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import torch |
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import torch.nn as nn |
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from torchvision import models |
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import torch.nn.functional as F |
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bce_loss = nn.BCEWithLogitsLoss(reduction="mean") |
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def muti_loss_fusion(preds, target): |
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loss0 = 0.0 |
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loss = 0.0 |
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for i in range(0, len(preds)): |
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if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: |
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tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True) |
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loss = loss + bce_loss(preds[i], tmp_target) |
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else: |
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loss = loss + bce_loss(preds[i], target) |
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if i == 0: |
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loss0 = loss |
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return loss0, loss |
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fea_loss = nn.MSELoss(reduction="mean") |
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kl_loss = nn.KLDivLoss(reduction="mean") |
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l1_loss = nn.L1Loss(reduction="mean") |
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smooth_l1_loss = nn.SmoothL1Loss(reduction="mean") |
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def muti_loss_fusion_kl(preds, target, dfs, fs, mode='MSE'): |
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loss0 = 0.0 |
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loss = 0.0 |
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for i in range(0, len(preds)): |
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if preds[i].shape[2] != target.shape[2] or preds[i].shape[3] != target.shape[3]: |
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tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True) |
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loss = loss + bce_loss(preds[i], tmp_target) |
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else: |
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loss = loss + bce_loss(preds[i], target) |
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if i == 0: |
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loss0 = loss |
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for i in range(0, len(dfs)): |
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df = dfs[i] |
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fs_i = fs[i] |
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if mode == 'MSE': |
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loss = loss + fea_loss(df, fs_i) |
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elif mode == 'KL': |
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loss = loss + kl_loss(F.log_softmax(df, dim=1), F.softmax(fs_i, dim=1)) |
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elif mode == 'MAE': |
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loss = loss + l1_loss(df, fs_i) |
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elif mode == 'SmoothL1': |
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loss = loss + smooth_l1_loss(df, fs_i) |
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return loss0, loss |
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class REBNCONV(nn.Module): |
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def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1): |
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super(REBNCONV, self).__init__() |
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self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride) |
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self.bn_s1 = nn.BatchNorm2d(out_ch) |
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self.relu_s1 = nn.ReLU(inplace=True) |
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def forward(self, x): |
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hx = x |
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xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) |
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return xout |
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def _upsample_like(src, tar): |
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src = F.interpolate(src, size=tar.shape[2:], mode='bilinear', align_corners=False) |
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return src |
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class RSU7(nn.Module): |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): |
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super(RSU7, self).__init__() |
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self.in_ch = in_ch |
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self.mid_ch = mid_ch |
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self.out_ch = out_ch |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) |
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) |
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) |
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self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
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def forward(self, x): |
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b, c, h, w = x.shape |
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hx = x |
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hxin = self.rebnconvin(hx) |
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hx1 = self.rebnconv1(hxin) |
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hx = self.pool1(hx1) |
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hx2 = self.rebnconv2(hx) |
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hx = self.pool2(hx2) |
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hx3 = self.rebnconv3(hx) |
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hx = self.pool3(hx3) |
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hx4 = self.rebnconv4(hx) |
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hx = self.pool4(hx4) |
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hx5 = self.rebnconv5(hx) |
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hx = self.pool5(hx5) |
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hx6 = self.rebnconv6(hx) |
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hx7 = self.rebnconv7(hx6) |
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hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) |
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hx6dup = _upsample_like(hx6d, hx5) |
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hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) |
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hx5dup = _upsample_like(hx5d, hx4) |
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hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) |
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hx4dup = _upsample_like(hx4d, hx3) |
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) |
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hx3dup = _upsample_like(hx3d, hx2) |
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) |
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hx2dup = _upsample_like(hx2d, hx1) |
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) |
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return hx1d + hxin |
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class RSU6(nn.Module): |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3): |
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super(RSU6, self).__init__() |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) |
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) |
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) |
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self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
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def forward(self, x): |
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hx = x |
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hxin = self.rebnconvin(hx) |
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hx1 = self.rebnconv1(hxin) |
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hx = self.pool1(hx1) |
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hx2 = self.rebnconv2(hx) |
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hx = self.pool2(hx2) |
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hx3 = self.rebnconv3(hx) |
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hx = self.pool3(hx3) |
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hx4 = self.rebnconv4(hx) |
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hx = self.pool4(hx4) |
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hx5 = self.rebnconv5(hx) |
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hx6 = self.rebnconv6(hx5) |
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hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) |
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hx5dup = _upsample_like(hx5d, hx4) |
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hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) |
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hx4dup = _upsample_like(hx4d, hx3) |
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) |
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hx3dup = _upsample_like(hx3d, hx2) |
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) |
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hx2dup = _upsample_like(hx2d, hx1) |
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) |
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return hx1d + hxin |
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class RSU5(nn.Module): |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3): |
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super(RSU5, self).__init__() |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) |
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) |
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) |
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self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
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def forward(self, x): |
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hx = x |
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hxin = self.rebnconvin(hx) |
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hx1 = self.rebnconv1(hxin) |
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hx = self.pool1(hx1) |
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hx2 = self.rebnconv2(hx) |
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hx = self.pool2(hx2) |
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hx3 = self.rebnconv3(hx) |
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hx = self.pool3(hx3) |
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hx4 = self.rebnconv4(hx) |
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hx5 = self.rebnconv5(hx4) |
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hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) |
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hx4dup = _upsample_like(hx4d, hx3) |
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hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) |
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hx3dup = _upsample_like(hx3d, hx2) |
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) |
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hx2dup = _upsample_like(hx2d, hx1) |
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) |
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return hx1d + hxin |
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class RSU4(nn.Module): |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3): |
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super(RSU4, self).__init__() |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) |
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) |
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self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
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def forward(self, x): |
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hx = x |
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hxin = self.rebnconvin(hx) |
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hx1 = self.rebnconv1(hxin) |
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hx = self.pool1(hx1) |
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hx2 = self.rebnconv2(hx) |
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hx = self.pool2(hx2) |
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hx3 = self.rebnconv3(hx) |
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hx4 = self.rebnconv4(hx3) |
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hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) |
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hx3dup = _upsample_like(hx3d, hx2) |
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hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) |
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hx2dup = _upsample_like(hx2d, hx1) |
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hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) |
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return hx1d + hxin |
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class RSU4F(nn.Module): |
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3): |
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super(RSU4F, self).__init__() |
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self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) |
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self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) |
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self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) |
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self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) |
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self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) |
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self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) |
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self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) |
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self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
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def forward(self, x): |
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hx = x |
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hxin = self.rebnconvin(hx) |
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hx1 = self.rebnconv1(hxin) |
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hx2 = self.rebnconv2(hx1) |
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hx3 = self.rebnconv3(hx2) |
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hx4 = self.rebnconv4(hx3) |
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hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) |
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hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) |
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hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) |
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return hx1d + hxin |
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class myrebnconv(nn.Module): |
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def __init__(self, in_ch=3, |
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out_ch=1, |
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kernel_size=3, |
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stride=1, |
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padding=1, |
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dilation=1, |
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groups=1): |
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super(myrebnconv, self).__init__() |
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self.conv = nn.Conv2d(in_ch, |
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out_ch, |
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kernel_size=kernel_size, |
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stride=stride, |
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padding=padding, |
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dilation=dilation, |
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groups=groups) |
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self.bn = nn.BatchNorm2d(out_ch) |
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self.rl = nn.ReLU(inplace=True) |
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def forward(self, x): |
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return self.rl(self.bn(self.conv(x))) |
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class ISNetGTEncoder(nn.Module): |
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def __init__(self, in_ch=1, out_ch=1): |
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super(ISNetGTEncoder, self).__init__() |
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self.conv_in = myrebnconv(in_ch, 16, 3, stride=2, padding=1) |
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self.stage1 = RSU7(16, 16, 64) |
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self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage2 = RSU6(64, 16, 64) |
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self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage3 = RSU5(64, 32, 128) |
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self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage4 = RSU4(128, 32, 256) |
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self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage5 = RSU4F(256, 64, 512) |
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self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage6 = RSU4F(512, 64, 512) |
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self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) |
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self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) |
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self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) |
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self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) |
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self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) |
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self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) |
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@staticmethod |
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def compute_loss(args): |
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preds, targets = args |
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return muti_loss_fusion(preds, targets) |
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def forward(self, x): |
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hx = x |
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hxin = self.conv_in(hx) |
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hx1 = self.stage1(hxin) |
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hx = self.pool12(hx1) |
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hx2 = self.stage2(hx) |
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hx = self.pool23(hx2) |
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hx3 = self.stage3(hx) |
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hx = self.pool34(hx3) |
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hx4 = self.stage4(hx) |
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hx = self.pool45(hx4) |
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hx5 = self.stage5(hx) |
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hx = self.pool56(hx5) |
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hx6 = self.stage6(hx) |
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d1 = self.side1(hx1) |
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d1 = _upsample_like(d1, x) |
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d2 = self.side2(hx2) |
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d2 = _upsample_like(d2, x) |
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d3 = self.side3(hx3) |
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d3 = _upsample_like(d3, x) |
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d4 = self.side4(hx4) |
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d4 = _upsample_like(d4, x) |
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d5 = self.side5(hx5) |
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d5 = _upsample_like(d5, x) |
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d6 = self.side6(hx6) |
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d6 = _upsample_like(d6, x) |
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return [d1, d2, d3, d4, d5, d6], [hx1, hx2, hx3, hx4, hx5, hx6] |
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class ISNetDIS(nn.Module): |
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def __init__(self, in_ch=3, out_ch=1): |
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super(ISNetDIS, self).__init__() |
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self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1) |
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self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage1 = RSU7(64, 32, 64) |
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self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage2 = RSU6(64, 32, 128) |
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self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage3 = RSU5(128, 64, 256) |
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self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage4 = RSU4(256, 128, 512) |
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self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage5 = RSU4F(512, 256, 512) |
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self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) |
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self.stage6 = RSU4F(512, 256, 512) |
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self.stage5d = RSU4F(1024, 256, 512) |
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self.stage4d = RSU4(1024, 128, 256) |
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self.stage3d = RSU5(512, 64, 128) |
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self.stage2d = RSU6(256, 32, 64) |
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self.stage1d = RSU7(128, 16, 64) |
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self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) |
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self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) |
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self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) |
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self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) |
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self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) |
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self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) |
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@staticmethod |
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def compute_loss_kl(preds, targets, dfs, fs, mode='MSE'): |
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return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode) |
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@staticmethod |
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def compute_loss(args): |
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if len(args) == 3: |
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ds, dfs, labels = args |
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return muti_loss_fusion(ds, labels) |
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else: |
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ds, dfs, labels, fs = args |
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return muti_loss_fusion_kl(ds, labels, dfs, fs, mode="MSE") |
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def forward(self, x): |
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hx = x |
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hxin = self.conv_in(hx) |
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hx = self.pool_in(hxin) |
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hx1 = self.stage1(hxin) |
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hx = self.pool12(hx1) |
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hx2 = self.stage2(hx) |
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hx = self.pool23(hx2) |
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hx3 = self.stage3(hx) |
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hx = self.pool34(hx3) |
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hx4 = self.stage4(hx) |
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hx = self.pool45(hx4) |
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hx5 = self.stage5(hx) |
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hx = self.pool56(hx5) |
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hx6 = self.stage6(hx) |
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hx6up = _upsample_like(hx6, hx5) |
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hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) |
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hx5dup = _upsample_like(hx5d, hx4) |
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hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) |
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hx4dup = _upsample_like(hx4d, hx3) |
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hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) |
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hx3dup = _upsample_like(hx3d, hx2) |
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hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) |
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hx2dup = _upsample_like(hx2d, hx1) |
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hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) |
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d1 = self.side1(hx1d) |
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d1 = _upsample_like(d1, x) |
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d2 = self.side2(hx2d) |
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d2 = _upsample_like(d2, x) |
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d3 = self.side3(hx3d) |
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d3 = _upsample_like(d3, x) |
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d4 = self.side4(hx4d) |
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d4 = _upsample_like(d4, x) |
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d5 = self.side5(hx5d) |
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d5 = _upsample_like(d5, x) |
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d6 = self.side6(hx6) |
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d6 = _upsample_like(d6, x) |
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return [d1, d2, d3, d4, d5, d6], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] |
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