def __init__()

in models/modules/static_layers.py [0:0]

• 39 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, in_channels, out_channels,
                 kernel_size=3, stride=1, expand_ratio=6, mid_channels=None, act_func='relu6', use_se=False, channels_per_group=1):
        super(MBInvertedConvLayer, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels

        self.kernel_size = kernel_size
        self.stride = stride
        self.expand_ratio = expand_ratio
        self.mid_channels = mid_channels
        self.act_func = act_func
        self.use_se = use_se

        self.channels_per_group = channels_per_group

        if self.mid_channels is None:
            feature_dim = round(self.in_channels * self.expand_ratio)
        else:
            feature_dim = self.mid_channels

        if self.expand_ratio == 1:
            self.inverted_bottleneck = None
        else:
            self.inverted_bottleneck = nn.Sequential(OrderedDict([
                ('conv', nn.Conv2d(self.in_channels, feature_dim, 1, 1, 0, bias=False)),
                ('bn', nn.BatchNorm2d(feature_dim)),
                ('act', build_activation(self.act_func, inplace=True)),
            ]))

        assert feature_dim % self.channels_per_group == 0
        active_groups = feature_dim // self.channels_per_group
        pad = get_same_padding(self.kernel_size)
        depth_conv_modules = [
            ('conv', nn.Conv2d(feature_dim, feature_dim, kernel_size, stride, pad, groups=active_groups, bias=False)),
            ('bn', nn.BatchNorm2d(feature_dim)),
            ('act', build_activation(self.act_func, inplace=True))
        ]
        if self.use_se:
            depth_conv_modules.append(('se', SELayer(feature_dim)))
        self.depth_conv = nn.Sequential(OrderedDict(depth_conv_modules))

        self.point_linear = nn.Sequential(OrderedDict([
            ('conv', nn.Conv2d(feature_dim, out_channels, 1, 1, 0, bias=False)),
            ('bn', nn.BatchNorm2d(out_channels)),
        ]))