def __init__()

in imnet_finetune/pnasnet.py [0:0]

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

    def __init__(self, num_classes=1001):
        super(PNASNet5Large, self).__init__()
        self.num_classes = num_classes
        self.conv_0 = nn.Sequential(OrderedDict([
            ('conv', nn.Conv2d(3, 96, kernel_size=3, stride=2, bias=False)),
            ('bn', nn.BatchNorm2d(96, eps=0.001))
        ]))
        self.cell_stem_0 = CellStem0(in_channels_left=96, out_channels_left=54,
                                     in_channels_right=96,
                                     out_channels_right=54)
        self.cell_stem_1 = Cell(in_channels_left=96, out_channels_left=108,
                                in_channels_right=270, out_channels_right=108,
                                match_prev_layer_dimensions=True,
                                is_reduction=True)
        self.cell_0 = Cell(in_channels_left=270, out_channels_left=216,
                           in_channels_right=540, out_channels_right=216,
                           match_prev_layer_dimensions=True)
        self.cell_1 = Cell(in_channels_left=540, out_channels_left=216,
                           in_channels_right=1080, out_channels_right=216)
        self.cell_2 = Cell(in_channels_left=1080, out_channels_left=216,
                           in_channels_right=1080, out_channels_right=216)
        self.cell_3 = Cell(in_channels_left=1080, out_channels_left=216,
                           in_channels_right=1080, out_channels_right=216)
        self.cell_4 = Cell(in_channels_left=1080, out_channels_left=432,
                           in_channels_right=1080, out_channels_right=432,
                           is_reduction=True, zero_pad=True)
        self.cell_5 = Cell(in_channels_left=1080, out_channels_left=432,
                           in_channels_right=2160, out_channels_right=432,
                           match_prev_layer_dimensions=True)
        self.cell_6 = Cell(in_channels_left=2160, out_channels_left=432,
                           in_channels_right=2160, out_channels_right=432)
        self.cell_7 = Cell(in_channels_left=2160, out_channels_left=432,
                           in_channels_right=2160, out_channels_right=432)
        self.cell_8 = Cell(in_channels_left=2160, out_channels_left=864,
                           in_channels_right=2160, out_channels_right=864,
                           is_reduction=True)
        self.cell_9 = Cell(in_channels_left=2160, out_channels_left=864,
                           in_channels_right=4320, out_channels_right=864,
                           match_prev_layer_dimensions=True)
        self.cell_10 = Cell(in_channels_left=4320, out_channels_left=864,
                            in_channels_right=4320, out_channels_right=864)
        self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864,
                            in_channels_right=4320, out_channels_right=864)
        self.relu = nn.ReLU()
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.5)
        self.last_linear = nn.Linear(4320, num_classes)