From f95fabcf6e3124ae18808ac52cf1c7ec649b3ce2 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Fri, 31 May 2024 09:06:42 -0600 Subject: [PATCH 01/29] DeepGlobePy --- configs/_base_/datasets/deepGlobe.py | 69 ++++++++++++++++++ configs/_base_/models/deeplabv3_r50-d8.py | 5 +- ...abv3_r50-d8_4xb2-40k_deepglobe-512x1024.py | 8 ++ docs/en/stat.py | 0 docs/zh_cn/stat.py | 0 mmseg/configs/_base_/datasets/deepGlobe.py | 69 ++++++++++++++++++ .../configs/_base_/schedules/schedule_40k.py | 39 ++++------ mmseg/datasets/__init__.py | 2 + mmseg/datasets/deepGlobe.py | 48 ++++++++++++ .../isic2016_task1/tools/prepare_dataset.py | 0 .../isic2017_task1/tools/prepare_dataset.py | 0 ...6_unet_1xb16-0.0001-20k_vampire-512x512.py | 0 ...16_unet_1xb16-0.001-20k_vampire-512x512.py | 0 ...d16_unet_1xb16-0.01-20k_vampire-512x512.py | 0 .../vampire/configs/vampire_512x512.py | 0 .../vampire/datasets/__init__.py | 0 .../vampire/datasets/vampire_dataset.py | 0 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deletions(-) create mode 100644 configs/_base_/datasets/deepGlobe.py create mode 100644 configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py mode change 100755 => 100644 docs/en/stat.py mode change 100755 => 100644 docs/zh_cn/stat.py create mode 100644 mmseg/configs/_base_/datasets/deepGlobe.py create mode 100644 mmseg/datasets/deepGlobe.py mode change 100755 => 100644 projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/dermoscopy/isic2017_task1/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/configs/vampire_512x512.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/__init__.py mode change 100755 => 100644 projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/vampire_dataset.py mode change 100755 => 100644 projects/medical/2d_image/fundus_photography/dr_hagis/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/fundus_photography/orvs/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/histopathology/breast_cancer_cell_seg/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/histopathology/conic2022_seg/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/histopathology/consep/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_ravir-512x512.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_ravir-512x512.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_ravir-512x512.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/ravir_512x512.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/__init__.py mode change 100755 => 100644 projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/ravir_dataset.py mode change 100755 => 100644 projects/medical/2d_image/microscopy_images/bactteria_detection/tools/prepare_dataset.py mode change 100755 => 100644 projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py mode change 100755 => 100644 setup.py mode change 100755 => 100644 tests/data/biomedical.nii.gz mode change 100755 => 100644 tests/data/biomedical_ann.nii.gz mode change 100755 => 100644 tests/data/dataset.json mode change 100755 => 100644 tests/data/dsdl_seg/config.py mode change 100755 => 100644 tests/data/dsdl_seg/defs/class-dom.yaml mode change 100755 => 100644 tests/data/dsdl_seg/defs/segmentation-def.yaml mode change 100755 => 100644 tests/data/dsdl_seg/set-train/train.yaml mode change 100755 => 100644 tests/data/dsdl_seg/set-train/train_samples.json mode change 100755 => 100644 tools/dist_test.sh mode change 100755 => 100644 tools/dist_train.sh mode change 100755 => 100644 tools/slurm_test.sh mode change 100755 => 100644 tools/slurm_train.sh diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py new file mode 100644 index 0000000000..e9330a91e7 --- /dev/null +++ b/configs/_base_/datasets/deepGlobe.py @@ -0,0 +1,69 @@ +#configs/_base_/datasets/deepGlobe.py +#mmseseg/configs/_base_/datasets/deepGlobe.py +# dataset settings +dataset_type = 'DeepGlobeDataset' +data_root = 'data/deepglobe_ds/' +crop_size = (512, 1024) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 1024), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=6, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=6, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/models/deeplabv3_r50-d8.py b/configs/_base_/models/deeplabv3_r50-d8.py index 22efe9a6ca..725ae1afd0 100644 --- a/configs/_base_/models/deeplabv3_r50-d8.py +++ b/configs/_base_/models/deeplabv3_r50-d8.py @@ -1,3 +1,4 @@ +#configs/_base_/models/deeplabv3_r50-d8.py # model settings norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( @@ -29,7 +30,7 @@ channels=512, dilations=(1, 12, 24, 36), dropout_ratio=0.1, - num_classes=19, + num_classes=7, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( @@ -42,7 +43,7 @@ num_convs=1, concat_input=False, dropout_ratio=0.1, - num_classes=19, + num_classes=7, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( diff --git a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py new file mode 100644 index 0000000000..3694832b98 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -0,0 +1,8 @@ +#configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/deepGlobe.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 1024) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) diff --git a/docs/en/stat.py b/docs/en/stat.py old mode 100755 new mode 100644 diff --git a/docs/zh_cn/stat.py b/docs/zh_cn/stat.py old mode 100755 new mode 100644 diff --git a/mmseg/configs/_base_/datasets/deepGlobe.py b/mmseg/configs/_base_/datasets/deepGlobe.py new file mode 100644 index 0000000000..e9330a91e7 --- /dev/null +++ b/mmseg/configs/_base_/datasets/deepGlobe.py @@ -0,0 +1,69 @@ +#configs/_base_/datasets/deepGlobe.py +#mmseseg/configs/_base_/datasets/deepGlobe.py +# dataset settings +dataset_type = 'DeepGlobeDataset' +data_root = 'data/deepglobe_ds/' +crop_size = (512, 1024) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 1024), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=6, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=6, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'), + pipeline=test_pipeline)) +test_dataloader = val_dataloader + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/mmseg/configs/_base_/schedules/schedule_40k.py b/mmseg/configs/_base_/schedules/schedule_40k.py index b4b2ea42b5..f007a5d280 100644 --- a/mmseg/configs/_base_/schedules/schedule_40k.py +++ b/mmseg/configs/_base_/schedules/schedule_40k.py @@ -1,34 +1,25 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, - LoggerHook, ParamSchedulerHook) -from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper -from mmengine.optim.scheduler.lr_scheduler import PolyLR -from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop -from torch.optim.sgd import SGD - -from mmseg.engine import SegVisualizationHook - +#configs/_base_/schedules/schedule_40k.py # optimizer -optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005) -optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None) - +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy param_scheduler = [ dict( - type=PolyLR, + type='PolyLR', eta_min=1e-4, power=0.9, begin=0, - end=40000, + end=10000, by_epoch=False) ] # training schedule for 40k -train_cfg = dict(type=IterBasedTrainLoop, max_iters=40000, val_interval=4000) -val_cfg = dict(type=ValLoop) -test_cfg = dict(type=TestLoop) +train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') default_hooks = dict( - timer=dict(type=IterTimerHook), - logger=dict(type=LoggerHook, interval=50, log_metric_by_epoch=False), - param_scheduler=dict(type=ParamSchedulerHook), - checkpoint=dict(type=CheckpointHook, by_epoch=False, interval=4000), - sampler_seed=dict(type=DistSamplerSeedHook), - visualization=dict(type=SegVisualizationHook)) + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index f8ad750d76..48c04220f5 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -1,3 +1,4 @@ +#mmseg/datasets/__init__.py # Copyright (c) OpenMMLab. All rights reserved. # yapf: disable from .ade import ADE20KDataset @@ -26,6 +27,7 @@ from .refuge import REFUGEDataset from .stare import STAREDataset from .synapse import SynapseDataset +from .deepGlobe import DeepGlobeDataset # yapf: disable from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad, BioMedical3DRandomCrop, BioMedical3DRandomFlip, diff --git a/mmseg/datasets/deepGlobe.py b/mmseg/datasets/deepGlobe.py new file mode 100644 index 0000000000..e09004254e --- /dev/null +++ b/mmseg/datasets/deepGlobe.py @@ -0,0 +1,48 @@ +#mmseg/datasets/deepGlobe.py +# Copyright (c) OpenMMLab. All rights reserved. +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset +import json +import os +from datetime import datetime + +import geopandas as gpd +import numpy as np +import pandas as pd +import torch +import torch.utils.data as tdata + +@DATASETS.register_module() +class DeepGlobeDataset(BaseSegDataset): + """Deep Globe Dataset. + + The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is + fixed to '_t.png' for Cityscapes dataset. + """ + METAINFO = dict( + classes=('Urban', 'Agriculture', 'Range', 'Forest', 'Water', 'Barren', + 'Unknown'), + palette=[[0,255,255], [255,255,0], [255,0,255], [0,255,0], + [0,0,255], [255,255,255], [1,1,1] + ]) + + class_dict={ + "1": "Urban", + "2": "Agriculture", + "3": "Range", + "4": "Forest", + "5": "Water", + "6": "Barren", + "7": "Unknown" + } + color_map = [ + [0,255,255], [255,255,0], [255,0,255], [0,255,0], + [0,0,255], [255,255,255], [1,1,1] + ] + + def __init__(self, + img_suffix='_sat.jpg', + seg_map_suffix='_mask.png', + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs) diff --git a/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py b/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py old mode 100755 new mode 100644 diff --git a/projects/medical/2d_image/dermoscopy/isic2017_task1/tools/prepare_dataset.py b/projects/medical/2d_image/dermoscopy/isic2017_task1/tools/prepare_dataset.py old mode 100755 new mode 100644 diff --git a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py old mode 100755 new mode 100644 diff --git a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py 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old mode 100755 new mode 100644 diff --git a/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py b/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py old mode 100755 new mode 100644 diff --git a/setup.py b/setup.py old mode 100755 new mode 100644 diff --git a/tests/data/biomedical.nii.gz b/tests/data/biomedical.nii.gz old mode 100755 new mode 100644 diff --git a/tests/data/biomedical_ann.nii.gz b/tests/data/biomedical_ann.nii.gz old mode 100755 new mode 100644 diff --git a/tests/data/dataset.json b/tests/data/dataset.json old mode 100755 new mode 100644 diff --git a/tests/data/dsdl_seg/config.py b/tests/data/dsdl_seg/config.py old mode 100755 new mode 100644 diff --git a/tests/data/dsdl_seg/defs/class-dom.yaml b/tests/data/dsdl_seg/defs/class-dom.yaml old mode 100755 new mode 100644 diff --git a/tests/data/dsdl_seg/defs/segmentation-def.yaml b/tests/data/dsdl_seg/defs/segmentation-def.yaml old mode 100755 new mode 100644 diff --git a/tests/data/dsdl_seg/set-train/train.yaml b/tests/data/dsdl_seg/set-train/train.yaml old mode 100755 new mode 100644 diff --git a/tests/data/dsdl_seg/set-train/train_samples.json b/tests/data/dsdl_seg/set-train/train_samples.json old mode 100755 new mode 100644 diff --git a/tools/dist_test.sh b/tools/dist_test.sh old mode 100755 new mode 100644 diff --git a/tools/dist_train.sh b/tools/dist_train.sh old mode 100755 new mode 100644 diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh old mode 100755 new mode 100644 diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh old mode 100755 new mode 100644 From 34cd949742e6a46bc98e21e1f555d71a99d36ab4 Mon Sep 17 00:00:00 2001 From: Andrea Yela <73191386+AnYelg@users.noreply.github.com> Date: Fri, 31 May 2024 15:05:10 -0600 Subject: [PATCH 02/29] Resize images --- configs/_base_/datasets/deepGlobe.py | 6 +++--- mmseg/configs/_base_/datasets/deepGlobe.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py index e9330a91e7..711dee8efe 100644 --- a/configs/_base_/datasets/deepGlobe.py +++ b/configs/_base_/datasets/deepGlobe.py @@ -3,13 +3,13 @@ # dataset settings dataset_type = 'DeepGlobeDataset' data_root = 'data/deepglobe_ds/' -crop_size = (512, 1024) +crop_size = (256, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', - scale=(2048, 1024), + scale=(1024, 512), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), @@ -19,7 +19,7 @@ ] test_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='Resize', scale=(1024, 512), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), diff --git a/mmseg/configs/_base_/datasets/deepGlobe.py b/mmseg/configs/_base_/datasets/deepGlobe.py index e9330a91e7..711dee8efe 100644 --- a/mmseg/configs/_base_/datasets/deepGlobe.py +++ b/mmseg/configs/_base_/datasets/deepGlobe.py @@ -3,13 +3,13 @@ # dataset settings dataset_type = 'DeepGlobeDataset' data_root = 'data/deepglobe_ds/' -crop_size = (512, 1024) +crop_size = (256, 512) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', - scale=(2048, 1024), + scale=(1024, 512), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), @@ -19,7 +19,7 @@ ] test_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(2048, 1024), keep_ratio=True), + dict(type='Resize', scale=(1024, 512), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), From 61a68cebae9472aba7a37f3bdaaf5d9ce9b9cafb Mon Sep 17 00:00:00 2001 From: Andrea Yela <73191386+AnYelg@users.noreply.github.com> Date: Fri, 31 May 2024 17:04:47 -0600 Subject: [PATCH 03/29] Upload of right size --- configs/_base_/datasets/deepGlobe.py | 6 +++--- mmseg/configs/_base_/datasets/deepGlobe.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py index 711dee8efe..3813cb3763 100644 --- a/configs/_base_/datasets/deepGlobe.py +++ b/configs/_base_/datasets/deepGlobe.py @@ -3,13 +3,13 @@ # dataset settings dataset_type = 'DeepGlobeDataset' data_root = 'data/deepglobe_ds/' -crop_size = (256, 512) +crop_size = (256, 256) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', - scale=(1024, 512), + scale=(256, 256), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), @@ -19,7 +19,7 @@ ] test_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(1024, 512), keep_ratio=True), + dict(type='Resize', scale=(256, 256), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), diff --git a/mmseg/configs/_base_/datasets/deepGlobe.py b/mmseg/configs/_base_/datasets/deepGlobe.py index 711dee8efe..3813cb3763 100644 --- a/mmseg/configs/_base_/datasets/deepGlobe.py +++ b/mmseg/configs/_base_/datasets/deepGlobe.py @@ -3,13 +3,13 @@ # dataset settings dataset_type = 'DeepGlobeDataset' data_root = 'data/deepglobe_ds/' -crop_size = (256, 512) +crop_size = (256, 256) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', - scale=(1024, 512), + scale=(256, 256), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), @@ -19,7 +19,7 @@ ] test_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(1024, 512), keep_ratio=True), + dict(type='Resize', scale=(256, 256), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), From 7d21d817c8ba2753417eb72787b2e8d600904e68 Mon Sep 17 00:00:00 2001 From: Andrea Yela <73191386+AnYelg@users.noreply.github.com> Date: Fri, 31 May 2024 18:10:55 -0600 Subject: [PATCH 04/29] Adding unet --- configs/_base_/datasets/deepGlobe.py | 2 +- .../models/pspnet_unet_deepglobe_s5-d16.py | 58 ++++++++++++++++ configs/_base_/schedules/schedule_40k.py | 4 +- ...abv3_r50-d8_4xb2-40k_deepglobe-512x1024.py | 2 +- ...5-d16_pspnet_4xb4-40k_deepglobe-256x256.py | 9 +++ mmseg/configs/_base_/datasets/deepGlobe.py | 69 ------------------- 6 files changed, 71 insertions(+), 73 deletions(-) create mode 100644 configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py create mode 100644 configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py delete mode 100644 mmseg/configs/_base_/datasets/deepGlobe.py diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py index 3813cb3763..75c1f2cfb6 100644 --- a/configs/_base_/datasets/deepGlobe.py +++ b/configs/_base_/datasets/deepGlobe.py @@ -42,7 +42,7 @@ ]) ] train_dataloader = dict( - batch_size=6, + batch_size=4, num_workers=2, persistent_workers=True, sampler=dict(type='InfiniteSampler', shuffle=True), diff --git a/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py new file mode 100644 index 0000000000..f14a2ca089 --- /dev/null +++ b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py @@ -0,0 +1,58 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained=None, + backbone=dict( + type='UNet', + in_channels=3, + base_channels=64, + num_stages=5, + strides=(1, 1, 1, 1, 1), + enc_num_convs=(2, 2, 2, 2, 2), + dec_num_convs=(2, 2, 2, 2), + downsamples=(True, True, True, True), + enc_dilations=(1, 1, 1, 1, 1), + dec_dilations=(1, 1, 1, 1), + with_cp=False, + conv_cfg=None, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU'), + upsample_cfg=dict(type='InterpConv'), + norm_eval=False), + decode_head=dict( + type='PSPHead', + in_channels=64, + in_index=4, + channels=16, + pool_scales=(1, 2, 3, 6), + dropout_ratio=0.1, + num_classes=2, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=3, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=256, stride=170)) diff --git a/configs/_base_/schedules/schedule_40k.py b/configs/_base_/schedules/schedule_40k.py index 4b823339a2..5512eb4e60 100644 --- a/configs/_base_/schedules/schedule_40k.py +++ b/configs/_base_/schedules/schedule_40k.py @@ -8,11 +8,11 @@ eta_min=1e-4, power=0.9, begin=0, - end=40000, + end=10000, by_epoch=False) ] # training schedule for 40k -train_cfg = dict(type='IterBasedTrainLoop', max_iters=40000, val_interval=4000) +train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') default_hooks = dict( diff --git a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py index 3694832b98..583091b4db 100644 --- a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py +++ b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -3,6 +3,6 @@ '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/deepGlobe.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' ] -crop_size = (512, 1024) +crop_size = (256, 256) data_preprocessor = dict(size=crop_size) model = dict(data_preprocessor=data_preprocessor) diff --git a/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py new file mode 100644 index 0000000000..a37534cd09 --- /dev/null +++ b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/pspnet_unet_deepglobe_s5-d16.py', '../_base_/datasets/deepGlobe.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (256, 256) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + test_cfg=dict(crop_size=(256, 256), stride=(85, 85))) diff --git a/mmseg/configs/_base_/datasets/deepGlobe.py b/mmseg/configs/_base_/datasets/deepGlobe.py deleted file mode 100644 index 3813cb3763..0000000000 --- a/mmseg/configs/_base_/datasets/deepGlobe.py +++ /dev/null @@ -1,69 +0,0 @@ -#configs/_base_/datasets/deepGlobe.py -#mmseseg/configs/_base_/datasets/deepGlobe.py -# dataset settings -dataset_type = 'DeepGlobeDataset' -data_root = 'data/deepglobe_ds/' -crop_size = (256, 256) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - type='RandomResize', - scale=(256, 256), - ratio_range=(0.5, 2.0), - keep_ratio=True), - dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), - dict(type='RandomFlip', prob=0.5), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs') -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(256, 256), keep_ratio=True), - # add loading annotation after ``Resize`` because ground truth - # does not need to do resize data transform - dict(type='LoadAnnotations'), - dict(type='PackSegInputs') -] -img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] -tta_pipeline = [ - dict(type='LoadImageFromFile', backend_args=None), - dict( - type='TestTimeAug', - transforms=[ - [ - dict(type='Resize', scale_factor=r, keep_ratio=True) - for r in img_ratios - ], - [ - dict(type='RandomFlip', prob=0., direction='horizontal'), - dict(type='RandomFlip', prob=1., direction='horizontal') - ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] - ]) -] -train_dataloader = dict( - batch_size=6, - num_workers=2, - persistent_workers=True, - sampler=dict(type='InfiniteSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_prefix=dict( - img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'), - pipeline=train_pipeline)) -val_dataloader = dict( - batch_size=6, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_prefix=dict( - img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'), - pipeline=test_pipeline)) -test_dataloader = val_dataloader - -val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) -test_evaluator = val_evaluator From 5d42080b4f5a603ebcf25cd19e1cd07af3caec1a Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Fri, 31 May 2024 20:33:29 -0600 Subject: [PATCH 05/29] Mean and Std added --- configs/_base_/models/deeplabv3_r50-d8.py | 4 +- .../models/deeplabv3plus_r50-d8_deepglobe.py | 54 +++ .../models/pspnet_unet_deepglobe_s5-d16.py | 8 +- ...plus_r50-d8_4xb2-40k_deepglobe-512x1024.py | 8 + .../vis_data/20240531_185612.json | 4 + .../20240531_185612/vis_data/config.py | 297 ++++++++++++++++ .../20240531_185612/vis_data/scalars.json | 4 + .../20240531_185824/vis_data/config.py | 327 ++++++++++++++++++ .../20240531_190210/vis_data/config.py | 327 ++++++++++++++++++ .../vis_data/20240531_190343.json | 6 + .../20240531_190343/vis_data/config.py | 327 ++++++++++++++++++ .../20240531_190343/vis_data/scalars.json | 6 + .../20240531_190554/vis_data/config.py | 327 ++++++++++++++++++ .../20240531_190654/vis_data/config.py | 327 ++++++++++++++++++ .../20240531_191031/vis_data/config.py | 327 ++++++++++++++++++ .../vis_data/20240531_191309.json | 57 +++ .../20240531_191309/vis_data/config.py | 327 ++++++++++++++++++ .../20240531_191309/vis_data/scalars.json | 57 +++ .../vis_data/20240531_193140.json | 210 +++++++++++ .../20240531_193140/vis_data/config.py | 299 ++++++++++++++++ .../20240531_193140/vis_data/scalars.json | 210 +++++++++++ .../vis_data/20240531_200633.json | 210 +++++++++++ .../20240531_200633/vis_data/config.py | 299 ++++++++++++++++ .../20240531_200633/vis_data/scalars.json | 210 +++++++++++ ...abv3_r50-d8_4xb2-40k_deepglobe-512x1024.py | 297 ++++++++++++++++ ...plus_r50-d8_4xb2-40k_deepglobe-512x1024.py | 299 ++++++++++++++++ mmsegmentationEQ2/work-dir/last_checkpoint | 1 + ...5-d16_pspnet_4xb4-40k_deepglobe-256x256.py | 327 ++++++++++++++++++ 28 files changed, 5150 insertions(+), 6 deletions(-) create mode 100644 configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py create mode 100644 configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/20240531_193140.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/20240531_200633.json create mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/config.py create mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/scalars.json create mode 100644 mmsegmentationEQ2/work-dir/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py create mode 100644 mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py create mode 100644 mmsegmentationEQ2/work-dir/last_checkpoint create mode 100644 mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py diff --git a/configs/_base_/models/deeplabv3_r50-d8.py b/configs/_base_/models/deeplabv3_r50-d8.py index 725ae1afd0..36cc34947a 100644 --- a/configs/_base_/models/deeplabv3_r50-d8.py +++ b/configs/_base_/models/deeplabv3_r50-d8.py @@ -3,8 +3,8 @@ norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], + mean=[0.4082, 0.3791, 0.2815], + std=[0.1351, 0.1022, 0.0931], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) diff --git a/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py new file mode 100644 index 0000000000..1d63ef467f --- /dev/null +++ b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py @@ -0,0 +1,54 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[0.4082, 0.3791, 0.2815], + std=[0.1351, 0.1022, 0.0931], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='DepthwiseSeparableASPPHead', + in_channels=2048, + in_index=3, + channels=512, + dilations=(1, 12, 24, 36), + c1_in_channels=256, + c1_channels=48, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py index f14a2ca089..8cefb930fe 100644 --- a/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py +++ b/configs/_base_/models/pspnet_unet_deepglobe_s5-d16.py @@ -2,8 +2,8 @@ norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], + mean=[0.4082, 0.3791, 0.2815], + std=[0.1351, 0.1022, 0.0931], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) @@ -35,14 +35,14 @@ channels=16, pool_scales=(1, 2, 3, 6), dropout_ratio=0.1, - num_classes=2, + num_classes=7, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), auxiliary_head=dict( type='FCNHead', - in_channels=1024, + in_channels=128, in_index=3, channels=256, num_convs=1, diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py new file mode 100644 index 0000000000..bbd63a3be1 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8_deepglobe.py', + '../_base_/datasets/deepglobe.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (256, 256) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json new file mode 100644 index 0000000000..c721825dd1 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json @@ -0,0 +1,4 @@ +{"lr": 0.009956325915795412, "data_time": 0.0019856929779052735, "loss": 1.4917661011219026, "decode.loss_ce": 1.0774178504943848, "decode.acc_seg": 28.853071212768555, "aux.loss_ce": 0.41434821486473083, "aux.acc_seg": 27.412687301635742, "time": 0.15990304946899414, "iter": 50, "memory": 5333, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0018914222717285156, "loss": 1.7780531525611878, "decode.loss_ce": 1.3381518483161927, "decode.acc_seg": 44.7864875793457, "aux.loss_ce": 0.4399012625217438, "aux.acc_seg": 58.39480972290039, "time": 0.15970003604888916, "iter": 100, "memory": 2026, "step": 100} +{"lr": 0.00986712825274952, "data_time": 0.0018882989883422852, "loss": 1.7831645488739014, "decode.loss_ce": 1.3502689361572267, "decode.acc_seg": 53.84635925292969, "aux.loss_ce": 0.4328956037759781, "aux.acc_seg": 71.783447265625, "time": 0.16029999256134034, "iter": 150, "memory": 2026, "step": 150} +{"lr": 0.009822495508277105, "data_time": 0.001996755599975586, "loss": 1.6858153462409973, "decode.loss_ce": 1.2507937788963317, "decode.acc_seg": 61.574554443359375, "aux.loss_ce": 0.4350215882062912, "aux.acc_seg": 51.27716064453125, "time": 0.16009950637817383, "iter": 200, "memory": 2026, "step": 200} diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py new file mode 100644 index 0000000000..779dbfc650 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py @@ -0,0 +1,297 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dilations=( + 1, + 12, + 24, + 36, + ), + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + type='ASPPHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json new file mode 100644 index 0000000000..c721825dd1 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json @@ -0,0 +1,4 @@ +{"lr": 0.009956325915795412, "data_time": 0.0019856929779052735, "loss": 1.4917661011219026, "decode.loss_ce": 1.0774178504943848, "decode.acc_seg": 28.853071212768555, "aux.loss_ce": 0.41434821486473083, "aux.acc_seg": 27.412687301635742, "time": 0.15990304946899414, "iter": 50, "memory": 5333, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0018914222717285156, "loss": 1.7780531525611878, "decode.loss_ce": 1.3381518483161927, "decode.acc_seg": 44.7864875793457, "aux.loss_ce": 0.4399012625217438, "aux.acc_seg": 58.39480972290039, "time": 0.15970003604888916, "iter": 100, "memory": 2026, "step": 100} +{"lr": 0.00986712825274952, "data_time": 0.0018882989883422852, "loss": 1.7831645488739014, "decode.loss_ce": 1.3502689361572267, "decode.acc_seg": 53.84635925292969, "aux.loss_ce": 0.4328956037759781, "aux.acc_seg": 71.783447265625, "time": 0.16029999256134034, "iter": 150, "memory": 2026, "step": 150} +{"lr": 0.009822495508277105, "data_time": 0.001996755599975586, "loss": 1.6858153462409973, "decode.loss_ce": 1.2507937788963317, "decode.acc_seg": 61.574554443359375, "aux.loss_ce": 0.4350215882062912, "aux.acc_seg": 51.27716064453125, "time": 0.16009950637817383, "iter": 200, "memory": 2026, "step": 200} diff --git a/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py new file mode 100644 index 0000000000..f0f2107d2d --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=2, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py new file mode 100644 index 0000000000..3da4bf85b8 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json new file mode 100644 index 0000000000..74c76e4688 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json @@ -0,0 +1,6 @@ +{"lr": 0.009956325915795412, "data_time": 0.0017998695373535156, "loss": 1.6646920680999755, "decode.loss_ce": 1.1981685936450959, "decode.acc_seg": 76.98594665527344, "aux.loss_ce": 0.46652348041534425, "aux.acc_seg": 76.02849578857422, "time": 0.24519989490509034, "iter": 50, "memory": 5384, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0017005205154418945, "loss": 1.6861127138137817, "decode.loss_ce": 1.1947236716747285, "decode.acc_seg": 42.63398361206055, "aux.loss_ce": 0.4913890391588211, "aux.acc_seg": 44.73920440673828, "time": 0.2460998773574829, "iter": 100, "memory": 2587, "step": 100} +{"lr": 0.00986712825274952, "data_time": 0.0018002510070800782, "loss": 1.639373207092285, "decode.loss_ce": 1.1630970656871795, "decode.acc_seg": 79.20752716064453, "aux.loss_ce": 0.47627614736557006, "aux.acc_seg": 78.85543060302734, "time": 0.24520010948181153, "iter": 150, "memory": 2587, "step": 150} +{"lr": 0.009822495508277105, "data_time": 0.0015007734298706054, "loss": 1.5109625697135924, "decode.loss_ce": 1.0720477163791657, "decode.acc_seg": 19.41878318786621, "aux.loss_ce": 0.43891484290361404, "aux.acc_seg": 22.12300682067871, "time": 0.25390303134918213, "iter": 200, "memory": 2587, "step": 200} +{"lr": 0.009777839986082375, "data_time": 0.0017011642456054687, "loss": 1.6361542820930481, "decode.loss_ce": 1.1623711287975311, "decode.acc_seg": 46.691837310791016, "aux.loss_ce": 0.473783141374588, "aux.acc_seg": 49.031089782714844, "time": 0.254599928855896, "iter": 250, "memory": 2587, "step": 250} +{"lr": 0.009733161557641541, "data_time": 0.0019950389862060545, "loss": 1.6020457327365876, "decode.loss_ce": 1.1411132007837295, "decode.acc_seg": 62.18528747558594, "aux.loss_ce": 0.46093251556158066, "aux.acc_seg": 62.52021789550781, "time": 0.25039970874786377, "iter": 300, "memory": 2587, "step": 300} diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py new file mode 100644 index 0000000000..1ee8cce552 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=128, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json new file mode 100644 index 0000000000..74c76e4688 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json @@ -0,0 +1,6 @@ +{"lr": 0.009956325915795412, "data_time": 0.0017998695373535156, "loss": 1.6646920680999755, "decode.loss_ce": 1.1981685936450959, "decode.acc_seg": 76.98594665527344, "aux.loss_ce": 0.46652348041534425, "aux.acc_seg": 76.02849578857422, "time": 0.24519989490509034, "iter": 50, "memory": 5384, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0017005205154418945, "loss": 1.6861127138137817, "decode.loss_ce": 1.1947236716747285, "decode.acc_seg": 42.63398361206055, "aux.loss_ce": 0.4913890391588211, "aux.acc_seg": 44.73920440673828, "time": 0.2460998773574829, "iter": 100, "memory": 2587, "step": 100} +{"lr": 0.00986712825274952, "data_time": 0.0018002510070800782, "loss": 1.639373207092285, "decode.loss_ce": 1.1630970656871795, "decode.acc_seg": 79.20752716064453, "aux.loss_ce": 0.47627614736557006, "aux.acc_seg": 78.85543060302734, "time": 0.24520010948181153, "iter": 150, "memory": 2587, "step": 150} +{"lr": 0.009822495508277105, "data_time": 0.0015007734298706054, "loss": 1.5109625697135924, "decode.loss_ce": 1.0720477163791657, "decode.acc_seg": 19.41878318786621, "aux.loss_ce": 0.43891484290361404, "aux.acc_seg": 22.12300682067871, "time": 0.25390303134918213, "iter": 200, "memory": 2587, "step": 200} +{"lr": 0.009777839986082375, "data_time": 0.0017011642456054687, "loss": 1.6361542820930481, "decode.loss_ce": 1.1623711287975311, "decode.acc_seg": 46.691837310791016, "aux.loss_ce": 0.473783141374588, "aux.acc_seg": 49.031089782714844, "time": 0.254599928855896, "iter": 250, "memory": 2587, "step": 250} +{"lr": 0.009733161557641541, "data_time": 0.0019950389862060545, "loss": 1.6020457327365876, "decode.loss_ce": 1.1411132007837295, "decode.acc_seg": 62.18528747558594, "aux.loss_ce": 0.46093251556158066, "aux.acc_seg": 62.52021789550781, "time": 0.25039970874786377, "iter": 300, "memory": 2587, "step": 300} diff --git a/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py new file mode 100644 index 0000000000..14720e1413 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py new file mode 100644 index 0000000000..446965d11b --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=2048, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py new file mode 100644 index 0000000000..3da4bf85b8 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json new file mode 100644 index 0000000000..e6714bbd21 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json @@ -0,0 +1,57 @@ +{"lr": 0.009956325915795412, "data_time": 0.0014963388442993165, "loss": 1.8483109831809998, "decode.loss_ce": 1.331848531961441, "decode.acc_seg": 55.11040496826172, "aux.loss_ce": 0.5164624512195587, "aux.acc_seg": 54.60165023803711, "time": 0.2458038806915283, "iter": 50, "memory": 5384, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0018981456756591796, "loss": 1.7798760533332825, "decode.loss_ce": 1.2660165190696717, "decode.acc_seg": 60.45057678222656, 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b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py new file mode 100644 index 0000000000..1ee8cce552 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=128, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json new file mode 100644 index 0000000000..e6714bbd21 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json @@ -0,0 +1,57 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+ ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + c1_channels=48, + c1_in_channels=256, + channels=512, + dilations=( + 1, + 12, + 24, + 36, + ), + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + type='DepthwiseSeparableASPPHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json new file mode 100644 index 0000000000..9c09caa947 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.009956325915795412, "data_time": 0.001997065544128418, "loss": 1.9265463590621947, "decode.loss_ce": 1.3989190936088562, "decode.acc_seg": 50.13331604003906, "aux.loss_ce": 0.5276272594928741, "aux.acc_seg": 37.92593002319336, "time": 0.13590002059936523, "iter": 50, "memory": 2758, "step": 50} +{"lr": 0.009911738346653688, "data_time": 0.0021010637283325195, "loss": 1.2555165827274322, "decode.loss_ce": 0.8888391077518463, "decode.acc_seg": 76.25853729248047, "aux.loss_ce": 0.3666774734854698, "aux.acc_seg": 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interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 0.4082, + 0.3791, + 0.2815, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 0.1351, + 0.1022, + 0.0931, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + c1_channels=48, + c1_in_channels=256, + channels=512, + dilations=( + 1, + 12, + 24, + 36, + ), + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + type='DepthwiseSeparableASPPHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git 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"data_time": 0.0020024776458740234, "loss": 0.4459540516138077, "decode.loss_ce": 0.2949722409248352, "decode.acc_seg": 84.75885772705078, "aux.loss_ce": 0.15098181068897248, "aux.acc_seg": 81.20121002197266, "time": 0.13850021362304688, "iter": 9900, "memory": 2158, "step": 9900} +{"lr": 0.0001840905681883749, "data_time": 0.0021030664443969726, "loss": 0.5935711234807968, "decode.loss_ce": 0.3962235927581787, "decode.acc_seg": 85.27488708496094, "aux.loss_ce": 0.19734752252697946, "aux.acc_seg": 79.67033386230469, "time": 0.1472994565963745, "iter": 9950, "memory": 2158, "step": 9950} +{"lr": 0.0001, "data_time": 0.002106976509094238, "loss": 0.5856386065483093, "decode.loss_ce": 0.383250430226326, "decode.acc_seg": 90.42201232910156, "aux.loss_ce": 0.20238817259669303, "aux.acc_seg": 85.8167724609375, "time": 0.14720311164855956, "iter": 10000, "memory": 2158, "step": 10000} +{"aAcc": 82.13, "mIoU": 51.6, "mAcc": 62.97, "data_time": 0.0015940779731387184, "time": 0.07122682957422166, "step": 10000} diff --git a/mmsegmentationEQ2/work-dir/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/mmsegmentationEQ2/work-dir/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py new file mode 100644 index 0000000000..779dbfc650 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -0,0 +1,297 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + dilations=( + 1, + 12, + 24, + 36, + ), + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + type='ASPPHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py b/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py new file mode 100644 index 0000000000..8e286f18e6 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -0,0 +1,299 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 0.4082, + 0.3791, + 0.2815, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 0.1351, + 0.1022, + 0.0931, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 0.4082, + 0.3791, + 0.2815, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 0.1351, + 0.1022, + 0.0931, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + c1_channels=48, + c1_in_channels=256, + channels=512, + dilations=( + 1, + 12, + 24, + 36, + ), + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + type='DepthwiseSeparableASPPHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/last_checkpoint b/mmsegmentationEQ2/work-dir/last_checkpoint new file mode 100644 index 0000000000..c87d9aafc5 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/last_checkpoint @@ -0,0 +1 @@ +D:\Github\mmsegmentationEQ2\mmsegmentationEQ2\work-dir\iter_10000.pth \ No newline at end of file diff --git a/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py b/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py new file mode 100644 index 0000000000..1ee8cce552 --- /dev/null +++ b/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py @@ -0,0 +1,327 @@ +crop_size = ( + 256, + 256, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/deepglobe_ds/' +dataset_type = 'DeepGlobeDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=128, + in_index=3, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + num_convs=1, + type='FCNHead'), + backbone=dict( + act_cfg=dict(type='ReLU'), + base_channels=64, + conv_cfg=None, + dec_dilations=( + 1, + 1, + 1, + 1, + ), + dec_num_convs=( + 2, + 2, + 2, + 2, + ), + downsamples=( + True, + True, + True, + True, + ), + enc_dilations=( + 1, + 1, + 1, + 1, + 1, + ), + enc_num_convs=( + 2, + 2, + 2, + 2, + 2, + ), + in_channels=3, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=5, + strides=( + 1, + 1, + 1, + 1, + 1, + ), + type='UNet', + upsample_cfg=dict(type='InterpConv'), + with_cp=False), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 256, + 256, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=16, + dropout_ratio=0.1, + in_channels=64, + in_index=4, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=7, + pool_scales=( + 1, + 2, + 3, + 6, + ), + type='PSPHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 256, + 256, + ), mode='slide', stride=( + 85, + 85, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train_sat', + seg_map_path='ann_dir/train_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 256, + 256, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 256, + 256, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=6, + dataset=dict( + data_prefix=dict( + img_path='img_dir/val_sat', + seg_map_path='ann_dir/val_mask_grayscale'), + data_root='data/deepglobe_ds/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 256, + 256, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='DeepGlobeDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'mmsegmentationEQ2/work-dir' From 07e20e350ab2f3121ea3b716b794bb47a16db5fe Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Fri, 31 May 2024 20:36:43 -0600 Subject: [PATCH 06/29] rm work-dir --- .../vis_data/20240531_185612.json | 4 - .../20240531_185612/vis_data/config.py | 297 ---------------- .../20240531_185612/vis_data/scalars.json | 4 - .../20240531_185824/vis_data/config.py | 327 ------------------ .../20240531_190210/vis_data/config.py | 327 ------------------ .../vis_data/20240531_190343.json | 6 - .../20240531_190343/vis_data/config.py | 327 ------------------ .../20240531_190343/vis_data/scalars.json | 6 - .../20240531_190554/vis_data/config.py | 327 ------------------ .../20240531_190654/vis_data/config.py | 327 ------------------ .../20240531_191031/vis_data/config.py | 327 ------------------ .../vis_data/20240531_191309.json | 57 --- .../20240531_191309/vis_data/config.py | 327 ------------------ .../20240531_191309/vis_data/scalars.json | 57 --- .../vis_data/20240531_193140.json | 210 ----------- .../20240531_193140/vis_data/config.py | 299 ---------------- .../20240531_193140/vis_data/scalars.json | 210 ----------- .../vis_data/20240531_200633.json | 210 ----------- .../20240531_200633/vis_data/config.py | 299 ---------------- .../20240531_200633/vis_data/scalars.json | 210 ----------- ...abv3_r50-d8_4xb2-40k_deepglobe-512x1024.py | 297 ---------------- ...plus_r50-d8_4xb2-40k_deepglobe-512x1024.py | 299 ---------------- mmsegmentationEQ2/work-dir/last_checkpoint | 1 - ...5-d16_pspnet_4xb4-40k_deepglobe-256x256.py | 327 ------------------ 24 files changed, 5082 deletions(-) delete mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/20240531_193140.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/20240531_200633.json delete mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/config.py delete mode 100644 mmsegmentationEQ2/work-dir/20240531_200633/vis_data/scalars.json delete mode 100644 mmsegmentationEQ2/work-dir/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py delete mode 100644 mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py delete mode 100644 mmsegmentationEQ2/work-dir/last_checkpoint delete mode 100644 mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json deleted file mode 100644 index c721825dd1..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/20240531_185612.json +++ /dev/null @@ -1,4 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0019856929779052735, "loss": 1.4917661011219026, "decode.loss_ce": 1.0774178504943848, "decode.acc_seg": 28.853071212768555, "aux.loss_ce": 0.41434821486473083, "aux.acc_seg": 27.412687301635742, "time": 0.15990304946899414, "iter": 50, "memory": 5333, "step": 50} -{"lr": 0.009911738346653688, "data_time": 0.0018914222717285156, "loss": 1.7780531525611878, "decode.loss_ce": 1.3381518483161927, "decode.acc_seg": 44.7864875793457, "aux.loss_ce": 0.4399012625217438, "aux.acc_seg": 58.39480972290039, "time": 0.15970003604888916, "iter": 100, "memory": 2026, "step": 100} -{"lr": 0.00986712825274952, "data_time": 0.0018882989883422852, "loss": 1.7831645488739014, "decode.loss_ce": 1.3502689361572267, "decode.acc_seg": 53.84635925292969, "aux.loss_ce": 0.4328956037759781, "aux.acc_seg": 71.783447265625, "time": 0.16029999256134034, "iter": 150, "memory": 2026, "step": 150} -{"lr": 0.009822495508277105, "data_time": 0.001996755599975586, "loss": 1.6858153462409973, "decode.loss_ce": 1.2507937788963317, "decode.acc_seg": 61.574554443359375, "aux.loss_ce": 0.4350215882062912, "aux.acc_seg": 51.27716064453125, "time": 0.16009950637817383, "iter": 200, "memory": 2026, "step": 200} diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py deleted file mode 100644 index 779dbfc650..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/config.py +++ /dev/null @@ -1,297 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - contract_dilation=True, - depth=50, - dilations=( - 1, - 1, - 2, - 4, - ), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=4, - out_indices=( - 0, - 1, - 2, - 3, - ), - strides=( - 1, - 2, - 1, - 1, - ), - style='pytorch', - type='ResNetV1c'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=512, - dilations=( - 1, - 12, - 24, - 36, - ), - dropout_ratio=0.1, - in_channels=2048, - in_index=3, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - type='ASPPHead'), - pretrained='open-mmlab://resnet50_v1c', - test_cfg=dict(mode='whole'), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json deleted file mode 100644 index c721825dd1..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_185612/vis_data/scalars.json +++ /dev/null @@ -1,4 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0019856929779052735, "loss": 1.4917661011219026, "decode.loss_ce": 1.0774178504943848, "decode.acc_seg": 28.853071212768555, "aux.loss_ce": 0.41434821486473083, "aux.acc_seg": 27.412687301635742, "time": 0.15990304946899414, "iter": 50, "memory": 5333, "step": 50} -{"lr": 0.009911738346653688, "data_time": 0.0018914222717285156, "loss": 1.7780531525611878, "decode.loss_ce": 1.3381518483161927, "decode.acc_seg": 44.7864875793457, "aux.loss_ce": 0.4399012625217438, "aux.acc_seg": 58.39480972290039, "time": 0.15970003604888916, "iter": 100, "memory": 2026, "step": 100} -{"lr": 0.00986712825274952, "data_time": 0.0018882989883422852, "loss": 1.7831645488739014, "decode.loss_ce": 1.3502689361572267, "decode.acc_seg": 53.84635925292969, "aux.loss_ce": 0.4328956037759781, "aux.acc_seg": 71.783447265625, "time": 0.16029999256134034, "iter": 150, "memory": 2026, "step": 150} -{"lr": 0.009822495508277105, "data_time": 0.001996755599975586, "loss": 1.6858153462409973, "decode.loss_ce": 1.2507937788963317, "decode.acc_seg": 61.574554443359375, "aux.loss_ce": 0.4350215882062912, "aux.acc_seg": 51.27716064453125, "time": 0.16009950637817383, "iter": 200, "memory": 2026, "step": 200} diff --git a/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py deleted file mode 100644 index f0f2107d2d..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_185824/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=2, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py deleted file mode 100644 index 3da4bf85b8..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190210/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json deleted file mode 100644 index 74c76e4688..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/20240531_190343.json +++ /dev/null @@ -1,6 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0017998695373535156, "loss": 1.6646920680999755, "decode.loss_ce": 1.1981685936450959, "decode.acc_seg": 76.98594665527344, "aux.loss_ce": 0.46652348041534425, "aux.acc_seg": 76.02849578857422, "time": 0.24519989490509034, "iter": 50, "memory": 5384, "step": 50} -{"lr": 0.009911738346653688, "data_time": 0.0017005205154418945, "loss": 1.6861127138137817, "decode.loss_ce": 1.1947236716747285, "decode.acc_seg": 42.63398361206055, "aux.loss_ce": 0.4913890391588211, "aux.acc_seg": 44.73920440673828, "time": 0.2460998773574829, "iter": 100, "memory": 2587, "step": 100} -{"lr": 0.00986712825274952, "data_time": 0.0018002510070800782, "loss": 1.639373207092285, "decode.loss_ce": 1.1630970656871795, "decode.acc_seg": 79.20752716064453, "aux.loss_ce": 0.47627614736557006, "aux.acc_seg": 78.85543060302734, "time": 0.24520010948181153, "iter": 150, "memory": 2587, "step": 150} -{"lr": 0.009822495508277105, "data_time": 0.0015007734298706054, "loss": 1.5109625697135924, "decode.loss_ce": 1.0720477163791657, "decode.acc_seg": 19.41878318786621, "aux.loss_ce": 0.43891484290361404, "aux.acc_seg": 22.12300682067871, "time": 0.25390303134918213, "iter": 200, "memory": 2587, "step": 200} -{"lr": 0.009777839986082375, "data_time": 0.0017011642456054687, "loss": 1.6361542820930481, "decode.loss_ce": 1.1623711287975311, "decode.acc_seg": 46.691837310791016, "aux.loss_ce": 0.473783141374588, "aux.acc_seg": 49.031089782714844, "time": 0.254599928855896, "iter": 250, "memory": 2587, "step": 250} -{"lr": 0.009733161557641541, "data_time": 0.0019950389862060545, "loss": 1.6020457327365876, "decode.loss_ce": 1.1411132007837295, "decode.acc_seg": 62.18528747558594, "aux.loss_ce": 0.46093251556158066, "aux.acc_seg": 62.52021789550781, "time": 0.25039970874786377, "iter": 300, "memory": 2587, "step": 300} diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py deleted file mode 100644 index 1ee8cce552..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=128, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json deleted file mode 100644 index 74c76e4688..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190343/vis_data/scalars.json +++ /dev/null @@ -1,6 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0017998695373535156, "loss": 1.6646920680999755, "decode.loss_ce": 1.1981685936450959, "decode.acc_seg": 76.98594665527344, "aux.loss_ce": 0.46652348041534425, "aux.acc_seg": 76.02849578857422, "time": 0.24519989490509034, "iter": 50, "memory": 5384, "step": 50} -{"lr": 0.009911738346653688, "data_time": 0.0017005205154418945, "loss": 1.6861127138137817, "decode.loss_ce": 1.1947236716747285, "decode.acc_seg": 42.63398361206055, "aux.loss_ce": 0.4913890391588211, "aux.acc_seg": 44.73920440673828, "time": 0.2460998773574829, "iter": 100, "memory": 2587, "step": 100} -{"lr": 0.00986712825274952, "data_time": 0.0018002510070800782, "loss": 1.639373207092285, "decode.loss_ce": 1.1630970656871795, "decode.acc_seg": 79.20752716064453, "aux.loss_ce": 0.47627614736557006, "aux.acc_seg": 78.85543060302734, "time": 0.24520010948181153, "iter": 150, "memory": 2587, "step": 150} -{"lr": 0.009822495508277105, "data_time": 0.0015007734298706054, "loss": 1.5109625697135924, "decode.loss_ce": 1.0720477163791657, "decode.acc_seg": 19.41878318786621, "aux.loss_ce": 0.43891484290361404, "aux.acc_seg": 22.12300682067871, "time": 0.25390303134918213, "iter": 200, "memory": 2587, "step": 200} -{"lr": 0.009777839986082375, "data_time": 0.0017011642456054687, "loss": 1.6361542820930481, "decode.loss_ce": 1.1623711287975311, "decode.acc_seg": 46.691837310791016, "aux.loss_ce": 0.473783141374588, "aux.acc_seg": 49.031089782714844, "time": 0.254599928855896, "iter": 250, "memory": 2587, "step": 250} -{"lr": 0.009733161557641541, "data_time": 0.0019950389862060545, "loss": 1.6020457327365876, "decode.loss_ce": 1.1411132007837295, "decode.acc_seg": 62.18528747558594, "aux.loss_ce": 0.46093251556158066, "aux.acc_seg": 62.52021789550781, "time": 0.25039970874786377, "iter": 300, "memory": 2587, "step": 300} diff --git a/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py deleted file mode 100644 index 14720e1413..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190554/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=256, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py deleted file mode 100644 index 446965d11b..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_190654/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=2048, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py deleted file mode 100644 index 3da4bf85b8..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_191031/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json deleted file mode 100644 index e6714bbd21..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/20240531_191309.json +++ /dev/null @@ -1,57 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0014963388442993165, "loss": 1.8483109831809998, "decode.loss_ce": 1.331848531961441, "decode.acc_seg": 55.11040496826172, "aux.loss_ce": 0.5164624512195587, "aux.acc_seg": 54.60165023803711, "time": 0.2458038806915283, "iter": 50, "memory": 5384, "step": 50} -{"lr": 0.009911738346653688, "data_time": 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0.24639651775360108, "iter": 2750, "memory": 2587, "step": 2750} diff --git a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py b/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py deleted file mode 100644 index 1ee8cce552..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/config.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=128, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_191309/vis_data/scalars.json 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@@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - contract_dilation=True, - depth=50, - dilations=( - 1, - 1, - 2, - 4, - ), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=4, - out_indices=( - 0, - 1, - 2, - 3, - ), - strides=( - 1, - 2, - 1, - 1, - ), - style='pytorch', - type='ResNetV1c'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - c1_channels=48, - c1_in_channels=256, - channels=512, - dilations=( - 1, - 12, - 24, - 36, - ), - dropout_ratio=0.1, - in_channels=2048, - in_index=3, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - type='DepthwiseSeparableASPPHead'), - pretrained='open-mmlab://resnet50_v1c', - test_cfg=dict(mode='whole'), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json deleted file mode 100644 index 9c09caa947..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_193140/vis_data/scalars.json +++ /dev/null @@ -1,210 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.001997065544128418, "loss": 1.9265463590621947, "decode.loss_ce": 1.3989190936088562, "decode.acc_seg": 50.13331604003906, "aux.loss_ce": 0.5276272594928741, "aux.acc_seg": 37.92593002319336, "time": 0.13590002059936523, "iter": 50, "memory": 2758, "step": 50} -{"lr": 0.009911738346653688, "data_time": 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seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 0.1351, - 0.1022, - 0.0931, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - contract_dilation=True, - depth=50, - dilations=( - 1, - 1, - 2, - 4, - ), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=4, - out_indices=( - 0, - 1, - 2, - 3, - ), - strides=( - 1, - 2, - 1, - 1, - ), - style='pytorch', - type='ResNetV1c'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 0.4082, - 0.3791, - 0.2815, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 0.1351, - 0.1022, - 0.0931, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - c1_channels=48, - c1_in_channels=256, - channels=512, - dilations=( - 1, - 12, - 24, - 36, - ), - dropout_ratio=0.1, - in_channels=2048, - in_index=3, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - type='DepthwiseSeparableASPPHead'), - pretrained='open-mmlab://resnet50_v1c', - test_cfg=dict(mode='whole'), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/20240531_200633/vis_data/scalars.json b/mmsegmentationEQ2/work-dir/20240531_200633/vis_data/scalars.json deleted file mode 100644 index 28d73e108d..0000000000 --- a/mmsegmentationEQ2/work-dir/20240531_200633/vis_data/scalars.json +++ /dev/null @@ -1,210 +0,0 @@ -{"lr": 0.009956325915795412, "data_time": 0.0019952535629272463, "loss": 1.7342483878135682, "decode.loss_ce": 1.2658996105194091, "decode.acc_seg": 86.26676940917969, "aux.loss_ce": 0.46834878623485565, "aux.acc_seg": 86.26676940917969, "time": 0.13660020828247071, "iter": 50, "memory": 5163, "step": 50} -{"lr": 0.009911738346653688, "data_time": 0.002091789245605469, "loss": 1.4407753229141236, "decode.loss_ce": 1.0334229171276093, "decode.acc_seg": 83.80162048339844, "aux.loss_ce": 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type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - contract_dilation=True, - depth=50, - dilations=( - 1, - 1, - 2, - 4, - ), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=4, - out_indices=( - 0, - 1, - 2, - 3, - ), - strides=( - 1, - 2, - 1, - 1, - ), - style='pytorch', - type='ResNetV1c'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=512, - dilations=( - 1, - 12, - 24, - 36, - ), - dropout_ratio=0.1, - in_channels=2048, - in_index=3, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - type='ASPPHead'), - pretrained='open-mmlab://resnet50_v1c', - test_cfg=dict(mode='whole'), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py b/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py deleted file mode 100644 index 8e286f18e6..0000000000 --- a/mmsegmentationEQ2/work-dir/deeplabv3plus_r50-d8_4xb2-40k_deepglobe-512x1024.py +++ /dev/null @@ -1,299 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 0.4082, - 0.3791, - 0.2815, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 0.1351, - 0.1022, - 0.0931, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=1024, - in_index=2, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - contract_dilation=True, - depth=50, - dilations=( - 1, - 1, - 2, - 4, - ), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=4, - out_indices=( - 0, - 1, - 2, - 3, - ), - strides=( - 1, - 2, - 1, - 1, - ), - style='pytorch', - type='ResNetV1c'), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 0.4082, - 0.3791, - 0.2815, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 0.1351, - 0.1022, - 0.0931, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - c1_channels=48, - c1_in_channels=256, - channels=512, - dilations=( - 1, - 12, - 24, - 36, - ), - dropout_ratio=0.1, - in_channels=2048, - in_index=3, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - type='DepthwiseSeparableASPPHead'), - pretrained='open-mmlab://resnet50_v1c', - test_cfg=dict(mode='whole'), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' diff --git a/mmsegmentationEQ2/work-dir/last_checkpoint b/mmsegmentationEQ2/work-dir/last_checkpoint deleted file mode 100644 index c87d9aafc5..0000000000 --- a/mmsegmentationEQ2/work-dir/last_checkpoint +++ /dev/null @@ -1 +0,0 @@ -D:\Github\mmsegmentationEQ2\mmsegmentationEQ2\work-dir\iter_10000.pth \ No newline at end of file diff --git a/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py b/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py deleted file mode 100644 index 1ee8cce552..0000000000 --- a/mmsegmentationEQ2/work-dir/unet-s5-d16_pspnet_4xb4-40k_deepglobe-256x256.py +++ /dev/null @@ -1,327 +0,0 @@ -crop_size = ( - 256, - 256, -) -data_preprocessor = dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor') -data_root = 'data/deepglobe_ds/' -dataset_type = 'DeepGlobeDataset' -default_hooks = dict( - checkpoint=dict(by_epoch=False, interval=4000, type='CheckpointHook'), - logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), - param_scheduler=dict(type='ParamSchedulerHook'), - sampler_seed=dict(type='DistSamplerSeedHook'), - timer=dict(type='IterTimerHook'), - visualization=dict(type='SegVisualizationHook')) -default_scope = 'mmseg' -env_cfg = dict( - cudnn_benchmark=True, - dist_cfg=dict(backend='nccl'), - mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) -img_ratios = [ - 0.5, - 0.75, - 1.0, - 1.25, - 1.5, - 1.75, -] -launcher = 'none' -load_from = None -log_level = 'INFO' -log_processor = dict(by_epoch=False) -model = dict( - auxiliary_head=dict( - align_corners=False, - channels=256, - concat_input=False, - dropout_ratio=0.1, - in_channels=128, - in_index=3, - loss_decode=dict( - loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - num_convs=1, - type='FCNHead'), - backbone=dict( - act_cfg=dict(type='ReLU'), - base_channels=64, - conv_cfg=None, - dec_dilations=( - 1, - 1, - 1, - 1, - ), - dec_num_convs=( - 2, - 2, - 2, - 2, - ), - downsamples=( - True, - True, - True, - True, - ), - enc_dilations=( - 1, - 1, - 1, - 1, - 1, - ), - enc_num_convs=( - 2, - 2, - 2, - 2, - 2, - ), - in_channels=3, - norm_cfg=dict(requires_grad=True, type='SyncBN'), - norm_eval=False, - num_stages=5, - strides=( - 1, - 1, - 1, - 1, - 1, - ), - type='UNet', - upsample_cfg=dict(type='InterpConv'), - with_cp=False), - data_preprocessor=dict( - bgr_to_rgb=True, - mean=[ - 123.675, - 116.28, - 103.53, - ], - pad_val=0, - seg_pad_val=255, - size=( - 256, - 256, - ), - std=[ - 58.395, - 57.12, - 57.375, - ], - type='SegDataPreProcessor'), - decode_head=dict( - align_corners=False, - channels=16, - dropout_ratio=0.1, - in_channels=64, - in_index=4, - loss_decode=dict( - loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), - norm_cfg=dict(requires_grad=True, type='SyncBN'), - num_classes=7, - pool_scales=( - 1, - 2, - 3, - 6, - ), - type='PSPHead'), - pretrained=None, - test_cfg=dict(crop_size=( - 256, - 256, - ), mode='slide', stride=( - 85, - 85, - )), - train_cfg=dict(), - type='EncoderDecoder') -norm_cfg = dict(requires_grad=True, type='SyncBN') -optim_wrapper = dict( - clip_grad=None, - optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), - type='OptimWrapper') -optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) -param_scheduler = [ - dict( - begin=0, - by_epoch=False, - end=10000, - eta_min=0.0001, - power=0.9, - type='PolyLR'), -] -resume = False -test_cfg = dict(type='TestLoop') -test_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -test_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), -] -train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) -train_dataloader = dict( - batch_size=4, - dataset=dict( - data_prefix=dict( - img_path='img_dir/train_sat', - seg_map_path='ann_dir/train_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict( - cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=2, - persistent_workers=True, - sampler=dict(shuffle=True, type='InfiniteSampler')) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations'), - dict( - keep_ratio=True, - ratio_range=( - 0.5, - 2.0, - ), - scale=( - 256, - 256, - ), - type='RandomResize'), - dict(cat_max_ratio=0.75, crop_size=( - 256, - 256, - ), type='RandomCrop'), - dict(prob=0.5, type='RandomFlip'), - dict(type='PhotoMetricDistortion'), - dict(type='PackSegInputs'), -] -tta_model = dict(type='SegTTAModel') -tta_pipeline = [ - dict(backend_args=None, type='LoadImageFromFile'), - dict( - transforms=[ - [ - dict(keep_ratio=True, scale_factor=0.5, type='Resize'), - dict(keep_ratio=True, scale_factor=0.75, type='Resize'), - dict(keep_ratio=True, scale_factor=1.0, type='Resize'), - dict(keep_ratio=True, scale_factor=1.25, type='Resize'), - dict(keep_ratio=True, scale_factor=1.5, type='Resize'), - dict(keep_ratio=True, scale_factor=1.75, type='Resize'), - ], - [ - dict(direction='horizontal', prob=0.0, type='RandomFlip'), - dict(direction='horizontal', prob=1.0, type='RandomFlip'), - ], - [ - dict(type='LoadAnnotations'), - ], - [ - dict(type='PackSegInputs'), - ], - ], - type='TestTimeAug'), -] -val_cfg = dict(type='ValLoop') -val_dataloader = dict( - batch_size=6, - dataset=dict( - data_prefix=dict( - img_path='img_dir/val_sat', - seg_map_path='ann_dir/val_mask_grayscale'), - data_root='data/deepglobe_ds/', - pipeline=[ - dict(type='LoadImageFromFile'), - dict(keep_ratio=True, scale=( - 256, - 256, - ), type='Resize'), - dict(type='LoadAnnotations'), - dict(type='PackSegInputs'), - ], - type='DeepGlobeDataset'), - num_workers=4, - persistent_workers=True, - sampler=dict(shuffle=False, type='DefaultSampler')) -val_evaluator = dict( - iou_metrics=[ - 'mIoU', - ], type='IoUMetric') -vis_backends = [ - dict(type='LocalVisBackend'), -] -visualizer = dict( - name='visualizer', - type='SegLocalVisualizer', - vis_backends=[ - dict(type='LocalVisBackend'), - ]) -work_dir = 'mmsegmentationEQ2/work-dir' From a30aea1f53196443c78294b6fd71bdf7f1f79ce4 Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Mon, 3 Jun 2024 17:44:34 -0600 Subject: [PATCH 07/29] CCNET --- .../{ccnet_r50-d8.py => ccnet_r50-d8_deepglobe_5.py} | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) rename configs/_base_/models/{ccnet_r50-d8.py => ccnet_r50-d8_deepglobe_5.py} (92%) diff --git a/configs/_base_/models/ccnet_r50-d8.py b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py similarity index 92% rename from configs/_base_/models/ccnet_r50-d8.py rename to configs/_base_/models/ccnet_r50-d8_deepglobe_5.py index 575d8eb459..6392d71bc4 100644 --- a/configs/_base_/models/ccnet_r50-d8.py +++ b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py @@ -2,8 +2,8 @@ norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], + mean=[0.4082, 0.3791, 0.2815], + std=[0.1351, 0.1022, 0.0931], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) @@ -29,7 +29,7 @@ channels=512, recurrence=2, dropout_ratio=0.1, - num_classes=19, + num_classes=7, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( @@ -42,7 +42,7 @@ num_convs=1, concat_input=False, dropout_ratio=0.1, - num_classes=19, + num_classes=7, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( From 230379d3071d9c4747455363aa0a2cffc79348f8 Mon Sep 17 00:00:00 2001 From: Andrea Yela <73191386+AnYelg@users.noreply.github.com> Date: Mon, 3 Jun 2024 17:54:23 -0600 Subject: [PATCH 08/29] FCN Model --- configs/_base_/models/fcn_r50-d8deepglobe.py | 53 +++++++++++++++++++ .../fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py | 7 +++ 2 files changed, 60 insertions(+) create mode 100644 configs/_base_/models/fcn_r50-d8deepglobe.py create mode 100644 configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py diff --git a/configs/_base_/models/fcn_r50-d8deepglobe.py b/configs/_base_/models/fcn_r50-d8deepglobe.py new file mode 100644 index 0000000000..3e033fe47d --- /dev/null +++ b/configs/_base_/models/fcn_r50-d8deepglobe.py @@ -0,0 +1,53 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[0.4082, 0.3791, 0.2815], + std=[0.1351, 0.1022, 0.0931], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='FCNHead', + in_channels=2048, + in_index=3, + channels=512, + num_convs=2, + concat_input=True, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py b/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py new file mode 100644 index 0000000000..23f642087f --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-80k_deepglobe-512x1024.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8deepglobe.py', '../_base_/datasets/deepGlobe.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (256, 256) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) From 3340e2c47c1ff319c6186e4d34a9de188f3954f4 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Mon, 3 Jun 2024 17:59:00 -0600 Subject: [PATCH 09/29] hr18 --- configs/_base_/models/fcn_hr18_deepGlobe.py | 60 +++++++++++++++++++ .../fcn_hr18_4xb2-40k_deepglobe-512x1024.py | 7 +++ 2 files changed, 67 insertions(+) create mode 100644 configs/_base_/models/fcn_hr18_deepGlobe.py create mode 100644 configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py diff --git a/configs/_base_/models/fcn_hr18_deepGlobe.py b/configs/_base_/models/fcn_hr18_deepGlobe.py new file mode 100644 index 0000000000..0faaa1e567 --- /dev/null +++ b/configs/_base_/models/fcn_hr18_deepGlobe.py @@ -0,0 +1,60 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[0.4082, 0.3791, 0.2815], + std=[ 0.1351, 0.1022, 0.0931], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + type='HRNet', + norm_cfg=norm_cfg, + norm_eval=False, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(18, 36)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(18, 36, 72)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(18, 36, 72, 144)))), + decode_head=dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + channels=sum([18, 36, 72, 144]), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py b/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py new file mode 100644 index 0000000000..e5f8eecd63 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-40k_deepglobe-512x1024.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/fcn_hr18_deepGlobe.py', '../_base_/datasets/deepGlobe.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (256, 256) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) From 9d33f4dd6974af95733c8ad96756cb80fef84a41 Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Mon, 3 Jun 2024 18:23:24 -0600 Subject: [PATCH 10/29] CCNET config --- configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py index 84fc51a6b3..55ee6208d0 100644 --- a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py +++ b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py @@ -1,7 +1,7 @@ _base_ = [ - '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/models/ccnet_r50-d8_deepglobe_5.py', '../_base_/datasets/deepGlobe.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' ] -crop_size = (512, 1024) +crop_size = (256, 256) data_preprocessor = dict(size=crop_size) model = dict(data_preprocessor=data_preprocessor) From 160704f397e62abb439f72f94f4f511a0b0ac11f Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Mon, 3 Jun 2024 18:23:24 -0600 Subject: [PATCH 11/29] CCNET config --- configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py index 84fc51a6b3..55ee6208d0 100644 --- a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py +++ b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py @@ -1,7 +1,7 @@ _base_ = [ - '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', + '../_base_/models/ccnet_r50-d8_deepglobe_5.py', '../_base_/datasets/deepGlobe.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' ] -crop_size = (512, 1024) +crop_size = (256, 256) data_preprocessor = dict(size=crop_size) model = dict(data_preprocessor=data_preprocessor) From 7b800d40918a30db84b5e487bdf876ddd586b4ce Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Mon, 3 Jun 2024 18:28:03 -0600 Subject: [PATCH 12/29] CCNET v.2 --- ...pes-512x1024.py => ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename configs/ccnet/{ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py => ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py} (100%) diff --git a/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py rename to configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py From 5c23ab1c386efc27a47f6b588e282e35f5a13d61 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Mon, 3 Jun 2024 19:25:38 -0600 Subject: [PATCH 13/29] class --- mmseg/utils/class_names.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py index 644e955966..f2c88a560a 100644 --- a/mmseg/utils/class_names.py +++ b/mmseg/utils/class_names.py @@ -463,6 +463,12 @@ def bdd100k_classes(): 'bicycle' ] +def deepGlobe_classes(): + return[ + 'Urban', 'Agriculture', 'Range', 'Forest', 'Water', 'Barren', + 'Unknown' + ] + def bdd100k_palette(): """bdd100k palette for external use(same with cityscapes)""" @@ -487,9 +493,15 @@ def hsidrive_palette(): [0, 0, 255], [102, 51, 0], [255, 255, 0], [0, 207, 250], [255, 166, 0], [0, 204, 204]] +def deepGlobe_palette(): + """DeepGlobe palette for external use.""" + return [[0,255,255], [255,255,0], [255,0,255], [0,255,0], + [0,0,255], [255,255,255], [1,1,1]] + dataset_aliases = { 'cityscapes': ['cityscapes'], + 'deepGlobe': ['deepGlobe'], 'ade': ['ade', 'ade20k'], 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'], 'pcontext': ['pcontext', 'pascal_context', 'voc2010'], From a9597f359a8be9b6914685c9edfad72c257ba62a Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Mon, 3 Jun 2024 20:39:06 -0600 Subject: [PATCH 14/29] dataconfig --- mmseg/datasets/__init__.py | 2 +- mmseg/utils/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index 48c04220f5..907bbf8cef 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -63,5 +63,5 @@ 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset', 'HSIDrive20Dataset' + 'NYUDataset', 'HSIDrive20Dataset', 'DeepGlobeDataset' ] diff --git a/mmseg/utils/__init__.py b/mmseg/utils/__init__.py index 0a2af58c6e..c74f610500 100644 --- a/mmseg/utils/__init__.py +++ b/mmseg/utils/__init__.py @@ -2,7 +2,7 @@ # yapf: disable from .class_names import (ade_classes, ade_palette, bdd100k_classes, bdd100k_palette, cityscapes_classes, - cityscapes_palette, cocostuff_classes, + cityscapes_palette, deepGlobe_classes, deepGlobe_palette, cocostuff_classes, cocostuff_palette, dataset_aliases, get_classes, get_palette, isaid_classes, isaid_palette, loveda_classes, loveda_palette, potsdam_classes, From caf83e3f225efb90321ee2ebe2c9367d47279d72 Mon Sep 17 00:00:00 2001 From: Emilio Sibaja Date: Tue, 4 Jun 2024 10:30:15 -0600 Subject: [PATCH 15/29] Mean and STD change, dataset added, scale resized changes for the urban and unknown label to work properly --- configs/_base_/datasets/deepGlobe.py | 4 ++-- configs/_base_/models/ccnet_r50-d8_deepglobe_5.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py index 75c1f2cfb6..c6c64e2b4d 100644 --- a/configs/_base_/datasets/deepGlobe.py +++ b/configs/_base_/datasets/deepGlobe.py @@ -9,7 +9,7 @@ dict(type='LoadAnnotations'), dict( type='RandomResize', - scale=(256, 256), + scale=(512, 512), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), @@ -19,7 +19,7 @@ ] test_pipeline = [ dict(type='LoadImageFromFile'), - dict(type='Resize', scale=(256, 256), keep_ratio=True), + dict(type='Resize', scale=(512, 512), keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), diff --git a/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py index 6392d71bc4..e8909c99a6 100644 --- a/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py +++ b/configs/_base_/models/ccnet_r50-d8_deepglobe_5.py @@ -3,7 +3,7 @@ data_preprocessor = dict( type='SegDataPreProcessor', mean=[0.4082, 0.3791, 0.2815], - std=[0.1351, 0.1022, 0.0931], + std=[0.1451, 0.1116, 0.1013], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) From 39f019f08b2f50279a32e3b1adbe0e8e9fe4abca Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:02:07 -0600 Subject: [PATCH 16/29] tensorboard --- configs/_base_/default_runtime.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py index 272b4d2467..96aa87149a 100644 --- a/configs/_base_/default_runtime.py +++ b/configs/_base_/default_runtime.py @@ -4,7 +4,9 @@ mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl'), ) -vis_backends = [dict(type='LocalVisBackend')] +vis_backends = [dict(type='LocalVisBackend'), + dict(type='TensorboardVisBackend')] + visualizer = dict( type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer') log_processor = dict(by_epoch=False) From 347dca0950731b30fabcc3728b80c9e270a9ca97 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:08:48 -0600 Subject: [PATCH 17/29] batch --- configs/_base_/datasets/deepGlobe.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py index c6c64e2b4d..4e6f44bdf5 100644 --- a/configs/_base_/datasets/deepGlobe.py +++ b/configs/_base_/datasets/deepGlobe.py @@ -42,8 +42,8 @@ ]) ] train_dataloader = dict( - batch_size=4, - num_workers=2, + batch_size=32, + num_workers=4, persistent_workers=True, sampler=dict(type='InfiniteSampler', shuffle=True), dataset=dict( @@ -53,7 +53,7 @@ img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'), pipeline=train_pipeline)) val_dataloader = dict( - batch_size=6, + batch_size=16, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), From f4f3331b9477a14389c60c728f31e990c451df51 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:55:39 -0600 Subject: [PATCH 18/29] GcNet-DeepLab --- configs/_base_/models/deeplabv3_r50-d8.py | 2 +- .../models/deeplabv3plus_r50-d8_deepglobe.py | 2 +- .../_base_/models/gcnet_r50-d8_deepGlobe.py | 54 +++++++++++++++++++ ...cnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py | 7 +++ 4 files changed, 63 insertions(+), 2 deletions(-) create mode 100644 configs/_base_/models/gcnet_r50-d8_deepGlobe.py create mode 100644 configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py diff --git a/configs/_base_/models/deeplabv3_r50-d8.py b/configs/_base_/models/deeplabv3_r50-d8.py index 36cc34947a..caa74a096b 100644 --- a/configs/_base_/models/deeplabv3_r50-d8.py +++ b/configs/_base_/models/deeplabv3_r50-d8.py @@ -4,7 +4,7 @@ data_preprocessor = dict( type='SegDataPreProcessor', mean=[0.4082, 0.3791, 0.2815], - std=[0.1351, 0.1022, 0.0931], + std=[ 0.1451, 0.1116, 0.1013], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) diff --git a/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py index 1d63ef467f..2451ce45a3 100644 --- a/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py +++ b/configs/_base_/models/deeplabv3plus_r50-d8_deepglobe.py @@ -3,7 +3,7 @@ data_preprocessor = dict( type='SegDataPreProcessor', mean=[0.4082, 0.3791, 0.2815], - std=[0.1351, 0.1022, 0.0931], + std=[ 0.1451, 0.1116, 0.1013], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) diff --git a/configs/_base_/models/gcnet_r50-d8_deepGlobe.py b/configs/_base_/models/gcnet_r50-d8_deepGlobe.py new file mode 100644 index 0000000000..e7e509f5e4 --- /dev/null +++ b/configs/_base_/models/gcnet_r50-d8_deepGlobe.py @@ -0,0 +1,54 @@ +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[0.4082, 0.3791, 0.2815], + std=[ 0.1451, 0.1116, 0.1013], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained='open-mmlab://resnet50_v1c', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 2, 4), + strides=(1, 2, 1, 1), + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + contract_dilation=True), + decode_head=dict( + type='GCHead', + in_channels=2048, + in_index=3, + channels=512, + ratio=1 / 4., + pooling_type='att', + fusion_types=('channel_add', ), + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=1024, + in_index=2, + channels=256, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=7, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py b/configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py new file mode 100644 index 0000000000..c359f534ba --- /dev/null +++ b/configs/gcnet/gcnet_r50-d8_4xb2-40k_deepGlobe-512x1024.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/deepGlobe.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (256, 256) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) From 97091a544ae2438ea8835d117918f141af3db882 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:57:05 -0600 Subject: [PATCH 19/29] modelfix --- .../deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py index 583091b4db..ed3da73684 100644 --- a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py +++ b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py @@ -1,6 +1,6 @@ #configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py _base_ = [ - '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/deepGlobe.py', + '../_base_/models/deeplabv3_r50-d8_deepGlobe.py', '../_base_/datasets/deepGlobe.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' ] crop_size = (256, 256) From 66d35693820c57c24829ded1f059a1e0e51e9f98 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Wed, 5 Jun 2024 10:19:29 -0600 Subject: [PATCH 20/29] model_test --- tools/model_test.py | 58 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 tools/model_test.py diff --git a/tools/model_test.py b/tools/model_test.py new file mode 100644 index 0000000000..e1b03ffd93 --- /dev/null +++ b/tools/model_test.py @@ -0,0 +1,58 @@ +import mmcv +import os +from mmseg.apis import inference_model, init_model +import numpy as np +import cv2 + +# Configuration and checkpoint files +config_file = 'D:/Github/mmsegmentationEQ2/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py' +checkpoint_file = 'D:/CCNET_data/CCNET/iter_10000.pth' + +# Initialize the segmentor +model = init_model(config_file, checkpoint_file, device='cuda:0') + +# Custom palette +palette = [[0, 255, 255], [255, 255, 0], [255, 0, 255], [0, 255, 0], [0, 0, 255], [255, 255, 255], [0, 0, 0]] + +# Convert palette to numpy array for easy indexing +palette = np.array(palette, dtype=np.uint8) + +# Directory containing the image(s) for inference +image_dir = 'D:/Test_model' + +# Directory to save the results +output_dir = 'D:/Test_restults' + +# Create the output directory if it does not exist +os.makedirs(output_dir, exist_ok=True) + +# Iterate over all images in the directory +for image_name in os.listdir(image_dir): + img_path = os.path.join(image_dir, image_name) + # Run inference + result = inference_model(model, img_path) + + # Extract the predicted mask from SegDataSample + result_mask = result.pred_sem_seg.data.cpu().numpy().astype(np.uint8) + + # Debug: Print shape and type of result_mask + print(f"Processing {image_name}: result_mask shape {result_mask.shape}, dtype {result_mask.dtype}") + + # Remove the extra dimension from result_mask + result_mask = result_mask.squeeze(0) + + # Map the single-channel mask to a 3-channel image using the palette + color_mask = palette[result_mask] + + # Debug: Print shape and type of color_mask + print(f"Processing {image_name}: color_mask shape {color_mask.shape}, dtype {color_mask.dtype}") + + # Check if color_mask is valid for saving + if color_mask is not None and color_mask.size > 0: + # Save the color mask image + output_path = os.path.join(output_dir, image_name) + mmcv.imwrite(color_mask, output_path) + else: + print(f"Skipping {image_name} due to invalid mask") + +print('Inference completed and results saved to:',output_dir) \ No newline at end of file From 26064f83d66d812b8f86f6887707dcab5dc5c177 Mon Sep 17 00:00:00 2001 From: A01781042 <90709790+A01781042@users.noreply.github.com> Date: Wed, 5 Jun 2024 10:21:00 -0600 Subject: [PATCH 21/29] del --- tools/model_test.py | 58 --------------------------------------------- 1 file changed, 58 deletions(-) delete mode 100644 tools/model_test.py diff --git a/tools/model_test.py b/tools/model_test.py deleted file mode 100644 index e1b03ffd93..0000000000 --- a/tools/model_test.py +++ /dev/null @@ -1,58 +0,0 @@ -import mmcv -import os -from mmseg.apis import inference_model, init_model -import numpy as np -import cv2 - -# Configuration and checkpoint files -config_file = 'D:/Github/mmsegmentationEQ2/configs/ccnet/ccnet_r50-d8_4xb2-40k_deepglobe-256x256.py' -checkpoint_file = 'D:/CCNET_data/CCNET/iter_10000.pth' - -# Initialize the segmentor -model = init_model(config_file, checkpoint_file, device='cuda:0') - -# Custom palette -palette = [[0, 255, 255], [255, 255, 0], [255, 0, 255], [0, 255, 0], [0, 0, 255], [255, 255, 255], [0, 0, 0]] - -# Convert palette to numpy array for easy indexing -palette = np.array(palette, dtype=np.uint8) - -# Directory containing the image(s) for inference -image_dir = 'D:/Test_model' - -# Directory to save the results -output_dir = 'D:/Test_restults' - -# Create the output directory if it does not exist -os.makedirs(output_dir, exist_ok=True) - -# Iterate over all images in the directory -for image_name in os.listdir(image_dir): - img_path = os.path.join(image_dir, image_name) - # Run inference - result = inference_model(model, img_path) - - # Extract the predicted mask from SegDataSample - result_mask = result.pred_sem_seg.data.cpu().numpy().astype(np.uint8) - - # Debug: Print shape and type of result_mask - print(f"Processing {image_name}: result_mask shape {result_mask.shape}, dtype {result_mask.dtype}") - - # Remove the extra dimension from result_mask - result_mask = result_mask.squeeze(0) - - # Map the single-channel mask to a 3-channel image using the palette - color_mask = palette[result_mask] - - # Debug: Print shape and type of color_mask - print(f"Processing {image_name}: color_mask shape {color_mask.shape}, dtype {color_mask.dtype}") - - # Check if color_mask is valid for saving - if color_mask is not None and color_mask.size > 0: - # Save the color mask image - output_path = os.path.join(output_dir, image_name) - mmcv.imwrite(color_mask, output_path) - else: - print(f"Skipping {image_name} due to invalid mask") - -print('Inference completed and results saved to:',output_dir) \ No newline at end of file From 9c28a6b1a229ef742dfe3439844af2d72a3d896f Mon Sep 17 00:00:00 2001 From: Andrea Yela <73191386+AnYelg@users.noreply.github.com> Date: Wed, 5 Jun 2024 11:02:46 -0600 Subject: [PATCH 22/29] Predicciones1 --- PrediccionesFCN/100694_sat.png | Bin 0 -> 6158 bytes PrediccionesFCN/102122_sat.png | Bin 0 -> 4413 bytes PrediccionesFCN/10233_sat.png | Bin 0 -> 3725 bytes PrediccionesFCN/103665_sat.png | Bin 0 -> 5063 bytes PrediccionesFCN/103730_sat.png | Bin 0 -> 10702 bytes PrediccionesFCN/104113_sat.png | Bin 0 -> 6536 bytes PrediccionesFCN/10452_sat.png | Bin 0 -> 17764 bytes PrediccionesFCN/10901_sat.png | Bin 0 -> 11305 bytes PrediccionesFCN/111335_sat.png | Bin 0 -> 9906 bytes PrediccionesFCN/114433_sat.png | Bin 0 -> 15553 bytes 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