[Refine] Remove redundant code

docs/zh/examples/amgnet.md

@@ -103,17 +103,17 @@ unzip data.zip
 
 === "airfoil"
 
-    ``` py linenums="61"
+    ``` py linenums="55"
     --8<--
-    examples/amgnet/amgnet_airfoil.py:61:62
+    examples/amgnet/amgnet_airfoil.py:55:56
     --8<--
     ```
 
 === "cylinder"
 
-    ``` py linenums="61"
+    ``` py linenums="55"
     --8<--
-    examples/amgnet/amgnet_cylinder.py:61:62
+    examples/amgnet/amgnet_cylinder.py:55:56
     --8<--
     ```
 

examples/NLS-MB/NLS-MB_optical_rogue_wave.py

@@ -233,15 +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,
-        geom=geom,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -259,14 +254,12 @@ def train(cfg: DictConfig):
         model,
         constraint,
         OUTPUT_DIR,
-        optimizer_lbfgs,
-        None,
-        EPOCHS,
-        cfg.TRAIN.lbfgs.iters_per_epoch,
+        optimizer=optimizer_lbfgs,
+        epochs=EPOCHS,
+        iters_per_epoch=cfg.TRAIN.lbfgs.iters_per_epoch,
         eval_during_train=cfg.TRAIN.lbfgs.eval_during_train,
         eval_freq=cfg.TRAIN.lbfgs.eval_freq,
         equation=equation,
-        geom=geom,
         validator=validator,
     )
     # train model
@@ -341,12 +334,9 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        eval_freq=cfg.TRAIN.eval_freq,
         equation=equation,
-        geom=geom,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+        cfg=cfg,
     )
     solver.eval()
 
@@ -374,7 +364,7 @@ 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

@@ -210,15 +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,
-        geom=geom,
         validator=validator,
+        cfg=cfg,
     )
     # train model
     solver.train()
@@ -237,13 +232,11 @@ def train(cfg: DictConfig):
         constraint,
         OUTPUT_DIR,
         optimizer_lbfgs,
-        None,
-        EPOCHS,
-        cfg.TRAIN.lbfgs.iters_per_epoch,
+        epochs=EPOCHS,
+        iters_per_epoch=cfg.TRAIN.lbfgs.iters_per_epoch,
         eval_during_train=cfg.TRAIN.lbfgs.eval_during_train,
         eval_freq=cfg.TRAIN.lbfgs.eval_freq,
         equation=equation,
-        geom=geom,
         validator=validator,
     )
     # train model
@@ -318,12 +311,9 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        eval_freq=cfg.TRAIN.eval_freq,
         equation=equation,
-        geom=geom,
         validator=validator,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+        cfg=cfg,
     )
     solver.eval()
 
@@ -351,7 +341,7 @@ 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

@@ -1,19 +1,20 @@
+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: output_NLS-MB_rogue_wave/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
   job:
     name: ${mode} # name of logfile
     chdir: false # keep current working directory unchanged
-    config:
-      override_dirname:
-        exclude_keys:
-          - TRAIN.checkpoint_path
-          - TRAIN.pretrained_model_path
-          - EVAL.pretrained_model_path
-          - mode
-          - output_dir
-          - log_freq
   callbacks:
     init_callback:
       _target_: ppsci.utils.callbacks.InitCallback

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

@@ -1,19 +1,20 @@
+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: output_NLS-MB_soliton/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
   job:
     name: ${mode} # name of logfile
     chdir: false # keep current working directory unchanged
-    config:
-      override_dirname:
-        exclude_keys:
-          - TRAIN.checkpoint_path
-          - TRAIN.pretrained_model_path
-          - EVAL.pretrained_model_path
-          - mode
-          - output_dir
-          - log_freq
   callbacks:
     init_callback:
       _target_: ppsci.utils.callbacks.InitCallback

examples/RegAE/RegAE.py

@@ -14,23 +14,15 @@
 
 from __future__ import annotations
 
-from os import path as osp
-
 import hydra
 import paddle
 from omegaconf import DictConfig
 from paddle.nn import functional as F
 
 import ppsci
-from ppsci.utils import logger
 
 
 def train(cfg: DictConfig):
-    # set random seed for reproducibility
-    ppsci.utils.misc.set_random_seed(cfg.seed)
-    # initialize logger
-    logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
-
     # set model
     model = ppsci.arch.AutoEncoder(**cfg.MODEL)
 
@@ -99,16 +91,9 @@ 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()
@@ -117,11 +102,6 @@ def loss_expr(output_dict, label_dict, weight_dict=None):
 
 
 def evaluate(cfg: DictConfig):
-    # set random seed for reproducibility
-    ppsci.utils.misc.set_random_seed(cfg.seed)
-    # initialize logger
-    logger.init_logger("ppsci", osp.join(cfg.output_dir, "eval.log"), "info")
-
     # set model
     model = ppsci.arch.AutoEncoder(**cfg.MODEL)
 
@@ -151,11 +131,8 @@ 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

@@ -1,19 +1,23 @@
+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: output_RegAE/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
   job:
     name: ${mode} # name of logfile
     chdir: false # keep current working directory unchanged
-    config:
-      override_dirname:
-        exclude_keys:
-          - TRAIN.checkpoint_path
-          - TRAIN.pretrained_model_path
-          - EVAL.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}

examples/allen_cahn/allen_cahn_causal.py

@@ -158,22 +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,
-        seed=cfg.seed,
+        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
@@ -222,11 +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
@@ -248,7 +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

@@ -156,22 +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,
-        seed=cfg.seed,
+        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
@@ -220,11 +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
@@ -246,7 +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