!8574 recitify warpctc script

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

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

@@ -61,12 +61,18 @@ class _CaptchaDataset:
         return image, label
 
 
+def transpose_hwc2whc(image):
+    """transpose image from HWC to WHC"""
+    image = np.transpose(image, (1, 0, 2))
+    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
 
      Args:
-        dataset_path(int): dataset path
+        dataset_path(str): dataset path
         batch_size(int): batch size of generated dataset, default is 1
         num_shards(int): number of devices
         shard_id(int): rank id
@@ -79,12 +85,13 @@ def create_dataset(dataset_path, batch_size=1, num_shards=1, shard_id=0, device_
         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()
+        c.TypeCast(mstype.float16)
     ]
     label_trans = [
         c.TypeCast(mstype.int32)
     ]
     ds = ds.map(operations=image_trans, input_columns=["image"], num_parallel_workers=8)
+    ds = ds.map(operations=transpose_hwc2whc, input_columns=["image"], num_parallel_workers=8)
     ds = ds.map(operations=label_trans, input_columns=["label"], num_parallel_workers=8)
 
     ds = ds.batch(batch_size, drop_remainder=True)

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

@@ -50,7 +50,7 @@ class WarpCTCAccuracy(nn.Metric):
 
     def eval(self):
         if self._total_num == 0:
-            raise RuntimeError('Accuary can not be calculated, because the number of samples is 0.')
+            raise RuntimeError('Accuracy can not be calculated, because the number of samples is 0.')
         return self._correct_num / self._total_num
 
     def _is_eq(self, pred_lbl, target):

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

@@ -33,13 +33,13 @@ class StackedRNN(nn.Cell):
         hidden_size(int): the hidden size in LSTM layers, default is 512
      """
 
-    def __init__(self, input_size, batch_size=64, hidden_size=512):
+    def __init__(self, input_size, batch_size=64, hidden_size=512, num_class=11):
         super(StackedRNN, self).__init__()
-        self.batch_size = batch_size
         self.input_size = input_size
-        self.num_classes = 11
-        self.reshape = P.Reshape()
-        self.cast = P.Cast()
+        self.batch_size = batch_size
+        self.hidden_size = hidden_size
+        self.num_class = num_class
+
         k = (1 / hidden_size) ** 0.5
 
         self.rnn1 = P.DynamicRNN(forget_bias=0.0)
@@ -58,32 +58,23 @@ class StackedRNN(nn.Cell):
         self.c1 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16))
         self.c2 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16))
 
-        self.fc_weight = np.random.random((self.num_classes, hidden_size)).astype(np.float32)
-        self.fc_bias = np.random.random(self.num_classes).astype(np.float32)
-
-        self.fc = nn.Dense(in_channels=hidden_size, out_channels=self.num_classes, weight_init=Tensor(self.fc_weight),
-                           bias_init=Tensor(self.fc_bias))
+        self.fc_weight = Tensor(np.random.random((hidden_size, num_class)).astype(np.float16))
+        self.fc_bias = Tensor(np.random.random(self.num_class).astype(np.float16))
 
-        self.fc.to_float(mstype.float32)
-        self.expand_dims = P.ExpandDims()
-        self.concat = P.Concat()
+        self.cast = P.Cast()
+        self.reshape = P.Reshape()
         self.transpose = P.Transpose()
-        self.squeeze = P.Squeeze(axis=0)
+        self.matmul = nn.MatMul()
 
     def construct(self, x):
-        x = self.cast(x, mstype.float16)
-        x = self.transpose(x, (3, 0, 2, 1))
+        x = self.transpose(x, (1, 0, 2, 3))
         x = self.reshape(x, (-1, self.batch_size, self.input_size))
 
         y1, _, _, _, _, _, _, _ = self.rnn1(x, self.w1, self.b1, None, self.h1, self.c1)
         y2, _, _, _, _, _, _, _ = self.rnn2(y1, self.w2, self.b2, None, self.h2, self.c2)
 
-        output = ()
-        for i in range(F.shape(x)[0]):
-            y2_after_fc = self.fc(self.squeeze(y2[i:i + 1:1]))
-            y2_after_fc = self.expand_dims(y2_after_fc, 0)
-            output += (y2_after_fc,)
-        output = self.concat(output)
+        # [time_step, bs, hidden_size] * [hidden_size, num_class] + [num_class]
+        output = self.matmul(y2, self.fc_weight) + self.fc_bias
         return output
 
 

model_zoo/official/cv/warpctc/train.py

@@ -51,14 +51,11 @@ if args_opt.platform == 'Ascend':
 if __name__ == '__main__':
     lr_scale = 1
     if args_opt.run_distribute:
+        init()
         if args_opt.platform == 'Ascend':
-            init()
-            lr_scale = 1
             device_num = int(os.environ.get("RANK_SIZE"))
             rank = int(os.environ.get("RANK_ID"))
         else:
-            init()
-            lr_scale = 1
             device_num = get_group_size()
             rank = get_rank()
         context.reset_auto_parallel_context()