# Codes are borrowed from # https://github.com/xuebinqin/DIS/blob/main/IS-Net/models/isnet.py import torch import torch.nn as nn import torch.nn.functional as F from torchvision import models bce_loss = nn.BCEWithLogitsLoss(reduction="mean") def muti_loss_fusion(preds, target): loss0 = 0.0 loss = 0.0 for i in range(0, len(preds)): if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: 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(reduction="mean") kl_loss = nn.KLDivLoss(reduction="mean") l1_loss = nn.L1Loss(reduction="mean") smooth_l1_loss = nn.SmoothL1Loss(reduction="mean") def muti_loss_fusion_kl(preds, target, dfs, fs, mode="MSE"): loss0 = 0.0 loss = 0.0 for i in range(0, len(preds)): if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: 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)): df = dfs[i] fs_i = fs[i] if mode == "MSE": loss = loss + fea_loss( df, fs_i ) ### add the mse loss of features as additional constraints elif mode == "KL": loss = loss + kl_loss(F.log_softmax(df, dim=1), F.softmax(fs_i, dim=1)) elif mode == "MAE": loss = loss + l1_loss(df, fs_i) elif mode == "SmoothL1": loss = loss + smooth_l1_loss(df, fs_i) 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.interpolate(src, size=tar.shape[2:], mode="bilinear", align_corners=False) 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) @staticmethod def compute_loss(args): preds, targets = args 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 [torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)], [hx1, hx2, hx3, hx4, hx5, hx6] return [d1, d2, d3, d4, d5, 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) @staticmethod def compute_loss_kl(preds, targets, dfs, fs, mode="MSE"): return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode) @staticmethod def compute_loss(args): if len(args) == 3: ds, dfs, labels = args return muti_loss_fusion(ds, labels) else: ds, dfs, labels, fs = args return muti_loss_fusion_kl(ds, labels, dfs, fs, mode="MSE") 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 [torch.sigmoid(d1), torch.sigmoid(d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(d5), torch.sigmoid(d6)], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] return [d1, d2, d3, d4, d5, d6], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]