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| import torch | |
| import torch.nn as nn | |
| from torchvision import models | |
| import torch.nn.functional as F | |
| import numpy as np | |
| class sobel_net(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv_opx = nn.Conv2d(1, 1, 3, bias=False) | |
| self.conv_opy = nn.Conv2d(1, 1, 3, bias=False) | |
| sobel_kernelx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype='float32').reshape((1, 1, 3, 3)) | |
| sobel_kernely = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype='float32').reshape((1, 1, 3, 3)) | |
| self.conv_opx.weight.data = torch.from_numpy(sobel_kernelx) | |
| self.conv_opy.weight.data = torch.from_numpy(sobel_kernely) | |
| for p in self.parameters(): | |
| p.requires_grad = False | |
| def forward(self, im): # input rgb | |
| x = (0.299 * im[:, 0, :, :] + 0.587 * im[:, 1, :, :] + 0.114 * im[:, 2, :, :]).unsqueeze(1) # rgb2gray | |
| gradx = self.conv_opx(x) | |
| grady = self.conv_opy(x) | |
| x = (gradx ** 2 + grady ** 2) ** 0.5 | |
| x = (x - x.min()) / (x.max() - x.min()) | |
| x = F.pad(x, (1, 1, 1, 1)) | |
| x = torch.cat([im, x], dim=1) | |
| return x | |
| class REBNCONV(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=3, dirate=1): | |
| super(REBNCONV, self).__init__() | |
| self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate) | |
| self.bn_s1 = nn.BatchNorm2d(out_ch) | |
| self.relu_s1 = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| #print(x.device) | |
| hx = x | |
| xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) | |
| return xout | |
| ## upsample tensor 'src' to have the same spatial size with tensor 'tar' | |
| def _upsample_like(src, tar): | |
| src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=False) | |
| return src | |
| ### RSU-7 ### | |
| class RSU7(nn.Module): # UNet07DRES(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3): | |
| super(RSU7, self).__init__() | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) | |
| self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) | |
| self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) | |
| self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.rebnconvin(hx) | |
| hx1 = self.rebnconv1(hxin) | |
| hx = self.pool1(hx1) | |
| hx2 = self.rebnconv2(hx) | |
| hx = self.pool2(hx2) | |
| hx3 = self.rebnconv3(hx) | |
| hx = self.pool3(hx3) | |
| hx4 = self.rebnconv4(hx) | |
| hx = self.pool4(hx4) | |
| hx5 = self.rebnconv5(hx) | |
| hx = self.pool5(hx5) | |
| hx6 = self.rebnconv6(hx) | |
| hx7 = self.rebnconv7(hx6) | |
| hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) | |
| hx6dup = _upsample_like(hx6d, hx5) | |
| hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) | |
| hx5dup = _upsample_like(hx5d, hx4) | |
| hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) | |
| hx4dup = _upsample_like(hx4d, hx3) | |
| hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) | |
| return hx1d + hxin | |
| ### RSU-6 ### | |
| class RSU6(nn.Module): # UNet06DRES(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3): | |
| super(RSU6, self).__init__() | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) | |
| self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) | |
| self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) | |
| self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.rebnconvin(hx) | |
| hx1 = self.rebnconv1(hxin) | |
| hx = self.pool1(hx1) | |
| hx2 = self.rebnconv2(hx) | |
| hx = self.pool2(hx2) | |
| hx3 = self.rebnconv3(hx) | |
| hx = self.pool3(hx3) | |
| hx4 = self.rebnconv4(hx) | |
| hx = self.pool4(hx4) | |
| hx5 = self.rebnconv5(hx) | |
| hx6 = self.rebnconv6(hx5) | |
| hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) | |
| hx5dup = _upsample_like(hx5d, hx4) | |
| hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) | |
| hx4dup = _upsample_like(hx4d, hx3) | |
| hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) | |
| return hx1d + hxin | |
| ### RSU-5 ### | |
| class RSU5(nn.Module): # UNet05DRES(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3): | |
| super(RSU5, self).__init__() | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) | |
| self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) | |
| self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) | |
| self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.rebnconvin(hx) | |
| hx1 = self.rebnconv1(hxin) | |
| hx = self.pool1(hx1) | |
| hx2 = self.rebnconv2(hx) | |
| hx = self.pool2(hx2) | |
| hx3 = self.rebnconv3(hx) | |
| hx = self.pool3(hx3) | |
| hx4 = self.rebnconv4(hx) | |
| hx5 = self.rebnconv5(hx4) | |
| hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) | |
| hx4dup = _upsample_like(hx4d, hx3) | |
| hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) | |
| return hx1d + hxin | |
| ### RSU-4 ### | |
| class RSU4(nn.Module): # UNet04DRES(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3): | |
| super(RSU4, self).__init__() | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) | |
| self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) | |
| self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) | |
| self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) | |
| self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) | |
| self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.rebnconvin(hx) | |
| hx1 = self.rebnconv1(hxin) | |
| hx = self.pool1(hx1) | |
| hx2 = self.rebnconv2(hx) | |
| hx = self.pool2(hx2) | |
| hx3 = self.rebnconv3(hx) | |
| hx4 = self.rebnconv4(hx3) | |
| hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) | |
| return hx1d + hxin | |
| ### RSU-4F ### | |
| class RSU4F(nn.Module): # UNet04FRES(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3): | |
| super(RSU4F, self).__init__() | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) | |
| self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) | |
| self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) | |
| self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) | |
| self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) | |
| self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) | |
| self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) | |
| self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.rebnconvin(hx) | |
| hx1 = self.rebnconv1(hxin) | |
| hx2 = self.rebnconv2(hx1) | |
| hx3 = self.rebnconv3(hx2) | |
| hx4 = self.rebnconv4(hx3) | |
| hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) | |
| hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) | |
| hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) | |
| return hx1d + hxin | |
| ##### U^2-Net #### | |
| class U2NET(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=1): | |
| super(U2NET, self).__init__() | |
| self.edge = sobel_net() | |
| self.stage1 = RSU7(in_ch, 32, 64) | |
| self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage2 = RSU6(64, 32, 128) | |
| self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage3 = RSU5(128, 64, 256) | |
| self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage4 = RSU4(256, 128, 512) | |
| self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage5 = RSU4F(512, 256, 512) | |
| self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage6 = RSU4F(512, 256, 512) | |
| # decoder | |
| self.stage5d = RSU4F(1024, 256, 512) | |
| self.stage4d = RSU4(1024, 128, 256) | |
| self.stage3d = RSU5(512, 64, 128) | |
| self.stage2d = RSU6(256, 32, 64) | |
| self.stage1d = RSU7(128, 16, 64) | |
| self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) | |
| self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) | |
| self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) | |
| self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) | |
| self.outconv = nn.Conv2d(6, out_ch, 1) | |
| def forward(self, x): | |
| x = self.edge(x) | |
| hx = x | |
| # stage 1 | |
| hx1 = self.stage1(hx) | |
| hx = self.pool12(hx1) | |
| # stage 2 | |
| hx2 = self.stage2(hx) | |
| hx = self.pool23(hx2) | |
| # stage 3 | |
| hx3 = self.stage3(hx) | |
| hx = self.pool34(hx3) | |
| # stage 4 | |
| hx4 = self.stage4(hx) | |
| hx = self.pool45(hx4) | |
| # stage 5 | |
| hx5 = self.stage5(hx) | |
| hx = self.pool56(hx5) | |
| # stage 6 | |
| hx6 = self.stage6(hx) | |
| hx6up = _upsample_like(hx6, hx5) | |
| # -------------------- decoder -------------------- | |
| hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) | |
| hx5dup = _upsample_like(hx5d, hx4) | |
| hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) | |
| hx4dup = _upsample_like(hx4d, hx3) | |
| hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) | |
| # side output | |
| d1 = self.side1(hx1d) | |
| d2 = self.side2(hx2d) | |
| d2 = _upsample_like(d2, d1) | |
| d3 = self.side3(hx3d) | |
| d3 = _upsample_like(d3, d1) | |
| d4 = self.side4(hx4d) | |
| d4 = _upsample_like(d4, d1) | |
| d5 = self.side5(hx5d) | |
| d5 = _upsample_like(d5, d1) | |
| d6 = self.side6(hx6) | |
| d6 = _upsample_like(d6, d1) | |
| d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) | |
| return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid( | |
| d4), torch.sigmoid(d5), torch.sigmoid(d6) | |
| ### U^2-Net small ### | |
| class U2NETP(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=1): | |
| super(U2NETP, self).__init__() | |
| self.stage1 = RSU7(in_ch, 16, 64) | |
| self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage2 = RSU6(64, 16, 64) | |
| self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage3 = RSU5(64, 16, 64) | |
| self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage4 = RSU4(64, 16, 64) | |
| self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage5 = RSU4F(64, 16, 64) | |
| self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage6 = RSU4F(64, 16, 64) | |
| # decoder | |
| self.stage5d = RSU4F(128, 16, 64) | |
| self.stage4d = RSU4(128, 16, 64) | |
| self.stage3d = RSU5(128, 16, 64) | |
| self.stage2d = RSU6(128, 16, 64) | |
| self.stage1d = RSU7(128, 16, 64) | |
| self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side3 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side4 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side5 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side6 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.outconv = nn.Conv2d(6, out_ch, 1) | |
| def forward(self, x): | |
| hx = x | |
| # stage 1 | |
| hx1 = self.stage1(hx) | |
| hx = self.pool12(hx1) | |
| # stage 2 | |
| hx2 = self.stage2(hx) | |
| hx = self.pool23(hx2) | |
| # stage 3 | |
| hx3 = self.stage3(hx) | |
| hx = self.pool34(hx3) | |
| # stage 4 | |
| hx4 = self.stage4(hx) | |
| hx = self.pool45(hx4) | |
| # stage 5 | |
| hx5 = self.stage5(hx) | |
| hx = self.pool56(hx5) | |
| # stage 6 | |
| hx6 = self.stage6(hx) | |
| hx6up = _upsample_like(hx6, hx5) | |
| # decoder | |
| hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) | |
| hx5dup = _upsample_like(hx5d, hx4) | |
| hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) | |
| hx4dup = _upsample_like(hx4d, hx3) | |
| hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) | |
| hx3dup = _upsample_like(hx3d, hx2) | |
| hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) | |
| hx2dup = _upsample_like(hx2d, hx1) | |
| hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) | |
| # side output | |
| d1 = self.side1(hx1d) | |
| d2 = self.side2(hx2d) | |
| d2 = _upsample_like(d2, d1) | |
| d3 = self.side3(hx3d) | |
| d3 = _upsample_like(d3, d1) | |
| d4 = self.side4(hx4d) | |
| d4 = _upsample_like(d4, d1) | |
| d5 = self.side5(hx5d) | |
| d5 = _upsample_like(d5, d1) | |
| d6 = self.side6(hx6) | |
| d6 = _upsample_like(d6, d1) | |
| d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) | |
| return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid( | |
| d4), torch.sigmoid(d5), torch.sigmoid(d6) | |
| def get_parameter_number(net): | |
| total_num = sum(p.numel() for p in net.parameters()) | |
| trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad) | |
| return {'Total': total_num, 'Trainable': trainable_num} | |
| if __name__ == '__main__': | |
| net = U2NET(4, 1)#.cuda() | |
| print(get_parameter_number(net)) # 69090500 加attention后69442032 | |
| with torch.no_grad(): | |
| inputs = torch.zeros(1, 3, 256, 256)#.cuda() | |
| outs = net(inputs) | |
| print(outs[0].shape) # torch.Size([2, 3, 256, 256]) torch.Size([2, 2, 256, 256]) | |