DenseNet网络模型官方代码详解(Pytorch版本)

(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition1): _Transition( (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (denseblock2): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition2): _Transition( (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)

) (denseblock3): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)

) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer( (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer( (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer( (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer( (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) )

(denselayer17): _DenseLayer( (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer18): _DenseLayer( (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer19): _DenseLayer( (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer20): _DenseLayer( (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer21): _DenseLayer( (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer22): _DenseLayer( (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer23): _DenseLayer( (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer24): _DenseLayer( (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) )

(transition3): _Transition( (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (denseblock4): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

(relu1): ReLU(inplace=True) (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer( (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer( (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer( (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer( (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)