first commit

ppocr/modeling/backbones/rec_resnet_45.py

@@ -0,0 +1,150 @@
+# 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/FangShancheng/ABINet/tree/main/modules
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import ParamAttr
+from paddle.nn.initializer import KaimingNormal
+import paddle.nn as nn
+import paddle.nn.functional as F
+import numpy as np
+import math
+
+__all__ = ["ResNet45"]
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return nn.Conv2D(
+        in_planes,
+        out_planes,
+        kernel_size=1,
+        stride=1,
+        weight_attr=ParamAttr(initializer=KaimingNormal()),
+        bias_attr=False,
+    )
+
+
+def conv3x3(in_channel, out_channel, stride=1):
+    return nn.Conv2D(
+        in_channel,
+        out_channel,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        weight_attr=ParamAttr(initializer=KaimingNormal()),
+        bias_attr=False,
+    )
+
+
+class BasicBlock(nn.Layer):
+    expansion = 1
+
+    def __init__(self, in_channels, channels, stride=1, downsample=None):
+        super().__init__()
+        self.conv1 = conv1x1(in_channels, channels)
+        self.bn1 = nn.BatchNorm2D(channels)
+        self.relu = nn.ReLU()
+        self.conv2 = conv3x3(channels, channels, stride)
+        self.bn2 = nn.BatchNorm2D(channels)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet45(nn.Layer):
+    def __init__(
+        self,
+        in_channels=3,
+        block=BasicBlock,
+        layers=[3, 4, 6, 6, 3],
+        strides=[2, 1, 2, 1, 1],
+    ):
+        self.inplanes = 32
+        super(ResNet45, self).__init__()
+        self.conv1 = nn.Conv2D(
+            in_channels,
+            32,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            weight_attr=ParamAttr(initializer=KaimingNormal()),
+            bias_attr=False,
+        )
+        self.bn1 = nn.BatchNorm2D(32)
+        self.relu = nn.ReLU()
+
+        self.layer1 = self._make_layer(block, 32, layers[0], stride=strides[0])
+        self.layer2 = self._make_layer(block, 64, layers[1], stride=strides[1])
+        self.layer3 = self._make_layer(block, 128, layers[2], stride=strides[2])
+        self.layer4 = self._make_layer(block, 256, layers[3], stride=strides[3])
+        self.layer5 = self._make_layer(block, 512, layers[4], stride=strides[4])
+        self.out_channels = 512
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            # downsample = True
+            downsample = nn.Sequential(
+                nn.Conv2D(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    weight_attr=ParamAttr(initializer=KaimingNormal()),
+                    bias_attr=False,
+                ),
+                nn.BatchNorm2D(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.layer5(x)
+        return x