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| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class VGG_FeatureExtractor(nn.Module): | |
| """ FeatureExtractor of CRNN (https://arxiv.org/pdf/1507.05717.pdf) """ | |
| def __init__(self, input_channel, output_channel=512): | |
| super(VGG_FeatureExtractor, self).__init__() | |
| self.output_channel = [int(output_channel / 8), int(output_channel / 4), | |
| int(output_channel / 2), output_channel] # [64, 128, 256, 512] | |
| self.ConvNet = nn.Sequential( | |
| nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), | |
| nn.MaxPool2d(2, 2), # 64x16x50 | |
| nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True), | |
| nn.MaxPool2d(2, 2), # 128x8x25 | |
| nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True), # 256x8x25 | |
| nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True), | |
| nn.MaxPool2d((2, 1), (2, 1)), # 256x4x25 | |
| nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False), | |
| nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), # 512x4x25 | |
| nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False), | |
| nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), | |
| nn.MaxPool2d((2, 1), (2, 1)), # 512x2x25 | |
| nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True)) # 512x1x24 | |
| def forward(self, input): | |
| return self.ConvNet(input) | |
| class RCNN_FeatureExtractor(nn.Module): | |
| """ FeatureExtractor of GRCNN (https://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf) """ | |
| def __init__(self, input_channel, output_channel=512): | |
| super(RCNN_FeatureExtractor, self).__init__() | |
| self.output_channel = [int(output_channel / 8), int(output_channel / 4), | |
| int(output_channel / 2), output_channel] # [64, 128, 256, 512] | |
| self.ConvNet = nn.Sequential( | |
| nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), | |
| nn.MaxPool2d(2, 2), # 64 x 16 x 50 | |
| GRCL(self.output_channel[0], self.output_channel[0], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, 2), # 64 x 8 x 25 | |
| GRCL(self.output_channel[0], self.output_channel[1], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, (2, 1), (0, 1)), # 128 x 4 x 26 | |
| GRCL(self.output_channel[1], self.output_channel[2], num_iteration=5, kernel_size=3, pad=1), | |
| nn.MaxPool2d(2, (2, 1), (0, 1)), # 256 x 2 x 27 | |
| nn.Conv2d(self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False), | |
| nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True)) # 512 x 1 x 26 | |
| def forward(self, input): | |
| return self.ConvNet(input) | |
| class ResNet_FeatureExtractor(nn.Module): | |
| """ FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) """ | |
| def __init__(self, input_channel, output_channel=512): | |
| super(ResNet_FeatureExtractor, self).__init__() | |
| self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3]) | |
| def forward(self, input): | |
| return self.ConvNet(input) | |
| # For Gated RCNN | |
| class GRCL(nn.Module): | |
| def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad): | |
| super(GRCL, self).__init__() | |
| self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False) | |
| self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False) | |
| self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False) | |
| self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False) | |
| self.BN_x_init = nn.BatchNorm2d(output_channel) | |
| self.num_iteration = num_iteration | |
| self.GRCL = [GRCL_unit(output_channel) for _ in range(num_iteration)] | |
| self.GRCL = nn.Sequential(*self.GRCL) | |
| def forward(self, input): | |
| """ The input of GRCL is consistant over time t, which is denoted by u(0) | |
| thus wgf_u / wf_u is also consistant over time t. | |
| """ | |
| wgf_u = self.wgf_u(input) | |
| wf_u = self.wf_u(input) | |
| x = F.relu(self.BN_x_init(wf_u)) | |
| for i in range(self.num_iteration): | |
| x = self.GRCL[i](wgf_u, self.wgr_x(x), wf_u, self.wr_x(x)) | |
| return x | |
| class GRCL_unit(nn.Module): | |
| def __init__(self, output_channel): | |
| super(GRCL_unit, self).__init__() | |
| self.BN_gfu = nn.BatchNorm2d(output_channel) | |
| self.BN_grx = nn.BatchNorm2d(output_channel) | |
| self.BN_fu = nn.BatchNorm2d(output_channel) | |
| self.BN_rx = nn.BatchNorm2d(output_channel) | |
| self.BN_Gx = nn.BatchNorm2d(output_channel) | |
| def forward(self, wgf_u, wgr_x, wf_u, wr_x): | |
| G_first_term = self.BN_gfu(wgf_u) | |
| G_second_term = self.BN_grx(wgr_x) | |
| G = F.sigmoid(G_first_term + G_second_term) | |
| x_first_term = self.BN_fu(wf_u) | |
| x_second_term = self.BN_Gx(self.BN_rx(wr_x) * G) | |
| x = F.relu(x_first_term + x_second_term) | |
| return x | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| 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(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def _conv3x3(self, in_planes, out_planes, stride=1): | |
| "3x3 convolution with padding" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=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.Module): | |
| def __init__(self, input_channel, output_channel, block, layers): | |
| 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, bias=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, bias=False) | |
| self.bn0_2 = nn.BatchNorm2d(self.inplanes) | |
| self.relu = nn.ReLU(inplace=True) | |
| 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, bias=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, bias=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, bias=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), bias=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, bias=False) | |
| self.bn4_2 = nn.BatchNorm2d(self.output_channel_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=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i 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 | |