2023/09/17 10:21:35 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] CUDA available: True numpy_random_seed: 472233255 GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB CUDA_HOME: /home/sunjiahao/cuda-11.7 NVCC: Cuda compilation tools, release 11.7, V11.7.64 GCC: gcc (GCC) 9.4.0 PyTorch: 2.0.1+cu117 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.7 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.5 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.15.2+cu117 OpenCV: 4.8.0 MMEngine: 0.8.4 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 472233255 Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/09/17 10:21:35 - mmengine - INFO - Config: backend_args = None class_names = [ 'Car', 'Pedestrian', 'Cyclist', ] custom_hooks = [ dict( disable_after_epoch=11, disable_aug_list=[ 'GlobalRotScaleTrans', 'RandomFlip3D', 'ObjectSample', ], type='DisableAugHook'), ] custom_imports = dict( allow_failed_imports=False, imports=[ 'projects.DSVT.dsvt', ]) data_root = 'data/waymo/kitti_format/' dataset_type = 'WaymoDataset' db_sampler = dict( backend_args=None, classes=[ 'Car', 'Pedestrian', 'Cyclist', ], data_root='data/waymo/kitti_format/', info_path='data/waymo/kitti_format/waymo_dbinfos_train.pkl', points_loader=dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=[ 0, 1, 2, 3, 4, ]), prepare=dict( filter_by_difficulty=[ -1, ], filter_by_min_points=dict(Car=5, Cyclist=5, Pedestrian=5)), rate=1.0, sample_groups=dict(Car=15, Cyclist=10, Pedestrian=10)) default_hooks = dict( checkpoint=dict(interval=1, type='CheckpointHook'), logger=dict(interval=50, type='LoggerHook'), param_scheduler=dict(type='ParamSchedulerHook'), sampler_seed=dict(type='DistSamplerSeedHook'), timer=dict(type='IterTimerHook'), visualization=dict(type='Det3DVisualizationHook')) default_scope = 'mmdet3d' env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) grid_size = [ 468, 468, 1, ] input_modality = dict(use_camera=False, use_lidar=True) launcher = 'pytorch' load_from = None log_level = 'INFO' log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) lr = 1e-05 metainfo = dict(classes=[ 'Car', 'Pedestrian', 'Cyclist', ]) model = dict( backbone=dict( blocks_nums=[ 1, 2, 2, ], in_channels=192, layer_strides=[ 1, 2, 2, ], out_channels=[ 128, 128, 256, ], type='ResSECOND'), bbox_head=dict( bbox_coder=dict( code_size=7, max_num=500, out_size_factor=1, pc_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], post_center_range=[ -80, -80, -10.0, 80, 80, 10.0, ], score_threshold=0.1, type='DSVTBBoxCoder', voxel_size=[ 0.32, 0.32, ]), common_heads=dict( dim=( 3, 2, ), height=( 1, 2, ), iou=( 1, 2, ), reg=( 2, 2, ), rot=( 2, 2, )), conv_cfg=dict(type='Conv2d'), in_channels=384, loss_bbox=dict(loss_weight=2.0, reduction='mean', type='mmdet.L1Loss'), loss_cls=dict( loss_weight=1.0, reduction='mean', type='mmdet.GaussianFocalLoss'), loss_iou=dict(loss_weight=1.0, reduction='sum', type='mmdet.L1Loss'), loss_reg_iou=dict( loss_weight=2.0, reduction='mean', type='mmdet3d.DIoU3DLoss'), norm_bbox=True, norm_cfg=dict(eps=0.001, momentum=0.01, type='BN2d'), separate_head=dict( final_kernel=3, init_bias=-2.19, norm_cfg=dict(eps=0.001, momentum=0.01, type='BN2d'), type='SeparateHead'), share_conv_channel=64, tasks=[ dict(class_names=[ 'Car', 'Pedestrian', 'Cyclist', ], num_class=3), ], type='DSVTCenterHead'), data_preprocessor=dict(type='Det3DDataPreprocessor', voxel=False), map2bev=dict( num_bev_feats=192, output_shape=[ 468, 468, 1, ], type='PointPillarsScatter3D'), middle_encoder=dict( activation='gelu', conv_out_channel=192, dim_feedforward=[ 384, ], dim_model=[ 192, ], dropout=0.0, input_layer=dict( dim_model=[ 192, ], downsample_stride=[], hybrid_factor=[ 2, 2, 1, ], normalize_pos=False, set_info=[ [ 36, 4, ], ], shift_list=[ [ [ 0, 0, 0, ], [ 6, 6, 0, ], ], ], sparse_shape=[ 468, 468, 1, ], window_shape=[ [ 12, 12, 1, ], ]), nhead=[ 8, ], output_shape=[ 468, 468, ], set_info=[ [ 36, 4, ], ], stage_num=1, type='DSVTMiddleEncoder'), neck=dict( in_channels=[ 128, 128, 256, ], norm_cfg=dict(eps=0.001, momentum=0.01, type='BN'), out_channels=[ 128, 128, 128, ], type='SECONDFPN', upsample_cfg=dict(bias=False, type='deconv'), upsample_strides=[ 1, 2, 4, ], use_conv_for_no_stride=False), test_cfg=dict( iou_rectifier=[ [ 0.68, 0.71, 0.65, ], ], max_per_img=500, max_pool_nms=False, min_radius=[ 4, 12, 10, 1, 0.85, 0.175, ], multi_class_nms=True, nms_thr=[ [ 0.7, 0.6, 0.55, ], ], nms_type='rotate', out_size_factor=1, pc_range=[ -80, -80, ], post_max_size=[ [ 500, 500, 500, ], ], pre_max_size=[ [ 4096, 4096, 4096, ], ], voxel_size=[ 0.32, 0.32, ]), train_cfg=dict( code_weights=[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ], dense_reg=1, gaussian_overlap=0.1, grid_size=[ 468, 468, 1, ], max_objs=500, min_radius=2, out_size_factor=1, point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], voxel_size=[ 0.32, 0.32, 6, ]), type='DSVT', voxel_encoder=dict( grid_size=[ 468, 468, 1, ], num_filters=[ 192, 192, ], num_point_features=5, point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DynamicPillarVFE3D', use_absolute_xyz=True, use_norm=True, voxel_size=[ 0.32, 0.32, 6, ], with_distance=False)) optim_wrapper = dict( clip_grad=dict(max_norm=10, norm_type=2), optimizer=dict( betas=( 0.9, 0.99, ), lr=1e-05, type='AdamW', weight_decay=0.05), type='OptimWrapper') param_scheduler = [ dict( T_max=1.2, begin=0, by_epoch=True, convert_to_iter_based=True, end=1.2, eta_min=0.001, type='CosineAnnealingLR'), dict( T_max=10.8, begin=1.2, by_epoch=True, convert_to_iter_based=True, end=12, eta_min=1e-09, type='CosineAnnealingLR'), dict( T_max=1.2, begin=0, by_epoch=True, convert_to_iter_based=True, end=1.2, eta_min=0.85, type='CosineAnnealingMomentum'), dict( T_max=10.8, begin=1.2, by_epoch=True, convert_to_iter_based=True, end=12, eta_min=0.95, type='CosineAnnealingMomentum'), ] point_cloud_range = [ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ] resume = False sync_bn = 'torch' test_cfg = dict() test_dataloader = dict( batch_size=4, dataset=dict( ann_file='waymo_wo_cam_ins_infos_val.pkl', backend_args=None, box_type_3d='LiDAR', data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'), data_root='data/waymo/kitti_format/', metainfo=dict(classes=[ 'Car', 'Pedestrian', 'Cyclist', ]), modality=dict(use_camera=False, use_lidar=True), pipeline=[ dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=5), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTPointsRangeFilter'), dict( keys=[ 'points', ], meta_keys=[ 'box_type_3d', 'sample_idx', 'context_name', 'timestamp', ], type='Pack3DDetInputs'), ], test_mode=True, type='WaymoDataset'), drop_last=False, num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) test_evaluator = dict( ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl', backend_args=None, convert_kitti_format=False, type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin') test_pipeline = [ dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=5), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTPointsRangeFilter'), dict( keys=[ 'points', ], meta_keys=[ 'box_type_3d', 'sample_idx', 'context_name', 'timestamp', ], type='Pack3DDetInputs'), ] train_cfg = dict(by_epoch=True, max_epochs=12, val_interval=1) train_dataloader = dict( batch_size=1, dataset=dict( ann_file='waymo_wo_cam_ins_infos_train.pkl', backend_args=None, box_type_3d='LiDAR', data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'), data_root='data/waymo/kitti_format/', load_interval=5, metainfo=dict(classes=[ 'Car', 'Pedestrian', 'Cyclist', ]), modality=dict(use_camera=False, use_lidar=True), pipeline=[ dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=5), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( db_sampler=dict( backend_args=None, classes=[ 'Car', 'Pedestrian', 'Cyclist', ], data_root='data/waymo/kitti_format/', info_path='data/waymo/kitti_format/waymo_dbinfos_train.pkl', points_loader=dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=[ 0, 1, 2, 3, 4, ]), prepare=dict( filter_by_difficulty=[ -1, ], filter_by_min_points=dict( Car=5, Cyclist=5, Pedestrian=5)), rate=1.0, sample_groups=dict(Car=15, Cyclist=10, Pedestrian=10)), type='ObjectSample'), dict( flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5, sync_2d=False, type='RandomFlip3D'), dict( rot_range=[ -0.78539816, 0.78539816, ], scale_ratio_range=[ 0.95, 1.05, ], translation_std=[ 0.5, 0.5, 0.5, ], type='GlobalRotScaleTrans'), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTPointsRangeFilter'), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTObjectRangeFilter'), dict(type='PointShuffle'), dict( keys=[ 'points', 'gt_bboxes_3d', 'gt_labels_3d', ], type='Pack3DDetInputs'), ], test_mode=False, type='WaymoDataset'), num_workers=4, persistent_workers=True, sampler=dict(shuffle=True, type='DefaultSampler')) train_pipeline = [ dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=5), dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), dict( db_sampler=dict( backend_args=None, classes=[ 'Car', 'Pedestrian', 'Cyclist', ], data_root='data/waymo/kitti_format/', info_path='data/waymo/kitti_format/waymo_dbinfos_train.pkl', points_loader=dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=[ 0, 1, 2, 3, 4, ]), prepare=dict( filter_by_difficulty=[ -1, ], filter_by_min_points=dict(Car=5, Cyclist=5, Pedestrian=5)), rate=1.0, sample_groups=dict(Car=15, Cyclist=10, Pedestrian=10)), type='ObjectSample'), dict( flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5, sync_2d=False, type='RandomFlip3D'), dict( rot_range=[ -0.78539816, 0.78539816, ], scale_ratio_range=[ 0.95, 1.05, ], translation_std=[ 0.5, 0.5, 0.5, ], type='GlobalRotScaleTrans'), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTPointsRangeFilter'), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTObjectRangeFilter'), dict(type='PointShuffle'), dict( keys=[ 'points', 'gt_bboxes_3d', 'gt_labels_3d', ], type='Pack3DDetInputs'), ] val_cfg = dict() val_dataloader = dict( batch_size=4, dataset=dict( ann_file='waymo_wo_cam_ins_infos_val.pkl', backend_args=None, box_type_3d='LiDAR', data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'), data_root='data/waymo/kitti_format/', metainfo=dict(classes=[ 'Car', 'Pedestrian', 'Cyclist', ]), modality=dict(use_camera=False, use_lidar=True), pipeline=[ dict( backend_args=None, coord_type='LIDAR', load_dim=6, norm_elongation=True, norm_intensity=True, type='LoadPointsFromFile', use_dim=5), dict( point_cloud_range=[ -74.88, -74.88, -2, 74.88, 74.88, 4.0, ], type='DSVTPointsRangeFilter'), dict( keys=[ 'points', ], meta_keys=[ 'box_type_3d', 'sample_idx', 'context_name', 'timestamp', ], type='Pack3DDetInputs'), ], test_mode=True, type='WaymoDataset'), drop_last=False, num_workers=4, persistent_workers=True, sampler=dict(shuffle=False, type='DefaultSampler')) val_evaluator = dict( ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl', backend_args=None, convert_kitti_format=False, type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin') vis_backends = [ dict(type='LocalVisBackend'), dict(type='WandbVisBackend'), ] visualizer = dict( name='visualizer', type='Det3DLocalVisualizer', vis_backends=[ dict(type='LocalVisBackend'), dict(type='WandbVisBackend'), ]) voxel_size = [ 0.32, 0.32, 6, ] work_dir = 'exps/dsvt_sycbn_fix-os-aug_init-from-mm3d' 2023/09/17 10:21:48 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook (NORMAL ) DisableAugHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val: (VERY_HIGH ) RuntimeInfoHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) Det3DVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_val: (VERY_HIGH ) RuntimeInfoHook -------------------- after_train: (VERY_HIGH ) RuntimeInfoHook (VERY_LOW ) CheckpointHook -------------------- before_test: (VERY_HIGH ) RuntimeInfoHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) Det3DVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_test: (VERY_HIGH ) RuntimeInfoHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/09/17 10:22:04 - mmengine - INFO - load 4352197 Car database infos in DataBaseSampler 2023/09/17 10:22:04 - mmengine - INFO - load 2037626 Pedestrian database infos in DataBaseSampler 2023/09/17 10:22:04 - mmengine - INFO - load 49518 Cyclist database infos in DataBaseSampler 2023/09/17 10:22:13 - mmengine - INFO - After filter database: 2023/09/17 10:22:13 - mmengine - INFO - load 4078706 Car database infos in DataBaseSampler 2023/09/17 10:22:13 - mmengine - INFO - load 1889987 Pedestrian database infos in DataBaseSampler 2023/09/17 10:22:13 - mmengine - INFO - load 47534 Cyclist database infos in DataBaseSampler 2023/09/17 10:24:02 - mmengine - INFO - Sample size will be reduced to 1/5 ofthe original data sample 2023/09/17 10:24:22 - mmengine - INFO - ------------------------------ 2023/09/17 10:24:22 - mmengine - INFO - The length of training dataset: 31617 2023/09/17 10:24:22 - mmengine - INFO - The number of instances per category in the dataset: +------------+--------+ | category | number | +------------+--------+ | Car | 870437 | | Pedestrian | 407621 | | Cyclist | 9933 | +------------+--------+ 2023/09/17 10:25:11 - mmengine - INFO - ------------------------------ 2023/09/17 10:25:11 - mmengine - INFO - The length of test dataset: 39987 2023/09/17 10:25:11 - mmengine - INFO - The number of instances per category in the dataset: +------------+---------+ | category | number | +------------+---------+ | Car | 1123373 | | Pedestrian | 494739 | | Cyclist | 12398 | +------------+---------+ Name of parameter - Initialization information voxel_encoder.pfn_layers.0.linear.weight - torch.Size([96, 11]): The value is the same before and after calling `init_weights` of DSVT voxel_encoder.pfn_layers.0.norm.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of DSVT voxel_encoder.pfn_layers.0.norm.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of DSVT voxel_encoder.pfn_layers.1.linear.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT voxel_encoder.pfn_layers.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT voxel_encoder.pfn_layers.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.0.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.0.1.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.0.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.1.1.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.0.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.2.1.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.0.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.0.weight - torch.Size([192, 2]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.3.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.input_layer.posembed_layers.0.3.1.position_embedding_head.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.0.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.0.encoder_list.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.0.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.1.encoder_list.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.0.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.2.encoder_list.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.0.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.self_attn.in_proj_weight - torch.Size([576, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.self_attn.in_proj_bias - torch.Size([576]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.self_attn.out_proj.weight - torch.Size([192, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.self_attn.out_proj.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.linear1.weight - torch.Size([384, 192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.linear1.bias - torch.Size([384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.linear2.weight - torch.Size([192, 384]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.linear2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.norm1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.norm1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.norm2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.win_attn.norm2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.norm.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.stage_0.3.encoder_list.1.norm.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.0.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.0.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.1.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.1.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.2.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.2.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.3.weight - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT middle_encoder.residual_norm_stage_0.3.bias - torch.Size([192]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.conv1.weight - torch.Size([128, 192, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.downsample_layer.0.weight - torch.Size([128, 192, 1, 1]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.downsample_layer.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.0.downsample_layer.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.0.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.downsample_layer.0.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.downsample_layer.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.0.downsample_layer.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.conv1.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.conv2.weight - torch.Size([128, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.bn2.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.1.2.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.conv1.weight - torch.Size([256, 128, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.downsample_layer.0.weight - torch.Size([256, 128, 1, 1]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.downsample_layer.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.0.downsample_layer.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.conv1.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.conv2.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.bn2.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT backbone.blocks.2.2.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.0.0.weight - torch.Size([128, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 neck.deblocks.0.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.0.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.1.0.weight - torch.Size([128, 128, 2, 2]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 neck.deblocks.1.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.1.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.2.0.weight - torch.Size([256, 128, 4, 4]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 neck.deblocks.2.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT neck.deblocks.2.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of DSVT bbox_head.shared_conv.conv.weight - torch.Size([64, 384, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.shared_conv.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.shared_conv.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.reg.0.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.reg.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.reg.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.reg.1.weight - torch.Size([2, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.reg.1.bias - torch.Size([2]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.height.0.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.height.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.height.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.height.1.weight - torch.Size([1, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.height.1.bias - torch.Size([1]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.dim.0.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.dim.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.dim.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.dim.1.weight - torch.Size([3, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.dim.1.bias - torch.Size([3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.rot.0.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.rot.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.rot.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.rot.1.weight - torch.Size([2, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.rot.1.bias - torch.Size([2]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.iou.0.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.iou.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.iou.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.iou.1.weight - torch.Size([1, 64, 3, 3]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.iou.1.bias - torch.Size([1]): Initialized by user-defined `init_weights` in DSVTCenterHead bbox_head.task_heads.0.heatmap.0.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.heatmap.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.heatmap.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.heatmap.1.weight - torch.Size([3, 64, 3, 3]): The value is the same before and after calling `init_weights` of DSVT bbox_head.task_heads.0.heatmap.1.bias - torch.Size([3]): Initialized by user-defined `init_weights` in DSVTCenterHead 2023/09/17 10:25:13 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/09/17 10:25:13 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/09/17 10:25:13 - mmengine - INFO - Checkpoints will be saved to /home/sunjiahao/projects/mmdetection3d/exps/dsvt_sycbn_fix-os-aug_init-from-mm3d. 2023/09/17 10:25:52 - mmengine - INFO - Epoch(train) [1][ 50/3953] lr: 1.0261e-05 eta: 10:25:33 time: 0.7921 data_time: 0.1226 memory: 8645 grad_norm: 288.6008 loss: 38.2172 task0.loss_heatmap: 15.6981 task0.loss_bbox: 19.3335 task0.loss_iou: 1.1626 task0.loss_reg_iou: 2.0230 2023/09/17 10:26:18 - mmengine - INFO - Epoch(train) [1][ 100/3953] lr: 1.1064e-05 eta: 8:31:12 time: 0.5039 data_time: 0.0034 memory: 8836 grad_norm: 155.2932 loss: 26.3643 task0.loss_heatmap: 10.9627 task0.loss_bbox: 12.8018 task0.loss_iou: 0.7022 task0.loss_reg_iou: 1.8976 2023/09/17 10:26:43 - mmengine - INFO - Epoch(train) [1][ 150/3953] lr: 1.2408e-05 eta: 7:53:04 time: 0.5049 data_time: 0.0032 memory: 8106 grad_norm: 101.2156 loss: 20.0524 task0.loss_heatmap: 7.7415 task0.loss_bbox: 10.0606 task0.loss_iou: 0.4854 task0.loss_reg_iou: 1.7649 2023/09/17 10:27:09 - mmengine - INFO - Epoch(train) [1][ 200/3953] lr: 1.4293e-05 eta: 7:36:10 time: 0.5170 data_time: 0.0033 memory: 8489 grad_norm: 71.5777 loss: 16.1107 task0.loss_heatmap: 5.8221 task0.loss_bbox: 8.3010 task0.loss_iou: 0.3625 task0.loss_reg_iou: 1.6250 2023/09/17 10:27:34 - mmengine - INFO - Epoch(train) [1][ 250/3953] lr: 1.6715e-05 eta: 7:24:07 time: 0.5058 data_time: 0.0034 memory: 8316 grad_norm: 51.6312 loss: 14.1620 task0.loss_heatmap: 4.7137 task0.loss_bbox: 7.5434 task0.loss_iou: 0.3149 task0.loss_reg_iou: 1.5899 2023/09/17 10:28:00 - mmengine - INFO - Epoch(train) [1][ 300/3953] lr: 1.9673e-05 eta: 7:16:44 time: 0.5120 data_time: 0.0035 memory: 8040 grad_norm: 38.3862 loss: 12.6169 task0.loss_heatmap: 3.8045 task0.loss_bbox: 7.0152 task0.loss_iou: 0.2744 task0.loss_reg_iou: 1.5229 2023/09/17 10:28:25 - mmengine - INFO - Epoch(train) [1][ 350/3953] lr: 2.3164e-05 eta: 7:10:37 time: 0.5056 data_time: 0.0035 memory: 8561 grad_norm: 30.8317 loss: 11.9300 task0.loss_heatmap: 3.4794 task0.loss_bbox: 6.6877 task0.loss_iou: 0.2681 task0.loss_reg_iou: 1.4948 2023/09/17 10:28:51 - mmengine - INFO - Epoch(train) [1][ 400/3953] lr: 2.7182e-05 eta: 7:07:57 time: 0.5261 data_time: 0.0035 memory: 8316 grad_norm: 27.1046 loss: 11.6035 task0.loss_heatmap: 3.2145 task0.loss_bbox: 6.6513 task0.loss_iou: 0.2612 task0.loss_reg_iou: 1.4765 2023/09/17 10:29:18 - mmengine - INFO - Epoch(train) [1][ 450/3953] lr: 3.1724e-05 eta: 7:05:50 time: 0.5267 data_time: 0.0034 memory: 8408 grad_norm: 28.4180 loss: 11.2865 task0.loss_heatmap: 3.0945 task0.loss_bbox: 6.4941 task0.loss_iou: 0.2686 task0.loss_reg_iou: 1.4294 2023/09/17 10:29:43 - mmengine - INFO - Epoch(train) [1][ 500/3953] lr: 3.6786e-05 eta: 7:03:13 time: 0.5162 data_time: 0.0034 memory: 8519 grad_norm: 30.1911 loss: 10.8469 task0.loss_heatmap: 3.0125 task0.loss_bbox: 6.1976 task0.loss_iou: 0.2545 task0.loss_reg_iou: 1.3824 2023/09/17 10:30:10 - mmengine - INFO - Epoch(train) [1][ 550/3953] lr: 4.2360e-05 eta: 7:02:07 time: 0.5319 data_time: 0.0034 memory: 9244 grad_norm: 29.5771 loss: 10.2659 task0.loss_heatmap: 2.7724 task0.loss_bbox: 5.9411 task0.loss_iou: 0.2522 task0.loss_reg_iou: 1.3002 2023/09/17 10:30:35 - mmengine - INFO - Epoch(train) [1][ 600/3953] lr: 4.8442e-05 eta: 6:59:37 time: 0.5086 data_time: 0.0033 memory: 8214 grad_norm: 31.1935 loss: 9.9739 task0.loss_heatmap: 2.6532 task0.loss_bbox: 5.7969 task0.loss_iou: 0.2517 task0.loss_reg_iou: 1.2721 2023/09/17 10:31:01 - mmengine - INFO - Epoch(train) [1][ 650/3953] lr: 5.5025e-05 eta: 6:57:26 time: 0.5086 data_time: 0.0034 memory: 8716 grad_norm: 35.0419 loss: 9.7614 task0.loss_heatmap: 2.5291 task0.loss_bbox: 5.7274 task0.loss_iou: 0.2564 task0.loss_reg_iou: 1.2486 2023/09/17 10:31:26 - mmengine - INFO - Epoch(train) [1][ 700/3953] lr: 6.2101e-05 eta: 6:55:43 time: 0.5127 data_time: 0.0036 memory: 8226 grad_norm: 33.6264 loss: 9.4050 task0.loss_heatmap: 2.3766 task0.loss_bbox: 5.5762 task0.loss_iou: 0.2460 task0.loss_reg_iou: 1.2062 2023/09/17 10:31:52 - mmengine - INFO - Epoch(train) [1][ 750/3953] lr: 6.9662e-05 eta: 6:54:25 time: 0.5170 data_time: 0.0035 memory: 8791 grad_norm: 33.0950 loss: 9.2612 task0.loss_heatmap: 2.2911 task0.loss_bbox: 5.5356 task0.loss_iou: 0.2433 task0.loss_reg_iou: 1.1911 2023/09/17 10:32:18 - mmengine - INFO - Epoch(train) [1][ 800/3953] lr: 7.7701e-05 eta: 6:52:40 time: 0.5057 data_time: 0.0035 memory: 8042 grad_norm: 33.8821 loss: 9.1690 task0.loss_heatmap: 2.1763 task0.loss_bbox: 5.5543 task0.loss_iou: 0.2458 task0.loss_reg_iou: 1.1926 2023/09/17 10:32:43 - mmengine - INFO - Epoch(train) [1][ 850/3953] lr: 8.6208e-05 eta: 6:51:27 time: 0.5141 data_time: 0.0033 memory: 8521 grad_norm: 35.6729 loss: 8.8579 task0.loss_heatmap: 2.1238 task0.loss_bbox: 5.3559 task0.loss_iou: 0.2421 task0.loss_reg_iou: 1.1360 2023/09/17 10:33:09 - mmengine - INFO - Epoch(train) [1][ 900/3953] lr: 9.5175e-05 eta: 6:50:07 time: 0.5091 data_time: 0.0034 memory: 8575 grad_norm: 35.4511 loss: 8.5146 task0.loss_heatmap: 2.0164 task0.loss_bbox: 5.1490 task0.loss_iou: 0.2452 task0.loss_reg_iou: 1.1040 2023/09/17 10:33:35 - mmengine - INFO - Epoch(train) [1][ 950/3953] lr: 1.0459e-04 eta: 6:49:26 time: 0.5229 data_time: 0.0034 memory: 8200 grad_norm: 36.6914 loss: 8.6415 task0.loss_heatmap: 2.0184 task0.loss_bbox: 5.2328 task0.loss_iou: 0.2534 task0.loss_reg_iou: 1.1370 2023/09/17 10:34:01 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 10:34:01 - mmengine - INFO - Epoch(train) [1][1000/3953] lr: 1.1445e-04 eta: 6:48:40 time: 0.5203 data_time: 0.0033 memory: 8595 grad_norm: 37.6633 loss: 8.4963 task0.loss_heatmap: 1.9337 task0.loss_bbox: 5.2001 task0.loss_iou: 0.2464 task0.loss_reg_iou: 1.1161 2023/09/17 10:34:26 - mmengine - INFO - Epoch(train) [1][1050/3953] lr: 1.2473e-04 eta: 6:47:23 time: 0.5052 data_time: 0.0033 memory: 8559 grad_norm: 35.1983 loss: 8.2495 task0.loss_heatmap: 1.8243 task0.loss_bbox: 5.0723 task0.loss_iou: 0.2480 task0.loss_reg_iou: 1.1049 2023/09/17 10:34:52 - mmengine - INFO - Epoch(train) [1][1100/3953] lr: 1.3543e-04 eta: 6:46:23 time: 0.5107 data_time: 0.0034 memory: 7878 grad_norm: 36.3883 loss: 8.2762 task0.loss_heatmap: 1.8875 task0.loss_bbox: 5.0627 task0.loss_iou: 0.2468 task0.loss_reg_iou: 1.0792 2023/09/17 10:35:17 - mmengine - INFO - Epoch(train) [1][1150/3953] lr: 1.4653e-04 eta: 6:45:06 time: 0.5011 data_time: 0.0035 memory: 8309 grad_norm: 36.9365 loss: 8.0704 task0.loss_heatmap: 1.8453 task0.loss_bbox: 4.9072 task0.loss_iou: 0.2487 task0.loss_reg_iou: 1.0691 2023/09/17 10:35:42 - mmengine - INFO - Epoch(train) [1][1200/3953] lr: 1.5803e-04 eta: 6:44:05 time: 0.5072 data_time: 0.0032 memory: 8332 grad_norm: 34.8834 loss: 7.9555 task0.loss_heatmap: 1.7977 task0.loss_bbox: 4.8838 task0.loss_iou: 0.2367 task0.loss_reg_iou: 1.0373 2023/09/17 10:36:09 - mmengine - INFO - Epoch(train) [1][1250/3953] lr: 1.6991e-04 eta: 6:43:47 time: 0.5289 data_time: 0.0034 memory: 8213 grad_norm: 34.2848 loss: 7.8825 task0.loss_heatmap: 1.7850 task0.loss_bbox: 4.8201 task0.loss_iou: 0.2478 task0.loss_reg_iou: 1.0296 2023/09/17 10:36:34 - mmengine - INFO - Epoch(train) [1][1300/3953] lr: 1.8216e-04 eta: 6:42:56 time: 0.5105 data_time: 0.0034 memory: 7984 grad_norm: 33.9214 loss: 7.6102 task0.loss_heatmap: 1.6353 task0.loss_bbox: 4.7170 task0.loss_iou: 0.2414 task0.loss_reg_iou: 1.0165 2023/09/17 10:37:00 - mmengine - INFO - Epoch(train) [1][1350/3953] lr: 1.9476e-04 eta: 6:42:23 time: 0.5198 data_time: 0.0033 memory: 8503 grad_norm: 34.4813 loss: 7.4028 task0.loss_heatmap: 1.5628 task0.loss_bbox: 4.5899 task0.loss_iou: 0.2495 task0.loss_reg_iou: 1.0006 2023/09/17 10:37:27 - mmengine - INFO - Epoch(train) [1][1400/3953] lr: 2.0770e-04 eta: 6:42:33 time: 0.5463 data_time: 0.0035 memory: 8333 grad_norm: 32.7201 loss: 7.6849 task0.loss_heatmap: 1.6273 task0.loss_bbox: 4.7513 task0.loss_iou: 0.2580 task0.loss_reg_iou: 1.0483 2023/09/17 10:37:53 - mmengine - INFO - Epoch(train) [1][1450/3953] lr: 2.2096e-04 eta: 6:42:00 time: 0.5205 data_time: 0.0035 memory: 8264 grad_norm: 32.1531 loss: 7.3571 task0.loss_heatmap: 1.5860 task0.loss_bbox: 4.5594 task0.loss_iou: 0.2326 task0.loss_reg_iou: 0.9792 2023/09/17 10:38:19 - mmengine - INFO - Epoch(train) [1][1500/3953] lr: 2.3454e-04 eta: 6:41:28 time: 0.5203 data_time: 0.0032 memory: 8436 grad_norm: 30.4141 loss: 7.4634 task0.loss_heatmap: 1.5887 task0.loss_bbox: 4.6339 task0.loss_iou: 0.2416 task0.loss_reg_iou: 0.9993 2023/09/17 10:38:45 - mmengine - INFO - Epoch(train) [1][1550/3953] lr: 2.4842e-04 eta: 6:40:29 time: 0.5026 data_time: 0.0033 memory: 8289 grad_norm: 32.4187 loss: 7.2129 task0.loss_heatmap: 1.4732 task0.loss_bbox: 4.5050 task0.loss_iou: 0.2380 task0.loss_reg_iou: 0.9967 2023/09/17 10:39:11 - mmengine - INFO - Epoch(train) [1][1600/3953] lr: 2.6257e-04 eta: 6:40:06 time: 0.5255 data_time: 0.0034 memory: 8229 grad_norm: 30.3098 loss: 7.1601 task0.loss_heatmap: 1.4607 task0.loss_bbox: 4.4853 task0.loss_iou: 0.2442 task0.loss_reg_iou: 0.9699 2023/09/17 10:39:38 - mmengine - INFO - Epoch(train) [1][1650/3953] lr: 2.7699e-04 eta: 6:40:06 time: 0.5431 data_time: 0.0033 memory: 8560 grad_norm: 30.3519 loss: 6.8295 task0.loss_heatmap: 1.3884 task0.loss_bbox: 4.2737 task0.loss_iou: 0.2338 task0.loss_reg_iou: 0.9337 2023/09/17 10:40:04 - mmengine - INFO - Epoch(train) [1][1700/3953] lr: 2.9166e-04 eta: 6:39:26 time: 0.5134 data_time: 0.0035 memory: 8147 grad_norm: 27.3221 loss: 6.9662 task0.loss_heatmap: 1.4133 task0.loss_bbox: 4.3704 task0.loss_iou: 0.2379 task0.loss_reg_iou: 0.9446 2023/09/17 10:40:30 - mmengine - INFO - Epoch(train) [1][1750/3953] lr: 3.0657e-04 eta: 6:38:52 time: 0.5186 data_time: 0.0035 memory: 8371 grad_norm: 28.2811 loss: 7.1235 task0.loss_heatmap: 1.3721 task0.loss_bbox: 4.5008 task0.loss_iou: 0.2408 task0.loss_reg_iou: 1.0099 2023/09/17 10:40:56 - mmengine - INFO - Epoch(train) [1][1800/3953] lr: 3.2169e-04 eta: 6:38:20 time: 0.5188 data_time: 0.0034 memory: 8005 grad_norm: 29.1215 loss: 7.0128 task0.loss_heatmap: 1.3754 task0.loss_bbox: 4.4287 task0.loss_iou: 0.2406 task0.loss_reg_iou: 0.9681 2023/09/17 10:41:21 - mmengine - INFO - Epoch(train) [1][1850/3953] lr: 3.3702e-04 eta: 6:37:34 time: 0.5082 data_time: 0.0041 memory: 8568 grad_norm: 25.4783 loss: 6.8528 task0.loss_heatmap: 1.3311 task0.loss_bbox: 4.3221 task0.loss_iou: 0.2414 task0.loss_reg_iou: 0.9582 2023/09/17 10:41:47 - mmengine - INFO - Epoch(train) [1][1900/3953] lr: 3.5252e-04 eta: 6:37:07 time: 0.5225 data_time: 0.0035 memory: 8302 grad_norm: 26.2725 loss: 6.7573 task0.loss_heatmap: 1.3260 task0.loss_bbox: 4.2460 task0.loss_iou: 0.2362 task0.loss_reg_iou: 0.9491 2023/09/17 10:42:12 - mmengine - INFO - Epoch(train) [1][1950/3953] lr: 3.6820e-04 eta: 6:36:22 time: 0.5066 data_time: 0.0037 memory: 8121 grad_norm: 24.8427 loss: 6.7418 task0.loss_heatmap: 1.2463 task0.loss_bbox: 4.2701 task0.loss_iou: 0.2426 task0.loss_reg_iou: 0.9828 2023/09/17 10:42:38 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 10:42:38 - mmengine - INFO - Epoch(train) [1][2000/3953] lr: 3.8402e-04 eta: 6:35:53 time: 0.5206 data_time: 0.0037 memory: 8079 grad_norm: 24.6883 loss: 6.8664 task0.loss_heatmap: 1.3311 task0.loss_bbox: 4.3166 task0.loss_iou: 0.2482 task0.loss_reg_iou: 0.9705 2023/09/17 10:43:04 - mmengine - INFO - Epoch(train) [1][2050/3953] lr: 3.9998e-04 eta: 6:35:09 time: 0.5071 data_time: 0.0035 memory: 9062 grad_norm: 24.8408 loss: 6.5418 task0.loss_heatmap: 1.2460 task0.loss_bbox: 4.1226 task0.loss_iou: 0.2368 task0.loss_reg_iou: 0.9365 2023/09/17 10:43:30 - mmengine - INFO - Epoch(train) [1][2100/3953] lr: 4.1605e-04 eta: 6:34:41 time: 0.5206 data_time: 0.0035 memory: 8319 grad_norm: 25.0483 loss: 6.6996 task0.loss_heatmap: 1.2997 task0.loss_bbox: 4.2267 task0.loss_iou: 0.2320 task0.loss_reg_iou: 0.9412 2023/09/17 10:43:55 - mmengine - INFO - Epoch(train) [1][2150/3953] lr: 4.3222e-04 eta: 6:34:02 time: 0.5097 data_time: 0.0035 memory: 8770 grad_norm: 23.2984 loss: 6.5805 task0.loss_heatmap: 1.2630 task0.loss_bbox: 4.1614 task0.loss_iou: 0.2293 task0.loss_reg_iou: 0.9268 2023/09/17 10:44:21 - mmengine - INFO - Epoch(train) [1][2200/3953] lr: 4.4847e-04 eta: 6:33:34 time: 0.5206 data_time: 0.0035 memory: 8541 grad_norm: 24.5039 loss: 6.3550 task0.loss_heatmap: 1.1915 task0.loss_bbox: 4.0263 task0.loss_iou: 0.2309 task0.loss_reg_iou: 0.9064 2023/09/17 10:44:47 - mmengine - INFO - Epoch(train) [1][2250/3953] lr: 4.6479e-04 eta: 6:32:54 time: 0.5081 data_time: 0.0036 memory: 8632 grad_norm: 25.0765 loss: 6.3307 task0.loss_heatmap: 1.1707 task0.loss_bbox: 4.0296 task0.loss_iou: 0.2201 task0.loss_reg_iou: 0.9102 2023/09/17 10:45:13 - mmengine - INFO - Epoch(train) [1][2300/3953] lr: 4.8114e-04 eta: 6:32:32 time: 0.5258 data_time: 0.0038 memory: 8043 grad_norm: 27.3864 loss: 6.5235 task0.loss_heatmap: 1.2503 task0.loss_bbox: 4.1003 task0.loss_iou: 0.2389 task0.loss_reg_iou: 0.9339 2023/09/17 10:45:39 - mmengine - INFO - Epoch(train) [1][2350/3953] lr: 4.9753e-04 eta: 6:31:53 time: 0.5087 data_time: 0.0034 memory: 8604 grad_norm: 24.6034 loss: 6.5168 task0.loss_heatmap: 1.1985 task0.loss_bbox: 4.1458 task0.loss_iou: 0.2328 task0.loss_reg_iou: 0.9398 2023/09/17 10:46:04 - mmengine - INFO - Epoch(train) [1][2400/3953] lr: 5.1392e-04 eta: 6:31:22 time: 0.5161 data_time: 0.0035 memory: 8612 grad_norm: 24.0578 loss: 6.4883 task0.loss_heatmap: 1.2335 task0.loss_bbox: 4.0786 task0.loss_iou: 0.2352 task0.loss_reg_iou: 0.9410 2023/09/17 10:46:30 - mmengine - INFO - Epoch(train) [1][2450/3953] lr: 5.3030e-04 eta: 6:30:50 time: 0.5147 data_time: 0.0033 memory: 8725 grad_norm: 24.4564 loss: 6.2149 task0.loss_heatmap: 1.1624 task0.loss_bbox: 3.9298 task0.loss_iou: 0.2280 task0.loss_reg_iou: 0.8947 2023/09/17 10:46:55 - mmengine - INFO - Epoch(train) [1][2500/3953] lr: 5.4665e-04 eta: 6:30:11 time: 0.5066 data_time: 0.0035 memory: 8046 grad_norm: 24.6686 loss: 6.1685 task0.loss_heatmap: 1.1217 task0.loss_bbox: 3.9077 task0.loss_iou: 0.2353 task0.loss_reg_iou: 0.9038 2023/09/17 10:47:21 - mmengine - INFO - Epoch(train) [1][2550/3953] lr: 5.6296e-04 eta: 6:29:41 time: 0.5162 data_time: 0.0034 memory: 8283 grad_norm: 25.2781 loss: 6.1186 task0.loss_heatmap: 1.0863 task0.loss_bbox: 3.9203 task0.loss_iou: 0.2284 task0.loss_reg_iou: 0.8836 2023/09/17 10:47:47 - mmengine - INFO - Epoch(train) [1][2600/3953] lr: 5.7920e-04 eta: 6:29:04 time: 0.5089 data_time: 0.0033 memory: 8394 grad_norm: 24.1518 loss: 6.4481 task0.loss_heatmap: 1.1732 task0.loss_bbox: 4.0724 task0.loss_iou: 0.2417 task0.loss_reg_iou: 0.9608 2023/09/17 10:48:13 - mmengine - INFO - Epoch(train) [1][2650/3953] lr: 5.9536e-04 eta: 6:28:37 time: 0.5188 data_time: 0.0034 memory: 8200 grad_norm: 25.4446 loss: 6.1407 task0.loss_heatmap: 1.0646 task0.loss_bbox: 3.9469 task0.loss_iou: 0.2235 task0.loss_reg_iou: 0.9056 2023/09/17 10:48:39 - mmengine - INFO - Epoch(train) [1][2700/3953] lr: 6.1143e-04 eta: 6:28:10 time: 0.5196 data_time: 0.0034 memory: 8827 grad_norm: 25.7314 loss: 6.1793 task0.loss_heatmap: 1.1408 task0.loss_bbox: 3.8986 task0.loss_iou: 0.2290 task0.loss_reg_iou: 0.9108 2023/09/17 10:49:04 - mmengine - INFO - Epoch(train) [1][2750/3953] lr: 6.2737e-04 eta: 6:27:36 time: 0.5112 data_time: 0.0034 memory: 7963 grad_norm: 25.3421 loss: 6.0333 task0.loss_heatmap: 1.0657 task0.loss_bbox: 3.8848 task0.loss_iou: 0.2090 task0.loss_reg_iou: 0.8739 2023/09/17 10:49:30 - mmengine - INFO - Epoch(train) [1][2800/3953] lr: 6.4319e-04 eta: 6:27:11 time: 0.5211 data_time: 0.0035 memory: 8324 grad_norm: 29.1355 loss: 5.7990 task0.loss_heatmap: 1.0127 task0.loss_bbox: 3.6916 task0.loss_iou: 0.2133 task0.loss_reg_iou: 0.8814 2023/09/17 10:49:56 - mmengine - INFO - Epoch(train) [1][2850/3953] lr: 6.5885e-04 eta: 6:26:41 time: 0.5161 data_time: 0.0033 memory: 8499 grad_norm: 28.7634 loss: 5.9184 task0.loss_heatmap: 1.0379 task0.loss_bbox: 3.7909 task0.loss_iou: 0.2172 task0.loss_reg_iou: 0.8724 2023/09/17 10:50:25 - mmengine - INFO - Epoch(train) [1][2900/3953] lr: 6.7434e-04 eta: 6:26:59 time: 0.5773 data_time: 0.0034 memory: 8031 grad_norm: 26.0623 loss: 6.0325 task0.loss_heatmap: 1.0255 task0.loss_bbox: 3.8757 task0.loss_iou: 0.2253 task0.loss_reg_iou: 0.9059 2023/09/17 10:50:51 - mmengine - INFO - Epoch(train) [1][2950/3953] lr: 6.8965e-04 eta: 6:26:33 time: 0.5221 data_time: 0.0033 memory: 8451 grad_norm: 24.9966 loss: 5.9314 task0.loss_heatmap: 1.0558 task0.loss_bbox: 3.7665 task0.loss_iou: 0.2288 task0.loss_reg_iou: 0.8803 2023/09/17 10:51:17 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 10:51:17 - mmengine - INFO - Epoch(train) [1][3000/3953] lr: 7.0475e-04 eta: 6:26:10 time: 0.5256 data_time: 0.0033 memory: 8832 grad_norm: 27.1623 loss: 5.8317 task0.loss_heatmap: 0.9910 task0.loss_bbox: 3.7247 task0.loss_iou: 0.2279 task0.loss_reg_iou: 0.8881 2023/09/17 10:51:43 - mmengine - INFO - Epoch(train) [1][3050/3953] lr: 7.1964e-04 eta: 6:25:33 time: 0.5057 data_time: 0.0032 memory: 8805 grad_norm: 25.0107 loss: 5.8027 task0.loss_heatmap: 0.9906 task0.loss_bbox: 3.7119 task0.loss_iou: 0.2228 task0.loss_reg_iou: 0.8773 2023/09/17 10:52:08 - mmengine - INFO - Epoch(train) [1][3100/3953] lr: 7.3429e-04 eta: 6:25:01 time: 0.5133 data_time: 0.0032 memory: 8700 grad_norm: 28.0601 loss: 5.9261 task0.loss_heatmap: 1.0119 task0.loss_bbox: 3.7795 task0.loss_iou: 0.2319 task0.loss_reg_iou: 0.9028 2023/09/17 10:52:34 - mmengine - INFO - Epoch(train) [1][3150/3953] lr: 7.4869e-04 eta: 6:24:26 time: 0.5082 data_time: 0.0034 memory: 8595 grad_norm: 26.6273 loss: 5.8137 task0.loss_heatmap: 0.9871 task0.loss_bbox: 3.7047 task0.loss_iou: 0.2248 task0.loss_reg_iou: 0.8971 2023/09/17 10:52:59 - mmengine - INFO - Epoch(train) [1][3200/3953] lr: 7.6282e-04 eta: 6:23:48 time: 0.5029 data_time: 0.0033 memory: 8316 grad_norm: 29.8977 loss: 5.7098 task0.loss_heatmap: 1.0087 task0.loss_bbox: 3.6093 task0.loss_iou: 0.2263 task0.loss_reg_iou: 0.8655 2023/09/17 10:53:25 - mmengine - INFO - Epoch(train) [1][3250/3953] lr: 7.7667e-04 eta: 6:23:18 time: 0.5151 data_time: 0.0033 memory: 8032 grad_norm: 28.8952 loss: 5.5640 task0.loss_heatmap: 0.9274 task0.loss_bbox: 3.5650 task0.loss_iou: 0.2245 task0.loss_reg_iou: 0.8472 2023/09/17 10:53:51 - mmengine - INFO - Epoch(train) [1][3300/3953] lr: 7.9022e-04 eta: 6:22:58 time: 0.5301 data_time: 0.0032 memory: 8136 grad_norm: 27.1047 loss: 5.4440 task0.loss_heatmap: 0.9424 task0.loss_bbox: 3.4829 task0.loss_iou: 0.2046 task0.loss_reg_iou: 0.8141 2023/09/17 10:54:16 - mmengine - INFO - Epoch(train) [1][3350/3953] lr: 8.0345e-04 eta: 6:22:22 time: 0.5057 data_time: 0.0034 memory: 8203 grad_norm: 26.3624 loss: 5.6903 task0.loss_heatmap: 0.9744 task0.loss_bbox: 3.6373 task0.loss_iou: 0.2275 task0.loss_reg_iou: 0.8511 2023/09/17 10:54:42 - mmengine - INFO - Epoch(train) [1][3400/3953] lr: 8.1637e-04 eta: 6:21:48 time: 0.5071 data_time: 0.0033 memory: 8473 grad_norm: 28.7272 loss: 5.8230 task0.loss_heatmap: 1.0161 task0.loss_bbox: 3.6917 task0.loss_iou: 0.2294 task0.loss_reg_iou: 0.8857 2023/09/17 10:55:07 - mmengine - INFO - Epoch(train) [1][3450/3953] lr: 8.2894e-04 eta: 6:21:11 time: 0.5028 data_time: 0.0032 memory: 8314 grad_norm: 30.2283 loss: 5.7137 task0.loss_heatmap: 1.0024 task0.loss_bbox: 3.6111 task0.loss_iou: 0.2264 task0.loss_reg_iou: 0.8739 2023/09/17 10:55:32 - mmengine - INFO - Epoch(train) [1][3500/3953] lr: 8.4115e-04 eta: 6:20:36 time: 0.5056 data_time: 0.0032 memory: 8600 grad_norm: 26.9506 loss: 5.5799 task0.loss_heatmap: 0.9726 task0.loss_bbox: 3.5265 task0.loss_iou: 0.2205 task0.loss_reg_iou: 0.8603 2023/09/17 10:55:58 - mmengine - INFO - Epoch(train) [1][3550/3953] lr: 8.5300e-04 eta: 6:20:13 time: 0.5246 data_time: 0.0033 memory: 8138 grad_norm: 29.2824 loss: 5.5868 task0.loss_heatmap: 0.9416 task0.loss_bbox: 3.5747 task0.loss_iou: 0.2244 task0.loss_reg_iou: 0.8461 2023/09/17 10:56:23 - mmengine - INFO - Epoch(train) [1][3600/3953] lr: 8.6446e-04 eta: 6:19:37 time: 0.5034 data_time: 0.0033 memory: 8473 grad_norm: 29.4793 loss: 5.5532 task0.loss_heatmap: 0.9430 task0.loss_bbox: 3.5561 task0.loss_iou: 0.2221 task0.loss_reg_iou: 0.8320 2023/09/17 10:56:49 - mmengine - INFO - Epoch(train) [1][3650/3953] lr: 8.7553e-04 eta: 6:19:08 time: 0.5150 data_time: 0.0034 memory: 8449 grad_norm: 26.1118 loss: 5.7269 task0.loss_heatmap: 1.0114 task0.loss_bbox: 3.6221 task0.loss_iou: 0.2204 task0.loss_reg_iou: 0.8730 2023/09/17 10:57:14 - mmengine - INFO - Epoch(train) [1][3700/3953] lr: 8.8619e-04 eta: 6:18:33 time: 0.5037 data_time: 0.0034 memory: 8709 grad_norm: 27.9108 loss: 5.4356 task0.loss_heatmap: 0.9091 task0.loss_bbox: 3.4773 task0.loss_iou: 0.2101 task0.loss_reg_iou: 0.8392 2023/09/17 10:57:40 - mmengine - INFO - Epoch(train) [1][3750/3953] lr: 8.9644e-04 eta: 6:18:03 time: 0.5138 data_time: 0.0033 memory: 8436 grad_norm: 25.2728 loss: 5.3424 task0.loss_heatmap: 0.8875 task0.loss_bbox: 3.3959 task0.loss_iou: 0.2221 task0.loss_reg_iou: 0.8369 2023/09/17 10:58:06 - mmengine - INFO - Epoch(train) [1][3800/3953] lr: 9.0626e-04 eta: 6:17:33 time: 0.5112 data_time: 0.0034 memory: 8363 grad_norm: 25.9303 loss: 5.4202 task0.loss_heatmap: 0.9226 task0.loss_bbox: 3.4547 task0.loss_iou: 0.2094 task0.loss_reg_iou: 0.8335 2023/09/17 10:58:31 - mmengine - INFO - Epoch(train) [1][3850/3953] lr: 9.1563e-04 eta: 6:17:03 time: 0.5128 data_time: 0.0034 memory: 8382 grad_norm: 27.9852 loss: 5.3136 task0.loss_heatmap: 0.8979 task0.loss_bbox: 3.3926 task0.loss_iou: 0.2060 task0.loss_reg_iou: 0.8171 2023/09/17 10:58:57 - mmengine - INFO - Epoch(train) [1][3900/3953] lr: 9.2456e-04 eta: 6:16:36 time: 0.5173 data_time: 0.0034 memory: 8727 grad_norm: 25.7036 loss: 5.4588 task0.loss_heatmap: 0.9078 task0.loss_bbox: 3.4891 task0.loss_iou: 0.2186 task0.loss_reg_iou: 0.8434 2023/09/17 10:59:22 - mmengine - INFO - Epoch(train) [1][3950/3953] lr: 9.3303e-04 eta: 6:15:59 time: 0.4978 data_time: 0.0038 memory: 8180 grad_norm: 27.8411 loss: 5.4139 task0.loss_heatmap: 0.8517 task0.loss_bbox: 3.4697 task0.loss_iou: 0.2249 task0.loss_reg_iou: 0.8676 2023/09/17 10:59:24 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 10:59:24 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/09/17 10:59:46 - mmengine - INFO - Epoch(val) [1][ 50/1250] eta: 0:07:58 time: 0.3986 data_time: 0.0251 memory: 8767 2023/09/17 11:00:05 - mmengine - INFO - Epoch(val) [1][ 100/1250] eta: 0:07:30 time: 0.3845 data_time: 0.0047 memory: 4043 2023/09/17 11:00:24 - mmengine - INFO - Epoch(val) [1][ 150/1250] eta: 0:07:08 time: 0.3855 data_time: 0.0049 memory: 4062 2023/09/17 11:00:43 - mmengine - INFO - Epoch(val) [1][ 200/1250] eta: 0:06:46 time: 0.3787 data_time: 0.0049 memory: 4051 2023/09/17 11:01:01 - mmengine - INFO - Epoch(val) [1][ 250/1250] eta: 0:06:20 time: 0.3557 data_time: 0.0045 memory: 4033 2023/09/17 11:01:20 - mmengine - INFO - Epoch(val) [1][ 300/1250] eta: 0:06:01 time: 0.3824 data_time: 0.0045 memory: 4050 2023/09/17 11:01:39 - mmengine - INFO - Epoch(val) [1][ 350/1250] eta: 0:05:42 time: 0.3803 data_time: 0.0045 memory: 4057 2023/09/17 11:01:59 - mmengine - INFO - Epoch(val) [1][ 400/1250] eta: 0:05:24 time: 0.3861 data_time: 0.0046 memory: 4060 2023/09/17 11:02:19 - mmengine - INFO - Epoch(val) [1][ 450/1250] eta: 0:05:07 time: 0.4032 data_time: 0.0042 memory: 4048 2023/09/17 11:02:37 - mmengine - INFO - Epoch(val) [1][ 500/1250] eta: 0:04:46 time: 0.3664 data_time: 0.0047 memory: 4041 2023/09/17 11:02:55 - mmengine - INFO - Epoch(val) [1][ 550/1250] eta: 0:04:26 time: 0.3669 data_time: 0.0046 memory: 4042 2023/09/17 11:03:14 - mmengine - INFO - Epoch(val) [1][ 600/1250] eta: 0:04:06 time: 0.3687 data_time: 0.0052 memory: 4057 2023/09/17 11:03:34 - mmengine - INFO - Epoch(val) [1][ 650/1250] eta: 0:03:48 time: 0.4045 data_time: 0.0046 memory: 4065 2023/09/17 11:03:54 - mmengine - INFO - Epoch(val) [1][ 700/1250] eta: 0:03:30 time: 0.3917 data_time: 0.0048 memory: 4042 2023/09/17 11:04:13 - mmengine - INFO - Epoch(val) [1][ 750/1250] eta: 0:03:11 time: 0.3961 data_time: 0.0046 memory: 4051 2023/09/17 11:04:32 - mmengine - INFO - Epoch(val) [1][ 800/1250] eta: 0:02:52 time: 0.3705 data_time: 0.0046 memory: 4055 2023/09/17 11:04:51 - mmengine - INFO - Epoch(val) [1][ 850/1250] eta: 0:02:33 time: 0.3856 data_time: 0.0049 memory: 4058 2023/09/17 11:05:09 - mmengine - INFO - Epoch(val) [1][ 900/1250] eta: 0:02:13 time: 0.3580 data_time: 0.0047 memory: 4049 2023/09/17 11:05:27 - mmengine - INFO - Epoch(val) [1][ 950/1250] eta: 0:01:54 time: 0.3667 data_time: 0.0049 memory: 4054 2023/09/17 11:05:46 - mmengine - INFO - Epoch(val) [1][1000/1250] eta: 0:01:35 time: 0.3774 data_time: 0.0047 memory: 4056 2023/09/17 11:06:05 - mmengine - INFO - Epoch(val) [1][1050/1250] eta: 0:01:16 time: 0.3771 data_time: 0.0046 memory: 4043 2023/09/17 11:06:23 - mmengine - INFO - Epoch(val) [1][1100/1250] eta: 0:00:56 time: 0.3552 data_time: 0.0047 memory: 4032 2023/09/17 11:06:42 - mmengine - INFO - Epoch(val) [1][1150/1250] eta: 0:00:37 time: 0.3814 data_time: 0.0045 memory: 4041 2023/09/17 11:07:01 - mmengine - INFO - Epoch(val) [1][1200/1250] eta: 0:00:18 time: 0.3796 data_time: 0.0046 memory: 4047 2023/09/17 11:07:20 - mmengine - INFO - Epoch(val) [1][1250/1250] eta: 0:00:00 time: 0.3883 data_time: 0.0048 memory: 4055 2023/09/17 11:07:28 - mmengine - INFO - Start converting ... 2023/09/17 11:13:37 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.305111] [mAPH 0.299407] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.261783] [mAPH 0.256881] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.454385] [mAPH 0.304014] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.386795] [mAPH 0.258622] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.332695] [mAPH 0.293812] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.31993] [mAPH 0.282539] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.583114] [mAPH 0.572732] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.571399] [mAPH 0.56122] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.197647] [mAPH 0.19369] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.174371] [mAPH 0.170872] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.0547903] [mAPH 0.0532048] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.0396829] [mAPH 0.0385318] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.540165] [mAPH 0.370521] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.494161] [mAPH 0.338764] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.433369] [mAPH 0.283266] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.376954] [mAPH 0.246327] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.304633] [mAPH 0.190383] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.22361] [mAPH 0.139712] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.452933] [mAPH 0.404] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.449685] [mAPH 0.401103] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.294222] [mAPH 0.263842] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.277056] [mAPH 0.248448] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.138311] [mAPH 0.10784] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.128615] [mAPH 0.10028] Eval Using 229s 2023/09/17 11:13:38 - mmengine - INFO - Epoch(val) [1][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.3051 Waymo metric/Vehicle/L1 mAPH: 0.2994 Waymo metric/Vehicle/L2 mAP: 0.2618 Waymo metric/Vehicle/L2 mAPH: 0.2569 Waymo metric/Pedestrian/L1 mAP: 0.4544 Waymo metric/Pedestrian/L1 mAPH: 0.3040 Waymo metric/Pedestrian/L2 mAP: 0.3868 Waymo metric/Pedestrian/L2 mAPH: 0.2586 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.3327 Waymo metric/Cyclist/L1 mAPH: 0.2938 Waymo metric/Cyclist/L2 mAP: 0.3199 Waymo metric/Cyclist/L2 mAPH: 0.2825 Waymo metric/Overall/L1 mAP: 0.3641 Waymo metric/Overall/L1 mAPH: 0.2991 Waymo metric/Overall/L2 mAP: 0.3228 Waymo metric/Overall/L2 mAPH: 0.2660 data_time: 0.0055 time: 0.3796 2023/09/17 11:14:03 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 11:14:04 - mmengine - INFO - Epoch(train) [2][ 50/3953] lr: 9.4149e-04 eta: 6:15:32 time: 0.5214 data_time: 0.0050 memory: 7927 grad_norm: 29.5544 loss: 5.4077 task0.loss_heatmap: 0.9067 task0.loss_bbox: 3.4430 task0.loss_iou: 0.2121 task0.loss_reg_iou: 0.8460 2023/09/17 11:14:37 - mmengine - INFO - Epoch(train) [2][ 100/3953] lr: 9.4898e-04 eta: 6:16:18 time: 0.6535 data_time: 0.0042 memory: 8305 grad_norm: 27.0615 loss: 5.4191 task0.loss_heatmap: 0.9447 task0.loss_bbox: 3.4196 task0.loss_iou: 0.2165 task0.loss_reg_iou: 0.8382 2023/09/17 11:15:03 - mmengine - INFO - Epoch(train) [2][ 150/3953] lr: 9.5598e-04 eta: 6:15:55 time: 0.5258 data_time: 0.0040 memory: 8097 grad_norm: 24.5735 loss: 5.3323 task0.loss_heatmap: 0.8924 task0.loss_bbox: 3.3957 task0.loss_iou: 0.2150 task0.loss_reg_iou: 0.8293 2023/09/17 11:15:29 - mmengine - INFO - Epoch(train) [2][ 200/3953] lr: 9.6249e-04 eta: 6:15:29 time: 0.5220 data_time: 0.0041 memory: 8616 grad_norm: 23.2102 loss: 5.7310 task0.loss_heatmap: 0.9697 task0.loss_bbox: 3.6314 task0.loss_iou: 0.2287 task0.loss_reg_iou: 0.9012 2023/09/17 11:15:55 - mmengine - INFO - Epoch(train) [2][ 250/3953] lr: 9.6850e-04 eta: 6:15:00 time: 0.5137 data_time: 0.0041 memory: 8509 grad_norm: 24.9293 loss: 5.2234 task0.loss_heatmap: 0.8676 task0.loss_bbox: 3.3197 task0.loss_iou: 0.2132 task0.loss_reg_iou: 0.8228 2023/09/17 11:16:21 - mmengine - INFO - Epoch(train) [2][ 300/3953] lr: 9.7400e-04 eta: 6:14:30 time: 0.5126 data_time: 0.0042 memory: 8481 grad_norm: 27.8395 loss: 5.3387 task0.loss_heatmap: 0.8929 task0.loss_bbox: 3.4196 task0.loss_iou: 0.1994 task0.loss_reg_iou: 0.8267 2023/09/17 11:16:46 - mmengine - INFO - Epoch(train) [2][ 350/3953] lr: 9.7898e-04 eta: 6:13:59 time: 0.5099 data_time: 0.0041 memory: 8039 grad_norm: 25.7642 loss: 5.2578 task0.loss_heatmap: 0.8972 task0.loss_bbox: 3.3116 task0.loss_iou: 0.2073 task0.loss_reg_iou: 0.8417 2023/09/17 11:17:12 - mmengine - INFO - Epoch(train) [2][ 400/3953] lr: 9.8345e-04 eta: 6:13:29 time: 0.5125 data_time: 0.0039 memory: 8515 grad_norm: 23.2283 loss: 5.3746 task0.loss_heatmap: 0.8612 task0.loss_bbox: 3.4556 task0.loss_iou: 0.2173 task0.loss_reg_iou: 0.8406 2023/09/17 11:17:37 - mmengine - INFO - Epoch(train) [2][ 450/3953] lr: 9.8739e-04 eta: 6:12:58 time: 0.5106 data_time: 0.0038 memory: 8111 grad_norm: 24.0048 loss: 5.4153 task0.loss_heatmap: 0.9100 task0.loss_bbox: 3.4469 task0.loss_iou: 0.2138 task0.loss_reg_iou: 0.8445 2023/09/17 11:18:03 - mmengine - INFO - Epoch(train) [2][ 500/3953] lr: 9.9080e-04 eta: 6:12:29 time: 0.5131 data_time: 0.0039 memory: 8556 grad_norm: 23.9043 loss: 5.1582 task0.loss_heatmap: 0.8519 task0.loss_bbox: 3.2940 task0.loss_iou: 0.2100 task0.loss_reg_iou: 0.8022 2023/09/17 11:18:29 - mmengine - INFO - Epoch(train) [2][ 550/3953] lr: 9.9368e-04 eta: 6:12:01 time: 0.5159 data_time: 0.0040 memory: 9107 grad_norm: 24.9956 loss: 5.2532 task0.loss_heatmap: 0.8808 task0.loss_bbox: 3.3462 task0.loss_iou: 0.2045 task0.loss_reg_iou: 0.8218 2023/09/17 11:18:54 - mmengine - INFO - Epoch(train) [2][ 600/3953] lr: 9.9602e-04 eta: 6:11:31 time: 0.5127 data_time: 0.0040 memory: 8393 grad_norm: 23.0309 loss: 5.2088 task0.loss_heatmap: 0.8407 task0.loss_bbox: 3.3098 task0.loss_iou: 0.2207 task0.loss_reg_iou: 0.8377 2023/09/17 11:19:20 - mmengine - INFO - Epoch(train) [2][ 650/3953] lr: 9.9782e-04 eta: 6:10:58 time: 0.5040 data_time: 0.0041 memory: 8403 grad_norm: 24.8313 loss: 5.1546 task0.loss_heatmap: 0.8173 task0.loss_bbox: 3.2882 task0.loss_iou: 0.2178 task0.loss_reg_iou: 0.8314 2023/09/17 11:19:45 - mmengine - INFO - Epoch(train) [2][ 700/3953] lr: 9.9909e-04 eta: 6:10:30 time: 0.5149 data_time: 0.0039 memory: 8717 grad_norm: 25.4258 loss: 5.1092 task0.loss_heatmap: 0.8289 task0.loss_bbox: 3.2516 task0.loss_iou: 0.2123 task0.loss_reg_iou: 0.8164 2023/09/17 11:20:11 - mmengine - INFO - Epoch(train) [2][ 750/3953] lr: 9.9981e-04 eta: 6:10:02 time: 0.5156 data_time: 0.0039 memory: 8615 grad_norm: 23.9502 loss: 5.1434 task0.loss_heatmap: 0.8419 task0.loss_bbox: 3.2663 task0.loss_iou: 0.2186 task0.loss_reg_iou: 0.8166 2023/09/17 11:20:37 - mmengine - INFO - Epoch(train) [2][ 800/3953] lr: 1.0000e-03 eta: 6:09:37 time: 0.5227 data_time: 0.0039 memory: 8759 grad_norm: 26.8036 loss: 5.1329 task0.loss_heatmap: 0.8151 task0.loss_bbox: 3.2947 task0.loss_iou: 0.2065 task0.loss_reg_iou: 0.8167 2023/09/17 11:21:03 - mmengine - INFO - Epoch(train) [2][ 850/3953] lr: 1.0000e-03 eta: 6:09:07 time: 0.5083 data_time: 0.0038 memory: 8243 grad_norm: 25.9016 loss: 5.1449 task0.loss_heatmap: 0.8388 task0.loss_bbox: 3.2851 task0.loss_iou: 0.2107 task0.loss_reg_iou: 0.8103 2023/09/17 11:21:28 - mmengine - INFO - Epoch(train) [2][ 900/3953] lr: 9.9998e-04 eta: 6:08:33 time: 0.5030 data_time: 0.0042 memory: 8046 grad_norm: 26.5300 loss: 5.2138 task0.loss_heatmap: 0.8608 task0.loss_bbox: 3.2964 task0.loss_iou: 0.2199 task0.loss_reg_iou: 0.8368 2023/09/17 11:21:54 - mmengine - INFO - Epoch(train) [2][ 950/3953] lr: 9.9997e-04 eta: 6:08:06 time: 0.5171 data_time: 0.0039 memory: 8519 grad_norm: 21.2792 loss: 5.2677 task0.loss_heatmap: 0.8991 task0.loss_bbox: 3.3293 task0.loss_iou: 0.2094 task0.loss_reg_iou: 0.8299 2023/09/17 11:22:19 - mmengine - INFO - Epoch(train) [2][1000/3953] lr: 9.9994e-04 eta: 6:07:37 time: 0.5118 data_time: 0.0038 memory: 8091 grad_norm: 22.7993 loss: 5.1391 task0.loss_heatmap: 0.8756 task0.loss_bbox: 3.2668 task0.loss_iou: 0.2045 task0.loss_reg_iou: 0.7922 2023/09/17 11:22:43 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 11:22:45 - mmengine - INFO - Epoch(train) [2][1050/3953] lr: 9.9991e-04 eta: 6:07:07 time: 0.5104 data_time: 0.0039 memory: 8097 grad_norm: 20.9741 loss: 4.9849 task0.loss_heatmap: 0.7998 task0.loss_bbox: 3.1833 task0.loss_iou: 0.2045 task0.loss_reg_iou: 0.7974 2023/09/17 11:23:11 - mmengine - INFO - Epoch(train) [2][1100/3953] lr: 9.9987e-04 eta: 6:06:40 time: 0.5143 data_time: 0.0038 memory: 8364 grad_norm: 23.6839 loss: 5.0440 task0.loss_heatmap: 0.8155 task0.loss_bbox: 3.2268 task0.loss_iou: 0.2055 task0.loss_reg_iou: 0.7961 2023/09/17 11:23:36 - mmengine - INFO - Epoch(train) [2][1150/3953] lr: 9.9983e-04 eta: 6:06:08 time: 0.5045 data_time: 0.0038 memory: 8436 grad_norm: 22.3484 loss: 5.0463 task0.loss_heatmap: 0.7764 task0.loss_bbox: 3.2337 task0.loss_iou: 0.2091 task0.loss_reg_iou: 0.8271 2023/09/17 11:24:01 - mmengine - INFO - Epoch(train) [2][1200/3953] lr: 9.9977e-04 eta: 6:05:34 time: 0.5016 data_time: 0.0040 memory: 8320 grad_norm: 22.4833 loss: 4.8962 task0.loss_heatmap: 0.7879 task0.loss_bbox: 3.0993 task0.loss_iou: 0.2061 task0.loss_reg_iou: 0.8030 2023/09/17 11:24:26 - mmengine - INFO - Epoch(train) [2][1250/3953] lr: 9.9971e-04 eta: 6:05:04 time: 0.5065 data_time: 0.0038 memory: 8551 grad_norm: 22.7920 loss: 4.8662 task0.loss_heatmap: 0.8088 task0.loss_bbox: 3.0846 task0.loss_iou: 0.1952 task0.loss_reg_iou: 0.7775 2023/09/17 11:24:52 - mmengine - INFO - Epoch(train) [2][1300/3953] lr: 9.9965e-04 eta: 6:04:33 time: 0.5078 data_time: 0.0039 memory: 7922 grad_norm: 21.0723 loss: 5.1268 task0.loss_heatmap: 0.8374 task0.loss_bbox: 3.2543 task0.loss_iou: 0.2057 task0.loss_reg_iou: 0.8294 2023/09/17 11:25:17 - mmengine - INFO - Epoch(train) [2][1350/3953] lr: 9.9958e-04 eta: 6:04:02 time: 0.5044 data_time: 0.0042 memory: 8585 grad_norm: 22.5434 loss: 4.7872 task0.loss_heatmap: 0.7457 task0.loss_bbox: 3.0441 task0.loss_iou: 0.2025 task0.loss_reg_iou: 0.7949 2023/09/17 11:25:42 - mmengine - INFO - Epoch(train) [2][1400/3953] lr: 9.9950e-04 eta: 6:03:32 time: 0.5079 data_time: 0.0038 memory: 8477 grad_norm: 20.5991 loss: 4.9291 task0.loss_heatmap: 0.7816 task0.loss_bbox: 3.1417 task0.loss_iou: 0.2025 task0.loss_reg_iou: 0.8033 2023/09/17 11:26:08 - mmengine - INFO - Epoch(train) [2][1450/3953] lr: 9.9941e-04 eta: 6:03:05 time: 0.5175 data_time: 0.0037 memory: 8289 grad_norm: 23.2451 loss: 4.9958 task0.loss_heatmap: 0.7865 task0.loss_bbox: 3.1696 task0.loss_iou: 0.2147 task0.loss_reg_iou: 0.8250 2023/09/17 11:26:34 - mmengine - INFO - Epoch(train) [2][1500/3953] lr: 9.9932e-04 eta: 6:02:42 time: 0.5253 data_time: 0.0038 memory: 8402 grad_norm: 20.6008 loss: 4.6913 task0.loss_heatmap: 0.7503 task0.loss_bbox: 2.9986 task0.loss_iou: 0.1927 task0.loss_reg_iou: 0.7497 2023/09/17 11:27:01 - mmengine - INFO - Epoch(train) [2][1550/3953] lr: 9.9922e-04 eta: 6:02:22 time: 0.5343 data_time: 0.0036 memory: 8022 grad_norm: 21.6879 loss: 4.8496 task0.loss_heatmap: 0.8052 task0.loss_bbox: 3.0798 task0.loss_iou: 0.1976 task0.loss_reg_iou: 0.7670 2023/09/17 11:27:31 - mmengine - INFO - Epoch(train) [2][1600/3953] lr: 9.9911e-04 eta: 6:02:28 time: 0.6011 data_time: 0.0036 memory: 8614 grad_norm: 20.6047 loss: 4.8266 task0.loss_heatmap: 0.7864 task0.loss_bbox: 3.0611 task0.loss_iou: 0.2030 task0.loss_reg_iou: 0.7761 2023/09/17 11:27:57 - mmengine - INFO - Epoch(train) [2][1650/3953] lr: 9.9900e-04 eta: 6:01:59 time: 0.5117 data_time: 0.0037 memory: 8635 grad_norm: 20.1632 loss: 4.9970 task0.loss_heatmap: 0.8089 task0.loss_bbox: 3.1723 task0.loss_iou: 0.2088 task0.loss_reg_iou: 0.8069 2023/09/17 11:28:22 - mmengine - INFO - Epoch(train) [2][1700/3953] lr: 9.9888e-04 eta: 6:01:31 time: 0.5154 data_time: 0.0036 memory: 8720 grad_norm: 21.1768 loss: 5.0802 task0.loss_heatmap: 0.8147 task0.loss_bbox: 3.2632 task0.loss_iou: 0.1972 task0.loss_reg_iou: 0.8050 2023/09/17 11:28:48 - mmengine - INFO - Epoch(train) [2][1750/3953] lr: 9.9876e-04 eta: 6:01:02 time: 0.5106 data_time: 0.0040 memory: 8157 grad_norm: 22.3697 loss: 4.7909 task0.loss_heatmap: 0.7345 task0.loss_bbox: 3.0867 task0.loss_iou: 0.1977 task0.loss_reg_iou: 0.7720 2023/09/17 11:29:13 - mmengine - INFO - Epoch(train) [2][1800/3953] lr: 9.9862e-04 eta: 6:00:31 time: 0.5047 data_time: 0.0039 memory: 8522 grad_norm: 20.8721 loss: 4.8048 task0.loss_heatmap: 0.7596 task0.loss_bbox: 3.0527 task0.loss_iou: 0.1998 task0.loss_reg_iou: 0.7926 2023/09/17 11:29:39 - mmengine - INFO - Epoch(train) [2][1850/3953] lr: 9.9848e-04 eta: 6:00:02 time: 0.5106 data_time: 0.0039 memory: 8370 grad_norm: 19.6541 loss: 4.8024 task0.loss_heatmap: 0.7753 task0.loss_bbox: 3.0432 task0.loss_iou: 0.2012 task0.loss_reg_iou: 0.7828 2023/09/17 11:30:04 - mmengine - INFO - Epoch(train) [2][1900/3953] lr: 9.9834e-04 eta: 5:59:35 time: 0.5147 data_time: 0.0037 memory: 8206 grad_norm: 20.0113 loss: 4.8070 task0.loss_heatmap: 0.7819 task0.loss_bbox: 3.0400 task0.loss_iou: 0.2061 task0.loss_reg_iou: 0.7790 2023/09/17 11:30:30 - mmengine - INFO - Epoch(train) [2][1950/3953] lr: 9.9818e-04 eta: 5:59:06 time: 0.5104 data_time: 0.0037 memory: 8596 grad_norm: 21.2076 loss: 4.9217 task0.loss_heatmap: 0.7753 task0.loss_bbox: 3.1401 task0.loss_iou: 0.2034 task0.loss_reg_iou: 0.8029 2023/09/17 11:30:55 - mmengine - INFO - Epoch(train) [2][2000/3953] lr: 9.9802e-04 eta: 5:58:36 time: 0.5073 data_time: 0.0037 memory: 8563 grad_norm: 19.5433 loss: 4.9432 task0.loss_heatmap: 0.8012 task0.loss_bbox: 3.1463 task0.loss_iou: 0.2012 task0.loss_reg_iou: 0.7944 2023/09/17 11:31:20 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 11:31:21 - mmengine - INFO - Epoch(train) [2][2050/3953] lr: 9.9786e-04 eta: 5:58:09 time: 0.5149 data_time: 0.0040 memory: 7982 grad_norm: 20.8150 loss: 4.8176 task0.loss_heatmap: 0.7702 task0.loss_bbox: 3.0763 task0.loss_iou: 0.1943 task0.loss_reg_iou: 0.7768 2023/09/17 11:31:47 - mmengine - INFO - Epoch(train) [2][2100/3953] lr: 9.9768e-04 eta: 5:57:40 time: 0.5099 data_time: 0.0036 memory: 8644 grad_norm: 20.6349 loss: 4.8859 task0.loss_heatmap: 0.7723 task0.loss_bbox: 3.0981 task0.loss_iou: 0.2126 task0.loss_reg_iou: 0.8028 2023/09/17 11:32:12 - mmengine - INFO - Epoch(train) [2][2150/3953] lr: 9.9750e-04 eta: 5:57:11 time: 0.5105 data_time: 0.0038 memory: 8684 grad_norm: 19.7295 loss: 4.9010 task0.loss_heatmap: 0.7852 task0.loss_bbox: 3.1031 task0.loss_iou: 0.2175 task0.loss_reg_iou: 0.7952 2023/09/17 11:32:38 - mmengine - INFO - Epoch(train) [2][2200/3953] lr: 9.9731e-04 eta: 5:56:45 time: 0.5181 data_time: 0.0037 memory: 8768 grad_norm: 19.0590 loss: 4.8240 task0.loss_heatmap: 0.7608 task0.loss_bbox: 3.0581 task0.loss_iou: 0.1988 task0.loss_reg_iou: 0.8064 2023/09/17 11:33:04 - mmengine - INFO - Epoch(train) [2][2250/3953] lr: 9.9712e-04 eta: 5:56:16 time: 0.5098 data_time: 0.0036 memory: 8140 grad_norm: 19.3219 loss: 4.7504 task0.loss_heatmap: 0.7728 task0.loss_bbox: 3.0259 task0.loss_iou: 0.1939 task0.loss_reg_iou: 0.7577 2023/09/17 11:33:29 - mmengine - INFO - Epoch(train) [2][2300/3953] lr: 9.9692e-04 eta: 5:55:45 time: 0.5021 data_time: 0.0036 memory: 8526 grad_norm: 20.2009 loss: 4.6741 task0.loss_heatmap: 0.7566 task0.loss_bbox: 2.9722 task0.loss_iou: 0.1933 task0.loss_reg_iou: 0.7521 2023/09/17 11:33:54 - mmengine - INFO - Epoch(train) [2][2350/3953] lr: 9.9671e-04 eta: 5:55:17 time: 0.5110 data_time: 0.0040 memory: 8785 grad_norm: 18.7111 loss: 4.7668 task0.loss_heatmap: 0.7645 task0.loss_bbox: 3.0335 task0.loss_iou: 0.1906 task0.loss_reg_iou: 0.7782 2023/09/17 11:34:20 - mmengine - INFO - Epoch(train) [2][2400/3953] lr: 9.9650e-04 eta: 5:54:48 time: 0.5088 data_time: 0.0037 memory: 8511 grad_norm: 19.2472 loss: 4.6055 task0.loss_heatmap: 0.7334 task0.loss_bbox: 2.9199 task0.loss_iou: 0.1897 task0.loss_reg_iou: 0.7624 2023/09/17 11:34:45 - mmengine - INFO - Epoch(train) [2][2450/3953] lr: 9.9628e-04 eta: 5:54:18 time: 0.5058 data_time: 0.0036 memory: 8190 grad_norm: 19.2750 loss: 4.6345 task0.loss_heatmap: 0.7824 task0.loss_bbox: 2.9212 task0.loss_iou: 0.1851 task0.loss_reg_iou: 0.7458 2023/09/17 11:35:11 - mmengine - INFO - Epoch(train) [2][2500/3953] lr: 9.9605e-04 eta: 5:53:54 time: 0.5234 data_time: 0.0036 memory: 8778 grad_norm: 20.5608 loss: 4.6452 task0.loss_heatmap: 0.7199 task0.loss_bbox: 2.9763 task0.loss_iou: 0.1855 task0.loss_reg_iou: 0.7636 2023/09/17 11:35:37 - mmengine - INFO - Epoch(train) [2][2550/3953] lr: 9.9582e-04 eta: 5:53:27 time: 0.5167 data_time: 0.0036 memory: 8193 grad_norm: 19.3009 loss: 4.6031 task0.loss_heatmap: 0.7275 task0.loss_bbox: 2.9188 task0.loss_iou: 0.1949 task0.loss_reg_iou: 0.7619 2023/09/17 11:36:02 - mmengine - INFO - Epoch(train) [2][2600/3953] lr: 9.9558e-04 eta: 5:52:58 time: 0.5086 data_time: 0.0035 memory: 8831 grad_norm: 18.5614 loss: 4.6998 task0.loss_heatmap: 0.7514 task0.loss_bbox: 2.9834 task0.loss_iou: 0.1959 task0.loss_reg_iou: 0.7691 2023/09/17 11:36:28 - mmengine - INFO - Epoch(train) [2][2650/3953] lr: 9.9533e-04 eta: 5:52:30 time: 0.5100 data_time: 0.0036 memory: 8550 grad_norm: 18.9556 loss: 4.7163 task0.loss_heatmap: 0.7790 task0.loss_bbox: 2.9426 task0.loss_iou: 0.2115 task0.loss_reg_iou: 0.7832 2023/09/17 11:36:54 - mmengine - INFO - Epoch(train) [2][2700/3953] lr: 9.9507e-04 eta: 5:52:04 time: 0.5168 data_time: 0.0035 memory: 8165 grad_norm: 18.1304 loss: 4.5530 task0.loss_heatmap: 0.6889 task0.loss_bbox: 2.9055 task0.loss_iou: 0.1974 task0.loss_reg_iou: 0.7612 2023/09/17 11:37:19 - mmengine - INFO - Epoch(train) [2][2750/3953] lr: 9.9481e-04 eta: 5:51:34 time: 0.5072 data_time: 0.0037 memory: 8461 grad_norm: 18.9269 loss: 4.6896 task0.loss_heatmap: 0.7510 task0.loss_bbox: 2.9691 task0.loss_iou: 0.1988 task0.loss_reg_iou: 0.7708 2023/09/17 11:37:44 - mmengine - INFO - Epoch(train) [2][2800/3953] lr: 9.9455e-04 eta: 5:51:05 time: 0.5051 data_time: 0.0038 memory: 8223 grad_norm: 19.1555 loss: 4.5310 task0.loss_heatmap: 0.7037 task0.loss_bbox: 2.8885 task0.loss_iou: 0.1895 task0.loss_reg_iou: 0.7493 2023/09/17 11:38:10 - mmengine - INFO - Epoch(train) [2][2850/3953] lr: 9.9427e-04 eta: 5:50:35 time: 0.5057 data_time: 0.0036 memory: 8543 grad_norm: 19.6398 loss: 4.7961 task0.loss_heatmap: 0.7899 task0.loss_bbox: 3.0203 task0.loss_iou: 0.2026 task0.loss_reg_iou: 0.7832 2023/09/17 11:38:35 - mmengine - INFO - Epoch(train) [2][2900/3953] lr: 9.9399e-04 eta: 5:50:07 time: 0.5085 data_time: 0.0037 memory: 8492 grad_norm: 19.3391 loss: 4.6095 task0.loss_heatmap: 0.7604 task0.loss_bbox: 2.9239 task0.loss_iou: 0.1902 task0.loss_reg_iou: 0.7351 2023/09/17 11:39:01 - mmengine - INFO - Epoch(train) [2][2950/3953] lr: 9.9370e-04 eta: 5:49:41 time: 0.5171 data_time: 0.0037 memory: 8056 grad_norm: 18.2368 loss: 4.7157 task0.loss_heatmap: 0.7847 task0.loss_bbox: 2.9918 task0.loss_iou: 0.1862 task0.loss_reg_iou: 0.7531 2023/09/17 11:39:26 - mmengine - INFO - Epoch(train) [2][3000/3953] lr: 9.9341e-04 eta: 5:49:13 time: 0.5109 data_time: 0.0035 memory: 8661 grad_norm: 20.3287 loss: 4.6747 task0.loss_heatmap: 0.7118 task0.loss_bbox: 2.9915 task0.loss_iou: 0.1969 task0.loss_reg_iou: 0.7744 2023/09/17 11:39:50 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 11:39:52 - mmengine - INFO - Epoch(train) [2][3050/3953] lr: 9.9311e-04 eta: 5:48:43 time: 0.5043 data_time: 0.0035 memory: 8233 grad_norm: 19.1106 loss: 4.6448 task0.loss_heatmap: 0.7110 task0.loss_bbox: 2.9450 task0.loss_iou: 0.2069 task0.loss_reg_iou: 0.7819 2023/09/17 11:40:17 - mmengine - INFO - Epoch(train) [2][3100/3953] lr: 9.9280e-04 eta: 5:48:13 time: 0.5041 data_time: 0.0036 memory: 8591 grad_norm: 19.0734 loss: 4.5942 task0.loss_heatmap: 0.7322 task0.loss_bbox: 2.9123 task0.loss_iou: 0.1861 task0.loss_reg_iou: 0.7635 2023/09/17 11:40:47 - mmengine - INFO - Epoch(train) [2][3150/3953] lr: 9.9249e-04 eta: 5:48:10 time: 0.5958 data_time: 0.0036 memory: 8223 grad_norm: 19.1035 loss: 4.5730 task0.loss_heatmap: 0.7218 task0.loss_bbox: 2.9031 task0.loss_iou: 0.1907 task0.loss_reg_iou: 0.7573 2023/09/17 11:41:13 - mmengine - INFO - Epoch(train) [2][3200/3953] lr: 9.9216e-04 eta: 5:47:47 time: 0.5279 data_time: 0.0035 memory: 8124 grad_norm: 18.8807 loss: 4.6350 task0.loss_heatmap: 0.7335 task0.loss_bbox: 2.9382 task0.loss_iou: 0.1935 task0.loss_reg_iou: 0.7697 2023/09/17 11:41:38 - mmengine - INFO - Epoch(train) [2][3250/3953] lr: 9.9184e-04 eta: 5:47:17 time: 0.5049 data_time: 0.0036 memory: 8814 grad_norm: 18.7776 loss: 4.6510 task0.loss_heatmap: 0.7197 task0.loss_bbox: 2.9669 task0.loss_iou: 0.1929 task0.loss_reg_iou: 0.7715 2023/09/17 11:42:04 - mmengine - INFO - Epoch(train) [2][3300/3953] lr: 9.9150e-04 eta: 5:46:50 time: 0.5135 data_time: 0.0038 memory: 8363 grad_norm: 20.4983 loss: 4.3478 task0.loss_heatmap: 0.6517 task0.loss_bbox: 2.7954 task0.loss_iou: 0.1846 task0.loss_reg_iou: 0.7160 2023/09/17 11:42:30 - mmengine - INFO - Epoch(train) [2][3350/3953] lr: 9.9116e-04 eta: 5:46:23 time: 0.5138 data_time: 0.0037 memory: 7932 grad_norm: 18.2059 loss: 4.5432 task0.loss_heatmap: 0.7087 task0.loss_bbox: 2.9131 task0.loss_iou: 0.1830 task0.loss_reg_iou: 0.7383 2023/09/17 11:42:56 - mmengine - INFO - Epoch(train) [2][3400/3953] lr: 9.9081e-04 eta: 5:45:57 time: 0.5173 data_time: 0.0036 memory: 8505 grad_norm: 19.6813 loss: 4.3474 task0.loss_heatmap: 0.6729 task0.loss_bbox: 2.7790 task0.loss_iou: 0.1854 task0.loss_reg_iou: 0.7102 2023/09/17 11:43:21 - mmengine - INFO - Epoch(train) [2][3450/3953] lr: 9.9046e-04 eta: 5:45:28 time: 0.5085 data_time: 0.0035 memory: 8514 grad_norm: 19.6765 loss: 4.5772 task0.loss_heatmap: 0.7454 task0.loss_bbox: 2.8849 task0.loss_iou: 0.1976 task0.loss_reg_iou: 0.7493 2023/09/17 11:43:47 - mmengine - INFO - Epoch(train) [2][3500/3953] lr: 9.9010e-04 eta: 5:45:02 time: 0.5144 data_time: 0.0035 memory: 8374 grad_norm: 18.1028 loss: 4.5238 task0.loss_heatmap: 0.7024 task0.loss_bbox: 2.8841 task0.loss_iou: 0.1928 task0.loss_reg_iou: 0.7444 2023/09/17 11:44:12 - mmengine - INFO - Epoch(train) [2][3550/3953] lr: 9.8973e-04 eta: 5:44:31 time: 0.4992 data_time: 0.0035 memory: 8536 grad_norm: 18.9179 loss: 4.5376 task0.loss_heatmap: 0.7285 task0.loss_bbox: 2.8383 task0.loss_iou: 0.2084 task0.loss_reg_iou: 0.7625 2023/09/17 11:44:37 - mmengine - INFO - Epoch(train) [2][3600/3953] lr: 9.8936e-04 eta: 5:44:03 time: 0.5090 data_time: 0.0035 memory: 8067 grad_norm: 18.4400 loss: 4.4410 task0.loss_heatmap: 0.7044 task0.loss_bbox: 2.7944 task0.loss_iou: 0.1901 task0.loss_reg_iou: 0.7521 2023/09/17 11:45:02 - mmengine - INFO - Epoch(train) [2][3650/3953] lr: 9.8898e-04 eta: 5:43:33 time: 0.5033 data_time: 0.0035 memory: 8497 grad_norm: 18.3693 loss: 4.5597 task0.loss_heatmap: 0.7332 task0.loss_bbox: 2.8870 task0.loss_iou: 0.1944 task0.loss_reg_iou: 0.7451 2023/09/17 11:45:28 - mmengine - INFO - Epoch(train) [2][3700/3953] lr: 9.8859e-04 eta: 5:43:04 time: 0.5062 data_time: 0.0036 memory: 8846 grad_norm: 18.6551 loss: 4.4636 task0.loss_heatmap: 0.6673 task0.loss_bbox: 2.8550 task0.loss_iou: 0.1890 task0.loss_reg_iou: 0.7523 2023/09/17 11:45:52 - mmengine - INFO - Epoch(train) [2][3750/3953] lr: 9.8819e-04 eta: 5:42:30 time: 0.4844 data_time: 0.0036 memory: 8379 grad_norm: 19.1031 loss: 4.4167 task0.loss_heatmap: 0.7039 task0.loss_bbox: 2.7760 task0.loss_iou: 0.1949 task0.loss_reg_iou: 0.7419 2023/09/17 11:46:17 - mmengine - INFO - Epoch(train) [2][3800/3953] lr: 9.8779e-04 eta: 5:42:01 time: 0.5069 data_time: 0.0035 memory: 8726 grad_norm: 18.6464 loss: 4.6563 task0.loss_heatmap: 0.7171 task0.loss_bbox: 2.9615 task0.loss_iou: 0.2032 task0.loss_reg_iou: 0.7745 2023/09/17 11:46:42 - mmengine - INFO - Epoch(train) [2][3850/3953] lr: 9.8739e-04 eta: 5:41:29 time: 0.4901 data_time: 0.0035 memory: 8145 grad_norm: 17.8410 loss: 4.4429 task0.loss_heatmap: 0.7080 task0.loss_bbox: 2.8233 task0.loss_iou: 0.1822 task0.loss_reg_iou: 0.7293 2023/09/17 11:47:07 - mmengine - INFO - Epoch(train) [2][3900/3953] lr: 9.8697e-04 eta: 5:41:00 time: 0.5053 data_time: 0.0036 memory: 8367 grad_norm: 19.3313 loss: 4.4305 task0.loss_heatmap: 0.6676 task0.loss_bbox: 2.8438 task0.loss_iou: 0.1882 task0.loss_reg_iou: 0.7310 2023/09/17 11:47:32 - mmengine - INFO - Epoch(train) [2][3950/3953] lr: 9.8655e-04 eta: 5:40:28 time: 0.4934 data_time: 0.0034 memory: 8236 grad_norm: 17.8067 loss: 4.4321 task0.loss_heatmap: 0.6952 task0.loss_bbox: 2.8006 task0.loss_iou: 0.1871 task0.loss_reg_iou: 0.7492 2023/09/17 11:47:33 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 11:47:33 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/09/17 11:47:55 - mmengine - INFO - Epoch(val) [2][ 50/1250] eta: 0:07:34 time: 0.3786 data_time: 0.0060 memory: 7586 2023/09/17 11:48:14 - mmengine - INFO - Epoch(val) [2][ 100/1250] eta: 0:07:18 time: 0.3847 data_time: 0.0044 memory: 4043 2023/09/17 11:48:33 - mmengine - INFO - Epoch(val) [2][ 150/1250] eta: 0:07:00 time: 0.3848 data_time: 0.0045 memory: 4062 2023/09/17 11:48:52 - mmengine - INFO - Epoch(val) [2][ 200/1250] eta: 0:06:40 time: 0.3784 data_time: 0.0046 memory: 4051 2023/09/17 11:49:10 - mmengine - INFO - Epoch(val) [2][ 250/1250] eta: 0:06:16 time: 0.3549 data_time: 0.0043 memory: 4033 2023/09/17 11:49:29 - mmengine - INFO - Epoch(val) [2][ 300/1250] eta: 0:05:58 time: 0.3809 data_time: 0.0043 memory: 4050 2023/09/17 11:49:48 - mmengine - INFO - Epoch(val) [2][ 350/1250] eta: 0:05:39 time: 0.3817 data_time: 0.0045 memory: 4057 2023/09/17 11:50:07 - mmengine - INFO - Epoch(val) [2][ 400/1250] eta: 0:05:22 time: 0.3867 data_time: 0.0044 memory: 4060 2023/09/17 11:50:27 - mmengine - INFO - Epoch(val) [2][ 450/1250] eta: 0:05:05 time: 0.4026 data_time: 0.0042 memory: 4048 2023/09/17 11:50:46 - mmengine - INFO - Epoch(val) [2][ 500/1250] eta: 0:04:45 time: 0.3671 data_time: 0.0045 memory: 4041 2023/09/17 11:51:04 - mmengine - INFO - Epoch(val) [2][ 550/1250] eta: 0:04:25 time: 0.3676 data_time: 0.0043 memory: 4042 2023/09/17 11:51:23 - mmengine - INFO - Epoch(val) [2][ 600/1250] eta: 0:04:05 time: 0.3686 data_time: 0.0046 memory: 4057 2023/09/17 11:51:43 - mmengine - INFO - Epoch(val) [2][ 650/1250] eta: 0:03:48 time: 0.4061 data_time: 0.0045 memory: 4065 2023/09/17 11:52:03 - mmengine - INFO - Epoch(val) [2][ 700/1250] eta: 0:03:29 time: 0.3918 data_time: 0.0044 memory: 4042 2023/09/17 11:52:22 - mmengine - INFO - Epoch(val) [2][ 750/1250] eta: 0:03:11 time: 0.3980 data_time: 0.0047 memory: 4051 2023/09/17 11:52:41 - mmengine - INFO - Epoch(val) [2][ 800/1250] eta: 0:02:51 time: 0.3695 data_time: 0.0045 memory: 4055 2023/09/17 11:53:00 - mmengine - INFO - Epoch(val) [2][ 850/1250] eta: 0:02:32 time: 0.3848 data_time: 0.0045 memory: 4058 2023/09/17 11:53:18 - mmengine - INFO - Epoch(val) [2][ 900/1250] eta: 0:02:13 time: 0.3562 data_time: 0.0043 memory: 4049 2023/09/17 11:53:36 - mmengine - INFO - Epoch(val) [2][ 950/1250] eta: 0:01:53 time: 0.3652 data_time: 0.0045 memory: 4054 2023/09/17 11:53:55 - mmengine - INFO - Epoch(val) [2][1000/1250] eta: 0:01:34 time: 0.3755 data_time: 0.0045 memory: 4056 2023/09/17 11:54:14 - mmengine - INFO - Epoch(val) [2][1050/1250] eta: 0:01:15 time: 0.3769 data_time: 0.0044 memory: 4043 2023/09/17 11:54:32 - mmengine - INFO - Epoch(val) [2][1100/1250] eta: 0:00:56 time: 0.3539 data_time: 0.0043 memory: 4032 2023/09/17 11:54:51 - mmengine - INFO - Epoch(val) [2][1150/1250] eta: 0:00:37 time: 0.3822 data_time: 0.0047 memory: 4041 2023/09/17 11:55:10 - mmengine - INFO - Epoch(val) [2][1200/1250] eta: 0:00:18 time: 0.3798 data_time: 0.0044 memory: 4047 2023/09/17 11:55:29 - mmengine - INFO - Epoch(val) [2][1250/1250] eta: 0:00:00 time: 0.3876 data_time: 0.0045 memory: 4055 2023/09/17 11:55:33 - mmengine - INFO - Start converting ... 2023/09/17 12:03:46 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.131877] [mAPH 0.129791] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.113462] [mAPH 0.111656] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.506248] [mAPH 0.385672] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.436263] [mAPH 0.331774] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.430265] [mAPH 0.409875] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.414141] [mAPH 0.394507] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.165806] [mAPH 0.163817] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.162309] [mAPH 0.160358] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.155005] [mAPH 0.152339] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.13689] [mAPH 0.134523] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.0704715] [mAPH 0.0682993] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.0514203] [mAPH 0.0498249] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.601257] [mAPH 0.468806] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.55675] [mAPH 0.433977] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.484669] [mAPH 0.363216] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.427249] [mAPH 0.319764] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.319283] [mAPH 0.220877] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.236779] [mAPH 0.163532] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.564696] [mAPH 0.540062] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.560647] [mAPH 0.536189] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.40964] [mAPH 0.386394] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.3865] [mAPH 0.364558] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.179114] [mAPH 0.169016] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.166779] [mAPH 0.157371] Eval Using 331s 2023/09/17 12:03:47 - mmengine - INFO - Epoch(val) [2][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.1319 Waymo metric/Vehicle/L1 mAPH: 0.1298 Waymo metric/Vehicle/L2 mAP: 0.1135 Waymo metric/Vehicle/L2 mAPH: 0.1117 Waymo metric/Pedestrian/L1 mAP: 0.5062 Waymo metric/Pedestrian/L1 mAPH: 0.3857 Waymo metric/Pedestrian/L2 mAP: 0.4363 Waymo metric/Pedestrian/L2 mAPH: 0.3318 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.4303 Waymo metric/Cyclist/L1 mAPH: 0.4099 Waymo metric/Cyclist/L2 mAP: 0.4141 Waymo metric/Cyclist/L2 mAPH: 0.3945 Waymo metric/Overall/L1 mAP: 0.3561 Waymo metric/Overall/L1 mAPH: 0.3084 Waymo metric/Overall/L2 mAP: 0.3213 Waymo metric/Overall/L2 mAPH: 0.2793 data_time: 0.0045 time: 0.3785 2023/09/17 12:04:12 - mmengine - INFO - Epoch(train) [3][ 50/3953] lr: 9.8610e-04 eta: 5:39:57 time: 0.5026 data_time: 0.0056 memory: 8032 grad_norm: 18.6851 loss: 4.5858 task0.loss_heatmap: 0.7260 task0.loss_bbox: 2.9082 task0.loss_iou: 0.1868 task0.loss_reg_iou: 0.7648 2023/09/17 12:04:34 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:04:37 - mmengine - INFO - Epoch(train) [3][ 100/3953] lr: 9.8566e-04 eta: 5:39:29 time: 0.5075 data_time: 0.0045 memory: 8207 grad_norm: 17.3604 loss: 4.3264 task0.loss_heatmap: 0.6702 task0.loss_bbox: 2.7607 task0.loss_iou: 0.1803 task0.loss_reg_iou: 0.7153 2023/09/17 12:05:02 - mmengine - INFO - Epoch(train) [3][ 150/3953] lr: 9.8522e-04 eta: 5:38:59 time: 0.4977 data_time: 0.0048 memory: 8907 grad_norm: 18.0746 loss: 4.4423 task0.loss_heatmap: 0.6798 task0.loss_bbox: 2.8194 task0.loss_iou: 0.1969 task0.loss_reg_iou: 0.7462 2023/09/17 12:05:27 - mmengine - INFO - Epoch(train) [3][ 200/3953] lr: 9.8478e-04 eta: 5:38:27 time: 0.4929 data_time: 0.0040 memory: 8064 grad_norm: 18.9201 loss: 4.4514 task0.loss_heatmap: 0.6782 task0.loss_bbox: 2.8390 task0.loss_iou: 0.1893 task0.loss_reg_iou: 0.7447 2023/09/17 12:05:52 - mmengine - INFO - Epoch(train) [3][ 250/3953] lr: 9.8432e-04 eta: 5:37:57 time: 0.4992 data_time: 0.0039 memory: 8455 grad_norm: 16.9657 loss: 4.4917 task0.loss_heatmap: 0.7049 task0.loss_bbox: 2.8509 task0.loss_iou: 0.1959 task0.loss_reg_iou: 0.7400 2023/09/17 12:06:22 - mmengine - INFO - Epoch(train) [3][ 300/3953] lr: 9.8386e-04 eta: 5:37:52 time: 0.6034 data_time: 0.0037 memory: 8483 grad_norm: 17.9736 loss: 4.4673 task0.loss_heatmap: 0.7431 task0.loss_bbox: 2.8176 task0.loss_iou: 0.1824 task0.loss_reg_iou: 0.7242 2023/09/17 12:06:48 - mmengine - INFO - Epoch(train) [3][ 350/3953] lr: 9.8340e-04 eta: 5:37:25 time: 0.5118 data_time: 0.0037 memory: 8257 grad_norm: 18.6627 loss: 4.5272 task0.loss_heatmap: 0.7093 task0.loss_bbox: 2.8612 task0.loss_iou: 0.1933 task0.loss_reg_iou: 0.7634 2023/09/17 12:07:13 - mmengine - INFO - Epoch(train) [3][ 400/3953] lr: 9.8292e-04 eta: 5:36:55 time: 0.4987 data_time: 0.0037 memory: 8131 grad_norm: 18.5113 loss: 4.6293 task0.loss_heatmap: 0.7291 task0.loss_bbox: 2.9428 task0.loss_iou: 0.1917 task0.loss_reg_iou: 0.7657 2023/09/17 12:07:38 - mmengine - INFO - Epoch(train) [3][ 450/3953] lr: 9.8244e-04 eta: 5:36:27 time: 0.5077 data_time: 0.0037 memory: 8341 grad_norm: 18.2149 loss: 4.4832 task0.loss_heatmap: 0.7144 task0.loss_bbox: 2.8550 task0.loss_iou: 0.1825 task0.loss_reg_iou: 0.7313 2023/09/17 12:08:03 - mmengine - INFO - Epoch(train) [3][ 500/3953] lr: 9.8196e-04 eta: 5:35:56 time: 0.4948 data_time: 0.0037 memory: 8119 grad_norm: 18.6576 loss: 4.4781 task0.loss_heatmap: 0.6995 task0.loss_bbox: 2.8587 task0.loss_iou: 0.1869 task0.loss_reg_iou: 0.7329 2023/09/17 12:08:27 - mmengine - INFO - Epoch(train) [3][ 550/3953] lr: 9.8146e-04 eta: 5:35:25 time: 0.4907 data_time: 0.0036 memory: 8491 grad_norm: 17.3653 loss: 4.3878 task0.loss_heatmap: 0.6886 task0.loss_bbox: 2.7556 task0.loss_iou: 0.1930 task0.loss_reg_iou: 0.7506 2023/09/17 12:08:52 - mmengine - INFO - Epoch(train) [3][ 600/3953] lr: 9.8096e-04 eta: 5:34:53 time: 0.4927 data_time: 0.0037 memory: 8363 grad_norm: 19.7087 loss: 4.3447 task0.loss_heatmap: 0.6520 task0.loss_bbox: 2.7585 task0.loss_iou: 0.1920 task0.loss_reg_iou: 0.7423 2023/09/17 12:09:17 - mmengine - INFO - Epoch(train) [3][ 650/3953] lr: 9.8046e-04 eta: 5:34:24 time: 0.5001 data_time: 0.0036 memory: 8442 grad_norm: 19.4657 loss: 4.3032 task0.loss_heatmap: 0.6611 task0.loss_bbox: 2.7393 task0.loss_iou: 0.1866 task0.loss_reg_iou: 0.7162 2023/09/17 12:09:42 - mmengine - INFO - Epoch(train) [3][ 700/3953] lr: 9.7994e-04 eta: 5:33:54 time: 0.4970 data_time: 0.0036 memory: 8284 grad_norm: 18.4566 loss: 4.4541 task0.loss_heatmap: 0.7082 task0.loss_bbox: 2.8242 task0.loss_iou: 0.1863 task0.loss_reg_iou: 0.7354 2023/09/17 12:10:07 - mmengine - INFO - Epoch(train) [3][ 750/3953] lr: 9.7942e-04 eta: 5:33:23 time: 0.4936 data_time: 0.0036 memory: 8583 grad_norm: 18.9412 loss: 4.3083 task0.loss_heatmap: 0.6831 task0.loss_bbox: 2.7231 task0.loss_iou: 0.1890 task0.loss_reg_iou: 0.7131 2023/09/17 12:10:32 - mmengine - INFO - Epoch(train) [3][ 800/3953] lr: 9.7890e-04 eta: 5:32:54 time: 0.5013 data_time: 0.0036 memory: 8318 grad_norm: 19.2366 loss: 4.4842 task0.loss_heatmap: 0.7072 task0.loss_bbox: 2.8270 task0.loss_iou: 0.1920 task0.loss_reg_iou: 0.7579 2023/09/17 12:10:56 - mmengine - INFO - Epoch(train) [3][ 850/3953] lr: 9.7837e-04 eta: 5:32:22 time: 0.4874 data_time: 0.0036 memory: 8281 grad_norm: 17.1423 loss: 4.5870 task0.loss_heatmap: 0.7301 task0.loss_bbox: 2.9105 task0.loss_iou: 0.1876 task0.loss_reg_iou: 0.7589 2023/09/17 12:11:21 - mmengine - INFO - Epoch(train) [3][ 900/3953] lr: 9.7783e-04 eta: 5:31:55 time: 0.5092 data_time: 0.0037 memory: 8455 grad_norm: 17.5442 loss: 4.3318 task0.loss_heatmap: 0.6581 task0.loss_bbox: 2.7849 task0.loss_iou: 0.1779 task0.loss_reg_iou: 0.7110 2023/09/17 12:11:46 - mmengine - INFO - Epoch(train) [3][ 950/3953] lr: 9.7728e-04 eta: 5:31:24 time: 0.4931 data_time: 0.0037 memory: 8128 grad_norm: 18.8165 loss: 4.4373 task0.loss_heatmap: 0.7272 task0.loss_bbox: 2.8053 task0.loss_iou: 0.1865 task0.loss_reg_iou: 0.7183 2023/09/17 12:12:11 - mmengine - INFO - Epoch(train) [3][1000/3953] lr: 9.7673e-04 eta: 5:30:56 time: 0.5023 data_time: 0.0039 memory: 8398 grad_norm: 20.7539 loss: 4.4827 task0.loss_heatmap: 0.7112 task0.loss_bbox: 2.8520 task0.loss_iou: 0.1815 task0.loss_reg_iou: 0.7381 2023/09/17 12:12:36 - mmengine - INFO - Epoch(train) [3][1050/3953] lr: 9.7617e-04 eta: 5:30:24 time: 0.4878 data_time: 0.0036 memory: 8064 grad_norm: 18.6609 loss: 4.3123 task0.loss_heatmap: 0.7020 task0.loss_bbox: 2.7012 task0.loss_iou: 0.1879 task0.loss_reg_iou: 0.7213 2023/09/17 12:12:57 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:13:00 - mmengine - INFO - Epoch(train) [3][1100/3953] lr: 9.7561e-04 eta: 5:29:54 time: 0.4942 data_time: 0.0038 memory: 8151 grad_norm: 18.9080 loss: 4.3716 task0.loss_heatmap: 0.6845 task0.loss_bbox: 2.7960 task0.loss_iou: 0.1853 task0.loss_reg_iou: 0.7058 2023/09/17 12:13:25 - mmengine - INFO - Epoch(train) [3][1150/3953] lr: 9.7504e-04 eta: 5:29:24 time: 0.4952 data_time: 0.0037 memory: 8437 grad_norm: 17.4864 loss: 4.2483 task0.loss_heatmap: 0.6397 task0.loss_bbox: 2.7068 task0.loss_iou: 0.1854 task0.loss_reg_iou: 0.7164 2023/09/17 12:13:50 - mmengine - INFO - Epoch(train) [3][1200/3953] lr: 9.7446e-04 eta: 5:28:54 time: 0.4956 data_time: 0.0037 memory: 8347 grad_norm: 18.1621 loss: 4.3876 task0.loss_heatmap: 0.6978 task0.loss_bbox: 2.7697 task0.loss_iou: 0.1869 task0.loss_reg_iou: 0.7331 2023/09/17 12:14:15 - mmengine - INFO - Epoch(train) [3][1250/3953] lr: 9.7388e-04 eta: 5:28:25 time: 0.4990 data_time: 0.0037 memory: 8792 grad_norm: 18.8779 loss: 4.2058 task0.loss_heatmap: 0.6258 task0.loss_bbox: 2.6988 task0.loss_iou: 0.1795 task0.loss_reg_iou: 0.7017 2023/09/17 12:14:40 - mmengine - INFO - Epoch(train) [3][1300/3953] lr: 9.7329e-04 eta: 5:27:56 time: 0.5010 data_time: 0.0039 memory: 8663 grad_norm: 20.8286 loss: 4.4269 task0.loss_heatmap: 0.6955 task0.loss_bbox: 2.8195 task0.loss_iou: 0.1807 task0.loss_reg_iou: 0.7312 2023/09/17 12:15:04 - mmengine - INFO - Epoch(train) [3][1350/3953] lr: 9.7269e-04 eta: 5:27:25 time: 0.4892 data_time: 0.0039 memory: 8086 grad_norm: 20.1459 loss: 4.2701 task0.loss_heatmap: 0.6818 task0.loss_bbox: 2.7159 task0.loss_iou: 0.1784 task0.loss_reg_iou: 0.6940 2023/09/17 12:15:29 - mmengine - INFO - Epoch(train) [3][1400/3953] lr: 9.7209e-04 eta: 5:26:55 time: 0.4935 data_time: 0.0037 memory: 8439 grad_norm: 22.1153 loss: 4.4439 task0.loss_heatmap: 0.6502 task0.loss_bbox: 2.8259 task0.loss_iou: 0.2057 task0.loss_reg_iou: 0.7621 2023/09/17 12:15:54 - mmengine - INFO - Epoch(train) [3][1450/3953] lr: 9.7148e-04 eta: 5:26:25 time: 0.4938 data_time: 0.0037 memory: 8051 grad_norm: 18.0662 loss: 4.2648 task0.loss_heatmap: 0.6755 task0.loss_bbox: 2.6843 task0.loss_iou: 0.1862 task0.loss_reg_iou: 0.7189 2023/09/17 12:16:18 - mmengine - INFO - Epoch(train) [3][1500/3953] lr: 9.7086e-04 eta: 5:25:55 time: 0.4927 data_time: 0.0038 memory: 8623 grad_norm: 17.2775 loss: 4.2797 task0.loss_heatmap: 0.6395 task0.loss_bbox: 2.7516 task0.loss_iou: 0.1763 task0.loss_reg_iou: 0.7123 2023/09/17 12:16:44 - mmengine - INFO - Epoch(train) [3][1550/3953] lr: 9.7024e-04 eta: 5:25:30 time: 0.5164 data_time: 0.0041 memory: 8323 grad_norm: 18.6072 loss: 4.3382 task0.loss_heatmap: 0.6660 task0.loss_bbox: 2.7343 task0.loss_iou: 0.1919 task0.loss_reg_iou: 0.7461 2023/09/17 12:17:09 - mmengine - INFO - Epoch(train) [3][1600/3953] lr: 9.6961e-04 eta: 5:25:00 time: 0.4948 data_time: 0.0039 memory: 8309 grad_norm: 17.6693 loss: 4.2140 task0.loss_heatmap: 0.6489 task0.loss_bbox: 2.6823 task0.loss_iou: 0.1840 task0.loss_reg_iou: 0.6988 2023/09/17 12:17:34 - mmengine - INFO - Epoch(train) [3][1650/3953] lr: 9.6898e-04 eta: 5:24:31 time: 0.4962 data_time: 0.0040 memory: 8298 grad_norm: 16.8219 loss: 4.2555 task0.loss_heatmap: 0.6311 task0.loss_bbox: 2.7445 task0.loss_iou: 0.1746 task0.loss_reg_iou: 0.7053 2023/09/17 12:17:59 - mmengine - INFO - Epoch(train) [3][1700/3953] lr: 9.6834e-04 eta: 5:24:03 time: 0.5041 data_time: 0.0039 memory: 8572 grad_norm: 18.3777 loss: 4.1378 task0.loss_heatmap: 0.6154 task0.loss_bbox: 2.6533 task0.loss_iou: 0.1794 task0.loss_reg_iou: 0.6897 2023/09/17 12:18:24 - mmengine - INFO - Epoch(train) [3][1750/3953] lr: 9.6769e-04 eta: 5:23:35 time: 0.5017 data_time: 0.0038 memory: 8510 grad_norm: 21.0370 loss: 4.1274 task0.loss_heatmap: 0.6181 task0.loss_bbox: 2.6130 task0.loss_iou: 0.1833 task0.loss_reg_iou: 0.7130 2023/09/17 12:18:50 - mmengine - INFO - Epoch(train) [3][1800/3953] lr: 9.6704e-04 eta: 5:23:11 time: 0.5204 data_time: 0.0037 memory: 8219 grad_norm: 17.5124 loss: 4.2384 task0.loss_heatmap: 0.6571 task0.loss_bbox: 2.6878 task0.loss_iou: 0.1779 task0.loss_reg_iou: 0.7155 2023/09/17 12:19:19 - mmengine - INFO - Epoch(train) [3][1850/3953] lr: 9.6638e-04 eta: 5:22:59 time: 0.5833 data_time: 0.0039 memory: 8654 grad_norm: 21.0350 loss: 4.2839 task0.loss_heatmap: 0.6815 task0.loss_bbox: 2.6971 task0.loss_iou: 0.1872 task0.loss_reg_iou: 0.7180 2023/09/17 12:19:45 - mmengine - INFO - Epoch(train) [3][1900/3953] lr: 9.6571e-04 eta: 5:22:32 time: 0.5098 data_time: 0.0037 memory: 8170 grad_norm: 20.1167 loss: 4.1587 task0.loss_heatmap: 0.6586 task0.loss_bbox: 2.6420 task0.loss_iou: 0.1730 task0.loss_reg_iou: 0.6850 2023/09/17 12:20:09 - mmengine - INFO - Epoch(train) [3][1950/3953] lr: 9.6504e-04 eta: 5:22:03 time: 0.4954 data_time: 0.0037 memory: 7937 grad_norm: 20.6119 loss: 4.1662 task0.loss_heatmap: 0.6293 task0.loss_bbox: 2.6438 task0.loss_iou: 0.1869 task0.loss_reg_iou: 0.7062 2023/09/17 12:20:34 - mmengine - INFO - Epoch(train) [3][2000/3953] lr: 9.6436e-04 eta: 5:21:33 time: 0.4942 data_time: 0.0037 memory: 9225 grad_norm: 19.0617 loss: 4.1872 task0.loss_heatmap: 0.6562 task0.loss_bbox: 2.6380 task0.loss_iou: 0.1852 task0.loss_reg_iou: 0.7079 2023/09/17 12:21:00 - mmengine - INFO - Epoch(train) [3][2050/3953] lr: 9.6367e-04 eta: 5:21:07 time: 0.5117 data_time: 0.0038 memory: 8567 grad_norm: 17.8113 loss: 4.2710 task0.loss_heatmap: 0.7056 task0.loss_bbox: 2.6956 task0.loss_iou: 0.1763 task0.loss_reg_iou: 0.6935 2023/09/17 12:21:22 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:21:25 - mmengine - INFO - Epoch(train) [3][2100/3953] lr: 9.6298e-04 eta: 5:20:39 time: 0.5019 data_time: 0.0037 memory: 8230 grad_norm: 17.9471 loss: 4.1890 task0.loss_heatmap: 0.6387 task0.loss_bbox: 2.6930 task0.loss_iou: 0.1693 task0.loss_reg_iou: 0.6880 2023/09/17 12:21:50 - mmengine - INFO - Epoch(train) [3][2150/3953] lr: 9.6228e-04 eta: 5:20:11 time: 0.5016 data_time: 0.0036 memory: 8239 grad_norm: 19.2824 loss: 4.3174 task0.loss_heatmap: 0.6277 task0.loss_bbox: 2.7568 task0.loss_iou: 0.1849 task0.loss_reg_iou: 0.7480 2023/09/17 12:22:15 - mmengine - INFO - Epoch(train) [3][2200/3953] lr: 9.6158e-04 eta: 5:19:44 time: 0.5081 data_time: 0.0038 memory: 8497 grad_norm: 17.0228 loss: 4.4065 task0.loss_heatmap: 0.6831 task0.loss_bbox: 2.8284 task0.loss_iou: 0.1828 task0.loss_reg_iou: 0.7121 2023/09/17 12:22:40 - mmengine - INFO - Epoch(train) [3][2250/3953] lr: 9.6087e-04 eta: 5:19:15 time: 0.4937 data_time: 0.0038 memory: 8141 grad_norm: 17.7327 loss: 4.0949 task0.loss_heatmap: 0.6410 task0.loss_bbox: 2.6185 task0.loss_iou: 0.1713 task0.loss_reg_iou: 0.6641 2023/09/17 12:23:05 - mmengine - INFO - Epoch(train) [3][2300/3953] lr: 9.6015e-04 eta: 5:18:47 time: 0.5008 data_time: 0.0038 memory: 8241 grad_norm: 18.6618 loss: 4.0081 task0.loss_heatmap: 0.5981 task0.loss_bbox: 2.5856 task0.loss_iou: 0.1641 task0.loss_reg_iou: 0.6603 2023/09/17 12:23:30 - mmengine - INFO - Epoch(train) [3][2350/3953] lr: 9.5943e-04 eta: 5:18:19 time: 0.4996 data_time: 0.0037 memory: 8081 grad_norm: 21.0171 loss: 4.2738 task0.loss_heatmap: 0.6139 task0.loss_bbox: 2.7311 task0.loss_iou: 0.1877 task0.loss_reg_iou: 0.7411 2023/09/17 12:23:55 - mmengine - INFO - Epoch(train) [3][2400/3953] lr: 9.5870e-04 eta: 5:17:49 time: 0.4908 data_time: 0.0038 memory: 8282 grad_norm: 17.8773 loss: 4.2067 task0.loss_heatmap: 0.6729 task0.loss_bbox: 2.6586 task0.loss_iou: 0.1832 task0.loss_reg_iou: 0.6920 2023/09/17 12:24:20 - mmengine - INFO - Epoch(train) [3][2450/3953] lr: 9.5797e-04 eta: 5:17:21 time: 0.5011 data_time: 0.0039 memory: 8184 grad_norm: 18.9125 loss: 4.3599 task0.loss_heatmap: 0.6541 task0.loss_bbox: 2.7866 task0.loss_iou: 0.1824 task0.loss_reg_iou: 0.7368 2023/09/17 12:24:45 - mmengine - INFO - Epoch(train) [3][2500/3953] lr: 9.5723e-04 eta: 5:16:52 time: 0.4965 data_time: 0.0038 memory: 8464 grad_norm: 19.4562 loss: 4.3698 task0.loss_heatmap: 0.6723 task0.loss_bbox: 2.7610 task0.loss_iou: 0.1857 task0.loss_reg_iou: 0.7509 2023/09/17 12:25:10 - mmengine - INFO - Epoch(train) [3][2550/3953] lr: 9.5648e-04 eta: 5:16:25 time: 0.5026 data_time: 0.0040 memory: 8559 grad_norm: 21.3802 loss: 4.1040 task0.loss_heatmap: 0.6332 task0.loss_bbox: 2.6063 task0.loss_iou: 0.1766 task0.loss_reg_iou: 0.6879 2023/09/17 12:25:35 - mmengine - INFO - Epoch(train) [3][2600/3953] lr: 9.5572e-04 eta: 5:15:56 time: 0.4968 data_time: 0.0037 memory: 7983 grad_norm: 20.1115 loss: 4.0672 task0.loss_heatmap: 0.6191 task0.loss_bbox: 2.5698 task0.loss_iou: 0.1799 task0.loss_reg_iou: 0.6985 2023/09/17 12:26:00 - mmengine - INFO - Epoch(train) [3][2650/3953] lr: 9.5496e-04 eta: 5:15:28 time: 0.5026 data_time: 0.0036 memory: 8288 grad_norm: 18.9790 loss: 4.0186 task0.loss_heatmap: 0.6267 task0.loss_bbox: 2.5496 task0.loss_iou: 0.1700 task0.loss_reg_iou: 0.6724 2023/09/17 12:26:25 - mmengine - INFO - Epoch(train) [3][2700/3953] lr: 9.5420e-04 eta: 5:15:01 time: 0.5025 data_time: 0.0038 memory: 8346 grad_norm: 18.9288 loss: 4.0653 task0.loss_heatmap: 0.6595 task0.loss_bbox: 2.5759 task0.loss_iou: 0.1635 task0.loss_reg_iou: 0.6665 2023/09/17 12:26:50 - mmengine - INFO - Epoch(train) [3][2750/3953] lr: 9.5343e-04 eta: 5:14:32 time: 0.4967 data_time: 0.0037 memory: 8164 grad_norm: 17.1377 loss: 4.0887 task0.loss_heatmap: 0.6291 task0.loss_bbox: 2.6054 task0.loss_iou: 0.1747 task0.loss_reg_iou: 0.6795 2023/09/17 12:27:15 - mmengine - INFO - Epoch(train) [3][2800/3953] lr: 9.5265e-04 eta: 5:14:06 time: 0.5070 data_time: 0.0039 memory: 8045 grad_norm: 18.2382 loss: 4.0698 task0.loss_heatmap: 0.6210 task0.loss_bbox: 2.6103 task0.loss_iou: 0.1679 task0.loss_reg_iou: 0.6706 2023/09/17 12:27:40 - mmengine - INFO - Epoch(train) [3][2850/3953] lr: 9.5186e-04 eta: 5:13:37 time: 0.4945 data_time: 0.0037 memory: 8075 grad_norm: 21.8966 loss: 4.0985 task0.loss_heatmap: 0.5930 task0.loss_bbox: 2.6182 task0.loss_iou: 0.1804 task0.loss_reg_iou: 0.7069 2023/09/17 12:28:05 - mmengine - INFO - Epoch(train) [3][2900/3953] lr: 9.5107e-04 eta: 5:13:10 time: 0.5070 data_time: 0.0040 memory: 8618 grad_norm: 21.5228 loss: 4.2430 task0.loss_heatmap: 0.6520 task0.loss_bbox: 2.6855 task0.loss_iou: 0.1832 task0.loss_reg_iou: 0.7223 2023/09/17 12:28:30 - mmengine - INFO - Epoch(train) [3][2950/3953] lr: 9.5028e-04 eta: 5:12:41 time: 0.4940 data_time: 0.0037 memory: 8456 grad_norm: 18.4824 loss: 3.9713 task0.loss_heatmap: 0.5788 task0.loss_bbox: 2.5333 task0.loss_iou: 0.1772 task0.loss_reg_iou: 0.6820 2023/09/17 12:28:55 - mmengine - INFO - Epoch(train) [3][3000/3953] lr: 9.4947e-04 eta: 5:12:14 time: 0.5017 data_time: 0.0037 memory: 8935 grad_norm: 18.3031 loss: 4.0581 task0.loss_heatmap: 0.6008 task0.loss_bbox: 2.5848 task0.loss_iou: 0.1796 task0.loss_reg_iou: 0.6929 2023/09/17 12:29:19 - mmengine - INFO - Epoch(train) [3][3050/3953] lr: 9.4866e-04 eta: 5:11:43 time: 0.4796 data_time: 0.0037 memory: 8125 grad_norm: 18.7810 loss: 4.0475 task0.loss_heatmap: 0.5979 task0.loss_bbox: 2.5972 task0.loss_iou: 0.1758 task0.loss_reg_iou: 0.6766 2023/09/17 12:29:41 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:29:44 - mmengine - INFO - Epoch(train) [3][3100/3953] lr: 9.4785e-04 eta: 5:11:15 time: 0.4985 data_time: 0.0038 memory: 8806 grad_norm: 18.8294 loss: 4.2784 task0.loss_heatmap: 0.6601 task0.loss_bbox: 2.7222 task0.loss_iou: 0.1830 task0.loss_reg_iou: 0.7132 2023/09/17 12:30:09 - mmengine - INFO - Epoch(train) [3][3150/3953] lr: 9.4703e-04 eta: 5:10:48 time: 0.5040 data_time: 0.0037 memory: 8325 grad_norm: 18.0544 loss: 4.0163 task0.loss_heatmap: 0.6219 task0.loss_bbox: 2.5432 task0.loss_iou: 0.1705 task0.loss_reg_iou: 0.6806 2023/09/17 12:30:35 - mmengine - INFO - Epoch(train) [3][3200/3953] lr: 9.4620e-04 eta: 5:10:24 time: 0.5267 data_time: 0.0039 memory: 8444 grad_norm: 20.3447 loss: 4.2636 task0.loss_heatmap: 0.6639 task0.loss_bbox: 2.7070 task0.loss_iou: 0.1779 task0.loss_reg_iou: 0.7148 2023/09/17 12:31:00 - mmengine - INFO - Epoch(train) [3][3250/3953] lr: 9.4537e-04 eta: 5:09:57 time: 0.4999 data_time: 0.0039 memory: 8541 grad_norm: 21.7542 loss: 4.1319 task0.loss_heatmap: 0.6104 task0.loss_bbox: 2.6292 task0.loss_iou: 0.1818 task0.loss_reg_iou: 0.7106 2023/09/17 12:31:25 - mmengine - INFO - Epoch(train) [3][3300/3953] lr: 9.4453e-04 eta: 5:09:27 time: 0.4888 data_time: 0.0039 memory: 8543 grad_norm: 19.7360 loss: 3.9937 task0.loss_heatmap: 0.5958 task0.loss_bbox: 2.5357 task0.loss_iou: 0.1713 task0.loss_reg_iou: 0.6908 2023/09/17 12:31:54 - mmengine - INFO - Epoch(train) [3][3350/3953] lr: 9.4368e-04 eta: 5:09:12 time: 0.5791 data_time: 0.0040 memory: 8012 grad_norm: 19.2746 loss: 4.0442 task0.loss_heatmap: 0.6150 task0.loss_bbox: 2.5934 task0.loss_iou: 0.1668 task0.loss_reg_iou: 0.6690 2023/09/17 12:32:20 - mmengine - INFO - Epoch(train) [3][3400/3953] lr: 9.4283e-04 eta: 5:08:48 time: 0.5215 data_time: 0.0037 memory: 8173 grad_norm: 18.4940 loss: 4.0924 task0.loss_heatmap: 0.6500 task0.loss_bbox: 2.5889 task0.loss_iou: 0.1776 task0.loss_reg_iou: 0.6760 2023/09/17 12:32:45 - mmengine - INFO - Epoch(train) [3][3450/3953] lr: 9.4197e-04 eta: 5:08:20 time: 0.5007 data_time: 0.0037 memory: 8462 grad_norm: 20.8639 loss: 4.1259 task0.loss_heatmap: 0.6023 task0.loss_bbox: 2.6387 task0.loss_iou: 0.1803 task0.loss_reg_iou: 0.7046 2023/09/17 12:33:10 - mmengine - INFO - Epoch(train) [3][3500/3953] lr: 9.4111e-04 eta: 5:07:54 time: 0.5057 data_time: 0.0037 memory: 8387 grad_norm: 19.1469 loss: 4.0577 task0.loss_heatmap: 0.6148 task0.loss_bbox: 2.5725 task0.loss_iou: 0.1832 task0.loss_reg_iou: 0.6873 2023/09/17 12:33:35 - mmengine - INFO - Epoch(train) [3][3550/3953] lr: 9.4024e-04 eta: 5:07:26 time: 0.5021 data_time: 0.0037 memory: 8425 grad_norm: 19.1819 loss: 3.8671 task0.loss_heatmap: 0.5687 task0.loss_bbox: 2.4800 task0.loss_iou: 0.1635 task0.loss_reg_iou: 0.6550 2023/09/17 12:34:00 - mmengine - INFO - Epoch(train) [3][3600/3953] lr: 9.3937e-04 eta: 5:06:59 time: 0.5025 data_time: 0.0038 memory: 8038 grad_norm: 17.4847 loss: 4.1451 task0.loss_heatmap: 0.6502 task0.loss_bbox: 2.6278 task0.loss_iou: 0.1749 task0.loss_reg_iou: 0.6921 2023/09/17 12:34:25 - mmengine - INFO - Epoch(train) [3][3650/3953] lr: 9.3849e-04 eta: 5:06:32 time: 0.5002 data_time: 0.0038 memory: 8582 grad_norm: 20.3090 loss: 4.2222 task0.loss_heatmap: 0.6348 task0.loss_bbox: 2.6782 task0.loss_iou: 0.1863 task0.loss_reg_iou: 0.7229 2023/09/17 12:34:50 - mmengine - INFO - Epoch(train) [3][3700/3953] lr: 9.3760e-04 eta: 5:06:02 time: 0.4876 data_time: 0.0037 memory: 8766 grad_norm: 20.7965 loss: 4.1771 task0.loss_heatmap: 0.6433 task0.loss_bbox: 2.6613 task0.loss_iou: 0.1746 task0.loss_reg_iou: 0.6978 2023/09/17 12:35:14 - mmengine - INFO - Epoch(train) [3][3750/3953] lr: 9.3671e-04 eta: 5:05:33 time: 0.4933 data_time: 0.0037 memory: 8190 grad_norm: 21.3049 loss: 4.0176 task0.loss_heatmap: 0.6083 task0.loss_bbox: 2.5597 task0.loss_iou: 0.1636 task0.loss_reg_iou: 0.6860 2023/09/17 12:35:39 - mmengine - INFO - Epoch(train) [3][3800/3953] lr: 9.3581e-04 eta: 5:05:05 time: 0.4917 data_time: 0.0038 memory: 8444 grad_norm: 20.3507 loss: 3.9439 task0.loss_heatmap: 0.6286 task0.loss_bbox: 2.4936 task0.loss_iou: 0.1570 task0.loss_reg_iou: 0.6646 2023/09/17 12:36:04 - mmengine - INFO - Epoch(train) [3][3850/3953] lr: 9.3490e-04 eta: 5:04:36 time: 0.4949 data_time: 0.0037 memory: 8283 grad_norm: 18.6438 loss: 4.0378 task0.loss_heatmap: 0.6299 task0.loss_bbox: 2.5591 task0.loss_iou: 0.1686 task0.loss_reg_iou: 0.6801 2023/09/17 12:36:29 - mmengine - INFO - Epoch(train) [3][3900/3953] lr: 9.3399e-04 eta: 5:04:09 time: 0.4971 data_time: 0.0037 memory: 8243 grad_norm: 19.3458 loss: 4.0382 task0.loss_heatmap: 0.6138 task0.loss_bbox: 2.5490 task0.loss_iou: 0.1787 task0.loss_reg_iou: 0.6967 2023/09/17 12:36:54 - mmengine - INFO - Epoch(train) [3][3950/3953] lr: 9.3308e-04 eta: 5:03:41 time: 0.4979 data_time: 0.0039 memory: 8139 grad_norm: 18.6038 loss: 3.9917 task0.loss_heatmap: 0.5786 task0.loss_bbox: 2.5546 task0.loss_iou: 0.1741 task0.loss_reg_iou: 0.6844 2023/09/17 12:36:55 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:36:55 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/09/17 12:37:17 - mmengine - INFO - Epoch(val) [3][ 50/1250] eta: 0:07:39 time: 0.3828 data_time: 0.0066 memory: 7811 2023/09/17 12:37:37 - mmengine - INFO - Epoch(val) [3][ 100/1250] eta: 0:07:23 time: 0.3884 data_time: 0.0051 memory: 4043 2023/09/17 12:37:56 - mmengine - INFO - Epoch(val) [3][ 150/1250] eta: 0:07:05 time: 0.3885 data_time: 0.0054 memory: 4062 2023/09/17 12:38:15 - mmengine - INFO - Epoch(val) [3][ 200/1250] eta: 0:06:44 time: 0.3811 data_time: 0.0052 memory: 4051 2023/09/17 12:38:33 - mmengine - INFO - Epoch(val) [3][ 250/1250] eta: 0:06:19 time: 0.3582 data_time: 0.0047 memory: 4033 2023/09/17 12:38:52 - mmengine - INFO - Epoch(val) [3][ 300/1250] eta: 0:06:01 time: 0.3843 data_time: 0.0051 memory: 4050 2023/09/17 12:39:11 - mmengine - INFO - Epoch(val) [3][ 350/1250] eta: 0:05:43 time: 0.3845 data_time: 0.0047 memory: 4057 2023/09/17 12:39:31 - mmengine - INFO - Epoch(val) [3][ 400/1250] eta: 0:05:24 time: 0.3884 data_time: 0.0049 memory: 4060 2023/09/17 12:39:51 - mmengine - INFO - Epoch(val) [3][ 450/1250] eta: 0:05:07 time: 0.4040 data_time: 0.0043 memory: 4048 2023/09/17 12:40:09 - mmengine - INFO - Epoch(val) [3][ 500/1250] eta: 0:04:47 time: 0.3692 data_time: 0.0050 memory: 4041 2023/09/17 12:40:28 - mmengine - INFO - Epoch(val) [3][ 550/1250] eta: 0:04:27 time: 0.3689 data_time: 0.0046 memory: 4042 2023/09/17 12:40:46 - mmengine - INFO - Epoch(val) [3][ 600/1250] eta: 0:04:07 time: 0.3703 data_time: 0.0049 memory: 4057 2023/09/17 12:41:07 - mmengine - INFO - Epoch(val) [3][ 650/1250] eta: 0:03:49 time: 0.4064 data_time: 0.0049 memory: 4065 2023/09/17 12:41:26 - mmengine - INFO - Epoch(val) [3][ 700/1250] eta: 0:03:30 time: 0.3941 data_time: 0.0048 memory: 4042 2023/09/17 12:41:46 - mmengine - INFO - Epoch(val) [3][ 750/1250] eta: 0:03:12 time: 0.3984 data_time: 0.0048 memory: 4051 2023/09/17 12:42:05 - mmengine - INFO - Epoch(val) [3][ 800/1250] eta: 0:02:52 time: 0.3735 data_time: 0.0051 memory: 4055 2023/09/17 12:42:24 - mmengine - INFO - Epoch(val) [3][ 850/1250] eta: 0:02:33 time: 0.3871 data_time: 0.0050 memory: 4058 2023/09/17 12:42:42 - mmengine - INFO - Epoch(val) [3][ 900/1250] eta: 0:02:13 time: 0.3590 data_time: 0.0047 memory: 4049 2023/09/17 12:43:01 - mmengine - INFO - Epoch(val) [3][ 950/1250] eta: 0:01:54 time: 0.3668 data_time: 0.0047 memory: 4054 2023/09/17 12:43:20 - mmengine - INFO - Epoch(val) [3][1000/1250] eta: 0:01:35 time: 0.3773 data_time: 0.0051 memory: 4056 2023/09/17 12:43:39 - mmengine - INFO - Epoch(val) [3][1050/1250] eta: 0:01:16 time: 0.3815 data_time: 0.0052 memory: 4043 2023/09/17 12:43:57 - mmengine - INFO - Epoch(val) [3][1100/1250] eta: 0:00:57 time: 0.3590 data_time: 0.0049 memory: 4032 2023/09/17 12:44:16 - mmengine - INFO - Epoch(val) [3][1150/1250] eta: 0:00:38 time: 0.3848 data_time: 0.0048 memory: 4041 2023/09/17 12:44:35 - mmengine - INFO - Epoch(val) [3][1200/1250] eta: 0:00:19 time: 0.3812 data_time: 0.0046 memory: 4047 2023/09/17 12:44:54 - mmengine - INFO - Epoch(val) [3][1250/1250] eta: 0:00:00 time: 0.3879 data_time: 0.0046 memory: 4055 2023/09/17 12:44:58 - mmengine - INFO - Start converting ... 2023/09/17 12:51:56 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.563639] [mAPH 0.557459] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.490017] [mAPH 0.484612] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.649604] [mAPH 0.520753] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.568083] [mAPH 0.454309] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.60402] [mAPH 0.584798] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.581143] [mAPH 0.562644] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.816925] [mAPH 0.809977] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.803471] [mAPH 0.796624] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.519603] [mAPH 0.512873] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.465396] [mAPH 0.459342] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.240043] [mAPH 0.233987] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.177386] [mAPH 0.172862] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.730898] [mAPH 0.595759] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.686699] [mAPH 0.558875] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.633774] [mAPH 0.505703] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.563916] [mAPH 0.449093] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.485739] [mAPH 0.361177] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.368061] [mAPH 0.272955] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.74367] [mAPH 0.724282] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.738333] [mAPH 0.719083] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.548422] [mAPH 0.529119] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.516937] [mAPH 0.498736] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.357287] [mAPH 0.333373] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.332567] [mAPH 0.310304] Eval Using 294s 2023/09/17 12:51:57 - mmengine - INFO - Epoch(val) [3][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.5636 Waymo metric/Vehicle/L1 mAPH: 0.5575 Waymo metric/Vehicle/L2 mAP: 0.4900 Waymo metric/Vehicle/L2 mAPH: 0.4846 Waymo metric/Pedestrian/L1 mAP: 0.6496 Waymo metric/Pedestrian/L1 mAPH: 0.5208 Waymo metric/Pedestrian/L2 mAP: 0.5681 Waymo metric/Pedestrian/L2 mAPH: 0.4543 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6040 Waymo metric/Cyclist/L1 mAPH: 0.5848 Waymo metric/Cyclist/L2 mAP: 0.5811 Waymo metric/Cyclist/L2 mAPH: 0.5626 Waymo metric/Overall/L1 mAP: 0.6058 Waymo metric/Overall/L1 mAPH: 0.5543 Waymo metric/Overall/L2 mAP: 0.5464 Waymo metric/Overall/L2 mAPH: 0.5005 data_time: 0.0049 time: 0.3810 2023/09/17 12:52:22 - mmengine - INFO - Epoch(train) [4][ 50/3953] lr: 9.3210e-04 eta: 5:03:11 time: 0.4947 data_time: 0.0054 memory: 8659 grad_norm: 17.6331 loss: 4.2141 task0.loss_heatmap: 0.6554 task0.loss_bbox: 2.6769 task0.loss_iou: 0.1758 task0.loss_reg_iou: 0.7060 2023/09/17 12:52:46 - mmengine - INFO - Epoch(train) [4][ 100/3953] lr: 9.3117e-04 eta: 5:02:42 time: 0.4914 data_time: 0.0043 memory: 8270 grad_norm: 18.1585 loss: 4.0030 task0.loss_heatmap: 0.5861 task0.loss_bbox: 2.5655 task0.loss_iou: 0.1732 task0.loss_reg_iou: 0.6782 2023/09/17 12:53:07 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 12:53:12 - mmengine - INFO - Epoch(train) [4][ 150/3953] lr: 9.3023e-04 eta: 5:02:17 time: 0.5129 data_time: 0.0040 memory: 8230 grad_norm: 18.0506 loss: 4.0384 task0.loss_heatmap: 0.6203 task0.loss_bbox: 2.5724 task0.loss_iou: 0.1677 task0.loss_reg_iou: 0.6781 2023/09/17 12:53:36 - mmengine - INFO - Epoch(train) [4][ 200/3953] lr: 9.2929e-04 eta: 5:01:48 time: 0.4897 data_time: 0.0038 memory: 8356 grad_norm: 18.8324 loss: 4.1242 task0.loss_heatmap: 0.6417 task0.loss_bbox: 2.6201 task0.loss_iou: 0.1747 task0.loss_reg_iou: 0.6877 2023/09/17 12:54:01 - mmengine - INFO - Epoch(train) [4][ 250/3953] lr: 9.2835e-04 eta: 5:01:19 time: 0.4904 data_time: 0.0038 memory: 8624 grad_norm: 17.3685 loss: 4.1637 task0.loss_heatmap: 0.6733 task0.loss_bbox: 2.6437 task0.loss_iou: 0.1770 task0.loss_reg_iou: 0.6697 2023/09/17 12:54:26 - mmengine - INFO - Epoch(train) [4][ 300/3953] lr: 9.2740e-04 eta: 5:00:51 time: 0.4906 data_time: 0.0040 memory: 8307 grad_norm: 17.6896 loss: 3.9203 task0.loss_heatmap: 0.5652 task0.loss_bbox: 2.5102 task0.loss_iou: 0.1757 task0.loss_reg_iou: 0.6692 2023/09/17 12:54:51 - mmengine - INFO - Epoch(train) [4][ 350/3953] lr: 9.2644e-04 eta: 5:00:23 time: 0.5000 data_time: 0.0041 memory: 8382 grad_norm: 19.2588 loss: 3.9211 task0.loss_heatmap: 0.6048 task0.loss_bbox: 2.4885 task0.loss_iou: 0.1688 task0.loss_reg_iou: 0.6590 2023/09/17 12:55:15 - mmengine - INFO - Epoch(train) [4][ 400/3953] lr: 9.2548e-04 eta: 4:59:54 time: 0.4838 data_time: 0.0038 memory: 8317 grad_norm: 18.5239 loss: 3.9667 task0.loss_heatmap: 0.5938 task0.loss_bbox: 2.5267 task0.loss_iou: 0.1741 task0.loss_reg_iou: 0.6721 2023/09/17 12:55:40 - mmengine - INFO - Epoch(train) [4][ 450/3953] lr: 9.2451e-04 eta: 4:59:26 time: 0.5000 data_time: 0.0038 memory: 8487 grad_norm: 17.3418 loss: 4.0161 task0.loss_heatmap: 0.6314 task0.loss_bbox: 2.5390 task0.loss_iou: 0.1692 task0.loss_reg_iou: 0.6766 2023/09/17 12:56:08 - mmengine - INFO - Epoch(train) [4][ 500/3953] lr: 9.2353e-04 eta: 4:59:07 time: 0.5572 data_time: 0.0037 memory: 8244 grad_norm: 18.4071 loss: 3.9670 task0.loss_heatmap: 0.6135 task0.loss_bbox: 2.5031 task0.loss_iou: 0.1745 task0.loss_reg_iou: 0.6758 2023/09/17 12:56:35 - mmengine - INFO - Epoch(train) [4][ 550/3953] lr: 9.2255e-04 eta: 4:58:47 time: 0.5454 data_time: 0.0037 memory: 8493 grad_norm: 19.3790 loss: 3.9413 task0.loss_heatmap: 0.5800 task0.loss_bbox: 2.4920 task0.loss_iou: 0.1786 task0.loss_reg_iou: 0.6907 2023/09/17 12:57:00 - mmengine - INFO - Epoch(train) [4][ 600/3953] lr: 9.2156e-04 eta: 4:58:18 time: 0.4942 data_time: 0.0042 memory: 7939 grad_norm: 18.6238 loss: 4.2107 task0.loss_heatmap: 0.6427 task0.loss_bbox: 2.6546 task0.loss_iou: 0.1851 task0.loss_reg_iou: 0.7283 2023/09/17 12:57:25 - mmengine - INFO - Epoch(train) [4][ 650/3953] lr: 9.2057e-04 eta: 4:57:51 time: 0.4995 data_time: 0.0036 memory: 8588 grad_norm: 17.5070 loss: 4.1463 task0.loss_heatmap: 0.6468 task0.loss_bbox: 2.6407 task0.loss_iou: 0.1686 task0.loss_reg_iou: 0.6901 2023/09/17 12:57:50 - mmengine - INFO - Epoch(train) [4][ 700/3953] lr: 9.1957e-04 eta: 4:57:26 time: 0.5154 data_time: 0.0036 memory: 8540 grad_norm: 16.8141 loss: 3.9311 task0.loss_heatmap: 0.6073 task0.loss_bbox: 2.4954 task0.loss_iou: 0.1704 task0.loss_reg_iou: 0.6579 2023/09/17 12:58:15 - mmengine - INFO - Epoch(train) [4][ 750/3953] lr: 9.1857e-04 eta: 4:56:58 time: 0.4950 data_time: 0.0037 memory: 8443 grad_norm: 19.2417 loss: 3.8594 task0.loss_heatmap: 0.5611 task0.loss_bbox: 2.4431 task0.loss_iou: 0.1754 task0.loss_reg_iou: 0.6799 2023/09/17 12:58:40 - mmengine - INFO - Epoch(train) [4][ 800/3953] lr: 9.1756e-04 eta: 4:56:31 time: 0.5028 data_time: 0.0036 memory: 8130 grad_norm: 17.9953 loss: 3.9581 task0.loss_heatmap: 0.5973 task0.loss_bbox: 2.5230 task0.loss_iou: 0.1712 task0.loss_reg_iou: 0.6666 2023/09/17 12:59:05 - mmengine - INFO - Epoch(train) [4][ 850/3953] lr: 9.1655e-04 eta: 4:56:05 time: 0.5041 data_time: 0.0040 memory: 8587 grad_norm: 20.3667 loss: 4.2366 task0.loss_heatmap: 0.6805 task0.loss_bbox: 2.6842 task0.loss_iou: 0.1763 task0.loss_reg_iou: 0.6956 2023/09/17 12:59:30 - mmengine - INFO - Epoch(train) [4][ 900/3953] lr: 9.1553e-04 eta: 4:55:37 time: 0.4934 data_time: 0.0038 memory: 8491 grad_norm: 18.6285 loss: 3.7580 task0.loss_heatmap: 0.5463 task0.loss_bbox: 2.3987 task0.loss_iou: 0.1667 task0.loss_reg_iou: 0.6464 2023/09/17 12:59:55 - mmengine - INFO - Epoch(train) [4][ 950/3953] lr: 9.1450e-04 eta: 4:55:09 time: 0.4989 data_time: 0.0038 memory: 8426 grad_norm: 18.5826 loss: 3.8506 task0.loss_heatmap: 0.5769 task0.loss_bbox: 2.4516 task0.loss_iou: 0.1646 task0.loss_reg_iou: 0.6575 2023/09/17 13:00:20 - mmengine - INFO - Epoch(train) [4][1000/3953] lr: 9.1347e-04 eta: 4:54:42 time: 0.4978 data_time: 0.0037 memory: 8615 grad_norm: 18.2519 loss: 3.9683 task0.loss_heatmap: 0.5870 task0.loss_bbox: 2.5214 task0.loss_iou: 0.1754 task0.loss_reg_iou: 0.6845 2023/09/17 13:00:45 - mmengine - INFO - Epoch(train) [4][1050/3953] lr: 9.1243e-04 eta: 4:54:14 time: 0.4929 data_time: 0.0037 memory: 7876 grad_norm: 18.2582 loss: 3.9950 task0.loss_heatmap: 0.6209 task0.loss_bbox: 2.5298 task0.loss_iou: 0.1716 task0.loss_reg_iou: 0.6727 2023/09/17 13:01:10 - mmengine - INFO - Epoch(train) [4][1100/3953] lr: 9.1139e-04 eta: 4:53:47 time: 0.5026 data_time: 0.0037 memory: 8212 grad_norm: 17.7897 loss: 3.8808 task0.loss_heatmap: 0.6254 task0.loss_bbox: 2.4469 task0.loss_iou: 0.1657 task0.loss_reg_iou: 0.6429 2023/09/17 13:01:30 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:01:35 - mmengine - INFO - Epoch(train) [4][1150/3953] lr: 9.1034e-04 eta: 4:53:20 time: 0.4951 data_time: 0.0036 memory: 8420 grad_norm: 18.4608 loss: 3.9037 task0.loss_heatmap: 0.5957 task0.loss_bbox: 2.4970 task0.loss_iou: 0.1614 task0.loss_reg_iou: 0.6495 2023/09/17 13:02:00 - mmengine - INFO - Epoch(train) [4][1200/3953] lr: 9.0929e-04 eta: 4:52:53 time: 0.5047 data_time: 0.0039 memory: 8396 grad_norm: 17.9213 loss: 3.9177 task0.loss_heatmap: 0.5907 task0.loss_bbox: 2.5001 task0.loss_iou: 0.1652 task0.loss_reg_iou: 0.6617 2023/09/17 13:02:25 - mmengine - INFO - Epoch(train) [4][1250/3953] lr: 9.0823e-04 eta: 4:52:26 time: 0.4991 data_time: 0.0039 memory: 8204 grad_norm: 17.7268 loss: 4.0119 task0.loss_heatmap: 0.6339 task0.loss_bbox: 2.5506 task0.loss_iou: 0.1726 task0.loss_reg_iou: 0.6549 2023/09/17 13:02:49 - mmengine - INFO - Epoch(train) [4][1300/3953] lr: 9.0716e-04 eta: 4:51:58 time: 0.4912 data_time: 0.0037 memory: 8381 grad_norm: 17.3834 loss: 3.9688 task0.loss_heatmap: 0.6039 task0.loss_bbox: 2.5466 task0.loss_iou: 0.1656 task0.loss_reg_iou: 0.6527 2023/09/17 13:03:14 - mmengine - INFO - Epoch(train) [4][1350/3953] lr: 9.0609e-04 eta: 4:51:31 time: 0.5023 data_time: 0.0039 memory: 8452 grad_norm: 17.5741 loss: 3.9697 task0.loss_heatmap: 0.5893 task0.loss_bbox: 2.5372 task0.loss_iou: 0.1691 task0.loss_reg_iou: 0.6740 2023/09/17 13:03:40 - mmengine - INFO - Epoch(train) [4][1400/3953] lr: 9.0502e-04 eta: 4:51:05 time: 0.5023 data_time: 0.0038 memory: 8828 grad_norm: 18.2350 loss: 4.0359 task0.loss_heatmap: 0.5960 task0.loss_bbox: 2.5571 task0.loss_iou: 0.1766 task0.loss_reg_iou: 0.7062 2023/09/17 13:04:06 - mmengine - INFO - Epoch(train) [4][1450/3953] lr: 9.0393e-04 eta: 4:50:40 time: 0.5220 data_time: 0.0037 memory: 8212 grad_norm: 18.2185 loss: 3.9275 task0.loss_heatmap: 0.6049 task0.loss_bbox: 2.4979 task0.loss_iou: 0.1661 task0.loss_reg_iou: 0.6586 2023/09/17 13:04:30 - mmengine - INFO - Epoch(train) [4][1500/3953] lr: 9.0285e-04 eta: 4:50:12 time: 0.4870 data_time: 0.0042 memory: 8525 grad_norm: 18.7861 loss: 3.8966 task0.loss_heatmap: 0.5727 task0.loss_bbox: 2.4988 task0.loss_iou: 0.1623 task0.loss_reg_iou: 0.6627 2023/09/17 13:04:55 - mmengine - INFO - Epoch(train) [4][1550/3953] lr: 9.0175e-04 eta: 4:49:45 time: 0.5050 data_time: 0.0041 memory: 8273 grad_norm: 18.1266 loss: 3.9144 task0.loss_heatmap: 0.6141 task0.loss_bbox: 2.4952 task0.loss_iou: 0.1656 task0.loss_reg_iou: 0.6395 2023/09/17 13:05:20 - mmengine - INFO - Epoch(train) [4][1600/3953] lr: 9.0066e-04 eta: 4:49:19 time: 0.5002 data_time: 0.0040 memory: 8453 grad_norm: 16.2948 loss: 3.8776 task0.loss_heatmap: 0.5891 task0.loss_bbox: 2.4596 task0.loss_iou: 0.1685 task0.loss_reg_iou: 0.6603 2023/09/17 13:05:45 - mmengine - INFO - Epoch(train) [4][1650/3953] lr: 8.9955e-04 eta: 4:48:50 time: 0.4872 data_time: 0.0040 memory: 8498 grad_norm: 18.5233 loss: 4.0459 task0.loss_heatmap: 0.6273 task0.loss_bbox: 2.5680 task0.loss_iou: 0.1730 task0.loss_reg_iou: 0.6777 2023/09/17 13:06:09 - mmengine - INFO - Epoch(train) [4][1700/3953] lr: 8.9844e-04 eta: 4:48:22 time: 0.4890 data_time: 0.0039 memory: 8247 grad_norm: 17.3617 loss: 4.0071 task0.loss_heatmap: 0.5994 task0.loss_bbox: 2.5500 task0.loss_iou: 0.1715 task0.loss_reg_iou: 0.6863 2023/09/17 13:06:34 - mmengine - INFO - Epoch(train) [4][1750/3953] lr: 8.9733e-04 eta: 4:47:55 time: 0.4993 data_time: 0.0039 memory: 8448 grad_norm: 17.7440 loss: 3.8536 task0.loss_heatmap: 0.5728 task0.loss_bbox: 2.4401 task0.loss_iou: 0.1689 task0.loss_reg_iou: 0.6718 2023/09/17 13:06:59 - mmengine - INFO - Epoch(train) [4][1800/3953] lr: 8.9621e-04 eta: 4:47:29 time: 0.5061 data_time: 0.0040 memory: 8046 grad_norm: 18.4002 loss: 3.9169 task0.loss_heatmap: 0.5812 task0.loss_bbox: 2.4894 task0.loss_iou: 0.1706 task0.loss_reg_iou: 0.6756 2023/09/17 13:07:24 - mmengine - INFO - Epoch(train) [4][1850/3953] lr: 8.9509e-04 eta: 4:47:02 time: 0.5010 data_time: 0.0038 memory: 8318 grad_norm: 17.2473 loss: 3.9419 task0.loss_heatmap: 0.5691 task0.loss_bbox: 2.5191 task0.loss_iou: 0.1698 task0.loss_reg_iou: 0.6839 2023/09/17 13:07:49 - mmengine - INFO - Epoch(train) [4][1900/3953] lr: 8.9396e-04 eta: 4:46:35 time: 0.5000 data_time: 0.0038 memory: 8177 grad_norm: 16.8615 loss: 3.9106 task0.loss_heatmap: 0.5736 task0.loss_bbox: 2.4976 task0.loss_iou: 0.1680 task0.loss_reg_iou: 0.6714 2023/09/17 13:08:14 - mmengine - INFO - Epoch(train) [4][1950/3953] lr: 8.9282e-04 eta: 4:46:08 time: 0.5004 data_time: 0.0038 memory: 8385 grad_norm: 17.6825 loss: 3.8675 task0.loss_heatmap: 0.5901 task0.loss_bbox: 2.4477 task0.loss_iou: 0.1684 task0.loss_reg_iou: 0.6614 2023/09/17 13:08:40 - mmengine - INFO - Epoch(train) [4][2000/3953] lr: 8.9168e-04 eta: 4:45:43 time: 0.5119 data_time: 0.0039 memory: 8684 grad_norm: 17.5619 loss: 3.8523 task0.loss_heatmap: 0.5549 task0.loss_bbox: 2.4498 task0.loss_iou: 0.1759 task0.loss_reg_iou: 0.6718 2023/09/17 13:09:09 - mmengine - INFO - Epoch(train) [4][2050/3953] lr: 8.9053e-04 eta: 4:45:25 time: 0.5704 data_time: 0.0038 memory: 7997 grad_norm: 17.1979 loss: 3.9156 task0.loss_heatmap: 0.6096 task0.loss_bbox: 2.4832 task0.loss_iou: 0.1663 task0.loss_reg_iou: 0.6565 2023/09/17 13:09:35 - mmengine - INFO - Epoch(train) [4][2100/3953] lr: 8.8938e-04 eta: 4:45:01 time: 0.5295 data_time: 0.0038 memory: 8495 grad_norm: 17.9280 loss: 3.7835 task0.loss_heatmap: 0.5549 task0.loss_bbox: 2.4261 task0.loss_iou: 0.1619 task0.loss_reg_iou: 0.6406 2023/09/17 13:09:56 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:10:00 - mmengine - INFO - Epoch(train) [4][2150/3953] lr: 8.8823e-04 eta: 4:44:35 time: 0.4997 data_time: 0.0040 memory: 8389 grad_norm: 19.9221 loss: 3.9434 task0.loss_heatmap: 0.5959 task0.loss_bbox: 2.4991 task0.loss_iou: 0.1709 task0.loss_reg_iou: 0.6775 2023/09/17 13:10:25 - mmengine - INFO - Epoch(train) [4][2200/3953] lr: 8.8706e-04 eta: 4:44:08 time: 0.5020 data_time: 0.0039 memory: 8450 grad_norm: 17.5091 loss: 3.9647 task0.loss_heatmap: 0.6102 task0.loss_bbox: 2.5232 task0.loss_iou: 0.1665 task0.loss_reg_iou: 0.6649 2023/09/17 13:10:50 - mmengine - INFO - Epoch(train) [4][2250/3953] lr: 8.8590e-04 eta: 4:43:41 time: 0.4957 data_time: 0.0038 memory: 7838 grad_norm: 16.8904 loss: 3.8278 task0.loss_heatmap: 0.5825 task0.loss_bbox: 2.4516 task0.loss_iou: 0.1614 task0.loss_reg_iou: 0.6323 2023/09/17 13:11:15 - mmengine - INFO - Epoch(train) [4][2300/3953] lr: 8.8472e-04 eta: 4:43:14 time: 0.5013 data_time: 0.0037 memory: 8515 grad_norm: 17.5048 loss: 3.8654 task0.loss_heatmap: 0.5795 task0.loss_bbox: 2.4435 task0.loss_iou: 0.1745 task0.loss_reg_iou: 0.6680 2023/09/17 13:11:40 - mmengine - INFO - Epoch(train) [4][2350/3953] lr: 8.8355e-04 eta: 4:42:48 time: 0.5056 data_time: 0.0037 memory: 8056 grad_norm: 17.3550 loss: 3.7966 task0.loss_heatmap: 0.5787 task0.loss_bbox: 2.4304 task0.loss_iou: 0.1570 task0.loss_reg_iou: 0.6306 2023/09/17 13:12:05 - mmengine - INFO - Epoch(train) [4][2400/3953] lr: 8.8236e-04 eta: 4:42:21 time: 0.4992 data_time: 0.0037 memory: 8365 grad_norm: 18.5729 loss: 3.8133 task0.loss_heatmap: 0.5515 task0.loss_bbox: 2.4130 task0.loss_iou: 0.1742 task0.loss_reg_iou: 0.6746 2023/09/17 13:12:30 - mmengine - INFO - Epoch(train) [4][2450/3953] lr: 8.8118e-04 eta: 4:41:54 time: 0.4965 data_time: 0.0037 memory: 8586 grad_norm: 16.3913 loss: 3.8780 task0.loss_heatmap: 0.5896 task0.loss_bbox: 2.4551 task0.loss_iou: 0.1656 task0.loss_reg_iou: 0.6678 2023/09/17 13:12:55 - mmengine - INFO - Epoch(train) [4][2500/3953] lr: 8.7998e-04 eta: 4:41:26 time: 0.4896 data_time: 0.0037 memory: 8149 grad_norm: 17.0023 loss: 3.8697 task0.loss_heatmap: 0.5901 task0.loss_bbox: 2.4620 task0.loss_iou: 0.1626 task0.loss_reg_iou: 0.6549 2023/09/17 13:13:19 - mmengine - INFO - Epoch(train) [4][2550/3953] lr: 8.7878e-04 eta: 4:40:59 time: 0.4977 data_time: 0.0037 memory: 7927 grad_norm: 19.3612 loss: 3.8234 task0.loss_heatmap: 0.5447 task0.loss_bbox: 2.4576 task0.loss_iou: 0.1598 task0.loss_reg_iou: 0.6613 2023/09/17 13:13:45 - mmengine - INFO - Epoch(train) [4][2600/3953] lr: 8.7758e-04 eta: 4:40:33 time: 0.5086 data_time: 0.0038 memory: 8515 grad_norm: 17.9430 loss: 3.7618 task0.loss_heatmap: 0.5774 task0.loss_bbox: 2.3823 task0.loss_iou: 0.1596 task0.loss_reg_iou: 0.6424 2023/09/17 13:14:10 - mmengine - INFO - Epoch(train) [4][2650/3953] lr: 8.7637e-04 eta: 4:40:07 time: 0.5047 data_time: 0.0038 memory: 7974 grad_norm: 15.9191 loss: 3.7764 task0.loss_heatmap: 0.5860 task0.loss_bbox: 2.3924 task0.loss_iou: 0.1607 task0.loss_reg_iou: 0.6373 2023/09/17 13:14:35 - mmengine - INFO - Epoch(train) [4][2700/3953] lr: 8.7516e-04 eta: 4:39:40 time: 0.4974 data_time: 0.0037 memory: 8419 grad_norm: 17.5910 loss: 3.8437 task0.loss_heatmap: 0.6092 task0.loss_bbox: 2.4313 task0.loss_iou: 0.1595 task0.loss_reg_iou: 0.6437 2023/09/17 13:15:00 - mmengine - INFO - Epoch(train) [4][2750/3953] lr: 8.7394e-04 eta: 4:39:13 time: 0.5009 data_time: 0.0038 memory: 8372 grad_norm: 16.4220 loss: 3.9597 task0.loss_heatmap: 0.5805 task0.loss_bbox: 2.5013 task0.loss_iou: 0.1873 task0.loss_reg_iou: 0.6906 2023/09/17 13:15:25 - mmengine - INFO - Epoch(train) [4][2800/3953] lr: 8.7272e-04 eta: 4:38:47 time: 0.5010 data_time: 0.0037 memory: 8224 grad_norm: 20.6013 loss: 3.9328 task0.loss_heatmap: 0.6090 task0.loss_bbox: 2.4784 task0.loss_iou: 0.1719 task0.loss_reg_iou: 0.6735 2023/09/17 13:15:50 - mmengine - INFO - Epoch(train) [4][2850/3953] lr: 8.7149e-04 eta: 4:38:19 time: 0.4934 data_time: 0.0037 memory: 8535 grad_norm: 18.7342 loss: 3.8012 task0.loss_heatmap: 0.5750 task0.loss_bbox: 2.4199 task0.loss_iou: 0.1625 task0.loss_reg_iou: 0.6439 2023/09/17 13:16:15 - mmengine - INFO - Epoch(train) [4][2900/3953] lr: 8.7025e-04 eta: 4:37:52 time: 0.4963 data_time: 0.0037 memory: 8020 grad_norm: 17.6038 loss: 3.7439 task0.loss_heatmap: 0.5602 task0.loss_bbox: 2.3786 task0.loss_iou: 0.1634 task0.loss_reg_iou: 0.6416 2023/09/17 13:16:40 - mmengine - INFO - Epoch(train) [4][2950/3953] lr: 8.6901e-04 eta: 4:37:27 time: 0.5111 data_time: 0.0038 memory: 8622 grad_norm: 18.6232 loss: 3.9655 task0.loss_heatmap: 0.5600 task0.loss_bbox: 2.5494 task0.loss_iou: 0.1732 task0.loss_reg_iou: 0.6830 2023/09/17 13:17:05 - mmengine - INFO - Epoch(train) [4][3000/3953] lr: 8.6777e-04 eta: 4:37:00 time: 0.5009 data_time: 0.0037 memory: 8608 grad_norm: 16.9953 loss: 3.9784 task0.loss_heatmap: 0.5943 task0.loss_bbox: 2.5483 task0.loss_iou: 0.1671 task0.loss_reg_iou: 0.6687 2023/09/17 13:17:30 - mmengine - INFO - Epoch(train) [4][3050/3953] lr: 8.6652e-04 eta: 4:36:33 time: 0.4905 data_time: 0.0039 memory: 8565 grad_norm: 16.8934 loss: 3.7934 task0.loss_heatmap: 0.5363 task0.loss_bbox: 2.4041 task0.loss_iou: 0.1738 task0.loss_reg_iou: 0.6793 2023/09/17 13:17:55 - mmengine - INFO - Epoch(train) [4][3100/3953] lr: 8.6527e-04 eta: 4:36:08 time: 0.5152 data_time: 0.0038 memory: 8196 grad_norm: 16.6185 loss: 3.8705 task0.loss_heatmap: 0.5963 task0.loss_bbox: 2.4633 task0.loss_iou: 0.1618 task0.loss_reg_iou: 0.6490 2023/09/17 13:18:16 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:18:21 - mmengine - INFO - Epoch(train) [4][3150/3953] lr: 8.6401e-04 eta: 4:35:42 time: 0.5075 data_time: 0.0043 memory: 8426 grad_norm: 16.3514 loss: 3.9481 task0.loss_heatmap: 0.5970 task0.loss_bbox: 2.5321 task0.loss_iou: 0.1622 task0.loss_reg_iou: 0.6568 2023/09/17 13:18:46 - mmengine - INFO - Epoch(train) [4][3200/3953] lr: 8.6275e-04 eta: 4:35:16 time: 0.5021 data_time: 0.0042 memory: 8553 grad_norm: 16.3604 loss: 3.8264 task0.loss_heatmap: 0.5894 task0.loss_bbox: 2.4396 task0.loss_iou: 0.1564 task0.loss_reg_iou: 0.6409 2023/09/17 13:19:11 - mmengine - INFO - Epoch(train) [4][3250/3953] lr: 8.6148e-04 eta: 4:34:49 time: 0.5006 data_time: 0.0041 memory: 8493 grad_norm: 15.8978 loss: 3.7857 task0.loss_heatmap: 0.5959 task0.loss_bbox: 2.3906 task0.loss_iou: 0.1608 task0.loss_reg_iou: 0.6384 2023/09/17 13:19:36 - mmengine - INFO - Epoch(train) [4][3300/3953] lr: 8.6020e-04 eta: 4:34:23 time: 0.5048 data_time: 0.0042 memory: 8686 grad_norm: 16.5658 loss: 3.8628 task0.loss_heatmap: 0.5896 task0.loss_bbox: 2.4550 task0.loss_iou: 0.1653 task0.loss_reg_iou: 0.6528 2023/09/17 13:20:01 - mmengine - INFO - Epoch(train) [4][3350/3953] lr: 8.5893e-04 eta: 4:33:56 time: 0.4959 data_time: 0.0045 memory: 8296 grad_norm: 16.8482 loss: 3.8067 task0.loss_heatmap: 0.5912 task0.loss_bbox: 2.3960 task0.loss_iou: 0.1657 task0.loss_reg_iou: 0.6538 2023/09/17 13:20:26 - mmengine - INFO - Epoch(train) [4][3400/3953] lr: 8.5764e-04 eta: 4:33:29 time: 0.4995 data_time: 0.0041 memory: 8659 grad_norm: 18.4250 loss: 3.7971 task0.loss_heatmap: 0.6059 task0.loss_bbox: 2.3979 task0.loss_iou: 0.1613 task0.loss_reg_iou: 0.6319 2023/09/17 13:20:51 - mmengine - INFO - Epoch(train) [4][3450/3953] lr: 8.5635e-04 eta: 4:33:03 time: 0.4975 data_time: 0.0041 memory: 8106 grad_norm: 18.0933 loss: 3.7561 task0.loss_heatmap: 0.5473 task0.loss_bbox: 2.4060 task0.loss_iou: 0.1640 task0.loss_reg_iou: 0.6389 2023/09/17 13:21:16 - mmengine - INFO - Epoch(train) [4][3500/3953] lr: 8.5506e-04 eta: 4:32:36 time: 0.5001 data_time: 0.0042 memory: 8183 grad_norm: 16.9705 loss: 3.6971 task0.loss_heatmap: 0.5627 task0.loss_bbox: 2.3528 task0.loss_iou: 0.1567 task0.loss_reg_iou: 0.6248 2023/09/17 13:21:44 - mmengine - INFO - Epoch(train) [4][3550/3953] lr: 8.5376e-04 eta: 4:32:17 time: 0.5699 data_time: 0.0041 memory: 7992 grad_norm: 18.3707 loss: 3.8115 task0.loss_heatmap: 0.5765 task0.loss_bbox: 2.4446 task0.loss_iou: 0.1544 task0.loss_reg_iou: 0.6361 2023/09/17 13:22:13 - mmengine - INFO - Epoch(train) [4][3600/3953] lr: 8.5246e-04 eta: 4:31:57 time: 0.5641 data_time: 0.0041 memory: 8682 grad_norm: 16.5689 loss: 3.7961 task0.loss_heatmap: 0.5608 task0.loss_bbox: 2.4269 task0.loss_iou: 0.1652 task0.loss_reg_iou: 0.6431 2023/09/17 13:22:38 - mmengine - INFO - Epoch(train) [4][3650/3953] lr: 8.5115e-04 eta: 4:31:31 time: 0.5043 data_time: 0.0042 memory: 8488 grad_norm: 16.6520 loss: 3.5518 task0.loss_heatmap: 0.5318 task0.loss_bbox: 2.2644 task0.loss_iou: 0.1518 task0.loss_reg_iou: 0.6037 2023/09/17 13:23:03 - mmengine - INFO - Epoch(train) [4][3700/3953] lr: 8.4984e-04 eta: 4:31:04 time: 0.5026 data_time: 0.0042 memory: 8061 grad_norm: 15.2033 loss: 3.8048 task0.loss_heatmap: 0.5884 task0.loss_bbox: 2.4225 task0.loss_iou: 0.1584 task0.loss_reg_iou: 0.6354 2023/09/17 13:23:27 - mmengine - INFO - Epoch(train) [4][3750/3953] lr: 8.4853e-04 eta: 4:30:37 time: 0.4902 data_time: 0.0042 memory: 8190 grad_norm: 17.5197 loss: 3.8160 task0.loss_heatmap: 0.6087 task0.loss_bbox: 2.4180 task0.loss_iou: 0.1598 task0.loss_reg_iou: 0.6296 2023/09/17 13:23:52 - mmengine - INFO - Epoch(train) [4][3800/3953] lr: 8.4720e-04 eta: 4:30:09 time: 0.4909 data_time: 0.0042 memory: 8315 grad_norm: 18.0695 loss: 3.8173 task0.loss_heatmap: 0.5664 task0.loss_bbox: 2.4252 task0.loss_iou: 0.1627 task0.loss_reg_iou: 0.6630 2023/09/17 13:24:17 - mmengine - INFO - Epoch(train) [4][3850/3953] lr: 8.4588e-04 eta: 4:29:43 time: 0.5042 data_time: 0.0041 memory: 8109 grad_norm: 17.4994 loss: 3.7616 task0.loss_heatmap: 0.5964 task0.loss_bbox: 2.3521 task0.loss_iou: 0.1673 task0.loss_reg_iou: 0.6459 2023/09/17 13:24:43 - mmengine - INFO - Epoch(train) [4][3900/3953] lr: 8.4455e-04 eta: 4:29:18 time: 0.5095 data_time: 0.0042 memory: 8712 grad_norm: 16.0082 loss: 3.7673 task0.loss_heatmap: 0.5999 task0.loss_bbox: 2.3720 task0.loss_iou: 0.1609 task0.loss_reg_iou: 0.6345 2023/09/17 13:25:07 - mmengine - INFO - Epoch(train) [4][3950/3953] lr: 8.4321e-04 eta: 4:28:51 time: 0.4940 data_time: 0.0043 memory: 8356 grad_norm: 17.9955 loss: 3.8610 task0.loss_heatmap: 0.6088 task0.loss_bbox: 2.4223 task0.loss_iou: 0.1679 task0.loss_reg_iou: 0.6621 2023/09/17 13:25:09 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:25:09 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/09/17 13:25:31 - mmengine - INFO - Epoch(val) [4][ 50/1250] eta: 0:07:40 time: 0.3834 data_time: 0.0067 memory: 7047 2023/09/17 13:25:51 - mmengine - INFO - Epoch(val) [4][ 100/1250] eta: 0:07:23 time: 0.3874 data_time: 0.0049 memory: 4043 2023/09/17 13:26:10 - mmengine - INFO - Epoch(val) [4][ 150/1250] eta: 0:07:05 time: 0.3890 data_time: 0.0050 memory: 4062 2023/09/17 13:26:29 - mmengine - INFO - Epoch(val) [4][ 200/1250] eta: 0:06:44 time: 0.3798 data_time: 0.0049 memory: 4051 2023/09/17 13:26:47 - mmengine - INFO - Epoch(val) [4][ 250/1250] eta: 0:06:19 time: 0.3584 data_time: 0.0043 memory: 4033 2023/09/17 13:27:06 - mmengine - INFO - Epoch(val) [4][ 300/1250] eta: 0:06:01 time: 0.3826 data_time: 0.0043 memory: 4050 2023/09/17 13:27:25 - mmengine - INFO - Epoch(val) [4][ 350/1250] eta: 0:05:42 time: 0.3843 data_time: 0.0045 memory: 4057 2023/09/17 13:27:45 - mmengine - INFO - Epoch(val) [4][ 400/1250] eta: 0:05:24 time: 0.3889 data_time: 0.0047 memory: 4060 2023/09/17 13:28:05 - mmengine - INFO - Epoch(val) [4][ 450/1250] eta: 0:05:07 time: 0.4050 data_time: 0.0044 memory: 4048 2023/09/17 13:28:23 - mmengine - INFO - Epoch(val) [4][ 500/1250] eta: 0:04:46 time: 0.3674 data_time: 0.0048 memory: 4041 2023/09/17 13:28:42 - mmengine - INFO - Epoch(val) [4][ 550/1250] eta: 0:04:27 time: 0.3700 data_time: 0.0046 memory: 4042 2023/09/17 13:29:00 - mmengine - INFO - Epoch(val) [4][ 600/1250] eta: 0:04:07 time: 0.3700 data_time: 0.0048 memory: 4057 2023/09/17 13:29:21 - mmengine - INFO - Epoch(val) [4][ 650/1250] eta: 0:03:49 time: 0.4082 data_time: 0.0049 memory: 4065 2023/09/17 13:29:40 - mmengine - INFO - Epoch(val) [4][ 700/1250] eta: 0:03:30 time: 0.3940 data_time: 0.0046 memory: 4042 2023/09/17 13:30:00 - mmengine - INFO - Epoch(val) [4][ 750/1250] eta: 0:03:12 time: 0.3979 data_time: 0.0049 memory: 4051 2023/09/17 13:30:19 - mmengine - INFO - Epoch(val) [4][ 800/1250] eta: 0:02:52 time: 0.3727 data_time: 0.0049 memory: 4055 2023/09/17 13:30:38 - mmengine - INFO - Epoch(val) [4][ 850/1250] eta: 0:02:33 time: 0.3881 data_time: 0.0049 memory: 4058 2023/09/17 13:30:56 - mmengine - INFO - Epoch(val) [4][ 900/1250] eta: 0:02:13 time: 0.3596 data_time: 0.0048 memory: 4049 2023/09/17 13:31:15 - mmengine - INFO - Epoch(val) [4][ 950/1250] eta: 0:01:54 time: 0.3673 data_time: 0.0049 memory: 4054 2023/09/17 13:31:34 - mmengine - INFO - Epoch(val) [4][1000/1250] eta: 0:01:35 time: 0.3791 data_time: 0.0049 memory: 4056 2023/09/17 13:31:53 - mmengine - INFO - Epoch(val) [4][1050/1250] eta: 0:01:16 time: 0.3786 data_time: 0.0047 memory: 4043 2023/09/17 13:32:11 - mmengine - INFO - Epoch(val) [4][1100/1250] eta: 0:00:57 time: 0.3569 data_time: 0.0048 memory: 4032 2023/09/17 13:32:30 - mmengine - INFO - Epoch(val) [4][1150/1250] eta: 0:00:38 time: 0.3829 data_time: 0.0047 memory: 4041 2023/09/17 13:32:49 - mmengine - INFO - Epoch(val) [4][1200/1250] eta: 0:00:19 time: 0.3817 data_time: 0.0045 memory: 4047 2023/09/17 13:33:08 - mmengine - INFO - Epoch(val) [4][1250/1250] eta: 0:00:00 time: 0.3879 data_time: 0.0048 memory: 4055 2023/09/17 13:33:12 - mmengine - INFO - Start converting ... 2023/09/17 13:41:10 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39986 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.548035] [mAPH 0.542129] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.477076] [mAPH 0.471895] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.67093] [mAPH 0.572488] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.591973] [mAPH 0.504515] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.616942] [mAPH 0.599222] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.593626] [mAPH 0.57657] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.803513] [mAPH 0.796376] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.790354] [mAPH 0.783324] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.500384] [mAPH 0.494194] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.448763] [mAPH 0.443176] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.222837] [mAPH 0.218073] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.1652] [mAPH 0.161621] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.744024] [mAPH 0.647667] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.69977] [mAPH 0.607609] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.654322] [mAPH 0.553837] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.58709] [mAPH 0.496656] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.524184] [mAPH 0.419225] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.404755] [mAPH 0.322077] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.74354] [mAPH 0.724535] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.738202] [mAPH 0.719332] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.557524] [mAPH 0.54163] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.525634] [mAPH 0.510643] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.395795] [mAPH 0.376133] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.36839] [mAPH 0.350084] Eval Using 329s 2023/09/17 13:41:11 - mmengine - INFO - Epoch(val) [4][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.5480 Waymo metric/Vehicle/L1 mAPH: 0.5421 Waymo metric/Vehicle/L2 mAP: 0.4771 Waymo metric/Vehicle/L2 mAPH: 0.4719 Waymo metric/Pedestrian/L1 mAP: 0.6709 Waymo metric/Pedestrian/L1 mAPH: 0.5725 Waymo metric/Pedestrian/L2 mAP: 0.5920 Waymo metric/Pedestrian/L2 mAPH: 0.5045 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6169 Waymo metric/Cyclist/L1 mAPH: 0.5992 Waymo metric/Cyclist/L2 mAP: 0.5936 Waymo metric/Cyclist/L2 mAPH: 0.5766 Waymo metric/Overall/L1 mAP: 0.6120 Waymo metric/Overall/L1 mAPH: 0.5713 Waymo metric/Overall/L2 mAP: 0.5542 Waymo metric/Overall/L2 mAPH: 0.5177 data_time: 0.0048 time: 0.3808 2023/09/17 13:41:35 - mmengine - INFO - Epoch(train) [5][ 50/3953] lr: 8.4179e-04 eta: 4:28:22 time: 0.4894 data_time: 0.0059 memory: 7962 grad_norm: 16.2080 loss: 3.8427 task0.loss_heatmap: 0.5791 task0.loss_bbox: 2.4511 task0.loss_iou: 0.1653 task0.loss_reg_iou: 0.6472 2023/09/17 13:42:00 - mmengine - INFO - Epoch(train) [5][ 100/3953] lr: 8.4045e-04 eta: 4:27:55 time: 0.4933 data_time: 0.0042 memory: 8705 grad_norm: 16.7876 loss: 3.6259 task0.loss_heatmap: 0.5493 task0.loss_bbox: 2.3149 task0.loss_iou: 0.1515 task0.loss_reg_iou: 0.6103 2023/09/17 13:42:25 - mmengine - INFO - Epoch(train) [5][ 150/3953] lr: 8.3910e-04 eta: 4:27:28 time: 0.5010 data_time: 0.0043 memory: 8352 grad_norm: 17.8901 loss: 3.7259 task0.loss_heatmap: 0.5572 task0.loss_bbox: 2.3509 task0.loss_iou: 0.1687 task0.loss_reg_iou: 0.6490 2023/09/17 13:42:44 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:42:50 - mmengine - INFO - Epoch(train) [5][ 200/3953] lr: 8.3774e-04 eta: 4:27:02 time: 0.5005 data_time: 0.0042 memory: 8410 grad_norm: 17.1835 loss: 3.7470 task0.loss_heatmap: 0.5863 task0.loss_bbox: 2.3786 task0.loss_iou: 0.1542 task0.loss_reg_iou: 0.6279 2023/09/17 13:43:15 - mmengine - INFO - Epoch(train) [5][ 250/3953] lr: 8.3638e-04 eta: 4:26:35 time: 0.4939 data_time: 0.0043 memory: 8145 grad_norm: 19.0734 loss: 3.7606 task0.loss_heatmap: 0.5830 task0.loss_bbox: 2.3759 task0.loss_iou: 0.1594 task0.loss_reg_iou: 0.6423 2023/09/17 13:43:40 - mmengine - INFO - Epoch(train) [5][ 300/3953] lr: 8.3502e-04 eta: 4:26:07 time: 0.4915 data_time: 0.0042 memory: 8160 grad_norm: 18.2051 loss: 3.6729 task0.loss_heatmap: 0.5520 task0.loss_bbox: 2.3337 task0.loss_iou: 0.1567 task0.loss_reg_iou: 0.6306 2023/09/17 13:44:04 - mmengine - INFO - Epoch(train) [5][ 350/3953] lr: 8.3365e-04 eta: 4:25:41 time: 0.4950 data_time: 0.0041 memory: 8232 grad_norm: 17.2210 loss: 3.7008 task0.loss_heatmap: 0.5318 task0.loss_bbox: 2.3714 task0.loss_iou: 0.1600 task0.loss_reg_iou: 0.6375 2023/09/17 13:44:29 - mmengine - INFO - Epoch(train) [5][ 400/3953] lr: 8.3228e-04 eta: 4:25:14 time: 0.4984 data_time: 0.0042 memory: 8335 grad_norm: 17.8193 loss: 3.7131 task0.loss_heatmap: 0.5922 task0.loss_bbox: 2.3336 task0.loss_iou: 0.1596 task0.loss_reg_iou: 0.6277 2023/09/17 13:44:55 - mmengine - INFO - Epoch(train) [5][ 450/3953] lr: 8.3090e-04 eta: 4:24:49 time: 0.5141 data_time: 0.0041 memory: 8029 grad_norm: 16.8817 loss: 3.9723 task0.loss_heatmap: 0.6226 task0.loss_bbox: 2.5333 task0.loss_iou: 0.1644 task0.loss_reg_iou: 0.6519 2023/09/17 13:45:20 - mmengine - INFO - Epoch(train) [5][ 500/3953] lr: 8.2952e-04 eta: 4:24:23 time: 0.5043 data_time: 0.0041 memory: 8274 grad_norm: 16.7764 loss: 3.6893 task0.loss_heatmap: 0.5595 task0.loss_bbox: 2.3445 task0.loss_iou: 0.1577 task0.loss_reg_iou: 0.6276 2023/09/17 13:45:45 - mmengine - INFO - Epoch(train) [5][ 550/3953] lr: 8.2814e-04 eta: 4:23:56 time: 0.4983 data_time: 0.0050 memory: 8546 grad_norm: 17.3020 loss: 3.6521 task0.loss_heatmap: 0.5569 task0.loss_bbox: 2.3260 task0.loss_iou: 0.1554 task0.loss_reg_iou: 0.6138 2023/09/17 13:46:10 - mmengine - INFO - Epoch(train) [5][ 600/3953] lr: 8.2675e-04 eta: 4:23:29 time: 0.4909 data_time: 0.0039 memory: 8630 grad_norm: 16.5090 loss: 3.6732 task0.loss_heatmap: 0.5525 task0.loss_bbox: 2.3420 task0.loss_iou: 0.1533 task0.loss_reg_iou: 0.6254 2023/09/17 13:46:34 - mmengine - INFO - Epoch(train) [5][ 650/3953] lr: 8.2535e-04 eta: 4:23:02 time: 0.4896 data_time: 0.0039 memory: 8302 grad_norm: 16.1379 loss: 3.5637 task0.loss_heatmap: 0.5314 task0.loss_bbox: 2.2525 task0.loss_iou: 0.1597 task0.loss_reg_iou: 0.6201 2023/09/17 13:46:59 - mmengine - INFO - Epoch(train) [5][ 700/3953] lr: 8.2395e-04 eta: 4:22:36 time: 0.5035 data_time: 0.0040 memory: 8978 grad_norm: 17.8047 loss: 3.8621 task0.loss_heatmap: 0.5594 task0.loss_bbox: 2.4626 task0.loss_iou: 0.1745 task0.loss_reg_iou: 0.6657 2023/09/17 13:47:29 - mmengine - INFO - Epoch(train) [5][ 750/3953] lr: 8.2255e-04 eta: 4:22:18 time: 0.5962 data_time: 0.0040 memory: 8240 grad_norm: 16.0196 loss: 3.6816 task0.loss_heatmap: 0.5698 task0.loss_bbox: 2.3312 task0.loss_iou: 0.1578 task0.loss_reg_iou: 0.6228 2023/09/17 13:47:55 - mmengine - INFO - Epoch(train) [5][ 800/3953] lr: 8.2114e-04 eta: 4:21:53 time: 0.5121 data_time: 0.0038 memory: 8319 grad_norm: 15.9664 loss: 3.8035 task0.loss_heatmap: 0.6034 task0.loss_bbox: 2.3921 task0.loss_iou: 0.1632 task0.loss_reg_iou: 0.6448 2023/09/17 13:48:20 - mmengine - INFO - Epoch(train) [5][ 850/3953] lr: 8.1973e-04 eta: 4:21:28 time: 0.5159 data_time: 0.0038 memory: 8451 grad_norm: 15.7617 loss: 3.5999 task0.loss_heatmap: 0.5213 task0.loss_bbox: 2.3078 task0.loss_iou: 0.1536 task0.loss_reg_iou: 0.6172 2023/09/17 13:48:46 - mmengine - INFO - Epoch(train) [5][ 900/3953] lr: 8.1831e-04 eta: 4:21:02 time: 0.5049 data_time: 0.0040 memory: 8593 grad_norm: 16.0062 loss: 3.9049 task0.loss_heatmap: 0.5756 task0.loss_bbox: 2.4385 task0.loss_iou: 0.1903 task0.loss_reg_iou: 0.7005 2023/09/17 13:49:11 - mmengine - INFO - Epoch(train) [5][ 950/3953] lr: 8.1689e-04 eta: 4:20:36 time: 0.5050 data_time: 0.0039 memory: 8394 grad_norm: 15.5395 loss: 3.6630 task0.loss_heatmap: 0.5274 task0.loss_bbox: 2.3209 task0.loss_iou: 0.1612 task0.loss_reg_iou: 0.6535 2023/09/17 13:49:36 - mmengine - INFO - Epoch(train) [5][1000/3953] lr: 8.1547e-04 eta: 4:20:10 time: 0.4966 data_time: 0.0039 memory: 8512 grad_norm: 17.7643 loss: 3.7344 task0.loss_heatmap: 0.5532 task0.loss_bbox: 2.3807 task0.loss_iou: 0.1565 task0.loss_reg_iou: 0.6439 2023/09/17 13:50:02 - mmengine - INFO - Epoch(train) [5][1050/3953] lr: 8.1404e-04 eta: 4:19:44 time: 0.5135 data_time: 0.0039 memory: 8147 grad_norm: 17.6004 loss: 3.7076 task0.loss_heatmap: 0.5572 task0.loss_bbox: 2.3473 task0.loss_iou: 0.1618 task0.loss_reg_iou: 0.6413 2023/09/17 13:50:27 - mmengine - INFO - Epoch(train) [5][1100/3953] lr: 8.1260e-04 eta: 4:19:18 time: 0.5020 data_time: 0.0039 memory: 8262 grad_norm: 18.9591 loss: 3.7310 task0.loss_heatmap: 0.5611 task0.loss_bbox: 2.3616 task0.loss_iou: 0.1639 task0.loss_reg_iou: 0.6444 2023/09/17 13:50:52 - mmengine - INFO - Epoch(train) [5][1150/3953] lr: 8.1116e-04 eta: 4:18:53 time: 0.5148 data_time: 0.0039 memory: 8492 grad_norm: 17.1457 loss: 3.7698 task0.loss_heatmap: 0.5615 task0.loss_bbox: 2.4110 task0.loss_iou: 0.1603 task0.loss_reg_iou: 0.6369 2023/09/17 13:51:11 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:51:17 - mmengine - INFO - Epoch(train) [5][1200/3953] lr: 8.0972e-04 eta: 4:18:25 time: 0.4843 data_time: 0.0040 memory: 8585 grad_norm: 15.7393 loss: 3.7809 task0.loss_heatmap: 0.5767 task0.loss_bbox: 2.4054 task0.loss_iou: 0.1622 task0.loss_reg_iou: 0.6366 2023/09/17 13:51:42 - mmengine - INFO - Epoch(train) [5][1250/3953] lr: 8.0828e-04 eta: 4:18:00 time: 0.5164 data_time: 0.0038 memory: 8173 grad_norm: 16.0798 loss: 3.8383 task0.loss_heatmap: 0.5915 task0.loss_bbox: 2.4458 task0.loss_iou: 0.1596 task0.loss_reg_iou: 0.6413 2023/09/17 13:52:08 - mmengine - INFO - Epoch(train) [5][1300/3953] lr: 8.0683e-04 eta: 4:17:34 time: 0.5024 data_time: 0.0039 memory: 8591 grad_norm: 16.4788 loss: 3.8948 task0.loss_heatmap: 0.5856 task0.loss_bbox: 2.5066 task0.loss_iou: 0.1605 task0.loss_reg_iou: 0.6422 2023/09/17 13:52:34 - mmengine - INFO - Epoch(train) [5][1350/3953] lr: 8.0537e-04 eta: 4:17:10 time: 0.5213 data_time: 0.0038 memory: 8458 grad_norm: 17.4439 loss: 3.8303 task0.loss_heatmap: 0.5701 task0.loss_bbox: 2.4397 task0.loss_iou: 0.1650 task0.loss_reg_iou: 0.6554 2023/09/17 13:52:59 - mmengine - INFO - Epoch(train) [5][1400/3953] lr: 8.0391e-04 eta: 4:16:44 time: 0.5025 data_time: 0.0038 memory: 8429 grad_norm: 17.0423 loss: 3.7676 task0.loss_heatmap: 0.5849 task0.loss_bbox: 2.3832 task0.loss_iou: 0.1620 task0.loss_reg_iou: 0.6375 2023/09/17 13:53:23 - mmengine - INFO - Epoch(train) [5][1450/3953] lr: 8.0245e-04 eta: 4:16:17 time: 0.4901 data_time: 0.0038 memory: 8068 grad_norm: 15.3013 loss: 3.7131 task0.loss_heatmap: 0.5534 task0.loss_bbox: 2.3620 task0.loss_iou: 0.1580 task0.loss_reg_iou: 0.6397 2023/09/17 13:53:48 - mmengine - INFO - Epoch(train) [5][1500/3953] lr: 8.0098e-04 eta: 4:15:51 time: 0.5039 data_time: 0.0039 memory: 8457 grad_norm: 16.4689 loss: 3.7130 task0.loss_heatmap: 0.5663 task0.loss_bbox: 2.3573 task0.loss_iou: 0.1625 task0.loss_reg_iou: 0.6269 2023/09/17 13:54:14 - mmengine - INFO - Epoch(train) [5][1550/3953] lr: 7.9951e-04 eta: 4:15:25 time: 0.5103 data_time: 0.0039 memory: 8994 grad_norm: 19.3925 loss: 3.8263 task0.loss_heatmap: 0.6092 task0.loss_bbox: 2.3897 task0.loss_iou: 0.1647 task0.loss_reg_iou: 0.6626 2023/09/17 13:54:38 - mmengine - INFO - Epoch(train) [5][1600/3953] lr: 7.9804e-04 eta: 4:14:58 time: 0.4845 data_time: 0.0041 memory: 8416 grad_norm: 17.9256 loss: 3.7874 task0.loss_heatmap: 0.5927 task0.loss_bbox: 2.3950 task0.loss_iou: 0.1640 task0.loss_reg_iou: 0.6357 2023/09/17 13:55:03 - mmengine - INFO - Epoch(train) [5][1650/3953] lr: 7.9656e-04 eta: 4:14:31 time: 0.4957 data_time: 0.0037 memory: 9045 grad_norm: 17.6795 loss: 3.7345 task0.loss_heatmap: 0.5544 task0.loss_bbox: 2.3832 task0.loss_iou: 0.1596 task0.loss_reg_iou: 0.6374 2023/09/17 13:55:28 - mmengine - INFO - Epoch(train) [5][1700/3953] lr: 7.9507e-04 eta: 4:14:05 time: 0.5005 data_time: 0.0038 memory: 7989 grad_norm: 16.6969 loss: 3.7515 task0.loss_heatmap: 0.6077 task0.loss_bbox: 2.3590 task0.loss_iou: 0.1531 task0.loss_reg_iou: 0.6317 2023/09/17 13:55:54 - mmengine - INFO - Epoch(train) [5][1750/3953] lr: 7.9359e-04 eta: 4:13:39 time: 0.5106 data_time: 0.0039 memory: 7987 grad_norm: 16.4164 loss: 3.6633 task0.loss_heatmap: 0.5501 task0.loss_bbox: 2.3445 task0.loss_iou: 0.1502 task0.loss_reg_iou: 0.6185 2023/09/17 13:56:18 - mmengine - INFO - Epoch(train) [5][1800/3953] lr: 7.9210e-04 eta: 4:13:13 time: 0.4947 data_time: 0.0039 memory: 8345 grad_norm: 17.8894 loss: 3.7306 task0.loss_heatmap: 0.5522 task0.loss_bbox: 2.3738 task0.loss_iou: 0.1598 task0.loss_reg_iou: 0.6449 2023/09/17 13:56:44 - mmengine - INFO - Epoch(train) [5][1850/3953] lr: 7.9060e-04 eta: 4:12:47 time: 0.5123 data_time: 0.0039 memory: 8264 grad_norm: 18.0671 loss: 3.6458 task0.loss_heatmap: 0.5581 task0.loss_bbox: 2.3092 task0.loss_iou: 0.1546 task0.loss_reg_iou: 0.6239 2023/09/17 13:57:09 - mmengine - INFO - Epoch(train) [5][1900/3953] lr: 7.8910e-04 eta: 4:12:21 time: 0.4972 data_time: 0.0038 memory: 8621 grad_norm: 18.2329 loss: 3.8132 task0.loss_heatmap: 0.5576 task0.loss_bbox: 2.4368 task0.loss_iou: 0.1654 task0.loss_reg_iou: 0.6535 2023/09/17 13:57:34 - mmengine - INFO - Epoch(train) [5][1950/3953] lr: 7.8760e-04 eta: 4:11:55 time: 0.5027 data_time: 0.0038 memory: 8544 grad_norm: 16.9402 loss: 3.6115 task0.loss_heatmap: 0.5350 task0.loss_bbox: 2.3016 task0.loss_iou: 0.1566 task0.loss_reg_iou: 0.6183 2023/09/17 13:57:59 - mmengine - INFO - Epoch(train) [5][2000/3953] lr: 7.8609e-04 eta: 4:11:28 time: 0.4984 data_time: 0.0039 memory: 8664 grad_norm: 16.2927 loss: 3.6683 task0.loss_heatmap: 0.5580 task0.loss_bbox: 2.3174 task0.loss_iou: 0.1613 task0.loss_reg_iou: 0.6315 2023/09/17 13:58:24 - mmengine - INFO - Epoch(train) [5][2050/3953] lr: 7.8458e-04 eta: 4:11:03 time: 0.5071 data_time: 0.0039 memory: 8809 grad_norm: 16.6445 loss: 3.8915 task0.loss_heatmap: 0.6277 task0.loss_bbox: 2.4581 task0.loss_iou: 0.1636 task0.loss_reg_iou: 0.6421 2023/09/17 13:58:49 - mmengine - INFO - Epoch(train) [5][2100/3953] lr: 7.8307e-04 eta: 4:10:36 time: 0.4992 data_time: 0.0040 memory: 8155 grad_norm: 15.8997 loss: 3.6673 task0.loss_heatmap: 0.5648 task0.loss_bbox: 2.3035 task0.loss_iou: 0.1679 task0.loss_reg_iou: 0.6311 2023/09/17 13:59:14 - mmengine - INFO - Epoch(train) [5][2150/3953] lr: 7.8155e-04 eta: 4:10:10 time: 0.5011 data_time: 0.0040 memory: 8490 grad_norm: 17.0787 loss: 3.6028 task0.loss_heatmap: 0.5292 task0.loss_bbox: 2.2981 task0.loss_iou: 0.1577 task0.loss_reg_iou: 0.6178 2023/09/17 13:59:33 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 13:59:39 - mmengine - INFO - Epoch(train) [5][2200/3953] lr: 7.8003e-04 eta: 4:09:44 time: 0.5031 data_time: 0.0039 memory: 8364 grad_norm: 14.8554 loss: 3.6345 task0.loss_heatmap: 0.5570 task0.loss_bbox: 2.3010 task0.loss_iou: 0.1539 task0.loss_reg_iou: 0.6225 2023/09/17 14:00:07 - mmengine - INFO - Epoch(train) [5][2250/3953] lr: 7.7850e-04 eta: 4:09:22 time: 0.5438 data_time: 0.0040 memory: 8464 grad_norm: 16.8789 loss: 3.6429 task0.loss_heatmap: 0.5458 task0.loss_bbox: 2.3059 task0.loss_iou: 0.1639 task0.loss_reg_iou: 0.6272 2023/09/17 14:00:35 - mmengine - INFO - Epoch(train) [5][2300/3953] lr: 7.7697e-04 eta: 4:09:02 time: 0.5763 data_time: 0.0039 memory: 8494 grad_norm: 16.8636 loss: 3.7335 task0.loss_heatmap: 0.5352 task0.loss_bbox: 2.4028 task0.loss_iou: 0.1581 task0.loss_reg_iou: 0.6374 2023/09/17 14:01:00 - mmengine - INFO - Epoch(train) [5][2350/3953] lr: 7.7544e-04 eta: 4:08:35 time: 0.4990 data_time: 0.0039 memory: 8173 grad_norm: 16.5818 loss: 3.7013 task0.loss_heatmap: 0.5485 task0.loss_bbox: 2.3646 task0.loss_iou: 0.1566 task0.loss_reg_iou: 0.6316 2023/09/17 14:01:25 - mmengine - INFO - Epoch(train) [5][2400/3953] lr: 7.7390e-04 eta: 4:08:09 time: 0.5020 data_time: 0.0039 memory: 8127 grad_norm: 18.4697 loss: 3.6125 task0.loss_heatmap: 0.5182 task0.loss_bbox: 2.3153 task0.loss_iou: 0.1518 task0.loss_reg_iou: 0.6272 2023/09/17 14:01:50 - mmengine - INFO - Epoch(train) [5][2450/3953] lr: 7.7236e-04 eta: 4:07:43 time: 0.4994 data_time: 0.0042 memory: 8503 grad_norm: 17.5469 loss: 3.7797 task0.loss_heatmap: 0.5727 task0.loss_bbox: 2.4090 task0.loss_iou: 0.1590 task0.loss_reg_iou: 0.6389 2023/09/17 14:02:16 - mmengine - INFO - Epoch(train) [5][2500/3953] lr: 7.7081e-04 eta: 4:07:17 time: 0.5024 data_time: 0.0038 memory: 8447 grad_norm: 15.7139 loss: 3.4076 task0.loss_heatmap: 0.5073 task0.loss_bbox: 2.1694 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5825 2023/09/17 14:02:41 - mmengine - INFO - Epoch(train) [5][2550/3953] lr: 7.6927e-04 eta: 4:06:51 time: 0.5035 data_time: 0.0039 memory: 8404 grad_norm: 18.2686 loss: 3.7885 task0.loss_heatmap: 0.5766 task0.loss_bbox: 2.4062 task0.loss_iou: 0.1615 task0.loss_reg_iou: 0.6442 2023/09/17 14:03:06 - mmengine - INFO - Epoch(train) [5][2600/3953] lr: 7.6771e-04 eta: 4:06:25 time: 0.4980 data_time: 0.0038 memory: 8228 grad_norm: 17.1620 loss: 3.6889 task0.loss_heatmap: 0.5891 task0.loss_bbox: 2.3326 task0.loss_iou: 0.1523 task0.loss_reg_iou: 0.6149 2023/09/17 14:03:31 - mmengine - INFO - Epoch(train) [5][2650/3953] lr: 7.6616e-04 eta: 4:05:58 time: 0.4997 data_time: 0.0039 memory: 8241 grad_norm: 15.6954 loss: 3.6692 task0.loss_heatmap: 0.5609 task0.loss_bbox: 2.3230 task0.loss_iou: 0.1553 task0.loss_reg_iou: 0.6299 2023/09/17 14:03:56 - mmengine - INFO - Epoch(train) [5][2700/3953] lr: 7.6460e-04 eta: 4:05:32 time: 0.5031 data_time: 0.0038 memory: 8275 grad_norm: 16.9960 loss: 3.7347 task0.loss_heatmap: 0.5719 task0.loss_bbox: 2.3737 task0.loss_iou: 0.1581 task0.loss_reg_iou: 0.6309 2023/09/17 14:04:21 - mmengine - INFO - Epoch(train) [5][2750/3953] lr: 7.6304e-04 eta: 4:05:06 time: 0.5032 data_time: 0.0038 memory: 8457 grad_norm: 15.9465 loss: 3.6055 task0.loss_heatmap: 0.5503 task0.loss_bbox: 2.2842 task0.loss_iou: 0.1554 task0.loss_reg_iou: 0.6157 2023/09/17 14:04:46 - mmengine - INFO - Epoch(train) [5][2800/3953] lr: 7.6147e-04 eta: 4:04:40 time: 0.5002 data_time: 0.0040 memory: 8444 grad_norm: 16.7399 loss: 3.6847 task0.loss_heatmap: 0.5526 task0.loss_bbox: 2.3516 task0.loss_iou: 0.1578 task0.loss_reg_iou: 0.6227 2023/09/17 14:05:11 - mmengine - INFO - Epoch(train) [5][2850/3953] lr: 7.5990e-04 eta: 4:04:15 time: 0.5071 data_time: 0.0040 memory: 8390 grad_norm: 16.3485 loss: 3.6609 task0.loss_heatmap: 0.5375 task0.loss_bbox: 2.3516 task0.loss_iou: 0.1550 task0.loss_reg_iou: 0.6169 2023/09/17 14:05:36 - mmengine - INFO - Epoch(train) [5][2900/3953] lr: 7.5833e-04 eta: 4:03:48 time: 0.4886 data_time: 0.0041 memory: 8320 grad_norm: 16.4445 loss: 3.6453 task0.loss_heatmap: 0.5412 task0.loss_bbox: 2.3334 task0.loss_iou: 0.1533 task0.loss_reg_iou: 0.6175 2023/09/17 14:06:02 - mmengine - INFO - Epoch(train) [5][2950/3953] lr: 7.5675e-04 eta: 4:03:24 time: 0.5289 data_time: 0.0041 memory: 8282 grad_norm: 17.5826 loss: 3.5925 task0.loss_heatmap: 0.5391 task0.loss_bbox: 2.2900 task0.loss_iou: 0.1543 task0.loss_reg_iou: 0.6091 2023/09/17 14:06:27 - mmengine - INFO - Epoch(train) [5][3000/3953] lr: 7.5517e-04 eta: 4:02:57 time: 0.4982 data_time: 0.0040 memory: 8155 grad_norm: 16.9228 loss: 3.6467 task0.loss_heatmap: 0.5439 task0.loss_bbox: 2.3248 task0.loss_iou: 0.1517 task0.loss_reg_iou: 0.6263 2023/09/17 14:06:52 - mmengine - INFO - Epoch(train) [5][3050/3953] lr: 7.5359e-04 eta: 4:02:31 time: 0.4997 data_time: 0.0039 memory: 7884 grad_norm: 17.3580 loss: 3.5079 task0.loss_heatmap: 0.5061 task0.loss_bbox: 2.2257 task0.loss_iou: 0.1604 task0.loss_reg_iou: 0.6157 2023/09/17 14:07:17 - mmengine - INFO - Epoch(train) [5][3100/3953] lr: 7.5200e-04 eta: 4:02:05 time: 0.4959 data_time: 0.0039 memory: 8070 grad_norm: 15.9812 loss: 3.5884 task0.loss_heatmap: 0.5237 task0.loss_bbox: 2.2618 task0.loss_iou: 0.1644 task0.loss_reg_iou: 0.6384 2023/09/17 14:07:41 - mmengine - INFO - Epoch(train) [5][3150/3953] lr: 7.5041e-04 eta: 4:01:38 time: 0.4907 data_time: 0.0038 memory: 8528 grad_norm: 16.6671 loss: 3.8255 task0.loss_heatmap: 0.6102 task0.loss_bbox: 2.4038 task0.loss_iou: 0.1672 task0.loss_reg_iou: 0.6444 2023/09/17 14:08:00 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:08:06 - mmengine - INFO - Epoch(train) [5][3200/3953] lr: 7.4881e-04 eta: 4:01:11 time: 0.4971 data_time: 0.0038 memory: 8521 grad_norm: 19.0355 loss: 3.6226 task0.loss_heatmap: 0.5223 task0.loss_bbox: 2.3055 task0.loss_iou: 0.1609 task0.loss_reg_iou: 0.6339 2023/09/17 14:08:31 - mmengine - INFO - Epoch(train) [5][3250/3953] lr: 7.4722e-04 eta: 4:00:44 time: 0.4861 data_time: 0.0039 memory: 8001 grad_norm: 16.5967 loss: 3.8666 task0.loss_heatmap: 0.6058 task0.loss_bbox: 2.4508 task0.loss_iou: 0.1562 task0.loss_reg_iou: 0.6537 2023/09/17 14:08:55 - mmengine - INFO - Epoch(train) [5][3300/3953] lr: 7.4562e-04 eta: 4:00:18 time: 0.4922 data_time: 0.0038 memory: 8066 grad_norm: 16.0159 loss: 3.6841 task0.loss_heatmap: 0.5556 task0.loss_bbox: 2.3501 task0.loss_iou: 0.1612 task0.loss_reg_iou: 0.6172 2023/09/17 14:09:20 - mmengine - INFO - Epoch(train) [5][3350/3953] lr: 7.4401e-04 eta: 3:59:50 time: 0.4856 data_time: 0.0038 memory: 8286 grad_norm: 18.3661 loss: 3.5913 task0.loss_heatmap: 0.5114 task0.loss_bbox: 2.3050 task0.loss_iou: 0.1534 task0.loss_reg_iou: 0.6215 2023/09/17 14:09:44 - mmengine - INFO - Epoch(train) [5][3400/3953] lr: 7.4240e-04 eta: 3:59:24 time: 0.4903 data_time: 0.0038 memory: 8430 grad_norm: 16.4793 loss: 3.5740 task0.loss_heatmap: 0.5514 task0.loss_bbox: 2.2706 task0.loss_iou: 0.1499 task0.loss_reg_iou: 0.6022 2023/09/17 14:10:08 - mmengine - INFO - Epoch(train) [5][3450/3953] lr: 7.4079e-04 eta: 3:58:56 time: 0.4813 data_time: 0.0037 memory: 8376 grad_norm: 16.7497 loss: 3.6654 task0.loss_heatmap: 0.5479 task0.loss_bbox: 2.3314 task0.loss_iou: 0.1584 task0.loss_reg_iou: 0.6277 2023/09/17 14:10:32 - mmengine - INFO - Epoch(train) [5][3500/3953] lr: 7.3918e-04 eta: 3:58:29 time: 0.4847 data_time: 0.0037 memory: 8087 grad_norm: 19.0532 loss: 3.6962 task0.loss_heatmap: 0.5509 task0.loss_bbox: 2.3328 task0.loss_iou: 0.1656 task0.loss_reg_iou: 0.6469 2023/09/17 14:10:57 - mmengine - INFO - Epoch(train) [5][3550/3953] lr: 7.3756e-04 eta: 3:58:02 time: 0.4826 data_time: 0.0037 memory: 8609 grad_norm: 15.4347 loss: 3.4871 task0.loss_heatmap: 0.5225 task0.loss_bbox: 2.2077 task0.loss_iou: 0.1537 task0.loss_reg_iou: 0.6032 2023/09/17 14:11:21 - mmengine - INFO - Epoch(train) [5][3600/3953] lr: 7.3594e-04 eta: 3:57:35 time: 0.4942 data_time: 0.0038 memory: 8141 grad_norm: 18.2110 loss: 3.6712 task0.loss_heatmap: 0.5534 task0.loss_bbox: 2.3326 task0.loss_iou: 0.1564 task0.loss_reg_iou: 0.6288 2023/09/17 14:11:45 - mmengine - INFO - Epoch(train) [5][3650/3953] lr: 7.3432e-04 eta: 3:57:08 time: 0.4848 data_time: 0.0037 memory: 8604 grad_norm: 17.0694 loss: 3.5083 task0.loss_heatmap: 0.5139 task0.loss_bbox: 2.2458 task0.loss_iou: 0.1502 task0.loss_reg_iou: 0.5984 2023/09/17 14:12:10 - mmengine - INFO - Epoch(train) [5][3700/3953] lr: 7.3269e-04 eta: 3:56:42 time: 0.4993 data_time: 0.0037 memory: 8243 grad_norm: 16.5632 loss: 3.6215 task0.loss_heatmap: 0.5444 task0.loss_bbox: 2.3124 task0.loss_iou: 0.1523 task0.loss_reg_iou: 0.6124 2023/09/17 14:12:35 - mmengine - INFO - Epoch(train) [5][3750/3953] lr: 7.3106e-04 eta: 3:56:16 time: 0.5001 data_time: 0.0037 memory: 8244 grad_norm: 17.6174 loss: 3.6655 task0.loss_heatmap: 0.5369 task0.loss_bbox: 2.3212 task0.loss_iou: 0.1632 task0.loss_reg_iou: 0.6441 2023/09/17 14:13:04 - mmengine - INFO - Epoch(train) [5][3800/3953] lr: 7.2943e-04 eta: 3:55:55 time: 0.5696 data_time: 0.0037 memory: 8133 grad_norm: 15.3355 loss: 3.5774 task0.loss_heatmap: 0.5056 task0.loss_bbox: 2.2993 task0.loss_iou: 0.1557 task0.loss_reg_iou: 0.6169 2023/09/17 14:13:30 - mmengine - INFO - Epoch(train) [5][3850/3953] lr: 7.2779e-04 eta: 3:55:30 time: 0.5219 data_time: 0.0037 memory: 9077 grad_norm: 16.3060 loss: 3.6109 task0.loss_heatmap: 0.5636 task0.loss_bbox: 2.2772 task0.loss_iou: 0.1567 task0.loss_reg_iou: 0.6133 2023/09/17 14:13:55 - mmengine - INFO - Epoch(train) [5][3900/3953] lr: 7.2615e-04 eta: 3:55:04 time: 0.5043 data_time: 0.0037 memory: 8250 grad_norm: 18.0428 loss: 3.6606 task0.loss_heatmap: 0.5664 task0.loss_bbox: 2.3198 task0.loss_iou: 0.1549 task0.loss_reg_iou: 0.6195 2023/09/17 14:14:20 - mmengine - INFO - Epoch(train) [5][3950/3953] lr: 7.2451e-04 eta: 3:54:38 time: 0.4963 data_time: 0.0039 memory: 8148 grad_norm: 16.6059 loss: 3.7413 task0.loss_heatmap: 0.5922 task0.loss_bbox: 2.3636 task0.loss_iou: 0.1582 task0.loss_reg_iou: 0.6274 2023/09/17 14:14:22 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:14:22 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/09/17 14:14:43 - mmengine - INFO - Epoch(val) [5][ 50/1250] eta: 0:07:35 time: 0.3796 data_time: 0.0063 memory: 7986 2023/09/17 14:15:02 - mmengine - INFO - Epoch(val) [5][ 100/1250] eta: 0:07:20 time: 0.3858 data_time: 0.0047 memory: 4043 2023/09/17 14:15:22 - mmengine - INFO - Epoch(val) [5][ 150/1250] eta: 0:07:02 time: 0.3875 data_time: 0.0048 memory: 4062 2023/09/17 14:15:41 - mmengine - INFO - Epoch(val) [5][ 200/1250] eta: 0:06:42 time: 0.3809 data_time: 0.0048 memory: 4051 2023/09/17 14:15:59 - mmengine - INFO - Epoch(val) [5][ 250/1250] eta: 0:06:17 time: 0.3556 data_time: 0.0044 memory: 4033 2023/09/17 14:16:18 - mmengine - INFO - Epoch(val) [5][ 300/1250] eta: 0:05:59 time: 0.3839 data_time: 0.0044 memory: 4050 2023/09/17 14:16:37 - mmengine - INFO - Epoch(val) [5][ 350/1250] eta: 0:05:41 time: 0.3825 data_time: 0.0045 memory: 4057 2023/09/17 14:16:56 - mmengine - INFO - Epoch(val) [5][ 400/1250] eta: 0:05:23 time: 0.3883 data_time: 0.0046 memory: 4060 2023/09/17 14:17:17 - mmengine - INFO - Epoch(val) [5][ 450/1250] eta: 0:05:06 time: 0.4079 data_time: 0.0043 memory: 4048 2023/09/17 14:17:36 - mmengine - INFO - Epoch(val) [5][ 500/1250] eta: 0:04:46 time: 0.3728 data_time: 0.0047 memory: 4041 2023/09/17 14:17:54 - mmengine - INFO - Epoch(val) [5][ 550/1250] eta: 0:04:27 time: 0.3719 data_time: 0.0046 memory: 4042 2023/09/17 14:18:13 - mmengine - INFO - Epoch(val) [5][ 600/1250] eta: 0:04:07 time: 0.3745 data_time: 0.0047 memory: 4057 2023/09/17 14:18:33 - mmengine - INFO - Epoch(val) [5][ 650/1250] eta: 0:03:49 time: 0.4065 data_time: 0.0046 memory: 4065 2023/09/17 14:18:53 - mmengine - INFO - Epoch(val) [5][ 700/1250] eta: 0:03:31 time: 0.3932 data_time: 0.0045 memory: 4042 2023/09/17 14:19:13 - mmengine - INFO - Epoch(val) [5][ 750/1250] eta: 0:03:12 time: 0.3989 data_time: 0.0045 memory: 4051 2023/09/17 14:19:31 - mmengine - INFO - Epoch(val) [5][ 800/1250] eta: 0:02:52 time: 0.3727 data_time: 0.0046 memory: 4055 2023/09/17 14:19:51 - mmengine - INFO - Epoch(val) [5][ 850/1250] eta: 0:02:33 time: 0.3862 data_time: 0.0046 memory: 4058 2023/09/17 14:20:09 - mmengine - INFO - Epoch(val) [5][ 900/1250] eta: 0:02:13 time: 0.3591 data_time: 0.0045 memory: 4049 2023/09/17 14:20:27 - mmengine - INFO - Epoch(val) [5][ 950/1250] eta: 0:01:54 time: 0.3670 data_time: 0.0045 memory: 4054 2023/09/17 14:20:46 - mmengine - INFO - Epoch(val) [5][1000/1250] eta: 0:01:35 time: 0.3777 data_time: 0.0046 memory: 4056 2023/09/17 14:21:05 - mmengine - INFO - Epoch(val) [5][1050/1250] eta: 0:01:16 time: 0.3791 data_time: 0.0045 memory: 4043 2023/09/17 14:21:23 - mmengine - INFO - Epoch(val) [5][1100/1250] eta: 0:00:57 time: 0.3561 data_time: 0.0046 memory: 4032 2023/09/17 14:21:42 - mmengine - INFO - Epoch(val) [5][1150/1250] eta: 0:00:38 time: 0.3836 data_time: 0.0046 memory: 4041 2023/09/17 14:22:01 - mmengine - INFO - Epoch(val) [5][1200/1250] eta: 0:00:19 time: 0.3811 data_time: 0.0045 memory: 4047 2023/09/17 14:22:20 - mmengine - INFO - Epoch(val) [5][1250/1250] eta: 0:00:00 time: 0.3887 data_time: 0.0046 memory: 4055 2023/09/17 14:22:24 - mmengine - INFO - Start converting ... 2023/09/17 14:30:28 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.649132] [mAPH 0.642408] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.567926] [mAPH 0.561972] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.703693] [mAPH 0.600951] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.621289] [mAPH 0.529457] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.65224] [mAPH 0.63549] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.627846] [mAPH 0.611712] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.868709] [mAPH 0.862143] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.855048] [mAPH 0.848572] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.620402] [mAPH 0.612558] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.559045] [mAPH 0.551912] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.354114] [mAPH 0.34626] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.265025] [mAPH 0.259046] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.770643] [mAPH 0.672351] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.727814] [mAPH 0.633696] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.688677] [mAPH 0.579265] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.618345] [mAPH 0.519362] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.574797] [mAPH 0.462768] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.442905] [mAPH 0.35496] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.761708] [mAPH 0.745549] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.756242] [mAPH 0.740198] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.599488] [mAPH 0.58267] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.565625] [mAPH 0.54974] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.459668] [mAPH 0.439077] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.428168] [mAPH 0.40907] Eval Using 357s 2023/09/17 14:30:29 - mmengine - INFO - Epoch(val) [5][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.6491 Waymo metric/Vehicle/L1 mAPH: 0.6424 Waymo metric/Vehicle/L2 mAP: 0.5679 Waymo metric/Vehicle/L2 mAPH: 0.5620 Waymo metric/Pedestrian/L1 mAP: 0.7037 Waymo metric/Pedestrian/L1 mAPH: 0.6010 Waymo metric/Pedestrian/L2 mAP: 0.6213 Waymo metric/Pedestrian/L2 mAPH: 0.5295 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6522 Waymo metric/Cyclist/L1 mAPH: 0.6355 Waymo metric/Cyclist/L2 mAP: 0.6278 Waymo metric/Cyclist/L2 mAPH: 0.6117 Waymo metric/Overall/L1 mAP: 0.6684 Waymo metric/Overall/L1 mAPH: 0.6263 Waymo metric/Overall/L2 mAP: 0.6057 Waymo metric/Overall/L2 mAPH: 0.5677 data_time: 0.0046 time: 0.3808 2023/09/17 14:30:54 - mmengine - INFO - Epoch(train) [6][ 50/3953] lr: 7.2277e-04 eta: 3:54:11 time: 0.5104 data_time: 0.0052 memory: 9006 grad_norm: 16.4774 loss: 3.7062 task0.loss_heatmap: 0.5908 task0.loss_bbox: 2.3389 task0.loss_iou: 0.1535 task0.loss_reg_iou: 0.6229 2023/09/17 14:31:19 - mmengine - INFO - Epoch(train) [6][ 100/3953] lr: 7.2112e-04 eta: 3:53:45 time: 0.5026 data_time: 0.0038 memory: 8456 grad_norm: 17.4663 loss: 3.6218 task0.loss_heatmap: 0.5118 task0.loss_bbox: 2.3234 task0.loss_iou: 0.1568 task0.loss_reg_iou: 0.6297 2023/09/17 14:31:45 - mmengine - INFO - Epoch(train) [6][ 150/3953] lr: 7.1947e-04 eta: 3:53:19 time: 0.5024 data_time: 0.0038 memory: 8206 grad_norm: 15.4955 loss: 3.7063 task0.loss_heatmap: 0.5577 task0.loss_bbox: 2.3660 task0.loss_iou: 0.1547 task0.loss_reg_iou: 0.6278 2023/09/17 14:32:09 - mmengine - INFO - Epoch(train) [6][ 200/3953] lr: 7.1781e-04 eta: 3:52:53 time: 0.4965 data_time: 0.0036 memory: 8513 grad_norm: 15.3325 loss: 3.5052 task0.loss_heatmap: 0.5391 task0.loss_bbox: 2.2188 task0.loss_iou: 0.1502 task0.loss_reg_iou: 0.5971 2023/09/17 14:32:27 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:32:35 - mmengine - INFO - Epoch(train) [6][ 250/3953] lr: 7.1616e-04 eta: 3:52:27 time: 0.5056 data_time: 0.0036 memory: 8483 grad_norm: 16.8149 loss: 3.6425 task0.loss_heatmap: 0.5298 task0.loss_bbox: 2.3236 task0.loss_iou: 0.1609 task0.loss_reg_iou: 0.6283 2023/09/17 14:33:00 - mmengine - INFO - Epoch(train) [6][ 300/3953] lr: 7.1450e-04 eta: 3:52:01 time: 0.4978 data_time: 0.0036 memory: 8432 grad_norm: 15.5797 loss: 3.5432 task0.loss_heatmap: 0.5166 task0.loss_bbox: 2.2657 task0.loss_iou: 0.1539 task0.loss_reg_iou: 0.6069 2023/09/17 14:33:24 - mmengine - INFO - Epoch(train) [6][ 350/3953] lr: 7.1283e-04 eta: 3:51:34 time: 0.4876 data_time: 0.0041 memory: 8390 grad_norm: 16.9900 loss: 3.5902 task0.loss_heatmap: 0.5427 task0.loss_bbox: 2.2627 task0.loss_iou: 0.1602 task0.loss_reg_iou: 0.6247 2023/09/17 14:33:49 - mmengine - INFO - Epoch(train) [6][ 400/3953] lr: 7.1117e-04 eta: 3:51:08 time: 0.4921 data_time: 0.0040 memory: 7950 grad_norm: 18.3315 loss: 3.7500 task0.loss_heatmap: 0.5387 task0.loss_bbox: 2.3800 task0.loss_iou: 0.1706 task0.loss_reg_iou: 0.6607 2023/09/17 14:34:13 - mmengine - INFO - Epoch(train) [6][ 450/3953] lr: 7.0950e-04 eta: 3:50:41 time: 0.4841 data_time: 0.0039 memory: 8350 grad_norm: 15.8694 loss: 3.6435 task0.loss_heatmap: 0.5675 task0.loss_bbox: 2.2993 task0.loss_iou: 0.1569 task0.loss_reg_iou: 0.6198 2023/09/17 14:34:38 - mmengine - INFO - Epoch(train) [6][ 500/3953] lr: 7.0783e-04 eta: 3:50:15 time: 0.4978 data_time: 0.0037 memory: 8663 grad_norm: 16.6676 loss: 3.5214 task0.loss_heatmap: 0.5312 task0.loss_bbox: 2.2410 task0.loss_iou: 0.1469 task0.loss_reg_iou: 0.6024 2023/09/17 14:35:02 - mmengine - INFO - Epoch(train) [6][ 550/3953] lr: 7.0615e-04 eta: 3:49:48 time: 0.4962 data_time: 0.0037 memory: 8534 grad_norm: 16.0402 loss: 3.6612 task0.loss_heatmap: 0.5868 task0.loss_bbox: 2.3029 task0.loss_iou: 0.1534 task0.loss_reg_iou: 0.6180 2023/09/17 14:35:27 - mmengine - INFO - Epoch(train) [6][ 600/3953] lr: 7.0447e-04 eta: 3:49:21 time: 0.4850 data_time: 0.0037 memory: 8403 grad_norm: 17.1717 loss: 3.6464 task0.loss_heatmap: 0.5406 task0.loss_bbox: 2.3297 task0.loss_iou: 0.1498 task0.loss_reg_iou: 0.6264 2023/09/17 14:35:51 - mmengine - INFO - Epoch(train) [6][ 650/3953] lr: 7.0279e-04 eta: 3:48:55 time: 0.4876 data_time: 0.0037 memory: 8333 grad_norm: 16.6131 loss: 3.5083 task0.loss_heatmap: 0.5161 task0.loss_bbox: 2.2275 task0.loss_iou: 0.1534 task0.loss_reg_iou: 0.6113 2023/09/17 14:36:16 - mmengine - INFO - Epoch(train) [6][ 700/3953] lr: 7.0111e-04 eta: 3:48:28 time: 0.4891 data_time: 0.0037 memory: 8149 grad_norm: 15.7778 loss: 3.6426 task0.loss_heatmap: 0.5484 task0.loss_bbox: 2.3200 task0.loss_iou: 0.1535 task0.loss_reg_iou: 0.6206 2023/09/17 14:36:40 - mmengine - INFO - Epoch(train) [6][ 750/3953] lr: 6.9942e-04 eta: 3:48:02 time: 0.4947 data_time: 0.0038 memory: 8491 grad_norm: 16.1677 loss: 3.6224 task0.loss_heatmap: 0.5569 task0.loss_bbox: 2.2877 task0.loss_iou: 0.1541 task0.loss_reg_iou: 0.6236 2023/09/17 14:37:05 - mmengine - INFO - Epoch(train) [6][ 800/3953] lr: 6.9774e-04 eta: 3:47:35 time: 0.4887 data_time: 0.0038 memory: 8313 grad_norm: 16.9532 loss: 3.6918 task0.loss_heatmap: 0.5542 task0.loss_bbox: 2.3274 task0.loss_iou: 0.1615 task0.loss_reg_iou: 0.6487 2023/09/17 14:37:29 - mmengine - INFO - Epoch(train) [6][ 850/3953] lr: 6.9604e-04 eta: 3:47:09 time: 0.4940 data_time: 0.0038 memory: 8251 grad_norm: 15.6871 loss: 3.5747 task0.loss_heatmap: 0.5165 task0.loss_bbox: 2.2867 task0.loss_iou: 0.1521 task0.loss_reg_iou: 0.6195 2023/09/17 14:37:55 - mmengine - INFO - Epoch(train) [6][ 900/3953] lr: 6.9435e-04 eta: 3:46:43 time: 0.5013 data_time: 0.0037 memory: 8227 grad_norm: 15.3745 loss: 3.5730 task0.loss_heatmap: 0.5411 task0.loss_bbox: 2.2668 task0.loss_iou: 0.1566 task0.loss_reg_iou: 0.6085 2023/09/17 14:38:21 - mmengine - INFO - Epoch(train) [6][ 950/3953] lr: 6.9265e-04 eta: 3:46:19 time: 0.5369 data_time: 0.0037 memory: 8373 grad_norm: 17.6636 loss: 3.6451 task0.loss_heatmap: 0.5262 task0.loss_bbox: 2.3276 task0.loss_iou: 0.1609 task0.loss_reg_iou: 0.6304 2023/09/17 14:38:49 - mmengine - INFO - Epoch(train) [6][1000/3953] lr: 6.9096e-04 eta: 3:45:57 time: 0.5562 data_time: 0.0037 memory: 7974 grad_norm: 15.5299 loss: 3.5838 task0.loss_heatmap: 0.5386 task0.loss_bbox: 2.3008 task0.loss_iou: 0.1458 task0.loss_reg_iou: 0.5986 2023/09/17 14:39:15 - mmengine - INFO - Epoch(train) [6][1050/3953] lr: 6.8925e-04 eta: 3:45:31 time: 0.5072 data_time: 0.0038 memory: 8282 grad_norm: 15.7862 loss: 3.5434 task0.loss_heatmap: 0.5356 task0.loss_bbox: 2.2496 task0.loss_iou: 0.1528 task0.loss_reg_iou: 0.6054 2023/09/17 14:39:39 - mmengine - INFO - Epoch(train) [6][1100/3953] lr: 6.8755e-04 eta: 3:45:05 time: 0.4920 data_time: 0.0037 memory: 8465 grad_norm: 15.1548 loss: 3.5557 task0.loss_heatmap: 0.5549 task0.loss_bbox: 2.2510 task0.loss_iou: 0.1504 task0.loss_reg_iou: 0.5994 2023/09/17 14:40:04 - mmengine - INFO - Epoch(train) [6][1150/3953] lr: 6.8584e-04 eta: 3:44:39 time: 0.4984 data_time: 0.0037 memory: 8322 grad_norm: 16.0685 loss: 3.5870 task0.loss_heatmap: 0.5479 task0.loss_bbox: 2.2634 task0.loss_iou: 0.1592 task0.loss_reg_iou: 0.6165 2023/09/17 14:40:29 - mmengine - INFO - Epoch(train) [6][1200/3953] lr: 6.8413e-04 eta: 3:44:13 time: 0.4996 data_time: 0.0038 memory: 8103 grad_norm: 15.6836 loss: 3.4330 task0.loss_heatmap: 0.5018 task0.loss_bbox: 2.1778 task0.loss_iou: 0.1476 task0.loss_reg_iou: 0.6059 2023/09/17 14:40:46 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:40:54 - mmengine - INFO - Epoch(train) [6][1250/3953] lr: 6.8242e-04 eta: 3:43:46 time: 0.4903 data_time: 0.0037 memory: 8388 grad_norm: 16.1782 loss: 3.4405 task0.loss_heatmap: 0.4965 task0.loss_bbox: 2.1996 task0.loss_iou: 0.1473 task0.loss_reg_iou: 0.5972 2023/09/17 14:41:18 - mmengine - INFO - Epoch(train) [6][1300/3953] lr: 6.8071e-04 eta: 3:43:20 time: 0.4894 data_time: 0.0037 memory: 8619 grad_norm: 16.7960 loss: 3.7051 task0.loss_heatmap: 0.5407 task0.loss_bbox: 2.3690 task0.loss_iou: 0.1567 task0.loss_reg_iou: 0.6387 2023/09/17 14:41:42 - mmengine - INFO - Epoch(train) [6][1350/3953] lr: 6.7899e-04 eta: 3:42:53 time: 0.4889 data_time: 0.0037 memory: 8500 grad_norm: 16.2691 loss: 3.5355 task0.loss_heatmap: 0.5413 task0.loss_bbox: 2.2182 task0.loss_iou: 0.1562 task0.loss_reg_iou: 0.6197 2023/09/17 14:42:08 - mmengine - INFO - Epoch(train) [6][1400/3953] lr: 6.7727e-04 eta: 3:42:28 time: 0.5042 data_time: 0.0037 memory: 8451 grad_norm: 16.7388 loss: 3.5987 task0.loss_heatmap: 0.5205 task0.loss_bbox: 2.3080 task0.loss_iou: 0.1554 task0.loss_reg_iou: 0.6147 2023/09/17 14:42:33 - mmengine - INFO - Epoch(train) [6][1450/3953] lr: 6.7555e-04 eta: 3:42:01 time: 0.4963 data_time: 0.0039 memory: 8099 grad_norm: 14.6778 loss: 3.5223 task0.loss_heatmap: 0.5378 task0.loss_bbox: 2.2337 task0.loss_iou: 0.1495 task0.loss_reg_iou: 0.6013 2023/09/17 14:42:57 - mmengine - INFO - Epoch(train) [6][1500/3953] lr: 6.7383e-04 eta: 3:41:35 time: 0.4903 data_time: 0.0038 memory: 8025 grad_norm: 16.1991 loss: 3.5642 task0.loss_heatmap: 0.5464 task0.loss_bbox: 2.2700 task0.loss_iou: 0.1461 task0.loss_reg_iou: 0.6017 2023/09/17 14:43:22 - mmengine - INFO - Epoch(train) [6][1550/3953] lr: 6.7210e-04 eta: 3:41:08 time: 0.4902 data_time: 0.0038 memory: 8610 grad_norm: 16.0024 loss: 3.5411 task0.loss_heatmap: 0.5452 task0.loss_bbox: 2.2505 task0.loss_iou: 0.1488 task0.loss_reg_iou: 0.5966 2023/09/17 14:43:46 - mmengine - INFO - Epoch(train) [6][1600/3953] lr: 6.7037e-04 eta: 3:40:42 time: 0.4891 data_time: 0.0037 memory: 8815 grad_norm: 16.4529 loss: 3.5975 task0.loss_heatmap: 0.5384 task0.loss_bbox: 2.2692 task0.loss_iou: 0.1600 task0.loss_reg_iou: 0.6300 2023/09/17 14:44:10 - mmengine - INFO - Epoch(train) [6][1650/3953] lr: 6.6864e-04 eta: 3:40:15 time: 0.4850 data_time: 0.0037 memory: 8573 grad_norm: 16.5242 loss: 3.4296 task0.loss_heatmap: 0.5088 task0.loss_bbox: 2.1717 task0.loss_iou: 0.1561 task0.loss_reg_iou: 0.5929 2023/09/17 14:44:35 - mmengine - INFO - Epoch(train) [6][1700/3953] lr: 6.6691e-04 eta: 3:39:49 time: 0.4957 data_time: 0.0037 memory: 8996 grad_norm: 16.7913 loss: 3.4990 task0.loss_heatmap: 0.5004 task0.loss_bbox: 2.2310 task0.loss_iou: 0.1538 task0.loss_reg_iou: 0.6138 2023/09/17 14:45:00 - mmengine - INFO - Epoch(train) [6][1750/3953] lr: 6.6517e-04 eta: 3:39:23 time: 0.4984 data_time: 0.0036 memory: 8513 grad_norm: 15.8122 loss: 3.4969 task0.loss_heatmap: 0.5145 task0.loss_bbox: 2.2407 task0.loss_iou: 0.1459 task0.loss_reg_iou: 0.5959 2023/09/17 14:45:25 - mmengine - INFO - Epoch(train) [6][1800/3953] lr: 6.6344e-04 eta: 3:38:57 time: 0.4974 data_time: 0.0039 memory: 8519 grad_norm: 15.4801 loss: 3.5423 task0.loss_heatmap: 0.5357 task0.loss_bbox: 2.2672 task0.loss_iou: 0.1407 task0.loss_reg_iou: 0.5987 2023/09/17 14:45:50 - mmengine - INFO - Epoch(train) [6][1850/3953] lr: 6.6170e-04 eta: 3:38:31 time: 0.4962 data_time: 0.0038 memory: 8045 grad_norm: 14.4912 loss: 3.5604 task0.loss_heatmap: 0.5407 task0.loss_bbox: 2.2668 task0.loss_iou: 0.1503 task0.loss_reg_iou: 0.6026 2023/09/17 14:46:15 - mmengine - INFO - Epoch(train) [6][1900/3953] lr: 6.5996e-04 eta: 3:38:05 time: 0.4965 data_time: 0.0038 memory: 8684 grad_norm: 15.9642 loss: 3.5752 task0.loss_heatmap: 0.5420 task0.loss_bbox: 2.2451 task0.loss_iou: 0.1627 task0.loss_reg_iou: 0.6255 2023/09/17 14:46:39 - mmengine - INFO - Epoch(train) [6][1950/3953] lr: 6.5821e-04 eta: 3:37:39 time: 0.4989 data_time: 0.0037 memory: 8960 grad_norm: 17.0928 loss: 3.4530 task0.loss_heatmap: 0.4937 task0.loss_bbox: 2.2033 task0.loss_iou: 0.1497 task0.loss_reg_iou: 0.6063 2023/09/17 14:47:04 - mmengine - INFO - Epoch(train) [6][2000/3953] lr: 6.5647e-04 eta: 3:37:12 time: 0.4867 data_time: 0.0038 memory: 8241 grad_norm: 15.3391 loss: 3.5335 task0.loss_heatmap: 0.5329 task0.loss_bbox: 2.2491 task0.loss_iou: 0.1474 task0.loss_reg_iou: 0.6041 2023/09/17 14:47:29 - mmengine - INFO - Epoch(train) [6][2050/3953] lr: 6.5472e-04 eta: 3:36:47 time: 0.5004 data_time: 0.0037 memory: 8829 grad_norm: 16.9897 loss: 3.6954 task0.loss_heatmap: 0.5463 task0.loss_bbox: 2.3478 task0.loss_iou: 0.1631 task0.loss_reg_iou: 0.6382 2023/09/17 14:47:53 - mmengine - INFO - Epoch(train) [6][2100/3953] lr: 6.5297e-04 eta: 3:36:20 time: 0.4902 data_time: 0.0038 memory: 8152 grad_norm: 15.8637 loss: 3.5333 task0.loss_heatmap: 0.5207 task0.loss_bbox: 2.2430 task0.loss_iou: 0.1565 task0.loss_reg_iou: 0.6130 2023/09/17 14:48:18 - mmengine - INFO - Epoch(train) [6][2150/3953] lr: 6.5121e-04 eta: 3:35:53 time: 0.4847 data_time: 0.0038 memory: 8130 grad_norm: 16.2717 loss: 3.5133 task0.loss_heatmap: 0.5078 task0.loss_bbox: 2.2564 task0.loss_iou: 0.1469 task0.loss_reg_iou: 0.6022 2023/09/17 14:48:43 - mmengine - INFO - Epoch(train) [6][2200/3953] lr: 6.4946e-04 eta: 3:35:28 time: 0.5013 data_time: 0.0038 memory: 8599 grad_norm: 16.8395 loss: 3.5265 task0.loss_heatmap: 0.5485 task0.loss_bbox: 2.2165 task0.loss_iou: 0.1514 task0.loss_reg_iou: 0.6102 2023/09/17 14:49:00 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:49:08 - mmengine - INFO - Epoch(train) [6][2250/3953] lr: 6.4770e-04 eta: 3:35:02 time: 0.4988 data_time: 0.0038 memory: 8620 grad_norm: 17.0884 loss: 3.5966 task0.loss_heatmap: 0.5507 task0.loss_bbox: 2.2705 task0.loss_iou: 0.1557 task0.loss_reg_iou: 0.6196 2023/09/17 14:49:32 - mmengine - INFO - Epoch(train) [6][2300/3953] lr: 6.4594e-04 eta: 3:34:36 time: 0.4933 data_time: 0.0037 memory: 8292 grad_norm: 15.6108 loss: 3.5586 task0.loss_heatmap: 0.5283 task0.loss_bbox: 2.2627 task0.loss_iou: 0.1530 task0.loss_reg_iou: 0.6146 2023/09/17 14:49:56 - mmengine - INFO - Epoch(train) [6][2350/3953] lr: 6.4418e-04 eta: 3:34:08 time: 0.4760 data_time: 0.0039 memory: 8498 grad_norm: 17.2455 loss: 3.4395 task0.loss_heatmap: 0.4769 task0.loss_bbox: 2.1965 task0.loss_iou: 0.1568 task0.loss_reg_iou: 0.6093 2023/09/17 14:50:21 - mmengine - INFO - Epoch(train) [6][2400/3953] lr: 6.4242e-04 eta: 3:33:42 time: 0.4912 data_time: 0.0038 memory: 8243 grad_norm: 15.9417 loss: 3.4024 task0.loss_heatmap: 0.4990 task0.loss_bbox: 2.1655 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.5899 2023/09/17 14:50:46 - mmengine - INFO - Epoch(train) [6][2450/3953] lr: 6.4066e-04 eta: 3:33:17 time: 0.5119 data_time: 0.0039 memory: 8471 grad_norm: 15.1379 loss: 3.5668 task0.loss_heatmap: 0.5199 task0.loss_bbox: 2.2795 task0.loss_iou: 0.1552 task0.loss_reg_iou: 0.6122 2023/09/17 14:51:14 - mmengine - INFO - Epoch(train) [6][2500/3953] lr: 6.3889e-04 eta: 3:32:54 time: 0.5461 data_time: 0.0038 memory: 8442 grad_norm: 16.0088 loss: 3.4832 task0.loss_heatmap: 0.5088 task0.loss_bbox: 2.2340 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5922 2023/09/17 14:51:40 - mmengine - INFO - Epoch(train) [6][2550/3953] lr: 6.3712e-04 eta: 3:32:30 time: 0.5290 data_time: 0.0038 memory: 8736 grad_norm: 15.8389 loss: 3.4803 task0.loss_heatmap: 0.4887 task0.loss_bbox: 2.2173 task0.loss_iou: 0.1560 task0.loss_reg_iou: 0.6182 2023/09/17 14:52:05 - mmengine - INFO - Epoch(train) [6][2600/3953] lr: 6.3535e-04 eta: 3:32:04 time: 0.4949 data_time: 0.0039 memory: 8685 grad_norm: 15.0493 loss: 3.5234 task0.loss_heatmap: 0.5152 task0.loss_bbox: 2.2375 task0.loss_iou: 0.1565 task0.loss_reg_iou: 0.6142 2023/09/17 14:52:29 - mmengine - INFO - Epoch(train) [6][2650/3953] lr: 6.3358e-04 eta: 3:31:37 time: 0.4856 data_time: 0.0040 memory: 8181 grad_norm: 17.1586 loss: 3.5831 task0.loss_heatmap: 0.5541 task0.loss_bbox: 2.2642 task0.loss_iou: 0.1513 task0.loss_reg_iou: 0.6135 2023/09/17 14:52:54 - mmengine - INFO - Epoch(train) [6][2700/3953] lr: 6.3181e-04 eta: 3:31:11 time: 0.4922 data_time: 0.0038 memory: 8363 grad_norm: 15.7559 loss: 3.5847 task0.loss_heatmap: 0.5215 task0.loss_bbox: 2.3024 task0.loss_iou: 0.1441 task0.loss_reg_iou: 0.6167 2023/09/17 14:53:18 - mmengine - INFO - Epoch(train) [6][2750/3953] lr: 6.3003e-04 eta: 3:30:45 time: 0.4950 data_time: 0.0037 memory: 8742 grad_norm: 15.1433 loss: 3.4800 task0.loss_heatmap: 0.5454 task0.loss_bbox: 2.1919 task0.loss_iou: 0.1485 task0.loss_reg_iou: 0.5943 2023/09/17 14:53:43 - mmengine - INFO - Epoch(train) [6][2800/3953] lr: 6.2825e-04 eta: 3:30:19 time: 0.4965 data_time: 0.0039 memory: 8640 grad_norm: 16.8108 loss: 3.5621 task0.loss_heatmap: 0.5368 task0.loss_bbox: 2.2577 task0.loss_iou: 0.1516 task0.loss_reg_iou: 0.6160 2023/09/17 14:54:09 - mmengine - INFO - Epoch(train) [6][2850/3953] lr: 6.2648e-04 eta: 3:29:54 time: 0.5174 data_time: 0.0038 memory: 8397 grad_norm: 16.7533 loss: 3.4806 task0.loss_heatmap: 0.4956 task0.loss_bbox: 2.2223 task0.loss_iou: 0.1542 task0.loss_reg_iou: 0.6085 2023/09/17 14:54:34 - mmengine - INFO - Epoch(train) [6][2900/3953] lr: 6.2469e-04 eta: 3:29:28 time: 0.4972 data_time: 0.0044 memory: 8235 grad_norm: 16.5716 loss: 3.5871 task0.loss_heatmap: 0.5363 task0.loss_bbox: 2.2843 task0.loss_iou: 0.1548 task0.loss_reg_iou: 0.6117 2023/09/17 14:54:59 - mmengine - INFO - Epoch(train) [6][2950/3953] lr: 6.2291e-04 eta: 3:29:03 time: 0.5067 data_time: 0.0042 memory: 8695 grad_norm: 15.2645 loss: 3.6662 task0.loss_heatmap: 0.5670 task0.loss_bbox: 2.3287 task0.loss_iou: 0.1538 task0.loss_reg_iou: 0.6167 2023/09/17 14:55:24 - mmengine - INFO - Epoch(train) [6][3000/3953] lr: 6.2113e-04 eta: 3:28:37 time: 0.5017 data_time: 0.0043 memory: 8578 grad_norm: 15.8775 loss: 3.5236 task0.loss_heatmap: 0.5544 task0.loss_bbox: 2.2209 task0.loss_iou: 0.1468 task0.loss_reg_iou: 0.6016 2023/09/17 14:55:50 - mmengine - INFO - Epoch(train) [6][3050/3953] lr: 6.1934e-04 eta: 3:28:11 time: 0.5030 data_time: 0.0041 memory: 8258 grad_norm: 16.1303 loss: 3.5719 task0.loss_heatmap: 0.5370 task0.loss_bbox: 2.2793 task0.loss_iou: 0.1462 task0.loss_reg_iou: 0.6094 2023/09/17 14:56:14 - mmengine - INFO - Epoch(train) [6][3100/3953] lr: 6.1756e-04 eta: 3:27:45 time: 0.4911 data_time: 0.0042 memory: 8543 grad_norm: 17.9670 loss: 3.5931 task0.loss_heatmap: 0.5337 task0.loss_bbox: 2.2858 task0.loss_iou: 0.1590 task0.loss_reg_iou: 0.6147 2023/09/17 14:56:38 - mmengine - INFO - Epoch(train) [6][3150/3953] lr: 6.1577e-04 eta: 3:27:18 time: 0.4848 data_time: 0.0039 memory: 8471 grad_norm: 17.3912 loss: 3.5419 task0.loss_heatmap: 0.4983 task0.loss_bbox: 2.2593 task0.loss_iou: 0.1609 task0.loss_reg_iou: 0.6234 2023/09/17 14:57:03 - mmengine - INFO - Epoch(train) [6][3200/3953] lr: 6.1398e-04 eta: 3:26:53 time: 0.4968 data_time: 0.0041 memory: 8413 grad_norm: 16.2531 loss: 3.4613 task0.loss_heatmap: 0.4990 task0.loss_bbox: 2.2180 task0.loss_iou: 0.1428 task0.loss_reg_iou: 0.6015 2023/09/17 14:57:21 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 14:57:28 - mmengine - INFO - Epoch(train) [6][3250/3953] lr: 6.1218e-04 eta: 3:26:27 time: 0.4969 data_time: 0.0039 memory: 8393 grad_norm: 18.7778 loss: 3.4169 task0.loss_heatmap: 0.4760 task0.loss_bbox: 2.1893 task0.loss_iou: 0.1507 task0.loss_reg_iou: 0.6008 2023/09/17 14:57:53 - mmengine - INFO - Epoch(train) [6][3300/3953] lr: 6.1039e-04 eta: 3:26:00 time: 0.4929 data_time: 0.0040 memory: 8349 grad_norm: 16.2924 loss: 3.4600 task0.loss_heatmap: 0.5267 task0.loss_bbox: 2.1896 task0.loss_iou: 0.1511 task0.loss_reg_iou: 0.5926 2023/09/17 14:58:17 - mmengine - INFO - Epoch(train) [6][3350/3953] lr: 6.0860e-04 eta: 3:25:34 time: 0.4885 data_time: 0.0041 memory: 8350 grad_norm: 17.4343 loss: 3.5005 task0.loss_heatmap: 0.5042 task0.loss_bbox: 2.2373 task0.loss_iou: 0.1503 task0.loss_reg_iou: 0.6087 2023/09/17 14:58:42 - mmengine - INFO - Epoch(train) [6][3400/3953] lr: 6.0680e-04 eta: 3:25:08 time: 0.4923 data_time: 0.0039 memory: 8228 grad_norm: 15.6899 loss: 3.4810 task0.loss_heatmap: 0.5414 task0.loss_bbox: 2.1821 task0.loss_iou: 0.1572 task0.loss_reg_iou: 0.6003 2023/09/17 14:59:07 - mmengine - INFO - Epoch(train) [6][3450/3953] lr: 6.0500e-04 eta: 3:24:42 time: 0.4994 data_time: 0.0040 memory: 8169 grad_norm: 15.1797 loss: 3.4115 task0.loss_heatmap: 0.5020 task0.loss_bbox: 2.1839 task0.loss_iou: 0.1467 task0.loss_reg_iou: 0.5789 2023/09/17 14:59:31 - mmengine - INFO - Epoch(train) [6][3500/3953] lr: 6.0320e-04 eta: 3:24:16 time: 0.4925 data_time: 0.0038 memory: 8174 grad_norm: 15.5621 loss: 3.4657 task0.loss_heatmap: 0.5429 task0.loss_bbox: 2.1775 task0.loss_iou: 0.1534 task0.loss_reg_iou: 0.5918 2023/09/17 14:59:56 - mmengine - INFO - Epoch(train) [6][3550/3953] lr: 6.0140e-04 eta: 3:23:51 time: 0.5018 data_time: 0.0038 memory: 8749 grad_norm: 16.6620 loss: 3.3832 task0.loss_heatmap: 0.4890 task0.loss_bbox: 2.1673 task0.loss_iou: 0.1447 task0.loss_reg_iou: 0.5822 2023/09/17 15:00:21 - mmengine - INFO - Epoch(train) [6][3600/3953] lr: 5.9960e-04 eta: 3:23:24 time: 0.4933 data_time: 0.0039 memory: 8576 grad_norm: 16.5344 loss: 3.5890 task0.loss_heatmap: 0.5425 task0.loss_bbox: 2.2959 task0.loss_iou: 0.1504 task0.loss_reg_iou: 0.6002 2023/09/17 15:00:47 - mmengine - INFO - Epoch(train) [6][3650/3953] lr: 5.9780e-04 eta: 3:22:59 time: 0.5070 data_time: 0.0039 memory: 8443 grad_norm: 15.8323 loss: 3.3789 task0.loss_heatmap: 0.4946 task0.loss_bbox: 2.1658 task0.loss_iou: 0.1432 task0.loss_reg_iou: 0.5752 2023/09/17 15:01:12 - mmengine - INFO - Epoch(train) [6][3700/3953] lr: 5.9599e-04 eta: 3:22:34 time: 0.5080 data_time: 0.0041 memory: 8660 grad_norm: 15.2652 loss: 3.5666 task0.loss_heatmap: 0.5522 task0.loss_bbox: 2.2648 task0.loss_iou: 0.1525 task0.loss_reg_iou: 0.5971 2023/09/17 15:01:36 - mmengine - INFO - Epoch(train) [6][3750/3953] lr: 5.9418e-04 eta: 3:22:07 time: 0.4825 data_time: 0.0039 memory: 9222 grad_norm: 16.6562 loss: 3.4513 task0.loss_heatmap: 0.5080 task0.loss_bbox: 2.2018 task0.loss_iou: 0.1432 task0.loss_reg_iou: 0.5984 2023/09/17 15:02:01 - mmengine - INFO - Epoch(train) [6][3800/3953] lr: 5.9238e-04 eta: 3:21:41 time: 0.4974 data_time: 0.0040 memory: 8291 grad_norm: 15.6164 loss: 3.4613 task0.loss_heatmap: 0.4892 task0.loss_bbox: 2.2275 task0.loss_iou: 0.1496 task0.loss_reg_iou: 0.5949 2023/09/17 15:02:26 - mmengine - INFO - Epoch(train) [6][3850/3953] lr: 5.9057e-04 eta: 3:21:16 time: 0.5011 data_time: 0.0039 memory: 8589 grad_norm: 16.6150 loss: 3.5018 task0.loss_heatmap: 0.5042 task0.loss_bbox: 2.2417 task0.loss_iou: 0.1525 task0.loss_reg_iou: 0.6033 2023/09/17 15:02:51 - mmengine - INFO - Epoch(train) [6][3900/3953] lr: 5.8876e-04 eta: 3:20:50 time: 0.4916 data_time: 0.0039 memory: 8364 grad_norm: 16.5653 loss: 3.5607 task0.loss_heatmap: 0.5267 task0.loss_bbox: 2.2885 task0.loss_iou: 0.1471 task0.loss_reg_iou: 0.5984 2023/09/17 15:03:16 - mmengine - INFO - Epoch(train) [6][3950/3953] lr: 5.8695e-04 eta: 3:20:24 time: 0.4999 data_time: 0.0039 memory: 8249 grad_norm: 15.6715 loss: 3.5757 task0.loss_heatmap: 0.5374 task0.loss_bbox: 2.2792 task0.loss_iou: 0.1562 task0.loss_reg_iou: 0.6028 2023/09/17 15:03:17 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:03:17 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/09/17 15:03:39 - mmengine - INFO - Epoch(val) [6][ 50/1250] eta: 0:07:41 time: 0.3847 data_time: 0.0081 memory: 7527 2023/09/17 15:03:58 - mmengine - INFO - Epoch(val) [6][ 100/1250] eta: 0:07:24 time: 0.3881 data_time: 0.0050 memory: 4043 2023/09/17 15:04:18 - mmengine - INFO - Epoch(val) [6][ 150/1250] eta: 0:07:06 time: 0.3891 data_time: 0.0049 memory: 4062 2023/09/17 15:04:37 - mmengine - INFO - Epoch(val) [6][ 200/1250] eta: 0:06:45 time: 0.3824 data_time: 0.0052 memory: 4051 2023/09/17 15:04:55 - mmengine - INFO - Epoch(val) [6][ 250/1250] eta: 0:06:20 time: 0.3583 data_time: 0.0046 memory: 4033 2023/09/17 15:05:14 - mmengine - INFO - Epoch(val) [6][ 300/1250] eta: 0:06:02 time: 0.3842 data_time: 0.0048 memory: 4050 2023/09/17 15:05:33 - mmengine - INFO - Epoch(val) [6][ 350/1250] eta: 0:05:43 time: 0.3843 data_time: 0.0051 memory: 4057 2023/09/17 15:05:53 - mmengine - INFO - Epoch(val) [6][ 400/1250] eta: 0:05:25 time: 0.3906 data_time: 0.0051 memory: 4060 2023/09/17 15:06:13 - mmengine - INFO - Epoch(val) [6][ 450/1250] eta: 0:05:08 time: 0.4049 data_time: 0.0045 memory: 4048 2023/09/17 15:06:32 - mmengine - INFO - Epoch(val) [6][ 500/1250] eta: 0:04:47 time: 0.3695 data_time: 0.0051 memory: 4041 2023/09/17 15:06:50 - mmengine - INFO - Epoch(val) [6][ 550/1250] eta: 0:04:27 time: 0.3722 data_time: 0.0049 memory: 4042 2023/09/17 15:07:09 - mmengine - INFO - Epoch(val) [6][ 600/1250] eta: 0:04:08 time: 0.3706 data_time: 0.0049 memory: 4057 2023/09/17 15:07:29 - mmengine - INFO - Epoch(val) [6][ 650/1250] eta: 0:03:50 time: 0.4090 data_time: 0.0048 memory: 4065 2023/09/17 15:07:49 - mmengine - INFO - Epoch(val) [6][ 700/1250] eta: 0:03:31 time: 0.3937 data_time: 0.0048 memory: 4042 2023/09/17 15:08:09 - mmengine - INFO - Epoch(val) [6][ 750/1250] eta: 0:03:12 time: 0.3990 data_time: 0.0049 memory: 4051 2023/09/17 15:08:28 - mmengine - INFO - Epoch(val) [6][ 800/1250] eta: 0:02:53 time: 0.3714 data_time: 0.0050 memory: 4055 2023/09/17 15:08:47 - mmengine - INFO - Epoch(val) [6][ 850/1250] eta: 0:02:33 time: 0.3873 data_time: 0.0054 memory: 4058 2023/09/17 15:09:05 - mmengine - INFO - Epoch(val) [6][ 900/1250] eta: 0:02:14 time: 0.3581 data_time: 0.0048 memory: 4049 2023/09/17 15:09:23 - mmengine - INFO - Epoch(val) [6][ 950/1250] eta: 0:01:54 time: 0.3668 data_time: 0.0050 memory: 4054 2023/09/17 15:09:42 - mmengine - INFO - Epoch(val) [6][1000/1250] eta: 0:01:35 time: 0.3785 data_time: 0.0050 memory: 4056 2023/09/17 15:10:01 - mmengine - INFO - Epoch(val) [6][1050/1250] eta: 0:01:16 time: 0.3799 data_time: 0.0048 memory: 4043 2023/09/17 15:10:19 - mmengine - INFO - Epoch(val) [6][1100/1250] eta: 0:00:57 time: 0.3566 data_time: 0.0050 memory: 4032 2023/09/17 15:10:38 - mmengine - INFO - Epoch(val) [6][1150/1250] eta: 0:00:38 time: 0.3843 data_time: 0.0049 memory: 4041 2023/09/17 15:10:57 - mmengine - INFO - Epoch(val) [6][1200/1250] eta: 0:00:19 time: 0.3813 data_time: 0.0048 memory: 4047 2023/09/17 15:11:17 - mmengine - INFO - Epoch(val) [6][1250/1250] eta: 0:00:00 time: 0.3895 data_time: 0.0051 memory: 4055 2023/09/17 15:11:21 - mmengine - INFO - Start converting ... 2023/09/17 15:18:47 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.612291] [mAPH 0.606756] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.534484] [mAPH 0.529612] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.731849] [mAPH 0.636563] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.650308] [mAPH 0.564186] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.671543] [mAPH 0.656118] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.646333] [mAPH 0.631487] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.84842] [mAPH 0.842377] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.835122] [mAPH 0.829164] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.581099] [mAPH 0.574845] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.523127] [mAPH 0.517459] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.302545] [mAPH 0.297198] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.225125] [mAPH 0.221092] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.798059] [mAPH 0.708786] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.756761] [mAPH 0.670144] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.717676] [mAPH 0.617277] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.647495] [mAPH 0.556291] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.595995] [mAPH 0.487634] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.463985] [mAPH 0.377395] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.778655] [mAPH 0.76343] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.77307] [mAPH 0.757954] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.63002] [mAPH 0.615321] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.59391] [mAPH 0.580061] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.465233] [mAPH 0.445879] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.433197] [mAPH 0.415162] Eval Using 300s 2023/09/17 15:18:47 - mmengine - INFO - Epoch(val) [6][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.6123 Waymo metric/Vehicle/L1 mAPH: 0.6068 Waymo metric/Vehicle/L2 mAP: 0.5345 Waymo metric/Vehicle/L2 mAPH: 0.5296 Waymo metric/Pedestrian/L1 mAP: 0.7318 Waymo metric/Pedestrian/L1 mAPH: 0.6366 Waymo metric/Pedestrian/L2 mAP: 0.6503 Waymo metric/Pedestrian/L2 mAPH: 0.5642 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6715 Waymo metric/Cyclist/L1 mAPH: 0.6561 Waymo metric/Cyclist/L2 mAP: 0.6463 Waymo metric/Cyclist/L2 mAPH: 0.6315 Waymo metric/Overall/L1 mAP: 0.6719 Waymo metric/Overall/L1 mAPH: 0.6331 Waymo metric/Overall/L2 mAP: 0.6104 Waymo metric/Overall/L2 mAPH: 0.5751 data_time: 0.0051 time: 0.3813 2023/09/17 15:19:12 - mmengine - INFO - Epoch(train) [7][ 50/3953] lr: 5.8503e-04 eta: 3:19:57 time: 0.5007 data_time: 0.0057 memory: 9024 grad_norm: 15.5685 loss: 3.4005 task0.loss_heatmap: 0.5083 task0.loss_bbox: 2.1562 task0.loss_iou: 0.1496 task0.loss_reg_iou: 0.5865 2023/09/17 15:19:37 - mmengine - INFO - Epoch(train) [7][ 100/3953] lr: 5.8321e-04 eta: 3:19:30 time: 0.4913 data_time: 0.0045 memory: 8622 grad_norm: 16.9189 loss: 3.4619 task0.loss_heatmap: 0.5087 task0.loss_bbox: 2.2008 task0.loss_iou: 0.1478 task0.loss_reg_iou: 0.6047 2023/09/17 15:20:02 - mmengine - INFO - Epoch(train) [7][ 150/3953] lr: 5.8140e-04 eta: 3:19:05 time: 0.5063 data_time: 0.0043 memory: 8137 grad_norm: 15.8249 loss: 3.4511 task0.loss_heatmap: 0.5194 task0.loss_bbox: 2.2119 task0.loss_iou: 0.1435 task0.loss_reg_iou: 0.5763 2023/09/17 15:20:27 - mmengine - INFO - Epoch(train) [7][ 200/3953] lr: 5.7958e-04 eta: 3:18:39 time: 0.4954 data_time: 0.0041 memory: 8518 grad_norm: 15.2837 loss: 3.3409 task0.loss_heatmap: 0.4959 task0.loss_bbox: 2.1304 task0.loss_iou: 0.1408 task0.loss_reg_iou: 0.5737 2023/09/17 15:20:52 - mmengine - INFO - Epoch(train) [7][ 250/3953] lr: 5.7777e-04 eta: 3:18:14 time: 0.5031 data_time: 0.0039 memory: 8227 grad_norm: 16.1706 loss: 3.5462 task0.loss_heatmap: 0.5395 task0.loss_bbox: 2.2563 task0.loss_iou: 0.1491 task0.loss_reg_iou: 0.6014 2023/09/17 15:21:08 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:21:17 - mmengine - INFO - Epoch(train) [7][ 300/3953] lr: 5.7595e-04 eta: 3:17:48 time: 0.4924 data_time: 0.0039 memory: 8056 grad_norm: 16.4378 loss: 3.4524 task0.loss_heatmap: 0.5519 task0.loss_bbox: 2.1760 task0.loss_iou: 0.1450 task0.loss_reg_iou: 0.5795 2023/09/17 15:21:42 - mmengine - INFO - Epoch(train) [7][ 350/3953] lr: 5.7413e-04 eta: 3:17:22 time: 0.4973 data_time: 0.0040 memory: 8053 grad_norm: 16.7439 loss: 3.5300 task0.loss_heatmap: 0.5161 task0.loss_bbox: 2.2486 task0.loss_iou: 0.1538 task0.loss_reg_iou: 0.6115 2023/09/17 15:22:07 - mmengine - INFO - Epoch(train) [7][ 400/3953] lr: 5.7231e-04 eta: 3:16:56 time: 0.5050 data_time: 0.0039 memory: 8700 grad_norm: 16.2367 loss: 3.4417 task0.loss_heatmap: 0.5099 task0.loss_bbox: 2.1975 task0.loss_iou: 0.1432 task0.loss_reg_iou: 0.5911 2023/09/17 15:22:32 - mmengine - INFO - Epoch(train) [7][ 450/3953] lr: 5.7049e-04 eta: 3:16:31 time: 0.5025 data_time: 0.0039 memory: 8036 grad_norm: 15.9413 loss: 3.4639 task0.loss_heatmap: 0.4869 task0.loss_bbox: 2.2344 task0.loss_iou: 0.1479 task0.loss_reg_iou: 0.5947 2023/09/17 15:22:57 - mmengine - INFO - Epoch(train) [7][ 500/3953] lr: 5.6867e-04 eta: 3:16:05 time: 0.4966 data_time: 0.0038 memory: 8532 grad_norm: 17.8561 loss: 3.6240 task0.loss_heatmap: 0.5219 task0.loss_bbox: 2.3217 task0.loss_iou: 0.1575 task0.loss_reg_iou: 0.6229 2023/09/17 15:23:22 - mmengine - INFO - Epoch(train) [7][ 550/3953] lr: 5.6684e-04 eta: 3:15:39 time: 0.4971 data_time: 0.0039 memory: 8089 grad_norm: 16.6347 loss: 3.4857 task0.loss_heatmap: 0.5187 task0.loss_bbox: 2.2188 task0.loss_iou: 0.1534 task0.loss_reg_iou: 0.5949 2023/09/17 15:23:46 - mmengine - INFO - Epoch(train) [7][ 600/3953] lr: 5.6502e-04 eta: 3:15:13 time: 0.4910 data_time: 0.0039 memory: 8182 grad_norm: 15.7497 loss: 3.4327 task0.loss_heatmap: 0.5026 task0.loss_bbox: 2.1905 task0.loss_iou: 0.1472 task0.loss_reg_iou: 0.5924 2023/09/17 15:24:12 - mmengine - INFO - Epoch(train) [7][ 650/3953] lr: 5.6320e-04 eta: 3:14:48 time: 0.5024 data_time: 0.0038 memory: 8296 grad_norm: 15.4271 loss: 3.3287 task0.loss_heatmap: 0.4695 task0.loss_bbox: 2.1358 task0.loss_iou: 0.1420 task0.loss_reg_iou: 0.5814 2023/09/17 15:24:37 - mmengine - INFO - Epoch(train) [7][ 700/3953] lr: 5.6137e-04 eta: 3:14:22 time: 0.5015 data_time: 0.0039 memory: 8186 grad_norm: 16.2316 loss: 3.5350 task0.loss_heatmap: 0.5563 task0.loss_bbox: 2.2381 task0.loss_iou: 0.1444 task0.loss_reg_iou: 0.5962 2023/09/17 15:25:02 - mmengine - INFO - Epoch(train) [7][ 750/3953] lr: 5.5954e-04 eta: 3:13:56 time: 0.4985 data_time: 0.0040 memory: 8582 grad_norm: 16.9482 loss: 3.4301 task0.loss_heatmap: 0.5145 task0.loss_bbox: 2.1844 task0.loss_iou: 0.1462 task0.loss_reg_iou: 0.5849 2023/09/17 15:25:27 - mmengine - INFO - Epoch(train) [7][ 800/3953] lr: 5.5772e-04 eta: 3:13:31 time: 0.5039 data_time: 0.0041 memory: 8389 grad_norm: 16.5051 loss: 3.5216 task0.loss_heatmap: 0.5272 task0.loss_bbox: 2.2485 task0.loss_iou: 0.1529 task0.loss_reg_iou: 0.5930 2023/09/17 15:25:52 - mmengine - INFO - Epoch(train) [7][ 850/3953] lr: 5.5589e-04 eta: 3:13:06 time: 0.5112 data_time: 0.0041 memory: 8666 grad_norm: 15.0997 loss: 3.6034 task0.loss_heatmap: 0.5440 task0.loss_bbox: 2.3002 task0.loss_iou: 0.1494 task0.loss_reg_iou: 0.6098 2023/09/17 15:26:18 - mmengine - INFO - Epoch(train) [7][ 900/3953] lr: 5.5406e-04 eta: 3:12:40 time: 0.5083 data_time: 0.0043 memory: 8715 grad_norm: 16.0247 loss: 3.5165 task0.loss_heatmap: 0.5421 task0.loss_bbox: 2.2395 task0.loss_iou: 0.1449 task0.loss_reg_iou: 0.5901 2023/09/17 15:26:42 - mmengine - INFO - Epoch(train) [7][ 950/3953] lr: 5.5223e-04 eta: 3:12:14 time: 0.4919 data_time: 0.0041 memory: 8380 grad_norm: 15.5413 loss: 3.5390 task0.loss_heatmap: 0.5503 task0.loss_bbox: 2.2616 task0.loss_iou: 0.1461 task0.loss_reg_iou: 0.5810 2023/09/17 15:27:07 - mmengine - INFO - Epoch(train) [7][1000/3953] lr: 5.5040e-04 eta: 3:11:49 time: 0.4947 data_time: 0.0039 memory: 8187 grad_norm: 17.1249 loss: 3.4912 task0.loss_heatmap: 0.5397 task0.loss_bbox: 2.2016 task0.loss_iou: 0.1502 task0.loss_reg_iou: 0.5997 2023/09/17 15:27:32 - mmengine - INFO - Epoch(train) [7][1050/3953] lr: 5.4857e-04 eta: 3:11:23 time: 0.4932 data_time: 0.0040 memory: 8351 grad_norm: 17.2548 loss: 3.5005 task0.loss_heatmap: 0.5114 task0.loss_bbox: 2.2306 task0.loss_iou: 0.1505 task0.loss_reg_iou: 0.6080 2023/09/17 15:27:57 - mmengine - INFO - Epoch(train) [7][1100/3953] lr: 5.4674e-04 eta: 3:10:57 time: 0.4960 data_time: 0.0038 memory: 8232 grad_norm: 15.7845 loss: 3.3824 task0.loss_heatmap: 0.4770 task0.loss_bbox: 2.1634 task0.loss_iou: 0.1470 task0.loss_reg_iou: 0.5950 2023/09/17 15:28:23 - mmengine - INFO - Epoch(train) [7][1150/3953] lr: 5.4491e-04 eta: 3:10:32 time: 0.5177 data_time: 0.0038 memory: 8102 grad_norm: 16.3545 loss: 3.5473 task0.loss_heatmap: 0.5309 task0.loss_bbox: 2.2706 task0.loss_iou: 0.1465 task0.loss_reg_iou: 0.5994 2023/09/17 15:28:50 - mmengine - INFO - Epoch(train) [7][1200/3953] lr: 5.4308e-04 eta: 3:10:08 time: 0.5399 data_time: 0.0038 memory: 8603 grad_norm: 17.4331 loss: 3.4053 task0.loss_heatmap: 0.5051 task0.loss_bbox: 2.1604 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.5917 2023/09/17 15:29:17 - mmengine - INFO - Epoch(train) [7][1250/3953] lr: 5.4124e-04 eta: 3:09:45 time: 0.5459 data_time: 0.0037 memory: 8424 grad_norm: 17.4303 loss: 3.3859 task0.loss_heatmap: 0.4929 task0.loss_bbox: 2.1518 task0.loss_iou: 0.1458 task0.loss_reg_iou: 0.5954 2023/09/17 15:29:33 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:29:42 - mmengine - INFO - Epoch(train) [7][1300/3953] lr: 5.3941e-04 eta: 3:09:19 time: 0.4985 data_time: 0.0037 memory: 8106 grad_norm: 17.5618 loss: 3.5391 task0.loss_heatmap: 0.5351 task0.loss_bbox: 2.2451 task0.loss_iou: 0.1489 task0.loss_reg_iou: 0.6100 2023/09/17 15:30:07 - mmengine - INFO - Epoch(train) [7][1350/3953] lr: 5.3758e-04 eta: 3:08:54 time: 0.5069 data_time: 0.0038 memory: 8309 grad_norm: 17.2445 loss: 3.5124 task0.loss_heatmap: 0.5332 task0.loss_bbox: 2.2321 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5988 2023/09/17 15:30:32 - mmengine - INFO - Epoch(train) [7][1400/3953] lr: 5.3574e-04 eta: 3:08:28 time: 0.4971 data_time: 0.0038 memory: 8181 grad_norm: 16.5318 loss: 3.3456 task0.loss_heatmap: 0.4747 task0.loss_bbox: 2.1500 task0.loss_iou: 0.1440 task0.loss_reg_iou: 0.5769 2023/09/17 15:30:57 - mmengine - INFO - Epoch(train) [7][1450/3953] lr: 5.3391e-04 eta: 3:08:02 time: 0.4937 data_time: 0.0036 memory: 8467 grad_norm: 16.4023 loss: 3.3249 task0.loss_heatmap: 0.4846 task0.loss_bbox: 2.1244 task0.loss_iou: 0.1429 task0.loss_reg_iou: 0.5731 2023/09/17 15:31:21 - mmengine - INFO - Epoch(train) [7][1500/3953] lr: 5.3207e-04 eta: 3:07:36 time: 0.4888 data_time: 0.0038 memory: 7954 grad_norm: 16.2573 loss: 3.3202 task0.loss_heatmap: 0.4903 task0.loss_bbox: 2.0957 task0.loss_iou: 0.1482 task0.loss_reg_iou: 0.5861 2023/09/17 15:31:47 - mmengine - INFO - Epoch(train) [7][1550/3953] lr: 5.3023e-04 eta: 3:07:11 time: 0.5107 data_time: 0.0037 memory: 8484 grad_norm: 17.2280 loss: 3.2593 task0.loss_heatmap: 0.4522 task0.loss_bbox: 2.0981 task0.loss_iou: 0.1409 task0.loss_reg_iou: 0.5682 2023/09/17 15:32:12 - mmengine - INFO - Epoch(train) [7][1600/3953] lr: 5.2840e-04 eta: 3:06:45 time: 0.5074 data_time: 0.0037 memory: 8834 grad_norm: 16.1966 loss: 3.4926 task0.loss_heatmap: 0.5318 task0.loss_bbox: 2.2157 task0.loss_iou: 0.1479 task0.loss_reg_iou: 0.5972 2023/09/17 15:32:37 - mmengine - INFO - Epoch(train) [7][1650/3953] lr: 5.2656e-04 eta: 3:06:20 time: 0.5008 data_time: 0.0038 memory: 8256 grad_norm: 15.7567 loss: 3.4388 task0.loss_heatmap: 0.5172 task0.loss_bbox: 2.1713 task0.loss_iou: 0.1526 task0.loss_reg_iou: 0.5976 2023/09/17 15:33:02 - mmengine - INFO - Epoch(train) [7][1700/3953] lr: 5.2472e-04 eta: 3:05:54 time: 0.4914 data_time: 0.0037 memory: 8283 grad_norm: 16.4050 loss: 3.3775 task0.loss_heatmap: 0.5167 task0.loss_bbox: 2.1283 task0.loss_iou: 0.1539 task0.loss_reg_iou: 0.5787 2023/09/17 15:33:27 - mmengine - INFO - Epoch(train) [7][1750/3953] lr: 5.2289e-04 eta: 3:05:28 time: 0.5034 data_time: 0.0037 memory: 9223 grad_norm: 15.4546 loss: 3.4822 task0.loss_heatmap: 0.5107 task0.loss_bbox: 2.2278 task0.loss_iou: 0.1450 task0.loss_reg_iou: 0.5987 2023/09/17 15:33:51 - mmengine - INFO - Epoch(train) [7][1800/3953] lr: 5.2105e-04 eta: 3:05:02 time: 0.4851 data_time: 0.0038 memory: 8570 grad_norm: 16.2620 loss: 3.4432 task0.loss_heatmap: 0.5183 task0.loss_bbox: 2.1825 task0.loss_iou: 0.1519 task0.loss_reg_iou: 0.5904 2023/09/17 15:34:16 - mmengine - INFO - Epoch(train) [7][1850/3953] lr: 5.1921e-04 eta: 3:04:36 time: 0.5002 data_time: 0.0037 memory: 8012 grad_norm: 15.2717 loss: 3.4829 task0.loss_heatmap: 0.5128 task0.loss_bbox: 2.2250 task0.loss_iou: 0.1471 task0.loss_reg_iou: 0.5979 2023/09/17 15:34:41 - mmengine - INFO - Epoch(train) [7][1900/3953] lr: 5.1737e-04 eta: 3:04:10 time: 0.4902 data_time: 0.0037 memory: 8542 grad_norm: 16.2236 loss: 3.3738 task0.loss_heatmap: 0.5104 task0.loss_bbox: 2.1308 task0.loss_iou: 0.1454 task0.loss_reg_iou: 0.5872 2023/09/17 15:35:05 - mmengine - INFO - Epoch(train) [7][1950/3953] lr: 5.1553e-04 eta: 3:03:44 time: 0.4868 data_time: 0.0037 memory: 8457 grad_norm: 15.2909 loss: 3.4972 task0.loss_heatmap: 0.5445 task0.loss_bbox: 2.2161 task0.loss_iou: 0.1464 task0.loss_reg_iou: 0.5902 2023/09/17 15:35:29 - mmengine - INFO - Epoch(train) [7][2000/3953] lr: 5.1369e-04 eta: 3:03:18 time: 0.4866 data_time: 0.0038 memory: 8173 grad_norm: 16.6387 loss: 3.3604 task0.loss_heatmap: 0.4675 task0.loss_bbox: 2.1479 task0.loss_iou: 0.1503 task0.loss_reg_iou: 0.5947 2023/09/17 15:35:54 - mmengine - INFO - Epoch(train) [7][2050/3953] lr: 5.1185e-04 eta: 3:02:52 time: 0.4957 data_time: 0.0037 memory: 8416 grad_norm: 15.4719 loss: 3.3196 task0.loss_heatmap: 0.4890 task0.loss_bbox: 2.1158 task0.loss_iou: 0.1379 task0.loss_reg_iou: 0.5769 2023/09/17 15:36:19 - mmengine - INFO - Epoch(train) [7][2100/3953] lr: 5.1001e-04 eta: 3:02:26 time: 0.4925 data_time: 0.0038 memory: 8775 grad_norm: 17.2351 loss: 3.6074 task0.loss_heatmap: 0.5760 task0.loss_bbox: 2.2564 task0.loss_iou: 0.1592 task0.loss_reg_iou: 0.6159 2023/09/17 15:36:43 - mmengine - INFO - Epoch(train) [7][2150/3953] lr: 5.0818e-04 eta: 3:02:00 time: 0.4904 data_time: 0.0038 memory: 8273 grad_norm: 17.3604 loss: 3.4275 task0.loss_heatmap: 0.5075 task0.loss_bbox: 2.1730 task0.loss_iou: 0.1520 task0.loss_reg_iou: 0.5951 2023/09/17 15:37:08 - mmengine - INFO - Epoch(train) [7][2200/3953] lr: 5.0634e-04 eta: 3:01:35 time: 0.4994 data_time: 0.0037 memory: 8845 grad_norm: 15.5153 loss: 3.4363 task0.loss_heatmap: 0.5250 task0.loss_bbox: 2.1890 task0.loss_iou: 0.1461 task0.loss_reg_iou: 0.5762 2023/09/17 15:37:33 - mmengine - INFO - Epoch(train) [7][2250/3953] lr: 5.0450e-04 eta: 3:01:09 time: 0.5055 data_time: 0.0036 memory: 9061 grad_norm: 15.7228 loss: 3.4432 task0.loss_heatmap: 0.5014 task0.loss_bbox: 2.1947 task0.loss_iou: 0.1471 task0.loss_reg_iou: 0.6000 2023/09/17 15:37:49 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:37:58 - mmengine - INFO - Epoch(train) [7][2300/3953] lr: 5.0266e-04 eta: 3:00:44 time: 0.4995 data_time: 0.0038 memory: 8035 grad_norm: 16.7256 loss: 3.3660 task0.loss_heatmap: 0.4956 task0.loss_bbox: 2.1530 task0.loss_iou: 0.1389 task0.loss_reg_iou: 0.5785 2023/09/17 15:38:23 - mmengine - INFO - Epoch(train) [7][2350/3953] lr: 5.0082e-04 eta: 3:00:18 time: 0.4905 data_time: 0.0038 memory: 8468 grad_norm: 17.5788 loss: 3.4845 task0.loss_heatmap: 0.5189 task0.loss_bbox: 2.2204 task0.loss_iou: 0.1501 task0.loss_reg_iou: 0.5951 2023/09/17 15:38:47 - mmengine - INFO - Epoch(train) [7][2400/3953] lr: 4.9898e-04 eta: 2:59:52 time: 0.4867 data_time: 0.0037 memory: 8588 grad_norm: 16.7360 loss: 3.3848 task0.loss_heatmap: 0.5102 task0.loss_bbox: 2.1561 task0.loss_iou: 0.1408 task0.loss_reg_iou: 0.5777 2023/09/17 15:39:12 - mmengine - INFO - Epoch(train) [7][2450/3953] lr: 4.9714e-04 eta: 2:59:26 time: 0.4870 data_time: 0.0038 memory: 8371 grad_norm: 15.4760 loss: 3.3928 task0.loss_heatmap: 0.5128 task0.loss_bbox: 2.1562 task0.loss_iou: 0.1425 task0.loss_reg_iou: 0.5813 2023/09/17 15:39:38 - mmengine - INFO - Epoch(train) [7][2500/3953] lr: 4.9530e-04 eta: 2:59:01 time: 0.5183 data_time: 0.0037 memory: 7997 grad_norm: 17.3223 loss: 3.3625 task0.loss_heatmap: 0.4872 task0.loss_bbox: 2.1364 task0.loss_iou: 0.1519 task0.loss_reg_iou: 0.5871 2023/09/17 15:40:02 - mmengine - INFO - Epoch(train) [7][2550/3953] lr: 4.9346e-04 eta: 2:58:35 time: 0.4880 data_time: 0.0037 memory: 8237 grad_norm: 15.9407 loss: 3.4285 task0.loss_heatmap: 0.5279 task0.loss_bbox: 2.1714 task0.loss_iou: 0.1458 task0.loss_reg_iou: 0.5834 2023/09/17 15:40:26 - mmengine - INFO - Epoch(train) [7][2600/3953] lr: 4.9162e-04 eta: 2:58:09 time: 0.4888 data_time: 0.0037 memory: 8446 grad_norm: 15.7883 loss: 3.3838 task0.loss_heatmap: 0.4887 task0.loss_bbox: 2.1717 task0.loss_iou: 0.1469 task0.loss_reg_iou: 0.5765 2023/09/17 15:40:51 - mmengine - INFO - Epoch(train) [7][2650/3953] lr: 4.8978e-04 eta: 2:57:43 time: 0.4911 data_time: 0.0038 memory: 8522 grad_norm: 16.5763 loss: 3.4958 task0.loss_heatmap: 0.5088 task0.loss_bbox: 2.2262 task0.loss_iou: 0.1514 task0.loss_reg_iou: 0.6094 2023/09/17 15:41:18 - mmengine - INFO - Epoch(train) [7][2700/3953] lr: 4.8794e-04 eta: 2:57:19 time: 0.5436 data_time: 0.0037 memory: 8260 grad_norm: 17.0276 loss: 3.3382 task0.loss_heatmap: 0.5040 task0.loss_bbox: 2.1099 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.5762 2023/09/17 15:41:46 - mmengine - INFO - Epoch(train) [7][2750/3953] lr: 4.8610e-04 eta: 2:56:55 time: 0.5456 data_time: 0.0037 memory: 8456 grad_norm: 16.4860 loss: 3.6302 task0.loss_heatmap: 0.5683 task0.loss_bbox: 2.3103 task0.loss_iou: 0.1542 task0.loss_reg_iou: 0.5974 2023/09/17 15:42:11 - mmengine - INFO - Epoch(train) [7][2800/3953] lr: 4.8426e-04 eta: 2:56:30 time: 0.5041 data_time: 0.0038 memory: 8723 grad_norm: 15.9725 loss: 3.5390 task0.loss_heatmap: 0.5484 task0.loss_bbox: 2.2440 task0.loss_iou: 0.1460 task0.loss_reg_iou: 0.6006 2023/09/17 15:42:36 - mmengine - INFO - Epoch(train) [7][2850/3953] lr: 4.8242e-04 eta: 2:56:05 time: 0.5026 data_time: 0.0038 memory: 8352 grad_norm: 17.2061 loss: 3.4097 task0.loss_heatmap: 0.5207 task0.loss_bbox: 2.1390 task0.loss_iou: 0.1548 task0.loss_reg_iou: 0.5952 2023/09/17 15:43:02 - mmengine - INFO - Epoch(train) [7][2900/3953] lr: 4.8059e-04 eta: 2:55:39 time: 0.5128 data_time: 0.0037 memory: 8340 grad_norm: 16.2430 loss: 3.4892 task0.loss_heatmap: 0.5077 task0.loss_bbox: 2.2429 task0.loss_iou: 0.1470 task0.loss_reg_iou: 0.5916 2023/09/17 15:43:27 - mmengine - INFO - Epoch(train) [7][2950/3953] lr: 4.7875e-04 eta: 2:55:14 time: 0.5034 data_time: 0.0037 memory: 8234 grad_norm: 16.3407 loss: 3.2347 task0.loss_heatmap: 0.4963 task0.loss_bbox: 2.0507 task0.loss_iou: 0.1362 task0.loss_reg_iou: 0.5515 2023/09/17 15:43:51 - mmengine - INFO - Epoch(train) [7][3000/3953] lr: 4.7691e-04 eta: 2:54:48 time: 0.4854 data_time: 0.0037 memory: 8475 grad_norm: 16.1968 loss: 3.4987 task0.loss_heatmap: 0.5205 task0.loss_bbox: 2.2340 task0.loss_iou: 0.1501 task0.loss_reg_iou: 0.5941 2023/09/17 15:44:15 - mmengine - INFO - Epoch(train) [7][3050/3953] lr: 4.7507e-04 eta: 2:54:22 time: 0.4868 data_time: 0.0038 memory: 8433 grad_norm: 16.5977 loss: 3.4121 task0.loss_heatmap: 0.5045 task0.loss_bbox: 2.1837 task0.loss_iou: 0.1466 task0.loss_reg_iou: 0.5773 2023/09/17 15:44:40 - mmengine - INFO - Epoch(train) [7][3100/3953] lr: 4.7323e-04 eta: 2:53:56 time: 0.4990 data_time: 0.0038 memory: 8289 grad_norm: 15.9106 loss: 3.5489 task0.loss_heatmap: 0.5472 task0.loss_bbox: 2.2490 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.6047 2023/09/17 15:45:05 - mmengine - INFO - Epoch(train) [7][3150/3953] lr: 4.7140e-04 eta: 2:53:31 time: 0.5003 data_time: 0.0037 memory: 8352 grad_norm: 15.6069 loss: 3.2997 task0.loss_heatmap: 0.4773 task0.loss_bbox: 2.1122 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5654 2023/09/17 15:45:30 - mmengine - INFO - Epoch(train) [7][3200/3953] lr: 4.6956e-04 eta: 2:53:05 time: 0.4850 data_time: 0.0038 memory: 8458 grad_norm: 16.4128 loss: 3.3414 task0.loss_heatmap: 0.4798 task0.loss_bbox: 2.1144 task0.loss_iou: 0.1554 task0.loss_reg_iou: 0.5917 2023/09/17 15:45:54 - mmengine - INFO - Epoch(train) [7][3250/3953] lr: 4.6773e-04 eta: 2:52:39 time: 0.4978 data_time: 0.0038 memory: 8077 grad_norm: 16.1860 loss: 3.4080 task0.loss_heatmap: 0.4874 task0.loss_bbox: 2.1715 task0.loss_iou: 0.1501 task0.loss_reg_iou: 0.5990 2023/09/17 15:46:10 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:46:19 - mmengine - INFO - Epoch(train) [7][3300/3953] lr: 4.6589e-04 eta: 2:52:13 time: 0.4966 data_time: 0.0038 memory: 8280 grad_norm: 17.3310 loss: 3.2908 task0.loss_heatmap: 0.4789 task0.loss_bbox: 2.0870 task0.loss_iou: 0.1440 task0.loss_reg_iou: 0.5810 2023/09/17 15:46:44 - mmengine - INFO - Epoch(train) [7][3350/3953] lr: 4.6405e-04 eta: 2:51:48 time: 0.5002 data_time: 0.0038 memory: 8467 grad_norm: 16.0287 loss: 3.4141 task0.loss_heatmap: 0.5085 task0.loss_bbox: 2.1801 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5807 2023/09/17 15:47:10 - mmengine - INFO - Epoch(train) [7][3400/3953] lr: 4.6222e-04 eta: 2:51:22 time: 0.5054 data_time: 0.0038 memory: 8626 grad_norm: 16.0907 loss: 3.3733 task0.loss_heatmap: 0.5137 task0.loss_bbox: 2.1324 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5788 2023/09/17 15:47:34 - mmengine - INFO - Epoch(train) [7][3450/3953] lr: 4.6039e-04 eta: 2:50:56 time: 0.4875 data_time: 0.0038 memory: 8305 grad_norm: 15.9223 loss: 3.5037 task0.loss_heatmap: 0.5416 task0.loss_bbox: 2.2097 task0.loss_iou: 0.1536 task0.loss_reg_iou: 0.5988 2023/09/17 15:47:59 - mmengine - INFO - Epoch(train) [7][3500/3953] lr: 4.5855e-04 eta: 2:50:31 time: 0.4922 data_time: 0.0038 memory: 8065 grad_norm: 16.9505 loss: 3.4053 task0.loss_heatmap: 0.5199 task0.loss_bbox: 2.1495 task0.loss_iou: 0.1465 task0.loss_reg_iou: 0.5893 2023/09/17 15:48:24 - mmengine - INFO - Epoch(train) [7][3550/3953] lr: 4.5672e-04 eta: 2:50:05 time: 0.5019 data_time: 0.0039 memory: 8517 grad_norm: 17.6989 loss: 3.4198 task0.loss_heatmap: 0.5118 task0.loss_bbox: 2.1715 task0.loss_iou: 0.1467 task0.loss_reg_iou: 0.5898 2023/09/17 15:48:49 - mmengine - INFO - Epoch(train) [7][3600/3953] lr: 4.5489e-04 eta: 2:49:40 time: 0.4997 data_time: 0.0038 memory: 8502 grad_norm: 17.0653 loss: 3.3129 task0.loss_heatmap: 0.4585 task0.loss_bbox: 2.1320 task0.loss_iou: 0.1454 task0.loss_reg_iou: 0.5769 2023/09/17 15:49:14 - mmengine - INFO - Epoch(train) [7][3650/3953] lr: 4.5306e-04 eta: 2:49:14 time: 0.4984 data_time: 0.0038 memory: 8961 grad_norm: 15.9441 loss: 3.3936 task0.loss_heatmap: 0.5134 task0.loss_bbox: 2.1488 task0.loss_iou: 0.1503 task0.loss_reg_iou: 0.5810 2023/09/17 15:49:39 - mmengine - INFO - Epoch(train) [7][3700/3953] lr: 4.5122e-04 eta: 2:48:49 time: 0.5036 data_time: 0.0038 memory: 8453 grad_norm: 17.2752 loss: 3.3955 task0.loss_heatmap: 0.4986 task0.loss_bbox: 2.1635 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5852 2023/09/17 15:50:04 - mmengine - INFO - Epoch(train) [7][3750/3953] lr: 4.4939e-04 eta: 2:48:23 time: 0.4954 data_time: 0.0037 memory: 8224 grad_norm: 19.2790 loss: 3.3950 task0.loss_heatmap: 0.4897 task0.loss_bbox: 2.1738 task0.loss_iou: 0.1446 task0.loss_reg_iou: 0.5869 2023/09/17 15:50:28 - mmengine - INFO - Epoch(train) [7][3800/3953] lr: 4.4756e-04 eta: 2:47:57 time: 0.4945 data_time: 0.0037 memory: 8540 grad_norm: 15.2651 loss: 3.3294 task0.loss_heatmap: 0.4946 task0.loss_bbox: 2.1225 task0.loss_iou: 0.1411 task0.loss_reg_iou: 0.5712 2023/09/17 15:50:53 - mmengine - INFO - Epoch(train) [7][3850/3953] lr: 4.4573e-04 eta: 2:47:31 time: 0.4873 data_time: 0.0038 memory: 8305 grad_norm: 16.0531 loss: 3.3446 task0.loss_heatmap: 0.4759 task0.loss_bbox: 2.1300 task0.loss_iou: 0.1487 task0.loss_reg_iou: 0.5900 2023/09/17 15:51:17 - mmengine - INFO - Epoch(train) [7][3900/3953] lr: 4.4391e-04 eta: 2:47:05 time: 0.4886 data_time: 0.0037 memory: 8232 grad_norm: 16.6597 loss: 3.3807 task0.loss_heatmap: 0.4762 task0.loss_bbox: 2.1863 task0.loss_iou: 0.1429 task0.loss_reg_iou: 0.5754 2023/09/17 15:51:41 - mmengine - INFO - Epoch(train) [7][3950/3953] lr: 4.4208e-04 eta: 2:46:39 time: 0.4829 data_time: 0.0038 memory: 8286 grad_norm: 16.1977 loss: 3.3608 task0.loss_heatmap: 0.4891 task0.loss_bbox: 2.1487 task0.loss_iou: 0.1426 task0.loss_reg_iou: 0.5804 2023/09/17 15:51:43 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 15:51:43 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/09/17 15:52:04 - mmengine - INFO - Epoch(val) [7][ 50/1250] eta: 0:07:35 time: 0.3797 data_time: 0.0064 memory: 7426 2023/09/17 15:52:24 - mmengine - INFO - Epoch(val) [7][ 100/1250] eta: 0:07:20 time: 0.3870 data_time: 0.0046 memory: 4043 2023/09/17 15:52:43 - mmengine - INFO - Epoch(val) [7][ 150/1250] eta: 0:07:03 time: 0.3876 data_time: 0.0046 memory: 4062 2023/09/17 15:53:02 - mmengine - INFO - Epoch(val) [7][ 200/1250] eta: 0:06:42 time: 0.3809 data_time: 0.0047 memory: 4051 2023/09/17 15:53:20 - mmengine - INFO - Epoch(val) [7][ 250/1250] eta: 0:06:18 time: 0.3566 data_time: 0.0044 memory: 4033 2023/09/17 15:53:39 - mmengine - INFO - Epoch(val) [7][ 300/1250] eta: 0:06:00 time: 0.3842 data_time: 0.0045 memory: 4050 2023/09/17 15:53:59 - mmengine - INFO - Epoch(val) [7][ 350/1250] eta: 0:05:42 time: 0.3855 data_time: 0.0047 memory: 4057 2023/09/17 15:54:18 - mmengine - INFO - Epoch(val) [7][ 400/1250] eta: 0:05:24 time: 0.3897 data_time: 0.0047 memory: 4060 2023/09/17 15:54:39 - mmengine - INFO - Epoch(val) [7][ 450/1250] eta: 0:05:07 time: 0.4101 data_time: 0.0046 memory: 4048 2023/09/17 15:54:57 - mmengine - INFO - Epoch(val) [7][ 500/1250] eta: 0:04:47 time: 0.3732 data_time: 0.0048 memory: 4041 2023/09/17 15:55:16 - mmengine - INFO - Epoch(val) [7][ 550/1250] eta: 0:04:27 time: 0.3711 data_time: 0.0046 memory: 4042 2023/09/17 15:55:34 - mmengine - INFO - Epoch(val) [7][ 600/1250] eta: 0:04:07 time: 0.3708 data_time: 0.0048 memory: 4057 2023/09/17 15:55:55 - mmengine - INFO - Epoch(val) [7][ 650/1250] eta: 0:03:50 time: 0.4070 data_time: 0.0046 memory: 4065 2023/09/17 15:56:14 - mmengine - INFO - Epoch(val) [7][ 700/1250] eta: 0:03:31 time: 0.3931 data_time: 0.0045 memory: 4042 2023/09/17 15:56:34 - mmengine - INFO - Epoch(val) [7][ 750/1250] eta: 0:03:12 time: 0.3971 data_time: 0.0044 memory: 4051 2023/09/17 15:56:53 - mmengine - INFO - Epoch(val) [7][ 800/1250] eta: 0:02:52 time: 0.3717 data_time: 0.0051 memory: 4055 2023/09/17 15:57:12 - mmengine - INFO - Epoch(val) [7][ 850/1250] eta: 0:02:33 time: 0.3884 data_time: 0.0050 memory: 4058 2023/09/17 15:57:30 - mmengine - INFO - Epoch(val) [7][ 900/1250] eta: 0:02:14 time: 0.3596 data_time: 0.0045 memory: 4049 2023/09/17 15:57:49 - mmengine - INFO - Epoch(val) [7][ 950/1250] eta: 0:01:54 time: 0.3678 data_time: 0.0046 memory: 4054 2023/09/17 15:58:08 - mmengine - INFO - Epoch(val) [7][1000/1250] eta: 0:01:35 time: 0.3779 data_time: 0.0045 memory: 4056 2023/09/17 15:58:27 - mmengine - INFO - Epoch(val) [7][1050/1250] eta: 0:01:16 time: 0.3801 data_time: 0.0046 memory: 4043 2023/09/17 15:58:44 - mmengine - INFO - Epoch(val) [7][1100/1250] eta: 0:00:57 time: 0.3575 data_time: 0.0046 memory: 4032 2023/09/17 15:59:04 - mmengine - INFO - Epoch(val) [7][1150/1250] eta: 0:00:38 time: 0.3848 data_time: 0.0047 memory: 4041 2023/09/17 15:59:23 - mmengine - INFO - Epoch(val) [7][1200/1250] eta: 0:00:19 time: 0.3823 data_time: 0.0046 memory: 4047 2023/09/17 15:59:42 - mmengine - INFO - Epoch(val) [7][1250/1250] eta: 0:00:00 time: 0.3870 data_time: 0.0045 memory: 4055 2023/09/17 15:59:45 - mmengine - INFO - Start converting ... 2023/09/17 16:07:11 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.654241] [mAPH 0.649056] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.573603] [mAPH 0.568999] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.738343] [mAPH 0.648954] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.6564] [mAPH 0.575464] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.689736] [mAPH 0.674821] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.664088] [mAPH 0.649731] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.870735] [mAPH 0.865557] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.857278] [mAPH 0.852168] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.631637] [mAPH 0.625798] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.570555] [mAPH 0.565228] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.354439] [mAPH 0.348406] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.266082] [mAPH 0.261464] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.802001] [mAPH 0.721515] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.761152] [mAPH 0.682905] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.725931] [mAPH 0.629731] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.655601] [mAPH 0.568032] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.610481] [mAPH 0.497291] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.475922] [mAPH 0.385568] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.796027] [mAPH 0.781982] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.790312] [mAPH 0.776368] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.641206] [mAPH 0.626577] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.605272] [mAPH 0.591444] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.491971] [mAPH 0.469735] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.458238] [mAPH 0.437504] Eval Using 326s 2023/09/17 16:07:12 - mmengine - INFO - Epoch(val) [7][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.6542 Waymo metric/Vehicle/L1 mAPH: 0.6491 Waymo metric/Vehicle/L2 mAP: 0.5736 Waymo metric/Vehicle/L2 mAPH: 0.5690 Waymo metric/Pedestrian/L1 mAP: 0.7383 Waymo metric/Pedestrian/L1 mAPH: 0.6490 Waymo metric/Pedestrian/L2 mAP: 0.6564 Waymo metric/Pedestrian/L2 mAPH: 0.5755 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6897 Waymo metric/Cyclist/L1 mAPH: 0.6748 Waymo metric/Cyclist/L2 mAP: 0.6641 Waymo metric/Cyclist/L2 mAPH: 0.6497 Waymo metric/Overall/L1 mAP: 0.6941 Waymo metric/Overall/L1 mAPH: 0.6576 Waymo metric/Overall/L2 mAP: 0.6314 Waymo metric/Overall/L2 mAPH: 0.5981 data_time: 0.0047 time: 0.3812 2023/09/17 16:07:37 - mmengine - INFO - Epoch(train) [8][ 50/3953] lr: 4.4014e-04 eta: 2:46:12 time: 0.5040 data_time: 0.0052 memory: 8589 grad_norm: 17.8906 loss: 3.4296 task0.loss_heatmap: 0.5028 task0.loss_bbox: 2.1836 task0.loss_iou: 0.1441 task0.loss_reg_iou: 0.5991 2023/09/17 16:08:02 - mmengine - INFO - Epoch(train) [8][ 100/3953] lr: 4.3832e-04 eta: 2:45:47 time: 0.5062 data_time: 0.0038 memory: 8114 grad_norm: 16.6626 loss: 3.4125 task0.loss_heatmap: 0.4929 task0.loss_bbox: 2.1817 task0.loss_iou: 0.1430 task0.loss_reg_iou: 0.5949 2023/09/17 16:08:27 - mmengine - INFO - Epoch(train) [8][ 150/3953] lr: 4.3649e-04 eta: 2:45:22 time: 0.5029 data_time: 0.0038 memory: 8292 grad_norm: 16.3399 loss: 3.2886 task0.loss_heatmap: 0.5130 task0.loss_bbox: 2.0713 task0.loss_iou: 0.1392 task0.loss_reg_iou: 0.5652 2023/09/17 16:08:52 - mmengine - INFO - Epoch(train) [8][ 200/3953] lr: 4.3467e-04 eta: 2:44:56 time: 0.4901 data_time: 0.0037 memory: 8276 grad_norm: 17.3830 loss: 3.3536 task0.loss_heatmap: 0.4827 task0.loss_bbox: 2.1564 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5697 2023/09/17 16:09:16 - mmengine - INFO - Epoch(train) [8][ 250/3953] lr: 4.3284e-04 eta: 2:44:30 time: 0.4886 data_time: 0.0037 memory: 8308 grad_norm: 16.5150 loss: 3.4510 task0.loss_heatmap: 0.5166 task0.loss_bbox: 2.2032 task0.loss_iou: 0.1453 task0.loss_reg_iou: 0.5859 2023/09/17 16:09:41 - mmengine - INFO - Epoch(train) [8][ 300/3953] lr: 4.3102e-04 eta: 2:44:04 time: 0.4830 data_time: 0.0037 memory: 8150 grad_norm: 17.2595 loss: 3.4402 task0.loss_heatmap: 0.5268 task0.loss_bbox: 2.1826 task0.loss_iou: 0.1434 task0.loss_reg_iou: 0.5874 2023/09/17 16:09:55 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:10:05 - mmengine - INFO - Epoch(train) [8][ 350/3953] lr: 4.2920e-04 eta: 2:43:38 time: 0.4963 data_time: 0.0037 memory: 8085 grad_norm: 16.6698 loss: 3.3510 task0.loss_heatmap: 0.5052 task0.loss_bbox: 2.1182 task0.loss_iou: 0.1462 task0.loss_reg_iou: 0.5813 2023/09/17 16:10:31 - mmengine - INFO - Epoch(train) [8][ 400/3953] lr: 4.2738e-04 eta: 2:43:13 time: 0.5149 data_time: 0.0041 memory: 8513 grad_norm: 16.0985 loss: 3.3520 task0.loss_heatmap: 0.4500 task0.loss_bbox: 2.1681 task0.loss_iou: 0.1479 task0.loss_reg_iou: 0.5860 2023/09/17 16:10:56 - mmengine - INFO - Epoch(train) [8][ 450/3953] lr: 4.2556e-04 eta: 2:42:48 time: 0.5008 data_time: 0.0041 memory: 8281 grad_norm: 16.0813 loss: 3.4119 task0.loss_heatmap: 0.4941 task0.loss_bbox: 2.1856 task0.loss_iou: 0.1445 task0.loss_reg_iou: 0.5877 2023/09/17 16:11:21 - mmengine - INFO - Epoch(train) [8][ 500/3953] lr: 4.2374e-04 eta: 2:42:22 time: 0.4986 data_time: 0.0038 memory: 8616 grad_norm: 16.3329 loss: 3.2755 task0.loss_heatmap: 0.4598 task0.loss_bbox: 2.1046 task0.loss_iou: 0.1377 task0.loss_reg_iou: 0.5734 2023/09/17 16:11:45 - mmengine - INFO - Epoch(train) [8][ 550/3953] lr: 4.2192e-04 eta: 2:41:56 time: 0.4816 data_time: 0.0037 memory: 8565 grad_norm: 15.3818 loss: 3.3259 task0.loss_heatmap: 0.5051 task0.loss_bbox: 2.1013 task0.loss_iou: 0.1474 task0.loss_reg_iou: 0.5721 2023/09/17 16:12:10 - mmengine - INFO - Epoch(train) [8][ 600/3953] lr: 4.2011e-04 eta: 2:41:30 time: 0.4908 data_time: 0.0037 memory: 8593 grad_norm: 17.1408 loss: 3.2516 task0.loss_heatmap: 0.4565 task0.loss_bbox: 2.0755 task0.loss_iou: 0.1455 task0.loss_reg_iou: 0.5742 2023/09/17 16:12:34 - mmengine - INFO - Epoch(train) [8][ 650/3953] lr: 4.1829e-04 eta: 2:41:05 time: 0.4879 data_time: 0.0037 memory: 8462 grad_norm: 16.2344 loss: 3.3798 task0.loss_heatmap: 0.4818 task0.loss_bbox: 2.1814 task0.loss_iou: 0.1414 task0.loss_reg_iou: 0.5751 2023/09/17 16:12:58 - mmengine - INFO - Epoch(train) [8][ 700/3953] lr: 4.1648e-04 eta: 2:40:39 time: 0.4854 data_time: 0.0037 memory: 8297 grad_norm: 17.4064 loss: 3.3435 task0.loss_heatmap: 0.4815 task0.loss_bbox: 2.1280 task0.loss_iou: 0.1459 task0.loss_reg_iou: 0.5881 2023/09/17 16:13:24 - mmengine - INFO - Epoch(train) [8][ 750/3953] lr: 4.1466e-04 eta: 2:40:13 time: 0.5066 data_time: 0.0037 memory: 8296 grad_norm: 17.1160 loss: 3.3730 task0.loss_heatmap: 0.5126 task0.loss_bbox: 2.1343 task0.loss_iou: 0.1453 task0.loss_reg_iou: 0.5809 2023/09/17 16:13:48 - mmengine - INFO - Epoch(train) [8][ 800/3953] lr: 4.1285e-04 eta: 2:39:47 time: 0.4824 data_time: 0.0037 memory: 8262 grad_norm: 17.0747 loss: 3.3003 task0.loss_heatmap: 0.4762 task0.loss_bbox: 2.0870 task0.loss_iou: 0.1494 task0.loss_reg_iou: 0.5877 2023/09/17 16:14:13 - mmengine - INFO - Epoch(train) [8][ 850/3953] lr: 4.1104e-04 eta: 2:39:22 time: 0.4926 data_time: 0.0037 memory: 8922 grad_norm: 16.3965 loss: 3.3718 task0.loss_heatmap: 0.4942 task0.loss_bbox: 2.1420 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.5875 2023/09/17 16:14:37 - mmengine - INFO - Epoch(train) [8][ 900/3953] lr: 4.0923e-04 eta: 2:38:56 time: 0.4987 data_time: 0.0038 memory: 8330 grad_norm: 16.0994 loss: 3.4713 task0.loss_heatmap: 0.4851 task0.loss_bbox: 2.2373 task0.loss_iou: 0.1516 task0.loss_reg_iou: 0.5972 2023/09/17 16:15:02 - mmengine - INFO - Epoch(train) [8][ 950/3953] lr: 4.0742e-04 eta: 2:38:30 time: 0.4939 data_time: 0.0037 memory: 8402 grad_norm: 17.0071 loss: 3.3801 task0.loss_heatmap: 0.5057 task0.loss_bbox: 2.1543 task0.loss_iou: 0.1415 task0.loss_reg_iou: 0.5785 2023/09/17 16:15:27 - mmengine - INFO - Epoch(train) [8][1000/3953] lr: 4.0561e-04 eta: 2:38:05 time: 0.4872 data_time: 0.0038 memory: 8552 grad_norm: 17.1633 loss: 3.3428 task0.loss_heatmap: 0.4820 task0.loss_bbox: 2.1278 task0.loss_iou: 0.1485 task0.loss_reg_iou: 0.5845 2023/09/17 16:15:52 - mmengine - INFO - Epoch(train) [8][1050/3953] lr: 4.0381e-04 eta: 2:37:39 time: 0.5009 data_time: 0.0038 memory: 8536 grad_norm: 16.6375 loss: 3.3338 task0.loss_heatmap: 0.4699 task0.loss_bbox: 2.1285 task0.loss_iou: 0.1491 task0.loss_reg_iou: 0.5863 2023/09/17 16:16:16 - mmengine - INFO - Epoch(train) [8][1100/3953] lr: 4.0200e-04 eta: 2:37:13 time: 0.4925 data_time: 0.0037 memory: 8019 grad_norm: 17.1832 loss: 3.3283 task0.loss_heatmap: 0.4764 task0.loss_bbox: 2.1267 task0.loss_iou: 0.1452 task0.loss_reg_iou: 0.5800 2023/09/17 16:16:41 - mmengine - INFO - Epoch(train) [8][1150/3953] lr: 4.0020e-04 eta: 2:36:48 time: 0.4917 data_time: 0.0037 memory: 8308 grad_norm: 16.2766 loss: 3.3890 task0.loss_heatmap: 0.5146 task0.loss_bbox: 2.1469 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5826 2023/09/17 16:17:06 - mmengine - INFO - Epoch(train) [8][1200/3953] lr: 3.9840e-04 eta: 2:36:22 time: 0.5000 data_time: 0.0037 memory: 8439 grad_norm: 16.6389 loss: 3.3011 task0.loss_heatmap: 0.4813 task0.loss_bbox: 2.0984 task0.loss_iou: 0.1456 task0.loss_reg_iou: 0.5758 2023/09/17 16:17:31 - mmengine - INFO - Epoch(train) [8][1250/3953] lr: 3.9660e-04 eta: 2:35:57 time: 0.5001 data_time: 0.0038 memory: 8476 grad_norm: 17.2230 loss: 3.2347 task0.loss_heatmap: 0.4697 task0.loss_bbox: 2.0577 task0.loss_iou: 0.1417 task0.loss_reg_iou: 0.5655 2023/09/17 16:17:56 - mmengine - INFO - Epoch(train) [8][1300/3953] lr: 3.9480e-04 eta: 2:35:31 time: 0.4944 data_time: 0.0037 memory: 8214 grad_norm: 15.7951 loss: 3.2392 task0.loss_heatmap: 0.4795 task0.loss_bbox: 2.0635 task0.loss_iou: 0.1399 task0.loss_reg_iou: 0.5564 2023/09/17 16:18:10 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:18:20 - mmengine - INFO - Epoch(train) [8][1350/3953] lr: 3.9300e-04 eta: 2:35:06 time: 0.4970 data_time: 0.0038 memory: 8324 grad_norm: 17.9490 loss: 3.1946 task0.loss_heatmap: 0.4488 task0.loss_bbox: 2.0414 task0.loss_iou: 0.1387 task0.loss_reg_iou: 0.5657 2023/09/17 16:18:46 - mmengine - INFO - Epoch(train) [8][1400/3953] lr: 3.9120e-04 eta: 2:34:41 time: 0.5111 data_time: 0.0038 memory: 8487 grad_norm: 16.8193 loss: 3.4053 task0.loss_heatmap: 0.5105 task0.loss_bbox: 2.1529 task0.loss_iou: 0.1494 task0.loss_reg_iou: 0.5924 2023/09/17 16:19:12 - mmengine - INFO - Epoch(train) [8][1450/3953] lr: 3.8941e-04 eta: 2:34:16 time: 0.5197 data_time: 0.0038 memory: 8333 grad_norm: 16.7786 loss: 3.1727 task0.loss_heatmap: 0.4619 task0.loss_bbox: 2.0011 task0.loss_iou: 0.1483 task0.loss_reg_iou: 0.5614 2023/09/17 16:19:39 - mmengine - INFO - Epoch(train) [8][1500/3953] lr: 3.8762e-04 eta: 2:33:51 time: 0.5370 data_time: 0.0038 memory: 8227 grad_norm: 16.5498 loss: 3.2905 task0.loss_heatmap: 0.4759 task0.loss_bbox: 2.1019 task0.loss_iou: 0.1417 task0.loss_reg_iou: 0.5711 2023/09/17 16:20:05 - mmengine - INFO - Epoch(train) [8][1550/3953] lr: 3.8582e-04 eta: 2:33:26 time: 0.5156 data_time: 0.0037 memory: 8223 grad_norm: 16.0450 loss: 3.3506 task0.loss_heatmap: 0.4990 task0.loss_bbox: 2.1288 task0.loss_iou: 0.1452 task0.loss_reg_iou: 0.5776 2023/09/17 16:20:30 - mmengine - INFO - Epoch(train) [8][1600/3953] lr: 3.8403e-04 eta: 2:33:01 time: 0.5042 data_time: 0.0038 memory: 8203 grad_norm: 16.5851 loss: 3.3535 task0.loss_heatmap: 0.5052 task0.loss_bbox: 2.1400 task0.loss_iou: 0.1422 task0.loss_reg_iou: 0.5661 2023/09/17 16:20:55 - mmengine - INFO - Epoch(train) [8][1650/3953] lr: 3.8225e-04 eta: 2:32:36 time: 0.4964 data_time: 0.0037 memory: 8222 grad_norm: 16.8143 loss: 3.4360 task0.loss_heatmap: 0.5247 task0.loss_bbox: 2.1766 task0.loss_iou: 0.1458 task0.loss_reg_iou: 0.5889 2023/09/17 16:21:20 - mmengine - INFO - Epoch(train) [8][1700/3953] lr: 3.8046e-04 eta: 2:32:10 time: 0.5056 data_time: 0.0038 memory: 8188 grad_norm: 15.8235 loss: 3.3162 task0.loss_heatmap: 0.4895 task0.loss_bbox: 2.1154 task0.loss_iou: 0.1428 task0.loss_reg_iou: 0.5685 2023/09/17 16:21:44 - mmengine - INFO - Epoch(train) [8][1750/3953] lr: 3.7867e-04 eta: 2:31:45 time: 0.4887 data_time: 0.0038 memory: 8034 grad_norm: 17.1493 loss: 3.2311 task0.loss_heatmap: 0.4655 task0.loss_bbox: 2.0595 task0.loss_iou: 0.1422 task0.loss_reg_iou: 0.5640 2023/09/17 16:22:09 - mmengine - INFO - Epoch(train) [8][1800/3953] lr: 3.7689e-04 eta: 2:31:19 time: 0.4874 data_time: 0.0037 memory: 8086 grad_norm: 16.8193 loss: 3.2981 task0.loss_heatmap: 0.4848 task0.loss_bbox: 2.0956 task0.loss_iou: 0.1444 task0.loss_reg_iou: 0.5734 2023/09/17 16:22:33 - mmengine - INFO - Epoch(train) [8][1850/3953] lr: 3.7511e-04 eta: 2:30:53 time: 0.4926 data_time: 0.0038 memory: 8433 grad_norm: 17.0980 loss: 3.3384 task0.loss_heatmap: 0.4906 task0.loss_bbox: 2.1208 task0.loss_iou: 0.1462 task0.loss_reg_iou: 0.5810 2023/09/17 16:22:58 - mmengine - INFO - Epoch(train) [8][1900/3953] lr: 3.7333e-04 eta: 2:30:28 time: 0.4952 data_time: 0.0037 memory: 8324 grad_norm: 17.4142 loss: 3.3051 task0.loss_heatmap: 0.4862 task0.loss_bbox: 2.0848 task0.loss_iou: 0.1480 task0.loss_reg_iou: 0.5860 2023/09/17 16:23:23 - mmengine - INFO - Epoch(train) [8][1950/3953] lr: 3.7155e-04 eta: 2:30:02 time: 0.4950 data_time: 0.0038 memory: 8346 grad_norm: 16.2845 loss: 3.2374 task0.loss_heatmap: 0.4683 task0.loss_bbox: 2.0621 task0.loss_iou: 0.1380 task0.loss_reg_iou: 0.5690 2023/09/17 16:23:48 - mmengine - INFO - Epoch(train) [8][2000/3953] lr: 3.6977e-04 eta: 2:29:37 time: 0.5101 data_time: 0.0038 memory: 8070 grad_norm: 17.1871 loss: 3.4437 task0.loss_heatmap: 0.5129 task0.loss_bbox: 2.2068 task0.loss_iou: 0.1435 task0.loss_reg_iou: 0.5806 2023/09/17 16:24:14 - mmengine - INFO - Epoch(train) [8][2050/3953] lr: 3.6799e-04 eta: 2:29:12 time: 0.5141 data_time: 0.0038 memory: 8250 grad_norm: 17.5037 loss: 3.3009 task0.loss_heatmap: 0.4641 task0.loss_bbox: 2.1072 task0.loss_iou: 0.1464 task0.loss_reg_iou: 0.5832 2023/09/17 16:24:38 - mmengine - INFO - Epoch(train) [8][2100/3953] lr: 3.6622e-04 eta: 2:28:46 time: 0.4836 data_time: 0.0037 memory: 7861 grad_norm: 16.1722 loss: 3.3143 task0.loss_heatmap: 0.4799 task0.loss_bbox: 2.1278 task0.loss_iou: 0.1437 task0.loss_reg_iou: 0.5629 2023/09/17 16:25:03 - mmengine - INFO - Epoch(train) [8][2150/3953] lr: 3.6445e-04 eta: 2:28:20 time: 0.4900 data_time: 0.0037 memory: 8437 grad_norm: 15.9727 loss: 3.3322 task0.loss_heatmap: 0.4840 task0.loss_bbox: 2.1426 task0.loss_iou: 0.1387 task0.loss_reg_iou: 0.5668 2023/09/17 16:25:28 - mmengine - INFO - Epoch(train) [8][2200/3953] lr: 3.6268e-04 eta: 2:27:55 time: 0.4993 data_time: 0.0038 memory: 8340 grad_norm: 16.7876 loss: 3.2220 task0.loss_heatmap: 0.4553 task0.loss_bbox: 2.0613 task0.loss_iou: 0.1465 task0.loss_reg_iou: 0.5588 2023/09/17 16:25:52 - mmengine - INFO - Epoch(train) [8][2250/3953] lr: 3.6091e-04 eta: 2:27:29 time: 0.4927 data_time: 0.0037 memory: 8609 grad_norm: 17.7594 loss: 3.1610 task0.loss_heatmap: 0.4363 task0.loss_bbox: 2.0164 task0.loss_iou: 0.1420 task0.loss_reg_iou: 0.5663 2023/09/17 16:26:17 - mmengine - INFO - Epoch(train) [8][2300/3953] lr: 3.5915e-04 eta: 2:27:04 time: 0.4985 data_time: 0.0038 memory: 8583 grad_norm: 17.3633 loss: 3.3170 task0.loss_heatmap: 0.4743 task0.loss_bbox: 2.1161 task0.loss_iou: 0.1469 task0.loss_reg_iou: 0.5797 2023/09/17 16:26:32 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:26:42 - mmengine - INFO - Epoch(train) [8][2350/3953] lr: 3.5738e-04 eta: 2:26:38 time: 0.4977 data_time: 0.0038 memory: 8283 grad_norm: 17.1034 loss: 3.4581 task0.loss_heatmap: 0.4867 task0.loss_bbox: 2.2487 task0.loss_iou: 0.1411 task0.loss_reg_iou: 0.5816 2023/09/17 16:27:06 - mmengine - INFO - Epoch(train) [8][2400/3953] lr: 3.5562e-04 eta: 2:26:12 time: 0.4775 data_time: 0.0038 memory: 8191 grad_norm: 17.0234 loss: 3.1631 task0.loss_heatmap: 0.4498 task0.loss_bbox: 2.0206 task0.loss_iou: 0.1412 task0.loss_reg_iou: 0.5514 2023/09/17 16:27:30 - mmengine - INFO - Epoch(train) [8][2450/3953] lr: 3.5386e-04 eta: 2:25:46 time: 0.4808 data_time: 0.0038 memory: 8420 grad_norm: 17.1675 loss: 3.3236 task0.loss_heatmap: 0.5148 task0.loss_bbox: 2.1020 task0.loss_iou: 0.1375 task0.loss_reg_iou: 0.5694 2023/09/17 16:27:55 - mmengine - INFO - Epoch(train) [8][2500/3953] lr: 3.5210e-04 eta: 2:25:21 time: 0.4974 data_time: 0.0038 memory: 8584 grad_norm: 16.1302 loss: 3.2632 task0.loss_heatmap: 0.4855 task0.loss_bbox: 2.0717 task0.loss_iou: 0.1414 task0.loss_reg_iou: 0.5645 2023/09/17 16:28:20 - mmengine - INFO - Epoch(train) [8][2550/3953] lr: 3.5034e-04 eta: 2:24:55 time: 0.4979 data_time: 0.0038 memory: 7921 grad_norm: 16.8442 loss: 3.2895 task0.loss_heatmap: 0.4562 task0.loss_bbox: 2.1026 task0.loss_iou: 0.1444 task0.loss_reg_iou: 0.5863 2023/09/17 16:28:45 - mmengine - INFO - Epoch(train) [8][2600/3953] lr: 3.4859e-04 eta: 2:24:30 time: 0.4924 data_time: 0.0037 memory: 8247 grad_norm: 15.9714 loss: 3.3205 task0.loss_heatmap: 0.4698 task0.loss_bbox: 2.1298 task0.loss_iou: 0.1425 task0.loss_reg_iou: 0.5784 2023/09/17 16:29:09 - mmengine - INFO - Epoch(train) [8][2650/3953] lr: 3.4684e-04 eta: 2:24:04 time: 0.4881 data_time: 0.0038 memory: 8456 grad_norm: 16.0063 loss: 3.1994 task0.loss_heatmap: 0.4643 task0.loss_bbox: 2.0497 task0.loss_iou: 0.1341 task0.loss_reg_iou: 0.5513 2023/09/17 16:29:33 - mmengine - INFO - Epoch(train) [8][2700/3953] lr: 3.4509e-04 eta: 2:23:38 time: 0.4884 data_time: 0.0039 memory: 8515 grad_norm: 16.9306 loss: 3.3249 task0.loss_heatmap: 0.5163 task0.loss_bbox: 2.1080 task0.loss_iou: 0.1369 task0.loss_reg_iou: 0.5637 2023/09/17 16:29:58 - mmengine - INFO - Epoch(train) [8][2750/3953] lr: 3.4334e-04 eta: 2:23:12 time: 0.4834 data_time: 0.0037 memory: 8237 grad_norm: 16.3909 loss: 3.2729 task0.loss_heatmap: 0.4876 task0.loss_bbox: 2.0876 task0.loss_iou: 0.1397 task0.loss_reg_iou: 0.5580 2023/09/17 16:30:22 - mmengine - INFO - Epoch(train) [8][2800/3953] lr: 3.4159e-04 eta: 2:22:47 time: 0.4875 data_time: 0.0038 memory: 8567 grad_norm: 16.6602 loss: 3.1494 task0.loss_heatmap: 0.4552 task0.loss_bbox: 2.0062 task0.loss_iou: 0.1392 task0.loss_reg_iou: 0.5488 2023/09/17 16:30:47 - mmengine - INFO - Epoch(train) [8][2850/3953] lr: 3.3985e-04 eta: 2:22:21 time: 0.5028 data_time: 0.0038 memory: 8274 grad_norm: 16.0799 loss: 3.2596 task0.loss_heatmap: 0.4534 task0.loss_bbox: 2.0894 task0.loss_iou: 0.1458 task0.loss_reg_iou: 0.5710 2023/09/17 16:31:12 - mmengine - INFO - Epoch(train) [8][2900/3953] lr: 3.3811e-04 eta: 2:21:56 time: 0.4997 data_time: 0.0037 memory: 8178 grad_norm: 16.4376 loss: 3.1749 task0.loss_heatmap: 0.4248 task0.loss_bbox: 2.0538 task0.loss_iou: 0.1349 task0.loss_reg_iou: 0.5614 2023/09/17 16:31:37 - mmengine - INFO - Epoch(train) [8][2950/3953] lr: 3.3637e-04 eta: 2:21:30 time: 0.4978 data_time: 0.0038 memory: 8312 grad_norm: 16.4040 loss: 3.1837 task0.loss_heatmap: 0.4711 task0.loss_bbox: 2.0278 task0.loss_iou: 0.1363 task0.loss_reg_iou: 0.5485 2023/09/17 16:32:04 - mmengine - INFO - Epoch(train) [8][3000/3953] lr: 3.3463e-04 eta: 2:21:06 time: 0.5421 data_time: 0.0038 memory: 8311 grad_norm: 16.3655 loss: 3.2468 task0.loss_heatmap: 0.4867 task0.loss_bbox: 2.0634 task0.loss_iou: 0.1382 task0.loss_reg_iou: 0.5585 2023/09/17 16:32:30 - mmengine - INFO - Epoch(train) [8][3050/3953] lr: 3.3290e-04 eta: 2:20:41 time: 0.5183 data_time: 0.0042 memory: 8665 grad_norm: 17.1454 loss: 3.2829 task0.loss_heatmap: 0.4728 task0.loss_bbox: 2.0967 task0.loss_iou: 0.1428 task0.loss_reg_iou: 0.5706 2023/09/17 16:32:55 - mmengine - INFO - Epoch(train) [8][3100/3953] lr: 3.3116e-04 eta: 2:20:16 time: 0.4974 data_time: 0.0039 memory: 8576 grad_norm: 17.4017 loss: 3.3593 task0.loss_heatmap: 0.5196 task0.loss_bbox: 2.1090 task0.loss_iou: 0.1490 task0.loss_reg_iou: 0.5817 2023/09/17 16:33:19 - mmengine - INFO - Epoch(train) [8][3150/3953] lr: 3.2943e-04 eta: 2:19:50 time: 0.4880 data_time: 0.0038 memory: 8117 grad_norm: 16.2100 loss: 3.2200 task0.loss_heatmap: 0.4701 task0.loss_bbox: 2.0587 task0.loss_iou: 0.1363 task0.loss_reg_iou: 0.5548 2023/09/17 16:33:44 - mmengine - INFO - Epoch(train) [8][3200/3953] lr: 3.2770e-04 eta: 2:19:25 time: 0.4992 data_time: 0.0038 memory: 8299 grad_norm: 16.4492 loss: 3.2327 task0.loss_heatmap: 0.5062 task0.loss_bbox: 2.0330 task0.loss_iou: 0.1369 task0.loss_reg_iou: 0.5567 2023/09/17 16:34:08 - mmengine - INFO - Epoch(train) [8][3250/3953] lr: 3.2598e-04 eta: 2:18:59 time: 0.4748 data_time: 0.0037 memory: 8049 grad_norm: 16.8190 loss: 3.2300 task0.loss_heatmap: 0.4654 task0.loss_bbox: 2.0596 task0.loss_iou: 0.1418 task0.loss_reg_iou: 0.5632 2023/09/17 16:34:33 - mmengine - INFO - Epoch(train) [8][3300/3953] lr: 3.2426e-04 eta: 2:18:33 time: 0.4966 data_time: 0.0038 memory: 8304 grad_norm: 15.7839 loss: 3.2681 task0.loss_heatmap: 0.5071 task0.loss_bbox: 2.0668 task0.loss_iou: 0.1348 task0.loss_reg_iou: 0.5594 2023/09/17 16:34:47 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:34:57 - mmengine - INFO - Epoch(train) [8][3350/3953] lr: 3.2253e-04 eta: 2:18:07 time: 0.4857 data_time: 0.0038 memory: 8280 grad_norm: 16.3476 loss: 3.2756 task0.loss_heatmap: 0.4705 task0.loss_bbox: 2.0882 task0.loss_iou: 0.1477 task0.loss_reg_iou: 0.5691 2023/09/17 16:35:22 - mmengine - INFO - Epoch(train) [8][3400/3953] lr: 3.2082e-04 eta: 2:17:42 time: 0.4879 data_time: 0.0037 memory: 8429 grad_norm: 17.1188 loss: 3.2593 task0.loss_heatmap: 0.4721 task0.loss_bbox: 2.0802 task0.loss_iou: 0.1415 task0.loss_reg_iou: 0.5655 2023/09/17 16:35:46 - mmengine - INFO - Epoch(train) [8][3450/3953] lr: 3.1910e-04 eta: 2:17:16 time: 0.4928 data_time: 0.0038 memory: 8149 grad_norm: 15.7477 loss: 3.2937 task0.loss_heatmap: 0.4839 task0.loss_bbox: 2.0935 task0.loss_iou: 0.1489 task0.loss_reg_iou: 0.5674 2023/09/17 16:36:11 - mmengine - INFO - Epoch(train) [8][3500/3953] lr: 3.1739e-04 eta: 2:16:51 time: 0.4964 data_time: 0.0038 memory: 8440 grad_norm: 16.4298 loss: 3.2444 task0.loss_heatmap: 0.4883 task0.loss_bbox: 2.0575 task0.loss_iou: 0.1408 task0.loss_reg_iou: 0.5578 2023/09/17 16:36:36 - mmengine - INFO - Epoch(train) [8][3550/3953] lr: 3.1567e-04 eta: 2:16:25 time: 0.4993 data_time: 0.0037 memory: 8269 grad_norm: 15.8134 loss: 3.1846 task0.loss_heatmap: 0.4625 task0.loss_bbox: 2.0250 task0.loss_iou: 0.1394 task0.loss_reg_iou: 0.5576 2023/09/17 16:37:01 - mmengine - INFO - Epoch(train) [8][3600/3953] lr: 3.1397e-04 eta: 2:16:00 time: 0.4899 data_time: 0.0039 memory: 8730 grad_norm: 17.2858 loss: 3.3386 task0.loss_heatmap: 0.4963 task0.loss_bbox: 2.1073 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5902 2023/09/17 16:37:25 - mmengine - INFO - Epoch(train) [8][3650/3953] lr: 3.1226e-04 eta: 2:15:34 time: 0.4980 data_time: 0.0039 memory: 8892 grad_norm: 16.4541 loss: 3.2700 task0.loss_heatmap: 0.4831 task0.loss_bbox: 2.0756 task0.loss_iou: 0.1451 task0.loss_reg_iou: 0.5662 2023/09/17 16:37:51 - mmengine - INFO - Epoch(train) [8][3700/3953] lr: 3.1056e-04 eta: 2:15:09 time: 0.5005 data_time: 0.0038 memory: 8403 grad_norm: 16.9671 loss: 3.2765 task0.loss_heatmap: 0.5093 task0.loss_bbox: 2.0689 task0.loss_iou: 0.1397 task0.loss_reg_iou: 0.5587 2023/09/17 16:38:15 - mmengine - INFO - Epoch(train) [8][3750/3953] lr: 3.0885e-04 eta: 2:14:43 time: 0.4940 data_time: 0.0042 memory: 8421 grad_norm: 16.6229 loss: 3.2221 task0.loss_heatmap: 0.4660 task0.loss_bbox: 2.0614 task0.loss_iou: 0.1380 task0.loss_reg_iou: 0.5568 2023/09/17 16:38:41 - mmengine - INFO - Epoch(train) [8][3800/3953] lr: 3.0716e-04 eta: 2:14:18 time: 0.5081 data_time: 0.0043 memory: 8780 grad_norm: 16.8652 loss: 3.2140 task0.loss_heatmap: 0.4873 task0.loss_bbox: 2.0290 task0.loss_iou: 0.1402 task0.loss_reg_iou: 0.5574 2023/09/17 16:39:06 - mmengine - INFO - Epoch(train) [8][3850/3953] lr: 3.0546e-04 eta: 2:13:53 time: 0.5065 data_time: 0.0044 memory: 8134 grad_norm: 17.0034 loss: 3.2429 task0.loss_heatmap: 0.4588 task0.loss_bbox: 2.0771 task0.loss_iou: 0.1404 task0.loss_reg_iou: 0.5665 2023/09/17 16:39:31 - mmengine - INFO - Epoch(train) [8][3900/3953] lr: 3.0377e-04 eta: 2:13:28 time: 0.4954 data_time: 0.0046 memory: 8522 grad_norm: 15.9935 loss: 3.2127 task0.loss_heatmap: 0.4984 task0.loss_bbox: 2.0198 task0.loss_iou: 0.1387 task0.loss_reg_iou: 0.5559 2023/09/17 16:39:56 - mmengine - INFO - Epoch(train) [8][3950/3953] lr: 3.0208e-04 eta: 2:13:02 time: 0.4965 data_time: 0.0045 memory: 8651 grad_norm: 17.4829 loss: 3.2939 task0.loss_heatmap: 0.4702 task0.loss_bbox: 2.1015 task0.loss_iou: 0.1488 task0.loss_reg_iou: 0.5733 2023/09/17 16:39:57 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:39:57 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/09/17 16:40:18 - mmengine - INFO - Epoch(val) [8][ 50/1250] eta: 0:07:39 time: 0.3831 data_time: 0.0073 memory: 7063 2023/09/17 16:40:38 - mmengine - INFO - Epoch(val) [8][ 100/1250] eta: 0:07:22 time: 0.3873 data_time: 0.0051 memory: 4043 2023/09/17 16:40:57 - mmengine - INFO - Epoch(val) [8][ 150/1250] eta: 0:07:04 time: 0.3860 data_time: 0.0046 memory: 4062 2023/09/17 16:41:16 - mmengine - INFO - Epoch(val) [8][ 200/1250] eta: 0:06:43 time: 0.3797 data_time: 0.0047 memory: 4051 2023/09/17 16:41:34 - mmengine - INFO - Epoch(val) [8][ 250/1250] eta: 0:06:18 time: 0.3562 data_time: 0.0044 memory: 4033 2023/09/17 16:41:53 - mmengine - INFO - Epoch(val) [8][ 300/1250] eta: 0:06:00 time: 0.3857 data_time: 0.0044 memory: 4050 2023/09/17 16:42:12 - mmengine - INFO - Epoch(val) [8][ 350/1250] eta: 0:05:42 time: 0.3851 data_time: 0.0048 memory: 4057 2023/09/17 16:42:32 - mmengine - INFO - Epoch(val) [8][ 400/1250] eta: 0:05:24 time: 0.3880 data_time: 0.0047 memory: 4060 2023/09/17 16:42:52 - mmengine - INFO - Epoch(val) [8][ 450/1250] eta: 0:05:07 time: 0.4039 data_time: 0.0043 memory: 4048 2023/09/17 16:43:11 - mmengine - INFO - Epoch(val) [8][ 500/1250] eta: 0:04:48 time: 0.3905 data_time: 0.0046 memory: 4041 2023/09/17 16:43:30 - mmengine - INFO - Epoch(val) [8][ 550/1250] eta: 0:04:28 time: 0.3701 data_time: 0.0046 memory: 4042 2023/09/17 16:43:49 - mmengine - INFO - Epoch(val) [8][ 600/1250] eta: 0:04:08 time: 0.3754 data_time: 0.0050 memory: 4057 2023/09/17 16:44:09 - mmengine - INFO - Epoch(val) [8][ 650/1250] eta: 0:03:50 time: 0.4100 data_time: 0.0047 memory: 4065 2023/09/17 16:44:29 - mmengine - INFO - Epoch(val) [8][ 700/1250] eta: 0:03:32 time: 0.3967 data_time: 0.0048 memory: 4042 2023/09/17 16:44:49 - mmengine - INFO - Epoch(val) [8][ 750/1250] eta: 0:03:13 time: 0.4022 data_time: 0.0045 memory: 4051 2023/09/17 16:45:08 - mmengine - INFO - Epoch(val) [8][ 800/1250] eta: 0:02:53 time: 0.3730 data_time: 0.0049 memory: 4055 2023/09/17 16:45:27 - mmengine - INFO - Epoch(val) [8][ 850/1250] eta: 0:02:34 time: 0.3863 data_time: 0.0046 memory: 4058 2023/09/17 16:45:45 - mmengine - INFO - Epoch(val) [8][ 900/1250] eta: 0:02:14 time: 0.3638 data_time: 0.0048 memory: 4049 2023/09/17 16:46:04 - mmengine - INFO - Epoch(val) [8][ 950/1250] eta: 0:01:55 time: 0.3730 data_time: 0.0049 memory: 4054 2023/09/17 16:46:23 - mmengine - INFO - Epoch(val) [8][1000/1250] eta: 0:01:35 time: 0.3820 data_time: 0.0048 memory: 4056 2023/09/17 16:46:42 - mmengine - INFO - Epoch(val) [8][1050/1250] eta: 0:01:16 time: 0.3783 data_time: 0.0046 memory: 4043 2023/09/17 16:47:00 - mmengine - INFO - Epoch(val) [8][1100/1250] eta: 0:00:57 time: 0.3564 data_time: 0.0047 memory: 4032 2023/09/17 16:47:19 - mmengine - INFO - Epoch(val) [8][1150/1250] eta: 0:00:38 time: 0.3845 data_time: 0.0046 memory: 4041 2023/09/17 16:47:38 - mmengine - INFO - Epoch(val) [8][1200/1250] eta: 0:00:19 time: 0.3817 data_time: 0.0046 memory: 4047 2023/09/17 16:47:58 - mmengine - INFO - Epoch(val) [8][1250/1250] eta: 0:00:00 time: 0.3889 data_time: 0.0048 memory: 4055 2023/09/17 16:48:00 - mmengine - INFO - Start converting ... 2023/09/17 16:55:22 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.654612] [mAPH 0.649689] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.573511] [mAPH 0.569154] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.752886] [mAPH 0.668882] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.672061] [mAPH 0.59484] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.696166] [mAPH 0.681166] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.669888] [mAPH 0.655444] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.873146] [mAPH 0.868109] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.859888] [mAPH 0.854918] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.637295] [mAPH 0.631563] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.575856] [mAPH 0.57063] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.348998] [mAPH 0.344017] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.261045] [mAPH 0.257242] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.806038] [mAPH 0.730798] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.767012] [mAPH 0.693945] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.741888] [mAPH 0.650969] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.671725] [mAPH 0.588132] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.640879] [mAPH 0.534943] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.503856] [mAPH 0.417973] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.787886] [mAPH 0.775142] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.782229] [mAPH 0.769576] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.646502] [mAPH 0.63201] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.609361] [mAPH 0.595688] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.535547] [mAPH 0.508029] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.498656] [mAPH 0.472992] Eval Using 301s 2023/09/17 16:55:22 - mmengine - INFO - Epoch(val) [8][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.6546 Waymo metric/Vehicle/L1 mAPH: 0.6497 Waymo metric/Vehicle/L2 mAP: 0.5735 Waymo metric/Vehicle/L2 mAPH: 0.5692 Waymo metric/Pedestrian/L1 mAP: 0.7529 Waymo metric/Pedestrian/L1 mAPH: 0.6689 Waymo metric/Pedestrian/L2 mAP: 0.6721 Waymo metric/Pedestrian/L2 mAPH: 0.5948 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.6962 Waymo metric/Cyclist/L1 mAPH: 0.6812 Waymo metric/Cyclist/L2 mAP: 0.6699 Waymo metric/Cyclist/L2 mAPH: 0.6554 Waymo metric/Overall/L1 mAP: 0.7012 Waymo metric/Overall/L1 mAPH: 0.6666 Waymo metric/Overall/L2 mAP: 0.6385 Waymo metric/Overall/L2 mAPH: 0.6065 data_time: 0.0048 time: 0.3827 2023/09/17 16:55:47 - mmengine - INFO - Epoch(train) [9][ 50/3953] lr: 3.0029e-04 eta: 2:12:35 time: 0.4958 data_time: 0.0058 memory: 8152 grad_norm: 16.7636 loss: 3.1903 task0.loss_heatmap: 0.4506 task0.loss_bbox: 2.0475 task0.loss_iou: 0.1377 task0.loss_reg_iou: 0.5546 2023/09/17 16:56:12 - mmengine - INFO - Epoch(train) [9][ 100/3953] lr: 2.9860e-04 eta: 2:12:10 time: 0.5060 data_time: 0.0045 memory: 8279 grad_norm: 16.5498 loss: 3.2216 task0.loss_heatmap: 0.4867 task0.loss_bbox: 2.0420 task0.loss_iou: 0.1393 task0.loss_reg_iou: 0.5536 2023/09/17 16:56:39 - mmengine - INFO - Epoch(train) [9][ 150/3953] lr: 2.9692e-04 eta: 2:11:45 time: 0.5255 data_time: 0.0045 memory: 8524 grad_norm: 16.9433 loss: 3.1664 task0.loss_heatmap: 0.4484 task0.loss_bbox: 2.0262 task0.loss_iou: 0.1350 task0.loss_reg_iou: 0.5567 2023/09/17 16:57:06 - mmengine - INFO - Epoch(train) [9][ 200/3953] lr: 2.9524e-04 eta: 2:11:21 time: 0.5376 data_time: 0.0048 memory: 8293 grad_norm: 16.2586 loss: 3.3382 task0.loss_heatmap: 0.4958 task0.loss_bbox: 2.1312 task0.loss_iou: 0.1448 task0.loss_reg_iou: 0.5665 2023/09/17 16:57:31 - mmengine - INFO - Epoch(train) [9][ 250/3953] lr: 2.9356e-04 eta: 2:10:56 time: 0.5044 data_time: 0.0045 memory: 8090 grad_norm: 16.1018 loss: 3.1650 task0.loss_heatmap: 0.4582 task0.loss_bbox: 2.0226 task0.loss_iou: 0.1395 task0.loss_reg_iou: 0.5447 2023/09/17 16:57:56 - mmengine - INFO - Epoch(train) [9][ 300/3953] lr: 2.9189e-04 eta: 2:10:30 time: 0.5075 data_time: 0.0044 memory: 8003 grad_norm: 17.1737 loss: 3.2504 task0.loss_heatmap: 0.4776 task0.loss_bbox: 2.0792 task0.loss_iou: 0.1387 task0.loss_reg_iou: 0.5549 2023/09/17 16:58:21 - mmengine - INFO - Epoch(train) [9][ 350/3953] lr: 2.9022e-04 eta: 2:10:05 time: 0.4901 data_time: 0.0044 memory: 8199 grad_norm: 15.5949 loss: 3.1797 task0.loss_heatmap: 0.4780 task0.loss_bbox: 2.0122 task0.loss_iou: 0.1394 task0.loss_reg_iou: 0.5501 2023/09/17 16:58:34 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 16:58:46 - mmengine - INFO - Epoch(train) [9][ 400/3953] lr: 2.8855e-04 eta: 2:09:40 time: 0.5072 data_time: 0.0040 memory: 8170 grad_norm: 16.4200 loss: 3.1626 task0.loss_heatmap: 0.4853 task0.loss_bbox: 2.0044 task0.loss_iou: 0.1311 task0.loss_reg_iou: 0.5418 2023/09/17 16:59:12 - mmengine - INFO - Epoch(train) [9][ 450/3953] lr: 2.8688e-04 eta: 2:09:14 time: 0.5114 data_time: 0.0040 memory: 8772 grad_norm: 16.4290 loss: 3.1918 task0.loss_heatmap: 0.4766 task0.loss_bbox: 2.0260 task0.loss_iou: 0.1373 task0.loss_reg_iou: 0.5519 2023/09/17 16:59:37 - mmengine - INFO - Epoch(train) [9][ 500/3953] lr: 2.8522e-04 eta: 2:08:49 time: 0.4981 data_time: 0.0039 memory: 8172 grad_norm: 16.4409 loss: 3.2063 task0.loss_heatmap: 0.4655 task0.loss_bbox: 2.0413 task0.loss_iou: 0.1403 task0.loss_reg_iou: 0.5592 2023/09/17 17:00:02 - mmengine - INFO - Epoch(train) [9][ 550/3953] lr: 2.8356e-04 eta: 2:08:24 time: 0.5000 data_time: 0.0041 memory: 8236 grad_norm: 16.6264 loss: 3.2017 task0.loss_heatmap: 0.4471 task0.loss_bbox: 2.0623 task0.loss_iou: 0.1407 task0.loss_reg_iou: 0.5515 2023/09/17 17:00:26 - mmengine - INFO - Epoch(train) [9][ 600/3953] lr: 2.8190e-04 eta: 2:07:58 time: 0.4917 data_time: 0.0040 memory: 8506 grad_norm: 16.5240 loss: 3.1132 task0.loss_heatmap: 0.4490 task0.loss_bbox: 1.9857 task0.loss_iou: 0.1376 task0.loss_reg_iou: 0.5410 2023/09/17 17:00:51 - mmengine - INFO - Epoch(train) [9][ 650/3953] lr: 2.8025e-04 eta: 2:07:33 time: 0.4928 data_time: 0.0039 memory: 8419 grad_norm: 16.1108 loss: 3.1849 task0.loss_heatmap: 0.4554 task0.loss_bbox: 2.0460 task0.loss_iou: 0.1340 task0.loss_reg_iou: 0.5495 2023/09/17 17:01:16 - mmengine - INFO - Epoch(train) [9][ 700/3953] lr: 2.7860e-04 eta: 2:07:07 time: 0.4955 data_time: 0.0039 memory: 8274 grad_norm: 16.8223 loss: 3.1928 task0.loss_heatmap: 0.4526 task0.loss_bbox: 2.0478 task0.loss_iou: 0.1368 task0.loss_reg_iou: 0.5555 2023/09/17 17:01:41 - mmengine - INFO - Epoch(train) [9][ 750/3953] lr: 2.7695e-04 eta: 2:06:42 time: 0.5011 data_time: 0.0040 memory: 8147 grad_norm: 17.6983 loss: 3.1587 task0.loss_heatmap: 0.4775 task0.loss_bbox: 1.9969 task0.loss_iou: 0.1356 task0.loss_reg_iou: 0.5487 2023/09/17 17:02:05 - mmengine - INFO - Epoch(train) [9][ 800/3953] lr: 2.7531e-04 eta: 2:06:16 time: 0.4924 data_time: 0.0040 memory: 8339 grad_norm: 16.0724 loss: 3.2545 task0.loss_heatmap: 0.4944 task0.loss_bbox: 2.0612 task0.loss_iou: 0.1413 task0.loss_reg_iou: 0.5576 2023/09/17 17:02:30 - mmengine - INFO - Epoch(train) [9][ 850/3953] lr: 2.7366e-04 eta: 2:05:51 time: 0.5011 data_time: 0.0041 memory: 8258 grad_norm: 17.3238 loss: 3.1272 task0.loss_heatmap: 0.4400 task0.loss_bbox: 2.0054 task0.loss_iou: 0.1367 task0.loss_reg_iou: 0.5451 2023/09/17 17:02:55 - mmengine - INFO - Epoch(train) [9][ 900/3953] lr: 2.7202e-04 eta: 2:05:26 time: 0.4946 data_time: 0.0041 memory: 7928 grad_norm: 16.8240 loss: 3.2427 task0.loss_heatmap: 0.4769 task0.loss_bbox: 2.0676 task0.loss_iou: 0.1417 task0.loss_reg_iou: 0.5565 2023/09/17 17:03:20 - mmengine - INFO - Epoch(train) [9][ 950/3953] lr: 2.7039e-04 eta: 2:05:00 time: 0.5015 data_time: 0.0040 memory: 8259 grad_norm: 17.0601 loss: 3.1681 task0.loss_heatmap: 0.4666 task0.loss_bbox: 2.0180 task0.loss_iou: 0.1334 task0.loss_reg_iou: 0.5501 2023/09/17 17:03:45 - mmengine - INFO - Epoch(train) [9][1000/3953] lr: 2.6876e-04 eta: 2:04:35 time: 0.5057 data_time: 0.0039 memory: 8282 grad_norm: 16.2371 loss: 3.2479 task0.loss_heatmap: 0.5148 task0.loss_bbox: 2.0434 task0.loss_iou: 0.1373 task0.loss_reg_iou: 0.5524 2023/09/17 17:04:10 - mmengine - INFO - Epoch(train) [9][1050/3953] lr: 2.6713e-04 eta: 2:04:10 time: 0.4960 data_time: 0.0038 memory: 8685 grad_norm: 16.5396 loss: 3.0787 task0.loss_heatmap: 0.4298 task0.loss_bbox: 1.9824 task0.loss_iou: 0.1310 task0.loss_reg_iou: 0.5354 2023/09/17 17:04:35 - mmengine - INFO - Epoch(train) [9][1100/3953] lr: 2.6550e-04 eta: 2:03:44 time: 0.4894 data_time: 0.0039 memory: 8078 grad_norm: 18.0352 loss: 3.2190 task0.loss_heatmap: 0.4683 task0.loss_bbox: 2.0413 task0.loss_iou: 0.1423 task0.loss_reg_iou: 0.5671 2023/09/17 17:05:00 - mmengine - INFO - Epoch(train) [9][1150/3953] lr: 2.6388e-04 eta: 2:03:19 time: 0.5042 data_time: 0.0039 memory: 8481 grad_norm: 16.2063 loss: 3.1839 task0.loss_heatmap: 0.4610 task0.loss_bbox: 2.0305 task0.loss_iou: 0.1364 task0.loss_reg_iou: 0.5560 2023/09/17 17:05:25 - mmengine - INFO - Epoch(train) [9][1200/3953] lr: 2.6226e-04 eta: 2:02:53 time: 0.4992 data_time: 0.0038 memory: 8468 grad_norm: 16.8892 loss: 3.1775 task0.loss_heatmap: 0.4714 task0.loss_bbox: 2.0053 task0.loss_iou: 0.1396 task0.loss_reg_iou: 0.5612 2023/09/17 17:05:50 - mmengine - INFO - Epoch(train) [9][1250/3953] lr: 2.6064e-04 eta: 2:02:28 time: 0.4994 data_time: 0.0039 memory: 8118 grad_norm: 17.3097 loss: 3.1028 task0.loss_heatmap: 0.4600 task0.loss_bbox: 1.9629 task0.loss_iou: 0.1339 task0.loss_reg_iou: 0.5460 2023/09/17 17:06:15 - mmengine - INFO - Epoch(train) [9][1300/3953] lr: 2.5903e-04 eta: 2:02:03 time: 0.5000 data_time: 0.0038 memory: 8440 grad_norm: 15.3839 loss: 3.1137 task0.loss_heatmap: 0.4669 task0.loss_bbox: 1.9725 task0.loss_iou: 0.1357 task0.loss_reg_iou: 0.5386 2023/09/17 17:06:40 - mmengine - INFO - Epoch(train) [9][1350/3953] lr: 2.5742e-04 eta: 2:01:37 time: 0.5014 data_time: 0.0039 memory: 8019 grad_norm: 17.2842 loss: 3.1463 task0.loss_heatmap: 0.4622 task0.loss_bbox: 2.0028 task0.loss_iou: 0.1346 task0.loss_reg_iou: 0.5467 2023/09/17 17:06:53 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:07:05 - mmengine - INFO - Epoch(train) [9][1400/3953] lr: 2.5581e-04 eta: 2:01:12 time: 0.5090 data_time: 0.0041 memory: 8635 grad_norm: 17.4543 loss: 3.2759 task0.loss_heatmap: 0.5066 task0.loss_bbox: 2.0670 task0.loss_iou: 0.1412 task0.loss_reg_iou: 0.5610 2023/09/17 17:07:30 - mmengine - INFO - Epoch(train) [9][1450/3953] lr: 2.5421e-04 eta: 2:00:47 time: 0.4969 data_time: 0.0040 memory: 8564 grad_norm: 16.6853 loss: 3.2374 task0.loss_heatmap: 0.4863 task0.loss_bbox: 2.0500 task0.loss_iou: 0.1368 task0.loss_reg_iou: 0.5644 2023/09/17 17:07:56 - mmengine - INFO - Epoch(train) [9][1500/3953] lr: 2.5261e-04 eta: 2:00:22 time: 0.5119 data_time: 0.0039 memory: 8383 grad_norm: 16.4970 loss: 3.1685 task0.loss_heatmap: 0.4498 task0.loss_bbox: 2.0336 task0.loss_iou: 0.1407 task0.loss_reg_iou: 0.5444 2023/09/17 17:08:21 - mmengine - INFO - Epoch(train) [9][1550/3953] lr: 2.5101e-04 eta: 1:59:57 time: 0.5025 data_time: 0.0040 memory: 8227 grad_norm: 17.8257 loss: 3.1773 task0.loss_heatmap: 0.4502 task0.loss_bbox: 2.0295 task0.loss_iou: 0.1400 task0.loss_reg_iou: 0.5577 2023/09/17 17:08:46 - mmengine - INFO - Epoch(train) [9][1600/3953] lr: 2.4941e-04 eta: 1:59:31 time: 0.4940 data_time: 0.0040 memory: 8123 grad_norm: 15.2174 loss: 3.1439 task0.loss_heatmap: 0.4429 task0.loss_bbox: 2.0131 task0.loss_iou: 0.1382 task0.loss_reg_iou: 0.5498 2023/09/17 17:09:11 - mmengine - INFO - Epoch(train) [9][1650/3953] lr: 2.4782e-04 eta: 1:59:06 time: 0.5147 data_time: 0.0040 memory: 8101 grad_norm: 16.6019 loss: 3.2108 task0.loss_heatmap: 0.4615 task0.loss_bbox: 2.0590 task0.loss_iou: 0.1371 task0.loss_reg_iou: 0.5532 2023/09/17 17:09:39 - mmengine - INFO - Epoch(train) [9][1700/3953] lr: 2.4624e-04 eta: 1:58:42 time: 0.5524 data_time: 0.0039 memory: 8101 grad_norm: 17.6852 loss: 3.1405 task0.loss_heatmap: 0.4361 task0.loss_bbox: 2.0218 task0.loss_iou: 0.1362 task0.loss_reg_iou: 0.5465 2023/09/17 17:10:06 - mmengine - INFO - Epoch(train) [9][1750/3953] lr: 2.4465e-04 eta: 1:58:17 time: 0.5377 data_time: 0.0040 memory: 8477 grad_norm: 16.6561 loss: 3.1868 task0.loss_heatmap: 0.4427 task0.loss_bbox: 2.0474 task0.loss_iou: 0.1401 task0.loss_reg_iou: 0.5567 2023/09/17 17:10:31 - mmengine - INFO - Epoch(train) [9][1800/3953] lr: 2.4307e-04 eta: 1:57:52 time: 0.5049 data_time: 0.0043 memory: 8088 grad_norm: 17.3045 loss: 3.2189 task0.loss_heatmap: 0.4652 task0.loss_bbox: 2.0527 task0.loss_iou: 0.1414 task0.loss_reg_iou: 0.5597 2023/09/17 17:10:58 - mmengine - INFO - Epoch(train) [9][1850/3953] lr: 2.4150e-04 eta: 1:57:27 time: 0.5322 data_time: 0.0041 memory: 8456 grad_norm: 17.0331 loss: 3.2555 task0.loss_heatmap: 0.4867 task0.loss_bbox: 2.0696 task0.loss_iou: 0.1389 task0.loss_reg_iou: 0.5603 2023/09/17 17:11:23 - mmengine - INFO - Epoch(train) [9][1900/3953] lr: 2.3992e-04 eta: 1:57:02 time: 0.5091 data_time: 0.0046 memory: 8878 grad_norm: 17.4971 loss: 3.3066 task0.loss_heatmap: 0.4955 task0.loss_bbox: 2.1088 task0.loss_iou: 0.1369 task0.loss_reg_iou: 0.5654 2023/09/17 17:11:49 - mmengine - INFO - Epoch(train) [9][1950/3953] lr: 2.3836e-04 eta: 1:56:37 time: 0.5117 data_time: 0.0043 memory: 8451 grad_norm: 16.5479 loss: 3.2255 task0.loss_heatmap: 0.4820 task0.loss_bbox: 2.0400 task0.loss_iou: 0.1444 task0.loss_reg_iou: 0.5592 2023/09/17 17:12:14 - mmengine - INFO - Epoch(train) [9][2000/3953] lr: 2.3679e-04 eta: 1:56:12 time: 0.5044 data_time: 0.0043 memory: 8271 grad_norm: 18.0438 loss: 3.1799 task0.loss_heatmap: 0.4839 task0.loss_bbox: 2.0061 task0.loss_iou: 0.1384 task0.loss_reg_iou: 0.5515 2023/09/17 17:12:39 - mmengine - INFO - Epoch(train) [9][2050/3953] lr: 2.3523e-04 eta: 1:55:46 time: 0.4925 data_time: 0.0042 memory: 7893 grad_norm: 16.7797 loss: 3.0668 task0.loss_heatmap: 0.4403 task0.loss_bbox: 1.9420 task0.loss_iou: 0.1395 task0.loss_reg_iou: 0.5451 2023/09/17 17:13:04 - mmengine - INFO - Epoch(train) [9][2100/3953] lr: 2.3367e-04 eta: 1:55:21 time: 0.4998 data_time: 0.0038 memory: 8329 grad_norm: 18.2799 loss: 3.1453 task0.loss_heatmap: 0.4362 task0.loss_bbox: 2.0077 task0.loss_iou: 0.1395 task0.loss_reg_iou: 0.5619 2023/09/17 17:13:28 - mmengine - INFO - Epoch(train) [9][2150/3953] lr: 2.3211e-04 eta: 1:54:56 time: 0.4909 data_time: 0.0039 memory: 8158 grad_norm: 16.4726 loss: 3.1741 task0.loss_heatmap: 0.4894 task0.loss_bbox: 2.0025 task0.loss_iou: 0.1371 task0.loss_reg_iou: 0.5451 2023/09/17 17:13:53 - mmengine - INFO - Epoch(train) [9][2200/3953] lr: 2.3056e-04 eta: 1:54:30 time: 0.4943 data_time: 0.0044 memory: 8168 grad_norm: 16.8400 loss: 3.2141 task0.loss_heatmap: 0.4637 task0.loss_bbox: 2.0666 task0.loss_iou: 0.1372 task0.loss_reg_iou: 0.5467 2023/09/17 17:14:19 - mmengine - INFO - Epoch(train) [9][2250/3953] lr: 2.2901e-04 eta: 1:54:05 time: 0.5085 data_time: 0.0040 memory: 8430 grad_norm: 18.3089 loss: 3.0708 task0.loss_heatmap: 0.4325 task0.loss_bbox: 1.9539 task0.loss_iou: 0.1338 task0.loss_reg_iou: 0.5506 2023/09/17 17:14:44 - mmengine - INFO - Epoch(train) [9][2300/3953] lr: 2.2747e-04 eta: 1:53:40 time: 0.5047 data_time: 0.0041 memory: 8419 grad_norm: 16.8952 loss: 3.1673 task0.loss_heatmap: 0.4445 task0.loss_bbox: 2.0356 task0.loss_iou: 0.1341 task0.loss_reg_iou: 0.5531 2023/09/17 17:15:09 - mmengine - INFO - Epoch(train) [9][2350/3953] lr: 2.2593e-04 eta: 1:53:14 time: 0.5043 data_time: 0.0040 memory: 8304 grad_norm: 17.8553 loss: 3.1416 task0.loss_heatmap: 0.4439 task0.loss_bbox: 2.0154 task0.loss_iou: 0.1389 task0.loss_reg_iou: 0.5434 2023/09/17 17:15:22 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:15:34 - mmengine - INFO - Epoch(train) [9][2400/3953] lr: 2.2439e-04 eta: 1:52:49 time: 0.5061 data_time: 0.0041 memory: 8310 grad_norm: 17.3530 loss: 3.1578 task0.loss_heatmap: 0.4276 task0.loss_bbox: 2.0422 task0.loss_iou: 0.1359 task0.loss_reg_iou: 0.5521 2023/09/17 17:15:59 - mmengine - INFO - Epoch(train) [9][2450/3953] lr: 2.2286e-04 eta: 1:52:24 time: 0.4924 data_time: 0.0040 memory: 8476 grad_norm: 17.2284 loss: 3.2744 task0.loss_heatmap: 0.4820 task0.loss_bbox: 2.0837 task0.loss_iou: 0.1419 task0.loss_reg_iou: 0.5668 2023/09/17 17:16:24 - mmengine - INFO - Epoch(train) [9][2500/3953] lr: 2.2133e-04 eta: 1:51:58 time: 0.4948 data_time: 0.0043 memory: 8130 grad_norm: 17.6239 loss: 3.0781 task0.loss_heatmap: 0.4424 task0.loss_bbox: 1.9694 task0.loss_iou: 0.1322 task0.loss_reg_iou: 0.5340 2023/09/17 17:16:49 - mmengine - INFO - Epoch(train) [9][2550/3953] lr: 2.1980e-04 eta: 1:51:33 time: 0.5037 data_time: 0.0042 memory: 8206 grad_norm: 17.4120 loss: 3.1086 task0.loss_heatmap: 0.4434 task0.loss_bbox: 1.9818 task0.loss_iou: 0.1401 task0.loss_reg_iou: 0.5434 2023/09/17 17:17:14 - mmengine - INFO - Epoch(train) [9][2600/3953] lr: 2.1828e-04 eta: 1:51:08 time: 0.4962 data_time: 0.0040 memory: 8356 grad_norm: 17.7843 loss: 3.1117 task0.loss_heatmap: 0.4369 task0.loss_bbox: 1.9933 task0.loss_iou: 0.1357 task0.loss_reg_iou: 0.5457 2023/09/17 17:17:39 - mmengine - INFO - Epoch(train) [9][2650/3953] lr: 2.1676e-04 eta: 1:50:43 time: 0.5109 data_time: 0.0040 memory: 8487 grad_norm: 17.0482 loss: 3.1592 task0.loss_heatmap: 0.4609 task0.loss_bbox: 2.0077 task0.loss_iou: 0.1403 task0.loss_reg_iou: 0.5504 2023/09/17 17:18:05 - mmengine - INFO - Epoch(train) [9][2700/3953] lr: 2.1525e-04 eta: 1:50:18 time: 0.5120 data_time: 0.0044 memory: 8190 grad_norm: 17.6637 loss: 3.1679 task0.loss_heatmap: 0.4429 task0.loss_bbox: 2.0474 task0.loss_iou: 0.1318 task0.loss_reg_iou: 0.5459 2023/09/17 17:18:30 - mmengine - INFO - Epoch(train) [9][2750/3953] lr: 2.1374e-04 eta: 1:49:52 time: 0.5117 data_time: 0.0043 memory: 8416 grad_norm: 16.7944 loss: 3.1530 task0.loss_heatmap: 0.4371 task0.loss_bbox: 2.0275 task0.loss_iou: 0.1396 task0.loss_reg_iou: 0.5487 2023/09/17 17:18:56 - mmengine - INFO - Epoch(train) [9][2800/3953] lr: 2.1223e-04 eta: 1:49:27 time: 0.5027 data_time: 0.0040 memory: 8309 grad_norm: 17.5553 loss: 3.1340 task0.loss_heatmap: 0.4725 task0.loss_bbox: 1.9868 task0.loss_iou: 0.1347 task0.loss_reg_iou: 0.5400 2023/09/17 17:19:21 - mmengine - INFO - Epoch(train) [9][2850/3953] lr: 2.1073e-04 eta: 1:49:02 time: 0.4985 data_time: 0.0043 memory: 8542 grad_norm: 16.6027 loss: 3.1833 task0.loss_heatmap: 0.4563 task0.loss_bbox: 2.0389 task0.loss_iou: 0.1363 task0.loss_reg_iou: 0.5519 2023/09/17 17:19:46 - mmengine - INFO - Epoch(train) [9][2900/3953] lr: 2.0923e-04 eta: 1:48:37 time: 0.5055 data_time: 0.0042 memory: 8272 grad_norm: 16.4568 loss: 3.1539 task0.loss_heatmap: 0.4636 task0.loss_bbox: 2.0153 task0.loss_iou: 0.1359 task0.loss_reg_iou: 0.5390 2023/09/17 17:20:11 - mmengine - INFO - Epoch(train) [9][2950/3953] lr: 2.0774e-04 eta: 1:48:11 time: 0.5060 data_time: 0.0045 memory: 8473 grad_norm: 17.6317 loss: 3.1993 task0.loss_heatmap: 0.4687 task0.loss_bbox: 2.0385 task0.loss_iou: 0.1389 task0.loss_reg_iou: 0.5532 2023/09/17 17:20:36 - mmengine - INFO - Epoch(train) [9][3000/3953] lr: 2.0625e-04 eta: 1:47:46 time: 0.5057 data_time: 0.0044 memory: 8497 grad_norm: 16.8749 loss: 3.1791 task0.loss_heatmap: 0.4665 task0.loss_bbox: 2.0197 task0.loss_iou: 0.1374 task0.loss_reg_iou: 0.5555 2023/09/17 17:21:02 - mmengine - INFO - Epoch(train) [9][3050/3953] lr: 2.0476e-04 eta: 1:47:21 time: 0.5041 data_time: 0.0045 memory: 8497 grad_norm: 17.3202 loss: 3.1069 task0.loss_heatmap: 0.4280 task0.loss_bbox: 1.9899 task0.loss_iou: 0.1392 task0.loss_reg_iou: 0.5498 2023/09/17 17:21:27 - mmengine - INFO - Epoch(train) [9][3100/3953] lr: 2.0328e-04 eta: 1:46:56 time: 0.5065 data_time: 0.0041 memory: 9082 grad_norm: 18.6499 loss: 3.0916 task0.loss_heatmap: 0.4377 task0.loss_bbox: 1.9764 task0.loss_iou: 0.1335 task0.loss_reg_iou: 0.5439 2023/09/17 17:21:53 - mmengine - INFO - Epoch(train) [9][3150/3953] lr: 2.0180e-04 eta: 1:46:31 time: 0.5133 data_time: 0.0039 memory: 8697 grad_norm: 19.6243 loss: 3.2296 task0.loss_heatmap: 0.4789 task0.loss_bbox: 2.0508 task0.loss_iou: 0.1400 task0.loss_reg_iou: 0.5599 2023/09/17 17:22:20 - mmengine - INFO - Epoch(train) [9][3200/3953] lr: 2.0032e-04 eta: 1:46:06 time: 0.5427 data_time: 0.0040 memory: 8699 grad_norm: 17.3220 loss: 3.2196 task0.loss_heatmap: 0.4667 task0.loss_bbox: 2.0795 task0.loss_iou: 0.1362 task0.loss_reg_iou: 0.5372 2023/09/17 17:22:47 - mmengine - INFO - Epoch(train) [9][3250/3953] lr: 1.9885e-04 eta: 1:45:41 time: 0.5402 data_time: 0.0039 memory: 8191 grad_norm: 17.0186 loss: 3.0629 task0.loss_heatmap: 0.4465 task0.loss_bbox: 1.9547 task0.loss_iou: 0.1340 task0.loss_reg_iou: 0.5276 2023/09/17 17:23:12 - mmengine - INFO - Epoch(train) [9][3300/3953] lr: 1.9739e-04 eta: 1:45:16 time: 0.5048 data_time: 0.0039 memory: 8706 grad_norm: 17.2062 loss: 3.1779 task0.loss_heatmap: 0.4508 task0.loss_bbox: 2.0474 task0.loss_iou: 0.1361 task0.loss_reg_iou: 0.5435 2023/09/17 17:23:37 - mmengine - INFO - Epoch(train) [9][3350/3953] lr: 1.9593e-04 eta: 1:44:51 time: 0.4908 data_time: 0.0039 memory: 8672 grad_norm: 17.1812 loss: 3.2240 task0.loss_heatmap: 0.4677 task0.loss_bbox: 2.0561 task0.loss_iou: 0.1388 task0.loss_reg_iou: 0.5613 2023/09/17 17:23:50 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:24:02 - mmengine - INFO - Epoch(train) [9][3400/3953] lr: 1.9447e-04 eta: 1:44:25 time: 0.5006 data_time: 0.0042 memory: 8262 grad_norm: 17.2341 loss: 3.1281 task0.loss_heatmap: 0.4879 task0.loss_bbox: 1.9704 task0.loss_iou: 0.1372 task0.loss_reg_iou: 0.5327 2023/09/17 17:24:27 - mmengine - INFO - Epoch(train) [9][3450/3953] lr: 1.9301e-04 eta: 1:44:00 time: 0.5048 data_time: 0.0038 memory: 7882 grad_norm: 16.4175 loss: 3.1731 task0.loss_heatmap: 0.4378 task0.loss_bbox: 2.0514 task0.loss_iou: 0.1381 task0.loss_reg_iou: 0.5458 2023/09/17 17:24:51 - mmengine - INFO - Epoch(train) [9][3500/3953] lr: 1.9156e-04 eta: 1:43:35 time: 0.4906 data_time: 0.0039 memory: 8762 grad_norm: 17.4502 loss: 3.2167 task0.loss_heatmap: 0.4746 task0.loss_bbox: 2.0545 task0.loss_iou: 0.1409 task0.loss_reg_iou: 0.5466 2023/09/17 17:25:16 - mmengine - INFO - Epoch(train) [9][3550/3953] lr: 1.9012e-04 eta: 1:43:09 time: 0.4928 data_time: 0.0038 memory: 8129 grad_norm: 16.9503 loss: 3.2171 task0.loss_heatmap: 0.4716 task0.loss_bbox: 2.0625 task0.loss_iou: 0.1371 task0.loss_reg_iou: 0.5459 2023/09/17 17:25:41 - mmengine - INFO - Epoch(train) [9][3600/3953] lr: 1.8868e-04 eta: 1:42:44 time: 0.4899 data_time: 0.0039 memory: 8387 grad_norm: 17.2569 loss: 3.2162 task0.loss_heatmap: 0.4831 task0.loss_bbox: 2.0443 task0.loss_iou: 0.1367 task0.loss_reg_iou: 0.5521 2023/09/17 17:26:05 - mmengine - INFO - Epoch(train) [9][3650/3953] lr: 1.8724e-04 eta: 1:42:18 time: 0.4863 data_time: 0.0041 memory: 8308 grad_norm: 17.2431 loss: 3.0375 task0.loss_heatmap: 0.4193 task0.loss_bbox: 1.9556 task0.loss_iou: 0.1323 task0.loss_reg_iou: 0.5302 2023/09/17 17:26:30 - mmengine - INFO - Epoch(train) [9][3700/3953] lr: 1.8580e-04 eta: 1:41:53 time: 0.4965 data_time: 0.0039 memory: 8289 grad_norm: 18.0855 loss: 3.1569 task0.loss_heatmap: 0.4419 task0.loss_bbox: 2.0138 task0.loss_iou: 0.1410 task0.loss_reg_iou: 0.5602 2023/09/17 17:26:55 - mmengine - INFO - Epoch(train) [9][3750/3953] lr: 1.8438e-04 eta: 1:41:27 time: 0.5016 data_time: 0.0039 memory: 8505 grad_norm: 16.4790 loss: 3.0661 task0.loss_heatmap: 0.4330 task0.loss_bbox: 1.9636 task0.loss_iou: 0.1345 task0.loss_reg_iou: 0.5349 2023/09/17 17:27:20 - mmengine - INFO - Epoch(train) [9][3800/3953] lr: 1.8295e-04 eta: 1:41:02 time: 0.4982 data_time: 0.0040 memory: 9000 grad_norm: 17.0775 loss: 3.0597 task0.loss_heatmap: 0.4351 task0.loss_bbox: 1.9571 task0.loss_iou: 0.1338 task0.loss_reg_iou: 0.5337 2023/09/17 17:27:45 - mmengine - INFO - Epoch(train) [9][3850/3953] lr: 1.8153e-04 eta: 1:40:37 time: 0.4961 data_time: 0.0038 memory: 8250 grad_norm: 16.4808 loss: 3.0348 task0.loss_heatmap: 0.4100 task0.loss_bbox: 1.9514 task0.loss_iou: 0.1347 task0.loss_reg_iou: 0.5387 2023/09/17 17:28:09 - mmengine - INFO - Epoch(train) [9][3900/3953] lr: 1.8011e-04 eta: 1:40:11 time: 0.4889 data_time: 0.0037 memory: 8509 grad_norm: 16.4603 loss: 3.1539 task0.loss_heatmap: 0.4716 task0.loss_bbox: 1.9897 task0.loss_iou: 0.1392 task0.loss_reg_iou: 0.5533 2023/09/17 17:28:34 - mmengine - INFO - Epoch(train) [9][3950/3953] lr: 1.7870e-04 eta: 1:39:46 time: 0.4991 data_time: 0.0040 memory: 8956 grad_norm: 17.3449 loss: 3.1798 task0.loss_heatmap: 0.4674 task0.loss_bbox: 2.0280 task0.loss_iou: 0.1345 task0.loss_reg_iou: 0.5498 2023/09/17 17:28:35 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:28:35 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/09/17 17:28:57 - mmengine - INFO - Epoch(val) [9][ 50/1250] eta: 0:07:35 time: 0.3798 data_time: 0.0062 memory: 7215 2023/09/17 17:29:17 - mmengine - INFO - Epoch(val) [9][ 100/1250] eta: 0:07:22 time: 0.3895 data_time: 0.0047 memory: 4043 2023/09/17 17:29:36 - mmengine - INFO - Epoch(val) [9][ 150/1250] eta: 0:07:03 time: 0.3862 data_time: 0.0045 memory: 4062 2023/09/17 17:29:55 - mmengine - INFO - Epoch(val) [9][ 200/1250] eta: 0:06:43 time: 0.3828 data_time: 0.0046 memory: 4051 2023/09/17 17:30:13 - mmengine - INFO - Epoch(val) [9][ 250/1250] eta: 0:06:19 time: 0.3594 data_time: 0.0045 memory: 4033 2023/09/17 17:30:32 - mmengine - INFO - Epoch(val) [9][ 300/1250] eta: 0:06:01 time: 0.3832 data_time: 0.0043 memory: 4050 2023/09/17 17:30:52 - mmengine - INFO - Epoch(val) [9][ 350/1250] eta: 0:05:42 time: 0.3827 data_time: 0.0044 memory: 4057 2023/09/17 17:31:11 - mmengine - INFO - Epoch(val) [9][ 400/1250] eta: 0:05:24 time: 0.3895 data_time: 0.0047 memory: 4060 2023/09/17 17:31:31 - mmengine - INFO - Epoch(val) [9][ 450/1250] eta: 0:05:07 time: 0.4071 data_time: 0.0045 memory: 4048 2023/09/17 17:31:50 - mmengine - INFO - Epoch(val) [9][ 500/1250] eta: 0:04:47 time: 0.3702 data_time: 0.0048 memory: 4041 2023/09/17 17:32:08 - mmengine - INFO - Epoch(val) [9][ 550/1250] eta: 0:04:27 time: 0.3705 data_time: 0.0046 memory: 4042 2023/09/17 17:32:27 - mmengine - INFO - Epoch(val) [9][ 600/1250] eta: 0:04:07 time: 0.3735 data_time: 0.0052 memory: 4057 2023/09/17 17:32:48 - mmengine - INFO - Epoch(val) [9][ 650/1250] eta: 0:03:49 time: 0.4078 data_time: 0.0047 memory: 4065 2023/09/17 17:33:07 - mmengine - INFO - Epoch(val) [9][ 700/1250] eta: 0:03:31 time: 0.3944 data_time: 0.0051 memory: 4042 2023/09/17 17:33:27 - mmengine - INFO - Epoch(val) [9][ 750/1250] eta: 0:03:12 time: 0.3980 data_time: 0.0046 memory: 4051 2023/09/17 17:33:46 - mmengine - INFO - Epoch(val) [9][ 800/1250] eta: 0:02:52 time: 0.3723 data_time: 0.0045 memory: 4055 2023/09/17 17:34:05 - mmengine - INFO - Epoch(val) [9][ 850/1250] eta: 0:02:33 time: 0.3855 data_time: 0.0046 memory: 4058 2023/09/17 17:34:23 - mmengine - INFO - Epoch(val) [9][ 900/1250] eta: 0:02:13 time: 0.3581 data_time: 0.0046 memory: 4049 2023/09/17 17:34:41 - mmengine - INFO - Epoch(val) [9][ 950/1250] eta: 0:01:54 time: 0.3665 data_time: 0.0046 memory: 4054 2023/09/17 17:35:00 - mmengine - INFO - Epoch(val) [9][1000/1250] eta: 0:01:35 time: 0.3776 data_time: 0.0046 memory: 4056 2023/09/17 17:35:19 - mmengine - INFO - Epoch(val) [9][1050/1250] eta: 0:01:16 time: 0.3796 data_time: 0.0050 memory: 4043 2023/09/17 17:35:37 - mmengine - INFO - Epoch(val) [9][1100/1250] eta: 0:00:57 time: 0.3565 data_time: 0.0045 memory: 4032 2023/09/17 17:35:56 - mmengine - INFO - Epoch(val) [9][1150/1250] eta: 0:00:38 time: 0.3826 data_time: 0.0045 memory: 4041 2023/09/17 17:36:15 - mmengine - INFO - Epoch(val) [9][1200/1250] eta: 0:00:19 time: 0.3811 data_time: 0.0046 memory: 4047 2023/09/17 17:36:35 - mmengine - INFO - Epoch(val) [9][1250/1250] eta: 0:00:00 time: 0.3864 data_time: 0.0044 memory: 4055 2023/09/17 17:36:37 - mmengine - INFO - Start converting ... 2023/09/17 17:44:00 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.707648] [mAPH 0.702445] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.622475] [mAPH 0.617823] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.771738] [mAPH 0.686246] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.69225] [mAPH 0.613791] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.711871] [mAPH 0.696937] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.685411] [mAPH 0.671019] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.907829] [mAPH 0.902681] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.89461] [mAPH 0.889523] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.692095] [mAPH 0.68615] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.62706] [mAPH 0.621607] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.423496] [mAPH 0.417912] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.320173] [mAPH 0.315847] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.827046] [mAPH 0.747672] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.788992] [mAPH 0.711509] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.759756] [mAPH 0.672295] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.691918] [mAPH 0.611419] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.658224] [mAPH 0.554138] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.521634] [mAPH 0.436396] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.812971] [mAPH 0.799885] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.807137] [mAPH 0.794144] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.657422] [mAPH 0.644092] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.621112] [mAPH 0.608561] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.526378] [mAPH 0.493461] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.490641] [mAPH 0.459982] Eval Using 326s 2023/09/17 17:44:01 - mmengine - INFO - Epoch(val) [9][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.7076 Waymo metric/Vehicle/L1 mAPH: 0.7024 Waymo metric/Vehicle/L2 mAP: 0.6225 Waymo metric/Vehicle/L2 mAPH: 0.6178 Waymo metric/Pedestrian/L1 mAP: 0.7717 Waymo metric/Pedestrian/L1 mAPH: 0.6862 Waymo metric/Pedestrian/L2 mAP: 0.6923 Waymo metric/Pedestrian/L2 mAPH: 0.6138 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.7119 Waymo metric/Cyclist/L1 mAPH: 0.6969 Waymo metric/Cyclist/L2 mAP: 0.6854 Waymo metric/Cyclist/L2 mAPH: 0.6710 Waymo metric/Overall/L1 mAP: 0.7304 Waymo metric/Overall/L1 mAPH: 0.6952 Waymo metric/Overall/L2 mAP: 0.6667 Waymo metric/Overall/L2 mAPH: 0.6342 data_time: 0.0047 time: 0.3808 2023/09/17 17:44:25 - mmengine - INFO - Epoch(train) [10][ 50/3953] lr: 1.7721e-04 eta: 1:39:19 time: 0.4929 data_time: 0.0053 memory: 8071 grad_norm: 17.5351 loss: 3.1492 task0.loss_heatmap: 0.4838 task0.loss_bbox: 1.9801 task0.loss_iou: 0.1404 task0.loss_reg_iou: 0.5449 2023/09/17 17:44:50 - mmengine - INFO - Epoch(train) [10][ 100/3953] lr: 1.7581e-04 eta: 1:38:54 time: 0.4924 data_time: 0.0039 memory: 8420 grad_norm: 16.5593 loss: 3.1653 task0.loss_heatmap: 0.4697 task0.loss_bbox: 2.0012 task0.loss_iou: 0.1406 task0.loss_reg_iou: 0.5538 2023/09/17 17:45:14 - mmengine - INFO - Epoch(train) [10][ 150/3953] lr: 1.7441e-04 eta: 1:38:28 time: 0.4881 data_time: 0.0038 memory: 8151 grad_norm: 16.8552 loss: 3.1646 task0.loss_heatmap: 0.4692 task0.loss_bbox: 2.0052 task0.loss_iou: 0.1359 task0.loss_reg_iou: 0.5543 2023/09/17 17:45:39 - mmengine - INFO - Epoch(train) [10][ 200/3953] lr: 1.7302e-04 eta: 1:38:03 time: 0.4929 data_time: 0.0038 memory: 8074 grad_norm: 17.1236 loss: 3.2284 task0.loss_heatmap: 0.4995 task0.loss_bbox: 2.0400 task0.loss_iou: 0.1364 task0.loss_reg_iou: 0.5525 2023/09/17 17:46:04 - mmengine - INFO - Epoch(train) [10][ 250/3953] lr: 1.7163e-04 eta: 1:37:37 time: 0.4960 data_time: 0.0037 memory: 8335 grad_norm: 17.3594 loss: 3.1432 task0.loss_heatmap: 0.4528 task0.loss_bbox: 2.0153 task0.loss_iou: 0.1348 task0.loss_reg_iou: 0.5402 2023/09/17 17:46:28 - mmengine - INFO - Epoch(train) [10][ 300/3953] lr: 1.7024e-04 eta: 1:37:12 time: 0.4821 data_time: 0.0042 memory: 8418 grad_norm: 16.9751 loss: 3.0317 task0.loss_heatmap: 0.4409 task0.loss_bbox: 1.9385 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5221 2023/09/17 17:46:53 - mmengine - INFO - Epoch(train) [10][ 350/3953] lr: 1.6886e-04 eta: 1:36:46 time: 0.5080 data_time: 0.0038 memory: 8616 grad_norm: 17.2078 loss: 3.0750 task0.loss_heatmap: 0.4588 task0.loss_bbox: 1.9542 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5317 2023/09/17 17:47:21 - mmengine - INFO - Epoch(train) [10][ 400/3953] lr: 1.6748e-04 eta: 1:36:22 time: 0.5458 data_time: 0.0040 memory: 8575 grad_norm: 17.8135 loss: 3.2248 task0.loss_heatmap: 0.4768 task0.loss_bbox: 2.0607 task0.loss_iou: 0.1346 task0.loss_reg_iou: 0.5527 2023/09/17 17:47:33 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:47:48 - mmengine - INFO - Epoch(train) [10][ 450/3953] lr: 1.6611e-04 eta: 1:35:57 time: 0.5356 data_time: 0.0040 memory: 8061 grad_norm: 16.1461 loss: 3.0143 task0.loss_heatmap: 0.4345 task0.loss_bbox: 1.9210 task0.loss_iou: 0.1319 task0.loss_reg_iou: 0.5270 2023/09/17 17:48:12 - mmengine - INFO - Epoch(train) [10][ 500/3953] lr: 1.6475e-04 eta: 1:35:32 time: 0.4876 data_time: 0.0039 memory: 8436 grad_norm: 18.5194 loss: 3.0712 task0.loss_heatmap: 0.4509 task0.loss_bbox: 1.9456 task0.loss_iou: 0.1348 task0.loss_reg_iou: 0.5400 2023/09/17 17:48:37 - mmengine - INFO - Epoch(train) [10][ 550/3953] lr: 1.6338e-04 eta: 1:35:06 time: 0.4970 data_time: 0.0039 memory: 8372 grad_norm: 17.7796 loss: 3.0592 task0.loss_heatmap: 0.4369 task0.loss_bbox: 1.9587 task0.loss_iou: 0.1315 task0.loss_reg_iou: 0.5322 2023/09/17 17:49:02 - mmengine - INFO - Epoch(train) [10][ 600/3953] lr: 1.6203e-04 eta: 1:34:41 time: 0.4980 data_time: 0.0039 memory: 8467 grad_norm: 18.0015 loss: 3.1188 task0.loss_heatmap: 0.4609 task0.loss_bbox: 1.9762 task0.loss_iou: 0.1353 task0.loss_reg_iou: 0.5464 2023/09/17 17:49:26 - mmengine - INFO - Epoch(train) [10][ 650/3953] lr: 1.6067e-04 eta: 1:34:16 time: 0.4937 data_time: 0.0038 memory: 8192 grad_norm: 17.8412 loss: 3.1435 task0.loss_heatmap: 0.4360 task0.loss_bbox: 2.0312 task0.loss_iou: 0.1352 task0.loss_reg_iou: 0.5411 2023/09/17 17:49:51 - mmengine - INFO - Epoch(train) [10][ 700/3953] lr: 1.5932e-04 eta: 1:33:50 time: 0.5004 data_time: 0.0039 memory: 8146 grad_norm: 17.2789 loss: 3.2070 task0.loss_heatmap: 0.4794 task0.loss_bbox: 2.0448 task0.loss_iou: 0.1348 task0.loss_reg_iou: 0.5480 2023/09/17 17:50:16 - mmengine - INFO - Epoch(train) [10][ 750/3953] lr: 1.5798e-04 eta: 1:33:25 time: 0.5006 data_time: 0.0038 memory: 8528 grad_norm: 16.8086 loss: 3.1731 task0.loss_heatmap: 0.4647 task0.loss_bbox: 2.0236 task0.loss_iou: 0.1360 task0.loss_reg_iou: 0.5487 2023/09/17 17:50:41 - mmengine - INFO - Epoch(train) [10][ 800/3953] lr: 1.5664e-04 eta: 1:33:00 time: 0.4909 data_time: 0.0039 memory: 8408 grad_norm: 18.8405 loss: 3.0847 task0.loss_heatmap: 0.4440 task0.loss_bbox: 1.9700 task0.loss_iou: 0.1362 task0.loss_reg_iou: 0.5344 2023/09/17 17:51:05 - mmengine - INFO - Epoch(train) [10][ 850/3953] lr: 1.5530e-04 eta: 1:32:34 time: 0.4888 data_time: 0.0039 memory: 8446 grad_norm: 18.0710 loss: 2.9780 task0.loss_heatmap: 0.4270 task0.loss_bbox: 1.8957 task0.loss_iou: 0.1301 task0.loss_reg_iou: 0.5253 2023/09/17 17:51:30 - mmengine - INFO - Epoch(train) [10][ 900/3953] lr: 1.5397e-04 eta: 1:32:09 time: 0.4933 data_time: 0.0038 memory: 8497 grad_norm: 18.1862 loss: 3.0512 task0.loss_heatmap: 0.4270 task0.loss_bbox: 1.9554 task0.loss_iou: 0.1333 task0.loss_reg_iou: 0.5356 2023/09/17 17:51:55 - mmengine - INFO - Epoch(train) [10][ 950/3953] lr: 1.5265e-04 eta: 1:31:43 time: 0.4993 data_time: 0.0039 memory: 8219 grad_norm: 18.2325 loss: 3.1527 task0.loss_heatmap: 0.4448 task0.loss_bbox: 2.0321 task0.loss_iou: 0.1351 task0.loss_reg_iou: 0.5407 2023/09/17 17:52:20 - mmengine - INFO - Epoch(train) [10][1000/3953] lr: 1.5133e-04 eta: 1:31:18 time: 0.4994 data_time: 0.0038 memory: 8920 grad_norm: 16.7032 loss: 3.0657 task0.loss_heatmap: 0.4565 task0.loss_bbox: 1.9433 task0.loss_iou: 0.1313 task0.loss_reg_iou: 0.5345 2023/09/17 17:52:45 - mmengine - INFO - Epoch(train) [10][1050/3953] lr: 1.5001e-04 eta: 1:30:53 time: 0.4932 data_time: 0.0038 memory: 8067 grad_norm: 18.0300 loss: 3.0664 task0.loss_heatmap: 0.4345 task0.loss_bbox: 1.9626 task0.loss_iou: 0.1312 task0.loss_reg_iou: 0.5382 2023/09/17 17:53:09 - mmengine - INFO - Epoch(train) [10][1100/3953] lr: 1.4870e-04 eta: 1:30:27 time: 0.4887 data_time: 0.0039 memory: 8395 grad_norm: 17.6453 loss: 3.1329 task0.loss_heatmap: 0.4582 task0.loss_bbox: 1.9864 task0.loss_iou: 0.1418 task0.loss_reg_iou: 0.5466 2023/09/17 17:53:34 - mmengine - INFO - Epoch(train) [10][1150/3953] lr: 1.4739e-04 eta: 1:30:02 time: 0.4920 data_time: 0.0038 memory: 8664 grad_norm: 18.5364 loss: 2.9771 task0.loss_heatmap: 0.3878 task0.loss_bbox: 1.9353 task0.loss_iou: 0.1320 task0.loss_reg_iou: 0.5220 2023/09/17 17:53:58 - mmengine - INFO - Epoch(train) [10][1200/3953] lr: 1.4609e-04 eta: 1:29:36 time: 0.4908 data_time: 0.0038 memory: 8482 grad_norm: 17.7408 loss: 2.9785 task0.loss_heatmap: 0.4036 task0.loss_bbox: 1.9095 task0.loss_iou: 0.1374 task0.loss_reg_iou: 0.5280 2023/09/17 17:54:23 - mmengine - INFO - Epoch(train) [10][1250/3953] lr: 1.4479e-04 eta: 1:29:11 time: 0.4949 data_time: 0.0037 memory: 8343 grad_norm: 17.4685 loss: 2.9646 task0.loss_heatmap: 0.4180 task0.loss_bbox: 1.8891 task0.loss_iou: 0.1337 task0.loss_reg_iou: 0.5237 2023/09/17 17:54:48 - mmengine - INFO - Epoch(train) [10][1300/3953] lr: 1.4350e-04 eta: 1:28:46 time: 0.4982 data_time: 0.0038 memory: 8182 grad_norm: 18.6340 loss: 3.0042 task0.loss_heatmap: 0.4154 task0.loss_bbox: 1.9261 task0.loss_iou: 0.1296 task0.loss_reg_iou: 0.5331 2023/09/17 17:55:14 - mmengine - INFO - Epoch(train) [10][1350/3953] lr: 1.4221e-04 eta: 1:28:21 time: 0.5102 data_time: 0.0039 memory: 8163 grad_norm: 17.9760 loss: 3.1294 task0.loss_heatmap: 0.4622 task0.loss_bbox: 1.9957 task0.loss_iou: 0.1328 task0.loss_reg_iou: 0.5387 2023/09/17 17:55:39 - mmengine - INFO - Epoch(train) [10][1400/3953] lr: 1.4093e-04 eta: 1:27:55 time: 0.5079 data_time: 0.0039 memory: 8920 grad_norm: 16.8188 loss: 3.1350 task0.loss_heatmap: 0.4666 task0.loss_bbox: 1.9910 task0.loss_iou: 0.1369 task0.loss_reg_iou: 0.5404 2023/09/17 17:55:51 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 17:56:04 - mmengine - INFO - Epoch(train) [10][1450/3953] lr: 1.3965e-04 eta: 1:27:30 time: 0.4969 data_time: 0.0039 memory: 8101 grad_norm: 17.3543 loss: 3.1360 task0.loss_heatmap: 0.4690 task0.loss_bbox: 1.9898 task0.loss_iou: 0.1376 task0.loss_reg_iou: 0.5396 2023/09/17 17:56:29 - mmengine - INFO - Epoch(train) [10][1500/3953] lr: 1.3838e-04 eta: 1:27:05 time: 0.5093 data_time: 0.0038 memory: 8210 grad_norm: 18.5055 loss: 3.1460 task0.loss_heatmap: 0.4617 task0.loss_bbox: 1.9992 task0.loss_iou: 0.1372 task0.loss_reg_iou: 0.5478 2023/09/17 17:56:54 - mmengine - INFO - Epoch(train) [10][1550/3953] lr: 1.3711e-04 eta: 1:26:39 time: 0.4864 data_time: 0.0038 memory: 8461 grad_norm: 17.3605 loss: 3.1881 task0.loss_heatmap: 0.4804 task0.loss_bbox: 2.0259 task0.loss_iou: 0.1367 task0.loss_reg_iou: 0.5451 2023/09/17 17:57:19 - mmengine - INFO - Epoch(train) [10][1600/3953] lr: 1.3585e-04 eta: 1:26:14 time: 0.5128 data_time: 0.0038 memory: 9284 grad_norm: 17.0653 loss: 3.0245 task0.loss_heatmap: 0.4372 task0.loss_bbox: 1.9089 task0.loss_iou: 0.1356 task0.loss_reg_iou: 0.5429 2023/09/17 17:57:44 - mmengine - INFO - Epoch(train) [10][1650/3953] lr: 1.3459e-04 eta: 1:25:49 time: 0.5033 data_time: 0.0039 memory: 8506 grad_norm: 18.5470 loss: 3.1372 task0.loss_heatmap: 0.4682 task0.loss_bbox: 1.9958 task0.loss_iou: 0.1332 task0.loss_reg_iou: 0.5400 2023/09/17 17:58:10 - mmengine - INFO - Epoch(train) [10][1700/3953] lr: 1.3334e-04 eta: 1:25:24 time: 0.5012 data_time: 0.0040 memory: 8442 grad_norm: 18.2220 loss: 3.1745 task0.loss_heatmap: 0.4790 task0.loss_bbox: 2.0088 task0.loss_iou: 0.1405 task0.loss_reg_iou: 0.5463 2023/09/17 17:58:35 - mmengine - INFO - Epoch(train) [10][1750/3953] lr: 1.3209e-04 eta: 1:24:59 time: 0.4989 data_time: 0.0039 memory: 8004 grad_norm: 18.4199 loss: 3.0513 task0.loss_heatmap: 0.4508 task0.loss_bbox: 1.9357 task0.loss_iou: 0.1336 task0.loss_reg_iou: 0.5312 2023/09/17 17:58:59 - mmengine - INFO - Epoch(train) [10][1800/3953] lr: 1.3085e-04 eta: 1:24:33 time: 0.4867 data_time: 0.0039 memory: 8372 grad_norm: 18.5064 loss: 3.0486 task0.loss_heatmap: 0.4303 task0.loss_bbox: 1.9543 task0.loss_iou: 0.1322 task0.loss_reg_iou: 0.5318 2023/09/17 17:59:24 - mmengine - INFO - Epoch(train) [10][1850/3953] lr: 1.2961e-04 eta: 1:24:08 time: 0.4987 data_time: 0.0038 memory: 8540 grad_norm: 17.0553 loss: 3.1434 task0.loss_heatmap: 0.4777 task0.loss_bbox: 1.9890 task0.loss_iou: 0.1405 task0.loss_reg_iou: 0.5363 2023/09/17 17:59:51 - mmengine - INFO - Epoch(train) [10][1900/3953] lr: 1.2838e-04 eta: 1:23:43 time: 0.5516 data_time: 0.0039 memory: 8174 grad_norm: 17.7945 loss: 3.0391 task0.loss_heatmap: 0.4601 task0.loss_bbox: 1.9096 task0.loss_iou: 0.1368 task0.loss_reg_iou: 0.5326 2023/09/17 18:00:18 - mmengine - INFO - Epoch(train) [10][1950/3953] lr: 1.2715e-04 eta: 1:23:18 time: 0.5261 data_time: 0.0038 memory: 8501 grad_norm: 18.4869 loss: 3.1573 task0.loss_heatmap: 0.4603 task0.loss_bbox: 2.0115 task0.loss_iou: 0.1328 task0.loss_reg_iou: 0.5527 2023/09/17 18:00:43 - mmengine - INFO - Epoch(train) [10][2000/3953] lr: 1.2592e-04 eta: 1:22:53 time: 0.5021 data_time: 0.0039 memory: 8239 grad_norm: 18.4892 loss: 3.0434 task0.loss_heatmap: 0.4342 task0.loss_bbox: 1.9293 task0.loss_iou: 0.1407 task0.loss_reg_iou: 0.5391 2023/09/17 18:01:08 - mmengine - INFO - Epoch(train) [10][2050/3953] lr: 1.2471e-04 eta: 1:22:28 time: 0.4986 data_time: 0.0038 memory: 8626 grad_norm: 17.4440 loss: 3.1141 task0.loss_heatmap: 0.4525 task0.loss_bbox: 1.9890 task0.loss_iou: 0.1343 task0.loss_reg_iou: 0.5383 2023/09/17 18:01:32 - mmengine - INFO - Epoch(train) [10][2100/3953] lr: 1.2349e-04 eta: 1:22:02 time: 0.4945 data_time: 0.0038 memory: 8344 grad_norm: 17.1344 loss: 3.0912 task0.loss_heatmap: 0.4300 task0.loss_bbox: 1.9816 task0.loss_iou: 0.1354 task0.loss_reg_iou: 0.5442 2023/09/17 18:01:57 - mmengine - INFO - Epoch(train) [10][2150/3953] lr: 1.2228e-04 eta: 1:21:37 time: 0.4930 data_time: 0.0039 memory: 8057 grad_norm: 17.6319 loss: 3.2198 task0.loss_heatmap: 0.4780 task0.loss_bbox: 2.0556 task0.loss_iou: 0.1361 task0.loss_reg_iou: 0.5500 2023/09/17 18:02:22 - mmengine - INFO - Epoch(train) [10][2200/3953] lr: 1.2108e-04 eta: 1:21:12 time: 0.4915 data_time: 0.0038 memory: 8233 grad_norm: 17.2663 loss: 3.0077 task0.loss_heatmap: 0.4343 task0.loss_bbox: 1.9217 task0.loss_iou: 0.1332 task0.loss_reg_iou: 0.5185 2023/09/17 18:02:46 - mmengine - INFO - Epoch(train) [10][2250/3953] lr: 1.1988e-04 eta: 1:20:46 time: 0.4844 data_time: 0.0038 memory: 7941 grad_norm: 17.8646 loss: 2.9903 task0.loss_heatmap: 0.4306 task0.loss_bbox: 1.9070 task0.loss_iou: 0.1269 task0.loss_reg_iou: 0.5258 2023/09/17 18:03:11 - mmengine - INFO - Epoch(train) [10][2300/3953] lr: 1.1869e-04 eta: 1:20:21 time: 0.5056 data_time: 0.0038 memory: 8422 grad_norm: 17.8308 loss: 3.1553 task0.loss_heatmap: 0.4364 task0.loss_bbox: 2.0429 task0.loss_iou: 0.1311 task0.loss_reg_iou: 0.5449 2023/09/17 18:03:36 - mmengine - INFO - Epoch(train) [10][2350/3953] lr: 1.1750e-04 eta: 1:19:56 time: 0.4967 data_time: 0.0038 memory: 8312 grad_norm: 17.4112 loss: 2.9986 task0.loss_heatmap: 0.4192 task0.loss_bbox: 1.9298 task0.loss_iou: 0.1287 task0.loss_reg_iou: 0.5210 2023/09/17 18:04:01 - mmengine - INFO - Epoch(train) [10][2400/3953] lr: 1.1632e-04 eta: 1:19:30 time: 0.5012 data_time: 0.0038 memory: 8568 grad_norm: 17.8027 loss: 2.9589 task0.loss_heatmap: 0.3847 task0.loss_bbox: 1.9107 task0.loss_iou: 0.1347 task0.loss_reg_iou: 0.5288 2023/09/17 18:04:13 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:04:26 - mmengine - INFO - Epoch(train) [10][2450/3953] lr: 1.1514e-04 eta: 1:19:05 time: 0.4993 data_time: 0.0038 memory: 8006 grad_norm: 18.8046 loss: 3.1263 task0.loss_heatmap: 0.4250 task0.loss_bbox: 2.0128 task0.loss_iou: 0.1343 task0.loss_reg_iou: 0.5542 2023/09/17 18:04:51 - mmengine - INFO - Epoch(train) [10][2500/3953] lr: 1.1397e-04 eta: 1:18:40 time: 0.4984 data_time: 0.0039 memory: 8128 grad_norm: 18.0566 loss: 3.0340 task0.loss_heatmap: 0.4309 task0.loss_bbox: 1.9316 task0.loss_iou: 0.1385 task0.loss_reg_iou: 0.5330 2023/09/17 18:05:16 - mmengine - INFO - Epoch(train) [10][2550/3953] lr: 1.1281e-04 eta: 1:18:14 time: 0.4950 data_time: 0.0039 memory: 8386 grad_norm: 17.5795 loss: 3.1611 task0.loss_heatmap: 0.4780 task0.loss_bbox: 2.0080 task0.loss_iou: 0.1372 task0.loss_reg_iou: 0.5378 2023/09/17 18:05:41 - mmengine - INFO - Epoch(train) [10][2600/3953] lr: 1.1165e-04 eta: 1:17:49 time: 0.4970 data_time: 0.0039 memory: 8503 grad_norm: 18.7166 loss: 3.0776 task0.loss_heatmap: 0.4527 task0.loss_bbox: 1.9442 task0.loss_iou: 0.1386 task0.loss_reg_iou: 0.5421 2023/09/17 18:06:05 - mmengine - INFO - Epoch(train) [10][2650/3953] lr: 1.1049e-04 eta: 1:17:24 time: 0.4954 data_time: 0.0038 memory: 8124 grad_norm: 17.8090 loss: 3.0487 task0.loss_heatmap: 0.4177 task0.loss_bbox: 1.9596 task0.loss_iou: 0.1310 task0.loss_reg_iou: 0.5403 2023/09/17 18:06:30 - mmengine - INFO - Epoch(train) [10][2700/3953] lr: 1.0934e-04 eta: 1:16:59 time: 0.4972 data_time: 0.0038 memory: 8166 grad_norm: 17.5093 loss: 3.0198 task0.loss_heatmap: 0.4136 task0.loss_bbox: 1.9301 task0.loss_iou: 0.1384 task0.loss_reg_iou: 0.5377 2023/09/17 18:06:55 - mmengine - INFO - Epoch(train) [10][2750/3953] lr: 1.0819e-04 eta: 1:16:33 time: 0.5020 data_time: 0.0038 memory: 8446 grad_norm: 17.4999 loss: 3.0645 task0.loss_heatmap: 0.4356 task0.loss_bbox: 1.9648 task0.loss_iou: 0.1336 task0.loss_reg_iou: 0.5305 2023/09/17 18:07:21 - mmengine - INFO - Epoch(train) [10][2800/3953] lr: 1.0705e-04 eta: 1:16:08 time: 0.5085 data_time: 0.0038 memory: 8171 grad_norm: 17.2128 loss: 3.1405 task0.loss_heatmap: 0.4610 task0.loss_bbox: 2.0001 task0.loss_iou: 0.1354 task0.loss_reg_iou: 0.5440 2023/09/17 18:07:46 - mmengine - INFO - Epoch(train) [10][2850/3953] lr: 1.0592e-04 eta: 1:15:43 time: 0.5016 data_time: 0.0038 memory: 8439 grad_norm: 18.1676 loss: 3.0597 task0.loss_heatmap: 0.4434 task0.loss_bbox: 1.9466 task0.loss_iou: 0.1338 task0.loss_reg_iou: 0.5359 2023/09/17 18:08:11 - mmengine - INFO - Epoch(train) [10][2900/3953] lr: 1.0479e-04 eta: 1:15:18 time: 0.5010 data_time: 0.0038 memory: 8479 grad_norm: 16.9351 loss: 3.1521 task0.loss_heatmap: 0.4590 task0.loss_bbox: 2.0109 task0.loss_iou: 0.1379 task0.loss_reg_iou: 0.5443 2023/09/17 18:08:36 - mmengine - INFO - Epoch(train) [10][2950/3953] lr: 1.0366e-04 eta: 1:14:52 time: 0.5002 data_time: 0.0039 memory: 8214 grad_norm: 18.4490 loss: 3.1839 task0.loss_heatmap: 0.4637 task0.loss_bbox: 2.0285 task0.loss_iou: 0.1363 task0.loss_reg_iou: 0.5555 2023/09/17 18:09:01 - mmengine - INFO - Epoch(train) [10][3000/3953] lr: 1.0254e-04 eta: 1:14:27 time: 0.4920 data_time: 0.0038 memory: 8688 grad_norm: 17.4773 loss: 3.0608 task0.loss_heatmap: 0.4427 task0.loss_bbox: 1.9439 task0.loss_iou: 0.1353 task0.loss_reg_iou: 0.5389 2023/09/17 18:09:25 - mmengine - INFO - Epoch(train) [10][3050/3953] lr: 1.0143e-04 eta: 1:14:02 time: 0.4906 data_time: 0.0038 memory: 8276 grad_norm: 17.9874 loss: 3.0623 task0.loss_heatmap: 0.4604 task0.loss_bbox: 1.9414 task0.loss_iou: 0.1313 task0.loss_reg_iou: 0.5291 2023/09/17 18:09:50 - mmengine - INFO - Epoch(train) [10][3100/3953] lr: 1.0032e-04 eta: 1:13:36 time: 0.4980 data_time: 0.0039 memory: 8668 grad_norm: 19.0067 loss: 3.0518 task0.loss_heatmap: 0.4294 task0.loss_bbox: 1.9564 task0.loss_iou: 0.1341 task0.loss_reg_iou: 0.5320 2023/09/17 18:10:14 - mmengine - INFO - Epoch(train) [10][3150/3953] lr: 9.9221e-05 eta: 1:13:11 time: 0.4877 data_time: 0.0038 memory: 8768 grad_norm: 18.6648 loss: 3.0351 task0.loss_heatmap: 0.4489 task0.loss_bbox: 1.9290 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5270 2023/09/17 18:10:39 - mmengine - INFO - Epoch(train) [10][3200/3953] lr: 9.8123e-05 eta: 1:12:45 time: 0.4828 data_time: 0.0039 memory: 8182 grad_norm: 17.5517 loss: 2.9960 task0.loss_heatmap: 0.4412 task0.loss_bbox: 1.8959 task0.loss_iou: 0.1312 task0.loss_reg_iou: 0.5277 2023/09/17 18:11:04 - mmengine - INFO - Epoch(train) [10][3250/3953] lr: 9.7032e-05 eta: 1:12:20 time: 0.4992 data_time: 0.0039 memory: 8447 grad_norm: 18.1622 loss: 3.0945 task0.loss_heatmap: 0.4733 task0.loss_bbox: 1.9530 task0.loss_iou: 0.1344 task0.loss_reg_iou: 0.5338 2023/09/17 18:11:29 - mmengine - INFO - Epoch(train) [10][3300/3953] lr: 9.5945e-05 eta: 1:11:55 time: 0.5029 data_time: 0.0039 memory: 8735 grad_norm: 18.7540 loss: 3.2371 task0.loss_heatmap: 0.4693 task0.loss_bbox: 2.0754 task0.loss_iou: 0.1403 task0.loss_reg_iou: 0.5522 2023/09/17 18:11:53 - mmengine - INFO - Epoch(train) [10][3350/3953] lr: 9.4864e-05 eta: 1:11:30 time: 0.4910 data_time: 0.0038 memory: 8544 grad_norm: 17.2170 loss: 3.0395 task0.loss_heatmap: 0.4253 task0.loss_bbox: 1.9420 task0.loss_iou: 0.1372 task0.loss_reg_iou: 0.5350 2023/09/17 18:12:19 - mmengine - INFO - Epoch(train) [10][3400/3953] lr: 9.3789e-05 eta: 1:11:04 time: 0.5067 data_time: 0.0038 memory: 8332 grad_norm: 17.5783 loss: 3.0214 task0.loss_heatmap: 0.4228 task0.loss_bbox: 1.9373 task0.loss_iou: 0.1301 task0.loss_reg_iou: 0.5312 2023/09/17 18:12:31 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:12:46 - mmengine - INFO - Epoch(train) [10][3450/3953] lr: 9.2719e-05 eta: 1:10:40 time: 0.5522 data_time: 0.0038 memory: 8196 grad_norm: 17.1499 loss: 2.9891 task0.loss_heatmap: 0.4132 task0.loss_bbox: 1.9118 task0.loss_iou: 0.1319 task0.loss_reg_iou: 0.5321 2023/09/17 18:13:12 - mmengine - INFO - Epoch(train) [10][3500/3953] lr: 9.1655e-05 eta: 1:10:15 time: 0.5171 data_time: 0.0038 memory: 8192 grad_norm: 16.6918 loss: 3.0555 task0.loss_heatmap: 0.4512 task0.loss_bbox: 1.9342 task0.loss_iou: 0.1347 task0.loss_reg_iou: 0.5353 2023/09/17 18:13:37 - mmengine - INFO - Epoch(train) [10][3550/3953] lr: 9.0596e-05 eta: 1:09:49 time: 0.5015 data_time: 0.0038 memory: 8438 grad_norm: 18.4742 loss: 3.0655 task0.loss_heatmap: 0.4356 task0.loss_bbox: 1.9575 task0.loss_iou: 0.1318 task0.loss_reg_iou: 0.5406 2023/09/17 18:14:02 - mmengine - INFO - Epoch(train) [10][3600/3953] lr: 8.9543e-05 eta: 1:09:24 time: 0.4949 data_time: 0.0038 memory: 8176 grad_norm: 17.6858 loss: 2.9087 task0.loss_heatmap: 0.4143 task0.loss_bbox: 1.8450 task0.loss_iou: 0.1325 task0.loss_reg_iou: 0.5169 2023/09/17 18:14:27 - mmengine - INFO - Epoch(train) [10][3650/3953] lr: 8.8495e-05 eta: 1:08:59 time: 0.4945 data_time: 0.0038 memory: 8667 grad_norm: 16.2167 loss: 3.0771 task0.loss_heatmap: 0.4606 task0.loss_bbox: 1.9441 task0.loss_iou: 0.1365 task0.loss_reg_iou: 0.5359 2023/09/17 18:14:51 - mmengine - INFO - Epoch(train) [10][3700/3953] lr: 8.7453e-05 eta: 1:08:33 time: 0.4927 data_time: 0.0039 memory: 8202 grad_norm: 18.8102 loss: 3.1228 task0.loss_heatmap: 0.4621 task0.loss_bbox: 1.9871 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.5422 2023/09/17 18:15:16 - mmengine - INFO - Epoch(train) [10][3750/3953] lr: 8.6416e-05 eta: 1:08:08 time: 0.5016 data_time: 0.0039 memory: 8402 grad_norm: 16.6771 loss: 3.1147 task0.loss_heatmap: 0.4458 task0.loss_bbox: 1.9968 task0.loss_iou: 0.1361 task0.loss_reg_iou: 0.5361 2023/09/17 18:15:41 - mmengine - INFO - Epoch(train) [10][3800/3953] lr: 8.5385e-05 eta: 1:07:43 time: 0.4982 data_time: 0.0042 memory: 8496 grad_norm: 18.3941 loss: 2.9411 task0.loss_heatmap: 0.3977 task0.loss_bbox: 1.8985 task0.loss_iou: 0.1292 task0.loss_reg_iou: 0.5156 2023/09/17 18:16:06 - mmengine - INFO - Epoch(train) [10][3850/3953] lr: 8.4360e-05 eta: 1:07:18 time: 0.4967 data_time: 0.0040 memory: 8362 grad_norm: 17.6749 loss: 3.1083 task0.loss_heatmap: 0.4736 task0.loss_bbox: 1.9590 task0.loss_iou: 0.1365 task0.loss_reg_iou: 0.5392 2023/09/17 18:16:31 - mmengine - INFO - Epoch(train) [10][3900/3953] lr: 8.3340e-05 eta: 1:06:52 time: 0.4862 data_time: 0.0040 memory: 8449 grad_norm: 18.2074 loss: 3.0198 task0.loss_heatmap: 0.4504 task0.loss_bbox: 1.9152 task0.loss_iou: 0.1325 task0.loss_reg_iou: 0.5217 2023/09/17 18:16:55 - mmengine - INFO - Epoch(train) [10][3950/3953] lr: 8.2326e-05 eta: 1:06:27 time: 0.4895 data_time: 0.0038 memory: 8615 grad_norm: 18.2649 loss: 3.1055 task0.loss_heatmap: 0.4575 task0.loss_bbox: 1.9776 task0.loss_iou: 0.1362 task0.loss_reg_iou: 0.5341 2023/09/17 18:16:56 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:16:56 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/09/17 18:17:18 - mmengine - INFO - Epoch(val) [10][ 50/1250] eta: 0:07:37 time: 0.3809 data_time: 0.0062 memory: 7768 2023/09/17 18:17:38 - mmengine - INFO - Epoch(val) [10][ 100/1250] eta: 0:07:21 time: 0.3862 data_time: 0.0046 memory: 4043 2023/09/17 18:17:57 - mmengine - INFO - Epoch(val) [10][ 150/1250] eta: 0:07:02 time: 0.3861 data_time: 0.0045 memory: 4062 2023/09/17 18:18:16 - mmengine - INFO - Epoch(val) [10][ 200/1250] eta: 0:06:42 time: 0.3789 data_time: 0.0046 memory: 4051 2023/09/17 18:18:34 - mmengine - INFO - Epoch(val) [10][ 250/1250] eta: 0:06:17 time: 0.3556 data_time: 0.0043 memory: 4033 2023/09/17 18:18:53 - mmengine - INFO - Epoch(val) [10][ 300/1250] eta: 0:05:59 time: 0.3818 data_time: 0.0042 memory: 4050 2023/09/17 18:19:12 - mmengine - INFO - Epoch(val) [10][ 350/1250] eta: 0:05:40 time: 0.3818 data_time: 0.0044 memory: 4057 2023/09/17 18:19:31 - mmengine - INFO - Epoch(val) [10][ 400/1250] eta: 0:05:22 time: 0.3879 data_time: 0.0044 memory: 4060 2023/09/17 18:19:52 - mmengine - INFO - Epoch(val) [10][ 450/1250] eta: 0:05:06 time: 0.4096 data_time: 0.0045 memory: 4048 2023/09/17 18:20:10 - mmengine - INFO - Epoch(val) [10][ 500/1250] eta: 0:04:46 time: 0.3717 data_time: 0.0048 memory: 4041 2023/09/17 18:20:29 - mmengine - INFO - Epoch(val) [10][ 550/1250] eta: 0:04:26 time: 0.3718 data_time: 0.0045 memory: 4042 2023/09/17 18:20:47 - mmengine - INFO - Epoch(val) [10][ 600/1250] eta: 0:04:07 time: 0.3710 data_time: 0.0046 memory: 4057 2023/09/17 18:21:08 - mmengine - INFO - Epoch(val) [10][ 650/1250] eta: 0:03:49 time: 0.4071 data_time: 0.0045 memory: 4065 2023/09/17 18:21:27 - mmengine - INFO - Epoch(val) [10][ 700/1250] eta: 0:03:30 time: 0.3940 data_time: 0.0045 memory: 4042 2023/09/17 18:21:47 - mmengine - INFO - Epoch(val) [10][ 750/1250] eta: 0:03:12 time: 0.3980 data_time: 0.0045 memory: 4051 2023/09/17 18:22:06 - mmengine - INFO - Epoch(val) [10][ 800/1250] eta: 0:02:52 time: 0.3728 data_time: 0.0046 memory: 4055 2023/09/17 18:22:26 - mmengine - INFO - Epoch(val) [10][ 850/1250] eta: 0:02:33 time: 0.3896 data_time: 0.0047 memory: 4058 2023/09/17 18:22:44 - mmengine - INFO - Epoch(val) [10][ 900/1250] eta: 0:02:13 time: 0.3629 data_time: 0.0046 memory: 4049 2023/09/17 18:23:02 - mmengine - INFO - Epoch(val) [10][ 950/1250] eta: 0:01:54 time: 0.3665 data_time: 0.0045 memory: 4054 2023/09/17 18:23:21 - mmengine - INFO - Epoch(val) [10][1000/1250] eta: 0:01:35 time: 0.3782 data_time: 0.0047 memory: 4056 2023/09/17 18:23:40 - mmengine - INFO - Epoch(val) [10][1050/1250] eta: 0:01:16 time: 0.3803 data_time: 0.0047 memory: 4043 2023/09/17 18:23:58 - mmengine - INFO - Epoch(val) [10][1100/1250] eta: 0:00:57 time: 0.3587 data_time: 0.0046 memory: 4032 2023/09/17 18:24:17 - mmengine - INFO - Epoch(val) [10][1150/1250] eta: 0:00:38 time: 0.3890 data_time: 0.0052 memory: 4041 2023/09/17 18:24:37 - mmengine - INFO - Epoch(val) [10][1200/1250] eta: 0:00:19 time: 0.3862 data_time: 0.0046 memory: 4047 2023/09/17 18:24:56 - mmengine - INFO - Epoch(val) [10][1250/1250] eta: 0:00:00 time: 0.3857 data_time: 0.0045 memory: 4055 2023/09/17 18:24:58 - mmengine - INFO - Start converting ... 2023/09/17 18:32:44 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.70382] [mAPH 0.698872] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.620075] [mAPH 0.615649] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.769399] [mAPH 0.689586] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.690782] [mAPH 0.616753] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.715694] [mAPH 0.701334] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.689032] [mAPH 0.675195] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.905238] [mAPH 0.900359] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.891934] [mAPH 0.887114] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.684444] [mAPH 0.678879] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.621102] [mAPH 0.61599] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.419077] [mAPH 0.413605] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.317523] [mAPH 0.313261] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.822744] [mAPH 0.750607] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.785742] [mAPH 0.715256] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.756224] [mAPH 0.671946] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.687481] [mAPH 0.609657] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.661836] [mAPH 0.559412] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.526943] [mAPH 0.442475] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.810174] [mAPH 0.797241] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.804357] [mAPH 0.791516] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.669569] [mAPH 0.656409] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.632025] [mAPH 0.619615] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.538556] [mAPH 0.51004] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.502394] [mAPH 0.475763] Eval Using 342s 2023/09/17 18:32:44 - mmengine - INFO - Epoch(val) [10][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.7038 Waymo metric/Vehicle/L1 mAPH: 0.6989 Waymo metric/Vehicle/L2 mAP: 0.6201 Waymo metric/Vehicle/L2 mAPH: 0.6156 Waymo metric/Pedestrian/L1 mAP: 0.7694 Waymo metric/Pedestrian/L1 mAPH: 0.6896 Waymo metric/Pedestrian/L2 mAP: 0.6908 Waymo metric/Pedestrian/L2 mAPH: 0.6168 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.7157 Waymo metric/Cyclist/L1 mAPH: 0.7013 Waymo metric/Cyclist/L2 mAP: 0.6890 Waymo metric/Cyclist/L2 mAPH: 0.6752 Waymo metric/Overall/L1 mAP: 0.7296 Waymo metric/Overall/L1 mAPH: 0.6966 Waymo metric/Overall/L2 mAP: 0.6666 Waymo metric/Overall/L2 mAPH: 0.6359 data_time: 0.0046 time: 0.3813 2023/09/17 18:33:09 - mmengine - INFO - Epoch(train) [11][ 50/3953] lr: 8.1257e-05 eta: 1:06:00 time: 0.4998 data_time: 0.0054 memory: 8397 grad_norm: 17.4655 loss: 2.9066 task0.loss_heatmap: 0.3849 task0.loss_bbox: 1.8731 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5185 2023/09/17 18:33:34 - mmengine - INFO - Epoch(train) [11][ 100/3953] lr: 8.0254e-05 eta: 1:05:35 time: 0.5047 data_time: 0.0041 memory: 7948 grad_norm: 18.2377 loss: 3.1084 task0.loss_heatmap: 0.4378 task0.loss_bbox: 1.9883 task0.loss_iou: 0.1367 task0.loss_reg_iou: 0.5455 2023/09/17 18:34:00 - mmengine - INFO - Epoch(train) [11][ 150/3953] lr: 7.9258e-05 eta: 1:05:10 time: 0.5050 data_time: 0.0168 memory: 8404 grad_norm: 18.8974 loss: 2.9543 task0.loss_heatmap: 0.4097 task0.loss_bbox: 1.8902 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.5230 2023/09/17 18:34:24 - mmengine - INFO - Epoch(train) [11][ 200/3953] lr: 7.8267e-05 eta: 1:04:44 time: 0.4945 data_time: 0.0039 memory: 8447 grad_norm: 18.0431 loss: 3.0669 task0.loss_heatmap: 0.4750 task0.loss_bbox: 1.9311 task0.loss_iou: 0.1349 task0.loss_reg_iou: 0.5259 2023/09/17 18:34:49 - mmengine - INFO - Epoch(train) [11][ 250/3953] lr: 7.7281e-05 eta: 1:04:19 time: 0.4963 data_time: 0.0040 memory: 8271 grad_norm: 18.4802 loss: 3.0165 task0.loss_heatmap: 0.4516 task0.loss_bbox: 1.9059 task0.loss_iou: 0.1327 task0.loss_reg_iou: 0.5264 2023/09/17 18:35:14 - mmengine - INFO - Epoch(train) [11][ 300/3953] lr: 7.6302e-05 eta: 1:03:54 time: 0.4981 data_time: 0.0045 memory: 8583 grad_norm: 17.9941 loss: 3.0622 task0.loss_heatmap: 0.4399 task0.loss_bbox: 1.9549 task0.loss_iou: 0.1316 task0.loss_reg_iou: 0.5357 2023/09/17 18:35:39 - mmengine - INFO - Epoch(train) [11][ 350/3953] lr: 7.5328e-05 eta: 1:03:29 time: 0.5058 data_time: 0.0042 memory: 8078 grad_norm: 17.2275 loss: 3.0713 task0.loss_heatmap: 0.4433 task0.loss_bbox: 1.9640 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.5326 2023/09/17 18:36:04 - mmengine - INFO - Epoch(train) [11][ 400/3953] lr: 7.4360e-05 eta: 1:03:03 time: 0.4896 data_time: 0.0043 memory: 8346 grad_norm: 17.0537 loss: 2.9570 task0.loss_heatmap: 0.4278 task0.loss_bbox: 1.8751 task0.loss_iou: 0.1332 task0.loss_reg_iou: 0.5210 2023/09/17 18:36:29 - mmengine - INFO - Epoch(train) [11][ 450/3953] lr: 7.3397e-05 eta: 1:02:38 time: 0.4924 data_time: 0.0043 memory: 8125 grad_norm: 18.2322 loss: 3.0738 task0.loss_heatmap: 0.4371 task0.loss_bbox: 1.9737 task0.loss_iou: 0.1355 task0.loss_reg_iou: 0.5275 2023/09/17 18:36:38 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:36:53 - mmengine - INFO - Epoch(train) [11][ 500/3953] lr: 7.2441e-05 eta: 1:02:13 time: 0.4931 data_time: 0.0038 memory: 8334 grad_norm: 17.7414 loss: 2.9445 task0.loss_heatmap: 0.4237 task0.loss_bbox: 1.8712 task0.loss_iou: 0.1309 task0.loss_reg_iou: 0.5186 2023/09/17 18:37:18 - mmengine - INFO - Epoch(train) [11][ 550/3953] lr: 7.1490e-05 eta: 1:01:47 time: 0.4938 data_time: 0.0040 memory: 8579 grad_norm: 18.2876 loss: 3.0302 task0.loss_heatmap: 0.4481 task0.loss_bbox: 1.9195 task0.loss_iou: 0.1339 task0.loss_reg_iou: 0.5288 2023/09/17 18:37:45 - mmengine - INFO - Epoch(train) [11][ 600/3953] lr: 7.0545e-05 eta: 1:01:23 time: 0.5386 data_time: 0.0038 memory: 8311 grad_norm: 18.4025 loss: 3.0158 task0.loss_heatmap: 0.4413 task0.loss_bbox: 1.9250 task0.loss_iou: 0.1278 task0.loss_reg_iou: 0.5218 2023/09/17 18:38:12 - mmengine - INFO - Epoch(train) [11][ 650/3953] lr: 6.9605e-05 eta: 1:00:58 time: 0.5321 data_time: 0.0038 memory: 8394 grad_norm: 16.0535 loss: 2.9428 task0.loss_heatmap: 0.4232 task0.loss_bbox: 1.8749 task0.loss_iou: 0.1329 task0.loss_reg_iou: 0.5118 2023/09/17 18:38:38 - mmengine - INFO - Epoch(train) [11][ 700/3953] lr: 6.8672e-05 eta: 1:00:33 time: 0.5343 data_time: 0.0040 memory: 8384 grad_norm: 18.7379 loss: 3.0543 task0.loss_heatmap: 0.4287 task0.loss_bbox: 1.9529 task0.loss_iou: 0.1387 task0.loss_reg_iou: 0.5340 2023/09/17 18:39:03 - mmengine - INFO - Epoch(train) [11][ 750/3953] lr: 6.7744e-05 eta: 1:00:07 time: 0.4997 data_time: 0.0039 memory: 8646 grad_norm: 18.4762 loss: 3.0082 task0.loss_heatmap: 0.4219 task0.loss_bbox: 1.9221 task0.loss_iou: 0.1346 task0.loss_reg_iou: 0.5296 2023/09/17 18:39:27 - mmengine - INFO - Epoch(train) [11][ 800/3953] lr: 6.6823e-05 eta: 0:59:42 time: 0.4821 data_time: 0.0039 memory: 8232 grad_norm: 18.9060 loss: 2.9413 task0.loss_heatmap: 0.4205 task0.loss_bbox: 1.8736 task0.loss_iou: 0.1304 task0.loss_reg_iou: 0.5169 2023/09/17 18:39:52 - mmengine - INFO - Epoch(train) [11][ 850/3953] lr: 6.5907e-05 eta: 0:59:17 time: 0.4849 data_time: 0.0039 memory: 8807 grad_norm: 18.2944 loss: 2.9593 task0.loss_heatmap: 0.3986 task0.loss_bbox: 1.9098 task0.loss_iou: 0.1280 task0.loss_reg_iou: 0.5229 2023/09/17 18:40:16 - mmengine - INFO - Epoch(train) [11][ 900/3953] lr: 6.4997e-05 eta: 0:58:51 time: 0.4885 data_time: 0.0038 memory: 7995 grad_norm: 18.2286 loss: 2.9129 task0.loss_heatmap: 0.4120 task0.loss_bbox: 1.8534 task0.loss_iou: 0.1288 task0.loss_reg_iou: 0.5187 2023/09/17 18:40:41 - mmengine - INFO - Epoch(train) [11][ 950/3953] lr: 6.4093e-05 eta: 0:58:26 time: 0.4899 data_time: 0.0039 memory: 8256 grad_norm: 17.8460 loss: 3.0126 task0.loss_heatmap: 0.4173 task0.loss_bbox: 1.9408 task0.loss_iou: 0.1299 task0.loss_reg_iou: 0.5245 2023/09/17 18:41:05 - mmengine - INFO - Epoch(train) [11][1000/3953] lr: 6.3195e-05 eta: 0:58:01 time: 0.4951 data_time: 0.0039 memory: 8307 grad_norm: 17.5334 loss: 3.0360 task0.loss_heatmap: 0.4310 task0.loss_bbox: 1.9349 task0.loss_iou: 0.1361 task0.loss_reg_iou: 0.5340 2023/09/17 18:41:30 - mmengine - INFO - Epoch(train) [11][1050/3953] lr: 6.2302e-05 eta: 0:57:35 time: 0.4958 data_time: 0.0039 memory: 7829 grad_norm: 17.0281 loss: 3.0580 task0.loss_heatmap: 0.4600 task0.loss_bbox: 1.9325 task0.loss_iou: 0.1322 task0.loss_reg_iou: 0.5332 2023/09/17 18:41:54 - mmengine - INFO - Epoch(train) [11][1100/3953] lr: 6.1416e-05 eta: 0:57:10 time: 0.4809 data_time: 0.0040 memory: 7844 grad_norm: 17.4373 loss: 2.9503 task0.loss_heatmap: 0.4105 task0.loss_bbox: 1.8842 task0.loss_iou: 0.1312 task0.loss_reg_iou: 0.5243 2023/09/17 18:42:19 - mmengine - INFO - Epoch(train) [11][1150/3953] lr: 6.0536e-05 eta: 0:56:45 time: 0.4927 data_time: 0.0039 memory: 8091 grad_norm: 16.9742 loss: 2.9799 task0.loss_heatmap: 0.4482 task0.loss_bbox: 1.8910 task0.loss_iou: 0.1240 task0.loss_reg_iou: 0.5166 2023/09/17 18:42:44 - mmengine - INFO - Epoch(train) [11][1200/3953] lr: 5.9661e-05 eta: 0:56:20 time: 0.5020 data_time: 0.0039 memory: 8713 grad_norm: 17.5982 loss: 3.0595 task0.loss_heatmap: 0.4379 task0.loss_bbox: 1.9564 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.5339 2023/09/17 18:43:08 - mmengine - INFO - Epoch(train) [11][1250/3953] lr: 5.8793e-05 eta: 0:55:54 time: 0.4833 data_time: 0.0038 memory: 8457 grad_norm: 17.1207 loss: 3.0527 task0.loss_heatmap: 0.4247 task0.loss_bbox: 1.9624 task0.loss_iou: 0.1333 task0.loss_reg_iou: 0.5323 2023/09/17 18:43:33 - mmengine - INFO - Epoch(train) [11][1300/3953] lr: 5.7930e-05 eta: 0:55:29 time: 0.4873 data_time: 0.0039 memory: 8829 grad_norm: 17.8805 loss: 2.9199 task0.loss_heatmap: 0.3995 task0.loss_bbox: 1.8683 task0.loss_iou: 0.1293 task0.loss_reg_iou: 0.5228 2023/09/17 18:43:58 - mmengine - INFO - Epoch(train) [11][1350/3953] lr: 5.7074e-05 eta: 0:55:04 time: 0.5020 data_time: 0.0039 memory: 8092 grad_norm: 19.5465 loss: 3.0304 task0.loss_heatmap: 0.4378 task0.loss_bbox: 1.9310 task0.loss_iou: 0.1334 task0.loss_reg_iou: 0.5282 2023/09/17 18:44:22 - mmengine - INFO - Epoch(train) [11][1400/3953] lr: 5.6223e-05 eta: 0:54:38 time: 0.4927 data_time: 0.0039 memory: 7969 grad_norm: 17.8719 loss: 3.0063 task0.loss_heatmap: 0.4397 task0.loss_bbox: 1.9074 task0.loss_iou: 0.1297 task0.loss_reg_iou: 0.5296 2023/09/17 18:44:47 - mmengine - INFO - Epoch(train) [11][1450/3953] lr: 5.5379e-05 eta: 0:54:13 time: 0.4873 data_time: 0.0038 memory: 8658 grad_norm: 18.4569 loss: 3.0058 task0.loss_heatmap: 0.4146 task0.loss_bbox: 1.9292 task0.loss_iou: 0.1360 task0.loss_reg_iou: 0.5259 2023/09/17 18:44:57 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:45:12 - mmengine - INFO - Epoch(train) [11][1500/3953] lr: 5.4540e-05 eta: 0:53:48 time: 0.5040 data_time: 0.0039 memory: 8427 grad_norm: 18.2699 loss: 3.0439 task0.loss_heatmap: 0.4468 task0.loss_bbox: 1.9326 task0.loss_iou: 0.1342 task0.loss_reg_iou: 0.5302 2023/09/17 18:45:38 - mmengine - INFO - Epoch(train) [11][1550/3953] lr: 5.3708e-05 eta: 0:53:23 time: 0.5148 data_time: 0.0038 memory: 8268 grad_norm: 17.7513 loss: 3.0801 task0.loss_heatmap: 0.4279 task0.loss_bbox: 1.9697 task0.loss_iou: 0.1417 task0.loss_reg_iou: 0.5408 2023/09/17 18:46:02 - mmengine - INFO - Epoch(train) [11][1600/3953] lr: 5.2881e-05 eta: 0:52:57 time: 0.4846 data_time: 0.0039 memory: 8688 grad_norm: 18.6011 loss: 2.9702 task0.loss_heatmap: 0.3992 task0.loss_bbox: 1.9144 task0.loss_iou: 0.1285 task0.loss_reg_iou: 0.5281 2023/09/17 18:46:26 - mmengine - INFO - Epoch(train) [11][1650/3953] lr: 5.2061e-05 eta: 0:52:32 time: 0.4835 data_time: 0.0038 memory: 8329 grad_norm: 17.6291 loss: 2.9047 task0.loss_heatmap: 0.4154 task0.loss_bbox: 1.8444 task0.loss_iou: 0.1320 task0.loss_reg_iou: 0.5129 2023/09/17 18:46:50 - mmengine - INFO - Epoch(train) [11][1700/3953] lr: 5.1246e-05 eta: 0:52:07 time: 0.4885 data_time: 0.0041 memory: 8032 grad_norm: 18.3626 loss: 3.1478 task0.loss_heatmap: 0.4563 task0.loss_bbox: 1.9986 task0.loss_iou: 0.1428 task0.loss_reg_iou: 0.5500 2023/09/17 18:47:16 - mmengine - INFO - Epoch(train) [11][1750/3953] lr: 5.0438e-05 eta: 0:51:41 time: 0.5060 data_time: 0.0044 memory: 8659 grad_norm: 17.7223 loss: 2.8647 task0.loss_heatmap: 0.4142 task0.loss_bbox: 1.8113 task0.loss_iou: 0.1322 task0.loss_reg_iou: 0.5070 2023/09/17 18:47:41 - mmengine - INFO - Epoch(train) [11][1800/3953] lr: 4.9636e-05 eta: 0:51:16 time: 0.4986 data_time: 0.0045 memory: 8020 grad_norm: 18.7054 loss: 3.0228 task0.loss_heatmap: 0.4544 task0.loss_bbox: 1.9255 task0.loss_iou: 0.1309 task0.loss_reg_iou: 0.5121 2023/09/17 18:48:05 - mmengine - INFO - Epoch(train) [11][1850/3953] lr: 4.8840e-05 eta: 0:50:51 time: 0.4914 data_time: 0.0046 memory: 8503 grad_norm: 18.8623 loss: 2.9637 task0.loss_heatmap: 0.4257 task0.loss_bbox: 1.8890 task0.loss_iou: 0.1269 task0.loss_reg_iou: 0.5221 2023/09/17 18:48:30 - mmengine - INFO - Epoch(train) [11][1900/3953] lr: 4.8050e-05 eta: 0:50:26 time: 0.5021 data_time: 0.0047 memory: 8143 grad_norm: 16.7223 loss: 3.0071 task0.loss_heatmap: 0.4363 task0.loss_bbox: 1.9200 task0.loss_iou: 0.1274 task0.loss_reg_iou: 0.5235 2023/09/17 18:48:56 - mmengine - INFO - Epoch(train) [11][1950/3953] lr: 4.7266e-05 eta: 0:50:01 time: 0.5057 data_time: 0.0044 memory: 8419 grad_norm: 17.8844 loss: 3.0364 task0.loss_heatmap: 0.4365 task0.loss_bbox: 1.9329 task0.loss_iou: 0.1324 task0.loss_reg_iou: 0.5347 2023/09/17 18:49:20 - mmengine - INFO - Epoch(train) [11][2000/3953] lr: 4.6489e-05 eta: 0:49:35 time: 0.4943 data_time: 0.0044 memory: 8246 grad_norm: 17.8373 loss: 3.0130 task0.loss_heatmap: 0.4161 task0.loss_bbox: 1.9369 task0.loss_iou: 0.1356 task0.loss_reg_iou: 0.5244 2023/09/17 18:49:46 - mmengine - INFO - Epoch(train) [11][2050/3953] lr: 4.5717e-05 eta: 0:49:10 time: 0.5023 data_time: 0.0045 memory: 8816 grad_norm: 18.0732 loss: 3.1209 task0.loss_heatmap: 0.4612 task0.loss_bbox: 1.9886 task0.loss_iou: 0.1335 task0.loss_reg_iou: 0.5376 2023/09/17 18:50:13 - mmengine - INFO - Epoch(train) [11][2100/3953] lr: 4.4952e-05 eta: 0:48:45 time: 0.5390 data_time: 0.0044 memory: 8539 grad_norm: 17.5938 loss: 3.0603 task0.loss_heatmap: 0.4756 task0.loss_bbox: 1.9243 task0.loss_iou: 0.1309 task0.loss_reg_iou: 0.5295 2023/09/17 18:50:40 - mmengine - INFO - Epoch(train) [11][2150/3953] lr: 4.4192e-05 eta: 0:48:20 time: 0.5585 data_time: 0.0044 memory: 8476 grad_norm: 18.9363 loss: 2.9411 task0.loss_heatmap: 0.4189 task0.loss_bbox: 1.8712 task0.loss_iou: 0.1297 task0.loss_reg_iou: 0.5213 2023/09/17 18:51:07 - mmengine - INFO - Epoch(train) [11][2200/3953] lr: 4.3439e-05 eta: 0:47:55 time: 0.5341 data_time: 0.0045 memory: 8455 grad_norm: 17.6585 loss: 3.0537 task0.loss_heatmap: 0.4511 task0.loss_bbox: 1.9391 task0.loss_iou: 0.1337 task0.loss_reg_iou: 0.5299 2023/09/17 18:51:33 - mmengine - INFO - Epoch(train) [11][2250/3953] lr: 4.2692e-05 eta: 0:47:30 time: 0.5127 data_time: 0.0044 memory: 8669 grad_norm: 16.7090 loss: 3.0540 task0.loss_heatmap: 0.4376 task0.loss_bbox: 1.9483 task0.loss_iou: 0.1373 task0.loss_reg_iou: 0.5308 2023/09/17 18:51:58 - mmengine - INFO - Epoch(train) [11][2300/3953] lr: 4.1952e-05 eta: 0:47:05 time: 0.4941 data_time: 0.0046 memory: 8551 grad_norm: 18.9379 loss: 3.0300 task0.loss_heatmap: 0.4430 task0.loss_bbox: 1.9208 task0.loss_iou: 0.1313 task0.loss_reg_iou: 0.5349 2023/09/17 18:52:23 - mmengine - INFO - Epoch(train) [11][2350/3953] lr: 4.1217e-05 eta: 0:46:40 time: 0.5002 data_time: 0.0044 memory: 8322 grad_norm: 17.8529 loss: 2.9649 task0.loss_heatmap: 0.4302 task0.loss_bbox: 1.8753 task0.loss_iou: 0.1316 task0.loss_reg_iou: 0.5279 2023/09/17 18:52:48 - mmengine - INFO - Epoch(train) [11][2400/3953] lr: 4.0489e-05 eta: 0:46:15 time: 0.5140 data_time: 0.0044 memory: 8504 grad_norm: 17.7130 loss: 3.0753 task0.loss_heatmap: 0.4452 task0.loss_bbox: 1.9607 task0.loss_iou: 0.1335 task0.loss_reg_iou: 0.5359 2023/09/17 18:53:13 - mmengine - INFO - Epoch(train) [11][2450/3953] lr: 3.9767e-05 eta: 0:45:49 time: 0.5024 data_time: 0.0044 memory: 8052 grad_norm: 17.2758 loss: 2.9534 task0.loss_heatmap: 0.4260 task0.loss_bbox: 1.8823 task0.loss_iou: 0.1288 task0.loss_reg_iou: 0.5164 2023/09/17 18:53:24 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 18:53:38 - mmengine - INFO - Epoch(train) [11][2500/3953] lr: 3.9051e-05 eta: 0:45:24 time: 0.4959 data_time: 0.0044 memory: 8691 grad_norm: 18.9935 loss: 3.1012 task0.loss_heatmap: 0.4655 task0.loss_bbox: 1.9714 task0.loss_iou: 0.1350 task0.loss_reg_iou: 0.5293 2023/09/17 18:54:03 - mmengine - INFO - Epoch(train) [11][2550/3953] lr: 3.8341e-05 eta: 0:44:59 time: 0.5000 data_time: 0.0046 memory: 8442 grad_norm: 17.5685 loss: 3.0712 task0.loss_heatmap: 0.4435 task0.loss_bbox: 1.9593 task0.loss_iou: 0.1345 task0.loss_reg_iou: 0.5340 2023/09/17 18:54:28 - mmengine - INFO - Epoch(train) [11][2600/3953] lr: 3.7638e-05 eta: 0:44:34 time: 0.5003 data_time: 0.0043 memory: 8145 grad_norm: 18.0758 loss: 3.0134 task0.loss_heatmap: 0.4344 task0.loss_bbox: 1.9097 task0.loss_iou: 0.1331 task0.loss_reg_iou: 0.5362 2023/09/17 18:54:53 - mmengine - INFO - Epoch(train) [11][2650/3953] lr: 3.6941e-05 eta: 0:44:08 time: 0.4986 data_time: 0.0042 memory: 8357 grad_norm: 18.4657 loss: 3.0436 task0.loss_heatmap: 0.4190 task0.loss_bbox: 1.9534 task0.loss_iou: 0.1338 task0.loss_reg_iou: 0.5374 2023/09/17 18:55:18 - mmengine - INFO - Epoch(train) [11][2700/3953] lr: 3.6250e-05 eta: 0:43:43 time: 0.5040 data_time: 0.0042 memory: 8468 grad_norm: 17.9191 loss: 3.0502 task0.loss_heatmap: 0.4494 task0.loss_bbox: 1.9370 task0.loss_iou: 0.1333 task0.loss_reg_iou: 0.5306 2023/09/17 18:55:44 - mmengine - INFO - Epoch(train) [11][2750/3953] lr: 3.5565e-05 eta: 0:43:18 time: 0.5100 data_time: 0.0042 memory: 8275 grad_norm: 18.1831 loss: 3.0040 task0.loss_heatmap: 0.4422 task0.loss_bbox: 1.9107 task0.loss_iou: 0.1303 task0.loss_reg_iou: 0.5207 2023/09/17 18:56:09 - mmengine - INFO - Epoch(train) [11][2800/3953] lr: 3.4887e-05 eta: 0:42:53 time: 0.5003 data_time: 0.0043 memory: 8180 grad_norm: 17.2586 loss: 3.1153 task0.loss_heatmap: 0.4532 task0.loss_bbox: 1.9835 task0.loss_iou: 0.1381 task0.loss_reg_iou: 0.5405 2023/09/17 18:56:34 - mmengine - INFO - Epoch(train) [11][2850/3953] lr: 3.4215e-05 eta: 0:42:28 time: 0.5073 data_time: 0.0042 memory: 8583 grad_norm: 18.0972 loss: 3.0842 task0.loss_heatmap: 0.4305 task0.loss_bbox: 1.9881 task0.loss_iou: 0.1341 task0.loss_reg_iou: 0.5315 2023/09/17 18:56:59 - mmengine - INFO - Epoch(train) [11][2900/3953] lr: 3.3549e-05 eta: 0:42:02 time: 0.4980 data_time: 0.0043 memory: 8261 grad_norm: 18.7917 loss: 3.0324 task0.loss_heatmap: 0.4301 task0.loss_bbox: 1.9396 task0.loss_iou: 0.1324 task0.loss_reg_iou: 0.5303 2023/09/17 18:57:24 - mmengine - INFO - Epoch(train) [11][2950/3953] lr: 3.2890e-05 eta: 0:41:37 time: 0.5036 data_time: 0.0044 memory: 8545 grad_norm: 17.6439 loss: 3.1153 task0.loss_heatmap: 0.4792 task0.loss_bbox: 1.9747 task0.loss_iou: 0.1334 task0.loss_reg_iou: 0.5280 2023/09/17 18:57:49 - mmengine - INFO - Epoch(train) [11][3000/3953] lr: 3.2237e-05 eta: 0:41:12 time: 0.4921 data_time: 0.0042 memory: 8382 grad_norm: 18.0941 loss: 3.0215 task0.loss_heatmap: 0.4310 task0.loss_bbox: 1.9316 task0.loss_iou: 0.1305 task0.loss_reg_iou: 0.5285 2023/09/17 18:58:14 - mmengine - INFO - Epoch(train) [11][3050/3953] lr: 3.1590e-05 eta: 0:40:47 time: 0.4985 data_time: 0.0040 memory: 8036 grad_norm: 18.6607 loss: 2.9907 task0.loss_heatmap: 0.4251 task0.loss_bbox: 1.9038 task0.loss_iou: 0.1355 task0.loss_reg_iou: 0.5262 2023/09/17 18:58:40 - mmengine - INFO - Epoch(train) [11][3100/3953] lr: 3.0950e-05 eta: 0:40:22 time: 0.5118 data_time: 0.0041 memory: 8464 grad_norm: 18.1124 loss: 2.9424 task0.loss_heatmap: 0.4029 task0.loss_bbox: 1.8864 task0.loss_iou: 0.1306 task0.loss_reg_iou: 0.5225 2023/09/17 18:59:04 - mmengine - INFO - Epoch(train) [11][3150/3953] lr: 3.0316e-05 eta: 0:39:56 time: 0.4973 data_time: 0.0040 memory: 8116 grad_norm: 19.1925 loss: 3.0799 task0.loss_heatmap: 0.4488 task0.loss_bbox: 1.9720 task0.loss_iou: 0.1348 task0.loss_reg_iou: 0.5243 2023/09/17 18:59:30 - mmengine - INFO - Epoch(train) [11][3200/3953] lr: 2.9688e-05 eta: 0:39:31 time: 0.5156 data_time: 0.0041 memory: 8174 grad_norm: 16.8176 loss: 2.9937 task0.loss_heatmap: 0.4123 task0.loss_bbox: 1.9257 task0.loss_iou: 0.1317 task0.loss_reg_iou: 0.5239 2023/09/17 18:59:55 - mmengine - INFO - Epoch(train) [11][3250/3953] lr: 2.9067e-05 eta: 0:39:06 time: 0.4985 data_time: 0.0040 memory: 8325 grad_norm: 18.1875 loss: 3.0068 task0.loss_heatmap: 0.4491 task0.loss_bbox: 1.9018 task0.loss_iou: 0.1345 task0.loss_reg_iou: 0.5215 2023/09/17 19:00:20 - mmengine - INFO - Epoch(train) [11][3300/3953] lr: 2.8452e-05 eta: 0:38:41 time: 0.5018 data_time: 0.0042 memory: 8333 grad_norm: 17.4652 loss: 3.0593 task0.loss_heatmap: 0.4530 task0.loss_bbox: 1.9516 task0.loss_iou: 0.1305 task0.loss_reg_iou: 0.5242 2023/09/17 19:00:45 - mmengine - INFO - Epoch(train) [11][3350/3953] lr: 2.7844e-05 eta: 0:38:16 time: 0.4998 data_time: 0.0039 memory: 8308 grad_norm: 17.3942 loss: 3.0541 task0.loss_heatmap: 0.4273 task0.loss_bbox: 1.9634 task0.loss_iou: 0.1332 task0.loss_reg_iou: 0.5303 2023/09/17 19:01:10 - mmengine - INFO - Epoch(train) [11][3400/3953] lr: 2.7241e-05 eta: 0:37:50 time: 0.4980 data_time: 0.0040 memory: 8533 grad_norm: 17.4916 loss: 3.1063 task0.loss_heatmap: 0.4566 task0.loss_bbox: 1.9870 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5325 2023/09/17 19:01:35 - mmengine - INFO - Epoch(train) [11][3450/3953] lr: 2.6646e-05 eta: 0:37:25 time: 0.4879 data_time: 0.0042 memory: 8250 grad_norm: 20.4886 loss: 2.9315 task0.loss_heatmap: 0.4193 task0.loss_bbox: 1.8702 task0.loss_iou: 0.1289 task0.loss_reg_iou: 0.5131 2023/09/17 19:01:45 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:02:00 - mmengine - INFO - Epoch(train) [11][3500/3953] lr: 2.6056e-05 eta: 0:37:00 time: 0.5029 data_time: 0.0043 memory: 8559 grad_norm: 17.8552 loss: 2.9985 task0.loss_heatmap: 0.4615 task0.loss_bbox: 1.8921 task0.loss_iou: 0.1276 task0.loss_reg_iou: 0.5173 2023/09/17 19:02:25 - mmengine - INFO - Epoch(train) [11][3550/3953] lr: 2.5473e-05 eta: 0:36:35 time: 0.5027 data_time: 0.0041 memory: 8315 grad_norm: 17.3257 loss: 3.0681 task0.loss_heatmap: 0.4430 task0.loss_bbox: 1.9617 task0.loss_iou: 0.1343 task0.loss_reg_iou: 0.5290 2023/09/17 19:02:50 - mmengine - INFO - Epoch(train) [11][3600/3953] lr: 2.4897e-05 eta: 0:36:09 time: 0.4990 data_time: 0.0041 memory: 8201 grad_norm: 17.5241 loss: 3.1375 task0.loss_heatmap: 0.4972 task0.loss_bbox: 1.9636 task0.loss_iou: 0.1398 task0.loss_reg_iou: 0.5369 2023/09/17 19:03:17 - mmengine - INFO - Epoch(train) [11][3650/3953] lr: 2.4327e-05 eta: 0:35:44 time: 0.5501 data_time: 0.0042 memory: 8433 grad_norm: 18.6728 loss: 3.0127 task0.loss_heatmap: 0.4182 task0.loss_bbox: 1.9341 task0.loss_iou: 0.1326 task0.loss_reg_iou: 0.5279 2023/09/17 19:03:45 - mmengine - INFO - Epoch(train) [11][3700/3953] lr: 2.3763e-05 eta: 0:35:19 time: 0.5423 data_time: 0.0044 memory: 8334 grad_norm: 17.3779 loss: 3.0633 task0.loss_heatmap: 0.4533 task0.loss_bbox: 1.9434 task0.loss_iou: 0.1352 task0.loss_reg_iou: 0.5314 2023/09/17 19:04:11 - mmengine - INFO - Epoch(train) [11][3750/3953] lr: 2.3206e-05 eta: 0:34:54 time: 0.5288 data_time: 0.0045 memory: 8196 grad_norm: 19.2485 loss: 3.0598 task0.loss_heatmap: 0.4361 task0.loss_bbox: 1.9593 task0.loss_iou: 0.1308 task0.loss_reg_iou: 0.5337 2023/09/17 19:04:36 - mmengine - INFO - Epoch(train) [11][3800/3953] lr: 2.2655e-05 eta: 0:34:29 time: 0.4988 data_time: 0.0044 memory: 8143 grad_norm: 17.5070 loss: 3.0687 task0.loss_heatmap: 0.4357 task0.loss_bbox: 1.9624 task0.loss_iou: 0.1321 task0.loss_reg_iou: 0.5385 2023/09/17 19:05:01 - mmengine - INFO - Epoch(train) [11][3850/3953] lr: 2.2111e-05 eta: 0:34:04 time: 0.5007 data_time: 0.0043 memory: 8329 grad_norm: 18.5182 loss: 2.9527 task0.loss_heatmap: 0.4190 task0.loss_bbox: 1.8836 task0.loss_iou: 0.1302 task0.loss_reg_iou: 0.5199 2023/09/17 19:05:26 - mmengine - INFO - Epoch(train) [11][3900/3953] lr: 2.1573e-05 eta: 0:33:39 time: 0.4998 data_time: 0.0042 memory: 8393 grad_norm: 17.3528 loss: 2.9109 task0.loss_heatmap: 0.3930 task0.loss_bbox: 1.8637 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.5228 2023/09/17 19:05:51 - mmengine - INFO - Epoch(train) [11][3950/3953] lr: 2.1042e-05 eta: 0:33:13 time: 0.5003 data_time: 0.0044 memory: 8659 grad_norm: 18.2373 loss: 3.0758 task0.loss_heatmap: 0.4646 task0.loss_bbox: 1.9506 task0.loss_iou: 0.1340 task0.loss_reg_iou: 0.5266 2023/09/17 19:05:53 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:05:53 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/09/17 19:06:15 - mmengine - INFO - Epoch(val) [11][ 50/1250] eta: 0:07:38 time: 0.3824 data_time: 0.0068 memory: 6890 2023/09/17 19:06:34 - mmengine - INFO - Epoch(val) [11][ 100/1250] eta: 0:07:22 time: 0.3877 data_time: 0.0053 memory: 4043 2023/09/17 19:06:54 - mmengine - INFO - Epoch(val) [11][ 150/1250] eta: 0:07:04 time: 0.3880 data_time: 0.0052 memory: 4062 2023/09/17 19:07:13 - mmengine - INFO - Epoch(val) [11][ 200/1250] eta: 0:06:44 time: 0.3823 data_time: 0.0053 memory: 4051 2023/09/17 19:07:31 - mmengine - INFO - Epoch(val) [11][ 250/1250] eta: 0:06:19 time: 0.3567 data_time: 0.0047 memory: 4033 2023/09/17 19:07:50 - mmengine - INFO - Epoch(val) [11][ 300/1250] eta: 0:06:01 time: 0.3835 data_time: 0.0049 memory: 4050 2023/09/17 19:08:09 - mmengine - INFO - Epoch(val) [11][ 350/1250] eta: 0:05:42 time: 0.3837 data_time: 0.0050 memory: 4057 2023/09/17 19:08:29 - mmengine - INFO - Epoch(val) [11][ 400/1250] eta: 0:05:24 time: 0.3928 data_time: 0.0056 memory: 4060 2023/09/17 19:08:49 - mmengine - INFO - Epoch(val) [11][ 450/1250] eta: 0:05:07 time: 0.4079 data_time: 0.0051 memory: 4048 2023/09/17 19:09:07 - mmengine - INFO - Epoch(val) [11][ 500/1250] eta: 0:04:47 time: 0.3692 data_time: 0.0052 memory: 4041 2023/09/17 19:09:26 - mmengine - INFO - Epoch(val) [11][ 550/1250] eta: 0:04:27 time: 0.3712 data_time: 0.0049 memory: 4042 2023/09/17 19:09:45 - mmengine - INFO - Epoch(val) [11][ 600/1250] eta: 0:04:07 time: 0.3710 data_time: 0.0051 memory: 4057 2023/09/17 19:10:05 - mmengine - INFO - Epoch(val) [11][ 650/1250] eta: 0:03:50 time: 0.4076 data_time: 0.0049 memory: 4065 2023/09/17 19:10:25 - mmengine - INFO - Epoch(val) [11][ 700/1250] eta: 0:03:31 time: 0.3969 data_time: 0.0050 memory: 4042 2023/09/17 19:10:45 - mmengine - INFO - Epoch(val) [11][ 750/1250] eta: 0:03:12 time: 0.3998 data_time: 0.0053 memory: 4051 2023/09/17 19:11:03 - mmengine - INFO - Epoch(val) [11][ 800/1250] eta: 0:02:53 time: 0.3726 data_time: 0.0054 memory: 4055 2023/09/17 19:11:23 - mmengine - INFO - Epoch(val) [11][ 850/1250] eta: 0:02:33 time: 0.3867 data_time: 0.0052 memory: 4058 2023/09/17 19:11:41 - mmengine - INFO - Epoch(val) [11][ 900/1250] eta: 0:02:14 time: 0.3607 data_time: 0.0051 memory: 4049 2023/09/17 19:11:59 - mmengine - INFO - Epoch(val) [11][ 950/1250] eta: 0:01:54 time: 0.3683 data_time: 0.0051 memory: 4054 2023/09/17 19:12:18 - mmengine - INFO - Epoch(val) [11][1000/1250] eta: 0:01:35 time: 0.3806 data_time: 0.0052 memory: 4056 2023/09/17 19:12:37 - mmengine - INFO - Epoch(val) [11][1050/1250] eta: 0:01:16 time: 0.3785 data_time: 0.0050 memory: 4043 2023/09/17 19:12:55 - mmengine - INFO - Epoch(val) [11][1100/1250] eta: 0:00:57 time: 0.3570 data_time: 0.0050 memory: 4032 2023/09/17 19:13:14 - mmengine - INFO - Epoch(val) [11][1150/1250] eta: 0:00:38 time: 0.3850 data_time: 0.0051 memory: 4041 2023/09/17 19:13:33 - mmengine - INFO - Epoch(val) [11][1200/1250] eta: 0:00:19 time: 0.3825 data_time: 0.0051 memory: 4047 2023/09/17 19:13:53 - mmengine - INFO - Epoch(val) [11][1250/1250] eta: 0:00:00 time: 0.3879 data_time: 0.0052 memory: 4055 2023/09/17 19:13:57 - mmengine - INFO - Start converting ... 2023/09/17 19:21:11 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39987 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.730533] [mAPH 0.725578] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.646404] [mAPH 0.641917] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.780328] [mAPH 0.701842] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.702013] [mAPH 0.629276] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.718358] [mAPH 0.70492] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.691706] [mAPH 0.678753] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.90938] [mAPH 0.904842] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.896268] [mAPH 0.891782] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.71188] [mAPH 0.706249] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.647767] [mAPH 0.642561] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.474166] [mAPH 0.467833] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.363187] [mAPH 0.358166] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.83148] [mAPH 0.761713] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.793722] [mAPH 0.725554] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.765713] [mAPH 0.683088] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.697568] [mAPH 0.621129] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.678742] [mAPH 0.575212] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.543771] [mAPH 0.457731] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.811887] [mAPH 0.799608] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.806203] [mAPH 0.794023] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.670683] [mAPH 0.657707] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.633487] [mAPH 0.621309] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.545918] [mAPH 0.522205] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.508768] [mAPH 0.486646] Eval Using 322s 2023/09/17 19:21:12 - mmengine - INFO - Epoch(val) [11][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.7305 Waymo metric/Vehicle/L1 mAPH: 0.7256 Waymo metric/Vehicle/L2 mAP: 0.6464 Waymo metric/Vehicle/L2 mAPH: 0.6419 Waymo metric/Pedestrian/L1 mAP: 0.7803 Waymo metric/Pedestrian/L1 mAPH: 0.7018 Waymo metric/Pedestrian/L2 mAP: 0.7020 Waymo metric/Pedestrian/L2 mAPH: 0.6293 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.7184 Waymo metric/Cyclist/L1 mAPH: 0.7049 Waymo metric/Cyclist/L2 mAP: 0.6917 Waymo metric/Cyclist/L2 mAPH: 0.6788 Waymo metric/Overall/L1 mAP: 0.7431 Waymo metric/Overall/L1 mAPH: 0.7108 Waymo metric/Overall/L2 mAP: 0.6800 Waymo metric/Overall/L2 mAPH: 0.6500 data_time: 0.0052 time: 0.3816 2023/09/17 19:21:12 - mmengine - INFO - Disable ObjectSample 2023/09/17 19:21:12 - mmengine - INFO - Disable RandomFlip3D 2023/09/17 19:21:12 - mmengine - INFO - Disable GlobalRotScaleTrans 2023/09/17 19:21:48 - mmengine - INFO - Epoch(train) [12][ 50/3953] lr: 2.0486e-05 eta: 0:32:48 time: 0.7112 data_time: 0.2043 memory: 8202 grad_norm: 20.3062 loss: 2.9701 task0.loss_heatmap: 0.5233 task0.loss_bbox: 1.8135 task0.loss_iou: 0.1341 task0.loss_reg_iou: 0.4992 2023/09/17 19:22:13 - mmengine - INFO - Epoch(train) [12][ 100/3953] lr: 1.9968e-05 eta: 0:32:22 time: 0.5043 data_time: 0.0046 memory: 8001 grad_norm: 24.0639 loss: 2.6434 task0.loss_heatmap: 0.4487 task0.loss_bbox: 1.6165 task0.loss_iou: 0.1250 task0.loss_reg_iou: 0.4531 2023/09/17 19:22:38 - mmengine - INFO - Epoch(train) [12][ 150/3953] lr: 1.9457e-05 eta: 0:31:57 time: 0.5120 data_time: 0.0047 memory: 8207 grad_norm: 21.2339 loss: 2.6883 task0.loss_heatmap: 0.4406 task0.loss_bbox: 1.6649 task0.loss_iou: 0.1269 task0.loss_reg_iou: 0.4559 2023/09/17 19:23:04 - mmengine - INFO - Epoch(train) [12][ 200/3953] lr: 1.8952e-05 eta: 0:31:32 time: 0.5150 data_time: 0.0045 memory: 8269 grad_norm: 22.7303 loss: 2.5025 task0.loss_heatmap: 0.4293 task0.loss_bbox: 1.5237 task0.loss_iou: 0.1116 task0.loss_reg_iou: 0.4380 2023/09/17 19:23:29 - mmengine - INFO - Epoch(train) [12][ 250/3953] lr: 1.8453e-05 eta: 0:31:07 time: 0.5018 data_time: 0.0046 memory: 9000 grad_norm: 21.6113 loss: 2.6238 task0.loss_heatmap: 0.4174 task0.loss_bbox: 1.6275 task0.loss_iou: 0.1228 task0.loss_reg_iou: 0.4562 2023/09/17 19:23:55 - mmengine - INFO - Epoch(train) [12][ 300/3953] lr: 1.7961e-05 eta: 0:30:42 time: 0.5059 data_time: 0.0045 memory: 8447 grad_norm: 22.9636 loss: 2.6552 task0.loss_heatmap: 0.4177 task0.loss_bbox: 1.6518 task0.loss_iou: 0.1206 task0.loss_reg_iou: 0.4651 2023/09/17 19:24:20 - mmengine - INFO - Epoch(train) [12][ 350/3953] lr: 1.7476e-05 eta: 0:30:16 time: 0.5012 data_time: 0.0044 memory: 8483 grad_norm: 19.7803 loss: 2.5517 task0.loss_heatmap: 0.3688 task0.loss_bbox: 1.6026 task0.loss_iou: 0.1251 task0.loss_reg_iou: 0.4552 2023/09/17 19:24:45 - mmengine - INFO - Epoch(train) [12][ 400/3953] lr: 1.6997e-05 eta: 0:29:51 time: 0.5023 data_time: 0.0044 memory: 8754 grad_norm: 20.1630 loss: 2.6002 task0.loss_heatmap: 0.4371 task0.loss_bbox: 1.5837 task0.loss_iou: 0.1237 task0.loss_reg_iou: 0.4556 2023/09/17 19:25:09 - mmengine - INFO - Epoch(train) [12][ 450/3953] lr: 1.6525e-05 eta: 0:29:26 time: 0.4905 data_time: 0.0045 memory: 8087 grad_norm: 21.0682 loss: 2.4899 task0.loss_heatmap: 0.4050 task0.loss_bbox: 1.5278 task0.loss_iou: 0.1156 task0.loss_reg_iou: 0.4415 2023/09/17 19:25:34 - mmengine - INFO - Epoch(train) [12][ 500/3953] lr: 1.6059e-05 eta: 0:29:01 time: 0.4958 data_time: 0.0043 memory: 8257 grad_norm: 21.5658 loss: 2.7369 task0.loss_heatmap: 0.4777 task0.loss_bbox: 1.6856 task0.loss_iou: 0.1176 task0.loss_reg_iou: 0.4560 2023/09/17 19:25:43 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:26:00 - mmengine - INFO - Epoch(train) [12][ 550/3953] lr: 1.5600e-05 eta: 0:28:36 time: 0.5136 data_time: 0.0044 memory: 8379 grad_norm: 20.3617 loss: 2.7258 task0.loss_heatmap: 0.4611 task0.loss_bbox: 1.6732 task0.loss_iou: 0.1225 task0.loss_reg_iou: 0.4690 2023/09/17 19:26:25 - mmengine - INFO - Epoch(train) [12][ 600/3953] lr: 1.5147e-05 eta: 0:28:10 time: 0.5076 data_time: 0.0046 memory: 8088 grad_norm: 21.6858 loss: 2.6915 task0.loss_heatmap: 0.4707 task0.loss_bbox: 1.6320 task0.loss_iou: 0.1197 task0.loss_reg_iou: 0.4692 2023/09/17 19:26:50 - mmengine - INFO - Epoch(train) [12][ 650/3953] lr: 1.4701e-05 eta: 0:27:45 time: 0.5046 data_time: 0.0046 memory: 8432 grad_norm: 20.4596 loss: 2.5944 task0.loss_heatmap: 0.4104 task0.loss_bbox: 1.6196 task0.loss_iou: 0.1181 task0.loss_reg_iou: 0.4464 2023/09/17 19:27:15 - mmengine - INFO - Epoch(train) [12][ 700/3953] lr: 1.4262e-05 eta: 0:27:20 time: 0.4973 data_time: 0.0044 memory: 7929 grad_norm: 19.1192 loss: 2.7721 task0.loss_heatmap: 0.5110 task0.loss_bbox: 1.6756 task0.loss_iou: 0.1257 task0.loss_reg_iou: 0.4598 2023/09/17 19:27:41 - mmengine - INFO - Epoch(train) [12][ 750/3953] lr: 1.3829e-05 eta: 0:26:55 time: 0.5059 data_time: 0.0046 memory: 8265 grad_norm: 20.5262 loss: 2.6588 task0.loss_heatmap: 0.4663 task0.loss_bbox: 1.6290 task0.loss_iou: 0.1213 task0.loss_reg_iou: 0.4422 2023/09/17 19:28:06 - mmengine - INFO - Epoch(train) [12][ 800/3953] lr: 1.3402e-05 eta: 0:26:29 time: 0.5034 data_time: 0.0045 memory: 8516 grad_norm: 18.8490 loss: 2.6188 task0.loss_heatmap: 0.4106 task0.loss_bbox: 1.6343 task0.loss_iou: 0.1255 task0.loss_reg_iou: 0.4484 2023/09/17 19:28:31 - mmengine - INFO - Epoch(train) [12][ 850/3953] lr: 1.2983e-05 eta: 0:26:04 time: 0.5119 data_time: 0.0046 memory: 7978 grad_norm: 25.6212 loss: 2.7587 task0.loss_heatmap: 0.4667 task0.loss_bbox: 1.7012 task0.loss_iou: 0.1210 task0.loss_reg_iou: 0.4697 2023/09/17 19:28:57 - mmengine - INFO - Epoch(train) [12][ 900/3953] lr: 1.2569e-05 eta: 0:25:39 time: 0.5178 data_time: 0.0047 memory: 8587 grad_norm: 19.7746 loss: 2.8091 task0.loss_heatmap: 0.5177 task0.loss_bbox: 1.7082 task0.loss_iou: 0.1196 task0.loss_reg_iou: 0.4636 2023/09/17 19:29:23 - mmengine - INFO - Epoch(train) [12][ 950/3953] lr: 1.2163e-05 eta: 0:25:14 time: 0.5064 data_time: 0.0043 memory: 8310 grad_norm: 19.6452 loss: 2.7392 task0.loss_heatmap: 0.4702 task0.loss_bbox: 1.6810 task0.loss_iou: 0.1216 task0.loss_reg_iou: 0.4664 2023/09/17 19:29:48 - mmengine - INFO - Epoch(train) [12][1000/3953] lr: 1.1763e-05 eta: 0:24:49 time: 0.5017 data_time: 0.0043 memory: 8780 grad_norm: 24.6387 loss: 2.7944 task0.loss_heatmap: 0.5002 task0.loss_bbox: 1.6922 task0.loss_iou: 0.1276 task0.loss_reg_iou: 0.4744 2023/09/17 19:30:13 - mmengine - INFO - Epoch(train) [12][1050/3953] lr: 1.1369e-05 eta: 0:24:23 time: 0.5097 data_time: 0.0044 memory: 8537 grad_norm: 22.6232 loss: 2.7525 task0.loss_heatmap: 0.4592 task0.loss_bbox: 1.6881 task0.loss_iou: 0.1279 task0.loss_reg_iou: 0.4772 2023/09/17 19:30:38 - mmengine - INFO - Epoch(train) [12][1100/3953] lr: 1.0983e-05 eta: 0:23:58 time: 0.5017 data_time: 0.0043 memory: 8251 grad_norm: 21.4530 loss: 2.6081 task0.loss_heatmap: 0.4210 task0.loss_bbox: 1.6197 task0.loss_iou: 0.1237 task0.loss_reg_iou: 0.4437 2023/09/17 19:31:03 - mmengine - INFO - Epoch(train) [12][1150/3953] lr: 1.0603e-05 eta: 0:23:33 time: 0.4860 data_time: 0.0045 memory: 8064 grad_norm: 22.9593 loss: 2.5513 task0.loss_heatmap: 0.4446 task0.loss_bbox: 1.5584 task0.loss_iou: 0.1152 task0.loss_reg_iou: 0.4331 2023/09/17 19:31:28 - mmengine - INFO - Epoch(train) [12][1200/3953] lr: 1.0229e-05 eta: 0:23:08 time: 0.5076 data_time: 0.0043 memory: 8165 grad_norm: 19.5514 loss: 2.5344 task0.loss_heatmap: 0.4052 task0.loss_bbox: 1.5600 task0.loss_iou: 0.1279 task0.loss_reg_iou: 0.4413 2023/09/17 19:31:53 - mmengine - INFO - Epoch(train) [12][1250/3953] lr: 9.8621e-06 eta: 0:22:43 time: 0.5051 data_time: 0.0043 memory: 8051 grad_norm: 21.1524 loss: 2.6968 task0.loss_heatmap: 0.4462 task0.loss_bbox: 1.6541 task0.loss_iou: 0.1273 task0.loss_reg_iou: 0.4692 2023/09/17 19:32:18 - mmengine - INFO - Epoch(train) [12][1300/3953] lr: 9.5019e-06 eta: 0:22:17 time: 0.4963 data_time: 0.0044 memory: 8478 grad_norm: 22.5771 loss: 2.7753 task0.loss_heatmap: 0.4482 task0.loss_bbox: 1.7155 task0.loss_iou: 0.1330 task0.loss_reg_iou: 0.4786 2023/09/17 19:32:43 - mmengine - INFO - Epoch(train) [12][1350/3953] lr: 9.1483e-06 eta: 0:21:52 time: 0.4956 data_time: 0.0042 memory: 8325 grad_norm: 18.5493 loss: 2.7713 task0.loss_heatmap: 0.5036 task0.loss_bbox: 1.6766 task0.loss_iou: 0.1230 task0.loss_reg_iou: 0.4681 2023/09/17 19:33:08 - mmengine - INFO - Epoch(train) [12][1400/3953] lr: 8.8013e-06 eta: 0:21:27 time: 0.4993 data_time: 0.0042 memory: 8241 grad_norm: 19.5513 loss: 2.5854 task0.loss_heatmap: 0.3842 task0.loss_bbox: 1.6174 task0.loss_iou: 0.1237 task0.loss_reg_iou: 0.4601 2023/09/17 19:33:33 - mmengine - INFO - Epoch(train) [12][1450/3953] lr: 8.4610e-06 eta: 0:21:02 time: 0.4966 data_time: 0.0044 memory: 8169 grad_norm: 21.4402 loss: 2.7097 task0.loss_heatmap: 0.4642 task0.loss_bbox: 1.6589 task0.loss_iou: 0.1224 task0.loss_reg_iou: 0.4642 2023/09/17 19:33:58 - mmengine - INFO - Epoch(train) [12][1500/3953] lr: 8.1273e-06 eta: 0:20:36 time: 0.5033 data_time: 0.0043 memory: 8475 grad_norm: 18.9775 loss: 2.6261 task0.loss_heatmap: 0.4306 task0.loss_bbox: 1.6204 task0.loss_iou: 0.1209 task0.loss_reg_iou: 0.4541 2023/09/17 19:34:06 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:34:23 - mmengine - INFO - Epoch(train) [12][1550/3953] lr: 7.8003e-06 eta: 0:20:11 time: 0.4936 data_time: 0.0042 memory: 8309 grad_norm: 19.2149 loss: 2.6318 task0.loss_heatmap: 0.3821 task0.loss_bbox: 1.6680 task0.loss_iou: 0.1217 task0.loss_reg_iou: 0.4599 2023/09/17 19:34:48 - mmengine - INFO - Epoch(train) [12][1600/3953] lr: 7.4800e-06 eta: 0:19:46 time: 0.5042 data_time: 0.0041 memory: 8370 grad_norm: 20.8724 loss: 2.6486 task0.loss_heatmap: 0.4100 task0.loss_bbox: 1.6606 task0.loss_iou: 0.1218 task0.loss_reg_iou: 0.4562 2023/09/17 19:35:13 - mmengine - INFO - Epoch(train) [12][1650/3953] lr: 7.1663e-06 eta: 0:19:21 time: 0.4981 data_time: 0.0041 memory: 8273 grad_norm: 18.9681 loss: 2.6907 task0.loss_heatmap: 0.4882 task0.loss_bbox: 1.6353 task0.loss_iou: 0.1200 task0.loss_reg_iou: 0.4472 2023/09/17 19:35:37 - mmengine - INFO - Epoch(train) [12][1700/3953] lr: 6.8593e-06 eta: 0:18:56 time: 0.4936 data_time: 0.0042 memory: 7825 grad_norm: 18.7083 loss: 2.6269 task0.loss_heatmap: 0.4062 task0.loss_bbox: 1.6338 task0.loss_iou: 0.1225 task0.loss_reg_iou: 0.4645 2023/09/17 19:36:03 - mmengine - INFO - Epoch(train) [12][1750/3953] lr: 6.5590e-06 eta: 0:18:30 time: 0.5101 data_time: 0.0044 memory: 8628 grad_norm: 20.9001 loss: 2.8036 task0.loss_heatmap: 0.4926 task0.loss_bbox: 1.7297 task0.loss_iou: 0.1169 task0.loss_reg_iou: 0.4644 2023/09/17 19:36:28 - mmengine - INFO - Epoch(train) [12][1800/3953] lr: 6.2654e-06 eta: 0:18:05 time: 0.5075 data_time: 0.0044 memory: 8293 grad_norm: 22.5292 loss: 2.6085 task0.loss_heatmap: 0.4350 task0.loss_bbox: 1.5956 task0.loss_iou: 0.1243 task0.loss_reg_iou: 0.4536 2023/09/17 19:36:54 - mmengine - INFO - Epoch(train) [12][1850/3953] lr: 5.9784e-06 eta: 0:17:40 time: 0.5074 data_time: 0.0046 memory: 8444 grad_norm: 20.1626 loss: 2.7512 task0.loss_heatmap: 0.4357 task0.loss_bbox: 1.7134 task0.loss_iou: 0.1272 task0.loss_reg_iou: 0.4748 2023/09/17 19:37:19 - mmengine - INFO - Epoch(train) [12][1900/3953] lr: 5.6982e-06 eta: 0:17:15 time: 0.5042 data_time: 0.0046 memory: 8008 grad_norm: 19.2896 loss: 2.6455 task0.loss_heatmap: 0.4441 task0.loss_bbox: 1.6256 task0.loss_iou: 0.1278 task0.loss_reg_iou: 0.4481 2023/09/17 19:37:44 - mmengine - INFO - Epoch(train) [12][1950/3953] lr: 5.4246e-06 eta: 0:16:49 time: 0.4917 data_time: 0.0045 memory: 8077 grad_norm: 19.6649 loss: 2.4924 task0.loss_heatmap: 0.3882 task0.loss_bbox: 1.5457 task0.loss_iou: 0.1207 task0.loss_reg_iou: 0.4378 2023/09/17 19:38:09 - mmengine - INFO - Epoch(train) [12][2000/3953] lr: 5.1577e-06 eta: 0:16:24 time: 0.4983 data_time: 0.0046 memory: 8673 grad_norm: 19.6285 loss: 2.6898 task0.loss_heatmap: 0.3957 task0.loss_bbox: 1.7037 task0.loss_iou: 0.1235 task0.loss_reg_iou: 0.4669 2023/09/17 19:38:34 - mmengine - INFO - Epoch(train) [12][2050/3953] lr: 4.8975e-06 eta: 0:15:59 time: 0.5100 data_time: 0.0045 memory: 8446 grad_norm: 21.2194 loss: 2.6041 task0.loss_heatmap: 0.4258 task0.loss_bbox: 1.5937 task0.loss_iou: 0.1287 task0.loss_reg_iou: 0.4559 2023/09/17 19:38:59 - mmengine - INFO - Epoch(train) [12][2100/3953] lr: 4.6440e-06 eta: 0:15:34 time: 0.4924 data_time: 0.0044 memory: 8398 grad_norm: 20.3527 loss: 2.5115 task0.loss_heatmap: 0.3907 task0.loss_bbox: 1.5726 task0.loss_iou: 0.1164 task0.loss_reg_iou: 0.4318 2023/09/17 19:39:23 - mmengine - INFO - Epoch(train) [12][2150/3953] lr: 4.3973e-06 eta: 0:15:09 time: 0.4949 data_time: 0.0046 memory: 8068 grad_norm: 24.3011 loss: 2.7913 task0.loss_heatmap: 0.5454 task0.loss_bbox: 1.6518 task0.loss_iou: 0.1314 task0.loss_reg_iou: 0.4627 2023/09/17 19:39:49 - mmengine - INFO - Epoch(train) [12][2200/3953] lr: 4.1572e-06 eta: 0:14:43 time: 0.5022 data_time: 0.0044 memory: 8475 grad_norm: 21.8890 loss: 2.8470 task0.loss_heatmap: 0.5693 task0.loss_bbox: 1.6731 task0.loss_iou: 0.1277 task0.loss_reg_iou: 0.4768 2023/09/17 19:40:14 - mmengine - INFO - Epoch(train) [12][2250/3953] lr: 3.9239e-06 eta: 0:14:18 time: 0.5174 data_time: 0.0043 memory: 8830 grad_norm: 20.4446 loss: 2.7231 task0.loss_heatmap: 0.4825 task0.loss_bbox: 1.6549 task0.loss_iou: 0.1234 task0.loss_reg_iou: 0.4622 2023/09/17 19:40:40 - mmengine - INFO - Epoch(train) [12][2300/3953] lr: 3.6972e-06 eta: 0:13:53 time: 0.5059 data_time: 0.0045 memory: 8502 grad_norm: 21.2420 loss: 2.7765 task0.loss_heatmap: 0.5205 task0.loss_bbox: 1.6701 task0.loss_iou: 0.1244 task0.loss_reg_iou: 0.4615 2023/09/17 19:41:05 - mmengine - INFO - Epoch(train) [12][2350/3953] lr: 3.4773e-06 eta: 0:13:28 time: 0.4967 data_time: 0.0045 memory: 8466 grad_norm: 20.2065 loss: 2.8767 task0.loss_heatmap: 0.5166 task0.loss_bbox: 1.7530 task0.loss_iou: 0.1299 task0.loss_reg_iou: 0.4771 2023/09/17 19:41:30 - mmengine - INFO - Epoch(train) [12][2400/3953] lr: 3.2641e-06 eta: 0:13:03 time: 0.5105 data_time: 0.0043 memory: 8468 grad_norm: 23.3349 loss: 2.7365 task0.loss_heatmap: 0.5264 task0.loss_bbox: 1.6433 task0.loss_iou: 0.1242 task0.loss_reg_iou: 0.4426 2023/09/17 19:41:56 - mmengine - INFO - Epoch(train) [12][2450/3953] lr: 3.0576e-06 eta: 0:12:37 time: 0.5167 data_time: 0.0043 memory: 8115 grad_norm: 19.1471 loss: 3.0062 task0.loss_heatmap: 0.5812 task0.loss_bbox: 1.7991 task0.loss_iou: 0.1283 task0.loss_reg_iou: 0.4976 2023/09/17 19:42:22 - mmengine - INFO - Epoch(train) [12][2500/3953] lr: 2.8579e-06 eta: 0:12:12 time: 0.5144 data_time: 0.0045 memory: 8470 grad_norm: 20.1869 loss: 2.8886 task0.loss_heatmap: 0.5145 task0.loss_bbox: 1.7723 task0.loss_iou: 0.1262 task0.loss_reg_iou: 0.4757 2023/09/17 19:42:30 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:42:46 - mmengine - INFO - Epoch(train) [12][2550/3953] lr: 2.6649e-06 eta: 0:11:47 time: 0.4887 data_time: 0.0043 memory: 8289 grad_norm: 19.0754 loss: 2.4517 task0.loss_heatmap: 0.4093 task0.loss_bbox: 1.5065 task0.loss_iou: 0.1120 task0.loss_reg_iou: 0.4239 2023/09/17 19:43:11 - mmengine - INFO - Epoch(train) [12][2600/3953] lr: 2.4786e-06 eta: 0:11:22 time: 0.4925 data_time: 0.0042 memory: 7925 grad_norm: 23.0893 loss: 2.6492 task0.loss_heatmap: 0.4320 task0.loss_bbox: 1.6312 task0.loss_iou: 0.1215 task0.loss_reg_iou: 0.4646 2023/09/17 19:43:35 - mmengine - INFO - Epoch(train) [12][2650/3953] lr: 2.2991e-06 eta: 0:10:56 time: 0.4879 data_time: 0.0041 memory: 7971 grad_norm: 20.7217 loss: 2.7523 task0.loss_heatmap: 0.4616 task0.loss_bbox: 1.6998 task0.loss_iou: 0.1243 task0.loss_reg_iou: 0.4665 2023/09/17 19:44:00 - mmengine - INFO - Epoch(train) [12][2700/3953] lr: 2.1262e-06 eta: 0:10:31 time: 0.4893 data_time: 0.0042 memory: 8310 grad_norm: 19.1174 loss: 2.7739 task0.loss_heatmap: 0.4251 task0.loss_bbox: 1.7432 task0.loss_iou: 0.1260 task0.loss_reg_iou: 0.4795 2023/09/17 19:44:25 - mmengine - INFO - Epoch(train) [12][2750/3953] lr: 1.9602e-06 eta: 0:10:06 time: 0.5059 data_time: 0.0043 memory: 8368 grad_norm: 19.4826 loss: 2.9297 task0.loss_heatmap: 0.5269 task0.loss_bbox: 1.7927 task0.loss_iou: 0.1295 task0.loss_reg_iou: 0.4805 2023/09/17 19:44:50 - mmengine - INFO - Epoch(train) [12][2800/3953] lr: 1.8009e-06 eta: 0:09:41 time: 0.4982 data_time: 0.0042 memory: 8154 grad_norm: 19.7381 loss: 2.6472 task0.loss_heatmap: 0.4081 task0.loss_bbox: 1.6612 task0.loss_iou: 0.1228 task0.loss_reg_iou: 0.4550 2023/09/17 19:45:14 - mmengine - INFO - Epoch(train) [12][2850/3953] lr: 1.6483e-06 eta: 0:09:16 time: 0.4887 data_time: 0.0043 memory: 8468 grad_norm: 18.9070 loss: 2.4264 task0.loss_heatmap: 0.3805 task0.loss_bbox: 1.5003 task0.loss_iou: 0.1126 task0.loss_reg_iou: 0.4330 2023/09/17 19:45:40 - mmengine - INFO - Epoch(train) [12][2900/3953] lr: 1.5024e-06 eta: 0:08:50 time: 0.5050 data_time: 0.0044 memory: 8204 grad_norm: 18.6062 loss: 2.5722 task0.loss_heatmap: 0.3860 task0.loss_bbox: 1.5977 task0.loss_iou: 0.1230 task0.loss_reg_iou: 0.4656 2023/09/17 19:46:04 - mmengine - INFO - Epoch(train) [12][2950/3953] lr: 1.3634e-06 eta: 0:08:25 time: 0.4938 data_time: 0.0043 memory: 8197 grad_norm: 19.1572 loss: 2.6484 task0.loss_heatmap: 0.4407 task0.loss_bbox: 1.6220 task0.loss_iou: 0.1237 task0.loss_reg_iou: 0.4619 2023/09/17 19:46:29 - mmengine - INFO - Epoch(train) [12][3000/3953] lr: 1.2310e-06 eta: 0:08:00 time: 0.4974 data_time: 0.0045 memory: 7857 grad_norm: 21.3090 loss: 2.8266 task0.loss_heatmap: 0.4678 task0.loss_bbox: 1.7612 task0.loss_iou: 0.1235 task0.loss_reg_iou: 0.4742 2023/09/17 19:46:54 - mmengine - INFO - Epoch(train) [12][3050/3953] lr: 1.1054e-06 eta: 0:07:35 time: 0.4855 data_time: 0.0042 memory: 8218 grad_norm: 24.4956 loss: 3.0661 task0.loss_heatmap: 0.5835 task0.loss_bbox: 1.8677 task0.loss_iou: 0.1317 task0.loss_reg_iou: 0.4831 2023/09/17 19:47:18 - mmengine - INFO - Epoch(train) [12][3100/3953] lr: 9.8660e-07 eta: 0:07:09 time: 0.4953 data_time: 0.0042 memory: 8446 grad_norm: 22.0158 loss: 2.5034 task0.loss_heatmap: 0.3894 task0.loss_bbox: 1.5482 task0.loss_iou: 0.1200 task0.loss_reg_iou: 0.4457 2023/09/17 19:47:43 - mmengine - INFO - Epoch(train) [12][3150/3953] lr: 8.7452e-07 eta: 0:06:44 time: 0.4948 data_time: 0.0043 memory: 8633 grad_norm: 19.3692 loss: 2.5004 task0.loss_heatmap: 0.4039 task0.loss_bbox: 1.5368 task0.loss_iou: 0.1147 task0.loss_reg_iou: 0.4450 2023/09/17 19:48:08 - mmengine - INFO - Epoch(train) [12][3200/3953] lr: 7.6921e-07 eta: 0:06:19 time: 0.4928 data_time: 0.0043 memory: 8628 grad_norm: 22.3079 loss: 2.6828 task0.loss_heatmap: 0.4410 task0.loss_bbox: 1.6635 task0.loss_iou: 0.1259 task0.loss_reg_iou: 0.4523 2023/09/17 19:48:32 - mmengine - INFO - Epoch(train) [12][3250/3953] lr: 6.7065e-07 eta: 0:05:54 time: 0.4926 data_time: 0.0044 memory: 8188 grad_norm: 19.3225 loss: 2.8416 task0.loss_heatmap: 0.5011 task0.loss_bbox: 1.7457 task0.loss_iou: 0.1217 task0.loss_reg_iou: 0.4731 2023/09/17 19:48:57 - mmengine - INFO - Epoch(train) [12][3300/3953] lr: 5.7885e-07 eta: 0:05:29 time: 0.4958 data_time: 0.0043 memory: 8529 grad_norm: 18.6726 loss: 2.8163 task0.loss_heatmap: 0.4400 task0.loss_bbox: 1.7599 task0.loss_iou: 0.1284 task0.loss_reg_iou: 0.4879 2023/09/17 19:49:22 - mmengine - INFO - Epoch(train) [12][3350/3953] lr: 4.9381e-07 eta: 0:05:03 time: 0.4969 data_time: 0.0044 memory: 8275 grad_norm: 19.2894 loss: 2.5559 task0.loss_heatmap: 0.4059 task0.loss_bbox: 1.5855 task0.loss_iou: 0.1161 task0.loss_reg_iou: 0.4484 2023/09/17 19:49:47 - mmengine - INFO - Epoch(train) [12][3400/3953] lr: 4.1553e-07 eta: 0:04:38 time: 0.4928 data_time: 0.0042 memory: 8296 grad_norm: 20.8383 loss: 2.6858 task0.loss_heatmap: 0.4509 task0.loss_bbox: 1.6542 task0.loss_iou: 0.1181 task0.loss_reg_iou: 0.4625 2023/09/17 19:50:11 - mmengine - INFO - Epoch(train) [12][3450/3953] lr: 3.4402e-07 eta: 0:04:13 time: 0.4810 data_time: 0.0042 memory: 8082 grad_norm: 22.3702 loss: 2.6325 task0.loss_heatmap: 0.4379 task0.loss_bbox: 1.6191 task0.loss_iou: 0.1300 task0.loss_reg_iou: 0.4455 2023/09/17 19:50:35 - mmengine - INFO - Epoch(train) [12][3500/3953] lr: 2.7927e-07 eta: 0:03:48 time: 0.4899 data_time: 0.0045 memory: 8146 grad_norm: 23.6884 loss: 2.6496 task0.loss_heatmap: 0.4585 task0.loss_bbox: 1.6183 task0.loss_iou: 0.1179 task0.loss_reg_iou: 0.4548 2023/09/17 19:50:44 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:51:00 - mmengine - INFO - Epoch(train) [12][3550/3953] lr: 2.2128e-07 eta: 0:03:23 time: 0.4993 data_time: 0.0045 memory: 7752 grad_norm: 18.4150 loss: 2.6787 task0.loss_heatmap: 0.4264 task0.loss_bbox: 1.6698 task0.loss_iou: 0.1246 task0.loss_reg_iou: 0.4580 2023/09/17 19:51:25 - mmengine - INFO - Epoch(train) [12][3600/3953] lr: 1.7006e-07 eta: 0:02:57 time: 0.4952 data_time: 0.0042 memory: 8021 grad_norm: 20.2490 loss: 2.6952 task0.loss_heatmap: 0.4689 task0.loss_bbox: 1.6592 task0.loss_iou: 0.1128 task0.loss_reg_iou: 0.4543 2023/09/17 19:51:50 - mmengine - INFO - Epoch(train) [12][3650/3953] lr: 1.2561e-07 eta: 0:02:32 time: 0.4994 data_time: 0.0041 memory: 8546 grad_norm: 21.5802 loss: 2.4341 task0.loss_heatmap: 0.4038 task0.loss_bbox: 1.4993 task0.loss_iou: 0.1146 task0.loss_reg_iou: 0.4164 2023/09/17 19:52:15 - mmengine - INFO - Epoch(train) [12][3700/3953] lr: 8.7924e-08 eta: 0:02:07 time: 0.4934 data_time: 0.0042 memory: 8460 grad_norm: 19.2245 loss: 2.7815 task0.loss_heatmap: 0.4440 task0.loss_bbox: 1.7288 task0.loss_iou: 0.1256 task0.loss_reg_iou: 0.4831 2023/09/17 19:52:40 - mmengine - INFO - Epoch(train) [12][3750/3953] lr: 5.7006e-08 eta: 0:01:42 time: 0.5115 data_time: 0.0042 memory: 8124 grad_norm: 20.8187 loss: 2.7486 task0.loss_heatmap: 0.5100 task0.loss_bbox: 1.6466 task0.loss_iou: 0.1216 task0.loss_reg_iou: 0.4704 2023/09/17 19:53:05 - mmengine - INFO - Epoch(train) [12][3800/3953] lr: 3.2855e-08 eta: 0:01:17 time: 0.5025 data_time: 0.0045 memory: 8305 grad_norm: 19.9588 loss: 2.6553 task0.loss_heatmap: 0.4470 task0.loss_bbox: 1.6146 task0.loss_iou: 0.1255 task0.loss_reg_iou: 0.4683 2023/09/17 19:53:30 - mmengine - INFO - Epoch(train) [12][3850/3953] lr: 1.5474e-08 eta: 0:00:51 time: 0.4972 data_time: 0.0043 memory: 8479 grad_norm: 22.3239 loss: 2.7140 task0.loss_heatmap: 0.4633 task0.loss_bbox: 1.6701 task0.loss_iou: 0.1237 task0.loss_reg_iou: 0.4569 2023/09/17 19:53:55 - mmengine - INFO - Epoch(train) [12][3900/3953] lr: 4.8603e-09 eta: 0:00:26 time: 0.5000 data_time: 0.0044 memory: 8095 grad_norm: 21.1350 loss: 2.7780 task0.loss_heatmap: 0.4863 task0.loss_bbox: 1.6977 task0.loss_iou: 0.1263 task0.loss_reg_iou: 0.4677 2023/09/17 19:54:20 - mmengine - INFO - Epoch(train) [12][3950/3953] lr: 1.0156e-09 eta: 0:00:01 time: 0.4951 data_time: 0.0042 memory: 8526 grad_norm: 20.2039 loss: 2.7266 task0.loss_heatmap: 0.5007 task0.loss_bbox: 1.6471 task0.loss_iou: 0.1200 task0.loss_reg_iou: 0.4588 2023/09/17 19:54:22 - mmengine - INFO - Exp name: dsvt_voxel032_res-second_secfpn_8xb1-cyclic-12e_waymoD5-3d-3class_20230917_102130 2023/09/17 19:54:22 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/09/17 19:54:42 - mmengine - INFO - Epoch(val) [12][ 50/1250] eta: 0:07:34 time: 0.3787 data_time: 0.0064 memory: 8044 2023/09/17 19:55:02 - mmengine - INFO - Epoch(val) [12][ 100/1250] eta: 0:07:21 time: 0.3887 data_time: 0.0050 memory: 4043 2023/09/17 19:55:21 - mmengine - INFO - Epoch(val) [12][ 150/1250] eta: 0:07:02 time: 0.3862 data_time: 0.0050 memory: 4062 2023/09/17 19:55:40 - mmengine - INFO - Epoch(val) [12][ 200/1250] eta: 0:06:42 time: 0.3786 data_time: 0.0049 memory: 4051 2023/09/17 19:55:58 - mmengine - INFO - Epoch(val) [12][ 250/1250] eta: 0:06:17 time: 0.3552 data_time: 0.0048 memory: 4033 2023/09/17 19:56:17 - mmengine - INFO - Epoch(val) [12][ 300/1250] eta: 0:05:59 time: 0.3804 data_time: 0.0046 memory: 4050 2023/09/17 19:56:36 - mmengine - INFO - Epoch(val) [12][ 350/1250] eta: 0:05:40 time: 0.3813 data_time: 0.0048 memory: 4057 2023/09/17 19:56:55 - mmengine - INFO - Epoch(val) [12][ 400/1250] eta: 0:05:22 time: 0.3895 data_time: 0.0049 memory: 4060 2023/09/17 19:57:16 - mmengine - INFO - Epoch(val) [12][ 450/1250] eta: 0:05:06 time: 0.4065 data_time: 0.0048 memory: 4048 2023/09/17 19:57:34 - mmengine - INFO - Epoch(val) [12][ 500/1250] eta: 0:04:46 time: 0.3687 data_time: 0.0049 memory: 4041 2023/09/17 19:57:53 - mmengine - INFO - Epoch(val) [12][ 550/1250] eta: 0:04:26 time: 0.3693 data_time: 0.0048 memory: 4042 2023/09/17 19:58:11 - mmengine - INFO - Epoch(val) [12][ 600/1250] eta: 0:04:06 time: 0.3682 data_time: 0.0047 memory: 4057 2023/09/17 19:58:31 - mmengine - INFO - Epoch(val) [12][ 650/1250] eta: 0:03:48 time: 0.4048 data_time: 0.0046 memory: 4065 2023/09/17 19:58:51 - mmengine - INFO - Epoch(val) [12][ 700/1250] eta: 0:03:30 time: 0.3910 data_time: 0.0047 memory: 4042 2023/09/17 19:59:11 - mmengine - INFO - Epoch(val) [12][ 750/1250] eta: 0:03:11 time: 0.3972 data_time: 0.0048 memory: 4051 2023/09/17 19:59:29 - mmengine - INFO - Epoch(val) [12][ 800/1250] eta: 0:02:52 time: 0.3721 data_time: 0.0047 memory: 4055 2023/09/17 19:59:49 - mmengine - INFO - Epoch(val) [12][ 850/1250] eta: 0:02:32 time: 0.3858 data_time: 0.0048 memory: 4058 2023/09/17 20:00:07 - mmengine - INFO - Epoch(val) [12][ 900/1250] eta: 0:02:13 time: 0.3589 data_time: 0.0048 memory: 4049 2023/09/17 20:00:25 - mmengine - INFO - Epoch(val) [12][ 950/1250] eta: 0:01:54 time: 0.3655 data_time: 0.0049 memory: 4054 2023/09/17 20:00:44 - mmengine - INFO - Epoch(val) [12][1000/1250] eta: 0:01:35 time: 0.3777 data_time: 0.0047 memory: 4056 2023/09/17 20:01:03 - mmengine - INFO - Epoch(val) [12][1050/1250] eta: 0:01:16 time: 0.3774 data_time: 0.0050 memory: 4043 2023/09/17 20:01:20 - mmengine - INFO - Epoch(val) [12][1100/1250] eta: 0:00:56 time: 0.3547 data_time: 0.0048 memory: 4032 2023/09/17 20:01:40 - mmengine - INFO - Epoch(val) [12][1150/1250] eta: 0:00:37 time: 0.3834 data_time: 0.0049 memory: 4041 2023/09/17 20:01:59 - mmengine - INFO - Epoch(val) [12][1200/1250] eta: 0:00:18 time: 0.3808 data_time: 0.0049 memory: 4047 2023/09/17 20:02:18 - mmengine - INFO - Epoch(val) [12][1250/1250] eta: 0:00:00 time: 0.3881 data_time: 0.0048 memory: 4055 2023/09/17 20:02:22 - mmengine - INFO - Start converting ... 2023/09/17 20:09:18 - mmengine - INFO - Multi-thread version modified by Lue Fan from commit 17f070076dad149766357b31e25d27cf8b5da6ac 39978 examples found. OBJECT_TYPE_TYPE_VEHICLE_LEVEL_1: [mAP 0.75255] [mAPH 0.747558] OBJECT_TYPE_TYPE_VEHICLE_LEVEL_2: [mAP 0.667622] [mAPH 0.663084] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_1: [mAP 0.791542] [mAPH 0.716668] OBJECT_TYPE_TYPE_PEDESTRIAN_LEVEL_2: [mAP 0.713486] [mAPH 0.643403] OBJECT_TYPE_TYPE_SIGN_LEVEL_1: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_SIGN_LEVEL_2: [mAP 0] [mAPH 0] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_1: [mAP 0.72077] [mAPH 0.708553] OBJECT_TYPE_TYPE_CYCLIST_LEVEL_2: [mAP 0.693748] [mAPH 0.681981] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_1: [mAP 0.918402] [mAPH 0.913961] RANGE_TYPE_VEHICLE_[0, 30)_LEVEL_2: [mAP 0.905627] [mAPH 0.90124] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_1: [mAP 0.736392] [mAPH 0.730772] RANGE_TYPE_VEHICLE_[30, 50)_LEVEL_2: [mAP 0.671197] [mAPH 0.665984] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_1: [mAP 0.513401] [mAPH 0.506765] RANGE_TYPE_VEHICLE_[50, +inf)_LEVEL_2: [mAP 0.395546] [mAPH 0.390236] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_1: [mAP 0.840726] [mAPH 0.775005] RANGE_TYPE_PEDESTRIAN_[0, 30)_LEVEL_2: [mAP 0.802641] [mAPH 0.738226] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_1: [mAP 0.776954] [mAPH 0.696244] RANGE_TYPE_PEDESTRIAN_[30, 50)_LEVEL_2: [mAP 0.709281] [mAPH 0.6343] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_1: [mAP 0.693619] [mAPH 0.594595] RANGE_TYPE_PEDESTRIAN_[50, +inf)_LEVEL_2: [mAP 0.559213] [mAPH 0.475911] RANGE_TYPE_SIGN_[0, 30)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[0, 30)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[30, 50)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_1: [mAP 0] [mAPH 0] RANGE_TYPE_SIGN_[50, +inf)_LEVEL_2: [mAP 0] [mAPH 0] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_1: [mAP 0.812011] [mAPH 0.801081] RANGE_TYPE_CYCLIST_[0, 30)_LEVEL_2: [mAP 0.806185] [mAPH 0.795333] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_1: [mAP 0.676067] [mAPH 0.664298] RANGE_TYPE_CYCLIST_[30, 50)_LEVEL_2: [mAP 0.63765] [mAPH 0.626542] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_1: [mAP 0.556127] [mAPH 0.534861] RANGE_TYPE_CYCLIST_[50, +inf)_LEVEL_2: [mAP 0.518019] [mAPH 0.498192] Eval Using 306s 2023/09/17 20:09:19 - mmengine - INFO - Epoch(val) [12][1250/1250] Waymo metric/Vehicle/L1 mAP: 0.7526 Waymo metric/Vehicle/L1 mAPH: 0.7476 Waymo metric/Vehicle/L2 mAP: 0.6676 Waymo metric/Vehicle/L2 mAPH: 0.6631 Waymo metric/Pedestrian/L1 mAP: 0.7915 Waymo metric/Pedestrian/L1 mAPH: 0.7167 Waymo metric/Pedestrian/L2 mAP: 0.7135 Waymo metric/Pedestrian/L2 mAPH: 0.6434 Waymo metric/Sign/L1 mAP: 0.0000 Waymo metric/Sign/L1 mAPH: 0.0000 Waymo metric/Sign/L2 mAP: 0.0000 Waymo metric/Sign/L2 mAPH: 0.0000 Waymo metric/Cyclist/L1 mAP: 0.7208 Waymo metric/Cyclist/L1 mAPH: 0.7086 Waymo metric/Cyclist/L2 mAP: 0.6937 Waymo metric/Cyclist/L2 mAPH: 0.6820 Waymo metric/Overall/L1 mAP: 0.7550 Waymo metric/Overall/L1 mAPH: 0.7243 Waymo metric/Overall/L2 mAP: 0.6916 Waymo metric/Overall/L2 mAPH: 0.6628 data_time: 0.0049 time: 0.3795