| 124 | |
| 125 | |
| 126 | class ConvStack(nn.Module): |
| 127 | def __init__(self, |
| 128 | dim_in: List[Optional[int]], |
| 129 | dim_res_blocks: List[int], |
| 130 | dim_out: List[Optional[int]], |
| 131 | resamplers: Union[Literal['pixel_shuffle', 'nearest', 'bilinear', 'conv_transpose', 'pixel_unshuffle', 'avg_pool', 'max_pool'], List], |
| 132 | dim_times_res_block_hidden: int = 1, |
| 133 | num_res_blocks: int = 1, |
| 134 | res_block_in_norm: Literal['layer_norm', 'group_norm' , 'instance_norm', 'none'] = 'layer_norm', |
| 135 | res_block_hidden_norm: Literal['layer_norm', 'group_norm' , 'instance_norm', 'none'] = 'group_norm', |
| 136 | activation: Literal['relu', 'leaky_relu', 'silu', 'elu'] = 'relu', |
| 137 | ): |
| 138 | super().__init__() |
| 139 | self.input_blocks = nn.ModuleList([ |
| 140 | nn.Conv2d(dim_in_, dim_res_block_, kernel_size=1, stride=1, padding=0) if dim_in_ is not None else nn.Identity() |
| 141 | for dim_in_, dim_res_block_ in zip(dim_in if isinstance(dim_in, Sequence) else itertools.repeat(dim_in), dim_res_blocks) |
| 142 | ]) |
| 143 | self.resamplers = nn.ModuleList([ |
| 144 | Resampler(dim_prev, dim_succ, scale_factor=2, type_=resampler) |
| 145 | for i, (dim_prev, dim_succ, resampler) in enumerate(zip( |
| 146 | dim_res_blocks[:-1], |
| 147 | dim_res_blocks[1:], |
| 148 | resamplers if isinstance(resamplers, Sequence) else itertools.repeat(resamplers) |
| 149 | )) |
| 150 | ]) |
| 151 | self.res_blocks = nn.ModuleList([ |
| 152 | nn.Sequential( |
| 153 | *( |
| 154 | ResidualConvBlock( |
| 155 | dim_res_block_, dim_res_block_, dim_times_res_block_hidden * dim_res_block_, |
| 156 | activation=activation, in_norm=res_block_in_norm, hidden_norm=res_block_hidden_norm |
| 157 | ) for _ in range(num_res_blocks[i] if isinstance(num_res_blocks, list) else num_res_blocks) |
| 158 | ) |
| 159 | ) for i, dim_res_block_ in enumerate(dim_res_blocks) |
| 160 | ]) |
| 161 | self.output_blocks = nn.ModuleList([ |
| 162 | nn.Conv2d(dim_res_block_, dim_out_, kernel_size=1, stride=1, padding=0) if dim_out_ is not None else nn.Identity() |
| 163 | for dim_out_, dim_res_block_ in zip(dim_out if isinstance(dim_out, Sequence) else itertools.repeat(dim_out), dim_res_blocks) |
| 164 | ]) |
| 165 | |
| 166 | def enable_gradient_checkpointing(self): |
| 167 | for i in range(len(self.resamplers)): |
| 168 | self.resamplers[i] = wrap_module_with_gradient_checkpointing(self.resamplers[i]) |
| 169 | for i in range(len(self.res_blocks)): |
| 170 | for j in range(len(self.res_blocks[i])): |
| 171 | self.res_blocks[i][j] = wrap_module_with_gradient_checkpointing(self.res_blocks[i][j]) |
| 172 | |
| 173 | def forward(self, in_features: List[torch.Tensor]): |
| 174 | out_features = [] |
| 175 | for i in range(len(self.res_blocks)): |
| 176 | feature = self.input_blocks[i](in_features[i]) |
| 177 | if i == 0: |
| 178 | x = feature |
| 179 | elif feature is not None: |
| 180 | x = x + feature |
| 181 | x = self.res_blocks[i](x) |
| 182 | out_features.append(self.output_blocks[i](x)) |
| 183 | if i < len(self.res_blocks) - 1: |