change repeat to repeat_interleave

mindocr/models/backbones/mindcv_models/repmlp.py

@@ -228,8 +228,9 @@ def local_inject(self):
         self.fc3.bias.data = fc3_bias
 
     def _convert_conv_to_fc(self, conv_kernel, conv_bias):
-        I = ops.eye(self.h * self.w).repeat(1, self.S).reshape(self.h * self.w, self.S, self.h, self.w)  # noqa: E741
-        fc_k = ops.Conv2D(I, conv_kernel, pad=(conv_kernel.size(2) // 2, conv_kernel.size(3) // 2), group=self.S)
+        # noqa: E741
+        x = ops.eye(self.h * self.w).repeat_interleave(1, self.S).reshape(self.h * self.w, self.S, self.h, self.w)
+        fc_k = ops.Conv2D(x, conv_kernel, pad=(conv_kernel.size(2) // 2, conv_kernel.size(3) // 2), group=self.S)
         fc_k = fc_k.reshape(self.h * self.w, self.S * self.h * self.w).t()
         fc_bias = conv_bias.repeat_interleave(self.h * self.w)
         return fc_k, fc_bias

mindocr/models/heads/kie_relationextraction_head.py

@@ -49,12 +49,12 @@ def __init__(self, hidden_size=768, hidden_dropout_prob=0.1, use_float16: bool =
     def construct(self, hidden_states, question, question_label, answer, answer_label):
         __, _, hidden_size = hidden_states.shape
         q_label_repr = self.entity_emb(question_label)
-        question = question.expand_dims(-1).repeat(hidden_size, -1)
+        question = question.expand_dims(-1).repeat_interleave(hidden_size, -1)
         tmp_hidden_states = ops.gather_d(hidden_states, 1, question)
         q_repr = ops.concat((tmp_hidden_states, q_label_repr), axis=-1)
 
         a_label_repr = self.entity_emb(answer_label)
-        answer = answer.expand_dims(-1).repeat(hidden_size, -1)
+        answer = answer.expand_dims(-1).repeat_interleave(hidden_size, -1)
         tmp_hidden_states = ops.gather_d(hidden_states, 1, answer)
         a_repr = ops.concat((tmp_hidden_states, a_label_repr), axis=-1)
 

mindocr/models/heads/rec_robustscanner_head.py

@@ -83,7 +83,7 @@ def construct(self, query, key, value, h, w, valid_width_masks=None):
                 logits_i = logits[i].squeeze(0)  # (c, h, w)
                 logits_i = logits_i.view((-1, w))  # (c*h, w)
                 ch = c * h
-                valid_width_mask = valid_width_mask.repeat(ch, axis=0)  # (c*h, w)
+                valid_width_mask = valid_width_mask.repeat_interleave(ch, 0)  # (c*h, w)
                 valid_width_mask = ops.cast(valid_width_mask, ms.bool_)
                 logits_i = ops.select(valid_width_mask, logits_i, float('-inf'))  # (c*h, w)
                 logits_list.append(logits_i.view((c, h, w)))  # (c, h, w)

mindocr/models/heads/rec_sar_head.py

@@ -232,7 +232,7 @@ def _2d_attention(self,
                 attn_weight_i = attn_weight_i.transpose((0, 1, 3, 2))  # (T, h, c, w)
                 attn_weight_i = attn_weight_i.view((-1, w))  # (T*h*c, w)
                 Tch = T * h * c
-                valid_width_mask = valid_width_mask.repeat(Tch, axis=0)  # (T*h*c, w)
+                valid_width_mask = valid_width_mask.repeat_interleave(Tch, 0)  # (T*h*c, w)
                 valid_width_mask = ops.cast(valid_width_mask, ms.bool_)
                 attn_weight_i = ops.select(
                     valid_width_mask, attn_weight_i.astype(ms.float32), float('-inf'))  # (T*h*c, w)

mindocr/models/heads/rec_visionlan_head.py

@@ -162,7 +162,7 @@ def __init__(self, n_dim: int = 512, N_max_character: int = 25, n_position: int
     def construct(self, enc_output):
         # enc_output: b,256,512
         reading_order = Tensor(np.arange(self.character_len), dtype=ms.int64)
-        reading_order = mnp.repeat(reading_order[None, ...], enc_output.shape[0], 0)    # (S,) -> (B, S)
+        reading_order = ops.repeat_interleave(reading_order[None, ...], enc_output.shape[0], 0)    # (S,) -> (B, S)
         reading_order = self.f0_embedding(reading_order)      # b,max_len,512
         # calculate attention
         t = self.w0(reading_order.transpose((0, 2, 1)))     # b,512,256