support default config node

Author: HydrogenSulfate

examples/NLS-MB/NLS-MB_optical_rogue_wave.py

@@ -233,14 +233,10 @@ def train(cfg: DictConfig):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        cfg.output_dir,
-        optimizer,
-        epochs=cfg.TRAIN.epochs,
-        iters_per_epoch=cfg.TRAIN.iters_per_epoch,
-        eval_during_train=cfg.TRAIN.eval_during_train,
-        eval_freq=cfg.TRAIN.eval_freq,
+        optimizer=optimizer,
         equation=equation,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -258,14 +254,11 @@ def train(cfg: DictConfig):
         model,
         constraint,
         OUTPUT_DIR,
-        optimizer_lbfgs,
-        None,
-        EPOCHS,
-        cfg.TRAIN.lbfgs.iters_per_epoch,
-        eval_during_train=cfg.TRAIN.lbfgs.eval_during_train,
-        eval_freq=cfg.TRAIN.lbfgs.eval_freq,
+        optimizer=optimizer_lbfgs,
+        epochs=EPOCHS,
         equation=equation,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -339,11 +332,9 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        eval_freq=cfg.TRAIN.eval_freq,
         equation=equation,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+        cfg=cfg,
     )
     solver.eval()
 
@@ -371,7 +362,7 @@ def export(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        pretrained_model_path=cfg.INFER.pretrained_model_path,
+        cfg=cfg,
     )
     # export model
     from paddle.static import InputSpec

examples/NLS-MB/NLS-MB_optical_soliton.py

@@ -210,14 +210,10 @@ def train(cfg: DictConfig):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        cfg.output_dir,
-        optimizer,
-        epochs=cfg.TRAIN.epochs,
-        iters_per_epoch=cfg.TRAIN.iters_per_epoch,
-        eval_during_train=cfg.TRAIN.eval_during_train,
-        eval_freq=cfg.TRAIN.eval_freq,
+        optimizer=optimizer,
         equation=equation,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -236,13 +232,11 @@ def train(cfg: DictConfig):
         constraint,
         OUTPUT_DIR,
         optimizer_lbfgs,
-        None,
         EPOCHS,
         cfg.TRAIN.lbfgs.iters_per_epoch,
-        eval_during_train=cfg.TRAIN.lbfgs.eval_during_train,
-        eval_freq=cfg.TRAIN.lbfgs.eval_freq,
         equation=equation,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -316,11 +310,9 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        eval_freq=cfg.TRAIN.eval_freq,
         equation=equation,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+        cfg=cfg,
     )
     solver.eval()
 
@@ -348,7 +340,7 @@ def export(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        pretrained_model_path=cfg.INFER.pretrained_model_path,
+        cfg=cfg,
     )
     # export model
     from paddle.static import InputSpec

examples/NLS-MB/conf/NLS-MB_rogue_wave.yaml

@@ -1,3 +1,12 @@
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - _self_
+
 hydra:
   run:
     # dynamic output directory according to running time and override name

examples/NLS-MB/conf/NLS-MB_soliton.yaml

@@ -1,3 +1,12 @@
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - _self_
+
 hydra:
   run:
     # dynamic output directory according to running time and override name

examples/RegAE/RegAE.py

@@ -88,16 +88,9 @@ def loss_expr(output_dict, label_dict, weight_dict=None):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        cfg.output_dir,
-        optimizer,
-        None,
-        cfg.TRAIN.epochs,
-        cfg.TRAIN.iters_per_epoch,
-        save_freq=cfg.TRAIN.save_freq,
-        eval_during_train=cfg.TRAIN.eval_during_train,
-        eval_freq=cfg.TRAIN.eval_freq,
+        optimizer=optimizer,
         validator=validator,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -135,11 +128,8 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        None,
-        output_dir=cfg.output_dir,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
     # evaluate after finished training
     solver.eval()

examples/RegAE/conf/RegAE.yaml

@@ -1,3 +1,12 @@
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - _self_
+
 hydra:
   run:
     # dynamic output directory according to running time and override name

examples/advection/conf/adv_plain.yaml

@@ -0,0 +1,96 @@
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - _self_
+
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    dir: outputs_advection_plain/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  job:
+    name: ${mode} # name of logfile
+    chdir: false # keep current working direcotry unchaned
+    config:
+      override_dirname:
+        exclude_keys:
+          - TRAIN.checkpoint_path
+          - TRAIN.pretrained_model_path
+          - EVAL.pretrained_model_path
+          - INFER.pretrained_model_path
+          - mode
+          - output_dir
+          - log_freq
+  callbacks:
+    init_callback:
+      _target_: ppsci.utils.callbacks.InitCallback
+  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: 100
+
+DATA_PATH: ./dataset/allen_cahn.mat
+
+# model settings
+MODEL:
+  input_keys: [t, x]
+  output_keys: [u]
+  num_layers: 4
+  hidden_size: 256
+  activation: tanh
+  periods:
+    t: [2*np.pi, False]
+
+# training settings
+TRAIN:
+  epochs: 200
+  iters_per_epoch: 1000
+  save_freq: 10
+  eval_during_train: true
+  eval_freq: 1
+  lr_scheduler:
+    epochs: ${TRAIN.epochs}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: 1.0e-3
+    gamma: 0.9
+    decay_steps: 2000
+    by_epoch: false
+  batch_size: 4096
+  pretrained_model_path: null
+  checkpoint_path: null
+  ema:
+    decay: 0.9
+    avg_freq: 1
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: null
+  eval_with_no_grad: true
+  batch_size: 4096
+
+# inference settings
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/AllenCahn/allen_cahn_plain_pretrained.pdparams
+  export_path: ./inference/allen_cahn
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${INFER.export_path}.onnx
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: true
+  min_subgraph_size: 5
+  gpu_mem: 2000
+  gpu_id: 0
+  max_batch_size: 1024
+  num_cpu_threads: 10
+  batch_size: 1024

examples/allen_cahn/allen_cahn_causal.py

@@ -158,21 +158,9 @@ def gen_label_batch(input_batch):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        cfg.output_dir,
-        optimizer,
-        lr_scheduler,
-        cfg.TRAIN.epochs,
-        cfg.TRAIN.iters_per_epoch,
-        save_freq=cfg.TRAIN.save_freq,
-        log_freq=cfg.log_freq,
-        eval_during_train=True,
-        eval_freq=cfg.TRAIN.eval_freq,
+        optimizer=optimizer,
         equation=equation,
         validator=validator,
-        pretrained_model_path=cfg.TRAIN.pretrained_model_path,
-        checkpoint_path=cfg.TRAIN.checkpoint_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
-        use_tbd=True,
         cfg=cfg,
     )
     # train model
@@ -221,11 +209,8 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        log_freq=cfg.log_freq,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
 
     # evaluate after finished training
@@ -247,7 +232,7 @@ def export(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        pretrained_model_path=cfg.INFER.pretrained_model_path,
+        cfg=cfg,
     )
     # export model
     from paddle.static import InputSpec

examples/allen_cahn/allen_cahn_plain.py

@@ -156,21 +156,9 @@ def gen_label_batch(input_batch):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        cfg.output_dir,
-        optimizer,
-        lr_scheduler,
-        cfg.TRAIN.epochs,
-        cfg.TRAIN.iters_per_epoch,
-        save_freq=cfg.TRAIN.save_freq,
-        log_freq=cfg.log_freq,
-        eval_during_train=True,
-        eval_freq=cfg.TRAIN.eval_freq,
+        optimizer=optimizer,
         equation=equation,
         validator=validator,
-        pretrained_model_path=cfg.TRAIN.pretrained_model_path,
-        checkpoint_path=cfg.TRAIN.checkpoint_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
-        use_tbd=True,
         cfg=cfg,
     )
     # train model
@@ -219,11 +207,8 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        log_freq=cfg.log_freq,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
 
     # evaluate after finished training
@@ -245,7 +230,7 @@ def export(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        pretrained_model_path=cfg.INFER.pretrained_model_path,
+        cfg=cfg,
     )
     # export model
     from paddle.static import InputSpec