[Fix] Fix batch indexing failed in phylstm2 (#941)

* fix batch indexing failed in list:

* fix chapter number of adv_cvit.md(test=document_fix)

Author: HydrogenSulfate

docs/zh/examples/adv_cvit.md

@@ -145,7 +145,7 @@ examples/adv/adv_cvit.py:117:125
 --8<--
 ```
 
-### 3.7 模型训练、评估
+### 3.6 模型训练、评估
 
 完成上述设置之后，只需要将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`，然后启动训练、评估。
 

docs/zh/examples/phylstm.md

@@ -106,19 +106,19 @@ examples/phylstm/phylstm2.py:37:100
 
 设置训练数据集和损失计算函数，返回字段，代码如下所示：
 
-``` py linenums="119"
+``` py linenums="120"
 --8<--
-examples/phylstm/phylstm2.py:119:145
+examples/phylstm/phylstm2.py:120:146
 --8<--
 ```
 
 ### 3.4 评估器构建
 
 设置评估数据集和损失计算函数，返回字段，代码如下所示：
 
-``` py linenums="147"
+``` py linenums="148"
 --8<--
-examples/phylstm/phylstm2.py:147:174
+examples/phylstm/phylstm2.py:148:170
 --8<--
 ```
 
@@ -136,27 +136,27 @@ examples/phylstm/conf/phylstm2.yaml:39:39
 
 训练过程会调用优化器来更新模型参数，此处选择 `Adam` 优化器并设定 `learning_rate` 为 1e-3。
 
-``` py linenums="177"
+``` py linenums="172"
 --8<--
-examples/phylstm/phylstm2.py:177:177
+examples/phylstm/phylstm2.py:172:173
 --8<--
 ```
 
 ### 3.7 模型训练与评估
 
 完成上述设置之后，只需要将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`。
 
-``` py linenums="178"
+``` py linenums="174"
 --8<--
-examples/phylstm/phylstm2.py:178:192
+examples/phylstm/phylstm2.py:174:180
 --8<--
 ```
 
 最后启动训练、评估即可：
 
-``` py linenums="194"
+``` py linenums="182"
 --8<--
-examples/phylstm/phylstm2.py:194:197
+examples/phylstm/phylstm2.py:182:185
 --8<--
 ```
 

examples/phylstm/functions.py

@@ -181,4 +181,19 @@ def get(self, epochs=1):
                     np.asarray(0.0, dtype=paddle.get_default_dtype())
                 )
 
+        def to_numpy_dict(dct):
+            return {k: np.asarray(v, dtype="float32") for k, v in dct.items()}
+
+        input_dict_train = to_numpy_dict(input_dict_train)
+        for k, v in input_dict_train.items():
+            print(f"input_dict_train {k} {type(v)}")
+        label_dict_train = to_numpy_dict(label_dict_train)
+        for k, v in label_dict_train.items():
+            print(f"label_dict_train {k} {type(v)}")
+        input_dict_val = to_numpy_dict(input_dict_val)
+        for k, v in input_dict_val.items():
+            print(f"input_dict_val {k} {type(v)}")
+        label_dict_val = to_numpy_dict(label_dict_val)
+        for k, v in label_dict_val.items():
+            print(f"label_dict_val {k} {type(v)}")
         return input_dict_train, label_dict_train, input_dict_val, label_dict_val

examples/phylstm/phylstm2.py

@@ -109,6 +109,7 @@ def train(cfg: DictConfig):
     model.register_output_transform(functions.transform_out)
 
     dataset_obj = functions.Dataset(eta, eta_t, g, ag, ag_c, lift, phi_t)
+
     (
         input_dict_train,
         label_dict_train,
@@ -151,11 +152,6 @@ def train(cfg: DictConfig):
                 "input": input_dict_val,
                 "label": label_dict_val,
             },
-            "sampler": {
-                "name": "BatchSampler",
-                "drop_last": False,
-                "shuffle": False,
-            },
             "batch_size": 1,
             "num_workers": 0,
         },
@@ -178,17 +174,9 @@ def train(cfg: DictConfig):
     solver = ppsci.solver.Solver(
         model,
         constraint_pde,
-        cfg.output_dir,
-        optimizer,
-        None,
-        cfg.TRAIN.epochs,
-        cfg.TRAIN.iters_per_epoch,
-        save_freq=cfg.TRAIN.save_freq,
-        log_freq=cfg.log_freq,
-        seed=cfg.seed,
+        optimizer=optimizer,
         validator=validator_pde,
-        checkpoint_path=cfg.TRAIN.checkpoint_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
 
     # train model
@@ -278,6 +266,7 @@ def evaluate(cfg: DictConfig):
     model.register_output_transform(functions.transform_out)
 
     dataset_obj = functions.Dataset(eta, eta_t, g, ag, ag_c, lift, phi_t)
+
     (
         _,
         _,
@@ -292,11 +281,6 @@ def evaluate(cfg: DictConfig):
                 "input": input_dict_val,
                 "label": label_dict_val,
             },
-            "sampler": {
-                "name": "BatchSampler",
-                "drop_last": False,
-                "shuffle": False,
-            },
             "batch_size": 1,
             "num_workers": 0,
         },
@@ -317,11 +301,8 @@ def evaluate(cfg: DictConfig):
     # initialize solver
     solver = ppsci.solver.Solver(
         model,
-        output_dir=cfg.output_dir,
-        seed=cfg.seed,
         validator=validator_pde,
-        pretrained_model_path=cfg.EVAL.pretrained_model_path,
-        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+        cfg=cfg,
     )
     # evaluate
     solver.eval()