DenseNet(
(features): Sequential(
(init_conv): Conv2d(3, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(denseblock_1): _DenseBlock(
(denselayer_1): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(24, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_2): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(36, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_3): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_4): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(60, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_5): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(72, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_6): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(84, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_7): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_8): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(108, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_9): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_10): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(132, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_11): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_12): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(156, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(156, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_13): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(168, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_14): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(180, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(180, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_15): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_16): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(204, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(204, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(transition_1): _Transition(
(conv): Conv(
(norm): BatchNorm2d(216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(216, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock_2): _DenseBlock(
(denselayer_1): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(108, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_2): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_3): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(132, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_4): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_5): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(156, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(156, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_6): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(168, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_7): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(180, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(180, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_8): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_9): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(204, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(204, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_10): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(216, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_11): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(228, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(228, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_12): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(240, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_13): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(252, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(252, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_14): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(264, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_15): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(276, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(276, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_16): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(transition_2): _Transition(
(conv): Conv(
(norm): BatchNorm2d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(300, 150, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
)
(denseblock_3): _DenseBlock(
(denselayer_1): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(150, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(150, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_2): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(162, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(162, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_3): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(174, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(174, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_4): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(186, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(186, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_5): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(198, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(198, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_6): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(210, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(210, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_7): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(222, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(222, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_8): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(234, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(234, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_9): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(246, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(246, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_10): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(258, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(258, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_11): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(270, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(270, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_12): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(282, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(282, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_13): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(294, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(294, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_14): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(306, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(306, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_15): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(318, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(318, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(denselayer_16): _DenseLayer(
(conv_1): Conv(
(norm): BatchNorm2d(330, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(330, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(conv_2): Conv(
(norm): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
)
(norm_last): BatchNorm2d(342, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu_last): ReLU(inplace)
(pool_last): AvgPool2d(kernel_size=8, stride=8, padding=0)
)
(classifier): Linear(in_features=342, out_features=10, bias=True)
)
FLOPs: 292.38M, Params: 0.77M
