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log_train_20221209-023159.txt
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log_train_20221209-023159.txt
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2022-12-09 02:31:59,469 INFO **********************Start logging**********************
2022-12-09 02:31:59,469 INFO CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
2022-12-09 02:31:59,470 INFO total_batch_size: 16
2022-12-09 02:31:59,470 INFO cfg_file cfgs/waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1.yaml
2022-12-09 02:31:59,471 INFO batch_size 2
2022-12-09 02:31:59,471 INFO epochs 30
2022-12-09 02:31:59,471 INFO workers 4
2022-12-09 02:31:59,471 INFO extra_tag default
2022-12-09 02:31:59,471 INFO ckpt None
2022-12-09 02:31:59,471 INFO pretrained_model None
2022-12-09 02:31:59,471 INFO launcher slurm
2022-12-09 02:31:59,471 INFO tcp_port 33715
2022-12-09 02:31:59,471 INFO sync_bn False
2022-12-09 02:31:59,471 INFO fix_random_seed False
2022-12-09 02:31:59,471 INFO ckpt_save_interval 1
2022-12-09 02:31:59,471 INFO local_rank 0
2022-12-09 02:31:59,472 INFO max_ckpt_save_num 30
2022-12-09 02:31:59,472 INFO merge_all_iters_to_one_epoch False
2022-12-09 02:31:59,472 INFO set_cfgs None
2022-12-09 02:31:59,472 INFO max_waiting_mins 0
2022-12-09 02:31:59,472 INFO start_epoch 0
2022-12-09 02:31:59,472 INFO num_epochs_to_eval 0
2022-12-09 02:31:59,472 INFO save_to_file False
2022-12-09 02:31:59,472 INFO cfg.ROOT_DIR: /scratch/cluster/yanght/Projects/Amazon/OpenPCDet/BEVGen/Baseline/VoxSeT_exp/VoxSeT_dev3
2022-12-09 02:31:59,472 INFO cfg.LOCAL_RANK: 0
2022-12-09 02:31:59,472 INFO cfg.CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
2022-12-09 02:31:59,472 INFO
cfg.DATA_CONFIG = edict()
2022-12-09 02:31:59,472 INFO cfg.DATA_CONFIG.DATASET: WaymoDataset
2022-12-09 02:31:59,472 INFO cfg.DATA_CONFIG.DATA_PATH: /scratch/cluster/yanght/Dataset/Project_Dataset/Detection/waymo/
2022-12-09 02:31:59,472 INFO cfg.DATA_CONFIG.PROCESSED_DATA_TAG: waymo_processed_data_v0_5_0
2022-12-09 02:31:59,472 INFO cfg.DATA_CONFIG.NUM_SWEEPS: 1
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-74.24, -74.24, -2, 74.24, 74.24, 4.0]
2022-12-09 02:31:59,473 INFO
cfg.DATA_CONFIG.DATA_SPLIT = edict()
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val
2022-12-09 02:31:59,473 INFO
cfg.DATA_CONFIG.SAMPLED_INTERVAL = edict()
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.SAMPLED_INTERVAL.train: 1
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.SAMPLED_INTERVAL.test: 1
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.DISABLE_NLZ_FLAG_ON_POINTS: True
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.USE_SHARED_MEMORY: False
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.SHARED_MEMORY_FILE_LIMIT: 35000
2022-12-09 02:31:59,473 INFO
cfg.DATA_CONFIG.DATA_AUGMENTOR = edict()
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
2022-12-09 02:31:59,473 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'gt_sampling', 'USE_ROAD_PLANE': False, 'DB_INFO_PATH': ['waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl'], 'USE_SHARED_MEMORY': False, 'DB_DATA_PATH': ['waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy'], 'PREPARE': {'filter_by_min_points': ['Vehicle:5', 'Pedestrian:5', 'Cyclist:5'], 'filter_by_difficulty': [-1]}, 'SAMPLE_GROUPS': ['Vehicle:15', 'Pedestrian:10', 'Cyclist:10'], 'NUM_POINT_FEATURES': 5, 'REMOVE_EXTRA_WIDTH': [0.0, 0.0, 0.0], 'LIMIT_WHOLE_SCENE': True}, {'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['x', 'y']}, {'NAME': 'random_world_rotation', 'WORLD_ROT_ANGLE': [-0.78539816, 0.78539816]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.95, 1.05]}]
2022-12-09 02:31:59,473 INFO
cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict()
2022-12-09 02:31:59,474 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
2022-12-09 02:31:59,474 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity', 'elongation']
2022-12-09 02:31:59,474 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity', 'elongation']
2022-12-09 02:31:59,474 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}, {'NAME': 'shuffle_points', 'SHUFFLE_ENABLED': {'train': True, 'test': True}}, {'NAME': 'transform_points_to_voxels', 'VOXEL_SIZE': [0.32, 0.32, 6.0], 'MAX_POINTS_PER_VOXEL': 20, 'MAX_NUMBER_OF_VOXELS': {'train': 150000, 'test': 150000}}, {'NAME': 'sample_points', 'AT_MOST': True, 'NUM_POINTS': {'train': 80826, 'test': 80826}}]
2022-12-09 02:31:59,474 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml
2022-12-09 02:31:59,474 INFO
cfg.MODEL = edict()
2022-12-09 02:31:59,474 INFO cfg.MODEL.NAME: CenterPoint
2022-12-09 02:31:59,474 INFO
cfg.MODEL.VFE = edict()
2022-12-09 02:31:59,474 INFO cfg.MODEL.VFE.NAME: VoxSeT
2022-12-09 02:31:59,474 INFO cfg.MODEL.VFE.INPUT_DIM: 16
2022-12-09 02:31:59,474 INFO cfg.MODEL.VFE.NUM_LATENTS: [4, 4, 4, 4]
2022-12-09 02:31:59,475 INFO cfg.MODEL.VFE.OUTPUT_DIM: 128
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.BACKBONE_3D.NAME: PillarRes18BackBone8xHybridRefineBEV
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.EXP_SEG = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.BACKBONE_3D.EXP_SEG.NAME: UNet2D
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.EXP_SEG.LOSS_CONFIG = edict()
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.EXP_SEG.LOSS_CONFIG.LOSS_WEIGHTS = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.BACKBONE_3D.EXP_SEG.LOSS_CONFIG.LOSS_WEIGHTS.bev_weight: 1.0
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.IMP_SEG = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.BACKBONE_3D.IMP_SEG.NAME: ImplicitNet2d
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.IMP_SEG.LOSS_CONFIG = edict()
2022-12-09 02:31:59,475 INFO
cfg.MODEL.BACKBONE_3D.IMP_SEG.LOSS_CONFIG.LOSS_WEIGHTS = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.BACKBONE_3D.IMP_SEG.LOSS_CONFIG.LOSS_WEIGHTS.smp_weight: 1.0
2022-12-09 02:31:59,475 INFO
cfg.MODEL.POINT_HEAD = edict()
2022-12-09 02:31:59,475 INFO cfg.MODEL.POINT_HEAD.NAME: PointHeadSimple
2022-12-09 02:31:59,475 INFO cfg.MODEL.POINT_HEAD.CLS_FC: [128, 128]
2022-12-09 02:31:59,475 INFO cfg.MODEL.POINT_HEAD.PART_FC: []
2022-12-09 02:31:59,476 INFO cfg.MODEL.POINT_HEAD.CLASS_AGNOSTIC: True
2022-12-09 02:31:59,476 INFO
cfg.MODEL.POINT_HEAD.TARGET_CONFIG = edict()
2022-12-09 02:31:59,476 INFO cfg.MODEL.POINT_HEAD.TARGET_CONFIG.GT_EXTRA_WIDTH: [0.1, 0.1, 0.1]
2022-12-09 02:31:59,476 INFO
cfg.MODEL.POINT_HEAD.LOSS_CONFIG = edict()
2022-12-09 02:31:59,476 INFO
cfg.MODEL.POINT_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict()
2022-12-09 02:31:59,476 INFO cfg.MODEL.POINT_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.point_cls_weight: 1.0
2022-12-09 02:31:59,476 INFO
cfg.MODEL.BACKBONE_2D = edict()
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.NAME: BaseBEVBackbone
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [3, 3]
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [1, 2]
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [128, 128]
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2]
2022-12-09 02:31:59,476 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [128, 128]
2022-12-09 02:31:59,476 INFO
cfg.MODEL.DENSE_HEAD = edict()
2022-12-09 02:31:59,476 INFO cfg.MODEL.DENSE_HEAD.NAME: CenterHead
2022-12-09 02:31:59,476 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False
2022-12-09 02:31:59,476 INFO cfg.MODEL.DENSE_HEAD.CLASS_NAMES_EACH_HEAD: [['Vehicle', 'Pedestrian', 'Cyclist']]
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SHARED_CONV_CHANNEL: 64
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.USE_BIAS_BEFORE_NORM: True
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.NUM_HM_CONV: 2
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG = edict()
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_ORDER: ['center', 'center_z', 'dim', 'rot']
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT = edict()
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center = edict()
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center.out_channels: 2
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center.num_conv: 2
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center_z = edict()
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center_z.out_channels: 1
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center_z.num_conv: 2
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.dim = edict()
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.dim.out_channels: 3
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.dim.num_conv: 2
2022-12-09 02:31:59,477 INFO
cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.rot = edict()
2022-12-09 02:31:59,477 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.rot.out_channels: 2
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.rot.num_conv: 2
2022-12-09 02:31:59,478 INFO
cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG = edict()
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.FEATURE_MAP_STRIDE: 1
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NUM_MAX_OBJS: 500
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.GAUSSIAN_OVERLAP: 0.1
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MIN_RADIUS: 2
2022-12-09 02:31:59,478 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG = edict()
2022-12-09 02:31:59,478 INFO
cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict()
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.loc_weight: 2.0
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
2022-12-09 02:31:59,478 INFO
cfg.MODEL.DENSE_HEAD.POST_PROCESSING = edict()
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.SCORE_THRESH: 0.1
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.POST_CENTER_LIMIT_RANGE: [-80, -80, -10.0, 80, 80, 10.0]
2022-12-09 02:31:59,478 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.MAX_OBJ_PER_SAMPLE: 500
2022-12-09 02:31:59,478 INFO
cfg.MODEL.DENSE_HEAD.POST_PROCESSING.NMS_CONFIG = edict()
2022-12-09 02:31:59,479 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.NMS_CONFIG.NMS_TYPE: nms_gpu
2022-12-09 02:31:59,479 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.NMS_CONFIG.NMS_THRESH: 0.7
2022-12-09 02:31:59,479 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.NMS_CONFIG.NMS_PRE_MAXSIZE: 4096
2022-12-09 02:31:59,479 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.NMS_CONFIG.NMS_POST_MAXSIZE: 500
2022-12-09 02:31:59,479 INFO
cfg.MODEL.POST_PROCESSING = edict()
2022-12-09 02:31:59,479 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
2022-12-09 02:31:59,479 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: waymo
2022-12-09 02:31:59,479 INFO
cfg.OPTIMIZATION = edict()
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 2
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 30
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.LR: 0.006
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85]
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.PCT_START: 0.4
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45]
2022-12-09 02:31:59,479 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1
2022-12-09 02:31:59,480 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07
2022-12-09 02:31:59,480 INFO cfg.OPTIMIZATION.LR_WARMUP: False
2022-12-09 02:31:59,480 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1
2022-12-09 02:31:59,480 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
2022-12-09 02:31:59,480 INFO cfg.TAG: centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1
2022-12-09 02:31:59,480 INFO cfg.EXP_GROUP_PATH: waymo_models/gen2DSS
2022-12-09 02:32:25,808 INFO Database filter by min points Vehicle: 1194368 => 1019923
2022-12-09 02:32:25,996 INFO Database filter by min points Pedestrian: 1114091 => 943716
2022-12-09 02:32:26,022 INFO Database filter by min points Cyclist: 53344 => 47529
2022-12-09 02:32:26,280 INFO Database filter by difficulty Vehicle: 1019923 => 1019923
2022-12-09 02:32:26,518 INFO Database filter by difficulty Pedestrian: 943716 => 943716
2022-12-09 02:32:26,529 INFO Database filter by difficulty Cyclist: 47529 => 47529
2022-12-09 02:32:27,027 INFO Loading Waymo dataset
2022-12-09 02:34:43,257 INFO Total skipped info 0
2022-12-09 02:34:43,258 INFO Total samples for Waymo dataset: 158081
2022-12-09 02:34:43,258 INFO prepairing pose table for multi-sweeps data
2022-12-09 02:34:50,694 INFO DistributedDataParallel(
(module): CenterPoint(
(vfe): VoxSeT(
(input_embed): MLP(
(layers): ModuleList(
(0): Linear(in_features=5, out_features=16, bias=True)
(1): Linear(in_features=16, out_features=16, bias=True)
)
)
(pe0): PositionalEncodingFourier(
(token_projection): Linear(in_features=192, out_features=16, bias=True)
)
(pe1): PositionalEncodingFourier(
(token_projection): Linear(in_features=192, out_features=32, bias=True)
)
(pe2): PositionalEncodingFourier(
(token_projection): Linear(in_features=192, out_features=64, bias=True)
)
(pe3): PositionalEncodingFourier(
(token_projection): Linear(in_features=192, out_features=128, bias=True)
)
(mlp_vsa_layer_0): MLP_VSA_Layer(
(pre_mlp): Sequential(
(0): Linear(in_features=16, out_features=16, bias=True)
(1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=16, out_features=16, bias=True)
(4): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=16, out_features=16, bias=True)
(7): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(score): Linear(in_features=16, out_features=4, bias=True)
(conv_ffn): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(norm): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(mhsa): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=16, out_features=16, bias=True)
)
)
(mlp_vsa_layer_1): MLP_VSA_Layer(
(pre_mlp): Sequential(
(0): Linear(in_features=32, out_features=32, bias=True)
(1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=32, out_features=32, bias=True)
(4): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=32, out_features=32, bias=True)
(7): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(score): Linear(in_features=32, out_features=4, bias=True)
(conv_ffn): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(norm): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(mhsa): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=32, out_features=32, bias=True)
)
)
(mlp_vsa_layer_2): MLP_VSA_Layer(
(pre_mlp): Sequential(
(0): Linear(in_features=64, out_features=64, bias=True)
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=64, out_features=64, bias=True)
(4): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=64, out_features=64, bias=True)
(7): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(score): Linear(in_features=64, out_features=4, bias=True)
(conv_ffn): Sequential(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(norm): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(mhsa): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
)
)
(mlp_vsa_layer_3): MLP_VSA_Layer(
(pre_mlp): Sequential(
(0): Linear(in_features=128, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=128, out_features=128, bias=True)
(4): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=128, out_features=128, bias=True)
(7): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(score): Linear(in_features=128, out_features=4, bias=True)
(conv_ffn): Sequential(
(0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
(norm): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(mhsa): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
)
(post_mlp): Sequential(
(0): Linear(in_features=256, out_features=128, bias=True)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=128, out_features=128, bias=True)
(4): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=128, out_features=128, bias=True)
(7): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
(backbone_3d): PillarRes18BackBone8xHybridRefineBEV(
(conv_collapse_exp): SparseSequential(
(0): SparseConv2d(128, 128, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], output_padding=[0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(unet2d): UNet2D(
(inc): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
(down1): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(down2): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(down3): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(down4): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(1024, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(1024, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(up1): Up(
(up): ConvTranspose2d(1024, 512, kernel_size=(2, 2), stride=(2, 2))
(conv): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(512, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(up2): Up(
(up): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))
(conv): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(up3): Up(
(up): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))
(conv): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(up4): Up(
(up): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))
(conv): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(outc): OutConv(
(conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))
)
)
(conv_mask_exp): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
(conv_merge_mask_exp): Sequential(
(0): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv_collapse_imp): SparseSequential(
(0): SparseConv2d(128, 128, kernel_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], output_padding=[0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv_down_3to4_imp): Sequential(
(0): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(implicit_net2d): ImplicitNet2d(
(encoder): MultiScaleEncoder2D(
(conv_input): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
(down1): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(down2): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
(down3): Down(
(maxpool_conv): Sequential(
(0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
)
(decoder): Sequential(
(0): Linear(in_features=448, out_features=128, bias=True)
(1): ReLU()
(2): Linear(in_features=128, out_features=128, bias=True)
(3): ReLU()
(4): Linear(in_features=128, out_features=128, bias=True)
(5): ReLU()
(6): Linear(in_features=128, out_features=1, bias=True)
)
)
(conv_mask_imp): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
(conv_merge_mask_imp): Sequential(
(0): Sequential(
(0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(conv_merge_bev): Sequential(
(0): Sequential(
(0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(cls_loss_func): SigmoidFocalClassificationLoss()
)
(map_to_bev_module): None
(pfe): None
(backbone_2d): BaseBEVBackbone(
(blocks): ModuleList(
(0): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
)
(1): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
)
)
(deblocks): ModuleList(
(0): Sequential(
(0): ConvTranspose2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(dense_head): CenterHead(
(shared_conv): Sequential(
(0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(heads_list): ModuleList(
(0): SeparateHead(
(center): Sequential(
(0): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(center_z): Sequential(
(0): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(dim): Sequential(
(0): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(rot): Sequential(
(0): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(hm): Sequential(
(0): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
)
)
(hm_loss_func): FocalLossCenterNet()
(reg_loss_func): RegLossCenterNet()
)
(point_head): PointHeadSimple(
(cls_loss_func): SigmoidFocalClassificationLoss()
(cls_layers): Sequential(
(0): Linear(in_features=128, out_features=128, bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=128, out_features=128, bias=False)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=128, out_features=1, bias=True)
)
)
(roi_head): None
)
)
2022-12-09 02:34:50,700 INFO **********************Start training waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1(default)**********************
2022-12-13 06:17:14,717 INFO **********************End training waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1(default)**********************
2022-12-13 06:17:14,717 INFO **********************Start evaluation waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1(default)**********************
2022-12-13 06:17:14,718 INFO Loading Waymo dataset
2022-12-13 06:17:55,401 INFO Total skipped info 0
2022-12-13 06:17:55,401 INFO Total samples for Waymo dataset: 39987
2022-12-13 06:17:55,401 INFO prepairing pose table for multi-sweeps data
2022-12-13 06:17:55,487 INFO ==> Loading parameters from checkpoint /scratch/cluster/yanght/Projects/Amazon/OpenPCDet/BEVGen/Baseline/VoxSeT_exp/VoxSeT_dev3/output/waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1/default/ckpt/checkpoint_epoch_30.pth to CPU
2022-12-13 06:18:01,810 INFO ==> Checkpoint trained from version: none
2022-12-13 06:18:01,897 INFO ==> Done (loaded 591/591)
2022-12-13 06:18:01,912 INFO *************** EPOCH 30 EVALUATION *****************
2022-12-13 06:37:05,626 INFO *************** Performance of EPOCH 30 *****************
2022-12-13 06:37:05,626 INFO Generate label finished(sec_per_example: 0.0286 second).
2022-12-13 06:37:05,627 INFO recall_roi_0.3: 0.000000
2022-12-13 06:37:05,627 INFO recall_rcnn_0.3: 0.848979
2022-12-13 06:37:05,627 INFO recall_roi_0.5: 0.000000
2022-12-13 06:37:05,627 INFO recall_rcnn_0.5: 0.806706
2022-12-13 06:37:05,627 INFO recall_roi_0.7: 0.000000
2022-12-13 06:37:05,627 INFO recall_rcnn_0.7: 0.570117
2022-12-13 06:37:05,649 INFO Average predicted number of objects(39987 samples): 123.529
2022-12-13 08:27:07,579 INFO
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1/AP: 0.7829
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1/APH: 0.7782
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1/APL: 0.7829
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/AP: 0.7023
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/APH: 0.6980
OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2/APL: 0.7023
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1/AP: 0.8105
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1/APH: 0.7412
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1/APL: 0.8105
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/AP: 0.7361
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/APH: 0.6713
OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2/APL: 0.7361
OBJECT_TYPE_TYPE_SIGN_LEVEL_1/AP: 0.0000
OBJECT_TYPE_TYPE_SIGN_LEVEL_1/APH: 0.0000
OBJECT_TYPE_TYPE_SIGN_LEVEL_1/APL: 0.0000
OBJECT_TYPE_TYPE_SIGN_LEVEL_2/AP: 0.0000
OBJECT_TYPE_TYPE_SIGN_LEVEL_2/APH: 0.0000
OBJECT_TYPE_TYPE_SIGN_LEVEL_2/APL: 0.0000
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1/AP: 0.7457
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1/APH: 0.7335
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1/APL: 0.7457
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/AP: 0.7190
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/APH: 0.7072
OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2/APL: 0.7190
2022-12-13 08:27:07,601 INFO Result is save to /scratch/cluster/yanght/Projects/Amazon/OpenPCDet/BEVGen/Baseline/VoxSeT_exp/VoxSeT_dev3/output/waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1/default/eval/eval_with_train/epoch_30/val
2022-12-13 08:27:07,601 INFO ****************Evaluation done.*****************
2022-12-13 08:27:07,700 INFO Epoch 30 has been evaluated
2022-12-13 08:27:37,737 INFO **********************End evaluation waymo_models/gen2DSS/centerpoint_voxset_1x_gen2dss_hybrid_train100_testSI1_lr0006_smpW1(default)**********************