(
self,
in_channels: int = 3,
channels: List[int] = [320, 640, 1280, 1280],
num_res_blocks: int = 2,
downscale_factor: int = 8,
)
| 294 | """ |
| 295 | |
| 296 | def __init__( |
| 297 | self, |
| 298 | in_channels: int = 3, |
| 299 | channels: List[int] = [320, 640, 1280, 1280], |
| 300 | num_res_blocks: int = 2, |
| 301 | downscale_factor: int = 8, |
| 302 | ): |
| 303 | super().__init__() |
| 304 | |
| 305 | in_channels = in_channels * downscale_factor**2 |
| 306 | |
| 307 | self.unshuffle = nn.PixelUnshuffle(downscale_factor) |
| 308 | self.conv_in = nn.Conv2d(in_channels, channels[0], kernel_size=3, padding=1) |
| 309 | |
| 310 | self.body = nn.ModuleList( |
| 311 | [ |
| 312 | AdapterBlock(channels[0], channels[0], num_res_blocks), |
| 313 | *[ |
| 314 | AdapterBlock(channels[i - 1], channels[i], num_res_blocks, down=True) |
| 315 | for i in range(1, len(channels)) |
| 316 | ], |
| 317 | ] |
| 318 | ) |
| 319 | |
| 320 | self.total_downscale_factor = downscale_factor * 2 ** (len(channels) - 1) |
| 321 | |
| 322 | def forward(self, x: torch.Tensor) -> List[torch.Tensor]: |
| 323 | r""" |
nothing calls this directly
no test coverage detected