(self, output_dim: int = 16, input_dim=None, stride: int = 1, groups=1)
| 101 | |
| 102 | class CausalEncoderBlock(nn.Module): |
| 103 | def __init__(self, output_dim: int = 16, input_dim=None, stride: int = 1, groups=1): |
| 104 | super().__init__() |
| 105 | input_dim = input_dim or output_dim // 2 |
| 106 | self.block = nn.Sequential( |
| 107 | CausalResidualUnit(input_dim, dilation=1, groups=groups), |
| 108 | CausalResidualUnit(input_dim, dilation=3, groups=groups), |
| 109 | CausalResidualUnit(input_dim, dilation=9, groups=groups), |
| 110 | Snake1d(input_dim), |
| 111 | WNCausalConv1d( |
| 112 | input_dim, |
| 113 | output_dim, |
| 114 | kernel_size=2 * stride, |
| 115 | stride=stride, |
| 116 | padding=math.ceil(stride / 2), |
| 117 | output_padding=stride % 2, |
| 118 | ), |
| 119 | ) |
| 120 | |
| 121 | def forward(self, x): |
| 122 | return self.block(x) |
nothing calls this directly
no test coverage detected