Add export and inference for amgnet

Author: Hydralune

examples/amgnet/deploy/python_infer/amgn_predictor.py renamed to examples/amgnet/amgn_predictor.py

@@ -1,67 +1,67 @@
-defaults:
-  - ppsci_default
-  - TRAIN: train_default
-  - TRAIN/ema: ema_default
-  - TRAIN/swa: swa_default
-  - EVAL: eval_default
-  - INFER: infer_default
-  - hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
-  - _self_
-
-hydra:
-  run:
-    # dynamic output directory according to running time and override name
-    dir: outputs_amgnet_airfoil/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
-  job:
-    name: ${mode} # name of logfile
-    chdir: false # keep current working directory unchanged
-  sweep:
-    # output directory for multirun
-    dir: ${hydra.run.dir}
-    subdir: ./
-
-# general settings
-mode: train # running mode: train/eval
-seed: 42
-output_dir: ${hydra:run.dir}
-log_freq: 20
-
-# set working condition
-
-# set training data path
-TRAIN_DATA_DIR: "./data/NACA0012_interpolate/outputs_train"
-TRAIN_MESH_GRAPH_PATH: "./data/NACA0012_interpolate/mesh_fine.su2"
-
-# set evaluate data path
-EVAL_DATA_DIR: "./data/NACA0012_interpolate/outputs_test"
-EVAL_MESH_GRAPH_PATH: "./data/NACA0012_interpolate/mesh_fine.su2"
-
-# model settings
-MODEL:
-  input_keys: ["input"]
-  output_keys: ["pred"]
-  input_dim: 5
-  output_dim: 3
-  latent_dim: 128
-  num_layers: 2
-  message_passing_aggregator: "sum"
-  message_passing_steps: 6
-  speed: "norm"
-
-# training settings
-TRAIN:
-  epochs: 500
-  iters_per_epoch: 42
-  save_freq: 50
-  eval_during_train: true
-  eval_freq: 50
-  learning_rate: 5.0e-4
-  batch_size: 4
-  pretrained_model_path: null
-  checkpoint_path: null
-
-# evaluation settings
-EVAL:
-  batch_size: 1
-  pretrained_model_path: null
-  eval_with_no_grad: true
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
+  - _self_
+
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    dir: outputs_amgnet_airfoil/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  job:
+    name: ${mode} # name of logfile
+    chdir: false # keep current working directory unchanged
+  sweep:
+    # output directory for multirun
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: train # running mode: train/eval
+seed: 42
+output_dir: ${hydra:run.dir}
+log_freq: 20
+
+# set working condition
+
+# set training data path
+TRAIN_DATA_DIR: "./data/NACA0012_interpolate/outputs_train"
+TRAIN_MESH_GRAPH_PATH: "./data/NACA0012_interpolate/mesh_fine.su2"
+
+# set evaluate data path
+EVAL_DATA_DIR: "./data/NACA0012_interpolate/outputs_test"
+EVAL_MESH_GRAPH_PATH: "./data/NACA0012_interpolate/mesh_fine.su2"
+
+# model settings
+MODEL:
+  input_keys: ["input"]
+  output_keys: ["pred"]
+  input_dim: 5
+  output_dim: 3
+  latent_dim: 128
+  num_layers: 2
+  message_passing_aggregator: "sum"
+  message_passing_steps: 6
+  speed: "norm"
+
+# training settings
+TRAIN:
+  epochs: 500
+  iters_per_epoch: 42
+  save_freq: 50
+  eval_during_train: true
+  eval_freq: 50
+  learning_rate: 5.0e-4
+  batch_size: 4
+  pretrained_model_path: null
+  checkpoint_path: null
+
+# evaluation settings
+EVAL:
+  batch_size: 1
+  pretrained_model_path: null
+  eval_with_no_grad: true

examples/amgnet/conf/amgnet_airfoil.yaml

@@ -1,4 +1,4 @@
-git+https://github.com/PaddlePaddle/PGL.git
-matplotlib
-pyamg
-scipy
+git+https://github.com/PaddlePaddle/PGL.git
+matplotlib
+pyamg
+scipy