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)
)