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cagroup3d-win10-scannet
File size: 62,959 Bytes 
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2023-04-02 19:19:00,193   INFO  **********************Start logging**********************
2023-04-02 19:19:00,193   INFO  CUDA_VISIBLE_DEVICES=ALL
2023-04-02 19:19:00,194   INFO  total_batch_size: 16
2023-04-02 19:19:00,194   INFO  cfg_file         cfgs/scannet_models/CAGroup3D.yaml
2023-04-02 19:19:00,195   INFO  batch_size       16
2023-04-02 19:19:00,196   INFO  epochs           9
2023-04-02 19:19:00,196   INFO  workers          4
2023-04-02 19:19:00,197   INFO  extra_tag        cagroup3d-win10-scannet-train
2023-04-02 19:19:00,197   INFO  ckpt             ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth
2023-04-02 19:19:00,198   INFO  pretrained_model ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth
2023-04-02 19:19:00,199   INFO  launcher         pytorch
2023-04-02 19:19:00,199   INFO  tcp_port         18888
2023-04-02 19:19:00,200   INFO  sync_bn          False
2023-04-02 19:19:00,200   INFO  fix_random_seed  True
2023-04-02 19:19:00,201   INFO  ckpt_save_interval 1
2023-04-02 19:19:00,201   INFO  max_ckpt_save_num 30
2023-04-02 19:19:00,202   INFO  merge_all_iters_to_one_epoch False
2023-04-02 19:19:00,202   INFO  set_cfgs         None
2023-04-02 19:19:00,203   INFO  max_waiting_mins 0
2023-04-02 19:19:00,203   INFO  start_epoch      0
2023-04-02 19:19:00,204   INFO  num_epochs_to_eval 0
2023-04-02 19:19:00,204   INFO  save_to_file     False
2023-04-02 19:19:00,205   INFO  cfg.ROOT_DIR: C:\CITYU\CS5182\proj\CAGroup3D
2023-04-02 19:19:00,205   INFO  cfg.LOCAL_RANK: 0
2023-04-02 19:19:00,206   INFO  cfg.CLASS_NAMES: ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin']
2023-04-02 19:19:00,207   INFO  
cfg.DATA_CONFIG = edict()
2023-04-02 19:19:00,207   INFO  cfg.DATA_CONFIG.DATASET: ScannetDataset
2023-04-02 19:19:00,208   INFO  cfg.DATA_CONFIG.DATA_PATH: ../data/scannet_data/ScanNetV2
2023-04-02 19:19:00,208   INFO  cfg.DATA_CONFIG.PROCESSED_DATA_TAG: scannet_processed_data_v0_5_0
2023-04-02 19:19:00,209   INFO  cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-40, -40, -10, 40, 40, 10]
2023-04-02 19:19:00,209   INFO  
cfg.DATA_CONFIG.DATA_SPLIT = edict()
2023-04-02 19:19:00,210   INFO  cfg.DATA_CONFIG.DATA_SPLIT.train: train
2023-04-02 19:19:00,210   INFO  cfg.DATA_CONFIG.DATA_SPLIT.test: val
2023-04-02 19:19:00,211   INFO  
cfg.DATA_CONFIG.REPEAT = edict()
2023-04-02 19:19:00,211   INFO  cfg.DATA_CONFIG.REPEAT.train: 10
2023-04-02 19:19:00,212   INFO  cfg.DATA_CONFIG.REPEAT.test: 1
2023-04-02 19:19:00,213   INFO  
cfg.DATA_CONFIG.INFO_PATH = edict()
2023-04-02 19:19:00,213   INFO  cfg.DATA_CONFIG.INFO_PATH.train: ['scannet_infos_train.pkl']
2023-04-02 19:19:00,214   INFO  cfg.DATA_CONFIG.INFO_PATH.test: ['scannet_infos_val.pkl']
2023-04-02 19:19:00,214   INFO  cfg.DATA_CONFIG.GET_ITEM_LIST: ['points', 'instance_mask', 'semantic_mask']
2023-04-02 19:19:00,215   INFO  cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True
2023-04-02 19:19:00,215   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN = edict()
2023-04-02 19:19:00,216   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.DISABLE_AUG_LIST: ['placeholder']
2023-04-02 19:19:00,216   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}, {'NAME': 'point_seg_class_mapping', 'valid_cat_ids': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39], 'max_cat_id': 40}, {'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['x', 'y']}, {'NAME': 'random_world_rotation', 'WORLD_ROT_ANGLE': [-0.087266, 0.087266]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.9, 1.1]}, {'NAME': 'random_world_translation', 'ALONG_AXIS_LIST': ['x', 'y', 'z'], 'NOISE_TRANSLATE_STD': 0.1}]
2023-04-02 19:19:00,217   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST = edict()
2023-04-02 19:19:00,218   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.DISABLE_AUG_LIST: ['placeholder']
2023-04-02 19:19:00,218   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}]
2023-04-02 19:19:00,219   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict()
2023-04-02 19:19:00,219   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
2023-04-02 19:19:00,220   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}]
2023-04-02 19:19:00,220   INFO  
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict()
2023-04-02 19:19:00,221   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
2023-04-02 19:19:00,222   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
2023-04-02 19:19:00,222   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
2023-04-02 19:19:00,223   INFO  cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}]
2023-04-02 19:19:00,223   INFO  cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/scannet_dataset.yaml
2023-04-02 19:19:00,224   INFO  cfg.VOXEL_SIZE: 0.02
2023-04-02 19:19:00,224   INFO  cfg.N_CLASSES: 18
2023-04-02 19:19:00,224   INFO  cfg.SEMANTIC_THR: 0.15
2023-04-02 19:19:00,225   INFO  
cfg.MODEL = edict()
2023-04-02 19:19:00,225   INFO  cfg.MODEL.NAME: CAGroup3D
2023-04-02 19:19:00,226   INFO  cfg.MODEL.VOXEL_SIZE: 0.02
2023-04-02 19:19:00,226   INFO  cfg.MODEL.SEMANTIC_MIN_THR: 0.05
2023-04-02 19:19:00,227   INFO  cfg.MODEL.SEMANTIC_ITER_VALUE: 0.02
2023-04-02 19:19:00,227   INFO  cfg.MODEL.SEMANTIC_THR: 0.15
2023-04-02 19:19:00,227   INFO  
cfg.MODEL.BACKBONE_3D = edict()
2023-04-02 19:19:00,228   INFO  cfg.MODEL.BACKBONE_3D.NAME: BiResNet
2023-04-02 19:19:00,228   INFO  cfg.MODEL.BACKBONE_3D.IN_CHANNELS: 3
2023-04-02 19:19:00,229   INFO  cfg.MODEL.BACKBONE_3D.OUT_CHANNELS: 64
2023-04-02 19:19:00,229   INFO  
cfg.MODEL.DENSE_HEAD = edict()
2023-04-02 19:19:00,230   INFO  cfg.MODEL.DENSE_HEAD.NAME: CAGroup3DHead
2023-04-02 19:19:00,230   INFO  cfg.MODEL.DENSE_HEAD.IN_CHANNELS: [64, 128, 256, 512]
2023-04-02 19:19:00,231   INFO  cfg.MODEL.DENSE_HEAD.OUT_CHANNELS: 64
2023-04-02 19:19:00,231   INFO  cfg.MODEL.DENSE_HEAD.SEMANTIC_THR: 0.15
2023-04-02 19:19:00,232   INFO  cfg.MODEL.DENSE_HEAD.VOXEL_SIZE: 0.02
2023-04-02 19:19:00,233   INFO  cfg.MODEL.DENSE_HEAD.N_CLASSES: 18
2023-04-02 19:19:00,233   INFO  cfg.MODEL.DENSE_HEAD.N_REG_OUTS: 6
2023-04-02 19:19:00,233   INFO  cfg.MODEL.DENSE_HEAD.CLS_KERNEL: 9
2023-04-02 19:19:00,234   INFO  cfg.MODEL.DENSE_HEAD.WITH_YAW: False
2023-04-02 19:19:00,234   INFO  cfg.MODEL.DENSE_HEAD.USE_SEM_SCORE: False
2023-04-02 19:19:00,235   INFO  cfg.MODEL.DENSE_HEAD.EXPAND_RATIO: 3
2023-04-02 19:19:00,235   INFO  
cfg.MODEL.DENSE_HEAD.ASSIGNER = edict()
2023-04-02 19:19:00,236   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.NAME: CAGroup3DAssigner
2023-04-02 19:19:00,237   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.LIMIT: 27
2023-04-02 19:19:00,237   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.TOPK: 18
2023-04-02 19:19:00,237   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.N_SCALES: 4
2023-04-02 19:19:00,238   INFO  
cfg.MODEL.DENSE_HEAD.LOSS_OFFSET = edict()
2023-04-02 19:19:00,239   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.NAME: SmoothL1Loss
2023-04-02 19:19:00,239   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.BETA: 0.04
2023-04-02 19:19:00,240   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.REDUCTION: sum
2023-04-02 19:19:00,240   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.LOSS_WEIGHT: 1.0
2023-04-02 19:19:00,240   INFO  
cfg.MODEL.DENSE_HEAD.LOSS_BBOX = edict()
2023-04-02 19:19:00,241   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.NAME: IoU3DLoss
2023-04-02 19:19:00,242   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.WITH_YAW: False
2023-04-02 19:19:00,242   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.LOSS_WEIGHT: 1.0
2023-04-02 19:19:00,243   INFO  
cfg.MODEL.DENSE_HEAD.NMS_CONFIG = edict()
2023-04-02 19:19:00,243   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.SCORE_THR: 0.01
2023-04-02 19:19:00,244   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.NMS_PRE: 1000
2023-04-02 19:19:00,244   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.IOU_THR: 0.5
2023-04-02 19:19:00,245   INFO  
cfg.MODEL.ROI_HEAD = edict()
2023-04-02 19:19:00,246   INFO  cfg.MODEL.ROI_HEAD.NAME: CAGroup3DRoIHead
2023-04-02 19:19:00,246   INFO  cfg.MODEL.ROI_HEAD.NUM_CLASSES: 18
2023-04-02 19:19:00,247   INFO  cfg.MODEL.ROI_HEAD.MIDDLE_FEATURE_SOURCE: [3]
2023-04-02 19:19:00,247   INFO  cfg.MODEL.ROI_HEAD.GRID_SIZE: 7
2023-04-02 19:19:00,247   INFO  cfg.MODEL.ROI_HEAD.VOXEL_SIZE: 0.02
2023-04-02 19:19:00,248   INFO  cfg.MODEL.ROI_HEAD.COORD_KEY: 2
2023-04-02 19:19:00,248   INFO  cfg.MODEL.ROI_HEAD.MLPS: [[64, 128, 128]]
2023-04-02 19:19:00,249   INFO  cfg.MODEL.ROI_HEAD.CODE_SIZE: 6
2023-04-02 19:19:00,249   INFO  cfg.MODEL.ROI_HEAD.ENCODE_SINCOS: False
2023-04-02 19:19:00,250   INFO  cfg.MODEL.ROI_HEAD.ROI_PER_IMAGE: 128
2023-04-02 19:19:00,250   INFO  cfg.MODEL.ROI_HEAD.ROI_FG_RATIO: 0.9
2023-04-02 19:19:00,251   INFO  cfg.MODEL.ROI_HEAD.REG_FG_THRESH: 0.3
2023-04-02 19:19:00,251   INFO  cfg.MODEL.ROI_HEAD.ROI_CONV_KERNEL: 5
2023-04-02 19:19:00,251   INFO  cfg.MODEL.ROI_HEAD.ENLARGE_RATIO: False
2023-04-02 19:19:00,252   INFO  cfg.MODEL.ROI_HEAD.USE_IOU_LOSS: False
2023-04-02 19:19:00,252   INFO  cfg.MODEL.ROI_HEAD.USE_GRID_OFFSET: False
2023-04-02 19:19:00,253   INFO  cfg.MODEL.ROI_HEAD.USE_SIMPLE_POOLING: True
2023-04-02 19:19:00,253   INFO  cfg.MODEL.ROI_HEAD.USE_CENTER_POOLING: True
2023-04-02 19:19:00,254   INFO  
cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS = edict()
2023-04-02 19:19:00,254   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_CLS_WEIGHT: 1.0
2023-04-02 19:19:00,254   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_REG_WEIGHT: 1.0
2023-04-02 19:19:00,255   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_IOU_WEIGHT: 1.0
2023-04-02 19:19:00,255   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.CODE_WEIGHT: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
2023-04-02 19:19:00,256   INFO  
cfg.MODEL.POST_PROCESSING = edict()
2023-04-02 19:19:00,256   INFO  cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.25, 0.5]
2023-04-02 19:19:00,257   INFO  cfg.MODEL.POST_PROCESSING.EVAL_METRIC: scannet
2023-04-02 19:19:00,257   INFO  
cfg.OPTIMIZATION = edict()
2023-04-02 19:19:00,258   INFO  cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 16
2023-04-02 19:19:00,258   INFO  cfg.OPTIMIZATION.NUM_EPOCHS: 1
2023-04-02 19:19:00,259   INFO  cfg.OPTIMIZATION.OPTIMIZER: adamW
2023-04-02 19:19:00,259   INFO  cfg.OPTIMIZATION.LR: 0.001
2023-04-02 19:19:00,259   INFO  cfg.OPTIMIZATION.WEIGHT_DECAY: 0.0001
2023-04-02 19:19:00,260   INFO  cfg.OPTIMIZATION.DECAY_STEP_LIST: [7, 9]
2023-04-02 19:19:00,261   INFO  cfg.OPTIMIZATION.LR_DECAY: 0.1
2023-04-02 19:19:00,261   INFO  cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
2023-04-02 19:19:00,261   INFO  cfg.OPTIMIZATION.PCT_START: 0.4
2023-04-02 19:19:00,262   INFO  cfg.OPTIMIZATION.DIV_FACTOR: 10
2023-04-02 19:19:00,262   INFO  cfg.OPTIMIZATION.LR_CLIP: 1e-07
2023-04-02 19:19:00,263   INFO  cfg.OPTIMIZATION.LR_WARMUP: False
2023-04-02 19:19:00,263   INFO  cfg.OPTIMIZATION.WARMUP_EPOCH: 1
2023-04-02 19:19:00,264   INFO  cfg.TAG: CAGroup3D
2023-04-02 19:19:00,264   INFO  cfg.EXP_GROUP_PATH: scannet_models
2023-04-02 19:19:00,295   INFO  Loading SCANNET dataset
2023-04-02 19:19:00,413   INFO  Total samples for SCANNET dataset: 1201
2023-04-02 19:19:03,525   INFO  ==> Loading parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU
2023-04-02 19:19:04,589   INFO  ==> Checkpoint trained from version: pcdet+0.5.2+0000000
2023-04-02 19:19:04,732   INFO  ==> Done (loaded 838/838)
2023-04-02 19:19:04,914   INFO  ==> Loading parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU
2023-04-02 19:19:06,073   INFO  ==> Loading optimizer parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU
2023-04-02 19:19:06,413   INFO  ==> Done
2023-04-02 19:19:06,797   INFO  DistributedDataParallel(
  (module): CAGroup3D(
    (vfe): None
    (backbone_3d): BiResNet(
      (conv1): Sequential(
        (0): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): MinkowskiReLU()
        (3): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): MinkowskiReLU()
      )
      (relu): MinkowskiReLU()
      (layer1): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (layer2): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (layer3): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (layer4): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (compression3): Sequential(
        (0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (compression4): Sequential(
        (0): MinkowskiConvolution(in=512, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (down3): Sequential(
        (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (down4): Sequential(
        (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): MinkowskiReLU()
        (3): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
        (4): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (layer3_): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (layer4_): Sequential(
        (0): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
        (1): BasicBlock(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
        )
      )
      (layer5_): Sequential(
        (0): Bottleneck(
          (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv3): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm3): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (layer5): Sequential(
        (0): Bottleneck(
          (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
          (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv3): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (norm3): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): MinkowskiReLU()
          (downsample): Sequential(
            (0): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
            (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (spp): DAPPM(
        (scale1): Sequential(
          (0): MinkowskiAvgPooling(kernel_size=[5, 5, 5], stride=[2, 2, 2], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiReLU()
          (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (scale2): Sequential(
          (0): MinkowskiAvgPooling(kernel_size=[9, 9, 9], stride=[4, 4, 4], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiReLU()
          (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (scale3): Sequential(
          (0): MinkowskiAvgPooling(kernel_size=[17, 17, 17], stride=[8, 8, 8], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiReLU()
          (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (scale4): Sequential(
          (0): MinkowskiAvgPooling(kernel_size=[33, 33, 33], stride=[16, 16, 16], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiReLU()
          (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (scale0): Sequential(
          (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (process1): Sequential(
          (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (process2): Sequential(
          (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (process3): Sequential(
          (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (process4): Sequential(
          (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (compression): Sequential(
          (0): MinkowskiBatchNorm(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=640, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
        (shortcut): Sequential(
          (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MinkowskiReLU()
          (2): MinkowskiConvolution(in=1024, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        )
      )
      (out): Sequential(
        (0): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): MinkowskiReLU()
        (3): MinkowskiConvolution(in=256, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): MinkowskiReLU()
      )
    )
    (map_to_bev_module): None
    (pfe): None
    (backbone_2d): None
    (dense_head): CAGroup3DHead(
      (loss_centerness): CrossEntropy()
      (loss_bbox): IoU3DLoss()
      (loss_cls): FocalLoss()
      (loss_sem): FocalLoss()
      (loss_offset): SmoothL1Loss()
      (offset_block): Sequential(
        (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): MinkowskiELU()
        (3): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (5): MinkowskiELU()
        (6): MinkowskiConvolution(in=64, out=3, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
      )
      (feature_offset): Sequential(
        (0): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): MinkowskiELU()
      )
      (semantic_conv): MinkowskiConvolution(in=64, out=18, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
      (centerness_conv): MinkowskiConvolution(in=64, out=1, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
      (reg_conv): MinkowskiConvolution(in=64, out=6, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
      (cls_conv): MinkowskiConvolution(in=64, out=18, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
      (scales): ModuleList(
        (0): Scale()
        (1): Scale()
        (2): Scale()
        (3): Scale()
        (4): Scale()
        (5): Scale()
        (6): Scale()
        (7): Scale()
        (8): Scale()
        (9): Scale()
        (10): Scale()
        (11): Scale()
        (12): Scale()
        (13): Scale()
        (14): Scale()
        (15): Scale()
        (16): Scale()
        (17): Scale()
      )
      (cls_individual_out): ModuleList(
        (0): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (1): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (2): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (3): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (4): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (5): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (6): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (7): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (8): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (9): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (10): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (11): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (12): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (13): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (14): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (15): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (16): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (17): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
      )
      (cls_individual_up): ModuleList(
        (0): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (1): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (2): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (3): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (4): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (5): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (6): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (7): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (8): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (9): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (10): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (11): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (12): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (13): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (14): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (15): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (16): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
        (17): ModuleList(
          (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
          (1): Sequential(
            (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (1): MinkowskiELU()
          )
        )
      )
      (cls_individual_fuse): ModuleList(
        (0): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (1): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (2): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (3): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (4): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (5): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (6): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (7): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (8): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (9): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (10): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (11): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (12): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (13): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (14): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (15): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (16): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (17): Sequential(
          (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
      )
      (cls_individual_expand_out): ModuleList(
        (0): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (1): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (2): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (3): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (4): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (5): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (6): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (7): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (8): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (9): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (10): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (11): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (12): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (13): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (14): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (15): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (16): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
        (17): Sequential(
          (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): MinkowskiELU()
        )
      )
    )
    (point_head): None
    (roi_head): CAGroup3DRoIHead(
      (proposal_target_layer): ProposalTargetLayer()
      (reg_loss_func): WeightedSmoothL1Loss()
      (roi_grid_pool_layers): ModuleList(
        (0): SimplePoolingLayer(
          (grid_conv): MinkowskiConvolution(in=64, out=128, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
          (grid_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (grid_relu): MinkowskiELU()
          (pooling_conv): MinkowskiConvolution(in=128, out=128, kernel_size=[7, 7, 7], stride=[1, 1, 1], dilation=[1, 1, 1])
          (pooling_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (reg_fc_layers): Sequential(
        (0): Linear(in_features=128, out_features=256, bias=False)
        (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
        (3): Dropout(p=0.3, inplace=False)
        (4): Linear(in_features=256, out_features=256, bias=False)
        (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (6): ReLU()
      )
      (reg_pred_layer): Linear(in_features=256, out_features=6, bias=True)
    )
  )
)
2023-04-02 19:19:06,861   INFO  **********************Start training scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)**********************
2023-04-03 02:48:23,235   INFO  Epoch [ 9][  50]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6437654900550842, loss_bbox: 0.24313656240701675, loss_cls: 0.12552095234394073, loss_sem: 0.0908023527264595, loss_vote: 0.4054640585184097, one_stage_loss: 1.5086894202232362, rcnn_loss_reg: 0.18907397538423537, loss_two_stage: 0.18907397538423537, 
2023-04-03 10:39:11,381   INFO  Epoch [ 9][ 100]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6452190101146698, loss_bbox: 0.2506902211904526, loss_cls: 0.13058093473315238, loss_sem: 0.09085427075624466, loss_vote: 0.3957407087087631, one_stage_loss: 1.5130851411819457, rcnn_loss_reg: 0.18459385246038437, loss_two_stage: 0.18459385246038437, 
2023-04-03 18:37:23,817   INFO  Epoch [ 9][ 150]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6455395030975342, loss_bbox: 0.26197255998849867, loss_cls: 0.13516157254576683, loss_sem: 0.09203423753380775, loss_vote: 0.3918899363279343, one_stage_loss: 1.5265977954864502, rcnn_loss_reg: 0.19318852871656417, loss_two_stage: 0.19318852871656417, 
2023-04-04 02:50:19,460   INFO  Epoch [ 9][ 200]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6455828678607941, loss_bbox: 0.26617022305727006, loss_cls: 0.13564770326018333, loss_sem: 0.09159276977181435, loss_vote: 0.3895376515388489, one_stage_loss: 1.5285312223434449, rcnn_loss_reg: 0.19834407955408095, loss_two_stage: 0.19834407955408095, 
2023-04-04 10:54:09,210   INFO  Epoch [ 9][ 250]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6433031773567199, loss_bbox: 0.2684298923611641, loss_cls: 0.1351591469347477, loss_sem: 0.0898670071363449, loss_vote: 0.38588442504405973, one_stage_loss: 1.5226436424255372, rcnn_loss_reg: 0.19586090356111527, loss_two_stage: 0.19586090356111527, 
2023-04-04 18:54:23,838   INFO  Epoch [ 9][ 300]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6456489896774292, loss_bbox: 0.26939463675022124, loss_cls: 0.13399980276823042, loss_sem: 0.08941386982798577, loss_vote: 0.38932142794132235, one_stage_loss: 1.5277787280082702, rcnn_loss_reg: 0.19743700653314591, loss_two_stage: 0.19743700653314591, 
2023-04-05 03:03:32,943   INFO  Epoch [ 9][ 350]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6423018944263458, loss_bbox: 0.2762458199262619, loss_cls: 0.13613657861948014, loss_sem: 0.08952155798673629, loss_vote: 0.3827694195508957, one_stage_loss: 1.526975281238556, rcnn_loss_reg: 0.2028840947151184, loss_two_stage: 0.2028840947151184, 
2023-04-05 11:14:13,601   INFO  Epoch [ 9][ 400]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6456181585788727, loss_bbox: 0.27737378895282744, loss_cls: 0.1374327675998211, loss_sem: 0.08987010061740876, loss_vote: 0.3778589928150177, one_stage_loss: 1.5281538224220277, rcnn_loss_reg: 0.19922083646059036, loss_two_stage: 0.19922083646059036, 
2023-04-05 19:27:24,824   INFO  Epoch [ 9][ 450]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6425622916221618, loss_bbox: 0.27846161276102066, loss_cls: 0.1391485698521137, loss_sem: 0.09056971445679665, loss_vote: 0.3740858173370361, one_stage_loss: 1.524828016757965, rcnn_loss_reg: 0.2066230583190918, loss_two_stage: 0.2066230583190918, 
2023-04-06 03:42:50,486   INFO  Epoch [ 9][ 500]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6431539249420166, loss_bbox: 0.29076395750045775, loss_cls: 0.14162053808569908, loss_sem: 0.09268154501914978, loss_vote: 0.37324252247810363, one_stage_loss: 1.5414625024795532, rcnn_loss_reg: 0.21089414656162261, loss_two_stage: 0.21089414656162261, 
2023-04-06 12:02:31,235   INFO  Epoch [ 9][ 550]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6435322630405426, loss_bbox: 0.2865727955102921, loss_cls: 0.14131027206778526, loss_sem: 0.08974318355321884, loss_vote: 0.36962947726249695, one_stage_loss: 1.5307879900932313, rcnn_loss_reg: 0.21048259049654006, loss_two_stage: 0.21048259049654006, 
2023-04-06 20:02:56,351   INFO  Epoch [ 9][ 600]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6414503383636475, loss_bbox: 0.29402961790561677, loss_cls: 0.14405513793230057, loss_sem: 0.09276293635368348, loss_vote: 0.34642710983753205, one_stage_loss: 1.5187251472473144, rcnn_loss_reg: 0.21440828204154969, loss_two_stage: 0.21440828204154969, 
2023-04-07 04:17:17,115   INFO  Epoch [ 9][ 650]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6445023834705352, loss_bbox: 0.29507197201251983, loss_cls: 0.1420440413057804, loss_sem: 0.08918493255972862, loss_vote: 0.36039295852184294, one_stage_loss: 1.5311962819099427, rcnn_loss_reg: 0.21248085170984268, loss_two_stage: 0.21248085170984268, 
2023-04-07 12:27:17,390   INFO  Epoch [ 9][ 700]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6422169864177704, loss_bbox: 0.29913002490997315, loss_cls: 0.1449529528617859, loss_sem: 0.09117808878421783, loss_vote: 0.35617750346660615, one_stage_loss: 1.5336555647850036, rcnn_loss_reg: 0.21863267749547957, loss_two_stage: 0.21863267749547957, 
2023-04-07 20:27:53,668   INFO  Epoch [ 9][ 750]/[ 751] : lr:  1.000e-04, sem_thr: 0.05, loss_centerness: 0.6417906320095063, loss_bbox: 0.30550686359405516, loss_cls: 0.14678879588842392, loss_sem: 0.09153819352388382, loss_vote: 0.33501013278961184, one_stage_loss: 1.5206346249580383, rcnn_loss_reg: 0.21843280464410783, loss_two_stage: 0.21843280464410783, 
2023-04-07 20:40:13,681   INFO  **********************End training scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)**********************



2023-04-07 20:40:13,682   INFO  **********************Start evaluation scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)**********************
2023-04-07 20:40:13,683   INFO  Loading SCANNET dataset
2023-04-07 20:40:13,727   INFO  Total samples for SCANNET dataset: 312
2023-04-07 20:40:13,734   INFO  ==> Loading parameters from checkpoint C:\CITYU\CS5182\proj\CAGroup3D\output\scannet_models\CAGroup3D\cagroup3d-win10-scannet-train\ckpt\checkpoint_epoch_9.pth to CPU
2023-04-07 20:40:15,066   INFO  ==> Checkpoint trained from version: pcdet+0.5.2+4ae8a35+pyde9d900
2023-04-07 20:40:15,285   INFO  ==> Done (loaded 838/838)
2023-04-07 20:40:15,536   INFO  *************** EPOCH 9 EVALUATION *****************
2023-04-07 23:42:31,883   INFO  *************** Performance of EPOCH 9 *****************
2023-04-07 23:42:31,884   INFO  Generate label finished(sec_per_example: 35.0515 second).
2023-04-07 23:42:31,884   INFO  recall_roi_0.25: 0.000000
2023-04-07 23:42:31,885   INFO  recall_rcnn_0.25: 0.000000
2023-04-07 23:42:31,885   INFO  recall_roi_0.5: 0.000000
2023-04-07 23:42:31,886   INFO  recall_rcnn_0.5: 0.000000
2023-04-07 23:42:31,886   INFO  Average predicted number of objects(312 samples): 615.288
2023-04-07 23:42:50,866   INFO  {'cabinet_AP_0.25': 0.48540377616882324, 'bed_AP_0.25': 0.8844786882400513, 'chair_AP_0.25': 0.9513316750526428, 'sofa_AP_0.25': 0.897523820400238, 'table_AP_0.25': 0.6726281046867371, 'door_AP_0.25': 0.6615963578224182, 'window_AP_0.25': 0.6129509806632996, 'bookshelf_AP_0.25': 0.6147690415382385, 'picture_AP_0.25': 0.3527411222457886, 'counter_AP_0.25': 0.6677877902984619, 'desk_AP_0.25': 0.8030293583869934, 'curtain_AP_0.25': 0.6958670020103455, 'refrigerator_AP_0.25': 0.5268049240112305, 'showercurtrain_AP_0.25': 0.7306337356567383, 'toilet_AP_0.25': 0.9988548159599304, 'sink_AP_0.25': 0.7495102286338806, 'bathtub_AP_0.25': 0.8837810754776001, 'garbagebin_AP_0.25': 0.6311063170433044, 'mAP_0.25': 0.7122666239738464, 'cabinet_rec_0.25': 0.9005376344086021, 'bed_rec_0.25': 0.9135802469135802, 'chair_rec_0.25': 0.9692982456140351, 'sofa_rec_0.25': 0.979381443298969, 'table_rec_0.25': 0.8514285714285714, 'door_rec_0.25': 0.9079229122055674, 'window_rec_0.25': 0.900709219858156, 'bookshelf_rec_0.25': 0.8701298701298701, 'picture_rec_0.25': 0.6576576576576577, 'counter_rec_0.25': 0.9423076923076923, 'desk_rec_0.25': 0.9606299212598425, 'curtain_rec_0.25': 0.8656716417910447, 'refrigerator_rec_0.25': 0.8947368421052632, 'showercurtrain_rec_0.25': 0.9642857142857143, 'toilet_rec_0.25': 1.0, 'sink_rec_0.25': 0.8367346938775511, 'bathtub_rec_0.25': 0.9032258064516129, 'garbagebin_rec_0.25': 0.8622641509433963, 'mAR_0.25': 0.8989167924742847, 'cabinet_AP_0.50': 0.3409128785133362, 'bed_AP_0.50': 0.8349310159683228, 'chair_AP_0.50': 0.8949430584907532, 'sofa_AP_0.50': 0.8081077337265015, 'table_AP_0.50': 0.6181142330169678, 'door_AP_0.50': 0.5156012773513794, 'window_AP_0.50': 0.31114932894706726, 'bookshelf_AP_0.50': 0.5378049612045288, 'picture_AP_0.50': 0.22183111310005188, 'counter_AP_0.50': 0.3777454197406769, 'desk_AP_0.50': 0.6232985854148865, 'curtain_AP_0.50': 0.40846431255340576, 'refrigerator_AP_0.50': 0.44400057196617126, 'showercurtrain_AP_0.50': 0.43062731623649597, 'toilet_AP_0.50': 0.9467570781707764, 'sink_AP_0.50': 0.5182515382766724, 'bathtub_AP_0.50': 0.8415195941925049, 'garbagebin_AP_0.50': 0.5481723546981812, 'mAP_0.50': 0.5679017901420593, 'cabinet_rec_0.50': 0.6935483870967742, 'bed_rec_0.50': 0.8641975308641975, 'chair_rec_0.50': 0.9232456140350878, 'sofa_rec_0.50': 0.9175257731958762, 'table_rec_0.50': 0.7571428571428571, 'door_rec_0.50': 0.734475374732334, 'window_rec_0.50': 0.5921985815602837, 'bookshelf_rec_0.50': 0.7662337662337663, 'picture_rec_0.50': 0.42342342342342343, 'counter_rec_0.50': 0.6153846153846154, 'desk_rec_0.50': 0.8346456692913385, 'curtain_rec_0.50': 0.6119402985074627, 'refrigerator_rec_0.50': 0.7719298245614035, 'showercurtrain_rec_0.50': 0.6071428571428571, 'toilet_rec_0.50': 0.9482758620689655, 'sink_rec_0.50': 0.6428571428571429, 'bathtub_rec_0.50': 0.8709677419354839, 'garbagebin_rec_0.50': 0.7169811320754716, 'mAR_0.50': 0.7384509140060744}
2023-04-07 23:42:50,872   INFO  Result is save to C:\CITYU\CS5182\proj\CAGroup3D\output\scannet_models\CAGroup3D\cagroup3d-win10-scannet-train\eval\eval_with_train\epoch_9\val
2023-04-07 23:42:50,873   INFO  ****************Evaluation done.*****************
2023-04-07 23:42:51,201   INFO  Epoch 9 has been evaluated
2023-04-07 23:43:21,209   INFO  **********************End evaluation scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)**********************