def __init__()

in models/src/wavegrad/upsampling.py [0:0]

• 44 lines of code
• 2 McCabe index (conditional complexity)

    def __init__(self, in_channels, out_channels, factor, dilations):
        super(UpsamplingBlock, self).__init__()
        self.first_block_main_branch = torch.nn.ModuleDict(
            {
                "upsampling": torch.nn.Sequential(
                    *[
                        torch.nn.LeakyReLU(0.2),
                        InterpolationBlock(
                            scale_factor=factor, mode="linear", align_corners=False
                        ),
                        Conv1dWithInitialization(
                            in_channels=in_channels,
                            out_channels=out_channels,
                            kernel_size=3,
                            stride=1,
                            padding=dilations[0],
                            dilation=dilations[0],
                        ),
                    ]
                ),
                "modulation": BasicModulationBlock(out_channels, dilation=dilations[1]),
            }
        )
        self.first_block_residual_branch = torch.nn.Sequential(
            *[
                Conv1dWithInitialization(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_size=1,
                    stride=1,
                ),
                InterpolationBlock(
                    scale_factor=factor, mode="linear", align_corners=False
                ),
            ]
        )
        self.second_block_main_branch = torch.nn.ModuleDict(
            {
                f"modulation_{idx}": BasicModulationBlock(
                    out_channels, dilation=dilations[2 + idx]
                )
                for idx in range(2)
            }
        )