def __init_

def init()

in models/mobilenet_v1.py [0:0]
85 lines of code
1 McCabe index (conditional complexity)

    def __init__(self):
        super().__init__()

        self.model_0_0 = torch.nn.modules.conv.Conv2d(3, 32, 3, 2, 1, bias=False)
        self.model_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self.model_0_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_1_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self.model_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self.model_1_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_1_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self.model_1_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self.model_1_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_2_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 2, 1, groups=64, bias=False)
        self.model_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self.model_2_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_2_3 = torch.nn.modules.conv.Conv2d(64, 128, 1, 1, 0, bias=False)
        self.model_2_4 = torch.nn.modules.batchnorm.BatchNorm2d(128)
        self.model_2_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_3_0 = torch.nn.modules.conv.Conv2d(128, 128, 3, 1, 1, groups=128, bias=False)
        self.model_3_1 = torch.nn.modules.batchnorm.BatchNorm2d(128)
        self.model_3_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_3_3 = torch.nn.modules.conv.Conv2d(128, 128, 1, 1, 0, bias=False)
        self.model_3_4 = torch.nn.modules.batchnorm.BatchNorm2d(128)
        self.model_3_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_4_0 = torch.nn.modules.conv.Conv2d(128, 128, 3, 2, 1, groups=128, bias=False)
        self.model_4_1 = torch.nn.modules.batchnorm.BatchNorm2d(128)
        self.model_4_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_4_3 = torch.nn.modules.conv.Conv2d(128, 256, 1, 1, 0, bias=False)
        self.model_4_4 = torch.nn.modules.batchnorm.BatchNorm2d(256)
        self.model_4_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_5_0 = torch.nn.modules.conv.Conv2d(256, 256, 3, 1, 1, groups=256, bias=False)
        self.model_5_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
        self.model_5_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_5_3 = torch.nn.modules.conv.Conv2d(256, 256, 1, 1, 0, bias=False)
        self.model_5_4 = torch.nn.modules.batchnorm.BatchNorm2d(256)
        self.model_5_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_6_0 = torch.nn.modules.conv.Conv2d(256, 256, 3, 2, 1, groups=256, bias=False)
        self.model_6_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
        self.model_6_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_6_3 = torch.nn.modules.conv.Conv2d(256, 512, 1, 1, 0, bias=False)
        self.model_6_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_6_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_7_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
        self.model_7_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_7_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_7_3 = torch.nn.modules.conv.Conv2d(512, 512, 1, 1, 0, bias=False)
        self.model_7_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_7_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_8_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
        self.model_8_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_8_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_8_3 = torch.nn.modules.conv.Conv2d(512, 512, 1, 1, 0, bias=False)
        self.model_8_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_8_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_9_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
        self.model_9_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_9_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_9_3 = torch.nn.modules.conv.Conv2d(512, 512, 1, 1, 0, bias=False)
        self.model_9_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_9_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_10_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
        self.model_10_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_10_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_10_3 = torch.nn.modules.conv.Conv2d(512, 512, 1, 1, 0, bias=False)
        self.model_10_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_10_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_11_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 1, 1, groups=512, bias=False)
        self.model_11_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_11_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_11_3 = torch.nn.modules.conv.Conv2d(512, 512, 1, 1, 0, bias=False)
        self.model_11_4 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_11_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_12_0 = torch.nn.modules.conv.Conv2d(512, 512, 3, 2, 1, groups=512, bias=False)
        self.model_12_1 = torch.nn.modules.batchnorm.BatchNorm2d(512)
        self.model_12_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_12_3 = torch.nn.modules.conv.Conv2d(512, 1024, 1, 1, 0, bias=False)
        self.model_12_4 = torch.nn.modules.batchnorm.BatchNorm2d(1024)
        self.model_12_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_13_0 = torch.nn.modules.conv.Conv2d(1024, 1024, 3, 1, 1, groups=1024, bias=False)
        self.model_13_1 = torch.nn.modules.batchnorm.BatchNorm2d(1024)
        self.model_13_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_13_3 = torch.nn.modules.conv.Conv2d(1024, 1024, 1, 1, 0, bias=False)
        self.model_13_4 = torch.nn.modules.batchnorm.BatchNorm2d(1024)
        self.model_13_5 = torch.nn.modules.activation.ReLU(inplace=True)
        self.model_14 = torch.nn.modules.pooling.AvgPool2d(7)
        self.fc = torch.nn.modules.linear.Linear(1024, 1000)
def __init__()

def init()
