# 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/blob/main/davarocr/davar_rcg/models/backbones/ResNetRFL.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
kaiming_init_ = KaimingNormal()
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
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.BatchNorm(planes)
self.conv2 = self._conv3x3(planes, planes)
self.bn2 = nn.BatchNorm(planes)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
def _conv3x3(self, in_planes, out_planes, stride=1):
return nn.Conv2D(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
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 ResNetRFL(nn.Layer):
def __init__(self,
in_channels,
out_channels=512,
use_cnt=True,
use_seq=True):
"""
Args:
in_channels (int): input channel
out_channels (int): output channel
"""
super(ResNetRFL, self).__init__()
assert use_cnt or use_seq
self.use_cnt, self.use_seq = use_cnt, use_seq
self.backbone = RFLBase(in_channels)
self.out_channels = out_channels
self.out_channels_block = [
int(self.out_channels / 4), int(self.out_channels / 2),
self.out_channels, self.out_channels
]
block = BasicBlock
layers = [1, 2, 5, 3]
self.inplanes = int(self.out_channels // 2)
self.relu = nn.ReLU()
if self.use_seq:
self.maxpool3 = nn.MaxPool2D(
kernel_size=2, stride=(2, 1), padding=(0, 1))
self.layer3 = self._make_layer(
block, self.out_channels_block[2], layers[2], stride=1)
self.conv3 = nn.Conv2D(
self.out_channels_block[2],
self.out_channels_block[2],
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn3 = nn.BatchNorm(self.out_channels_block[2])
self.layer4 = self._make_layer(
block, self.out_channels_block[3], layers[3], stride=1)
self.conv4_1 = nn.Conv2D(
self.out_channels_block[3],
self.out_channels_block[3],
kernel_size=2,
stride=(2, 1),
padding=(0, 1),
bias_attr=False)
self.bn4_1 = nn.BatchNorm(self.out_channels_block[3])
self.conv4_2 = nn.Conv2D(
self.out_channels_block[3],
self.out_channels_block[3],
kernel_size=2,
stride=1,
padding=0,
bias_attr=False)
self.bn4_2 = nn.BatchNorm(self.out_channels_block[3])
if self.use_cnt:
self.inplanes = int(self.out_channels // 2)
self.v_maxpool3 = nn.MaxPool2D(
kernel_size=2, stride=(2, 1), padding=(0, 1))
self.v_layer3 = self._make_layer(
block, self.out_channels_block[2], layers[2], stride=1)
self.v_conv3 = nn.Conv2D(
self.out_channels_block[2],
self.out_channels_block[2],
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.v_bn3 = nn.BatchNorm(self.out_channels_block[2])
self.v_layer4 = self._make_layer(
block, self.out_channels_block[3], layers[3], stride=1)
self.v_conv4_1 = nn.Conv2D(
self.out_channels_block[3],
self.out_channels_block[3],
kernel_size=2,
stride=(2, 1),
padding=(0, 1),
bias_attr=False)
self.v_bn4_1 = nn.BatchNorm(self.out_channels_block[3])
self.v_conv4_2 = nn.Conv2D(
self.out_channels_block[3],
self.out_channels_block[3],
kernel_size=2,
stride=1,
padding=0,
bias_attr=False)
self.v_bn4_2 = nn.BatchNorm(self.out_channels_block[3])
def _make_layer(self, block, planes, blocks, stride=1):
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,
bias_attr=False),
nn.BatchNorm(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, inputs):
x_1 = self.backbone(inputs)
if self.use_cnt:
v_x = self.v_maxpool3(x_1)
v_x = self.v_layer3(v_x)
v_x = self.v_conv3(v_x)
v_x = self.v_bn3(v_x)
visual_feature_2 = self.relu(v_x)
v_x = self.v_layer4(visual_feature_2)
v_x = self.v_conv4_1(v_x)
v_x = self.v_bn4_1(v_x)
v_x = self.relu(v_x)
v_x = self.v_conv4_2(v_x)
v_x = self.v_bn4_2(v_x)
visual_feature_3 = self.relu(v_x)
else:
visual_feature_3 = None
if self.use_seq:
x = self.maxpool3(x_1)
x = self.layer3(x)
x = self.conv3(x)
x = self.bn3(x)
x_2 = self.relu(x)
x = self.layer4(x_2)
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_3 = self.relu(x)
else:
x_3 = None
return [visual_feature_3, x_3]
class ResNetBase(nn.Layer):
def __init__(self, in_channels, out_channels, block, layers):
super(ResNetBase, self).__init__()
self.out_channels_block = [
int(out_channels / 4), int(out_channels / 2), out_channels,
out_channels
]
self.inplanes = int(out_channels / 8)
self.conv0_1 = nn.Conv2D(
in_channels,
int(out_channels / 16),
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn0_1 = nn.BatchNorm(int(out_channels / 16))
self.conv0_2 = nn.Conv2D(
int(out_channels / 16),
self.inplanes,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn0_2 = nn.BatchNorm(self.inplanes)
self.relu = nn.ReLU()
self.maxpool1 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.layer1 = self._make_layer(block, self.out_channels_block[0],
layers[0])
self.conv1 = nn.Conv2D(
self.out_channels_block[0],
self.out_channels_block[0],
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn1 = nn.BatchNorm(self.out_channels_block[0])
self.maxpool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.layer2 = self._make_layer(
block, self.out_channels_block[1], layers[1], stride=1)
self.conv2 = nn.Conv2D(
self.out_channels_block[1],
self.out_channels_block[1],
kernel_size=3,
stride=1,
padding=1,
bias_attr=False)
self.bn2 = nn.BatchNorm(self.out_channels_block[1])
def _make_layer(self, block, planes, blocks, stride=1):
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,
bias_attr=False),
nn.BatchNorm(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)
return x
class RFLBase(nn.Layer):
""" Reciprocal feature learning share backbone network"""
def __init__(self, in_channels, out_channels=512):
super(RFLBase, self).__init__()
self.ConvNet = ResNetBase(in_channels, out_channels, BasicBlock,
[1, 2, 5, 3])
def forward(self, inputs):
return self.ConvNet(inputs)