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ckpt/checkpoint_epoch_13.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:c95b812c9a7ab8a87e4c22365a35dd961625d8e554074e691b6ec6322a53817e
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size 1174230627
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eval/eval_with_train/tensorboard_val/events.out.tfevents.1680598726.DESKTOP-OROUQQR
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version https://git-lfs.github.com/spec/v1
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oid sha256:7dc6edf41196b5f4293868f90693a60a48909b272d700583b661b95a27d73f8f
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size 40
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log_train_20230402-184711.txt
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| 1 |
+
2023-04-02 18:47:11,122 INFO **********************Start logging**********************
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| 2 |
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2023-04-02 18:47:11,123 INFO CUDA_VISIBLE_DEVICES=ALL
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| 3 |
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2023-04-02 18:47:11,123 INFO total_batch_size: 16
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| 4 |
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2023-04-02 18:47:11,124 INFO cfg_file cfgs/sunrgbd_models/CAGroup3D.yaml
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| 5 |
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2023-04-02 18:47:11,125 INFO batch_size 16
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| 6 |
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2023-04-02 18:47:11,126 INFO epochs 13
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| 7 |
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2023-04-02 18:47:11,127 INFO workers 4
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| 8 |
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2023-04-02 18:47:11,128 INFO extra_tag cagroup3d-win10-sunrgbd-train
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| 9 |
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2023-04-02 18:47:11,130 INFO ckpt ../output/sunrgbd_models/CAGroup3D/cagroup3d-win10-sunrgbd-train-good/ckpt/checkpoint_epoch_12.pth
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| 10 |
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2023-04-02 18:47:11,132 INFO pretrained_model ../output/sunrgbd_models/CAGroup3D/cagroup3d-win10-sunrgbd-train-good/ckpt/checkpoint_epoch_12.pth
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| 11 |
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2023-04-02 18:47:11,133 INFO launcher pytorch
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| 12 |
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2023-04-02 18:47:11,134 INFO tcp_port 18888
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| 13 |
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2023-04-02 18:47:11,136 INFO sync_bn False
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| 14 |
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2023-04-02 18:47:11,138 INFO fix_random_seed True
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| 15 |
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2023-04-02 18:47:11,139 INFO ckpt_save_interval 1
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| 16 |
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2023-04-02 18:47:11,140 INFO max_ckpt_save_num 30
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| 17 |
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2023-04-02 18:47:11,141 INFO merge_all_iters_to_one_epoch False
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| 18 |
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2023-04-02 18:47:11,142 INFO set_cfgs None
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| 19 |
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2023-04-02 18:47:11,143 INFO max_waiting_mins 0
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| 20 |
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2023-04-02 18:47:11,144 INFO start_epoch 0
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| 21 |
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2023-04-02 18:47:11,145 INFO num_epochs_to_eval 0
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| 22 |
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2023-04-02 18:47:11,147 INFO save_to_file False
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| 23 |
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2023-04-02 18:47:11,148 INFO cfg.ROOT_DIR: C:\PINKAMENA\CITYU\CS5182\proj\CAGroup3D
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| 24 |
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2023-04-02 18:47:11,148 INFO cfg.LOCAL_RANK: 0
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| 25 |
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2023-04-02 18:47:11,149 INFO cfg.CLASS_NAMES: ['bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser', 'night_stand', 'bookshelf', 'bathtub']
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| 26 |
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2023-04-02 18:47:11,151 INFO
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| 27 |
+
cfg.DATA_CONFIG = edict()
|
| 28 |
+
2023-04-02 18:47:11,153 INFO cfg.DATA_CONFIG.DATASET: SunrgbdDataset
|
| 29 |
+
2023-04-02 18:47:11,155 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/sunrgbd_data/sunrgbd
|
| 30 |
+
2023-04-02 18:47:11,155 INFO cfg.DATA_CONFIG.PROCESSED_DATA_TAG: sunrgbd_processed_data_v0_5_0
|
| 31 |
+
2023-04-02 18:47:11,158 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-40, -40, -10, 40, 40, 10]
|
| 32 |
+
2023-04-02 18:47:11,159 INFO
|
| 33 |
+
cfg.DATA_CONFIG.DATA_SPLIT = edict()
|
| 34 |
+
2023-04-02 18:47:11,161 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train
|
| 35 |
+
2023-04-02 18:47:11,161 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val
|
| 36 |
+
2023-04-02 18:47:11,163 INFO
|
| 37 |
+
cfg.DATA_CONFIG.REPEAT = edict()
|
| 38 |
+
2023-04-02 18:47:11,164 INFO cfg.DATA_CONFIG.REPEAT.train: 4
|
| 39 |
+
2023-04-02 18:47:11,165 INFO cfg.DATA_CONFIG.REPEAT.test: 1
|
| 40 |
+
2023-04-02 18:47:11,166 INFO
|
| 41 |
+
cfg.DATA_CONFIG.INFO_PATH = edict()
|
| 42 |
+
2023-04-02 18:47:11,167 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['sunrgbd_infos_train.pkl']
|
| 43 |
+
2023-04-02 18:47:11,169 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['sunrgbd_infos_val.pkl']
|
| 44 |
+
2023-04-02 18:47:11,170 INFO cfg.DATA_CONFIG.GET_ITEM_LIST: ['points']
|
| 45 |
+
2023-04-02 18:47:11,171 INFO cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True
|
| 46 |
+
2023-04-02 18:47:11,172 INFO
|
| 47 |
+
cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN = edict()
|
| 48 |
+
2023-04-02 18:47:11,174 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.DISABLE_AUG_LIST: ['placeholder']
|
| 49 |
+
2023-04-02 18:47:11,175 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 |
+
2023-04-02 18:47:11,179 INFO
|
| 51 |
+
cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST = edict()
|
| 52 |
+
2023-04-02 18:47:11,180 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.DISABLE_AUG_LIST: ['placeholder']
|
| 53 |
+
2023-04-02 18:47:11,182 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.AUG_CONFIG_LIST: [{'NAME': 'indoor_point_sample', 'num_points': 100000}]
|
| 54 |
+
2023-04-02 18:47:11,184 INFO
|
| 55 |
+
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict()
|
| 56 |
+
2023-04-02 18:47:11,189 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
|
| 57 |
+
2023-04-02 18:47:11,191 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'indoor_point_sample', 'num_points': 50000}]
|
| 58 |
+
2023-04-02 18:47:11,192 INFO
|
| 59 |
+
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict()
|
| 60 |
+
2023-04-02 18:47:11,193 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
|
| 61 |
+
2023-04-02 18:47:11,194 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
|
| 62 |
+
2023-04-02 18:47:11,195 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'r', 'g', 'b']
|
| 63 |
+
2023-04-02 18:47:11,197 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': False}]
|
| 64 |
+
2023-04-02 18:47:11,201 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/sunrgbd_dataset.yaml
|
| 65 |
+
2023-04-02 18:47:11,202 INFO cfg.VOXEL_SIZE: 0.02
|
| 66 |
+
2023-04-02 18:47:11,202 INFO cfg.N_CLASSES: 10
|
| 67 |
+
2023-04-02 18:47:11,203 INFO cfg.SEMANTIC_THR: 0.15
|
| 68 |
+
2023-04-02 18:47:11,203 INFO
|
| 69 |
+
cfg.MODEL = edict()
|
| 70 |
+
2023-04-02 18:47:11,205 INFO cfg.MODEL.NAME: CAGroup3D
|
| 71 |
+
2023-04-02 18:47:11,205 INFO cfg.MODEL.VOXEL_SIZE: 0.02
|
| 72 |
+
2023-04-02 18:47:11,206 INFO cfg.MODEL.SEMANTIC_MIN_THR: 0.05
|
| 73 |
+
2023-04-02 18:47:11,207 INFO cfg.MODEL.SEMANTIC_ITER_VALUE: 0.02
|
| 74 |
+
2023-04-02 18:47:11,208 INFO cfg.MODEL.SEMANTIC_THR: 0.15
|
| 75 |
+
2023-04-02 18:47:11,208 INFO
|
| 76 |
+
cfg.MODEL.BACKBONE_3D = edict()
|
| 77 |
+
2023-04-02 18:47:11,210 INFO cfg.MODEL.BACKBONE_3D.NAME: BiResNet
|
| 78 |
+
2023-04-02 18:47:11,211 INFO cfg.MODEL.BACKBONE_3D.IN_CHANNELS: 3
|
| 79 |
+
2023-04-02 18:47:11,215 INFO cfg.MODEL.BACKBONE_3D.OUT_CHANNELS: 64
|
| 80 |
+
2023-04-02 18:47:11,215 INFO
|
| 81 |
+
cfg.MODEL.DENSE_HEAD = edict()
|
| 82 |
+
2023-04-02 18:47:11,217 INFO cfg.MODEL.DENSE_HEAD.NAME: CAGroup3DHead
|
| 83 |
+
2023-04-02 18:47:11,218 INFO cfg.MODEL.DENSE_HEAD.IN_CHANNELS: [64, 128, 256, 512]
|
| 84 |
+
2023-04-02 18:47:11,218 INFO cfg.MODEL.DENSE_HEAD.OUT_CHANNELS: 64
|
| 85 |
+
2023-04-02 18:47:11,220 INFO cfg.MODEL.DENSE_HEAD.SEMANTIC_THR: 0.15
|
| 86 |
+
2023-04-02 18:47:11,220 INFO cfg.MODEL.DENSE_HEAD.VOXEL_SIZE: 0.02
|
| 87 |
+
2023-04-02 18:47:11,221 INFO cfg.MODEL.DENSE_HEAD.N_CLASSES: 10
|
| 88 |
+
2023-04-02 18:47:11,223 INFO cfg.MODEL.DENSE_HEAD.N_REG_OUTS: 8
|
| 89 |
+
2023-04-02 18:47:11,224 INFO cfg.MODEL.DENSE_HEAD.CLS_KERNEL: 9
|
| 90 |
+
2023-04-02 18:47:11,224 INFO cfg.MODEL.DENSE_HEAD.WITH_YAW: True
|
| 91 |
+
2023-04-02 18:47:11,225 INFO cfg.MODEL.DENSE_HEAD.USE_SEM_SCORE: False
|
| 92 |
+
2023-04-02 18:47:11,227 INFO cfg.MODEL.DENSE_HEAD.EXPAND_RATIO: 3
|
| 93 |
+
2023-04-02 18:47:11,231 INFO
|
| 94 |
+
cfg.MODEL.DENSE_HEAD.ASSIGNER = edict()
|
| 95 |
+
2023-04-02 18:47:11,234 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.NAME: CAGroup3DAssigner
|
| 96 |
+
2023-04-02 18:47:11,234 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.LIMIT: 27
|
| 97 |
+
2023-04-02 18:47:11,234 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.TOPK: 18
|
| 98 |
+
2023-04-02 18:47:11,236 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.N_SCALES: 4
|
| 99 |
+
2023-04-02 18:47:11,238 INFO
|
| 100 |
+
cfg.MODEL.DENSE_HEAD.LOSS_OFFSET = edict()
|
| 101 |
+
2023-04-02 18:47:11,240 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.NAME: SmoothL1Loss
|
| 102 |
+
2023-04-02 18:47:11,241 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.BETA: 0.04
|
| 103 |
+
2023-04-02 18:47:11,241 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.REDUCTION: sum
|
| 104 |
+
2023-04-02 18:47:11,243 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.LOSS_WEIGHT: 0.2
|
| 105 |
+
2023-04-02 18:47:11,244 INFO
|
| 106 |
+
cfg.MODEL.DENSE_HEAD.LOSS_BBOX = edict()
|
| 107 |
+
2023-04-02 18:47:11,246 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.NAME: IoU3DLoss
|
| 108 |
+
2023-04-02 18:47:11,247 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.WITH_YAW: True
|
| 109 |
+
2023-04-02 18:47:11,249 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.LOSS_WEIGHT: 1.0
|
| 110 |
+
2023-04-02 18:47:11,250 INFO
|
| 111 |
+
cfg.MODEL.DENSE_HEAD.NMS_CONFIG = edict()
|
| 112 |
+
2023-04-02 18:47:11,251 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.SCORE_THR: 0.01
|
| 113 |
+
2023-04-02 18:47:11,253 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.NMS_PRE: 1000
|
| 114 |
+
2023-04-02 18:47:11,254 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.IOU_THR: 0.5
|
| 115 |
+
2023-04-02 18:47:11,254 INFO
|
| 116 |
+
cfg.MODEL.ROI_HEAD = edict()
|
| 117 |
+
2023-04-02 18:47:11,256 INFO cfg.MODEL.ROI_HEAD.NAME: CAGroup3DRoIHead
|
| 118 |
+
2023-04-02 18:47:11,257 INFO cfg.MODEL.ROI_HEAD.NUM_CLASSES: 10
|
| 119 |
+
2023-04-02 18:47:11,258 INFO cfg.MODEL.ROI_HEAD.MIDDLE_FEATURE_SOURCE: [3]
|
| 120 |
+
2023-04-02 18:47:11,260 INFO cfg.MODEL.ROI_HEAD.GRID_SIZE: 7
|
| 121 |
+
2023-04-02 18:47:11,262 INFO cfg.MODEL.ROI_HEAD.VOXEL_SIZE: 0.02
|
| 122 |
+
2023-04-02 18:47:11,263 INFO cfg.MODEL.ROI_HEAD.COORD_KEY: 2
|
| 123 |
+
2023-04-02 18:47:11,264 INFO cfg.MODEL.ROI_HEAD.MLPS: [[64, 128, 128]]
|
| 124 |
+
2023-04-02 18:47:11,265 INFO cfg.MODEL.ROI_HEAD.CODE_SIZE: 7
|
| 125 |
+
2023-04-02 18:47:11,267 INFO cfg.MODEL.ROI_HEAD.ENCODE_SINCOS: True
|
| 126 |
+
2023-04-02 18:47:11,269 INFO cfg.MODEL.ROI_HEAD.ROI_PER_IMAGE: 128
|
| 127 |
+
2023-04-02 18:47:11,271 INFO cfg.MODEL.ROI_HEAD.ROI_FG_RATIO: 0.9
|
| 128 |
+
2023-04-02 18:47:11,272 INFO cfg.MODEL.ROI_HEAD.REG_FG_THRESH: 0.3
|
| 129 |
+
2023-04-02 18:47:11,275 INFO cfg.MODEL.ROI_HEAD.ROI_CONV_KERNEL: 5
|
| 130 |
+
2023-04-02 18:47:11,276 INFO cfg.MODEL.ROI_HEAD.ENLARGE_RATIO: False
|
| 131 |
+
2023-04-02 18:47:11,277 INFO cfg.MODEL.ROI_HEAD.USE_IOU_LOSS: True
|
| 132 |
+
2023-04-02 18:47:11,277 INFO cfg.MODEL.ROI_HEAD.USE_GRID_OFFSET: False
|
| 133 |
+
2023-04-02 18:47:11,279 INFO cfg.MODEL.ROI_HEAD.USE_SIMPLE_POOLING: True
|
| 134 |
+
2023-04-02 18:47:11,280 INFO cfg.MODEL.ROI_HEAD.USE_CENTER_POOLING: True
|
| 135 |
+
2023-04-02 18:47:11,282 INFO
|
| 136 |
+
cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS = edict()
|
| 137 |
+
2023-04-02 18:47:11,283 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_CLS_WEIGHT: 1.0
|
| 138 |
+
2023-04-02 18:47:11,284 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_REG_WEIGHT: 0.5
|
| 139 |
+
2023-04-02 18:47:11,285 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_IOU_WEIGHT: 1.0
|
| 140 |
+
2023-04-02 18:47:11,286 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 |
+
2023-04-02 18:47:11,288 INFO
|
| 142 |
+
cfg.MODEL.POST_PROCESSING = edict()
|
| 143 |
+
2023-04-02 18:47:11,290 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.25, 0.5]
|
| 144 |
+
2023-04-02 18:47:11,292 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: scannet
|
| 145 |
+
2023-04-02 18:47:11,293 INFO
|
| 146 |
+
cfg.OPTIMIZATION = edict()
|
| 147 |
+
2023-04-02 18:47:11,295 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 16
|
| 148 |
+
2023-04-02 18:47:11,296 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 1
|
| 149 |
+
2023-04-02 18:47:11,296 INFO cfg.OPTIMIZATION.OPTIMIZER: adamW
|
| 150 |
+
2023-04-02 18:47:11,298 INFO cfg.OPTIMIZATION.LR: 0.001
|
| 151 |
+
2023-04-02 18:47:11,298 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.0001
|
| 152 |
+
2023-04-02 18:47:11,299 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [8, 11]
|
| 153 |
+
2023-04-02 18:47:11,300 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1
|
| 154 |
+
2023-04-02 18:47:11,301 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
|
| 155 |
+
2023-04-02 18:47:11,302 INFO cfg.OPTIMIZATION.PCT_START: 0.4
|
| 156 |
+
2023-04-02 18:47:11,303 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10
|
| 157 |
+
2023-04-02 18:47:11,306 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07
|
| 158 |
+
2023-04-02 18:47:11,307 INFO cfg.OPTIMIZATION.LR_WARMUP: False
|
| 159 |
+
2023-04-02 18:47:11,309 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1
|
| 160 |
+
2023-04-02 18:47:11,310 INFO cfg.TAG: CAGroup3D
|
| 161 |
+
2023-04-02 18:47:11,311 INFO cfg.EXP_GROUP_PATH: sunrgbd_models
|
| 162 |
+
2023-04-02 18:47:11,474 INFO Loading SUNRGBD dataset
|
| 163 |
+
2023-04-02 18:47:11,731 INFO Total samples for SUNRGBD dataset: 5285
|
| 164 |
+
2023-04-02 18:47:14,571 INFO ==> Loading parameters from checkpoint ../output/sunrgbd_models/CAGroup3D/cagroup3d-win10-sunrgbd-train-good/ckpt/checkpoint_epoch_12.pth to CPU
|
| 165 |
+
2023-04-02 18:47:15,954 INFO ==> Checkpoint trained from version: pcdet+0.5.2+0000000
|
| 166 |
+
2023-04-02 18:47:16,119 INFO ==> Done (loaded 638/638)
|
| 167 |
+
2023-04-02 18:47:16,286 INFO ==> Loading parameters from checkpoint ../output/sunrgbd_models/CAGroup3D/cagroup3d-win10-sunrgbd-train-good/ckpt/checkpoint_epoch_12.pth to CPU
|
| 168 |
+
2023-04-02 18:47:17,535 INFO ==> Loading optimizer parameters from checkpoint ../output/sunrgbd_models/CAGroup3D/cagroup3d-win10-sunrgbd-train-good/ckpt/checkpoint_epoch_12.pth to CPU
|
| 169 |
+
2023-04-02 18:47:17,866 INFO ==> Done
|
| 170 |
+
2023-04-02 18:47:18,267 INFO DistributedDataParallel(
|
| 171 |
+
(module): CAGroup3D(
|
| 172 |
+
(vfe): None
|
| 173 |
+
(backbone_3d): BiResNet(
|
| 174 |
+
(conv1): Sequential(
|
| 175 |
+
(0): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 176 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 177 |
+
(2): MinkowskiReLU()
|
| 178 |
+
(3): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 179 |
+
(4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 180 |
+
(5): MinkowskiReLU()
|
| 181 |
+
)
|
| 182 |
+
(relu): MinkowskiReLU()
|
| 183 |
+
(layer1): Sequential(
|
| 184 |
+
(0): BasicBlock(
|
| 185 |
+
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 186 |
+
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 187 |
+
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 188 |
+
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 189 |
+
(relu): MinkowskiReLU()
|
| 190 |
+
(downsample): Sequential(
|
| 191 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 192 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 193 |
+
)
|
| 194 |
+
)
|
| 195 |
+
(1): BasicBlock(
|
| 196 |
+
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 197 |
+
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 198 |
+
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 199 |
+
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 200 |
+
(relu): MinkowskiReLU()
|
| 201 |
+
)
|
| 202 |
+
)
|
| 203 |
+
(layer2): Sequential(
|
| 204 |
+
(0): BasicBlock(
|
| 205 |
+
(conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 206 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 207 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 208 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 209 |
+
(relu): MinkowskiReLU()
|
| 210 |
+
(downsample): Sequential(
|
| 211 |
+
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 212 |
+
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 213 |
+
)
|
| 214 |
+
)
|
| 215 |
+
(1): BasicBlock(
|
| 216 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 217 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 218 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 219 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 220 |
+
(relu): MinkowskiReLU()
|
| 221 |
+
)
|
| 222 |
+
)
|
| 223 |
+
(layer3): Sequential(
|
| 224 |
+
(0): BasicBlock(
|
| 225 |
+
(conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 226 |
+
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 227 |
+
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 228 |
+
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 229 |
+
(relu): MinkowskiReLU()
|
| 230 |
+
(downsample): Sequential(
|
| 231 |
+
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 232 |
+
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 233 |
+
)
|
| 234 |
+
)
|
| 235 |
+
(1): BasicBlock(
|
| 236 |
+
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 237 |
+
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 238 |
+
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 239 |
+
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 240 |
+
(relu): MinkowskiReLU()
|
| 241 |
+
)
|
| 242 |
+
)
|
| 243 |
+
(layer4): Sequential(
|
| 244 |
+
(0): BasicBlock(
|
| 245 |
+
(conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 246 |
+
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 247 |
+
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 248 |
+
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 249 |
+
(relu): MinkowskiReLU()
|
| 250 |
+
(downsample): Sequential(
|
| 251 |
+
(0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 252 |
+
(1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 253 |
+
)
|
| 254 |
+
)
|
| 255 |
+
(1): BasicBlock(
|
| 256 |
+
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 257 |
+
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 258 |
+
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 259 |
+
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 260 |
+
(relu): MinkowskiReLU()
|
| 261 |
+
)
|
| 262 |
+
)
|
| 263 |
+
(compression3): Sequential(
|
| 264 |
+
(0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 265 |
+
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 266 |
+
)
|
| 267 |
+
(compression4): Sequential(
|
| 268 |
+
(0): MinkowskiConvolution(in=512, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 269 |
+
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 270 |
+
)
|
| 271 |
+
(down3): Sequential(
|
| 272 |
+
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 273 |
+
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 274 |
+
)
|
| 275 |
+
(down4): Sequential(
|
| 276 |
+
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 277 |
+
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 278 |
+
(2): MinkowskiReLU()
|
| 279 |
+
(3): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 280 |
+
(4): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 281 |
+
)
|
| 282 |
+
(layer3_): Sequential(
|
| 283 |
+
(0): BasicBlock(
|
| 284 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 285 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 286 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 287 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 288 |
+
(relu): MinkowskiReLU()
|
| 289 |
+
)
|
| 290 |
+
(1): BasicBlock(
|
| 291 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 292 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 293 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 294 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 295 |
+
(relu): MinkowskiReLU()
|
| 296 |
+
)
|
| 297 |
+
)
|
| 298 |
+
(layer4_): Sequential(
|
| 299 |
+
(0): BasicBlock(
|
| 300 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 301 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 302 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 303 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 304 |
+
(relu): MinkowskiReLU()
|
| 305 |
+
)
|
| 306 |
+
(1): BasicBlock(
|
| 307 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 308 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 309 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 310 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 311 |
+
(relu): MinkowskiReLU()
|
| 312 |
+
)
|
| 313 |
+
)
|
| 314 |
+
(layer5_): Sequential(
|
| 315 |
+
(0): Bottleneck(
|
| 316 |
+
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 317 |
+
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 318 |
+
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 319 |
+
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 320 |
+
(conv3): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 321 |
+
(norm3): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 322 |
+
(relu): MinkowskiReLU()
|
| 323 |
+
(downsample): Sequential(
|
| 324 |
+
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 325 |
+
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 326 |
+
)
|
| 327 |
+
)
|
| 328 |
+
)
|
| 329 |
+
(layer5): Sequential(
|
| 330 |
+
(0): Bottleneck(
|
| 331 |
+
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 332 |
+
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 333 |
+
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 334 |
+
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 335 |
+
(conv3): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 336 |
+
(norm3): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 337 |
+
(relu): MinkowskiReLU()
|
| 338 |
+
(downsample): Sequential(
|
| 339 |
+
(0): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 340 |
+
(1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 341 |
+
)
|
| 342 |
+
)
|
| 343 |
+
)
|
| 344 |
+
(spp): DAPPM(
|
| 345 |
+
(scale1): Sequential(
|
| 346 |
+
(0): MinkowskiAvgPooling(kernel_size=[5, 5, 5], stride=[2, 2, 2], 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 |
+
(scale2): Sequential(
|
| 352 |
+
(0): MinkowskiAvgPooling(kernel_size=[9, 9, 9], stride=[4, 4, 4], 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 |
+
(scale3): Sequential(
|
| 358 |
+
(0): MinkowskiAvgPooling(kernel_size=[17, 17, 17], stride=[8, 8, 8], 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 |
+
(scale4): Sequential(
|
| 364 |
+
(0): MinkowskiAvgPooling(kernel_size=[33, 33, 33], stride=[16, 16, 16], dilation=[1, 1, 1])
|
| 365 |
+
(1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 366 |
+
(2): MinkowskiReLU()
|
| 367 |
+
(3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 368 |
+
)
|
| 369 |
+
(scale0): Sequential(
|
| 370 |
+
(0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 371 |
+
(1): MinkowskiReLU()
|
| 372 |
+
(2): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 373 |
+
)
|
| 374 |
+
(process1): Sequential(
|
| 375 |
+
(0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 376 |
+
(1): MinkowskiReLU()
|
| 377 |
+
(2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 378 |
+
)
|
| 379 |
+
(process2): Sequential(
|
| 380 |
+
(0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 381 |
+
(1): MinkowskiReLU()
|
| 382 |
+
(2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 383 |
+
)
|
| 384 |
+
(process3): Sequential(
|
| 385 |
+
(0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 386 |
+
(1): MinkowskiReLU()
|
| 387 |
+
(2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 388 |
+
)
|
| 389 |
+
(process4): Sequential(
|
| 390 |
+
(0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 391 |
+
(1): MinkowskiReLU()
|
| 392 |
+
(2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 393 |
+
)
|
| 394 |
+
(compression): Sequential(
|
| 395 |
+
(0): MinkowskiBatchNorm(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 396 |
+
(1): MinkowskiReLU()
|
| 397 |
+
(2): MinkowskiConvolution(in=640, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 398 |
+
)
|
| 399 |
+
(shortcut): Sequential(
|
| 400 |
+
(0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 401 |
+
(1): MinkowskiReLU()
|
| 402 |
+
(2): MinkowskiConvolution(in=1024, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 403 |
+
)
|
| 404 |
+
)
|
| 405 |
+
(out): Sequential(
|
| 406 |
+
(0): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
|
| 407 |
+
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 408 |
+
(2): MinkowskiReLU()
|
| 409 |
+
(3): MinkowskiConvolution(in=256, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 410 |
+
(4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 411 |
+
(5): MinkowskiReLU()
|
| 412 |
+
)
|
| 413 |
+
)
|
| 414 |
+
(map_to_bev_module): None
|
| 415 |
+
(pfe): None
|
| 416 |
+
(backbone_2d): None
|
| 417 |
+
(dense_head): CAGroup3DHead(
|
| 418 |
+
(loss_centerness): CrossEntropy()
|
| 419 |
+
(loss_bbox): IoU3DLoss()
|
| 420 |
+
(loss_cls): FocalLoss()
|
| 421 |
+
(loss_sem): FocalLoss()
|
| 422 |
+
(loss_offset): SmoothL1Loss()
|
| 423 |
+
(offset_block): Sequential(
|
| 424 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 425 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 426 |
+
(2): MinkowskiELU()
|
| 427 |
+
(3): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 428 |
+
(4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 429 |
+
(5): MinkowskiELU()
|
| 430 |
+
(6): MinkowskiConvolution(in=64, out=9, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 431 |
+
)
|
| 432 |
+
(feature_offset): Sequential(
|
| 433 |
+
(0): MinkowskiConvolution(in=64, out=192, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 434 |
+
(1): MinkowskiBatchNorm(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 435 |
+
(2): MinkowskiELU()
|
| 436 |
+
)
|
| 437 |
+
(semantic_conv): MinkowskiConvolution(in=64, out=10, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 438 |
+
(centerness_conv): MinkowskiConvolution(in=64, out=1, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 439 |
+
(reg_conv): MinkowskiConvolution(in=64, out=8, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 440 |
+
(cls_conv): MinkowskiConvolution(in=64, out=10, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 441 |
+
(scales): ModuleList(
|
| 442 |
+
(0): Scale()
|
| 443 |
+
(1): Scale()
|
| 444 |
+
(2): Scale()
|
| 445 |
+
(3): Scale()
|
| 446 |
+
(4): Scale()
|
| 447 |
+
(5): Scale()
|
| 448 |
+
(6): Scale()
|
| 449 |
+
(7): Scale()
|
| 450 |
+
(8): Scale()
|
| 451 |
+
(9): Scale()
|
| 452 |
+
)
|
| 453 |
+
(cls_individual_out): ModuleList(
|
| 454 |
+
(0): Sequential(
|
| 455 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 456 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 457 |
+
(2): MinkowskiELU()
|
| 458 |
+
)
|
| 459 |
+
(1): Sequential(
|
| 460 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 461 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 462 |
+
(2): MinkowskiELU()
|
| 463 |
+
)
|
| 464 |
+
(2): Sequential(
|
| 465 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 466 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 467 |
+
(2): MinkowskiELU()
|
| 468 |
+
)
|
| 469 |
+
(3): Sequential(
|
| 470 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 471 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 472 |
+
(2): MinkowskiELU()
|
| 473 |
+
)
|
| 474 |
+
(4): Sequential(
|
| 475 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 476 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 477 |
+
(2): MinkowskiELU()
|
| 478 |
+
)
|
| 479 |
+
(5): Sequential(
|
| 480 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 481 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 482 |
+
(2): MinkowskiELU()
|
| 483 |
+
)
|
| 484 |
+
(6): Sequential(
|
| 485 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 486 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 487 |
+
(2): MinkowskiELU()
|
| 488 |
+
)
|
| 489 |
+
(7): Sequential(
|
| 490 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 491 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 492 |
+
(2): MinkowskiELU()
|
| 493 |
+
)
|
| 494 |
+
(8): Sequential(
|
| 495 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 496 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 497 |
+
(2): MinkowskiELU()
|
| 498 |
+
)
|
| 499 |
+
(9): Sequential(
|
| 500 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 501 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 502 |
+
(2): MinkowskiELU()
|
| 503 |
+
)
|
| 504 |
+
)
|
| 505 |
+
(cls_individual_up): ModuleList(
|
| 506 |
+
(0): ModuleList(
|
| 507 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 508 |
+
(1): Sequential(
|
| 509 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 510 |
+
(1): MinkowskiELU()
|
| 511 |
+
)
|
| 512 |
+
)
|
| 513 |
+
(1): ModuleList(
|
| 514 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 515 |
+
(1): Sequential(
|
| 516 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 517 |
+
(1): MinkowskiELU()
|
| 518 |
+
)
|
| 519 |
+
)
|
| 520 |
+
(2): ModuleList(
|
| 521 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 522 |
+
(1): Sequential(
|
| 523 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 524 |
+
(1): MinkowskiELU()
|
| 525 |
+
)
|
| 526 |
+
)
|
| 527 |
+
(3): ModuleList(
|
| 528 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 529 |
+
(1): Sequential(
|
| 530 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 531 |
+
(1): MinkowskiELU()
|
| 532 |
+
)
|
| 533 |
+
)
|
| 534 |
+
(4): ModuleList(
|
| 535 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 536 |
+
(1): Sequential(
|
| 537 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 538 |
+
(1): MinkowskiELU()
|
| 539 |
+
)
|
| 540 |
+
)
|
| 541 |
+
(5): ModuleList(
|
| 542 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 543 |
+
(1): Sequential(
|
| 544 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 545 |
+
(1): MinkowskiELU()
|
| 546 |
+
)
|
| 547 |
+
)
|
| 548 |
+
(6): ModuleList(
|
| 549 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 550 |
+
(1): Sequential(
|
| 551 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 552 |
+
(1): MinkowskiELU()
|
| 553 |
+
)
|
| 554 |
+
)
|
| 555 |
+
(7): ModuleList(
|
| 556 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 557 |
+
(1): Sequential(
|
| 558 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 559 |
+
(1): MinkowskiELU()
|
| 560 |
+
)
|
| 561 |
+
)
|
| 562 |
+
(8): ModuleList(
|
| 563 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 564 |
+
(1): Sequential(
|
| 565 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 566 |
+
(1): MinkowskiELU()
|
| 567 |
+
)
|
| 568 |
+
)
|
| 569 |
+
(9): ModuleList(
|
| 570 |
+
(0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1])
|
| 571 |
+
(1): Sequential(
|
| 572 |
+
(0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 573 |
+
(1): MinkowskiELU()
|
| 574 |
+
)
|
| 575 |
+
)
|
| 576 |
+
)
|
| 577 |
+
(cls_individual_fuse): ModuleList(
|
| 578 |
+
(0): Sequential(
|
| 579 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 580 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 581 |
+
(2): MinkowskiELU()
|
| 582 |
+
)
|
| 583 |
+
(1): Sequential(
|
| 584 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 585 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 586 |
+
(2): MinkowskiELU()
|
| 587 |
+
)
|
| 588 |
+
(2): Sequential(
|
| 589 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 590 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 591 |
+
(2): MinkowskiELU()
|
| 592 |
+
)
|
| 593 |
+
(3): Sequential(
|
| 594 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 595 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 596 |
+
(2): MinkowskiELU()
|
| 597 |
+
)
|
| 598 |
+
(4): Sequential(
|
| 599 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 600 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 601 |
+
(2): MinkowskiELU()
|
| 602 |
+
)
|
| 603 |
+
(5): Sequential(
|
| 604 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 605 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 606 |
+
(2): MinkowskiELU()
|
| 607 |
+
)
|
| 608 |
+
(6): Sequential(
|
| 609 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 610 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 611 |
+
(2): MinkowskiELU()
|
| 612 |
+
)
|
| 613 |
+
(7): Sequential(
|
| 614 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 615 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 616 |
+
(2): MinkowskiELU()
|
| 617 |
+
)
|
| 618 |
+
(8): Sequential(
|
| 619 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 620 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 621 |
+
(2): MinkowskiELU()
|
| 622 |
+
)
|
| 623 |
+
(9): Sequential(
|
| 624 |
+
(0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 625 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 626 |
+
(2): MinkowskiELU()
|
| 627 |
+
)
|
| 628 |
+
)
|
| 629 |
+
(cls_individual_expand_out): ModuleList(
|
| 630 |
+
(0): Sequential(
|
| 631 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 632 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 633 |
+
(2): MinkowskiELU()
|
| 634 |
+
)
|
| 635 |
+
(1): Sequential(
|
| 636 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 637 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 638 |
+
(2): MinkowskiELU()
|
| 639 |
+
)
|
| 640 |
+
(2): Sequential(
|
| 641 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 642 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 643 |
+
(2): MinkowskiELU()
|
| 644 |
+
)
|
| 645 |
+
(3): Sequential(
|
| 646 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 647 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 648 |
+
(2): MinkowskiELU()
|
| 649 |
+
)
|
| 650 |
+
(4): Sequential(
|
| 651 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 652 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 653 |
+
(2): MinkowskiELU()
|
| 654 |
+
)
|
| 655 |
+
(5): Sequential(
|
| 656 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 657 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 658 |
+
(2): MinkowskiELU()
|
| 659 |
+
)
|
| 660 |
+
(6): Sequential(
|
| 661 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 662 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 663 |
+
(2): MinkowskiELU()
|
| 664 |
+
)
|
| 665 |
+
(7): Sequential(
|
| 666 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 667 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 668 |
+
(2): MinkowskiELU()
|
| 669 |
+
)
|
| 670 |
+
(8): Sequential(
|
| 671 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 672 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 673 |
+
(2): MinkowskiELU()
|
| 674 |
+
)
|
| 675 |
+
(9): Sequential(
|
| 676 |
+
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 677 |
+
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 678 |
+
(2): MinkowskiELU()
|
| 679 |
+
)
|
| 680 |
+
)
|
| 681 |
+
)
|
| 682 |
+
(point_head): None
|
| 683 |
+
(roi_head): CAGroup3DRoIHead(
|
| 684 |
+
(iou_loss_computer): IoU3DLoss()
|
| 685 |
+
(proposal_target_layer): ProposalTargetLayer()
|
| 686 |
+
(reg_loss_func): WeightedSmoothL1Loss()
|
| 687 |
+
(roi_grid_pool_layers): ModuleList(
|
| 688 |
+
(0): SimplePoolingLayer(
|
| 689 |
+
(grid_conv): MinkowskiConvolution(in=64, out=128, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 690 |
+
(grid_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 691 |
+
(grid_relu): MinkowskiELU()
|
| 692 |
+
(pooling_conv): MinkowskiConvolution(in=128, out=128, kernel_size=[7, 7, 7], stride=[1, 1, 1], dilation=[1, 1, 1])
|
| 693 |
+
(pooling_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 694 |
+
)
|
| 695 |
+
)
|
| 696 |
+
(reg_fc_layers): Sequential(
|
| 697 |
+
(0): Linear(in_features=128, out_features=256, bias=False)
|
| 698 |
+
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 699 |
+
(2): ReLU()
|
| 700 |
+
(3): Dropout(p=0.3, inplace=False)
|
| 701 |
+
(4): Linear(in_features=256, out_features=256, bias=False)
|
| 702 |
+
(5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 703 |
+
(6): ReLU()
|
| 704 |
+
)
|
| 705 |
+
(reg_pred_layer): Linear(in_features=256, out_features=8, bias=True)
|
| 706 |
+
)
|
| 707 |
+
)
|
| 708 |
+
)
|
| 709 |
+
2023-04-02 18:47:18,392 INFO **********************Start training sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd-train)**********************
|
| 710 |
+
2023-04-02 21:02:53,692 INFO Epoch [13][ 50]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6623631048202515, loss_bbox: 0.35187224864959715, loss_cls: 0.18067386567592622, loss_sem: 0.27758967235684395, loss_vote: 0.11847812041640282, one_stage_loss: 1.5909770154953002, rcnn_loss_reg: 0.3226509618759155, rcnn_loss_iou: 0.37351417541503906, loss_two_stage: 0.6961651408672332,
|
| 711 |
+
2023-04-02 23:14:58,782 INFO Epoch [13][ 100]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6630406963825226, loss_bbox: 0.35230674386024474, loss_cls: 0.1816549304127693, loss_sem: 0.22412991568446158, loss_vote: 0.11678819626569747, one_stage_loss: 1.5379204940795899, rcnn_loss_reg: 0.3200619313120842, rcnn_loss_iou: 0.3759835082292557, loss_two_stage: 0.6960454404354095,
|
| 712 |
+
2023-04-03 01:31:22,578 INFO Epoch [13][ 150]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6641919124126434, loss_bbox: 0.34244911730289457, loss_cls: 0.17572991371154786, loss_sem: 0.164187930226326, loss_vote: 0.11165566861629486, one_stage_loss: 1.4582145309448242, rcnn_loss_reg: 0.3245995166897774, rcnn_loss_iou: 0.3702506846189499, loss_two_stage: 0.6948502039909363,
|
| 713 |
+
2023-04-03 03:15:28,156 INFO Epoch [13][ 200]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.661133303642273, loss_bbox: 0.3516665494441986, loss_cls: 0.1864103177189827, loss_sem: 0.19939401865005493, loss_vote: 0.12085840627551078, one_stage_loss: 1.5194626092910766, rcnn_loss_reg: 0.3308644261956215, rcnn_loss_iou: 0.37599210619926454, loss_two_stage: 0.7068565285205841,
|
| 714 |
+
2023-04-03 04:48:30,413 INFO Epoch [13][ 250]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6646712601184845, loss_bbox: 0.34947874903678894, loss_cls: 0.18044402152299882, loss_sem: 0.14906390145421028, loss_vote: 0.11759307235479355, one_stage_loss: 1.4612509989738465, rcnn_loss_reg: 0.31861241459846495, rcnn_loss_iou: 0.3731016290187836, loss_two_stage: 0.6917140460014344,
|
| 715 |
+
2023-04-03 06:21:26,423 INFO Epoch [13][ 300]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6643083465099334, loss_bbox: 0.3536418664455414, loss_cls: 0.17994892954826355, loss_sem: 0.1544986192882061, loss_vote: 0.11901766777038575, one_stage_loss: 1.4714154267311097, rcnn_loss_reg: 0.32731219202280043, rcnn_loss_iou: 0.376601088643074, loss_two_stage: 0.7039132845401764,
|
| 716 |
+
2023-04-03 07:54:18,231 INFO Epoch [13][ 350]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6622652173042297, loss_bbox: 0.34778221607208254, loss_cls: 0.17725018173456192, loss_sem: 0.26385487884283065, loss_vote: 0.11609601065516471, one_stage_loss: 1.5672485136985779, rcnn_loss_reg: 0.3184669044613838, rcnn_loss_iou: 0.36683365106582644, loss_two_stage: 0.6853005504608154,
|
| 717 |
+
2023-04-03 09:30:14,314 INFO Epoch [13][ 400]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.661707843542099, loss_bbox: 0.35418744444847106, loss_cls: 0.18439087867736817, loss_sem: 0.25267973288893697, loss_vote: 0.11465635925531387, one_stage_loss: 1.5676222562789917, rcnn_loss_reg: 0.32181145310401915, rcnn_loss_iou: 0.37672561407089233, loss_two_stage: 0.6985370683670044,
|
| 718 |
+
2023-04-03 11:03:56,627 INFO Epoch [13][ 450]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6624130165576935, loss_bbox: 0.35507028639316557, loss_cls: 0.18141891568899154, loss_sem: 0.1587126612663269, loss_vote: 0.11347746297717094, one_stage_loss: 1.4710923361778259, rcnn_loss_reg: 0.3236926472187042, rcnn_loss_iou: 0.37554241478443146, loss_two_stage: 0.6992350625991821,
|
| 719 |
+
2023-04-03 12:35:12,815 INFO Epoch [13][ 500]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6629520893096924, loss_bbox: 0.3519792276620865, loss_cls: 0.17892935872077942, loss_sem: 0.17500929594039916, loss_vote: 0.11417005106806755, one_stage_loss: 1.483040030002594, rcnn_loss_reg: 0.3217777442932129, rcnn_loss_iou: 0.37734968066215513, loss_two_stage: 0.6991274237632752,
|
| 720 |
+
2023-04-03 14:10:09,972 INFO Epoch [13][ 550]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6637387633323669, loss_bbox: 0.34735734045505523, loss_cls: 0.18063713282346724, loss_sem: 0.13179368287324905, loss_vote: 0.12056573927402496, one_stage_loss: 1.4440926504135132, rcnn_loss_reg: 0.33059713900089266, rcnn_loss_iou: 0.37435609817504883, loss_two_stage: 0.7049532413482666,
|
| 721 |
+
2023-04-03 15:41:21,969 INFO Epoch [13][ 600]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.662292810678482, loss_bbox: 0.35720852434635164, loss_cls: 0.17849652022123336, loss_sem: 0.12923728227615355, loss_vote: 0.11629633039236069, one_stage_loss: 1.4435314631462097, rcnn_loss_reg: 0.3326093548536301, rcnn_loss_iou: 0.37626142144203184, loss_two_stage: 0.7088707709312438,
|
| 722 |
+
2023-04-03 17:11:28,683 INFO Epoch [13][ 650]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6618774938583374, loss_bbox: 0.35374693453311923, loss_cls: 0.18276749283075333, loss_sem: 0.144855744689703, loss_vote: 0.11687358900904656, one_stage_loss: 1.460121262073517, rcnn_loss_reg: 0.32241543173789977, rcnn_loss_iou: 0.3724362623691559, loss_two_stage: 0.6948516941070557,
|
| 723 |
+
2023-04-03 18:44:33,632 INFO Epoch [13][ 700]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6633472311496734, loss_bbox: 0.35507338523864745, loss_cls: 0.1834974604845047, loss_sem: 0.16129221200942992, loss_vote: 0.11933488368988038, one_stage_loss: 1.482545187473297, rcnn_loss_reg: 0.32722929924726485, rcnn_loss_iou: 0.37687767803668976, loss_two_stage: 0.7041069781780243,
|
| 724 |
+
2023-04-03 20:14:36,146 INFO Epoch [13][ 750]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6644761955738068, loss_bbox: 0.35398071110248563, loss_cls: 0.18030155092477798, loss_sem: 0.17029531091451644, loss_vote: 0.11823550701141357, one_stage_loss: 1.4872892904281616, rcnn_loss_reg: 0.33191990315914155, rcnn_loss_iou: 0.37786650359630586, loss_two_stage: 0.7097864115238189,
|
| 725 |
+
2023-04-03 21:43:57,721 INFO Epoch [13][ 800]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6641488337516784, loss_bbox: 0.3623661398887634, loss_cls: 0.17759602785110473, loss_sem: 0.13985075324773788, loss_vote: 0.12048821434378625, one_stage_loss: 1.4644499826431274, rcnn_loss_reg: 0.32571767300367355, rcnn_loss_iou: 0.37867982625961305, loss_two_stage: 0.7043974995613098,
|
| 726 |
+
2023-04-03 23:14:42,693 INFO Epoch [13][ 850]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6634637904167175, loss_bbox: 0.3510202074050903, loss_cls: 0.1817261689901352, loss_sem: 0.17562781766057015, loss_vote: 0.11219386965036392, one_stage_loss: 1.4840318632125855, rcnn_loss_reg: 0.3145780658721924, rcnn_loss_iou: 0.3710771632194519, loss_two_stage: 0.6856552314758301,
|
| 727 |
+
2023-04-04 00:46:57,991 INFO Epoch [13][ 900]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6604310703277588, loss_bbox: 0.35609620809555054, loss_cls: 0.18157291144132615, loss_sem: 0.17424873754382134, loss_vote: 0.1169225138425827, one_stage_loss: 1.4892714548110961, rcnn_loss_reg: 0.3271025702357292, rcnn_loss_iou: 0.37567222356796265, loss_two_stage: 0.7027747964859009,
|
| 728 |
+
2023-04-04 02:46:38,860 INFO Epoch [13][ 950]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6638546955585479, loss_bbox: 0.34598992109298704, loss_cls: 0.17896713733673095, loss_sem: 0.14396111875772477, loss_vote: 0.11255239754915237, one_stage_loss: 1.445325255393982, rcnn_loss_reg: 0.3274132317304611, rcnn_loss_iou: 0.37157038986682894, loss_two_stage: 0.69898362159729,
|
| 729 |
+
2023-04-04 05:08:44,516 INFO Epoch [13][1000]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6622758531570434, loss_bbox: 0.3549597650766373, loss_cls: 0.17880465477705001, loss_sem: 0.16847254008054732, loss_vote: 0.11574765816330909, one_stage_loss: 1.4802604627609253, rcnn_loss_reg: 0.32289525389671325, rcnn_loss_iou: 0.3747403818368912, loss_two_stage: 0.6976356363296509,
|
| 730 |
+
2023-04-04 07:33:09,320 INFO Epoch [13][1050]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6620133376121521, loss_bbox: 0.3527708554267883, loss_cls: 0.17823042571544648, loss_sem: 0.13636301800608636, loss_vote: 0.11551862224936485, one_stage_loss: 1.444896252155304, rcnn_loss_reg: 0.3306643870472908, rcnn_loss_iou: 0.38059409976005554, loss_two_stage: 0.7112584865093231,
|
| 731 |
+
2023-04-04 09:58:58,318 INFO Epoch [13][1100]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6630631291866302, loss_bbox: 0.34507942259311675, loss_cls: 0.1765292030572891, loss_sem: 0.17366553276777266, loss_vote: 0.11843317538499833, one_stage_loss: 1.476770441532135, rcnn_loss_reg: 0.32106285572052, rcnn_loss_iou: 0.3712770110368729, loss_two_stage: 0.6923398649692536,
|
| 732 |
+
2023-04-04 11:50:03,203 INFO Epoch [13][1150]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6624673461914062, loss_bbox: 0.35551845014095307, loss_cls: 0.18390144944190978, loss_sem: 0.1476328657567501, loss_vote: 0.11500328212976456, one_stage_loss: 1.4645233917236329, rcnn_loss_reg: 0.3294647446274757, rcnn_loss_iou: 0.3782592761516571, loss_two_stage: 0.7077240252494812,
|
| 733 |
+
2023-04-04 13:20:31,850 INFO Epoch [13][1200]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.663930526971817, loss_bbox: 0.35358040988445283, loss_cls: 0.17962905526161194, loss_sem: 0.1480906042456627, loss_vote: 0.11691673502326011, one_stage_loss: 1.462147331237793, rcnn_loss_reg: 0.31809635043144224, rcnn_loss_iou: 0.3736869865655899, loss_two_stage: 0.6917833364009858,
|
| 734 |
+
2023-04-04 14:50:35,089 INFO Epoch [13][1250]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6616610872745514, loss_bbox: 0.3473211169242859, loss_cls: 0.17929236128926276, loss_sem: 0.13846698969602586, loss_vote: 0.10878140345215798, one_stage_loss: 1.435522973537445, rcnn_loss_reg: 0.3105960166454315, rcnn_loss_iou: 0.36950829446315764, loss_two_stage: 0.6801043093204499,
|
| 735 |
+
2023-04-04 16:20:45,500 INFO Epoch [13][1300]/[1322] : lr: 1.000e-05, sem_thr: 0.05, loss_centerness: 0.6631780481338501, loss_bbox: 0.35142988979816436, loss_cls: 0.18354893177747728, loss_sem: 0.1891991038620472, loss_vote: 0.10815204933285713, one_stage_loss: 1.4955080199241637, rcnn_loss_reg: 0.3235346841812134, rcnn_loss_iou: 0.3715806418657303, loss_two_stage: 0.6951153266429901,
|
| 736 |
+
2023-04-04 16:58:46,259 INFO **********************End training sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd-train)**********************
|
| 737 |
+
|
| 738 |
+
|
| 739 |
+
|
| 740 |
+
2023-04-04 16:58:46,261 INFO **********************Start evaluation sunrgbd_models/CAGroup3D(cagroup3d-win10-sunrgbd-train)**********************
|
| 741 |
+
2023-04-04 16:58:46,262 INFO Loading SUNRGBD dataset
|
| 742 |
+
2023-04-04 16:58:46,521 INFO Total samples for SUNRGBD dataset: 5050
|
| 743 |
+
2023-04-04 16:58:46,528 INFO ==> Loading parameters from checkpoint C:\PINKAMENA\CITYU\CS5182\proj\CAGroup3D\output\sunrgbd_models\CAGroup3D\cagroup3d-win10-sunrgbd-train\ckpt\checkpoint_epoch_13.pth to CPU
|
| 744 |
+
2023-04-04 16:58:47,139 INFO ==> Checkpoint trained from version: pcdet+0.5.2+18bc5f5+py9059037
|
| 745 |
+
2023-04-04 16:58:47,218 INFO ==> Done (loaded 638/638)
|
| 746 |
+
2023-04-04 16:58:47,318 INFO *************** EPOCH 13 EVALUATION *****************
|
tensorboard/events.out.tfevents.1680432431.DESKTOP-OROUQQR
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:8757b023e44e386daf0bc5afd1f9b84c9791075e8211029a490618e3a8b3edbe
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size 980826
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