/tmp/chandorkar_10482232
Wed May 14 06:03:14 PM CEST 2025
copying data..
load env
Wed May 14 06:03:16 PM CEST 2025
starting run..
no change     /beegfs/chandorkar/miniforge3/condabin/conda
no change     /beegfs/chandorkar/miniforge3/bin/conda
no change     /beegfs/chandorkar/miniforge3/bin/conda-env
no change     /beegfs/chandorkar/miniforge3/bin/activate
no change     /beegfs/chandorkar/miniforge3/bin/deactivate
no change     /beegfs/chandorkar/miniforge3/etc/profile.d/conda.sh
no change     /beegfs/chandorkar/miniforge3/etc/fish/conf.d/conda.fish
no change     /beegfs/chandorkar/miniforge3/shell/condabin/Conda.psm1
no change     /beegfs/chandorkar/miniforge3/shell/condabin/conda-hook.ps1
no change     /beegfs/chandorkar/miniforge3/lib/python3.10/site-packages/xontrib/conda.xsh
no change     /beegfs/chandorkar/miniforge3/etc/profile.d/conda.csh
modified      /beegfs/chandorkar/.bashrc

==> For changes to take effect, close and re-open your current shell. <==

Added mamba to /beegfs/chandorkar/.bashrc

==> For changes to take effect, close and re-open your current shell. <==

Run 'mamba init' to be able to run mamba activate/deactivate
and start a new shell session. Or use conda to activate/deactivate.

train_cfg : {'assigner': [{'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_pos_iou': 0.35, 'ignore_iof_thr': -1}, {'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_pos_iou': 0.35, 'ignore_iof_thr': -1}, {'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.6, 'neg_iou_thr': 0.45, 'min_pos_iou': 0.45, 'ignore_iof_thr': -1}], 'allowed_border': 0, 'pos_weight': -1, 'debug': False}
VoxelNet(
  1.099 M, 100.000% Params, 24.443 GFLOPs, 100.000% FLOPs, 
  (data_preprocessor): Det3DDataPreprocessor(
    0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, 
    (voxel_layer): VoxelizationByGridShape(voxel_size=[0.16, 0.16, 4], grid_shape=[432, 496, 1], point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1], max_num_points=100, max_voxels=(16000, 40000), deterministic=True)
  )
  (backbone): PELEENET_SECOND(
    0.472 M, 42.936% Params, 19.825 GFLOPs, 81.105% FLOPs, 
    (blocks): ModuleList(
      (0): Sequential(
        0.041 M, 3.712% Params, 8.821 GFLOPs, 36.089% FLOPs, 
        (0): _DenseBlock(
          0.041 M, 3.712% Params, 8.821 GFLOPs, 36.089% FLOPs, 
          (denselayer1): _DenseLayer(
            0.009 M, 0.830% Params, 1.971 GFLOPs, 8.065% FLOPs, 
            (layer1_1): _ConvLayer(
              0.001 M, 0.096% Params, 0.23 GFLOPs, 0.940% FLOPs, 
              (conv): Conv2d(0.001 M, 0.093% Params, 0.219 GFLOPs, 0.898% FLOPs, 64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.001 M, 0.096% Params, 0.23 GFLOPs, 0.940% FLOPs, 
              (conv): Conv2d(0.001 M, 0.093% Params, 0.219 GFLOPs, 0.898% FLOPs, 64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
          )
          (denselayer2): _DenseLayer(
            0.01 M, 0.923% Params, 2.191 GFLOPs, 8.963% FLOPs, 
            (layer1_1): _ConvLayer(
              0.002 M, 0.143% Params, 0.339 GFLOPs, 1.389% FLOPs, 
              (conv): Conv2d(0.002 M, 0.140% Params, 0.329 GFLOPs, 1.346% FLOPs, 96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.002 M, 0.143% Params, 0.339 GFLOPs, 1.389% FLOPs, 
              (conv): Conv2d(0.002 M, 0.140% Params, 0.329 GFLOPs, 1.346% FLOPs, 96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
          )
          (denselayer3): _DenseLayer(
            0.011 M, 1.016% Params, 2.41 GFLOPs, 9.860% FLOPs, 
            (layer1_1): _ConvLayer(
              0.002 M, 0.189% Params, 0.449 GFLOPs, 1.837% FLOPs, 
              (conv): Conv2d(0.002 M, 0.186% Params, 0.439 GFLOPs, 1.795% FLOPs, 128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.002 M, 0.189% Params, 0.449 GFLOPs, 1.837% FLOPs, 
              (conv): Conv2d(0.002 M, 0.186% Params, 0.439 GFLOPs, 1.795% FLOPs, 128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.504 GFLOPs, 2.062% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.494 GFLOPs, 2.020% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.028% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
            )
          )
          (denselayer_last0): _ConvLayer(
            0.01 M, 0.943% Params, 2.235 GFLOPs, 9.145% FLOPs, 
            (conv): Conv2d(0.01 M, 0.932% Params, 2.194 GFLOPs, 8.977% FLOPs, 160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.027 GFLOPs, 0.112% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            (actvn): ReLU(0.0 M, 0.000% Params, 0.014 GFLOPs, 0.056% FLOPs, inplace=True)
          )
          (avgpool2d_last0): AvgPool2d(0.0 M, 0.000% Params, 0.014 GFLOPs, 0.056% FLOPs, kernel_size=2, stride=2, padding=0)
        )
      )
      (1): Sequential(
        0.129 M, 11.700% Params, 6.933 GFLOPs, 28.364% FLOPs, 
        (0): _DenseBlock(
          0.129 M, 11.700% Params, 6.933 GFLOPs, 28.364% FLOPs, 
          (denselayer1): _DenseLayer(
            0.016 M, 1.441% Params, 0.855 GFLOPs, 3.496% FLOPs, 
            (layer1_1): _ConvLayer(
              0.002 M, 0.192% Params, 0.115 GFLOPs, 0.470% FLOPs, 
              (conv): Conv2d(0.002 M, 0.186% Params, 0.11 GFLOPs, 0.449% FLOPs, 64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.002 M, 0.192% Params, 0.115 GFLOPs, 0.470% FLOPs, 
              (conv): Conv2d(0.002 M, 0.186% Params, 0.11 GFLOPs, 0.449% FLOPs, 64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.126 GFLOPs, 0.515% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.123 GFLOPs, 0.505% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
          )
          (denselayer2): _DenseLayer(
            0.018 M, 1.627% Params, 0.964 GFLOPs, 3.945% FLOPs, 
            (layer1_1): _ConvLayer(
              0.003 M, 0.285% Params, 0.17 GFLOPs, 0.694% FLOPs, 
              (conv): Conv2d(0.003 M, 0.279% Params, 0.165 GFLOPs, 0.673% FLOPs, 96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.003 M, 0.285% Params, 0.17 GFLOPs, 0.694% FLOPs, 
              (conv): Conv2d(0.003 M, 0.279% Params, 0.165 GFLOPs, 0.673% FLOPs, 96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.126 GFLOPs, 0.515% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.123 GFLOPs, 0.505% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
          )
          (denselayer3): _DenseLayer(
            0.02 M, 1.814% Params, 1.074 GFLOPs, 4.394% FLOPs, 
            (layer1_1): _ConvLayer(
              0.004 M, 0.378% Params, 0.225 GFLOPs, 0.919% FLOPs, 
              (conv): Conv2d(0.004 M, 0.373% Params, 0.219 GFLOPs, 0.898% FLOPs, 128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.004 M, 0.378% Params, 0.225 GFLOPs, 0.919% FLOPs, 
              (conv): Conv2d(0.004 M, 0.373% Params, 0.219 GFLOPs, 0.898% FLOPs, 128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.126 GFLOPs, 0.515% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.123 GFLOPs, 0.505% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
          )
          (denselayer4): _DenseLayer(
            0.022 M, 2.000% Params, 1.184 GFLOPs, 4.842% FLOPs, 
            (layer1_1): _ConvLayer(
              0.005 M, 0.472% Params, 0.279 GFLOPs, 1.143% FLOPs, 
              (conv): Conv2d(0.005 M, 0.466% Params, 0.274 GFLOPs, 1.122% FLOPs, 160, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.005 M, 0.472% Params, 0.279 GFLOPs, 1.143% FLOPs, 
              (conv): Conv2d(0.005 M, 0.466% Params, 0.274 GFLOPs, 1.122% FLOPs, 160, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.126 GFLOPs, 0.515% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.123 GFLOPs, 0.505% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
          )
          (denselayer5): _DenseLayer(
            0.024 M, 2.186% Params, 1.293 GFLOPs, 5.291% FLOPs, 
            (layer1_1): _ConvLayer(
              0.006 M, 0.565% Params, 0.334 GFLOPs, 1.368% FLOPs, 
              (conv): Conv2d(0.006 M, 0.559% Params, 0.329 GFLOPs, 1.346% FLOPs, 192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.006 M, 0.565% Params, 0.334 GFLOPs, 1.368% FLOPs, 
              (conv): Conv2d(0.006 M, 0.559% Params, 0.329 GFLOPs, 1.346% FLOPs, 192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.006% Params, 0.003 GFLOPs, 0.014% FLOPs, 32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.007% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.005 M, 0.422% Params, 0.249 GFLOPs, 1.020% FLOPs, 
              (conv): Conv2d(0.005 M, 0.419% Params, 0.247 GFLOPs, 1.010% FLOPs, 32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.126 GFLOPs, 0.515% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.123 GFLOPs, 0.505% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.002 GFLOPs, 0.007% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
          )
          (denselayer_last1): _ConvLayer(
            0.029 M, 2.632% Params, 1.556 GFLOPs, 6.368% FLOPs, 
            (conv): Conv2d(0.029 M, 2.608% Params, 1.536 GFLOPs, 6.284% FLOPs, 224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm): BatchNorm2d(0.0 M, 0.023% Params, 0.014 GFLOPs, 0.056% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            (actvn): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.028% FLOPs, inplace=True)
          )
          (avgpool2d_last1): AvgPool2d(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.028% FLOPs, kernel_size=2, stride=2, padding=0)
        )
      )
      (2): Sequential(
        0.303 M, 27.524% Params, 4.071 GFLOPs, 16.653% FLOPs, 
        (0): _DenseBlock(
          0.303 M, 27.524% Params, 4.071 GFLOPs, 16.653% FLOPs, 
          (denselayer1): _DenseLayer(
            0.037 M, 3.409% Params, 0.504 GFLOPs, 2.063% FLOPs, 
            (layer1_1): _ConvLayer(
              0.008 M, 0.757% Params, 0.112 GFLOPs, 0.459% FLOPs, 
              (conv): Conv2d(0.008 M, 0.745% Params, 0.11 GFLOPs, 0.449% FLOPs, 128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.008 M, 0.757% Params, 0.112 GFLOPs, 0.459% FLOPs, 
              (conv): Conv2d(0.008 M, 0.745% Params, 0.11 GFLOPs, 0.449% FLOPs, 128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.031 GFLOPs, 0.129% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.031 GFLOPs, 0.126% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
          )
          (denselayer2): _DenseLayer(
            0.042 M, 3.782% Params, 0.559 GFLOPs, 2.287% FLOPs, 
            (layer1_1): _ConvLayer(
              0.01 M, 0.943% Params, 0.14 GFLOPs, 0.572% FLOPs, 
              (conv): Conv2d(0.01 M, 0.932% Params, 0.137 GFLOPs, 0.561% FLOPs, 160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.01 M, 0.943% Params, 0.14 GFLOPs, 0.572% FLOPs, 
              (conv): Conv2d(0.01 M, 0.932% Params, 0.137 GFLOPs, 0.561% FLOPs, 160, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.031 GFLOPs, 0.129% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.031 GFLOPs, 0.126% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
          )
          (denselayer3): _DenseLayer(
            0.046 M, 4.154% Params, 0.614 GFLOPs, 2.512% FLOPs, 
            (layer1_1): _ConvLayer(
              0.012 M, 1.130% Params, 0.167 GFLOPs, 0.684% FLOPs, 
              (conv): Conv2d(0.012 M, 1.118% Params, 0.165 GFLOPs, 0.673% FLOPs, 192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.012 M, 1.130% Params, 0.167 GFLOPs, 0.684% FLOPs, 
              (conv): Conv2d(0.012 M, 1.118% Params, 0.165 GFLOPs, 0.673% FLOPs, 192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.031 GFLOPs, 0.129% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.031 GFLOPs, 0.126% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
          )
          (denselayer4): _DenseLayer(
            0.05 M, 4.527% Params, 0.669 GFLOPs, 2.736% FLOPs, 
            (layer1_1): _ConvLayer(
              0.014 M, 1.316% Params, 0.195 GFLOPs, 0.796% FLOPs, 
              (conv): Conv2d(0.014 M, 1.304% Params, 0.192 GFLOPs, 0.785% FLOPs, 224, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.014 M, 1.316% Params, 0.195 GFLOPs, 0.796% FLOPs, 
              (conv): Conv2d(0.014 M, 1.304% Params, 0.192 GFLOPs, 0.785% FLOPs, 224, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.031 GFLOPs, 0.129% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.031 GFLOPs, 0.126% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
          )
          (denselayer5): _DenseLayer(
            0.054 M, 4.899% Params, 0.724 GFLOPs, 2.960% FLOPs, 
            (layer1_1): _ConvLayer(
              0.017 M, 1.502% Params, 0.222 GFLOPs, 0.908% FLOPs, 
              (conv): Conv2d(0.016 M, 1.490% Params, 0.219 GFLOPs, 0.898% FLOPs, 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer1_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_1): _ConvLayer(
              0.017 M, 1.502% Params, 0.222 GFLOPs, 0.908% FLOPs, 
              (conv): Conv2d(0.016 M, 1.490% Params, 0.219 GFLOPs, 0.898% FLOPs, 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.012% Params, 0.002 GFLOPs, 0.007% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.004% FLOPs, inplace=True)
            )
            (layer2_2): _ConvLayer(
              0.009 M, 0.841% Params, 0.124 GFLOPs, 0.508% FLOPs, 
              (conv): Conv2d(0.009 M, 0.838% Params, 0.123 GFLOPs, 0.505% FLOPs, 64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
            (layer2_3): _ConvLayer(
              0.002 M, 0.213% Params, 0.031 GFLOPs, 0.129% FLOPs, 
              (conv): Conv2d(0.002 M, 0.210% Params, 0.031 GFLOPs, 0.126% FLOPs, 16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (norm): BatchNorm2d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.002% FLOPs, 16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (actvn): ReLU(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.001% FLOPs, inplace=True)
            )
          )
          (denselayer_last2): _ConvLayer(
            0.074 M, 6.754% Params, 0.998 GFLOPs, 4.082% FLOPs, 
            (conv): Conv2d(0.074 M, 6.707% Params, 0.987 GFLOPs, 4.039% FLOPs, 288, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm): BatchNorm2d(0.001 M, 0.047% Params, 0.007 GFLOPs, 0.028% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            (actvn): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, inplace=True)
          )
          (avgpool2d_last2): AvgPool2d(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.014% FLOPs, kernel_size=2, stride=2, padding=0)
        )
      )
    )
  )
  init_cfg={'type': 'Kaiming', 'layer': 'Conv2d'}
  (neck): SECONDFPN(
    0.599 M, 54.473% Params, 3.134 GFLOPs, 12.820% FLOPs, 
    (deblocks): ModuleList(
      (0): Sequential(
        0.008 M, 0.769% Params, 0.459 GFLOPs, 1.879% FLOPs, 
        (0): ConvTranspose2d(0.008 M, 0.745% Params, 0.439 GFLOPs, 1.795% FLOPs, 64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(0.0 M, 0.023% Params, 0.014 GFLOPs, 0.056% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.028% FLOPs, inplace=True)
      )
      (1): Sequential(
        0.066 M, 5.985% Params, 0.898 GFLOPs, 3.675% FLOPs, 
        (0): ConvTranspose2d(0.066 M, 5.962% Params, 0.878 GFLOPs, 3.591% FLOPs, 128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
        (1): BatchNorm2d(0.0 M, 0.023% Params, 0.014 GFLOPs, 0.056% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.028% FLOPs, inplace=True)
      )
      (2): Sequential(
        0.525 M, 47.719% Params, 1.776 GFLOPs, 7.265% FLOPs, 
        (0): ConvTranspose2d(0.524 M, 47.695% Params, 1.755 GFLOPs, 7.181% FLOPs, 256, 128, kernel_size=(4, 4), stride=(4, 4), bias=False)
        (1): BatchNorm2d(0.0 M, 0.023% Params, 0.014 GFLOPs, 0.056% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.028% FLOPs, inplace=True)
      )
    )
  )
  init_cfg=[{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
  (bbox_head): Anchor3DHead(
    0.028 M, 2.522% Params, 1.485 GFLOPs, 6.075% FLOPs, 
    (loss_cls): FocalLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
    (loss_bbox): SmoothL1Loss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
    (loss_dir): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
    (conv_cls): Conv2d(0.007 M, 0.630% Params, 0.371 GFLOPs, 1.519% FLOPs, 384, 18, kernel_size=(1, 1), stride=(1, 1))
    (conv_reg): Conv2d(0.016 M, 1.471% Params, 0.866 GFLOPs, 3.544% FLOPs, 384, 42, kernel_size=(1, 1), stride=(1, 1))
    (conv_dir_cls): Conv2d(0.005 M, 0.420% Params, 0.247 GFLOPs, 1.012% FLOPs, 384, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  init_cfg={'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'conv_cls', 'std': 0.01, 'bias_prob': 0.01}}
  (voxel_layer): Voxelization(voxel_size=[0.16, 0.16, 4], point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1], max_num_points=32, max_voxels=(20000, 20000), deterministic=True)
  (voxel_encoder): PillarFeatureNet(
    0.001 M, 0.070% Params, 0.0 GFLOPs, 0.000% FLOPs, 
    (pfn_layers): ModuleList(
      (0): PFNLayer(
        0.001 M, 0.070% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (norm): BatchNorm1d(0.0 M, 0.012% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (linear): Linear(0.001 M, 0.058% Params, 0.0 GFLOPs, 0.000% FLOPs, in_features=10, out_features=64, bias=False)
      )
    )
  )
  (middle_encoder): PointPillarsScatter(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
)
==============================
Input shape: (40000, 4)
Flops: 24.44 GFLOPs
Params: 1.1 M
==============================
!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
/beegfs/chandorkar/projects/mmdetection3d/mmdet3d/models/dense_heads/anchor3d_head.py:94: UserWarning: dir_offset and dir_limit_offset will be depressed and be incorporated into box coder in the future
  warnings.warn(
clean up done at /tmp/chandorkar_10482232
