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cloth_segmentation/networks/u2net.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class REBNCONV(nn.Module):
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def __init__(self, in_ch=3, out_ch=3, dirate=1):
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super(REBNCONV, self).__init__()
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self.conv_s1 = nn.Conv2d(
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in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate
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)
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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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## upsample tensor 'src' to have the same spatial size with tensor 'tar'
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def _upsample_like(src, tar):
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src = F.upsample(src, size=tar.shape[2:], mode="bilinear")
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return src
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### RSU-7 ###
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class RSU7(nn.Module): # UNet07DRES(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU7, 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.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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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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"""
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del hx1, hx2, hx3, hx4, hx5, hx6, hx7
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del hx6d, hx5d, hx3d, hx2d
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del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup
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"""
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return hx1d + hxin
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### RSU-6 ###
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class RSU6(nn.Module): # UNet06DRES(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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"""
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del hx1, hx2, hx3, hx4, hx5, hx6
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del hx5d, hx4d, hx3d, hx2d
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del hx2dup, hx3dup, hx4dup, hx5dup
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"""
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return hx1d + hxin
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### RSU-5 ###
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class RSU5(nn.Module): # UNet05DRES(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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"""
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del hx1, hx2, hx3, hx4, hx5
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del hx4d, hx3d, hx2d
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del hx2dup, hx3dup, hx4dup
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"""
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return hx1d + hxin
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### RSU-4 ###
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class RSU4(nn.Module): # UNet04DRES(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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"""
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del hx1, hx2, hx3, hx4
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del hx3d, hx2d
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del hx2dup, hx3dup
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"""
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return hx1d + hxin
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### RSU-4F ###
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class RSU4F(nn.Module): # UNet04FRES(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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| 336 |
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hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
|
| 337 |
-
hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
|
| 338 |
-
hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
|
| 339 |
-
|
| 340 |
-
"""
|
| 341 |
-
del hx1, hx2, hx3, hx4
|
| 342 |
-
del hx3d, hx2d
|
| 343 |
-
"""
|
| 344 |
-
|
| 345 |
-
return hx1d + hxin
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
##### U^2-Net ####
|
| 349 |
-
class U2NET(nn.Module):
|
| 350 |
-
def __init__(self, in_ch=3, out_ch=1):
|
| 351 |
-
super(U2NET, self).__init__()
|
| 352 |
-
|
| 353 |
-
self.stage1 = RSU7(in_ch, 32, 64)
|
| 354 |
-
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 355 |
-
|
| 356 |
-
self.stage2 = RSU6(64, 32, 128)
|
| 357 |
-
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 358 |
-
|
| 359 |
-
self.stage3 = RSU5(128, 64, 256)
|
| 360 |
-
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 361 |
-
|
| 362 |
-
self.stage4 = RSU4(256, 128, 512)
|
| 363 |
-
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 364 |
-
|
| 365 |
-
self.stage5 = RSU4F(512, 256, 512)
|
| 366 |
-
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 367 |
-
|
| 368 |
-
self.stage6 = RSU4F(512, 256, 512)
|
| 369 |
-
|
| 370 |
-
# decoder
|
| 371 |
-
self.stage5d = RSU4F(1024, 256, 512)
|
| 372 |
-
self.stage4d = RSU4(1024, 128, 256)
|
| 373 |
-
self.stage3d = RSU5(512, 64, 128)
|
| 374 |
-
self.stage2d = RSU6(256, 32, 64)
|
| 375 |
-
self.stage1d = RSU7(128, 16, 64)
|
| 376 |
-
|
| 377 |
-
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 378 |
-
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 379 |
-
self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
|
| 380 |
-
self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
|
| 381 |
-
self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
|
| 382 |
-
self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
|
| 383 |
-
|
| 384 |
-
self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
|
| 385 |
-
|
| 386 |
-
def forward(self, x):
|
| 387 |
-
|
| 388 |
-
hx = x
|
| 389 |
-
|
| 390 |
-
# stage 1
|
| 391 |
-
hx1 = self.stage1(hx)
|
| 392 |
-
hx = self.pool12(hx1)
|
| 393 |
-
|
| 394 |
-
# stage 2
|
| 395 |
-
hx2 = self.stage2(hx)
|
| 396 |
-
hx = self.pool23(hx2)
|
| 397 |
-
|
| 398 |
-
# stage 3
|
| 399 |
-
hx3 = self.stage3(hx)
|
| 400 |
-
hx = self.pool34(hx3)
|
| 401 |
-
|
| 402 |
-
# stage 4
|
| 403 |
-
hx4 = self.stage4(hx)
|
| 404 |
-
hx = self.pool45(hx4)
|
| 405 |
-
|
| 406 |
-
# stage 5
|
| 407 |
-
hx5 = self.stage5(hx)
|
| 408 |
-
hx = self.pool56(hx5)
|
| 409 |
-
|
| 410 |
-
# stage 6
|
| 411 |
-
hx6 = self.stage6(hx)
|
| 412 |
-
hx6up = _upsample_like(hx6, hx5)
|
| 413 |
-
|
| 414 |
-
# -------------------- decoder --------------------
|
| 415 |
-
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
|
| 416 |
-
hx5dup = _upsample_like(hx5d, hx4)
|
| 417 |
-
|
| 418 |
-
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
|
| 419 |
-
hx4dup = _upsample_like(hx4d, hx3)
|
| 420 |
-
|
| 421 |
-
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
|
| 422 |
-
hx3dup = _upsample_like(hx3d, hx2)
|
| 423 |
-
|
| 424 |
-
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
|
| 425 |
-
hx2dup = _upsample_like(hx2d, hx1)
|
| 426 |
-
|
| 427 |
-
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
|
| 428 |
-
|
| 429 |
-
# side output
|
| 430 |
-
d1 = self.side1(hx1d)
|
| 431 |
-
|
| 432 |
-
d2 = self.side2(hx2d)
|
| 433 |
-
d2 = _upsample_like(d2, d1)
|
| 434 |
-
|
| 435 |
-
d3 = self.side3(hx3d)
|
| 436 |
-
d3 = _upsample_like(d3, d1)
|
| 437 |
-
|
| 438 |
-
d4 = self.side4(hx4d)
|
| 439 |
-
d4 = _upsample_like(d4, d1)
|
| 440 |
-
|
| 441 |
-
d5 = self.side5(hx5d)
|
| 442 |
-
d5 = _upsample_like(d5, d1)
|
| 443 |
-
|
| 444 |
-
d6 = self.side6(hx6)
|
| 445 |
-
d6 = _upsample_like(d6, d1)
|
| 446 |
-
|
| 447 |
-
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
| 448 |
-
|
| 449 |
-
"""
|
| 450 |
-
del hx1, hx2, hx3, hx4, hx5, hx6
|
| 451 |
-
del hx5d, hx4d, hx3d, hx2d, hx1d
|
| 452 |
-
del hx6up, hx5dup, hx4dup, hx3dup, hx2dup
|
| 453 |
-
"""
|
| 454 |
-
|
| 455 |
-
return d0, d1, d2, d3, d4, d5, d6
|
| 456 |
-
|
| 457 |
-
|
| 458 |
-
### U^2-Net small ###
|
| 459 |
-
class U2NETP(nn.Module):
|
| 460 |
-
def __init__(self, in_ch=3, out_ch=1):
|
| 461 |
-
super(U2NETP, self).__init__()
|
| 462 |
-
|
| 463 |
-
self.stage1 = RSU7(in_ch, 16, 64)
|
| 464 |
-
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 465 |
-
|
| 466 |
-
self.stage2 = RSU6(64, 16, 64)
|
| 467 |
-
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 468 |
-
|
| 469 |
-
self.stage3 = RSU5(64, 16, 64)
|
| 470 |
-
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 471 |
-
|
| 472 |
-
self.stage4 = RSU4(64, 16, 64)
|
| 473 |
-
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 474 |
-
|
| 475 |
-
self.stage5 = RSU4F(64, 16, 64)
|
| 476 |
-
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
| 477 |
-
|
| 478 |
-
self.stage6 = RSU4F(64, 16, 64)
|
| 479 |
-
|
| 480 |
-
# decoder
|
| 481 |
-
self.stage5d = RSU4F(128, 16, 64)
|
| 482 |
-
self.stage4d = RSU4(128, 16, 64)
|
| 483 |
-
self.stage3d = RSU5(128, 16, 64)
|
| 484 |
-
self.stage2d = RSU6(128, 16, 64)
|
| 485 |
-
self.stage1d = RSU7(128, 16, 64)
|
| 486 |
-
|
| 487 |
-
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 488 |
-
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 489 |
-
self.side3 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 490 |
-
self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 491 |
-
self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 492 |
-
self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
|
| 493 |
-
|
| 494 |
-
self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
|
| 495 |
-
|
| 496 |
-
def forward(self, x):
|
| 497 |
-
|
| 498 |
-
hx = x
|
| 499 |
-
|
| 500 |
-
# stage 1
|
| 501 |
-
hx1 = self.stage1(hx)
|
| 502 |
-
hx = self.pool12(hx1)
|
| 503 |
-
|
| 504 |
-
# stage 2
|
| 505 |
-
hx2 = self.stage2(hx)
|
| 506 |
-
hx = self.pool23(hx2)
|
| 507 |
-
|
| 508 |
-
# stage 3
|
| 509 |
-
hx3 = self.stage3(hx)
|
| 510 |
-
hx = self.pool34(hx3)
|
| 511 |
-
|
| 512 |
-
# stage 4
|
| 513 |
-
hx4 = self.stage4(hx)
|
| 514 |
-
hx = self.pool45(hx4)
|
| 515 |
-
|
| 516 |
-
# stage 5
|
| 517 |
-
hx5 = self.stage5(hx)
|
| 518 |
-
hx = self.pool56(hx5)
|
| 519 |
-
|
| 520 |
-
# stage 6
|
| 521 |
-
hx6 = self.stage6(hx)
|
| 522 |
-
hx6up = _upsample_like(hx6, hx5)
|
| 523 |
-
|
| 524 |
-
# decoder
|
| 525 |
-
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
|
| 526 |
-
hx5dup = _upsample_like(hx5d, hx4)
|
| 527 |
-
|
| 528 |
-
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
|
| 529 |
-
hx4dup = _upsample_like(hx4d, hx3)
|
| 530 |
-
|
| 531 |
-
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
|
| 532 |
-
hx3dup = _upsample_like(hx3d, hx2)
|
| 533 |
-
|
| 534 |
-
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
|
| 535 |
-
hx2dup = _upsample_like(hx2d, hx1)
|
| 536 |
-
|
| 537 |
-
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
|
| 538 |
-
|
| 539 |
-
# side output
|
| 540 |
-
d1 = self.side1(hx1d)
|
| 541 |
-
|
| 542 |
-
d2 = self.side2(hx2d)
|
| 543 |
-
d2 = _upsample_like(d2, d1)
|
| 544 |
-
|
| 545 |
-
d3 = self.side3(hx3d)
|
| 546 |
-
d3 = _upsample_like(d3, d1)
|
| 547 |
-
|
| 548 |
-
d4 = self.side4(hx4d)
|
| 549 |
-
d4 = _upsample_like(d4, d1)
|
| 550 |
-
|
| 551 |
-
d5 = self.side5(hx5d)
|
| 552 |
-
d5 = _upsample_like(d5, d1)
|
| 553 |
-
|
| 554 |
-
d6 = self.side6(hx6)
|
| 555 |
-
d6 = _upsample_like(d6, d1)
|
| 556 |
-
|
| 557 |
-
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
| 558 |
-
|
| 559 |
-
"""
|
| 560 |
-
del hx1, hx2, hx3, hx4, hx5, hx6
|
| 561 |
-
del hx5d, hx4d, hx3d, hx2d, hx1d
|
| 562 |
-
del hx6up, hx5dup, hx4dup, hx3dup, hx2dup
|
| 563 |
-
"""
|
| 564 |
-
|
| 565 |
-
return d0, d1, d2, d3, d4, d5, d6
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