/tmp/chandorkar_11608751
Sat Jun 21 12:09:04 AM CEST 2025
copying data..
load env
WARNING: The 2022a stack is deprecated!
         It has been built against CentOS7 and using it on Alma Linux 9 can
cause problems,
         e.g. due to missing system libraries or version ABI missmatches.
         You can try to continue, but remain aware of potential issues.

         Use the 2023a (or newer) software stacks, or one of the _AL9 software
stacks

Sat Jun 21 12:09:07 AM CEST 2025
starting run..
no change     /beegfs/chandorkar/miniforge3/condabin/conda
no change     /beegfs/chandorkar/miniforge3/bin/conda
no change     /beegfs/chandorkar/miniforge3/bin/conda-env
no change     /beegfs/chandorkar/miniforge3/bin/activate
no change     /beegfs/chandorkar/miniforge3/bin/deactivate
no change     /beegfs/chandorkar/miniforge3/etc/profile.d/conda.sh
no change     /beegfs/chandorkar/miniforge3/etc/fish/conf.d/conda.fish
no change     /beegfs/chandorkar/miniforge3/shell/condabin/Conda.psm1
no change     /beegfs/chandorkar/miniforge3/shell/condabin/conda-hook.ps1
no change     /beegfs/chandorkar/miniforge3/lib/python3.10/site-packages/xontrib/conda.xsh
no change     /beegfs/chandorkar/miniforge3/etc/profile.d/conda.csh
modified      /beegfs/chandorkar/.bashrc

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

Added mamba to /beegfs/chandorkar/.bashrc

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

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

train_cfg : {'assigner': [{'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_pos_iou': 0.35, 'ignore_iof_thr': -1}, {'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_pos_iou': 0.35, 'ignore_iof_thr': -1}, {'type': 'Max3DIoUAssigner', 'iou_calculator': {'type': 'mmdet3d.BboxOverlapsNearest3D'}, 'pos_iou_thr': 0.6, 'neg_iou_thr': 0.45, 'min_pos_iou': 0.45, 'ignore_iof_thr': -1}], 'allowed_border': 0, 'pos_weight': -1, 'debug': False}
VoxelNet(
  4.835 M, 100.000% Params, 34.327 GFLOPs, 100.000% FLOPs, 
  (data_preprocessor): Det3DDataPreprocessor(
    0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, 
    (voxel_layer): VoxelizationByGridShape(voxel_size=[0.16, 0.16, 4], grid_shape=[432, 496, 1], point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1], max_num_points=32, max_voxels=(16000, 40000), deterministic=True)
  )
  (backbone): SECOND(
    4.208 M, 87.026% Params, 29.708 GFLOPs, 86.546% FLOPs, 
    (blocks): ModuleList(
      (0): Sequential(
        0.148 M, 3.060% Params, 7.94 GFLOPs, 23.131% FLOPs, 
        (0): Conv2d(0.037 M, 0.762% Params, 1.975 GFLOPs, 5.753% FLOPs, 64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.020% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.010% FLOPs, inplace=True)
        (3): Conv2d(0.037 M, 0.762% Params, 1.975 GFLOPs, 5.753% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.020% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.010% FLOPs, inplace=True)
        (6): Conv2d(0.037 M, 0.762% Params, 1.975 GFLOPs, 5.753% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.020% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.010% FLOPs, inplace=True)
        (9): Conv2d(0.037 M, 0.762% Params, 1.975 GFLOPs, 5.753% FLOPs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): BatchNorm2d(0.0 M, 0.003% Params, 0.007 GFLOPs, 0.020% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(0.0 M, 0.000% Params, 0.003 GFLOPs, 0.010% FLOPs, inplace=True)
      )
      (1): Sequential(
        0.813 M, 16.806% Params, 10.892 GFLOPs, 31.730% FLOPs, 
        (0): Conv2d(0.074 M, 1.525% Params, 0.987 GFLOPs, 2.876% FLOPs, 64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
        (3): Conv2d(0.147 M, 3.050% Params, 1.975 GFLOPs, 5.753% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
        (6): Conv2d(0.147 M, 3.050% Params, 1.975 GFLOPs, 5.753% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
        (9): Conv2d(0.147 M, 3.050% Params, 1.975 GFLOPs, 5.753% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
        (12): Conv2d(0.147 M, 3.050% Params, 1.975 GFLOPs, 5.753% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
        (15): Conv2d(0.147 M, 3.050% Params, 1.975 GFLOPs, 5.753% FLOPs, 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): BatchNorm2d(0.0 M, 0.005% Params, 0.003 GFLOPs, 0.010% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(0.0 M, 0.000% Params, 0.002 GFLOPs, 0.005% FLOPs, inplace=True)
      )
      (2): Sequential(
        3.247 M, 67.160% Params, 10.876 GFLOPs, 31.685% FLOPs, 
        (0): Conv2d(0.295 M, 6.100% Params, 0.987 GFLOPs, 2.876% FLOPs, 128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
        (3): Conv2d(0.59 M, 12.199% Params, 1.975 GFLOPs, 5.753% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (5): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
        (6): Conv2d(0.59 M, 12.199% Params, 1.975 GFLOPs, 5.753% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (7): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (8): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
        (9): Conv2d(0.59 M, 12.199% Params, 1.975 GFLOPs, 5.753% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (10): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (11): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
        (12): Conv2d(0.59 M, 12.199% Params, 1.975 GFLOPs, 5.753% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (13): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (14): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
        (15): Conv2d(0.59 M, 12.199% Params, 1.975 GFLOPs, 5.753% FLOPs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (16): BatchNorm2d(0.001 M, 0.011% Params, 0.002 GFLOPs, 0.005% FLOPs, 256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (17): ReLU(0.0 M, 0.000% Params, 0.001 GFLOPs, 0.002% FLOPs, inplace=True)
      )
    )
  )
  init_cfg={'type': 'Kaiming', 'layer': 'Conv2d'}
  (neck): SECONDFPN(
    0.599 M, 12.385% Params, 3.134 GFLOPs, 9.128% FLOPs, 
    (deblocks): ModuleList(
      (0): Sequential(
        0.008 M, 0.175% Params, 0.459 GFLOPs, 1.338% FLOPs, 
        (0): ConvTranspose2d(0.008 M, 0.169% Params, 0.439 GFLOPs, 1.278% FLOPs, 64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(0.0 M, 0.005% Params, 0.014 GFLOPs, 0.040% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.020% FLOPs, inplace=True)
      )
      (1): Sequential(
        0.066 M, 1.361% Params, 0.898 GFLOPs, 2.617% FLOPs, 
        (0): ConvTranspose2d(0.066 M, 1.355% Params, 0.878 GFLOPs, 2.557% FLOPs, 128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
        (1): BatchNorm2d(0.0 M, 0.005% Params, 0.014 GFLOPs, 0.040% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.020% FLOPs, inplace=True)
      )
      (2): Sequential(
        0.525 M, 10.849% Params, 1.776 GFLOPs, 5.173% FLOPs, 
        (0): ConvTranspose2d(0.524 M, 10.844% Params, 1.755 GFLOPs, 5.114% FLOPs, 256, 128, kernel_size=(4, 4), stride=(4, 4), bias=False)
        (1): BatchNorm2d(0.0 M, 0.005% Params, 0.014 GFLOPs, 0.040% FLOPs, 128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(0.0 M, 0.000% Params, 0.007 GFLOPs, 0.020% FLOPs, inplace=True)
      )
    )
  )
  init_cfg=[{'type': 'Kaiming', 'layer': 'ConvTranspose2d'}, {'type': 'Constant', 'layer': 'NaiveSyncBatchNorm2d', 'val': 1.0}]
  (bbox_head): Anchor3DHead(
    0.028 M, 0.573% Params, 1.485 GFLOPs, 4.326% FLOPs, 
    (loss_cls): FocalLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
    (loss_bbox): SmoothL1Loss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
    (loss_dir): CrossEntropyLoss(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, avg_non_ignore=False)
    (conv_cls): Conv2d(0.007 M, 0.143% Params, 0.371 GFLOPs, 1.081% FLOPs, 384, 18, kernel_size=(1, 1), stride=(1, 1))
    (conv_reg): Conv2d(0.016 M, 0.334% Params, 0.866 GFLOPs, 2.523% FLOPs, 384, 42, kernel_size=(1, 1), stride=(1, 1))
    (conv_dir_cls): Conv2d(0.005 M, 0.096% Params, 0.247 GFLOPs, 0.721% FLOPs, 384, 12, kernel_size=(1, 1), stride=(1, 1))
  )
  init_cfg={'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'conv_cls', 'std': 0.01, 'bias_prob': 0.01}}
  (voxel_layer): Voxelization(voxel_size=[0.16, 0.16, 4], point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1], max_num_points=32, max_voxels=(20000, 20000), deterministic=True)
  (voxel_encoder): PillarFeatureNet(
    0.001 M, 0.016% Params, 0.0 GFLOPs, 0.000% FLOPs, 
    (pfn_layers): ModuleList(
      (0): PFNLayer(
        0.001 M, 0.016% Params, 0.0 GFLOPs, 0.000% FLOPs, 
        (norm): BatchNorm1d(0.0 M, 0.003% Params, 0.0 GFLOPs, 0.000% FLOPs, 64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (linear): Linear(0.001 M, 0.013% Params, 0.0 GFLOPs, 0.000% FLOPs, in_features=10, out_features=64, bias=False)
      )
    )
  )
  (middle_encoder): PointPillarsScatter(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
)
==============================
Input shape: (40000, 4)
Flops: 34.33 GFLOPs
Params: 4.83 M
==============================
!!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
/beegfs/chandorkar/projects/mmdetection3d/mmdet3d/models/dense_heads/anchor3d_head.py:94: UserWarning: dir_offset and dir_limit_offset will be depressed and be incorporated into box coder in the future
  warnings.warn(
clean up done at /tmp/chandorkar_11608751
