| 139 | |
| 140 | class UNetBlock(nn.Module): |
| 141 | def __init__(self, in_channels, out_channels, down=True): |
| 142 | super().__init__() |
| 143 | self.down = down |
| 144 | |
| 145 | if down: |
| 146 | self.conv = nn.Sequential( |
| 147 | nn.Conv2d(in_channels, out_channels, 3, padding=1), |
| 148 | nn.BatchNorm2d(out_channels), |
| 149 | nn.ReLU(inplace=True), |
| 150 | nn.Conv2d(out_channels, out_channels, 3, padding=1), |
| 151 | nn.BatchNorm2d(out_channels), |
| 152 | nn.ReLU(inplace=True) |
| 153 | ) |
| 154 | self.pool = nn.MaxPool2d(2) |
| 155 | else: |
| 156 | self.conv = nn.Sequential( |
| 157 | nn.Conv2d(in_channels, out_channels, 3, padding=1), |
| 158 | nn.BatchNorm2d(out_channels), |
| 159 | nn.ReLU(inplace=True), |
| 160 | nn.Conv2d(out_channels, out_channels, 3, padding=1), |
| 161 | nn.BatchNorm2d(out_channels), |
| 162 | nn.ReLU(inplace=True) |
| 163 | ) |
| 164 | self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2) |
| 165 | |
| 166 | def forward(self, x, skip=None): |
| 167 | if self.down: |