Apply UNet Encoder to image. Args: input: The input image. Returns: The output multi-level feature map from encoder.
(self, input: torch.Tensor)
| 92 | self.convs_down.append(convs_down_i) |
| 93 | |
| 94 | def forward(self, input: torch.Tensor) -> list[torch.Tensor]: |
| 95 | """Apply UNet Encoder to image. |
| 96 | |
| 97 | Args: |
| 98 | input: The input image. |
| 99 | |
| 100 | Returns: |
| 101 | The output multi-level feature map from encoder. |
| 102 | """ |
| 103 | features = [] |
| 104 | |
| 105 | feat_i = self.conv_in(input) |
| 106 | features.append(feat_i) |
| 107 | |
| 108 | for conv_down in self.convs_down: |
| 109 | feat_i = conv_down(feat_i) |
| 110 | features.append(feat_i) |
| 111 | |
| 112 | return features |
| 113 | |
| 114 | @property |
| 115 | def out_width(self) -> int: |
nothing calls this directly
no outgoing calls
no test coverage detected