(dim_in: int, dim_out: int)
| 95 | padding = (dilation * (kernel_size - 1)) // 2 |
| 96 | |
| 97 | def _create_block(dim_in: int, dim_out: int) -> list[nn.Module]: |
| 98 | layers = [ |
| 99 | norm_layer_2d(dim_in, norm_type, num_groups=norm_num_groups), |
| 100 | actvn, |
| 101 | ] |
| 102 | |
| 103 | layers.append( |
| 104 | nn.Conv2d( |
| 105 | dim_in, |
| 106 | dim_out, |
| 107 | kernel_size=kernel_size, |
| 108 | stride=1, |
| 109 | dilation=dilation, |
| 110 | padding=padding, |
| 111 | ) |
| 112 | ) |
| 113 | return layers |
| 114 | |
| 115 | residual = nn.Sequential( |
| 116 | *_create_block(dim_in, dim_hidden), |
no test coverage detected