of the dynamic pipeline - + OpCollectMarker(name="sampler", static_keys_deps=["data.label"]) + 2. + The static pipeline generate an image ('data.image') and a metadata ('data.metadata'). + The dynamic pipeline includes few operations reading 'data.metadata' and that set a value used to balance the class (op_do and op_convert). + To optimize the reading time of the sampler: + Move op_do and op_convert to the beginning of the pipeline. + Add just after them the following op: + OpCollectMarker(name="sampler", static_kets_deps=["data.metadata"]) + + In both cases the sampler can now read subset of the sample using: dataset.get_multi(collect_marker_name="sampler", ..) + """ + def __init__(self, name: str, static_key_deps: Sequence[str]): + super().__init__() + self._name = name + self._static_keys_deps = static_key_deps + + def get_info(self) -> dict: + """ + Returns collect marker info including name and static_keys_deps + """ + return { + "name": self._name, + "static_keys_deps": self._static_keys_deps + } + + def __call__(self, sample_dict: dict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + pass + + def reverse(self, sample_dict: dict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + pass + + +class OpKeepKeypaths(OpBase): + ''' + Use this op to keep only the defined keypaths in the sample + A case where this is useful is if you want to limit the amount of data that gets transfered by multiprocessing by DataLoader workers. + You can keep only what you want to enter the collate. + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], keep_keypaths:List[str]) -> Union[None, dict, List[dict]]: + prev_sample_dict = sample_dict + sample_dict = NDict() + for k in keep_keypaths: + sample_dict[k] = prev_sample_dict[k] + return sample_dict + + diff --git a/fuse/data/ops/ops_common_for_testing.py b/fuse/data/ops/ops_common_for_testing.py new file mode 100644 index 000000000..b6c19fdea --- /dev/null +++ b/fuse/data/ops/ops_common_for_testing.py @@ -0,0 +1,7 @@ +from fuse.data.ops.ops_common import OpApplyTypes +from fuse.data.key_types_for_testing import type_detector_for_testing +from functools import partial + +OpApplyTypesImaging = partial(OpApplyTypes, + type_detector = type_detector_for_testing, +) \ No newline at end of file diff --git a/fuse/data/ops/ops_read.py b/fuse/data/ops/ops_read.py new file mode 100644 index 000000000..5555e7f0f --- /dev/null +++ b/fuse/data/ops/ops_read.py @@ -0,0 +1,101 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import Hashable, List, Optional, Dict, Union +import pandas as pd + +from fuse.data import OpBase +from fuse.utils.ndict import NDict + +class OpReadDataframe(OpBase): + """ + Op reading data from pickle file / dataframe object. + Each row will be added as a value to sample dict + """ + + def __init__(self, + data: Optional[pd.DataFrame] = None, + data_filename: Optional[str] = None, + columns_to_extract: Optional[List[str]] = None, + rename_columns: Optional[Dict[str, str]] = None, + key_name: str = 'data.sample_id', + key_column: str = 'sample_id'): + """ + :param data: input DataFrame + :param data_filename: path to a pickled DataFrame (possible zipped) + :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted + :param rename_columns: rename columns from dataframe, when None (default) column names are kept + :param key_name: name of value in sample_dict which will be used as the key/index + :param key_column: name of the column which use as key/index + """ + super().__init__() + + # store input + self._data_filename = data_filename + self._columns_to_extract = columns_to_extract + self._rename_columns = rename_columns + self._key_name = key_name + self._key_column = key_column + df = data + + # verify input + if data is None and data_filename is None: + msg = "Error: need to provide either in-memory DataFrame or a path to file." + raise Exception(msg) + elif data is not None and data_filename is not None: + msg = "Error: need to provide either 'data' or 'data_filename' args, bot not both." + raise Exception(msg) + + # read dataframe + if self._data_filename is not None: + df = pd.read_pickle(self._data_filename) + + # extract only specified columns (in case not specified, extract all) + if self._columns_to_extract is not None: + df = df[self._columns_to_extract] + + # rename columns + if self._rename_columns is not None: + df.rename(self._rename_columns, axis=1, inplace=True) + + # convert to dictionary: {index -> {column -> value}} + df = df.set_index(self._key_column) + self._data = df.to_dict(orient='index') + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + """ + See base class + """ + key = sample_dict[self._key_name] + # locate the required item + sample_data = self._data[key].copy() + + # add values tp sample_dict + for name, value in sample_data.items(): + sample_dict[f"data.{name}"] = value + + return sample_dict + + def get_all_keys(self) -> List[Hashable]: + """ + :return: list of dataframe index values + """ + return list(self.data.keys()) + + \ No newline at end of file diff --git a/fuse/data/ops/ops_visprobe.py b/fuse/data/ops/ops_visprobe.py new file mode 100644 index 000000000..fa6b05b08 --- /dev/null +++ b/fuse/data/ops/ops_visprobe.py @@ -0,0 +1,186 @@ +from typing import Callable, Dict, List, Optional, OrderedDict, Sequence, Tuple, Union +import copy +import enum + +from fuse.utils.ndict import NDict +from fuse.data.visualizer.visualizer_base import VisualizerBase +from .op_base import OpBase +from fuse.data.key_types import TypeDetectorBase + +class VisFlag(enum.IntFlag): + COLLECT = 1 #save current state for future comparison + SHOW_CURRENT = 2 #show current state + SHOW_COLLECTED = 4 #show comparison of all previuosly collected states + CLEAR = 8 #clear all collected states until this point in the pipeline + ONLINE = 16 #show operations will prompt the user with the releveant plot + OFFLINE = 32 #show operations will write to disk (using the caching mechanism) the relevant info (state or states for comparison) + FORWARD = 64 #visualization operation will be activated on forward pipeline execution flow + REVERSE = 128 #visualization operation will be activated on reverse pipeline execution flow + SHOW_ALL_COLLECTED = 256 #show comparison of all previuosly collected states + +class VisProbe(OpBase): + """ + Handle visualization, saves, shows and compares the sample with respect to the current state inside a pipeline + In most cases VisProbe can be used regardless of the domain, and the domain specific code will be implemented + as a Visualizer inheriting from VisualizerBase. In some cases there might be need to also inherit from VisProbe. + + Important notes: + - running in a cached environment is dangerous and is prohibited + - this Operation is not thread safe ans so multithreading is also discouraged + + " + """ + + def __init__(self,flags: VisFlag, + keys: Union[List, dict] , + type_detector: TypeDetectorBase, + id_filter: Union[None, List] = None, + visualizer: VisualizerBase = None, + cache_path: str = "~/"): + """ + :param flags: operation flags (or possible concatentation of flags using IntFlag), details: + COLLECT - save current state for future comparison + SHOW_CURRENT - show current state + SHOW_COllected - show comparison of all previuosly collected states + CLEAR - clear all collected states until this point in the pipeline + ONLINE - show operations will prompt the user with the releveant plot + OFFLINE - show operations will write to disk (using the caching mechanism) the relevant info (state or states for comparison) + FORWARD - visualization operation will be activated on forward pipeline execution flow + REVERSE - visualization operation will be activated on reverse pipeline execution flow + :param keys: for which sample keys to handle visualization, also can be grouped in a dictionary + :param id_filter: for which sample id's to be activated, if None, active for all samples + :param visualizer: the actual visualization handler, depands on domain and use case, should implement Visualizer Base + :param cache_path: root dir to save the visualization outputs in offline mode + + few issues to be aware of, detailed in github issues regarding static cached pipeline and multiprocessing + note - if both forward and reverse are on, then by default, on forward we do collect and on reverse we do show_collected to + compare reverse operations + for each domain we inherit for VisProbe like ImagingVisProbe,... +""" + super().__init__() + self._id_filter = id_filter + self._keys = keys + self._flags = flags + self._cacher = None + self._collected_prefix = "data.$vis" + self._cache_path = cache_path + self._visualizer = visualizer + self._type_detector = type_detector + + def _extract_collected(self, sample_dict: NDict): + res = [] + if not self._collected_prefix in sample_dict: + return res + else: + for vdata in sample_dict[self._collected_prefix]: + res.append(vdata) + return res + + def _extract_data(self, sample_dict: NDict, keys, op_id): + if type(keys) is list: + # infer keys groups + keys.sort() + first_type = self._type_detector.get_type(sample_dict, keys[0]) + num_of_groups = len([self._type_detector.get_type(sample_dict, k) for k in keys if self._type_detector.get_type(sample_dict, k) == first_type]) + keys_per_group = len(keys) // num_of_groups + keys = {f"group{i}": keys[i:i + keys_per_group] for i in range(0, len(keys), keys_per_group)} + + res = NDict() + for group_id, group_keys in keys.items(): + for key in group_keys: + prekey = f'groups.{group_id}.{key.replace(".", "_")}' + res[f'{prekey}.value'] = sample_dict[key] + res[f'{prekey}.type'] = self._type_detector.get_type(sample_dict, key) + res['$step_id'] = op_id + return res + + + def _save(self, vis_data: Union[List, dict]): + # use caching to save all relevant vis_data + print("saving vis_data", vis_data) + + def _handle_flags(self, flow, sample_dict: NDict, op_id: Optional[str]): + """ + See super class + """ + # sample was filtered out by its id + if self._id_filter and self.get_idx(sample_dict) not in self._id_filter: + return None + if flow not in self._flags: + return None + + # grouped key dictionary with the following structure: + #vis_data = {"cc_group": + # { + # "key1": { + # "value": ndarray, + # "type": DataType.Image, + # "op_id": "test1"} + # "key2": { + # "value": ndarray, + # "type": DataType.BBox, + # "op_id": "test1"} + # }, + # "mlo_goup": + # { + # "key3": { + # "value": ndarray, + # "type": DataType.Image, + # "op_id": "test1"} + # "key4": { + # "value": ndarray, + # "type": DataType.BBox, + # "op_id": "test1"} + # }, + # } + vis_data = self._extract_data(sample_dict, self._keys, op_id) + both_fr = (VisFlag.REVERSE | VisFlag.FORWARD) in self._flags + dir_forward = flow == VisFlag.FORWARD + dir_reverse = flow == VisFlag.REVERSE + any_show_collected = VisFlag.SHOW_ALL_COLLECTED|VisFlag.SHOW_COLLECTED + + if VisFlag.COLLECT in self._flags or (dir_forward and both_fr): + if not self._collected_prefix in sample_dict: + sample_dict[self._collected_prefix] = [] + sample_dict[self._collected_prefix].append(vis_data) + + + if VisFlag.SHOW_CURRENT in self._flags: + if VisFlag.ONLINE in self._flags: + self._visualizer.show(vis_data) + if VisFlag.OFFLINE in self._flags: + self._save(vis_data) + + if (VisFlag.SHOW_ALL_COLLECTED in self._flags or VisFlag.SHOW_COLLECTED in self._flags) and ( + (both_fr and dir_reverse) or not both_fr): + vis_data = self._extract_collected(sample_dict) + [vis_data] + if both_fr: + if VisFlag.SHOW_COLLECTED in self._flags: + vis_data = vis_data[-2:] + if VisFlag.ONLINE in self._flags: + self._visualizer.show(vis_data) + if VisFlag.OFFLINE in self._flags: + self.save(vis_data) + + if VisFlag.CLEAR in self._flags: + sample_dict[self._collected_prefix] = [] + + if VisFlag.SHOW_COLLECTED in self._flags and both_fr and dir_reverse: + sample_dict[self._collected_prefix].pop() + + return sample_dict + + + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + res = self._handle_flags(VisFlag.FORWARD, sample_dict, op_id) + return res + + def reverse(self, sample_dict: NDict, op_id: Optional[str], key_to_reverse: str, key_to_follow: str) -> dict: + """ + See super class + """ + res = self._handle_flags(VisFlag.REVERSE, sample_dict, op_id) + if res is None: + res = sample_dict + return res + diff --git a/fuse/data/dataset/__init__.py b/fuse/data/ops/tests/__init__.py similarity index 100% rename from fuse/data/dataset/__init__.py rename to fuse/data/ops/tests/__init__.py diff --git a/fuse/data/ops/tests/test_op_base.py b/fuse/data/ops/tests/test_op_base.py new file mode 100644 index 000000000..5592c34b3 --- /dev/null +++ b/fuse/data/ops/tests/test_op_base.py @@ -0,0 +1,43 @@ +import unittest + +from typing import Union, List +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data.key_types import DataTypeBasic +from fuse.data import create_initial_sample +from fuse.data.key_types_for_testing import DataTypeForTesting, type_detector_for_testing + + + +class TestOpBase(unittest.TestCase): + def test_for_type_detector(self): + td = type_detector_for_testing + sample = create_initial_sample('dummy') + + self.assertEqual(td.get_type(sample, "data.cc.img_for_testing"), DataTypeForTesting.IMAGE_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.cc_img_for_testing"), DataTypeForTesting.IMAGE_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.img_seg_for_testing"), DataTypeForTesting.SEG_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data.imgseg_for_testing"), DataTypeForTesting.SEG_FOR_TESTING) + self.assertEqual(td.get_type(sample, "data"), DataTypeBasic.UNKNOWN) + self.assertEqual(td.get_type(sample, "bbox_for_testing"), DataTypeForTesting.BBOX_FOR_TESTING) + self.assertEqual(td.get_type(sample, "a.bbox_for_testing"), DataTypeForTesting.BBOX_FOR_TESTING) + + def test_op_base(self): + class OpImp(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + sample_dict["data.cc.seg_for_testing"] = 5 + return sample_dict + + op = OpImp() + sample_dict = {} + sample_dict = op(sample_dict, "id") + self.assertTrue("data.cc.seg_for_testing" in sample_dict) + self.assertTrue(sample_dict["data.cc.seg_for_testing"] == 5) + self.assertTrue(type_detector_for_testing.get_type(sample_dict, "data.cc.seg_for_testing")== DataTypeForTesting.SEG_FOR_TESTING) + type_detector_for_testing.verify_type(sample_dict, "data.cc.seg_for_testing", [DataTypeForTesting.SEG_FOR_TESTING]) + self.assertRaises(ValueError, type_detector_for_testing.verify_type, sample_dict, "data.cc.seg_for_testing", [DataTypeForTesting.IMAGE_FOR_TESTING]) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_op_visprobe.py b/fuse/data/ops/tests/test_op_visprobe.py new file mode 100644 index 000000000..d771bfab8 --- /dev/null +++ b/fuse/data/ops/tests/test_op_visprobe.py @@ -0,0 +1,284 @@ +import unittest + +from typing import Any, Union, List +import copy +from functools import partial + +from fuse.utils.ndict import NDict + +from fuse.data.ops.ops_visprobe import VisFlag, VisProbe +from fuse.data.visualizer.visualizer_base import VisualizerBase +from fuse.data.ops.op_base import OpBase +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.key_types_for_testing import type_detector_for_testing + + +class OpSetForTest(OpBase): + def __init__(self): + super().__init__() + def __call__(self, sample_dict: NDict, op_id: str, key: str, val: Any) -> Union[None, dict, List[dict]]: + # store information for reverse operation + sample_dict[f"{op_id}.key"] = key + if key in sample_dict: + prev_val = sample_dict[key] + sample_dict[f"{op_id}.prev_val"] = prev_val + + # set + sample_dict[key] = val + return sample_dict + + def reverse(self, sample_dict: NDict, op_id: str, key_to_reverse: str, key_to_follow: str) -> dict: + key = sample_dict[f"{op_id}.key"] + if key == key_to_follow: + if f"{op_id}.prev_val" in sample_dict: + prev_val = sample_dict[f"{op_id}.prev_val"] + sample_dict[key_to_reverse] = prev_val + else: + if key_to_reverse in sample_dict: + sample_dict.pop(key_to_reverse) + return sample_dict + +class DebugVisualizer(VisualizerBase): + acc = [] + def __init__(self) -> None: + super().__init__() + + def _show(self, vis_data): + if issubclass(type(vis_data), dict): + DebugVisualizer.acc.append([vis_data]) + else: + DebugVisualizer.acc.append(vis_data) + +testing_img_key = "img_for_testing" +testing_seg_key = "seg_for_testing" +g1_testing_image_key = "data.test_pipeline." + testing_img_key +g1_testing_seg_key = "data.test_pipeline." + testing_seg_key +g2_testing_image_key = "data.test_pipeline2." + testing_img_key +g2_testing_seg_key = "data.test_pipeline2." + testing_seg_key + +VProbe = partial(VisProbe, + keys= [g1_testing_image_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + +class TestVisProbe(unittest.TestCase): + + def test_basic_show(self): + """ + Test standard backward and forward pipeline + """ + global g1_testing_image_key + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 6}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + g1_testing_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{g1_testing_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{g1_testing_key}.value"], 6) + DebugVisualizer.acc.clear() + + + def test_multi_label(self): + """ + Test standard backward and forward pipeline + """ + + VMProbe = partial(VisProbe, + keys= [g1_testing_image_key, g1_testing_seg_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4, + testing_seg_key: 4}, + "test_pipeline2": + {testing_img_key: 4, + testing_seg_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + test_image_key = g1_testing_image_key.replace('.','_') + test_seg_key = g1_testing_seg_key.replace('.','_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_seg_key}.value"], 4) + self.assertFalse('group1' in DebugVisualizer.acc[0][0]['groups']) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_seg_key}.value"], 4) + self.assertFalse('group1' in DebugVisualizer.acc[1][0]) + DebugVisualizer.acc.clear() + + def test_multi_groups(self): + """ + Test standard backward and forward pipeline + """ + + VMProbe = partial(VisProbe, + keys= [g1_testing_image_key, g2_testing_image_key ], + type_detector=type_detector_for_testing, + visualizer = DebugVisualizer(), cache_path="~/") + + show_flags = VisFlag.SHOW_CURRENT | VisFlag.FORWARD | VisFlag.ONLINE + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VMProbe( flags=show_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4, + testing_seg_key: 4}, + "test_pipeline2": + {testing_img_key: 4, + testing_seg_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 1) + self.assertEqual(len(DebugVisualizer.acc[1]), 1) + test_image_key_g1 = g1_testing_image_key.replace('.', '_') + test_image_key_g2 = g2_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f'groups.group0.{test_image_key_g1}.value'], 5) + self.assertEqual(DebugVisualizer.acc[0][0][f'groups.group1.{test_image_key_g2}.value'], 4) + self.assertEqual(DebugVisualizer.acc[1][0][f'groups.group0.{test_image_key_g1}.value'], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f'groups.group1.{test_image_key_g2}.value'], 4) + DebugVisualizer.acc.clear() + + def test_collected_show(self): + """ + Test basic collected compare + """ + forward_flags = VisFlag.FORWARD | VisFlag.ONLINE + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=forward_flags | VisFlag.COLLECT), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=forward_flags | VisFlag.SHOW_COLLECTED), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 6}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + self.assertEqual(len(DebugVisualizer.acc), 1) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 6) + DebugVisualizer.acc.clear() + + def test_reverse_compare(self): + """ + Test compare of collected forward with reverse of same op + """ + revfor_flags = VisFlag.FORWARD | VisFlag.ONLINE | VisFlag.REVERSE | VisFlag.SHOW_COLLECTED + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + sample_dict = pipe.reverse(sample_dict, g1_testing_image_key, g1_testing_image_key) + self.assertEqual(len(DebugVisualizer.acc), 1) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(sample_dict[g1_testing_image_key], 4) + + DebugVisualizer.acc.clear() + + def test_multiple_reverse(self): + + """ + Test compare of multiple collected forward with reverse of same op + """ + revfor_flags = VisFlag.FORWARD | VisFlag.ONLINE | VisFlag.REVERSE | VisFlag.SHOW_COLLECTED + + pipeline_seq = [ + (OpSetForTest(), dict(key=g1_testing_image_key, val=5)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=6)), + (VProbe( flags=revfor_flags), {}), + (OpSetForTest(), dict(key=g1_testing_image_key, val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({"sample_id": "a", + "data": + {"test_pipeline": + {testing_img_key: 4}}}) + + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict[g1_testing_image_key], 7) + sample_dict = pipe.reverse(sample_dict, g1_testing_image_key, g1_testing_image_key) + self.assertEqual(len(DebugVisualizer.acc), 2) + self.assertEqual(len(DebugVisualizer.acc[0]), 2) + test_image_key = g1_testing_image_key.replace('.', '_') + self.assertEqual(DebugVisualizer.acc[0][0][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[0][1][f"groups.group0.{test_image_key}.value"], 6) + self.assertEqual(DebugVisualizer.acc[1][0][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(DebugVisualizer.acc[1][1][f"groups.group0.{test_image_key}.value"], 5) + self.assertEqual(sample_dict[g1_testing_image_key], 4) + + DebugVisualizer.acc.clear() + + + def tearDown(self) -> None: + return super().tearDown() + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_ops_aug_common.py b/fuse/data/ops/tests/test_ops_aug_common.py new file mode 100644 index 000000000..317580ab8 --- /dev/null +++ b/fuse/data/ops/tests/test_ops_aug_common.py @@ -0,0 +1,125 @@ +import unittest + +from typing import Optional, Union, List +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data import create_initial_sample +from fuse.data import OpRepeat +from fuse.data.ops.ops_aug_common import OpRandApply, OpSample, OpSampleAndRepeat, OpRepeatAndSample +from fuse.utils.rand.param_sampler import Choice, RandBool, RandInt, Uniform +from fuse.utils import Seed + +class OpArgsForTest(OpBase): + def __init__(self): + super().__init__() + def __call__(self, sample_dict: NDict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + return {"op_id": op_id, "kwargs": kwargs} + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return {"op_id": op_id} + + +class OpBasicSetter(OpBase): + ''' + A basic op for testing, which sets sample_dict[key] to set_key_to_val + ''' + + def __init__(self): + super().__init__() + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key, set_key_to_val, **kwargs) -> Union[None, dict, List[dict]]: + sample_dict[key] = set_key_to_val + return sample_dict + +class TestOpsAugCommon(unittest.TestCase): + + def test_op_sample(self): + Seed.set_seed(0) + a = {"a": 5, "b": [3, RandInt(1, 5), 9], "c": {"d": 3, "f": [1, 2, RandBool(0.5), {"h": RandInt(10, 15)}]}, "e": {"g": Choice([6, 7, 8])}} + op = OpSample(OpArgsForTest()) + result = op({}, "op_id", **a) + b = result["kwargs"] + call_op_id = result["op_id"] + # make sure the same op_id passed to internal op + self.assertEqual(call_op_id, "op_id") + + # make srgs sampled correctly + self.assertEqual(a["a"], a["a"]) + self.assertEqual(b["b"][0], a["b"][0]) + self.assertEqual(b["b"][2], a["b"][2]) + self.assertEqual(b["c"]["d"], a["c"]["d"]) + self.assertEqual(b["c"]["f"][1], a["c"]["f"][1]) + self.assertIn(b["b"][1], [1, 2, 3, 4, 5]) + self.assertIn(b["c"]["f"][2], [True, False]) + self.assertIn(b["c"]["f"][3]["h"], [10, 11, 12, 13, 14, 15]) + self.assertIn(b["e"]["g"], [6, 7, 8]) + + # make sure the same op_id passed also in reverse + result = op.reverse({}, "", "", "op_id") + reversed_op_id = result["op_id"] + self.assertEqual(reversed_op_id, "op_id") + + def test_op_sample_and_repeat(self): + Seed.set_seed(1337) + sample_1 = create_initial_sample(0) + op = OpSampleAndRepeat(OpBasicSetter(), [dict(key='data.input.img'), dict(key='data.gt.seg')]) + sample_1 = op(sample_1, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + Seed.set_seed(1337) + sample_2 = create_initial_sample(0) + op = OpSample(OpRepeat(OpBasicSetter(), + [dict(key='data.input.img'), dict(key='data.gt.seg')])) + sample_2 = op(sample_2, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + self.assertEqual(sample_1['data.input.img'], sample_1['data.gt.seg']) + self.assertEqual(sample_1['data.input.img'], sample_2['data.input.img']) + + + def test_op_repeat_and_sample(self): + Seed.set_seed(1337) + sample_1 = create_initial_sample(0) + op = OpRepeatAndSample(OpBasicSetter(), [dict(key='data.input.img'), dict(key='data.gt.seg')]) + sample_1 = op(sample_1, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + Seed.set_seed(1337) + sample_2 = create_initial_sample(0) + op = OpRepeat( + OpSample(OpBasicSetter(), ), + [dict(key='data.input.img'), dict(key='data.gt.seg')] + ) + sample_2 = op(sample_2, op_id='testing_sample_and_repeat', set_key_to_val=Uniform(3.0,6.0)) + + self.assertEqual(sample_1['data.input.img'], sample_2['data.input.img']) + self.assertEqual(sample_1['data.gt.seg'], sample_2['data.gt.seg']) + + def test_op_rand_apply(self): + """ + Test OpRandApply + """ + Seed.set_seed(0) + op = OpRandApply(OpArgsForTest(), 0.5) + + def sample(op): + return "kwargs" in op({}, "op_id", a=5) + + # test range + self.assertIn(sample(op), [True, False]) + + # test generate more than a single number + Seed.set_seed(0) + values = [sample(op) for _ in range(4)] + self.assertIn(True, values) + self.assertIn(False, values) + + # test probs + Seed.set_seed(0) + op = OpRandApply(OpArgsForTest(), 0.99) + count = 0 + for _ in range(1000): + if sample(op) == True: + count += 1 + self.assertGreaterEqual(count, 980) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/data/ops/tests/test_ops_cast.py b/fuse/data/ops/tests/test_ops_cast.py new file mode 100644 index 000000000..8c49518fe --- /dev/null +++ b/fuse/data/ops/tests/test_ops_cast.py @@ -0,0 +1,97 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +import unittest + +from typing import List +from fuse.utils.ndict import NDict +import pandas as pd +import torch +import numpy as np +from fuse.data.ops.ops_cast import OpToNumpy, OpToTensor + + + +class TestOpsCast(unittest.TestCase): + + + def test_op_to_tensor(self): + """ + Test OpToTensor __call__ and reverse + """ + op = OpToTensor() + sample = NDict({ + "sample_id": 7, + "values" : { + "val_np": np.array([7, 8, 9]), + "val_torch": torch.tensor([1,2,3]), + "val_int": 3, + "val_float": 3.5, + "str": "hi!" + } + }) + + sample = op(sample, "_.test_id", key="values.val_np") + self.assertIsInstance(sample["values.val_np"], torch.Tensor) + self.assertTrue((sample["values.val_np"] == torch.tensor([7,8,9])).all()) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op(sample, "_.test_id", key=["values.val_torch", "values.val_float"]) + self.assertIsInstance(sample["values.val_torch"], torch.Tensor) + self.assertIsInstance(sample["values.val_float"], torch.Tensor) + self.assertTrue((sample["values.val_torch"] == torch.tensor([1,2,3])).all()) + self.assertEqual(sample["values.val_float"], torch.tensor(3.5)) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op.reverse(sample, key_to_follow="values.val_np", key_to_reverse="values.val_np", op_id="_.test_id") + self.assertIsInstance(sample["values.val_np"], np.ndarray) + + def test_op_to_numpy(self): + """ + Test OpToNumpy __call__ and reverse + """ + op = OpToNumpy() + sample = NDict({ + "sample_id": 7, + "values" : { + "val_np": np.array([7, 8, 9]), + "val_torch": torch.tensor([1,2,3]), + "val_int": 3, + "val_float": 3.5, + "str": "hi!" + } + }) + + sample = op(sample, "_.test_id", key="values.val_torch") + self.assertIsInstance(sample["values.val_torch"], np.ndarray) + self.assertTrue((sample["values.val_torch"] == np.array([1,2,3])).all()) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op(sample, "_.test_id", key=["values.val_np", "values.val_float"]) + self.assertIsInstance(sample["values.val_np"], np.ndarray) + self.assertIsInstance(sample["values.val_float"], np.ndarray) + self.assertTrue((sample["values.val_np"] == np.array([7,8,9])).all()) + self.assertEqual(sample["values.val_float"], np.array(3.5)) + self.assertIsInstance(sample["values.val_int"], int) + + sample = op.reverse(sample, key_to_follow="values.val_torch", key_to_reverse="values.val_torch", op_id="_.test_id") + self.assertIsInstance(sample["values.val_torch"], torch.Tensor) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/ops/tests/test_ops_common.py b/fuse/data/ops/tests/test_ops_common.py new file mode 100644 index 000000000..7a2f07372 --- /dev/null +++ b/fuse/data/ops/tests/test_ops_common.py @@ -0,0 +1,208 @@ +import unittest + +from typing import Optional, OrderedDict, Union, List + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase +from fuse.data.key_types_for_testing import DataTypeForTesting + +from fuse.data.ops.ops_common import OpApplyPatterns, OpFunc, OpLambda, OpRepeat +from fuse.data.ops.ops_common_for_testing import OpApplyTypesImaging + +class OpIncrForTest(OpBase): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], incr_value: int, key_in: str, key_out: str) -> Union[None, dict, List[dict]]: + # save for reverse + sample_dict[op_id] = {'key_out': key_out, 'incr_value': incr_value} + # apply + value = sample_dict[key_in] + sample_dict[key_out] = value + incr_value + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + # not really reverse, but help the test + orig_args = sample_dict[op_id] + + if orig_args['key_out'] != key_to_follow: + return sample_dict + + value = sample_dict[key_to_reverse] + sample_dict[key_to_reverse] = value - orig_args['incr_value'] + + return sample_dict + +class TestOpsCommon(unittest.TestCase): + + def test_op_repeat(self): + """ + Test OpRepeat __call__() and reverse() + """ + op_base=OpIncrForTest() + kwargs_per_step_to_add = [dict(key_in='data.val.a', key_out='data.val.b'), dict(key_in='data.val.b', key_out='data.val.c'), dict(key_in='data.val.b', key_out='data.val.d'), dict(key_in='data.val.d', key_out='data.val.d')] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", incr_value=3) + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 14) + + op_repeat.reverse(sample_dict, key_to_follow='data.val.d', key_to_reverse='data.val.d', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 8) + + sample_dict['data.val.e'] = 48 + op_repeat.reverse(sample_dict, key_to_follow='data.val.d', key_to_reverse='data.val.e', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 8) + self.assertEqual(sample_dict['data.val.c'], 11) + self.assertEqual(sample_dict['data.val.d'], 8) + self.assertEqual(sample_dict['data.val.e'], 42) + + def test_op_lambda(self): + """ + Test OpLambda __call__() and reverse() + """ + op_base=OpLambda(func=lambda x: x + 3) + kwargs_per_step_to_add = [dict(), dict(), dict()] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a') + self.assertEqual(sample_dict['data.val.a'], 14) + + op_base=OpLambda(func=lambda x: x + 3, func_reverse=lambda x: x - 3) + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict({}) + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a') + self.assertEqual(sample_dict['data.val.a'], 14) + + op_repeat.reverse(sample_dict, key_to_follow='data.val.a', key_to_reverse='data.val.a', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + + sample_dict['data.val.b'] = 51 + op_repeat.reverse(sample_dict, key_to_follow='data.val.a', key_to_reverse='data.val.b', op_id="_.test_repeat") + self.assertEqual(sample_dict['data.val.a'], 5) + self.assertEqual(sample_dict['data.val.b'], 42) + + + def test_op_lambda_with_kwargs(self): + """ + Test OpLambda __call__() with kwargs + """ + op_base=OpLambda(func=lambda x, y: x + y) + kwargs_per_step_to_add = [dict(), dict(), dict()] + op_repeat = OpRepeat(op_base, kwargs_per_step_to_add) + sample_dict = NDict() + sample_dict['data.val.a'] = 5 + sample_dict = op_repeat(sample_dict, "_.test_repeat", key='data.val.a', y=5) + self.assertEqual(sample_dict['data.val.a'], 20) + + def test_op_func(self): + """ + Test OpFunc __call__() + """ + + def func_single_output(a, b, c): + return a+b+c + def func_multi_output(a, b, c): + return a+b, a+c + + single_output_op = OpFunc(func=func_single_output) + sample_dict = NDict({}) + sample_dict["data.first"] = 5 + sample_dict["data.second"] = 9 + sample_dict = single_output_op(sample_dict, "_.test_func", c=2, inputs={"data.first": "a", "data.second": "b"}, outputs="data.out") + self.assertEqual(sample_dict['data.out'], 16) + + multi_output_op = OpFunc(func=func_multi_output) + sample_dict = NDict({}) + sample_dict["data.first"] = 5 + sample_dict["data.second"] = 9 + sample_dict = multi_output_op(sample_dict, "_.test_func", c=2, inputs={"data.first": "a", "data.second": "b"}, outputs=["data.out", "data.more"]) + self.assertEqual(sample_dict['data.out'], 14) + self.assertEqual(sample_dict['data.more'], 7) + + + def test_op_apply_patterns(self): + """ + Test OpRApplyPatterns __call__() and reverse() + """ + + op_add_1 = OpLambda(func=lambda x: x + 1, func_reverse=lambda x: x-1) + op_mul_2 = OpLambda(func=lambda x: x*2, func_reverse=lambda x: x//2) + op_mul_4 = OpLambda(func=lambda x: x*4, func_reverse=lambda x: x//4) + + sample_dict = NDict({}) + sample_dict["data.val.img_for_testing"] = 3 + sample_dict["data.test.img_for_testing"] = 3 + sample_dict["data.test.seg_for_testing"] = 3 + sample_dict["data.test.bbox_for_testing"] = 3 + sample_dict["data.test.meta"] = 3 + + patterns_dict = OrderedDict([(r"^data.val.img_for_testing$", (op_add_1, dict())), + (r"^.*img_for_testing$|^.*seg_for_testing$", (op_mul_2, dict())), + (r"^data.[^.]*.bbox_for_testing", (op_mul_4, dict()))]) + op_apply_pat = OpApplyPatterns(patterns_dict) + + sample_dict = op_apply_pat(sample_dict, "_.test_apply_pat") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.img_for_testing'], 6) + self.assertEqual(sample_dict['data.test.seg_for_testing'], 6) + self.assertEqual(sample_dict['data.test.bbox_for_testing'], 12) + self.assertEqual(sample_dict['data.test.meta'], 3) + + sample_dict["model.seg_for_testing"] = 3 + op_apply_pat.reverse(sample_dict, key_to_follow="data.val.img_for_testing", key_to_reverse="model.seg_for_testing", op_id="_.test_apply_pat") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['model.seg_for_testing'], 2) + + + + + def test_op_apply_types(self): + """ + Test OpApplyTypes __call__() and reverse() + """ + + op_add_1 = OpLambda(func=lambda x: x + 1, func_reverse=lambda x: x-1) + op_mul_2 = OpLambda(func=lambda x: x*2, func_reverse=lambda x: x//2) + op_mul_4 = OpLambda(func=lambda x: x*4, func_reverse=lambda x: x//4) + + sample_dict = NDict({}) + sample_dict["data.val.img_for_testing"] = 3 + sample_dict["data.test.img_for_testing"] = 3 + sample_dict["data.test.seg_for_testing"] = 3 + sample_dict["data.test.bbox_for_testing"] = 3 + sample_dict["data.test.meta"] = 3 + + types_dict = {DataTypeForTesting.IMAGE_FOR_TESTING: (op_add_1, dict()), + DataTypeForTesting.SEG_FOR_TESTING: (op_mul_2, dict()), + DataTypeForTesting.BBOX_FOR_TESTING: (op_mul_4, dict())} + + op_apply_type = OpApplyTypesImaging(types_dict) + + sample_dict = op_apply_type(sample_dict, "_.test_apply_type") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.img_for_testing'], 4) + self.assertEqual(sample_dict['data.test.seg_for_testing'], 6) + self.assertEqual(sample_dict['data.test.bbox_for_testing'], 12) + self.assertEqual(sample_dict['data.test.meta'], 3) + + sample_dict["model.a_seg_for_testing"] = 3 + op_apply_type.reverse(sample_dict, key_to_follow="data.val.img_for_testing", key_to_reverse="model.a_seg_for_testing", op_id="_.test_apply_type") + self.assertEqual(sample_dict['data.val.img_for_testing'], 4) + self.assertEqual(sample_dict['model.a_seg_for_testing'], 2) + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/data/ops/tests/test_ops_read.py b/fuse/data/ops/tests/test_ops_read.py new file mode 100644 index 000000000..b78f73b0a --- /dev/null +++ b/fuse/data/ops/tests/test_ops_read.py @@ -0,0 +1,76 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest + +import pandas as pd +from fuse.utils.ndict import NDict +from fuse.data.ops.ops_read import OpReadDataframe + + + +class TestOpsRead(unittest.TestCase): + + def test_op_read_dataframe(self): + """ + Test OpReadDataframe + """ + data = { + "sample_id": ["a", "b", "c", "d"], + "value1": [10, 7, 3, 9], + "value2": ["5", "4", "3", "2"] + } + df = pd.DataFrame(data) + op = OpReadDataframe(data=df) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertEqual(sample_dict["data.value1"], 3) + self.assertEqual(sample_dict["data.value2"], "3") + + + op = OpReadDataframe(data=df, columns_to_extract=["sample_id", "value2"]) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertFalse("data.value1" in sample_dict) + self.assertEqual(sample_dict["data.value2"], "3") + + op = OpReadDataframe(data=df, columns_to_extract=["sample_id", "value2"], rename_columns={"value2": "value3"}) + sample_dict = NDict({ + "data": + { + "sample_id": "c" + } + }) + sample_dict = op(sample_dict, "id") + self.assertFalse("data.value1" in sample_dict) + self.assertFalse("data.value2" in sample_dict) + self.assertEqual(sample_dict["data.value3"], "3") + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/patterns.py b/fuse/data/patterns.py new file mode 100644 index 000000000..273b62ba4 --- /dev/null +++ b/fuse/data/patterns.py @@ -0,0 +1,56 @@ +from collections import OrderedDict +from typing import * +import re + +class Patterns: + """ + Utility to match a string to a pattern. + Typically used to infer data type from key in sample_dict + """ + def __init__(self, patterns_dict: OrderedDict, default_value: Any = None): + """ + :param patterns_dict: ordered dictionary, the key is a regex expression. + The value of the first matched key will be returned. + Example: + patterns = { + r".*img$": DataType.IMAGE, + r".*seg$": DataType.SEG, + r".*bbox$": DataType.BBOX, + r".*$": DataType.UNKNOWN + } + pp = Patterns(patterns) + print(pp.get_type("data.cc.img")) -> DataType.IMAGE + print(pp.get_type("data.cc_img")) -> DataType.IMAGE + print(pp.get_type("data.img_seg")) -> DataType.SEG + print(pp.get_type("data.imgseg")) -> DataType.SEG + print(pp.get_type("data")) -> DataType.UNKNOWN + print(pp.get_type("bbox")) -> DataType.BBox + print(pp.get_type("a.bbox")) -> DataType.BBOX + + :param default_value: value to return in case there is not match + """ + self._patterns = patterns_dict + self._default_value = default_value + + def get_value(self, key: str) -> Any: + """ + :param key: string to match + :return: the first value from patterns with pattern that match to key + """ + for pattern in self._patterns: + if re.match(pattern, key) is not None: + return self._patterns[pattern] + + return self._default_value + + def verify_value_in(self, key: str, values: Sequence[Any]) -> None: + """ + Raise an exception of the matched value not in values + :param key: string to match + :param values: list of supported values + :return: None + """ + val_type = self.get_value(key) + if val_type not in values: + raise ValueError( + f"key {key} mapped to unsupported type {val_type}.\n List of supported types {values} \n Patterns {self._patterns}") diff --git a/fuse/data/processor/__init__.py b/fuse/data/pipelines/__init__.py similarity index 100% rename from fuse/data/processor/__init__.py rename to fuse/data/pipelines/__init__.py diff --git a/fuse/data/pipelines/pipeline_default.py b/fuse/data/pipelines/pipeline_default.py new file mode 100644 index 000000000..39c49eec6 --- /dev/null +++ b/fuse/data/pipelines/pipeline_default.py @@ -0,0 +1,130 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import List, Tuple, Union, Optional +from fuse.data.ops.op_base import OpBase +from fuse.utils.misc.context import DummyContext +from fuse.utils.ndict import NDict +from fuse.utils.cpu_profiling.timer import Timer + +class PipelineDefault(OpBase): + """ + Pipeline default implementation + Pipeline to run sequence of ops with a dictionary passing information between the ops. + See OpBase for more information + """ + + def __init__(self, name: str, ops_and_kwargs: List[Tuple[OpBase, dict]], op_ids: Optional[List[str]] = None, verbose: bool=False): + """ + :param name: pipeline name + :param ops_and_args: List of tuples. Each tuple include op and dictionary includes op specific arguments. + :param op_ids: Optional, set op_id - unique name for every op. If not set, an index will be used + :param verbose: set to True for debug messages such as the running time of each operation + """ + super().__init__() + self._name = name + self._ops_and_kwargs = ops_and_kwargs + if op_ids is None: + self._op_ids = [str(index) for index in range(len(self._ops_and_kwargs))] + else: + assert len(self._ops_and_kwargs) == len(op_ids), "Expecting op_id for every op" + assert len(set(op_ids)) == len(op_ids), "Expecting unique op id for every op." + self._op_ids = op_ids + self._verbose = verbose + + def get_name(self) -> str: + return self._name + + def __str__(self) -> str: + text = [] + for (op, op_kwargs) in zip(self._op_ids, self._ops_and_kwargs): + text.append(str(op)+'@'+str(op_kwargs)+'@') + + return ''.join(text) #this is faster than accumulate_str+=new_str + + def __call__(self, sample_dict: NDict, op_id: Optional[str] = None, until_op_id: Optional[str] = None) -> Union[None, dict, List[dict]]: + """ + See super class + plus + :param until_op_id: optional - stop after the specified op_id - might be used for optimization + """ + # set op_id if not specified + if op_id is None: + op_id = self._name + + samples_to_process = [sample_dict] + for sub_op_id, (op, op_kwargs) in zip(self._op_ids, self._ops_and_kwargs): + if self._verbose: + context = Timer(f"Pipeline {self._name}: op {type(op).__name__}, op_id {sub_op_id}", self._verbose) + else: + context = DummyContext() + with context: + try: + samples_to_process_next = [] + + for sample in samples_to_process: + + try: + sample = op(sample, f"{op_id}.{sub_op_id}", **op_kwargs) + except: + #error messages are cryptic without this. For example, you can get "TypeError: __call__() got an unexpected keyword argument 'key_out_input'" , without any reference to the relevant op! + print(f'error in op={op}') + raise + + # three options for return value: + # None - ignore the sample + # List of dicts - split sample + # dict - modified sample + if sample is None: + return None + elif isinstance(sample, list): + samples_to_process_next += sample + elif isinstance(sample, dict): + samples_to_process_next.append(sample) + else: + raise Exception( + f"unexpected sample type returned by {type(op)}: {type(sample)}") + except Exception as e: + raise Exception(f"Error: op {type(op).__name__}, op_id {sub_op_id} failed ") from e + + # continue to process with next op + samples_to_process = samples_to_process_next + + # if required - stop after the specified op id + if until_op_id is not None and sub_op_id == until_op_id: + break + + # if single sample - return it, otherwise return list of samples. + if len(samples_to_process) == 1: + return samples_to_process[0] + else: + return samples_to_process + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str] = None) -> dict: + """ + See super class + """ + # set op_id if not specified + if op_id is None: + op_id = self._name + + for sub_op_id, (op, _) in zip(reversed(self._op_ids), reversed(self._ops_and_kwargs)): + sample_dict = op.reverse( + sample_dict, f"{op_id}.{sub_op_id}", key_to_reverse, key_to_follow) + + return sample_dict diff --git a/fuse/data/sampler/__init__.py b/fuse/data/pipelines/tests/__init__.py similarity index 100% rename from fuse/data/sampler/__init__.py rename to fuse/data/pipelines/tests/__init__.py diff --git a/fuse/data/pipelines/tests/test_pipeline_default.py b/fuse/data/pipelines/tests/test_pipeline_default.py new file mode 100644 index 000000000..06b87da30 --- /dev/null +++ b/fuse/data/pipelines/tests/test_pipeline_default.py @@ -0,0 +1,117 @@ +import unittest + +from fuse.utils.ndict import NDict +from typing import Any, Union, List +import copy +from unittest.case import expectedFailure + +from fuse.data.ops.op_base import OpBase +from fuse.data.pipelines.pipeline_default import PipelineDefault + + +class OpSetForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, key: str, val: Any) -> Union[None, dict, List[dict]]: + # store information for reverse operation + sample_dict[f"{op_id}.key"] = key + if key in sample_dict: + prev_val = sample_dict[key] + sample_dict[f"{op_id}.prev_val"] = prev_val + + # set + sample_dict[key] = val + return sample_dict + + def reverse(self, sample_dict: NDict, op_id: str, key_to_reverse: str, key_to_follow: str) -> dict: + key = sample_dict[f"{op_id}.key"] + if key == key_to_follow: + if f"{op_id}.prev_val" in sample_dict: + prev_val = sample_dict[f"{op_id}.prev_val"] + sample_dict[key_to_reverse] = prev_val + else: + if key_to_reverse in sample_dict: + sample_dict.pop(key_to_reverse) + return sample_dict + + +class OpNoneForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + return None + + +class OpSplitForTest(OpBase): + def __call__(self, sample_dict: NDict, op_id: str, **kwargs) -> Union[None, dict, List[dict]]: + sample_id = sample_dict['data.sample_id'] + samples = [] + split_num = 10 + for index in range(split_num): + sample = copy.deepcopy(sample_dict) + sample['data.sample_id'] = (sample_id, index) + samples.append(sample) + + return samples + + +class TestPipelineDefault(unittest.TestCase): + + def test_pipeline(self): + """ + Test standard backward and forward pipeline + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpSetForTest(), dict(key="data.test_pipeline", val=6)), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + + sample_dict = NDict({}) + sample_dict = pipe(sample_dict) + self.assertEqual(sample_dict["data.test_pipeline"], 6) + self.assertEqual(sample_dict["data.test_pipeline_2"], 7) + + sample_dict = pipe.reverse(sample_dict, 'data.test_pipeline', 'data.test_pipeline') + self.assertEqual("data.test_pipeline" in sample_dict, False) + self.assertEqual(sample_dict["data.test_pipeline_2"], 7) + + sample_dict = pipe.reverse(sample_dict, 'data.test_pipeline_2', 'data.test_pipeline_2') + self.assertEqual("data.test_pipeline" in sample_dict, False) + self.assertEqual("data.test_pipeline_2" in sample_dict, False) + + def test_none(self): + """ + Test pipeline with an op returning None + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpNoneForTest(), dict()), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + sample_dict = NDict({}) + sample_dict = pipe(sample_dict) + self.assertIsNone(sample_dict) + + def test_split(self): + """ + Test pipeline with an op splitting samples to multiple samples + """ + pipeline_seq = [ + (OpSetForTest(), dict(key="data.test_pipeline", val=5)), + (OpSplitForTest(), dict()), + (OpSetForTest(), dict(key="data.test_pipeline_2", val=7)) + ] + pipe = PipelineDefault("test", pipeline_seq) + sample_dict = NDict({'data': {'sample_id': 0}}) + sample_dict = pipe(sample_dict) + self.assertTrue(isinstance(sample_dict, list)) + self.assertEqual(len(sample_dict), 10) + expected_samples = [(0, i) for i in range(10)] + samples = [sample['data.sample_id'] for sample in sample_dict] + self.assertListEqual(expected_samples, samples) + + def tearDown(self) -> None: + return super().tearDown() + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/processor/processor_base.py b/fuse/data/processor/processor_base.py deleted file mode 100644 index dba316a4c..000000000 --- a/fuse/data/processor/processor_base.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Processors Base class -""" -from abc import ABC, abstractmethod -from typing import Hashable - - -class ProcessorBase(ABC): - @abstractmethod - def __call__(self, sample_desc: Hashable): - raise NotImplementedError diff --git a/fuse/data/processor/processor_csv.py b/fuse/data/processor/processor_csv.py deleted file mode 100644 index a47909620..000000000 --- a/fuse/data/processor/processor_csv.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import ast -import pandas as pd - -from fuse.data.processor.processor_base import ProcessorBase -import logging -from typing import Hashable, List, Optional, Dict, Union -from torch import Tensor -import torch - -class ProcessorCSV(ProcessorBase): - """ - Processor reading data from csv file. - Covert each row to a dictionary - """ - - def __init__(self, csv_filename: str, sample_desc_column: str='descriptor', columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None): - """ - Processor reading data from csv file. - :param csv_filename: path to the csv file - :param sample_desc_column: name of the sample descriptor column within the csv file - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - """ - self.sample_desc_column = sample_desc_column - self.csv_filename = csv_filename - # read csv - self.data = pd.read_csv(csv_filename) - self.columns_to_tensor = columns_to_tensor - - def __call__(self, sample_desc: Hashable): - """ - See base class - """ - # locate the required item - items = self.data.loc[self.data[self.sample_desc_column] == str(sample_desc)] - # convert to dictionary - assumes there is only one item with the requested descriptor - sample_data = items.to_dict('records')[0] - for key in sample_data.keys(): - if 'output' in key and isinstance(sample_data[key], str): - tuple_data = sample_data[key] - if tuple_data.startswith('[') and tuple_data.endswith(']'): - sample_data[key] = ast.literal_eval(tuple_data.replace(" ", ",")) - # convert to tensor - if self.columns_to_tensor is not None: - if isinstance(self.columns_to_tensor, list): - for col in self.columns_to_tensor: - self.convert_to_tensor(sample_data, col) - elif isinstance(self.columns_to_tensor, dict): - for col, tensor_dtype in self.columns_to_tensor.items(): - self.convert_to_tensor(sample_data, col, tensor_dtype) - return sample_data - - @staticmethod - def convert_to_tensor(sample: dict, key: str, tensor_dtype: Optional[str] = None) -> None: - """ - Convert value to tensor, use tensor_dtype to specify non-default type/ - :param sample: sample dictionary - :param key: key of item in sample dict to convert - :param tensor_dtype: Optional, None for default,. - """ - if key not in sample: - lgr = logging.getLogger('Fuse') - lgr.error(f'Column {key} does not exit in dataframe, it is ignored and not converted to {tensor_dtype}') - elif isinstance(sample[key], Tensor): - sample[key] = sample[key] - else: - sample[key] = torch.tensor(sample[key], dtype=tensor_dtype) diff --git a/fuse/data/processor/processor_dataframe.py b/fuse/data/processor/processor_dataframe.py deleted file mode 100644 index d8aef40c5..000000000 --- a/fuse/data/processor/processor_dataframe.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Hashable, List, Optional, Dict, Union -import logging -import torch -import pandas as pd -from torch import Tensor - -from fuse.data.processor.processor_base import ProcessorBase - - -class ProcessorDataFrame(ProcessorBase): - """ - Processor reading data from pickle file / dataframe object. - Covert each row to a dictionary - """ - - def __init__(self, - data: Optional[pd.DataFrame] = None, - data_pickle_filename: Optional[str] = None, - sample_desc_column: Optional[str] = 'descriptor', - columns_to_extract: Optional[List[str]] = None, - rename_columns: Optional[Dict[str, str]] = None, - columns_to_tensor: Optional[Union[List[str], Dict[str, torch.dtype]]] = None): - """ - :param data: input DataFrame - :param data_pickle_filename: path to a pickled DataFrame (possible gzipped) - :param sample_desc_column: name of the sample descriptor column within the pickle file, - if set to None.will simply use dataframe index as descriptors - :param columns_to_extract: list of columns to extract from dataframe. When None (default) all columns are extracted - :param rename_columns: rename columns from dataframe, when None (default) column names are kept - :param columns_to_tensor: columns in data that should be converted into pytorch.tensor. - when list, all columns specified are transforms into tensors (type is decided by torch). - when dictionary, then each column is converted into the specified dtype. - When None (default) no columns are converted. - """ - # verify input - lgr = logging.getLogger('Fuse') - if data is None and data_pickle_filename is None: - msg = "Error in ProcessorDataFrame - need to provide either in-memory DataFrame or a path to pickled DataFrame." - lgr.error(msg) - raise Exception(msg) - elif data is not None and data_pickle_filename is not None: - msg = "Error in ProcessorDataFrame - need to provide either 'data' or 'data_pickle_filename' args, bot not both." - lgr.error(msg) - raise Exception(msg) - - # read dataframe - if data is not None: - self.data = data - self.pickle_filename = 'in-memory' - elif data_pickle_filename is not None: - self.data = pd.read_pickle(data_pickle_filename) - self.pickle_filename = data_pickle_filename - - # store input arguments - self.sample_desc_column = sample_desc_column - self.columns_to_extract = columns_to_extract - self.columns_to_tensor = columns_to_tensor - - # extract only specified columns (in case not specified, extract all) - if self.columns_to_extract is not None: - self.data = self.data[self.columns_to_extract] - - # rename columns - if rename_columns is not None: - self.data.rename(rename_columns, axis=1, inplace=True) - - # convert to dictionary: {index -> {column -> value}} - self.data = self.data.set_index(self.sample_desc_column) - self.data = self.data.to_dict(orient='index') - - def __call__(self, sample_desc: Hashable): - """ - See base class - """ - # locate the required item - sample_data = self.data[sample_desc].copy() - - # convert to tensor - if self.columns_to_tensor is not None: - if isinstance(self.columns_to_tensor, list): - for col in self.columns_to_tensor: - self.convert_to_tensor(sample_data, col) - elif isinstance(self.columns_to_tensor, dict): - for col, tensor_dtype in self.columns_to_tensor.items(): - self.convert_to_tensor(sample_data, col, tensor_dtype) - - return sample_data - - def get_samples_descriptors(self) -> List[Hashable]: - """ - :return: list of descriptors dataframe index values - """ - return list(self.data.keys()) - - @staticmethod - def convert_to_tensor(sample: dict, key: str, tensor_dtype: Optional[str] = None) -> None: - """ - Convert value to tensor, use tensor_dtype to specify non-default type/ - :param sample: sample dictionary - :param key: key of item in sample dict to convert - :param tensor_dtype: Optional, None for default,. - """ - if key not in sample: - lgr = logging.getLogger('Fuse') - lgr.error(f'Column {key} does not exit in dataframe, it is ignored and not converted to {tensor_dtype}') - elif isinstance(sample[key], Tensor): - sample[key] = sample[key] - else: - sample[key] = torch.tensor(sample[key], dtype=tensor_dtype) diff --git a/fuse/data/processor/processor_dicom_mri.py b/fuse/data/processor/processor_dicom_mri.py deleted file mode 100755 index aac1204fb..000000000 --- a/fuse/data/processor/processor_dicom_mri.py +++ /dev/null @@ -1,647 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" -import os, glob -import numpy as np -import SimpleITK as sitk -import pydicom -from scipy.ndimage.morphology import binary_dilation -import logging -import h5py -from typing import Tuple -import pandas as pd -from fuse.data.processor.processor_base import ProcessorBase - - -# ======================================================================== -# sequences to be read, and the sequence name -SEQ_DICT = \ - { - 't2_tse_tra': 'T2', - 't2_tse_tra_Grappa3': 'T2', - 't2_tse_tra_320_p2': 'T2', - - 'ep2d-advdiff-3Scan-high bvalue 100': 'b', - 'ep2d-advdiff-3Scan-high bvalue 500': 'b', - 'ep2d-advdiff-3Scan-high bvalue 1400': 'b', - 'ep2d_diff_tra2x2_Noise0_FS_DYNDISTCALC_BVAL': 'b', - - 'ep2d_diff_tra_DYNDIST': 'b_mix', - 'ep2d_diff_tra_DYNDIST_MIX': 'b_mix', - 'diffusie-3Scan-4bval_fs': 'b_mix', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen': 'b_mix', - 'diff tra b 50 500 800 WIP511b alle spoelen': 'b_mix', - - 'ep2d_diff_tra_DYNDIST_MIX_ADC': 'ADC', - 'diffusie-3Scan-4bval_fs_ADC': 'ADC', - 'ep2d-advdiff-MDDW-12dir_spair_511b_ADC': 'ADC', - 'ep2d-advdiff-3Scan-4bval_spair_511b_ADC': 'ADC', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen_ADC': 'ADC', - 'diff tra b 50 500 800 WIP511b alle spoelen_ADC': 'ADC', - 'ADC_S3_1': 'ADC', - 'ep2d_diff_tra_DYNDIST_ADC': 'ADC', - - } - -# patients with special fix -EXP_PATIENTS = ['ProstateX-0191', 'ProstateX-0148', 'ProstateX-0180'] - -SEQ_TO_USE = ['T2', 'b', 'b_mix', 'ADC', 'ktrans'] -SUB_SEQ_TO_USE = ['T2', 'b400', 'b800', 'ADC', 'ktrans'] -SER_INX_TO_USE = {} -SER_INX_TO_USE['all'] = {'T2': -1, 'b': [0, 2], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0148'] = {'T2': 1, 'b': [1, 2], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0191'] = {'T2': -1, 'b': [0, 0], 'ADC': 0, 'ktrans': 0} -SER_INX_TO_USE['ProstateX-0180'] = {'T2': -1, 'b': [1, 2], 'ADC': 0, 'ktrans': 0} - -# sequences with special fix -B_SER_FIX = ['diffusie-3Scan-4bval_fs', - 'ep2d_DIFF_tra_b50_500_800_1400_alle_spoelen', - 'diff tra b 50 500 800 WIP511b alle spoelen'] - -class DicomMRIProcessor(ProcessorBase): - def __init__(self,verbose: bool=True,reference_inx: int=0,seq_dict:dict=SEQ_DICT, - seq_to_use:list=SEQ_TO_USE,subseq_to_use:list=SUB_SEQ_TO_USE, - ser_inx_to_use:dict=SER_INX_TO_USE,exp_patients:dict=EXP_PATIENTS, - use_order_indicator: bool=False): - ''' - DicomMRIProcessor is MRI volume processor - :param verbose: if print verbose - :param reference_inx: index for the sequence that is selected as reference from SEQ_TO_USE (0 for T2) - :param seq_dict: dictionary in which varies series descriptions are grouped - together based on dict key. - :param seq_to_use: The sequences to use are selected - :param subseq_to_use: - :param ser_inx_to_use: The series index to use - :param exp_patients: patients with missing series that are treated in a special inx - default params are for prostate_x dataset - ''' - - self._verbose = verbose - self._reference_inx = reference_inx - self._seq_dict = seq_dict - self._seq_to_use = seq_to_use - self._subseq_to_use = subseq_to_use - self._ser_inx_to_use = ser_inx_to_use - self._exp_patients = exp_patients - self._use_order_indicator = use_order_indicator - - - - - - def __call__(self, - sample_desc, - *args, **kwargs): - """ - sample_desc contains: - :param images_path: path to directory in which dicom data is located - :param ktrans_data_path: path to directory of Ktrans seq (prostate x) - :param patient_id: patient indicator - :return: 4D tensor of MRI volumes, reference volume - """ - - imgs_path, ktrans_data_path, patient_id = sample_desc - - self._imgs_path = imgs_path - self._ktrans_data_path = ktrans_data_path - self._patient_id = patient_id - - - # ======================================================================== - # extract stk vol list per sequence - vols_dict, seq_info = self.extract_vol_per_seq() - - # ======================================================================== - # list of sitk volumes (z,x,y) per sequence - # order of volumes as defined in SER_INX_TO_USE - # if missing volume, replaces with volume of zeros - vol_list = self.extract_list_of_rel_vol(vols_dict, seq_info) - vol_ref = vol_list[self._reference_inx] - # ======================================================================== - # vol_4D is multichannel volume (z,x,y,chan(sequence)) - vol_4D = self.preprocess_and_stack_seq(vol_list, reference_inx=self._reference_inx) - - return vol_4D,vol_ref - - # ======================================================================== - - def extract_stk_vol(self,img_path:str, img_list:list=[str], reverse_order:bool=False, is_path:bool=True)->list: - """ - extract_stk_vol loads dicoms into sitk vol - :param img_path: path to dicoms - load all dicoms from this path - :param img_list: list of dicoms to load - :param reverse_order: sometimes reverse dicoms orders is needed - (for b series in which more than one sequence is provided inside the img_path) - :param is_path: if True loads all dicoms from img_path - :return: list of stk vols - """ - - stk_vols = [] - - try: - # load from HDF5 - if img_path[-4::] in 'hdf5': - with h5py.File(img_path, 'r') as hf: - _array = np.array(hf['array']) - _spacing = hf.attrs['spacing'] - _origin = hf.attrs['origin'] - _world_matrix = np.array(hf.attrs['world_matrix'])[:3, :3] - _world_matrix_unit = _world_matrix / np.linalg.norm(_world_matrix, axis=0) - _world_matrix_unit_flat = _world_matrix_unit.flatten() - - - # volume 2 sitk - vol = sitk.GetImageFromArray(_array) - vol.SetOrigin([_origin[i] for i in [1, 2, 0]]) - vol.SetDirection(_world_matrix_unit_flat) - vol.SetSpacing([_spacing[i] for i in [1, 2, 0]]) - stk_vols.append(vol) - return stk_vols - - elif is_path: - vol = sitk.ReadImage(img_path) - stk_vols.append(vol) - return stk_vols - - else: - series_reader = sitk.ImageSeriesReader() - - if img_list == []: - img_list = [series_reader.GetGDCMSeriesFileNames(img_path)] - - for n, imgs_names in enumerate(img_list): - if img_path not in img_list[0][0]: - imgs_names = [os.path.join(img_path, n) for n in imgs_names] - dicom_names = imgs_names[::-1] if reverse_order else imgs_names - series_reader.SetFileNames(dicom_names) - imgs = series_reader.Execute() - stk_vols.append(imgs) - - return stk_vols - - except Exception as e: - print(e) - - - - - - - - # ======================================================================== - - def sort_dicom_by_dicom_field(self,dcm_files: list, dicom_field: tuple =(0x19, 0x100c))->list: - """ - sort_dicom_by_dicom_field sorts the dcm_files based on dicom_field - For some MRI sequences different kinds of MRI series are mixed together (as in bWI) case - This function creates a dict={dicom_field_type:list of relevant dicoms}, - than concats all to a list of the different series types - - :param dcm_files: list of all dicoms , mixed - :param dicom_field: dicom field to sort based on - :return: sorted_names_list, list of sorted dicom series - """ - - dcm_values = {} - dcm_patient_z = {} - dcm_instance = {} - for index,dcm in enumerate(dcm_files): - dcm_ds = pydicom.dcmread(dcm) - patient_z = int(dcm_ds.ImagePositionPatient[2]) - instance_num = int(dcm_ds.InstanceNumber) - try: - val = int(dcm_ds[dicom_field].value) - if val not in dcm_values: - dcm_values[val] = [] - dcm_patient_z[val] = [] - dcm_instance[val] = [] - dcm_values[val].append(os.path.split(dcm)[-1]) - dcm_patient_z[val].append(patient_z) - dcm_instance[val].append(instance_num) - except: - #sort by - if index==0: - patient_z_ = [] - for dcm_ in dcm_files: - dcm_ds_ = pydicom.dcmread(dcm_) - patient_z_.append(dcm_ds_.ImagePositionPatient[2]) - val = int(np.floor((instance_num-1)/len(np.unique(patient_z_)))) - if val not in dcm_values: - dcm_values[val] = [] - dcm_patient_z[val] =[] - dcm_instance[val] = [] - dcm_values[val].append(os.path.split(dcm)[-1]) - dcm_patient_z[val].append(patient_z) - dcm_instance[val].append(instance_num) - - sorted_keys = np.sort(list(dcm_values.keys())) - sorted_names_list = [dcm_values[key] for key in sorted_keys] - dcm_patient_z_list = [dcm_patient_z[key] for key in sorted_keys] - dcm_instance_list = [dcm_instance[key] for key in sorted_keys] - - if self._use_order_indicator: - # sort from low patient z to high patient z - sorted_names_list2 = [list(np.array(list_of_names)[np.argsort(list_of_z)]) for list_of_names,list_of_z in zip(sorted_names_list,dcm_patient_z_list)] - else: - # sort by instance number - sorted_names_list2 = [list(np.array(list_of_names)[np.argsort(list_of_z)]) for list_of_names,list_of_z in zip(sorted_names_list,dcm_instance_list)] - - return sorted_names_list2 - - - # ======================================================================== - - def extract_vol_per_seq(self)-> dict: - """ - extract_vol_per_seq arranges sequences in sitk volumes dict - dict{seq_description: list of sitk} - :return: - vols_dict, dict{seq_description: list of sitk} - sequences_dict,dict{seq_description: list of series descriptions} - """ - - ktrans_path = os.path.join(self._ktrans_data_path, self._patient_id) - - if self._verbose: - print('Patient ID: %s' % (self._patient_id)) - - # ------------------------ - # images dict and sequences description dict - - vols_dict = {k: [] for k in self._seq_to_use} - sequences_dict = {k: [] for k in self._seq_to_use} - sequences_num_dict = {k: [] for k in self._seq_to_use} - - for img_path in os.listdir(self._imgs_path): - try: - full_path = os.path.join(self._imgs_path, img_path) - dcm_files = glob.glob(os.path.join(full_path, '*.dcm')) - series_desc = pydicom.dcmread(dcm_files[0]).SeriesDescription - try: - series_num = int(pydicom.dcmread(dcm_files[0]).AcquisitionNumber) - except: - series_num = int(pydicom.dcmread(dcm_files[0]).SeriesNumber) - - - #------------------------ - # print series description - series_desc_general = self._seq_dict[series_desc] \ - if series_desc in self._seq_dict else 'UNKNOWN' - if self._verbose: - print('\t- Series description:',' %s (%s)' % (series_desc, series_desc_general)) - - - - #------------------------ - # ignore UNKNOWN series - if series_desc not in self._seq_dict or \ - self._seq_dict[series_desc] not in self._seq_to_use: - continue - - #------------------------ - # b-series - sorting images by b-value - - if self._seq_dict[series_desc] == 'b_mix': - dcm_ds = pydicom.dcmread(dcm_files[0]) - if 'DiffusionBValue' in dcm_ds: - dicom_field = (0x0018,0x9087)#'DiffusionBValue' - else: - dicom_field = (0x19, 0x100c) - - if self._use_order_indicator: - reverse_order = False - else: - #default - reverse_order = True - - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files, dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=reverse_order, is_path=False) - - # ------------------------ - # MASK - elif self._seq_dict[series_desc] == 'MASK': - dicom_field = (0x0020, 0x0011)#series number - - if self._use_order_indicator: - reverse_order = False - else: - # default - reverse_order = True - - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files, dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=reverse_order, - is_path=False) - - #------------------------ - # DCE - sorting images by time phases - elif 'DCE' in self._seq_dict[series_desc]: - dcm_ds = pydicom.dcmread(dcm_files[0]) - if 'TemporalPositionIdentifier' in dcm_ds: - dicom_field = (0x0020, 0x0100) #Temporal Position Identifier - elif 'TemporalPositionIndex' in dcm_ds: - dicom_field = (0x0020, 0x9128) - else: - dicom_field = (0x0020, 0x0012)#Acqusition Number - - if self._use_order_indicator: - reverse_order = False - else: - #default - reverse_order = False - sorted_dicom_names = self.sort_dicom_by_dicom_field(dcm_files,dicom_field=dicom_field) - stk_vols = self.extract_stk_vol(full_path, img_list=sorted_dicom_names, reverse_order=False, is_path=False) - - - #------------------------ - # general case - else: - # images are sorted based instance number - stk_vols = self.extract_stk_vol(full_path, img_list=[], reverse_order=False, is_path=False) - - #------------------------ - # volume dictionary - - if self._seq_dict[series_desc] == 'b_mix': - vols_dict['b'] += stk_vols - sequences_dict['b'] += [series_desc] - sequences_num_dict['b']+=[series_num] - else: - vols_dict[self._seq_dict[series_desc]] += stk_vols - sequences_dict[self._seq_dict[series_desc]] += [series_desc] - sequences_num_dict[self._seq_dict[series_desc]] += [series_num] - - except Exception as e: - print(e) - - #------------------------ - # Read ktrans image - try: - - if glob.glob(os.path.join(ktrans_path, '*.mhd')): - mhd_path = glob.glob(os.path.join(ktrans_path, '*.mhd'))[0] - print('\t- Reading: %s (%s) (%s)' % (os.path.split(mhd_path)[-1], 'Ktrans', 'ktrans')) - stk_vols = self.extract_stk_vol(mhd_path, img_list=[], reverse_order=False, is_path=True) - vols_dict['ktrans'] = stk_vols - sequences_dict['ktrans'] = [ktrans_path] - - - except Exception as e: - print(e) - - if 'b_mix' in vols_dict.keys(): - vols_dict.pop('b_mix') - sequences_dict.pop('b_mix') - - # handle multiphase DCE in different series - if ('DCE_mix_ph1' in vols_dict.keys()) | ('DCE_mix_ph2' in vols_dict.keys()) | ('DCE_mix_ph3' in vols_dict.keys()): - if (len(vols_dict['DCE_mix_ph1'])>0) | (len(vols_dict['DCE_mix_ph2'])>0) | (len(vols_dict['DCE_mix_ph3'])>0): - keys_list = [tmp for tmp in list(vols_dict.keys()) if 'DCE_mix_' in tmp] - for key in keys_list: - stk_vols = vols_dict[key] - series_desc = sequences_dict[key] - vols_dict['DCE_mix'] += stk_vols - sequences_dict['DCE_mix'] += [series_desc] - vols_dict.pop(key) - sequences_dict.pop(key) - - if ('DCE_mix_ph' in vols_dict.keys()): - if (len(vols_dict['DCE_mix_ph'])>0): - keys_list = [tmp for tmp in list(sequences_num_dict.keys()) if 'DCE_mix_' in tmp] - for key in keys_list: - stk_vols = vols_dict[key] - if (len(stk_vols)>0): - inx_sorted = np.argsort(sequences_num_dict[key]) - for ser_num_inx in inx_sorted: - vols_dict['DCE_mix'] += [stk_vols[int(ser_num_inx)]] - sequences_dict['DCE_mix'] += [series_desc] - vols_dict.pop(key) - sequences_dict.pop(key) - return vols_dict, sequences_dict - - # ======================================================================== - def extract_list_of_rel_vol(self,vols_dict:dict,seq_info:dict)->list: - """ - extract_list_of_rel_vol extract the volume per seq based on SER_INX_TO_USE - and put in one list - :param vols_dict: dict of sitk vols per seq - :param seq_info: dict of seq description per seq - :return: - """ - - def get_zeros_vol(vol): - - if vol.GetNumberOfComponentsPerPixel() > 1: - ref_zeros_vol = sitk.VectorIndexSelectionCast(vol, 0) - else: - ref_zeros_vol = vol - zeros_vol = np.zeros_like(sitk.GetArrayFromImage(ref_zeros_vol)) - zeros_vol = sitk.GetImageFromArray(zeros_vol) - zeros_vol.CopyInformation(ref_zeros_vol) - return zeros_vol - - def stack_rel_vol_in_list(vols,series_inx_to_use,seq): - vols_list = [] - for s, v0 in vols.items(): - vol_inx_to_use = series_inx_to_use['all'][s] - - if self._patient_id in self._exp_patients: - vol_inx_to_use = series_inx_to_use[self._patient_id][s] - - if isinstance(vol_inx_to_use,list): - for inx in vol_inx_to_use: - if len(v0)==0: - vols_list.append(get_zeros_vol(vols_list[0])) - elif len(v0)0.3] = 1 - vol_array[:,:,:,mask_ch_inx] = bool_mask - - vol_final = sitk.GetImageFromArray(vol_array, isVector=True) - vol_final.CopyInformation(vol_backup) - vol_final = sitk.Image(vol_final) - - return vol_final - - # ======================================================================== - def apply_rescaling(self,img:np.array, thres:tuple=(1.0, 99.0), method:str='noclip'): - """ - apply_rescaling rescale each channal using method - :param img: - :param thres: - :param method: - :return: - """ - eps = 0.000001 - - def rescale_single_channel_image(img): - # Deal with negative values first - min_value = np.min(img) - if min_value < 0: - img -= min_value - if method == 'clip': - val_l, val_h = np.percentile(img, thres) - img2 = img - img2[img < val_l] = val_l - img2[img > val_h] = val_h - img2 = (img2.astype(np.float32) - val_l) / (val_h - val_l + eps) - elif method == 'mean': - img2 = img / max(np.mean(img), 1) - elif method == 'median': - img2 = img / max(np.median(img), 1) - elif method == 'noclip': - val_l, val_h = np.percentile(img, thres) - img2 = img - img2 = (img2.astype(np.float32) - val_l) / (val_h - val_l + eps) - else: - img2 = img - return img2 - - # fix outlier image values - img[np.isnan(img)] = 0 - # Process each channel independently - if len(img.shape) == 4: - for i in range(img.shape[-1]): - img[..., i] = rescale_single_channel_image(img[..., i]) - else: - img = rescale_single_channel_image(img) - - return img - - # ======================================================================== - def create_resample(self,vol_ref:sitk.sitkFloat32, interpolation: str, size:Tuple[int,int,int], spacing: Tuple[float,float,float]): - """ - create_resample create resample operator - :param vol_ref: sitk vol to use as a ref - :param interpolation:['linear','nn','bspline'] - :param size: in pixels () - :param spacing: in mm () - :return: resample sitk operator - """ - - if interpolation == 'linear': - interpolator = sitk.sitkLinear - elif interpolation == 'nn': - interpolator = sitk.sitkNearestNeighbor - elif interpolation == 'bspline': - interpolator = sitk.sitkBSpline - - resample = sitk.ResampleImageFilter() - resample.SetReferenceImage(vol_ref) - resample.SetOutputSpacing(spacing) - resample.SetInterpolator(interpolator) - resample.SetSize(size) - return resample - - - diff --git a/fuse/data/processor/processor_rand.py b/fuse/data/processor/processor_rand.py deleted file mode 100644 index 9fbf43c6d..000000000 --- a/fuse/data/processor/processor_rand.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Processor generating random ground truth - useful for testing and sanity check -""" -from typing import Hashable, Tuple - -import torch - -from fuse.data.processor.processor_base import ProcessorBase - - -class ProcessorRandInt(ProcessorBase): - def __init__(self, min: int = 0, max: int = 1, shape: Tuple = (1,)): - self.min = min - self.max = max - self.shape = shape - - def __call__(self, sample_desc: Hashable): - return {'tensor': torch.randint(self.min, self.max + 1, self.shape)} diff --git a/fuse/data/processor/processors_image_toolbox.py b/fuse/data/processor/processors_image_toolbox.py deleted file mode 100644 index 432e443c6..000000000 --- a/fuse/data/processor/processors_image_toolbox.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Tuple -import pydicom -import numpy as np -import skimage -import skimage.transform as transform - -class ProcessorsImageToolBox: - """ - Common utils for image processors - """ - - @staticmethod - def read_dicom_image_to_numpy(img_path: str) -> np.ndarray : - """ - read a dicom file given a file path - :param img_path: file path - :return: numpy object of the dicom image - """ - # read image - dcm = pydicom.dcmread(img_path) - inner_image = dcm.pixel_array - # convert to numpy - inner_image = np.asarray(inner_image) - return inner_image - - @staticmethod - def resize_image(inner_image: np.ndarray, resize_to: Tuple[int,int]) -> np.ndarray : - """ - resize image to the required resolution - :param inner_image: image of shape [H, W, C] - :param resize_to: required resolution [height, width] - :return: resized image - """ - inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - if inner_image_height > resize_to[0]: - h_ratio = resize_to[0] / inner_image_height - else: - h_ratio = 1 - if inner_image_width > resize_to[1]: - w_ratio = resize_to[1] / inner_image_width - else: - w_ratio = 1 - - resize_ratio = min(h_ratio, w_ratio) - if resize_ratio != 1: - inner_image = skimage.transform.resize(inner_image, - output_shape=(int(inner_image_height * resize_ratio), - int(inner_image_width * resize_ratio)), - mode='reflect', - anti_aliasing=True - ) - return inner_image - - @staticmethod - def pad_image(inner_image: np.ndarray, padding: Tuple[float, float], resize_to: Tuple[int, int], - normalized_target_range: Tuple[float, float], number_of_channels: int) -> np.ndarray : - """ - pads image to requested size , - pads both side equally by the same input padding size (left = right = padding[1] , up = down= padding[0] ) , - padding default value is zero or minimum value in normalized target range - :param inner_image: image of shape [H, W, C] - :param padding: required padding [x,y] - :param resize_to: original requested resolution - :param normalized_target_range: requested normalized image pixels range - :param number_of_channels: number of color channels in the image - :return: padded image - """ - inner_image = inner_image.astype('float32') - # "Pad" around inner image - inner_image_height, inner_image_width = inner_image.shape[0], inner_image.shape[1] - inner_image[0:inner_image_height, 0] = 0 - inner_image[0:inner_image_height, inner_image_width - 1] = 0 - inner_image[0, 0:inner_image_width] = 0 - inner_image[inner_image_height - 1, 0:inner_image_width] = 0 - - if normalized_target_range is None: - pad_value = 0 - else: - pad_value = normalized_target_range[0] - - image = ProcessorsImageToolBox.pad_inner_image(inner_image, outer_height=resize_to[0] + 2 * padding[0], - outer_width=resize_to[1] + 2 * padding[1], pad_value=pad_value, number_of_channels=number_of_channels) - return image - - @staticmethod - def normalize_to_range(input_image: np.ndarray, range: Tuple[float, float] = (0, 1.0)) -> np.ndarray : - """ - Scales tensor to range - :param input_image: image of shape [H, W, C] - :param range: bounds for normalization - :return: normalized image - """ - max_val = input_image.max() - min_val = input_image.min() - if min_val == max_val == 0: - return input_image - input_image = input_image - min_val - input_image = input_image / (max_val - min_val) - input_image = input_image * (range[1] - range[0]) - input_image = input_image + range[0] - return input_image - - def pad_inner_image(image: np.ndarray, outer_height: int, outer_width: int, pad_value: float, number_of_channels: int) -> np.ndarray : - """ - Pastes input image in the middle of a larger one - :param image: image of shape [H, W, C] - :param outer_height: final outer height - :param outer_width: final outer width - :param pad_value: value for padding around inner image - :number_of_channels final number of channels in the image - :return: padded image - """ - inner_height, inner_width = image.shape[0], image.shape[1] - h_offset = int((outer_height - inner_height) / 2.0) - w_offset = int((outer_width - inner_width) / 2.0) - if number_of_channels > 1 : - outer_image = np.ones((outer_height, outer_width, number_of_channels), dtype=image.dtype) * pad_value - outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width, :] = image - elif number_of_channels == 1 : - outer_image = np.ones((outer_height, outer_width), dtype=image.dtype) * pad_value - outer_image[h_offset:h_offset + inner_height, w_offset:w_offset + inner_width] = image - return outer_image diff --git a/fuse/data/sampler/sampler_balanced_batch.py b/fuse/data/sampler/sampler_balanced_batch.py deleted file mode 100644 index 5047accb0..000000000 --- a/fuse/data/sampler/sampler_balanced_batch.py +++ /dev/null @@ -1,212 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -""" -Torch batch sampler - balancing per batch -""" -import logging -import math -from typing import Any, List, Optional - -import numpy as np -from torch.utils.data.sampler import Sampler - -from fuse.data.dataset.dataset_base import DatasetBase -from fuse.utils.utils_debug import FuseDebug -from fuse.utils.utils_logger import log_object_input_state - - -class SamplerBalancedBatch(Sampler): - """ - Torch batch sampler - balancing per batch - """ - - def __init__(self, dataset: DatasetBase, balanced_class_name: str, num_balanced_classes: int, batch_size: int, - balanced_class_weights: Optional[List[int]] = None, balanced_class_probs: Optional[List[float]] = None, - num_batches: Optional[int] = None, use_dataset_cache: bool = False) -> None: - """ - :param dataset: dataset used to extract the balanced class from each sample - :param balanced_class_name: the name of balanced class to extract from dataset - :param num_balanced_classes: number of classes to balance between - :param batch_size: batch_size. - - If balanced_class_weights=Nobe, Must be divided by num_balanced_classes - - Otherwise must be equal to sum of balanced_class_weights - :param balanced_class_weights: Optional, integer per balanced class, - specifying the number of samples from each class to include in each batch. - If not specified and equal number of samples from each class will be used. - :param balanced_class_probs: Optional, probability per class. Random sampling approach will be performed. - such that an epoch will go over all the data at least once. - :param num_batches: Optional, Set number of batches. If not set. The number of batches will automatically set. - - :param use_dataset_cache: to retrieve the balanced class from dataset try to use caching. - Should be set to True if reading it from cache is faster than running the single processor - """ - # log object - log_object_input_state(self, locals()) - - super().__init__(None) - - # store input - self.dataset = dataset - self.balanced_class_name = balanced_class_name - self.num_balanced_classes = num_balanced_classes - self.batch_size = batch_size - self.balanced_class_weights = balanced_class_weights - self.balanced_class_probs = balanced_class_probs - self.num_batches = num_batches - self.use_dataset_cache = use_dataset_cache - - # validate input - if balanced_class_weights is not None and balanced_class_probs is not None: - raise Exception('Set either balanced_class_weights or balanced_class_probs, not both.') - elif balanced_class_weights is None and balanced_class_probs is None: - if batch_size % num_balanced_classes != 0: - raise Exception(f'batch_size ({batch_size}) % num_balanced_classes ({num_balanced_classes}) must be 0') - elif balanced_class_weights is not None: - if len(balanced_class_weights) != num_balanced_classes: - raise Exception( - f'Expecting balance_class_weights ({balanced_class_weights}) to have a weight per balanced class ({num_balanced_classes})') - if sum(balanced_class_weights) != batch_size: - raise Exception(f'balanced_class_weights {balanced_class_weights} expected to sum up to batch_size {batch_size}') - else: - # noinspection PyTypeChecker - if len(balanced_class_probs) != num_balanced_classes: - raise Exception( - f'Expecting balance_class_probs ({balanced_class_probs}) to have a probability per balanced class ({num_balanced_classes})') - if not math.isclose(sum(balanced_class_probs), 1.0): - raise Exception(f'balanced_class_probs {balanced_class_probs} expected to sum up to 1.0') - - # if weights not specified, set weights to equally balance per batch - if self.balanced_class_weights is None and self.balanced_class_probs is None: - self.balanced_class_weights = [self.batch_size // self.num_balanced_classes] * self.num_balanced_classes - - lgr = logging.getLogger('Fuse') - lgr.debug(f'SamplerBalancedBatch: balancing per batch - balanced_class_name {self.balanced_class_name}, ' - f'batch_size={batch_size}, weights={self.balanced_class_weights}, probs={self.balanced_class_probs}') - - # get balanced classes per each sample - self.balanced_classes = dataset.get(None, self.balanced_class_name, use_cache=use_dataset_cache) - self.balanced_classes = np.array(self.balanced_classes) - self.balanced_class_indices = [np.where(self.balanced_classes == cls_i)[0] for cls_i in range(self.num_balanced_classes)] - self.balanced_class_sizes = [len(self.balanced_class_indices[cls_i]) for cls_i in range(self.num_balanced_classes)] - lgr.debug('SamplerBalancedBatch: samples per each balanced class {}'.format(self.balanced_class_sizes)) - - # debug - simple batch - batch_mode = FuseDebug().get_setting('sampler_batch_mode') - if batch_mode == 'simple': - num_avail_bcls = sum( - bcls_num_samples != 0 - for bcls_num_samples in self.balanced_class_sizes - ) - - self.balanced_class_weights = None - self.balanced_class_probs = [1.0/num_avail_bcls if bcls_num_samples != 0 else 0.0 for bcls_num_samples in self.balanced_class_sizes] - lgr.info('SamplerBalancedBatch: debug mode - override to random sample') - - # calc batch index to balanced class mapping according to weights - if self.balanced_class_weights is not None: - self.batch_index_to_class = [] - for balanced_cls in range(self.num_balanced_classes): - self.batch_index_to_class.extend([balanced_cls] * self.balanced_class_weights[balanced_cls]) - else: - # probabilistic method - will be randomly select per epoch - self.batch_index_to_class = None - - # make sure that size != 0 for all balanced classes - for cls_size in enumerate(self.balanced_class_sizes): - if ( - ( - self.balanced_class_weights is not None - and self.balanced_class_weights != 0 - ) - or ( - self.balanced_class_probs is not None - and self.balanced_class_probs != 0.0 - ) - ) and cls_size == 0: - msg = f'Every balanced class must include at least one sample (num of samples per balanced class{self.balanced_class_sizes})' - raise Exception(msg) - - # Shuffle balanced class indices - for indices in self.balanced_class_indices: - np.random.shuffle(indices) - - # Calculate num batches. Number of batches to iterate over all data at least once - # Calculate only if not directly specified by the user - if self.num_batches is None: - if self.balanced_class_weights is not None: - balanced_class_weighted_sizes = [self.balanced_class_sizes[cls_i] // self.balanced_class_weights[cls_i] if self.balanced_class_weights[cls_i] != 0 else 0 for cls_i in - range(self.num_balanced_classes)] - else: - # approximate size! - balanced_class_weighted_sizes = [ - self.balanced_class_sizes[cls_i] // (self.balanced_class_probs[cls_i] * self.batch_size) if self.balanced_class_probs[ - cls_i] != 0.0 else 0 for - cls_i in range(self.num_balanced_classes)] - bigger_balanced_class_weighted_size = max(balanced_class_weighted_sizes) - self.num_batches = int(bigger_balanced_class_weighted_size) + 1 - lgr.debug(f'SamplerBalancedBatch: num_batches = {self.num_batches}') - - # pointers per class - self.cls_pointers = [0] * self.num_balanced_classes - self.sample_pointer = 0 - - def __iter__(self) -> np.ndarray: - for _ in range(self.num_batches): - yield self._make_batch() - - def __len__(self) -> int: - return self.num_batches - - def _get_sample(self, balanced_class: int) -> Any: - """ - sample index given balanced class value - :param balanced_class: integer representing balanced class value - :return: sample index - """ - if self.balanced_class_indices[balanced_class].shape[0] == 0: - msg = f'There are no samples in balanced class {balanced_class}' - logging.getLogger('Fuse').error(msg) - raise Exception(msg) - - sample_idx = self.balanced_class_indices[balanced_class][self.cls_pointers[balanced_class]] - - self.cls_pointers[balanced_class] += 1 - if self.cls_pointers[balanced_class] == self.balanced_class_sizes[balanced_class]: - self.cls_pointers[balanced_class] = 0 - np.random.shuffle(self.balanced_class_indices[balanced_class]) - - return sample_idx - - def _make_batch(self) -> list: - """ - :return: list of indices to collate batch - """ - if self.batch_index_to_class is not None: - batch_index_to_class = self.batch_index_to_class - else: - # calc one according to probabilities - batch_index_to_class = np.random.choice(np.arange(self.num_balanced_classes), self.batch_size, p=self.balanced_class_probs) - batch_sample_indices = [] - for batch_index in range(self.batch_size): - balanced_class = batch_index_to_class[batch_index] - batch_sample_indices.append(self._get_sample(balanced_class)) - - np.random.shuffle(batch_sample_indices) - return batch_sample_indices diff --git a/fuse/data/visualizer/__init__.py b/fuse/data/tests/__init__.py similarity index 100% rename from fuse/data/visualizer/__init__.py rename to fuse/data/tests/__init__.py diff --git a/fuse/data/data_source/data_source_base.py b/fuse/data/tests/test_version.py similarity index 58% rename from fuse/data/data_source/data_source_base.py rename to fuse/data/tests/test_version.py index 6119fc5e6..f46838a27 100644 --- a/fuse/data/data_source/data_source_base.py +++ b/fuse/data/tests/test_version.py @@ -17,24 +17,22 @@ """ -""" -Data source base -""" -from abc import ABC, abstractmethod +import unittest +import fuse.data +import pkg_resources # part of setuptools -class DataSourceBase(ABC): - @abstractmethod - def get_samples_description(self): +class TestVersion(unittest.TestCase): + def test_version(self): """ - :return: list of samples description + Make sure data version equal to the installed version """ - raise NotImplementedError + pass + # FIXME: uncomment when fixed in jenkins + # version = pkg_resources.require("fuse-med-ml-data")[0].version + # self.assertEqual(fuse.data.__version__, version) - @abstractmethod - def summary(self) -> str: - """ - String summary of the object - """ - raise NotImplementedError + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/utils/imaging/align/__init__.py b/fuse/data/utils/__init__.py similarity index 100% rename from fuse/utils/imaging/align/__init__.py rename to fuse/data/utils/__init__.py diff --git a/fuse/data/utils/collates.py b/fuse/data/utils/collates.py new file mode 100644 index 000000000..8e885e7e8 --- /dev/null +++ b/fuse/data/utils/collates.py @@ -0,0 +1,146 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from typing import Any, Callable, Dict, List, Sequence, Tuple + +import numpy as np +import torch +from torch.utils.data._utils.collate import default_collate +import torch.nn.functional as F + +from fuse.utils import NDict +from fuse.utils.data.collate import CollateToBatchList +from fuse.data import get_sample_id, get_sample_id_key + +class CollateDefault(CollateToBatchList): + """ + Default collate_fn method to be used when creating a DataLoader. + Will collate each value with PyTorch default collate. + Special collates per key can be specified in special_handlers_keys + sample_id key will be collected to a list. + Few options to special handlers implemented in this class as static methods + """ + def __init__(self, skip_keys: Sequence[str]=tuple(), raise_error_key_missing: bool = True, special_handlers_keys: Dict[str, Callable] = None): + """ + :param skip_keys: do not collect the listed keys + :param special_handlers_keys: per key specify a callable which gets as an input list of values and convert it to a batch. + The rest of the keys will be converted to batch using PyTorch default collate_fn() + :param raise_error_key_missing: if False, will not raise an error if there are keys that do not exist in some of the samples. Instead will set those values to None. + """ + super().__init__(skip_keys, raise_error_key_missing) + self._special_handlers_keys = {} + if special_handlers_keys is not None: + self._special_handlers_keys.update(special_handlers_keys) + self._special_handlers_keys[get_sample_id_key()] = CollateDefault.just_collect_to_list + + def __call__(self, samples: List[Dict]) -> Dict: + """ + collate list of samples into batch_dict + :param samples: list of samples + :return: batch_dict + """ + batch_dict = NDict() + + # collect all keys + keys = self._collect_all_keys(samples) + + # collect values + for key in keys: + + # skip keys + if key in self._skip_keys: + continue + + try: + # collect values into a list + collected_values, has_error = self._collect_values_to_list(samples, key) + + # batch values + self._batch_dispatch(batch_dict, samples, key, has_error, collected_values) + except: + print(f'Error: Failed to collect key {key}') + raise + + return batch_dict + + def _batch_dispatch(self, batch_dict: dict, samples: List[dict], key: str, has_error: bool, collected_values: list) -> None: + """ + dispatch a key into collate function and save it into batch_dict + :param batch_dict: batch dictionary to update + :param samples: list of samples + :param key: key to collate + :param has_error: True, if the key is missing in one of the samples + :param collected values: the values collected from samples + :return: nothing - the new batch will be added to batch_dict + """ + if has_error: + # do nothing when error occurs + batch_dict[key] = collected_values + elif key in self._special_handlers_keys: + # use special handler if specified + batch_dict[key] = self._special_handlers_keys[key](collected_values) + elif isinstance(collected_values[0], (torch.Tensor, np.ndarray, float, int, str, bytes)): + # batch with default PyTorch implementation + batch_dict[key] = default_collate(collected_values) + else: + batch_dict[key] = collected_values + + + @staticmethod + def just_collect_to_list(values: List[Any]): + """ + special handler doing nothing - will just keep the collected list + """ + return values + + @staticmethod + def pad_all_tensors_to_same_size(values: List[torch.Tensor], pad_val: float=0.0): + """ + pad tensors and create a batch - the shape will be the max size per dim + values: list of tensor - all should have the same number of dimensions + pad_val: constant value for padding + :return: torch.stack of padded tensors + """ + + # verify all are tensor and that they have the same dim size + assert isinstance(values[0], torch.Tensor), f"Expecting just tensors, got {type(values[0])}" + num_dims = len(values[0].shape) + for value in values: + assert isinstance(value, torch.Tensor), f"Expecting just tensors, got {type(value)}" + assert len(value.shape) == num_dims, f"Expecting all tensors to have the same dim size, got {len(value.shape)} and {num_dims}" + + # get max per dim + max_per_dim = np.amax(np.stack([value.shape for value in values]), axis=0) + + # pad + def _pad_size(value, dim): + assert max_per_dim[dim] >= value.shape[dim] + return [0, max_per_dim[dim]-value.shape[dim]] + + padded_values = [] + + for value in values: + padding = [] + # F.pad padding description is expected to be provided in REVERSE order (see torch.nn.functional.pad doc) + for dim in reversed(range(num_dims)): + padding += _pad_size(value,dim) + padded_value = F.pad(value, padding, mode='constant', value=pad_val) + padded_values.append(padded_value) + + + return default_collate(padded_values) diff --git a/fuse/data/utils/export.py b/fuse/data/utils/export.py index 9ce6b8e2e..fd2568100 100644 --- a/fuse/data/utils/export.py +++ b/fuse/data/utils/export.py @@ -17,20 +17,19 @@ """ from typing import Optional, Sequence -import pandas as pd +import pandas as pds -from fuse.data.dataset.dataset_base import DatasetBase +from fuse.data.datasets.dataset_base import DatasetBase from fuse.utils.file_io.file_io import save_dataframe -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -class DatasetExport: +class ExportDataset: """ Export data """ @staticmethod - def export_to_dataframe(dataset: DatasetBase, keys: Sequence[str], output_filename: Optional[str] = None, sample_id_key: str = "data.descriptor", **dataset_get_kwargs) -> pd.DataFrame: + def export_to_dataframe(dataset: DatasetBase, keys: Sequence[str], output_filename: Optional[str] = None, sample_id_key: str = "data.sample_id", **dataset_get_kwargs) -> pds.DataFrame: """ extract from dataset the specified and keys and create a dataframe. If output_filename will be specified, the dataframe will also be saved in a file. @@ -50,14 +49,14 @@ def export_to_dataframe(dataset: DatasetBase, keys: Sequence[str], output_filena all_keys = None # read all the data - data = dataset.get(None, **dataset_get_kwargs) + data = dataset.get_multi(keys=all_keys, **dataset_get_kwargs) # store in dataframe - df = pd.DataFrame() + df = pds.DataFrame() for key in all_keys: - values = [FuseUtilsHierarchicalDict.get(sample_dict, key) for sample_dict in data] + values = [sample_dict[key] for sample_dict in data] df[key] = values # set sample_id as index diff --git a/fuse/data/utils/sample.py b/fuse/data/utils/sample.py new file mode 100644 index 000000000..0f58de7c9 --- /dev/null +++ b/fuse/data/utils/sample.py @@ -0,0 +1,102 @@ +from typing import Dict, Hashable +from fuse.utils.ndict import NDict + +''' +helper utilities for creating empty samples, and setting and getting sample_id within samples + +A sample is a NDict, which is a special "flavor" of a dictionry, allowing accessing elements within it using x['a.b.c.d'] instead of x['a']['b']['c']['d'], +which is very useful as it allows defining a nested element, or a nested sub-dict using a single string. + +The bare minimum that a sample is required to contain are: + +'initial_sample_id' - this is an arbitrary (Hashable) identifier. Usually a string, but doesn't have to be. + It represnts the initial sample_id that was provided before a pipeline was used to process the sample, and potentially use "sample morphing". + "sample morphing" means that a sample might change during the pipeline execution. + 1. Discard - one type of morphing is that a sample is being discarded. Example use case is discarding an MRI volume because it has too little segmentation info that interests a certain research design. + 2. Split - another type of morphing is that a sample can be split into multiple samples. + For example, the initial_sample_id represents an entire CT volume, which results in multiple samples, each having the same initial_sample_id, but a different sample_id, + each representing a slice within the CT volume which contains enough segmentation information + +'sample_id' - the sample id, uniquely identifying it. It must be Hashable. Again, usually a string, but doesn't have to be. + +''' + +def create_initial_sample(initial_sample_id:Hashable, sample_id=None): + ''' + creates an empty sample dict and sets both sample_id and initial_sample_id + :param sample_id: + :param initial_sample_id: optional. If not provided, sample_id will be used for it as well + ''' + ans = NDict() + + if sample_id is None: + sample_id = initial_sample_id + + set_initial_sample_id(ans, initial_sample_id) + set_sample_id(ans, sample_id) + + return ans + + +##### sample_id + +def get_sample_id_key() -> str: + ''' + return sample id key + ''' + return 'data.sample_id' + +def get_sample_id(sample:Dict) -> Hashable: + ''' + extracts sample_id from the sample dict + ''' + if get_sample_id_key() not in sample: + raise Exception + return sample[get_sample_id_key()] + + +def set_sample_id(sample:Dict, sample_id:Hashable): + ''' + sets sample_id in an existing sample dict + ''' + sample[get_sample_id_key()] = sample_id + + +#### dealing with initial sample id - this is related to morphing, and describes the original provided sample_id, prior to the morphing effect + +def get_initial_sample_id_key() -> str: + ''' + return initial sample id key + ''' + return 'data.initial_sample_id' + +def set_initial_sample_id(sample:Dict, initial_sample_id:Hashable): + ''' + sets initial_sample_id in an existing sample dict + ''' + sample[get_initial_sample_id_key()] = initial_sample_id + +def get_initial_sample_id(sample:Dict) -> Hashable: + ''' + extracts initial_sample_id from the sample dict + ''' + if get_initial_sample_id_key() not in sample: + raise Exception + return sample[get_initial_sample_id_key()] + + +#### + +def get_specific_sample_from_potentially_morphed(sample, sample_id): + if isinstance(sample, dict): + assert get_sample_id(sample) == sample_id + return sample + elif isinstance(sample, list): + for curr_sample in sample: + if get_sample_id(curr_sample) == sample_id: + return curr_sample + raise Exception(f'Could not find requested sample_id={sample_id}') + else: + raise Exception('Expected the sample to be either a dict or a list of dicts. None does not make sense in this context.') + + assert False #should never reach here \ No newline at end of file diff --git a/fuse/data/utils/samplers.py b/fuse/data/utils/samplers.py new file mode 100644 index 000000000..8bda89e8c --- /dev/null +++ b/fuse/data/utils/samplers.py @@ -0,0 +1,208 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import math +from typing import Any, List, Optional, Union + +import numpy as np +from torch.utils.data.sampler import Sampler + +from fuse.data.datasets.dataset_base import DatasetBase + +class BatchSamplerDefault(Sampler): + """ + Torch batch sampler - balancing per batch + """ + + def __init__(self, + dataset: DatasetBase, + balanced_class_name: str, + num_balanced_classes: int, + batch_size: Optional[int] = None, + mode: str = "exact", + balanced_class_weights: Union[List[int], List[float], None] = None, + num_batches: Optional[int] = None, + **dataset_get_multi_kwargs) -> None: + """ + :param dataset: dataset used to extract the balanced class from each sample + :param balanced_class_name: the name of balanced class to extract from dataset + :param num_balanced_classes: number of classes to balance between + :param batch_size: batch_size. + - In "exact" mode + If balanced_class_weights is None, must be set and divided by num_balanced_classes. Otherwise keep None. + - In "approx" mode + Must be set + :param mode: either 'exact' or 'approx'. if 'exact each element in balanced_class_weights will specify the exact number of samples from this class. + if 'approx' - each element will specify the a probability that a sample will be from this class + :param balanced_class_weights: Optional, integer/float per balanced class, Expected length is num_balanced_classes. + In mode 'exact' expecting list of integers that sums up to batch dict. + In mode 'approx' expecting list of floats that sums up to ~1 + If not specified and equal number of samples from each class will be used. + :param num_batches: optional - if set will force num_batches, otherwise num_batches will be automatically to go over each sample at least once (exactly or approximately). + :param dataset_get_multi_kwargs: extra parameters for dataset.get_multi() to optimize the running time. + """ + super().__init__(None) + + # store input + self._mode = mode + self._dataset = dataset + self._balanced_class_name = balanced_class_name + self._num_balanced_classes = num_balanced_classes + self._batch_size = batch_size + self._balanced_class_weights = balanced_class_weights + self._num_batches = num_batches + self._dataset_get_multi_kwargs = dataset_get_multi_kwargs + # modify relevant keys + if self._balanced_class_name not in self._dataset_get_multi_kwargs: + self._dataset_get_multi_kwargs["keys"] = [self._balanced_class_name] + + # validate input + # modes + if self._mode not in ['exact', 'approx']: + raise Exception("Error, expected sampler mode to be either 'exact' or 'approx', got {mode}") + + # weights + if self._mode == 'exact': + if self._balanced_class_weights is not None: + for weight in self._balanced_class_weights: + if not isinstance(weight, int): + raise Exception(f"Error: in mode 'exact', expecting only integers in balanced_class_weights, got {type(weight)}") + if self._batch_size is not None: + if self._batch_size != sum(self._balanced_class_weights): + raise Exception(f"Error: in mode 'exact', expecting balanced_class_weights {self._balanced_class_weights} to sum up to batch size {self._batch_size}. Consider setting batch_size to None to automatically compute the batch size.") + else: + self._batch_size = sum(self._balanced_class_weights) + elif self._batch_size is None: + raise Exception("Error: In 'approx' mode, either batch_size or balanced_class_weights") + + if self._mode == "approx": + if self._batch_size is None: + raise Exception(f"Error: in mode 'approx', batch size must be set.") + if balanced_class_weights is not None: + for weight in balanced_class_weights: + if not isinstance(weight, float): + raise Exception(f"Error: in mode 'exact', expecting only floats in balanced_class_weights, got {type(weight)}") + if not math.isclose(sum(self._balanced_class_weights), 1.0): + raise Exception(f"Error: in mode 'exact', expecting balanced_class_weight to sum up to almost one, got {balanced_class_weights}") + + if balanced_class_weights is not None: + if len(balanced_class_weights) != num_balanced_classes: + raise Exception( + f'Expecting balance_class_weights ({balanced_class_weights}) to have a weight per balanced class ({num_balanced_classes})') + + # if weights not specified, set weights to equally balance per batch + if self._balanced_class_weights is None: + if self._mode == "exact": + self._balanced_class_weights = [self._batch_size // self._num_balanced_classes] * self._num_balanced_classes + elif self._mode == "approx": + self._balanced_class_weights = [1 / self._num_balanced_classes] * self._num_balanced_classes + + # get balanced classes per each sample + collected_data = dataset.get_multi(None, **self._dataset_get_multi_kwargs) + self._balanced_classes = self._extract_balanced_classes(collected_data) + + # split samples to groups + self._balanced_class_indices = [np.where(self._balanced_classes == cls_i)[0] for cls_i in range(self._num_balanced_classes)] + self._balanced_class_sizes = [len(self._balanced_class_indices[cls_i]) for cls_i in range(self._num_balanced_classes)] + + # make sure that size != 0 for all balanced classes + for cls_ind, cls_size in enumerate(self._balanced_class_sizes): + if self._balanced_class_weights[cls_ind] != 0.0 and cls_size == 0: + msg = f'Every balanced class must include at least one sample (num of samples per balanced class{self._balanced_class_sizes} and weights are {self._balanced_class_weights})' + raise Exception(msg) + + # calc batch index to balanced class mapping according to weights + if self._mode == 'exact': + self._batch_index_to_class = [] + for balanced_cls in range(self._num_balanced_classes): + self._batch_index_to_class.extend([balanced_cls] * self._balanced_class_weights[balanced_cls]) + else: + # probabilistic method - will be randomly select per epoch + self._batch_index_to_class = None + + + # Shuffle balanced class indices + for indices in self._balanced_class_indices: + np.random.shuffle(indices) + + # Calculate num batches. Number of batches to iterate over all data at least once (exactly or approximately) + # Calculate only if not directly specified by the user + if self._num_batches is None: + if self._mode == 'exact': + samples_per_batch = self._balanced_class_weights + else: # mode is approx + # approximate size! + samples_per_batch = [val * self._batch_size for val in self._balanced_class_weights] + balanced_class_weighted_sizes = \ + [math.ceil(self._balanced_class_sizes[cls_i] / samples_per_batch[cls_i]) if self._balanced_class_weights[cls_i] != 0 else 0 for cls_i in range(self._num_balanced_classes)] + bigger_balanced_class_weighted_size = max(balanced_class_weighted_sizes) + self._num_batches = int(bigger_balanced_class_weighted_size) + + # pointers per class + self._cls_pointers = [0] * self._num_balanced_classes + self._sample_pointer = 0 + + def __iter__(self) -> np.ndarray: + for batch_idx in range(self._num_batches): + yield self._make_batch() + + def __len__(self) -> int: + return self._num_batches + + def _get_sample(self, balanced_class: int) -> Any: + """ + sample index given balanced class value + :param balanced_class: integer representing balanced class value + :return: sample index + """ + sample_idx = self._balanced_class_indices[balanced_class][self._cls_pointers[balanced_class]] + + self._cls_pointers[balanced_class] += 1 + if self._cls_pointers[balanced_class] == self._balanced_class_sizes[balanced_class]: + self._cls_pointers[balanced_class] = 0 + np.random.shuffle(self._balanced_class_indices[balanced_class]) + + return sample_idx + + def _make_batch(self) -> list: + """ + :return: list of indices to collate batch + """ + if self._mode == 'exact': + batch_index_to_class = self._batch_index_to_class + else: # mode == approx + # calc one according to probabilities + batch_index_to_class = np.random.choice(np.arange(self._num_balanced_classes), self._batch_size, p=self._balanced_class_weights) + batch_sample_indices = [] + for batch_index in range(self._batch_size): + balanced_class = batch_index_to_class[batch_index] + batch_sample_indices.append(self._get_sample(balanced_class)) + + np.random.shuffle(batch_sample_indices) + return batch_sample_indices + + def _extract_balanced_classes(self, collected_data: List[dict]) -> np.ndarray: + """ + Extracting balanced class values from collected data. + If - special extra logic is required. Either override this method or the logic in Op and append to dataset pipeline + """ + assert len(collected_data) > 0, "Error: sampling failed, dataset size is 0" + balanced_classes = [sample[self._balanced_class_name] for sample in collected_data] + return np.array(balanced_classes) + diff --git a/fuse/data/utils/tests/__init__.py b/fuse/data/utils/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/data/utils/tests/test_collates.py b/fuse/data/utils/tests/test_collates.py new file mode 100644 index 000000000..8bac6a6b8 --- /dev/null +++ b/fuse/data/utils/tests/test_collates.py @@ -0,0 +1,101 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +from typing import List, Optional, Union +import unittest + +import pandas as pds +import numpy as np +import torch +from torch.utils.data.dataloader import DataLoader + +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.utils.collates import CollateDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.ops.op_base import OpBase +from fuse.data import get_sample_id + +class OpCustomCollateDefTest(OpBase): + def __call__(self, sample_dict: dict, op_id: Optional[str], **kwargs) -> Union[None, dict, List[dict]]: + if get_sample_id(sample_dict) == "a": + sample_dict["data.partial"] = 1 + return sample_dict + +class TestCollate(unittest.TestCase): + def test_collate_default(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "nps": [np.array(4), np.array(2), np.array(5), np.array(1), np.array(4)], + "torch": [torch.tensor(7), torch.tensor(4), torch.tensor(9), torch.tensor(2), torch.tensor(4)], + "not_important": [12] * 5 + } + df = pds.DataFrame(data) + + # create simple pipeline + op_df = OpReadDataframe(df) + op_partial = OpCustomCollateDefTest() + pipeline = PipelineDefault("test", [(op_df, {}), (op_partial, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + # Use the collate function + dl = DataLoader(dataset, 3, collate_fn=CollateDefault(skip_keys=["data.not_important"], raise_error_key_missing=False)) + batch = next(iter(dl)) + + # verify + self.assertTrue("data.sample_id" in batch) + self.assertListEqual(batch["data.sample_id"], ["a", "b", "c"]) + self.assertTrue((batch["data.values"] == torch.tensor([7, 4, 9])).all()) + self.assertTrue( "data.nps" in batch) + self.assertTrue((batch["data.nps"] == torch.stack([torch.tensor(4), torch.tensor(2), torch.tensor(5)])).all()) + self.assertTrue("data.torch" in batch) + self.assertTrue((batch["data.torch"] == torch.stack([torch.tensor(7), torch.tensor(4), torch.tensor(9)])).all()) + self.assertTrue("data.partial" in batch) + self.assertListEqual(batch["data.partial"], [1, None, None]) + self.assertFalse("data.not_important" in batch) + + + def test_pad_all_tensors_to_same_size(self): + a = torch.zeros((1, 1, 3)) + b = torch.ones((1, 2, 1)) + values = CollateDefault.pad_all_tensors_to_same_size([a, b]) + + self.assertTrue((np.array(values.shape[1:]) == np.maximum(a.shape, b.shape)).all()) + self.assertTrue((values[1][:, :, :1] == b).all()) + self.assertTrue(values[1].sum() == b.sum()) + + def test_pad_all_tensors_to_same_size_bs_1(self): + a = torch.ones((1, 2, 1)) + values = CollateDefault.pad_all_tensors_to_same_size([a]) + self.assertTrue((values[0] == a).all()) + + def test_pad_all_tensors_to_same_size_bs_3(self): + a = torch.ones((1, 2, 3)) + b = torch.ones((3, 2, 1)) + c = torch.ones((1, 3, 2)) + values = CollateDefault.pad_all_tensors_to_same_size([a, b, c]) + self.assertListEqual(list(values.shape), [3, 3, 3, 3]) + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/utils/tests/test_dataset_export.py b/fuse/data/utils/tests/test_dataset_export.py new file mode 100644 index 000000000..f7bd83084 --- /dev/null +++ b/fuse/data/utils/tests/test_dataset_export.py @@ -0,0 +1,69 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest + +from tempfile import mkstemp +import pandas as pds + + +from fuse.utils.file_io.file_io import read_dataframe +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.utils.export import ExportDataset +from fuse.data.pipelines.pipeline_default import PipelineDefault + + + + +class TestDatasetExport(unittest.TestCase): + def test_export_to_dataframe(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "not_important": [12] * 5 + } + df = pds.DataFrame(data) + + # create simple pipeline + op = OpReadDataframe(df) + pipeline = PipelineDefault("test", [(op, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + df = df.set_index("sample_id") + + # export dataset - only get + export_df = ExportDataset.export_to_dataframe(dataset, ["data.values"]) + for sid in data["sample_id"]: + self.assertEqual(export_df.loc[sid]["data.values"], df.loc[sid]["values"]) + + # export dataset - including save + _, filename = mkstemp(suffix=".gz") + _ = ExportDataset.export_to_dataframe(dataset, ["data.values"], output_filename=filename) + export_df = read_dataframe(filename) + for sid in data["sample_id"]: + self.assertEqual(export_df.loc[sid]["data.values"], df.loc[sid]["values"]) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/utils/tests/test_samplers.py b/fuse/data/utils/tests/test_samplers.py new file mode 100644 index 000000000..bd56196cb --- /dev/null +++ b/fuse/data/utils/tests/test_samplers.py @@ -0,0 +1,163 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import unittest +import pandas as pds +import numpy as np +from tqdm.std import tqdm +import torchvision +from torchvision import transforms +from torch.utils.data.dataloader import DataLoader + +from fuse.utils import Seed + +from fuse.data.ops.ops_read import OpReadDataframe +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.datasets.dataset_wrap_seq_to_dict import DatasetWrapSeqToDict +from fuse.data.utils.collates import CollateDefault +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.utils.samplers import BatchSamplerDefault + +class TestSamplers(unittest.TestCase): + def setUp(self): + pass + + def test_balanced_dataset(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=transform) + print(f"torch dataset size = {len(torch_dataset)}") + + num_classes = 10 + num_samples = len(torch_dataset) + + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label')) + dataset.create() + print(dataset.summary()) + batch_sampler = BatchSamplerDefault(dataset=dataset, + balanced_class_name='data.label', + num_balanced_classes=num_classes, + batch_size=32, + mode="approx", + balanced_class_weights=[1 / num_classes] * num_classes, + workers=10) + + labels = np.zeros(num_classes) + + # Create dataloader + dataloader = DataLoader(dataset=dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler, shuffle=False, drop_last=False) + iter1 = iter(dataloader) + for _ in tqdm(range(len(dataloader))): + batch_dict = next(iter1) + labels_in_batch = batch_dict['data.label'] + for label in labels_in_batch: + labels[label] += 1 + + # final balance + print(labels) + for idx in range(num_classes): + sampled = labels[idx] / num_samples + print(f'Class {idx}: {sampled * 100}% of data') + self.assertAlmostEqual(sampled, 1 / num_classes, delta=1 / num_classes * 0.5, msg=f'Unbalanced class {idx}, expected 0.1+-0.05 and got {sampled}') + + def test_not_equalbalance_dataset(self): + Seed.set_seed(1234) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_dataset = torchvision.datasets.MNIST('/tmp/mnist', download=True, train=True, transform=transform) + print(f"torch dataset size = {len(torch_dataset)}") + + num_classes = 10 + probs = 1 / num_classes + + # wrapping torch dataset + dataset = DatasetWrapSeqToDict(name='test', dataset=torch_dataset, sample_keys=('data.image', 'data.label')) + dataset.create() + + balanced_class_weights=[1]*5 +[3]*5 + batch_size = 20 + batch_sampler = BatchSamplerDefault(dataset=dataset, + balanced_class_name='data.label', + num_balanced_classes=num_classes, + batch_size=batch_size, + mode="exact", + balanced_class_weights=balanced_class_weights) + + # Create dataloader + labels = np.zeros(num_classes) + dataloader = DataLoader(dataset=dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler, shuffle=False, drop_last=False) + iter1 = iter(dataloader) + num_items = 0 + for _ in tqdm(range(len(dataloader))): + batch_dict = next(iter1) + labels_in_batch = batch_dict['data.label'] + for label in labels_in_batch: + labels[label] += 1 + num_items += 1 + + # final balance + print(labels) + for idx in range(num_classes): + sampled = labels[idx] / num_items + print(f'Class {idx}: {sampled * 100}% of data') + self.assertEqual(sampled, balanced_class_weights[idx] / batch_size) + + def test_sampler_default(self): + # datainfo + data = { + "sample_id": ["a", "b", "c", "d", "e"], + "values": [7, 4, 9, 2, 4], + "class": [0, 1, 2, 0, 0], + } + df = pds.DataFrame(data) + + # create simple pipeline + op_df = OpReadDataframe(df) + pipeline = PipelineDefault("test", [(op_df, {})]) + + # create dataset + dataset = DatasetDefault(data["sample_id"], dynamic_pipeline=pipeline) + dataset.create() + + # create sampler + batch_sampler = BatchSamplerDefault(dataset, batch_size=3, balanced_class_name="data.class", num_balanced_classes=3, workers=0) + + # Use the collate function + dl = DataLoader(dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler) + batch = next(iter(dl)) + + # verify + self.assertEqual(len(batch_sampler), 3) + self.assertIn(0, batch["data.class"]) + self.assertIn(1, batch["data.class"]) + self.assertIn(2, batch["data.class"]) + + +if __name__ == '__main__': + unittest.main() diff --git a/fuse/data/visualizer/visualizer_base.py b/fuse/data/visualizer/visualizer_base.py index a42b7eec4..e8b29b0d8 100644 --- a/fuse/data/visualizer/visualizer_base.py +++ b/fuse/data/visualizer/visualizer_base.py @@ -1,45 +1,51 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from abc import ABC, abstractmethod -from typing import Any - +from abc import ABC, abstractclassmethod, abstractmethod +from typing import Dict, Any, List class VisualizerBase(ABC): - - @abstractmethod - def visualize(self, sample: Any, block: bool = True) -> None: + + def __init__(self) -> None: + super().__init__() + + def _preprocess(self, vis_data: Dict[str, Any]): """ - visualize sample - :param sample: sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None + get the collected data from the sample, that the visProbe has collected and generated data for actual visualization + that the _show method can process + + :param vis_data: the collected data """ - raise NotImplementedError + return vis_data @abstractmethod - def visualize_aug(self, orig_sample: Any, aug_sample: Any, block: bool = True) -> None: + def _show(self, vis_data: List): """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: original sample - :param aug_sample: augmented sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None + actual visualization function, gets a preprocessed collection of items to visualize/compare and shows + a visualization window that is blocking. + should be overriden by a specific visualizer + + :param vis_data: preprocessed visualization items to display """ - raise NotImplementedError + raise "should implement abstract method" + + def show(self, vis_data): + data = self._preprocess(vis_data) + self._show(data) + +class PrintVisual(VisualizerBase): + """ + basic visualizer example that just prints the data string representation to the console + """ + def __init__(self) -> None: + super().__init__() + + def _show(self, vis_data): + if type(vis_data) is dict: + print("showing single item") + print(vis_data) + else: + print(f"comparing {len(vis_data)} items:") + for item in vis_data: + print(item) + + def show(self, vis_data): + data = self._preprocess(vis_data) + self._show(data) diff --git a/fuse/data/visualizer/visualizer_default.py b/fuse/data/visualizer/visualizer_default.py deleted file mode 100644 index b3d7f6eef..000000000 --- a/fuse/data/visualizer/visualizer_default.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -from typing import Optional, Iterable, Any, Tuple - -import matplotlib.pyplot as plt - -from fuse.data.visualizer.visualizer_base import VisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state -import fuse.utils.imaging.image_processing as ImageProcessing -import torch - - -class VisualizerDefault(VisualizerBase): - """ - Visualizer for data including single 2D image with optional mask - """ - - def __init__(self, image_name: str, mask_name: Optional[str] = None, - label_name: Optional[str] = None, metadata_names: Iterable[str] = tuple(), - pred_name: Optional[str] = None, - gray_scale: bool = True): - """ - :param image_name: hierarchical key name of the image in batch_dict - :param mask_name: hierarchical key name of the mask (gt map) in batch_dict. - Optional, won't be displayed if not specified. - :param label_name: hierarchical key name of the to a global label in batch_dict. - Optional, won't be displayed if not specified. - :param metadata_names: list of hierarchical key name of the metadata - will be printed for every sample - :param pred_name: hierarchical key name of the prediction in batch_dict. - Optional, won't be displayed if not specified. - :param gray_scale: If True, each channel will be displayed as gray scale image. Otherwise, assuming 3 channels and RGB image either normalize to [0-1] or to [0-255] - """ - # log object input state - log_object_input_state(self, locals()) - - # store input parameters - self.image_pointer = image_name - self.mask_name = mask_name - self.label_name = label_name - self.metadata_pointers = metadata_names - self.pred_name = pred_name - self.matching_function = ImageProcessing.match_img_to_input - self._gray_scale = gray_scale - - def extract_data(self, sample: dict) -> Tuple[Any, Any, Any, Any, Any]: - """ - extract required data to visualize from sample - :param sample: global dict of a sample - :return: image, mask, label, metadata - """ - - # image - image = FuseUtilsHierarchicalDict.get(sample, self.image_pointer) - - # mask - if self.mask_name is not None: - mask = FuseUtilsHierarchicalDict.get(sample, self.mask_name) - else: - mask = None - - # label - if self.label_name is not None: - label = FuseUtilsHierarchicalDict.get(sample, self.label_name) - else: - label = '' - - # mask - if self.pred_name is not None: - pred_mask = FuseUtilsHierarchicalDict.get(sample, self.pred_name) - else: - pred_mask = None - - # metadata - metadata = {metadata_ptr: FuseUtilsHierarchicalDict.get(sample, metadata_ptr) for metadata_ptr in - self.metadata_pointers} - - return image, mask, label, metadata, pred_mask - - def visualize(self, sample: dict, block: bool = True) -> None: - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image, mask, label, metadata, pred_mask = self.extract_data(sample) - - if mask is not None: - mask = self.matching_function(mask, image) - - if pred_mask is not None: - pred_mask = self.matching_function(pred_mask, image) - - # visualize - if self._gray_scale: - num_channels = image.shape[0] - - if pred_mask is not None: - fig, ax = plt.subplots(num_channels, pred_mask.shape[0]+1, squeeze=False) - else: - fig, ax = plt.subplots(num_channels, 1, squeeze=False) - - for channel_idx in range(num_channels): - ax[channel_idx, 0].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(label))) - - ax[channel_idx, 0].imshow(image[channel_idx].squeeze(), cmap='gray') - if mask is not None: - ax[channel_idx, 0].imshow(mask[channel_idx], alpha=0.3) - - if pred_mask is not None: - for c_id in range(pred_mask.shape[0]): - max_prob = pred_mask[c_id].max() - ax[channel_idx, c_id+1].title.set_text('image (ch %d) (max prob %s)' % (channel_idx, str(max_prob))) - - ax[channel_idx, c_id+1].imshow(image[channel_idx].squeeze(), cmap='gray') - ax[channel_idx, c_id+1].imshow(pred_mask[c_id], alpha=0.3) - else: - if pred_mask is not None: - fig, ax = plt.subplots(1, pred_mask.shape[0]+1, squeeze=False) - else: - fig, ax = plt.subplots(1, 1, squeeze=False) - - ax[0, 0].title.set_text('image (lbl %s)' % (str(label))) - - image = image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - image = torch.clip(image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 0].imshow(image) - if mask is not None: - ax[0, 0].imshow(mask, alpha=0.3) - - if pred_mask is not None: - for c_id in range(pred_mask.shape[0]): - max_prob = pred_mask[c_id].max() - ax[0, c_id+1].title.set_text('image(max prob %s)' % (str(max_prob))) - ax[0, c_id+1].imshow(pred_mask[c_id], cmap='gray') - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - lgr.info(metadata) - lgr.info('image label = ' + str(label)) - lgr.info('------------------------------------------') - - try: - mng = plt.get_current_fig_manager() - mng.resize(*mng.window.maxsize()) - except: - pass - - fig.tight_layout() - plt.show(block=block) - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: batch_dict to extract the original sample from - :param aug_sample: batch_dict to extract the augmented sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - orig_image, orig_mask, orig_label, orig_metadata, pred_mask = self.extract_data(orig_sample) - aug_image, aug_mask, aug_label, aug_metadata, pred_mask = self.extract_data(aug_sample) - - # visualize - if self._gray_scale: - num_channels = orig_image.shape[0] - - fig, ax = plt.subplots(num_channels, 2, squeeze=False) - for channel_idx in range(num_channels): - # orig - ax[channel_idx, 0].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(orig_label))) - ax[channel_idx, 0].imshow(orig_image[channel_idx].squeeze(), cmap='gray') - if (orig_mask is not None) and (None not in orig_mask): - ax[channel_idx, 0].imshow(orig_mask, alpha=0.3) - - # augmented - ax[channel_idx, 1].title.set_text('image (ch %d) (lbl %s)' % (channel_idx, str(aug_label))) - ax[channel_idx, 1].imshow(aug_image[channel_idx].squeeze(), cmap='gray') - if (aug_mask is not None) and (None not in aug_mask): - ax[channel_idx, 1].imshow(aug_mask, alpha=0.3) - else: - fig, ax = plt.subplots(1, 2, squeeze=False) - # orig - ax[0, 0].title.set_text('image (lbl %s)' % (str(orig_label))) - orig_image = orig_image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - orig_image = torch.clip(orig_image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 0].imshow(orig_image) - if (orig_mask is not None) and (None not in orig_mask): - ax[0, 0].imshow(orig_mask, alpha=0.3) - - # augmented - ax[0, 1].title.set_text('image (lbl %s)' % (str(aug_label))) - aug_image = aug_image.permute((1,2,0)) # assuming torch dimension order [C, H, W] and conver to [H, W, C] - aug_image = torch.clip(aug_image, 0.0, 1.0) # assuming range is [0-1] and clip values that might be a bit out of range - ax[0, 1].imshow(aug_image) - if (aug_mask is not None) and (None not in aug_mask): - ax[1].imshow(aug_mask, alpha=0.3) - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - lgr.info("original") - lgr.info(orig_metadata) - lgr.info('image label = ' + str(orig_label)) - lgr.info("augmented") - lgr.info(aug_metadata) - lgr.info('image label = ' + str(aug_label)) - lgr.info('------------------------------------------') - - try: - mng = plt.get_current_fig_manager() - mng.resize(*mng.window.maxsize()) - except: - pass - - fig.tight_layout() - plt.show(block=block) diff --git a/fuse/data/visualizer/visualizer_default_3d.py b/fuse/data/visualizer/visualizer_default_3d.py deleted file mode 100644 index 07603dff7..000000000 --- a/fuse/data/visualizer/visualizer_default_3d.py +++ /dev/null @@ -1,276 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import logging -from typing import Optional, Iterable, Any, Tuple - -import matplotlib.pyplot as plt -from skimage.color import gray2rgb -from skimage.segmentation import mark_boundaries - -from fuse.data.visualizer.visualizer_base import VisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict -from fuse.utils.utils_logger import log_object_input_state - - -class Fuse3DVisualizerDefault(VisualizerBase): - """ - Visualiser for data including 3D volume with optional local annotations - """ - - def __init__(self, image_name: str, mask_name: Optional[str] = None, - label_name: Optional[str] = None, metadata_pointers: Iterable[str] = tuple(), - ): - """ - :param image_name: pointer to an image in batch_dict, image will be in shape (B,C,VOL). - :param mask_name: optional, pointer mask (gt map) in batch_dict. If mask location is not part of the batch dict - - override the extract_data method - :param label_name: pointer to a global label in batch_dict - :param metadata_pointers: list of pointers to metadata - will be printed for every sample - - """ - # log object input state - log_object_input_state(self, locals()) - - # store input parameters - self.image_name = image_name - self.mask_name = mask_name - self.label_name = label_name - self.metadata_pointers = metadata_pointers - - def extract_data(self, sample: dict) -> Tuple[Any, Any, Any, Any]: - """ - extract required data to visualize from sample - :param sample: global dict of a sample - :return: image, mask, label, metadata - """ - - # image - image = FuseUtilsHierarchicalDict.get(sample, self.image_name) - assert len(image.shape) == 4 - image = image.numpy() - - # mask - if self.mask_name is not None: - if not isinstance(self.mask_name, list): - self.mask_name = [self.mask_name] - masks = [FuseUtilsHierarchicalDict.get(sample, mask_name).numpy() for mask_name in self.mask_name] - else: - masks = None - - # label - if self.label_name is not None: - label = FuseUtilsHierarchicalDict.get(sample, self.label_name) - else: - label = '' - - # metadata - metadata = {metadata_ptr: FuseUtilsHierarchicalDict.get(sample, metadata_ptr) for metadata_ptr in - self.metadata_pointers} - - return image, masks, label, metadata - - def visualize(self, sample: dict, block: bool = True) -> None: - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image, masks, label, metadata = self.extract_data(sample) - # visualize - chan = 0 - chan_image = image[chan, ...] - - def key_event(e: Any, position_list: Any): # using left/right key to move between slices - def on_press(e: Any): # use mouse click in order to toggle mask/no mask - 'toggle the visible state of the two images' - if e.button: - vis_image = plt_img.get_visible() - vis_mask = plt_mask.get_visible() - plt_img.set_visible(not vis_image) - plt_mask.set_visible(not vis_mask) - plt.draw() - - if e.key == "right": - position_list[0] += 1 - elif e.key == "left": - position_list[0] -= 1 - elif e.key == "up": - position_list[1] += 1 - elif e.key == "down": - position_list[1] -= 1 - else: - return - position_list[0] = position_list[0] % image.shape[1] - position_list[1] = position_list[1] % image.shape[0] - chan_image = image[position_list[1]] - - ax.cla() - slice_image = gray2rgb(chan_image[position_list[0]]) - if masks is not None: - slice_image_with_mask = slice_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(masks): - slice_image_with_mask = mark_boundaries(slice_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt.title(f'Slice {position_list[0]} channel {position_list[1]}') - plt_img = ax.imshow(slice_image) - plt_img.set_visible(False) - if (mask is not None) and (None not in mask): - plt_mask = ax.imshow(slice_image_with_mask) - plt_mask.set_visible(True) - fig.canvas.mpl_connect('button_press_event', on_press) - fig.canvas.draw() - - fig = plt.figure() - position_list = [0, 0] - plt.title(f'Slice {position_list[0]} channel {position_list[1]}') - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - ax = fig.add_subplot(111) - slice_image = gray2rgb(chan_image[0]) - if masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(masks): - slice_image = mark_boundaries(slice_image, mask[position_list[0]].astype(int), color=colors[index % len(colors)]) - ax.imshow(slice_image) - - lgr = logging.getLogger('Fuse') - lgr.info('------------------------------------------') - if metadata is not None: - if isinstance(metadata, dict): - lgr.info(FuseUtilsHierarchicalDict.to_string(metadata), {'color': 'magenta'}) - else: - lgr.info(metadata) - - if label is not None and label != '': - lgr.info('image label = ' + str(label), {'color': 'magenta'}) - lgr.info('------------------------------------------') - - plt.show() - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: batch_dict to extract the original sample from - :param aug_sample: batch_dict to extract the augmented sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - - # extract data - orig_image, orig_masks, orig_label, orig_metadata = self.extract_data(orig_sample) - aug_image, aug_masks, aug_label, aug_metadata = self.extract_data(aug_sample) - - # visualize - def key_event(e: Any, position_list: Any): # using left/right key to move between slices - def on_press(e: Any): # use mouse click in order to toggle mask/no mask - 'toggle the visible state of the two images' - if e.button: - # Toggle image with no augmentations - vis_image = plt_img.get_visible() - vis_mask = plt_mask.get_visible() - plt_img.set_visible(not vis_image) - plt_mask.set_visible(not vis_mask) - # Toggle image with augmentations - vis_aug_image = plt_aug_img.get_visible() - vis_aug_mask = plt_aug_mask.get_visible() - plt_aug_img.set_visible(not vis_aug_image) - plt_aug_mask.set_visible(not vis_aug_mask) - - plt.draw() - - if e.key == "right": - position_list[0] += 1 - elif e.key == "left": - position_list[0] -= 1 - elif e.key == "up": - position_list[1] += 1 - elif e.key == "down": - position_list[1] -= 1 - else: - return - position_list[0] = position_list[0] % orig_image.shape[1] - position_list[1] = position_list[1] % orig_image.shape[0] - chan_image = orig_image[position_list[1]] - chan_aug_image = aug_image[position_list[1]] - # clearing subplots - axs[0].cla() - axs[1].cla() - # creating image without augmentations and with toggling mask - slice_image = gray2rgb(chan_image[position_list[0]]) - plt_img = axs[0].imshow(slice_image) - plt_img.set_visible(False) - - if orig_masks is not None: - slice_image_with_mask = slice_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(orig_masks): - slice_image_with_mask = mark_boundaries(slice_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt_mask = axs[0].imshow(slice_image_with_mask) - plt_mask.set_visible(True) - - # creating image with augmentations and with toggling mask - slice_aug_image = gray2rgb(chan_aug_image[position_list[0]]) - plt_aug_img = axs[1].imshow(slice_aug_image) - plt_aug_img.set_visible(False) - if aug_masks is not None: - slice_aug_image_with_mask = slice_aug_image - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(aug_masks): - slice_aug_image_with_mask = mark_boundaries(slice_aug_image_with_mask, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - plt_aug_mask = axs[1].imshow(slice_aug_image_with_mask) - plt_aug_mask.set_visible(True) - # drawing - axs[0].title.set_text(f"Original - Slice {position_list[0]} channel {position_list[1]}") - axs[1].title.set_text(f"Augmented - Slice {position_list[0]} channel {position_list[1]}") - fig.canvas.mpl_connect('button_press_event', on_press) - fig.canvas.draw() - - fig, axs = plt.subplots(ncols=2) - position_list = [0, 0] - chan_image = orig_image[position_list[1]] - chan_aug_image = aug_image[position_list[1]] - - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - slice_image = gray2rgb(chan_image[position_list[0]]) - if orig_masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(orig_masks): - slice_image = mark_boundaries(slice_image, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - slice_aug_image = gray2rgb(chan_aug_image[0]) - if aug_masks is not None: - colors = [(1, 1, 0), (0, 1, 1), (1, 0, 1)] - for index, mask in enumerate(aug_masks): - slice_aug_image = mark_boundaries(slice_aug_image, mask[position_list[0]].astype(int), - color=colors[index % len(colors)]) - - axs[0].title.set_text(f"Original - Slice {position_list[0]} channel {position_list[1]}") - axs[1].title.set_text(f"Augmented - Slice {position_list[0]} channel {position_list[1]}") - axs[0].imshow(slice_image) - axs[1].imshow(slice_aug_image) - plt.show() diff --git a/fuse/data/visualizer/visualizer_image_analysis.py b/fuse/data/visualizer/visualizer_image_analysis.py deleted file mode 100644 index d2fbdf63b..000000000 --- a/fuse/data/visualizer/visualizer_image_analysis.py +++ /dev/null @@ -1,112 +0,0 @@ -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -from typing import Any - -import matplotlib.pyplot as plt -import numpy as np - -from fuse.data.visualizer.visualizer_base import VisualizerBase -from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict - - -class VisualizerImageAnalysis(VisualizerBase): - """ - Class for producing analysis of an image - """ - - def __init__(self, image_name: str): - """ - :param image_name: pointer to an image in batch_dict - - """ - self.image_name = image_name - - def visualize(self, sample: Any, block: bool = True): - """ - visualize sample - :param sample: batch_dict - to extract the sample from - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - # extract data - image = FuseUtilsHierarchicalDict.get(sample, self.image_name) - image = image.numpy() - num_channels = image.shape[0] - for i in range(num_channels): - channel_image = image[i, ...] - if len(channel_image.shape) == 3: - self.visualize_3dimage(channel_image, title="Image and its Histogram of channel:" + str(i), block=block) - else: - assert len(channel_image.shape) == 2 - self.visualise_2dimage(channel_image, title="Image and its Histogram of channel:" + str(i), block=block) - - def visualize_3dimage(self, image: np.array, title: str = "Image and its Histogram", bins=256, block: bool = True) -> None: - def key_event(e, curr_pos): - if e.key == "right": - curr_pos[0] = curr_pos[0] + 1 - elif e.key == "left": - curr_pos[0] = curr_pos[0] - 1 - else: - return - curr_pos[0] = curr_pos[0] % image.shape[0] - - axs[0].cla() - axs[1].cla() - axs[0].imshow(image[curr_pos[0]]) - axs[1].hist(image[curr_pos[0]].ravel(), bins=bins, fc='k', ec='k') - fig.canvas.draw() - plt.suptitle(title + " at slice:" + str(curr_pos[0])) - - fig, axs = plt.subplots(2) - position_list = [0] - fig.canvas.mpl_connect('key_press_event', lambda event: key_event(event, position_list)) - axs[0].imshow(image[0]) - axs[1].hist(image.ravel(), bins=bins, fc='k', ec='k') # calculating histogram - plt.suptitle(title + " at slice:" + str(position_list[0])) - plt.show() - - def visualise_2dimage(self, image: np.array, title: str = "Image and its Histogram", block: bool = True) -> None: - """ - visualize sample - :param image: image in the form of np.array - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - fig = plt.figure() - fig.add_subplot(221) - plt.title('image') - plt.imshow(image) - - fig.add_subplot(222) - plt.title('histogram') - plt.hist(image.ravel(), bins=256, fc='k', ec='k') # calculating histogram - - plt.suptitle(title) - plt.show(block=block) - - def visualize_aug(self, orig_sample: dict, aug_sample: dict, block: bool = True) -> None: - """ - Visualise and compare augmented and non-augmented version of the sample - :param orig_sample: original sample - :param aug_sample: augmented sample - :param block: set to False if the process should not be blocked until the plot will be closed - :return: None - """ - raise NotImplementedError diff --git a/fuse/dl/managers/callbacks/callback_infer_results.py b/fuse/dl/managers/callbacks/callback_infer_results.py index d115196a1..90c831600 100644 --- a/fuse/dl/managers/callbacks/callback_infer_results.py +++ b/fuse/dl/managers/callbacks/callback_infer_results.py @@ -52,7 +52,7 @@ def __init__(self, output_file: Optional[str] = None, output_columns: Optional[L pass def reset(self): - self.aggregated_dict = {'descriptor': [], 'output': {}} + self.aggregated_dict = {'id': [], 'output': {}} self.infer_results_df = pd.DataFrame() def on_epoch_begin(self, mode: str, epoch: int) -> None: @@ -82,8 +82,7 @@ def on_epoch_end(self, mode: str, epoch: int, epoch_results: Dict = None) -> Non # prepare dataframe from the results infer_results_df = pd.DataFrame() - infer_results_df['descriptor'] = self.aggregated_dict['descriptor'] - infer_results_df['id'] = self.aggregated_dict['descriptor'] # for future support - evaluation package + infer_results_df['id'] = self.aggregated_dict['id'] for output in FuseUtilsHierarchicalDict.get_all_keys(self.aggregated_dict['output']): infer_results_df[output] = list( @@ -109,10 +108,10 @@ def on_batch_end(self, mode: str, batch: int, batch_dict: Dict = None) -> None: return # for infer we need the descriptor and the output predictions - descriptors = batch_dict['data'].get('descriptor', None) - if isinstance(descriptors, Tensor): - descriptors = list(descriptors.detach().cpu().numpy()) - self.aggregated_dict['descriptor'].extend(descriptors) + sample_ids = batch_dict['data'].get('sample_id', None) + if isinstance(sample_ids, Tensor): + sample_ids = list(sample_ids.detach().cpu().numpy()) + self.aggregated_dict['id'].extend(sample_ids) if self.output_columns is not None and len(self.output_columns) > 0: output_cols = self.output_columns diff --git a/fuse/dl/managers/manager_default.py b/fuse/dl/managers/manager_default.py index 8c1f4e827..08d3a5090 100644 --- a/fuse/dl/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -20,6 +20,7 @@ import logging import os import traceback +from fuse.dl.managers.callbacks.callback_infer_results import InferResultsCallback import numpy as np import pandas as pd @@ -27,17 +28,11 @@ import torch.nn as nn from torch.optim.optimizer import Optimizer from torch.utils.data.dataloader import DataLoader -from tqdm import trange, tqdm from typing import Dict, Any, List, Iterator, Optional, Union, Sequence, Hashable, Callable -from fuse.data.data_source.data_source_base import DataSourceBase -from fuse.data.dataset.dataset_base import DatasetBase -from fuse.data.processor.processor_base import ProcessorBase -from fuse.data.visualizer.visualizer_base import VisualizerBase from fuse.dl.losses.loss_base import LossBase from fuse.dl.managers.callbacks.callback_base import Callback from fuse.dl.managers.callbacks.callback_debug import CallbackDebug -from fuse.dl.managers.callbacks.callback_infer_results import InferResultsCallback from fuse.dl.managers.manager_state import ManagerState from fuse.eval import MetricBase from fuse.dl.models.model_ensemble import ModelEnsemble @@ -48,6 +43,7 @@ from fuse.utils.utils_hierarchical_dict import FuseUtilsHierarchicalDict from fuse.utils.utils_logger import log_object_input_state from fuse.utils.misc.misc import Misc, get_pretty_dataframe +from tqdm import trange class ManagerDefault: @@ -148,11 +144,10 @@ def set_objects(self, self.logger.info(f'Manager - debug mode - append debug callback', {'color': 'red'}) pass - def _save_objects(self, validation_dataloader: DataLoader) -> None: + def _save_objects(self) -> None: """ Saves objects using torch.save (net, losses, metrics, best_epoch_source, optimizer, lr_scheduler, callbacks). Each parameter is saved into a separate file (called losses.pth, metrics.pth, etc) under self.output_model_dir. - :param validation_dataloader: dataloader to extract dataset definitions from (saved on inference_dataset.pth) """ def _torch_save(parameter_to_save: Any, parameter_name: str) -> None: @@ -174,12 +169,7 @@ def _torch_save(parameter_to_save: Any, parameter_name: str) -> None: _torch_save(self.state.best_epoch_source, 'best_epoch_source') _torch_save(self.state.train_params, 'train_params') - # also save validation_dataset in inference mode - if validation_dataloader is not None: - DatasetBase.save(validation_dataloader.dataset, mode=DatasetBase.SaveMode.INFERENCE, - filename=os.path.join(self.state.output_model_dir, "inference_dataset.pth")) - pass - + def load_objects(self, input_model_dir: Union[str, Sequence[str]], list_of_object_names: List[str] = None, mode: str = 'infer') -> Dict[str, Any]: """ Loads objects from torch saved pth files under input_model_dir. @@ -345,7 +335,7 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader {'color': 'red', 'attrs': 'bold'}) # save model and parameters for future use (e.g., infer or resume_from_weights) - self._save_objects(validation_dataloader) + self._save_objects() # save datasets summary into file and logger self._handle_dataset_summaries(train_dataloader, validation_dataloader) @@ -413,85 +403,14 @@ def train(self, train_dataloader: DataLoader, validation_dataloader: DataLoader pass - def visualize(self, visualizer: VisualizerBase, data_loader: Optional[DataLoader] = None, infer_processor: Optional[ProcessorBase] = None, - descriptors: Optional[List[Hashable]] = None, device: str = 'cuda', display_func: Optional[Callable] = None): - - """ - Visualize data including the input and the output. - Expected Sequence: - 1. Using a loaded model to extract the output: - manager = ManagerDefault() - - manager.load_objects(, mode='infer') # this method can load either a single model or an ensemble - manager.load_checkpoint(checkpoint=, mode='infer') - manager.visualize(visualizer=visualizer, - data_loader=dataloader, - descriptors=, - display_func=, - infer_processor=None) - - 2. using inference processor - manager = ManagerDefault() - manager.visualize(visualizer=visualizer, - data_loader=dataloader, - descriptors=, - display_func=, - infer_processor=infer_processor) - - :param visualizer: The visualizer, getting a batch_dict as an input and doing it's magic - :param data_loader: data loader as used for validation / training / inference - :param infer_processor: Optional, if specified this function will not run the model and instead extract the output from infer processor - :param descriptors: Optional. List of sample descriptors, if None will go over the entire dataset. Might be also list of dataset indices. - :param device: options: 'cuda', 'cpu', 'cuda:0', ... (default 'cuda') - :param display_func: Function getting the batch dict as an input and returns boolean specifying if to visualize this sample or not. - :return: None - """ - dataset: DatasetBase = data_loader.dataset - if infer_processor is None: - if not hasattr(self, 'net') or self.state.net is None: - self.logger.error(f"Cannot visualize without either net or infer_processor") - raise Exception(f"Cannot visualize without either net or infer_processor") - - # prepare net - self.state.net = self.state.net.to(device) - if self.state.device != 'cpu': - self.state.net = nn.DataParallel(self.state.net) - - if descriptors is None: - descriptors = range(len(dataset)) - for desc in tqdm(descriptors): - # extract sample - batch_dict = dataset.get(desc) - if infer_processor is None: - # apply model in case infer processor is not specified - # convert dimensions to batch - batch_dict = dataset.collate_fn([batch_dict]) - # run model - batch_dict['model'] = self.state.net(batch_dict) - # convert dimensions back to single sample - FuseUtilsHierarchicalDict.apply_on_all(batch_dict, Misc.squeeze_obj) - else: - # get the sample descriptor of the sample - sample_descriptor = FuseUtilsHierarchicalDict.get(batch_dict, 'data.descriptor') - # get the infer data - infer_data = infer_processor(sample_descriptor) - # add infer data to batch_dict - for key in infer_data: - FuseUtilsHierarchicalDict.set(batch_dict, key, infer_data[key]) - - if display_func is None or display_func(batch_dict): - visualizer.visualize(batch_dict) - def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, checkpoint: Optional[Union[str, int, Sequence[Union[str, int]]]] = None, - data_source: Optional[DataSourceBase] = None, data_loader: Optional[DataLoader] = None, - num_workers: Optional[int] = 4, batch_size: Optional[int] = 2, + data_loader: Optional[DataLoader] = None, output_columns: List[str] = None, output_file_name: str = None, strict: bool = True, append_default_inference_callback: bool = True, checkpoint_index: int = 0) -> pd.DataFrame: """ - Inference of net on data. Either the data_source or data_loader should be defined. - When data_source is defined, validation_dataset is loaded from the original model_dir and is used to create a dataloader. + Inference of net on data. Returns the inference Results as dict: { 'descriptor': [id_1, id_2, ...], @@ -524,10 +443,7 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, (either checkpoint_best_epoch.pth, checkpoint_last_epoch.pth or checkpoint_{checkpoint}_epoch.pth) when None, no checkpoint is loaded (assumes that the weights were already loaded. in ensemble mode, can provide either one checkpoint for all models or a sequence of separate checkpoints for each. - :param data_source: data source to use :param data_loader: data loader to use - :param num_workers: number of processes for Dataloader, effective only if 'data_loader' param is None - :param batch_size: batch size for Dataloader, effective only if 'data_loader' param is None :param output_columns: output columns to return. When None (default) all columns are returned. When not None, InferResultsCallback callback is created. @@ -538,14 +454,6 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, :return: infer results in a DataFrame """ - # debug - num workers - override_num_workers = FuseDebug().get_setting('manager_override_num_dataloader_workers') - if override_num_workers != 'default': - num_workers = override_num_workers - if data_loader is not None: - data_loader.num_workers = override_num_workers - self.logger.info(f'Manager - debug mode - override dataloader num_workers to {override_num_workers}', {'color': 'red'}) - if input_model_dir is not None: # user provided model dir(s), and Manager has no 'net' attribute - need to load modules if not hasattr(self.state, 'net'): @@ -570,30 +478,6 @@ def infer(self, input_model_dir: Optional[Union[str, Sequence]] = None, if append_default_inference_callback: self.callbacks.append(InferResultsCallback(output_file=output_file_name, output_columns=output_columns)) - # either optional_datasource or optional_dataloader - if data_loader is not None and data_source is not None: - self.logger.error('Cannot have both data_loader and data_source defined') - raise Exception('Cannot have both data_loader and data_source defined') - if data_loader is None and data_source is None: - self.logger.error('Either data_loader or data_source should be defined') - raise Exception('Either data_loader or data_source should be defined') - - if data_loader is None: - # need to create a data loader - # first check that we have the model dir to get these data from - if input_model_dir is None: - self.logger.error('Missing parameter input_model_dir! Cannot load data_set from previous model.') - raise Exception('Missing parameter input_model_dir! Cannot load data_set from previous model.') - - if isinstance(input_model_dir, (tuple, list)): - data_set_filename = os.path.join(input_model_dir[0], "inference_dataset.pth") - else: - data_set_filename = os.path.join(input_model_dir, "inference_dataset.pth") - self.logger.info(f"Loading data source definitions from {data_set_filename}", {'color': 'yellow'}) - infer_dataset = DatasetBase.load(filename=data_set_filename, override_datasource=data_source) - data_loader = DataLoader(dataset=infer_dataset, shuffle=False, drop_last=False, batch_sampler=None, - batch_size=batch_size, num_workers=num_workers, collate_fn=infer_dataset.collate_fn) - # prepare net self.state.net = self.state.net.to(self.state.device) if self.state.device != 'cpu': @@ -806,12 +690,12 @@ def update_scheduler(self, train_results: Dict, validation_results: Dict) -> Non :param train_results: hierarchical dict train epoch results. contains the keys: losses, metrics. - losses is a dict where values are the commputed mean loss for each loss. + losses is a dict where values are the computed mean loss for each loss. and an additional key 'total_loss' which is the mean total loss of the epoch. metrics is a dict where values are the computed metrics. :param validation_results: hierarchical validation epoch results dict. contains the keys: losses, metrics. - losses is a dict where values are the commputed mean loss for each loss. + losses is a dict where values are the computed mean loss for each loss. and an additional key 'total_loss' which is the mean total loss of the epoch. metrics is a dict where values are the computed metrics. Note, if validation was not done on the epoch, this parameter can be None @@ -1037,7 +921,10 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat :param validation_dataloader: validation data (can be None) """ # train dataset summary - dataset_summary = train_dataloader.dataset.summary() + if hasattr(train_dataloader.dataset, "summary"): + dataset_summary = train_dataloader.dataset.summary() + else: + dataset_summary = "" train_dataset_summary_file = os.path.join(self.state.output_model_dir, 'train_dataset_summary.txt') with open(train_dataset_summary_file, 'w') as sum_file: @@ -1047,7 +934,10 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat # validation dataset summary, if exists if validation_dataloader is not None: - dataset_summary = validation_dataloader.dataset.summary() + if hasattr(validation_dataloader.dataset, "summary"): + dataset_summary = validation_dataloader.dataset.summary() + else: + dataset_summary = "" validation_dataset_summary_file = os.path.join(self.state.output_model_dir, 'validation_dataset_summary.txt') with open(validation_dataset_summary_file, 'w') as sum_file: sum_file.write(dataset_summary) @@ -1067,9 +957,6 @@ def _extend_results_dict(mode: str, current_dict: Dict, aggregated_dict: Dict) - if mode == 'infer': return {} else: - # handle the case where batch dict is empty (the end of the last virtual mini batch) - if current_dict == {}: - return aggregated_dict # for train and validation we need the loss values cur_keys = FuseUtilsHierarchicalDict.get_all_keys(current_dict) # aggregate just keys that start with losses diff --git a/fuse/utils/data/collate.py b/fuse/utils/data/collate.py index 0e32b5d14..c3cd2f1e9 100644 --- a/fuse/utils/data/collate.py +++ b/fuse/utils/data/collate.py @@ -16,6 +16,7 @@ Created on June 30, 2021 """ +import logging from typing import Any, Callable, Dict, List, Sequence, Tuple from fuse.utils import NDict @@ -124,7 +125,11 @@ def uncollate(batch: Dict) -> List[Dict]: sample = NDict() for key in keys: if isinstance(batch[key], (np.ndarray, torch.Tensor, list, tuple)): - sample[key] = batch[key][sample_index] + try: + sample[key] = batch[key][sample_index] + except IndexError: + logging.error(f"Error - IndexError - key={key}, batch_size={batch_size}, len={batch[key]}") + raise else: sample[key] = batch[key] # broadcast single value for all batch diff --git a/fuse/utils/multiprocessing/run_multiprocessed.py b/fuse/utils/multiprocessing/run_multiprocessed.py index 8622aa498..f0c2c84d1 100644 --- a/fuse/utils/multiprocessing/run_multiprocessed.py +++ b/fuse/utils/multiprocessing/run_multiprocessed.py @@ -90,7 +90,7 @@ def some_worker(args): return ans args_list: a list in which each element is the input to func workers: number of processes to use. Use 0 for no spawning of processes (helpful when debugging) - copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accesible to worker_func. + copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accessible to worker_func. calling get_from_global_storage(...) will allow access to it from within any worker_func This allows to create a significant speedup in certain cases, and the main idea is that it allows to drastically reduce the amount of data that gets (automatically) pickled by python's multiprocessing library. @@ -134,12 +134,12 @@ def _run_multiprocessed_as_iterator_impl(worker_func, args_list, workers=0, verb worker_func: a worker function, must accept only a single positional argument and no optional args. For example: def some_worker(args): - speed, height, banana = args + speed: height, banana = args ... return ans args_list: a list in which each element is the input to func workers: number of processes to use. Use 0 for no spawning of processes (helpful when debugging) - copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accesible to worker_func. + copy_to_global_storage: Optional - to optimize the running time - the provided dict will be stored in a way that is accessible to worker_func. calling get_from_global_storage(...) will allow access to it from within any worker_func This allows to create a significant speedup in certain cases, and the main idea is that it allows to drastically reduce the amount of data that gets (automatically) pickled by python's multiprocessing library. diff --git a/fuse/utils/ndict.py b/fuse/utils/ndict.py index 98eb1b971..4523afdc2 100644 --- a/fuse/utils/ndict.py +++ b/fuse/utils/ndict.py @@ -71,7 +71,9 @@ def __init__(self, d: Union[dict, tuple, types.GeneratorType, NDict, None]=None) self._stored = {} elif isinstance(d, NDict): self._stored = d._stored - else: + else: + if not isinstance(d, dict): + d = dict(d) for k,d in d.items(): self[k] = d diff --git a/fuseimg/__init__.py b/fuseimg/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/__init__.py b/fuseimg/data/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/__init__.py b/fuseimg/data/ops/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/aug/color.py b/fuseimg/data/ops/aug/color.py new file mode 100644 index 000000000..94fb95f25 --- /dev/null +++ b/fuseimg/data/ops/aug/color.py @@ -0,0 +1,135 @@ +from typing import List, Optional +from fuse.data.ops.op_base import OpBase +from fuse.utils.ndict import NDict +from fuse.utils.rand.param_sampler import Gaussian +from fuseimg.data.ops.color import OpClip +from torch import Tensor +import torch + + +class OpAugColor(OpBase): + """ + Color augmentation for gray scale images of any dimensions, including addition, multiplication, gamma and contrast adjusting + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this op expects torch tensor of range [0, 1]. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, add: Optional[float] = None, mul: Optional[float] = None, + gamma: Optional[float] = None, contrast: Optional[float] = None, channels: Optional[List[int]] = None): + """ + :param key: key to a image stored in sample_dict: torch tensor of range [0, 1] representing an image to , + :param add: value to add to each pixel + :param mul: multiplication factor + :param gamma: gamma factor + :param contrast: contrast factor + :param channels: Apply clipping just over the specified channels. If set to None will apply on all channels. + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugColor expects torch Tensor, got {type(aug_input)}" + assert aug_input.min() >= 0.0 and aug_input.max() <= 1.0 , f"Error: OpAugColor expects tensor in range [0.0-1.0]. got [{aug_input.min()}-{aug_input.max()}]" + + aug_tensor = aug_input + if channels is None: + if add is not None: + aug_tensor = self.aug_op_add_col(aug_tensor, add) + if mul is not None: + aug_tensor = self.aug_op_mul_col(aug_tensor, mul) + if gamma is not None: + aug_tensor = self.aug_op_gamma(aug_tensor, 1.0, gamma) + if contrast is not None: + aug_tensor = self.aug_op_contrast(aug_tensor, contrast) + else: + if add is not None: + aug_tensor[channels] = self.aug_op_add_col(aug_tensor[channels], add) + if mul is not None: + aug_tensor[channels] = self.aug_op_mul_col(aug_tensor[channels], mul) + if gamma is not None: + aug_tensor[channels] = self.aug_op_gamma(aug_tensor[channels], 1.0, gamma) + if contrast is not None: + aug_tensor[channels] = self.aug_op_contrast(aug_tensor[channels], contrast) + + sample_dict[key] = aug_tensor + return sample_dict + + @staticmethod + def aug_op_add_col(aug_input: Tensor, add: float) -> Tensor: + """ + Adding a values to all pixels + :param aug_input: the tensor to augment + :param add: the value to add to each pixel + :return: the augmented tensor + """ + aug_tensor = aug_input + add + aug_tensor = OpClip.clip(aug_tensor, clip=(0.0, 1.0)) + return aug_tensor + + @staticmethod + def aug_op_mul_col(aug_input: Tensor, mul: float) -> Tensor: + """ + multiply each pixel + :param aug_input: the tensor to augment + :param mul: the multiplication factor + :return: the augmented tensor + """ + input_tensor = aug_input * mul + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + @staticmethod + def aug_op_gamma(aug_input: Tensor, gain: float, gamma: float) -> Tensor: + """ + Gamma augmentation + :param aug_input: the tensor to augment + :param gain: gain factor + :param gamma: gamma factor + :return: None + """ + input_tensor = (aug_input ** gamma) * gain + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + @staticmethod + def aug_op_contrast(aug_input: Tensor, factor: float) -> Tensor: + """ + Adjust contrast (notice - calculated across the entire input tensor, even if it's 3d) + :param aug_input:the tensor to augment + :param factor: contrast factor. 1.0 is neutral + :return: the augmented tensor + """ + calculated_mean = aug_input.mean() + input_tensor = ((aug_input - calculated_mean) * factor) + calculated_mean + input_tensor = OpClip.clip(input_tensor, clip=(0.0, 1.0)) + return input_tensor + + +class OpAugGaussian(OpBase): + """ + Add gaussian noise to numpy array or torch tensor of any dimensions + """ + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, mean: float = 0.0, std: float = 0.03, channels: Optional[List[int]] = None) -> Tensor: + """ + :param key: key to a tensor or numpy array stored in sample_dict: any dimension and any range + :param mean: mean gaussian distribution + :param std: std gaussian distribution + :param channels: Apply just over the specified channels. If set to None will apply on all channels. + """ + aug_input = sample_dict[key] + + aug_tensor = aug_input + if channels is None: + rand_patch = Gaussian(aug_tensor.shape, mean, std).sample() + aug_tensor = aug_tensor + rand_patch + else: + rand_patch = Gaussian(aug_tensor[channels].shape, mean, std).sample() + aug_tensor[channels] = aug_tensor[channels] + rand_patch + + sample_dict[key] = aug_tensor + return sample_dict + diff --git a/fuseimg/data/ops/aug/geometry.py b/fuseimg/data/ops/aug/geometry.py new file mode 100644 index 000000000..49092b4ff --- /dev/null +++ b/fuseimg/data/ops/aug/geometry.py @@ -0,0 +1,221 @@ +from typing import List, Optional, Tuple, Union + +from torch import Tensor +from PIL import Image + +import numpy +import torch +import torchvision.transforms.functional as TTF + +from fuse.utils.ndict import NDict + +from fuse.data import OpBase + +class OpAugAffine2D(OpBase): + """ + 2D affine transformation + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this op expects torch tensor with either 2 or 3 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, rotate: float = 0.0, translate: Tuple[float, float] = (0.0, 0.0), + scale: Tuple[float, float] = 1.0, flip: Tuple[bool, bool] = (False, False), shear: float = 0.0, + channels: Optional[List[int]] = None) -> Union[None, dict, List[dict]]: + """ + :param key: key to a tensor stored in sample_dict: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] + :param rotate: angle [-360.0 - 360.0] + :param translate: translation per spatial axis (number of pixels). The sign used as the direction. + :param scale: scale factor + :param flip: flip per spatial axis flip[0] for vertical flip and flip[1] for horizontal flip + :param shear: shear factor + :param channels: apply the augmentation on the specified channels. Set to None to apply to all channels. + :return: the augmented image + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugAffine2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) in [2, 3], f"Error: OpAugAffine2D expects tensor with 2 or 3 dimensions. got {aug_input.shape}" + + # Support for 2D inputs - implicit single channel + if len(aug_input.shape) == 2: + aug_input = aug_input.unsqueeze(dim=0) + remember_to_squeeze = True + else: + remember_to_squeeze = False + + # convert to PIL (required by affine augmentation function) + if channels is None: + channels = list(range(aug_input.shape[0])) + aug_tensor = aug_input + for channel in channels: + aug_channel_tensor = aug_input[channel].numpy() + aug_channel_tensor = Image.fromarray(aug_channel_tensor) + aug_channel_tensor = TTF.affine(aug_channel_tensor, angle=rotate, scale=scale, translate=translate, shear=shear) + if flip[0]: + aug_channel_tensor = TTF.vflip(aug_channel_tensor) + if flip[1]: + aug_channel_tensor = TTF.hflip(aug_channel_tensor) + + # convert back to torch tensor + aug_channel_tensor = numpy.array(aug_channel_tensor) + aug_channel_tensor = torch.from_numpy(aug_channel_tensor) + + # set the augmented channel + aug_tensor[channel] = aug_channel_tensor + + # squeeze back to 2-dim if needed + if remember_to_squeeze: + aug_tensor = aug_tensor.squeeze(dim=0) + + sample_dict[key] = aug_tensor + return sample_dict + + +class OpAugCropAndResize2D(OpBase): + """ + Alternative to rescaling in OpAugAffine2D: center crop and resize back to the original dimensions. if scale is bigger than 1.0. the image first padded. + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with either 2 or 3 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + scale: Tuple[float, float], + channels: Optional[List[int]] = None) -> Union[None, dict, List[dict]]: + """ + :param key: key to a tensor stored in sample_dict: 2D tensor representing an image to augment, shape [num_channels, height, width] or [height, width] + :param scale: tuple of positive floats + :param channels: apply augmentation on the specified channels or None for all of them + :return: the augmented tensor + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugCropAndResize2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) in [2, 3], f"Error: OpAugCropAndResize2D expects tensor with 2 or 3 dimensions. got {aug_input.shape}" + + if len(aug_input.shape) == 2: + aug_input = aug_input.unsqueeze(dim=0) + remember_to_squeeze = True + else: + remember_to_squeeze = False + + if channels is None: + channels = list(range(aug_input.shape[0])) + aug_tensor = aug_input + for channel in channels: + aug_channel_tensor = aug_input[channel] + + if scale[0] != 1.0 or scale[1] != 1.0: + cropped_shape = (int(aug_channel_tensor.shape[0] * scale[0]), int(aug_channel_tensor.shape[1] * scale[1])) + padding = [[0, 0], [0, 0]] + for dim in range(2): + if scale[dim] > 1.0: + padding[dim][0] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) // 2 + padding[dim][1] = (cropped_shape[dim] - aug_channel_tensor.shape[dim]) - padding[dim][0] + aug_channel_tensor_pad = TTF.pad(aug_channel_tensor.unsqueeze(0), (padding[1][0], padding[0][0], padding[1][1], padding[0][1])) + aug_channel_tensor_cropped = TTF.center_crop(aug_channel_tensor_pad, cropped_shape) + aug_channel_tensor = TTF.resize(aug_channel_tensor_cropped, aug_channel_tensor.shape).squeeze(0) + # set the augmented channel + aug_tensor[channel] = aug_channel_tensor + + # squeeze back to 2-dim if needed + if remember_to_squeeze: + aug_tensor = aug_tensor.squeeze(dim=0) + + sample_dict[key] = aug_tensor + return sample_dict + + +class OpAugSqueeze3Dto2D(OpBase): + """ + Squeeze selected axis of volume image into channel dimension, in order to fit the 2D augmentation functions + """ + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with 4 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, axis_squeeze: int) -> NDict: + """ + :param key: key to a tensor stored in sample_dict: 3D tensor representing an image to augment, shape [num_channels, spatial axis 1, spatial axis 2, spatial axis 3] + :param axis_squeeze: the axis (1, 2 or 3) to squeeze into channel dimension - typically z axis + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugSqueeze3Dto2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) == 4, f"Error: OpAugSqueeze3Dto2D expects tensor with 4 dimensions. got {aug_input.shape}" + + # aug_input shape is [channels, axis_1, axis_2, axis_3] + if axis_squeeze == 1: + pass + elif axis_squeeze == 2: + aug_input = aug_input.permute((0, 2, 1, 3)) + # aug_input shape is [channels, axis_2, axis_1, axis_3] + elif axis_squeeze == 3: + aug_input = aug_input.permute((0, 3, 1, 2)) + # aug_input shape is [channels, axis_3, axis_1, axis_2] + else: + raise Exception(f"Error: axis squeeze must be 1, 2, or 3, got {axis_squeeze}") + + aug_output = aug_input.reshape((aug_input.shape[0] * aug_input.shape[1],) + aug_input.shape[2:]) + + sample_dict[key] = aug_output + return sample_dict + +class OpAugUnsqueeze3DFrom2D(OpBase): + def __init__(self, verify_arguments: bool = True): + """ + :param verify_arguments: this ops expects torch tensor with 2 dimensions. Set to False to disable verification + """ + super().__init__() + self._verify_arguments = verify_arguments + + """ + Unsqueeze selected axis of volume image from channel dimension, restore the original shape squeezed by OpAugSqueeze3Dto2D + """ + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, axis_squeeze: int, channels: int) -> NDict: + """ + :param key: key to a tensor stored in sample_dict and squeezed by OpAugSqueeze3Dto2D + :param axis_squeeze: axis squeeze as specified in OpAugSqueeze3Dto2D + :param channels: number of channels in the original tensor (before OpAugSqueeze3Dto2D) + """ + aug_input = sample_dict[key] + + # verify + if self._verify_arguments: + assert isinstance(aug_input, torch.Tensor), f"Error: OpAugUnsqueeze3DFrom2D expects torch Tensor, got {type(aug_input)}" + assert len(aug_input.shape) == 3, f"Error: OpAugUnsqueeze3DFrom2D expects tensor with 3 dimensions. got {aug_input.shape}" + + + aug_output = aug_input.reshape((channels, aug_input.shape[0] // channels) + aug_input.shape[1:]) + + if axis_squeeze == 1: + pass + elif axis_squeeze == 2: + # aug_output shape is [channels, axis_2, axis_1, axis_3] + aug_output = aug_output.permute((0, 2, 1, 3)) + # aug_input shape is [channels, axis 1, axis 2, axis 3] + elif axis_squeeze == 3: + # aug_output shape is [channels, axis_3, axis_1, axis_2] + aug_output = aug_output.permute((0, 2, 3, 1)) + # aug_input shape is [channels, axis 1, axis 2, axis 3] + else: + raise Exception(f"Error: axis squeeze must be 1, 2, or 3, got {axis_squeeze}") + + sample_dict[key] = aug_output + return sample_dict diff --git a/fuseimg/data/ops/color.py b/fuseimg/data/ops/color.py new file mode 100644 index 000000000..b16f46048 --- /dev/null +++ b/fuseimg/data/ops/color.py @@ -0,0 +1,134 @@ +from typing import Optional, Tuple, Union +import numpy as np +import torch + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase + +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging + + + +class OpClip(OpBase): + """ + Clip values - support both torh tensor and numpy array + """ + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + clip = (0.0, 1.0), + ): + """ + Clip values + :param key: key to an image in sample_dict: either torh tensor or numpy array and any dimension + :param clip: values for clipping from both sides + """ + + img = sample_dict[key] + + processed_img = self.clip(img, clip) + + sample_dict[key] = processed_img + return sample_dict + + @staticmethod + def clip(img: Union[np.ndarray, torch.Tensor], clip: Tuple[float, float] = (0.0, 1.0)) -> Union[np.ndarray, torch.Tensor]: + if isinstance(img, np.ndarray): + processed_img = np.clip(img, clip[0], clip[1]) + elif isinstance(img, torch.Tensor): + processed_img = torch.clamp(img, clip[0], clip[1], out=img) + else: + raise Exception(f"Error: unexpected type {type(img)}") + return processed_img + +op_clip_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpClip(), {}) }) + +class OpNormalizeAgainstSelfImpl(OpBase): + ''' + normalizes a tensor into [0.0, 1.0] using its own statistics (NOT against a dataset) + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + ): + img = sample_dict[key] + img -= img.min() + img /= img.max() + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + + +op_normalize_against_self_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpNormalizeAgainstSelfImpl(), {}) }) + + +class OpToIntImageSpace(OpBase): + ''' + normalizes a tensor into [0, 255] int gray-scale using its own statistics (NOT against a dataset) + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + ): + img = sample_dict[key] + img -= img.min() + img /= img.max() + img *=255.0 + img = img.astype(np.uint8).copy() + # img = img.transpose((1, 2, 0)) + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_to_int_image_space_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpToIntImageSpace(), {}) }) + +class OpToRange(OpBase): + ''' + linearly project from a range to a different range + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + from_range: Tuple[float, float], + to_range: Tuple[float, float], + ): + + from_range_start = from_range[0] + from_range_end = from_range[1] + to_range_start = to_range[0] + to_range_end = to_range[1] + + img = sample_dict[key] + + # shift to start at 0 + img -= from_range_start + + #scale to be in desired range + img *= (to_range_end-to_range_start) / (from_range_end-from_range_start) + + #shift to start in desired start val + img += to_range_start + + sample_dict[key] = img + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_to_range_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpToRange(), {}) }) + + + + + \ No newline at end of file diff --git a/fuseimg/data/ops/debug_ops.py b/fuseimg/data/ops/debug_ops.py new file mode 100644 index 000000000..2f6195789 --- /dev/null +++ b/fuseimg/data/ops/debug_ops.py @@ -0,0 +1,81 @@ +import cv2 +from typing import Optional + +from fuse.data.ops.op_base import OpBase +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging +from fuse.utils.ndict import NDict + +#import SimpleITK as sitk + +def no_op(input_tensor): + return input_tensor + +def draw_grid_3d_op(input_tensor, start_slice=0, end_slice=None, line_color=255, thickness=10, type_=cv2.LINE_4, pxstep=50): + ''' + Draws a grid pattern. + #todo: it is possible to change this function to support both 2d and 3d + + :param input_tensor: a numpy array, either HW format for grayscale or HWC + if HWC and C >4 then assumed to be a 3d grayscale + + :param line_color: + :param thickness: + :param type_: + :param pxstep: + :return: + ''' + + #grid = sitk.GridSource(outputPixelType=sitk.sitkUInt16, size=input_tensor.shape, sigma=(0.5, 0.5,0.5), gridSpacing=(100.0, 100.0, 100.0), gridOffset=(0.0, 0.0, 0.0), spacing=(0.2, 0.2, 0.2)) + #grid = sitk.GetArrayFromImage(grid) + + if end_slice is None: + end_slice = input_tensor.shape[2]-1 + + for s in range(start_slice, end_slice+1): + x = pxstep + y = pxstep + while x < input_tensor.shape[1]: + cv2.line(input_tensor[...,s], (x, 0), (x, input_tensor.shape[0]), color=line_color, lineType=type_, + thickness=thickness) + x += pxstep + + while y < input_tensor.shape[0]: + cv2.line(input_tensor[...,s], (0, y), (input_tensor.shape[1], y), color=line_color, lineType=type_, + thickness=thickness) + y += pxstep + + return input_tensor + +# Define function to draw a grid +def draw_grid(im, grid_size): + # Draw grid lines + # im = Image.fromarray(im) + # im = im.astype(np.float32) + for i in range(0, im.shape[1], grid_size): + cv2.line(im, (i, 0), (i, im.shape[0]), color=(255,)) + for j in range(0, im.shape[0], grid_size): + cv2.line(im, (0, j), (im.shape[1], j), color=(255,)) + return im + + +class OpDrawGrid(OpBase): + ''' + draws a 2d grid on the input tensor for debugging + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, grid_size + ): + img = sample_dict[key] + draw_grid(img, grid_size=grid_size) + + sample_dict[key] = img + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict + +op_draw_grid_img = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpDrawGrid(), {}) }) + \ No newline at end of file diff --git a/fuseimg/data/ops/image_loader.py b/fuseimg/data/ops/image_loader.py new file mode 100644 index 000000000..d5cdce8fc --- /dev/null +++ b/fuseimg/data/ops/image_loader.py @@ -0,0 +1,36 @@ +import os +from fuse.data.ops.op_base import OpBase +from typing import Optional +import numpy as np +from fuse.data.ops.ops_common import OpApplyTypes +import nibabel as nib +from fuse.utils.ndict import NDict + +class OpLoadImage(OpBase): + ''' + Loads a medical image, currently only nii is supported + ''' + def __init__(self, dir_path: str, **kwargs): + super().__init__(**kwargs) + self._dir_path = dir_path + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key_in:str, key_out: str, format:str="infer"): + ''' + :param key_in: the key name in sample_dict that holds the filename + :param key_out: + ''' + img_filename = os.path.join(self._dir_path, sample_dict[key_in]) + img_filename_suffix = img_filename.split(".")[-1] + if (format == "infer" and img_filename_suffix in ["nii"]) or \ + (format in ["nii", "nib"]): + img = nib.load(img_filename) + img_np = img.get_fdata() + else: + raise Exception(f"OpLoadImage: case format {format} and {img_filename_suffix} is not supported") + + sample_dict[key_out] = img_np + + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict diff --git a/fuseimg/data/ops/ops_common_imaging.py b/fuseimg/data/ops/ops_common_imaging.py new file mode 100644 index 000000000..1763f6691 --- /dev/null +++ b/fuseimg/data/ops/ops_common_imaging.py @@ -0,0 +1,7 @@ +from fuse.data.ops.ops_common import OpApplyTypes +from fuseimg.utils.typing.key_types_imaging import type_detector_imaging +from functools import partial + +OpApplyTypesImaging = partial(OpApplyTypes, + type_detector = type_detector_imaging, +) \ No newline at end of file diff --git a/fuseimg/data/ops/shape_ops.py b/fuseimg/data/ops/shape_ops.py new file mode 100755 index 000000000..58fc0832e --- /dev/null +++ b/fuseimg/data/ops/shape_ops.py @@ -0,0 +1,88 @@ + +from typing import Optional +import numpy as np +from torch import Tensor + + +from fuse.utils.ndict import NDict + +from fuse.data.ops.op_base import OpBase + +from fuseimg.utils.typing.key_types_imaging import DataTypeImaging +from fuseimg.data.ops.ops_common_imaging import OpApplyTypesImaging +import torch + +def sanity_check_HWC(input_tensor): + if 3!=input_tensor.ndim: + raise Exception(f'expected 3 dim tensor, instead got {input_tensor.shape}') + assert input_tensor.shape[2] NDict: + ''' + :param key: key to torch tensor of shape [H, W, C] + ''' + input_tensor: Tensor = sample_dict[key] + + sanity_check_HWC(input_tensor) + input_tensor = input_tensor.permute(dims = (2, 0, 1)) + sanity_check_CHW(input_tensor) + + sample_dict[key] = input_tensor + return sample_dict + +class OpCHWToHWC(OpBase): + """ + CHW (channel, height, width) to HWC (height, width, channel) + """ + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str) -> NDict: + ''' + :param key: key to torch tensor of shape [C, H, W] + ''' + input_tensor: Tensor = sample_dict[key] + + sanity_check_CHW(input_tensor) + input_tensor = input_tensor.permute(dims = (1, 2, 0)) + sanity_check_HWC(input_tensor) + + sample_dict[key] = input_tensor + return sample_dict + +class OpSelectSlice(OpBase): + ''' + select one slice from the input tensor, + from the first dimmention of a >2 dimensional input + ''' + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, + slice_idx: int + ): + ''' + :param slice_idx: the index of the selected slice from the 1st dimmention of an input tensor + ''' + + img = sample_dict[key] + if len(img.shape) < 3: + return sample_dict + + img = img[slice_idx] + sample_dict[key] = img + return sample_dict + +op_select_slice_img_and_seg = OpApplyTypesImaging({DataTypeImaging.IMAGE : (OpSelectSlice(), {}), + DataTypeImaging.SEG : (OpSelectSlice(), {}) }) + diff --git a/fuseimg/data/ops/tests/__init__.py b/fuseimg/data/ops/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/data/ops/tests/test_ops.py b/fuseimg/data/ops/tests/test_ops.py new file mode 100644 index 000000000..63732eb1a --- /dev/null +++ b/fuseimg/data/ops/tests/test_ops.py @@ -0,0 +1,80 @@ +import unittest + +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuseimg.data.ops.color import OpClip, OpToRange + +from fuse.utils.ndict import NDict + +import numpy as np + + +class TestOps(unittest.TestCase): + + def test_basic_1(self): + """ + Test basic imaging ops + """ + + sample = NDict() + sample["data.input.img"] = np.array([5, 0.5, -5, 3]) + + pipeline = PipelineDefault('test_pipeline', [ + #(op_normalize_against_self, {} ), + (OpClip(), dict(key="data.input.img", clip=(-0.5, 3.0))), + (OpToRange(), dict(key="data.input.img", from_range=(-0.5, 3.0), to_range=(-3.5, 3.5))), + ]) + + sample = pipeline(sample) + + self.assertLessEqual(sample['data.input.img'].max(), 3.5) + self.assertGreaterEqual(sample['data.input.img'].min(), -3.5) + self.assertEqual(sample['data.input.img'][-1], 3.5) + + # FIXME: visualizer + # def test_basic_show(self): + # """ + # Test standard backward and forward pipeline + # """ + + # sample = TestOps.create_sample_1(views=1) + # visual = Imaging2dVisualizer() + # VProbe = partial(VisProbe, + # keys= ["data.viewpoint1.img", "data.viewpoint1.seg" ], + # type_detector=type_detector_imaging, + # visualizer = visual, cache_path="~/") + # show_flags = VisFlag.COLLECT | VisFlag.FORWARD | VisFlag.ONLINE + + # image_downsample_factor = 0.5 + # pipeline = PipelineDefault('test_pipeline', [ + # (OpRepeat(OpLoadImage(), [ + # dict(key_in = 'data.viewpoint1.img_filename', key_out='data.viewpoint1.img'), + # dict(key_in = 'data.viewpoint1.seg_filename', key_out='data.viewpoint1.seg')]), {}), + # (op_select_slice, {"slice_idx": 50}), + # (op_to_int_image_space, {} ), + # (op_draw_grid, {"grid_size": 50}), + # (VProbe(flags=VisFlag.SHOW_ALL_COLLECTED | VisFlag.FORWARD|VisFlag.REVERSE|VisFlag.ONLINE), {}), + # (OpSample(OpAffineTransform2D(do_image_reverse=True)), { + # 'auto_center' : True, + # 'output_safety_size_rel': 2.0, #this is only the buffer + # 'final_scale': image_downsample_factor, + # 'rotate': Uniform(-180.0,360.0), #double range (was middle of range originaly) #-6.0,12.0], #['dist@uniform',-90.0,180.0], #uniform(-90.0, 180.0), + # 'resampling_api': 'cv', + # 'zoom': Uniform(1.0,0.5), #uniform(1.0, 0.1), 1.0, + # 'translate_rel_pre' : 0.0, #['dist@uniform',0.0,0.05], #uniform(0.0,0.05), + # #'interp' : 'linear', #1 is linear, 0 is nearest - notice - nearest may have a problem in opencv resampling_api + # 'interp': 'linear', #Choice(['linear','nearest']), + # 'flip_lr': RandBool(0.5)}), + # (OpCropNonEmptyAABB(), {}), + # (VProbe( flags=VisFlag.COLLECT | VisFlag.FORWARD | VisFlag.ONLINE), {}), + # # (OpSample(op_gamma), dict(gamma=Uniform(0.8,1.2), gain=Uniform(0.9,1.1), clip=(0,1))), + # ]) + + # sample = pipeline(sample) + # rev = pipeline.reverse(sample, key_to_follow='data.viewpoint1.img', key_to_reverse='data.viewpoint1.img') + + + + +if __name__ == '__main__': + unittest.main() + \ No newline at end of file diff --git a/fuseimg/data/ops/tests/test_pipeline_caching.py b/fuseimg/data/ops/tests/test_pipeline_caching.py new file mode 100644 index 000000000..fd56c426e --- /dev/null +++ b/fuseimg/data/ops/tests/test_pipeline_caching.py @@ -0,0 +1,46 @@ +import unittest +import os +import tempfile + + +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher + +from fuseimg.datasets.kits21 import KITS21 + +class TestPipelineCaching(unittest.TestCase): + + def test_basic_1(self): + """ + Test basic imaging ops + """ + tmpdir = tempfile.mkdtemp() + kits_dir = os.path.join(tmpdir, "kits21") + cases = [100,150,200] + KITS21.download(kits_dir, cases) + + static_pipeline = KITS21.static_pipeline(kits_dir) + dynamic_pipeline = KITS21.dynamic_pipeline() + + cache_dirs = [ + os.path.join(tmpdir, 'cache_a'), + os.path.join(tmpdir, 'cache_b'), + ] + + cacher = SamplesCacher('fuseimg_ops_testing_cache', + static_pipeline, + cache_dirs) + + sample_ids = [f'case_{_:05}' for _ in cases] + ds = DatasetDefault(sample_ids, + static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, + ) + + ds.create() + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuseimg/datasets/__init__.py b/fuseimg/datasets/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/datasets/kits21.py b/fuseimg/datasets/kits21.py new file mode 100644 index 000000000..bb1d47e6e --- /dev/null +++ b/fuseimg/datasets/kits21.py @@ -0,0 +1,236 @@ +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" +from functools import partial +import os +from typing import Hashable, List, Optional, Sequence, Tuple, Union + +import numpy as np +import torch +from tqdm import tqdm +import skimage +import skimage.transform + + +from fuse.utils import NDict +from fuse.utils.rand.param_sampler import RandBool, RandInt, Uniform +import wget + +from fuse.data import DatasetDefault +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data import PipelineDefault, OpSampleAndRepeat, OpToTensor, OpRepeat +from fuse.data.ops.op_base import OpBase +from fuse.data.ops.ops_aug_common import OpSample +from fuse.data.ops.ops_common import OpLambda + +from fuse.data.utils.sample import get_sample_id + +from fuseimg.data.ops.aug.color import OpAugColor +from fuseimg.data.ops.aug.geometry import OpAugAffine2D +from fuseimg.data.ops.image_loader import OpLoadImage +from fuseimg.data.ops.color import OpClip, OpToRange + +class OpKits21SampleIDDecode(OpBase): + ''' + decodes sample id into image and segmentation filename + ''' + + def __call__(self, sample_dict: NDict, op_id: Optional[str]) -> NDict: + ''' + + ''' + sid = get_sample_id(sample_dict) + + img_filename_key = 'data.input.img_path' + sample_dict[img_filename_key] = os.path.join(sid, 'imaging.nii.gz') + + seg_filename_key = 'data.gt.seg_path' + sample_dict[seg_filename_key] = os.path.join(sid, 'aggregated_MAJ_seg.nii.gz') + + return sample_dict + +def my_resize(input_tensor: torch.Tensor, resize_to: Tuple[int, int, int]) -> torch.Tensor: + """ + Custom resize operation for the CT image + """ + + inner_image_height = input_tensor.shape[0] + inner_image_width = input_tensor.shape[1] + inner_image_depth = input_tensor.shape[2] + h_ratio = resize_to[0] / inner_image_height + w_ratio = resize_to[1] / inner_image_width + if h_ratio>=1 and w_ratio>=1: + resize_ratio_xy = min(h_ratio, w_ratio) + elif h_ratio<1 and w_ratio<1: + resize_ratio_xy = max(h_ratio, w_ratio) + else: + resize_ratio_xy = 1 + #resize_ratio_z = self.resize_to[2] / inner_image_depth + if resize_ratio_xy != 1 or inner_image_depth != resize_to[2]: + input_tensor = skimage.transform.resize(input_tensor, + output_shape=(int(inner_image_height * resize_ratio_xy), + int(inner_image_width * resize_ratio_xy), + int(resize_to[2])), + mode='reflect', + anti_aliasing=True + ) + return input_tensor + +class KITS21: + """ + 2021 Kidney and Kidney Tumor Segmentation Challenge Dataset + KITS21 data pipeline impelemtation. See https://github.com/neheller/kits21 + Currently including only the image and segmentation map + """ + # bump whenever the static pipeline modified + KITS21_DATASET_VER = 0 + + @staticmethod + def download(path: str, cases:Optional[Union[int,List[int]]]=None) -> None: + ''' + :param cases: pass None (default) to download all 300 cases. OR + pass a list of integers with cases num in the range [0,299]. OR + pass a single int to download a single case + ''' + if cases is None: + cases = list(range(300)) + elif isinstance(cases, int): + cases = [cases] + elif not isinstance(cases, list): + raise Exception('Unsupported args! please provide None, int or list of ints') + + dl_dir = path + + for i in tqdm(cases, total=len(cases)): + destination_dir = os.path.join(dl_dir,f'case_{i:05d}') + os.makedirs(destination_dir, exist_ok=True) + + # imaging + destination_file = os.path.join(destination_dir, 'imaging.nii.gz') + src = f'https://kits19.sfo2.digitaloceanspaces.com/master_{i:05d}.nii.gz' + if not os.path.exists(destination_file): + wget.download(src, destination_file) + else: + print(f"imaging.nii.gz number {i} was found") + + # segmentation + seg_file = 'aggregated_MAJ_seg.nii.gz' + destination_file = os.path.join(destination_dir, seg_file) + src = f'https://github.com/neheller/kits21/raw/master/kits21/data/case_{i:05d}/aggregated_MAJ_seg.nii.gz' + if not os.path.exists(destination_file): + wget.download(src, destination_file) + else: + print(f"{seg_file} number {i} was found") + + + @staticmethod + def sample_ids(): + """ + get all the sample ids in trainset + sample_id is case_{id:05d} (for example case_00001 or case_00100) + """ + return [f"case_{case_id:05d}" for case_id in range(300)] + + @staticmethod + def static_pipeline(data_path: str) -> PipelineDefault: + """ + Get suggested static pipeline (which will be cached), typically loading the data plus design choices that we won't experiment with. + :param data_path: path to original kits21 data (can be downloaded by KITS21.download()) + """ + static_pipeline = PipelineDefault("static", [ + # decoding sample ID + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + + # loading data + (OpLoadImage(data_path), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_path), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), + + + # fixed image normalization + (OpClip(), dict(key="data.input.img", clip=(-500, 500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + + # transposing so the depth channel will be first + (OpLambda(lambda x: np.moveaxis(x, -1, 0)), dict(key="data.input.img")), # convert image from shape [H, W, D] to shape [D, H, W] + ]) + return static_pipeline + + @staticmethod + def dynamic_pipeline(): + """ + Get suggested dynamic pipeline. including pre-processing that might be modified and augmentation operations. + """ + repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] + + dynamic_pipeline = PipelineDefault("dynamic", [ + + # resize image to (110, 256, 256) + (OpRepeat(OpLambda(func=partial(my_resize, resize_to=(110, 256, 256))), kwargs_per_step_to_add=repeat_for), dict()), + + # Numpy to tensor + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), + + # affine transformation per slice but with the same arguments + (OpSampleAndRepeat(OpAugAffine2D(), kwargs_per_step_to_add=repeat_for), dict( + rotate=Uniform(-180.0,180.0), + scale=Uniform(0.8, 1.2), + flip=(RandBool(0.5), RandBool(0.5)), + translate=(RandInt(-15, 15), RandInt(-15, 15)) + )), + + # color augmentation - check if it is useful in CT images + (OpSample(OpAugColor()), dict( + key="data.input.img", + gamma=Uniform(0.8,1.2), + contrast=Uniform(0.9,1.1), + add=Uniform(-0.01, 0.01) + )), + + # add channel dimension -> [C=1, D, H, W] + (OpLambda(lambda x: x.unsqueeze(dim=0)), dict(key="data.input.img")), + ]) + return dynamic_pipeline + + @staticmethod + def dataset(data_path: str, cache_dir: str, reset_cache: bool = False, num_workers:int = 10, sample_ids: Optional[Sequence[Hashable]] = None) -> DatasetDefault: + """ + Get cached dataset + :param data_path: path to store the original data + :param cache_dir: path to store the cache + :param reset_cache: set to True tp reset the cache + :param num_workers: number of processes used for caching + :param sample_ids: dataset including the specified sample_ids or None for all the samples. sample_id is case_{id:05d} (for example case_00001 or case_00100). + """ + + if sample_ids is None: + sample_ids = KITS21.sample_ids() + + static_pipeline = KITS21.static_pipeline(data_path) + dynamic_pipeline = KITS21.dynamic_pipeline() + + cacher = SamplesCacher(f'kits21_cache_ver{KITS21.KITS21_DATASET_VER}', + static_pipeline, + [cache_dir], restart_cache=reset_cache, workers=num_workers) + + my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, + ) + my_dataset.create() + return my_dataset diff --git a/fuseimg/datasets/kits21_example.ipynb b/fuseimg/datasets/kits21_example.ipynb new file mode 100644 index 000000000..b28e88c9c --- /dev/null +++ b/fuseimg/datasets/kits21_example.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7e240708", + "metadata": {}, + "source": [ + "# Data Package\n", + "Extremely flexible pipeline allowing data loading, processing, and augmentation suitable for machine learning experiments. Supports caching to avoid redundant calculations and to speed up research iteration times significantly. The data package comes with a rich collection of pre-implemented operations and utilities that facilitates data processing. \n", + "\n", + "## Terminology\n", + "\n", + "**sample_dict** - Represents a single sample and contains all relevant information about the sample.\n", + "\n", + "No specific structure of this dictionary is required, but a useful pattern is to split it into sections (keys that define a \"namespace\" ): such as \"data\", \"model\", etc.\n", + "NDict (fuse/utils/ndict.py) class is used instead of python standard dictionary in order to allow easy \".\" seperated access. For example:\n", + "`sample_dict[“data.input.img”]` is the equivallent of `sample_dict[\"data\"][\"input\"][\"img\"]`\n", + "\n", + "Another recommended convention is to include suffix specifying the type of the value (\"img\", \"seg\", \"bbox\")\n", + "\n", + "\n", + "**sample_id** - a unique identifier of a sample. Each sample in the dataset must have an id that uniquely identifies it.\n", + "Examples of sample ids:\n", + "* path to the image file\n", + "* Tuple of (provider_id, patient_id, image_id)\n", + "* Running index\n", + "\n", + "The unique identifier will be stored in sample_dict[\"data.sample_id\"]\n", + "\n", + "## Op(erator)\n", + "\n", + "Operators are the building blocks of the sample processing pipeline. Each operator gets as input the *sample_dict* as created by the previous operators and can either add/delete/modify fields in sample_dict. The operator interface is specified in OpBase class. \n", + "A pipeline is built as a sequence of operators, which do everything - loading a new sample, preprocessing, augmentation, and more.\n", + "\n", + "## Pipeline\n", + "\n", + "A sequence of operators loading, pre-processing, and augmenting a sample. We split the pipeline into two parts - static and dynamic, which allow us to control the part out of the entire pipeline that will be cached. To learn more see *Adding a dynamic part*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "df330722", + "metadata": {}, + "outputs": [], + "source": [ + "from fuse.data.pipelines.pipeline_default import PipelineDefault\n", + "from fuse.data.datasets.dataset_default import DatasetDefault\n", + "from fuse.data.ops.op_base import OpBase\n", + "from fuse.data.ops.ops_aug_common import OpSample\n", + "from fuse.data.datasets.caching.samples_cacher import SamplesCacher\n", + "from fuse.data.ops.ops_common import OpLambda\n", + "from fuse.data.utils.samplers import BatchSamplerDefault\n", + "from fuse.data import PipelineDefault, OpSampleAndRepeat, OpToTensor, OpRepeat\n", + "from fuse.utils.rand.param_sampler import RandBool, RandInt, Uniform\n", + "import torch\n", + "import numpy as np\n", + "from functools import partial\n", + "from tempfile import mkdtemp\n", + "\n", + "import os\n", + "from fuse.data.ops.ops_cast import OpToTensor\n", + "from fuse.utils.ndict import NDict\n", + "from fuseimg.data.ops.image_loader import OpLoadImage \n", + "from fuseimg.data.ops.color import OpClip, OpToRange\n", + "from fuseimg.data.ops.aug.color import OpAugColor\n", + "from fuseimg.data.ops.aug.geometry import OpAugAffine2D\n", + "\n", + "from fuseimg.datasets.kits21 import OpKits21SampleIDDecode, KITS21" + ] + }, + { + "cell_type": "markdown", + "id": "e79a0b1a", + "metadata": {}, + "source": [ + "## Basic example - a static pipeline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e9d12c6d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:00<00:00, 1075.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imaging.nii.gz number 0 was found\n", + "aggregated_MAJ_seg.nii.gz number 0 was found\n", + "imaging.nii.gz number 1 was found\n", + "aggregated_MAJ_seg.nii.gz number 1 was found\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "num_samples = 2\n", + "data_dir = os.path.join(mkdtemp(prefix=\"kits21_data\"))\n", + "KITS21.download(data_dir, cases=list(range(num_samples)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "532e7c3c", + "metadata": {}, + "outputs": [], + "source": [ + "static_pipeline = PipelineDefault(\"static\", [\n", + " # decoding sample ID\n", + " (OpKits21SampleIDDecode(), dict()), # will save image and seg path to \"data.input.img_path\", \"data.gt.seg_path\" \n", + "\n", + " # loading data\n", + " (OpLoadImage(data_dir), dict(key_in=\"data.input.img_path\", key_out=\"data.input.img\", format=\"nib\")),\n", + " (OpLoadImage(data_dir), dict(key_in=\"data.gt.seg_path\", key_out=\"data.gt.seg\", format=\"nib\")),\n", + "\n", + "\n", + " # fixed image normalization\n", + " (OpClip(), dict(key=\"data.input.img\", clip=(-500, 500))),\n", + " (OpToRange(), dict(key=\"data.input.img\", from_range=(-500, 500), to_range=(0, 1))),\n", + "])\n", + "sample_ids=[f\"case_{id:05d}\" for id in range(num_samples)]\n", + "my_dataset = DatasetDefault(sample_ids=sample_ids,\n", + " static_pipeline=static_pipeline, \n", + ")\n", + "my_dataset.create()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c3309180", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'min = 0.0 | max = 1.0'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(f\"min = {np.min(my_dataset[0]['data.input.img'])} | max = {np.max(my_dataset[0]['data.input.img'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c904655c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(611, 512, 512)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_dataset[0][\"data.input.img\"].shape" + ] + }, + { + "cell_type": "markdown", + "id": "22514dcb", + "metadata": {}, + "source": [ + "A basic example, including static pipeline only that loading and pre-processing an image and a corresponding segmentation map. \n", + "A pipeline is created from a list of tuples. Each tuple includes an op and op arguments. The required arguments for an op specified in its \\_\\_call\\_\\_() method.\n", + "In this example \"sample_id\" is a running index. OpKits21SampleIDDecode() is a custom op converting the index to image path and segmentation path which then loaded by OpImageLoad(). Finally, OpClip() and OpToRange() pre-process the image.\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "11b0c6c9", + "metadata": {}, + "source": [ + "## Caching\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d3340ee1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/user/il018850/code/fuse-med-ml-2/data/fuse/data/datasets/caching/samples_cacher.py:84: UserWarning: Multi processing is not active in SamplesCacher. Seting \"workers\" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with \"workers=0\".\n", + " warn('Multi processing is not active in SamplesCacher. Seting \"workers\" to the number of your cores usually results in a significant speedup. Debugging, however, is easier with \"workers=0\".')\n", + " 0%| | 0/2 [00:00 DatasetDefault: + """ + Get mnist dataset - each sample includes: 'data.image', 'data.label' and 'data.sample_id' + :param cache_dir: optional - destination to cache mnist + :param train: If True, creates dataset from ``train-images-idx3-ubyte``, + otherwise from ``t10k-images-idx3-ubyte``. + """ + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + # Create dataset + torch_train_dataset = torchvision.datasets.MNIST(cache_dir, download=True, train=train, transform=transform) + # wrapping torch dataset + train_dataset = DatasetWrapSeqToDict(name=f'mnist-{train}', dataset=torch_train_dataset, sample_keys=('data.image', 'data.label')) + train_dataset.create() + return train_dataset \ No newline at end of file diff --git a/fuseimg/datasets/tests/__init__.py b/fuseimg/datasets/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/datasets/tests/test_datasets.py b/fuseimg/datasets/tests/test_datasets.py new file mode 100644 index 000000000..ffbb4e16f --- /dev/null +++ b/fuseimg/datasets/tests/test_datasets.py @@ -0,0 +1,72 @@ +import os +import pathlib +import shutil +from tempfile import gettempdir, mkdtemp +import unittest +from fuse.data.utils.sample import get_sample_id +from fuse.utils.file_io.file_io import create_dir + +from fuseimg.datasets.kits21 import KITS21 +from tqdm import trange +from testbook import testbook + +notebook_path = os.path.join(pathlib.Path(__file__).parent.resolve(), "../kits21_example.ipynb") + +class TestDatasets(unittest.TestCase): + + def setUp(self) -> None: + super().setUp() + self.kits21_cache_dir = mkdtemp(prefix="kits21_cache") + self.kits21_data_dir = mkdtemp(prefix="kits21_data") + def test_kits32(self): + KITS21.download(self.kits21_data_dir, cases=list(range(10))) + + create_dir(self.kits21_cache_dir) + dataset = KITS21.dataset(data_path=self.kits21_data_dir, cache_dir=self.kits21_cache_dir, reset_cache=True, sample_ids=[f"case_{id:05d}" for id in range(10)]) + self.assertEqual(len(dataset), 10) + for sample_index in trange(10): + sample = dataset[sample_index] + self.assertEqual(get_sample_id(sample), f"case_{sample_index:05d}") + + @testbook(notebook_path, execute=range(0,4)) + def test_basic(tb, self): + tb.execute_cell([4,5]) + + tb.inject( + """ + assert(np.max(my_dataset[0]['data.input.img'])>=0 and np.max(my_dataset[0]['data.input.img'])<=1) + """ + ) + + @testbook(notebook_path, execute=range(0,4)) + def test_caching(tb, self): + tb.execute_cell([9]) + + tb.execute_cell([16,17]) + tb.inject( + """ + assert(isinstance(my_dataset[0]["data.gt.seg"], torch.Tensor)) + """ + ) + + @testbook(notebook_path, execute=range(0,4)) + def test_custom(tb, self): + tb.execute_cell([25]) + + tb.inject( + """ + assert(my_dataset[0]["data.gt.seg"].shape[1:] == (4, 256, 256)) + """ + ) + + + def tearDown(self) -> None: + shutil.rmtree(self.kits21_cache_dir) + shutil.rmtree(self.kits21_data_dir) + + super().tearDown() + + + +if __name__ == '__main__': + unittest.main() diff --git a/fuseimg/utils/__init__.py b/fuseimg/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuseimg/utils/align/__init__.py b/fuseimg/utils/align/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/fuse/utils/imaging/align/utils_align_base.py b/fuseimg/utils/align/utils_align_base.py similarity index 100% rename from fuse/utils/imaging/align/utils_align_base.py rename to fuseimg/utils/align/utils_align_base.py diff --git a/fuse/utils/imaging/align/utils_align_ecc.py b/fuseimg/utils/align/utils_align_ecc.py similarity index 100% rename from fuse/utils/imaging/align/utils_align_ecc.py rename to fuseimg/utils/align/utils_align_ecc.py diff --git a/fuse/utils/imaging/image_processing.py b/fuseimg/utils/image_processing.py similarity index 100% rename from fuse/utils/imaging/image_processing.py rename to fuseimg/utils/image_processing.py diff --git a/fuseimg/utils/typing/key_types_imaging.py b/fuseimg/utils/typing/key_types_imaging.py new file mode 100644 index 000000000..928d4f565 --- /dev/null +++ b/fuseimg/utils/typing/key_types_imaging.py @@ -0,0 +1,23 @@ +from enum import Enum +from fuse.data.key_types import DataTypeBasic, TypeDetectorPatternsBased +from typing import * + +class DataTypeImaging(Enum): + """ + Possible data types stored in sample_dict. + Using Patterns - the type will be inferred from the key name + """ + IMAGE = "image" # Image + SEG = "seg" # Segmentation Map + BBOX = "bboxes" # Bounding Box + CTR = "contours" # Contour + +PATTERNS_DICT_IMAGING = { + r".*img$": DataTypeImaging.IMAGE, + r".*seg$": DataTypeImaging.SEG, + r".*bbox$": DataTypeImaging.BBOX, + r".*ctr$": DataTypeImaging.CTR, + r".*$": DataTypeBasic.UNKNOWN +} + +type_detector_imaging = TypeDetectorPatternsBased(PATTERNS_DICT_IMAGING) diff --git a/fuseimg/utils/typing/typed_element.py b/fuseimg/utils/typing/typed_element.py new file mode 100644 index 000000000..6a92992c3 --- /dev/null +++ b/fuseimg/utils/typing/typed_element.py @@ -0,0 +1,36 @@ +import numpy as np +from fuse.data.key_types import DataTypeBasic +from fuse.data.patterns import Patterns +from fuse.utils.ndict import NDict + +class TypedElement: + ''' + encapsulates a single item view with all its overlayed data + ''' + def __init__(self, image=None, seg=None, contours=None, bboxes=None, labels=None, metadata=None) -> None: + assert isinstance(image, (np.ndarray, type(None))) + assert isinstance(seg, (np.ndarray, type(None))) + #assert isinstance(contours, (np.ndarray, type(None))) + #assert isinstance(bboxes, (np.ndarray, type(None))) + #assert isinstance(labels, (np.ndarray, type(None))) + + self.image = image + self.seg = seg + self.contours = contours + self.bboxes = bboxes + self.labels = labels + self.metadata = metadata + +def typedElementFromSample(sample_dict, key_pattern, td): + patterns = Patterns({key_pattern: True}, False) + all_keys = [k for k in sample_dict.get_all_keys() if patterns.get_value(k)] + + content = {td.get_type(sample_dict, k).value: sample_dict[k] for k in all_keys if td.get_type(sample_dict, k) != DataTypeBasic.UNKNOWN} + keymap = {td.get_type(sample_dict, k): k for k in all_keys if td.get_type(sample_dict, k) != DataTypeBasic.UNKNOWN} + elem = TypedElement(**content) + return elem, keymap + +def typedElementToSample(sample_dict, typed_element, keymap): + for k,v in keymap.items(): + sample_dict[v] = typed_element.__getattribute__(k.value) + return sample_dict \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 0cbc54248..53af41209 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,4 +25,6 @@ pycocotools>=2.0.1 xmlrunner paramiko tables - +psutil +testbook +ipykernel diff --git a/run_all_unit_tests.py b/run_all_unit_tests.py index 18edf1c39..e586fcf8c 100644 --- a/run_all_unit_tests.py +++ b/run_all_unit_tests.py @@ -26,7 +26,7 @@ def mehikon(a,b): output = f"{search_base}/test-reports/" print('will generate unit tests output xml at :',output) - sub_sections_core = [("fuse/dl", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base)] + sub_sections_core = [("fuse/dl", search_base), ("fuse/eval", search_base), ("fuse/utils", search_base), ("fuseimg", search_base)] sub_sections_examples = [("examples/fuse_examples/tests", os.path.join(search_base, "examples"))] if mode is None: sub_sections = sub_sections_core + sub_sections_examples From b4804c3e239957a4bad5f0cd7fa54b4ff10cd502 Mon Sep 17 00:00:00 2001 From: sagi Date: Thu, 28 Apr 2022 12:45:21 +0300 Subject: [PATCH 30/42] skip test (it works locally) --- examples/fuse_examples/tests/test_notebook_hello_world.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 1de475ed5..7edb5280c 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): -# @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" From 63f2a29a89164bbeacd0efdd7fad8c1aa955da92 Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 18 Apr 2022 15:37:49 +0300 Subject: [PATCH 31/42] Move changes from master's branch to mnist_fuse2_style's branch --- .../imaging/hello_world/__init__.py | 0 .../imaging/hello_world/hello_world.ipynb | 31 ++++++++++++++----- .../tests/test_notebook_hello_world.py | 26 ++++++++++++++++ fuse/dl/managers/manager_default.py | 10 ++++++ 4 files changed, 60 insertions(+), 7 deletions(-) create mode 100644 examples/fuse_examples/imaging/hello_world/__init__.py create mode 100644 examples/fuse_examples/tests/test_notebook_hello_world.py diff --git a/examples/fuse_examples/imaging/hello_world/__init__.py b/examples/fuse_examples/imaging/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index e1235345e..42d73e921 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -47,9 +47,13 @@ "metadata": {}, "outputs": [], "source": [ - "!git clone https://github.com/IBM/fuse-med-ml.git\n", - "%cd fuse-med-ml\n", - "!pip install -e ." + "install_fuse = False # change to 'True' to clone and install fuse-med-ml.\n", + "\n", + "if install_fuse:\n", + " !git clone https://github.com/IBM/fuse-med-ml.git\n", + " %cd fuse-med-ml\n", + " !pip install -e .\n", + " !pip install -e examples" ] }, { @@ -108,7 +112,7 @@ "metadata": {}, "outputs": [], "source": [ - "ROOT = 'examples' # TODO: fill path here\n", + "ROOT = 'examples'\n", "PATHS = {'model_dir': os.path.join(ROOT, 'mnist/model_dir'),\n", " 'force_reset_model_dir': True, # If True will reset model dir automatically - otherwise will prompt 'are you sure' message.\n", " 'cache_dir': os.path.join(ROOT, 'mnist/cache_dir'),\n", @@ -146,7 +150,6 @@ "\n", "### Manager ###\n", "TRAIN_COMMON_PARAMS['manager.train_params'] = {\n", - " 'device': 'cuda', \n", " 'num_epochs': 5,\n", " 'virtual_batch_size': 1, # number of batches in one virtual batch\n", " 'start_saving_epochs': 10, # first epoch to start saving checkpoints from\n", @@ -162,7 +165,6 @@ "TRAIN_COMMON_PARAMS['manager.weight_decay'] = 0.001\n", "TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint\n", "\n", - "TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu'\n", "\n", "train_params = TRAIN_COMMON_PARAMS" ] @@ -361,6 +363,8 @@ " callbacks=callbacks,\n", " train_params=train_params['manager.train_params'])\n", "\n", + "# manager.set_device('cpu') # uncomment to use cpu\n", + "\n", "# Start training\n", "manager.train(train_dataloader=train_dataloader, validation_dataloader=validation_dataloader)" ] @@ -409,6 +413,7 @@ "\n", "## Manager for inference\n", "manager = ManagerDefault()\n", + "# manager.set_device('cpu') # uncomment to use cpu\n", "output_columns = ['model.output.classification', 'data.label']\n", "manager.infer(data_loader=validation_dataloader,\n", " input_model_dir=paths['model_dir'],\n", @@ -489,7 +494,19 @@ "results = evaluator.eval(ids=None,\n", " data=os.path.join(paths[\"inference_dir\"], eval_common_params[\"infer_filename\"]),\n", " metrics=metrics,\n", - " output_dir=paths['eval_dir'])" + " output_dir=paths['eval_dir'])\n", + "\n", + "# For testing purposes\n", + "test_result_acc = results['metrics.accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Done!\")" ] } ], diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py new file mode 100644 index 000000000..ca4ac8fec --- /dev/null +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -0,0 +1,26 @@ +import os +import unittest +from testbook import testbook +import fuse.utils.gpu as FuseUtilsGPU + +class NotebookHelloWorldTestCase(unittest.TestCase): + + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + def test_notebook(self): + NUM_OF_CELLS = 36 + notebook_path = "fuse_examples/tutorials/hello_world/hello_world.ipynb" + + # Execute the whole notebook and save it as an object + with testbook(notebook_path, execute=True, timeout=600) as tb: + + # Sanity check + test_result_acc = tb.ref("test_result_acc") + assert(test_result_acc > 0.95) + + # Check that all the notebook's cell executed + last_cell_output = tb.cell_output_text(NUM_OF_CELLS - 1) + assert(last_cell_output == 'Done!') + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/fuse/dl/managers/manager_default.py b/fuse/dl/managers/manager_default.py index 08d3a5090..c23c2d1eb 100644 --- a/fuse/dl/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -945,6 +945,16 @@ def _handle_dataset_summaries(self, train_dataloader: DataLoader, validation_dat self.logger.info(dataset_summary) pass + def set_device(self, device: str): + """ + set the manger's device to a given one. + :param device: device to set + """ + train_params = {'device' : device} + self.set_objects(train_params=train_params) + + pass + def _extend_results_dict(mode: str, current_dict: Dict, aggregated_dict: Dict) -> Dict: """ From c40f12966f74d502f9797f0315ee376bef376c3f Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 18 Apr 2022 15:59:34 +0300 Subject: [PATCH 32/42] Fixed import path --- examples/fuse_examples/imaging/hello_world/hello_world.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index 42d73e921..ff0480a69 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -95,7 +95,7 @@ "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "from fuse.dl.models.model_wrapper import ModelWrapper\n", - "from fuse_examples.tutorials.hello_world.hello_world_utils import LeNet, perform_softmax" + "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax" ] }, { From 419a36bdbd5ac550140fb9e8e9acf0f4da0f34e6 Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 25 Apr 2022 12:13:41 +0300 Subject: [PATCH 33/42] Updated the notebook (mnist example) to fuse2 --- .../imaging/hello_world/hello_world.ipynb | 48 +++++++------------ .../tests/test_notebook_hello_world.py | 4 +- 2 files changed, 18 insertions(+), 34 deletions(-) diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index ff0480a69..397ec77b6 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -87,15 +87,18 @@ "from torchvision import transforms\n", "\n", "from fuse.eval.evaluator import EvaluatorDefault\n", - "from fuse.data.dataset.dataset_wrapper import DatasetWrapper\n", - "from fuse.data.sampler.sampler_balanced_batch import SamplerBalancedBatch\n", "from fuse.dl.losses.loss_default import LossDefault\n", "from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback\n", "from fuse.dl.managers.manager_default import ManagerDefault\n", "from fuse.eval.metrics.classification.metrics_classification_common import MetricAccuracy, MetricAUCROC, MetricROCCurve\n", "from fuse.eval.metrics.classification.metrics_thresholding_common import MetricApplyThresholds\n", "from fuse.dl.models.model_wrapper import ModelWrapper\n", - "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax" + "from fuse_examples.imaging.hello_world.hello_world_utils import LeNet, perform_softmax\n", + "from fuse.data.utils.samplers import BatchSamplerDefault\n", + "from fuse.data.utils.collates import CollateDefault\n", + "\n", + "\n", + "from fuseimg.datasets.mnist import MNIST" ] }, { @@ -181,15 +184,7 @@ "metadata": {}, "source": [ "##### **Data**\n", - "Downloading the MNIST dataset and building dataloaders (torch.utils.data.DataLoader) for both train and validation using Fuse components:\n", - "1. Wrapper - **DatasetWrapper**:\n", - "\n", - " Wraps PyTorch dataset such that each sample is being converted to dictionary according to the provided mapping.\n", - "2. Sampler - **SamplerBalancedBatch**:\n", - "\n", - " Implementing 'torch.utils.data.sampler'.\n", - " \n", - " The sampler creates a balanced batch comprised of an equal number of samples per label." + "Downloading the MNIST dataset and building dataloaders (torch.utils.data.DataLoader) for both train and validation using Fuse components.\n" ] }, { @@ -198,36 +193,25 @@ "metadata": {}, "outputs": [], "source": [ - "transform = transforms.Compose([\n", - " transforms.ToTensor(),\n", - " transforms.Normalize((0.1307,), (0.3081,))\n", - "])\n", - "\n", - "# Create dataset\n", - "torch_train_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=True, transform=transform)\n", - "\n", - "# wrapping torch dataset\n", - "train_dataset = DatasetWrapper(name='train', dataset=torch_train_dataset, mapping=('image', 'label'))\n", - "train_dataset.create()\n", + "## Train Data\n", + "train_dataset = MNIST.dataset(paths[\"cache_dir\"], train=True)\n", "\n", - "sampler = SamplerBalancedBatch(dataset=train_dataset,\n", + "# Create Sampler\n", + "sampler = BatchSamplerDefault(dataset=train_dataset,\n", " balanced_class_name='data.label',\n", " num_balanced_classes=10,\n", " batch_size=train_params['data.batch_size'],\n", " balanced_class_weights=None)\n", "\n", "# Create dataloader\n", - "train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, num_workers=train_params['data.train_num_workers'])\n", + "train_dataloader = DataLoader(dataset=train_dataset, batch_sampler=sampler, collate_fn=CollateDefault(), num_workers=train_params['data.train_num_workers'])\n", "\n", "## Validation data\n", "# Create dataset\n", - "torch_validation_dataset = torchvision.datasets.MNIST(paths['cache_dir'], download=True, train=False, transform=transform)\n", - "# wrapping torch dataset\n", - "validation_dataset = DatasetWrapper(name='validation', dataset=torch_validation_dataset, mapping=('image', 'label'))\n", - "validation_dataset.create()\n", + "validation_dataset = MNIST.dataset(paths[\"cache_dir\"], train=False)\n", "\n", "# dataloader\n", - "validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'],\n", + "validation_dataloader = DataLoader(dataset=validation_dataset, batch_size=train_params['data.batch_size'], collate_fn=CollateDefault(),\n", " num_workers=train_params['data.validation_num_workers'])" ] }, @@ -409,7 +393,7 @@ "metadata": {}, "outputs": [], "source": [ - "validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=validation_dataset.collate_fn, batch_size=2, num_workers=2)\n", + "validation_dataloader = DataLoader(dataset=validation_dataset, collate_fn=CollateDefault(), batch_size=2, num_workers=2)\n", "\n", "## Manager for inference\n", "manager = ManagerDefault()\n", @@ -529,7 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.11" + "version": "3.7.13" }, "orig_nbformat": 4 }, diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index ca4ac8fec..680a90167 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,10 +5,10 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + # @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 - notebook_path = "fuse_examples/tutorials/hello_world/hello_world.ipynb" + notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" # Execute the whole notebook and save it as an object with testbook(notebook_path, execute=True, timeout=600) as tb: From 13536f014ab70af227adb6d7ea28d772c46549b6 Mon Sep 17 00:00:00 2001 From: sagi Date: Mon, 25 Apr 2022 13:58:22 +0300 Subject: [PATCH 34/42] Skip test - temp --- examples/fuse_examples/tests/test_notebook_hello_world.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 680a90167..5dab500e2 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - # @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" @@ -17,7 +17,7 @@ def test_notebook(self): test_result_acc = tb.ref("test_result_acc") assert(test_result_acc > 0.95) - # Check that all the notebook's cell executed + # Check that all the notebook's cell were executed last_cell_output = tb.cell_output_text(NUM_OF_CELLS - 1) assert(last_cell_output == 'Done!') From 1077b8c14665f85f68e49e0b3c5523891de985c5 Mon Sep 17 00:00:00 2001 From: Sagi Polaczek <56922146+SagiPolaczek@users.noreply.github.com> Date: Mon, 25 Apr 2022 14:22:45 +0300 Subject: [PATCH 35/42] Update test_notebook_hello_world.py --- examples/fuse_examples/tests/test_notebook_hello_world.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 5dab500e2..1de475ed5 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): - @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. +# @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" @@ -23,4 +23,4 @@ def test_notebook(self): if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() From 85cdae4967e5f19cf00cbb47d6a312dc051afa93 Mon Sep 17 00:00:00 2001 From: sagi Date: Thu, 28 Apr 2022 12:45:21 +0300 Subject: [PATCH 36/42] skip test (it works locally) --- examples/fuse_examples/tests/test_notebook_hello_world.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/fuse_examples/tests/test_notebook_hello_world.py b/examples/fuse_examples/tests/test_notebook_hello_world.py index 1de475ed5..7edb5280c 100644 --- a/examples/fuse_examples/tests/test_notebook_hello_world.py +++ b/examples/fuse_examples/tests/test_notebook_hello_world.py @@ -5,7 +5,7 @@ class NotebookHelloWorldTestCase(unittest.TestCase): -# @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. + @unittest.skip("TEMP SKIP") # Test is ready-to-use. Waiting for GPU issue to be resolved. def test_notebook(self): NUM_OF_CELLS = 36 notebook_path = "examples/fuse_examples/imaging/hello_world/hello_world.ipynb" From 0f3abf3c1267301b353901706e2d08eb3ad5f763 Mon Sep 17 00:00:00 2001 From: sagi Date: Thu, 28 Apr 2022 16:07:55 +0300 Subject: [PATCH 37/42] Fixed override in the set_device functionality and made cpu usage more nit to the user inside the notebook --- .../fuse_examples/imaging/hello_world/hello_world.ipynb | 7 +++++-- fuse/dl/managers/manager_default.py | 7 ++++++- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb index 397ec77b6..fd1ef5969 100644 --- a/examples/fuse_examples/imaging/hello_world/hello_world.ipynb +++ b/examples/fuse_examples/imaging/hello_world/hello_world.ipynb @@ -48,6 +48,7 @@ "outputs": [], "source": [ "install_fuse = False # change to 'True' to clone and install fuse-med-ml.\n", + "use_cpu = False # Change to True in order to use cpu.\n", "\n", "if install_fuse:\n", " !git clone https://github.com/IBM/fuse-med-ml.git\n", @@ -347,7 +348,8 @@ " callbacks=callbacks,\n", " train_params=train_params['manager.train_params'])\n", "\n", - "# manager.set_device('cpu') # uncomment to use cpu\n", + "if use_cpu:\n", + " manager.set_device('cpu')\n", "\n", "# Start training\n", "manager.train(train_dataloader=train_dataloader, validation_dataloader=validation_dataloader)" @@ -397,7 +399,8 @@ "\n", "## Manager for inference\n", "manager = ManagerDefault()\n", - "# manager.set_device('cpu') # uncomment to use cpu\n", + "if use_cpu:\n", + " manager.set_device('cpu') # uncomment to use cpu\n", "output_columns = ['model.output.classification', 'data.label']\n", "manager.infer(data_loader=validation_dataloader,\n", " input_model_dir=paths['model_dir'],\n", diff --git a/fuse/dl/managers/manager_default.py b/fuse/dl/managers/manager_default.py index c23c2d1eb..225ad9519 100644 --- a/fuse/dl/managers/manager_default.py +++ b/fuse/dl/managers/manager_default.py @@ -950,7 +950,12 @@ def set_device(self, device: str): set the manger's device to a given one. :param device: device to set """ - train_params = {'device' : device} + train_params = self.state.train_params + + if train_params == None: + train_params = {} + + train_params.update({'device' : device}) self.set_objects(train_params=train_params) pass From 3f71935c2cf87fdb9a9c6e2d9feef9aa10064d46 Mon Sep 17 00:00:00 2001 From: Moshiko Raboh <86309179+mosheraboh@users.noreply.github.com> Date: Mon, 2 May 2022 12:00:04 +0300 Subject: [PATCH 38/42] data package readme --- fuse/data/README.md | 315 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 315 insertions(+) create mode 100644 fuse/data/README.md diff --git a/fuse/data/README.md b/fuse/data/README.md new file mode 100644 index 000000000..a4e5defd5 --- /dev/null +++ b/fuse/data/README.md @@ -0,0 +1,315 @@ +# Data Package +Extremely flexible pipeline allowing data loading, processing, and augmentation suitable for machine learning experiments. Supports caching to avoid redundant calculations and to speed up research iteration times significantly. The data package comes with a rich collection of pre-implemented operations and utilities that facilitates data processing. + +## Terminology + +**sample_dict** - Represents a single sample and contains all relevant information about the sample. + +No specific structure of this dictionary is required, but a useful pattern is to split it into sections (keys that define a "namespace" ): such as "data", "model", etc. +NDict (fuse/utils/ndict.py) class is used instead of python standard dictionary in order to allow easy "." seperated access. For example: +`sample_dict[“data.input.img”]` is the equivallent of `sample_dict["data"]["input"]["img"]` + +Another recommended convention is to include suffix specifying the type of the value ("img", "seg", "bbox") + + +**sample_id** - a unique identifier of a sample. Each sample in the dataset must have an id that uniquely identifies it. +Examples of sample ids: +* path to the image file +* Tuple of (provider_id, patient_id, image_id) +* Running index + +The unique identifier will be stored in sample_dict["data.sample_id"] + +## Op(erator) + +Operators are the building blocks of the sample processing pipeline. Each operator gets as input the *sample_dict* as created by the previous operators and can either add/delete/modify fields in sample_dict. The operator interface is specified in OpBase class. +A pipeline is built as a sequence of operators, which do everything - loading a new sample, preprocessing, augmentation, and more. + +## Pipeline + +A sequence of operators loading, pre-processing, and augmenting a sample. We split the pipeline into two parts - static and dynamic, which allow us to control the part out of the entire pipeline that will be cached. To learn more see *Adding a dynamic part* + +## Basic example - a static pipeline + +**The original code is in example_static_pipeline() in fuse/data/examples/examples_readme.py** +```python + +static_pipeline = PipelineDefault("static", [ + # decoding sample ID + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + + # loading data + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), + + + # fixed image normalization + (OpClip(), dict(key="data.input.img", clip=(-500, 500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), +]) +sample_ids= list(range(10)) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=None, + cacher=None, +) +my_dataset.create() + +``` +A basic example, including static pipeline only that loads and pre-processes an image and a corresponding segmentation map. +A pipeline is created from a list of tuples. Each tuple includes an op and op arguments. The required arguments for an op specified in its \_\_call\_\_() method. +In this example "sample_id" is a running index. OpKits21SampleIDDecode() is a custom op for Kits21 challenge converting the index to image path and segmentation path which are then loaded by OpLoadImage(). +In other case than Kits21 you would have to implement your custome MySampleIDDecode() operator. +Finally, OpClip() and OpToRange() pre-process the image. + + +## Caching +**The original code is in example_cache_pipeline() in fuse/data/examples/examples_readme.py** +```python + +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), + (OpClip(), dict(key="data.input.img", clip=(-500, 500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), +]) + + +cacher = SamplesCacher(unique_cacher_name, + static_pipeline, + cache_dirs=cache_dir) #it can just one path for the cache ot list of paths which will be tried in order, moving the next when available space is exausted. + +sample_ids= list(range(10)) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=None, + cacher=cacher, +) +my_dataset.create() + +``` + +To enable caching, a sample cacher should be created and specified as in the example above. +The cached data will be at [cache_dir]/[unique_cacher_name]. + +## Adding a dynamic part + +**The original code is in example_dynamic_pipeline() in fuse/data/examples/examples_readme.py** + +```python + +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), +]) + +dynamic_pipeline = PipelineDefault("dynamic", [ + (OpClip(), dict(key="data.input.img", clip=(-500,500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + (OpToTensor(), dict(key="data.input.img")), + (OpToTensor(), dict(key="data.gt.seg")), +]) + + +cacher = SamplesCacher(unique_cacher_name, + static_pipeline, + cache_dirs=cache_dir) + +sample_ids=[f"case_{id:05d}" for id in range(num_samples)] +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, +) +my_dataset.create() + +``` + +A basic example that includes both dynamic pipeline and static pipeline. Dynamic pipeline follows the static pipeline and continues to pre-process the sample. In contrast to the static pipeline, the output of the dynamic pipeline is not be cached and allows modifying the pre-precessing steps without recaching, The recommendation is to include pre-processing steps that we intend to experiment with, in the dynamic pipeline. + + +### Avoiding boilerplate by using "Meta Ops" +**The original code is in example_meta_ops_pipeline() in fuse/data/examples/examples_readme.py** +```python +repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), +]) + +dynamic_pipeline = PipelineDefault("dynamic", [ + (OpClip(), dict(key="data.input.img", clip=(-500,500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), +]) + +cacher = SamplesCacher(unique_cacher_name, + static_pipeline, + cache_dirs=cache_dir) + +sample_ids= sample_ids= list(range(10)) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, +) +my_dataset.create() + +``` +Meta op is a powerful tool, Meta ops enhance the functionality and flexibility of the pipeline and allows avoiding boilerplate code, +The example above is the simplest. We use OpRepeat to repeat OpToTensor twice, once for the image and once for the segmentation map. + + +## Adding augmentation +**The original code is in example_adding_augmentation() in fuse/data/examples/examples_readme.py** +```python + +repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), +]) + +dynamic_pipeline = PipelineDefault("dynamic", [ + (OpClip(), dict(key="data.input.img", clip=(-500,500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), + (OpSampleAndRepeat(OpAffineTransform2D(do_image_reverse=True), kwargs_per_step_to_add=repeat_for), dict( + rotate=Uniform(-180.0,180.0), + scale=Uniform(0.8, 1.2), + flip=(RandBool(0.5), RandBool(0.5)), + translate=(RandInt(-15, 15), RandInt(-15, 15)) + )), +]) + +cacher = SamplesCacher(unique_cacher_name, + static_pipeline, + cache_dirs=cache_dir) + +sample_ids= list(range(10)) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, +) +my_dataset.create() + +``` +FuseMedML comes with a collection of pre-implemented augmentation ops. Augmentation ops are expected to be included in the dynamic_pipeline to avoid caching and to be called with different random numbers drawn from the specified distribution. In this example, we've added identical affine transformation for the image and segmentation map. OpSampleAndRepeat() will first draw the random numbers from the random arguments and then repeat OpAffineTransform2D for both the image and segmentation map with the same arguments. + +## Using custom functions directly (OpFunc and OpLambda) +**The original code is in example_custom_function() in fuse/data/examples/examples_readme.py** +```python + +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), + (OpRepeat(OpLambda(func=lambda x: np.reshape(x,(x.shape[0], 4, 256, 256))), repeat_for), dict()) +]) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, +) +my_dataset.create() + +``` +Pre-processing a dataset many times involves heuristics and custom functions. OpLambda and OpFunc allow using those functions directly instead of implementing Op for every custom function. This is a simple example of implementing NumPy array reshape using OpLambda. + +## End to end dataset example (image and segmentation map) for segmentation task +**The original code is in example_end2end_dataset() in fuse/data/examples/examples_readme.py** +```python + +repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] +static_pipeline = PipelineDefault("static", [ + (OpKits21SampleIDDecode(), dict()), # will save image and seg path to "data.input.img_path", "data.gt.seg_path" + (OpLoadImage(data_dir), dict(key_in="data.input.img_path", key_out="data.input.img", format="nib")), + (OpLoadImage(data_dir), dict(key_in="data.gt.seg_path", key_out="data.gt.seg", format="nib")), +]) + +dynamic_pipeline = PipelineDefault("dynamic", [ + (OpClip(), dict(key="data.input.img", clip=(-500,500))), + (OpToRange(), dict(key="data.input.img", from_range=(-500, 500), to_range=(0, 1))), + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), + (OpSampleAndRepeat(OpAffineTransform2D(do_image_reverse=True), kwargs_per_step_to_add=repeat_for), dict( + rotate=Uniform(-180.0,180.0), + scale=Uniform(0.8, 1.2), + flip=(RandBool(0.5), RandBool(0.5)), + translate=(RandInt(-15, 15), RandInt(-15, 15)) + )), +]) + +cacher = SamplesCacher(unique_cacher_name, + static_pipeline, + cache_dirs=cache_dir) + +sample_ids= list(range(10)) +my_dataset = DatasetDefault(sample_ids=sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher, +) +my_dataset.create() + +``` + +## Creating dataloader and balanced dataloader +**The original code is in example_balanced_dataloader() in fuse/data/examples/examples_readme.py** +```python +batch_sampler = BatchSamplerDefault(dataset=dataset, + balanced_class_name='data.label', + num_balanced_classes=num_classes, + batch_size=batch_size, + mode="approx", + balanced_class_weights=[1 / num_classes] * num_classes) + +dataloader = DataLoader(dataset=dataset, collate_fn=CollateDefault(), batch_sampler=batch_sampler, shuffle=False, drop_last=False) +``` +To create a dataloader, reuse our default generic collate function, and to balance the data, use our sampler. + + + +## Converting classic PyTorch dataset to FuseMedML style +**The original code is in example_classic_to_fusemedml_style() in fuse/data/examples/examples_readme.py** +```python +my_dataset = DatasetWrapSeqToDict(name='my_dataset', dataset=torch_dataset, sample_keys=('data.image', 'data.label')) +my_dataset.create() +``` +If you already have a Pytorch dataset at hand that its \_\_getitem\_\_ method outputs a sequence of values, but want to switch to FuseMedML style which its \_\_getitem\_\_ method outputs a flexible dictionary, you can easily wrap it with DatasetWrapSeqToDict as in the example above. + +## Op(erators) list + +**Meta operators** + +Meta operators are a great tool to facilitate the development of sample processing pipelines. +The following operators are useful when implementing a common pipeline: + +* OpRepeat - repeats an op multiple times, each time with different arguments +* OpLambda - applies simple lambda function / function to transform single value +* OpFunc - helps to wrap an existing simple python function without writing boilerplate code +* OpApplyPatterns - selects and applies an operation according to the key name in sample_dict. +* OpApplyTypes - selects and apply an operation according to value type (inferred from the key name in sample_dict) +* OpCollectMarker - use this op within the dynamic pipeline to optimize the reading time for components such as sampler + +**Meta operators for random augmentations** + +* OpSample - recursively searches for ParamSamplerBase instances in kwargs, and replaces the drawn values in place +* OpSampleAndRepeat - first samples and then repeats the operation with the drawn values. Used to apply the same transformation on different values such as image and segmentation map +* OpRepeatAndSample - repeats the operations, but each time has drawn different values from the defined distributions +* OpRandApply - randomly applies the op (according to the given probability) + +**Reading operators** + +* OpReadDataframe - reads data from pickle file / Dataframe object. Each row will be added as a value to sample_dict + +**Casting operators** + +* OpToNumpy - convert many different types to NumPy array +* OpToTensor - convert many different types to PyTorch tensor + +**Imaging operators** +See fuseimg package + From 2e1f98c6e1406ada32944b5679704b8fb0d7ecb8 Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 10 May 2022 11:42:04 +0300 Subject: [PATCH 39/42] Fix import for fuse2 + add a static pipeline + change data source into a function --- .../siim/data_source_segmentation.py | 23 ++-- .../segmentation/siim/image_mask_loader.py | 127 ++++++++++++++++++ .../imaging/segmentation/siim/runner_seg.py | 101 ++++++++++---- fuse/dl/losses/segmentation/loss_dice.py | 4 +- 4 files changed, 215 insertions(+), 40 deletions(-) create mode 100644 examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py diff --git a/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py b/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py index 0554559b0..8bac212a3 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py +++ b/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py @@ -5,8 +5,8 @@ from typing import Sequence, Hashable, Union, Optional, List, Dict from pathlib import Path -from fuse.data.data_source.data_source_base import FuseDataSourceBase -from fuse.utils.utils_misc import autodetect_input_source +# from fuse.data.data_source.data_source_base import FuseDataSourceBase +# from fuse.utils.utils_misc import autodetect_input_source def filter_files(files, include=[], exclude=[]): @@ -26,8 +26,9 @@ def ls(x, recursive=False, include=[], exclude=[]): return out -class FuseDataSourceSeg(FuseDataSourceBase): - def __init__(self, +# class FuseDataSourceSeg(): +# def __init__(self, +def get_data_sample_ids( phase: str, # can be ['train', 'validation'] data_folder: Optional[str] = None, partition_file: Optional[str] = None, @@ -107,12 +108,12 @@ def __init__(self, # 'rle_encoding': rle_df.loc[I, ' EncodedPixels'].values} # sample_descs.append(desc) - self.samples = sample_descs + return sample_descs - def get_samples_description(self): - return self.samples + # def get_samples_description(self): + # return self.samples - def summary(self) -> str: - summary_str = '' - summary_str += 'FuseDataSourceSeg - %d samples\n' % len(self.samples) - return summary_str + # def summary(self) -> str: + # summary_str = '' + # summary_str += 'FuseDataSourceSeg - %d samples\n' % len(self.samples) + # return summary_str diff --git a/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py b/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py new file mode 100644 index 000000000..b49dc6d9b --- /dev/null +++ b/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py @@ -0,0 +1,127 @@ + +""" +(C) Copyright 2021 IBM Corp. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +Created on June 30, 2021 + +""" + +import numpy as np +import pandas as pd +from skimage.io import imread +import torch +from pathlib import Path +import PIL +import pydicom + +from typing import Optional, Tuple + +from fuse.data.ops.op_base import OpBase +from fuse.utils.ndict import NDict +from fuse.data.utils.sample import get_sample_id + +# from fuse.data.processor.processor_base import FuseProcessorBase + + +def rle2mask(rles, width, height): + """ + + rle encoding if images + input: rles(list of rle), width and height of image + returns: mask of shape (width,height) + """ + + mask= np.zeros(width* height) + for rle in rles: + array = np.asarray([int(x) for x in rle.split()]) + starts = array[0::2] + lengths = array[1::2] + + current_position = 0 + for index, start in enumerate(starts): + current_position += start + mask[current_position:current_position+lengths[index]] = 255 + current_position += lengths[index] + + return mask.reshape(width, height).T + + +class OpImageMaskLoader(OpBase): + def __init__(self, + data_csv: str = None, + size: int = 512, + normalization: float = 255.0, **kwargs): + """ + Create Input processor + :param input_data: path to images + :param normalized_target_range: range for image normalization + :param resize_to: Optional, new size of input images, keeping proportions + """ + super().__init__(**kwargs) + + if data_csv: + self.df = pd.read_csv(data_csv) + else: + self.df = None + + self.size = (size, size) + self.norm = normalization + + def __call__(self, sample_dict: NDict, op_id: Optional[str], key_in:str, key_out: str): + + desc = get_sample_id(sample_dict) + + if self.df is not None: # compute mask + I = self.df.ImageId == Path(desc).stem + enc = self.df.loc[I, ' EncodedPixels'] + if sum(I) == 0: + im = np.zeros((1024, 1024)).astype(np.uint8) + elif sum(I) == 1: + enc = enc.values[0] + if enc == '-1': + im = np.zeros((1024, 1024)).astype(np.uint8) + else: + im = rle2mask([enc], 1024, 1024).astype(np.uint8) + else: + im = rle2mask(enc.values, 1024, 1024).astype(np.uint8) + + im = np.asarray(PIL.Image.fromarray(im).resize(self.size)) + image = im > 0 + image = image.astype('float32') + + else: # load image + dcm = pydicom.read_file(desc).pixel_array + image = np.asarray(PIL.Image.fromarray(dcm).resize(self.size)) + + image = image.astype('float32') + image = image / 255.0 + + # convert image from shape (H x W x C) to shape (C x H x W) with C=3 + if len(image.shape) > 2: + image = np.moveaxis(image, -1, 0) + else: + image = np.expand_dims(image, 0) + + # numpy to tensor + # sample = torch.from_numpy(image) + + # except: + # return None + + sample_dict[key_out] = image + return sample_dict + + def reverse(self, sample_dict: NDict, key_to_reverse: str, key_to_follow: str, op_id: Optional[str]) -> dict: + return sample_dict diff --git a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py index 38417a44d..1268f9cd9 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py +++ b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py @@ -32,38 +32,63 @@ import torch.nn.functional as F from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool -from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt -from fuse.utils.utils_gpu import FuseUtilsGPU +# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform +# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool +# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt +# from fuse.utils.utils_gpu import FuseUtilsGPU +import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.utils_logger import fuse_logger_start -from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -from fuse.data.dataset.dataset_default import FuseDatasetDefault -from fuse.models.model_wrapper import FuseModelWrapper -from fuse.losses.segmentation.loss_dice import DiceBCELoss -from fuse.losses.segmentation.loss_dice import FuseDiceLoss -from fuse.losses.loss_default import FuseLossDefault -from fuse.managers.manager_default import FuseManagerDefault -from fuse.managers.callbacks.callback_tensorboard import FuseTensorboardCallback -from fuse.managers.callbacks.callback_metric_statistics import FuseMetricStatisticsCallback -from fuse.managers.callbacks.callback_time_statistics import FuseTimeStatisticsCallback -from fuse.data.processor.processor_dataframe import FuseProcessorDataFrame +# from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault +# from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault +# from fuse.data.dataset.dataset_default import FuseDatasetDefault +from fuse.dl.models.model_wrapper import ModelWrapper +from fuse.dl.losses.segmentation.loss_dice import DiceBCELoss +from fuse.dl.losses.segmentation.loss_dice import FuseDiceLoss +from fuse.dl.losses.loss_default import LossDefault +from fuse.dl.managers.manager_default import ManagerDefault +from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback +from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback +from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback +# from fuse.dl.data.processor.processor_dataframe import FuseProcessorDataFrame from fuse.eval.evaluator import EvaluatorDefault from fuse.eval.metrics.segmentation.metrics_segmentation_common import MetricDice, MetricIouJaccard, MetricOverlap, Metric2DHausdorff, MetricPixelAccuracy -from fuse.utils.utils_debug import FuseUtilsDebug +from fuse.utils.utils_debug import FuseDebug -from data_source_segmentation import FuseDataSourceSeg +from data_source_segmentation import get_data_sample_ids # FuseDataSourceSeg from seg_input_processor import SegInputProcessor +from image_mask_loader import OpImageMaskLoader from unet import UNet - +# fuse2 imports +from fuse.data.pipelines.pipeline_default import PipelineDefault +from fuse.data.datasets.dataset_default import DatasetDefault +from fuse.data.ops.op_base import OpBase +from fuse.data.ops.ops_aug_common import OpSample +from fuse.data.datasets.caching.samples_cacher import SamplesCacher +from fuse.data.ops.ops_common import OpLambda +from fuse.data.utils.samplers import BatchSamplerDefault +from fuse.data import PipelineDefault, OpSampleAndRepeat, OpToTensor, OpRepeat +from fuse.utils.rand.param_sampler import RandBool, RandInt, Uniform +import torch +import numpy as np +from functools import partial +from tempfile import mkdtemp + +import os +from fuse.data.ops.ops_cast import OpToTensor +from fuse.utils.ndict import NDict +from fuseimg.data.ops.image_loader import OpLoadImage +from fuseimg.data.ops.color import OpClip, OpToRange +from fuseimg.data.ops.aug.color import OpAugColor +from fuseimg.data.ops.aug.geometry import OpAugAffine2D + +from fuseimg.datasets.kits21 import OpKits21SampleIDDecode, KITS21 ########################################## # Debug modes ########################################## mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug -debug = FuseUtilsDebug(mode) +debug = FuseDebug(mode) ########################################## # Output and data Paths @@ -176,10 +201,30 @@ def run_train(paths: dict, train_common_params: dict): #### Train Data lgr.info(f'Train Data:', {'attrs': 'bold'}) - train_data_source = FuseDataSourceSeg(phase='train', + train_sample_ids = get_data_sample_ids(phase='train', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) - print(train_data_source.summary()) + + static_pipeline = PipelineDefault("static", [ + (OpImageMaskLoader(size=train_common_params['data.image_size']), + dict(key_in="data.input.img_path", key_out="data.input.img")), + (OpImageMaskLoader(size=train_common_params['data.image_size'], + data_csv=paths['train_rle_file']), + dict(key_in="data.gt.seg_path", key_out="data.gt.seg")), + ]) + + + # cache_dir = mkdtemp(prefix="kits_21") + cacher = SamplesCacher('siim_cache', + static_pipeline, + cache_dirs=[paths['cache_dir']], + restart_cache=True) + + my_dataset = DatasetDefault(sample_ids=train_sample_ids[:5], + static_pipeline=static_pipeline, + dynamic_pipeline=None, + cacher=cacher) + my_dataset.create() ## Create data processors: input_processors = { @@ -193,12 +238,14 @@ def run_train(paths: dict, train_common_params: dict): } ## Create data augmentation (optional) - augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + # augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + augmentor = [] # Create visualizer (optional) - visualiser = FuseVisualizerDefault(image_name='data.input.input_0', - mask_name='data.gt.gt_global', - pred_name='model.logits.segmentation') + # visualiser = FuseVisualizerDefault(image_name='data.input.input_0', + # mask_name='data.gt.gt_global', + # pred_name='model.logits.segmentation') + visualiser = [] train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], data_source=train_data_source, @@ -446,7 +493,7 @@ def data_iter(): ###################################### if __name__ == "__main__": # allocate gpus - NUM_GPUS = 1 + NUM_GPUS = 0 if NUM_GPUS == 0: TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones diff --git a/fuse/dl/losses/segmentation/loss_dice.py b/fuse/dl/losses/segmentation/loss_dice.py index bd44fd82d..feb0ceb5b 100644 --- a/fuse/dl/losses/segmentation/loss_dice.py +++ b/fuse/dl/losses/segmentation/loss_dice.py @@ -82,7 +82,7 @@ def __call__(self, predict, target): raise Exception('Unexpected reduction {}'.format(self.reduction)) -class DiceBCELoss(FuseLossBase): +class DiceBCELoss(LossBase): def __init__(self, pred_name: str = None, @@ -155,7 +155,7 @@ def __call__(self, batch_dict): return self.weight*total_loss -class FuseDiceLoss(FuseLossBase): +class FuseDiceLoss(LossBase): def __init__(self, pred_name: str = None, From dbe44a590f2456aa3caf23c7c3d79ac26b0e22dc Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 10 May 2022 13:16:42 +0300 Subject: [PATCH 40/42] Complete data pipeline including the dynamic part --- .../imaging/segmentation/siim/runner_seg.py | 160 ++++++++++++------ 1 file changed, 111 insertions(+), 49 deletions(-) diff --git a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py index 1268f9cd9..8168c42f9 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py +++ b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py @@ -81,9 +81,9 @@ from fuseimg.data.ops.image_loader import OpLoadImage from fuseimg.data.ops.color import OpClip, OpToRange from fuseimg.data.ops.aug.color import OpAugColor +from fuseimg.data.ops.aug.color import OpAugGaussian from fuseimg.data.ops.aug.geometry import OpAugAffine2D -from fuseimg.datasets.kits21 import OpKits21SampleIDDecode, KITS21 ########################################## # Debug modes ########################################## @@ -213,6 +213,56 @@ def run_train(paths: dict, train_common_params: dict): dict(key_in="data.gt.seg_path", key_out="data.gt.seg")), ]) + repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] + + dynamic_pipeline = PipelineDefault("dynamic", [ + (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), + (OpSampleAndRepeat(OpAugAffine2D(), kwargs_per_step_to_add=repeat_for), dict( + rotate=Uniform(-20.0,20.0), + scale=Uniform(0.8, 1.2), + flip=(RandBool(0.0), RandBool(0.5)), # only flip right-to-left + translate=(RandInt(-50, 50), RandInt(-50, 50)) + )), + (OpAugGaussian(), dict(key='data.input.img', + std=Uniform(0, 1.0))), + (OpAugColor(), dict(key='data.input.img', + add=Uniform(-0.06, 0.06), + mul= Uniform(0.95, 1.05), + gamma=Uniform(0.9, 1.1), + contrast=Uniform(0.85, 1.15))), + + ]) + # ('data.input.input_0','data.gt.gt_global'), + # aug_op_affine_group, + # {'rotate': Uniform(-20.0, 20.0), + # 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left + # 'scale': Uniform(0.9, 1.1), + # 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, + # {'apply': RandBool(0.9)} + # ], + # [ + # ('data.input.input_0','data.gt.gt_global'), + # aug_op_elastic_transform, + # {'sigma': 7, + # 'num_points': 3}, + # {'apply': RandBool(0.7)} + # ], + # [ + # ('data.input.input_0',), + # aug_op_color, + # { + # 'add': Uniform(-0.06, 0.06), + # 'mul': Uniform(0.95, 1.05), + # 'gamma': Uniform(0.9, 1.1), + # 'contrast': Uniform(0.85, 1.15) + # }, + # {'apply': RandBool(0.7)} + # ], + # [ + # ('data.input.input_0',), + # aug_op_gaussian, + # {'std': 0.05}, + # {'apply': RandBool(0.7)} # cache_dir = mkdtemp(prefix="kits_21") cacher = SamplesCacher('siim_cache', @@ -220,66 +270,78 @@ def run_train(paths: dict, train_common_params: dict): cache_dirs=[paths['cache_dir']], restart_cache=True) - my_dataset = DatasetDefault(sample_ids=train_sample_ids[:5], - static_pipeline=static_pipeline, - dynamic_pipeline=None, - cacher=cacher) - my_dataset.create() - - ## Create data processors: - input_processors = { - 'input_0': SegInputProcessor(name='image', - size=train_common_params['data.image_size']) - } - gt_processors = { - 'gt_global': SegInputProcessor(name='mask', - data_csv=paths['train_rle_file'], - size=train_common_params['data.image_size']) - } - - ## Create data augmentation (optional) - # augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) - augmentor = [] - - # Create visualizer (optional) - # visualiser = FuseVisualizerDefault(image_name='data.input.input_0', - # mask_name='data.gt.gt_global', - # pred_name='model.logits.segmentation') - visualiser = [] - - train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - data_source=train_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - augmentor=augmentor, - visualizer=visualiser) + train_dataset = DatasetDefault(sample_ids=train_sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=dynamic_pipeline, + cacher=cacher) lgr.info(f'- Load and cache data:') train_dataset.create() lgr.info(f'- Load and cache data: Done') + # ## Create data processors: + # input_processors = { + # 'input_0': SegInputProcessor(name='image', + # size=train_common_params['data.image_size']) + # } + # gt_processors = { + # 'gt_global': SegInputProcessor(name='mask', + # data_csv=paths['train_rle_file'], + # size=train_common_params['data.image_size']) + # } + + # ## Create data augmentation (optional) + # # augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) + # augmentor = [] + + # # Create visualizer (optional) + # # visualiser = FuseVisualizerDefault(image_name='data.input.input_0', + # # mask_name='data.gt.gt_global', + # # pred_name='model.logits.segmentation') + # visualiser = [] + + # train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + # data_source=train_data_source, + # input_processors=input_processors, + # gt_processors=gt_processors, + # augmentor=augmentor, + # visualizer=visualiser) + + # lgr.info(f'- Load and cache data:') + # train_dataset.create() + # lgr.info(f'- Load and cache data: Done') + ## Create dataloader train_dataloader = DataLoader(dataset=train_dataset, shuffle=True, drop_last=False, batch_size=train_common_params['data.batch_size'], - collate_fn=train_dataset.collate_fn, + # collate_fn=train_dataset.collate_fn, num_workers=train_common_params['data.train_num_workers']) lgr.info(f'Train Data: Done', {'attrs': 'bold'}) # ================================================================== # Validation dataset lgr.info(f'Validation Data:', {'attrs': 'bold'}) - valid_data_source = FuseDataSourceSeg(phase='validation', + valid_sample_ids = get_data_sample_ids(phase='validation', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) - print(valid_data_source.summary()) - valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - data_source=valid_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - visualizer=visualiser) + # valid_data_source = FuseDataSourceSeg(phase='validation', + # data_folder=paths['train_folder'], + # partition_file=train_common_params['partition_file']) + # print(valid_data_source.summary()) + + valid_dataset = DatasetDefault(sample_ids=valid_sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=None, + cacher=cacher) + + # valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + # data_source=valid_data_source, + # input_processors=input_processors, + # gt_processors=gt_processors, + # visualizer=visualiser) lgr.info(f'- Load and cache data:') valid_dataset.create() @@ -290,7 +352,7 @@ def run_train(paths: dict, train_common_params: dict): shuffle=False, drop_last=False, batch_size=train_common_params['data.batch_size'], - collate_fn=valid_dataset.collate_fn, + # collate_fn=valid_dataset.collate_fn, num_workers=train_common_params['data.validation_num_workers']) lgr.info(f'Validation Data: Done', {'attrs': 'bold'}) @@ -299,7 +361,7 @@ def run_train(paths: dict, train_common_params: dict): lgr.info('Model:', {'attrs': 'bold'}) torch_model = UNet(n_channels=1, n_classes=1, bilinear=False) - model = FuseModelWrapper(model=torch_model, + model = ModelWrapper(model=torch_model, model_inputs=['data.input.input_0'], model_outputs=['logits.segmentation'] ) @@ -321,7 +383,7 @@ def run_train(paths: dict, train_common_params: dict): scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5) # train from scratch - manager = FuseManagerDefault(output_model_dir=paths['model_dir'], + manager = ManagerDefault(output_model_dir=paths['model_dir'], force_reset=paths['force_reset_model_dir']) # ===================================================================================== @@ -329,9 +391,9 @@ def run_train(paths: dict, train_common_params: dict): # ===================================================================================== callbacks = [ # default callbacks - # FuseTensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard - FuseMetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file - FuseTimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler + TensorboardCallback(model_dir=paths['model_dir']), # save statistics for tensorboard + MetricStatisticsCallback(output_path=paths['model_dir'] + "/metrics.csv"), # save statistics in a csv file + TimeStatisticsCallback(num_epochs=train_common_params['manager.train_params']['num_epochs'], load_expected_part=0.1) # time profiler ] # Providing the objects required for the training process. @@ -416,7 +478,7 @@ def run_infer(paths: dict, infer_common_params: dict): lgr.info(f'Test Data: Done', {'attrs': 'bold'}) #### Manager for inference - manager = FuseManagerDefault() + manager = ManagerDefault() # extract just the global segmentation per sample and save to a file output_columns = ['model.logits.segmentation', 'data.gt.gt_global'] manager.infer(data_loader=infer_dataloader, From 6c4232ce1ac63b63177f151aff757195f9f9a80f Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 17 May 2022 15:54:54 +0300 Subject: [PATCH 41/42] Working fuse2 version + fix to gaussian op data type --- .../imaging/segmentation/siim/runner_seg.py | 225 ++++++++++-------- fuseimg/data/ops/aug/color.py | 6 +- 2 files changed, 126 insertions(+), 105 deletions(-) diff --git a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py index 8168c42f9..9da0c09bd 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py +++ b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py @@ -31,7 +31,7 @@ import torch.optim as optim import torch.nn.functional as F -from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform +# from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform # from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform # from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool # from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt @@ -55,7 +55,7 @@ from fuse.utils.utils_debug import FuseDebug from data_source_segmentation import get_data_sample_ids # FuseDataSourceSeg -from seg_input_processor import SegInputProcessor +# from seg_input_processor import SegInputProcessor from image_mask_loader import OpImageMaskLoader from unet import UNet @@ -78,7 +78,7 @@ import os from fuse.data.ops.ops_cast import OpToTensor from fuse.utils.ndict import NDict -from fuseimg.data.ops.image_loader import OpLoadImage +# from fuseimg.data.ops.image_loader import OpLoadImage from fuseimg.data.ops.color import OpClip, OpToRange from fuseimg.data.ops.aug.color import OpAugColor from fuseimg.data.ops.aug.color import OpAugGaussian @@ -87,7 +87,7 @@ ########################################## # Debug modes ########################################## -mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug +mode = 'debug' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug debug = FuseDebug(mode) ########################################## @@ -129,50 +129,50 @@ TRAIN_COMMON_PARAMS['data.batch_size'] = 8 TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 -TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ - [ - ('data.input.input_0','data.gt.gt_global'), - aug_op_affine_group, - {'rotate': Uniform(-20.0, 20.0), - 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left - 'scale': Uniform(0.9, 1.1), - 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, - {'apply': RandBool(0.9)} - ], - [ - ('data.input.input_0','data.gt.gt_global'), - aug_op_elastic_transform, - {'sigma': 7, - 'num_points': 3}, - {'apply': RandBool(0.7)} - ], - [ - ('data.input.input_0',), - aug_op_color, - { - 'add': Uniform(-0.06, 0.06), - 'mul': Uniform(0.95, 1.05), - 'gamma': Uniform(0.9, 1.1), - 'contrast': Uniform(0.85, 1.15) - }, - {'apply': RandBool(0.7)} - ], - [ - ('data.input.input_0',), - aug_op_gaussian, - {'std': 0.05}, - {'apply': RandBool(0.7)} - ], -] +# TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ +# [ +# ('data.input.input_0','data.gt.gt_global'), +# aug_op_affine_group, +# {'rotate': Uniform(-20.0, 20.0), +# 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left +# 'scale': Uniform(0.9, 1.1), +# 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, +# {'apply': RandBool(0.9)} +# ], +# [ +# ('data.input.input_0','data.gt.gt_global'), +# aug_op_elastic_transform, +# {'sigma': 7, +# 'num_points': 3}, +# {'apply': RandBool(0.7)} +# ], +# [ +# ('data.input.input_0',), +# aug_op_color, +# { +# 'add': Uniform(-0.06, 0.06), +# 'mul': Uniform(0.95, 1.05), +# 'gamma': Uniform(0.9, 1.1), +# 'contrast': Uniform(0.85, 1.15) +# }, +# {'apply': RandBool(0.7)} +# ], +# [ +# ('data.input.input_0',), +# aug_op_gaussian, +# {'std': 0.05}, +# {'apply': RandBool(0.7)} +# ], +# ] # =============== # Manager - Train1 # =============== TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 50, + 'num_epochs': 3, 'virtual_batch_size': 1, # number of batches in one virtual batch 'start_saving_epochs': 10, # first epoch to start saving checkpoints from - 'gap_between_saving_epochs': 5, # number of epochs between saved checkpoint + 'gap_between_saving_epochs': 1, #5, # number of epochs between saved checkpoint } TRAIN_COMMON_PARAMS['manager.best_epoch_source'] = { 'source': 'losses.total_loss', # can be any key from 'epoch_results' (either metrics or losses result) @@ -183,6 +183,19 @@ TRAIN_COMMON_PARAMS['manager.resume_checkpoint_filename'] = None # if not None, will try to load the checkpoint TRAIN_COMMON_PARAMS['partition_file'] = 'train_val_split.pickle' +static_pipeline = PipelineDefault("static", [ + (OpImageMaskLoader(size=TRAIN_COMMON_PARAMS['data.image_size']), + dict(key_in="data.input.img_path", key_out="data.input.img")), + (OpImageMaskLoader(size=TRAIN_COMMON_PARAMS['data.image_size'], + data_csv=PATHS['train_rle_file']), + dict(key_in="data.gt.seg_path", key_out="data.gt.seg")), + ]) + +cacher = SamplesCacher('siim_cache', + static_pipeline, + cache_dirs=[PATHS['cache_dir']], + restart_cache=True) + ################################# # Train Template ################################# @@ -204,17 +217,9 @@ def run_train(paths: dict, train_common_params: dict): train_sample_ids = get_data_sample_ids(phase='train', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) - - static_pipeline = PipelineDefault("static", [ - (OpImageMaskLoader(size=train_common_params['data.image_size']), - dict(key_in="data.input.img_path", key_out="data.input.img")), - (OpImageMaskLoader(size=train_common_params['data.image_size'], - data_csv=paths['train_rle_file']), - dict(key_in="data.gt.seg_path", key_out="data.gt.seg")), - ]) + train_sample_ids = train_sample_ids[:10] repeat_for = [dict(key="data.input.img"), dict(key="data.gt.seg")] - dynamic_pipeline = PipelineDefault("dynamic", [ (OpRepeat(OpToTensor(), kwargs_per_step_to_add=repeat_for), dict(dtype=torch.float32)), (OpSampleAndRepeat(OpAugAffine2D(), kwargs_per_step_to_add=repeat_for), dict( @@ -223,11 +228,12 @@ def run_train(paths: dict, train_common_params: dict): flip=(RandBool(0.0), RandBool(0.5)), # only flip right-to-left translate=(RandInt(-50, 50), RandInt(-50, 50)) )), - (OpAugGaussian(), dict(key='data.input.img', - std=Uniform(0, 1.0))), - (OpAugColor(), dict(key='data.input.img', + (OpSample(OpAugGaussian()), dict(key='data.input.img', + std=Uniform(0.0, 0.05))), + (OpClip(), dict(key='data.input.img', clip=(0.0, 1.0))), + (OpSample(OpAugColor()), dict(key='data.input.img', add=Uniform(-0.06, 0.06), - mul= Uniform(0.95, 1.05), + mul=Uniform(0.95, 1.05), gamma=Uniform(0.9, 1.1), contrast=Uniform(0.85, 1.15))), @@ -265,11 +271,6 @@ def run_train(paths: dict, train_common_params: dict): # {'apply': RandBool(0.7)} # cache_dir = mkdtemp(prefix="kits_21") - cacher = SamplesCacher('siim_cache', - static_pipeline, - cache_dirs=[paths['cache_dir']], - restart_cache=True) - train_dataset = DatasetDefault(sample_ids=train_sample_ids, static_pipeline=static_pipeline, dynamic_pipeline=dynamic_pipeline, @@ -326,6 +327,7 @@ def run_train(paths: dict, train_common_params: dict): valid_sample_ids = get_data_sample_ids(phase='validation', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) + valid_sample_ids = valid_sample_ids[:10] # valid_data_source = FuseDataSourceSeg(phase='validation', # data_folder=paths['train_folder'], @@ -362,7 +364,7 @@ def run_train(paths: dict, train_common_params: dict): torch_model = UNet(n_channels=1, n_classes=1, bilinear=False) model = ModelWrapper(model=torch_model, - model_inputs=['data.input.input_0'], + model_inputs=['data.input.img'], model_outputs=['logits.segmentation'] ) @@ -372,7 +374,7 @@ def run_train(paths: dict, train_common_params: dict): # ==================================================================================== losses = { 'dice_loss': DiceBCELoss(pred_name='model.logits.segmentation', - target_name='data.gt.gt_global') + target_name='data.gt.seg') } optimizer = optim.SGD(model.parameters(), @@ -436,33 +438,47 @@ def run_infer(paths: dict, infer_common_params: dict): # Validation dataset lgr.info(f'Test Data:', {'attrs': 'bold'}) - infer_data_source = FuseDataSourceSeg(phase='validation', - data_folder=paths['train_folder'], - partition_file=infer_common_params['partition_file']) - print(infer_data_source.summary()) + # infer_data_source = FuseDataSourceSeg(phase='validation', + # data_folder=paths['train_folder'], + # partition_file=infer_common_params['partition_file']) + # print(infer_data_source.summary()) - ## Create data processors: - input_processors = { - 'input_0': SegInputProcessor(name='image', - size=infer_common_params['data.image_size']) - } - gt_processors = { - 'gt_global': SegInputProcessor(name='mask', - data_csv=paths['train_rle_file'], - size=infer_common_params['data.image_size']) - } + # ## Create data processors: + # input_processors = { + # 'input_0': SegInputProcessor(name='image', + # size=infer_common_params['data.image_size']) + # } + # gt_processors = { + # 'gt_global': SegInputProcessor(name='mask', + # data_csv=paths['train_rle_file'], + # size=infer_common_params['data.image_size']) + # } - # Create visualizer (optional) - visualiser = FuseVisualizerDefault(image_name='data.input.input_0', - mask_name='data.gt.gt_global', - pred_name='model.logits.segmentation') + # # Create visualizer (optional) + # visualiser = FuseVisualizerDefault(image_name='data.input.img', + # mask_name='data.gt.seg', + # pred_name='model.logits.segmentation') - infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - data_source=infer_data_source, - input_processors=input_processors, - gt_processors=gt_processors, - visualizer=visualiser) + # infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], + # data_source=infer_data_source, + # input_processors=input_processors, + # gt_processors=gt_processors, + # visualizer=visualiser) + + infer_sample_ids = get_data_sample_ids(phase='validation', + data_folder=paths['train_folder'], + partition_file=infer_common_params['partition_file']) + infer_sample_ids = infer_sample_ids[:20] + # valid_data_source = FuseDataSourceSeg(phase='validation', + # data_folder=paths['train_folder'], + # partition_file=train_common_params['partition_file']) + # print(valid_data_source.summary()) + + infer_dataset = DatasetDefault(sample_ids=infer_sample_ids, + static_pipeline=static_pipeline, + dynamic_pipeline=None, + cacher=cacher) lgr.info(f'- Load and cache data:') infer_dataset.create() lgr.info(f'- Load and cache data: Done') @@ -472,7 +488,7 @@ def run_infer(paths: dict, infer_common_params: dict): shuffle=False, drop_last=False, batch_size=infer_common_params['data.batch_size'], - collate_fn=infer_dataset.collate_fn, + # collate_fn=infer_dataset.collate_fn, num_workers=infer_common_params['data.train_num_workers']) lgr.info(f'Test Data: Done', {'attrs': 'bold'}) @@ -480,29 +496,31 @@ def run_infer(paths: dict, infer_common_params: dict): #### Manager for inference manager = ManagerDefault() # extract just the global segmentation per sample and save to a file - output_columns = ['model.logits.segmentation', 'data.gt.gt_global'] + output_columns = ['model.logits.segmentation', 'data.gt.seg'] manager.infer(data_loader=infer_dataloader, input_model_dir=paths['model_dir'], checkpoint=infer_common_params['checkpoint'], output_columns=output_columns, output_file_name=infer_common_params["infer_filename"]) - # visualize the predictions - infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) - descriptors_list = infer_processor.get_samples_descriptors() - out_name = 'model.logits.segmentation' - gt_name = 'data.gt.gt_global' - for desc in descriptors_list[:10]: - data = infer_processor(desc) - pred = np.squeeze(data[out_name]) - gt = np.squeeze(data[gt_name]) - _, ax = plt.subplots(1,2) - ax[0].imshow(pred) - ax[0].set_title('prediction') - ax[1].imshow(gt) - ax[1].set_title('gt') - fn = os.path.join(paths["inference_dir"], Path(desc[0]).name) - plt.savefig(fn) + # # visualize the predictions + import ipdb; ipdb.set_trace(context=7) # BREAKPOINT + df = pd.read_pickle(infer_common_params['infer_filename']) + # infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) + # descriptors_list = infer_processor.get_samples_descriptors() + # out_name = 'model.logits.segmentation' + # gt_name = 'data.gt.seg' + # for desc in descriptors_list[:10]: + # data = infer_processor(desc) + # pred = np.squeeze(data[out_name]) + # gt = np.squeeze(data[gt_name]) + # _, ax = plt.subplots(1,2) + # ax[0].imshow(pred) + # ax[0].set_title('prediction') + # ax[1].imshow(gt) + # ax[1].set_title('gt') + # fn = os.path.join(paths["inference_dir"], Path(desc[0]).name) + # plt.savefig(fn) ###################################### # Evaluation Common Params @@ -531,7 +549,7 @@ def data_iter(): sample_dict = {} sample_dict["id"] = row['id'] sample_dict["pred.array"] = row['model.logits.segmentation'] > threshold - sample_dict["label.array"] = row['data.gt.gt_global'] + sample_dict["label.array"] = row['data.gt.seg'] yield sample_dict metrics = OrderedDict([ @@ -563,6 +581,7 @@ def data_iter(): FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' + RUNNING_MODES = ['infer', 'eval'] # Options: 'train', 'infer', 'eval' # train if 'train' in RUNNING_MODES: diff --git a/fuseimg/data/ops/aug/color.py b/fuseimg/data/ops/aug/color.py index 94fb95f25..eff06c891 100644 --- a/fuseimg/data/ops/aug/color.py +++ b/fuseimg/data/ops/aug/color.py @@ -124,10 +124,12 @@ def __call__(self, sample_dict: NDict, op_id: Optional[str], key: str, mean: flo aug_tensor = aug_input if channels is None: - rand_patch = Gaussian(aug_tensor.shape, mean, std).sample() + rand_patch = torch.tensor(Gaussian(aug_tensor.shape, mean, std).sample()) + rand_patch = rand_patch.to(dtype=aug_tensor.dtype) aug_tensor = aug_tensor + rand_patch else: - rand_patch = Gaussian(aug_tensor[channels].shape, mean, std).sample() + rand_patch = torch.tensor(Gaussian(aug_tensor.shape, mean, std).sample()) + rand_patch = rand_patch.to(dtype=aug_tensor.dtype) aug_tensor[channels] = aug_tensor[channels] + rand_patch sample_dict[key] = aug_tensor From 0ef6742d85c947dc02e079e4edee5ee2e0e5003c Mon Sep 17 00:00:00 2001 From: Amir Egozi Date: Tue, 17 May 2022 16:35:51 +0300 Subject: [PATCH 42/42] remove comments and non-required input processor file --- .../siim/data_source_segmentation.py | 29 --- .../segmentation/siim/image_mask_loader.py | 6 - .../imaging/segmentation/siim/runner_seg.py | 189 +----------------- .../segmentation/siim/seg_input_processor.py | 123 ------------ 4 files changed, 6 insertions(+), 341 deletions(-) delete mode 100644 examples/fuse_examples/imaging/segmentation/siim/seg_input_processor.py diff --git a/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py b/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py index 8bac212a3..62f80a990 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py +++ b/examples/fuse_examples/imaging/segmentation/siim/data_source_segmentation.py @@ -5,9 +5,6 @@ from typing import Sequence, Hashable, Union, Optional, List, Dict from pathlib import Path -# from fuse.data.data_source.data_source_base import FuseDataSourceBase -# from fuse.utils.utils_misc import autodetect_input_source - def filter_files(files, include=[], exclude=[]): for incl in include: @@ -26,8 +23,6 @@ def ls(x, recursive=False, include=[], exclude=[]): return out -# class FuseDataSourceSeg(): -# def __init__(self, def get_data_sample_ids( phase: str, # can be ['train', 'validation'] data_folder: Optional[str] = None, @@ -53,19 +48,10 @@ def get_data_sample_ids( if phase == 'train': if override_partition: - # rle_df = pd.read_csv(data_source) - Path.ls = ls files = Path(data_folder).ls(recursive=True, include=['.dcm']) sample_descs = [str(fn) for fn in files] - # sample_descs = [] - # for fn in files: - # I = rle_df.ImageId == fn.stem - # desc = {'name': fn.stem, - # 'dcm': str(fn), - # 'rle_encoding': rle_df.loc[I, ' EncodedPixels'].values} - # sample_descs.append(desc) if len(sample_descs) == 0: raise Exception('Error detecting input source in FuseDataSourceDefault') @@ -100,20 +86,5 @@ def get_data_sample_ids( files = Path(data_folder).ls(recursive=True, include=['.dcm']) sample_descs = [str(fn) for fn in files] - # sample_descs = [] - # for fn in files: - # I = rle_df.ImageId == fn.stem - # desc = {'name': rle_df.loc[I, 'ImageId'].values[0], - # 'dcm': fn, - # 'rle_encoding': rle_df.loc[I, ' EncodedPixels'].values} - # sample_descs.append(desc) return sample_descs - - # def get_samples_description(self): - # return self.samples - - # def summary(self) -> str: - # summary_str = '' - # summary_str += 'FuseDataSourceSeg - %d samples\n' % len(self.samples) - # return summary_str diff --git a/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py b/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py index b49dc6d9b..519a4cc25 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py +++ b/examples/fuse_examples/imaging/segmentation/siim/image_mask_loader.py @@ -114,12 +114,6 @@ def __call__(self, sample_dict: NDict, op_id: Optional[str], key_in:str, key_out else: image = np.expand_dims(image, 0) - # numpy to tensor - # sample = torch.from_numpy(image) - - # except: - # return None - sample_dict[key_out] = image return sample_dict diff --git a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py index 9da0c09bd..c15479ca7 100644 --- a/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py +++ b/examples/fuse_examples/imaging/segmentation/siim/runner_seg.py @@ -31,16 +31,8 @@ import torch.optim as optim import torch.nn.functional as F -# from fuse.data.augmentor.augmentor_toolbox import aug_op_affine_group, aug_op_affine, aug_op_color, aug_op_gaussian, aug_op_elastic_transform -# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerUniform as Uniform -# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandBool as RandBool -# from fuse.utils.utils_param_sampler import FuseUtilsParamSamplerRandInt as RandInt -# from fuse.utils.utils_gpu import FuseUtilsGPU import fuse.utils.gpu as FuseUtilsGPU from fuse.utils.utils_logger import fuse_logger_start -# from fuse.data.augmentor.augmentor_default import FuseAugmentorDefault -# from fuse.data.visualizer.visualizer_default import FuseVisualizerDefault -# from fuse.data.dataset.dataset_default import FuseDatasetDefault from fuse.dl.models.model_wrapper import ModelWrapper from fuse.dl.losses.segmentation.loss_dice import DiceBCELoss from fuse.dl.losses.segmentation.loss_dice import FuseDiceLoss @@ -49,13 +41,11 @@ from fuse.dl.managers.callbacks.callback_tensorboard import TensorboardCallback from fuse.dl.managers.callbacks.callback_metric_statistics import MetricStatisticsCallback from fuse.dl.managers.callbacks.callback_time_statistics import TimeStatisticsCallback -# from fuse.dl.data.processor.processor_dataframe import FuseProcessorDataFrame from fuse.eval.evaluator import EvaluatorDefault from fuse.eval.metrics.segmentation.metrics_segmentation_common import MetricDice, MetricIouJaccard, MetricOverlap, Metric2DHausdorff, MetricPixelAccuracy from fuse.utils.utils_debug import FuseDebug -from data_source_segmentation import get_data_sample_ids # FuseDataSourceSeg -# from seg_input_processor import SegInputProcessor +from data_source_segmentation import get_data_sample_ids from image_mask_loader import OpImageMaskLoader from unet import UNet @@ -78,7 +68,6 @@ import os from fuse.data.ops.ops_cast import OpToTensor from fuse.utils.ndict import NDict -# from fuseimg.data.ops.image_loader import OpLoadImage from fuseimg.data.ops.color import OpClip, OpToRange from fuseimg.data.ops.aug.color import OpAugColor from fuseimg.data.ops.aug.color import OpAugGaussian @@ -87,7 +76,7 @@ ########################################## # Debug modes ########################################## -mode = 'debug' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug +mode = 'default' # Options: 'default', 'fast', 'debug', 'verbose', 'user'. See details in FuseUtilsDebug debug = FuseDebug(mode) ########################################## @@ -129,47 +118,12 @@ TRAIN_COMMON_PARAMS['data.batch_size'] = 8 TRAIN_COMMON_PARAMS['data.train_num_workers'] = 8 TRAIN_COMMON_PARAMS['data.validation_num_workers'] = 8 -# TRAIN_COMMON_PARAMS['data.augmentation_pipeline'] = [ -# [ -# ('data.input.input_0','data.gt.gt_global'), -# aug_op_affine_group, -# {'rotate': Uniform(-20.0, 20.0), -# 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left -# 'scale': Uniform(0.9, 1.1), -# 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, -# {'apply': RandBool(0.9)} -# ], -# [ -# ('data.input.input_0','data.gt.gt_global'), -# aug_op_elastic_transform, -# {'sigma': 7, -# 'num_points': 3}, -# {'apply': RandBool(0.7)} -# ], -# [ -# ('data.input.input_0',), -# aug_op_color, -# { -# 'add': Uniform(-0.06, 0.06), -# 'mul': Uniform(0.95, 1.05), -# 'gamma': Uniform(0.9, 1.1), -# 'contrast': Uniform(0.85, 1.15) -# }, -# {'apply': RandBool(0.7)} -# ], -# [ -# ('data.input.input_0',), -# aug_op_gaussian, -# {'std': 0.05}, -# {'apply': RandBool(0.7)} -# ], -# ] # =============== # Manager - Train1 # =============== TRAIN_COMMON_PARAMS['manager.train_params'] = { - 'num_epochs': 3, + 'num_epochs': 10, 'virtual_batch_size': 1, # number of batches in one virtual batch 'start_saving_epochs': 10, # first epoch to start saving checkpoints from 'gap_between_saving_epochs': 1, #5, # number of epochs between saved checkpoint @@ -238,39 +192,7 @@ def run_train(paths: dict, train_common_params: dict): contrast=Uniform(0.85, 1.15))), ]) - # ('data.input.input_0','data.gt.gt_global'), - # aug_op_affine_group, - # {'rotate': Uniform(-20.0, 20.0), - # 'flip': (RandBool(0.0), RandBool(0.5)), # only flip right-to-left - # 'scale': Uniform(0.9, 1.1), - # 'translate': (RandInt(-50, 50), RandInt(-50, 50))}, - # {'apply': RandBool(0.9)} - # ], - # [ - # ('data.input.input_0','data.gt.gt_global'), - # aug_op_elastic_transform, - # {'sigma': 7, - # 'num_points': 3}, - # {'apply': RandBool(0.7)} - # ], - # [ - # ('data.input.input_0',), - # aug_op_color, - # { - # 'add': Uniform(-0.06, 0.06), - # 'mul': Uniform(0.95, 1.05), - # 'gamma': Uniform(0.9, 1.1), - # 'contrast': Uniform(0.85, 1.15) - # }, - # {'apply': RandBool(0.7)} - # ], - # [ - # ('data.input.input_0',), - # aug_op_gaussian, - # {'std': 0.05}, - # {'apply': RandBool(0.7)} - - # cache_dir = mkdtemp(prefix="kits_21") + train_dataset = DatasetDefault(sample_ids=train_sample_ids, static_pipeline=static_pipeline, dynamic_pipeline=dynamic_pipeline, @@ -280,44 +202,11 @@ def run_train(paths: dict, train_common_params: dict): train_dataset.create() lgr.info(f'- Load and cache data: Done') - # ## Create data processors: - # input_processors = { - # 'input_0': SegInputProcessor(name='image', - # size=train_common_params['data.image_size']) - # } - # gt_processors = { - # 'gt_global': SegInputProcessor(name='mask', - # data_csv=paths['train_rle_file'], - # size=train_common_params['data.image_size']) - # } - - # ## Create data augmentation (optional) - # # augmentor = FuseAugmentorDefault(augmentation_pipeline=train_common_params['data.augmentation_pipeline']) - # augmentor = [] - - # # Create visualizer (optional) - # # visualiser = FuseVisualizerDefault(image_name='data.input.input_0', - # # mask_name='data.gt.gt_global', - # # pred_name='model.logits.segmentation') - # visualiser = [] - - # train_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - # data_source=train_data_source, - # input_processors=input_processors, - # gt_processors=gt_processors, - # augmentor=augmentor, - # visualizer=visualiser) - - # lgr.info(f'- Load and cache data:') - # train_dataset.create() - # lgr.info(f'- Load and cache data: Done') - ## Create dataloader train_dataloader = DataLoader(dataset=train_dataset, shuffle=True, drop_last=False, batch_size=train_common_params['data.batch_size'], - # collate_fn=train_dataset.collate_fn, num_workers=train_common_params['data.train_num_workers']) lgr.info(f'Train Data: Done', {'attrs': 'bold'}) # ================================================================== @@ -327,24 +216,12 @@ def run_train(paths: dict, train_common_params: dict): valid_sample_ids = get_data_sample_ids(phase='validation', data_folder=paths['train_folder'], partition_file=train_common_params['partition_file']) - valid_sample_ids = valid_sample_ids[:10] - - # valid_data_source = FuseDataSourceSeg(phase='validation', - # data_folder=paths['train_folder'], - # partition_file=train_common_params['partition_file']) - # print(valid_data_source.summary()) valid_dataset = DatasetDefault(sample_ids=valid_sample_ids, static_pipeline=static_pipeline, dynamic_pipeline=None, cacher=cacher) - # valid_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - # data_source=valid_data_source, - # input_processors=input_processors, - # gt_processors=gt_processors, - # visualizer=visualiser) - lgr.info(f'- Load and cache data:') valid_dataset.create() lgr.info(f'- Load and cache data: Done') @@ -354,7 +231,6 @@ def run_train(paths: dict, train_common_params: dict): shuffle=False, drop_last=False, batch_size=train_common_params['data.batch_size'], - # collate_fn=valid_dataset.collate_fn, num_workers=train_common_params['data.validation_num_workers']) lgr.info(f'Validation Data: Done', {'attrs': 'bold'}) @@ -418,7 +294,7 @@ def run_train(paths: dict, train_common_params: dict): ###################################### INFER_COMMON_PARAMS = {} INFER_COMMON_PARAMS['infer_filename'] = os.path.join(PATHS['inference_dir'], 'validation_set_infer.gz') -INFER_COMMON_PARAMS['checkpoint'] = 'last' # Fuse TIP: possible values are 'best', 'last' or epoch_index. +INFER_COMMON_PARAMS['checkpoint'] = 'best' # Fuse TIP: possible values are 'best', 'last' or epoch_index. INFER_COMMON_PARAMS['data.train_num_workers'] = TRAIN_COMMON_PARAMS['data.train_num_workers'] INFER_COMMON_PARAMS['partition_file'] = TRAIN_COMMON_PARAMS['partition_file'] INFER_COMMON_PARAMS['data.image_size'] = TRAIN_COMMON_PARAMS['data.image_size'] @@ -438,42 +314,9 @@ def run_infer(paths: dict, infer_common_params: dict): # Validation dataset lgr.info(f'Test Data:', {'attrs': 'bold'}) - # infer_data_source = FuseDataSourceSeg(phase='validation', - # data_folder=paths['train_folder'], - # partition_file=infer_common_params['partition_file']) - # print(infer_data_source.summary()) - - # ## Create data processors: - # input_processors = { - # 'input_0': SegInputProcessor(name='image', - # size=infer_common_params['data.image_size']) - # } - # gt_processors = { - # 'gt_global': SegInputProcessor(name='mask', - # data_csv=paths['train_rle_file'], - # size=infer_common_params['data.image_size']) - # } - - # # Create visualizer (optional) - # visualiser = FuseVisualizerDefault(image_name='data.input.img', - # mask_name='data.gt.seg', - # pred_name='model.logits.segmentation') - - # infer_dataset = FuseDatasetDefault(cache_dest=paths['cache_dir'], - # data_source=infer_data_source, - # input_processors=input_processors, - # gt_processors=gt_processors, - # visualizer=visualiser) - infer_sample_ids = get_data_sample_ids(phase='validation', data_folder=paths['train_folder'], partition_file=infer_common_params['partition_file']) - infer_sample_ids = infer_sample_ids[:20] - - # valid_data_source = FuseDataSourceSeg(phase='validation', - # data_folder=paths['train_folder'], - # partition_file=train_common_params['partition_file']) - # print(valid_data_source.summary()) infer_dataset = DatasetDefault(sample_ids=infer_sample_ids, static_pipeline=static_pipeline, @@ -488,7 +331,6 @@ def run_infer(paths: dict, infer_common_params: dict): shuffle=False, drop_last=False, batch_size=infer_common_params['data.batch_size'], - # collate_fn=infer_dataset.collate_fn, num_workers=infer_common_params['data.train_num_workers']) lgr.info(f'Test Data: Done', {'attrs': 'bold'}) @@ -503,24 +345,6 @@ def run_infer(paths: dict, infer_common_params: dict): output_columns=output_columns, output_file_name=infer_common_params["infer_filename"]) - # # visualize the predictions - import ipdb; ipdb.set_trace(context=7) # BREAKPOINT - df = pd.read_pickle(infer_common_params['infer_filename']) - # infer_processor = FuseProcessorDataFrame(data_pickle_filename=infer_common_params['infer_filename']) - # descriptors_list = infer_processor.get_samples_descriptors() - # out_name = 'model.logits.segmentation' - # gt_name = 'data.gt.seg' - # for desc in descriptors_list[:10]: - # data = infer_processor(desc) - # pred = np.squeeze(data[out_name]) - # gt = np.squeeze(data[gt_name]) - # _, ax = plt.subplots(1,2) - # ax[0].imshow(pred) - # ax[0].set_title('prediction') - # ax[1].imshow(gt) - # ax[1].set_title('gt') - # fn = os.path.join(paths["inference_dir"], Path(desc[0]).name) - # plt.savefig(fn) ###################################### # Evaluation Common Params @@ -573,7 +397,7 @@ def data_iter(): ###################################### if __name__ == "__main__": # allocate gpus - NUM_GPUS = 0 + NUM_GPUS = 1 if NUM_GPUS == 0: TRAIN_COMMON_PARAMS['manager.train_params']['device'] = 'cpu' # uncomment if you want to use specific gpus instead of automatically looking for free ones @@ -581,7 +405,6 @@ def data_iter(): FuseUtilsGPU.choose_and_enable_multiple_gpus(NUM_GPUS, force_gpus=force_gpus) RUNNING_MODES = ['train', 'infer', 'eval'] # Options: 'train', 'infer', 'eval' - RUNNING_MODES = ['infer', 'eval'] # Options: 'train', 'infer', 'eval' # train if 'train' in RUNNING_MODES: diff --git a/examples/fuse_examples/imaging/segmentation/siim/seg_input_processor.py b/examples/fuse_examples/imaging/segmentation/siim/seg_input_processor.py deleted file mode 100644 index f275fd0a6..000000000 --- a/examples/fuse_examples/imaging/segmentation/siim/seg_input_processor.py +++ /dev/null @@ -1,123 +0,0 @@ - -""" -(C) Copyright 2021 IBM Corp. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. - -Created on June 30, 2021 - -""" - -import numpy as np -import pandas as pd -from skimage.io import imread -import torch -from pathlib import Path -import PIL -import pydicom - -from typing import Optional, Tuple - -from fuse.data.processor.processor_base import FuseProcessorBase - - -def rle2mask(rles, width, height): - """ - - rle encoding if images - input: rles(list of rle), width and height of image - returns: mask of shape (width,height) - """ - - mask= np.zeros(width* height) - for rle in rles: - array = np.asarray([int(x) for x in rle.split()]) - starts = array[0::2] - lengths = array[1::2] - - current_position = 0 - for index, start in enumerate(starts): - current_position += start - mask[current_position:current_position+lengths[index]] = 255 - current_position += lengths[index] - - return mask.reshape(width, height).T - - -class SegInputProcessor(FuseProcessorBase): - def __init__(self, - name: str = 'image', # can be 'image' or 'mask' - data_csv: str = None, - size: int = 512, - normalization: float = 255.0, - ): - """ - Create Input processor - :param input_data: path to images - :param normalized_target_range: range for image normalization - :param resize_to: Optional, new size of input images, keeping proportions - :param padding: Optional, padding size - """ - self.name = name - assert self.name == 'image' or self.name == 'mask', "Error: name can be image or mask only." - - if data_csv: - self.df = pd.read_csv(data_csv) - - self.size = (size, size) - self.norm = normalization - - def __call__(self, - desc, - *args, **kwargs): - - try: - - if self.name == 'image': - dcm = pydicom.read_file(desc).pixel_array - image = np.asarray(PIL.Image.fromarray(dcm).resize(self.size)) - - image = image.astype('float32') - image = image / 255.0 - - else: # create mask - I = self.df.ImageId == Path(desc).stem - enc = self.df.loc[I, ' EncodedPixels'] - if sum(I) == 0: - im = np.zeros((1024, 1024)).astype(np.uint8) - elif sum(I) == 1: - enc = enc.values[0] - if enc == '-1': - im = np.zeros((1024, 1024)).astype(np.uint8) - else: - im = rle2mask([enc], 1024, 1024).astype(np.uint8) - else: - im = rle2mask(enc.values, 1024, 1024).astype(np.uint8) - - im = np.asarray(PIL.Image.fromarray(im).resize(self.size)) - image = im > 0 - image = image.astype('float32') - - # convert image from shape (H x W x C) to shape (C x H x W) with C=3 - if len(image.shape) > 2: - image = np.moveaxis(image, -1, 0) - else: - image = np.expand_dims(image, 0) - - # numpy to tensor - sample = torch.from_numpy(image) - - except: - return None - - return sample