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  • yolo.v2 darknet19结构

    Darknet19(
      (conv1s): Sequential(
        (0): Sequential(
          (0): Conv2d_BatchNorm(
            (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (1): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (2): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (3): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
        (4): Sequential(
          (0): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False)
          (1): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (2): Conv2d_BatchNorm(
            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (3): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (4): Conv2d_BatchNorm(
            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
          (5): Conv2d_BatchNorm(
            (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True)
            (relu): LeakyReLU(0.1, inplace)
          )
        )
      )

    (conv2): Sequential( (0): MaxPool2d(kernel_size
    =(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) (1): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (2): Conv2d_BatchNorm( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (3): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (4): Conv2d_BatchNorm( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (5): Conv2d_BatchNorm( (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) )

    (conv3): Sequential( (0): Conv2d_BatchNorm( (conv): Conv2d(
    1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) (1): Conv2d_BatchNorm( (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) ) (reorg): ReorgLayer( )

    (conv4): Sequential( (0): Conv2d_BatchNorm( (conv): Conv2d(
    3072, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.01, affine=True) (relu): LeakyReLU(0.1, inplace) ) )

    (conv5): Conv2d( (conv): Conv2d(
    1024, 125, kernel_size=(1, 1), stride=(1, 1)) )

    (global_average_pool): AvgPool2d(kernel_size
    =(1, 1), stride=(1, 1), padding=0, ceil_mode=False, count_include_pad=True) )
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  • 原文地址:https://www.cnblogs.com/buyizhiyou/p/9237527.html
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