mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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349 lines
11 KiB
Python
349 lines
11 KiB
Python
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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/hikopensource/DAVAR-Lab-OCR/blob/main/davarocr/davar_rcg/models/backbones/ResNetRFL.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.nn as nn
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
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kaiming_init_ = KaimingNormal()
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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class BasicBlock(nn.Layer):
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"""Res-net Basic Block"""
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expansion = 1
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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downsample=None,
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norm_type='BN',
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**kwargs):
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"""
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Args:
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inplanes (int): input channel
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planes (int): channels of the middle feature
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stride (int): stride of the convolution
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downsample (int): type of the down_sample
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norm_type (str): type of the normalization
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**kwargs (None): backup parameter
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"""
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super(BasicBlock, self).__init__()
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self.conv1 = self._conv3x3(inplanes, planes)
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self.bn1 = nn.BatchNorm(planes)
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self.conv2 = self._conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm(planes)
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self.relu = nn.ReLU()
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self.downsample = downsample
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self.stride = stride
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def _conv3x3(self, in_planes, out_planes, stride=1):
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return nn.Conv2D(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias_attr=False)
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNetRFL(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels=512,
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use_cnt=True,
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use_seq=True):
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"""
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Args:
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in_channels (int): input channel
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out_channels (int): output channel
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"""
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super(ResNetRFL, self).__init__()
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assert use_cnt or use_seq
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self.use_cnt, self.use_seq = use_cnt, use_seq
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self.backbone = RFLBase(in_channels)
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self.out_channels = out_channels
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self.out_channels_block = [
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int(self.out_channels / 4), int(self.out_channels / 2),
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self.out_channels, self.out_channels
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]
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block = BasicBlock
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layers = [1, 2, 5, 3]
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self.inplanes = int(self.out_channels // 2)
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self.relu = nn.ReLU()
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if self.use_seq:
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self.maxpool3 = nn.MaxPool2D(
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kernel_size=2, stride=(2, 1), padding=(0, 1))
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self.layer3 = self._make_layer(
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block, self.out_channels_block[2], layers[2], stride=1)
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self.conv3 = nn.Conv2D(
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self.out_channels_block[2],
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self.out_channels_block[2],
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.bn3 = nn.BatchNorm(self.out_channels_block[2])
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self.layer4 = self._make_layer(
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block, self.out_channels_block[3], layers[3], stride=1)
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self.conv4_1 = nn.Conv2D(
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self.out_channels_block[3],
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self.out_channels_block[3],
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kernel_size=2,
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stride=(2, 1),
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padding=(0, 1),
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bias_attr=False)
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self.bn4_1 = nn.BatchNorm(self.out_channels_block[3])
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self.conv4_2 = nn.Conv2D(
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self.out_channels_block[3],
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self.out_channels_block[3],
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kernel_size=2,
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stride=1,
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padding=0,
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bias_attr=False)
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self.bn4_2 = nn.BatchNorm(self.out_channels_block[3])
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if self.use_cnt:
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self.inplanes = int(self.out_channels // 2)
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self.v_maxpool3 = nn.MaxPool2D(
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kernel_size=2, stride=(2, 1), padding=(0, 1))
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self.v_layer3 = self._make_layer(
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block, self.out_channels_block[2], layers[2], stride=1)
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self.v_conv3 = nn.Conv2D(
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self.out_channels_block[2],
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self.out_channels_block[2],
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.v_bn3 = nn.BatchNorm(self.out_channels_block[2])
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self.v_layer4 = self._make_layer(
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block, self.out_channels_block[3], layers[3], stride=1)
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self.v_conv4_1 = nn.Conv2D(
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self.out_channels_block[3],
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self.out_channels_block[3],
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kernel_size=2,
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stride=(2, 1),
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padding=(0, 1),
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bias_attr=False)
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self.v_bn4_1 = nn.BatchNorm(self.out_channels_block[3])
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self.v_conv4_2 = nn.Conv2D(
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self.out_channels_block[3],
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self.out_channels_block[3],
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kernel_size=2,
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stride=1,
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padding=0,
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bias_attr=False)
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self.v_bn4_2 = nn.BatchNorm(self.out_channels_block[3])
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2D(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias_attr=False),
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nn.BatchNorm(planes * block.expansion), )
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layers = list()
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, inputs):
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x_1 = self.backbone(inputs)
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if self.use_cnt:
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v_x = self.v_maxpool3(x_1)
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v_x = self.v_layer3(v_x)
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v_x = self.v_conv3(v_x)
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v_x = self.v_bn3(v_x)
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visual_feature_2 = self.relu(v_x)
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v_x = self.v_layer4(visual_feature_2)
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v_x = self.v_conv4_1(v_x)
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v_x = self.v_bn4_1(v_x)
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v_x = self.relu(v_x)
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v_x = self.v_conv4_2(v_x)
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v_x = self.v_bn4_2(v_x)
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visual_feature_3 = self.relu(v_x)
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else:
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visual_feature_3 = None
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if self.use_seq:
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x = self.maxpool3(x_1)
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x = self.layer3(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x_2 = self.relu(x)
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x = self.layer4(x_2)
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x = self.conv4_1(x)
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x = self.bn4_1(x)
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x = self.relu(x)
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x = self.conv4_2(x)
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x = self.bn4_2(x)
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x_3 = self.relu(x)
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else:
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x_3 = None
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return [visual_feature_3, x_3]
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class ResNetBase(nn.Layer):
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def __init__(self, in_channels, out_channels, block, layers):
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super(ResNetBase, self).__init__()
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self.out_channels_block = [
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int(out_channels / 4), int(out_channels / 2), out_channels,
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out_channels
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]
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self.inplanes = int(out_channels / 8)
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self.conv0_1 = nn.Conv2D(
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in_channels,
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int(out_channels / 16),
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.bn0_1 = nn.BatchNorm(int(out_channels / 16))
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self.conv0_2 = nn.Conv2D(
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int(out_channels / 16),
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self.inplanes,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.bn0_2 = nn.BatchNorm(self.inplanes)
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self.relu = nn.ReLU()
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self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
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self.layer1 = self._make_layer(block, self.out_channels_block[0],
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layers[0])
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self.conv1 = nn.Conv2D(
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self.out_channels_block[0],
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self.out_channels_block[0],
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.bn1 = nn.BatchNorm(self.out_channels_block[0])
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self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
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self.layer2 = self._make_layer(
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block, self.out_channels_block[1], layers[1], stride=1)
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self.conv2 = nn.Conv2D(
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self.out_channels_block[1],
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self.out_channels_block[1],
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self.bn2 = nn.BatchNorm(self.out_channels_block[1])
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2D(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias_attr=False),
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nn.BatchNorm(planes * block.expansion), )
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layers = list()
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv0_1(x)
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x = self.bn0_1(x)
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x = self.relu(x)
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x = self.conv0_2(x)
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x = self.bn0_2(x)
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x = self.relu(x)
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x = self.maxpool1(x)
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x = self.layer1(x)
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool2(x)
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x = self.layer2(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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return x
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class RFLBase(nn.Layer):
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""" Reciprocal feature learning share backbone network"""
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def __init__(self, in_channels, out_channels=512):
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super(RFLBase, self).__init__()
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self.ConvNet = ResNetBase(in_channels, out_channels, BasicBlock,
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[1, 2, 5, 3])
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def forward(self, inputs):
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return self.ConvNet(inputs)
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