(self, input_features)
| 129 | self.sigmoid = nn.Sigmoid() |
| 130 | |
| 131 | def forward(self, input_features): |
| 132 | outputs = {} |
| 133 | |
| 134 | # decode |
| 135 | x = input_features[-1] |
| 136 | for i in range(self.level_in, -1, -1): |
| 137 | x = self.convs[('upconv', i, 0)](x) |
| 138 | x = [upsample(x)] |
| 139 | if self.use_skips and i > 0: |
| 140 | x += [input_features[i - 1]] |
| 141 | x = torch.cat(x, 1) |
| 142 | x = self.convs[('upconv', i, 1)](x) |
| 143 | if i in self.scales: |
| 144 | outputs[('disp', i)] = self.sigmoid(self.convs[('dispconv', i)](x)) |
| 145 | return outputs |
nothing calls this directly
no test coverage detected