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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch.nn as nn | |
| from mmcv.runner import BaseModule, Sequential | |
| import mmocr.utils as utils | |
| from mmocr.models.builder import BACKBONES | |
| from mmocr.models.textrecog.layers import BasicBlock | |
| class ResNet31OCR(BaseModule): | |
| """Implement ResNet backbone for text recognition, modified from | |
| `ResNet <https://arxiv.org/pdf/1512.03385.pdf>`_ | |
| Args: | |
| base_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. | |
| stage4_pool_cfg (dict): Dictionary to construct and configure | |
| pooling layer in stage 4. | |
| last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. | |
| """ | |
| def __init__(self, | |
| base_channels=3, | |
| layers=[1, 2, 5, 3], | |
| channels=[64, 128, 256, 256, 512, 512, 512], | |
| out_indices=None, | |
| stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)), | |
| last_stage_pool=False, | |
| init_cfg=[ | |
| dict(type='Kaiming', layer='Conv2d'), | |
| dict(type='Uniform', layer='BatchNorm2d') | |
| ]): | |
| super().__init__(init_cfg=init_cfg) | |
| assert isinstance(base_channels, int) | |
| assert utils.is_type_list(layers, int) | |
| assert utils.is_type_list(channels, int) | |
| assert out_indices is None or isinstance(out_indices, (list, tuple)) | |
| assert isinstance(last_stage_pool, bool) | |
| self.out_indices = out_indices | |
| self.last_stage_pool = last_stage_pool | |
| # conv 1 (Conv, Conv) | |
| self.conv1_1 = nn.Conv2d( | |
| base_channels, channels[0], kernel_size=3, stride=1, padding=1) | |
| self.bn1_1 = nn.BatchNorm2d(channels[0]) | |
| self.relu1_1 = nn.ReLU(inplace=True) | |
| self.conv1_2 = nn.Conv2d( | |
| channels[0], channels[1], kernel_size=3, stride=1, padding=1) | |
| self.bn1_2 = nn.BatchNorm2d(channels[1]) | |
| self.relu1_2 = nn.ReLU(inplace=True) | |
| # 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]) | |
| self.conv2 = nn.Conv2d( | |
| channels[2], channels[2], kernel_size=3, stride=1, padding=1) | |
| self.bn2 = nn.BatchNorm2d(channels[2]) | |
| self.relu2 = nn.ReLU(inplace=True) | |
| # 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]) | |
| self.conv3 = nn.Conv2d( | |
| channels[3], channels[3], kernel_size=3, stride=1, padding=1) | |
| self.bn3 = nn.BatchNorm2d(channels[3]) | |
| self.relu3 = nn.ReLU(inplace=True) | |
| # conv 4 (Max-pooling, Residual block, Conv) | |
| self.pool4 = nn.MaxPool2d(padding=0, ceil_mode=True, **stage4_pool_cfg) | |
| self.block4 = self._make_layer(channels[3], channels[4], layers[2]) | |
| self.conv4 = nn.Conv2d( | |
| channels[4], channels[4], kernel_size=3, stride=1, padding=1) | |
| self.bn4 = nn.BatchNorm2d(channels[4]) | |
| self.relu4 = nn.ReLU(inplace=True) | |
| # 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) # 1/16 | |
| self.block5 = self._make_layer(channels[4], channels[5], layers[3]) | |
| self.conv5 = nn.Conv2d( | |
| channels[5], channels[5], kernel_size=3, stride=1, padding=1) | |
| self.bn5 = nn.BatchNorm2d(channels[5]) | |
| self.relu5 = nn.ReLU(inplace=True) | |
| def _make_layer(self, input_channels, output_channels, blocks): | |
| layers = [] | |
| for _ in range(blocks): | |
| downsample = None | |
| if input_channels != output_channels: | |
| downsample = Sequential( | |
| nn.Conv2d( | |
| input_channels, | |
| output_channels, | |
| kernel_size=1, | |
| stride=1, | |
| bias=False), | |
| nn.BatchNorm2d(output_channels), | |
| ) | |
| layers.append( | |
| BasicBlock( | |
| input_channels, output_channels, downsample=downsample)) | |
| input_channels = output_channels | |
| return 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 | |