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6DammK9
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cagroup3d-win10-sunrgbd

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Files changed (6) hide show
  1. CAGroup3D.yaml +101 -0
  2. ckpt/checkpoint_epoch_1.pth +3 -0
  3. eval/eval_with_train/eval_list_val.txt +0 -0
  4. eval/eval_with_train/tensorboard_val/events.out.tfevents.1679930777.DESKTOP-OROUQQR +3 -0
  5. log_train_20230326-111334.txt +740 -0
  6. tensorboard/events.out.tfevents.1679800415.DESKTOP-OROUQQR +3 -0
CAGroup3D.yaml ADDED
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+ CLASS_NAMES: ['bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
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+ 'night_stand', 'bookshelf', 'bathtub']
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+
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+ DATA_CONFIG:
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+ _BASE_CONFIG_: cfgs/dataset_configs/sunrgbd_dataset.yaml
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+
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+ VOXEL_SIZE: &VOXEL_SIZE 0.02
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+ N_CLASSES: &N_CLASSES 10
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+ SEMANTIC_THR: &SEMANTIC_THR 0.15
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+
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+ MODEL:
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+ NAME: CAGroup3D
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+ VOXEL_SIZE: *VOXEL_SIZE
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+ SEMANTIC_MIN_THR: 0.05
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+ SEMANTIC_ITER_VALUE: 0.02
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+ SEMANTIC_THR: *SEMANTIC_THR
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+ BACKBONE_3D:
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+ NAME: BiResNet
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+ IN_CHANNELS: 3
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+ OUT_CHANNELS: 64
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+
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+ DENSE_HEAD:
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+ NAME: CAGroup3DHead
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+ IN_CHANNELS: [64, 128, 256, 512]
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+ OUT_CHANNELS: 64
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+ SEMANTIC_THR: *SEMANTIC_THR
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+ VOXEL_SIZE: *VOXEL_SIZE
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+ N_CLASSES: *N_CLASSES
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+ N_REG_OUTS: 8
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+ CLS_KERNEL: 9
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+ WITH_YAW: True
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+ USE_SEM_SCORE: False
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+ EXPAND_RATIO: 3
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+ ASSIGNER:
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+ NAME: CAGroup3DAssigner
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+ LIMIT: 27
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+ TOPK: 18
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+ N_SCALES: 4
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+ LOSS_OFFSET:
40
+ NAME: SmoothL1Loss
41
+ BETA: 0.04
42
+ REDUCTION: sum
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+ LOSS_WEIGHT: 0.2
44
+ LOSS_BBOX:
45
+ NAME: IoU3DLoss
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+ WITH_YAW: True
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+ LOSS_WEIGHT: 1.0
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+ NMS_CONFIG:
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+ SCORE_THR: 0.01
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+ NMS_PRE: 1000
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+ IOU_THR: 0.5
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+
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+ ROI_HEAD:
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+ NAME: CAGroup3DRoIHead
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+ NUM_CLASSES: *N_CLASSES
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+ MIDDLE_FEATURE_SOURCE: [3]
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+ GRID_SIZE: 7
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+ VOXEL_SIZE: *VOXEL_SIZE
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+ COORD_KEY: 2
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+ MLPS: [[64,128,128]]
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+ CODE_SIZE: 7
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+ ENCODE_SINCOS: True
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+ ROI_PER_IMAGE: 128
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+ ROI_FG_RATIO: 0.9
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+ REG_FG_THRESH: 0.3
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+ ROI_CONV_KERNEL: 5
67
+ ENLARGE_RATIO: False
68
+ USE_IOU_LOSS: True
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+ USE_GRID_OFFSET: False
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+ USE_SIMPLE_POOLING: True
71
+ USE_CENTER_POOLING: True
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+ LOSS_WEIGHTS:
73
+ RCNN_CLS_WEIGHT: 1.0 # no use
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+ RCNN_REG_WEIGHT: 0.5
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+ RCNN_IOU_WEIGHT: 1.0
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+ CODE_WEIGHT: [1., 1., 1., 1., 1., 1., 1., 1.]
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+
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+ POST_PROCESSING:
79
+ RECALL_THRESH_LIST: [0.25, 0.5]
80
+ EVAL_METRIC: scannet
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+
82
+ OPTIMIZATION:
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+ BATCH_SIZE_PER_GPU: 16 # 4x4 or 8x2
84
+ NUM_EPOCHS: 1 #14
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+
86
+ OPTIMIZER: adamW
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+ LR: 0.001
88
+ WEIGHT_DECAY: 0.0001
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+ DECAY_STEP_LIST: [8, 11]
90
+ LR_DECAY: 0.1
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+ GRAD_NORM_CLIP: 10
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+
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+ # no use
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+ PCT_START: 0.4
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+ DIV_FACTOR: 10
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+ LR_CLIP: 0.0000001
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+
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+ LR_WARMUP: False
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+ WARMUP_EPOCH: 1
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+
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+
ckpt/checkpoint_epoch_1.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:55bd1a6ec18bbe0fe2468969029108aa060ed302d821d3d134d9c0c4c7fcd453
3
+ size 1120252391
eval/eval_with_train/eval_list_val.txt ADDED
File without changes
eval/eval_with_train/tensorboard_val/events.out.tfevents.1679930777.DESKTOP-OROUQQR ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:76615c733dc754a87d0cda644e03560d5473d615fb42918246b6559fbbd4b755
3
+ size 40
log_train_20230326-111334.txt ADDED
@@ -0,0 +1,740 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 2023-03-26 11:13:34,846 INFO **********************Start logging**********************
2
+ 2023-03-26 11:13:34,846 INFO CUDA_VISIBLE_DEVICES=ALL
3
+ 2023-03-26 11:13:34,847 INFO total_batch_size: 16
4
+ 2023-03-26 11:13:34,847 INFO cfg_file cfgs/sunrgbd_models/CAGroup3D.yaml
5
+ 2023-03-26 11:13:34,848 INFO batch_size 16
6
+ 2023-03-26 11:13:34,849 INFO epochs 1
7
+ 2023-03-26 11:13:34,850 INFO workers 4
8
+ 2023-03-26 11:13:34,851 INFO extra_tag cagroup3d-win10-sunrgbd
9
+ 2023-03-26 11:13:34,851 INFO ckpt None
10
+ 2023-03-26 11:13:34,852 INFO pretrained_model None
11
+ 2023-03-26 11:13:34,853 INFO launcher pytorch
12
+ 2023-03-26 11:13:34,854 INFO tcp_port 18888
13
+ 2023-03-26 11:13:34,854 INFO sync_bn False
14
+ 2023-03-26 11:13:34,855 INFO fix_random_seed True
15
+ 2023-03-26 11:13:34,856 INFO ckpt_save_interval 1
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+ 2023-03-26 11:13:34,856 INFO max_ckpt_save_num 30
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+ 2023-03-26 11:13:34,857 INFO merge_all_iters_to_one_epoch False
18
+ 2023-03-26 11:13:34,858 INFO set_cfgs None
19
+ 2023-03-26 11:13:34,859 INFO max_waiting_mins 0
20
+ 2023-03-26 11:13:34,859 INFO start_epoch 0
21
+ 2023-03-26 11:13:34,860 INFO num_epochs_to_eval 0
22
+ 2023-03-26 11:13:34,860 INFO save_to_file False
23
+ 2023-03-26 11:13:34,861 INFO cfg.ROOT_DIR: C:\PINKAMENA\CITYU\CS5182\proj\CAGroup3D
24
+ 2023-03-26 11:13:34,862 INFO cfg.LOCAL_RANK: 0
25
+ 2023-03-26 11:13:34,863 INFO cfg.CLASS_NAMES: ['bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser', 'night_stand', 'bookshelf', 'bathtub']
26
+ 2023-03-26 11:13:34,864 INFO
27
+ cfg.DATA_CONFIG = edict()
28
+ 2023-03-26 11:13:34,865 INFO cfg.DATA_CONFIG.DATASET: SunrgbdDataset
29
+ 2023-03-26 11:13:34,866 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/sunrgbd_data/sunrgbd
30
+ 2023-03-26 11:13:34,866 INFO cfg.DATA_CONFIG.PROCESSED_DATA_TAG: sunrgbd_processed_data_v0_5_0
31
+ 2023-03-26 11:13:34,868 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-40, -40, -10, 40, 40, 10]
32
+ 2023-03-26 11:13:34,869 INFO
33
+ cfg.DATA_CONFIG.DATA_SPLIT = edict()
34
+ 2023-03-26 11:13:34,869 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train
35
+ 2023-03-26 11:13:34,870 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val
36
+ 2023-03-26 11:13:34,870 INFO
37
+ cfg.DATA_CONFIG.REPEAT = edict()
38
+ 2023-03-26 11:13:34,871 INFO cfg.DATA_CONFIG.REPEAT.train: 4
39
+ 2023-03-26 11:13:34,872 INFO cfg.DATA_CONFIG.REPEAT.test: 1
40
+ 2023-03-26 11:13:34,873 INFO
41
+ cfg.DATA_CONFIG.INFO_PATH = edict()
42
+ 2023-03-26 11:13:34,874 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['sunrgbd_infos_train.pkl']
43
+ 2023-03-26 11:13:34,875 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['sunrgbd_infos_val.pkl']
44
+ 2023-03-26 11:13:34,876 INFO cfg.DATA_CONFIG.GET_ITEM_LIST: ['points']
45
+ 2023-03-26 11:13:34,877 INFO cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True
46
+ 2023-03-26 11:13:34,877 INFO
47
+ cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN = edict()
48
+ 2023-03-26 11:13:34,878 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.DISABLE_AUG_LIST: ['placeholder']
49
+ 2023-03-26 11:13:34,879 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.AUG_CONFIG_LIST: [{'NAME': 'indoor_point_sample', 'num_points': 100000}, {'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['y']}, {'NAME': 'random_world_rotation_mmdet3d', 'WORLD_ROT_ANGLE': [-0.523599, 0.523599]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.85, 1.15]}, {'NAME': 'random_world_translation', 'ALONG_AXIS_LIST': ['x', 'y', 'z'], 'NOISE_TRANSLATE_STD': 0.1}]
50
+ 2023-03-26 11:13:34,881 INFO
51
+ cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST = edict()
52
+ 2023-03-26 11:13:34,882 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.DISABLE_AUG_LIST: ['placeholder']
53
+ 2023-03-26 11:13:34,884 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.AUG_CONFIG_LIST: [{'NAME': 'indoor_point_sample', 'num_points': 100000}]
54
+ 2023-03-26 11:13:34,884 INFO
55
+ cfg.DATA_CONFIG.DATA_AUGMENTOR = edict()
56
+ 2023-03-26 11:13:34,885 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
57
+ 2023-03-26 11:13:34,886 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'indoor_point_sample', 'num_points': 50000}]
58
+ 2023-03-26 11:13:34,887 INFO
59
+ cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict()
60
+ 2023-03-26 11:13:34,888 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
61
+ 2023-03-26 11:13:34,889 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
62
+ 2023-03-26 11:13:34,890 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
63
+ 2023-03-26 11:13:34,892 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': False}]
64
+ 2023-03-26 11:13:34,893 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/sunrgbd_dataset.yaml
65
+ 2023-03-26 11:13:34,894 INFO cfg.VOXEL_SIZE: 0.02
66
+ 2023-03-26 11:13:34,894 INFO cfg.N_CLASSES: 10
67
+ 2023-03-26 11:13:34,894 INFO cfg.SEMANTIC_THR: 0.15
68
+ 2023-03-26 11:13:34,895 INFO
69
+ cfg.MODEL = edict()
70
+ 2023-03-26 11:13:34,896 INFO cfg.MODEL.NAME: CAGroup3D
71
+ 2023-03-26 11:13:34,896 INFO cfg.MODEL.VOXEL_SIZE: 0.02
72
+ 2023-03-26 11:13:34,897 INFO cfg.MODEL.SEMANTIC_MIN_THR: 0.05
73
+ 2023-03-26 11:13:34,898 INFO cfg.MODEL.SEMANTIC_ITER_VALUE: 0.02
74
+ 2023-03-26 11:13:34,899 INFO cfg.MODEL.SEMANTIC_THR: 0.15
75
+ 2023-03-26 11:13:34,899 INFO
76
+ cfg.MODEL.BACKBONE_3D = edict()
77
+ 2023-03-26 11:13:34,900 INFO cfg.MODEL.BACKBONE_3D.NAME: BiResNet
78
+ 2023-03-26 11:13:34,900 INFO cfg.MODEL.BACKBONE_3D.IN_CHANNELS: 3
79
+ 2023-03-26 11:13:34,900 INFO cfg.MODEL.BACKBONE_3D.OUT_CHANNELS: 64
80
+ 2023-03-26 11:13:34,901 INFO
81
+ cfg.MODEL.DENSE_HEAD = edict()
82
+ 2023-03-26 11:13:34,902 INFO cfg.MODEL.DENSE_HEAD.NAME: CAGroup3DHead
83
+ 2023-03-26 11:13:34,902 INFO cfg.MODEL.DENSE_HEAD.IN_CHANNELS: [64, 128, 256, 512]
84
+ 2023-03-26 11:13:34,902 INFO cfg.MODEL.DENSE_HEAD.OUT_CHANNELS: 64
85
+ 2023-03-26 11:13:34,903 INFO cfg.MODEL.DENSE_HEAD.SEMANTIC_THR: 0.15
86
+ 2023-03-26 11:13:34,903 INFO cfg.MODEL.DENSE_HEAD.VOXEL_SIZE: 0.02
87
+ 2023-03-26 11:13:34,904 INFO cfg.MODEL.DENSE_HEAD.N_CLASSES: 10
88
+ 2023-03-26 11:13:34,904 INFO cfg.MODEL.DENSE_HEAD.N_REG_OUTS: 8
89
+ 2023-03-26 11:13:34,905 INFO cfg.MODEL.DENSE_HEAD.CLS_KERNEL: 9
90
+ 2023-03-26 11:13:34,906 INFO cfg.MODEL.DENSE_HEAD.WITH_YAW: True
91
+ 2023-03-26 11:13:34,907 INFO cfg.MODEL.DENSE_HEAD.USE_SEM_SCORE: False
92
+ 2023-03-26 11:13:34,907 INFO cfg.MODEL.DENSE_HEAD.EXPAND_RATIO: 3
93
+ 2023-03-26 11:13:34,908 INFO
94
+ cfg.MODEL.DENSE_HEAD.ASSIGNER = edict()
95
+ 2023-03-26 11:13:34,909 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.NAME: CAGroup3DAssigner
96
+ 2023-03-26 11:13:34,910 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.LIMIT: 27
97
+ 2023-03-26 11:13:34,910 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.TOPK: 18
98
+ 2023-03-26 11:13:34,911 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.N_SCALES: 4
99
+ 2023-03-26 11:13:34,911 INFO
100
+ cfg.MODEL.DENSE_HEAD.LOSS_OFFSET = edict()
101
+ 2023-03-26 11:13:34,912 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.NAME: SmoothL1Loss
102
+ 2023-03-26 11:13:34,912 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.BETA: 0.04
103
+ 2023-03-26 11:13:34,913 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.REDUCTION: sum
104
+ 2023-03-26 11:13:34,913 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.LOSS_WEIGHT: 0.2
105
+ 2023-03-26 11:13:34,914 INFO
106
+ cfg.MODEL.DENSE_HEAD.LOSS_BBOX = edict()
107
+ 2023-03-26 11:13:34,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.NAME: IoU3DLoss
108
+ 2023-03-26 11:13:34,915 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.WITH_YAW: True
109
+ 2023-03-26 11:13:34,916 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.LOSS_WEIGHT: 1.0
110
+ 2023-03-26 11:13:34,916 INFO
111
+ cfg.MODEL.DENSE_HEAD.NMS_CONFIG = edict()
112
+ 2023-03-26 11:13:34,917 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.SCORE_THR: 0.01
113
+ 2023-03-26 11:13:34,917 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.NMS_PRE: 1000
114
+ 2023-03-26 11:13:34,917 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.IOU_THR: 0.5
115
+ 2023-03-26 11:13:34,918 INFO
116
+ cfg.MODEL.ROI_HEAD = edict()
117
+ 2023-03-26 11:13:34,918 INFO cfg.MODEL.ROI_HEAD.NAME: CAGroup3DRoIHead
118
+ 2023-03-26 11:13:34,919 INFO cfg.MODEL.ROI_HEAD.NUM_CLASSES: 10
119
+ 2023-03-26 11:13:34,919 INFO cfg.MODEL.ROI_HEAD.MIDDLE_FEATURE_SOURCE: [3]
120
+ 2023-03-26 11:13:34,920 INFO cfg.MODEL.ROI_HEAD.GRID_SIZE: 7
121
+ 2023-03-26 11:13:34,921 INFO cfg.MODEL.ROI_HEAD.VOXEL_SIZE: 0.02
122
+ 2023-03-26 11:13:34,921 INFO cfg.MODEL.ROI_HEAD.COORD_KEY: 2
123
+ 2023-03-26 11:13:34,922 INFO cfg.MODEL.ROI_HEAD.MLPS: [[64, 128, 128]]
124
+ 2023-03-26 11:13:34,923 INFO cfg.MODEL.ROI_HEAD.CODE_SIZE: 7
125
+ 2023-03-26 11:13:34,924 INFO cfg.MODEL.ROI_HEAD.ENCODE_SINCOS: True
126
+ 2023-03-26 11:13:34,925 INFO cfg.MODEL.ROI_HEAD.ROI_PER_IMAGE: 128
127
+ 2023-03-26 11:13:34,926 INFO cfg.MODEL.ROI_HEAD.ROI_FG_RATIO: 0.9
128
+ 2023-03-26 11:13:34,926 INFO cfg.MODEL.ROI_HEAD.REG_FG_THRESH: 0.3
129
+ 2023-03-26 11:13:34,926 INFO cfg.MODEL.ROI_HEAD.ROI_CONV_KERNEL: 5
130
+ 2023-03-26 11:13:34,927 INFO cfg.MODEL.ROI_HEAD.ENLARGE_RATIO: False
131
+ 2023-03-26 11:13:34,927 INFO cfg.MODEL.ROI_HEAD.USE_IOU_LOSS: True
132
+ 2023-03-26 11:13:34,928 INFO cfg.MODEL.ROI_HEAD.USE_GRID_OFFSET: False
133
+ 2023-03-26 11:13:34,928 INFO cfg.MODEL.ROI_HEAD.USE_SIMPLE_POOLING: True
134
+ 2023-03-26 11:13:34,928 INFO cfg.MODEL.ROI_HEAD.USE_CENTER_POOLING: True
135
+ 2023-03-26 11:13:34,929 INFO
136
+ cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS = edict()
137
+ 2023-03-26 11:13:34,929 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_CLS_WEIGHT: 1.0
138
+ 2023-03-26 11:13:34,930 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_REG_WEIGHT: 0.5
139
+ 2023-03-26 11:13:34,930 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_IOU_WEIGHT: 1.0
140
+ 2023-03-26 11:13:34,931 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.CODE_WEIGHT: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
141
+ 2023-03-26 11:13:34,932 INFO
142
+ cfg.MODEL.POST_PROCESSING = edict()
143
+ 2023-03-26 11:13:34,933 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.25, 0.5]
144
+ 2023-03-26 11:13:34,934 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: scannet
145
+ 2023-03-26 11:13:34,934 INFO
146
+ cfg.OPTIMIZATION = edict()
147
+ 2023-03-26 11:13:34,935 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 16
148
+ 2023-03-26 11:13:34,935 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 1
149
+ 2023-03-26 11:13:34,936 INFO cfg.OPTIMIZATION.OPTIMIZER: adamW
150
+ 2023-03-26 11:13:34,936 INFO cfg.OPTIMIZATION.LR: 0.001
151
+ 2023-03-26 11:13:34,937 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.0001
152
+ 2023-03-26 11:13:34,937 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [8, 11]
153
+ 2023-03-26 11:13:34,938 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1
154
+ 2023-03-26 11:13:34,938 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
155
+ 2023-03-26 11:13:34,939 INFO cfg.OPTIMIZATION.PCT_START: 0.4
156
+ 2023-03-26 11:13:34,940 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10
157
+ 2023-03-26 11:13:34,941 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07
158
+ 2023-03-26 11:13:34,941 INFO cfg.OPTIMIZATION.LR_WARMUP: False
159
+ 2023-03-26 11:13:34,942 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1
160
+ 2023-03-26 11:13:34,942 INFO cfg.TAG: CAGroup3D
161
+ 2023-03-26 11:13:34,942 INFO cfg.EXP_GROUP_PATH: sunrgbd_models
162
+ 2023-03-26 11:13:35,054 INFO Loading SUNRGBD dataset
163
+ 2023-03-26 11:13:35,212 INFO Total samples for SUNRGBD dataset: 5285
164
+ 2023-03-26 11:13:36,687 INFO DistributedDataParallel(
165
+ (module): CAGroup3D(
166
+ (vfe): None
167
+ (backbone_3d): BiResNet(
168
+ (conv1): Sequential(
169
+ (0): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
170
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
171
+ (2): MinkowskiReLU()
172
+ (3): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
173
+ (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
174
+ (5): MinkowskiReLU()
175
+ )
176
+ (relu): MinkowskiReLU()
177
+ (layer1): Sequential(
178
+ (0): BasicBlock(
179
+ (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
180
+ (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
181
+ (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
182
+ (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
183
+ (relu): MinkowskiReLU()
184
+ (downsample): Sequential(
185
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
186
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
187
+ )
188
+ )
189
+ (1): BasicBlock(
190
+ (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
191
+ (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
192
+ (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
193
+ (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
194
+ (relu): MinkowskiReLU()
195
+ )
196
+ )
197
+ (layer2): Sequential(
198
+ (0): BasicBlock(
199
+ (conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
200
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
201
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
202
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
203
+ (relu): MinkowskiReLU()
204
+ (downsample): Sequential(
205
+ (0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
206
+ (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
207
+ )
208
+ )
209
+ (1): BasicBlock(
210
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
211
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
212
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
213
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
214
+ (relu): MinkowskiReLU()
215
+ )
216
+ )
217
+ (layer3): Sequential(
218
+ (0): BasicBlock(
219
+ (conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
220
+ (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
221
+ (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
222
+ (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
223
+ (relu): MinkowskiReLU()
224
+ (downsample): Sequential(
225
+ (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
226
+ (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
227
+ )
228
+ )
229
+ (1): BasicBlock(
230
+ (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
231
+ (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
232
+ (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
233
+ (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
234
+ (relu): MinkowskiReLU()
235
+ )
236
+ )
237
+ (layer4): Sequential(
238
+ (0): BasicBlock(
239
+ (conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
240
+ (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
241
+ (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
242
+ (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
243
+ (relu): MinkowskiReLU()
244
+ (downsample): Sequential(
245
+ (0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
246
+ (1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
247
+ )
248
+ )
249
+ (1): BasicBlock(
250
+ (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
251
+ (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
252
+ (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
253
+ (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
254
+ (relu): MinkowskiReLU()
255
+ )
256
+ )
257
+ (compression3): Sequential(
258
+ (0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
259
+ (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
260
+ )
261
+ (compression4): Sequential(
262
+ (0): MinkowskiConvolution(in=512, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
263
+ (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
264
+ )
265
+ (down3): Sequential(
266
+ (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
267
+ (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
268
+ )
269
+ (down4): Sequential(
270
+ (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
271
+ (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
272
+ (2): MinkowskiReLU()
273
+ (3): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
274
+ (4): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
275
+ )
276
+ (layer3_): Sequential(
277
+ (0): BasicBlock(
278
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
279
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
280
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
281
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
282
+ (relu): MinkowskiReLU()
283
+ )
284
+ (1): BasicBlock(
285
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
286
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
287
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
288
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
289
+ (relu): MinkowskiReLU()
290
+ )
291
+ )
292
+ (layer4_): Sequential(
293
+ (0): BasicBlock(
294
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
295
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
296
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
297
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
298
+ (relu): MinkowskiReLU()
299
+ )
300
+ (1): BasicBlock(
301
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
302
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
303
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
304
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
305
+ (relu): MinkowskiReLU()
306
+ )
307
+ )
308
+ (layer5_): Sequential(
309
+ (0): Bottleneck(
310
+ (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
311
+ (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
312
+ (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
313
+ (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
314
+ (conv3): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
315
+ (norm3): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
316
+ (relu): MinkowskiReLU()
317
+ (downsample): Sequential(
318
+ (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
319
+ (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
320
+ )
321
+ )
322
+ )
323
+ (layer5): Sequential(
324
+ (0): Bottleneck(
325
+ (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
326
+ (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
327
+ (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
328
+ (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
329
+ (conv3): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
330
+ (norm3): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
331
+ (relu): MinkowskiReLU()
332
+ (downsample): Sequential(
333
+ (0): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
334
+ (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
335
+ )
336
+ )
337
+ )
338
+ (spp): DAPPM(
339
+ (scale1): Sequential(
340
+ (0): MinkowskiAvgPooling(kernel_size=[5, 5, 5], stride=[2, 2, 2], dilation=[1, 1, 1])
341
+ (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
342
+ (2): MinkowskiReLU()
343
+ (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
344
+ )
345
+ (scale2): Sequential(
346
+ (0): MinkowskiAvgPooling(kernel_size=[9, 9, 9], stride=[4, 4, 4], dilation=[1, 1, 1])
347
+ (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
348
+ (2): MinkowskiReLU()
349
+ (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
350
+ )
351
+ (scale3): Sequential(
352
+ (0): MinkowskiAvgPooling(kernel_size=[17, 17, 17], stride=[8, 8, 8], dilation=[1, 1, 1])
353
+ (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
354
+ (2): MinkowskiReLU()
355
+ (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
356
+ )
357
+ (scale4): Sequential(
358
+ (0): MinkowskiAvgPooling(kernel_size=[33, 33, 33], stride=[16, 16, 16], dilation=[1, 1, 1])
359
+ (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
360
+ (2): MinkowskiReLU()
361
+ (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
362
+ )
363
+ (scale0): Sequential(
364
+ (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
365
+ (1): MinkowskiReLU()
366
+ (2): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
367
+ )
368
+ (process1): Sequential(
369
+ (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
370
+ (1): MinkowskiReLU()
371
+ (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
372
+ )
373
+ (process2): Sequential(
374
+ (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
375
+ (1): MinkowskiReLU()
376
+ (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
377
+ )
378
+ (process3): Sequential(
379
+ (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
380
+ (1): MinkowskiReLU()
381
+ (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
382
+ )
383
+ (process4): Sequential(
384
+ (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
385
+ (1): MinkowskiReLU()
386
+ (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
387
+ )
388
+ (compression): Sequential(
389
+ (0): MinkowskiBatchNorm(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
390
+ (1): MinkowskiReLU()
391
+ (2): MinkowskiConvolution(in=640, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
392
+ )
393
+ (shortcut): Sequential(
394
+ (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
395
+ (1): MinkowskiReLU()
396
+ (2): MinkowskiConvolution(in=1024, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
397
+ )
398
+ )
399
+ (out): Sequential(
400
+ (0): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
401
+ (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
402
+ (2): MinkowskiReLU()
403
+ (3): MinkowskiConvolution(in=256, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
404
+ (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
405
+ (5): MinkowskiReLU()
406
+ )
407
+ )
408
+ (map_to_bev_module): None
409
+ (pfe): None
410
+ (backbone_2d): None
411
+ (dense_head): CAGroup3DHead(
412
+ (loss_centerness): CrossEntropy()
413
+ (loss_bbox): IoU3DLoss()
414
+ (loss_cls): FocalLoss()
415
+ (loss_sem): FocalLoss()
416
+ (loss_offset): SmoothL1Loss()
417
+ (offset_block): Sequential(
418
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
419
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
420
+ (2): MinkowskiELU()
421
+ (3): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
422
+ (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
423
+ (5): MinkowskiELU()
424
+ (6): MinkowskiConvolution(in=64, out=9, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
425
+ )
426
+ (feature_offset): Sequential(
427
+ (0): MinkowskiConvolution(in=64, out=192, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
428
+ (1): MinkowskiBatchNorm(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
429
+ (2): MinkowskiELU()
430
+ )
431
+ (semantic_conv): MinkowskiConvolution(in=64, out=10, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
432
+ (centerness_conv): MinkowskiConvolution(in=64, out=1, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
433
+ (reg_conv): MinkowskiConvolution(in=64, out=8, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
434
+ (cls_conv): MinkowskiConvolution(in=64, out=10, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
435
+ (scales): ModuleList(
436
+ (0): Scale()
437
+ (1): Scale()
438
+ (2): Scale()
439
+ (3): Scale()
440
+ (4): Scale()
441
+ (5): Scale()
442
+ (6): Scale()
443
+ (7): Scale()
444
+ (8): Scale()
445
+ (9): Scale()
446
+ )
447
+ (cls_individual_out): ModuleList(
448
+ (0): Sequential(
449
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
450
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
451
+ (2): MinkowskiELU()
452
+ )
453
+ (1): Sequential(
454
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
455
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
456
+ (2): MinkowskiELU()
457
+ )
458
+ (2): Sequential(
459
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
460
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
461
+ (2): MinkowskiELU()
462
+ )
463
+ (3): Sequential(
464
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
465
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
466
+ (2): MinkowskiELU()
467
+ )
468
+ (4): Sequential(
469
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
470
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
471
+ (2): MinkowskiELU()
472
+ )
473
+ (5): Sequential(
474
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
475
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
476
+ (2): MinkowskiELU()
477
+ )
478
+ (6): Sequential(
479
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
480
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
481
+ (2): MinkowskiELU()
482
+ )
483
+ (7): Sequential(
484
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
485
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
486
+ (2): MinkowskiELU()
487
+ )
488
+ (8): Sequential(
489
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
490
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
491
+ (2): MinkowskiELU()
492
+ )
493
+ (9): Sequential(
494
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
495
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
496
+ (2): MinkowskiELU()
497
+ )
498
+ )
499
+ (cls_individual_up): ModuleList(
500
+ (0): ModuleList(
501
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
502
+ (1): Sequential(
503
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
504
+ (1): MinkowskiELU()
505
+ )
506
+ )
507
+ (1): ModuleList(
508
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
509
+ (1): Sequential(
510
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
511
+ (1): MinkowskiELU()
512
+ )
513
+ )
514
+ (2): ModuleList(
515
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
516
+ (1): Sequential(
517
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
518
+ (1): MinkowskiELU()
519
+ )
520
+ )
521
+ (3): ModuleList(
522
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
523
+ (1): Sequential(
524
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
525
+ (1): MinkowskiELU()
526
+ )
527
+ )
528
+ (4): ModuleList(
529
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
530
+ (1): Sequential(
531
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
532
+ (1): MinkowskiELU()
533
+ )
534
+ )
535
+ (5): ModuleList(
536
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
537
+ (1): Sequential(
538
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
539
+ (1): MinkowskiELU()
540
+ )
541
+ )
542
+ (6): ModuleList(
543
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
544
+ (1): Sequential(
545
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
546
+ (1): MinkowskiELU()
547
+ )
548
+ )
549
+ (7): ModuleList(
550
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
551
+ (1): Sequential(
552
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
553
+ (1): MinkowskiELU()
554
+ )
555
+ )
556
+ (8): ModuleList(
557
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
558
+ (1): Sequential(
559
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
560
+ (1): MinkowskiELU()
561
+ )
562
+ )
563
+ (9): ModuleList(
564
+ (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
565
+ (1): Sequential(
566
+ (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
567
+ (1): MinkowskiELU()
568
+ )
569
+ )
570
+ )
571
+ (cls_individual_fuse): ModuleList(
572
+ (0): Sequential(
573
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
574
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
575
+ (2): MinkowskiELU()
576
+ )
577
+ (1): Sequential(
578
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
579
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
580
+ (2): MinkowskiELU()
581
+ )
582
+ (2): Sequential(
583
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
584
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
585
+ (2): MinkowskiELU()
586
+ )
587
+ (3): Sequential(
588
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
589
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
590
+ (2): MinkowskiELU()
591
+ )
592
+ (4): Sequential(
593
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
594
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
595
+ (2): MinkowskiELU()
596
+ )
597
+ (5): Sequential(
598
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
599
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
600
+ (2): MinkowskiELU()
601
+ )
602
+ (6): Sequential(
603
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
604
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
605
+ (2): MinkowskiELU()
606
+ )
607
+ (7): Sequential(
608
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
609
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
610
+ (2): MinkowskiELU()
611
+ )
612
+ (8): Sequential(
613
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
614
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
615
+ (2): MinkowskiELU()
616
+ )
617
+ (9): Sequential(
618
+ (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
619
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
620
+ (2): MinkowskiELU()
621
+ )
622
+ )
623
+ (cls_individual_expand_out): ModuleList(
624
+ (0): Sequential(
625
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
626
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
627
+ (2): MinkowskiELU()
628
+ )
629
+ (1): Sequential(
630
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
631
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
632
+ (2): MinkowskiELU()
633
+ )
634
+ (2): Sequential(
635
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
636
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
637
+ (2): MinkowskiELU()
638
+ )
639
+ (3): Sequential(
640
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
641
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
642
+ (2): MinkowskiELU()
643
+ )
644
+ (4): Sequential(
645
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
646
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
647
+ (2): MinkowskiELU()
648
+ )
649
+ (5): Sequential(
650
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
651
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
652
+ (2): MinkowskiELU()
653
+ )
654
+ (6): Sequential(
655
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
656
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
657
+ (2): MinkowskiELU()
658
+ )
659
+ (7): Sequential(
660
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
661
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
662
+ (2): MinkowskiELU()
663
+ )
664
+ (8): Sequential(
665
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
666
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
667
+ (2): MinkowskiELU()
668
+ )
669
+ (9): Sequential(
670
+ (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
671
+ (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
672
+ (2): MinkowskiELU()
673
+ )
674
+ )
675
+ )
676
+ (point_head): None
677
+ (roi_head): CAGroup3DRoIHead(
678
+ (iou_loss_computer): IoU3DLoss()
679
+ (proposal_target_layer): ProposalTargetLayer()
680
+ (reg_loss_func): WeightedSmoothL1Loss()
681
+ (roi_grid_pool_layers): ModuleList(
682
+ (0): SimplePoolingLayer(
683
+ (grid_conv): MinkowskiConvolution(in=64, out=128, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
684
+ (grid_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
685
+ (grid_relu): MinkowskiELU()
686
+ (pooling_conv): MinkowskiConvolution(in=128, out=128, kernel_size=[7, 7, 7], stride=[1, 1, 1], dilation=[1, 1, 1])
687
+ (pooling_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
688
+ )
689
+ )
690
+ (reg_fc_layers): Sequential(
691
+ (0): Linear(in_features=128, out_features=256, bias=False)
692
+ (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
693
+ (2): ReLU()
694
+ (3): Dropout(p=0.3, inplace=False)
695
+ (4): Linear(in_features=256, out_features=256, bias=False)
696
+ (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
697
+ (6): ReLU()
698
+ )
699
+ (reg_pred_layer): Linear(in_features=256, out_features=8, bias=True)
700
+ )
701
+ )
702
+ )
703
+ 2023-03-26 11:13:36,794 INFO **********************Start training sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd)**********************
704
+ 2023-03-26 12:29:52,942 INFO Epoch [ 1][ 50]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.20667796790599824, loss_bbox: 0.2990968986600637, loss_cls: 0.3071140018105507, loss_sem: 1.0371560204029082, loss_vote: 0.4092518210411072, one_stage_loss: 2.259296736717224, rcnn_loss_reg: 0.06894166469573974, rcnn_loss_iou: 0.03426224589347839, loss_two_stage: 0.10320390939712525,
705
+ 2023-03-26 13:52:01,310 INFO Epoch [ 1][ 100]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5310189422965049, loss_bbox: 0.7474155166745186, loss_cls: 0.6665185099840164, loss_sem: 0.6469615936279297, loss_vote: 0.3759205311536789, one_stage_loss: 2.967835102081299, rcnn_loss_reg: 0.4916525638103485, rcnn_loss_iou: 0.31042004823684693, loss_two_stage: 0.8020726108551025,
706
+ 2023-03-26 15:16:34,527 INFO Epoch [ 1][ 150]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.47814485549926755, loss_bbox: 0.6697727379202842, loss_cls: 0.5062539026141166, loss_sem: 0.5720264983177185, loss_vote: 0.3539592534303665, one_stage_loss: 2.580157253742218, rcnn_loss_reg: 0.4772424042224884, rcnn_loss_iou: 0.2981147265434265, loss_two_stage: 0.7753571343421936,
707
+ 2023-03-26 16:42:56,533 INFO Epoch [ 1][ 200]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5514369648694992, loss_bbox: 0.7656048792600632, loss_cls: 0.5199696916341782, loss_sem: 0.5628203338384629, loss_vote: 0.3609760856628418, one_stage_loss: 2.7608079433441164, rcnn_loss_reg: 0.5857562351226807, rcnn_loss_iou: 0.35664750695228575, loss_two_stage: 0.9424037384986877,
708
+ 2023-03-26 18:04:56,578 INFO Epoch [ 1][ 250]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.47652084708213804, loss_bbox: 0.6971965312957764, loss_cls: 0.4520271807909012, loss_sem: 0.6041016298532486, loss_vote: 0.35814492881298066, one_stage_loss: 2.587991120815277, rcnn_loss_reg: 0.29248207330703735, rcnn_loss_iou: 0.1785850465297699, loss_two_stage: 0.47106712102890014,
709
+ 2023-03-26 19:28:39,683 INFO Epoch [ 1][ 300]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5167306911945343, loss_bbox: 0.7328562247753143, loss_cls: 0.45524279356002806, loss_sem: 0.515706689953804, loss_vote: 0.32769578754901885, one_stage_loss: 2.5482321834564208, rcnn_loss_reg: 0.35428098678588865, rcnn_loss_iou: 0.21939268827438355, loss_two_stage: 0.5736736750602722,
710
+ 2023-03-26 20:53:09,330 INFO Epoch [ 1][ 350]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.604567551612854, loss_bbox: 0.8178253293037414, loss_cls: 0.5145705115795135, loss_sem: 0.4814878588914871, loss_vote: 0.30948863834142687, one_stage_loss: 2.727939887046814, rcnn_loss_reg: 0.8254446721076966, rcnn_loss_iou: 0.5003487575054169, loss_two_stage: 1.3257934308052064,
711
+ 2023-03-26 22:14:45,021 INFO Epoch [ 1][ 400]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5232829931378364, loss_bbox: 0.729054206609726, loss_cls: 0.4304437929391861, loss_sem: 0.6052549344301223, loss_vote: 0.32101795822381973, one_stage_loss: 2.609053874015808, rcnn_loss_reg: 0.47852428913116457, rcnn_loss_iou: 0.29645866751670835, loss_two_stage: 0.7749829506874084,
712
+ 2023-03-26 23:39:29,069 INFO Epoch [ 1][ 450]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.4240903949737549, loss_bbox: 0.6023871430754661, loss_cls: 0.43437127619981764, loss_sem: 0.58591732442379, loss_vote: 0.31612290561199186, one_stage_loss: 2.362889051437378, rcnn_loss_reg: 0.3720583057403564, rcnn_loss_iou: 0.2311265003681183, loss_two_stage: 0.6031848061084747,
713
+ 2023-03-27 01:03:24,345 INFO Epoch [ 1][ 500]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.3820648092031479, loss_bbox: 0.5443513408303261, loss_cls: 0.36938557237386704, loss_sem: 0.6360691225528717, loss_vote: 0.31682781159877776, one_stage_loss: 2.2486986470222474, rcnn_loss_reg: 0.36717917561531066, rcnn_loss_iou: 0.23230359673500062, loss_two_stage: 0.5994827747344971,
714
+ 2023-03-27 02:25:21,026 INFO Epoch [ 1][ 550]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5708097213506699, loss_bbox: 0.7884046626091004, loss_cls: 0.5089094871282578, loss_sem: 0.5691659837961197, loss_vote: 0.3056716549396515, one_stage_loss: 2.742961506843567, rcnn_loss_reg: 0.6101349353790283, rcnn_loss_iou: 0.3731300795078278, loss_two_stage: 0.9832650113105774,
715
+ 2023-03-27 03:44:41,116 INFO Epoch [ 1][ 600]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5802704590559006, loss_bbox: 0.7960068082809448, loss_cls: 0.459269055724144, loss_sem: 0.5500217407941819, loss_vote: 0.29440259009599684, one_stage_loss: 2.6799706506729124, rcnn_loss_reg: 0.6698205232620239, rcnn_loss_iou: 0.40001817524433136, loss_two_stage: 1.069838695526123,
716
+ 2023-03-27 05:05:05,293 INFO Epoch [ 1][ 650]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5250428706407547, loss_bbox: 0.7363606292009354, loss_cls: 0.4330296140909195, loss_sem: 0.6324895197153091, loss_vote: 0.3019771608710289, one_stage_loss: 2.6288998174667357, rcnn_loss_reg: 0.4743223488330841, rcnn_loss_iou: 0.290767160654068, loss_two_stage: 0.7650895142555236,
717
+ 2023-03-27 06:23:27,892 INFO Epoch [ 1][ 700]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5444362837076188, loss_bbox: 0.7522995507717133, loss_cls: 0.4244315469264984, loss_sem: 0.45511561155319213, loss_vote: 0.3005108740925789, one_stage_loss: 2.476793870925903, rcnn_loss_reg: 0.5306920790672303, rcnn_loss_iou: 0.31801644802093504, loss_two_stage: 0.8487085247039795,
718
+ 2023-03-27 07:47:41,250 INFO Epoch [ 1][ 750]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6098733115196228, loss_bbox: 0.8267546522617341, loss_cls: 0.45813657343387604, loss_sem: 0.47525602877140044, loss_vote: 0.2934702724218369, one_stage_loss: 2.6634908294677735, rcnn_loss_reg: 0.7940043830871581, rcnn_loss_iou: 0.4807016372680664, loss_two_stage: 1.2747060203552245,
719
+ 2023-03-27 09:12:50,838 INFO Epoch [ 1][ 800]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5564212232828141, loss_bbox: 0.7674001061916351, loss_cls: 0.4770412653684616, loss_sem: 0.5462373447418213, loss_vote: 0.28620937079191205, one_stage_loss: 2.6333092975616457, rcnn_loss_reg: 0.633576123714447, rcnn_loss_iou: 0.390158035159111, loss_two_stage: 1.0237341594696046,
720
+ 2023-03-27 10:33:02,697 INFO Epoch [ 1][ 850]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6046576589345932, loss_bbox: 0.8187049305438996, loss_cls: 0.44491772472858426, loss_sem: 0.5175370579957962, loss_vote: 0.2872957721352577, one_stage_loss: 2.6731131219863893, rcnn_loss_reg: 0.7730563342571258, rcnn_loss_iou: 0.4753589242696762, loss_two_stage: 1.2484152507781983,
721
+ 2023-03-27 11:57:22,870 INFO Epoch [ 1][ 900]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6090051412582398, loss_bbox: 0.8095787620544433, loss_cls: 0.4284948009252548, loss_sem: 0.544955484867096, loss_vote: 0.289944207072258, one_stage_loss: 2.6819783878326415, rcnn_loss_reg: 0.8724133563041687, rcnn_loss_iou: 0.5291033411026, loss_two_stage: 1.4015166926383973,
722
+ 2023-03-27 13:17:06,861 INFO Epoch [ 1][ 950]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5685943412780762, loss_bbox: 0.789885265827179, loss_cls: 0.4120765841007233, loss_sem: 0.44937529861927034, loss_vote: 0.2983333826065063, one_stage_loss: 2.5182648515701294, rcnn_loss_reg: 0.532546899318695, rcnn_loss_iou: 0.33089754700660706, loss_two_stage: 0.863444447517395,
723
+ 2023-03-27 14:41:55,361 INFO Epoch [ 1][1000]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6334057796001434, loss_bbox: 0.8424523377418518, loss_cls: 0.4098082458972931, loss_sem: 0.40657821238040925, loss_vote: 0.2823134985566139, one_stage_loss: 2.5745581150054933, rcnn_loss_reg: 0.8423162472248077, rcnn_loss_iou: 0.514250785112381, loss_two_stage: 1.3565670371055603,
724
+ 2023-03-27 16:00:37,355 INFO Epoch [ 1][1050]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5406019860506057, loss_bbox: 0.7440398615598679, loss_cls: 0.39454961895942686, loss_sem: 0.5495830583572388, loss_vote: 0.2843384724855423, one_stage_loss: 2.5131130170822145, rcnn_loss_reg: 0.6645504760742188, rcnn_loss_iou: 0.4026562488079071, loss_two_stage: 1.0672067260742188,
725
+ 2023-03-27 17:20:17,667 INFO Epoch [ 1][1100]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5215873223543167, loss_bbox: 0.7336350619792938, loss_cls: 0.41735528588294984, loss_sem: 0.4811014491319656, loss_vote: 0.29286098569631575, one_stage_loss: 2.4465401220321654, rcnn_loss_reg: 0.40519655585289, rcnn_loss_iou: 0.2512069880962372, loss_two_stage: 0.656403546333313,
726
+ 2023-03-27 18:39:11,247 INFO Epoch [ 1][1150]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.5330367308855056, loss_bbox: 0.7439761543273926, loss_cls: 0.40116438329219817, loss_sem: 0.5646754199266434, loss_vote: 0.29715222597122193, one_stage_loss: 2.5400049018859865, rcnn_loss_reg: 0.6408816885948181, rcnn_loss_iou: 0.39092864990234377, loss_two_stage: 1.031810338497162,
727
+ 2023-03-27 20:02:50,511 INFO Epoch [ 1][1200]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6315625596046448, loss_bbox: 0.8405828297138214, loss_cls: 0.4183447903394699, loss_sem: 0.3946588611602783, loss_vote: 0.28028561860322954, one_stage_loss: 2.565434675216675, rcnn_loss_reg: 0.8700305473804474, rcnn_loss_iou: 0.5194281542301178, loss_two_stage: 1.3894586968421936,
728
+ 2023-03-27 21:25:10,486 INFO Epoch [ 1][1250]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6114491987228393, loss_bbox: 0.8168105685710907, loss_cls: 0.4101775807142258, loss_sem: 0.4728459024429321, loss_vote: 0.27880846709012985, one_stage_loss: 2.590091710090637, rcnn_loss_reg: 0.8118275630474091, rcnn_loss_iou: 0.4983943772315979, loss_two_stage: 1.3102219486236573,
729
+ 2023-03-27 22:47:39,311 INFO Epoch [ 1][1300]/[1322] : lr: 1.000e-03, sem_thr: 0.15, loss_centerness: 0.6145700442790986, loss_bbox: 0.81527019739151, loss_cls: 0.3963031381368637, loss_sem: 0.4105939191579819, loss_vote: 0.27149350941181183, one_stage_loss: 2.5082308149337766, rcnn_loss_reg: 0.8241646671295166, rcnn_loss_iou: 0.4978309834003449, loss_two_stage: 1.3219956469535827,
730
+ 2023-03-27 23:26:17,307 INFO **********************End training sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd)**********************
731
+
732
+
733
+
734
+ 2023-03-27 23:26:17,309 INFO **********************Start evaluation sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd)**********************
735
+ 2023-03-27 23:26:17,310 INFO Loading SUNRGBD dataset
736
+ 2023-03-27 23:26:17,476 INFO Total samples for SUNRGBD dataset: 5050
737
+ 2023-03-27 23:26:17,484 INFO ==> Loading parameters from checkpoint C:\PINKAMENA\CITYU\CS5182\proj\CAGroup3D\output\sunrgbd_models\CAGroup3D\cagroup3d-win10-sunrgbd\ckpt\checkpoint_epoch_1.pth to CPU
738
+ 2023-03-27 23:26:18,098 INFO ==> Checkpoint trained from version: pcdet+0.5.2+18bc5f5+py60edc0c
739
+ 2023-03-27 23:26:18,162 INFO ==> Done (loaded 638/638)
740
+ 2023-03-27 23:26:19,088 INFO *************** EPOCH 1 EVALUATION *****************
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