def __init__()

in evaluations/inception_v3.py [0:0]

• 62 lines of code
• 1 McCabe index (conditional complexity)

    def __init__(self, in_channels, tmp_channels):
        super().__init__()
        self.conv = Conv2dLayer(in_channels, 192, kh=1, kw=1)
        self.tower = torch.nn.Sequential(
            collections.OrderedDict(
                [
                    ("conv", Conv2dLayer(in_channels, tmp_channels, kh=1, kw=1)),
                    (
                        "conv_1",
                        Conv2dLayer(
                            tmp_channels, tmp_channels, kh=1, kw=7, padding=[0, 3]
                        ),
                    ),
                    (
                        "conv_2",
                        Conv2dLayer(tmp_channels, 192, kh=7, kw=1, padding=[3, 0]),
                    ),
                ]
            )
        )
        self.tower_1 = torch.nn.Sequential(
            collections.OrderedDict(
                [
                    ("conv", Conv2dLayer(in_channels, tmp_channels, kh=1, kw=1)),
                    (
                        "conv_1",
                        Conv2dLayer(
                            tmp_channels, tmp_channels, kh=7, kw=1, padding=[3, 0]
                        ),
                    ),
                    (
                        "conv_2",
                        Conv2dLayer(
                            tmp_channels, tmp_channels, kh=1, kw=7, padding=[0, 3]
                        ),
                    ),
                    (
                        "conv_3",
                        Conv2dLayer(
                            tmp_channels, tmp_channels, kh=7, kw=1, padding=[3, 0]
                        ),
                    ),
                    (
                        "conv_4",
                        Conv2dLayer(tmp_channels, 192, kh=1, kw=7, padding=[0, 3]),
                    ),
                ]
            )
        )
        self.tower_2 = torch.nn.Sequential(
            collections.OrderedDict(
                [
                    (
                        "pool",
                        torch.nn.AvgPool2d(
                            kernel_size=3, stride=1, padding=1, count_include_pad=False
                        ),
                    ),
                    ("conv", Conv2dLayer(in_channels, 192, kh=1, kw=1)),
                ]
            )
        )