(
self,
in_channels: int,
out_channels: int,
block_out_channels: List[int] = [64],
layers_per_block: int = 2,
resnet_groups: int = 32,
)
| 94 | """Implements the encoder side of the Autoencoder.""" |
| 95 | |
| 96 | def __init__( |
| 97 | self, |
| 98 | in_channels: int, |
| 99 | out_channels: int, |
| 100 | block_out_channels: List[int] = [64], |
| 101 | layers_per_block: int = 2, |
| 102 | resnet_groups: int = 32, |
| 103 | ): |
| 104 | super().__init__() |
| 105 | |
| 106 | self.conv_in = nn.Conv2d( |
| 107 | in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1 |
| 108 | ) |
| 109 | |
| 110 | channels = [block_out_channels[0]] + list(block_out_channels) |
| 111 | self.down_blocks = [ |
| 112 | EncoderDecoderBlock2D( |
| 113 | in_channels, |
| 114 | out_channels, |
| 115 | num_layers=layers_per_block, |
| 116 | resnet_groups=resnet_groups, |
| 117 | add_downsample=i < len(block_out_channels) - 1, |
| 118 | add_upsample=False, |
| 119 | ) |
| 120 | for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:])) |
| 121 | ] |
| 122 | |
| 123 | self.mid_blocks = [ |
| 124 | ResnetBlock2D( |
| 125 | in_channels=block_out_channels[-1], |
| 126 | out_channels=block_out_channels[-1], |
| 127 | groups=resnet_groups, |
| 128 | ), |
| 129 | Attention(block_out_channels[-1], resnet_groups), |
| 130 | ResnetBlock2D( |
| 131 | in_channels=block_out_channels[-1], |
| 132 | out_channels=block_out_channels[-1], |
| 133 | groups=resnet_groups, |
| 134 | ), |
| 135 | ] |
| 136 | |
| 137 | self.conv_norm_out = nn.GroupNorm( |
| 138 | resnet_groups, block_out_channels[-1], pytorch_compatible=True |
| 139 | ) |
| 140 | self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, 3, padding=1) |
| 141 | |
| 142 | def __call__(self, x): |
| 143 | x = self.conv_in(x) |
no test coverage detected