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# 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
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
__all__ = ["ResNet31"]
def conv3x3(in_channel, out_channel, stride=1, conv_weight_attr=None):
return nn.Conv2D(
in_channel,
out_channel,
kernel_size=3,
stride=stride,
padding=1,
weight_attr=conv_weight_attr,
bias_attr=False)
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self, in_channels, channels, stride=1, downsample=False, conv_weight_attr=None, bn_weight_attr=None):
super().__init__()
self.conv1 = conv3x3(in_channels, channels, stride,
conv_weight_attr=conv_weight_attr)
self.bn1 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
self.relu = nn.ReLU()
self.conv2 = conv3x3(channels, channels,
conv_weight_attr=conv_weight_attr)
self.bn2 = nn.BatchNorm2D(channels, weight_attr=bn_weight_attr)
self.downsample = downsample
if downsample:
self.downsample = nn.Sequential(
nn.Conv2D(
in_channels,
channels * self.expansion,
1,
stride,
weight_attr=conv_weight_attr,
bias_attr=False),
nn.BatchNorm2D(channels * self.expansion, weight_attr=bn_weight_attr))
else:
self.downsample = nn.Sequential()
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:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet31(nn.Layer):
'''
Args:
in_channels (int): Number of channels of input image tensor.
layers (list[int]): List of BasicBlock number for each stage.
channels (list[int]): List of out_channels of Conv2d layer.
out_indices (None | Sequence[int]): Indices of output stages.
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
init_type (None | str): the config to control the initialization.
'''
def __init__(self,
in_channels=3,
layers=[1, 2, 5, 3],
channels=[64, 128, 256, 256, 512, 512, 512],
out_indices=None,
last_stage_pool=False,
init_type=None):
super(ResNet31, self).__init__()
assert isinstance(in_channels, int)
assert isinstance(last_stage_pool, bool)
self.out_indices = out_indices
self.last_stage_pool = last_stage_pool
conv_weight_attr = None
bn_weight_attr = None
if init_type is not None:
support_dict = ['KaimingNormal']
assert init_type in support_dict, Exception(
"resnet31 only support {}".format(support_dict))
conv_weight_attr = nn.initializer.KaimingNormal()
bn_weight_attr = ParamAttr(initializer=nn.initializer.Uniform(), learning_rate=1)
# conv 1 (Conv Conv)
self.conv1_1 = nn.Conv2D(
in_channels, channels[0], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn1_1 = nn.BatchNorm2D(channels[0], weight_attr=bn_weight_attr)
self.relu1_1 = nn.ReLU()
self.conv1_2 = nn.Conv2D(
channels[0], channels[1], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn1_2 = nn.BatchNorm2D(channels[1], weight_attr=bn_weight_attr)
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.block2 = self._make_layer(channels[1], channels[2], layers[0],
conv_weight_attr=conv_weight_attr, bn_weight_attr=bn_weight_attr)
self.conv2 = nn.Conv2D(
channels[2], channels[2], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn2 = nn.BatchNorm2D(channels[2], weight_attr=bn_weight_attr)
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.block3 = self._make_layer(channels[2], channels[3], layers[1],
conv_weight_attr=conv_weight_attr, bn_weight_attr=bn_weight_attr)
self.conv3 = nn.Conv2D(
channels[3], channels[3], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn3 = nn.BatchNorm2D(channels[3], weight_attr=bn_weight_attr)
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.block4 = self._make_layer(channels[3], channels[4], layers[2],
conv_weight_attr=conv_weight_attr, bn_weight_attr=bn_weight_attr)
self.conv4 = nn.Conv2D(
channels[4], channels[4], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn4 = nn.BatchNorm2D(channels[4], weight_attr=bn_weight_attr)
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.block5 = self._make_layer(channels[4], channels[5], layers[3],
conv_weight_attr=conv_weight_attr, bn_weight_attr=bn_weight_attr)
self.conv5 = nn.Conv2D(
channels[5], channels[5], kernel_size=3, stride=1, padding=1, weight_attr=conv_weight_attr)
self.bn5 = nn.BatchNorm2D(channels[5], weight_attr=bn_weight_attr)
self.relu5 = nn.ReLU()
self.out_channels = channels[-1]
def _make_layer(self, input_channels, output_channels, blocks, conv_weight_attr=None, bn_weight_attr=None):
layers = []
for _ in range(blocks):
downsample = None
if input_channels != output_channels:
downsample = nn.Sequential(
nn.Conv2D(
input_channels,
output_channels,
kernel_size=1,
stride=1,
weight_attr=conv_weight_attr,
bias_attr=False),
nn.BatchNorm2D(output_channels, weight_attr=bn_weight_attr))
layers.append(
BasicBlock(
input_channels, output_channels, downsample=downsample,
conv_weight_attr=conv_weight_attr, bn_weight_attr=bn_weight_attr))
input_channels = output_channels
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1_1(x)
x = self.bn1_1(x)
x = self.relu1_1(x)
x = self.conv1_2(x)
x = self.bn1_2(x)
x = self.relu1_2(x)
outs = []
for i in range(4):
layer_index = i + 2
pool_layer = getattr(self, f'pool{layer_index}')
block_layer = getattr(self, f'block{layer_index}')
conv_layer = getattr(self, f'conv{layer_index}')
bn_layer = getattr(self, f'bn{layer_index}')
relu_layer = getattr(self, f'relu{layer_index}')
if pool_layer is not None:
x = pool_layer(x)
x = block_layer(x)
x = conv_layer(x)
x = bn_layer(x)
x = relu_layer(x)
outs.append(x)
if self.out_indices is not None:
return tuple([outs[i] for i in self.out_indices])
return x
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