Use ReverseV2 instead of ReverseSequence in CRNN for higher throughput

official/cv/crnn/src/crnn.py

@@ -231,10 +231,8 @@ class CRNNV1(nn.Cell):
         self.transpose = P.Transpose()
         self.squeeze = P.Squeeze(axis=0)
         self.vgg = VGG()
-        self.reverse_seq1 = P.ReverseSequence(batch_dim=1, seq_dim=0)
-        self.reverse_seq2 = P.ReverseSequence(batch_dim=1, seq_dim=0)
-        self.reverse_seq3 = P.ReverseSequence(batch_dim=1, seq_dim=0)
-        self.reverse_seq4 = P.ReverseSequence(batch_dim=1, seq_dim=0)
+        self.reverse_seq1 = P.ReverseV2(axis=[1])
+        self.reverse_seq2 = P.ReverseV2(axis=[1])
         self.seq_length = Tensor(np.ones((self.batch_size), np.int32) * config.num_step, mstype.int32)
         self.concat1 = P.Concat(axis=2)
         self.rnn_dropout = nn.Dropout(0.9)
@@ -245,10 +243,10 @@ class CRNNV1(nn.Cell):
 
         x = self.reshape(x, (self.batch_size, self.input_size, -1))
         x = self.transpose(x, (2, 0, 1))
-        bw_x = self.reverse_seq1(x, self.seq_length)
+        bw_x = self.reverse_seq1(x)
         y1, _, _, _, _, _, _, _ = self.rnn1(x, self.w1, self.b1, None, self.h1, self.c1)
         y1_bw, _, _, _, _, _, _, _ = self.rnn1_bw(bw_x, self.w1_bw, self.b1_bw, None, self.h1_bw, self.c1_bw)
-        y1_bw = self.reverse_seq2(y1_bw, self.seq_length)
+        y1_bw = self.reverse_seq2(y1_bw)
         y1_out = self.concat1((y1, y1_bw))
         if self.use_dropout:
             y1_out = self.rnn_dropout(y1_out)