/tmp/chandorkar_10428844
Mon May 12 04:05:27 PM CEST 2025
copying data..
load env
Mon May 12 04:05:29 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.

2025-05-12 16:05:41,310 - INFO - Distributed training: False
2025-05-12 16:05:41,310 - INFO - torch.backends.cudnn.benchmark: False
2025-05-12 16:05:41,476 - INFO - Finish RPN Initialization
2025-05-12 16:05:41,793 - INFO - num_classes: [1, 2, 2, 1, 2, 2]
2025-05-12 16:05:41,825 - INFO - Finish CenterHead Initialization
2025-05-12 16:05:54,184 - INFO - {'car': 5, 'truck': 5, 'bus': 5, 'trailer': 5, 'construction_vehicle': 5, 'traffic_cone': 5, 'barrier': 5, 'motorcycle': 5, 'bicycle': 5, 'pedestrian': 5}
2025-05-12 16:05:54,184 - INFO - [-1]
2025-05-12 16:05:57,144 - INFO - load 62964 traffic_cone database infos
2025-05-12 16:05:57,144 - INFO - load 65262 truck database infos
2025-05-12 16:05:57,144 - INFO - load 339949 car database infos
2025-05-12 16:05:57,144 - INFO - load 161928 pedestrian database infos
2025-05-12 16:05:57,144 - INFO - load 26297 ignore database infos
2025-05-12 16:05:57,144 - INFO - load 11050 construction_vehicle database infos
2025-05-12 16:05:57,144 - INFO - load 107507 barrier database infos
2025-05-12 16:05:57,144 - INFO - load 8846 motorcycle database infos
2025-05-12 16:05:57,144 - INFO - load 8185 bicycle database infos
2025-05-12 16:05:57,144 - INFO - load 12286 bus database infos
2025-05-12 16:05:57,144 - INFO - load 19202 trailer database infos
2025-05-12 16:05:58,349 - INFO - After filter database:
2025-05-12 16:05:58,350 - INFO - load 55823 traffic_cone database infos
2025-05-12 16:05:58,350 - INFO - load 60428 truck database infos
2025-05-12 16:05:58,350 - INFO - load 294575 car database infos
2025-05-12 16:05:58,350 - INFO - load 148872 pedestrian database infos
2025-05-12 16:05:58,350 - INFO - load 26297 ignore database infos
2025-05-12 16:05:58,350 - INFO - load 10591 construction_vehicle database infos
2025-05-12 16:05:58,350 - INFO - load 102093 barrier database infos
2025-05-12 16:05:58,350 - INFO - load 8055 motorcycle database infos
2025-05-12 16:05:58,350 - INFO - load 7533 bicycle database infos
2025-05-12 16:05:58,350 - INFO - load 11622 bus database infos
2025-05-12 16:05:58,350 - INFO - load 18104 trailer database infos
2025-05-12 16:05:58,410 - INFO - model structure: FastPillar(
  (reader): Identity()
  (backbone): RepMiddlePillarEncoder2X34_6631(
    (act): LeakyReLU(negative_slope=0.01)
    (pillar_pooling1): PillarMaxPooling_dense(
      (groups): PillarQueryAndGroup()
      (score_fn): Sequential(
        (0): Linear(in_features=64, out_features=64, bias=True)
        (1): LeakyReLU(negative_slope=0.01)
      )
      (shared_mlps): Sequential(
        (0): Linear(in_features=8, out_features=64, bias=False)
        (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): LeakyReLU(negative_slope=0.01)
      )
    )
    (conv2_first_block): Sequential(
      (0): RepVGGBlock(
        (nonlinearity): LeakyReLU(negative_slope=0.01)
        (se): Identity()
        (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (rbr_dense): Sequential(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv2): Sequential(
      (0): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (3): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (4): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (5): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv3_first_block): Sequential(
      (0): RepVGGBlock(
        (nonlinearity): LeakyReLU(negative_slope=0.01)
        (se): Identity()
        (rbr_dense): Sequential(
          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv3): Sequential(
      (0): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (3): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (4): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (5): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv4_first_block): Sequential(
      (0): RepVGGBlock(
        (nonlinearity): LeakyReLU(negative_slope=0.01)
        (se): Identity()
        (rbr_dense): Sequential(
          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv4): Sequential(
      (0): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv5_first_block): Sequential(
      (0): RepVGGBlock(
        (nonlinearity): LeakyReLU(negative_slope=0.01)
        (se): Identity()
        (rbr_dense): Sequential(
          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv5): Sequential(
      (0): RepDense2DBasicBlock(
        (conv1): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          (nonlinearity): LeakyReLU(negative_slope=0.01)
          (se): Identity()
          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
  )
  (neck): RPNV2(
    (act): LeakyReLU(negative_slope=0.01)
    (block_5): Sequential(
      (0): ZeroPad2d((1, 1, 1, 1))
      (1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(negative_slope=0.01)
      (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (6): LeakyReLU(negative_slope=0.01)
      (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (9): LeakyReLU(negative_slope=0.01)
      (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (12): LeakyReLU(negative_slope=0.01)
      (13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (15): LeakyReLU(negative_slope=0.01)
      (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (18): LeakyReLU(negative_slope=0.01)
    )
    (deblock_5): Sequential(
      (0): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
      (1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (2): LeakyReLU(negative_slope=0.01)
    )
    (deblock_4): Sequential(
      (0): ZeroPad2d((1, 1, 1, 1))
      (1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(negative_slope=0.01)
    )
    (block_4): Sequential(
      (0): ZeroPad2d((1, 1, 1, 1))
      (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(negative_slope=0.01)
      (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (6): LeakyReLU(negative_slope=0.01)
      (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (9): LeakyReLU(negative_slope=0.01)
      (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (12): LeakyReLU(negative_slope=0.01)
      (13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (15): LeakyReLU(negative_slope=0.01)
      (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (18): LeakyReLU(negative_slope=0.01)
    )
  )
  (bbox_head): CenterHead_FP(
    (act): LeakyReLU(negative_slope=0.01, inplace=True)
    (crit): FastFocalLoss()
    (crit_reg): RegLoss()
    (crit_iou): IouLoss()
    (crit_iou_reg): IouRegLoss()
    (shared_conv): Sequential(
      (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (tasks): ModuleList(
      (0): SepHead(
        (act): LeakyReLU(negative_slope=0.01)
        (reg): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (1-2): 2 x SepHead(
        (act): LeakyReLU(negative_slope=0.01)
        (reg): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (3): SepHead(
        (act): LeakyReLU(negative_slope=0.01)
        (reg): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (4-5): 2 x SepHead(
        (act): LeakyReLU(negative_slope=0.01)
        (reg): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(negative_slope=0.01)
          (3): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
    )
  )
)
no apex
No Tensorflow
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  None
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  None
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  None
RepVGG Block, identity =  BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
RepVGG Block, identity =  BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
Use HM Bias:  -2.19
10
2025-05-12 16:06:04,917 - INFO - finding looplift candidates
2025-05-12 16:06:05,012 - INFO - finding looplift candidates
FastPillar(
  23.28 M, 100.000% Params, 136.939 GMac, 100.097% MACs, 
  (reader): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
  (backbone): RepMiddlePillarEncoder2X34_6631(
    13.43 M, 57.697% Params, 105.549 GMac, 77.152% MACs, 
    (act): LeakyReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, negative_slope=0.01)
    (pillar_pooling1): PillarMaxPooling_dense(
      0.0 M, 0.021% Params, 0.189 GMac, 0.138% MACs, 
      (groups): PillarQueryAndGroup(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
      (score_fn): Sequential(
        0.0 M, 0.018% Params, 0.16 GMac, 0.117% MACs, 
        (0): Linear(0.0 M, 0.018% Params, 0.155 GMac, 0.113% MACs, in_features=64, out_features=64, bias=True)
        (1): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.004% MACs, negative_slope=0.01)
      )
      (shared_mlps): Sequential(
        0.0 M, 0.003% Params, 0.029 GMac, 0.021% MACs, 
        (0): Linear(0.0 M, 0.002% Params, 0.019 GMac, 0.014% MACs, in_features=8, out_features=64, bias=False)
        (1): BatchNorm1d(0.0 M, 0.001% Params, 0.005 GMac, 0.004% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.004% MACs, negative_slope=0.01)
      )
    )
    (conv2_first_block): Sequential(
      0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
      (0): RepVGGBlock(
        0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
        (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
        (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
        (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (rbr_dense): Sequential(
          0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
          (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
          (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv2): Sequential(
      0.5 M, 2.131% Params, 36.799 GMac, 26.898% MACs, 
      (0): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (3): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (4): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (5): RepDense2DBasicBlock(
        0.08 M, 0.355% Params, 6.133 GMac, 4.483% MACs, 
        (conv1): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.04 M, 0.178% Params, 3.067 GMac, 2.242% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.005 GMac, 0.003% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.04 M, 0.159% Params, 2.74 GMac, 2.002% MACs, 
            (conv): Conv2d(0.04 M, 0.158% Params, 2.73 GMac, 1.996% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.0 M, 0.018% Params, 0.313 GMac, 0.229% MACs, 
            (conv): Conv2d(0.0 M, 0.018% Params, 0.303 GMac, 0.222% MACs, 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.009 GMac, 0.007% MACs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv3_first_block): Sequential(
      0.08 M, 0.354% Params, 1.529 GMac, 1.117% MACs, 
      (0): RepVGGBlock(
        0.08 M, 0.354% Params, 1.529 GMac, 1.117% MACs, 
        (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
        (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
        (rbr_dense): Sequential(
          0.07 M, 0.318% Params, 1.37 GMac, 1.001% MACs, 
          (conv): Conv2d(0.07 M, 0.317% Params, 1.365 GMac, 0.998% MACs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          0.01 M, 0.036% Params, 0.156 GMac, 0.114% MACs, 
          (conv): Conv2d(0.01 M, 0.035% Params, 0.152 GMac, 0.111% MACs, 64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv3): Sequential(
      1.98 M, 8.485% Params, 36.6 GMac, 26.753% MACs, 
      (0): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (3): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (4): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (5): RepDense2DBasicBlock(
        0.33 M, 1.414% Params, 6.1 GMac, 4.459% MACs, 
        (conv1): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.16 M, 0.707% Params, 3.05 GMac, 2.229% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.15 M, 0.634% Params, 2.735 GMac, 1.999% MACs, 
            (conv): Conv2d(0.15 M, 0.633% Params, 2.73 GMac, 1.996% MACs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.02 M, 0.071% Params, 0.308 GMac, 0.225% MACs, 
            (conv): Conv2d(0.02 M, 0.070% Params, 0.303 GMac, 0.222% MACs, 128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.001% Params, 0.005 GMac, 0.003% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv4_first_block): Sequential(
      0.33 M, 1.412% Params, 1.523 GMac, 1.113% MACs, 
      (0): RepVGGBlock(
        0.33 M, 1.412% Params, 1.523 GMac, 1.113% MACs, 
        (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
        (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
        (rbr_dense): Sequential(
          0.3 M, 1.269% Params, 1.367 GMac, 1.000% MACs, 
          (conv): Conv2d(0.29 M, 1.267% Params, 1.365 GMac, 0.998% MACs, 128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          0.03 M, 0.143% Params, 0.154 GMac, 0.113% MACs, 
          (conv): Conv2d(0.03 M, 0.141% Params, 0.152 GMac, 0.111% MACs, 128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv4): Sequential(
      3.94 M, 16.930% Params, 18.25 GMac, 13.340% MACs, 
      (0): RepDense2DBasicBlock(
        1.31 M, 5.643% Params, 6.083 GMac, 4.447% MACs, 
        (conv1): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (1): RepDense2DBasicBlock(
        1.31 M, 5.643% Params, 6.083 GMac, 4.447% MACs, 
        (conv1): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
      (2): RepDense2DBasicBlock(
        1.31 M, 5.643% Params, 6.083 GMac, 4.447% MACs, 
        (conv1): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          0.66 M, 2.822% Params, 3.042 GMac, 2.223% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.001% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            0.59 M, 2.536% Params, 2.732 GMac, 1.997% MACs, 
            (conv): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.07 M, 0.284% Params, 0.306 GMac, 0.223% MACs, 
            (conv): Conv2d(0.07 M, 0.282% Params, 0.303 GMac, 0.222% MACs, 256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
    (conv5_first_block): Sequential(
      1.31 M, 5.639% Params, 1.52 GMac, 1.111% MACs, 
      (0): RepVGGBlock(
        1.31 M, 5.639% Params, 1.52 GMac, 1.111% MACs, 
        (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.000% MACs, negative_slope=0.01)
        (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
        (rbr_dense): Sequential(
          1.18 M, 5.072% Params, 1.366 GMac, 0.999% MACs, 
          (conv): Conv2d(1.18 M, 5.067% Params, 1.365 GMac, 0.998% MACs, 256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (rbr_1x1): Sequential(
          0.13 M, 0.567% Params, 0.153 GMac, 0.112% MACs, 
          (conv): Conv2d(0.13 M, 0.563% Params, 0.152 GMac, 0.111% MACs, 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
      )
    )
    (conv5): Sequential(
      5.25 M, 22.547% Params, 6.075 GMac, 4.441% MACs, 
      (0): RepDense2DBasicBlock(
        5.25 M, 22.547% Params, 6.075 GMac, 4.441% MACs, 
        (conv1): RepVGGBlock(
          2.62 M, 11.274% Params, 3.038 GMac, 2.220% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.000% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            2.36 M, 10.139% Params, 2.731 GMac, 1.996% MACs, 
            (conv): Conv2d(2.36 M, 10.134% Params, 2.73 GMac, 1.996% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.26 M, 1.130% Params, 0.305 GMac, 0.223% MACs, 
            (conv): Conv2d(0.26 M, 1.126% Params, 0.303 GMac, 0.222% MACs, 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
        (conv2): RepVGGBlock(
          2.62 M, 11.274% Params, 3.038 GMac, 2.220% MACs, 
          (nonlinearity): LeakyReLU(0.0 M, 0.000% Params, 0.001 GMac, 0.000% MACs, negative_slope=0.01)
          (se): Identity(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
          (rbr_identity): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (rbr_dense): Sequential(
            2.36 M, 10.139% Params, 2.731 GMac, 1.996% MACs, 
            (conv): Conv2d(2.36 M, 10.134% Params, 2.73 GMac, 1.996% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
          (rbr_1x1): Sequential(
            0.26 M, 1.130% Params, 0.305 GMac, 0.223% MACs, 
            (conv): Conv2d(0.26 M, 1.126% Params, 0.303 GMac, 0.222% MACs, 512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn): BatchNorm2d(0.0 M, 0.004% Params, 0.001 GMac, 0.001% MACs, 512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          )
        )
      )
    )
  )
  (neck): RPNV2(
    8.1 M, 34.795% Params, 23.2 GMac, 16.958% MACs, 
    (act): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
    (block_5): Sequential(
      4.13 M, 17.748% Params, 4.791 GMac, 3.502% MACs, 
      (0): ZeroPad2d(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, (1, 1, 1, 1))
      (1): Conv2d(1.18 M, 5.067% Params, 1.365 GMac, 0.998% MACs, 512, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (4): Conv2d(0.59 M, 2.534% Params, 0.683 GMac, 0.499% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (5): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (6): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (7): Conv2d(0.59 M, 2.534% Params, 0.683 GMac, 0.499% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (8): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (9): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (10): Conv2d(0.59 M, 2.534% Params, 0.683 GMac, 0.499% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (11): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (12): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (13): Conv2d(0.59 M, 2.534% Params, 0.683 GMac, 0.499% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (14): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (15): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (16): Conv2d(0.59 M, 2.534% Params, 0.683 GMac, 0.499% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (17): BatchNorm2d(0.0 M, 0.002% Params, 0.001 GMac, 0.000% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (18): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
    )
    (deblock_5): Sequential(
      0.13 M, 0.564% Params, 0.618 GMac, 0.452% MACs, 
      (0): ConvTranspose2d(0.13 M, 0.563% Params, 0.607 GMac, 0.443% MACs, 256, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
      (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.001% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (2): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
    )
    (deblock_4): Sequential(
      0.3 M, 1.268% Params, 1.376 GMac, 1.006% MACs, 
      (0): ZeroPad2d(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, (1, 1, 1, 1))
      (1): Conv2d(0.29 M, 1.267% Params, 1.365 GMac, 0.998% MACs, 256, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.001% MACs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
    )
    (block_4): Sequential(
      3.54 M, 15.215% Params, 16.405 GMac, 11.991% MACs, 
      (0): ZeroPad2d(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, (1, 1, 1, 1))
      (1): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)
      (2): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (3): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (4): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (5): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (6): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (7): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (8): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (9): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (10): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (11): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (12): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (13): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (14): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (15): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
      (16): Conv2d(0.59 M, 2.534% Params, 2.73 GMac, 1.996% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (17): BatchNorm2d(0.0 M, 0.002% Params, 0.002 GMac, 0.002% MACs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      (18): LeakyReLU(0.0 M, 0.000% Params, 0.01 GMac, 0.007% MACs, negative_slope=0.01)
    )
  )
  (bbox_head): CenterHead_FP(
    1.75 M, 7.508% Params, 8.19 GMac, 5.986% MACs, 
    (act): LeakyReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, negative_slope=0.01, inplace=True)
    (crit): FastFocalLoss(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
    (crit_reg): RegLoss(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
    (crit_iou): IouLoss(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
    (crit_iou_reg): IouRegLoss(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, )
    (shared_conv): Sequential(
      0.15 M, 0.634% Params, 0.684 GMac, 0.500% MACs, 
      (0): Conv2d(0.15 M, 0.634% Params, 0.683 GMac, 0.499% MACs, 256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, inplace=True)
    )
    (tasks): ModuleList(
      (0): SepHead(
        0.27 M, 1.144% Params, 1.249 GMac, 0.913% MACs, 
        (act): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
        (reg): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          0.04 M, 0.167% Params, 0.182 GMac, 0.133% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.007% Params, 0.008 GMac, 0.006% MACs, 64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (1-2): 2 x SepHead(
        0.27 M, 1.146% Params, 1.252 GMac, 0.915% MACs, 
        (act): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
        (reg): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          0.04 M, 0.167% Params, 0.182 GMac, 0.133% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.007% Params, 0.008 GMac, 0.006% MACs, 64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (3): SepHead(
        0.27 M, 1.144% Params, 1.249 GMac, 0.913% MACs, 
        (act): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
        (reg): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          0.04 M, 0.167% Params, 0.182 GMac, 0.133% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.007% Params, 0.008 GMac, 0.006% MACs, 64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
      (4-5): 2 x SepHead(
        0.27 M, 1.146% Params, 1.252 GMac, 0.915% MACs, 
        (act): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
        (reg): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (height): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (dim): Sequential(
          0.04 M, 0.167% Params, 0.182 GMac, 0.133% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.007% Params, 0.008 GMac, 0.006% MACs, 64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (rot): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (vel): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (iou): Sequential(
          0.04 M, 0.162% Params, 0.176 GMac, 0.129% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.002% Params, 0.003 GMac, 0.002% MACs, 64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
        (hm): Sequential(
          0.04 M, 0.164% Params, 0.179 GMac, 0.131% MACs, 
          (0): Conv2d(0.04 M, 0.159% Params, 0.171 GMac, 0.125% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm2d(0.0 M, 0.001% Params, 0.001 GMac, 0.000% MACs, 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): LeakyReLU(0.0 M, 0.000% Params, 0.002 GMac, 0.002% MACs, negative_slope=0.01)
          (3): Conv2d(0.0 M, 0.005% Params, 0.005 GMac, 0.004% MACs, 64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        )
      )
    )
  )
)
[encoded data, 159030 characters]
Input shape: (4000, 4)
Flops: 136.81 GMac
Params: 23.28 M
[encoded data, 101428 characters].8/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)
  return F.conv2d(input, weight, bias, self.stride,
/beegfs/chandorkar/miniforge3/envs/det3d/lib/python3.8/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[encoded data, 57654 characters]
Traceback (most recent call last):
  File "projects/FastPillars/tools/train.py", line 168, in <module>
    main()
  File "projects/FastPillars/tools/train.py", line 157, in main
    train_detector(
  File "/beegfs/chandorkar/projects/FastPillars/det3d/torchie/apis/train.py", line 340, in train_detector
    adasda
NameError: name 'adasda' is not defined
srun: error: gpu21001: task 0: Exited with exit code 1
clean up done at /tmp/chandorkar_10428844
