2023/05/12 23:36:16 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1090418059 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /nvme/share/cuda-11.7 NVCC: Cuda compilation tools, release 11.7, V11.7.64 GCC: gcc (GCC) 9.4.0 PyTorch: 2.0.0+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=2.0.0, 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: 0.15.1+cu117 OpenCV: 4.7.0 MMEngine: 0.7.2 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/05/12 23:36:20 - mmengine - INFO - Config: dataset_type = 'SemanticKittiDataset' data_root = 'data/semantickitti/' class_names = [ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ] labels_map = dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }) metainfo = dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259) input_modality = dict(use_lidar=True, use_camera=False) backend_args = None train_pipeline = [ dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti'), dict(type='PointSegClassMapping'), dict( type='RandomChoice', transforms=[[{ 'type': 'LaserMix', 'num_areas': [3, 4, 5, 6], 'pitch_angles': [-25, 3], 'pre_transform': [{ 'type': 'LoadPointsFromFile', 'coord_type': 'LIDAR', 'load_dim': 4, 'use_dim': 4 }, { 'type': 'LoadAnnotations3D', 'with_bbox_3d': False, 'with_label_3d': False, 'with_seg_3d': True, 'seg_3d_dtype': 'np.int32', 'seg_offset': 65536, 'dataset_type': 'semantickitti' }, { 'type': 'PointSegClassMapping' }], 'prob': 1 }], [{ 'type': 'PolarMix', 'instance_classes': [0, 1, 2, 3, 4, 5, 6, 7], 'swap_ratio': 0.5, 'rotate_paste_ratio': 1.0, 'pre_transform': [{ 'type': 'LoadPointsFromFile', 'coord_type': 'LIDAR', 'load_dim': 4, 'use_dim': 4 }, { 'type': 'LoadAnnotations3D', 'with_bbox_3d': False, 'with_label_3d': False, 'with_seg_3d': True, 'seg_3d_dtype': 'np.int32', 'seg_offset': 65536, 'dataset_type': 'semantickitti' }, { 'type': 'PointSegClassMapping' }], 'prob': 1 }]], prob=[0.5, 0.5]), dict( type='GlobalRotScaleTrans', rot_range=[0.0, 6.28318531], scale_ratio_range=[0.95, 1.05], translation_std=[0, 0, 0]), dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ] test_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict(type='Pack3DDetInputs', keys=['points']) ] eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict(type='Pack3DDetInputs', keys=['points']) ] tta_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict( type='TestTimeAug', transforms=[[{ 'type': 'RandomFlip3D', 'sync_2d': False, 'flip_ratio_bev_horizontal': 0.0, 'flip_ratio_bev_vertical': 0.0 }, { 'type': 'RandomFlip3D', 'sync_2d': False, 'flip_ratio_bev_horizontal': 0.0, 'flip_ratio_bev_vertical': 1.0 }, { 'type': 'RandomFlip3D', 'sync_2d': False, 'flip_ratio_bev_horizontal': 1.0, 'flip_ratio_bev_vertical': 0.0 }, { 'type': 'RandomFlip3D', 'sync_2d': False, 'flip_ratio_bev_horizontal': 1.0, 'flip_ratio_bev_vertical': 1.0 }], [{ 'type': 'GlobalRotScaleTrans', 'rot_range': [-0.78539816, -0.78539816], 'scale_ratio_range': [0.95, 0.95], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [-0.78539816, -0.78539816], 'scale_ratio_range': [1.0, 1.0], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [-0.78539816, -0.78539816], 'scale_ratio_range': [1.05, 1.05], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.0, 0.0], 'scale_ratio_range': [0.95, 0.95], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.0, 0.0], 'scale_ratio_range': [1.0, 1.0], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.0, 0.0], 'scale_ratio_range': [1.05, 1.05], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.78539816, 0.78539816], 'scale_ratio_range': [0.95, 0.95], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.78539816, 0.78539816], 'scale_ratio_range': [1.0, 1.0], 'translation_std': [0, 0, 0] }, { 'type': 'GlobalRotScaleTrans', 'rot_range': [0.78539816, 0.78539816], 'scale_ratio_range': [1.05, 1.05], 'translation_std': [0, 0, 0] }], [{ 'type': 'Pack3DDetInputs', 'keys': ['points'] }]]) ] train_dataloader = dict( batch_size=2, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='SemanticKittiDataset', data_root='data/semantickitti/', ann_file='semantickitti_infos_train.pkl', pipeline=[ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti'), dict(type='PointSegClassMapping'), dict( type='RandomChoice', transforms=[[{ 'type': 'LaserMix', 'num_areas': [3, 4, 5, 6], 'pitch_angles': [-25, 3], 'pre_transform': [{ 'type': 'LoadPointsFromFile', 'coord_type': 'LIDAR', 'load_dim': 4, 'use_dim': 4 }, { 'type': 'LoadAnnotations3D', 'with_bbox_3d': False, 'with_label_3d': False, 'with_seg_3d': True, 'seg_3d_dtype': 'np.int32', 'seg_offset': 65536, 'dataset_type': 'semantickitti' }, { 'type': 'PointSegClassMapping' }], 'prob': 1 }], [{ 'type': 'PolarMix', 'instance_classes': [0, 1, 2, 3, 4, 5, 6, 7], 'swap_ratio': 0.5, 'rotate_paste_ratio': 1.0, 'pre_transform': [{ 'type': 'LoadPointsFromFile', 'coord_type': 'LIDAR', 'load_dim': 4, 'use_dim': 4 }, { 'type': 'LoadAnnotations3D', 'with_bbox_3d': False, 'with_label_3d': False, 'with_seg_3d': True, 'seg_3d_dtype': 'np.int32', 'seg_offset': 65536, 'dataset_type': 'semantickitti' }, { 'type': 'PointSegClassMapping' }], 'prob': 1 }]], prob=[0.5, 0.5]), dict( type='GlobalRotScaleTrans', rot_range=[0.0, 6.28318531], scale_ratio_range=[0.95, 1.05], translation_std=[0, 0, 0]), dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ], metainfo=dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259), modality=dict(use_lidar=True, use_camera=False), ignore_index=19, backend_args=None)) test_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='SemanticKittiDataset', data_root='data/semantickitti/', ann_file='semantickitti_infos_val.pkl', pipeline=[ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict(type='Pack3DDetInputs', keys=['points']) ], metainfo=dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259), modality=dict(use_lidar=True, use_camera=False), ignore_index=19, test_mode=True, backend_args=None)) val_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='SemanticKittiDataset', data_root='data/semantickitti/', ann_file='semantickitti_infos_val.pkl', pipeline=[ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict(type='Pack3DDetInputs', keys=['points']) ], metainfo=dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259), modality=dict(use_lidar=True, use_camera=False), ignore_index=19, test_mode=True, backend_args=None)) val_evaluator = dict(type='SegMetric') test_evaluator = dict(type='SegMetric') vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='Det3DLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') tta_model = dict(type='Seg3DTTAModel') model = dict( type='MinkUNet', data_preprocessor=dict( type='Det3DDataPreprocessor', voxel=True, voxel_type='minkunet', batch_first=False, max_voxels=None, voxel_layer=dict( max_num_points=-1, point_cloud_range=[-100, -100, -20, 100, 100, 20], voxel_size=[0.05, 0.05, 0.05], max_voxels=(-1, -1))), backbone=dict( type='MinkUNetBackbone', in_channels=4, num_stages=4, base_channels=32, encoder_channels=[32, 64, 128, 256], encoder_blocks=[2, 3, 4, 6], decoder_channels=[256, 128, 96, 96], decoder_blocks=[2, 2, 2, 2], sparseconv_backends='torchsparse'), decode_head=dict( type='MinkUNetHead', channels=96, num_classes=19, dropout_ratio=0, loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True), ignore_index=19), train_cfg=dict(), test_cfg=dict()) lr = 0.008 optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.008, weight_decay=0.01), clip_grad=dict(max_norm=10, norm_type=2)) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='MultiStepLR', begin=0, end=36, by_epoch=True, milestones=[24, 32], gamma=0.1) ] auto_scale_lr = dict(enable=False, base_batch_size=32) default_scope = 'mmdet3d' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='Det3DVisualizationHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False launcher = 'pytorch' work_dir = './work_dirs/minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti' 2023/05/12 23:36:28 - 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 -------------------- 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_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_train: (VERY_LOW ) CheckpointHook -------------------- 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_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/05/12 23:36:32 - mmengine - WARNING - The prefix is not set in metric class SegMetric. Name of parameter - Initialization information backbone.conv_input.0.net.0.kernel - torch.Size([27, 4, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.0.kernel - torch.Size([8, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.0.kernel - torch.Size([8, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.0.kernel - torch.Size([27, 32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.0.kernel - torch.Size([32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.0.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.0.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.3.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.0.kernel - torch.Size([8, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.0.kernel - torch.Size([27, 64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.0.kernel - torch.Size([64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.3.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.4.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.0.kernel - torch.Size([8, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.0.kernel - torch.Size([27, 128, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.0.kernel - torch.Size([128, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.3.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.4.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.5.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.6.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.0.kernel - torch.Size([8, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.0.kernel - torch.Size([27, 384, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.0.kernel - torch.Size([384, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.0.kernel - torch.Size([8, 256, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.0.kernel - torch.Size([27, 192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.0.kernel - torch.Size([192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.0.kernel - torch.Size([8, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.0.kernel - torch.Size([27, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.0.kernel - torch.Size([128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.0.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.0.kernel - torch.Size([8, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.0.kernel - torch.Size([27, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.0.kernel - torch.Size([128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.0.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet decode_head.conv_seg.weight - torch.Size([19, 96]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/05/12 23:36:39 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/05/12 23:36:39 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/05/12 23:36:39 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/work_dirs/minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti. 2023/05/12 23:38:28 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 8.0000e-03 eta: 1 day, 2:05:58 time: 2.1848 data_time: 0.0069 memory: 4956 grad_norm: 0.7773 loss: 1.4572 loss_sem_seg: 1.4572 2023/05/12 23:40:18 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 8.0000e-03 eta: 1 day, 2:07:06 time: 2.1930 data_time: 0.0033 memory: 5061 grad_norm: 0.6851 loss: 1.0384 loss_sem_seg: 1.0384 2023/05/12 23:42:16 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 8.0000e-03 eta: 1 day, 2:49:32 time: 2.3746 data_time: 0.0031 memory: 4803 grad_norm: 0.6426 loss: 0.8785 loss_sem_seg: 0.8785 2023/05/12 23:44:15 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 8.0000e-03 eta: 1 day, 3:09:03 time: 2.3706 data_time: 0.0033 memory: 5718 grad_norm: 0.6582 loss: 0.7970 loss_sem_seg: 0.7970 2023/05/12 23:46:16 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 8.0000e-03 eta: 1 day, 3:26:06 time: 2.4135 data_time: 0.0033 memory: 4864 grad_norm: 0.7684 loss: 0.7647 loss_sem_seg: 0.7647 2023/05/12 23:48:16 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 8.0000e-03 eta: 1 day, 3:36:42 time: 2.4127 data_time: 0.0032 memory: 5401 grad_norm: 0.8167 loss: 0.6869 loss_sem_seg: 0.6869 2023/05/12 23:50:17 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 8.0000e-03 eta: 1 day, 3:43:04 time: 2.4066 data_time: 0.0032 memory: 5004 grad_norm: 0.8604 loss: 0.6718 loss_sem_seg: 0.6718 2023/05/12 23:52:17 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 8.0000e-03 eta: 1 day, 3:48:05 time: 2.4149 data_time: 0.0032 memory: 4829 grad_norm: 0.8243 loss: 0.6548 loss_sem_seg: 0.6548 2023/05/12 23:54:18 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 8.0000e-03 eta: 1 day, 3:51:48 time: 2.4182 data_time: 0.0033 memory: 5421 grad_norm: 0.7920 loss: 0.6126 loss_sem_seg: 0.6126 2023/05/12 23:56:19 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 8.0000e-03 eta: 1 day, 3:54:10 time: 2.4155 data_time: 0.0033 memory: 4796 grad_norm: 0.6269 loss: 0.6112 loss_sem_seg: 0.6112 2023/05/12 23:58:17 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 8.0000e-03 eta: 1 day, 3:52:01 time: 2.3574 data_time: 0.0033 memory: 4603 grad_norm: 0.6743 loss: 0.5709 loss_sem_seg: 0.5709 2023/05/13 00:00:17 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 8.0000e-03 eta: 1 day, 3:53:08 time: 2.4125 data_time: 0.0033 memory: 5327 grad_norm: 0.6312 loss: 0.5419 loss_sem_seg: 0.5419 2023/05/13 00:02:20 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 8.0000e-03 eta: 1 day, 3:56:21 time: 2.4600 data_time: 0.0032 memory: 5344 grad_norm: 0.6495 loss: 0.5515 loss_sem_seg: 0.5515 2023/05/13 00:04:22 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 8.0000e-03 eta: 1 day, 3:57:16 time: 2.4291 data_time: 0.0034 memory: 5047 grad_norm: 0.5785 loss: 0.5276 loss_sem_seg: 0.5276 2023/05/13 00:06:21 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 8.0000e-03 eta: 1 day, 3:55:30 time: 2.3807 data_time: 0.0033 memory: 4892 grad_norm: 0.6394 loss: 0.5169 loss_sem_seg: 0.5169 2023/05/13 00:08:23 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 8.0000e-03 eta: 1 day, 3:56:36 time: 2.4462 data_time: 0.0033 memory: 4775 grad_norm: 0.6307 loss: 0.5275 loss_sem_seg: 0.5275 2023/05/13 00:10:23 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 8.0000e-03 eta: 1 day, 3:54:58 time: 2.3890 data_time: 0.0034 memory: 4918 grad_norm: 0.6581 loss: 0.4991 loss_sem_seg: 0.4991 2023/05/13 00:12:14 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 8.0000e-03 eta: 1 day, 3:47:02 time: 2.2287 data_time: 0.0033 memory: 4725 grad_norm: 0.5149 loss: 0.4814 loss_sem_seg: 0.4814 2023/05/13 00:14:04 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 8.0000e-03 eta: 1 day, 3:38:30 time: 2.1953 data_time: 0.0033 memory: 5142 grad_norm: 0.5337 loss: 0.4743 loss_sem_seg: 0.4743 2023/05/13 00:15:56 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 00:15:56 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 8.0000e-03 eta: 1 day, 3:32:13 time: 2.2403 data_time: 0.0033 memory: 4929 grad_norm: 0.6004 loss: 0.4764 loss_sem_seg: 0.4764 2023/05/13 00:17:51 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 8.0000e-03 eta: 1 day, 3:28:16 time: 2.2975 data_time: 0.0034 memory: 5034 grad_norm: 0.5389 loss: 0.4859 loss_sem_seg: 0.4859 2023/05/13 00:19:39 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 8.0000e-03 eta: 1 day, 3:20:34 time: 2.1738 data_time: 0.0033 memory: 4709 grad_norm: 0.4334 loss: 0.4391 loss_sem_seg: 0.4391 2023/05/13 00:21:35 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 8.0000e-03 eta: 1 day, 3:17:49 time: 2.3199 data_time: 0.0031 memory: 4841 grad_norm: 0.5015 loss: 0.4391 loss_sem_seg: 0.4391 2023/05/13 00:23:25 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 00:23:25 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/05/13 00:24:15 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:06:57 time: 0.9102 data_time: 0.0030 memory: 4679 2023/05/13 00:24:59 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:06:03 time: 0.8670 data_time: 0.0021 memory: 915 2023/05/13 00:25:43 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:05:18 time: 0.8873 data_time: 0.0020 memory: 919 2023/05/13 00:26:27 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:04:32 time: 0.8679 data_time: 0.0021 memory: 907 2023/05/13 00:27:10 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:03:48 time: 0.8779 data_time: 0.0020 memory: 928 2023/05/13 00:27:53 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:03:03 time: 0.8463 data_time: 0.0020 memory: 883 2023/05/13 00:28:36 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:02:19 time: 0.8726 data_time: 0.0020 memory: 898 2023/05/13 00:29:17 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:01:34 time: 0.8083 data_time: 0.0020 memory: 903 2023/05/13 00:30:02 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:51 time: 0.9013 data_time: 0.0020 memory: 916 2023/05/13 00:30:44 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:07 time: 0.8421 data_time: 0.0021 memory: 902 2023/05/13 00:31:09 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9311 | 0.0000 | 0.0525 | 0.1408 | 0.1505 | 0.0395 | 0.1865 | 0.0000 | 0.8983 | 0.0781 | 0.7468 | 0.0000 | 0.8635 | 0.4574 | 0.8728 | 0.4999 | 0.7508 | 0.5434 | 0.2572 | 0.3931 | 0.8955 | 0.4429 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 00:31:09 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.9311 bicycle: 0.0000 motorcycle: 0.0525 truck: 0.1408 bus: 0.1505 person: 0.0395 bicyclist: 0.1865 motorcyclist: 0.0000 road: 0.8983 parking: 0.0781 sidewalk: 0.7468 other-ground: 0.0000 building: 0.8635 fence: 0.4574 vegetation: 0.8728 trunck: 0.4999 terrian: 0.7508 pole: 0.5434 traffic-sign: 0.2572 miou: 0.3931 acc: 0.8955 acc_cls: 0.4429 data_time: 0.0021 time: 0.8559 2023/05/13 00:33:11 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 8.0000e-03 eta: 1 day, 3:17:04 time: 2.4252 data_time: 0.0038 memory: 4669 grad_norm: 0.5029 loss: 0.4370 loss_sem_seg: 0.4370 2023/05/13 00:35:10 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 8.0000e-03 eta: 1 day, 3:16:17 time: 2.3931 data_time: 0.0032 memory: 4963 grad_norm: 0.4969 loss: 0.4364 loss_sem_seg: 0.4364 2023/05/13 00:37:12 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 8.0000e-03 eta: 1 day, 3:16:23 time: 2.4306 data_time: 0.0033 memory: 4799 grad_norm: 0.4114 loss: 0.4170 loss_sem_seg: 0.4170 2023/05/13 00:39:13 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 8.0000e-03 eta: 1 day, 3:16:22 time: 2.4326 data_time: 0.0033 memory: 5325 grad_norm: 0.5126 loss: 0.4236 loss_sem_seg: 0.4236 2023/05/13 00:41:14 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 8.0000e-03 eta: 1 day, 3:15:39 time: 2.4083 data_time: 0.0033 memory: 4955 grad_norm: 0.4218 loss: 0.4103 loss_sem_seg: 0.4103 2023/05/13 00:43:11 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 8.0000e-03 eta: 1 day, 3:13:21 time: 2.3440 data_time: 0.0034 memory: 4783 grad_norm: 0.4832 loss: 0.4329 loss_sem_seg: 0.4329 2023/05/13 00:45:13 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 8.0000e-03 eta: 1 day, 3:13:26 time: 2.4497 data_time: 0.0033 memory: 4745 grad_norm: 0.4800 loss: 0.3796 loss_sem_seg: 0.3796 2023/05/13 00:47:12 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 8.0000e-03 eta: 1 day, 3:11:48 time: 2.3765 data_time: 0.0032 memory: 4978 grad_norm: 0.4434 loss: 0.3986 loss_sem_seg: 0.3986 2023/05/13 00:49:14 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 8.0000e-03 eta: 1 day, 3:11:19 time: 2.4328 data_time: 0.0033 memory: 4980 grad_norm: 0.4476 loss: 0.4043 loss_sem_seg: 0.4043 2023/05/13 00:51:11 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 8.0000e-03 eta: 1 day, 3:08:56 time: 2.3429 data_time: 0.0032 memory: 4717 grad_norm: 0.3380 loss: 0.3992 loss_sem_seg: 0.3992 2023/05/13 00:53:04 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 8.0000e-03 eta: 1 day, 3:04:59 time: 2.2625 data_time: 0.0032 memory: 5295 grad_norm: 0.4109 loss: 0.4035 loss_sem_seg: 0.4035 2023/05/13 00:54:56 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 8.0000e-03 eta: 1 day, 3:00:46 time: 2.2427 data_time: 0.0032 memory: 5058 grad_norm: 0.3615 loss: 0.4002 loss_sem_seg: 0.4002 2023/05/13 00:56:45 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 8.0000e-03 eta: 1 day, 2:55:20 time: 2.1711 data_time: 0.0039 memory: 4656 grad_norm: 0.4198 loss: 0.3930 loss_sem_seg: 0.3930 2023/05/13 00:58:23 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 8.0000e-03 eta: 1 day, 2:46:10 time: 1.9534 data_time: 0.0034 memory: 5256 grad_norm: 0.3117 loss: 0.3915 loss_sem_seg: 0.3915 2023/05/13 01:00:23 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 8.0000e-03 eta: 1 day, 2:45:35 time: 2.4185 data_time: 0.0033 memory: 4782 grad_norm: 0.3784 loss: 0.3963 loss_sem_seg: 0.3963 2023/05/13 01:02:12 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 8.0000e-03 eta: 1 day, 2:40:38 time: 2.1684 data_time: 0.0034 memory: 4873 grad_norm: 0.3857 loss: 0.3816 loss_sem_seg: 0.3816 2023/05/13 01:02:20 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 01:03:57 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 8.0000e-03 eta: 1 day, 2:34:39 time: 2.0981 data_time: 0.0032 memory: 5417 grad_norm: 0.3383 loss: 0.3951 loss_sem_seg: 0.3951 2023/05/13 01:05:41 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 8.0000e-03 eta: 1 day, 2:28:39 time: 2.0843 data_time: 0.0033 memory: 4470 grad_norm: 0.3560 loss: 0.4165 loss_sem_seg: 0.4165 2023/05/13 01:07:25 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 8.0000e-03 eta: 1 day, 2:22:44 time: 2.0767 data_time: 0.0033 memory: 5091 grad_norm: 0.3802 loss: 0.3770 loss_sem_seg: 0.3770 2023/05/13 01:09:10 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 8.0000e-03 eta: 1 day, 2:17:19 time: 2.0961 data_time: 0.0032 memory: 5156 grad_norm: 0.3529 loss: 0.3584 loss_sem_seg: 0.3584 2023/05/13 01:11:04 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 8.0000e-03 eta: 1 day, 2:14:55 time: 2.2850 data_time: 0.0033 memory: 4906 grad_norm: 0.3748 loss: 0.3604 loss_sem_seg: 0.3604 2023/05/13 01:13:04 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 8.0000e-03 eta: 1 day, 2:14:15 time: 2.4014 data_time: 0.0034 memory: 5211 grad_norm: 0.3208 loss: 0.3911 loss_sem_seg: 0.3911 2023/05/13 01:15:05 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 8.0000e-03 eta: 1 day, 2:13:41 time: 2.4121 data_time: 0.0033 memory: 4663 grad_norm: 0.4043 loss: 0.3786 loss_sem_seg: 0.3786 2023/05/13 01:16:56 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 01:16:56 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/05/13 01:17:48 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:07:04 time: 0.9242 data_time: 0.0021 memory: 4892 2023/05/13 01:18:34 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:06:14 time: 0.9061 data_time: 0.0021 memory: 915 2023/05/13 01:19:17 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:05:22 time: 0.8635 data_time: 0.0021 memory: 919 2023/05/13 01:20:01 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:04:36 time: 0.8830 data_time: 0.0021 memory: 907 2023/05/13 01:20:46 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:03:52 time: 0.9101 data_time: 0.0020 memory: 928 2023/05/13 01:21:29 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:03:05 time: 0.8485 data_time: 0.0021 memory: 883 2023/05/13 01:22:11 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:02:20 time: 0.8429 data_time: 0.0022 memory: 898 2023/05/13 01:22:56 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:01:36 time: 0.8902 data_time: 0.0021 memory: 903 2023/05/13 01:23:40 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:52 time: 0.8835 data_time: 0.0021 memory: 916 2023/05/13 01:24:20 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:07 time: 0.8106 data_time: 0.0021 memory: 902 2023/05/13 01:24:45 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9318 | 0.0269 | 0.3660 | 0.2711 | 0.2106 | 0.3357 | 0.0064 | 0.0076 | 0.9099 | 0.3152 | 0.7841 | 0.0008 | 0.8929 | 0.6058 | 0.8656 | 0.6399 | 0.6955 | 0.5861 | 0.4313 | 0.4675 | 0.9020 | 0.5642 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 01:24:45 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.9318 bicycle: 0.0269 motorcycle: 0.3660 truck: 0.2711 bus: 0.2106 person: 0.3357 bicyclist: 0.0064 motorcyclist: 0.0076 road: 0.9099 parking: 0.3152 sidewalk: 0.7841 other-ground: 0.0008 building: 0.8929 fence: 0.6058 vegetation: 0.8656 trunck: 0.6399 terrian: 0.6955 pole: 0.5861 traffic-sign: 0.4313 miou: 0.4675 acc: 0.9020 acc_cls: 0.5642 data_time: 0.0021 time: 0.8320 2023/05/13 01:26:42 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 8.0000e-03 eta: 1 day, 2:11:40 time: 2.3446 data_time: 0.0039 memory: 4856 grad_norm: 0.3431 loss: 0.3761 loss_sem_seg: 0.3761 2023/05/13 01:28:43 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 8.0000e-03 eta: 1 day, 2:11:08 time: 2.4248 data_time: 0.0033 memory: 4637 grad_norm: 0.2736 loss: 0.3434 loss_sem_seg: 0.3434 2023/05/13 01:30:46 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 8.0000e-03 eta: 1 day, 2:10:52 time: 2.4506 data_time: 0.0033 memory: 5673 grad_norm: 0.3635 loss: 0.3709 loss_sem_seg: 0.3709 2023/05/13 01:32:39 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 8.0000e-03 eta: 1 day, 2:08:15 time: 2.2734 data_time: 0.0033 memory: 5106 grad_norm: 0.2873 loss: 0.3641 loss_sem_seg: 0.3641 2023/05/13 01:34:28 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 8.0000e-03 eta: 1 day, 2:04:19 time: 2.1694 data_time: 0.0034 memory: 5199 grad_norm: 0.3292 loss: 0.3471 loss_sem_seg: 0.3471 2023/05/13 01:36:07 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 8.0000e-03 eta: 1 day, 1:58:15 time: 1.9922 data_time: 0.0033 memory: 5141 grad_norm: 0.2801 loss: 0.3660 loss_sem_seg: 0.3660 2023/05/13 01:37:58 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 8.0000e-03 eta: 1 day, 1:55:01 time: 2.2100 data_time: 0.0033 memory: 5328 grad_norm: 0.3092 loss: 0.3499 loss_sem_seg: 0.3499 2023/05/13 01:39:56 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 8.0000e-03 eta: 1 day, 1:53:37 time: 2.3582 data_time: 0.0034 memory: 5127 grad_norm: 0.2723 loss: 0.3535 loss_sem_seg: 0.3535 2023/05/13 01:41:53 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 8.0000e-03 eta: 1 day, 1:52:03 time: 2.3456 data_time: 0.0035 memory: 4894 grad_norm: 0.2541 loss: 0.3336 loss_sem_seg: 0.3336 2023/05/13 01:43:54 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 8.0000e-03 eta: 1 day, 1:51:23 time: 2.4249 data_time: 0.0033 memory: 4888 grad_norm: 0.3282 loss: 0.3676 loss_sem_seg: 0.3676 2023/05/13 01:45:55 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 8.0000e-03 eta: 1 day, 1:50:34 time: 2.4153 data_time: 0.0032 memory: 4936 grad_norm: 0.2883 loss: 0.3476 loss_sem_seg: 0.3476 2023/05/13 01:47:58 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 8.0000e-03 eta: 1 day, 1:50:05 time: 2.4491 data_time: 0.0032 memory: 5368 grad_norm: 0.3119 loss: 0.3760 loss_sem_seg: 0.3760 2023/05/13 01:48:17 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 01:49:58 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 8.0000e-03 eta: 1 day, 1:49:00 time: 2.3996 data_time: 0.0034 memory: 4852 grad_norm: 0.3056 loss: 0.3380 loss_sem_seg: 0.3380 2023/05/13 01:51:59 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 8.0000e-03 eta: 1 day, 1:48:13 time: 2.4301 data_time: 0.0034 memory: 4859 grad_norm: 0.2763 loss: 0.3320 loss_sem_seg: 0.3320 2023/05/13 01:54:00 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 8.0000e-03 eta: 1 day, 1:47:11 time: 2.4098 data_time: 0.0034 memory: 4624 grad_norm: 0.2727 loss: 0.3309 loss_sem_seg: 0.3309 2023/05/13 01:55:57 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 8.0000e-03 eta: 1 day, 1:45:27 time: 2.3454 data_time: 0.0032 memory: 4727 grad_norm: 0.2842 loss: 0.3085 loss_sem_seg: 0.3085 2023/05/13 01:57:56 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 8.0000e-03 eta: 1 day, 1:44:12 time: 2.3932 data_time: 0.0033 memory: 4828 grad_norm: 0.3219 loss: 0.3104 loss_sem_seg: 0.3104 2023/05/13 01:59:56 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 8.0000e-03 eta: 1 day, 1:42:53 time: 2.3891 data_time: 0.0033 memory: 5061 grad_norm: 0.2777 loss: 0.3176 loss_sem_seg: 0.3176 2023/05/13 02:01:58 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 8.0000e-03 eta: 1 day, 1:42:00 time: 2.4335 data_time: 0.0034 memory: 4708 grad_norm: 0.3017 loss: 0.3379 loss_sem_seg: 0.3379 2023/05/13 02:03:58 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 8.0000e-03 eta: 1 day, 1:40:48 time: 2.4065 data_time: 0.0033 memory: 5253 grad_norm: 0.3128 loss: 0.3330 loss_sem_seg: 0.3330 2023/05/13 02:06:00 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 8.0000e-03 eta: 1 day, 1:39:51 time: 2.4338 data_time: 0.0034 memory: 4769 grad_norm: 0.2921 loss: 0.3270 loss_sem_seg: 0.3270 2023/05/13 02:07:59 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 8.0000e-03 eta: 1 day, 1:38:30 time: 2.3962 data_time: 0.0034 memory: 4967 grad_norm: 0.2621 loss: 0.3133 loss_sem_seg: 0.3133 2023/05/13 02:10:00 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 8.0000e-03 eta: 1 day, 1:37:14 time: 2.4058 data_time: 0.0034 memory: 4731 grad_norm: 0.2628 loss: 0.3072 loss_sem_seg: 0.3072 2023/05/13 02:11:48 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 02:11:48 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/05/13 02:12:37 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:06:34 time: 0.8601 data_time: 0.0021 memory: 4543 2023/05/13 02:13:18 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:05:40 time: 0.8073 data_time: 0.0020 memory: 915 2023/05/13 02:13:51 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:04:39 time: 0.6717 data_time: 0.0021 memory: 919 2023/05/13 02:14:24 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:03:50 time: 0.6452 data_time: 0.0020 memory: 907 2023/05/13 02:14:58 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:03:09 time: 0.6806 data_time: 0.0021 memory: 928 2023/05/13 02:15:33 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:02:32 time: 0.7140 data_time: 0.0020 memory: 883 2023/05/13 02:16:09 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:01:55 time: 0.7026 data_time: 0.0022 memory: 898 2023/05/13 02:16:48 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:01:20 time: 0.7920 data_time: 0.0021 memory: 903 2023/05/13 02:17:29 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:43 time: 0.8240 data_time: 0.0022 memory: 916 2023/05/13 02:18:09 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:06 time: 0.7846 data_time: 0.0022 memory: 902 2023/05/13 02:18:40 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9274 | 0.1103 | 0.0832 | 0.5264 | 0.2216 | 0.4992 | 0.5350 | 0.0010 | 0.9187 | 0.4343 | 0.7914 | 0.0070 | 0.8915 | 0.6040 | 0.8916 | 0.6289 | 0.7742 | 0.6088 | 0.4614 | 0.5219 | 0.9150 | 0.5922 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 02:18:40 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9274 bicycle: 0.1103 motorcycle: 0.0832 truck: 0.5264 bus: 0.2216 person: 0.4992 bicyclist: 0.5350 motorcyclist: 0.0010 road: 0.9187 parking: 0.4343 sidewalk: 0.7914 other-ground: 0.0070 building: 0.8915 fence: 0.6040 vegetation: 0.8916 trunck: 0.6289 terrian: 0.7742 pole: 0.6088 traffic-sign: 0.4614 miou: 0.5219 acc: 0.9150 acc_cls: 0.5922 data_time: 0.0021 time: 0.7861 2023/05/13 02:20:38 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 8.0000e-03 eta: 1 day, 1:34:01 time: 2.3644 data_time: 0.0043 memory: 4850 grad_norm: 0.2657 loss: 0.3184 loss_sem_seg: 0.3184 2023/05/13 02:22:39 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 8.0000e-03 eta: 1 day, 1:32:44 time: 2.4100 data_time: 0.0032 memory: 4825 grad_norm: 0.2107 loss: 0.3261 loss_sem_seg: 0.3261 2023/05/13 02:24:38 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 8.0000e-03 eta: 1 day, 1:31:12 time: 2.3828 data_time: 0.0034 memory: 4834 grad_norm: 0.2593 loss: 0.3248 loss_sem_seg: 0.3248 2023/05/13 02:26:40 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 8.0000e-03 eta: 1 day, 1:30:08 time: 2.4386 data_time: 0.0033 memory: 5002 grad_norm: 0.2553 loss: 0.3254 loss_sem_seg: 0.3254 2023/05/13 02:28:40 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 8.0000e-03 eta: 1 day, 1:28:45 time: 2.4040 data_time: 0.0033 memory: 5046 grad_norm: 0.2224 loss: 0.3079 loss_sem_seg: 0.3079 2023/05/13 02:30:38 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 8.0000e-03 eta: 1 day, 1:27:01 time: 2.3659 data_time: 0.0033 memory: 4681 grad_norm: 0.2868 loss: 0.3267 loss_sem_seg: 0.3267 2023/05/13 02:32:38 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 8.0000e-03 eta: 1 day, 1:25:31 time: 2.3920 data_time: 0.0033 memory: 5079 grad_norm: 0.2496 loss: 0.3490 loss_sem_seg: 0.3490 2023/05/13 02:34:39 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 8.0000e-03 eta: 1 day, 1:24:12 time: 2.4172 data_time: 0.0033 memory: 4969 grad_norm: 0.2014 loss: 0.3252 loss_sem_seg: 0.3252 2023/05/13 02:35:08 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 02:36:40 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 8.0000e-03 eta: 1 day, 1:22:53 time: 2.4210 data_time: 0.0034 memory: 4950 grad_norm: 0.2465 loss: 0.3238 loss_sem_seg: 0.3238 2023/05/13 02:38:40 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 8.0000e-03 eta: 1 day, 1:21:22 time: 2.3950 data_time: 0.0033 memory: 5430 grad_norm: 0.2382 loss: 0.3174 loss_sem_seg: 0.3174 2023/05/13 02:40:36 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 8.0000e-03 eta: 1 day, 1:19:16 time: 2.3246 data_time: 0.0033 memory: 4926 grad_norm: 0.2266 loss: 0.3091 loss_sem_seg: 0.3091 2023/05/13 02:42:39 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 8.0000e-03 eta: 1 day, 1:18:14 time: 2.4616 data_time: 0.0033 memory: 5005 grad_norm: 0.2644 loss: 0.3280 loss_sem_seg: 0.3280 2023/05/13 02:44:39 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 8.0000e-03 eta: 1 day, 1:16:39 time: 2.3918 data_time: 0.0033 memory: 4902 grad_norm: 0.2570 loss: 0.3108 loss_sem_seg: 0.3108 2023/05/13 02:46:41 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 8.0000e-03 eta: 1 day, 1:15:29 time: 2.4487 data_time: 0.0033 memory: 5260 grad_norm: 0.2145 loss: 0.3005 loss_sem_seg: 0.3005 2023/05/13 02:48:43 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 8.0000e-03 eta: 1 day, 1:14:09 time: 2.4296 data_time: 0.0033 memory: 5255 grad_norm: 0.2354 loss: 0.3171 loss_sem_seg: 0.3171 2023/05/13 02:50:44 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 8.0000e-03 eta: 1 day, 1:12:46 time: 2.4226 data_time: 0.0035 memory: 4898 grad_norm: 0.2308 loss: 0.3044 loss_sem_seg: 0.3044 2023/05/13 02:52:41 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 8.0000e-03 eta: 1 day, 1:10:52 time: 2.3551 data_time: 0.0034 memory: 5071 grad_norm: 0.2156 loss: 0.3014 loss_sem_seg: 0.3014 2023/05/13 02:54:29 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 8.0000e-03 eta: 1 day, 1:07:34 time: 2.1604 data_time: 0.0033 memory: 5054 grad_norm: 0.2234 loss: 0.2903 loss_sem_seg: 0.2903 2023/05/13 02:56:15 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 8.0000e-03 eta: 1 day, 1:03:57 time: 2.1100 data_time: 0.0033 memory: 4753 grad_norm: 0.2329 loss: 0.2895 loss_sem_seg: 0.2895 2023/05/13 02:58:07 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 8.0000e-03 eta: 1 day, 1:01:20 time: 2.2494 data_time: 0.0034 memory: 4899 grad_norm: 0.1916 loss: 0.3206 loss_sem_seg: 0.3206 2023/05/13 02:59:59 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 8.0000e-03 eta: 1 day, 0:58:35 time: 2.2241 data_time: 0.0034 memory: 4634 grad_norm: 0.2025 loss: 0.2868 loss_sem_seg: 0.2868 2023/05/13 03:01:48 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 8.0000e-03 eta: 1 day, 0:55:37 time: 2.1930 data_time: 0.0033 memory: 4637 grad_norm: 0.2078 loss: 0.3276 loss_sem_seg: 0.3276 2023/05/13 03:03:51 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 8.0000e-03 eta: 1 day, 0:54:31 time: 2.4638 data_time: 0.0035 memory: 4723 grad_norm: 0.2405 loss: 0.3188 loss_sem_seg: 0.3188 2023/05/13 03:05:42 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 03:05:42 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/05/13 03:06:33 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:06:56 time: 0.9067 data_time: 0.0022 memory: 4793 2023/05/13 03:07:17 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:06:07 time: 0.8927 data_time: 0.0022 memory: 915 2023/05/13 03:08:00 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:05:17 time: 0.8508 data_time: 0.0021 memory: 919 2023/05/13 03:08:42 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:04:29 time: 0.8364 data_time: 0.0021 memory: 907 2023/05/13 03:09:24 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:03:44 time: 0.8453 data_time: 0.0022 memory: 928 2023/05/13 03:10:07 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:03:00 time: 0.8549 data_time: 0.0021 memory: 883 2023/05/13 03:10:48 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:02:16 time: 0.8205 data_time: 0.0022 memory: 898 2023/05/13 03:11:33 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:01:34 time: 0.9060 data_time: 0.0021 memory: 903 2023/05/13 03:12:16 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:50 time: 0.8497 data_time: 0.0022 memory: 916 2023/05/13 03:13:01 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:07 time: 0.9005 data_time: 0.0021 memory: 902 2023/05/13 03:13:25 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9394 | 0.2900 | 0.4939 | 0.4864 | 0.2728 | 0.5910 | 0.3488 | 0.0291 | 0.9268 | 0.4193 | 0.8075 | 0.0006 | 0.8919 | 0.6332 | 0.8716 | 0.6770 | 0.7307 | 0.6227 | 0.4951 | 0.5541 | 0.9122 | 0.6638 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 03:13:25 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9394 bicycle: 0.2900 motorcycle: 0.4939 truck: 0.4864 bus: 0.2728 person: 0.5910 bicyclist: 0.3488 motorcyclist: 0.0291 road: 0.9268 parking: 0.4193 sidewalk: 0.8075 other-ground: 0.0006 building: 0.8919 fence: 0.6332 vegetation: 0.8716 trunck: 0.6770 terrian: 0.7307 pole: 0.6227 traffic-sign: 0.4951 miou: 0.5541 acc: 0.9122 acc_cls: 0.6638 data_time: 0.0021 time: 0.9175 2023/05/13 03:15:27 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 8.0000e-03 eta: 1 day, 0:51:44 time: 2.4370 data_time: 0.0037 memory: 5100 grad_norm: 0.2126 loss: 0.2924 loss_sem_seg: 0.2924 2023/05/13 03:17:26 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 8.0000e-03 eta: 1 day, 0:50:01 time: 2.3785 data_time: 0.0032 memory: 4826 grad_norm: 0.1843 loss: 0.3066 loss_sem_seg: 0.3066 2023/05/13 03:19:28 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 8.0000e-03 eta: 1 day, 0:48:45 time: 2.4485 data_time: 0.0034 memory: 5129 grad_norm: 0.2595 loss: 0.3002 loss_sem_seg: 0.3002 2023/05/13 03:21:29 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 8.0000e-03 eta: 1 day, 0:47:16 time: 2.4162 data_time: 0.0034 memory: 4715 grad_norm: 0.2172 loss: 0.3106 loss_sem_seg: 0.3106 2023/05/13 03:22:08 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 03:23:29 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 8.0000e-03 eta: 1 day, 0:45:35 time: 2.3858 data_time: 0.0034 memory: 4753 grad_norm: 0.2312 loss: 0.3211 loss_sem_seg: 0.3211 2023/05/13 03:25:28 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 8.0000e-03 eta: 1 day, 0:43:57 time: 2.3954 data_time: 0.0033 memory: 4706 grad_norm: 0.2110 loss: 0.3020 loss_sem_seg: 0.3020 2023/05/13 03:27:28 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 8.0000e-03 eta: 1 day, 0:42:14 time: 2.3844 data_time: 0.0033 memory: 4935 grad_norm: 0.2184 loss: 0.3206 loss_sem_seg: 0.3206 2023/05/13 03:29:29 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 8.0000e-03 eta: 1 day, 0:40:47 time: 2.4279 data_time: 0.0035 memory: 4782 grad_norm: 0.1888 loss: 0.2859 loss_sem_seg: 0.2859 2023/05/13 03:31:27 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 8.0000e-03 eta: 1 day, 0:38:53 time: 2.3545 data_time: 0.0036 memory: 5230 grad_norm: 0.2116 loss: 0.3063 loss_sem_seg: 0.3063 2023/05/13 03:33:15 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 8.0000e-03 eta: 1 day, 0:35:50 time: 2.1641 data_time: 0.0034 memory: 4654 grad_norm: 0.1917 loss: 0.2932 loss_sem_seg: 0.2932 2023/05/13 03:35:15 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 8.0000e-03 eta: 1 day, 0:34:12 time: 2.3992 data_time: 0.0035 memory: 5031 grad_norm: 0.2006 loss: 0.2855 loss_sem_seg: 0.2855 2023/05/13 03:37:04 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 8.0000e-03 eta: 1 day, 0:31:19 time: 2.1840 data_time: 0.0034 memory: 5035 grad_norm: 0.2140 loss: 0.3008 loss_sem_seg: 0.3008 2023/05/13 03:38:37 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 8.0000e-03 eta: 1 day, 0:26:33 time: 1.8567 data_time: 0.0033 memory: 5166 grad_norm: 0.2167 loss: 0.3023 loss_sem_seg: 0.3023 2023/05/13 03:40:12 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 8.0000e-03 eta: 1 day, 0:22:08 time: 1.9072 data_time: 0.0035 memory: 6017 grad_norm: 0.1973 loss: 0.3132 loss_sem_seg: 0.3132 2023/05/13 03:41:48 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 8.0000e-03 eta: 1 day, 0:17:49 time: 1.9146 data_time: 0.0033 memory: 4818 grad_norm: 0.2047 loss: 0.2754 loss_sem_seg: 0.2754 2023/05/13 03:43:30 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 8.0000e-03 eta: 1 day, 0:14:16 time: 2.0425 data_time: 0.0034 memory: 4750 grad_norm: 0.2288 loss: 0.2923 loss_sem_seg: 0.2923 2023/05/13 03:45:24 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 8.0000e-03 eta: 1 day, 0:12:01 time: 2.2749 data_time: 0.0037 memory: 4896 grad_norm: 0.2046 loss: 0.2831 loss_sem_seg: 0.2831 2023/05/13 03:47:26 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 8.0000e-03 eta: 1 day, 0:10:42 time: 2.4415 data_time: 0.0035 memory: 4960 grad_norm: 0.1981 loss: 0.2816 loss_sem_seg: 0.2816 2023/05/13 03:49:25 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 8.0000e-03 eta: 1 day, 0:09:00 time: 2.3725 data_time: 0.0036 memory: 4860 grad_norm: 0.1983 loss: 0.2836 loss_sem_seg: 0.2836 2023/05/13 03:51:26 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 8.0000e-03 eta: 1 day, 0:07:35 time: 2.4257 data_time: 0.0033 memory: 5007 grad_norm: 0.1900 loss: 0.2953 loss_sem_seg: 0.2953 2023/05/13 03:53:22 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 8.0000e-03 eta: 1 day, 0:05:34 time: 2.3183 data_time: 0.0035 memory: 4708 grad_norm: 0.1790 loss: 0.2939 loss_sem_seg: 0.2939 2023/05/13 03:55:23 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 8.0000e-03 eta: 1 day, 0:04:06 time: 2.4205 data_time: 0.0035 memory: 5339 grad_norm: 0.2435 loss: 0.2893 loss_sem_seg: 0.2893 2023/05/13 03:57:23 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 8.0000e-03 eta: 1 day, 0:02:34 time: 2.4086 data_time: 0.0036 memory: 4829 grad_norm: 0.1764 loss: 0.2866 loss_sem_seg: 0.2866 2023/05/13 03:59:14 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 03:59:14 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/05/13 04:00:06 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:06:54 time: 0.9037 data_time: 0.0022 memory: 5757 2023/05/13 04:00:49 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:05:59 time: 0.8537 data_time: 0.0022 memory: 915 2023/05/13 04:01:31 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:05:11 time: 0.8490 data_time: 0.0020 memory: 919 2023/05/13 04:02:15 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:04:29 time: 0.8855 data_time: 0.0021 memory: 907 2023/05/13 04:03:00 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:03:47 time: 0.8911 data_time: 0.0020 memory: 928 2023/05/13 04:03:43 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:03:02 time: 0.8630 data_time: 0.0021 memory: 883 2023/05/13 04:04:26 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:02:18 time: 0.8560 data_time: 0.0021 memory: 898 2023/05/13 04:05:11 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:01:35 time: 0.9046 data_time: 0.0021 memory: 903 2023/05/13 04:05:54 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:51 time: 0.8583 data_time: 0.0022 memory: 916 2023/05/13 04:06:35 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:07 time: 0.8274 data_time: 0.0022 memory: 902 2023/05/13 04:07:08 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9437 | 0.1885 | 0.5499 | 0.4535 | 0.3214 | 0.5770 | 0.6734 | 0.0135 | 0.9300 | 0.4032 | 0.8036 | 0.0228 | 0.8981 | 0.5945 | 0.8928 | 0.6632 | 0.7685 | 0.6355 | 0.4794 | 0.5691 | 0.9189 | 0.6659 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 04:07:08 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9437 bicycle: 0.1885 motorcycle: 0.5499 truck: 0.4535 bus: 0.3214 person: 0.5770 bicyclist: 0.6734 motorcyclist: 0.0135 road: 0.9300 parking: 0.4032 sidewalk: 0.8036 other-ground: 0.0228 building: 0.8981 fence: 0.5945 vegetation: 0.8928 trunck: 0.6632 terrian: 0.7685 pole: 0.6355 traffic-sign: 0.4794 miou: 0.5691 acc: 0.9189 acc_cls: 0.6659 data_time: 0.0021 time: 0.8317 2023/05/13 04:07:55 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 04:09:04 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 8.0000e-03 eta: 23:59:08 time: 2.3116 data_time: 0.0043 memory: 4988 grad_norm: 0.2284 loss: 0.2937 loss_sem_seg: 0.2937 2023/05/13 04:10:54 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 8.0000e-03 eta: 23:56:33 time: 2.2066 data_time: 0.0034 memory: 4763 grad_norm: 0.1668 loss: 0.2796 loss_sem_seg: 0.2796 2023/05/13 04:12:54 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 8.0000e-03 eta: 23:54:59 time: 2.4075 data_time: 0.0036 memory: 4886 grad_norm: 0.2128 loss: 0.3002 loss_sem_seg: 0.3002 2023/05/13 04:14:55 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 8.0000e-03 eta: 23:53:28 time: 2.4165 data_time: 0.0035 memory: 4800 grad_norm: 0.1797 loss: 0.2756 loss_sem_seg: 0.2756 2023/05/13 04:16:50 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 8.0000e-03 eta: 23:51:20 time: 2.2927 data_time: 0.0033 memory: 5316 grad_norm: 0.1662 loss: 0.2841 loss_sem_seg: 0.2841 2023/05/13 04:18:41 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 8.0000e-03 eta: 23:48:54 time: 2.2325 data_time: 0.0033 memory: 4696 grad_norm: 0.1744 loss: 0.3050 loss_sem_seg: 0.3050 2023/05/13 04:20:33 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 8.0000e-03 eta: 23:46:32 time: 2.2425 data_time: 0.0034 memory: 4676 grad_norm: 0.1769 loss: 0.2943 loss_sem_seg: 0.2943 2023/05/13 04:22:23 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 8.0000e-03 eta: 23:43:53 time: 2.1838 data_time: 0.0034 memory: 5311 grad_norm: 0.1789 loss: 0.2898 loss_sem_seg: 0.2898 2023/05/13 04:24:19 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 8.0000e-03 eta: 23:41:55 time: 2.3232 data_time: 0.0034 memory: 4739 grad_norm: 0.1676 loss: 0.2929 loss_sem_seg: 0.2929 2023/05/13 04:26:19 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 8.0000e-03 eta: 23:40:21 time: 2.4094 data_time: 0.0034 memory: 5042 grad_norm: 0.1959 loss: 0.2803 loss_sem_seg: 0.2803 2023/05/13 04:28:22 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 8.0000e-03 eta: 23:38:56 time: 2.4442 data_time: 0.0034 memory: 4961 grad_norm: 0.1899 loss: 0.2624 loss_sem_seg: 0.2624 2023/05/13 04:30:21 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 8.0000e-03 eta: 23:37:17 time: 2.3919 data_time: 0.0035 memory: 4727 grad_norm: 0.1776 loss: 0.2924 loss_sem_seg: 0.2924 2023/05/13 04:32:22 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 8.0000e-03 eta: 23:35:44 time: 2.4175 data_time: 0.0034 memory: 5119 grad_norm: 0.1695 loss: 0.2763 loss_sem_seg: 0.2763 2023/05/13 04:34:23 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 8.0000e-03 eta: 23:34:11 time: 2.4216 data_time: 0.0034 memory: 5671 grad_norm: 0.1617 loss: 0.2672 loss_sem_seg: 0.2672 2023/05/13 04:36:18 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 8.0000e-03 eta: 23:32:08 time: 2.3087 data_time: 0.0034 memory: 4777 grad_norm: 0.1874 loss: 0.2610 loss_sem_seg: 0.2610 2023/05/13 04:38:15 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 8.0000e-03 eta: 23:30:11 time: 2.3310 data_time: 0.0033 memory: 4786 grad_norm: 0.1540 loss: 0.2774 loss_sem_seg: 0.2774 2023/05/13 04:40:18 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 8.0000e-03 eta: 23:28:46 time: 2.4498 data_time: 0.0033 memory: 5287 grad_norm: 0.1844 loss: 0.2689 loss_sem_seg: 0.2689 2023/05/13 04:42:17 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 8.0000e-03 eta: 23:27:05 time: 2.3919 data_time: 0.0034 memory: 4734 grad_norm: 0.1842 loss: 0.2771 loss_sem_seg: 0.2771 2023/05/13 04:44:18 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 8.0000e-03 eta: 23:25:30 time: 2.4190 data_time: 0.0034 memory: 4674 grad_norm: 0.2083 loss: 0.2772 loss_sem_seg: 0.2772 2023/05/13 04:46:20 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 8.0000e-03 eta: 23:24:02 time: 2.4453 data_time: 0.0034 memory: 4819 grad_norm: 0.1638 loss: 0.2749 loss_sem_seg: 0.2749 2023/05/13 04:47:08 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 04:48:20 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 8.0000e-03 eta: 23:22:19 time: 2.3887 data_time: 0.0032 memory: 4829 grad_norm: 0.1785 loss: 0.2750 loss_sem_seg: 0.2750 2023/05/13 04:50:08 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 8.0000e-03 eta: 23:19:38 time: 2.1611 data_time: 0.0035 memory: 4825 grad_norm: 0.2084 loss: 0.2722 loss_sem_seg: 0.2722 2023/05/13 04:52:02 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 8.0000e-03 eta: 23:17:29 time: 2.2871 data_time: 0.0034 memory: 4757 grad_norm: 0.1773 loss: 0.2839 loss_sem_seg: 0.2839 2023/05/13 04:53:54 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 04:53:54 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/05/13 04:54:46 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:06:58 time: 0.9107 data_time: 0.0022 memory: 4732 2023/05/13 04:55:30 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:06:07 time: 0.8875 data_time: 0.0021 memory: 915 2023/05/13 04:56:15 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:05:21 time: 0.8885 data_time: 0.0021 memory: 919 2023/05/13 04:57:00 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:04:36 time: 0.8983 data_time: 0.0021 memory: 907 2023/05/13 04:57:45 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:03:52 time: 0.9005 data_time: 0.0022 memory: 928 2023/05/13 04:58:24 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:03:03 time: 0.7967 data_time: 0.0021 memory: 883 2023/05/13 04:59:03 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:02:17 time: 0.7651 data_time: 0.0022 memory: 898 2023/05/13 04:59:43 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:01:33 time: 0.8071 data_time: 0.0021 memory: 903 2023/05/13 05:00:22 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:50 time: 0.7737 data_time: 0.0021 memory: 916 2023/05/13 05:00:58 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:07 time: 0.7275 data_time: 0.0021 memory: 902 2023/05/13 05:01:21 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9598 | 0.2724 | 0.5715 | 0.4424 | 0.3305 | 0.4804 | 0.8180 | 0.0003 | 0.9242 | 0.3270 | 0.8005 | 0.0552 | 0.9087 | 0.6453 | 0.8801 | 0.6510 | 0.7529 | 0.6321 | 0.4890 | 0.5759 | 0.9162 | 0.6599 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 05:01:21 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9598 bicycle: 0.2724 motorcycle: 0.5715 truck: 0.4424 bus: 0.3305 person: 0.4804 bicyclist: 0.8180 motorcyclist: 0.0003 road: 0.9242 parking: 0.3270 sidewalk: 0.8005 other-ground: 0.0552 building: 0.9087 fence: 0.6453 vegetation: 0.8801 trunck: 0.6510 terrian: 0.7529 pole: 0.6321 traffic-sign: 0.4890 miou: 0.5759 acc: 0.9162 acc_cls: 0.6599 data_time: 0.0021 time: 0.7430 2023/05/13 05:03:13 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 8.0000e-03 eta: 23:13:41 time: 2.2338 data_time: 0.0041 memory: 4887 grad_norm: 0.1649 loss: 0.2516 loss_sem_seg: 0.2516 2023/05/13 05:05:10 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 8.0000e-03 eta: 23:11:46 time: 2.3390 data_time: 0.0033 memory: 4966 grad_norm: 0.1693 loss: 0.2727 loss_sem_seg: 0.2727 2023/05/13 05:07:08 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 8.0000e-03 eta: 23:09:57 time: 2.3670 data_time: 0.0034 memory: 4456 grad_norm: 0.1735 loss: 0.2663 loss_sem_seg: 0.2663 2023/05/13 05:09:10 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 8.0000e-03 eta: 23:08:24 time: 2.4320 data_time: 0.0034 memory: 4902 grad_norm: 0.1788 loss: 0.2744 loss_sem_seg: 0.2744 2023/05/13 05:11:11 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 8.0000e-03 eta: 23:06:48 time: 2.4200 data_time: 0.0033 memory: 4886 grad_norm: 0.1722 loss: 0.2644 loss_sem_seg: 0.2644 2023/05/13 05:13:11 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 8.0000e-03 eta: 23:05:08 time: 2.4066 data_time: 0.0033 memory: 5197 grad_norm: 0.1703 loss: 0.2633 loss_sem_seg: 0.2633 2023/05/13 05:15:13 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 8.0000e-03 eta: 23:03:34 time: 2.4332 data_time: 0.0034 memory: 4839 grad_norm: 0.1601 loss: 0.2637 loss_sem_seg: 0.2637 2023/05/13 05:17:12 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 8.0000e-03 eta: 23:01:51 time: 2.3951 data_time: 0.0032 memory: 5204 grad_norm: 0.1555 loss: 0.2568 loss_sem_seg: 0.2568 2023/05/13 05:19:14 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 8.0000e-03 eta: 23:00:16 time: 2.4303 data_time: 0.0033 memory: 4753 grad_norm: 0.1585 loss: 0.2829 loss_sem_seg: 0.2829 2023/05/13 05:21:12 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 8.0000e-03 eta: 22:58:23 time: 2.3573 data_time: 0.0033 memory: 4727 grad_norm: 0.1703 loss: 0.2822 loss_sem_seg: 0.2822 2023/05/13 05:23:13 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 8.0000e-03 eta: 22:56:45 time: 2.4167 data_time: 0.0033 memory: 5710 grad_norm: 0.1526 loss: 0.2837 loss_sem_seg: 0.2837 2023/05/13 05:25:15 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 8.0000e-03 eta: 22:55:11 time: 2.4395 data_time: 0.0033 memory: 5038 grad_norm: 0.1682 loss: 0.2844 loss_sem_seg: 0.2844 2023/05/13 05:27:12 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 8.0000e-03 eta: 22:53:17 time: 2.3528 data_time: 0.0035 memory: 4841 grad_norm: 0.1600 loss: 0.2595 loss_sem_seg: 0.2595 2023/05/13 05:29:01 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 8.0000e-03 eta: 22:50:43 time: 2.1714 data_time: 0.0034 memory: 5156 grad_norm: 0.1549 loss: 0.2555 loss_sem_seg: 0.2555 2023/05/13 05:30:57 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 8.0000e-03 eta: 22:48:44 time: 2.3277 data_time: 0.0034 memory: 5441 grad_norm: 0.1557 loss: 0.2807 loss_sem_seg: 0.2807 2023/05/13 05:32:59 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 8.0000e-03 eta: 22:47:06 time: 2.4276 data_time: 0.0032 memory: 5226 grad_norm: 0.1623 loss: 0.2691 loss_sem_seg: 0.2691 2023/05/13 05:33:57 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 05:35:00 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 8.0000e-03 eta: 22:45:31 time: 2.4364 data_time: 0.0033 memory: 5585 grad_norm: 0.1572 loss: 0.2682 loss_sem_seg: 0.2682 2023/05/13 05:36:59 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 8.0000e-03 eta: 22:43:41 time: 2.3730 data_time: 0.0032 memory: 5986 grad_norm: 0.1606 loss: 0.2523 loss_sem_seg: 0.2523 2023/05/13 05:39:00 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 8.0000e-03 eta: 22:42:02 time: 2.4197 data_time: 0.0033 memory: 5004 grad_norm: 0.1771 loss: 0.2750 loss_sem_seg: 0.2750 2023/05/13 05:40:52 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 8.0000e-03 eta: 22:39:44 time: 2.2433 data_time: 0.0034 memory: 4760 grad_norm: 0.1513 loss: 0.2722 loss_sem_seg: 0.2722 2023/05/13 05:42:44 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 8.0000e-03 eta: 22:37:26 time: 2.2365 data_time: 0.0032 memory: 4938 grad_norm: 0.1568 loss: 0.2670 loss_sem_seg: 0.2670 2023/05/13 05:44:36 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 8.0000e-03 eta: 22:35:06 time: 2.2328 data_time: 0.0034 memory: 4848 grad_norm: 0.1513 loss: 0.2779 loss_sem_seg: 0.2779 2023/05/13 05:46:28 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 8.0000e-03 eta: 22:32:51 time: 2.2480 data_time: 0.0034 memory: 5197 grad_norm: 0.1454 loss: 0.2877 loss_sem_seg: 0.2877 2023/05/13 05:48:19 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 05:48:19 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/05/13 05:49:09 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:06:42 time: 0.8778 data_time: 0.0021 memory: 5398 2023/05/13 05:49:53 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:05:59 time: 0.8823 data_time: 0.0021 memory: 915 2023/05/13 05:50:34 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:05:09 time: 0.8236 data_time: 0.0021 memory: 919 2023/05/13 05:51:17 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:04:25 time: 0.8504 data_time: 0.0020 memory: 907 2023/05/13 05:52:00 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:03:42 time: 0.8554 data_time: 0.0020 memory: 928 2023/05/13 05:52:40 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:02:57 time: 0.8167 data_time: 0.0022 memory: 883 2023/05/13 05:53:23 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:02:15 time: 0.8569 data_time: 0.0022 memory: 898 2023/05/13 05:54:07 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:01:33 time: 0.8690 data_time: 0.0021 memory: 903 2023/05/13 05:54:50 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:50 time: 0.8618 data_time: 0.0021 memory: 916 2023/05/13 05:55:34 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:07 time: 0.8916 data_time: 0.0021 memory: 902 2023/05/13 05:56:06 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9454 | 0.4144 | 0.6848 | 0.7330 | 0.3225 | 0.6896 | 0.6125 | 0.0533 | 0.9300 | 0.4598 | 0.8157 | 0.0091 | 0.8459 | 0.3616 | 0.9001 | 0.6956 | 0.7929 | 0.6420 | 0.4551 | 0.5981 | 0.9165 | 0.6843 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 05:56:06 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9454 bicycle: 0.4144 motorcycle: 0.6848 truck: 0.7330 bus: 0.3225 person: 0.6896 bicyclist: 0.6125 motorcyclist: 0.0533 road: 0.9300 parking: 0.4598 sidewalk: 0.8157 other-ground: 0.0091 building: 0.8459 fence: 0.3616 vegetation: 0.9001 trunck: 0.6956 terrian: 0.7929 pole: 0.6420 traffic-sign: 0.4551 miou: 0.5981 acc: 0.9165 acc_cls: 0.6843 data_time: 0.0020 time: 0.8857 2023/05/13 05:58:09 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 8.0000e-03 eta: 22:29:44 time: 2.4502 data_time: 0.0042 memory: 5216 grad_norm: 0.1568 loss: 0.2647 loss_sem_seg: 0.2647 2023/05/13 06:00:10 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 8.0000e-03 eta: 22:28:05 time: 2.4234 data_time: 0.0033 memory: 4824 grad_norm: 0.1689 loss: 0.2681 loss_sem_seg: 0.2681 2023/05/13 06:02:09 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 8.0000e-03 eta: 22:26:17 time: 2.3842 data_time: 0.0033 memory: 5087 grad_norm: 0.1666 loss: 0.2746 loss_sem_seg: 0.2746 2023/05/13 06:04:09 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 8.0000e-03 eta: 22:24:32 time: 2.3984 data_time: 0.0035 memory: 5356 grad_norm: 0.1556 loss: 0.2516 loss_sem_seg: 0.2516 2023/05/13 06:06:01 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 8.0000e-03 eta: 22:22:14 time: 2.2349 data_time: 0.0033 memory: 4929 grad_norm: 0.1582 loss: 0.2658 loss_sem_seg: 0.2658 2023/05/13 06:07:49 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 8.0000e-03 eta: 22:19:41 time: 2.1571 data_time: 0.0034 memory: 5067 grad_norm: 0.1589 loss: 0.2732 loss_sem_seg: 0.2732 2023/05/13 06:09:49 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 8.0000e-03 eta: 22:17:59 time: 2.4144 data_time: 0.0034 memory: 4475 grad_norm: 0.1503 loss: 0.2786 loss_sem_seg: 0.2786 2023/05/13 06:11:47 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 8.0000e-03 eta: 22:16:07 time: 2.3596 data_time: 0.0033 memory: 4855 grad_norm: 0.1705 loss: 0.2699 loss_sem_seg: 0.2699 2023/05/13 06:13:32 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 8.0000e-03 eta: 22:13:22 time: 2.0928 data_time: 0.0033 memory: 5014 grad_norm: 0.1665 loss: 0.2796 loss_sem_seg: 0.2796 2023/05/13 06:15:18 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 8.0000e-03 eta: 22:10:44 time: 2.1231 data_time: 0.0035 memory: 5007 grad_norm: 0.1448 loss: 0.2791 loss_sem_seg: 0.2791 2023/05/13 06:17:03 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 8.0000e-03 eta: 22:08:02 time: 2.0968 data_time: 0.0033 memory: 5112 grad_norm: 0.1465 loss: 0.2523 loss_sem_seg: 0.2523 2023/05/13 06:18:47 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 8.0000e-03 eta: 22:05:17 time: 2.0845 data_time: 0.0033 memory: 5184 grad_norm: 0.2068 loss: 0.2575 loss_sem_seg: 0.2575 2023/05/13 06:19:44 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 06:20:30 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 8.0000e-03 eta: 22:02:28 time: 2.0527 data_time: 0.0035 memory: 4751 grad_norm: 0.1388 loss: 0.2607 loss_sem_seg: 0.2607 2023/05/13 06:22:24 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 8.0000e-03 eta: 22:00:21 time: 2.2772 data_time: 0.0033 memory: 4977 grad_norm: 0.1540 loss: 0.2678 loss_sem_seg: 0.2678 2023/05/13 06:24:14 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 8.0000e-03 eta: 21:58:02 time: 2.2109 data_time: 0.0035 memory: 4824 grad_norm: 0.1644 loss: 0.2778 loss_sem_seg: 0.2778 2023/05/13 06:26:06 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 8.0000e-03 eta: 21:55:49 time: 2.2426 data_time: 0.0036 memory: 5023 grad_norm: 0.1504 loss: 0.2691 loss_sem_seg: 0.2691 2023/05/13 06:28:01 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 8.0000e-03 eta: 21:53:48 time: 2.3009 data_time: 0.0034 memory: 4815 grad_norm: 0.1527 loss: 0.2545 loss_sem_seg: 0.2545 2023/05/13 06:30:02 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 8.0000e-03 eta: 21:52:08 time: 2.4210 data_time: 0.0036 memory: 5027 grad_norm: 0.1516 loss: 0.2473 loss_sem_seg: 0.2473 2023/05/13 06:32:04 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 8.0000e-03 eta: 21:50:30 time: 2.4321 data_time: 0.0033 memory: 4734 grad_norm: 0.1592 loss: 0.2480 loss_sem_seg: 0.2480 2023/05/13 06:34:04 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 8.0000e-03 eta: 21:48:45 time: 2.4003 data_time: 0.0034 memory: 4892 grad_norm: 0.1397 loss: 0.2478 loss_sem_seg: 0.2478 2023/05/13 06:36:04 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 8.0000e-03 eta: 21:47:02 time: 2.4028 data_time: 0.0035 memory: 4945 grad_norm: 0.1417 loss: 0.2651 loss_sem_seg: 0.2651 2023/05/13 06:38:06 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 8.0000e-03 eta: 21:45:22 time: 2.4263 data_time: 0.0034 memory: 5145 grad_norm: 0.1405 loss: 0.2486 loss_sem_seg: 0.2486 2023/05/13 06:40:07 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 8.0000e-03 eta: 21:43:43 time: 2.4325 data_time: 0.0035 memory: 5360 grad_norm: 0.1434 loss: 0.2637 loss_sem_seg: 0.2637 2023/05/13 06:41:59 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 06:41:59 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/05/13 06:42:52 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:07:07 time: 0.9324 data_time: 0.0022 memory: 4988 2023/05/13 06:43:34 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:06:02 time: 0.8422 data_time: 0.0022 memory: 915 2023/05/13 06:44:12 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:05:03 time: 0.7594 data_time: 0.0021 memory: 919 2023/05/13 06:44:51 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:04:16 time: 0.7900 data_time: 0.0021 memory: 907 2023/05/13 06:45:30 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:03:32 time: 0.7764 data_time: 0.0021 memory: 928 2023/05/13 06:46:08 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:02:49 time: 0.7517 data_time: 0.0021 memory: 883 2023/05/13 06:46:51 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:02:09 time: 0.8610 data_time: 0.0022 memory: 898 2023/05/13 06:47:34 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:01:29 time: 0.8566 data_time: 0.0021 memory: 903 2023/05/13 06:48:17 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:48 time: 0.8567 data_time: 0.0021 memory: 916 2023/05/13 06:48:58 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:07 time: 0.8347 data_time: 0.0021 memory: 902 2023/05/13 06:49:23 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9433 | 0.4576 | 0.6504 | 0.8316 | 0.4174 | 0.6665 | 0.5952 | 0.0315 | 0.9286 | 0.3922 | 0.7965 | 0.0037 | 0.8910 | 0.5582 | 0.8827 | 0.7098 | 0.7419 | 0.6397 | 0.5233 | 0.6137 | 0.9142 | 0.7002 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 06:49:23 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9433 bicycle: 0.4576 motorcycle: 0.6504 truck: 0.8316 bus: 0.4174 person: 0.6665 bicyclist: 0.5952 motorcyclist: 0.0315 road: 0.9286 parking: 0.3922 sidewalk: 0.7965 other-ground: 0.0037 building: 0.8910 fence: 0.5582 vegetation: 0.8827 trunck: 0.7098 terrian: 0.7419 pole: 0.6397 traffic-sign: 0.5233 miou: 0.6137 acc: 0.9142 acc_cls: 0.7002 data_time: 0.0020 time: 0.8406 2023/05/13 06:51:24 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 8.0000e-03 eta: 21:40:31 time: 2.4288 data_time: 0.0041 memory: 4894 grad_norm: 0.1385 loss: 0.2647 loss_sem_seg: 0.2647 2023/05/13 06:53:25 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 8.0000e-03 eta: 21:38:47 time: 2.4073 data_time: 0.0034 memory: 4933 grad_norm: 0.1600 loss: 0.2607 loss_sem_seg: 0.2607 2023/05/13 06:55:26 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 8.0000e-03 eta: 21:37:06 time: 2.4269 data_time: 0.0036 memory: 5408 grad_norm: 0.1356 loss: 0.2452 loss_sem_seg: 0.2452 2023/05/13 06:57:27 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 8.0000e-03 eta: 21:35:24 time: 2.4231 data_time: 0.0034 memory: 4891 grad_norm: 0.1522 loss: 0.2599 loss_sem_seg: 0.2599 2023/05/13 06:59:28 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 8.0000e-03 eta: 21:33:41 time: 2.4138 data_time: 0.0035 memory: 5041 grad_norm: 0.1459 loss: 0.2523 loss_sem_seg: 0.2523 2023/05/13 07:01:28 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 8.0000e-03 eta: 21:31:56 time: 2.4080 data_time: 0.0038 memory: 4810 grad_norm: 0.1660 loss: 0.2654 loss_sem_seg: 0.2654 2023/05/13 07:03:17 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 8.0000e-03 eta: 21:29:34 time: 2.1810 data_time: 0.0036 memory: 4604 grad_norm: 0.1629 loss: 0.2605 loss_sem_seg: 0.2605 2023/05/13 07:05:07 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 8.0000e-03 eta: 21:27:15 time: 2.2033 data_time: 0.0035 memory: 4812 grad_norm: 0.1420 loss: 0.2706 loss_sem_seg: 0.2706 2023/05/13 07:06:18 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 07:06:59 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 8.0000e-03 eta: 21:25:03 time: 2.2385 data_time: 0.0035 memory: 4586 grad_norm: 0.1383 loss: 0.2547 loss_sem_seg: 0.2547 2023/05/13 07:08:52 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 8.0000e-03 eta: 21:22:54 time: 2.2614 data_time: 0.0034 memory: 5023 grad_norm: 0.1250 loss: 0.2611 loss_sem_seg: 0.2611 2023/05/13 07:10:55 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 8.0000e-03 eta: 21:21:16 time: 2.4467 data_time: 0.0034 memory: 4885 grad_norm: 0.1410 loss: 0.2571 loss_sem_seg: 0.2571 2023/05/13 07:12:56 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 8.0000e-03 eta: 21:19:35 time: 2.4316 data_time: 0.0036 memory: 4894 grad_norm: 0.1452 loss: 0.2710 loss_sem_seg: 0.2710 2023/05/13 07:14:58 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 8.0000e-03 eta: 21:17:55 time: 2.4371 data_time: 0.0037 memory: 5462 grad_norm: 0.1340 loss: 0.2419 loss_sem_seg: 0.2419 2023/05/13 07:16:59 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 8.0000e-03 eta: 21:16:11 time: 2.4176 data_time: 0.0035 memory: 5311 grad_norm: 0.1379 loss: 0.2567 loss_sem_seg: 0.2567 2023/05/13 07:18:56 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 8.0000e-03 eta: 21:14:17 time: 2.3483 data_time: 0.0036 memory: 4852 grad_norm: 0.1488 loss: 0.2533 loss_sem_seg: 0.2533 2023/05/13 07:20:57 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 8.0000e-03 eta: 21:12:31 time: 2.4047 data_time: 0.0035 memory: 5330 grad_norm: 0.1587 loss: 0.2720 loss_sem_seg: 0.2720 2023/05/13 07:22:54 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.0000e-03 eta: 21:10:36 time: 2.3524 data_time: 0.0034 memory: 5225 grad_norm: 0.1437 loss: 0.2504 loss_sem_seg: 0.2504 2023/05/13 07:24:45 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.0000e-03 eta: 21:08:21 time: 2.2163 data_time: 0.0033 memory: 5253 grad_norm: 0.1416 loss: 0.2536 loss_sem_seg: 0.2536 2023/05/13 07:26:42 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.0000e-03 eta: 21:06:25 time: 2.3381 data_time: 0.0035 memory: 4869 grad_norm: 0.1610 loss: 0.2676 loss_sem_seg: 0.2676 2023/05/13 07:28:44 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.0000e-03 eta: 21:04:44 time: 2.4381 data_time: 0.0034 memory: 4642 grad_norm: 0.1478 loss: 0.2457 loss_sem_seg: 0.2457 2023/05/13 07:30:45 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.0000e-03 eta: 21:03:00 time: 2.4206 data_time: 0.0032 memory: 5692 grad_norm: 0.1416 loss: 0.2538 loss_sem_seg: 0.2538 2023/05/13 07:32:45 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.0000e-03 eta: 21:01:12 time: 2.3948 data_time: 0.0037 memory: 4723 grad_norm: 0.1255 loss: 0.2392 loss_sem_seg: 0.2392 2023/05/13 07:34:42 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.0000e-03 eta: 20:59:18 time: 2.3550 data_time: 0.0034 memory: 4927 grad_norm: 0.1524 loss: 0.2690 loss_sem_seg: 0.2690 2023/05/13 07:36:32 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 07:36:32 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/05/13 07:37:25 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:07:06 time: 0.9285 data_time: 0.0022 memory: 5033 2023/05/13 07:38:10 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:06:11 time: 0.8897 data_time: 0.0022 memory: 915 2023/05/13 07:38:52 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:05:20 time: 0.8581 data_time: 0.0021 memory: 919 2023/05/13 07:39:38 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:04:36 time: 0.9004 data_time: 0.0021 memory: 907 2023/05/13 07:40:23 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:03:52 time: 0.9040 data_time: 0.0021 memory: 928 2023/05/13 07:41:07 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:03:06 time: 0.8807 data_time: 0.0022 memory: 883 2023/05/13 07:41:52 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:02:22 time: 0.9015 data_time: 0.0020 memory: 898 2023/05/13 07:42:36 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:01:37 time: 0.8911 data_time: 0.0021 memory: 903 2023/05/13 07:43:14 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:51 time: 0.7608 data_time: 0.0021 memory: 916 2023/05/13 07:43:55 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:07 time: 0.8159 data_time: 0.0020 memory: 902 2023/05/13 07:44:19 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9594 | 0.4767 | 0.6952 | 0.5262 | 0.5227 | 0.6959 | 0.7593 | 0.0205 | 0.9385 | 0.4515 | 0.8162 | 0.0584 | 0.8863 | 0.5323 | 0.8971 | 0.7215 | 0.7905 | 0.6320 | 0.4958 | 0.6251 | 0.9230 | 0.7236 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 07:44:19 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9594 bicycle: 0.4767 motorcycle: 0.6952 truck: 0.5262 bus: 0.5227 person: 0.6959 bicyclist: 0.7593 motorcyclist: 0.0205 road: 0.9385 parking: 0.4515 sidewalk: 0.8162 other-ground: 0.0584 building: 0.8863 fence: 0.5323 vegetation: 0.8971 trunck: 0.7215 terrian: 0.7905 pole: 0.6320 traffic-sign: 0.4958 miou: 0.6251 acc: 0.9230 acc_cls: 0.7236 data_time: 0.0020 time: 0.8158 2023/05/13 07:46:11 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.0000e-03 eta: 20:55:27 time: 2.2429 data_time: 0.0038 memory: 4889 grad_norm: 0.1319 loss: 0.2477 loss_sem_seg: 0.2477 2023/05/13 07:48:00 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0000e-03 eta: 20:53:08 time: 2.1862 data_time: 0.0034 memory: 5024 grad_norm: 0.1464 loss: 0.2560 loss_sem_seg: 0.2560 2023/05/13 07:49:52 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 8.0000e-03 eta: 20:50:55 time: 2.2259 data_time: 0.0032 memory: 5170 grad_norm: 0.1426 loss: 0.2659 loss_sem_seg: 0.2659 2023/05/13 07:51:53 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 8.0000e-03 eta: 20:49:13 time: 2.4327 data_time: 0.0034 memory: 5404 grad_norm: 0.1676 loss: 0.2777 loss_sem_seg: 0.2777 2023/05/13 07:53:20 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 07:53:54 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 8.0000e-03 eta: 20:47:27 time: 2.4110 data_time: 0.0034 memory: 5254 grad_norm: 0.1582 loss: 0.2597 loss_sem_seg: 0.2597 2023/05/13 07:55:54 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 8.0000e-03 eta: 20:45:40 time: 2.4008 data_time: 0.0034 memory: 4960 grad_norm: 0.1314 loss: 0.2511 loss_sem_seg: 0.2511 2023/05/13 07:57:55 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 8.0000e-03 eta: 20:43:54 time: 2.4128 data_time: 0.0033 memory: 4941 grad_norm: 0.1387 loss: 0.2399 loss_sem_seg: 0.2399 2023/05/13 07:59:55 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 8.0000e-03 eta: 20:42:08 time: 2.4164 data_time: 0.0033 memory: 5066 grad_norm: 0.1362 loss: 0.2457 loss_sem_seg: 0.2457 2023/05/13 08:01:51 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 8.0000e-03 eta: 20:40:09 time: 2.3185 data_time: 0.0033 memory: 4755 grad_norm: 0.1424 loss: 0.2542 loss_sem_seg: 0.2542 2023/05/13 08:03:37 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 8.0000e-03 eta: 20:37:42 time: 2.1210 data_time: 0.0033 memory: 4719 grad_norm: 0.1406 loss: 0.2348 loss_sem_seg: 0.2348 2023/05/13 08:05:39 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 8.0000e-03 eta: 20:35:59 time: 2.4387 data_time: 0.0034 memory: 4878 grad_norm: 0.1498 loss: 0.2550 loss_sem_seg: 0.2550 2023/05/13 08:07:40 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 8.0000e-03 eta: 20:34:14 time: 2.4189 data_time: 0.0034 memory: 4864 grad_norm: 0.1350 loss: 0.2468 loss_sem_seg: 0.2468 2023/05/13 08:09:41 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 8.0000e-03 eta: 20:32:28 time: 2.4150 data_time: 0.0034 memory: 4822 grad_norm: 0.1277 loss: 0.2476 loss_sem_seg: 0.2476 2023/05/13 08:11:42 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 8.0000e-03 eta: 20:30:42 time: 2.4129 data_time: 0.0033 memory: 5209 grad_norm: 0.1368 loss: 0.2394 loss_sem_seg: 0.2394 2023/05/13 08:13:42 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 8.0000e-03 eta: 20:28:53 time: 2.3970 data_time: 0.0033 memory: 5162 grad_norm: 0.1297 loss: 0.2538 loss_sem_seg: 0.2538 2023/05/13 08:15:44 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 8.0000e-03 eta: 20:27:11 time: 2.4463 data_time: 0.0035 memory: 4737 grad_norm: 0.1435 loss: 0.2456 loss_sem_seg: 0.2456 2023/05/13 08:17:43 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 8.0000e-03 eta: 20:25:21 time: 2.3928 data_time: 0.0035 memory: 4808 grad_norm: 0.1233 loss: 0.2542 loss_sem_seg: 0.2542 2023/05/13 08:19:43 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 8.0000e-03 eta: 20:23:32 time: 2.3914 data_time: 0.0035 memory: 4921 grad_norm: 0.1285 loss: 0.2536 loss_sem_seg: 0.2536 2023/05/13 08:21:19 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 8.0000e-03 eta: 20:20:39 time: 1.9262 data_time: 0.0034 memory: 4899 grad_norm: 0.1342 loss: 0.2373 loss_sem_seg: 0.2373 2023/05/13 08:22:55 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 8.0000e-03 eta: 20:17:46 time: 1.9093 data_time: 0.0033 memory: 4912 grad_norm: 0.1175 loss: 0.2454 loss_sem_seg: 0.2454 2023/05/13 08:24:26 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 8.0000e-03 eta: 20:14:41 time: 1.8170 data_time: 0.0034 memory: 5247 grad_norm: 0.1279 loss: 0.2550 loss_sem_seg: 0.2550 2023/05/13 08:25:53 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 8.0000e-03 eta: 20:11:27 time: 1.7471 data_time: 0.0032 memory: 4911 grad_norm: 0.1352 loss: 0.2505 loss_sem_seg: 0.2505 2023/05/13 08:27:18 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 8.0000e-03 eta: 20:08:09 time: 1.7056 data_time: 0.0033 memory: 4944 grad_norm: 0.1222 loss: 0.2521 loss_sem_seg: 0.2521 2023/05/13 08:28:37 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 08:28:37 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/05/13 08:29:13 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:04:30 time: 0.5889 data_time: 0.0021 memory: 4878 2023/05/13 08:29:44 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:04:07 time: 0.6225 data_time: 0.0021 memory: 915 2023/05/13 08:30:16 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:03:41 time: 0.6381 data_time: 0.0020 memory: 919 2023/05/13 08:30:47 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:03:10 time: 0.6187 data_time: 0.0021 memory: 907 2023/05/13 08:31:22 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:02:43 time: 0.6895 data_time: 0.0020 memory: 928 2023/05/13 08:31:53 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:02:11 time: 0.6269 data_time: 0.0021 memory: 883 2023/05/13 08:32:25 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:01:40 time: 0.6435 data_time: 0.0020 memory: 898 2023/05/13 08:32:58 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:01:09 time: 0.6467 data_time: 0.0020 memory: 903 2023/05/13 08:33:30 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:37 time: 0.6412 data_time: 0.0020 memory: 916 2023/05/13 08:34:02 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:05 time: 0.6424 data_time: 0.0020 memory: 902 2023/05/13 08:34:26 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9578 | 0.3623 | 0.7149 | 0.8154 | 0.6053 | 0.7077 | 0.6155 | 0.0178 | 0.9342 | 0.4736 | 0.8129 | 0.0280 | 0.9162 | 0.6533 | 0.8880 | 0.7265 | 0.7562 | 0.6548 | 0.4753 | 0.6377 | 0.9232 | 0.7056 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 08:34:26 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9578 bicycle: 0.3623 motorcycle: 0.7149 truck: 0.8154 bus: 0.6053 person: 0.7077 bicyclist: 0.6155 motorcyclist: 0.0178 road: 0.9342 parking: 0.4736 sidewalk: 0.8129 other-ground: 0.0280 building: 0.9162 fence: 0.6533 vegetation: 0.8880 trunck: 0.7265 terrian: 0.7562 pole: 0.6548 traffic-sign: 0.4753 miou: 0.6377 acc: 0.9232 acc_cls: 0.7056 data_time: 0.0020 time: 0.6713 2023/05/13 08:35:40 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 08:35:56 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 8.0000e-03 eta: 20:02:05 time: 1.8034 data_time: 0.0039 memory: 4912 grad_norm: 0.1231 loss: 0.2496 loss_sem_seg: 0.2496 2023/05/13 08:37:21 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 8.0000e-03 eta: 19:58:48 time: 1.6948 data_time: 0.0032 memory: 5145 grad_norm: 0.1328 loss: 0.2508 loss_sem_seg: 0.2508 2023/05/13 08:38:55 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 8.0000e-03 eta: 19:55:57 time: 1.8871 data_time: 0.0033 memory: 5154 grad_norm: 0.1153 loss: 0.2233 loss_sem_seg: 0.2233 2023/05/13 08:40:24 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 8.0000e-03 eta: 19:52:51 time: 1.7683 data_time: 0.0033 memory: 4737 grad_norm: 0.1419 loss: 0.2380 loss_sem_seg: 0.2380 2023/05/13 08:41:43 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 8.0000e-03 eta: 19:49:24 time: 1.5960 data_time: 0.0033 memory: 4941 grad_norm: 0.1415 loss: 0.2567 loss_sem_seg: 0.2567 2023/05/13 08:43:03 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 8.0000e-03 eta: 19:45:58 time: 1.5906 data_time: 0.0034 memory: 5148 grad_norm: 0.1331 loss: 0.2449 loss_sem_seg: 0.2449 2023/05/13 08:44:22 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 8.0000e-03 eta: 19:42:32 time: 1.5862 data_time: 0.0034 memory: 5326 grad_norm: 0.1239 loss: 0.2520 loss_sem_seg: 0.2520 2023/05/13 08:45:40 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 8.0000e-03 eta: 19:39:04 time: 1.5629 data_time: 0.0033 memory: 4389 grad_norm: 0.1325 loss: 0.2441 loss_sem_seg: 0.2441 2023/05/13 08:47:11 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 8.0000e-03 eta: 19:36:07 time: 1.8046 data_time: 0.0032 memory: 4976 grad_norm: 0.1455 loss: 0.2520 loss_sem_seg: 0.2520 2023/05/13 08:48:48 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 8.0000e-03 eta: 19:33:27 time: 1.9398 data_time: 0.0033 memory: 5078 grad_norm: 0.1223 loss: 0.2385 loss_sem_seg: 0.2385 2023/05/13 08:50:22 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 8.0000e-03 eta: 19:30:42 time: 1.8887 data_time: 0.0032 memory: 4981 grad_norm: 0.1246 loss: 0.2511 loss_sem_seg: 0.2511 2023/05/13 08:51:57 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 8.0000e-03 eta: 19:27:58 time: 1.9006 data_time: 0.0033 memory: 5234 grad_norm: 0.1219 loss: 0.2420 loss_sem_seg: 0.2420 2023/05/13 08:53:32 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 8.0000e-03 eta: 19:25:16 time: 1.9054 data_time: 0.0033 memory: 4927 grad_norm: 0.1435 loss: 0.2399 loss_sem_seg: 0.2399 2023/05/13 08:55:05 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 8.0000e-03 eta: 19:22:28 time: 1.8558 data_time: 0.0032 memory: 5223 grad_norm: 0.1241 loss: 0.2408 loss_sem_seg: 0.2408 2023/05/13 08:56:32 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 8.0000e-03 eta: 19:19:27 time: 1.7409 data_time: 0.0032 memory: 4988 grad_norm: 0.1387 loss: 0.2406 loss_sem_seg: 0.2406 2023/05/13 08:57:58 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 8.0000e-03 eta: 19:16:25 time: 1.7233 data_time: 0.0032 memory: 4847 grad_norm: 0.1404 loss: 0.2376 loss_sem_seg: 0.2376 2023/05/13 08:59:23 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 8.0000e-03 eta: 19:13:21 time: 1.6974 data_time: 0.0033 memory: 4518 grad_norm: 0.1196 loss: 0.2266 loss_sem_seg: 0.2266 2023/05/13 09:00:51 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 8.0000e-03 eta: 19:10:23 time: 1.7519 data_time: 0.0033 memory: 4821 grad_norm: 0.1340 loss: 0.2331 loss_sem_seg: 0.2331 2023/05/13 09:02:30 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 8.0000e-03 eta: 19:07:53 time: 1.9775 data_time: 0.0034 memory: 4697 grad_norm: 0.1268 loss: 0.2459 loss_sem_seg: 0.2459 2023/05/13 09:04:04 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 8.0000e-03 eta: 19:05:12 time: 1.8801 data_time: 0.0033 memory: 4848 grad_norm: 0.1477 loss: 0.2499 loss_sem_seg: 0.2499 2023/05/13 09:05:20 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 09:05:39 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 8.0000e-03 eta: 19:02:33 time: 1.8953 data_time: 0.0032 memory: 5463 grad_norm: 0.1344 loss: 0.2556 loss_sem_seg: 0.2556 2023/05/13 09:07:04 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 8.0000e-03 eta: 18:59:33 time: 1.7042 data_time: 0.0033 memory: 4963 grad_norm: 0.1158 loss: 0.2342 loss_sem_seg: 0.2342 2023/05/13 09:08:33 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 8.0000e-03 eta: 18:56:41 time: 1.7789 data_time: 0.0033 memory: 4962 grad_norm: 0.1304 loss: 0.2238 loss_sem_seg: 0.2238 2023/05/13 09:10:01 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 09:10:01 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/05/13 09:10:41 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:05:11 time: 0.6787 data_time: 0.0021 memory: 4760 2023/05/13 09:11:12 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:04:28 time: 0.6363 data_time: 0.0020 memory: 915 2023/05/13 09:11:48 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:04:01 time: 0.7041 data_time: 0.0020 memory: 919 2023/05/13 09:12:21 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:03:27 time: 0.6664 data_time: 0.0021 memory: 907 2023/05/13 09:12:53 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:02:52 time: 0.6468 data_time: 0.0021 memory: 928 2023/05/13 09:13:24 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:02:17 time: 0.6079 data_time: 0.0020 memory: 883 2023/05/13 09:13:56 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:01:44 time: 0.6447 data_time: 0.0020 memory: 898 2023/05/13 09:14:28 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:01:11 time: 0.6345 data_time: 0.0020 memory: 903 2023/05/13 09:15:00 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:38 time: 0.6549 data_time: 0.0021 memory: 916 2023/05/13 09:15:33 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:05 time: 0.6444 data_time: 0.0020 memory: 902 2023/05/13 09:15:56 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9449 | 0.4389 | 0.6164 | 0.5325 | 0.4698 | 0.7350 | 0.8118 | 0.0469 | 0.9414 | 0.5212 | 0.8275 | 0.0129 | 0.9155 | 0.6315 | 0.8909 | 0.6688 | 0.7761 | 0.6538 | 0.4555 | 0.6259 | 0.9254 | 0.7013 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 09:15:56 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9449 bicycle: 0.4389 motorcycle: 0.6164 truck: 0.5325 bus: 0.4698 person: 0.7350 bicyclist: 0.8118 motorcyclist: 0.0469 road: 0.9414 parking: 0.5212 sidewalk: 0.8275 other-ground: 0.0129 building: 0.9155 fence: 0.6315 vegetation: 0.8909 trunck: 0.6688 terrian: 0.7761 pole: 0.6538 traffic-sign: 0.4555 miou: 0.6259 acc: 0.9254 acc_cls: 0.7013 data_time: 0.0021 time: 0.6632 2023/05/13 09:17:30 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 8.0000e-03 eta: 18:51:39 time: 1.8735 data_time: 0.0039 memory: 4904 grad_norm: 0.1393 loss: 0.2406 loss_sem_seg: 0.2406 2023/05/13 09:19:05 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 8.0000e-03 eta: 18:49:03 time: 1.8974 data_time: 0.0034 memory: 4555 grad_norm: 0.1284 loss: 0.2309 loss_sem_seg: 0.2309 2023/05/13 09:20:39 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 8.0000e-03 eta: 18:46:26 time: 1.8892 data_time: 0.0034 memory: 4963 grad_norm: 0.1311 loss: 0.2450 loss_sem_seg: 0.2450 2023/05/13 09:22:14 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 8.0000e-03 eta: 18:43:52 time: 1.9016 data_time: 0.0033 memory: 4591 grad_norm: 0.1418 loss: 0.2603 loss_sem_seg: 0.2603 2023/05/13 09:23:47 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 8.0000e-03 eta: 18:41:11 time: 1.8460 data_time: 0.0033 memory: 4991 grad_norm: 0.1339 loss: 0.2345 loss_sem_seg: 0.2345 2023/05/13 09:25:23 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 8.0000e-03 eta: 18:38:40 time: 1.9220 data_time: 0.0034 memory: 5307 grad_norm: 0.1326 loss: 0.2342 loss_sem_seg: 0.2342 2023/05/13 09:26:57 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 8.0000e-03 eta: 18:36:04 time: 1.8854 data_time: 0.0033 memory: 5091 grad_norm: 0.1334 loss: 0.2377 loss_sem_seg: 0.2377 2023/05/13 09:28:23 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 8.0000e-03 eta: 18:33:12 time: 1.7220 data_time: 0.0032 memory: 4948 grad_norm: 0.1389 loss: 0.2413 loss_sem_seg: 0.2413 2023/05/13 09:29:49 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 8.0000e-03 eta: 18:30:19 time: 1.7158 data_time: 0.0033 memory: 4729 grad_norm: 0.1246 loss: 0.2391 loss_sem_seg: 0.2391 2023/05/13 09:31:14 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 8.0000e-03 eta: 18:27:26 time: 1.7062 data_time: 0.0033 memory: 5075 grad_norm: 0.1278 loss: 0.2390 loss_sem_seg: 0.2390 2023/05/13 09:32:41 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 8.0000e-03 eta: 18:24:38 time: 1.7389 data_time: 0.0032 memory: 4751 grad_norm: 0.1222 loss: 0.2443 loss_sem_seg: 0.2443 2023/05/13 09:34:14 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 8.0000e-03 eta: 18:22:03 time: 1.8656 data_time: 0.0032 memory: 4553 grad_norm: 0.1308 loss: 0.2296 loss_sem_seg: 0.2296 2023/05/13 09:35:49 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 8.0000e-03 eta: 18:19:32 time: 1.8982 data_time: 0.0033 memory: 4848 grad_norm: 0.1295 loss: 0.2442 loss_sem_seg: 0.2442 2023/05/13 09:37:17 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 8.0000e-03 eta: 18:16:46 time: 1.7470 data_time: 0.0032 memory: 5279 grad_norm: 0.1127 loss: 0.2263 loss_sem_seg: 0.2263 2023/05/13 09:38:45 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 8.0000e-03 eta: 18:14:02 time: 1.7684 data_time: 0.0033 memory: 5153 grad_norm: 0.1239 loss: 0.2239 loss_sem_seg: 0.2239 2023/05/13 09:40:21 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 8.0000e-03 eta: 18:11:34 time: 1.9142 data_time: 0.0034 memory: 4996 grad_norm: 0.1241 loss: 0.2428 loss_sem_seg: 0.2428 2023/05/13 09:41:45 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 09:41:57 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 8.0000e-03 eta: 18:09:07 time: 1.9195 data_time: 0.0034 memory: 4968 grad_norm: 0.1173 loss: 0.2058 loss_sem_seg: 0.2058 2023/05/13 09:43:31 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 8.0000e-03 eta: 18:06:38 time: 1.8879 data_time: 0.0033 memory: 4820 grad_norm: 0.1430 loss: 0.2247 loss_sem_seg: 0.2247 2023/05/13 09:45:06 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 8.0000e-03 eta: 18:04:08 time: 1.8884 data_time: 0.0034 memory: 5053 grad_norm: 0.1227 loss: 0.2481 loss_sem_seg: 0.2481 2023/05/13 09:46:41 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 8.0000e-03 eta: 18:01:41 time: 1.9076 data_time: 0.0036 memory: 5225 grad_norm: 0.1306 loss: 0.2408 loss_sem_seg: 0.2408 2023/05/13 09:48:15 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 8.0000e-03 eta: 17:59:11 time: 1.8753 data_time: 0.0033 memory: 4767 grad_norm: 0.1426 loss: 0.2361 loss_sem_seg: 0.2361 2023/05/13 09:49:50 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 8.0000e-03 eta: 17:56:44 time: 1.8959 data_time: 0.0032 memory: 4830 grad_norm: 0.1262 loss: 0.2339 loss_sem_seg: 0.2339 2023/05/13 09:51:23 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 8.0000e-03 eta: 17:54:15 time: 1.8760 data_time: 0.0033 memory: 5908 grad_norm: 0.1366 loss: 0.2414 loss_sem_seg: 0.2414 2023/05/13 09:52:50 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 09:52:50 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/05/13 09:53:31 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:05:12 time: 0.6814 data_time: 0.0022 memory: 4780 2023/05/13 09:54:03 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:04:32 time: 0.6526 data_time: 0.0021 memory: 915 2023/05/13 09:54:37 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:03:59 time: 0.6636 data_time: 0.0021 memory: 919 2023/05/13 09:55:08 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:03:23 time: 0.6359 data_time: 0.0021 memory: 907 2023/05/13 09:55:42 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:02:51 time: 0.6696 data_time: 0.0020 memory: 928 2023/05/13 09:56:15 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:02:18 time: 0.6607 data_time: 0.0021 memory: 883 2023/05/13 09:56:48 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:01:44 time: 0.6547 data_time: 0.0021 memory: 898 2023/05/13 09:57:21 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:01:12 time: 0.6699 data_time: 0.0020 memory: 903 2023/05/13 09:57:53 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:38 time: 0.6362 data_time: 0.0021 memory: 916 2023/05/13 09:58:26 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:05 time: 0.6695 data_time: 0.0020 memory: 902 2023/05/13 09:58:50 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9490 | 0.4420 | 0.7185 | 0.6558 | 0.3724 | 0.6833 | 0.8660 | 0.1018 | 0.9362 | 0.5379 | 0.8144 | 0.0996 | 0.9065 | 0.6470 | 0.8967 | 0.7038 | 0.7779 | 0.6451 | 0.4808 | 0.6439 | 0.9251 | 0.7290 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 09:58:50 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9490 bicycle: 0.4420 motorcycle: 0.7185 truck: 0.6558 bus: 0.3724 person: 0.6833 bicyclist: 0.8660 motorcyclist: 0.1018 road: 0.9362 parking: 0.5379 sidewalk: 0.8144 other-ground: 0.0996 building: 0.9065 fence: 0.6470 vegetation: 0.8967 trunck: 0.7038 terrian: 0.7779 pole: 0.6451 traffic-sign: 0.4808 miou: 0.6439 acc: 0.9251 acc_cls: 0.7290 data_time: 0.0020 time: 0.6878 2023/05/13 10:00:20 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 8.0000e-03 eta: 17:49:25 time: 1.8106 data_time: 0.0045 memory: 4882 grad_norm: 0.1251 loss: 0.2464 loss_sem_seg: 0.2464 2023/05/13 10:01:45 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 8.0000e-03 eta: 17:46:38 time: 1.6855 data_time: 0.0032 memory: 5144 grad_norm: 0.1293 loss: 0.2447 loss_sem_seg: 0.2447 2023/05/13 10:03:10 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 8.0000e-03 eta: 17:43:55 time: 1.7112 data_time: 0.0033 memory: 4944 grad_norm: 0.1263 loss: 0.2296 loss_sem_seg: 0.2296 2023/05/13 10:04:37 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 8.0000e-03 eta: 17:41:13 time: 1.7306 data_time: 0.0032 memory: 4669 grad_norm: 0.1286 loss: 0.2306 loss_sem_seg: 0.2306 2023/05/13 10:06:10 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 8.0000e-03 eta: 17:38:45 time: 1.8627 data_time: 0.0032 memory: 4637 grad_norm: 0.1325 loss: 0.2246 loss_sem_seg: 0.2246 2023/05/13 10:07:37 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 8.0000e-03 eta: 17:36:06 time: 1.7403 data_time: 0.0032 memory: 4931 grad_norm: 0.1276 loss: 0.2429 loss_sem_seg: 0.2429 2023/05/13 10:09:03 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 8.0000e-03 eta: 17:33:26 time: 1.7238 data_time: 0.0033 memory: 4953 grad_norm: 0.1107 loss: 0.2374 loss_sem_seg: 0.2374 2023/05/13 10:10:37 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 8.0000e-03 eta: 17:31:02 time: 1.8893 data_time: 0.0032 memory: 5064 grad_norm: 0.1273 loss: 0.2300 loss_sem_seg: 0.2300 2023/05/13 10:12:12 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 8.0000e-03 eta: 17:28:38 time: 1.8886 data_time: 0.0033 memory: 4699 grad_norm: 0.1200 loss: 0.2264 loss_sem_seg: 0.2264 2023/05/13 10:13:47 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 8.0000e-03 eta: 17:26:16 time: 1.9037 data_time: 0.0034 memory: 5239 grad_norm: 0.1119 loss: 0.2142 loss_sem_seg: 0.2142 2023/05/13 10:15:23 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 8.0000e-03 eta: 17:23:55 time: 1.9109 data_time: 0.0033 memory: 4786 grad_norm: 0.1147 loss: 0.2229 loss_sem_seg: 0.2229 2023/05/13 10:16:58 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 8.0000e-03 eta: 17:21:34 time: 1.9116 data_time: 0.0032 memory: 5062 grad_norm: 0.1222 loss: 0.2396 loss_sem_seg: 0.2396 2023/05/13 10:18:28 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 10:18:32 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 8.0000e-03 eta: 17:19:10 time: 1.8719 data_time: 0.0033 memory: 4883 grad_norm: 0.1487 loss: 0.2328 loss_sem_seg: 0.2328 2023/05/13 10:20:06 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 8.0000e-03 eta: 17:16:47 time: 1.8795 data_time: 0.0033 memory: 4983 grad_norm: 0.1278 loss: 0.2411 loss_sem_seg: 0.2411 2023/05/13 10:21:42 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 8.0000e-03 eta: 17:14:28 time: 1.9159 data_time: 0.0032 memory: 5224 grad_norm: 0.1170 loss: 0.2198 loss_sem_seg: 0.2198 2023/05/13 10:23:17 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 8.0000e-03 eta: 17:12:08 time: 1.9088 data_time: 0.0033 memory: 5069 grad_norm: 0.1167 loss: 0.2336 loss_sem_seg: 0.2336 2023/05/13 10:24:52 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 8.0000e-03 eta: 17:09:47 time: 1.8914 data_time: 0.0033 memory: 5695 grad_norm: 0.1168 loss: 0.2304 loss_sem_seg: 0.2304 2023/05/13 10:26:26 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 8.0000e-03 eta: 17:07:26 time: 1.8937 data_time: 0.0032 memory: 4896 grad_norm: 0.1370 loss: 0.2333 loss_sem_seg: 0.2333 2023/05/13 10:28:02 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 8.0000e-03 eta: 17:05:08 time: 1.9081 data_time: 0.0034 memory: 5136 grad_norm: 0.1239 loss: 0.2397 loss_sem_seg: 0.2397 2023/05/13 10:29:37 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 8.0000e-03 eta: 17:02:50 time: 1.9152 data_time: 0.0033 memory: 4680 grad_norm: 0.1188 loss: 0.2404 loss_sem_seg: 0.2404 2023/05/13 10:31:12 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 8.0000e-03 eta: 17:00:30 time: 1.8956 data_time: 0.0033 memory: 4845 grad_norm: 0.1313 loss: 0.2343 loss_sem_seg: 0.2343 2023/05/13 10:32:47 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 8.0000e-03 eta: 16:58:11 time: 1.8992 data_time: 0.0033 memory: 4741 grad_norm: 0.1434 loss: 0.2489 loss_sem_seg: 0.2489 2023/05/13 10:34:15 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 8.0000e-03 eta: 16:55:39 time: 1.7497 data_time: 0.0033 memory: 4790 grad_norm: 0.1255 loss: 0.2387 loss_sem_seg: 0.2387 2023/05/13 10:35:35 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 10:35:35 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/05/13 10:36:12 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:04:42 time: 0.6150 data_time: 0.0021 memory: 4991 2023/05/13 10:36:40 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:03:59 time: 0.5546 data_time: 0.0020 memory: 915 2023/05/13 10:37:06 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:03:22 time: 0.5234 data_time: 0.0020 memory: 919 2023/05/13 10:37:34 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:02:54 time: 0.5642 data_time: 0.0020 memory: 907 2023/05/13 10:37:49 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:02:12 time: 0.3055 data_time: 0.0020 memory: 928 2023/05/13 10:38:08 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:01:42 time: 0.3686 data_time: 0.0020 memory: 883 2023/05/13 10:38:24 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:01:13 time: 0.3232 data_time: 0.0020 memory: 898 2023/05/13 10:38:46 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:00:50 time: 0.4461 data_time: 0.0020 memory: 903 2023/05/13 10:39:09 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:27 time: 0.4520 data_time: 0.0021 memory: 916 2023/05/13 10:39:27 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:04 time: 0.3706 data_time: 0.0020 memory: 902 2023/05/13 10:40:20 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9630 | 0.4508 | 0.7035 | 0.4968 | 0.5002 | 0.7088 | 0.8126 | 0.0015 | 0.9306 | 0.4035 | 0.8128 | 0.0245 | 0.9066 | 0.6433 | 0.8854 | 0.6765 | 0.7590 | 0.6369 | 0.4326 | 0.6184 | 0.9208 | 0.6988 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 10:40:20 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9630 bicycle: 0.4508 motorcycle: 0.7035 truck: 0.4968 bus: 0.5002 person: 0.7088 bicyclist: 0.8126 motorcyclist: 0.0015 road: 0.9306 parking: 0.4035 sidewalk: 0.8128 other-ground: 0.0245 building: 0.9066 fence: 0.6433 vegetation: 0.8854 trunck: 0.6765 terrian: 0.7590 pole: 0.6369 traffic-sign: 0.4326 miou: 0.6184 acc: 0.9208 acc_cls: 0.6988 data_time: 0.0020 time: 0.3548 2023/05/13 10:41:39 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 8.0000e-03 eta: 16:50:34 time: 1.5835 data_time: 0.0044 memory: 4924 grad_norm: 0.1171 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/13 10:42:58 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 8.0000e-03 eta: 16:47:49 time: 1.5850 data_time: 0.0033 memory: 4744 grad_norm: 0.1248 loss: 0.2224 loss_sem_seg: 0.2224 2023/05/13 10:44:33 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 8.0000e-03 eta: 16:45:32 time: 1.8993 data_time: 0.0033 memory: 4809 grad_norm: 0.1246 loss: 0.2276 loss_sem_seg: 0.2276 2023/05/13 10:46:08 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.0000e-03 eta: 16:43:15 time: 1.9004 data_time: 0.0033 memory: 4889 grad_norm: 0.1173 loss: 0.2252 loss_sem_seg: 0.2252 2023/05/13 10:47:44 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.0000e-03 eta: 16:41:00 time: 1.9112 data_time: 0.0036 memory: 4932 grad_norm: 0.1261 loss: 0.2383 loss_sem_seg: 0.2383 2023/05/13 10:49:19 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 8.0000e-03 eta: 16:38:43 time: 1.8977 data_time: 0.0032 memory: 4693 grad_norm: 0.1273 loss: 0.2293 loss_sem_seg: 0.2293 2023/05/13 10:50:54 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 8.0000e-03 eta: 16:36:28 time: 1.9096 data_time: 0.0034 memory: 4963 grad_norm: 0.1364 loss: 0.2484 loss_sem_seg: 0.2484 2023/05/13 10:52:27 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 8.0000e-03 eta: 16:34:09 time: 1.8565 data_time: 0.0034 memory: 5014 grad_norm: 0.1128 loss: 0.2272 loss_sem_seg: 0.2272 2023/05/13 10:54:01 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 8.0000e-03 eta: 16:31:52 time: 1.8854 data_time: 0.0033 memory: 4751 grad_norm: 0.1107 loss: 0.2262 loss_sem_seg: 0.2262 2023/05/13 10:54:05 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 10:55:37 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 8.0000e-03 eta: 16:29:37 time: 1.9057 data_time: 0.0032 memory: 5080 grad_norm: 0.1193 loss: 0.2537 loss_sem_seg: 0.2537 2023/05/13 10:57:12 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 8.0000e-03 eta: 16:27:23 time: 1.9074 data_time: 0.0034 memory: 4936 grad_norm: 0.1134 loss: 0.2280 loss_sem_seg: 0.2280 2023/05/13 10:58:45 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 8.0000e-03 eta: 16:25:05 time: 1.8658 data_time: 0.0033 memory: 5086 grad_norm: 0.1187 loss: 0.2351 loss_sem_seg: 0.2351 2023/05/13 11:00:19 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 8.0000e-03 eta: 16:22:49 time: 1.8736 data_time: 0.0033 memory: 4747 grad_norm: 0.1271 loss: 0.2497 loss_sem_seg: 0.2497 2023/05/13 11:01:55 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 8.0000e-03 eta: 16:20:36 time: 1.9105 data_time: 0.0034 memory: 5055 grad_norm: 0.1248 loss: 0.2480 loss_sem_seg: 0.2480 2023/05/13 11:03:30 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 8.0000e-03 eta: 16:18:23 time: 1.9166 data_time: 0.0033 memory: 4906 grad_norm: 0.1306 loss: 0.2373 loss_sem_seg: 0.2373 2023/05/13 11:05:00 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 8.0000e-03 eta: 16:16:01 time: 1.7986 data_time: 0.0033 memory: 4807 grad_norm: 0.1209 loss: 0.2279 loss_sem_seg: 0.2279 2023/05/13 11:06:27 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 8.0000e-03 eta: 16:13:35 time: 1.7414 data_time: 0.0033 memory: 4727 grad_norm: 0.1179 loss: 0.2410 loss_sem_seg: 0.2410 2023/05/13 11:07:54 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 8.0000e-03 eta: 16:11:08 time: 1.7353 data_time: 0.0033 memory: 4921 grad_norm: 0.1080 loss: 0.2219 loss_sem_seg: 0.2219 2023/05/13 11:09:09 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 8.0000e-03 eta: 16:08:24 time: 1.5042 data_time: 0.0034 memory: 5226 grad_norm: 0.1091 loss: 0.2266 loss_sem_seg: 0.2266 2023/05/13 11:10:37 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 8.0000e-03 eta: 16:05:59 time: 1.7502 data_time: 0.0034 memory: 4863 grad_norm: 0.1218 loss: 0.2221 loss_sem_seg: 0.2221 2023/05/13 11:12:12 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 8.0000e-03 eta: 16:03:47 time: 1.9028 data_time: 0.0040 memory: 4844 grad_norm: 0.1270 loss: 0.2134 loss_sem_seg: 0.2134 2023/05/13 11:13:45 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 8.0000e-03 eta: 16:01:33 time: 1.8679 data_time: 0.0034 memory: 4902 grad_norm: 0.1138 loss: 0.2257 loss_sem_seg: 0.2257 2023/05/13 11:15:20 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 8.0000e-03 eta: 15:59:20 time: 1.8890 data_time: 0.0036 memory: 4562 grad_norm: 0.1364 loss: 0.2311 loss_sem_seg: 0.2311 2023/05/13 11:16:47 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 11:16:47 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/05/13 11:17:30 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:05:32 time: 0.7238 data_time: 0.0021 memory: 4672 2023/05/13 11:18:03 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:04:42 time: 0.6595 data_time: 0.0020 memory: 915 2023/05/13 11:18:36 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:04:04 time: 0.6584 data_time: 0.0020 memory: 919 2023/05/13 11:19:09 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:03:28 time: 0.6606 data_time: 0.0020 memory: 907 2023/05/13 11:19:43 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:02:55 time: 0.6840 data_time: 0.0021 memory: 928 2023/05/13 11:20:15 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:02:20 time: 0.6362 data_time: 0.0021 memory: 883 2023/05/13 11:20:48 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:01:46 time: 0.6610 data_time: 0.0021 memory: 898 2023/05/13 11:21:22 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:01:12 time: 0.6718 data_time: 0.0020 memory: 903 2023/05/13 11:21:54 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:39 time: 0.6394 data_time: 0.0021 memory: 916 2023/05/13 11:22:27 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:05 time: 0.6657 data_time: 0.0021 memory: 902 2023/05/13 11:22:50 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9521 | 0.5034 | 0.7401 | 0.7542 | 0.4803 | 0.5530 | 0.5029 | 0.0100 | 0.9370 | 0.4392 | 0.8111 | 0.0673 | 0.9113 | 0.6795 | 0.8714 | 0.6169 | 0.7011 | 0.6263 | 0.4308 | 0.6099 | 0.9145 | 0.6878 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 11:22:50 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9521 bicycle: 0.5034 motorcycle: 0.7401 truck: 0.7542 bus: 0.4803 person: 0.5530 bicyclist: 0.5029 motorcyclist: 0.0100 road: 0.9370 parking: 0.4392 sidewalk: 0.8111 other-ground: 0.0673 building: 0.9113 fence: 0.6795 vegetation: 0.8714 trunck: 0.6169 terrian: 0.7011 pole: 0.6263 traffic-sign: 0.4308 miou: 0.6099 acc: 0.9145 acc_cls: 0.6878 data_time: 0.0022 time: 0.6960 2023/05/13 11:24:26 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 8.0000e-03 eta: 15:55:10 time: 1.9292 data_time: 0.0040 memory: 5036 grad_norm: 0.1347 loss: 0.2300 loss_sem_seg: 0.2300 2023/05/13 11:26:01 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 8.0000e-03 eta: 15:53:00 time: 1.9026 data_time: 0.0033 memory: 5086 grad_norm: 0.1177 loss: 0.2418 loss_sem_seg: 0.2418 2023/05/13 11:27:37 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 8.0000e-03 eta: 15:50:51 time: 1.9239 data_time: 0.0032 memory: 4810 grad_norm: 0.1131 loss: 0.2241 loss_sem_seg: 0.2241 2023/05/13 11:29:14 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 8.0000e-03 eta: 15:48:42 time: 1.9265 data_time: 0.0032 memory: 5190 grad_norm: 0.1222 loss: 0.2240 loss_sem_seg: 0.2240 2023/05/13 11:30:48 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 8.0000e-03 eta: 15:46:30 time: 1.8809 data_time: 0.0033 memory: 4894 grad_norm: 0.1189 loss: 0.2337 loss_sem_seg: 0.2337 2023/05/13 11:30:59 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 11:32:23 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 8.0000e-03 eta: 15:44:20 time: 1.9046 data_time: 0.0035 memory: 4865 grad_norm: 0.1248 loss: 0.2194 loss_sem_seg: 0.2194 2023/05/13 11:33:56 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 8.0000e-03 eta: 15:42:08 time: 1.8681 data_time: 0.0033 memory: 4702 grad_norm: 0.1084 loss: 0.2131 loss_sem_seg: 0.2131 2023/05/13 11:35:31 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 8.0000e-03 eta: 15:39:58 time: 1.8966 data_time: 0.0033 memory: 4933 grad_norm: 0.1358 loss: 0.2401 loss_sem_seg: 0.2401 2023/05/13 11:37:03 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 8.0000e-03 eta: 15:37:43 time: 1.8294 data_time: 0.0033 memory: 5493 grad_norm: 0.1186 loss: 0.2213 loss_sem_seg: 0.2213 2023/05/13 11:38:28 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 8.0000e-03 eta: 15:35:19 time: 1.7114 data_time: 0.0033 memory: 4848 grad_norm: 0.1145 loss: 0.2462 loss_sem_seg: 0.2462 2023/05/13 11:39:46 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 8.0000e-03 eta: 15:32:44 time: 1.5572 data_time: 0.0033 memory: 5120 grad_norm: 0.1110 loss: 0.2218 loss_sem_seg: 0.2218 2023/05/13 11:41:03 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 8.0000e-03 eta: 15:30:09 time: 1.5444 data_time: 0.0033 memory: 5077 grad_norm: 0.1302 loss: 0.2185 loss_sem_seg: 0.2185 2023/05/13 11:42:36 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 8.0000e-03 eta: 15:27:57 time: 1.8448 data_time: 0.0033 memory: 5068 grad_norm: 0.1189 loss: 0.2395 loss_sem_seg: 0.2395 2023/05/13 11:44:11 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 8.0000e-03 eta: 15:25:49 time: 1.9140 data_time: 0.0037 memory: 5313 grad_norm: 0.1059 loss: 0.2089 loss_sem_seg: 0.2089 2023/05/13 11:45:47 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 8.0000e-03 eta: 15:23:42 time: 1.9142 data_time: 0.0036 memory: 5155 grad_norm: 0.1067 loss: 0.2280 loss_sem_seg: 0.2280 2023/05/13 11:47:22 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 8.0000e-03 eta: 15:21:35 time: 1.9025 data_time: 0.0034 memory: 4842 grad_norm: 0.1282 loss: 0.2483 loss_sem_seg: 0.2483 2023/05/13 11:48:59 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 8.0000e-03 eta: 15:19:30 time: 1.9353 data_time: 0.0033 memory: 4768 grad_norm: 0.1042 loss: 0.2250 loss_sem_seg: 0.2250 2023/05/13 11:50:33 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 8.0000e-03 eta: 15:17:20 time: 1.8742 data_time: 0.0033 memory: 4821 grad_norm: 0.1177 loss: 0.1985 loss_sem_seg: 0.1985 2023/05/13 11:52:07 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 8.0000e-03 eta: 15:15:12 time: 1.8914 data_time: 0.0033 memory: 5052 grad_norm: 0.1226 loss: 0.2269 loss_sem_seg: 0.2269 2023/05/13 11:53:41 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.0000e-03 eta: 15:13:04 time: 1.8830 data_time: 0.0033 memory: 5041 grad_norm: 0.1051 loss: 0.2214 loss_sem_seg: 0.2214 2023/05/13 11:55:16 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 8.0000e-03 eta: 15:10:56 time: 1.8955 data_time: 0.0032 memory: 4795 grad_norm: 0.1247 loss: 0.2215 loss_sem_seg: 0.2215 2023/05/13 11:56:51 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 8.0000e-03 eta: 15:08:50 time: 1.9006 data_time: 0.0032 memory: 4869 grad_norm: 0.1228 loss: 0.2222 loss_sem_seg: 0.2222 2023/05/13 11:58:27 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 8.0000e-03 eta: 15:06:45 time: 1.9206 data_time: 0.0034 memory: 5680 grad_norm: 0.1145 loss: 0.2296 loss_sem_seg: 0.2296 2023/05/13 11:59:55 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 11:59:55 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/05/13 12:00:37 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:05:23 time: 0.7055 data_time: 0.0021 memory: 4747 2023/05/13 12:01:11 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:04:43 time: 0.6816 data_time: 0.0020 memory: 915 2023/05/13 12:01:44 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:04:05 time: 0.6606 data_time: 0.0021 memory: 919 2023/05/13 12:02:17 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:03:29 time: 0.6675 data_time: 0.0021 memory: 907 2023/05/13 12:02:52 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:02:56 time: 0.6916 data_time: 0.0021 memory: 928 2023/05/13 12:03:25 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:02:22 time: 0.6720 data_time: 0.0021 memory: 883 2023/05/13 12:03:57 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:01:47 time: 0.6413 data_time: 0.0021 memory: 898 2023/05/13 12:04:29 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:01:12 time: 0.6276 data_time: 0.0021 memory: 903 2023/05/13 12:05:02 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:39 time: 0.6626 data_time: 0.0021 memory: 916 2023/05/13 12:05:37 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:06 time: 0.7025 data_time: 0.0020 memory: 902 2023/05/13 12:06:00 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9433 | 0.5112 | 0.6990 | 0.6191 | 0.3682 | 0.6952 | 0.8202 | 0.0211 | 0.9327 | 0.3802 | 0.8116 | 0.0400 | 0.8956 | 0.5761 | 0.8930 | 0.7071 | 0.7693 | 0.6368 | 0.4866 | 0.6214 | 0.9206 | 0.6931 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 12:06:00 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9433 bicycle: 0.5112 motorcycle: 0.6990 truck: 0.6191 bus: 0.3682 person: 0.6952 bicyclist: 0.8202 motorcyclist: 0.0211 road: 0.9327 parking: 0.3802 sidewalk: 0.8116 other-ground: 0.0400 building: 0.8956 fence: 0.5761 vegetation: 0.8930 trunck: 0.7071 terrian: 0.7693 pole: 0.6368 traffic-sign: 0.4866 miou: 0.6214 acc: 0.9206 acc_cls: 0.6931 data_time: 0.0020 time: 0.7180 2023/05/13 12:07:34 - mmengine - INFO - Epoch(train) [16][ 50/1196] lr: 8.0000e-03 eta: 15:02:42 time: 1.8938 data_time: 0.0039 memory: 5160 grad_norm: 0.1086 loss: 0.2219 loss_sem_seg: 0.2219 2023/05/13 12:07:53 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 12:09:08 - mmengine - INFO - Epoch(train) [16][ 100/1196] lr: 8.0000e-03 eta: 15:00:34 time: 1.8736 data_time: 0.0032 memory: 4787 grad_norm: 0.1260 loss: 0.2397 loss_sem_seg: 0.2397 2023/05/13 12:10:25 - mmengine - INFO - Epoch(train) [16][ 150/1196] lr: 8.0000e-03 eta: 14:58:04 time: 1.5420 data_time: 0.0032 memory: 5001 grad_norm: 0.1244 loss: 0.2140 loss_sem_seg: 0.2140 2023/05/13 12:11:41 - mmengine - INFO - Epoch(train) [16][ 200/1196] lr: 8.0000e-03 eta: 14:55:31 time: 1.5074 data_time: 0.0032 memory: 4694 grad_norm: 0.1309 loss: 0.2301 loss_sem_seg: 0.2301 2023/05/13 12:13:07 - mmengine - INFO - Epoch(train) [16][ 250/1196] lr: 8.0000e-03 eta: 14:53:14 time: 1.7282 data_time: 0.0032 memory: 4800 grad_norm: 0.1197 loss: 0.2248 loss_sem_seg: 0.2248 2023/05/13 12:14:34 - mmengine - INFO - Epoch(train) [16][ 300/1196] lr: 8.0000e-03 eta: 14:50:58 time: 1.7454 data_time: 0.0033 memory: 5160 grad_norm: 0.1203 loss: 0.2182 loss_sem_seg: 0.2182 2023/05/13 12:16:13 - mmengine - INFO - Epoch(train) [16][ 350/1196] lr: 8.0000e-03 eta: 14:48:59 time: 1.9763 data_time: 0.0033 memory: 4513 grad_norm: 0.1104 loss: 0.2280 loss_sem_seg: 0.2280 2023/05/13 12:17:48 - mmengine - INFO - Epoch(train) [16][ 400/1196] lr: 8.0000e-03 eta: 14:46:54 time: 1.8985 data_time: 0.0032 memory: 5201 grad_norm: 0.1225 loss: 0.2200 loss_sem_seg: 0.2200 2023/05/13 12:19:22 - mmengine - INFO - Epoch(train) [16][ 450/1196] lr: 8.0000e-03 eta: 14:44:48 time: 1.8889 data_time: 0.0032 memory: 4681 grad_norm: 0.1061 loss: 0.2129 loss_sem_seg: 0.2129 2023/05/13 12:20:57 - mmengine - INFO - Epoch(train) [16][ 500/1196] lr: 8.0000e-03 eta: 14:42:43 time: 1.8853 data_time: 0.0034 memory: 4837 grad_norm: 0.1166 loss: 0.2218 loss_sem_seg: 0.2218 2023/05/13 12:22:32 - mmengine - INFO - Epoch(train) [16][ 550/1196] lr: 8.0000e-03 eta: 14:40:38 time: 1.8952 data_time: 0.0033 memory: 4580 grad_norm: 0.1141 loss: 0.2300 loss_sem_seg: 0.2300 2023/05/13 12:24:06 - mmengine - INFO - Epoch(train) [16][ 600/1196] lr: 8.0000e-03 eta: 14:38:34 time: 1.8985 data_time: 0.0033 memory: 4741 grad_norm: 0.1147 loss: 0.2235 loss_sem_seg: 0.2235 2023/05/13 12:25:42 - mmengine - INFO - Epoch(train) [16][ 650/1196] lr: 8.0000e-03 eta: 14:36:31 time: 1.9140 data_time: 0.0034 memory: 4839 grad_norm: 0.1190 loss: 0.2401 loss_sem_seg: 0.2401 2023/05/13 12:27:18 - mmengine - INFO - Epoch(train) [16][ 700/1196] lr: 8.0000e-03 eta: 14:34:28 time: 1.9071 data_time: 0.0032 memory: 4995 grad_norm: 0.1109 loss: 0.2213 loss_sem_seg: 0.2213 2023/05/13 12:28:52 - mmengine - INFO - Epoch(train) [16][ 750/1196] lr: 8.0000e-03 eta: 14:32:24 time: 1.8962 data_time: 0.0032 memory: 5140 grad_norm: 0.1049 loss: 0.2236 loss_sem_seg: 0.2236 2023/05/13 12:30:27 - mmengine - INFO - Epoch(train) [16][ 800/1196] lr: 8.0000e-03 eta: 14:30:20 time: 1.8921 data_time: 0.0032 memory: 5027 grad_norm: 0.1040 loss: 0.2376 loss_sem_seg: 0.2376 2023/05/13 12:32:02 - mmengine - INFO - Epoch(train) [16][ 850/1196] lr: 8.0000e-03 eta: 14:28:16 time: 1.8942 data_time: 0.0032 memory: 4820 grad_norm: 0.1114 loss: 0.2169 loss_sem_seg: 0.2169 2023/05/13 12:33:38 - mmengine - INFO - Epoch(train) [16][ 900/1196] lr: 8.0000e-03 eta: 14:26:14 time: 1.9268 data_time: 0.0032 memory: 5081 grad_norm: 0.1251 loss: 0.2295 loss_sem_seg: 0.2295 2023/05/13 12:35:15 - mmengine - INFO - Epoch(train) [16][ 950/1196] lr: 8.0000e-03 eta: 14:24:14 time: 1.9380 data_time: 0.0032 memory: 4813 grad_norm: 0.1266 loss: 0.2250 loss_sem_seg: 0.2250 2023/05/13 12:36:46 - mmengine - INFO - Epoch(train) [16][1000/1196] lr: 8.0000e-03 eta: 14:22:05 time: 1.8140 data_time: 0.0033 memory: 4783 grad_norm: 0.1155 loss: 0.2214 loss_sem_seg: 0.2214 2023/05/13 12:38:05 - mmengine - INFO - Epoch(train) [16][1050/1196] lr: 8.0000e-03 eta: 14:19:42 time: 1.5785 data_time: 0.0033 memory: 4932 grad_norm: 0.1148 loss: 0.2198 loss_sem_seg: 0.2198 2023/05/13 12:38:20 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 12:39:25 - mmengine - INFO - Epoch(train) [16][1100/1196] lr: 8.0000e-03 eta: 14:17:21 time: 1.6017 data_time: 0.0032 memory: 5159 grad_norm: 0.1137 loss: 0.2244 loss_sem_seg: 0.2244 2023/05/13 12:40:42 - mmengine - INFO - Epoch(train) [16][1150/1196] lr: 8.0000e-03 eta: 14:14:57 time: 1.5534 data_time: 0.0034 memory: 4635 grad_norm: 0.1150 loss: 0.2274 loss_sem_seg: 0.2274 2023/05/13 12:41:41 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 12:41:41 - mmengine - INFO - Saving checkpoint at 16 epochs 2023/05/13 12:42:04 - mmengine - INFO - Epoch(val) [16][ 50/509] eta: 0:02:39 time: 0.3468 data_time: 0.0021 memory: 5014 2023/05/13 12:42:16 - mmengine - INFO - Epoch(val) [16][100/509] eta: 0:01:59 time: 0.2377 data_time: 0.0020 memory: 915 2023/05/13 12:42:37 - mmengine - INFO - Epoch(val) [16][150/509] eta: 0:01:58 time: 0.4089 data_time: 0.0020 memory: 919 2023/05/13 12:43:05 - mmengine - INFO - Epoch(val) [16][200/509] eta: 0:02:00 time: 0.5684 data_time: 0.0020 memory: 907 2023/05/13 12:43:33 - mmengine - INFO - Epoch(val) [16][250/509] eta: 0:01:50 time: 0.5626 data_time: 0.0021 memory: 928 2023/05/13 12:44:00 - mmengine - INFO - Epoch(val) [16][300/509] eta: 0:01:32 time: 0.5399 data_time: 0.0021 memory: 883 2023/05/13 12:44:29 - mmengine - INFO - Epoch(val) [16][350/509] eta: 0:01:13 time: 0.5664 data_time: 0.0021 memory: 898 2023/05/13 12:44:57 - mmengine - INFO - Epoch(val) [16][400/509] eta: 0:00:51 time: 0.5696 data_time: 0.0020 memory: 903 2023/05/13 12:45:25 - mmengine - INFO - Epoch(val) [16][450/509] eta: 0:00:28 time: 0.5661 data_time: 0.0020 memory: 916 2023/05/13 12:46:01 - mmengine - INFO - Epoch(val) [16][500/509] eta: 0:00:04 time: 0.7021 data_time: 0.0020 memory: 902 2023/05/13 12:46:54 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9660 | 0.4399 | 0.7034 | 0.3841 | 0.6418 | 0.7376 | 0.6898 | 0.0174 | 0.9327 | 0.4337 | 0.8144 | 0.0061 | 0.9139 | 0.6591 | 0.8869 | 0.6089 | 0.7644 | 0.6500 | 0.5057 | 0.6187 | 0.9226 | 0.6882 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 12:46:54 - mmengine - INFO - Epoch(val) [16][509/509] car: 0.9660 bicycle: 0.4399 motorcycle: 0.7034 truck: 0.3841 bus: 0.6418 person: 0.7376 bicyclist: 0.6898 motorcyclist: 0.0174 road: 0.9327 parking: 0.4337 sidewalk: 0.8144 other-ground: 0.0061 building: 0.9139 fence: 0.6591 vegetation: 0.8869 trunck: 0.6089 terrian: 0.7644 pole: 0.6500 traffic-sign: 0.5057 miou: 0.6187 acc: 0.9226 acc_cls: 0.6882 data_time: 0.0020 time: 0.6844 2023/05/13 12:48:30 - mmengine - INFO - Epoch(train) [17][ 50/1196] lr: 8.0000e-03 eta: 14:10:27 time: 1.9027 data_time: 0.0039 memory: 4668 grad_norm: 0.1159 loss: 0.2286 loss_sem_seg: 0.2286 2023/05/13 12:50:05 - mmengine - INFO - Epoch(train) [17][ 100/1196] lr: 8.0000e-03 eta: 14:08:25 time: 1.9019 data_time: 0.0036 memory: 5027 grad_norm: 0.1223 loss: 0.2230 loss_sem_seg: 0.2230 2023/05/13 12:51:40 - mmengine - INFO - Epoch(train) [17][ 150/1196] lr: 8.0000e-03 eta: 14:06:25 time: 1.9113 data_time: 0.0034 memory: 4930 grad_norm: 0.1038 loss: 0.2138 loss_sem_seg: 0.2138 2023/05/13 12:53:15 - mmengine - INFO - Epoch(train) [17][ 200/1196] lr: 8.0000e-03 eta: 14:04:23 time: 1.9033 data_time: 0.0037 memory: 5202 grad_norm: 0.1140 loss: 0.2064 loss_sem_seg: 0.2064 2023/05/13 12:54:50 - mmengine - INFO - Epoch(train) [17][ 250/1196] lr: 8.0000e-03 eta: 14:02:21 time: 1.8844 data_time: 0.0034 memory: 5008 grad_norm: 0.1125 loss: 0.2310 loss_sem_seg: 0.2310 2023/05/13 12:56:25 - mmengine - INFO - Epoch(train) [17][ 300/1196] lr: 8.0000e-03 eta: 14:00:20 time: 1.9044 data_time: 0.0034 memory: 4741 grad_norm: 0.1141 loss: 0.2055 loss_sem_seg: 0.2055 2023/05/13 12:57:59 - mmengine - INFO - Epoch(train) [17][ 350/1196] lr: 8.0000e-03 eta: 13:58:19 time: 1.8852 data_time: 0.0034 memory: 5164 grad_norm: 0.1105 loss: 0.2119 loss_sem_seg: 0.2119 2023/05/13 12:59:34 - mmengine - INFO - Epoch(train) [17][ 400/1196] lr: 8.0000e-03 eta: 13:56:17 time: 1.8913 data_time: 0.0034 memory: 5583 grad_norm: 0.1237 loss: 0.2285 loss_sem_seg: 0.2285 2023/05/13 13:01:08 - mmengine - INFO - Epoch(train) [17][ 450/1196] lr: 8.0000e-03 eta: 13:54:16 time: 1.8807 data_time: 0.0034 memory: 5695 grad_norm: 0.1129 loss: 0.2292 loss_sem_seg: 0.2292 2023/05/13 13:02:43 - mmengine - INFO - Epoch(train) [17][ 500/1196] lr: 8.0000e-03 eta: 13:52:15 time: 1.8977 data_time: 0.0033 memory: 4862 grad_norm: 0.1023 loss: 0.2185 loss_sem_seg: 0.2185 2023/05/13 13:04:18 - mmengine - INFO - Epoch(train) [17][ 550/1196] lr: 8.0000e-03 eta: 13:50:15 time: 1.9109 data_time: 0.0032 memory: 5061 grad_norm: 0.1205 loss: 0.2296 loss_sem_seg: 0.2296 2023/05/13 13:05:54 - mmengine - INFO - Epoch(train) [17][ 600/1196] lr: 8.0000e-03 eta: 13:48:16 time: 1.9247 data_time: 0.0034 memory: 5233 grad_norm: 0.1172 loss: 0.2202 loss_sem_seg: 0.2202 2023/05/13 13:07:30 - mmengine - INFO - Epoch(train) [17][ 650/1196] lr: 8.0000e-03 eta: 13:46:17 time: 1.9127 data_time: 0.0034 memory: 5185 grad_norm: 0.1401 loss: 0.2430 loss_sem_seg: 0.2430 2023/05/13 13:09:05 - mmengine - INFO - Epoch(train) [17][ 700/1196] lr: 8.0000e-03 eta: 13:44:17 time: 1.8971 data_time: 0.0033 memory: 4877 grad_norm: 0.1138 loss: 0.2256 loss_sem_seg: 0.2256 2023/05/13 13:10:41 - mmengine - INFO - Epoch(train) [17][ 750/1196] lr: 8.0000e-03 eta: 13:42:18 time: 1.9171 data_time: 0.0033 memory: 5079 grad_norm: 0.1149 loss: 0.2312 loss_sem_seg: 0.2312 2023/05/13 13:12:07 - mmengine - INFO - Epoch(train) [17][ 800/1196] lr: 8.0000e-03 eta: 13:40:08 time: 1.7260 data_time: 0.0033 memory: 5738 grad_norm: 0.1033 loss: 0.2237 loss_sem_seg: 0.2237 2023/05/13 13:13:33 - mmengine - INFO - Epoch(train) [17][ 850/1196] lr: 8.0000e-03 eta: 13:37:58 time: 1.7177 data_time: 0.0032 memory: 5021 grad_norm: 0.1102 loss: 0.2247 loss_sem_seg: 0.2247 2023/05/13 13:13:57 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 13:14:59 - mmengine - INFO - Epoch(train) [17][ 900/1196] lr: 8.0000e-03 eta: 13:35:48 time: 1.7233 data_time: 0.0033 memory: 4839 grad_norm: 0.1207 loss: 0.2154 loss_sem_seg: 0.2154 2023/05/13 13:16:28 - mmengine - INFO - Epoch(train) [17][ 950/1196] lr: 8.0000e-03 eta: 13:33:42 time: 1.7797 data_time: 0.0033 memory: 5463 grad_norm: 0.1012 loss: 0.2199 loss_sem_seg: 0.2199 2023/05/13 13:17:56 - mmengine - INFO - Epoch(train) [17][1000/1196] lr: 8.0000e-03 eta: 13:31:35 time: 1.7578 data_time: 0.0033 memory: 4830 grad_norm: 0.1491 loss: 0.2491 loss_sem_seg: 0.2491 2023/05/13 13:19:25 - mmengine - INFO - Epoch(train) [17][1050/1196] lr: 8.0000e-03 eta: 13:29:29 time: 1.7716 data_time: 0.0033 memory: 5190 grad_norm: 0.1361 loss: 0.2570 loss_sem_seg: 0.2570 2023/05/13 13:21:06 - mmengine - INFO - Epoch(train) [17][1100/1196] lr: 8.0000e-03 eta: 13:27:38 time: 2.0372 data_time: 0.0033 memory: 4779 grad_norm: 0.1145 loss: 0.2337 loss_sem_seg: 0.2337 2023/05/13 13:22:44 - mmengine - INFO - Epoch(train) [17][1150/1196] lr: 8.0000e-03 eta: 13:25:42 time: 1.9519 data_time: 0.0033 memory: 5221 grad_norm: 0.1055 loss: 0.2276 loss_sem_seg: 0.2276 2023/05/13 13:24:13 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 13:24:13 - mmengine - INFO - Saving checkpoint at 17 epochs 2023/05/13 13:24:54 - mmengine - INFO - Epoch(val) [17][ 50/509] eta: 0:05:18 time: 0.6938 data_time: 0.0020 memory: 4611 2023/05/13 13:25:29 - mmengine - INFO - Epoch(val) [17][100/509] eta: 0:04:44 time: 0.6988 data_time: 0.0020 memory: 915 2023/05/13 13:26:05 - mmengine - INFO - Epoch(val) [17][150/509] eta: 0:04:11 time: 0.7111 data_time: 0.0021 memory: 919 2023/05/13 13:26:41 - mmengine - INFO - Epoch(val) [17][200/509] eta: 0:03:37 time: 0.7172 data_time: 0.0021 memory: 907 2023/05/13 13:27:15 - mmengine - INFO - Epoch(val) [17][250/509] eta: 0:03:01 time: 0.6925 data_time: 0.0020 memory: 928 2023/05/13 13:27:49 - mmengine - INFO - Epoch(val) [17][300/509] eta: 0:02:26 time: 0.6790 data_time: 0.0021 memory: 883 2023/05/13 13:28:23 - mmengine - INFO - Epoch(val) [17][350/509] eta: 0:01:50 time: 0.6759 data_time: 0.0020 memory: 898 2023/05/13 13:28:57 - mmengine - INFO - Epoch(val) [17][400/509] eta: 0:01:15 time: 0.6867 data_time: 0.0020 memory: 903 2023/05/13 13:29:32 - mmengine - INFO - Epoch(val) [17][450/509] eta: 0:00:40 time: 0.6967 data_time: 0.0021 memory: 916 2023/05/13 13:30:09 - mmengine - INFO - Epoch(val) [17][500/509] eta: 0:00:06 time: 0.7436 data_time: 0.0021 memory: 902 2023/05/13 13:30:33 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9648 | 0.5116 | 0.7437 | 0.6846 | 0.5645 | 0.7385 | 0.8508 | 0.0723 | 0.9372 | 0.4872 | 0.8129 | 0.0249 | 0.9079 | 0.6593 | 0.8890 | 0.6103 | 0.7644 | 0.6220 | 0.4805 | 0.6488 | 0.9236 | 0.7264 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 13:30:33 - mmengine - INFO - Epoch(val) [17][509/509] car: 0.9648 bicycle: 0.5116 motorcycle: 0.7437 truck: 0.6846 bus: 0.5645 person: 0.7385 bicyclist: 0.8508 motorcyclist: 0.0723 road: 0.9372 parking: 0.4872 sidewalk: 0.8129 other-ground: 0.0249 building: 0.9079 fence: 0.6593 vegetation: 0.8890 trunck: 0.6103 terrian: 0.7644 pole: 0.6220 traffic-sign: 0.4805 miou: 0.6488 acc: 0.9236 acc_cls: 0.7264 data_time: 0.0021 time: 0.7695 2023/05/13 13:32:11 - mmengine - INFO - Epoch(train) [18][ 50/1196] lr: 8.0000e-03 eta: 13:22:00 time: 1.9666 data_time: 0.0039 memory: 5691 grad_norm: 0.1138 loss: 0.2328 loss_sem_seg: 0.2328 2023/05/13 13:33:47 - mmengine - INFO - Epoch(train) [18][ 100/1196] lr: 8.0000e-03 eta: 13:20:03 time: 1.9142 data_time: 0.0034 memory: 4799 grad_norm: 0.1222 loss: 0.2177 loss_sem_seg: 0.2177 2023/05/13 13:35:22 - mmengine - INFO - Epoch(train) [18][ 150/1196] lr: 8.0000e-03 eta: 13:18:05 time: 1.9114 data_time: 0.0033 memory: 5826 grad_norm: 0.1061 loss: 0.2326 loss_sem_seg: 0.2326 2023/05/13 13:37:00 - mmengine - INFO - Epoch(train) [18][ 200/1196] lr: 8.0000e-03 eta: 13:16:09 time: 1.9473 data_time: 0.0033 memory: 4645 grad_norm: 0.1231 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/13 13:38:37 - mmengine - INFO - Epoch(train) [18][ 250/1196] lr: 8.0000e-03 eta: 13:14:14 time: 1.9556 data_time: 0.0034 memory: 4786 grad_norm: 0.0988 loss: 0.2145 loss_sem_seg: 0.2145 2023/05/13 13:40:16 - mmengine - INFO - Epoch(train) [18][ 300/1196] lr: 8.0000e-03 eta: 13:12:20 time: 1.9658 data_time: 0.0033 memory: 4874 grad_norm: 0.1043 loss: 0.2084 loss_sem_seg: 0.2084 2023/05/13 13:41:55 - mmengine - INFO - Epoch(train) [18][ 350/1196] lr: 8.0000e-03 eta: 13:10:26 time: 1.9828 data_time: 0.0035 memory: 4930 grad_norm: 0.1053 loss: 0.2036 loss_sem_seg: 0.2036 2023/05/13 13:43:21 - mmengine - INFO - Epoch(train) [18][ 400/1196] lr: 8.0000e-03 eta: 13:08:19 time: 1.7156 data_time: 0.0033 memory: 4877 grad_norm: 0.1196 loss: 0.2094 loss_sem_seg: 0.2094 2023/05/13 13:44:50 - mmengine - INFO - Epoch(train) [18][ 450/1196] lr: 8.0000e-03 eta: 13:06:14 time: 1.7763 data_time: 0.0032 memory: 5338 grad_norm: 0.1082 loss: 0.2201 loss_sem_seg: 0.2201 2023/05/13 13:46:26 - mmengine - INFO - Epoch(train) [18][ 500/1196] lr: 8.0000e-03 eta: 13:04:18 time: 1.9254 data_time: 0.0033 memory: 4925 grad_norm: 0.1053 loss: 0.2094 loss_sem_seg: 0.2094 2023/05/13 13:47:56 - mmengine - INFO - Epoch(train) [18][ 550/1196] lr: 8.0000e-03 eta: 13:02:16 time: 1.8120 data_time: 0.0032 memory: 4671 grad_norm: 0.1067 loss: 0.2085 loss_sem_seg: 0.2085 2023/05/13 13:49:27 - mmengine - INFO - Epoch(train) [18][ 600/1196] lr: 8.0000e-03 eta: 13:00:14 time: 1.8186 data_time: 0.0033 memory: 4655 grad_norm: 0.1160 loss: 0.2161 loss_sem_seg: 0.2161 2023/05/13 13:50:56 - mmengine - INFO - Epoch(train) [18][ 650/1196] lr: 8.0000e-03 eta: 12:58:11 time: 1.7814 data_time: 0.0034 memory: 4688 grad_norm: 0.1125 loss: 0.2220 loss_sem_seg: 0.2220 2023/05/13 13:51:29 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 13:52:27 - mmengine - INFO - Epoch(train) [18][ 700/1196] lr: 8.0000e-03 eta: 12:56:09 time: 1.8057 data_time: 0.0033 memory: 5017 grad_norm: 0.1119 loss: 0.2181 loss_sem_seg: 0.2181 2023/05/13 13:54:08 - mmengine - INFO - Epoch(train) [18][ 750/1196] lr: 8.0000e-03 eta: 12:54:18 time: 2.0172 data_time: 0.0033 memory: 5089 grad_norm: 0.1101 loss: 0.2201 loss_sem_seg: 0.2201 2023/05/13 13:55:43 - mmengine - INFO - Epoch(train) [18][ 800/1196] lr: 8.0000e-03 eta: 12:52:22 time: 1.9021 data_time: 0.0033 memory: 4553 grad_norm: 0.1004 loss: 0.2022 loss_sem_seg: 0.2022 2023/05/13 13:57:21 - mmengine - INFO - Epoch(train) [18][ 850/1196] lr: 8.0000e-03 eta: 12:50:28 time: 1.9629 data_time: 0.0032 memory: 4820 grad_norm: 0.0933 loss: 0.2175 loss_sem_seg: 0.2175 2023/05/13 13:59:00 - mmengine - INFO - Epoch(train) [18][ 900/1196] lr: 8.0000e-03 eta: 12:48:36 time: 1.9846 data_time: 0.0035 memory: 5724 grad_norm: 0.1076 loss: 0.2106 loss_sem_seg: 0.2106 2023/05/13 14:00:40 - mmengine - INFO - Epoch(train) [18][ 950/1196] lr: 8.0000e-03 eta: 12:46:45 time: 2.0056 data_time: 0.0033 memory: 5231 grad_norm: 0.1414 loss: 0.2403 loss_sem_seg: 0.2403 2023/05/13 14:02:21 - mmengine - INFO - Epoch(train) [18][1000/1196] lr: 8.0000e-03 eta: 12:44:54 time: 2.0127 data_time: 0.0032 memory: 5079 grad_norm: 0.1018 loss: 0.2127 loss_sem_seg: 0.2127 2023/05/13 14:04:01 - mmengine - INFO - Epoch(train) [18][1050/1196] lr: 8.0000e-03 eta: 12:43:03 time: 2.0055 data_time: 0.0033 memory: 4807 grad_norm: 0.1107 loss: 0.2056 loss_sem_seg: 0.2056 2023/05/13 14:05:39 - mmengine - INFO - Epoch(train) [18][1100/1196] lr: 8.0000e-03 eta: 12:41:10 time: 1.9620 data_time: 0.0033 memory: 4932 grad_norm: 0.1090 loss: 0.2146 loss_sem_seg: 0.2146 2023/05/13 14:07:17 - mmengine - INFO - Epoch(train) [18][1150/1196] lr: 8.0000e-03 eta: 12:39:16 time: 1.9501 data_time: 0.0033 memory: 4852 grad_norm: 0.1162 loss: 0.2106 loss_sem_seg: 0.2106 2023/05/13 14:08:46 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 14:08:46 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/05/13 14:09:26 - mmengine - INFO - Epoch(val) [18][ 50/509] eta: 0:05:15 time: 0.6874 data_time: 0.0020 memory: 5139 2023/05/13 14:10:03 - mmengine - INFO - Epoch(val) [18][100/509] eta: 0:04:49 time: 0.7274 data_time: 0.0021 memory: 915 2023/05/13 14:10:36 - mmengine - INFO - Epoch(val) [18][150/509] eta: 0:04:09 time: 0.6715 data_time: 0.0020 memory: 919 2023/05/13 14:11:12 - mmengine - INFO - Epoch(val) [18][200/509] eta: 0:03:35 time: 0.7070 data_time: 0.0020 memory: 907 2023/05/13 14:11:48 - mmengine - INFO - Epoch(val) [18][250/509] eta: 0:03:02 time: 0.7262 data_time: 0.0021 memory: 928 2023/05/13 14:12:23 - mmengine - INFO - Epoch(val) [18][300/509] eta: 0:02:26 time: 0.6923 data_time: 0.0020 memory: 883 2023/05/13 14:12:57 - mmengine - INFO - Epoch(val) [18][350/509] eta: 0:01:51 time: 0.6940 data_time: 0.0020 memory: 898 2023/05/13 14:13:34 - mmengine - INFO - Epoch(val) [18][400/509] eta: 0:01:16 time: 0.7437 data_time: 0.0021 memory: 903 2023/05/13 14:14:05 - mmengine - INFO - Epoch(val) [18][450/509] eta: 0:00:40 time: 0.6019 data_time: 0.0021 memory: 916 2023/05/13 14:14:37 - mmengine - INFO - Epoch(val) [18][500/509] eta: 0:00:06 time: 0.6455 data_time: 0.0020 memory: 902 2023/05/13 14:14:59 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9547 | 0.5158 | 0.7412 | 0.6822 | 0.4795 | 0.7505 | 0.8270 | 0.0283 | 0.9370 | 0.4701 | 0.8138 | 0.0705 | 0.8986 | 0.6058 | 0.8817 | 0.5905 | 0.7491 | 0.6471 | 0.5254 | 0.6405 | 0.9191 | 0.7100 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 14:14:59 - mmengine - INFO - Epoch(val) [18][509/509] car: 0.9547 bicycle: 0.5158 motorcycle: 0.7412 truck: 0.6822 bus: 0.4795 person: 0.7505 bicyclist: 0.8270 motorcyclist: 0.0283 road: 0.9370 parking: 0.4701 sidewalk: 0.8138 other-ground: 0.0705 building: 0.8986 fence: 0.6058 vegetation: 0.8817 trunck: 0.5905 terrian: 0.7491 pole: 0.6471 traffic-sign: 0.5254 miou: 0.6405 acc: 0.9191 acc_cls: 0.7100 data_time: 0.0020 time: 0.6718 2023/05/13 14:16:32 - mmengine - INFO - Epoch(train) [19][ 50/1196] lr: 8.0000e-03 eta: 12:35:32 time: 1.8500 data_time: 0.0042 memory: 4717 grad_norm: 0.1019 loss: 0.2164 loss_sem_seg: 0.2164 2023/05/13 14:18:07 - mmengine - INFO - Epoch(train) [19][ 100/1196] lr: 8.0000e-03 eta: 12:33:36 time: 1.8959 data_time: 0.0033 memory: 5006 grad_norm: 0.1059 loss: 0.2096 loss_sem_seg: 0.2096 2023/05/13 14:19:43 - mmengine - INFO - Epoch(train) [19][ 150/1196] lr: 8.0000e-03 eta: 12:31:42 time: 1.9351 data_time: 0.0033 memory: 5558 grad_norm: 0.1008 loss: 0.2196 loss_sem_seg: 0.2196 2023/05/13 14:21:21 - mmengine - INFO - Epoch(train) [19][ 200/1196] lr: 8.0000e-03 eta: 12:29:48 time: 1.9476 data_time: 0.0033 memory: 5105 grad_norm: 0.1044 loss: 0.2163 loss_sem_seg: 0.2163 2023/05/13 14:22:53 - mmengine - INFO - Epoch(train) [19][ 250/1196] lr: 8.0000e-03 eta: 12:27:49 time: 1.8350 data_time: 0.0032 memory: 5114 grad_norm: 0.1169 loss: 0.2274 loss_sem_seg: 0.2274 2023/05/13 14:24:24 - mmengine - INFO - Epoch(train) [19][ 300/1196] lr: 8.0000e-03 eta: 12:25:50 time: 1.8231 data_time: 0.0032 memory: 5065 grad_norm: 0.1188 loss: 0.2191 loss_sem_seg: 0.2191 2023/05/13 14:25:55 - mmengine - INFO - Epoch(train) [19][ 350/1196] lr: 8.0000e-03 eta: 12:23:51 time: 1.8193 data_time: 0.0034 memory: 5602 grad_norm: 0.1063 loss: 0.2242 loss_sem_seg: 0.2242 2023/05/13 14:27:29 - mmengine - INFO - Epoch(train) [19][ 400/1196] lr: 8.0000e-03 eta: 12:21:55 time: 1.8936 data_time: 0.0033 memory: 5193 grad_norm: 0.1105 loss: 0.2118 loss_sem_seg: 0.2118 2023/05/13 14:29:08 - mmengine - INFO - Epoch(train) [19][ 450/1196] lr: 8.0000e-03 eta: 12:20:03 time: 1.9635 data_time: 0.0033 memory: 4677 grad_norm: 0.1100 loss: 0.2015 loss_sem_seg: 0.2015 2023/05/13 14:29:49 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 14:30:43 - mmengine - INFO - Epoch(train) [19][ 500/1196] lr: 8.0000e-03 eta: 12:18:08 time: 1.9145 data_time: 0.0035 memory: 4838 grad_norm: 0.1076 loss: 0.2179 loss_sem_seg: 0.2179 2023/05/13 14:32:20 - mmengine - INFO - Epoch(train) [19][ 550/1196] lr: 8.0000e-03 eta: 12:16:15 time: 1.9419 data_time: 0.0036 memory: 5224 grad_norm: 0.1082 loss: 0.2041 loss_sem_seg: 0.2041 2023/05/13 14:34:01 - mmengine - INFO - Epoch(train) [19][ 600/1196] lr: 8.0000e-03 eta: 12:14:26 time: 2.0191 data_time: 0.0034 memory: 5226 grad_norm: 0.1189 loss: 0.2114 loss_sem_seg: 0.2114 2023/05/13 14:35:41 - mmengine - INFO - Epoch(train) [19][ 650/1196] lr: 8.0000e-03 eta: 12:12:35 time: 1.9966 data_time: 0.0033 memory: 4952 grad_norm: 0.1158 loss: 0.2117 loss_sem_seg: 0.2117 2023/05/13 14:37:22 - mmengine - INFO - Epoch(train) [19][ 700/1196] lr: 8.0000e-03 eta: 12:10:46 time: 2.0124 data_time: 0.0034 memory: 4567 grad_norm: 0.1114 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/13 14:38:52 - mmengine - INFO - Epoch(train) [19][ 750/1196] lr: 8.0000e-03 eta: 12:08:46 time: 1.8020 data_time: 0.0035 memory: 4848 grad_norm: 0.1059 loss: 0.2190 loss_sem_seg: 0.2190 2023/05/13 14:40:14 - mmengine - INFO - Epoch(train) [19][ 800/1196] lr: 8.0000e-03 eta: 12:06:39 time: 1.6356 data_time: 0.0035 memory: 4748 grad_norm: 0.1208 loss: 0.2257 loss_sem_seg: 0.2257 2023/05/13 14:41:35 - mmengine - INFO - Epoch(train) [19][ 850/1196] lr: 8.0000e-03 eta: 12:04:32 time: 1.6293 data_time: 0.0032 memory: 4635 grad_norm: 0.1170 loss: 0.2401 loss_sem_seg: 0.2401 2023/05/13 14:42:58 - mmengine - INFO - Epoch(train) [19][ 900/1196] lr: 8.0000e-03 eta: 12:02:27 time: 1.6612 data_time: 0.0033 memory: 4883 grad_norm: 0.1021 loss: 0.2159 loss_sem_seg: 0.2159 2023/05/13 14:44:23 - mmengine - INFO - Epoch(train) [19][ 950/1196] lr: 8.0000e-03 eta: 12:00:23 time: 1.6924 data_time: 0.0033 memory: 4967 grad_norm: 0.0979 loss: 0.2206 loss_sem_seg: 0.2206 2023/05/13 14:45:55 - mmengine - INFO - Epoch(train) [19][1000/1196] lr: 8.0000e-03 eta: 11:58:27 time: 1.8467 data_time: 0.0033 memory: 5085 grad_norm: 0.1099 loss: 0.2118 loss_sem_seg: 0.2118 2023/05/13 14:47:26 - mmengine - INFO - Epoch(train) [19][1050/1196] lr: 8.0000e-03 eta: 11:56:29 time: 1.8143 data_time: 0.0032 memory: 4723 grad_norm: 0.1087 loss: 0.2134 loss_sem_seg: 0.2134 2023/05/13 14:49:04 - mmengine - INFO - Epoch(train) [19][1100/1196] lr: 8.0000e-03 eta: 11:54:37 time: 1.9622 data_time: 0.0032 memory: 5037 grad_norm: 0.1113 loss: 0.2137 loss_sem_seg: 0.2137 2023/05/13 14:50:42 - mmengine - INFO - Epoch(train) [19][1150/1196] lr: 8.0000e-03 eta: 11:52:46 time: 1.9539 data_time: 0.0033 memory: 4735 grad_norm: 0.1107 loss: 0.2244 loss_sem_seg: 0.2244 2023/05/13 14:52:11 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 14:52:11 - mmengine - INFO - Saving checkpoint at 19 epochs 2023/05/13 14:52:54 - mmengine - INFO - Epoch(val) [19][ 50/509] eta: 0:05:33 time: 0.7273 data_time: 0.0021 memory: 5741 2023/05/13 14:53:28 - mmengine - INFO - Epoch(val) [19][100/509] eta: 0:04:49 time: 0.6874 data_time: 0.0020 memory: 915 2023/05/13 14:54:03 - mmengine - INFO - Epoch(val) [19][150/509] eta: 0:04:12 time: 0.6920 data_time: 0.0021 memory: 919 2023/05/13 14:54:35 - mmengine - INFO - Epoch(val) [19][200/509] eta: 0:03:33 time: 0.6515 data_time: 0.0021 memory: 907 2023/05/13 14:55:06 - mmengine - INFO - Epoch(val) [19][250/509] eta: 0:02:55 time: 0.6249 data_time: 0.0020 memory: 928 2023/05/13 14:55:36 - mmengine - INFO - Epoch(val) [19][300/509] eta: 0:02:18 time: 0.5953 data_time: 0.0020 memory: 883 2023/05/13 14:56:06 - mmengine - INFO - Epoch(val) [19][350/509] eta: 0:01:43 time: 0.5965 data_time: 0.0021 memory: 898 2023/05/13 14:56:39 - mmengine - INFO - Epoch(val) [19][400/509] eta: 0:01:11 time: 0.6595 data_time: 0.0020 memory: 903 2023/05/13 14:57:09 - mmengine - INFO - Epoch(val) [19][450/509] eta: 0:00:38 time: 0.5901 data_time: 0.0020 memory: 916 2023/05/13 14:57:41 - mmengine - INFO - Epoch(val) [19][500/509] eta: 0:00:05 time: 0.6491 data_time: 0.0020 memory: 902 2023/05/13 14:58:03 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9532 | 0.5113 | 0.7680 | 0.6914 | 0.5034 | 0.7557 | 0.7186 | 0.0574 | 0.9316 | 0.3319 | 0.8100 | 0.0709 | 0.9020 | 0.6278 | 0.8693 | 0.6805 | 0.7125 | 0.6542 | 0.4934 | 0.6339 | 0.9133 | 0.7084 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 14:58:03 - mmengine - INFO - Epoch(val) [19][509/509] car: 0.9532 bicycle: 0.5113 motorcycle: 0.7680 truck: 0.6914 bus: 0.5034 person: 0.7557 bicyclist: 0.7186 motorcyclist: 0.0574 road: 0.9316 parking: 0.3319 sidewalk: 0.8100 other-ground: 0.0709 building: 0.9020 fence: 0.6278 vegetation: 0.8693 trunck: 0.6805 terrian: 0.7125 pole: 0.6542 traffic-sign: 0.4934 miou: 0.6339 acc: 0.9133 acc_cls: 0.7084 data_time: 0.0020 time: 0.6645 2023/05/13 14:59:34 - mmengine - INFO - Epoch(train) [20][ 50/1196] lr: 8.0000e-03 eta: 11:49:06 time: 1.8199 data_time: 0.0039 memory: 4864 grad_norm: 0.1096 loss: 0.2127 loss_sem_seg: 0.2127 2023/05/13 15:01:13 - mmengine - INFO - Epoch(train) [20][ 100/1196] lr: 8.0000e-03 eta: 11:47:15 time: 1.9667 data_time: 0.0033 memory: 5170 grad_norm: 0.1121 loss: 0.2153 loss_sem_seg: 0.2153 2023/05/13 15:02:50 - mmengine - INFO - Epoch(train) [20][ 150/1196] lr: 8.0000e-03 eta: 11:45:23 time: 1.9416 data_time: 0.0034 memory: 4903 grad_norm: 0.1139 loss: 0.2059 loss_sem_seg: 0.2059 2023/05/13 15:04:28 - mmengine - INFO - Epoch(train) [20][ 200/1196] lr: 8.0000e-03 eta: 11:43:33 time: 1.9717 data_time: 0.0033 memory: 5420 grad_norm: 0.1086 loss: 0.2058 loss_sem_seg: 0.2058 2023/05/13 15:06:08 - mmengine - INFO - Epoch(train) [20][ 250/1196] lr: 8.0000e-03 eta: 11:41:43 time: 1.9939 data_time: 0.0034 memory: 4886 grad_norm: 0.1076 loss: 0.2059 loss_sem_seg: 0.2059 2023/05/13 15:06:59 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 15:07:47 - mmengine - INFO - Epoch(train) [20][ 300/1196] lr: 8.0000e-03 eta: 11:39:54 time: 1.9905 data_time: 0.0033 memory: 4851 grad_norm: 0.1134 loss: 0.2163 loss_sem_seg: 0.2163 2023/05/13 15:09:28 - mmengine - INFO - Epoch(train) [20][ 350/1196] lr: 8.0000e-03 eta: 11:38:05 time: 2.0022 data_time: 0.0033 memory: 5185 grad_norm: 0.1139 loss: 0.2052 loss_sem_seg: 0.2052 2023/05/13 15:11:07 - mmengine - INFO - Epoch(train) [20][ 400/1196] lr: 8.0000e-03 eta: 11:36:16 time: 1.9950 data_time: 0.0034 memory: 4965 grad_norm: 0.1303 loss: 0.2315 loss_sem_seg: 0.2315 2023/05/13 15:12:45 - mmengine - INFO - Epoch(train) [20][ 450/1196] lr: 8.0000e-03 eta: 11:34:25 time: 1.9463 data_time: 0.0032 memory: 4882 grad_norm: 0.1193 loss: 0.2291 loss_sem_seg: 0.2291 2023/05/13 15:14:22 - mmengine - INFO - Epoch(train) [20][ 500/1196] lr: 8.0000e-03 eta: 11:32:34 time: 1.9468 data_time: 0.0036 memory: 5235 grad_norm: 0.0903 loss: 0.2002 loss_sem_seg: 0.2002 2023/05/13 15:16:01 - mmengine - INFO - Epoch(train) [20][ 550/1196] lr: 8.0000e-03 eta: 11:30:44 time: 1.9828 data_time: 0.0034 memory: 4778 grad_norm: 0.1023 loss: 0.2042 loss_sem_seg: 0.2042 2023/05/13 15:17:30 - mmengine - INFO - Epoch(train) [20][ 600/1196] lr: 8.0000e-03 eta: 11:28:46 time: 1.7775 data_time: 0.0033 memory: 5196 grad_norm: 0.1168 loss: 0.2181 loss_sem_seg: 0.2181 2023/05/13 15:19:03 - mmengine - INFO - Epoch(train) [20][ 650/1196] lr: 8.0000e-03 eta: 11:26:52 time: 1.8675 data_time: 0.0033 memory: 4759 grad_norm: 0.1057 loss: 0.2191 loss_sem_seg: 0.2191 2023/05/13 15:20:43 - mmengine - INFO - Epoch(train) [20][ 700/1196] lr: 8.0000e-03 eta: 11:25:02 time: 1.9886 data_time: 0.0036 memory: 5022 grad_norm: 0.1237 loss: 0.2210 loss_sem_seg: 0.2210 2023/05/13 15:22:23 - mmengine - INFO - Epoch(train) [20][ 750/1196] lr: 8.0000e-03 eta: 11:23:14 time: 1.9978 data_time: 0.0033 memory: 4867 grad_norm: 0.1060 loss: 0.2207 loss_sem_seg: 0.2207 2023/05/13 15:24:01 - mmengine - INFO - Epoch(train) [20][ 800/1196] lr: 8.0000e-03 eta: 11:21:24 time: 1.9626 data_time: 0.0033 memory: 4940 grad_norm: 0.1085 loss: 0.1944 loss_sem_seg: 0.1944 2023/05/13 15:25:38 - mmengine - INFO - Epoch(train) [20][ 850/1196] lr: 8.0000e-03 eta: 11:19:33 time: 1.9372 data_time: 0.0032 memory: 5153 grad_norm: 0.1061 loss: 0.2029 loss_sem_seg: 0.2029 2023/05/13 15:27:16 - mmengine - INFO - Epoch(train) [20][ 900/1196] lr: 8.0000e-03 eta: 11:17:43 time: 1.9651 data_time: 0.0033 memory: 4861 grad_norm: 0.1074 loss: 0.1959 loss_sem_seg: 0.1959 2023/05/13 15:28:53 - mmengine - INFO - Epoch(train) [20][ 950/1196] lr: 8.0000e-03 eta: 11:15:52 time: 1.9429 data_time: 0.0034 memory: 5142 grad_norm: 0.1091 loss: 0.2052 loss_sem_seg: 0.2052 2023/05/13 15:30:23 - mmengine - INFO - Epoch(train) [20][1000/1196] lr: 8.0000e-03 eta: 11:13:56 time: 1.8049 data_time: 0.0035 memory: 4916 grad_norm: 0.1078 loss: 0.2235 loss_sem_seg: 0.2235 2023/05/13 15:31:54 - mmengine - INFO - Epoch(train) [20][1050/1196] lr: 8.0000e-03 eta: 11:12:00 time: 1.8124 data_time: 0.0033 memory: 5078 grad_norm: 0.1121 loss: 0.2106 loss_sem_seg: 0.2106 2023/05/13 15:33:22 - mmengine - INFO - Epoch(train) [20][1100/1196] lr: 8.0000e-03 eta: 11:10:01 time: 1.7512 data_time: 0.0033 memory: 4568 grad_norm: 0.1101 loss: 0.2137 loss_sem_seg: 0.2137 2023/05/13 15:34:54 - mmengine - INFO - Epoch(train) [20][1150/1196] lr: 8.0000e-03 eta: 11:08:07 time: 1.8453 data_time: 0.0032 memory: 5028 grad_norm: 0.1359 loss: 0.2259 loss_sem_seg: 0.2259 2023/05/13 15:36:24 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 15:36:24 - mmengine - INFO - Saving checkpoint at 20 epochs 2023/05/13 15:37:08 - mmengine - INFO - Epoch(val) [20][ 50/509] eta: 0:05:42 time: 0.7466 data_time: 0.0022 memory: 5709 2023/05/13 15:37:42 - mmengine - INFO - Epoch(val) [20][100/509] eta: 0:04:51 time: 0.6795 data_time: 0.0021 memory: 915 2023/05/13 15:38:16 - mmengine - INFO - Epoch(val) [20][150/509] eta: 0:04:13 time: 0.6893 data_time: 0.0021 memory: 919 2023/05/13 15:38:52 - mmengine - INFO - Epoch(val) [20][200/509] eta: 0:03:38 time: 0.7182 data_time: 0.0021 memory: 907 2023/05/13 15:39:27 - mmengine - INFO - Epoch(val) [20][250/509] eta: 0:03:03 time: 0.7028 data_time: 0.0021 memory: 928 2023/05/13 15:40:03 - mmengine - INFO - Epoch(val) [20][300/509] eta: 0:02:27 time: 0.7092 data_time: 0.0021 memory: 883 2023/05/13 15:40:38 - mmengine - INFO - Epoch(val) [20][350/509] eta: 0:01:52 time: 0.7009 data_time: 0.0021 memory: 898 2023/05/13 15:41:14 - mmengine - INFO - Epoch(val) [20][400/509] eta: 0:01:17 time: 0.7277 data_time: 0.0021 memory: 903 2023/05/13 15:41:50 - mmengine - INFO - Epoch(val) [20][450/509] eta: 0:00:41 time: 0.7091 data_time: 0.0021 memory: 916 2023/05/13 15:42:29 - mmengine - INFO - Epoch(val) [20][500/509] eta: 0:00:06 time: 0.7845 data_time: 0.0021 memory: 902 2023/05/13 15:42:51 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9431 | 0.4997 | 0.6929 | 0.4332 | 0.2630 | 0.7826 | 0.8293 | 0.1245 | 0.9340 | 0.5595 | 0.8097 | 0.1049 | 0.9065 | 0.6094 | 0.8953 | 0.6708 | 0.7877 | 0.6409 | 0.5108 | 0.6315 | 0.9226 | 0.7517 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 15:42:51 - mmengine - INFO - Epoch(val) [20][509/509] car: 0.9431 bicycle: 0.4997 motorcycle: 0.6929 truck: 0.4332 bus: 0.2630 person: 0.7826 bicyclist: 0.8293 motorcyclist: 0.1245 road: 0.9340 parking: 0.5595 sidewalk: 0.8097 other-ground: 0.1049 building: 0.9065 fence: 0.6094 vegetation: 0.8953 trunck: 0.6708 terrian: 0.7877 pole: 0.6409 traffic-sign: 0.5108 miou: 0.6315 acc: 0.9226 acc_cls: 0.7517 data_time: 0.0021 time: 0.7836 2023/05/13 15:44:31 - mmengine - INFO - Epoch(train) [21][ 50/1196] lr: 8.0000e-03 eta: 11:04:38 time: 1.9912 data_time: 0.0041 memory: 5382 grad_norm: 0.1126 loss: 0.2256 loss_sem_seg: 0.2256 2023/05/13 15:45:30 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 15:46:09 - mmengine - INFO - Epoch(train) [21][ 100/1196] lr: 8.0000e-03 eta: 11:02:48 time: 1.9514 data_time: 0.0034 memory: 5083 grad_norm: 0.1062 loss: 0.2171 loss_sem_seg: 0.2171 2023/05/13 15:47:45 - mmengine - INFO - Epoch(train) [21][ 150/1196] lr: 8.0000e-03 eta: 11:00:58 time: 1.9358 data_time: 0.0033 memory: 5231 grad_norm: 0.1136 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/13 15:49:14 - mmengine - INFO - Epoch(train) [21][ 200/1196] lr: 8.0000e-03 eta: 10:59:01 time: 1.7746 data_time: 0.0033 memory: 4751 grad_norm: 0.1251 loss: 0.2179 loss_sem_seg: 0.2179 2023/05/13 15:50:50 - mmengine - INFO - Epoch(train) [21][ 250/1196] lr: 8.0000e-03 eta: 10:57:09 time: 1.9096 data_time: 0.0034 memory: 4935 grad_norm: 0.1059 loss: 0.2131 loss_sem_seg: 0.2131 2023/05/13 15:52:29 - mmengine - INFO - Epoch(train) [21][ 300/1196] lr: 8.0000e-03 eta: 10:55:21 time: 1.9892 data_time: 0.0034 memory: 4865 grad_norm: 0.1074 loss: 0.2085 loss_sem_seg: 0.2085 2023/05/13 15:54:08 - mmengine - INFO - Epoch(train) [21][ 350/1196] lr: 8.0000e-03 eta: 10:53:33 time: 1.9835 data_time: 0.0035 memory: 5231 grad_norm: 0.1031 loss: 0.2188 loss_sem_seg: 0.2188 2023/05/13 15:55:47 - mmengine - INFO - Epoch(train) [21][ 400/1196] lr: 8.0000e-03 eta: 10:51:44 time: 1.9708 data_time: 0.0034 memory: 4783 grad_norm: 0.1087 loss: 0.2192 loss_sem_seg: 0.2192 2023/05/13 15:57:25 - mmengine - INFO - Epoch(train) [21][ 450/1196] lr: 8.0000e-03 eta: 10:49:55 time: 1.9590 data_time: 0.0034 memory: 5040 grad_norm: 0.1104 loss: 0.2202 loss_sem_seg: 0.2202 2023/05/13 15:59:02 - mmengine - INFO - Epoch(train) [21][ 500/1196] lr: 8.0000e-03 eta: 10:48:05 time: 1.9429 data_time: 0.0033 memory: 5064 grad_norm: 0.1015 loss: 0.2198 loss_sem_seg: 0.2198 2023/05/13 16:00:40 - mmengine - INFO - Epoch(train) [21][ 550/1196] lr: 8.0000e-03 eta: 10:46:16 time: 1.9711 data_time: 0.0033 memory: 5046 grad_norm: 0.1140 loss: 0.2279 loss_sem_seg: 0.2279 2023/05/13 16:02:22 - mmengine - INFO - Epoch(train) [21][ 600/1196] lr: 8.0000e-03 eta: 10:44:29 time: 2.0230 data_time: 0.0036 memory: 5135 grad_norm: 0.1175 loss: 0.2279 loss_sem_seg: 0.2279 2023/05/13 16:03:53 - mmengine - INFO - Epoch(train) [21][ 650/1196] lr: 8.0000e-03 eta: 10:42:36 time: 1.8366 data_time: 0.0033 memory: 4655 grad_norm: 0.1185 loss: 0.2130 loss_sem_seg: 0.2130 2023/05/13 16:05:25 - mmengine - INFO - Epoch(train) [21][ 700/1196] lr: 8.0000e-03 eta: 10:40:42 time: 1.8298 data_time: 0.0034 memory: 5007 grad_norm: 0.1180 loss: 0.1999 loss_sem_seg: 0.1999 2023/05/13 16:06:55 - mmengine - INFO - Epoch(train) [21][ 750/1196] lr: 8.0000e-03 eta: 10:38:47 time: 1.8036 data_time: 0.0034 memory: 5101 grad_norm: 0.0949 loss: 0.2111 loss_sem_seg: 0.2111 2023/05/13 16:08:24 - mmengine - INFO - Epoch(train) [21][ 800/1196] lr: 8.0000e-03 eta: 10:36:51 time: 1.7674 data_time: 0.0032 memory: 4756 grad_norm: 0.0992 loss: 0.2088 loss_sem_seg: 0.2088 2023/05/13 16:10:02 - mmengine - INFO - Epoch(train) [21][ 850/1196] lr: 8.0000e-03 eta: 10:35:03 time: 1.9700 data_time: 0.0034 memory: 4999 grad_norm: 0.0990 loss: 0.2071 loss_sem_seg: 0.2071 2023/05/13 16:11:39 - mmengine - INFO - Epoch(train) [21][ 900/1196] lr: 8.0000e-03 eta: 10:33:13 time: 1.9392 data_time: 0.0033 memory: 4844 grad_norm: 0.0981 loss: 0.2071 loss_sem_seg: 0.2071 2023/05/13 16:13:19 - mmengine - INFO - Epoch(train) [21][ 950/1196] lr: 8.0000e-03 eta: 10:31:26 time: 1.9993 data_time: 0.0033 memory: 4773 grad_norm: 0.1128 loss: 0.2204 loss_sem_seg: 0.2204 2023/05/13 16:14:59 - mmengine - INFO - Epoch(train) [21][1000/1196] lr: 8.0000e-03 eta: 10:29:38 time: 1.9935 data_time: 0.0034 memory: 5097 grad_norm: 0.1068 loss: 0.2047 loss_sem_seg: 0.2047 2023/05/13 16:16:38 - mmengine - INFO - Epoch(train) [21][1050/1196] lr: 8.0000e-03 eta: 10:27:51 time: 1.9799 data_time: 0.0033 memory: 4967 grad_norm: 0.1100 loss: 0.2023 loss_sem_seg: 0.2023 2023/05/13 16:17:37 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 16:18:17 - mmengine - INFO - Epoch(train) [21][1100/1196] lr: 8.0000e-03 eta: 10:26:03 time: 1.9840 data_time: 0.0032 memory: 5109 grad_norm: 0.1266 loss: 0.2254 loss_sem_seg: 0.2254 2023/05/13 16:19:53 - mmengine - INFO - Epoch(train) [21][1150/1196] lr: 8.0000e-03 eta: 10:24:13 time: 1.9279 data_time: 0.0035 memory: 4755 grad_norm: 0.0967 loss: 0.2020 loss_sem_seg: 0.2020 2023/05/13 16:21:12 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 16:21:12 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/05/13 16:21:47 - mmengine - INFO - Epoch(val) [21][ 50/509] eta: 0:04:24 time: 0.5753 data_time: 0.0021 memory: 4754 2023/05/13 16:22:16 - mmengine - INFO - Epoch(val) [21][100/509] eta: 0:03:56 time: 0.5806 data_time: 0.0020 memory: 915 2023/05/13 16:22:50 - mmengine - INFO - Epoch(val) [21][150/509] eta: 0:03:39 time: 0.6775 data_time: 0.0021 memory: 919 2023/05/13 16:23:26 - mmengine - INFO - Epoch(val) [21][200/509] eta: 0:03:16 time: 0.7124 data_time: 0.0021 memory: 907 2023/05/13 16:24:03 - mmengine - INFO - Epoch(val) [21][250/509] eta: 0:02:50 time: 0.7377 data_time: 0.0021 memory: 928 2023/05/13 16:24:38 - mmengine - INFO - Epoch(val) [21][300/509] eta: 0:02:18 time: 0.6964 data_time: 0.0020 memory: 883 2023/05/13 16:25:13 - mmengine - INFO - Epoch(val) [21][350/509] eta: 0:01:46 time: 0.6995 data_time: 0.0020 memory: 898 2023/05/13 16:25:47 - mmengine - INFO - Epoch(val) [21][400/509] eta: 0:01:13 time: 0.6859 data_time: 0.0021 memory: 903 2023/05/13 16:26:23 - mmengine - INFO - Epoch(val) [21][450/509] eta: 0:00:39 time: 0.7144 data_time: 0.0021 memory: 916 2023/05/13 16:26:59 - mmengine - INFO - Epoch(val) [21][500/509] eta: 0:00:06 time: 0.7205 data_time: 0.0021 memory: 902 2023/05/13 16:27:22 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9669 | 0.4489 | 0.7634 | 0.7380 | 0.5784 | 0.7007 | 0.8653 | 0.0038 | 0.9382 | 0.5092 | 0.8223 | 0.1009 | 0.9060 | 0.6111 | 0.8726 | 0.5192 | 0.7324 | 0.6509 | 0.4958 | 0.6434 | 0.9175 | 0.7114 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 16:27:23 - mmengine - INFO - Epoch(val) [21][509/509] car: 0.9669 bicycle: 0.4489 motorcycle: 0.7634 truck: 0.7380 bus: 0.5784 person: 0.7007 bicyclist: 0.8653 motorcyclist: 0.0038 road: 0.9382 parking: 0.5092 sidewalk: 0.8223 other-ground: 0.1009 building: 0.9060 fence: 0.6111 vegetation: 0.8726 trunck: 0.5192 terrian: 0.7324 pole: 0.6509 traffic-sign: 0.4958 miou: 0.6434 acc: 0.9175 acc_cls: 0.7114 data_time: 0.0021 time: 0.7550 2023/05/13 16:29:01 - mmengine - INFO - Epoch(train) [22][ 50/1196] lr: 8.0000e-03 eta: 10:20:38 time: 1.9786 data_time: 0.0039 memory: 4991 grad_norm: 0.1020 loss: 0.2089 loss_sem_seg: 0.2089 2023/05/13 16:30:37 - mmengine - INFO - Epoch(train) [22][ 100/1196] lr: 8.0000e-03 eta: 10:18:48 time: 1.9207 data_time: 0.0032 memory: 5174 grad_norm: 0.1086 loss: 0.2018 loss_sem_seg: 0.2018 2023/05/13 16:32:14 - mmengine - INFO - Epoch(train) [22][ 150/1196] lr: 8.0000e-03 eta: 10:16:58 time: 1.9229 data_time: 0.0033 memory: 4836 grad_norm: 0.1033 loss: 0.2073 loss_sem_seg: 0.2073 2023/05/13 16:33:52 - mmengine - INFO - Epoch(train) [22][ 200/1196] lr: 8.0000e-03 eta: 10:15:10 time: 1.9634 data_time: 0.0033 memory: 5133 grad_norm: 0.1183 loss: 0.2164 loss_sem_seg: 0.2164 2023/05/13 16:35:31 - mmengine - INFO - Epoch(train) [22][ 250/1196] lr: 8.0000e-03 eta: 10:13:23 time: 1.9740 data_time: 0.0033 memory: 4888 grad_norm: 0.1140 loss: 0.2142 loss_sem_seg: 0.2142 2023/05/13 16:37:07 - mmengine - INFO - Epoch(train) [22][ 300/1196] lr: 8.0000e-03 eta: 10:11:34 time: 1.9394 data_time: 0.0033 memory: 5251 grad_norm: 0.1121 loss: 0.2049 loss_sem_seg: 0.2049 2023/05/13 16:38:38 - mmengine - INFO - Epoch(train) [22][ 350/1196] lr: 8.0000e-03 eta: 10:09:40 time: 1.8016 data_time: 0.0033 memory: 4963 grad_norm: 0.1084 loss: 0.2117 loss_sem_seg: 0.2117 2023/05/13 16:40:08 - mmengine - INFO - Epoch(train) [22][ 400/1196] lr: 8.0000e-03 eta: 10:07:47 time: 1.8047 data_time: 0.0033 memory: 4644 grad_norm: 0.1177 loss: 0.2282 loss_sem_seg: 0.2282 2023/05/13 16:41:36 - mmengine - INFO - Epoch(train) [22][ 450/1196] lr: 8.0000e-03 eta: 10:05:52 time: 1.7621 data_time: 0.0034 memory: 4895 grad_norm: 0.1093 loss: 0.2166 loss_sem_seg: 0.2166 2023/05/13 16:43:04 - mmengine - INFO - Epoch(train) [22][ 500/1196] lr: 8.0000e-03 eta: 10:03:57 time: 1.7637 data_time: 0.0033 memory: 4853 grad_norm: 0.1045 loss: 0.2219 loss_sem_seg: 0.2219 2023/05/13 16:44:27 - mmengine - INFO - Epoch(train) [22][ 550/1196] lr: 8.0000e-03 eta: 10:01:59 time: 1.6619 data_time: 0.0033 memory: 4628 grad_norm: 0.1042 loss: 0.2012 loss_sem_seg: 0.2012 2023/05/13 16:45:53 - mmengine - INFO - Epoch(train) [22][ 600/1196] lr: 8.0000e-03 eta: 10:00:03 time: 1.7155 data_time: 0.0035 memory: 5142 grad_norm: 0.1095 loss: 0.2332 loss_sem_seg: 0.2332 2023/05/13 16:47:18 - mmengine - INFO - Epoch(train) [22][ 650/1196] lr: 8.0000e-03 eta: 9:58:07 time: 1.7031 data_time: 0.0034 memory: 5196 grad_norm: 0.1183 loss: 0.1986 loss_sem_seg: 0.1986 2023/05/13 16:48:44 - mmengine - INFO - Epoch(train) [22][ 700/1196] lr: 8.0000e-03 eta: 9:56:11 time: 1.7169 data_time: 0.0033 memory: 5125 grad_norm: 0.1008 loss: 0.1985 loss_sem_seg: 0.1985 2023/05/13 16:50:24 - mmengine - INFO - Epoch(train) [22][ 750/1196] lr: 8.0000e-03 eta: 9:54:24 time: 1.9914 data_time: 0.0033 memory: 4824 grad_norm: 0.1113 loss: 0.2070 loss_sem_seg: 0.2070 2023/05/13 16:51:51 - mmengine - INFO - Epoch(train) [22][ 800/1196] lr: 8.0000e-03 eta: 9:52:30 time: 1.7411 data_time: 0.0033 memory: 5038 grad_norm: 0.1082 loss: 0.2139 loss_sem_seg: 0.2139 2023/05/13 16:53:23 - mmengine - INFO - Epoch(train) [22][ 850/1196] lr: 8.0000e-03 eta: 9:50:38 time: 1.8404 data_time: 0.0033 memory: 4957 grad_norm: 0.1082 loss: 0.1948 loss_sem_seg: 0.1948 2023/05/13 16:54:30 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 16:55:02 - mmengine - INFO - Epoch(train) [22][ 900/1196] lr: 8.0000e-03 eta: 9:48:52 time: 1.9811 data_time: 0.0034 memory: 4732 grad_norm: 0.1052 loss: 0.2098 loss_sem_seg: 0.2098 2023/05/13 16:56:41 - mmengine - INFO - Epoch(train) [22][ 950/1196] lr: 8.0000e-03 eta: 9:47:05 time: 1.9823 data_time: 0.0033 memory: 4874 grad_norm: 0.1110 loss: 0.2280 loss_sem_seg: 0.2280 2023/05/13 16:58:21 - mmengine - INFO - Epoch(train) [22][1000/1196] lr: 8.0000e-03 eta: 9:45:19 time: 1.9944 data_time: 0.0034 memory: 5156 grad_norm: 0.0994 loss: 0.2147 loss_sem_seg: 0.2147 2023/05/13 17:00:00 - mmengine - INFO - Epoch(train) [22][1050/1196] lr: 8.0000e-03 eta: 9:43:32 time: 1.9837 data_time: 0.0034 memory: 4698 grad_norm: 0.1113 loss: 0.2031 loss_sem_seg: 0.2031 2023/05/13 17:01:42 - mmengine - INFO - Epoch(train) [22][1100/1196] lr: 8.0000e-03 eta: 9:41:47 time: 2.0387 data_time: 0.0035 memory: 4856 grad_norm: 0.1090 loss: 0.2182 loss_sem_seg: 0.2182 2023/05/13 17:03:18 - mmengine - INFO - Epoch(train) [22][1150/1196] lr: 8.0000e-03 eta: 9:39:59 time: 1.9273 data_time: 0.0032 memory: 4815 grad_norm: 0.1062 loss: 0.2118 loss_sem_seg: 0.2118 2023/05/13 17:04:46 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 17:04:46 - mmengine - INFO - Saving checkpoint at 22 epochs 2023/05/13 17:05:27 - mmengine - INFO - Epoch(val) [22][ 50/509] eta: 0:05:20 time: 0.6976 data_time: 0.0021 memory: 5066 2023/05/13 17:05:58 - mmengine - INFO - Epoch(val) [22][100/509] eta: 0:04:33 time: 0.6393 data_time: 0.0021 memory: 915 2023/05/13 17:06:33 - mmengine - INFO - Epoch(val) [22][150/509] eta: 0:04:03 time: 0.6970 data_time: 0.0021 memory: 919 2023/05/13 17:07:09 - mmengine - INFO - Epoch(val) [22][200/509] eta: 0:03:32 time: 0.7156 data_time: 0.0020 memory: 907 2023/05/13 17:07:44 - mmengine - INFO - Epoch(val) [22][250/509] eta: 0:02:58 time: 0.7047 data_time: 0.0021 memory: 928 2023/05/13 17:08:19 - mmengine - INFO - Epoch(val) [22][300/509] eta: 0:02:24 time: 0.6919 data_time: 0.0021 memory: 883 2023/05/13 17:08:54 - mmengine - INFO - Epoch(val) [22][350/509] eta: 0:01:50 time: 0.7032 data_time: 0.0021 memory: 898 2023/05/13 17:09:30 - mmengine - INFO - Epoch(val) [22][400/509] eta: 0:01:15 time: 0.7107 data_time: 0.0020 memory: 903 2023/05/13 17:10:02 - mmengine - INFO - Epoch(val) [22][450/509] eta: 0:00:40 time: 0.6449 data_time: 0.0022 memory: 916 2023/05/13 17:10:33 - mmengine - INFO - Epoch(val) [22][500/509] eta: 0:00:06 time: 0.6276 data_time: 0.0020 memory: 902 2023/05/13 17:10:56 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9595 | 0.4851 | 0.7722 | 0.5569 | 0.6230 | 0.7520 | 0.8678 | 0.0596 | 0.9316 | 0.4056 | 0.8095 | 0.0271 | 0.9163 | 0.6668 | 0.8892 | 0.7048 | 0.7643 | 0.6269 | 0.4925 | 0.6479 | 0.9233 | 0.7067 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 17:10:56 - mmengine - INFO - Epoch(val) [22][509/509] car: 0.9595 bicycle: 0.4851 motorcycle: 0.7722 truck: 0.5569 bus: 0.6230 person: 0.7520 bicyclist: 0.8678 motorcyclist: 0.0596 road: 0.9316 parking: 0.4056 sidewalk: 0.8095 other-ground: 0.0271 building: 0.9163 fence: 0.6668 vegetation: 0.8892 trunck: 0.7048 terrian: 0.7643 pole: 0.6269 traffic-sign: 0.4925 miou: 0.6479 acc: 0.9233 acc_cls: 0.7067 data_time: 0.0021 time: 0.6625 2023/05/13 17:12:26 - mmengine - INFO - Epoch(train) [23][ 50/1196] lr: 8.0000e-03 eta: 9:36:27 time: 1.8044 data_time: 0.0046 memory: 4764 grad_norm: 0.1065 loss: 0.2195 loss_sem_seg: 0.2195 2023/05/13 17:13:54 - mmengine - INFO - Epoch(train) [23][ 100/1196] lr: 8.0000e-03 eta: 9:34:33 time: 1.7522 data_time: 0.0033 memory: 4818 grad_norm: 0.1052 loss: 0.2051 loss_sem_seg: 0.2051 2023/05/13 17:15:24 - mmengine - INFO - Epoch(train) [23][ 150/1196] lr: 8.0000e-03 eta: 9:32:41 time: 1.8005 data_time: 0.0032 memory: 4895 grad_norm: 0.0927 loss: 0.2108 loss_sem_seg: 0.2108 2023/05/13 17:17:06 - mmengine - INFO - Epoch(train) [23][ 200/1196] lr: 8.0000e-03 eta: 9:30:57 time: 2.0504 data_time: 0.0034 memory: 4696 grad_norm: 0.1151 loss: 0.2087 loss_sem_seg: 0.2087 2023/05/13 17:18:46 - mmengine - INFO - Epoch(train) [23][ 250/1196] lr: 8.0000e-03 eta: 9:29:11 time: 1.9940 data_time: 0.0032 memory: 5759 grad_norm: 0.1055 loss: 0.1978 loss_sem_seg: 0.1978 2023/05/13 17:20:25 - mmengine - INFO - Epoch(train) [23][ 300/1196] lr: 8.0000e-03 eta: 9:27:25 time: 1.9783 data_time: 0.0032 memory: 4695 grad_norm: 0.1090 loss: 0.2093 loss_sem_seg: 0.2093 2023/05/13 17:22:05 - mmengine - INFO - Epoch(train) [23][ 350/1196] lr: 8.0000e-03 eta: 9:25:39 time: 1.9961 data_time: 0.0034 memory: 4869 grad_norm: 0.1085 loss: 0.1971 loss_sem_seg: 0.1971 2023/05/13 17:23:33 - mmengine - INFO - Epoch(train) [23][ 400/1196] lr: 8.0000e-03 eta: 9:23:46 time: 1.7682 data_time: 0.0032 memory: 5476 grad_norm: 0.0953 loss: 0.2145 loss_sem_seg: 0.2145 2023/05/13 17:25:06 - mmengine - INFO - Epoch(train) [23][ 450/1196] lr: 8.0000e-03 eta: 9:21:56 time: 1.8636 data_time: 0.0033 memory: 5002 grad_norm: 0.1114 loss: 0.1942 loss_sem_seg: 0.1942 2023/05/13 17:26:43 - mmengine - INFO - Epoch(train) [23][ 500/1196] lr: 8.0000e-03 eta: 9:20:09 time: 1.9387 data_time: 0.0035 memory: 5072 grad_norm: 0.1136 loss: 0.1999 loss_sem_seg: 0.1999 2023/05/13 17:28:22 - mmengine - INFO - Epoch(train) [23][ 550/1196] lr: 8.0000e-03 eta: 9:18:22 time: 1.9652 data_time: 0.0034 memory: 5336 grad_norm: 0.1074 loss: 0.2043 loss_sem_seg: 0.2043 2023/05/13 17:30:01 - mmengine - INFO - Epoch(train) [23][ 600/1196] lr: 8.0000e-03 eta: 9:16:36 time: 1.9831 data_time: 0.0034 memory: 5186 grad_norm: 0.0994 loss: 0.2016 loss_sem_seg: 0.2016 2023/05/13 17:31:40 - mmengine - INFO - Epoch(train) [23][ 650/1196] lr: 8.0000e-03 eta: 9:14:50 time: 1.9762 data_time: 0.0036 memory: 4687 grad_norm: 0.0919 loss: 0.2191 loss_sem_seg: 0.2191 2023/05/13 17:32:55 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 17:33:17 - mmengine - INFO - Epoch(train) [23][ 700/1196] lr: 8.0000e-03 eta: 9:13:03 time: 1.9521 data_time: 0.0033 memory: 4688 grad_norm: 0.0970 loss: 0.2139 loss_sem_seg: 0.2139 2023/05/13 17:34:58 - mmengine - INFO - Epoch(train) [23][ 750/1196] lr: 8.0000e-03 eta: 9:11:18 time: 2.0116 data_time: 0.0033 memory: 5216 grad_norm: 0.1136 loss: 0.2294 loss_sem_seg: 0.2294 2023/05/13 17:36:34 - mmengine - INFO - Epoch(train) [23][ 800/1196] lr: 8.0000e-03 eta: 9:09:30 time: 1.9236 data_time: 0.0034 memory: 5259 grad_norm: 0.1094 loss: 0.2132 loss_sem_seg: 0.2132 2023/05/13 17:38:11 - mmengine - INFO - Epoch(train) [23][ 850/1196] lr: 8.0000e-03 eta: 9:07:43 time: 1.9397 data_time: 0.0033 memory: 5321 grad_norm: 0.0963 loss: 0.2101 loss_sem_seg: 0.2101 2023/05/13 17:39:46 - mmengine - INFO - Epoch(train) [23][ 900/1196] lr: 8.0000e-03 eta: 9:05:55 time: 1.9010 data_time: 0.0033 memory: 5074 grad_norm: 0.1153 loss: 0.2193 loss_sem_seg: 0.2193 2023/05/13 17:41:21 - mmengine - INFO - Epoch(train) [23][ 950/1196] lr: 8.0000e-03 eta: 9:04:07 time: 1.9063 data_time: 0.0034 memory: 5347 grad_norm: 0.1108 loss: 0.2063 loss_sem_seg: 0.2063 2023/05/13 17:42:56 - mmengine - INFO - Epoch(train) [23][1000/1196] lr: 8.0000e-03 eta: 9:02:19 time: 1.9025 data_time: 0.0033 memory: 5036 grad_norm: 0.1057 loss: 0.2177 loss_sem_seg: 0.2177 2023/05/13 17:44:26 - mmengine - INFO - Epoch(train) [23][1050/1196] lr: 8.0000e-03 eta: 9:00:28 time: 1.7910 data_time: 0.0033 memory: 5173 grad_norm: 0.0955 loss: 0.2121 loss_sem_seg: 0.2121 2023/05/13 17:45:53 - mmengine - INFO - Epoch(train) [23][1100/1196] lr: 8.0000e-03 eta: 8:58:35 time: 1.7464 data_time: 0.0033 memory: 4637 grad_norm: 0.1006 loss: 0.2009 loss_sem_seg: 0.2009 2023/05/13 17:47:20 - mmengine - INFO - Epoch(train) [23][1150/1196] lr: 8.0000e-03 eta: 8:56:43 time: 1.7403 data_time: 0.0032 memory: 5380 grad_norm: 0.0990 loss: 0.1957 loss_sem_seg: 0.1957 2023/05/13 17:48:41 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 17:48:41 - mmengine - INFO - Saving checkpoint at 23 epochs 2023/05/13 17:49:16 - mmengine - INFO - Epoch(val) [23][ 50/509] eta: 0:04:29 time: 0.5872 data_time: 0.0022 memory: 4882 2023/05/13 17:49:44 - mmengine - INFO - Epoch(val) [23][100/509] eta: 0:03:54 time: 0.5595 data_time: 0.0021 memory: 915 2023/05/13 17:50:17 - mmengine - INFO - Epoch(val) [23][150/509] eta: 0:03:34 time: 0.6473 data_time: 0.0020 memory: 919 2023/05/13 17:50:49 - mmengine - INFO - Epoch(val) [23][200/509] eta: 0:03:09 time: 0.6539 data_time: 0.0021 memory: 907 2023/05/13 17:51:23 - mmengine - INFO - Epoch(val) [23][250/509] eta: 0:02:41 time: 0.6789 data_time: 0.0020 memory: 928 2023/05/13 17:51:56 - mmengine - INFO - Epoch(val) [23][300/509] eta: 0:02:11 time: 0.6517 data_time: 0.0020 memory: 883 2023/05/13 17:52:26 - mmengine - INFO - Epoch(val) [23][350/509] eta: 0:01:39 time: 0.6090 data_time: 0.0020 memory: 898 2023/05/13 17:53:00 - mmengine - INFO - Epoch(val) [23][400/509] eta: 0:01:08 time: 0.6765 data_time: 0.0020 memory: 903 2023/05/13 17:53:28 - mmengine - INFO - Epoch(val) [23][450/509] eta: 0:00:36 time: 0.5564 data_time: 0.0020 memory: 916 2023/05/13 17:53:55 - mmengine - INFO - Epoch(val) [23][500/509] eta: 0:00:05 time: 0.5393 data_time: 0.0020 memory: 902 2023/05/13 17:54:21 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9694 | 0.5203 | 0.6783 | 0.3212 | 0.4336 | 0.7288 | 0.8698 | 0.0109 | 0.9320 | 0.5535 | 0.8146 | 0.0573 | 0.8935 | 0.5733 | 0.8894 | 0.6714 | 0.7624 | 0.6309 | 0.4644 | 0.6197 | 0.9204 | 0.7162 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 17:54:21 - mmengine - INFO - Epoch(val) [23][509/509] car: 0.9694 bicycle: 0.5203 motorcycle: 0.6783 truck: 0.3212 bus: 0.4336 person: 0.7288 bicyclist: 0.8698 motorcyclist: 0.0109 road: 0.9320 parking: 0.5535 sidewalk: 0.8146 other-ground: 0.0573 building: 0.8935 fence: 0.5733 vegetation: 0.8894 trunck: 0.6714 terrian: 0.7624 pole: 0.6309 traffic-sign: 0.4644 miou: 0.6197 acc: 0.9204 acc_cls: 0.7162 data_time: 0.0021 time: 0.5502 2023/05/13 17:55:50 - mmengine - INFO - Epoch(train) [24][ 50/1196] lr: 8.0000e-03 eta: 8:53:08 time: 1.7851 data_time: 0.0039 memory: 5120 grad_norm: 0.1116 loss: 0.2076 loss_sem_seg: 0.2076 2023/05/13 17:57:27 - mmengine - INFO - Epoch(train) [24][ 100/1196] lr: 8.0000e-03 eta: 8:51:22 time: 1.9431 data_time: 0.0033 memory: 5097 grad_norm: 0.1185 loss: 0.2318 loss_sem_seg: 0.2318 2023/05/13 17:59:02 - mmengine - INFO - Epoch(train) [24][ 150/1196] lr: 8.0000e-03 eta: 8:49:34 time: 1.8925 data_time: 0.0033 memory: 4874 grad_norm: 0.0912 loss: 0.2256 loss_sem_seg: 0.2256 2023/05/13 18:00:37 - mmengine - INFO - Epoch(train) [24][ 200/1196] lr: 8.0000e-03 eta: 8:47:46 time: 1.9013 data_time: 0.0032 memory: 4923 grad_norm: 0.1013 loss: 0.2151 loss_sem_seg: 0.2151 2023/05/13 18:02:14 - mmengine - INFO - Epoch(train) [24][ 250/1196] lr: 8.0000e-03 eta: 8:45:59 time: 1.9385 data_time: 0.0034 memory: 5164 grad_norm: 0.0994 loss: 0.2053 loss_sem_seg: 0.2053 2023/05/13 18:03:49 - mmengine - INFO - Epoch(train) [24][ 300/1196] lr: 8.0000e-03 eta: 8:44:12 time: 1.9063 data_time: 0.0033 memory: 4596 grad_norm: 0.0964 loss: 0.2011 loss_sem_seg: 0.2011 2023/05/13 18:05:23 - mmengine - INFO - Epoch(train) [24][ 350/1196] lr: 8.0000e-03 eta: 8:42:24 time: 1.8736 data_time: 0.0033 memory: 4971 grad_norm: 0.1061 loss: 0.2215 loss_sem_seg: 0.2215 2023/05/13 18:06:58 - mmengine - INFO - Epoch(train) [24][ 400/1196] lr: 8.0000e-03 eta: 8:40:36 time: 1.8990 data_time: 0.0035 memory: 4912 grad_norm: 0.1035 loss: 0.1828 loss_sem_seg: 0.1828 2023/05/13 18:08:34 - mmengine - INFO - Epoch(train) [24][ 450/1196] lr: 8.0000e-03 eta: 8:38:49 time: 1.9202 data_time: 0.0034 memory: 5508 grad_norm: 0.1026 loss: 0.1990 loss_sem_seg: 0.1990 2023/05/13 18:09:55 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 18:10:11 - mmengine - INFO - Epoch(train) [24][ 500/1196] lr: 8.0000e-03 eta: 8:37:03 time: 1.9338 data_time: 0.0033 memory: 5088 grad_norm: 0.0997 loss: 0.1970 loss_sem_seg: 0.1970 2023/05/13 18:11:45 - mmengine - INFO - Epoch(train) [24][ 550/1196] lr: 8.0000e-03 eta: 8:35:15 time: 1.8944 data_time: 0.0033 memory: 5174 grad_norm: 0.0870 loss: 0.1901 loss_sem_seg: 0.1901 2023/05/13 18:13:21 - mmengine - INFO - Epoch(train) [24][ 600/1196] lr: 8.0000e-03 eta: 8:33:28 time: 1.9139 data_time: 0.0034 memory: 4877 grad_norm: 0.1032 loss: 0.2111 loss_sem_seg: 0.2111 2023/05/13 18:14:57 - mmengine - INFO - Epoch(train) [24][ 650/1196] lr: 8.0000e-03 eta: 8:31:41 time: 1.9116 data_time: 0.0032 memory: 5118 grad_norm: 0.1063 loss: 0.2014 loss_sem_seg: 0.2014 2023/05/13 18:16:29 - mmengine - INFO - Epoch(train) [24][ 700/1196] lr: 8.0000e-03 eta: 8:29:53 time: 1.8520 data_time: 0.0033 memory: 4727 grad_norm: 0.0970 loss: 0.2024 loss_sem_seg: 0.2024 2023/05/13 18:17:57 - mmengine - INFO - Epoch(train) [24][ 750/1196] lr: 8.0000e-03 eta: 8:28:01 time: 1.7503 data_time: 0.0032 memory: 5074 grad_norm: 0.0935 loss: 0.1932 loss_sem_seg: 0.1932 2023/05/13 18:19:24 - mmengine - INFO - Epoch(train) [24][ 800/1196] lr: 8.0000e-03 eta: 8:26:10 time: 1.7362 data_time: 0.0032 memory: 4919 grad_norm: 0.1100 loss: 0.2067 loss_sem_seg: 0.2067 2023/05/13 18:20:50 - mmengine - INFO - Epoch(train) [24][ 850/1196] lr: 8.0000e-03 eta: 8:24:18 time: 1.7205 data_time: 0.0032 memory: 5156 grad_norm: 0.0916 loss: 0.2085 loss_sem_seg: 0.2085 2023/05/13 18:22:16 - mmengine - INFO - Epoch(train) [24][ 900/1196] lr: 8.0000e-03 eta: 8:22:27 time: 1.7309 data_time: 0.0034 memory: 4738 grad_norm: 0.1080 loss: 0.1952 loss_sem_seg: 0.1952 2023/05/13 18:23:55 - mmengine - INFO - Epoch(train) [24][ 950/1196] lr: 8.0000e-03 eta: 8:20:42 time: 1.9657 data_time: 0.0033 memory: 5096 grad_norm: 0.1088 loss: 0.2075 loss_sem_seg: 0.2075 2023/05/13 18:25:23 - mmengine - INFO - Epoch(train) [24][1000/1196] lr: 8.0000e-03 eta: 8:18:51 time: 1.7630 data_time: 0.0033 memory: 5016 grad_norm: 0.1192 loss: 0.2030 loss_sem_seg: 0.2030 2023/05/13 18:26:47 - mmengine - INFO - Epoch(train) [24][1050/1196] lr: 8.0000e-03 eta: 8:16:59 time: 1.6943 data_time: 0.0034 memory: 4747 grad_norm: 0.1140 loss: 0.2203 loss_sem_seg: 0.2203 2023/05/13 18:28:23 - mmengine - INFO - Epoch(train) [24][1100/1196] lr: 8.0000e-03 eta: 8:15:12 time: 1.9064 data_time: 0.0033 memory: 5025 grad_norm: 0.1052 loss: 0.2183 loss_sem_seg: 0.2183 2023/05/13 18:29:56 - mmengine - INFO - Epoch(train) [24][1150/1196] lr: 8.0000e-03 eta: 8:13:25 time: 1.8718 data_time: 0.0033 memory: 4837 grad_norm: 0.1167 loss: 0.2208 loss_sem_seg: 0.2208 2023/05/13 18:31:25 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 18:31:25 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/05/13 18:32:05 - mmengine - INFO - Epoch(val) [24][ 50/509] eta: 0:05:14 time: 0.6856 data_time: 0.0021 memory: 4728 2023/05/13 18:32:38 - mmengine - INFO - Epoch(val) [24][100/509] eta: 0:04:35 time: 0.6608 data_time: 0.0021 memory: 915 2023/05/13 18:33:10 - mmengine - INFO - Epoch(val) [24][150/509] eta: 0:03:58 time: 0.6475 data_time: 0.0020 memory: 919 2023/05/13 18:33:43 - mmengine - INFO - Epoch(val) [24][200/509] eta: 0:03:24 time: 0.6512 data_time: 0.0020 memory: 907 2023/05/13 18:34:16 - mmengine - INFO - Epoch(val) [24][250/509] eta: 0:02:51 time: 0.6659 data_time: 0.0020 memory: 928 2023/05/13 18:34:48 - mmengine - INFO - Epoch(val) [24][300/509] eta: 0:02:17 time: 0.6415 data_time: 0.0020 memory: 883 2023/05/13 18:35:22 - mmengine - INFO - Epoch(val) [24][350/509] eta: 0:01:44 time: 0.6665 data_time: 0.0020 memory: 898 2023/05/13 18:35:55 - mmengine - INFO - Epoch(val) [24][400/509] eta: 0:01:11 time: 0.6649 data_time: 0.0021 memory: 903 2023/05/13 18:36:28 - mmengine - INFO - Epoch(val) [24][450/509] eta: 0:00:38 time: 0.6600 data_time: 0.0021 memory: 916 2023/05/13 18:37:00 - mmengine - INFO - Epoch(val) [24][500/509] eta: 0:00:05 time: 0.6320 data_time: 0.0021 memory: 902 2023/05/13 18:37:22 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9538 | 0.4871 | 0.6988 | 0.7091 | 0.5247 | 0.7562 | 0.8743 | 0.0047 | 0.9351 | 0.5576 | 0.8074 | 0.0344 | 0.8788 | 0.5365 | 0.8855 | 0.6874 | 0.7514 | 0.6514 | 0.4870 | 0.6432 | 0.9173 | 0.7122 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 18:37:22 - mmengine - INFO - Epoch(val) [24][509/509] car: 0.9538 bicycle: 0.4871 motorcycle: 0.6988 truck: 0.7091 bus: 0.5247 person: 0.7562 bicyclist: 0.8743 motorcyclist: 0.0047 road: 0.9351 parking: 0.5576 sidewalk: 0.8074 other-ground: 0.0344 building: 0.8788 fence: 0.5365 vegetation: 0.8855 trunck: 0.6874 terrian: 0.7514 pole: 0.6514 traffic-sign: 0.4870 miou: 0.6432 acc: 0.9173 acc_cls: 0.7122 data_time: 0.0020 time: 0.6421 2023/05/13 18:38:58 - mmengine - INFO - Epoch(train) [25][ 50/1196] lr: 8.0000e-04 eta: 8:10:01 time: 1.9063 data_time: 0.0041 memory: 4481 grad_norm: 0.0789 loss: 0.1993 loss_sem_seg: 0.1993 2023/05/13 18:40:31 - mmengine - INFO - Epoch(train) [25][ 100/1196] lr: 8.0000e-04 eta: 8:08:13 time: 1.8678 data_time: 0.0033 memory: 4985 grad_norm: 0.0673 loss: 0.1891 loss_sem_seg: 0.1891 2023/05/13 18:42:06 - mmengine - INFO - Epoch(train) [25][ 150/1196] lr: 8.0000e-04 eta: 8:06:27 time: 1.9021 data_time: 0.0033 memory: 5714 grad_norm: 0.0606 loss: 0.1706 loss_sem_seg: 0.1706 2023/05/13 18:43:37 - mmengine - INFO - Epoch(train) [25][ 200/1196] lr: 8.0000e-04 eta: 8:04:38 time: 1.8147 data_time: 0.0032 memory: 5028 grad_norm: 0.0721 loss: 0.1803 loss_sem_seg: 0.1803 2023/05/13 18:44:57 - mmengine - INFO - Epoch(train) [25][ 250/1196] lr: 8.0000e-04 eta: 8:02:44 time: 1.6013 data_time: 0.0032 memory: 4865 grad_norm: 0.0633 loss: 0.1690 loss_sem_seg: 0.1690 2023/05/13 18:46:09 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 18:46:16 - mmengine - INFO - Epoch(train) [25][ 300/1196] lr: 8.0000e-04 eta: 8:00:50 time: 1.5745 data_time: 0.0033 memory: 5210 grad_norm: 0.0648 loss: 0.1666 loss_sem_seg: 0.1666 2023/05/13 18:47:34 - mmengine - INFO - Epoch(train) [25][ 350/1196] lr: 8.0000e-04 eta: 7:58:56 time: 1.5702 data_time: 0.0033 memory: 5002 grad_norm: 0.0611 loss: 0.1892 loss_sem_seg: 0.1892 2023/05/13 18:48:46 - mmengine - INFO - Epoch(train) [25][ 400/1196] lr: 8.0000e-04 eta: 7:56:59 time: 1.4437 data_time: 0.0034 memory: 4743 grad_norm: 0.0609 loss: 0.1781 loss_sem_seg: 0.1781 2023/05/13 18:50:03 - mmengine - INFO - Epoch(train) [25][ 450/1196] lr: 8.0000e-04 eta: 7:55:04 time: 1.5313 data_time: 0.0033 memory: 4824 grad_norm: 0.0615 loss: 0.1719 loss_sem_seg: 0.1719 2023/05/13 18:51:30 - mmengine - INFO - Epoch(train) [25][ 500/1196] lr: 8.0000e-04 eta: 7:53:14 time: 1.7413 data_time: 0.0033 memory: 5018 grad_norm: 0.0661 loss: 0.1823 loss_sem_seg: 0.1823 2023/05/13 18:52:56 - mmengine - INFO - Epoch(train) [25][ 550/1196] lr: 8.0000e-04 eta: 7:51:24 time: 1.7232 data_time: 0.0033 memory: 5165 grad_norm: 0.0588 loss: 0.1815 loss_sem_seg: 0.1815 2023/05/13 18:54:27 - mmengine - INFO - Epoch(train) [25][ 600/1196] lr: 8.0000e-04 eta: 7:49:36 time: 1.8145 data_time: 0.0032 memory: 4535 grad_norm: 0.0628 loss: 0.1703 loss_sem_seg: 0.1703 2023/05/13 18:55:51 - mmengine - INFO - Epoch(train) [25][ 650/1196] lr: 8.0000e-04 eta: 7:47:45 time: 1.6867 data_time: 0.0032 memory: 4842 grad_norm: 0.0610 loss: 0.1787 loss_sem_seg: 0.1787 2023/05/13 18:57:25 - mmengine - INFO - Epoch(train) [25][ 700/1196] lr: 8.0000e-04 eta: 7:45:58 time: 1.8698 data_time: 0.0033 memory: 4784 grad_norm: 0.0590 loss: 0.1676 loss_sem_seg: 0.1676 2023/05/13 18:58:58 - mmengine - INFO - Epoch(train) [25][ 750/1196] lr: 8.0000e-04 eta: 7:44:12 time: 1.8728 data_time: 0.0035 memory: 5272 grad_norm: 0.0635 loss: 0.1758 loss_sem_seg: 0.1758 2023/05/13 19:00:34 - mmengine - INFO - Epoch(train) [25][ 800/1196] lr: 8.0000e-04 eta: 7:42:26 time: 1.9155 data_time: 0.0034 memory: 4837 grad_norm: 0.0608 loss: 0.1740 loss_sem_seg: 0.1740 2023/05/13 19:02:09 - mmengine - INFO - Epoch(train) [25][ 850/1196] lr: 8.0000e-04 eta: 7:40:40 time: 1.8984 data_time: 0.0033 memory: 4874 grad_norm: 0.0661 loss: 0.1755 loss_sem_seg: 0.1755 2023/05/13 19:03:46 - mmengine - INFO - Epoch(train) [25][ 900/1196] lr: 8.0000e-04 eta: 7:38:55 time: 1.9303 data_time: 0.0036 memory: 4661 grad_norm: 0.0632 loss: 0.1598 loss_sem_seg: 0.1598 2023/05/13 19:05:21 - mmengine - INFO - Epoch(train) [25][ 950/1196] lr: 8.0000e-04 eta: 7:37:10 time: 1.9029 data_time: 0.0035 memory: 4806 grad_norm: 0.0614 loss: 0.1725 loss_sem_seg: 0.1725 2023/05/13 19:06:54 - mmengine - INFO - Epoch(train) [25][1000/1196] lr: 8.0000e-04 eta: 7:35:23 time: 1.8662 data_time: 0.0035 memory: 4780 grad_norm: 0.0679 loss: 0.1762 loss_sem_seg: 0.1762 2023/05/13 19:08:29 - mmengine - INFO - Epoch(train) [25][1050/1196] lr: 8.0000e-04 eta: 7:33:38 time: 1.8921 data_time: 0.0037 memory: 4744 grad_norm: 0.0660 loss: 0.1641 loss_sem_seg: 0.1641 2023/05/13 19:10:05 - mmengine - INFO - Epoch(train) [25][1100/1196] lr: 8.0000e-04 eta: 7:31:53 time: 1.9320 data_time: 0.0035 memory: 5300 grad_norm: 0.0641 loss: 0.1633 loss_sem_seg: 0.1633 2023/05/13 19:11:40 - mmengine - INFO - Epoch(train) [25][1150/1196] lr: 8.0000e-04 eta: 7:30:07 time: 1.8942 data_time: 0.0033 memory: 5023 grad_norm: 0.0605 loss: 0.1650 loss_sem_seg: 0.1650 2023/05/13 19:13:07 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 19:13:07 - mmengine - INFO - Saving checkpoint at 25 epochs 2023/05/13 19:13:49 - mmengine - INFO - Epoch(val) [25][ 50/509] eta: 0:05:22 time: 0.7037 data_time: 0.0021 memory: 4850 2023/05/13 19:14:21 - mmengine - INFO - Epoch(val) [25][100/509] eta: 0:04:38 time: 0.6563 data_time: 0.0020 memory: 915 2023/05/13 19:14:55 - mmengine - INFO - Epoch(val) [25][150/509] eta: 0:04:04 time: 0.6793 data_time: 0.0020 memory: 919 2023/05/13 19:15:27 - mmengine - INFO - Epoch(val) [25][200/509] eta: 0:03:26 time: 0.6348 data_time: 0.0021 memory: 907 2023/05/13 19:16:00 - mmengine - INFO - Epoch(val) [25][250/509] eta: 0:02:52 time: 0.6578 data_time: 0.0021 memory: 928 2023/05/13 19:16:34 - mmengine - INFO - Epoch(val) [25][300/509] eta: 0:02:19 time: 0.6687 data_time: 0.0020 memory: 883 2023/05/13 19:17:07 - mmengine - INFO - Epoch(val) [25][350/509] eta: 0:01:46 time: 0.6713 data_time: 0.0021 memory: 898 2023/05/13 19:17:40 - mmengine - INFO - Epoch(val) [25][400/509] eta: 0:01:12 time: 0.6581 data_time: 0.0021 memory: 903 2023/05/13 19:18:15 - mmengine - INFO - Epoch(val) [25][450/509] eta: 0:00:39 time: 0.6974 data_time: 0.0021 memory: 916 2023/05/13 19:18:50 - mmengine - INFO - Epoch(val) [25][500/509] eta: 0:00:06 time: 0.6928 data_time: 0.0020 memory: 902 2023/05/13 19:19:12 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9747 | 0.5546 | 0.8266 | 0.7752 | 0.7508 | 0.8023 | 0.8825 | 0.0263 | 0.9439 | 0.5416 | 0.8282 | 0.0632 | 0.9199 | 0.6837 | 0.8773 | 0.6818 | 0.7304 | 0.6557 | 0.5167 | 0.6861 | 0.9237 | 0.7575 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 19:19:12 - mmengine - INFO - Epoch(val) [25][509/509] car: 0.9747 bicycle: 0.5546 motorcycle: 0.8266 truck: 0.7752 bus: 0.7508 person: 0.8023 bicyclist: 0.8825 motorcyclist: 0.0263 road: 0.9439 parking: 0.5416 sidewalk: 0.8282 other-ground: 0.0632 building: 0.9199 fence: 0.6837 vegetation: 0.8773 trunck: 0.6818 terrian: 0.7304 pole: 0.6557 traffic-sign: 0.5167 miou: 0.6861 acc: 0.9237 acc_cls: 0.7575 data_time: 0.0020 time: 0.7193 2023/05/13 19:20:42 - mmengine - INFO - Epoch(train) [26][ 50/1196] lr: 8.0000e-04 eta: 7:26:42 time: 1.7985 data_time: 0.0039 memory: 4783 grad_norm: 0.0616 loss: 0.1640 loss_sem_seg: 0.1640 2023/05/13 19:22:09 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 19:22:09 - mmengine - INFO - Epoch(train) [26][ 100/1196] lr: 8.0000e-04 eta: 7:24:54 time: 1.7435 data_time: 0.0032 memory: 4933 grad_norm: 0.0631 loss: 0.1758 loss_sem_seg: 0.1758 2023/05/13 19:23:36 - mmengine - INFO - Epoch(train) [26][ 150/1196] lr: 8.0000e-04 eta: 7:23:04 time: 1.7233 data_time: 0.0033 memory: 4960 grad_norm: 0.0621 loss: 0.1507 loss_sem_seg: 0.1507 2023/05/13 19:25:00 - mmengine - INFO - Epoch(train) [26][ 200/1196] lr: 8.0000e-04 eta: 7:21:14 time: 1.6805 data_time: 0.0032 memory: 4874 grad_norm: 0.0619 loss: 0.1745 loss_sem_seg: 0.1745 2023/05/13 19:26:19 - mmengine - INFO - Epoch(train) [26][ 250/1196] lr: 8.0000e-04 eta: 7:19:22 time: 1.5849 data_time: 0.0033 memory: 4914 grad_norm: 0.0642 loss: 0.1576 loss_sem_seg: 0.1576 2023/05/13 19:27:52 - mmengine - INFO - Epoch(train) [26][ 300/1196] lr: 8.0000e-04 eta: 7:17:36 time: 1.8644 data_time: 0.0034 memory: 4786 grad_norm: 0.0624 loss: 0.1666 loss_sem_seg: 0.1666 2023/05/13 19:29:27 - mmengine - INFO - Epoch(train) [26][ 350/1196] lr: 8.0000e-04 eta: 7:15:51 time: 1.8892 data_time: 0.0033 memory: 4608 grad_norm: 0.0651 loss: 0.1624 loss_sem_seg: 0.1624 2023/05/13 19:31:01 - mmengine - INFO - Epoch(train) [26][ 400/1196] lr: 8.0000e-04 eta: 7:14:06 time: 1.8923 data_time: 0.0033 memory: 4983 grad_norm: 0.0648 loss: 0.1778 loss_sem_seg: 0.1778 2023/05/13 19:32:34 - mmengine - INFO - Epoch(train) [26][ 450/1196] lr: 8.0000e-04 eta: 7:12:20 time: 1.8652 data_time: 0.0033 memory: 4703 grad_norm: 0.0663 loss: 0.1677 loss_sem_seg: 0.1677 2023/05/13 19:34:09 - mmengine - INFO - Epoch(train) [26][ 500/1196] lr: 8.0000e-04 eta: 7:10:35 time: 1.8951 data_time: 0.0033 memory: 5434 grad_norm: 0.0632 loss: 0.1683 loss_sem_seg: 0.1683 2023/05/13 19:35:44 - mmengine - INFO - Epoch(train) [26][ 550/1196] lr: 8.0000e-04 eta: 7:08:50 time: 1.9040 data_time: 0.0036 memory: 4787 grad_norm: 0.0646 loss: 0.1674 loss_sem_seg: 0.1674 2023/05/13 19:37:20 - mmengine - INFO - Epoch(train) [26][ 600/1196] lr: 8.0000e-04 eta: 7:07:05 time: 1.9061 data_time: 0.0034 memory: 4941 grad_norm: 0.0622 loss: 0.1723 loss_sem_seg: 0.1723 2023/05/13 19:38:56 - mmengine - INFO - Epoch(train) [26][ 650/1196] lr: 8.0000e-04 eta: 7:05:21 time: 1.9191 data_time: 0.0033 memory: 4927 grad_norm: 0.0616 loss: 0.1629 loss_sem_seg: 0.1629 2023/05/13 19:40:32 - mmengine - INFO - Epoch(train) [26][ 700/1196] lr: 8.0000e-04 eta: 7:03:36 time: 1.9228 data_time: 0.0034 memory: 4908 grad_norm: 0.0694 loss: 0.1647 loss_sem_seg: 0.1647 2023/05/13 19:42:08 - mmengine - INFO - Epoch(train) [26][ 750/1196] lr: 8.0000e-04 eta: 7:01:52 time: 1.9139 data_time: 0.0033 memory: 4975 grad_norm: 0.0629 loss: 0.1685 loss_sem_seg: 0.1685 2023/05/13 19:43:42 - mmengine - INFO - Epoch(train) [26][ 800/1196] lr: 8.0000e-04 eta: 7:00:07 time: 1.8909 data_time: 0.0034 memory: 4710 grad_norm: 0.0626 loss: 0.1552 loss_sem_seg: 0.1552 2023/05/13 19:45:18 - mmengine - INFO - Epoch(train) [26][ 850/1196] lr: 8.0000e-04 eta: 6:58:22 time: 1.9088 data_time: 0.0033 memory: 4944 grad_norm: 0.0667 loss: 0.1693 loss_sem_seg: 0.1693 2023/05/13 19:46:52 - mmengine - INFO - Epoch(train) [26][ 900/1196] lr: 8.0000e-04 eta: 6:56:37 time: 1.8872 data_time: 0.0033 memory: 4909 grad_norm: 0.0601 loss: 0.1735 loss_sem_seg: 0.1735 2023/05/13 19:48:28 - mmengine - INFO - Epoch(train) [26][ 950/1196] lr: 8.0000e-04 eta: 6:54:53 time: 1.9289 data_time: 0.0033 memory: 5456 grad_norm: 0.0637 loss: 0.1609 loss_sem_seg: 0.1609 2023/05/13 19:50:03 - mmengine - INFO - Epoch(train) [26][1000/1196] lr: 8.0000e-04 eta: 6:53:08 time: 1.8945 data_time: 0.0034 memory: 4886 grad_norm: 0.0617 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 19:51:38 - mmengine - INFO - Epoch(train) [26][1050/1196] lr: 8.0000e-04 eta: 6:51:23 time: 1.8927 data_time: 0.0034 memory: 5047 grad_norm: 0.0659 loss: 0.1670 loss_sem_seg: 0.1670 2023/05/13 19:53:12 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 19:53:12 - mmengine - INFO - Epoch(train) [26][1100/1196] lr: 8.0000e-04 eta: 6:49:38 time: 1.8928 data_time: 0.0033 memory: 4954 grad_norm: 0.0628 loss: 0.1640 loss_sem_seg: 0.1640 2023/05/13 19:54:41 - mmengine - INFO - Epoch(train) [26][1150/1196] lr: 8.0000e-04 eta: 6:47:51 time: 1.7760 data_time: 0.0039 memory: 5668 grad_norm: 0.0633 loss: 0.1608 loss_sem_seg: 0.1608 2023/05/13 19:56:01 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 19:56:01 - mmengine - INFO - Saving checkpoint at 26 epochs 2023/05/13 19:56:31 - mmengine - INFO - Epoch(val) [26][ 50/509] eta: 0:03:35 time: 0.4687 data_time: 0.0020 memory: 5136 2023/05/13 19:56:51 - mmengine - INFO - Epoch(val) [26][100/509] eta: 0:02:59 time: 0.4096 data_time: 0.0020 memory: 915 2023/05/13 19:57:12 - mmengine - INFO - Epoch(val) [26][150/509] eta: 0:02:35 time: 0.4183 data_time: 0.0020 memory: 919 2023/05/13 19:57:36 - mmengine - INFO - Epoch(val) [26][200/509] eta: 0:02:17 time: 0.4790 data_time: 0.0020 memory: 907 2023/05/13 19:57:58 - mmengine - INFO - Epoch(val) [26][250/509] eta: 0:01:54 time: 0.4432 data_time: 0.0020 memory: 928 2023/05/13 19:58:15 - mmengine - INFO - Epoch(val) [26][300/509] eta: 0:01:28 time: 0.3288 data_time: 0.0021 memory: 883 2023/05/13 19:58:33 - mmengine - INFO - Epoch(val) [26][350/509] eta: 0:01:05 time: 0.3557 data_time: 0.0019 memory: 898 2023/05/13 19:58:50 - mmengine - INFO - Epoch(val) [26][400/509] eta: 0:00:44 time: 0.3561 data_time: 0.0019 memory: 903 2023/05/13 19:59:07 - mmengine - INFO - Epoch(val) [26][450/509] eta: 0:00:23 time: 0.3286 data_time: 0.0020 memory: 916 2023/05/13 19:59:26 - mmengine - INFO - Epoch(val) [26][500/509] eta: 0:00:03 time: 0.3771 data_time: 0.0020 memory: 902 2023/05/13 19:59:50 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9728 | 0.5421 | 0.8249 | 0.7800 | 0.7405 | 0.8046 | 0.8736 | 0.0581 | 0.9446 | 0.5332 | 0.8301 | 0.0255 | 0.9215 | 0.6900 | 0.8807 | 0.6916 | 0.7366 | 0.6525 | 0.5036 | 0.6846 | 0.9252 | 0.7524 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 19:59:50 - mmengine - INFO - Epoch(val) [26][509/509] car: 0.9728 bicycle: 0.5421 motorcycle: 0.8249 truck: 0.7800 bus: 0.7405 person: 0.8046 bicyclist: 0.8736 motorcyclist: 0.0581 road: 0.9446 parking: 0.5332 sidewalk: 0.8301 other-ground: 0.0255 building: 0.9215 fence: 0.6900 vegetation: 0.8807 trunck: 0.6916 terrian: 0.7366 pole: 0.6525 traffic-sign: 0.5036 miou: 0.6846 acc: 0.9252 acc_cls: 0.7524 data_time: 0.0020 time: 0.3953 2023/05/13 20:00:48 - mmengine - INFO - Epoch(train) [27][ 50/1196] lr: 8.0000e-04 eta: 6:44:14 time: 1.1756 data_time: 0.0040 memory: 4949 grad_norm: 0.0640 loss: 0.1752 loss_sem_seg: 0.1752 2023/05/13 20:01:48 - mmengine - INFO - Epoch(train) [27][ 100/1196] lr: 8.0000e-04 eta: 6:42:16 time: 1.1947 data_time: 0.0032 memory: 5109 grad_norm: 0.0663 loss: 0.1550 loss_sem_seg: 0.1550 2023/05/13 20:02:49 - mmengine - INFO - Epoch(train) [27][ 150/1196] lr: 8.0000e-04 eta: 6:40:19 time: 1.2226 data_time: 0.0032 memory: 4748 grad_norm: 0.0654 loss: 0.1742 loss_sem_seg: 0.1742 2023/05/13 20:03:49 - mmengine - INFO - Epoch(train) [27][ 200/1196] lr: 8.0000e-04 eta: 6:38:22 time: 1.1944 data_time: 0.0033 memory: 4786 grad_norm: 0.0619 loss: 0.1646 loss_sem_seg: 0.1646 2023/05/13 20:04:51 - mmengine - INFO - Epoch(train) [27][ 250/1196] lr: 8.0000e-04 eta: 6:36:25 time: 1.2330 data_time: 0.0033 memory: 5029 grad_norm: 0.0668 loss: 0.1673 loss_sem_seg: 0.1673 2023/05/13 20:05:51 - mmengine - INFO - Epoch(train) [27][ 300/1196] lr: 8.0000e-04 eta: 6:34:28 time: 1.2160 data_time: 0.0034 memory: 4804 grad_norm: 0.0670 loss: 0.1563 loss_sem_seg: 0.1563 2023/05/13 20:06:51 - mmengine - INFO - Epoch(train) [27][ 350/1196] lr: 8.0000e-04 eta: 6:32:31 time: 1.1954 data_time: 0.0033 memory: 5033 grad_norm: 0.0652 loss: 0.1704 loss_sem_seg: 0.1704 2023/05/13 20:07:52 - mmengine - INFO - Epoch(train) [27][ 400/1196] lr: 8.0000e-04 eta: 6:30:35 time: 1.2147 data_time: 0.0033 memory: 5116 grad_norm: 0.0655 loss: 0.1623 loss_sem_seg: 0.1623 2023/05/13 20:08:52 - mmengine - INFO - Epoch(train) [27][ 450/1196] lr: 8.0000e-04 eta: 6:28:39 time: 1.2009 data_time: 0.0035 memory: 5032 grad_norm: 0.0594 loss: 0.1564 loss_sem_seg: 0.1564 2023/05/13 20:09:53 - mmengine - INFO - Epoch(train) [27][ 500/1196] lr: 8.0000e-04 eta: 6:26:42 time: 1.2109 data_time: 0.0034 memory: 4793 grad_norm: 0.0642 loss: 0.1661 loss_sem_seg: 0.1661 2023/05/13 20:10:52 - mmengine - INFO - Epoch(train) [27][ 550/1196] lr: 8.0000e-04 eta: 6:24:46 time: 1.1983 data_time: 0.0035 memory: 5695 grad_norm: 0.0630 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 20:11:52 - mmengine - INFO - Epoch(train) [27][ 600/1196] lr: 8.0000e-04 eta: 6:22:50 time: 1.1875 data_time: 0.0035 memory: 4772 grad_norm: 0.0709 loss: 0.1635 loss_sem_seg: 0.1635 2023/05/13 20:12:51 - mmengine - INFO - Epoch(train) [27][ 650/1196] lr: 8.0000e-04 eta: 6:20:54 time: 1.1784 data_time: 0.0034 memory: 4923 grad_norm: 0.0600 loss: 0.1548 loss_sem_seg: 0.1548 2023/05/13 20:13:51 - mmengine - INFO - Epoch(train) [27][ 700/1196] lr: 8.0000e-04 eta: 6:18:59 time: 1.2016 data_time: 0.0035 memory: 4644 grad_norm: 0.0616 loss: 0.1651 loss_sem_seg: 0.1651 2023/05/13 20:14:50 - mmengine - INFO - Epoch(train) [27][ 750/1196] lr: 8.0000e-04 eta: 6:17:03 time: 1.1909 data_time: 0.0034 memory: 4828 grad_norm: 0.0660 loss: 0.1622 loss_sem_seg: 0.1622 2023/05/13 20:15:47 - mmengine - INFO - Epoch(train) [27][ 800/1196] lr: 8.0000e-04 eta: 6:15:07 time: 1.1370 data_time: 0.0033 memory: 4858 grad_norm: 0.0654 loss: 0.1584 loss_sem_seg: 0.1584 2023/05/13 20:16:35 - mmengine - INFO - Epoch(train) [27][ 850/1196] lr: 8.0000e-04 eta: 6:13:07 time: 0.9621 data_time: 0.0032 memory: 4668 grad_norm: 0.0633 loss: 0.1632 loss_sem_seg: 0.1632 2023/05/13 20:17:25 - mmengine - INFO - Epoch(train) [27][ 900/1196] lr: 8.0000e-04 eta: 6:11:09 time: 0.9861 data_time: 0.0034 memory: 4737 grad_norm: 0.0623 loss: 0.1713 loss_sem_seg: 0.1713 2023/05/13 20:17:29 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 20:18:15 - mmengine - INFO - Epoch(train) [27][ 950/1196] lr: 8.0000e-04 eta: 6:09:11 time: 1.0020 data_time: 0.0035 memory: 4695 grad_norm: 0.0637 loss: 0.1574 loss_sem_seg: 0.1574 2023/05/13 20:19:14 - mmengine - INFO - Epoch(train) [27][1000/1196] lr: 8.0000e-04 eta: 6:07:16 time: 1.1931 data_time: 0.0035 memory: 4721 grad_norm: 0.0639 loss: 0.1531 loss_sem_seg: 0.1531 2023/05/13 20:20:13 - mmengine - INFO - Epoch(train) [27][1050/1196] lr: 8.0000e-04 eta: 6:05:22 time: 1.1793 data_time: 0.0034 memory: 5178 grad_norm: 0.0635 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/13 20:21:11 - mmengine - INFO - Epoch(train) [27][1100/1196] lr: 8.0000e-04 eta: 6:03:27 time: 1.1538 data_time: 0.0034 memory: 4593 grad_norm: 0.0622 loss: 0.1683 loss_sem_seg: 0.1683 2023/05/13 20:22:09 - mmengine - INFO - Epoch(train) [27][1150/1196] lr: 8.0000e-04 eta: 6:01:32 time: 1.1560 data_time: 0.0034 memory: 5545 grad_norm: 0.0666 loss: 0.1576 loss_sem_seg: 0.1576 2023/05/13 20:23:04 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 20:23:04 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/05/13 20:23:24 - mmengine - INFO - Epoch(val) [27][ 50/509] eta: 0:02:10 time: 0.2837 data_time: 0.0021 memory: 4622 2023/05/13 20:23:38 - mmengine - INFO - Epoch(val) [27][100/509] eta: 0:01:55 time: 0.2799 data_time: 0.0020 memory: 915 2023/05/13 20:23:52 - mmengine - INFO - Epoch(val) [27][150/509] eta: 0:01:39 time: 0.2701 data_time: 0.0020 memory: 919 2023/05/13 20:24:05 - mmengine - INFO - Epoch(val) [27][200/509] eta: 0:01:25 time: 0.2703 data_time: 0.0020 memory: 907 2023/05/13 20:24:19 - mmengine - INFO - Epoch(val) [27][250/509] eta: 0:01:11 time: 0.2810 data_time: 0.0019 memory: 928 2023/05/13 20:24:33 - mmengine - INFO - Epoch(val) [27][300/509] eta: 0:00:57 time: 0.2664 data_time: 0.0020 memory: 883 2023/05/13 20:24:46 - mmengine - INFO - Epoch(val) [27][350/509] eta: 0:00:43 time: 0.2618 data_time: 0.0019 memory: 898 2023/05/13 20:24:59 - mmengine - INFO - Epoch(val) [27][400/509] eta: 0:00:29 time: 0.2708 data_time: 0.0020 memory: 903 2023/05/13 20:25:13 - mmengine - INFO - Epoch(val) [27][450/509] eta: 0:00:16 time: 0.2766 data_time: 0.0020 memory: 916 2023/05/13 20:25:27 - mmengine - INFO - Epoch(val) [27][500/509] eta: 0:00:02 time: 0.2783 data_time: 0.0020 memory: 902 2023/05/13 20:25:46 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9744 | 0.5585 | 0.8201 | 0.7909 | 0.7583 | 0.7967 | 0.8685 | 0.0767 | 0.9473 | 0.5247 | 0.8303 | 0.0263 | 0.9187 | 0.6808 | 0.8742 | 0.6854 | 0.7200 | 0.6564 | 0.4871 | 0.6840 | 0.9226 | 0.7534 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 20:25:46 - mmengine - INFO - Epoch(val) [27][509/509] car: 0.9744 bicycle: 0.5585 motorcycle: 0.8201 truck: 0.7909 bus: 0.7583 person: 0.7967 bicyclist: 0.8685 motorcyclist: 0.0767 road: 0.9473 parking: 0.5247 sidewalk: 0.8303 other-ground: 0.0263 building: 0.9187 fence: 0.6808 vegetation: 0.8742 trunck: 0.6854 terrian: 0.7200 pole: 0.6564 traffic-sign: 0.4871 miou: 0.6840 acc: 0.9226 acc_cls: 0.7534 data_time: 0.0020 time: 0.2888 2023/05/13 20:26:49 - mmengine - INFO - Epoch(train) [28][ 50/1196] lr: 8.0000e-04 eta: 5:57:55 time: 1.2524 data_time: 0.0045 memory: 4819 grad_norm: 0.0607 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/13 20:27:50 - mmengine - INFO - Epoch(train) [28][ 100/1196] lr: 8.0000e-04 eta: 5:56:02 time: 1.2249 data_time: 0.0033 memory: 4773 grad_norm: 0.0642 loss: 0.1599 loss_sem_seg: 0.1599 2023/05/13 20:28:51 - mmengine - INFO - Epoch(train) [28][ 150/1196] lr: 8.0000e-04 eta: 5:54:08 time: 1.2159 data_time: 0.0035 memory: 4985 grad_norm: 0.0645 loss: 0.1487 loss_sem_seg: 0.1487 2023/05/13 20:29:51 - mmengine - INFO - Epoch(train) [28][ 200/1196] lr: 8.0000e-04 eta: 5:52:16 time: 1.2117 data_time: 0.0035 memory: 5001 grad_norm: 0.0659 loss: 0.1680 loss_sem_seg: 0.1680 2023/05/13 20:30:51 - mmengine - INFO - Epoch(train) [28][ 250/1196] lr: 8.0000e-04 eta: 5:50:22 time: 1.1908 data_time: 0.0033 memory: 4957 grad_norm: 0.0642 loss: 0.1577 loss_sem_seg: 0.1577 2023/05/13 20:31:52 - mmengine - INFO - Epoch(train) [28][ 300/1196] lr: 8.0000e-04 eta: 5:48:30 time: 1.2127 data_time: 0.0034 memory: 5359 grad_norm: 0.0673 loss: 0.1625 loss_sem_seg: 0.1625 2023/05/13 20:32:54 - mmengine - INFO - Epoch(train) [28][ 350/1196] lr: 8.0000e-04 eta: 5:46:38 time: 1.2373 data_time: 0.0034 memory: 5118 grad_norm: 0.0624 loss: 0.1587 loss_sem_seg: 0.1587 2023/05/13 20:33:53 - mmengine - INFO - Epoch(train) [28][ 400/1196] lr: 8.0000e-04 eta: 5:44:45 time: 1.1803 data_time: 0.0033 memory: 4914 grad_norm: 0.0666 loss: 0.1653 loss_sem_seg: 0.1653 2023/05/13 20:34:53 - mmengine - INFO - Epoch(train) [28][ 450/1196] lr: 8.0000e-04 eta: 5:42:53 time: 1.2081 data_time: 0.0033 memory: 4925 grad_norm: 0.0585 loss: 0.1462 loss_sem_seg: 0.1462 2023/05/13 20:35:44 - mmengine - INFO - Epoch(train) [28][ 500/1196] lr: 8.0000e-04 eta: 5:40:58 time: 1.0281 data_time: 0.0035 memory: 5330 grad_norm: 0.0666 loss: 0.1613 loss_sem_seg: 0.1613 2023/05/13 20:36:33 - mmengine - INFO - Epoch(train) [28][ 550/1196] lr: 8.0000e-04 eta: 5:39:02 time: 0.9815 data_time: 0.0034 memory: 4867 grad_norm: 0.0696 loss: 0.1557 loss_sem_seg: 0.1557 2023/05/13 20:37:21 - mmengine - INFO - Epoch(train) [28][ 600/1196] lr: 8.0000e-04 eta: 5:37:07 time: 0.9523 data_time: 0.0033 memory: 5200 grad_norm: 0.0609 loss: 0.1543 loss_sem_seg: 0.1543 2023/05/13 20:38:09 - mmengine - INFO - Epoch(train) [28][ 650/1196] lr: 8.0000e-04 eta: 5:35:11 time: 0.9506 data_time: 0.0033 memory: 4755 grad_norm: 0.0701 loss: 0.1623 loss_sem_seg: 0.1623 2023/05/13 20:39:01 - mmengine - INFO - Epoch(train) [28][ 700/1196] lr: 8.0000e-04 eta: 5:33:18 time: 1.0511 data_time: 0.0036 memory: 5084 grad_norm: 0.0618 loss: 0.1484 loss_sem_seg: 0.1484 2023/05/13 20:39:07 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 20:39:38 - mmengine - INFO - Epoch(train) [28][ 750/1196] lr: 8.0000e-04 eta: 5:31:19 time: 0.7352 data_time: 0.0036 memory: 5147 grad_norm: 0.0630 loss: 0.1596 loss_sem_seg: 0.1596 2023/05/13 20:40:14 - mmengine - INFO - Epoch(train) [28][ 800/1196] lr: 8.0000e-04 eta: 5:29:21 time: 0.7150 data_time: 0.0037 memory: 5238 grad_norm: 0.0627 loss: 0.1628 loss_sem_seg: 0.1628 2023/05/13 20:40:50 - mmengine - INFO - Epoch(train) [28][ 850/1196] lr: 8.0000e-04 eta: 5:27:23 time: 0.7269 data_time: 0.0042 memory: 4643 grad_norm: 0.0653 loss: 0.1492 loss_sem_seg: 0.1492 2023/05/13 20:41:26 - mmengine - INFO - Epoch(train) [28][ 900/1196] lr: 8.0000e-04 eta: 5:25:25 time: 0.7245 data_time: 0.0052 memory: 4847 grad_norm: 0.0593 loss: 0.1618 loss_sem_seg: 0.1618 2023/05/13 20:42:02 - mmengine - INFO - Epoch(train) [28][ 950/1196] lr: 8.0000e-04 eta: 5:23:27 time: 0.7063 data_time: 0.0045 memory: 5089 grad_norm: 0.0599 loss: 0.1504 loss_sem_seg: 0.1504 2023/05/13 20:42:38 - mmengine - INFO - Epoch(train) [28][1000/1196] lr: 8.0000e-04 eta: 5:21:30 time: 0.7190 data_time: 0.0037 memory: 5104 grad_norm: 0.0668 loss: 0.1625 loss_sem_seg: 0.1625 2023/05/13 20:43:13 - mmengine - INFO - Epoch(train) [28][1050/1196] lr: 8.0000e-04 eta: 5:19:33 time: 0.7168 data_time: 0.0037 memory: 4704 grad_norm: 0.0618 loss: 0.1670 loss_sem_seg: 0.1670 2023/05/13 20:43:49 - mmengine - INFO - Epoch(train) [28][1100/1196] lr: 8.0000e-04 eta: 5:17:36 time: 0.7046 data_time: 0.0036 memory: 4719 grad_norm: 0.0721 loss: 0.1578 loss_sem_seg: 0.1578 2023/05/13 20:44:25 - mmengine - INFO - Epoch(train) [28][1150/1196] lr: 8.0000e-04 eta: 5:15:39 time: 0.7251 data_time: 0.0037 memory: 4861 grad_norm: 0.0610 loss: 0.1592 loss_sem_seg: 0.1592 2023/05/13 20:44:59 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 20:44:59 - mmengine - INFO - Saving checkpoint at 28 epochs 2023/05/13 20:45:16 - mmengine - INFO - Epoch(val) [28][ 50/509] eta: 0:01:27 time: 0.1904 data_time: 0.0023 memory: 4924 2023/05/13 20:45:25 - mmengine - INFO - Epoch(val) [28][100/509] eta: 0:01:15 time: 0.1768 data_time: 0.0022 memory: 915 2023/05/13 20:45:34 - mmengine - INFO - Epoch(val) [28][150/509] eta: 0:01:04 time: 0.1711 data_time: 0.0022 memory: 919 2023/05/13 20:45:42 - mmengine - INFO - Epoch(val) [28][200/509] eta: 0:00:54 time: 0.1610 data_time: 0.0022 memory: 907 2023/05/13 20:45:50 - mmengine - INFO - Epoch(val) [28][250/509] eta: 0:00:44 time: 0.1610 data_time: 0.0022 memory: 928 2023/05/13 20:45:58 - mmengine - INFO - Epoch(val) [28][300/509] eta: 0:00:35 time: 0.1653 data_time: 0.0022 memory: 883 2023/05/13 20:46:06 - mmengine - INFO - Epoch(val) [28][350/509] eta: 0:00:27 time: 0.1655 data_time: 0.0022 memory: 898 2023/05/13 20:46:15 - mmengine - INFO - Epoch(val) [28][400/509] eta: 0:00:18 time: 0.1772 data_time: 0.0022 memory: 903 2023/05/13 20:46:25 - mmengine - INFO - Epoch(val) [28][450/509] eta: 0:00:10 time: 0.1994 data_time: 0.0021 memory: 916 2023/05/13 20:46:42 - mmengine - INFO - Epoch(val) [28][500/509] eta: 0:00:01 time: 0.3412 data_time: 0.0018 memory: 902 2023/05/13 20:47:03 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9706 | 0.5360 | 0.8178 | 0.7879 | 0.7101 | 0.8069 | 0.8897 | 0.0697 | 0.9468 | 0.5824 | 0.8343 | 0.0421 | 0.9177 | 0.6788 | 0.8740 | 0.6633 | 0.7218 | 0.6500 | 0.5059 | 0.6845 | 0.9230 | 0.7478 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 20:47:03 - mmengine - INFO - Epoch(val) [28][509/509] car: 0.9706 bicycle: 0.5360 motorcycle: 0.8178 truck: 0.7879 bus: 0.7101 person: 0.8069 bicyclist: 0.8897 motorcyclist: 0.0697 road: 0.9468 parking: 0.5824 sidewalk: 0.8343 other-ground: 0.0421 building: 0.9177 fence: 0.6788 vegetation: 0.8740 trunck: 0.6633 terrian: 0.7218 pole: 0.6500 traffic-sign: 0.5059 miou: 0.6845 acc: 0.9230 acc_cls: 0.7478 data_time: 0.0018 time: 0.3442 2023/05/13 20:47:41 - mmengine - INFO - Epoch(train) [29][ 50/1196] lr: 8.0000e-04 eta: 5:11:57 time: 0.7553 data_time: 0.0044 memory: 4997 grad_norm: 0.0603 loss: 0.1689 loss_sem_seg: 0.1689 2023/05/13 20:48:19 - mmengine - INFO - Epoch(train) [29][ 100/1196] lr: 8.0000e-04 eta: 5:10:02 time: 0.7517 data_time: 0.0035 memory: 4908 grad_norm: 0.0623 loss: 0.1517 loss_sem_seg: 0.1517 2023/05/13 20:48:55 - mmengine - INFO - Epoch(train) [29][ 150/1196] lr: 8.0000e-04 eta: 5:08:06 time: 0.7269 data_time: 0.0034 memory: 5252 grad_norm: 0.0657 loss: 0.1531 loss_sem_seg: 0.1531 2023/05/13 20:49:32 - mmengine - INFO - Epoch(train) [29][ 200/1196] lr: 8.0000e-04 eta: 5:06:11 time: 0.7358 data_time: 0.0033 memory: 5126 grad_norm: 0.0644 loss: 0.1520 loss_sem_seg: 0.1520 2023/05/13 20:50:07 - mmengine - INFO - Epoch(train) [29][ 250/1196] lr: 8.0000e-04 eta: 5:04:16 time: 0.7067 data_time: 0.0034 memory: 4468 grad_norm: 0.0648 loss: 0.1486 loss_sem_seg: 0.1486 2023/05/13 20:50:43 - mmengine - INFO - Epoch(train) [29][ 300/1196] lr: 8.0000e-04 eta: 5:02:20 time: 0.7076 data_time: 0.0036 memory: 5593 grad_norm: 0.0673 loss: 0.1561 loss_sem_seg: 0.1561 2023/05/13 20:51:18 - mmengine - INFO - Epoch(train) [29][ 350/1196] lr: 8.0000e-04 eta: 5:00:26 time: 0.7096 data_time: 0.0035 memory: 4801 grad_norm: 0.0661 loss: 0.1510 loss_sem_seg: 0.1510 2023/05/13 20:51:56 - mmengine - INFO - Epoch(train) [29][ 400/1196] lr: 8.0000e-04 eta: 4:58:31 time: 0.7478 data_time: 0.0036 memory: 5474 grad_norm: 0.0707 loss: 0.1616 loss_sem_seg: 0.1616 2023/05/13 20:52:33 - mmengine - INFO - Epoch(train) [29][ 450/1196] lr: 8.0000e-04 eta: 4:56:38 time: 0.7555 data_time: 0.0036 memory: 4946 grad_norm: 0.0645 loss: 0.1631 loss_sem_seg: 0.1631 2023/05/13 20:53:11 - mmengine - INFO - Epoch(train) [29][ 500/1196] lr: 8.0000e-04 eta: 4:54:44 time: 0.7460 data_time: 0.0035 memory: 4762 grad_norm: 0.0657 loss: 0.1635 loss_sem_seg: 0.1635 2023/05/13 20:53:19 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 20:53:47 - mmengine - INFO - Epoch(train) [29][ 550/1196] lr: 8.0000e-04 eta: 4:52:50 time: 0.7301 data_time: 0.0035 memory: 4886 grad_norm: 0.0628 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/13 20:54:23 - mmengine - INFO - Epoch(train) [29][ 600/1196] lr: 8.0000e-04 eta: 4:50:57 time: 0.7230 data_time: 0.0034 memory: 4824 grad_norm: 0.0633 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 20:55:01 - mmengine - INFO - Epoch(train) [29][ 650/1196] lr: 8.0000e-04 eta: 4:49:04 time: 0.7514 data_time: 0.0034 memory: 4816 grad_norm: 0.0666 loss: 0.1586 loss_sem_seg: 0.1586 2023/05/13 20:55:37 - mmengine - INFO - Epoch(train) [29][ 700/1196] lr: 8.0000e-04 eta: 4:47:11 time: 0.7203 data_time: 0.0034 memory: 4985 grad_norm: 0.0648 loss: 0.1551 loss_sem_seg: 0.1551 2023/05/13 20:56:12 - mmengine - INFO - Epoch(train) [29][ 750/1196] lr: 8.0000e-04 eta: 4:45:18 time: 0.6982 data_time: 0.0034 memory: 4590 grad_norm: 0.0657 loss: 0.1497 loss_sem_seg: 0.1497 2023/05/13 20:56:49 - mmengine - INFO - Epoch(train) [29][ 800/1196] lr: 8.0000e-04 eta: 4:43:25 time: 0.7461 data_time: 0.0034 memory: 5411 grad_norm: 0.0645 loss: 0.1553 loss_sem_seg: 0.1553 2023/05/13 20:57:26 - mmengine - INFO - Epoch(train) [29][ 850/1196] lr: 8.0000e-04 eta: 4:41:33 time: 0.7402 data_time: 0.0034 memory: 5655 grad_norm: 0.0639 loss: 0.1549 loss_sem_seg: 0.1549 2023/05/13 20:58:03 - mmengine - INFO - Epoch(train) [29][ 900/1196] lr: 8.0000e-04 eta: 4:39:41 time: 0.7303 data_time: 0.0034 memory: 4910 grad_norm: 0.0683 loss: 0.1614 loss_sem_seg: 0.1614 2023/05/13 20:58:41 - mmengine - INFO - Epoch(train) [29][ 950/1196] lr: 8.0000e-04 eta: 4:37:49 time: 0.7604 data_time: 0.0036 memory: 5194 grad_norm: 0.0631 loss: 0.1603 loss_sem_seg: 0.1603 2023/05/13 20:59:17 - mmengine - INFO - Epoch(train) [29][1000/1196] lr: 8.0000e-04 eta: 4:35:58 time: 0.7223 data_time: 0.0035 memory: 4822 grad_norm: 0.0624 loss: 0.1500 loss_sem_seg: 0.1500 2023/05/13 20:59:54 - mmengine - INFO - Epoch(train) [29][1050/1196] lr: 8.0000e-04 eta: 4:34:06 time: 0.7402 data_time: 0.0035 memory: 5418 grad_norm: 0.0636 loss: 0.1531 loss_sem_seg: 0.1531 2023/05/13 21:00:32 - mmengine - INFO - Epoch(train) [29][1100/1196] lr: 8.0000e-04 eta: 4:32:15 time: 0.7569 data_time: 0.0036 memory: 5037 grad_norm: 0.0722 loss: 0.1588 loss_sem_seg: 0.1588 2023/05/13 21:01:08 - mmengine - INFO - Epoch(train) [29][1150/1196] lr: 8.0000e-04 eta: 4:30:24 time: 0.7186 data_time: 0.0035 memory: 4791 grad_norm: 0.0649 loss: 0.1532 loss_sem_seg: 0.1532 2023/05/13 21:01:41 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:01:41 - mmengine - INFO - Saving checkpoint at 29 epochs 2023/05/13 21:01:58 - mmengine - INFO - Epoch(val) [29][ 50/509] eta: 0:01:26 time: 0.1887 data_time: 0.0023 memory: 5092 2023/05/13 21:02:06 - mmengine - INFO - Epoch(val) [29][100/509] eta: 0:01:13 time: 0.1691 data_time: 0.0021 memory: 915 2023/05/13 21:02:10 - mmengine - INFO - Epoch(val) [29][150/509] eta: 0:00:52 time: 0.0828 data_time: 0.0021 memory: 919 2023/05/13 21:02:16 - mmengine - INFO - Epoch(val) [29][200/509] eta: 0:00:42 time: 0.1126 data_time: 0.0021 memory: 907 2023/05/13 21:02:23 - mmengine - INFO - Epoch(val) [29][250/509] eta: 0:00:36 time: 0.1440 data_time: 0.0022 memory: 928 2023/05/13 21:02:29 - mmengine - INFO - Epoch(val) [29][300/509] eta: 0:00:28 time: 0.1277 data_time: 0.0022 memory: 883 2023/05/13 21:02:36 - mmengine - INFO - Epoch(val) [29][350/509] eta: 0:00:21 time: 0.1318 data_time: 0.0022 memory: 898 2023/05/13 21:02:43 - mmengine - INFO - Epoch(val) [29][400/509] eta: 0:00:14 time: 0.1402 data_time: 0.0023 memory: 903 2023/05/13 21:02:50 - mmengine - INFO - Epoch(val) [29][450/509] eta: 0:00:08 time: 0.1354 data_time: 0.0022 memory: 916 2023/05/13 21:02:56 - mmengine - INFO - Epoch(val) [29][500/509] eta: 0:00:01 time: 0.1245 data_time: 0.0022 memory: 902 2023/05/13 21:03:21 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9691 | 0.5811 | 0.8272 | 0.7938 | 0.6860 | 0.7863 | 0.8683 | 0.0401 | 0.9466 | 0.5567 | 0.8366 | 0.0232 | 0.9186 | 0.6812 | 0.8780 | 0.6852 | 0.7280 | 0.6549 | 0.5009 | 0.6822 | 0.9243 | 0.7513 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 21:03:21 - mmengine - INFO - Epoch(val) [29][509/509] car: 0.9691 bicycle: 0.5811 motorcycle: 0.8272 truck: 0.7938 bus: 0.6860 person: 0.7863 bicyclist: 0.8683 motorcyclist: 0.0401 road: 0.9466 parking: 0.5567 sidewalk: 0.8366 other-ground: 0.0232 building: 0.9186 fence: 0.6812 vegetation: 0.8780 trunck: 0.6852 terrian: 0.7280 pole: 0.6549 traffic-sign: 0.5009 miou: 0.6822 acc: 0.9243 acc_cls: 0.7513 data_time: 0.0022 time: 0.1281 2023/05/13 21:04:10 - mmengine - INFO - Epoch(train) [30][ 50/1196] lr: 8.0000e-04 eta: 4:26:55 time: 0.9843 data_time: 0.0048 memory: 5465 grad_norm: 0.0679 loss: 0.1554 loss_sem_seg: 0.1554 2023/05/13 21:04:46 - mmengine - INFO - Epoch(train) [30][ 100/1196] lr: 8.0000e-04 eta: 4:25:04 time: 0.7210 data_time: 0.0040 memory: 5036 grad_norm: 0.0640 loss: 0.1594 loss_sem_seg: 0.1594 2023/05/13 21:05:22 - mmengine - INFO - Epoch(train) [30][ 150/1196] lr: 8.0000e-04 eta: 4:23:14 time: 0.7182 data_time: 0.0038 memory: 4845 grad_norm: 0.0623 loss: 0.1569 loss_sem_seg: 0.1569 2023/05/13 21:05:58 - mmengine - INFO - Epoch(train) [30][ 200/1196] lr: 8.0000e-04 eta: 4:21:24 time: 0.7145 data_time: 0.0038 memory: 5039 grad_norm: 0.0627 loss: 0.1551 loss_sem_seg: 0.1551 2023/05/13 21:06:34 - mmengine - INFO - Epoch(train) [30][ 250/1196] lr: 8.0000e-04 eta: 4:19:34 time: 0.7286 data_time: 0.0038 memory: 5596 grad_norm: 0.0657 loss: 0.1506 loss_sem_seg: 0.1506 2023/05/13 21:07:12 - mmengine - INFO - Epoch(train) [30][ 300/1196] lr: 8.0000e-04 eta: 4:17:44 time: 0.7520 data_time: 0.0037 memory: 5030 grad_norm: 0.0641 loss: 0.1482 loss_sem_seg: 0.1482 2023/05/13 21:07:24 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:07:49 - mmengine - INFO - Epoch(train) [30][ 350/1196] lr: 8.0000e-04 eta: 4:15:55 time: 0.7385 data_time: 0.0037 memory: 5211 grad_norm: 0.0691 loss: 0.1641 loss_sem_seg: 0.1641 2023/05/13 21:08:25 - mmengine - INFO - Epoch(train) [30][ 400/1196] lr: 8.0000e-04 eta: 4:14:06 time: 0.7306 data_time: 0.0036 memory: 4815 grad_norm: 0.0655 loss: 0.1564 loss_sem_seg: 0.1564 2023/05/13 21:09:01 - mmengine - INFO - Epoch(train) [30][ 450/1196] lr: 8.0000e-04 eta: 4:12:17 time: 0.7245 data_time: 0.0036 memory: 5721 grad_norm: 0.0640 loss: 0.1468 loss_sem_seg: 0.1468 2023/05/13 21:09:39 - mmengine - INFO - Epoch(train) [30][ 500/1196] lr: 8.0000e-04 eta: 4:10:28 time: 0.7415 data_time: 0.0036 memory: 5103 grad_norm: 0.0663 loss: 0.1608 loss_sem_seg: 0.1608 2023/05/13 21:10:15 - mmengine - INFO - Epoch(train) [30][ 550/1196] lr: 8.0000e-04 eta: 4:08:40 time: 0.7373 data_time: 0.0036 memory: 5173 grad_norm: 0.0676 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/13 21:10:52 - mmengine - INFO - Epoch(train) [30][ 600/1196] lr: 8.0000e-04 eta: 4:06:52 time: 0.7357 data_time: 0.0037 memory: 4942 grad_norm: 0.0620 loss: 0.1487 loss_sem_seg: 0.1487 2023/05/13 21:11:31 - mmengine - INFO - Epoch(train) [30][ 650/1196] lr: 8.0000e-04 eta: 4:05:04 time: 0.7676 data_time: 0.0035 memory: 4758 grad_norm: 0.0625 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/13 21:12:07 - mmengine - INFO - Epoch(train) [30][ 700/1196] lr: 8.0000e-04 eta: 4:03:16 time: 0.7238 data_time: 0.0037 memory: 5170 grad_norm: 0.0628 loss: 0.1615 loss_sem_seg: 0.1615 2023/05/13 21:12:43 - mmengine - INFO - Epoch(train) [30][ 750/1196] lr: 8.0000e-04 eta: 4:01:28 time: 0.7236 data_time: 0.0036 memory: 4919 grad_norm: 0.0647 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/13 21:13:18 - mmengine - INFO - Epoch(train) [30][ 800/1196] lr: 8.0000e-04 eta: 3:59:40 time: 0.7097 data_time: 0.0035 memory: 4974 grad_norm: 0.0684 loss: 0.1513 loss_sem_seg: 0.1513 2023/05/13 21:13:55 - mmengine - INFO - Epoch(train) [30][ 850/1196] lr: 8.0000e-04 eta: 3:57:53 time: 0.7246 data_time: 0.0035 memory: 5162 grad_norm: 0.0575 loss: 0.1411 loss_sem_seg: 0.1411 2023/05/13 21:14:32 - mmengine - INFO - Epoch(train) [30][ 900/1196] lr: 8.0000e-04 eta: 3:56:06 time: 0.7422 data_time: 0.0036 memory: 4569 grad_norm: 0.0636 loss: 0.1503 loss_sem_seg: 0.1503 2023/05/13 21:15:09 - mmengine - INFO - Epoch(train) [30][ 950/1196] lr: 8.0000e-04 eta: 3:54:19 time: 0.7522 data_time: 0.0037 memory: 4779 grad_norm: 0.0638 loss: 0.1517 loss_sem_seg: 0.1517 2023/05/13 21:15:45 - mmengine - INFO - Epoch(train) [30][1000/1196] lr: 8.0000e-04 eta: 3:52:32 time: 0.7208 data_time: 0.0038 memory: 4942 grad_norm: 0.0684 loss: 0.1501 loss_sem_seg: 0.1501 2023/05/13 21:16:21 - mmengine - INFO - Epoch(train) [30][1050/1196] lr: 8.0000e-04 eta: 3:50:46 time: 0.7132 data_time: 0.0037 memory: 4820 grad_norm: 0.0611 loss: 0.1571 loss_sem_seg: 0.1571 2023/05/13 21:16:57 - mmengine - INFO - Epoch(train) [30][1100/1196] lr: 8.0000e-04 eta: 3:48:59 time: 0.7213 data_time: 0.0037 memory: 4761 grad_norm: 0.0670 loss: 0.1623 loss_sem_seg: 0.1623 2023/05/13 21:17:34 - mmengine - INFO - Epoch(train) [30][1150/1196] lr: 8.0000e-04 eta: 3:47:13 time: 0.7305 data_time: 0.0036 memory: 5838 grad_norm: 0.0658 loss: 0.1533 loss_sem_seg: 0.1533 2023/05/13 21:18:09 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:18:09 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/05/13 21:18:25 - mmengine - INFO - Epoch(val) [30][ 50/509] eta: 0:01:28 time: 0.1920 data_time: 0.0023 memory: 4690 2023/05/13 21:18:33 - mmengine - INFO - Epoch(val) [30][100/509] eta: 0:01:14 time: 0.1721 data_time: 0.0021 memory: 915 2023/05/13 21:18:42 - mmengine - INFO - Epoch(val) [30][150/509] eta: 0:01:03 time: 0.1699 data_time: 0.0021 memory: 919 2023/05/13 21:18:51 - mmengine - INFO - Epoch(val) [30][200/509] eta: 0:00:54 time: 0.1764 data_time: 0.0021 memory: 907 2023/05/13 21:18:59 - mmengine - INFO - Epoch(val) [30][250/509] eta: 0:00:45 time: 0.1695 data_time: 0.0021 memory: 928 2023/05/13 21:19:07 - mmengine - INFO - Epoch(val) [30][300/509] eta: 0:00:36 time: 0.1587 data_time: 0.0021 memory: 883 2023/05/13 21:19:15 - mmengine - INFO - Epoch(val) [30][350/509] eta: 0:00:27 time: 0.1655 data_time: 0.0021 memory: 898 2023/05/13 21:19:24 - mmengine - INFO - Epoch(val) [30][400/509] eta: 0:00:18 time: 0.1699 data_time: 0.0021 memory: 903 2023/05/13 21:19:33 - mmengine - INFO - Epoch(val) [30][450/509] eta: 0:00:10 time: 0.1863 data_time: 0.0022 memory: 916 2023/05/13 21:19:50 - mmengine - INFO - Epoch(val) [30][500/509] eta: 0:00:01 time: 0.3407 data_time: 0.0018 memory: 902 2023/05/13 21:20:11 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9642 | 0.5533 | 0.8038 | 0.7411 | 0.6296 | 0.8013 | 0.8916 | 0.0289 | 0.9465 | 0.5340 | 0.8294 | 0.0470 | 0.9182 | 0.6771 | 0.8711 | 0.6818 | 0.7108 | 0.6527 | 0.4991 | 0.6727 | 0.9205 | 0.7404 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 21:20:11 - mmengine - INFO - Epoch(val) [30][509/509] car: 0.9642 bicycle: 0.5533 motorcycle: 0.8038 truck: 0.7411 bus: 0.6296 person: 0.8013 bicyclist: 0.8916 motorcyclist: 0.0289 road: 0.9465 parking: 0.5340 sidewalk: 0.8294 other-ground: 0.0470 building: 0.9182 fence: 0.6771 vegetation: 0.8711 trunck: 0.6818 terrian: 0.7108 pole: 0.6527 traffic-sign: 0.4991 miou: 0.6727 acc: 0.9205 acc_cls: 0.7404 data_time: 0.0018 time: 0.3460 2023/05/13 21:20:48 - mmengine - INFO - Epoch(train) [31][ 50/1196] lr: 8.0000e-04 eta: 3:43:50 time: 0.7341 data_time: 0.0044 memory: 4840 grad_norm: 0.0653 loss: 0.1602 loss_sem_seg: 0.1602 2023/05/13 21:21:24 - mmengine - INFO - Epoch(train) [31][ 100/1196] lr: 8.0000e-04 eta: 3:42:04 time: 0.7314 data_time: 0.0036 memory: 5074 grad_norm: 0.0683 loss: 0.1621 loss_sem_seg: 0.1621 2023/05/13 21:21:39 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:22:01 - mmengine - INFO - Epoch(train) [31][ 150/1196] lr: 8.0000e-04 eta: 3:40:19 time: 0.7330 data_time: 0.0035 memory: 4878 grad_norm: 0.0659 loss: 0.1594 loss_sem_seg: 0.1594 2023/05/13 21:22:37 - mmengine - INFO - Epoch(train) [31][ 200/1196] lr: 8.0000e-04 eta: 3:38:34 time: 0.7169 data_time: 0.0036 memory: 4965 grad_norm: 0.0661 loss: 0.1480 loss_sem_seg: 0.1480 2023/05/13 21:23:13 - mmengine - INFO - Epoch(train) [31][ 250/1196] lr: 8.0000e-04 eta: 3:36:49 time: 0.7194 data_time: 0.0035 memory: 5352 grad_norm: 0.0625 loss: 0.1547 loss_sem_seg: 0.1547 2023/05/13 21:23:49 - mmengine - INFO - Epoch(train) [31][ 300/1196] lr: 8.0000e-04 eta: 3:35:04 time: 0.7309 data_time: 0.0036 memory: 5386 grad_norm: 0.0677 loss: 0.1518 loss_sem_seg: 0.1518 2023/05/13 21:24:25 - mmengine - INFO - Epoch(train) [31][ 350/1196] lr: 8.0000e-04 eta: 3:33:19 time: 0.7200 data_time: 0.0036 memory: 4860 grad_norm: 0.0652 loss: 0.1518 loss_sem_seg: 0.1518 2023/05/13 21:25:01 - mmengine - INFO - Epoch(train) [31][ 400/1196] lr: 8.0000e-04 eta: 3:31:35 time: 0.7245 data_time: 0.0035 memory: 5155 grad_norm: 0.0653 loss: 0.1638 loss_sem_seg: 0.1638 2023/05/13 21:25:37 - mmengine - INFO - Epoch(train) [31][ 450/1196] lr: 8.0000e-04 eta: 3:29:50 time: 0.7217 data_time: 0.0036 memory: 5415 grad_norm: 0.0657 loss: 0.1691 loss_sem_seg: 0.1691 2023/05/13 21:26:12 - mmengine - INFO - Epoch(train) [31][ 500/1196] lr: 8.0000e-04 eta: 3:28:06 time: 0.6913 data_time: 0.0035 memory: 4697 grad_norm: 0.0637 loss: 0.1483 loss_sem_seg: 0.1483 2023/05/13 21:26:48 - mmengine - INFO - Epoch(train) [31][ 550/1196] lr: 8.0000e-04 eta: 3:26:22 time: 0.7250 data_time: 0.0035 memory: 5035 grad_norm: 0.0591 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/13 21:27:24 - mmengine - INFO - Epoch(train) [31][ 600/1196] lr: 8.0000e-04 eta: 3:24:38 time: 0.7242 data_time: 0.0035 memory: 5084 grad_norm: 0.0600 loss: 0.1480 loss_sem_seg: 0.1480 2023/05/13 21:28:02 - mmengine - INFO - Epoch(train) [31][ 650/1196] lr: 8.0000e-04 eta: 3:22:55 time: 0.7480 data_time: 0.0036 memory: 5039 grad_norm: 0.0623 loss: 0.1441 loss_sem_seg: 0.1441 2023/05/13 21:28:38 - mmengine - INFO - Epoch(train) [31][ 700/1196] lr: 8.0000e-04 eta: 3:21:11 time: 0.7179 data_time: 0.0034 memory: 4765 grad_norm: 0.0616 loss: 0.1449 loss_sem_seg: 0.1449 2023/05/13 21:29:16 - mmengine - INFO - Epoch(train) [31][ 750/1196] lr: 8.0000e-04 eta: 3:19:28 time: 0.7611 data_time: 0.0035 memory: 4959 grad_norm: 0.0651 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/13 21:29:52 - mmengine - INFO - Epoch(train) [31][ 800/1196] lr: 8.0000e-04 eta: 3:17:45 time: 0.7292 data_time: 0.0035 memory: 5311 grad_norm: 0.0701 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/13 21:30:29 - mmengine - INFO - Epoch(train) [31][ 850/1196] lr: 8.0000e-04 eta: 3:16:03 time: 0.7271 data_time: 0.0036 memory: 5551 grad_norm: 0.0653 loss: 0.1499 loss_sem_seg: 0.1499 2023/05/13 21:31:04 - mmengine - INFO - Epoch(train) [31][ 900/1196] lr: 8.0000e-04 eta: 3:14:20 time: 0.7161 data_time: 0.0036 memory: 5087 grad_norm: 0.0664 loss: 0.1581 loss_sem_seg: 0.1581 2023/05/13 21:31:41 - mmengine - INFO - Epoch(train) [31][ 950/1196] lr: 8.0000e-04 eta: 3:12:37 time: 0.7273 data_time: 0.0035 memory: 5122 grad_norm: 0.0719 loss: 0.1586 loss_sem_seg: 0.1586 2023/05/13 21:32:17 - mmengine - INFO - Epoch(train) [31][1000/1196] lr: 8.0000e-04 eta: 3:10:55 time: 0.7254 data_time: 0.0038 memory: 5186 grad_norm: 0.0709 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 21:32:52 - mmengine - INFO - Epoch(train) [31][1050/1196] lr: 8.0000e-04 eta: 3:09:13 time: 0.7067 data_time: 0.0036 memory: 4808 grad_norm: 0.0648 loss: 0.1569 loss_sem_seg: 0.1569 2023/05/13 21:33:24 - mmengine - INFO - Epoch(train) [31][1100/1196] lr: 8.0000e-04 eta: 3:07:30 time: 0.6215 data_time: 0.0035 memory: 5019 grad_norm: 0.0610 loss: 0.1510 loss_sem_seg: 0.1510 2023/05/13 21:33:35 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:33:53 - mmengine - INFO - Epoch(train) [31][1150/1196] lr: 8.0000e-04 eta: 3:05:47 time: 0.5914 data_time: 0.0035 memory: 5385 grad_norm: 0.0654 loss: 0.1538 loss_sem_seg: 0.1538 2023/05/13 21:34:20 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:34:20 - mmengine - INFO - Saving checkpoint at 31 epochs 2023/05/13 21:34:34 - mmengine - INFO - Epoch(val) [31][ 50/509] eta: 0:01:05 time: 0.1435 data_time: 0.0022 memory: 5204 2023/05/13 21:34:40 - mmengine - INFO - Epoch(val) [31][100/509] eta: 0:00:56 time: 0.1336 data_time: 0.0021 memory: 915 2023/05/13 21:34:47 - mmengine - INFO - Epoch(val) [31][150/509] eta: 0:00:48 time: 0.1251 data_time: 0.0021 memory: 919 2023/05/13 21:34:54 - mmengine - INFO - Epoch(val) [31][200/509] eta: 0:00:43 time: 0.1570 data_time: 0.0021 memory: 907 2023/05/13 21:34:59 - mmengine - INFO - Epoch(val) [31][250/509] eta: 0:00:33 time: 0.0850 data_time: 0.0021 memory: 928 2023/05/13 21:35:02 - mmengine - INFO - Epoch(val) [31][300/509] eta: 0:00:25 time: 0.0747 data_time: 0.0021 memory: 883 2023/05/13 21:35:06 - mmengine - INFO - Epoch(val) [31][350/509] eta: 0:00:18 time: 0.0771 data_time: 0.0021 memory: 898 2023/05/13 21:35:14 - mmengine - INFO - Epoch(val) [31][400/509] eta: 0:00:12 time: 0.1516 data_time: 0.0021 memory: 903 2023/05/13 21:35:22 - mmengine - INFO - Epoch(val) [31][450/509] eta: 0:00:07 time: 0.1665 data_time: 0.0022 memory: 916 2023/05/13 21:35:31 - mmengine - INFO - Epoch(val) [31][500/509] eta: 0:00:01 time: 0.1672 data_time: 0.0021 memory: 902 2023/05/13 21:36:14 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9724 | 0.5517 | 0.8259 | 0.8301 | 0.7360 | 0.8037 | 0.8777 | 0.0746 | 0.9431 | 0.5137 | 0.8283 | 0.0401 | 0.9163 | 0.6790 | 0.8775 | 0.6955 | 0.7285 | 0.6538 | 0.5093 | 0.6872 | 0.9231 | 0.7521 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 21:36:14 - mmengine - INFO - Epoch(val) [31][509/509] car: 0.9724 bicycle: 0.5517 motorcycle: 0.8259 truck: 0.8301 bus: 0.7360 person: 0.8037 bicyclist: 0.8777 motorcyclist: 0.0746 road: 0.9431 parking: 0.5137 sidewalk: 0.8283 other-ground: 0.0401 building: 0.9163 fence: 0.6790 vegetation: 0.8775 trunck: 0.6955 terrian: 0.7285 pole: 0.6538 traffic-sign: 0.5093 miou: 0.6872 acc: 0.9231 acc_cls: 0.7521 data_time: 0.0021 time: 0.1646 2023/05/13 21:36:50 - mmengine - INFO - Epoch(train) [32][ 50/1196] lr: 8.0000e-04 eta: 3:02:32 time: 0.7366 data_time: 0.0045 memory: 4820 grad_norm: 0.0609 loss: 0.1467 loss_sem_seg: 0.1467 2023/05/13 21:37:27 - mmengine - INFO - Epoch(train) [32][ 100/1196] lr: 8.0000e-04 eta: 3:00:50 time: 0.7264 data_time: 0.0035 memory: 5144 grad_norm: 0.0624 loss: 0.1495 loss_sem_seg: 0.1495 2023/05/13 21:38:05 - mmengine - INFO - Epoch(train) [32][ 150/1196] lr: 8.0000e-04 eta: 2:59:10 time: 0.7549 data_time: 0.0035 memory: 4814 grad_norm: 0.0608 loss: 0.1460 loss_sem_seg: 0.1460 2023/05/13 21:38:42 - mmengine - INFO - Epoch(train) [32][ 200/1196] lr: 8.0000e-04 eta: 2:57:29 time: 0.7590 data_time: 0.0034 memory: 4802 grad_norm: 0.0645 loss: 0.1520 loss_sem_seg: 0.1520 2023/05/13 21:39:18 - mmengine - INFO - Epoch(train) [32][ 250/1196] lr: 8.0000e-04 eta: 2:55:48 time: 0.7085 data_time: 0.0034 memory: 4899 grad_norm: 0.0635 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/13 21:39:54 - mmengine - INFO - Epoch(train) [32][ 300/1196] lr: 8.0000e-04 eta: 2:54:08 time: 0.7130 data_time: 0.0035 memory: 5027 grad_norm: 0.0663 loss: 0.1567 loss_sem_seg: 0.1567 2023/05/13 21:40:31 - mmengine - INFO - Epoch(train) [32][ 350/1196] lr: 8.0000e-04 eta: 2:52:27 time: 0.7383 data_time: 0.0036 memory: 4816 grad_norm: 0.0616 loss: 0.1571 loss_sem_seg: 0.1571 2023/05/13 21:41:06 - mmengine - INFO - Epoch(train) [32][ 400/1196] lr: 8.0000e-04 eta: 2:50:47 time: 0.7065 data_time: 0.0034 memory: 4961 grad_norm: 0.0640 loss: 0.1560 loss_sem_seg: 0.1560 2023/05/13 21:41:43 - mmengine - INFO - Epoch(train) [32][ 450/1196] lr: 8.0000e-04 eta: 2:49:07 time: 0.7450 data_time: 0.0034 memory: 4796 grad_norm: 0.0663 loss: 0.1546 loss_sem_seg: 0.1546 2023/05/13 21:42:19 - mmengine - INFO - Epoch(train) [32][ 500/1196] lr: 8.0000e-04 eta: 2:47:27 time: 0.7191 data_time: 0.0034 memory: 4547 grad_norm: 0.0624 loss: 0.1443 loss_sem_seg: 0.1443 2023/05/13 21:42:56 - mmengine - INFO - Epoch(train) [32][ 550/1196] lr: 8.0000e-04 eta: 2:45:48 time: 0.7353 data_time: 0.0034 memory: 4838 grad_norm: 0.0631 loss: 0.1578 loss_sem_seg: 0.1578 2023/05/13 21:43:31 - mmengine - INFO - Epoch(train) [32][ 600/1196] lr: 8.0000e-04 eta: 2:44:08 time: 0.6996 data_time: 0.0034 memory: 5314 grad_norm: 0.0664 loss: 0.1586 loss_sem_seg: 0.1586 2023/05/13 21:44:06 - mmengine - INFO - Epoch(train) [32][ 650/1196] lr: 8.0000e-04 eta: 2:42:28 time: 0.7087 data_time: 0.0035 memory: 4688 grad_norm: 0.0694 loss: 0.1542 loss_sem_seg: 0.1542 2023/05/13 21:44:43 - mmengine - INFO - Epoch(train) [32][ 700/1196] lr: 8.0000e-04 eta: 2:40:49 time: 0.7301 data_time: 0.0034 memory: 4777 grad_norm: 0.0680 loss: 0.1570 loss_sem_seg: 0.1570 2023/05/13 21:45:18 - mmengine - INFO - Epoch(train) [32][ 750/1196] lr: 8.0000e-04 eta: 2:39:10 time: 0.7119 data_time: 0.0034 memory: 4598 grad_norm: 0.0649 loss: 0.1533 loss_sem_seg: 0.1533 2023/05/13 21:45:55 - mmengine - INFO - Epoch(train) [32][ 800/1196] lr: 8.0000e-04 eta: 2:37:31 time: 0.7255 data_time: 0.0033 memory: 5201 grad_norm: 0.0639 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/13 21:46:32 - mmengine - INFO - Epoch(train) [32][ 850/1196] lr: 8.0000e-04 eta: 2:35:53 time: 0.7395 data_time: 0.0034 memory: 5459 grad_norm: 0.0627 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/13 21:47:09 - mmengine - INFO - Epoch(train) [32][ 900/1196] lr: 8.0000e-04 eta: 2:34:14 time: 0.7404 data_time: 0.0033 memory: 4588 grad_norm: 0.0726 loss: 0.1567 loss_sem_seg: 0.1567 2023/05/13 21:47:26 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:47:47 - mmengine - INFO - Epoch(train) [32][ 950/1196] lr: 8.0000e-04 eta: 2:32:36 time: 0.7641 data_time: 0.0033 memory: 4622 grad_norm: 0.0641 loss: 0.1562 loss_sem_seg: 0.1562 2023/05/13 21:48:23 - mmengine - INFO - Epoch(train) [32][1000/1196] lr: 8.0000e-04 eta: 2:30:58 time: 0.7283 data_time: 0.0034 memory: 5222 grad_norm: 0.0681 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/13 21:49:00 - mmengine - INFO - Epoch(train) [32][1050/1196] lr: 8.0000e-04 eta: 2:29:20 time: 0.7330 data_time: 0.0034 memory: 5605 grad_norm: 0.0661 loss: 0.1534 loss_sem_seg: 0.1534 2023/05/13 21:49:36 - mmengine - INFO - Epoch(train) [32][1100/1196] lr: 8.0000e-04 eta: 2:27:42 time: 0.7225 data_time: 0.0034 memory: 5037 grad_norm: 0.0656 loss: 0.1409 loss_sem_seg: 0.1409 2023/05/13 21:50:11 - mmengine - INFO - Epoch(train) [32][1150/1196] lr: 8.0000e-04 eta: 2:26:04 time: 0.7059 data_time: 0.0035 memory: 5108 grad_norm: 0.0649 loss: 0.1513 loss_sem_seg: 0.1513 2023/05/13 21:50:46 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 21:50:46 - mmengine - INFO - Saving checkpoint at 32 epochs 2023/05/13 21:51:01 - mmengine - INFO - Epoch(val) [32][ 50/509] eta: 0:01:21 time: 0.1773 data_time: 0.0022 memory: 5975 2023/05/13 21:51:09 - mmengine - INFO - Epoch(val) [32][100/509] eta: 0:01:10 time: 0.1696 data_time: 0.0022 memory: 915 2023/05/13 21:51:18 - mmengine - INFO - Epoch(val) [32][150/509] eta: 0:01:02 time: 0.1745 data_time: 0.0024 memory: 919 2023/05/13 21:51:27 - mmengine - INFO - Epoch(val) [32][200/509] eta: 0:00:54 time: 0.1777 data_time: 0.0023 memory: 907 2023/05/13 21:51:35 - mmengine - INFO - Epoch(val) [32][250/509] eta: 0:00:44 time: 0.1644 data_time: 0.0021 memory: 928 2023/05/13 21:51:43 - mmengine - INFO - Epoch(val) [32][300/509] eta: 0:00:35 time: 0.1623 data_time: 0.0020 memory: 883 2023/05/13 21:51:52 - mmengine - INFO - Epoch(val) [32][350/509] eta: 0:00:27 time: 0.1696 data_time: 0.0020 memory: 898 2023/05/13 21:52:00 - mmengine - INFO - Epoch(val) [32][400/509] eta: 0:00:18 time: 0.1684 data_time: 0.0020 memory: 903 2023/05/13 21:52:09 - mmengine - INFO - Epoch(val) [32][450/509] eta: 0:00:10 time: 0.1735 data_time: 0.0020 memory: 916 2023/05/13 21:52:26 - mmengine - INFO - Epoch(val) [32][500/509] eta: 0:00:01 time: 0.3473 data_time: 0.0017 memory: 902 2023/05/13 21:52:46 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9743 | 0.5731 | 0.8148 | 0.8000 | 0.7519 | 0.8003 | 0.8782 | 0.0534 | 0.9430 | 0.5288 | 0.8300 | 0.0068 | 0.9190 | 0.6780 | 0.8738 | 0.6758 | 0.7182 | 0.6538 | 0.5054 | 0.6831 | 0.9220 | 0.7503 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 21:52:46 - mmengine - INFO - Epoch(val) [32][509/509] car: 0.9743 bicycle: 0.5731 motorcycle: 0.8148 truck: 0.8000 bus: 0.7519 person: 0.8003 bicyclist: 0.8782 motorcyclist: 0.0534 road: 0.9430 parking: 0.5288 sidewalk: 0.8300 other-ground: 0.0068 building: 0.9190 fence: 0.6780 vegetation: 0.8738 trunck: 0.6758 terrian: 0.7182 pole: 0.6538 traffic-sign: 0.5054 miou: 0.6831 acc: 0.9220 acc_cls: 0.7503 data_time: 0.0017 time: 0.3485 2023/05/13 21:53:23 - mmengine - INFO - Epoch(train) [33][ 50/1196] lr: 8.0000e-05 eta: 2:22:58 time: 0.7389 data_time: 0.0042 memory: 4859 grad_norm: 0.0634 loss: 0.1552 loss_sem_seg: 0.1552 2023/05/13 21:53:59 - mmengine - INFO - Epoch(train) [33][ 100/1196] lr: 8.0000e-05 eta: 2:21:20 time: 0.7125 data_time: 0.0034 memory: 4620 grad_norm: 0.0646 loss: 0.1469 loss_sem_seg: 0.1469 2023/05/13 21:54:35 - mmengine - INFO - Epoch(train) [33][ 150/1196] lr: 8.0000e-05 eta: 2:19:43 time: 0.7217 data_time: 0.0034 memory: 4881 grad_norm: 0.0611 loss: 0.1499 loss_sem_seg: 0.1499 2023/05/13 21:55:11 - mmengine - INFO - Epoch(train) [33][ 200/1196] lr: 8.0000e-05 eta: 2:18:06 time: 0.7246 data_time: 0.0034 memory: 5122 grad_norm: 0.0625 loss: 0.1590 loss_sem_seg: 0.1590 2023/05/13 21:55:46 - mmengine - INFO - Epoch(train) [33][ 250/1196] lr: 8.0000e-05 eta: 2:16:29 time: 0.7076 data_time: 0.0034 memory: 4801 grad_norm: 0.0594 loss: 0.1593 loss_sem_seg: 0.1593 2023/05/13 21:56:22 - mmengine - INFO - Epoch(train) [33][ 300/1196] lr: 8.0000e-05 eta: 2:14:53 time: 0.7197 data_time: 0.0034 memory: 4679 grad_norm: 0.0617 loss: 0.1473 loss_sem_seg: 0.1473 2023/05/13 21:57:00 - mmengine - INFO - Epoch(train) [33][ 350/1196] lr: 8.0000e-05 eta: 2:13:16 time: 0.7455 data_time: 0.0034 memory: 5296 grad_norm: 0.0577 loss: 0.1375 loss_sem_seg: 0.1375 2023/05/13 21:57:37 - mmengine - INFO - Epoch(train) [33][ 400/1196] lr: 8.0000e-05 eta: 2:11:40 time: 0.7372 data_time: 0.0034 memory: 4879 grad_norm: 0.0574 loss: 0.1381 loss_sem_seg: 0.1381 2023/05/13 21:58:12 - mmengine - INFO - Epoch(train) [33][ 450/1196] lr: 8.0000e-05 eta: 2:10:04 time: 0.7158 data_time: 0.0034 memory: 4636 grad_norm: 0.0628 loss: 0.1499 loss_sem_seg: 0.1499 2023/05/13 21:58:48 - mmengine - INFO - Epoch(train) [33][ 500/1196] lr: 8.0000e-05 eta: 2:08:28 time: 0.7212 data_time: 0.0034 memory: 4564 grad_norm: 0.0611 loss: 0.1505 loss_sem_seg: 0.1505 2023/05/13 21:59:26 - mmengine - INFO - Epoch(train) [33][ 550/1196] lr: 8.0000e-05 eta: 2:06:52 time: 0.7461 data_time: 0.0034 memory: 5064 grad_norm: 0.0658 loss: 0.1449 loss_sem_seg: 0.1449 2023/05/13 22:00:02 - mmengine - INFO - Epoch(train) [33][ 600/1196] lr: 8.0000e-05 eta: 2:05:17 time: 0.7264 data_time: 0.0035 memory: 5171 grad_norm: 0.0608 loss: 0.1508 loss_sem_seg: 0.1508 2023/05/13 22:00:39 - mmengine - INFO - Epoch(train) [33][ 650/1196] lr: 8.0000e-05 eta: 2:03:41 time: 0.7330 data_time: 0.0035 memory: 4810 grad_norm: 0.0621 loss: 0.1509 loss_sem_seg: 0.1509 2023/05/13 22:01:15 - mmengine - INFO - Epoch(train) [33][ 700/1196] lr: 8.0000e-05 eta: 2:02:06 time: 0.7334 data_time: 0.0035 memory: 5011 grad_norm: 0.0556 loss: 0.1588 loss_sem_seg: 0.1588 2023/05/13 22:01:36 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:01:52 - mmengine - INFO - Epoch(train) [33][ 750/1196] lr: 8.0000e-05 eta: 2:00:31 time: 0.7258 data_time: 0.0034 memory: 5115 grad_norm: 0.0590 loss: 0.1479 loss_sem_seg: 0.1479 2023/05/13 22:02:28 - mmengine - INFO - Epoch(train) [33][ 800/1196] lr: 8.0000e-05 eta: 1:58:55 time: 0.7177 data_time: 0.0034 memory: 5006 grad_norm: 0.0602 loss: 0.1486 loss_sem_seg: 0.1486 2023/05/13 22:03:03 - mmengine - INFO - Epoch(train) [33][ 850/1196] lr: 8.0000e-05 eta: 1:57:20 time: 0.7061 data_time: 0.0035 memory: 4878 grad_norm: 0.0621 loss: 0.1490 loss_sem_seg: 0.1490 2023/05/13 22:03:40 - mmengine - INFO - Epoch(train) [33][ 900/1196] lr: 8.0000e-05 eta: 1:55:46 time: 0.7409 data_time: 0.0035 memory: 4469 grad_norm: 0.0586 loss: 0.1438 loss_sem_seg: 0.1438 2023/05/13 22:04:16 - mmengine - INFO - Epoch(train) [33][ 950/1196] lr: 8.0000e-05 eta: 1:54:11 time: 0.7227 data_time: 0.0034 memory: 4821 grad_norm: 0.0593 loss: 0.1456 loss_sem_seg: 0.1456 2023/05/13 22:04:47 - mmengine - INFO - Epoch(train) [33][1000/1196] lr: 8.0000e-05 eta: 1:52:36 time: 0.6088 data_time: 0.0034 memory: 5425 grad_norm: 0.0600 loss: 0.1489 loss_sem_seg: 0.1489 2023/05/13 22:05:16 - mmengine - INFO - Epoch(train) [33][1050/1196] lr: 8.0000e-05 eta: 1:51:01 time: 0.5953 data_time: 0.0034 memory: 5413 grad_norm: 0.0632 loss: 0.1482 loss_sem_seg: 0.1482 2023/05/13 22:05:45 - mmengine - INFO - Epoch(train) [33][1100/1196] lr: 8.0000e-05 eta: 1:49:26 time: 0.5748 data_time: 0.0034 memory: 4591 grad_norm: 0.0592 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/13 22:06:15 - mmengine - INFO - Epoch(train) [33][1150/1196] lr: 8.0000e-05 eta: 1:47:52 time: 0.5924 data_time: 0.0034 memory: 4691 grad_norm: 0.0605 loss: 0.1477 loss_sem_seg: 0.1477 2023/05/13 22:06:51 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:06:51 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/05/13 22:07:02 - mmengine - INFO - Epoch(val) [33][ 50/509] eta: 0:00:40 time: 0.0880 data_time: 0.0021 memory: 4790 2023/05/13 22:07:06 - mmengine - INFO - Epoch(val) [33][100/509] eta: 0:00:34 time: 0.0817 data_time: 0.0020 memory: 915 2023/05/13 22:07:12 - mmengine - INFO - Epoch(val) [33][150/509] eta: 0:00:35 time: 0.1308 data_time: 0.0021 memory: 919 2023/05/13 22:07:20 - mmengine - INFO - Epoch(val) [33][200/509] eta: 0:00:35 time: 0.1614 data_time: 0.0019 memory: 907 2023/05/13 22:07:29 - mmengine - INFO - Epoch(val) [33][250/509] eta: 0:00:32 time: 0.1684 data_time: 0.0019 memory: 928 2023/05/13 22:07:37 - mmengine - INFO - Epoch(val) [33][300/509] eta: 0:00:27 time: 0.1676 data_time: 0.0019 memory: 883 2023/05/13 22:07:45 - mmengine - INFO - Epoch(val) [33][350/509] eta: 0:00:21 time: 0.1656 data_time: 0.0019 memory: 898 2023/05/13 22:07:53 - mmengine - INFO - Epoch(val) [33][400/509] eta: 0:00:15 time: 0.1564 data_time: 0.0020 memory: 903 2023/05/13 22:08:02 - mmengine - INFO - Epoch(val) [33][450/509] eta: 0:00:08 time: 0.1680 data_time: 0.0020 memory: 916 2023/05/13 22:08:10 - mmengine - INFO - Epoch(val) [33][500/509] eta: 0:00:01 time: 0.1648 data_time: 0.0020 memory: 902 2023/05/13 22:08:49 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9732 | 0.5657 | 0.8181 | 0.8039 | 0.7372 | 0.8001 | 0.8848 | 0.0373 | 0.9461 | 0.5440 | 0.8323 | 0.0094 | 0.9193 | 0.6812 | 0.8743 | 0.6714 | 0.7211 | 0.6538 | 0.5019 | 0.6829 | 0.9228 | 0.7499 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 22:08:49 - mmengine - INFO - Epoch(val) [33][509/509] car: 0.9732 bicycle: 0.5657 motorcycle: 0.8181 truck: 0.8039 bus: 0.7372 person: 0.8001 bicyclist: 0.8848 motorcyclist: 0.0373 road: 0.9461 parking: 0.5440 sidewalk: 0.8323 other-ground: 0.0094 building: 0.9193 fence: 0.6812 vegetation: 0.8743 trunck: 0.6714 terrian: 0.7211 pole: 0.6538 traffic-sign: 0.5019 miou: 0.6829 acc: 0.9228 acc_cls: 0.7499 data_time: 0.0019 time: 0.1660 2023/05/13 22:09:26 - mmengine - INFO - Epoch(train) [34][ 50/1196] lr: 8.0000e-05 eta: 1:44:52 time: 0.7265 data_time: 0.0044 memory: 4654 grad_norm: 0.0600 loss: 0.1551 loss_sem_seg: 0.1551 2023/05/13 22:10:01 - mmengine - INFO - Epoch(train) [34][ 100/1196] lr: 8.0000e-05 eta: 1:43:18 time: 0.7145 data_time: 0.0036 memory: 4874 grad_norm: 0.0580 loss: 0.1419 loss_sem_seg: 0.1419 2023/05/13 22:10:37 - mmengine - INFO - Epoch(train) [34][ 150/1196] lr: 8.0000e-05 eta: 1:41:45 time: 0.7123 data_time: 0.0035 memory: 4934 grad_norm: 0.0602 loss: 0.1510 loss_sem_seg: 0.1510 2023/05/13 22:11:13 - mmengine - INFO - Epoch(train) [34][ 200/1196] lr: 8.0000e-05 eta: 1:40:12 time: 0.7249 data_time: 0.0035 memory: 4899 grad_norm: 0.0585 loss: 0.1390 loss_sem_seg: 0.1390 2023/05/13 22:11:50 - mmengine - INFO - Epoch(train) [34][ 250/1196] lr: 8.0000e-05 eta: 1:38:38 time: 0.7410 data_time: 0.0036 memory: 4669 grad_norm: 0.0549 loss: 0.1537 loss_sem_seg: 0.1537 2023/05/13 22:12:27 - mmengine - INFO - Epoch(train) [34][ 300/1196] lr: 8.0000e-05 eta: 1:37:05 time: 0.7336 data_time: 0.0034 memory: 4711 grad_norm: 0.0605 loss: 0.1433 loss_sem_seg: 0.1433 2023/05/13 22:13:04 - mmengine - INFO - Epoch(train) [34][ 350/1196] lr: 8.0000e-05 eta: 1:35:33 time: 0.7422 data_time: 0.0034 memory: 5891 grad_norm: 0.0621 loss: 0.1539 loss_sem_seg: 0.1539 2023/05/13 22:13:43 - mmengine - INFO - Epoch(train) [34][ 400/1196] lr: 8.0000e-05 eta: 1:34:00 time: 0.7689 data_time: 0.0034 memory: 5292 grad_norm: 0.0587 loss: 0.1511 loss_sem_seg: 0.1511 2023/05/13 22:14:20 - mmengine - INFO - Epoch(train) [34][ 450/1196] lr: 8.0000e-05 eta: 1:32:28 time: 0.7441 data_time: 0.0034 memory: 4821 grad_norm: 0.0636 loss: 0.1484 loss_sem_seg: 0.1484 2023/05/13 22:14:56 - mmengine - INFO - Epoch(train) [34][ 500/1196] lr: 8.0000e-05 eta: 1:30:55 time: 0.7167 data_time: 0.0034 memory: 4640 grad_norm: 0.0580 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 22:15:18 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:15:31 - mmengine - INFO - Epoch(train) [34][ 550/1196] lr: 8.0000e-05 eta: 1:29:23 time: 0.7094 data_time: 0.0035 memory: 4738 grad_norm: 0.0565 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/13 22:16:06 - mmengine - INFO - Epoch(train) [34][ 600/1196] lr: 8.0000e-05 eta: 1:27:51 time: 0.7023 data_time: 0.0035 memory: 5544 grad_norm: 0.0620 loss: 0.1506 loss_sem_seg: 0.1506 2023/05/13 22:16:44 - mmengine - INFO - Epoch(train) [34][ 650/1196] lr: 8.0000e-05 eta: 1:26:19 time: 0.7513 data_time: 0.0034 memory: 5299 grad_norm: 0.0569 loss: 0.1412 loss_sem_seg: 0.1412 2023/05/13 22:17:20 - mmengine - INFO - Epoch(train) [34][ 700/1196] lr: 8.0000e-05 eta: 1:24:47 time: 0.7207 data_time: 0.0034 memory: 5236 grad_norm: 0.0620 loss: 0.1565 loss_sem_seg: 0.1565 2023/05/13 22:17:56 - mmengine - INFO - Epoch(train) [34][ 750/1196] lr: 8.0000e-05 eta: 1:23:15 time: 0.7199 data_time: 0.0034 memory: 5122 grad_norm: 0.0613 loss: 0.1481 loss_sem_seg: 0.1481 2023/05/13 22:18:33 - mmengine - INFO - Epoch(train) [34][ 800/1196] lr: 8.0000e-05 eta: 1:21:44 time: 0.7398 data_time: 0.0033 memory: 4573 grad_norm: 0.0594 loss: 0.1441 loss_sem_seg: 0.1441 2023/05/13 22:19:08 - mmengine - INFO - Epoch(train) [34][ 850/1196] lr: 8.0000e-05 eta: 1:20:12 time: 0.7016 data_time: 0.0034 memory: 5297 grad_norm: 0.0646 loss: 0.1539 loss_sem_seg: 0.1539 2023/05/13 22:19:45 - mmengine - INFO - Epoch(train) [34][ 900/1196] lr: 8.0000e-05 eta: 1:18:41 time: 0.7475 data_time: 0.0034 memory: 4940 grad_norm: 0.0656 loss: 0.1553 loss_sem_seg: 0.1553 2023/05/13 22:20:21 - mmengine - INFO - Epoch(train) [34][ 950/1196] lr: 8.0000e-05 eta: 1:17:10 time: 0.7194 data_time: 0.0035 memory: 4719 grad_norm: 0.0631 loss: 0.1441 loss_sem_seg: 0.1441 2023/05/13 22:20:59 - mmengine - INFO - Epoch(train) [34][1000/1196] lr: 8.0000e-05 eta: 1:15:39 time: 0.7497 data_time: 0.0034 memory: 4790 grad_norm: 0.0603 loss: 0.1414 loss_sem_seg: 0.1414 2023/05/13 22:21:36 - mmengine - INFO - Epoch(train) [34][1050/1196] lr: 8.0000e-05 eta: 1:14:08 time: 0.7456 data_time: 0.0034 memory: 5115 grad_norm: 0.0666 loss: 0.1546 loss_sem_seg: 0.1546 2023/05/13 22:22:14 - mmengine - INFO - Epoch(train) [34][1100/1196] lr: 8.0000e-05 eta: 1:12:37 time: 0.7543 data_time: 0.0034 memory: 4987 grad_norm: 0.0610 loss: 0.1499 loss_sem_seg: 0.1499 2023/05/13 22:22:50 - mmengine - INFO - Epoch(train) [34][1150/1196] lr: 8.0000e-05 eta: 1:11:06 time: 0.7191 data_time: 0.0034 memory: 4774 grad_norm: 0.0559 loss: 0.1511 loss_sem_seg: 0.1511 2023/05/13 22:23:23 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:23:23 - mmengine - INFO - Saving checkpoint at 34 epochs 2023/05/13 22:23:38 - mmengine - INFO - Epoch(val) [34][ 50/509] eta: 0:01:17 time: 0.1682 data_time: 0.0020 memory: 5032 2023/05/13 22:23:46 - mmengine - INFO - Epoch(val) [34][100/509] eta: 0:01:08 time: 0.1656 data_time: 0.0020 memory: 915 2023/05/13 22:23:54 - mmengine - INFO - Epoch(val) [34][150/509] eta: 0:00:59 time: 0.1603 data_time: 0.0020 memory: 919 2023/05/13 22:24:03 - mmengine - INFO - Epoch(val) [34][200/509] eta: 0:00:51 time: 0.1695 data_time: 0.0019 memory: 907 2023/05/13 22:24:11 - mmengine - INFO - Epoch(val) [34][250/509] eta: 0:00:42 time: 0.1613 data_time: 0.0020 memory: 928 2023/05/13 22:24:19 - mmengine - INFO - Epoch(val) [34][300/509] eta: 0:00:34 time: 0.1533 data_time: 0.0019 memory: 883 2023/05/13 22:24:27 - mmengine - INFO - Epoch(val) [34][350/509] eta: 0:00:25 time: 0.1654 data_time: 0.0019 memory: 898 2023/05/13 22:24:35 - mmengine - INFO - Epoch(val) [34][400/509] eta: 0:00:17 time: 0.1663 data_time: 0.0021 memory: 903 2023/05/13 22:24:43 - mmengine - INFO - Epoch(val) [34][450/509] eta: 0:00:09 time: 0.1638 data_time: 0.0020 memory: 916 2023/05/13 22:24:57 - mmengine - INFO - Epoch(val) [34][500/509] eta: 0:00:01 time: 0.2806 data_time: 0.0018 memory: 902 2023/05/13 22:25:17 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9716 | 0.5633 | 0.8237 | 0.7892 | 0.7206 | 0.8050 | 0.8840 | 0.0449 | 0.9459 | 0.5431 | 0.8317 | 0.0122 | 0.9187 | 0.6787 | 0.8744 | 0.6835 | 0.7200 | 0.6552 | 0.5022 | 0.6825 | 0.9226 | 0.7498 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 22:25:17 - mmengine - INFO - Epoch(val) [34][509/509] car: 0.9716 bicycle: 0.5633 motorcycle: 0.8237 truck: 0.7892 bus: 0.7206 person: 0.8050 bicyclist: 0.8840 motorcyclist: 0.0449 road: 0.9459 parking: 0.5431 sidewalk: 0.8317 other-ground: 0.0122 building: 0.9187 fence: 0.6787 vegetation: 0.8744 trunck: 0.6835 terrian: 0.7200 pole: 0.6552 traffic-sign: 0.5022 miou: 0.6825 acc: 0.9226 acc_cls: 0.7498 data_time: 0.0017 time: 0.3214 2023/05/13 22:25:54 - mmengine - INFO - Epoch(train) [35][ 50/1196] lr: 8.0000e-05 eta: 1:08:13 time: 0.7418 data_time: 0.0045 memory: 5155 grad_norm: 0.0621 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/13 22:26:31 - mmengine - INFO - Epoch(train) [35][ 100/1196] lr: 8.0000e-05 eta: 1:06:43 time: 0.7333 data_time: 0.0035 memory: 4965 grad_norm: 0.0600 loss: 0.1501 loss_sem_seg: 0.1501 2023/05/13 22:27:08 - mmengine - INFO - Epoch(train) [35][ 150/1196] lr: 8.0000e-05 eta: 1:05:12 time: 0.7470 data_time: 0.0035 memory: 4923 grad_norm: 0.0578 loss: 0.1481 loss_sem_seg: 0.1481 2023/05/13 22:27:45 - mmengine - INFO - Epoch(train) [35][ 200/1196] lr: 8.0000e-05 eta: 1:03:42 time: 0.7336 data_time: 0.0036 memory: 4816 grad_norm: 0.0607 loss: 0.1541 loss_sem_seg: 0.1541 2023/05/13 22:28:21 - mmengine - INFO - Epoch(train) [35][ 250/1196] lr: 8.0000e-05 eta: 1:02:13 time: 0.7232 data_time: 0.0036 memory: 4727 grad_norm: 0.0589 loss: 0.1415 loss_sem_seg: 0.1415 2023/05/13 22:28:58 - mmengine - INFO - Epoch(train) [35][ 300/1196] lr: 8.0000e-05 eta: 1:00:43 time: 0.7368 data_time: 0.0035 memory: 4661 grad_norm: 0.0581 loss: 0.1424 loss_sem_seg: 0.1424 2023/05/13 22:29:24 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:29:34 - mmengine - INFO - Epoch(train) [35][ 350/1196] lr: 8.0000e-05 eta: 0:59:13 time: 0.7266 data_time: 0.0036 memory: 5203 grad_norm: 0.0580 loss: 0.1490 loss_sem_seg: 0.1490 2023/05/13 22:30:11 - mmengine - INFO - Epoch(train) [35][ 400/1196] lr: 8.0000e-05 eta: 0:57:44 time: 0.7375 data_time: 0.0036 memory: 4985 grad_norm: 0.0640 loss: 0.1464 loss_sem_seg: 0.1464 2023/05/13 22:30:47 - mmengine - INFO - Epoch(train) [35][ 450/1196] lr: 8.0000e-05 eta: 0:56:14 time: 0.7198 data_time: 0.0035 memory: 5010 grad_norm: 0.0629 loss: 0.1545 loss_sem_seg: 0.1545 2023/05/13 22:31:23 - mmengine - INFO - Epoch(train) [35][ 500/1196] lr: 8.0000e-05 eta: 0:54:45 time: 0.7191 data_time: 0.0034 memory: 5294 grad_norm: 0.0642 loss: 0.1470 loss_sem_seg: 0.1470 2023/05/13 22:31:59 - mmengine - INFO - Epoch(train) [35][ 550/1196] lr: 8.0000e-05 eta: 0:53:16 time: 0.7074 data_time: 0.0033 memory: 4842 grad_norm: 0.0607 loss: 0.1494 loss_sem_seg: 0.1494 2023/05/13 22:32:35 - mmengine - INFO - Epoch(train) [35][ 600/1196] lr: 8.0000e-05 eta: 0:51:47 time: 0.7300 data_time: 0.0034 memory: 4796 grad_norm: 0.0667 loss: 0.1442 loss_sem_seg: 0.1442 2023/05/13 22:33:12 - mmengine - INFO - Epoch(train) [35][ 650/1196] lr: 8.0000e-05 eta: 0:50:18 time: 0.7488 data_time: 0.0035 memory: 5421 grad_norm: 0.0658 loss: 0.1525 loss_sem_seg: 0.1525 2023/05/13 22:33:48 - mmengine - INFO - Epoch(train) [35][ 700/1196] lr: 8.0000e-05 eta: 0:48:50 time: 0.7141 data_time: 0.0035 memory: 4876 grad_norm: 0.0572 loss: 0.1542 loss_sem_seg: 0.1542 2023/05/13 22:34:25 - mmengine - INFO - Epoch(train) [35][ 750/1196] lr: 8.0000e-05 eta: 0:47:21 time: 0.7279 data_time: 0.0036 memory: 5065 grad_norm: 0.0608 loss: 0.1476 loss_sem_seg: 0.1476 2023/05/13 22:35:01 - mmengine - INFO - Epoch(train) [35][ 800/1196] lr: 8.0000e-05 eta: 0:45:53 time: 0.7250 data_time: 0.0035 memory: 4867 grad_norm: 0.0595 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/13 22:35:38 - mmengine - INFO - Epoch(train) [35][ 850/1196] lr: 8.0000e-05 eta: 0:44:24 time: 0.7369 data_time: 0.0034 memory: 4665 grad_norm: 0.0600 loss: 0.1423 loss_sem_seg: 0.1423 2023/05/13 22:36:09 - mmengine - INFO - Epoch(train) [35][ 900/1196] lr: 8.0000e-05 eta: 0:42:56 time: 0.6255 data_time: 0.0033 memory: 4912 grad_norm: 0.0599 loss: 0.1446 loss_sem_seg: 0.1446 2023/05/13 22:36:38 - mmengine - INFO - Epoch(train) [35][ 950/1196] lr: 8.0000e-05 eta: 0:41:28 time: 0.5856 data_time: 0.0033 memory: 4957 grad_norm: 0.0622 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/13 22:37:08 - mmengine - INFO - Epoch(train) [35][1000/1196] lr: 8.0000e-05 eta: 0:39:59 time: 0.5899 data_time: 0.0034 memory: 5172 grad_norm: 0.0627 loss: 0.1427 loss_sem_seg: 0.1427 2023/05/13 22:37:37 - mmengine - INFO - Epoch(train) [35][1050/1196] lr: 8.0000e-05 eta: 0:38:31 time: 0.5828 data_time: 0.0036 memory: 5001 grad_norm: 0.0593 loss: 0.1421 loss_sem_seg: 0.1421 2023/05/13 22:38:06 - mmengine - INFO - Epoch(train) [35][1100/1196] lr: 8.0000e-05 eta: 0:37:03 time: 0.5735 data_time: 0.0037 memory: 5126 grad_norm: 0.0584 loss: 0.1535 loss_sem_seg: 0.1535 2023/05/13 22:38:46 - mmengine - INFO - Epoch(train) [35][1150/1196] lr: 8.0000e-05 eta: 0:35:36 time: 0.8173 data_time: 0.0036 memory: 4678 grad_norm: 0.0599 loss: 0.1424 loss_sem_seg: 0.1424 2023/05/13 22:39:22 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:39:22 - mmengine - INFO - Saving checkpoint at 35 epochs 2023/05/13 22:39:38 - mmengine - INFO - Epoch(val) [35][ 50/509] eta: 0:01:24 time: 0.1835 data_time: 0.0021 memory: 4779 2023/05/13 22:39:46 - mmengine - INFO - Epoch(val) [35][100/509] eta: 0:01:12 time: 0.1710 data_time: 0.0020 memory: 915 2023/05/13 22:39:54 - mmengine - INFO - Epoch(val) [35][150/509] eta: 0:01:02 time: 0.1637 data_time: 0.0020 memory: 919 2023/05/13 22:40:03 - mmengine - INFO - Epoch(val) [35][200/509] eta: 0:00:53 time: 0.1705 data_time: 0.0020 memory: 907 2023/05/13 22:40:11 - mmengine - INFO - Epoch(val) [35][250/509] eta: 0:00:44 time: 0.1675 data_time: 0.0020 memory: 928 2023/05/13 22:40:19 - mmengine - INFO - Epoch(val) [35][300/509] eta: 0:00:35 time: 0.1631 data_time: 0.0020 memory: 883 2023/05/13 22:40:28 - mmengine - INFO - Epoch(val) [35][350/509] eta: 0:00:26 time: 0.1662 data_time: 0.0019 memory: 898 2023/05/13 22:40:36 - mmengine - INFO - Epoch(val) [35][400/509] eta: 0:00:18 time: 0.1696 data_time: 0.0019 memory: 903 2023/05/13 22:40:44 - mmengine - INFO - Epoch(val) [35][450/509] eta: 0:00:09 time: 0.1539 data_time: 0.0020 memory: 916 2023/05/13 22:41:00 - mmengine - INFO - Epoch(val) [35][500/509] eta: 0:00:01 time: 0.3180 data_time: 0.0017 memory: 902 2023/05/13 22:41:20 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9717 | 0.5660 | 0.8197 | 0.7804 | 0.7215 | 0.8067 | 0.8813 | 0.0440 | 0.9456 | 0.5483 | 0.8303 | 0.0120 | 0.9179 | 0.6776 | 0.8742 | 0.6818 | 0.7187 | 0.6541 | 0.4987 | 0.6816 | 0.9223 | 0.7491 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 22:41:20 - mmengine - INFO - Epoch(val) [35][509/509] car: 0.9717 bicycle: 0.5660 motorcycle: 0.8197 truck: 0.7804 bus: 0.7215 person: 0.8067 bicyclist: 0.8813 motorcyclist: 0.0440 road: 0.9456 parking: 0.5483 sidewalk: 0.8303 other-ground: 0.0120 building: 0.9179 fence: 0.6776 vegetation: 0.8742 trunck: 0.6818 terrian: 0.7187 pole: 0.6541 traffic-sign: 0.4987 miou: 0.6816 acc: 0.9223 acc_cls: 0.7491 data_time: 0.0017 time: 0.3417 2023/05/13 22:41:57 - mmengine - INFO - Epoch(train) [36][ 50/1196] lr: 8.0000e-05 eta: 0:32:48 time: 0.7340 data_time: 0.0043 memory: 4928 grad_norm: 0.0647 loss: 0.1395 loss_sem_seg: 0.1395 2023/05/13 22:42:33 - mmengine - INFO - Epoch(train) [36][ 100/1196] lr: 8.0000e-05 eta: 0:31:21 time: 0.7309 data_time: 0.0034 memory: 4668 grad_norm: 0.0590 loss: 0.1418 loss_sem_seg: 0.1418 2023/05/13 22:43:03 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:43:10 - mmengine - INFO - Epoch(train) [36][ 150/1196] lr: 8.0000e-05 eta: 0:29:54 time: 0.7324 data_time: 0.0034 memory: 5388 grad_norm: 0.0586 loss: 0.1408 loss_sem_seg: 0.1408 2023/05/13 22:43:47 - mmengine - INFO - Epoch(train) [36][ 200/1196] lr: 8.0000e-05 eta: 0:28:27 time: 0.7399 data_time: 0.0033 memory: 5258 grad_norm: 0.0618 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/13 22:44:25 - mmengine - INFO - Epoch(train) [36][ 250/1196] lr: 8.0000e-05 eta: 0:27:00 time: 0.7537 data_time: 0.0032 memory: 5004 grad_norm: 0.0620 loss: 0.1391 loss_sem_seg: 0.1391 2023/05/13 22:45:02 - mmengine - INFO - Epoch(train) [36][ 300/1196] lr: 8.0000e-05 eta: 0:25:34 time: 0.7390 data_time: 0.0033 memory: 4744 grad_norm: 0.0655 loss: 0.1493 loss_sem_seg: 0.1493 2023/05/13 22:45:39 - mmengine - INFO - Epoch(train) [36][ 350/1196] lr: 8.0000e-05 eta: 0:24:07 time: 0.7397 data_time: 0.0033 memory: 5189 grad_norm: 0.0616 loss: 0.1480 loss_sem_seg: 0.1480 2023/05/13 22:46:13 - mmengine - INFO - Epoch(train) [36][ 400/1196] lr: 8.0000e-05 eta: 0:22:41 time: 0.6970 data_time: 0.0034 memory: 4987 grad_norm: 0.0570 loss: 0.1440 loss_sem_seg: 0.1440 2023/05/13 22:46:51 - mmengine - INFO - Epoch(train) [36][ 450/1196] lr: 8.0000e-05 eta: 0:21:14 time: 0.7430 data_time: 0.0033 memory: 5225 grad_norm: 0.0627 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/13 22:47:28 - mmengine - INFO - Epoch(train) [36][ 500/1196] lr: 8.0000e-05 eta: 0:19:48 time: 0.7405 data_time: 0.0033 memory: 5098 grad_norm: 0.0586 loss: 0.1530 loss_sem_seg: 0.1530 2023/05/13 22:48:04 - mmengine - INFO - Epoch(train) [36][ 550/1196] lr: 8.0000e-05 eta: 0:18:22 time: 0.7312 data_time: 0.0034 memory: 5121 grad_norm: 0.0619 loss: 0.1491 loss_sem_seg: 0.1491 2023/05/13 22:48:41 - mmengine - INFO - Epoch(train) [36][ 600/1196] lr: 8.0000e-05 eta: 0:16:56 time: 0.7333 data_time: 0.0034 memory: 4865 grad_norm: 0.0612 loss: 0.1537 loss_sem_seg: 0.1537 2023/05/13 22:49:18 - mmengine - INFO - Epoch(train) [36][ 650/1196] lr: 8.0000e-05 eta: 0:15:30 time: 0.7401 data_time: 0.0034 memory: 4912 grad_norm: 0.0581 loss: 0.1486 loss_sem_seg: 0.1486 2023/05/13 22:49:54 - mmengine - INFO - Epoch(train) [36][ 700/1196] lr: 8.0000e-05 eta: 0:14:04 time: 0.7270 data_time: 0.0033 memory: 5220 grad_norm: 0.0586 loss: 0.1501 loss_sem_seg: 0.1501 2023/05/13 22:50:32 - mmengine - INFO - Epoch(train) [36][ 750/1196] lr: 8.0000e-05 eta: 0:12:39 time: 0.7498 data_time: 0.0034 memory: 4897 grad_norm: 0.0622 loss: 0.1576 loss_sem_seg: 0.1576 2023/05/13 22:51:09 - mmengine - INFO - Epoch(train) [36][ 800/1196] lr: 8.0000e-05 eta: 0:11:13 time: 0.7373 data_time: 0.0034 memory: 5288 grad_norm: 0.0619 loss: 0.1347 loss_sem_seg: 0.1347 2023/05/13 22:51:45 - mmengine - INFO - Epoch(train) [36][ 850/1196] lr: 8.0000e-05 eta: 0:09:48 time: 0.7293 data_time: 0.0034 memory: 4864 grad_norm: 0.0660 loss: 0.1390 loss_sem_seg: 0.1390 2023/05/13 22:52:22 - mmengine - INFO - Epoch(train) [36][ 900/1196] lr: 8.0000e-05 eta: 0:08:22 time: 0.7355 data_time: 0.0035 memory: 4840 grad_norm: 0.0605 loss: 0.1511 loss_sem_seg: 0.1511 2023/05/13 22:52:58 - mmengine - INFO - Epoch(train) [36][ 950/1196] lr: 8.0000e-05 eta: 0:06:57 time: 0.7182 data_time: 0.0033 memory: 4595 grad_norm: 0.0592 loss: 0.1431 loss_sem_seg: 0.1431 2023/05/13 22:53:34 - mmengine - INFO - Epoch(train) [36][1000/1196] lr: 8.0000e-05 eta: 0:05:32 time: 0.7252 data_time: 0.0033 memory: 4982 grad_norm: 0.0616 loss: 0.1430 loss_sem_seg: 0.1430 2023/05/13 22:54:11 - mmengine - INFO - Epoch(train) [36][1050/1196] lr: 8.0000e-05 eta: 0:04:07 time: 0.7447 data_time: 0.0033 memory: 5068 grad_norm: 0.0645 loss: 0.1582 loss_sem_seg: 0.1582 2023/05/13 22:54:49 - mmengine - INFO - Epoch(train) [36][1100/1196] lr: 8.0000e-05 eta: 0:02:42 time: 0.7494 data_time: 0.0034 memory: 4963 grad_norm: 0.0638 loss: 0.1558 loss_sem_seg: 0.1558 2023/05/13 22:55:19 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:55:26 - mmengine - INFO - Epoch(train) [36][1150/1196] lr: 8.0000e-05 eta: 0:01:17 time: 0.7431 data_time: 0.0033 memory: 5364 grad_norm: 0.0592 loss: 0.1441 loss_sem_seg: 0.1441 2023/05/13 22:55:59 - mmengine - INFO - Exp name: minkunet34_w32_torchsparse_8xb2-lpmix-3x_semantickitti_20230512_233601 2023/05/13 22:55:59 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/05/13 22:56:14 - mmengine - INFO - Epoch(val) [36][ 50/509] eta: 0:01:22 time: 0.1789 data_time: 0.0022 memory: 4513 2023/05/13 22:56:23 - mmengine - INFO - Epoch(val) [36][100/509] eta: 0:01:11 time: 0.1728 data_time: 0.0020 memory: 915 2023/05/13 22:56:31 - mmengine - INFO - Epoch(val) [36][150/509] eta: 0:01:01 time: 0.1612 data_time: 0.0019 memory: 919 2023/05/13 22:56:39 - mmengine - INFO - Epoch(val) [36][200/509] eta: 0:00:52 time: 0.1714 data_time: 0.0020 memory: 907 2023/05/13 22:56:48 - mmengine - INFO - Epoch(val) [36][250/509] eta: 0:00:44 time: 0.1673 data_time: 0.0019 memory: 928 2023/05/13 22:56:55 - mmengine - INFO - Epoch(val) [36][300/509] eta: 0:00:35 time: 0.1554 data_time: 0.0020 memory: 883 2023/05/13 22:57:04 - mmengine - INFO - Epoch(val) [36][350/509] eta: 0:00:26 time: 0.1626 data_time: 0.0019 memory: 898 2023/05/13 22:57:12 - mmengine - INFO - Epoch(val) [36][400/509] eta: 0:00:18 time: 0.1701 data_time: 0.0020 memory: 903 2023/05/13 22:57:21 - mmengine - INFO - Epoch(val) [36][450/509] eta: 0:00:09 time: 0.1677 data_time: 0.0020 memory: 916 2023/05/13 22:57:37 - mmengine - INFO - Epoch(val) [36][500/509] eta: 0:00:01 time: 0.3296 data_time: 0.0017 memory: 902 2023/05/13 22:57:56 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9716 | 0.5525 | 0.8183 | 0.7726 | 0.7233 | 0.8032 | 0.8902 | 0.0336 | 0.9460 | 0.5389 | 0.8311 | 0.0118 | 0.9189 | 0.6802 | 0.8729 | 0.6741 | 0.7161 | 0.6540 | 0.4974 | 0.6793 | 0.9219 | 0.7444 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/13 22:57:56 - mmengine - INFO - Epoch(val) [36][509/509] car: 0.9716 bicycle: 0.5525 motorcycle: 0.8183 truck: 0.7726 bus: 0.7233 person: 0.8032 bicyclist: 0.8902 motorcyclist: 0.0336 road: 0.9460 parking: 0.5389 sidewalk: 0.8311 other-ground: 0.0118 building: 0.9189 fence: 0.6802 vegetation: 0.8729 trunck: 0.6741 terrian: 0.7161 pole: 0.6540 traffic-sign: 0.4974 miou: 0.6793 acc: 0.9219 acc_cls: 0.7444 data_time: 0.0016 time: 0.3555