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cagroup3d-win10-scannet

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cagroup3d-win10-scannet
File size: 15,114 Bytes 
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2023-04-01 21:45:09,865   INFO  **********************Start logging**********************
2023-04-01 21:45:09,866   INFO  CUDA_VISIBLE_DEVICES=ALL
2023-04-01 21:45:09,866   INFO  total_batch_size: 16
2023-04-01 21:45:09,867   INFO  cfg_file         cfgs/scannet_models/CAGroup3D.yaml
2023-04-01 21:45:09,868   INFO  batch_size       16
2023-04-01 21:45:09,868   INFO  workers          4
2023-04-01 21:45:09,869   INFO  extra_tag        cagroup3d-win10-scannet-eval
2023-04-01 21:45:09,869   INFO  ckpt             ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train/ckpt/checkpoint_epoch_8.pth
2023-04-01 21:45:09,870   INFO  launcher         pytorch
2023-04-01 21:45:09,870   INFO  tcp_port         18888
2023-04-01 21:45:09,870   INFO  set_cfgs         None
2023-04-01 21:45:09,872   INFO  max_waiting_mins 30
2023-04-01 21:45:09,872   INFO  start_epoch      0
2023-04-01 21:45:09,872   INFO  eval_tag         default
2023-04-01 21:45:09,872   INFO  eval_all         False
2023-04-01 21:45:09,873   INFO  ckpt_dir         None
2023-04-01 21:45:09,873   INFO  save_to_file     False
2023-04-01 21:45:09,875   INFO  cfg.ROOT_DIR: C:\CITYU\CS5182\proj\CAGroup3D
2023-04-01 21:45:09,876   INFO  cfg.LOCAL_RANK: 0
2023-04-01 21:45:09,876   INFO  cfg.CLASS_NAMES: ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin']
2023-04-01 21:45:09,876   INFO  
cfg.DATA_CONFIG = edict()
2023-04-01 21:45:09,878   INFO  cfg.DATA_CONFIG.DATASET: ScannetDataset
2023-04-01 21:45:09,878   INFO  cfg.DATA_CONFIG.DATA_PATH: ../data/scannet_data/ScanNetV2
2023-04-01 21:45:09,879   INFO  cfg.DATA_CONFIG.PROCESSED_DATA_TAG: scannet_processed_data_v0_5_0
2023-04-01 21:45:09,879   INFO  cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-40, -40, -10, 40, 40, 10]
2023-04-01 21:45:09,880   INFO  
cfg.DATA_CONFIG.DATA_SPLIT = edict()
2023-04-01 21:45:09,880   INFO  cfg.DATA_CONFIG.DATA_SPLIT.train: train
2023-04-01 21:45:09,880   INFO  cfg.DATA_CONFIG.DATA_SPLIT.test: val
2023-04-01 21:45:09,882   INFO  
cfg.DATA_CONFIG.REPEAT = edict()
2023-04-01 21:45:09,883   INFO  cfg.DATA_CONFIG.REPEAT.train: 10
2023-04-01 21:45:09,883   INFO  cfg.DATA_CONFIG.REPEAT.test: 1
2023-04-01 21:45:09,884   INFO  
cfg.DATA_CONFIG.INFO_PATH = edict()
2023-04-01 21:45:09,884   INFO  cfg.DATA_CONFIG.INFO_PATH.train: ['scannet_infos_train.pkl']
2023-04-01 21:45:09,885   INFO  cfg.DATA_CONFIG.INFO_PATH.test: ['scannet_infos_val.pkl']
2023-04-01 21:45:09,886   INFO  cfg.DATA_CONFIG.GET_ITEM_LIST: ['points', 'instance_mask', 'semantic_mask']
2023-04-01 21:45:09,886   INFO  cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True
2023-04-01 21:45:09,887   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN = edict()
2023-04-01 21:45:09,887   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.DISABLE_AUG_LIST: ['placeholder']
2023-04-01 21:45:09,888   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}]
2023-04-01 21:45:09,890   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST = edict()
2023-04-01 21:45:09,890   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.DISABLE_AUG_LIST: ['placeholder']
2023-04-01 21:45:09,890   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}]
2023-04-01 21:45:09,890   INFO  
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict()
2023-04-01 21:45:09,892   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
2023-04-01 21:45:09,893   INFO  cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}]
2023-04-01 21:45:09,893   INFO  
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict()
2023-04-01 21:45:09,894   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
2023-04-01 21:45:09,895   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
2023-04-01 21:45:09,895   INFO  cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
2023-04-01 21:45:09,896   INFO  cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}]
2023-04-01 21:45:09,896   INFO  cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/scannet_dataset.yaml
2023-04-01 21:45:09,896   INFO  cfg.VOXEL_SIZE: 0.02
2023-04-01 21:45:09,896   INFO  cfg.N_CLASSES: 18
2023-04-01 21:45:09,898   INFO  cfg.SEMANTIC_THR: 0.15
2023-04-01 21:45:09,898   INFO  
cfg.MODEL = edict()
2023-04-01 21:45:09,899   INFO  cfg.MODEL.NAME: CAGroup3D
2023-04-01 21:45:09,899   INFO  cfg.MODEL.VOXEL_SIZE: 0.02
2023-04-01 21:45:09,900   INFO  cfg.MODEL.SEMANTIC_MIN_THR: 0.05
2023-04-01 21:45:09,900   INFO  cfg.MODEL.SEMANTIC_ITER_VALUE: 0.02
2023-04-01 21:45:09,900   INFO  cfg.MODEL.SEMANTIC_THR: 0.15
2023-04-01 21:45:09,901   INFO  
cfg.MODEL.BACKBONE_3D = edict()
2023-04-01 21:45:09,901   INFO  cfg.MODEL.BACKBONE_3D.NAME: BiResNet
2023-04-01 21:45:09,902   INFO  cfg.MODEL.BACKBONE_3D.IN_CHANNELS: 3
2023-04-01 21:45:09,902   INFO  cfg.MODEL.BACKBONE_3D.OUT_CHANNELS: 64
2023-04-01 21:45:09,902   INFO  
cfg.MODEL.DENSE_HEAD = edict()
2023-04-01 21:45:09,903   INFO  cfg.MODEL.DENSE_HEAD.NAME: CAGroup3DHead
2023-04-01 21:45:09,903   INFO  cfg.MODEL.DENSE_HEAD.IN_CHANNELS: [64, 128, 256, 512]
2023-04-01 21:45:09,904   INFO  cfg.MODEL.DENSE_HEAD.OUT_CHANNELS: 64
2023-04-01 21:45:09,904   INFO  cfg.MODEL.DENSE_HEAD.SEMANTIC_THR: 0.15
2023-04-01 21:45:09,905   INFO  cfg.MODEL.DENSE_HEAD.VOXEL_SIZE: 0.02
2023-04-01 21:45:09,905   INFO  cfg.MODEL.DENSE_HEAD.N_CLASSES: 18
2023-04-01 21:45:09,906   INFO  cfg.MODEL.DENSE_HEAD.N_REG_OUTS: 6
2023-04-01 21:45:09,906   INFO  cfg.MODEL.DENSE_HEAD.CLS_KERNEL: 9
2023-04-01 21:45:09,906   INFO  cfg.MODEL.DENSE_HEAD.WITH_YAW: False
2023-04-01 21:45:09,907   INFO  cfg.MODEL.DENSE_HEAD.USE_SEM_SCORE: False
2023-04-01 21:45:09,907   INFO  cfg.MODEL.DENSE_HEAD.EXPAND_RATIO: 3
2023-04-01 21:45:09,908   INFO  
cfg.MODEL.DENSE_HEAD.ASSIGNER = edict()
2023-04-01 21:45:09,908   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.NAME: CAGroup3DAssigner
2023-04-01 21:45:09,909   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.LIMIT: 27
2023-04-01 21:45:09,909   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.TOPK: 18
2023-04-01 21:45:09,909   INFO  cfg.MODEL.DENSE_HEAD.ASSIGNER.N_SCALES: 4
2023-04-01 21:45:09,910   INFO  
cfg.MODEL.DENSE_HEAD.LOSS_OFFSET = edict()
2023-04-01 21:45:09,910   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.NAME: SmoothL1Loss
2023-04-01 21:45:09,911   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.BETA: 0.04
2023-04-01 21:45:09,911   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.REDUCTION: sum
2023-04-01 21:45:09,912   INFO  cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.LOSS_WEIGHT: 1.0
2023-04-01 21:45:09,912   INFO  
cfg.MODEL.DENSE_HEAD.LOSS_BBOX = edict()
2023-04-01 21:45:09,913   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.NAME: IoU3DLoss
2023-04-01 21:45:09,913   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.WITH_YAW: False
2023-04-01 21:45:09,914   INFO  cfg.MODEL.DENSE_HEAD.LOSS_BBOX.LOSS_WEIGHT: 1.0
2023-04-01 21:45:09,914   INFO  
cfg.MODEL.DENSE_HEAD.NMS_CONFIG = edict()
2023-04-01 21:45:09,915   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.SCORE_THR: 0.01
2023-04-01 21:45:09,915   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.NMS_PRE: 1000
2023-04-01 21:45:09,916   INFO  cfg.MODEL.DENSE_HEAD.NMS_CONFIG.IOU_THR: 0.5
2023-04-01 21:45:09,916   INFO  
cfg.MODEL.ROI_HEAD = edict()
2023-04-01 21:45:09,917   INFO  cfg.MODEL.ROI_HEAD.NAME: CAGroup3DRoIHead
2023-04-01 21:45:09,917   INFO  cfg.MODEL.ROI_HEAD.NUM_CLASSES: 18
2023-04-01 21:45:09,918   INFO  cfg.MODEL.ROI_HEAD.MIDDLE_FEATURE_SOURCE: [3]
2023-04-01 21:45:09,918   INFO  cfg.MODEL.ROI_HEAD.GRID_SIZE: 7
2023-04-01 21:45:09,919   INFO  cfg.MODEL.ROI_HEAD.VOXEL_SIZE: 0.02
2023-04-01 21:45:09,919   INFO  cfg.MODEL.ROI_HEAD.COORD_KEY: 2
2023-04-01 21:45:09,919   INFO  cfg.MODEL.ROI_HEAD.MLPS: [[64, 128, 128]]
2023-04-01 21:45:09,920   INFO  cfg.MODEL.ROI_HEAD.CODE_SIZE: 6
2023-04-01 21:45:09,920   INFO  cfg.MODEL.ROI_HEAD.ENCODE_SINCOS: False
2023-04-01 21:45:09,921   INFO  cfg.MODEL.ROI_HEAD.ROI_PER_IMAGE: 128
2023-04-01 21:45:09,921   INFO  cfg.MODEL.ROI_HEAD.ROI_FG_RATIO: 0.9
2023-04-01 21:45:09,921   INFO  cfg.MODEL.ROI_HEAD.REG_FG_THRESH: 0.3
2023-04-01 21:45:09,922   INFO  cfg.MODEL.ROI_HEAD.ROI_CONV_KERNEL: 5
2023-04-01 21:45:09,922   INFO  cfg.MODEL.ROI_HEAD.ENLARGE_RATIO: False
2023-04-01 21:45:09,923   INFO  cfg.MODEL.ROI_HEAD.USE_IOU_LOSS: False
2023-04-01 21:45:09,923   INFO  cfg.MODEL.ROI_HEAD.USE_GRID_OFFSET: False
2023-04-01 21:45:09,923   INFO  cfg.MODEL.ROI_HEAD.USE_SIMPLE_POOLING: True
2023-04-01 21:45:09,923   INFO  cfg.MODEL.ROI_HEAD.USE_CENTER_POOLING: True
2023-04-01 21:45:09,924   INFO  
cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS = edict()
2023-04-01 21:45:09,925   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_CLS_WEIGHT: 1.0
2023-04-01 21:45:09,927   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_REG_WEIGHT: 1.0
2023-04-01 21:45:09,927   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_IOU_WEIGHT: 1.0
2023-04-01 21:45:09,928   INFO  cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.CODE_WEIGHT: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
2023-04-01 21:45:09,928   INFO  
cfg.MODEL.POST_PROCESSING = edict()
2023-04-01 21:45:09,928   INFO  cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.25, 0.5]
2023-04-01 21:45:09,929   INFO  cfg.MODEL.POST_PROCESSING.EVAL_METRIC: scannet
2023-04-01 21:45:09,929   INFO  
cfg.OPTIMIZATION = edict()
2023-04-01 21:45:09,931   INFO  cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 16
2023-04-01 21:45:09,931   INFO  cfg.OPTIMIZATION.NUM_EPOCHS: 1
2023-04-01 21:45:09,931   INFO  cfg.OPTIMIZATION.OPTIMIZER: adamW
2023-04-01 21:45:09,932   INFO  cfg.OPTIMIZATION.LR: 0.001
2023-04-01 21:45:09,932   INFO  cfg.OPTIMIZATION.WEIGHT_DECAY: 0.0001
2023-04-01 21:45:09,933   INFO  cfg.OPTIMIZATION.DECAY_STEP_LIST: [7, 9]
2023-04-01 21:45:09,933   INFO  cfg.OPTIMIZATION.LR_DECAY: 0.1
2023-04-01 21:45:09,933   INFO  cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
2023-04-01 21:45:09,934   INFO  cfg.OPTIMIZATION.PCT_START: 0.4
2023-04-01 21:45:09,934   INFO  cfg.OPTIMIZATION.DIV_FACTOR: 10
2023-04-01 21:45:09,935   INFO  cfg.OPTIMIZATION.LR_CLIP: 1e-07
2023-04-01 21:45:09,935   INFO  cfg.OPTIMIZATION.LR_WARMUP: False
2023-04-01 21:45:09,936   INFO  cfg.OPTIMIZATION.WARMUP_EPOCH: 1
2023-04-01 21:45:09,936   INFO  cfg.TAG: CAGroup3D
2023-04-01 21:45:09,937   INFO  cfg.EXP_GROUP_PATH: scannet_models
2023-04-01 21:45:09,937   INFO  Loading SCANNET dataset
2023-04-01 21:45:09,972   INFO  Total samples for SCANNET dataset: 312
2023-04-01 21:45:12,816   INFO  ==> Loading parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train/ckpt/checkpoint_epoch_8.pth to CPU
2023-04-01 21:45:13,846   INFO  ==> Checkpoint trained from version: pcdet+0.5.2+0000000
2023-04-01 21:45:13,984   INFO  ==> Done (loaded 838/838)
2023-04-01 21:45:14,393   INFO  *************** EPOCH 8 EVALUATION *****************
2023-04-02 00:01:12,334   INFO  *************** Performance of EPOCH 8 *****************
2023-04-02 00:01:12,334   INFO  Generate label finished(sec_per_example: 26.1406 second).
2023-04-02 00:01:12,335   INFO  recall_roi_0.25: 0.000000
2023-04-02 00:01:12,336   INFO  recall_rcnn_0.25: 0.000000
2023-04-02 00:01:12,336   INFO  recall_roi_0.5: 0.000000
2023-04-02 00:01:12,337   INFO  recall_rcnn_0.5: 0.000000
2023-04-02 00:01:12,337   INFO  Average predicted number of objects(312 samples): 490.029
2023-04-02 00:01:30,129   INFO  {'cabinet_AP_0.25': 0.5813855528831482, 'bed_AP_0.25': 0.8816422820091248, 'chair_AP_0.25': 0.9543977379798889, 'sofa_AP_0.25': 0.8942864537239075, 'table_AP_0.25': 0.6969487071037292, 'door_AP_0.25': 0.682966947555542, 'window_AP_0.25': 0.6282612085342407, 'bookshelf_AP_0.25': 0.7065274715423584, 'picture_AP_0.25': 0.3937835395336151, 'counter_AP_0.25': 0.7749427556991577, 'desk_AP_0.25': 0.84075927734375, 'curtain_AP_0.25': 0.7117773294448853, 'refrigerator_AP_0.25': 0.5583232641220093, 'showercurtrain_AP_0.25': 0.7335410118103027, 'toilet_AP_0.25': 1.0, 'sink_AP_0.25': 0.7548432350158691, 'bathtub_AP_0.25': 0.87208491563797, 'garbagebin_AP_0.25': 0.6607850790023804, 'mAP_0.25': 0.7404030561447144, 'cabinet_rec_0.25': 0.9112903225806451, 'bed_rec_0.25': 0.9259259259259259, 'chair_rec_0.25': 0.9714912280701754, 'sofa_rec_0.25': 0.9690721649484536, 'table_rec_0.25': 0.8485714285714285, 'door_rec_0.25': 0.9036402569593148, 'window_rec_0.25': 0.875886524822695, 'bookshelf_rec_0.25': 0.9090909090909091, 'picture_rec_0.25': 0.6441441441441441, 'counter_rec_0.25': 0.9038461538461539, 'desk_rec_0.25': 0.968503937007874, 'curtain_rec_0.25': 0.8656716417910447, 'refrigerator_rec_0.25': 0.8421052631578947, 'showercurtrain_rec_0.25': 0.9642857142857143, 'toilet_rec_0.25': 1.0, 'sink_rec_0.25': 0.8469387755102041, 'bathtub_rec_0.25': 0.9032258064516129, 'garbagebin_rec_0.25': 0.8849056603773585, 'mAR_0.25': 0.8965886587523083, 'cabinet_AP_0.50': 0.4341818690299988, 'bed_AP_0.50': 0.8374742865562439, 'chair_AP_0.50': 0.9089729189872742, 'sofa_AP_0.50': 0.8061330914497375, 'table_AP_0.50': 0.6511608958244324, 'door_AP_0.50': 0.5602805614471436, 'window_AP_0.50': 0.3997173607349396, 'bookshelf_AP_0.50': 0.5989829897880554, 'picture_AP_0.50': 0.28941845893859863, 'counter_AP_0.50': 0.4558248519897461, 'desk_AP_0.50': 0.6606391072273254, 'curtain_AP_0.50': 0.5577377676963806, 'refrigerator_AP_0.50': 0.5126804113388062, 'showercurtrain_AP_0.50': 0.48787981271743774, 'toilet_AP_0.50': 0.958685576915741, 'sink_AP_0.50': 0.5293706655502319, 'bathtub_AP_0.50': 0.7828920483589172, 'garbagebin_AP_0.50': 0.5928370952606201, 'mAP_0.50': 0.6124927997589111, 'cabinet_rec_0.50': 0.7446236559139785, 'bed_rec_0.50': 0.8765432098765432, 'chair_rec_0.50': 0.9305555555555556, 'sofa_rec_0.50': 0.9072164948453608, 'table_rec_0.50': 0.7742857142857142, 'door_rec_0.50': 0.7601713062098501, 'window_rec_0.50': 0.648936170212766, 'bookshelf_rec_0.50': 0.7792207792207793, 'picture_rec_0.50': 0.481981981981982, 'counter_rec_0.50': 0.6153846153846154, 'desk_rec_0.50': 0.84251968503937, 'curtain_rec_0.50': 0.7313432835820896, 'refrigerator_rec_0.50': 0.7719298245614035, 'showercurtrain_rec_0.50': 0.6428571428571429, 'toilet_rec_0.50': 0.9655172413793104, 'sink_rec_0.50': 0.6224489795918368, 'bathtub_rec_0.50': 0.8387096774193549, 'garbagebin_rec_0.50': 0.7754716981132076, 'mAR_0.50': 0.7616509453350477}
2023-04-02 00:01:30,138   INFO  Result is save to C:\CITYU\CS5182\proj\CAGroup3D\output\scannet_models\CAGroup3D\cagroup3d-win10-scannet-eval\eval\epoch_8\val\default
2023-04-02 00:01:30,139   INFO  ****************Evaluation done.*****************