# copyright (c) 2022 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/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/backbones/ResNet32.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.nn as nn
__all__ = ["ResNet32"]
conv_weight_attr = nn.initializer.KaimingNormal()
class ResNet32(nn.Layer):
"""
Feature Extractor is proposed in FAN Ref [1]
Ref [1]: Focusing Attention: Towards Accurate Text Recognition in Neural Images ICCV-2017
"""
def __init__(self, in_channels, out_channels=512):
"""
Args:
in_channels (int): input channel
output_channel (int): output channel
"""
super(ResNet32, self).__init__()
self.out_channels = out_channels
self.ConvNet = ResNet(in_channels, out_channels, BasicBlock, [1, 2, 5, 3])
def forward(self, inputs):
"""
Args:
inputs: input feature
Returns:
output feature
"""
return self.ConvNet(inputs)
class BasicBlock(nn.Layer):
"""Res-net Basic Block"""
expansion = 1
def __init__(self, inplanes, planes,
stride=1, downsample=None,
norm_type='BN', **kwargs):
"""
Args:
inplanes (int): input channel
planes (int): channels of the middle feature
stride (int): stride of the convolution
downsample (int): type of the down_sample
norm_type (str): type of the normalization
**kwargs (None): backup parameter
"""
super(BasicBlock, self).__init__()
self.conv1 = self._conv3x3(inplanes, planes)
self.bn1 = nn.BatchNorm2D(planes)
self.conv2 = self._conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2D(planes)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
def _conv3x3(self, in_planes, out_planes, stride=1):
"""
Args:
in_planes (int): input channel
out_planes (int): channels of the middle feature
stride (int): stride of the convolution
Returns:
nn.Layer: Conv2D with kernel = 3
"""
return nn.Conv2D(in_planes, out_planes,
kernel_size=3, stride=stride,
padding=1, weight_attr=conv_weight_attr,
bias_attr=False)
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 ResNet(nn.Layer):
"""Res-Net network structure"""
def __init__(self, input_channel,
output_channel, block, layers):
"""
Args:
input_channel (int): input channel
output_channel (int): output channel
block (BasicBlock): convolution block
layers (list): layers of the block
"""
super(ResNet, self).__init__()
self.output_channel_block = [int(output_channel / 4),
int(output_channel / 2),
output_channel,
output_channel]
self.inplanes = int(output_channel / 8)
self.conv0_1 = nn.Conv2D(input_channel, int(output_channel / 16),
kernel_size=3, stride=1,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn0_1 = nn.BatchNorm2D(int(output_channel / 16))
self.conv0_2 = nn.Conv2D(int(output_channel / 16), self.inplanes,
kernel_size=3, stride=1,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn0_2 = nn.BatchNorm2D(self.inplanes)
self.relu = nn.ReLU()
self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.layer1 = self._make_layer(block,
self.output_channel_block[0],
layers[0])
self.conv1 = nn.Conv2D(self.output_channel_block[0],
self.output_channel_block[0],
kernel_size=3, stride=1,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn1 = nn.BatchNorm2D(self.output_channel_block[0])
self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.layer2 = self._make_layer(block,
self.output_channel_block[1],
layers[1], stride=1)
self.conv2 = nn.Conv2D(self.output_channel_block[1],
self.output_channel_block[1],
kernel_size=3, stride=1,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False,)
self.bn2 = nn.BatchNorm2D(self.output_channel_block[1])
self.maxpool3 = nn.MaxPool2D(kernel_size=2,
stride=(2, 1),
padding=(0, 1))
self.layer3 = self._make_layer(block, self.output_channel_block[2],
layers[2], stride=1)
self.conv3 = nn.Conv2D(self.output_channel_block[2],
self.output_channel_block[2],
kernel_size=3, stride=1,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn3 = nn.BatchNorm2D(self.output_channel_block[2])
self.layer4 = self._make_layer(block, self.output_channel_block[3],
layers[3], stride=1)
self.conv4_1 = nn.Conv2D(self.output_channel_block[3],
self.output_channel_block[3],
kernel_size=2, stride=(2, 1),
padding=(0, 1),
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn4_1 = nn.BatchNorm2D(self.output_channel_block[3])
self.conv4_2 = nn.Conv2D(self.output_channel_block[3],
self.output_channel_block[3],
kernel_size=2, stride=1,
padding=0,
weight_attr=conv_weight_attr,
bias_attr=False)
self.bn4_2 = nn.BatchNorm2D(self.output_channel_block[3])
def _make_layer(self, block, planes, blocks, stride=1):
"""
Args:
block (block): convolution block
planes (int): input channels
blocks (list): layers of the block
stride (int): stride of the convolution
Returns:
nn.Sequential: the combination of the convolution block
"""
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2D(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride,
weight_attr=conv_weight_attr,
bias_attr=False),
nn.BatchNorm2D(planes * block.expansion),
)
layers = list()
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv0_1(x)
x = self.bn0_1(x)
x = self.relu(x)
x = self.conv0_2(x)
x = self.bn0_2(x)
x = self.relu(x)
x = self.maxpool1(x)
x = self.layer1(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool2(x)
x = self.layer2(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.maxpool3(x)
x = self.layer3(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.layer4(x)
x = self.conv4_1(x)
x = self.bn4_1(x)
x = self.relu(x)
x = self.conv4_2(x)
x = self.bn4_2(x)
x = self.relu(x)
return x