add refer to backbone and head of sar

ppocr/modeling/backbones/rec_resnet_31.py

+# 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.
+"""
+This code is refer from: 
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/layers/conv_layer.py
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/backbones/resnet31_ocr.py
+"""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -18,12 +37,12 @@ def conv3x3(in_channel, out_channel, stride=1):
         kernel_size=3,
         stride=stride,
         padding=1,
-        bias_attr=False
-    )
+        bias_attr=False)
 
 
 class BasicBlock(nn.Layer):
     expansion = 1
+
     def __init__(self, in_channels, channels, stride=1, downsample=False):
         super().__init__()
         self.conv1 = conv3x3(in_channels, channels, stride)
@@ -34,9 +53,13 @@ class BasicBlock(nn.Layer):
         self.downsample = downsample
         if downsample:
             self.downsample = nn.Sequential(
-                nn.Conv2D(in_channels, channels * self.expansion, 1, stride, bias_attr=False),
-                nn.BatchNorm2D(channels * self.expansion),
-            )
+                nn.Conv2D(
+                    in_channels,
+                    channels * self.expansion,
+                    1,
+                    stride,
+                    bias_attr=False),
+                nn.BatchNorm2D(channels * self.expansion), )
         else:
             self.downsample = nn.Sequential()
         self.stride = stride
@@ -69,6 +92,7 @@ class ResNet31(nn.Layer):
         out_indices (None | Sequence[int]): Indices of output stages.
         last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
     '''
+
     def __init__(self,
                  in_channels=3,
                  layers=[1, 2, 5, 3],
@@ -83,41 +107,51 @@ class ResNet31(nn.Layer):
         self.last_stage_pool = last_stage_pool
 
         # conv 1 (Conv Conv)
-        self.conv1_1 = nn.Conv2D(in_channels, channels[0], kernel_size=3, stride=1, padding=1)
+        self.conv1_1 = nn.Conv2D(
+            in_channels, channels[0], kernel_size=3, stride=1, padding=1)
         self.bn1_1 = nn.BatchNorm2D(channels[0])
         self.relu1_1 = nn.ReLU()
 
-        self.conv1_2 = nn.Conv2D(channels[0], channels[1], kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2D(
+            channels[0], channels[1], kernel_size=3, stride=1, padding=1)
         self.bn1_2 = nn.BatchNorm2D(channels[1])
         self.relu1_2 = nn.ReLU()
 
         # conv 2 (Max-pooling, Residual block, Conv)
-        self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self.pool2 = nn.MaxPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
         self.block2 = self._make_layer(channels[1], channels[2], layers[0])
-        self.conv2 = nn.Conv2D(channels[2], channels[2], kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2D(
+            channels[2], channels[2], kernel_size=3, stride=1, padding=1)
         self.bn2 = nn.BatchNorm2D(channels[2])
         self.relu2 = nn.ReLU()
 
         # conv 3 (Max-pooling, Residual block, Conv)
-        self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self.pool3 = nn.MaxPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
         self.block3 = self._make_layer(channels[2], channels[3], layers[1])
-        self.conv3 = nn.Conv2D(channels[3], channels[3], kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2D(
+            channels[3], channels[3], kernel_size=3, stride=1, padding=1)
         self.bn3 = nn.BatchNorm2D(channels[3])
         self.relu3 = nn.ReLU()
 
         # conv 4 (Max-pooling, Residual block, Conv)
-        self.pool4 = nn.MaxPool2D(kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True)
+        self.pool4 = nn.MaxPool2D(
+            kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True)
         self.block4 = self._make_layer(channels[3], channels[4], layers[2])
-        self.conv4 = nn.Conv2D(channels[4], channels[4], kernel_size=3, stride=1, padding=1)
+        self.conv4 = nn.Conv2D(
+            channels[4], channels[4], kernel_size=3, stride=1, padding=1)
         self.bn4 = nn.BatchNorm2D(channels[4])
         self.relu4 = nn.ReLU()
 
         # conv 5 ((Max-pooling), Residual block, Conv)
         self.pool5 = None
         if self.last_stage_pool:
-            self.pool5 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
+            self.pool5 = nn.MaxPool2D(
+                kernel_size=2, stride=2, padding=0, ceil_mode=True)
         self.block5 = self._make_layer(channels[4], channels[5], layers[3])
-        self.conv5 = nn.Conv2D(channels[5], channels[5], kernel_size=3, stride=1, padding=1)
+        self.conv5 = nn.Conv2D(
+            channels[5], channels[5], kernel_size=3, stride=1, padding=1)
         self.bn5 = nn.BatchNorm2D(channels[5])
         self.relu5 = nn.ReLU()
 
@@ -135,14 +169,14 @@ class ResNet31(nn.Layer):
                         kernel_size=1,
                         stride=1,
                         bias_attr=False),
-                    nn.BatchNorm2D(output_channels),
-                )
+                    nn.BatchNorm2D(output_channels), )
 
-            layers.append(BasicBlock(input_channels, output_channels, downsample=downsample))
+            layers.append(
+                BasicBlock(
+                    input_channels, output_channels, downsample=downsample))
             input_channels = output_channels
         return nn.Sequential(*layers)
 
-
     def forward(self, x):
         x = self.conv1_1(x)
         x = self.bn1_1(x)
@@ -166,7 +200,7 @@ class ResNet31(nn.Layer):
             x = block_layer(x)
             x = conv_layer(x)
             x = bn_layer(x)
-            x= relu_layer(x)
+            x = relu_layer(x)
 
             outs.append(x)
 

ppocr/modeling/heads/rec_sar_head.py

+# 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.
+"""
+This code is refer from: 
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/encoders/sar_encoder.py
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/decoders/sar_decoder.py
+"""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function