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| # -*- coding: utf-8 -*- | ||
| # @Time : 2019/12/4 14:54 | ||
| # @Author : zhoujun | ||
| import torch | ||
| from torch import nn | ||
|
|
||
| class DBHead(nn.Module): | ||
| def __init__(self, in_channels, out_channels, k = 50): | ||
| super().__init__() | ||
| self.k = k | ||
| self.binarize = nn.Sequential( | ||
| nn.Conv2d(in_channels, in_channels // 4, 3, padding=1), | ||
| nn.BatchNorm2d(in_channels // 4), | ||
| nn.ReLU(inplace=True), | ||
| nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 4, 2, 1), | ||
| nn.BatchNorm2d(in_channels // 4), | ||
| nn.ReLU(inplace=True), | ||
| nn.ConvTranspose2d(in_channels // 4, 1, 4, 2, 1), | ||
| ) | ||
| self.binarize.apply(self.weights_init) | ||
|
|
||
| self.thresh = self._init_thresh(in_channels) | ||
| self.thresh.apply(self.weights_init) | ||
|
|
||
| def forward(self, x): | ||
| shrink_maps = self.binarize(x) | ||
| threshold_maps = self.thresh(x) | ||
| if self.training: | ||
| binary_maps = self.step_function(shrink_maps.sigmoid(), threshold_maps) | ||
| y = torch.cat((shrink_maps, threshold_maps, binary_maps), dim=1) | ||
| else: | ||
| y = torch.cat((shrink_maps, threshold_maps), dim=1) | ||
| return y | ||
|
|
||
| def weights_init(self, m): | ||
| classname = m.__class__.__name__ | ||
| if classname.find('Conv') != -1: | ||
| nn.init.kaiming_normal_(m.weight.data) | ||
| elif classname.find('BatchNorm') != -1: | ||
| m.weight.data.fill_(1.) | ||
| m.bias.data.fill_(1e-4) | ||
|
|
||
| def _init_thresh(self, inner_channels, serial=False, smooth=False, bias=False): | ||
| in_channels = inner_channels | ||
| if serial: | ||
| in_channels += 1 | ||
| self.thresh = nn.Sequential( | ||
| nn.Conv2d(in_channels, inner_channels // 4, 3, padding=1, bias=bias), | ||
| nn.BatchNorm2d(inner_channels // 4), | ||
| nn.ReLU(inplace=True), | ||
| self._init_upsample(inner_channels // 4, inner_channels // 4, smooth=smooth, bias=bias), | ||
| nn.BatchNorm2d(inner_channels // 4), | ||
| nn.ReLU(inplace=True), | ||
| self._init_upsample(inner_channels // 4, 1, smooth=smooth, bias=bias), | ||
| nn.Sigmoid()) | ||
| return self.thresh | ||
|
|
||
| def _init_upsample(self, in_channels, out_channels, smooth=False, bias=False): | ||
| if smooth: | ||
| inter_out_channels = out_channels | ||
| if out_channels == 1: | ||
| inter_out_channels = in_channels | ||
| module_list = [ | ||
| nn.Upsample(scale_factor=2, mode='nearest'), | ||
| nn.Conv2d(in_channels, inter_out_channels, 3, 1, 1, bias=bias)] | ||
| if out_channels == 1: | ||
| module_list.append(nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=1, bias=True)) | ||
| return nn.Sequential(module_list) | ||
| else: | ||
| return nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1) | ||
|
|
||
| def step_function(self, x, y): | ||
| return torch.reciprocal(1 + torch.exp(-self.k * (x - y))) |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,152 @@ | ||
|
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||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
|
|
||
| from torchvision.models import resnet101 | ||
|
|
||
| import DBHead | ||
| import einops | ||
|
|
||
| class ImageMultiheadSelfAttention(nn.Module) : | ||
| def __init__(self, planes): | ||
| super(ImageMultiheadSelfAttention, self).__init__() | ||
| self.attn = nn.MultiheadAttention(planes, 8) | ||
| def forward(self, x) : | ||
| res = x | ||
| n, c, h, w = x.shape | ||
| x = einops.rearrange(x, 'n c h w -> (h w) n c') | ||
| x = self.attn(x, x, x)[0] | ||
| x = einops.rearrange(x, '(h w) n c -> n c h w', n = n, c = c, h = h, w = w) | ||
| return res + x | ||
|
|
||
| class double_conv(nn.Module): | ||
| def __init__(self, in_ch, mid_ch, out_ch, stride = 1, planes = 256): | ||
| super(double_conv, self).__init__() | ||
| self.planes = planes | ||
| # down = None | ||
| # if stride > 1 : | ||
| # down = nn.Sequential( | ||
| # nn.AvgPool2d(2, 2), | ||
| # nn.Conv2d(in_ch + mid_ch, self.planes * Bottleneck.expansion, kernel_size=1, stride=1, bias=False),nn.BatchNorm2d(self.planes * Bottleneck.expansion) | ||
| # ) | ||
| self.down = None | ||
| if stride > 1 : | ||
| self.down = nn.AvgPool2d(2,stride=2) | ||
| self.conv = nn.Sequential( | ||
| nn.Conv2d(in_ch + mid_ch, mid_ch, kernel_size=3, padding=1, stride = 1, bias=False), | ||
| nn.BatchNorm2d(mid_ch), | ||
| nn.ReLU(inplace=True), | ||
| #Bottleneck(mid_ch, self.planes, stride, down, 2, 1, avd = True, norm_layer = nn.BatchNorm2d), | ||
| nn.Conv2d(mid_ch, out_ch, kernel_size=3, stride = 1, padding=1, bias=False), | ||
| nn.BatchNorm2d(out_ch), | ||
| nn.ReLU(inplace=True), | ||
| ) | ||
|
|
||
| def forward(self, x): | ||
| if self.down is not None : | ||
| x = self.down(x) | ||
| x = self.conv(x) | ||
| return x | ||
|
|
||
| class double_conv_up(nn.Module): | ||
| def __init__(self, in_ch, mid_ch, out_ch, stride = 1, planes = 256): | ||
| super(double_conv_up, self).__init__() | ||
| self.planes = planes | ||
| # down = None | ||
| # if stride > 1 : | ||
| # down = nn.Sequential( | ||
| # nn.AvgPool2d(2, 2), | ||
| # nn.Conv2d(in_ch + mid_ch, self.planes * Bottleneck.expansion, kernel_size=1, stride=1, bias=False),nn.BatchNorm2d(self.planes * Bottleneck.expansion) | ||
| # ) | ||
| self.down = None | ||
| if stride > 1 : | ||
| self.down = nn.AvgPool2d(2,stride=2) | ||
| self.conv = nn.Sequential( | ||
| nn.Conv2d(in_ch + mid_ch, mid_ch, kernel_size=3, padding=1, stride = 1, bias=False), | ||
| nn.BatchNorm2d(mid_ch), | ||
| nn.ReLU(inplace=True), | ||
| #Bottleneck(mid_ch, self.planes, stride, down, 2, 1, avd = True, norm_layer = nn.BatchNorm2d), | ||
| nn.Conv2d(mid_ch, mid_ch, kernel_size=3, stride = 1, padding=1, bias=False), | ||
| nn.BatchNorm2d(mid_ch), | ||
| nn.ReLU(inplace=True), | ||
| nn.ConvTranspose2d(mid_ch, out_ch, kernel_size=4, stride = 2, padding=1, bias=False), | ||
| nn.BatchNorm2d(out_ch), | ||
| nn.ReLU(inplace=True), | ||
| ) | ||
|
|
||
| def forward(self, x): | ||
| if self.down is not None : | ||
| x = self.down(x) | ||
| x = self.conv(x) | ||
| return x | ||
|
|
||
| class TextDetection(nn.Module) : | ||
| def __init__(self, pretrained=None) : | ||
| super(TextDetection, self).__init__() | ||
| self.backbone = resnet101(pretrained=True if pretrained else False) | ||
|
|
||
| self.conv_db = DBHead.DBHead(64, 0) | ||
|
|
||
| self.conv_mask = nn.Sequential( | ||
| nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), | ||
| nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), | ||
| nn.Conv2d(64, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), | ||
| nn.Conv2d(32, 1, kernel_size=1), | ||
| nn.Sigmoid() | ||
| ) | ||
|
|
||
| self.down_conv1 = double_conv(0, 512, 512, 2) | ||
| self.down_conv2 = double_conv(0, 512, 512, 2) | ||
| self.down_conv3 = double_conv(0, 512, 512, 2) | ||
|
|
||
| self.upconv1 = double_conv_up(0, 512, 256) | ||
| self.upconv2 = double_conv_up(256, 512, 256) | ||
| self.upconv3 = double_conv_up(256, 512, 256) | ||
| self.upconv4 = double_conv_up(256, 512, 256, planes = 128) | ||
| self.upconv5 = double_conv_up(256, 256, 128, planes = 64) | ||
| self.upconv6 = double_conv_up(128, 128, 64, planes = 32) | ||
| self.upconv7 = double_conv_up(64, 64, 64, planes = 16) | ||
|
|
||
| self.proj_h4 = nn.Conv2d(64 * 4, 64, 1) | ||
| self.proj_h8 = nn.Conv2d(128 * 4, 128, 1) | ||
| self.proj_h16 = nn.Conv2d(256 * 4, 256, 1) | ||
| self.proj_h32 = nn.Conv2d(512 * 4, 512, 1) | ||
|
|
||
| def forward(self, x) : | ||
| x = self.backbone.conv1(x) | ||
| x = self.backbone.bn1(x) | ||
| x = self.backbone.relu(x) | ||
| x = self.backbone.maxpool(x) # 64@384 | ||
|
|
||
| h4 = self.backbone.layer1(x) # 64@384 | ||
| h8 = self.backbone.layer2(h4) # 128@192 | ||
| h16 = self.backbone.layer3(h8) # 256@96 | ||
| h32 = self.backbone.layer4(h16) # 512@48 | ||
|
|
||
| h4 = self.proj_h4(h4) | ||
| h8 = self.proj_h8(h8) | ||
| h16 = self.proj_h16(h16) | ||
| h32 = self.proj_h32(h32) | ||
|
|
||
| h64 = self.down_conv1(h32) # 512@24 | ||
| h128 = self.down_conv2(h64) # 512@12 | ||
| h256 = self.down_conv3(h128) # 512@6 | ||
|
|
||
| up256 = self.upconv1(h256) # 128@12 | ||
| up128 = self.upconv2(torch.cat([up256, h128], dim = 1)) # 64@24 | ||
| up64 = self.upconv3(torch.cat([up128, h64], dim = 1)) # 128@48 | ||
| up32 = self.upconv4(torch.cat([up64, h32], dim = 1)) # 64@96 | ||
| up16 = self.upconv5(torch.cat([up32, h16], dim = 1)) # 128@192 | ||
| up8 = self.upconv6(torch.cat([up16, h8], dim = 1)) # 64@384 | ||
| up4 = self.upconv7(torch.cat([up8, h4], dim = 1)) # 64@768 | ||
|
|
||
| return self.conv_db(up8), self.conv_mask(up4) | ||
|
|
||
| if __name__ == '__main__' : | ||
| device = torch.device("cuda:0") | ||
| net = TextDetection().to(device) | ||
| img = torch.randn(2, 3, 1024, 1024).to(device) | ||
| db, seg = net(img) | ||
| print(db.shape) | ||
| print(seg.shape) |
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