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| """ | |
| S. Fang, K. Li, J. Shao, and Z. Li, | |
| “SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images,” | |
| IEEE Geosci. Remote Sensing Lett., pp. 1-5, 2021, doi: 10.1109/LGRS.2021.3056416. | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| from opencd.registry import MODELS | |
| class conv_block_nested(nn.Module): | |
| def __init__(self, in_ch, mid_ch, out_ch): | |
| super(conv_block_nested, self).__init__() | |
| self.activation = nn.ReLU(inplace=True) | |
| self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True) | |
| self.bn1 = nn.BatchNorm2d(mid_ch) | |
| self.conv2 = nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1, bias=True) | |
| self.bn2 = nn.BatchNorm2d(out_ch) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| identity = x | |
| x = self.bn1(x) | |
| x = self.activation(x) | |
| x = self.conv2(x) | |
| x = self.bn2(x) | |
| output = self.activation(x + identity) | |
| return output | |
| class up(nn.Module): | |
| def __init__(self, in_ch, bilinear=False): | |
| super(up, self).__init__() | |
| if bilinear: | |
| self.up = nn.Upsample(scale_factor=2, | |
| mode='bilinear', | |
| align_corners=True) | |
| else: | |
| self.up = nn.ConvTranspose2d(in_ch, in_ch, 2, stride=2) | |
| def forward(self, x): | |
| x = self.up(x) | |
| return x | |
| class ChannelAttention(nn.Module): | |
| def __init__(self, in_channels, ratio = 16): | |
| super(ChannelAttention, self).__init__() | |
| self.avg_pool = nn.AdaptiveAvgPool2d(1) | |
| self.max_pool = nn.AdaptiveMaxPool2d(1) | |
| self.fc1 = nn.Conv2d(in_channels,in_channels//ratio,1,bias=False) | |
| self.relu1 = nn.ReLU() | |
| self.fc2 = nn.Conv2d(in_channels//ratio, in_channels,1,bias=False) | |
| self.sigmod = nn.Sigmoid() | |
| def forward(self,x): | |
| avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) | |
| max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) | |
| out = avg_out + max_out | |
| return self.sigmod(out) | |
| class SNUNet_ECAM(nn.Module): | |
| # SNUNet-CD with ECAM | |
| def __init__(self, in_channels, base_channel=32): | |
| super(SNUNet_ECAM, self).__init__() | |
| torch.nn.Module.dump_patches = True | |
| n1 = base_channel # the initial number of channels of feature map | |
| filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv0_0 = conv_block_nested(in_channels, filters[0], filters[0]) | |
| self.conv1_0 = conv_block_nested(filters[0], filters[1], filters[1]) | |
| self.Up1_0 = up(filters[1]) | |
| self.conv2_0 = conv_block_nested(filters[1], filters[2], filters[2]) | |
| self.Up2_0 = up(filters[2]) | |
| self.conv3_0 = conv_block_nested(filters[2], filters[3], filters[3]) | |
| self.Up3_0 = up(filters[3]) | |
| self.conv4_0 = conv_block_nested(filters[3], filters[4], filters[4]) | |
| self.Up4_0 = up(filters[4]) | |
| self.conv0_1 = conv_block_nested(filters[0] * 2 + filters[1], filters[0], filters[0]) | |
| self.conv1_1 = conv_block_nested(filters[1] * 2 + filters[2], filters[1], filters[1]) | |
| self.Up1_1 = up(filters[1]) | |
| self.conv2_1 = conv_block_nested(filters[2] * 2 + filters[3], filters[2], filters[2]) | |
| self.Up2_1 = up(filters[2]) | |
| self.conv3_1 = conv_block_nested(filters[3] * 2 + filters[4], filters[3], filters[3]) | |
| self.Up3_1 = up(filters[3]) | |
| self.conv0_2 = conv_block_nested(filters[0] * 3 + filters[1], filters[0], filters[0]) | |
| self.conv1_2 = conv_block_nested(filters[1] * 3 + filters[2], filters[1], filters[1]) | |
| self.Up1_2 = up(filters[1]) | |
| self.conv2_2 = conv_block_nested(filters[2] * 3 + filters[3], filters[2], filters[2]) | |
| self.Up2_2 = up(filters[2]) | |
| self.conv0_3 = conv_block_nested(filters[0] * 4 + filters[1], filters[0], filters[0]) | |
| self.conv1_3 = conv_block_nested(filters[1] * 4 + filters[2], filters[1], filters[1]) | |
| self.Up1_3 = up(filters[1]) | |
| self.conv0_4 = conv_block_nested(filters[0] * 5 + filters[1], filters[0], filters[0]) | |
| self.ca = ChannelAttention(filters[0] * 4, ratio=16) | |
| self.ca1 = ChannelAttention(filters[0], ratio=16 // 4) | |
| # self.conv_final = nn.Conv2d(filters[0] * 4, out_ch, kernel_size=1) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def forward(self, xA, xB): | |
| '''xA''' | |
| x0_0A = self.conv0_0(xA) | |
| x1_0A = self.conv1_0(self.pool(x0_0A)) | |
| x2_0A = self.conv2_0(self.pool(x1_0A)) | |
| x3_0A = self.conv3_0(self.pool(x2_0A)) | |
| # x4_0A = self.conv4_0(self.pool(x3_0A)) | |
| '''xB''' | |
| x0_0B = self.conv0_0(xB) | |
| x1_0B = self.conv1_0(self.pool(x0_0B)) | |
| x2_0B = self.conv2_0(self.pool(x1_0B)) | |
| x3_0B = self.conv3_0(self.pool(x2_0B)) | |
| x4_0B = self.conv4_0(self.pool(x3_0B)) | |
| x0_1 = self.conv0_1(torch.cat([x0_0A, x0_0B, self.Up1_0(x1_0B)], 1)) | |
| x1_1 = self.conv1_1(torch.cat([x1_0A, x1_0B, self.Up2_0(x2_0B)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0A, x0_0B, x0_1, self.Up1_1(x1_1)], 1)) | |
| x2_1 = self.conv2_1(torch.cat([x2_0A, x2_0B, self.Up3_0(x3_0B)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0A, x1_0B, x1_1, self.Up2_1(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0A, x0_0B, x0_1, x0_2, self.Up1_2(x1_2)], 1)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0A, x3_0B, self.Up4_0(x4_0B)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0A, x2_0B, x2_1, self.Up3_1(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0A, x1_0B, x1_1, x1_2, self.Up2_2(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0A, x0_0B, x0_1, x0_2, x0_3, self.Up1_3(x1_3)], 1)) | |
| out = torch.cat([x0_1, x0_2, x0_3, x0_4], 1) | |
| intra = torch.sum(torch.stack((x0_1, x0_2, x0_3, x0_4)), dim=0) | |
| ca1 = self.ca1(intra) | |
| out = self.ca(out) * (out + ca1.repeat(1, 4, 1, 1)) | |
| # out = self.conv_final(out) | |
| return (out, ) | |