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])