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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| bce_loss = nn.BCELoss(size_average=True) | |
| def muti_loss_fusion(preds, target): | |
| loss0 = 0.0 | |
| loss = 0.0 | |
| for i in range(0, len(preds)): | |
| # print("i: ", i, preds[i].shape) | |
| if (preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]): | |
| # tmp_target = _upsample_like(target,preds[i]) | |
| tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True) | |
| loss = loss + bce_loss(preds[i], tmp_target) | |
| else: | |
| loss = loss + bce_loss(preds[i], target) | |
| if (i == 0): | |
| loss0 = loss | |
| return loss0, loss | |
| fea_loss = nn.MSELoss(size_average=True) | |
| kl_loss = nn.KLDivLoss(size_average=True) | |
| l1_loss = nn.L1Loss(size_average=True) | |
| smooth_l1_loss = nn.SmoothL1Loss(size_average=True) | |
| def muti_loss_fusion_kl(preds, target, dfs, fs, mode='MSE'): | |
| loss0 = 0.0 | |
| loss = 0.0 | |
| for i in range(0, len(preds)): | |
| # print("i: ", i, preds[i].shape) | |
| if (preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]): | |
| # tmp_target = _upsample_like(target,preds[i]) | |
| tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True) | |
| loss = loss + bce_loss(preds[i], tmp_target) | |
| else: | |
| loss = loss + bce_loss(preds[i], target) | |
| if (i == 0): | |
| loss0 = loss | |
| for i in range(0, len(dfs)): | |
| if (mode == 'MSE'): | |
| loss = loss + fea_loss(dfs[i], fs[i]) ### add the mse loss of features as additional constraints | |
| # print("fea_loss: ", fea_loss(dfs[i],fs[i]).item()) | |
| elif (mode == 'KL'): | |
| loss = loss + kl_loss(F.log_softmax(dfs[i], dim=1), F.softmax(fs[i], dim=1)) | |
| # print("kl_loss: ", kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1)).item()) | |
| elif (mode == 'MAE'): | |
| loss = loss + l1_loss(dfs[i], fs[i]) | |
| # print("ls_loss: ", l1_loss(dfs[i],fs[i])) | |
| elif (mode == 'SmoothL1'): | |
| loss = loss + smooth_l1_loss(dfs[i], fs[i]) | |
| # print("SmoothL1: ", smooth_l1_loss(dfs[i],fs[i]).item()) | |
| return loss0, loss | |
| class REBNCONV(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1): | |
| super(REBNCONV, self).__init__() | |
| self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride) | |
| self.bn_s1 = nn.BatchNorm2d(out_ch) | |
| self.relu_s1 = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| 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.upsample(src, size=tar.shape[2:], mode='bilinear') | |
| return src | |
| ### RSU-7 ### | |
| class RSU7(nn.Module): | |
| def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): | |
| super(RSU7, self).__init__() | |
| self.in_ch = in_ch | |
| self.mid_ch = mid_ch | |
| self.out_ch = out_ch | |
| self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2 | |
| 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): | |
| b, c, h, w = x.shape | |
| 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): | |
| 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): | |
| 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): | |
| 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): | |
| 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 | |
| class myrebnconv(nn.Module): | |
| def __init__(self, in_ch=3, | |
| out_ch=1, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| dilation=1, | |
| groups=1): | |
| super(myrebnconv, self).__init__() | |
| self.conv = nn.Conv2d(in_ch, | |
| out_ch, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| dilation=dilation, | |
| groups=groups) | |
| self.bn = nn.BatchNorm2d(out_ch) | |
| self.rl = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| return self.rl(self.bn(self.conv(x))) | |
| class ISNetGTEncoder(nn.Module): | |
| def __init__(self, in_ch=1, out_ch=1): | |
| super(ISNetGTEncoder, self).__init__() | |
| self.conv_in = myrebnconv(in_ch, 16, 3, stride=2, padding=1) # nn.Conv2d(in_ch,64,3,stride=2,padding=1) | |
| self.stage1 = RSU7(16, 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, 32, 128) | |
| self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage4 = RSU4(128, 32, 256) | |
| self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage5 = RSU4F(256, 64, 512) | |
| self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage6 = RSU4F(512, 64, 512) | |
| 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) | |
| def compute_loss(self, preds, targets): | |
| return muti_loss_fusion(preds, targets) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.conv_in(hx) | |
| # hx = self.pool_in(hxin) | |
| # stage 1 | |
| hx1 = self.stage1(hxin) | |
| 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) | |
| # side output | |
| d1 = self.side1(hx1) | |
| d1 = _upsample_like(d1, x) | |
| d2 = self.side2(hx2) | |
| d2 = _upsample_like(d2, x) | |
| d3 = self.side3(hx3) | |
| d3 = _upsample_like(d3, x) | |
| d4 = self.side4(hx4) | |
| d4 = _upsample_like(d4, x) | |
| d5 = self.side5(hx5) | |
| d5 = _upsample_like(d5, x) | |
| d6 = self.side6(hx6) | |
| d6 = _upsample_like(d6, x) | |
| # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) | |
| return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)], [hx1, hx2, | |
| hx3, hx4, | |
| hx5, hx6] | |
| class ISNetDIS(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=1): | |
| super(ISNetDIS, self).__init__() | |
| self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1) | |
| self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True) | |
| self.stage1 = RSU7(64, 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,out_ch,1) | |
| def compute_loss_kl(self, preds, targets, dfs, fs, mode='MSE'): | |
| # return muti_loss_fusion(preds,targets) | |
| return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode) | |
| def compute_loss(self, preds, targets): | |
| # return muti_loss_fusion(preds,targets) | |
| return muti_loss_fusion(preds, targets) | |
| def forward(self, x): | |
| hx = x | |
| hxin = self.conv_in(hx) | |
| # hx = self.pool_in(hxin) | |
| # stage 1 | |
| hx1 = self.stage1(hxin) | |
| 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) | |
| d1 = _upsample_like(d1, x) | |
| d2 = self.side2(hx2d) | |
| d2 = _upsample_like(d2, x) | |
| d3 = self.side3(hx3d) | |
| d3 = _upsample_like(d3, x) | |
| d4 = self.side4(hx4d) | |
| d4 = _upsample_like(d4, x) | |
| d5 = self.side5(hx5d) | |
| d5 = _upsample_like(d5, x) | |
| d6 = self.side6(hx6) | |
| d6 = _upsample_like(d6, x) | |
| # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1)) | |
| return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)], [hx1d, hx2d, | |
| hx3d, hx4d, | |
| hx5d, hx6] | |