def __init__()

in ResNetBasic.py [0:0]

• 29 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self,block,list_of_num_layers, list_of_out_dims, num_classes=1000, only_trunk=False ):
        # list_of_num_layers specifies number of layers in each stage
        # list_of_out_dims specifies number of output channel for each stage
        super(ResNet,self).__init__()
        self.grads = []
        self.fmaps = []
        assert len(list_of_num_layers)==4, 'Can have only four stages'
        conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                               bias=False)
        bn1 = nn.BatchNorm2d(64)
        relu = nn.ReLU()
        pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        init_layer(conv1)
        init_layer(bn1)


        trunk = [conv1, bn1, relu, pool1]
        indim = 64
        for i in range(4):

            for j in range(list_of_num_layers[i]):
                half_res = (i>=1) and (j==0)
                B = block(indim, list_of_out_dims[i], half_res)
                trunk.append(B)
                indim = list_of_out_dims[i]



        self.only_trunk=only_trunk
        if not only_trunk:
            avgpool = nn.AvgPool2d(7)
            trunk.append(avgpool)

        self.trunk = nn.Sequential(*trunk)
        self.final_feat_dim = indim
        if not only_trunk:
            self.classifier = nn.Linear(indim, num_classes)
            self.classifier.bias.data.fill_(0)