fix batchnorm ,test=develop (#27036)

python/paddle/nn/functional/norm.py

@@ -176,14 +176,13 @@ def batch_norm(x,
     mean_out = running_mean
     variance_out = running_var
 
-    true_data_format = ['NC', 'NCL', 'NCHW', 'NCWH', 'NCDHW']
+    true_data_format = ['NC', 'NCL', 'NCHW', 'NCDHW']
     if data_format not in true_data_format:
         raise ValueError(
-            "data_format must be one of 'NC', 'NCL', 'NCHW', 'NCWH', 'NCDHW', but receive {}".
+            "data_format must be one of 'NC', 'NCL', 'NCHW', 'NCDHW', but receive {}".
             format(data_format))
 
-    if data_format != 'NCWH':
-        data_format = 'NCHW'
+    data_format = 'NCHW'
 
     if in_dygraph_mode():
         # for dygraph need tuple

python/paddle/nn/layer/norm.py

@@ -811,7 +811,7 @@ class BatchNorm2d(_BatchNormBase):
             If it is set to None or one attribute of ParamAttr, batch_norm
             will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
             If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
-        data_format(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC". Default: NCHW.
+        data_format(str, optional): Specify the input data format, the data format can be "NCHW". Default: NCHW.
         track_running_stats(bool, optional): Whether to use global mean and variance. In train period, 
             True will track global mean and variance used for inference. When inference, track_running_stats must be 
             True. Default: True.
@@ -844,10 +844,10 @@ class BatchNorm2d(_BatchNormBase):
     """
 
     def _check_data_format(self, input):
-        if input == 'NCHW' or input == 'NCWH':
+        if input == 'NCHW':
             self._data_format = input
         else:
-            raise ValueError('expected NCHW or NCWH for data_format input')
+            raise ValueError('expected NCHW for data_format input')
 
     def _check_input_dim(self, input):
         if len(input.shape) != 4: