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) )