feat(ppsci): support data_effient_nopt

Author: xiaoyewww

docs/zh/examples/data_efficient_nopt.md

@@ -7,19 +7,20 @@
     # Download possion_64 data from https://drive.google.com/drive/folders/1crIsTZGxZULWhrXkwGDiWF33W6RHxJkf
     # Download helmholtz_64 data from https://drive.google.com/drive/folders/1UjIaF6FsjmN_xlGGSUX-1K2V3EF2Zalw
 
-    # Update the file paths in `config/operators_possion.yaml` or `config/operators_helmholtz.yaml` to specify `train_path`, `val_path`, `test_path`, `scales_path`, and `train_rand_idx_path`.
-
-    # possion_64 pretrain
-    python pretrain_basic.py --run_name r0 --config pois-64-pretrain-e1_20_m3 --yaml_config ./config/operators_poisson.yaml
-
-    # possion_64 finetune
-    python pretrain_basic.py --run_name r0 --config pois-64-e5_15_b0 --yaml_config ./config/operators_poisson.yaml
-
-    # helmholtz_64 pretrain
-    python pretrain_basic.py --run_name r0 --config helm-64-pretrain-o1_20_m1 --yaml_config ./config/operators_helmholtz.yaml
-
-    # helmholtz_64 finetune
-    python pretrain_basic.py --run_name r0 --config helm-64-o5_15_ft5_r2 --yaml_config ./config/operators_helmholtz.yaml
+    # Update the file paths in `cexamples/data_efficient_nopt/config/data_efficient_nopt.yaml`, specify to mode in `train`
+    # UPdate the file paths in config/operators_poisson.yaml or config/operators_helmholtz.yaml, specify to `train_path`, `val_path`, `test_path`, `scales_path` and `train_rand_idx_path`
+
+    # possion_64 pretrain, specify as following:
+    #   run_name: r0
+    #   config: pois-64-pretrain-e1_20_m3
+    #   yaml_config: config/operators_poisson.yaml
+    python data_efficient_nopt.py
+
+    # helmholtz_64 pretrain, specify as following:
+    #   run_name: r0
+    #   config: helm-64-pretrain-o1_20_m1
+    #   yaml_config: config/operators_helmholtz.yaml
+    python data_efficient_nopt.py
     ```
 
 === "模型评估命令"
@@ -34,9 +35,14 @@
 
     ``` sh
     cd examples/data_efficient_nopt
-    # Update the file paths in `config/operators_possion.yaml` or `config/operators_helmholtz.yaml` to specify `train_path`, `test_path`, and `scales_path`.
+    # Update the file paths in `cexamples/data_efficient_nopt/config/data_efficient_nopt.yaml`, specify to mode in `infer`
     # Use a fine-tuned model as the checkpoint in 'exp' or utilize `model_convert.py` to convert the official checkpoint.
-    python3 inference_fno_helmholtz_poisson.py --config ./config/inference_poisson.yaml --ckpt_path <ckpt_path> --num_demos 1
+    # UPdate the file paths in config/inference_poisson.yaml or config/inference_poisson.yaml, specify to `train_path`, `test_path` and `scales_path`
+
+    # possion_64 inference, specify as following:
+    #   evaluation: config/inference_poisson.yaml
+    #   ckpt_path: <ckpt_path>
+    python data_efficient_nopt.py
     ```
 
 ## 1. 背景简介

examples/data_efficient_nopt/config/data_efficient_nopt.yaml

@@ -1,5 +1,5 @@
 # general settings
-mode: train # running mode: train/eval
+mode: train # running mode: train/infer
 seed: 42
 
 # training settings
@@ -10,7 +10,7 @@ yaml_config: config/operators_poisson.yaml
 sweep_id: ''
 
 # evaluation settings
-eval_config: config/inference_poisson.yaml
+infer_config: config/inference_poisson.yaml
 ckpt_path: data/pd_finetune_b01_m0_n8192.tar
 num_demos: 1
 tqdm: False

examples/data_efficient_nopt/data_efficient_nopt.py

@@ -30,7 +30,6 @@
 import yaml
 from einops import rearrange
 from omegaconf import DictConfig
-from pretrain_basic import l2_err
 from ruamel.yaml import YAML
 from ruamel.yaml.comments import CommentedMap as ruamelDict
 from scipy.stats import linregress
@@ -47,6 +46,14 @@
 from ppsci.data.dataset.data_efficient_nopt_dataset import PoisHelmDatasetLoader
 
 
+def l2_err(pred, target, spatial_dim=(-1, -2, -3)):
+    x = paddle.sum((pred - target) ** 2, axis=spatial_dim) / paddle.sum(
+        target**2, axis=spatial_dim
+    )
+    x = paddle.sqrt(x)
+    return paddle.mean(x)  # , dim=0)
+
+
 def grad_norm(parameters):
     with paddle.no_grad():
         total_norm = 0
@@ -961,7 +968,7 @@ def get_pred(cfg):
     )
 
 
-def evaluate(cfg: DictConfig):
+def inference(cfg: DictConfig):
     get_pred(cfg)
 
 
@@ -971,8 +978,8 @@ def evaluate(cfg: DictConfig):
 def main(cfg: DictConfig):
     if cfg.mode == "train":
         train(cfg)
-    elif cfg.mode == "eval":
-        evaluate(cfg)
+    elif cfg.mode == "infer":
+        inference(cfg)
     else:
         raise ValueError(
             f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"