(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = 1,
causal: bool = False,
norm: str = "none",
trim_right_ratio: float = 1.0,
norm_kwargs: Dict[str, Any] = {},
out_padding: Union[int, List[Tuple[int, int]]] = 0,
groups: int = 1,
)
| 71 | """ |
| 72 | |
| 73 | def __init__( |
| 74 | self, |
| 75 | in_channels: int, |
| 76 | out_channels: int, |
| 77 | kernel_size: Union[int, Tuple[int, int]], |
| 78 | stride: Union[int, Tuple[int, int]] = 1, |
| 79 | causal: bool = False, |
| 80 | norm: str = "none", |
| 81 | trim_right_ratio: float = 1.0, |
| 82 | norm_kwargs: Dict[str, Any] = {}, |
| 83 | out_padding: Union[int, List[Tuple[int, int]]] = 0, |
| 84 | groups: int = 1, |
| 85 | ): |
| 86 | super().__init__() |
| 87 | self.convtr = NormConvTranspose2d( |
| 88 | in_channels, |
| 89 | out_channels, |
| 90 | kernel_size, |
| 91 | stride, |
| 92 | causal=causal, |
| 93 | norm=norm, |
| 94 | norm_kwargs=norm_kwargs, |
| 95 | groups=groups, |
| 96 | ) |
| 97 | if isinstance(out_padding, int): |
| 98 | self.out_padding = [(out_padding, out_padding), (out_padding, out_padding)] |
| 99 | else: |
| 100 | self.out_padding = out_padding |
| 101 | self.causal = causal |
| 102 | self.trim_right_ratio = trim_right_ratio |
| 103 | assert ( |
| 104 | self.causal or self.trim_right_ratio == 1.0 |
| 105 | ), "`trim_right_ratio` != 1.0 only makes sense for causal convolutions" |
| 106 | assert self.trim_right_ratio >= 0.0 and self.trim_right_ratio <= 1.0 |
| 107 | |
| 108 | def forward(self, x): |
| 109 | kernel_size = self.convtr.convtr.kernel_size[0] |
nothing calls this directly
no test coverage detected