delete blank lines and modify forward_train

configs/rec/rec_mtb_nrtr.yml

@@ -46,7 +46,7 @@ Architecture:
     name: TransformerOptim
     d_model: 512
     num_encoder_layers: 6
-    beam_size: 10    # When Beam size is greater than 0, it means to use beam search when evaluation.
+    beam_size: 10 # When Beam size is greater than 0, it means to use beam search when evaluation.
 
 
 Loss:

ppocr/modeling/backbones/__init__.py

@@ -27,8 +27,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_fpn import ResNetFPN
         from .rec_mv1_enhance import MobileNetV1Enhance
         from .rec_nrtr_mtb import MTB
-        from .rec_swin import SwinTransformer
-        support_dict = ['MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', 'SwinTransformer']
+        support_dict = [
+            'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB'
+        ]
     elif model_type == "e2e":
         from .e2e_resnet_vd_pg import ResNet
         support_dict = ["ResNet"]

ppocr/modeling/backbones/rec_nrtr_mtb.py

@@ -1,5 +1,20 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from paddle import nn
 
+
 class MTB(nn.Layer):
     def __init__(self, cnn_num, in_channels):
         super(MTB, self).__init__()
@@ -8,17 +23,20 @@ def __init__(self, cnn_num, in_channels):
         self.cnn_num = cnn_num
         if self.cnn_num == 2:
             for i in range(self.cnn_num):
-                self.block.add_sublayer('conv_{}'.format(i), nn.Conv2D(
-                    in_channels = in_channels if i == 0 else 32*(2**(i-1)), 
-                    out_channels = 32*(2**i), 
-                    kernel_size = 3, 
-                    stride = 2, 
-                    padding=1))
+                self.block.add_sublayer(
+                    'conv_{}'.format(i),
+                    nn.Conv2D(
+                        in_channels=in_channels
+                        if i == 0 else 32 * (2**(i - 1)),
+                        out_channels=32 * (2**i),
+                        kernel_size=3,
+                        stride=2,
+                        padding=1))
                 self.block.add_sublayer('relu_{}'.format(i), nn.ReLU())
-                self.block.add_sublayer('bn_{}'.format(i), nn.BatchNorm2D(32*(2**i)))
+                self.block.add_sublayer('bn_{}'.format(i),
+                                        nn.BatchNorm2D(32 * (2**i)))
 
     def forward(self, images):
-
         x = self.block(images)
         if self.cnn_num == 2:
             # (b, w, h, c)

ppocr/modeling/heads/__init__.py

@@ -27,14 +27,13 @@ def build_head(config):
     from .rec_att_head import AttentionHead
     from .rec_srn_head import SRNHead
     from .rec_nrtr_optim_head import TransformerOptim
-   
+
     # cls head
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-
-        'SRNHead', 'PGHead', 'TransformerOptim', 'TableAttentionHead']
-
+        'SRNHead', 'PGHead', 'TransformerOptim', 'TableAttentionHead'
+    ]
 
     #table head
     from .table_att_head import TableAttentionHead

ppocr/modeling/heads/multiheadAttention.py

@@ -1,3 +1,17 @@
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle
 from paddle import nn
 import paddle.nn.functional as F
@@ -11,7 +25,7 @@
 
 
 class MultiheadAttentionOptim(nn.Layer):
-    r"""Allows the model to jointly attend to information
+    """Allows the model to jointly attend to information
     from different representation subspaces.
     See reference: Attention Is All You Need
 
@@ -23,37 +37,43 @@ class MultiheadAttentionOptim(nn.Layer):
         embed_dim: total dimension of the model
         num_heads: parallel attention layers, or heads
 
-    Examples::
-
-        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
-        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
     """
 
-    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False):
+    def __init__(self,
+                 embed_dim,
+                 num_heads,
+                 dropout=0.,
+                 bias=True,
+                 add_bias_kv=False,
+                 add_zero_attn=False):
         super(MultiheadAttentionOptim, self).__init__()
         self.embed_dim = embed_dim
         self.num_heads = num_heads
         self.dropout = dropout
         self.head_dim = embed_dim // num_heads
         assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        self.scaling = self.head_dim ** -0.5
-
+        self.scaling = self.head_dim**-0.5
         self.out_proj = Linear(embed_dim, embed_dim, bias_attr=bias)
-
         self._reset_parameters()
-
-        self.conv1 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
-        self.conv2 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
-        self.conv3 = paddle.nn.Conv2D(in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv1 = paddle.nn.Conv2D(
+            in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv2 = paddle.nn.Conv2D(
+            in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
+        self.conv3 = paddle.nn.Conv2D(
+            in_channels=embed_dim, out_channels=embed_dim, kernel_size=(1, 1))
 
     def _reset_parameters(self):
-
-
         xavier_uniform_(self.out_proj.weight)
 
-
-    def forward(self, query, key, value, key_padding_mask=None, incremental_state=None,
-                need_weights=True, static_kv=False, attn_mask=None):
+    def forward(self,
+                query,
+                key,
+                value,
+                key_padding_mask=None,
+                incremental_state=None,
+                need_weights=True,
+                static_kv=False,
+                attn_mask=None):
         """
         Inputs of forward function
             query: [target length, batch size, embed dim]
@@ -68,8 +88,6 @@ def forward(self, query, key, value, key_padding_mask=None, incremental_state=No
             attn_output: [target length, batch size, embed dim]
             attn_output_weights: [batch size, target length, sequence length]
         """
-
-
         tgt_len, bsz, embed_dim = query.shape
         assert embed_dim == self.embed_dim
         assert list(query.shape) == [tgt_len, bsz, embed_dim]
@@ -80,56 +98,61 @@ def forward(self, query, key, value, key_padding_mask=None, incremental_state=No
         v = self._in_proj_v(value)
         q *= self.scaling
 
-
-        q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-        k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-        v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose([1, 0, 2])
-
+        q = q.reshape([tgt_len, bsz * self.num_heads, self.head_dim]).transpose(
+            [1, 0, 2])
+        k = k.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
+            [1, 0, 2])
+        v = v.reshape([-1, bsz * self.num_heads, self.head_dim]).transpose(
+            [1, 0, 2])
 
         src_len = k.shape[1]
 
         if key_padding_mask is not None:
             assert key_padding_mask.shape[0] == bsz
             assert key_padding_mask.shape[1] == src_len
 
-
-        attn_output_weights = paddle.bmm(q, k.transpose([0,2,1]))
-        assert list(attn_output_weights.shape) == [bsz * self.num_heads, tgt_len, src_len]
+        attn_output_weights = paddle.bmm(q, k.transpose([0, 2, 1]))
+        assert list(attn_output_weights.
+                    shape) == [bsz * self.num_heads, tgt_len, src_len]
 
         if attn_mask is not None:
             attn_mask = attn_mask.unsqueeze(0)
             attn_output_weights += attn_mask
         if key_padding_mask is not None:
-            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
+            attn_output_weights = attn_output_weights.reshape(
+                [bsz, self.num_heads, tgt_len, src_len])
             key = key_padding_mask.unsqueeze(1).unsqueeze(2).astype('float32')
-
             y = paddle.full(shape=key.shape, dtype='float32', fill_value='-inf')
-
-            y = paddle.where(key==0.,key, y)
-
+            y = paddle.where(key == 0., key, y)
             attn_output_weights += y
-            attn_output_weights = attn_output_weights.reshape([bsz*self.num_heads, tgt_len, src_len])
+            attn_output_weights = attn_output_weights.reshape(
+                [bsz * self.num_heads, tgt_len, src_len])
 
         attn_output_weights = F.softmax(
-            attn_output_weights.astype('float32'), axis=-1,
-            dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16 else attn_output_weights.dtype)
-        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=self.training)
+            attn_output_weights.astype('float32'),
+            axis=-1,
+            dtype=paddle.float32 if attn_output_weights.dtype == paddle.float16
+            else attn_output_weights.dtype)
+        attn_output_weights = F.dropout(
+            attn_output_weights, p=self.dropout, training=self.training)
 
         attn_output = paddle.bmm(attn_output_weights, v)
-        assert list(attn_output.shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
-        attn_output = attn_output.transpose([1, 0,2]).reshape([tgt_len, bsz, embed_dim])
+        assert list(attn_output.
+                    shape) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn_output = attn_output.transpose([1, 0, 2]).reshape(
+            [tgt_len, bsz, embed_dim])
         attn_output = self.out_proj(attn_output)
 
         if need_weights:
             # average attention weights over heads
-            attn_output_weights = attn_output_weights.reshape([bsz, self.num_heads, tgt_len, src_len])
-            attn_output_weights = attn_output_weights.sum(axis=1) / self.num_heads
+            attn_output_weights = attn_output_weights.reshape(
+                [bsz, self.num_heads, tgt_len, src_len])
+            attn_output_weights = attn_output_weights.sum(
+                axis=1) / self.num_heads
         else:
             attn_output_weights = None
-
         return attn_output, attn_output_weights
 
-
     def _in_proj_q(self, query):
         query = query.transpose([1, 2, 0])
         query = paddle.unsqueeze(query, axis=2)
@@ -139,7 +162,6 @@ def _in_proj_q(self, query):
         return res
 
     def _in_proj_k(self, key):
-
         key = key.transpose([1, 2, 0])
         key = paddle.unsqueeze(key, axis=2)
         res = self.conv2(key)
@@ -148,8 +170,7 @@ def _in_proj_k(self, key):
         return res
 
     def _in_proj_v(self, value):
-
-        value = value.transpose([1,2,0])#(1, 2, 0)
+        value = value.transpose([1, 2, 0])  #(1, 2, 0)
         value = paddle.unsqueeze(value, axis=2)
         res = self.conv3(value)
         res = paddle.squeeze(res, axis=2)