fix bug

Author: GreatV

docs/zh/examples/viv.md

@@ -119,9 +119,9 @@ examples/fsi/viv.py:61:64
 
 接下来我们需要指定训练轮数和学习率，此处我们按实验经验，使用十万轮训练轮数，并每隔1000个epochs评估一次模型精度。
 
-``` yaml linenums="39"
+``` yaml linenums="40"
 --8<--
-examples/fsi/conf/viv.yaml:39:40
+examples/fsi/conf/viv.yaml:40:45
 --8<--
 ```
 

examples/fsi/conf/viv.yaml

@@ -25,6 +25,7 @@ seed: 42
 output_dir: ${hydra:run.dir}
 log_freq: 20
 
+VIV_DATA_PATH: "./VIV_Training_Neta100.mat"
 
 # model settings
 MODEL:
@@ -37,13 +38,14 @@ MODEL:
 # training settings
 TRAIN:
   epochs: 100000
-  eval_freq: 1000
   iters_per_epoch: 1
   save_freq: 1
   eval_during_train: true
-  optimizer:
-    epochs: 100000
-    iters_per_epoch: 1
+  eval_freq: 1000
+  batch_size: 100
+  lr_scheduler:
+    epochs: ${TRAIN.epochs}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
     learning_rate: 0.001
     step_size: 20000
     gamma: 0.9
@@ -53,3 +55,4 @@ TRAIN:
 # evaluation settings
 EVAL:
   pretrained_model_path: null
+  batch_size: 32

examples/fsi/viv.py

@@ -38,12 +38,12 @@ def train(cfg: DictConfig):
     train_dataloader_cfg = {
         "dataset": {
             "name": "MatDataset",
-            "file_path": "./VIV_Training_Neta100.mat",
+            "file_path": cfg.VIV_DATA_PATH,
             "input_keys": ("t_f",),
             "label_keys": ("eta", "f"),
             "weight_dict": {"eta": 100},
         },
-        "batch_size": 100,
+        "batch_size": cfg.TRAIN.batch_size,
         "sampler": {
             "name": "BatchSampler",
             "drop_last": False,
@@ -64,18 +64,18 @@ def train(cfg: DictConfig):
     }
 
     # set optimizer
-    lr_scheduler = ppsci.optimizer.lr_scheduler.Step(**cfg.TRAIN.optimizer)()
+    lr_scheduler = ppsci.optimizer.lr_scheduler.Step(**cfg.TRAIN.lr_scheduler)()
     optimizer = ppsci.optimizer.Adam(lr_scheduler)((model,) + tuple(equation.values()))
 
     # set validator
     valid_dataloader_cfg = {
         "dataset": {
             "name": "MatDataset",
-            "file_path": "./VIV_Training_Neta100.mat",
+            "file_path": cfg.VIV_DATA_PATH,
             "input_keys": ("t_f",),
             "label_keys": ("eta", "f"),
         },
-        "batch_size": 32,
+        "batch_size": cfg.EVAL.batch_size,
         "sampler": {
             "name": "BatchSampler",
             "drop_last": False,
@@ -93,7 +93,7 @@ def train(cfg: DictConfig):
 
     # set visualizer(optional)
     visu_mat = ppsci.utils.reader.load_mat_file(
-        "./VIV_Training_Neta100.mat",
+        cfg.VIV_DATA_PATH,
         ("t_f", "eta_gt", "f_gt"),
         alias_dict={"eta_gt": "eta", "f_gt": "f"},
     )
@@ -121,7 +121,7 @@ def train(cfg: DictConfig):
         lr_scheduler,
         cfg.TRAIN.epochs,
         cfg.TRAIN.iters_per_epoch,
-        eval_during_train=True,
+        eval_during_train=cfg.TRAIN.eval_during_train,
         eval_freq=cfg.TRAIN.eval_freq,
         equation=equation,
         validator=validator,
@@ -153,11 +153,11 @@ def evaluate(cfg: DictConfig):
     valid_dataloader_cfg = {
         "dataset": {
             "name": "MatDataset",
-            "file_path": "./VIV_Training_Neta100.mat",
+            "file_path": cfg.VIV_DATA_PATH,
             "input_keys": ("t_f",),
             "label_keys": ("eta", "f"),
         },
-        "batch_size": 32,
+        "batch_size": cfg.EVAL.batch_size,
         "sampler": {
             "name": "BatchSampler",
             "drop_last": False,
@@ -175,7 +175,7 @@ def evaluate(cfg: DictConfig):
 
     # set visualizer(optional)
     visu_mat = ppsci.utils.reader.load_mat_file(
-        "./VIV_Training_Neta100.mat",
+        cfg.VIV_DATA_PATH,
         ("t_f", "eta_gt", "f_gt"),
         alias_dict={"eta_gt": "eta", "f_gt": "f"},
     )
@@ -205,9 +205,9 @@ def evaluate(cfg: DictConfig):
         pretrained_model_path=cfg.EVAL.pretrained_model_path,
     )
 
-    # evaluate after finished training
+    # evaluate
     solver.eval()
-    # visualize prediction after finished training
+    # visualize prediction
     solver.visualize()