!13689 opt warpctc performance

From: @gengdongjie
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @c_34

model_zoo/official/cv/warpctc/src/dataset.py

@@ -67,12 +67,6 @@ def transpose_hwc2whc(image):
     return image
 
 
-def transpose_hwc2chw(image):
-    """transpose image from HWC to CHW"""
-    image = np.transpose(image, (2, 0, 1))
-    return image
-
-
 def create_dataset(dataset_path, batch_size=1, num_shards=1, shard_id=0, device_target='Ascend'):
     """
      create train or evaluation dataset for warpctc
@@ -93,14 +87,20 @@ def create_dataset(dataset_path, batch_size=1, num_shards=1, shard_id=0, device_
         vc.Resize((m.ceil(cf.captcha_height / 16) * 16, cf.captcha_width)),
         c.TypeCast(mstype.float16)
     ]
+    image_trans_gpu = [
+        vc.Rescale(1.0 / 255.0, 0.0),
+        vc.Normalize([0.9010, 0.9049, 0.9025], std=[0.1521, 0.1347, 0.1458]),
+        vc.Resize((m.ceil(cf.captcha_height / 16) * 16, cf.captcha_width)),
+        vc.HWC2CHW()
+    ]
     label_trans = [
         c.TypeCast(mstype.int32)
     ]
-    data_set = data_set.map(operations=image_trans, input_columns=["image"], num_parallel_workers=8)
     if device_target == 'Ascend':
+        data_set = data_set.map(operations=image_trans, input_columns=["image"], num_parallel_workers=8)
         data_set = data_set.map(operations=transpose_hwc2whc, input_columns=["image"], num_parallel_workers=8)
     else:
-        data_set = data_set.map(operations=transpose_hwc2chw, input_columns=["image"], num_parallel_workers=8)
+        data_set = data_set.map(operations=image_trans_gpu, input_columns=["image"], num_parallel_workers=8)
     data_set = data_set.map(operations=label_trans, input_columns=["label"], num_parallel_workers=8)
 
     data_set = data_set.batch(batch_size, drop_remainder=True)

model_zoo/official/cv/warpctc/src/warpctc.py

@@ -123,7 +123,6 @@ class StackedRNNForGPU(nn.Cell):
         self.transpose = P.Transpose()
 
     def construct(self, x):
-        x = self.cast(x, mstype.float32)
         x = self.transpose(x, (3, 0, 2, 1))
         x = self.reshape(x, (-1, self.batch_size, self.input_size))
         output, _ = self.lstm(x, (self.h, self.c))