# 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