2023/05/10 22:19:04 - 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: 262397269 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: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None deterministic: False diff_rank_seed: True Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/05/10 22:19:09 - 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, seed=None), 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='MinkUNetBackboneV2', 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=480, 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()) 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=True, 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 lr = 0.008 optim_wrapper = dict( type='AmpOptimWrapper', loss_scale='dynamic', optimizer=dict(type='AdamW', lr=0.008, weight_decay=0.01), clip_grad=dict(max_norm=10, norm_type=2)) param_scheduler = [ dict( type='MultiStepLR', begin=0, end=36, by_epoch=True, milestones=[24, 32], gamma=0.1) ] train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') randomness = dict(seed=None, deterministic=False, diff_rank_seed=True) auto_scale_lr = dict(enable=False, base_batch_size=16) launcher = 'pytorch' work_dir = 'exps/minkunet34v2_noseed1' 2023/05/10 22:19:14 - 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 -------------------- 2023/05/10 22:19:19 - 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, 480]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/05/10 22:19:24 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/05/10 22:19:24 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/05/10 22:19:24 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/exps/minkunet34v2_noseed1. 2023/05/10 22:19:59 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 8.0000e-03 eta: 8:21:52 time: 0.7002 data_time: 0.0068 memory: 3212 grad_norm: inf loss: 2.0690 loss_sem_seg: 2.0690 2023/05/10 22:20:34 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 8.0000e-03 eta: 8:25:45 time: 0.7127 data_time: 0.0038 memory: 3237 grad_norm: 1.0192 loss: 1.1268 loss_sem_seg: 1.1268 2023/05/10 22:21:31 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 8.0000e-03 eta: 10:08:22 time: 1.1394 data_time: 0.0038 memory: 3224 grad_norm: 0.7626 loss: 0.9879 loss_sem_seg: 0.9879 2023/05/10 22:22:26 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 8.0000e-03 eta: 10:52:08 time: 1.0999 data_time: 0.0034 memory: 3239 grad_norm: 0.7102 loss: 0.8836 loss_sem_seg: 0.8836 2023/05/10 22:23:31 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 8.0000e-03 eta: 11:45:25 time: 1.2918 data_time: 0.0035 memory: 3242 grad_norm: 0.8698 loss: 0.7859 loss_sem_seg: 0.7859 2023/05/10 22:24:37 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 8.0000e-03 eta: 12:24:09 time: 1.3218 data_time: 0.0034 memory: 3103 grad_norm: 1.0071 loss: 0.7784 loss_sem_seg: 0.7784 2023/05/10 22:25:43 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 8.0000e-03 eta: 12:51:23 time: 1.3207 data_time: 0.0034 memory: 3464 grad_norm: 0.8690 loss: 0.7349 loss_sem_seg: 0.7349 2023/05/10 22:26:50 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 8.0000e-03 eta: 13:12:33 time: 1.3320 data_time: 0.0037 memory: 3364 grad_norm: 0.8988 loss: 0.6479 loss_sem_seg: 0.6479 2023/05/10 22:27:56 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 8.0000e-03 eta: 13:28:30 time: 1.3289 data_time: 0.0034 memory: 3406 grad_norm: 0.9453 loss: 0.6408 loss_sem_seg: 0.6408 2023/05/10 22:29:03 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 8.0000e-03 eta: 13:41:21 time: 1.3330 data_time: 0.0033 memory: 3323 grad_norm: 0.9540 loss: 0.6017 loss_sem_seg: 0.6017 2023/05/10 22:30:08 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 8.0000e-03 eta: 13:50:19 time: 1.3125 data_time: 0.0034 memory: 3370 grad_norm: 0.9484 loss: 0.5883 loss_sem_seg: 0.5883 2023/05/10 22:31:13 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 8.0000e-03 eta: 13:56:20 time: 1.2905 data_time: 0.0033 memory: 3987 grad_norm: 0.8722 loss: 0.5721 loss_sem_seg: 0.5721 2023/05/10 22:32:19 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 8.0000e-03 eta: 14:02:57 time: 1.3219 data_time: 0.0033 memory: 3355 grad_norm: 0.7755 loss: 0.5721 loss_sem_seg: 0.5721 2023/05/10 22:33:23 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 8.0000e-03 eta: 14:06:36 time: 1.2848 data_time: 0.0034 memory: 3227 grad_norm: 0.8689 loss: 0.5424 loss_sem_seg: 0.5424 2023/05/10 22:34:29 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 8.0000e-03 eta: 14:11:09 time: 1.3172 data_time: 0.0033 memory: 3521 grad_norm: 0.8208 loss: 0.5625 loss_sem_seg: 0.5625 2023/05/10 22:35:34 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 8.0000e-03 eta: 14:14:19 time: 1.3019 data_time: 0.0034 memory: 3667 grad_norm: 0.6350 loss: 0.5378 loss_sem_seg: 0.5378 2023/05/10 22:36:40 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 8.0000e-03 eta: 14:17:28 time: 1.3138 data_time: 0.0034 memory: 3134 grad_norm: 0.5683 loss: 0.5007 loss_sem_seg: 0.5007 2023/05/10 22:37:48 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 8.0000e-03 eta: 14:22:17 time: 1.3683 data_time: 0.0035 memory: 3261 grad_norm: 0.7220 loss: 0.5222 loss_sem_seg: 0.5222 2023/05/10 22:39:11 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 8.0000e-03 eta: 14:37:07 time: 1.6568 data_time: 0.0034 memory: 3353 grad_norm: 0.7425 loss: 0.5028 loss_sem_seg: 0.5028 2023/05/10 22:40:35 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/10 22:40:35 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 8.0000e-03 eta: 14:51:07 time: 1.6791 data_time: 0.0033 memory: 3235 grad_norm: 0.7528 loss: 0.4930 loss_sem_seg: 0.4930 2023/05/10 22:41:58 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 8.0000e-03 eta: 15:03:05 time: 1.6620 data_time: 0.0034 memory: 3308 grad_norm: 0.7359 loss: 0.5017 loss_sem_seg: 0.5017 2023/05/10 22:43:21 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 8.0000e-03 eta: 15:13:59 time: 1.6664 data_time: 0.0034 memory: 3377 grad_norm: 0.6351 loss: 0.4827 loss_sem_seg: 0.4827 2023/05/10 22:44:45 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 8.0000e-03 eta: 15:23:52 time: 1.6683 data_time: 0.0034 memory: 3290 grad_norm: 0.5948 loss: 0.5045 loss_sem_seg: 0.5045 2023/05/10 22:46:02 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/10 22:46:02 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/05/10 22:46:47 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:06:10 time: 0.8071 data_time: 0.0029 memory: 3421 2023/05/10 22:47:27 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:05:26 time: 0.7878 data_time: 0.0021 memory: 1105 2023/05/10 22:48:07 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:04:46 time: 0.8020 data_time: 0.0021 memory: 1110 2023/05/10 22:48:46 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:04:06 time: 0.7905 data_time: 0.0020 memory: 1100 2023/05/10 22:49:21 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:03:20 time: 0.6916 data_time: 0.0021 memory: 1111 2023/05/10 22:49:55 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:02:38 time: 0.6766 data_time: 0.0020 memory: 1075 2023/05/10 22:50:29 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:01:58 time: 0.6755 data_time: 0.0020 memory: 1091 2023/05/10 22:50:57 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:01:18 time: 0.5654 data_time: 0.0021 memory: 1090 2023/05/10 22:51:24 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:41 time: 0.5521 data_time: 0.0022 memory: 1113 2023/05/10 22:51:51 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:06 time: 0.5341 data_time: 0.0020 memory: 1098 2023/05/10 22:52:12 - 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 | 0.8779 | 0.0659 | 0.1519 | 0.1908 | 0.0240 | 0.3644 | 0.2944 | 0.0017 | 0.8600 | 0.1471 | 0.7107 | 0.0000 | 0.8646 | 0.4136 | 0.8485 | 0.5903 | 0.7400 | 0.5876 | 0.3639 | 0.4262 | 0.8783 | 0.4957 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/10 22:52:12 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.8779 bicycle: 0.0659 motorcycle: 0.1519 truck: 0.1908 bus: 0.0240 person: 0.3644 bicyclist: 0.2944 motorcyclist: 0.0017 road: 0.8600 parking: 0.1471 sidewalk: 0.7107 other-ground: 0.0000 building: 0.8646 fence: 0.4136 vegetation: 0.8485 trunck: 0.5903 terrian: 0.7400 pole: 0.5876 traffic-sign: 0.3639 miou: 0.4262 acc: 0.8783 acc_cls: 0.4957 data_time: 0.0020 time: 0.5429 2023/05/10 22:53:16 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 8.0000e-03 eta: 15:29:44 time: 1.2877 data_time: 0.0044 memory: 3245 grad_norm: 0.6969 loss: 0.4790 loss_sem_seg: 0.4790 2023/05/10 22:54:25 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 8.0000e-03 eta: 15:29:51 time: 1.3795 data_time: 0.0033 memory: 3404 grad_norm: 0.5262 loss: 0.4535 loss_sem_seg: 0.4535 2023/05/10 22:55:41 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 8.0000e-03 eta: 15:33:13 time: 1.5097 data_time: 0.0033 memory: 3550 grad_norm: 0.5829 loss: 0.4852 loss_sem_seg: 0.4852 2023/05/10 22:57:04 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 8.0000e-03 eta: 15:40:02 time: 1.6619 data_time: 0.0033 memory: 3408 grad_norm: 0.5235 loss: 0.4587 loss_sem_seg: 0.4587 2023/05/10 22:58:28 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 8.0000e-03 eta: 15:46:58 time: 1.6897 data_time: 0.0034 memory: 3259 grad_norm: inf loss: 0.4273 loss_sem_seg: 0.4273 2023/05/10 22:59:52 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 8.0000e-03 eta: 15:53:08 time: 1.6810 data_time: 0.0035 memory: 3554 grad_norm: 0.5112 loss: 0.4352 loss_sem_seg: 0.4352 2023/05/10 23:01:17 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 8.0000e-03 eta: 15:59:15 time: 1.7009 data_time: 0.0034 memory: 3426 grad_norm: 0.5052 loss: 0.4555 loss_sem_seg: 0.4555 2023/05/10 23:02:41 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 8.0000e-03 eta: 16:04:25 time: 1.6782 data_time: 0.0034 memory: 3494 grad_norm: 0.5473 loss: 0.4623 loss_sem_seg: 0.4623 2023/05/10 23:04:05 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 8.0000e-03 eta: 16:09:09 time: 1.6776 data_time: 0.0034 memory: 3243 grad_norm: 0.5068 loss: 0.4112 loss_sem_seg: 0.4112 2023/05/10 23:05:29 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 8.0000e-03 eta: 16:13:33 time: 1.6779 data_time: 0.0035 memory: 3663 grad_norm: 0.5080 loss: 0.4383 loss_sem_seg: 0.4383 2023/05/10 23:06:52 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 8.0000e-03 eta: 16:17:13 time: 1.6583 data_time: 0.0035 memory: 3452 grad_norm: 0.5018 loss: 0.4323 loss_sem_seg: 0.4323 2023/05/10 23:08:16 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 8.0000e-03 eta: 16:21:07 time: 1.6849 data_time: 0.0034 memory: 3295 grad_norm: 0.4573 loss: 0.4061 loss_sem_seg: 0.4061 2023/05/10 23:09:37 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 8.0000e-03 eta: 16:23:23 time: 1.6123 data_time: 0.0034 memory: 3265 grad_norm: 0.5166 loss: 0.4122 loss_sem_seg: 0.4122 2023/05/10 23:10:52 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 8.0000e-03 eta: 16:23:19 time: 1.4943 data_time: 0.0034 memory: 3361 grad_norm: 0.4284 loss: 0.3923 loss_sem_seg: 0.3923 2023/05/10 23:12:05 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 8.0000e-03 eta: 16:22:54 time: 1.4777 data_time: 0.0034 memory: 3450 grad_norm: 0.4690 loss: 0.4243 loss_sem_seg: 0.4243 2023/05/10 23:13:19 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 8.0000e-03 eta: 16:22:21 time: 1.4715 data_time: 0.0035 memory: 3277 grad_norm: 0.4303 loss: 0.4151 loss_sem_seg: 0.4151 2023/05/10 23:13:25 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/10 23:14:31 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 8.0000e-03 eta: 16:21:21 time: 1.4477 data_time: 0.0034 memory: 3409 grad_norm: 0.4685 loss: 0.4021 loss_sem_seg: 0.4021 2023/05/10 23:15:47 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 8.0000e-03 eta: 16:21:26 time: 1.5137 data_time: 0.0032 memory: 3332 grad_norm: 0.4459 loss: 0.3638 loss_sem_seg: 0.3638 2023/05/10 23:17:15 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 8.0000e-03 eta: 16:25:17 time: 1.7554 data_time: 0.0033 memory: 3233 grad_norm: 0.4487 loss: 0.3915 loss_sem_seg: 0.3915 2023/05/10 23:18:38 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 8.0000e-03 eta: 16:27:18 time: 1.6529 data_time: 0.0035 memory: 3257 grad_norm: 0.3934 loss: 0.3929 loss_sem_seg: 0.3929 2023/05/10 23:20:01 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 8.0000e-03 eta: 16:29:27 time: 1.6714 data_time: 0.0034 memory: 3447 grad_norm: 0.4337 loss: 0.3785 loss_sem_seg: 0.3785 2023/05/10 23:21:24 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 8.0000e-03 eta: 16:31:16 time: 1.6602 data_time: 0.0036 memory: 3232 grad_norm: 0.3335 loss: 0.3887 loss_sem_seg: 0.3887 2023/05/10 23:22:49 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 8.0000e-03 eta: 16:33:26 time: 1.6924 data_time: 0.0034 memory: 3212 grad_norm: 0.4431 loss: 0.3893 loss_sem_seg: 0.3893 2023/05/10 23:24:05 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/10 23:24:05 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/05/10 23:24:52 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:06:09 time: 0.8042 data_time: 0.0021 memory: 3348 2023/05/10 23:25:31 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:05:22 time: 0.7751 data_time: 0.0022 memory: 1105 2023/05/10 23:26:07 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:04:37 time: 0.7360 data_time: 0.0022 memory: 1110 2023/05/10 23:26:45 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:03:56 time: 0.7451 data_time: 0.0024 memory: 1100 2023/05/10 23:27:17 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:03:11 time: 0.6431 data_time: 0.0021 memory: 1111 2023/05/10 23:27:48 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:02:30 time: 0.6268 data_time: 0.0021 memory: 1075 2023/05/10 23:28:20 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:01:52 time: 0.6306 data_time: 0.0022 memory: 1091 2023/05/10 23:28:49 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:01:15 time: 0.5856 data_time: 0.0021 memory: 1090 2023/05/10 23:29:18 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:40 time: 0.5897 data_time: 0.0021 memory: 1113 2023/05/10 23:29:48 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:06 time: 0.5837 data_time: 0.0022 memory: 1098 2023/05/10 23:30:10 - 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 | 0.8978 | 0.1365 | 0.3710 | 0.3419 | 0.1188 | 0.4246 | 0.5328 | 0.0228 | 0.8913 | 0.2326 | 0.7519 | 0.0007 | 0.8688 | 0.5658 | 0.8622 | 0.6024 | 0.7065 | 0.5958 | 0.4324 | 0.4925 | 0.8938 | 0.5642 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/10 23:30:10 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.8978 bicycle: 0.1365 motorcycle: 0.3710 truck: 0.3419 bus: 0.1188 person: 0.4246 bicyclist: 0.5328 motorcyclist: 0.0228 road: 0.8913 parking: 0.2326 sidewalk: 0.7519 other-ground: 0.0007 building: 0.8688 fence: 0.5658 vegetation: 0.8622 trunck: 0.6024 terrian: 0.7065 pole: 0.5958 traffic-sign: 0.4324 miou: 0.4925 acc: 0.8938 acc_cls: 0.5642 data_time: 0.0022 time: 0.5923 2023/05/10 23:31:13 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 8.0000e-03 eta: 16:30:58 time: 1.2681 data_time: 0.0042 memory: 3514 grad_norm: 0.4174 loss: 0.3906 loss_sem_seg: 0.3906 2023/05/10 23:32:25 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 8.0000e-03 eta: 16:29:16 time: 1.4294 data_time: 0.0036 memory: 3377 grad_norm: 0.3958 loss: 0.3752 loss_sem_seg: 0.3752 2023/05/10 23:33:41 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 8.0000e-03 eta: 16:28:47 time: 1.5187 data_time: 0.0035 memory: 3286 grad_norm: 0.4363 loss: 0.3757 loss_sem_seg: 0.3757 2023/05/10 23:35:05 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 8.0000e-03 eta: 16:30:29 time: 1.6884 data_time: 0.0034 memory: 3523 grad_norm: 0.3378 loss: 0.3575 loss_sem_seg: 0.3575 2023/05/10 23:36:28 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 8.0000e-03 eta: 16:31:35 time: 1.6511 data_time: 0.0036 memory: 3360 grad_norm: 0.4018 loss: 0.3639 loss_sem_seg: 0.3639 2023/05/10 23:37:50 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 8.0000e-03 eta: 16:32:36 time: 1.6515 data_time: 0.0035 memory: 3414 grad_norm: 0.3912 loss: 0.3624 loss_sem_seg: 0.3624 2023/05/10 23:39:14 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 8.0000e-03 eta: 16:33:44 time: 1.6687 data_time: 0.0034 memory: 3691 grad_norm: 0.3116 loss: 0.3621 loss_sem_seg: 0.3621 2023/05/10 23:40:36 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 8.0000e-03 eta: 16:34:33 time: 1.6498 data_time: 0.0034 memory: 3429 grad_norm: 0.3372 loss: 0.3627 loss_sem_seg: 0.3627 2023/05/10 23:42:00 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 8.0000e-03 eta: 16:35:33 time: 1.6717 data_time: 0.0035 memory: 3608 grad_norm: 0.3221 loss: 0.3743 loss_sem_seg: 0.3743 2023/05/10 23:43:22 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 8.0000e-03 eta: 16:36:18 time: 1.6569 data_time: 0.0034 memory: 3776 grad_norm: 0.3048 loss: 0.3555 loss_sem_seg: 0.3555 2023/05/10 23:44:48 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 8.0000e-03 eta: 16:37:35 time: 1.7098 data_time: 0.0035 memory: 3513 grad_norm: 0.3747 loss: 0.3395 loss_sem_seg: 0.3395 2023/05/10 23:46:11 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 8.0000e-03 eta: 16:38:15 time: 1.6644 data_time: 0.0035 memory: 3335 grad_norm: 0.3711 loss: 0.3550 loss_sem_seg: 0.3550 2023/05/10 23:46:24 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/10 23:47:33 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 8.0000e-03 eta: 16:38:35 time: 1.6378 data_time: 0.0034 memory: 3717 grad_norm: 0.3630 loss: 0.3511 loss_sem_seg: 0.3511 2023/05/10 23:48:45 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 8.0000e-03 eta: 16:36:48 time: 1.4490 data_time: 0.0036 memory: 3340 grad_norm: 0.3155 loss: 0.3686 loss_sem_seg: 0.3686 2023/05/10 23:49:58 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 8.0000e-03 eta: 16:35:01 time: 1.4445 data_time: 0.0034 memory: 3189 grad_norm: 0.2819 loss: 0.3372 loss_sem_seg: 0.3372 2023/05/10 23:51:10 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 8.0000e-03 eta: 16:33:17 time: 1.4501 data_time: 0.0033 memory: 3337 grad_norm: 0.3426 loss: 0.3366 loss_sem_seg: 0.3366 2023/05/10 23:52:23 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 8.0000e-03 eta: 16:31:36 time: 1.4526 data_time: 0.0034 memory: 3321 grad_norm: 0.3823 loss: 0.3462 loss_sem_seg: 0.3462 2023/05/10 23:53:36 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 8.0000e-03 eta: 16:30:03 time: 1.4626 data_time: 0.0034 memory: 3336 grad_norm: 0.2993 loss: 0.3530 loss_sem_seg: 0.3530 2023/05/10 23:55:03 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 8.0000e-03 eta: 16:31:19 time: 1.7483 data_time: 0.0035 memory: 3488 grad_norm: 0.2893 loss: 0.3365 loss_sem_seg: 0.3365 2023/05/10 23:56:27 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 8.0000e-03 eta: 16:31:45 time: 1.6697 data_time: 0.0034 memory: 3380 grad_norm: 0.3915 loss: 0.3517 loss_sem_seg: 0.3517 2023/05/10 23:57:49 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 8.0000e-03 eta: 16:31:57 time: 1.6519 data_time: 0.0034 memory: 3323 grad_norm: 0.2757 loss: 0.3398 loss_sem_seg: 0.3398 2023/05/10 23:59:12 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 8.0000e-03 eta: 16:32:10 time: 1.6576 data_time: 0.0035 memory: 3407 grad_norm: 0.2907 loss: 0.3365 loss_sem_seg: 0.3365 2023/05/11 00:00:35 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 8.0000e-03 eta: 16:32:16 time: 1.6503 data_time: 0.0035 memory: 3226 grad_norm: 0.2664 loss: 0.3435 loss_sem_seg: 0.3435 2023/05/11 00:01:50 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 00:01:50 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/05/11 00:02:36 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:06:00 time: 0.7849 data_time: 0.0021 memory: 3378 2023/05/11 00:03:15 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:05:22 time: 0.7901 data_time: 0.0021 memory: 1105 2023/05/11 00:03:54 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:04:41 time: 0.7764 data_time: 0.0021 memory: 1110 2023/05/11 00:04:32 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:04:00 time: 0.7648 data_time: 0.0021 memory: 1100 2023/05/11 00:05:07 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:03:17 time: 0.6945 data_time: 0.0021 memory: 1111 2023/05/11 00:05:41 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:02:36 time: 0.6679 data_time: 0.0021 memory: 1075 2023/05/11 00:06:11 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:01:55 time: 0.6169 data_time: 0.0021 memory: 1091 2023/05/11 00:06:41 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:01:17 time: 0.5889 data_time: 0.0021 memory: 1090 2023/05/11 00:07:10 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:41 time: 0.5902 data_time: 0.0021 memory: 1113 2023/05/11 00:07:41 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:06 time: 0.6069 data_time: 0.0021 memory: 1098 2023/05/11 00:08:02 - 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 | 0.9125 | 0.3144 | 0.5246 | 0.4782 | 0.1514 | 0.4419 | 0.6613 | 0.0179 | 0.9218 | 0.3059 | 0.7921 | 0.0051 | 0.8816 | 0.5648 | 0.8939 | 0.6489 | 0.7783 | 0.6304 | 0.4664 | 0.5469 | 0.9145 | 0.6448 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 00:08:02 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9125 bicycle: 0.3144 motorcycle: 0.5246 truck: 0.4782 bus: 0.1514 person: 0.4419 bicyclist: 0.6613 motorcyclist: 0.0179 road: 0.9218 parking: 0.3059 sidewalk: 0.7921 other-ground: 0.0051 building: 0.8816 fence: 0.5648 vegetation: 0.8939 trunck: 0.6489 terrian: 0.7783 pole: 0.6304 traffic-sign: 0.4664 miou: 0.5469 acc: 0.9145 acc_cls: 0.6448 data_time: 0.0021 time: 0.6148 2023/05/11 00:09:05 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 8.0000e-03 eta: 16:28:48 time: 1.2642 data_time: 0.0042 memory: 3387 grad_norm: 0.2756 loss: 0.3374 loss_sem_seg: 0.3374 2023/05/11 00:10:16 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 8.0000e-03 eta: 16:26:47 time: 1.4206 data_time: 0.0034 memory: 3530 grad_norm: 0.2870 loss: 0.3375 loss_sem_seg: 0.3375 2023/05/11 00:11:29 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 8.0000e-03 eta: 16:25:02 time: 1.4476 data_time: 0.0034 memory: 3288 grad_norm: 0.2972 loss: 0.3477 loss_sem_seg: 0.3477 2023/05/11 00:12:56 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 8.0000e-03 eta: 16:25:55 time: 1.7492 data_time: 0.0034 memory: 3322 grad_norm: 0.2809 loss: 0.3443 loss_sem_seg: 0.3443 2023/05/11 00:14:19 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 8.0000e-03 eta: 16:25:53 time: 1.6509 data_time: 0.0034 memory: 3246 grad_norm: 0.2724 loss: 0.3237 loss_sem_seg: 0.3237 2023/05/11 00:15:42 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 8.0000e-03 eta: 16:25:58 time: 1.6683 data_time: 0.0034 memory: 3353 grad_norm: 0.2820 loss: 0.3401 loss_sem_seg: 0.3401 2023/05/11 00:17:06 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 8.0000e-03 eta: 16:25:59 time: 1.6644 data_time: 0.0034 memory: 3464 grad_norm: 0.2873 loss: 0.3278 loss_sem_seg: 0.3278 2023/05/11 00:18:30 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 8.0000e-03 eta: 16:26:13 time: 1.6935 data_time: 0.0035 memory: 3391 grad_norm: 0.3169 loss: 0.3184 loss_sem_seg: 0.3184 2023/05/11 00:18:50 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 00:19:54 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 8.0000e-03 eta: 16:26:14 time: 1.6735 data_time: 0.0035 memory: 3264 grad_norm: 0.2478 loss: 0.3143 loss_sem_seg: 0.3143 2023/05/11 00:21:17 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 8.0000e-03 eta: 16:26:03 time: 1.6534 data_time: 0.0035 memory: 3431 grad_norm: 0.2378 loss: 0.3115 loss_sem_seg: 0.3115 2023/05/11 00:22:40 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 8.0000e-03 eta: 16:25:57 time: 1.6659 data_time: 0.0034 memory: 3477 grad_norm: 0.2390 loss: 0.3189 loss_sem_seg: 0.3189 2023/05/11 00:24:02 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 8.0000e-03 eta: 16:25:41 time: 1.6498 data_time: 0.0035 memory: 3218 grad_norm: 0.2568 loss: 0.3270 loss_sem_seg: 0.3270 2023/05/11 00:25:23 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 8.0000e-03 eta: 16:25:03 time: 1.6055 data_time: 0.0035 memory: 3255 grad_norm: 0.2852 loss: 0.3285 loss_sem_seg: 0.3285 2023/05/11 00:26:35 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 8.0000e-03 eta: 16:23:16 time: 1.4544 data_time: 0.0034 memory: 3388 grad_norm: 0.2664 loss: 0.3277 loss_sem_seg: 0.3277 2023/05/11 00:27:49 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 8.0000e-03 eta: 16:21:38 time: 1.4735 data_time: 0.0035 memory: 3483 grad_norm: 0.2533 loss: 0.2955 loss_sem_seg: 0.2955 2023/05/11 00:29:03 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 8.0000e-03 eta: 16:20:08 time: 1.4884 data_time: 0.0034 memory: 3163 grad_norm: 0.2620 loss: 0.3122 loss_sem_seg: 0.3122 2023/05/11 00:30:18 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 8.0000e-03 eta: 16:18:36 time: 1.4843 data_time: 0.0034 memory: 3355 grad_norm: 0.2488 loss: 0.3179 loss_sem_seg: 0.3179 2023/05/11 00:31:32 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 8.0000e-03 eta: 16:17:05 time: 1.4854 data_time: 0.0036 memory: 3460 grad_norm: 0.2599 loss: 0.3124 loss_sem_seg: 0.3124 2023/05/11 00:33:00 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 8.0000e-03 eta: 16:17:28 time: 1.7528 data_time: 0.0035 memory: 3280 grad_norm: 0.2248 loss: 0.3056 loss_sem_seg: 0.3056 2023/05/11 00:34:21 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 8.0000e-03 eta: 16:16:59 time: 1.6357 data_time: 0.0034 memory: 3251 grad_norm: 0.2443 loss: 0.3184 loss_sem_seg: 0.3184 2023/05/11 00:35:44 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 8.0000e-03 eta: 16:16:40 time: 1.6613 data_time: 0.0034 memory: 3626 grad_norm: 0.2715 loss: 0.2995 loss_sem_seg: 0.2995 2023/05/11 00:37:06 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 8.0000e-03 eta: 16:16:08 time: 1.6350 data_time: 0.0034 memory: 3181 grad_norm: 0.2296 loss: 0.3161 loss_sem_seg: 0.3161 2023/05/11 00:38:29 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 8.0000e-03 eta: 16:15:45 time: 1.6570 data_time: 0.0035 memory: 3320 grad_norm: 0.2301 loss: 0.3054 loss_sem_seg: 0.3054 2023/05/11 00:39:45 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 00:39:45 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/05/11 00:40:31 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:06:07 time: 0.7999 data_time: 0.0021 memory: 3654 2023/05/11 00:41:11 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:05:26 time: 0.7964 data_time: 0.0021 memory: 1105 2023/05/11 00:41:51 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:04:45 time: 0.7906 data_time: 0.0021 memory: 1110 2023/05/11 00:42:27 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:04:01 time: 0.7351 data_time: 0.0021 memory: 1100 2023/05/11 00:43:02 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:03:17 time: 0.6939 data_time: 0.0021 memory: 1111 2023/05/11 00:43:33 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:02:34 time: 0.6241 data_time: 0.0021 memory: 1075 2023/05/11 00:44:00 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:01:53 time: 0.5359 data_time: 0.0021 memory: 1091 2023/05/11 00:44:27 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:01:15 time: 0.5314 data_time: 0.0021 memory: 1090 2023/05/11 00:44:53 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:39 time: 0.5275 data_time: 0.0021 memory: 1113 2023/05/11 00:45:19 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:05 time: 0.5178 data_time: 0.0021 memory: 1098 2023/05/11 00:45:51 - 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 | 0.9182 | 0.4451 | 0.2722 | 0.1444 | 0.2257 | 0.5682 | 0.7233 | 0.0254 | 0.9237 | 0.3848 | 0.7984 | 0.0071 | 0.8737 | 0.4989 | 0.8917 | 0.6643 | 0.7777 | 0.6328 | 0.4976 | 0.5407 | 0.9129 | 0.6218 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 00:45:51 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9182 bicycle: 0.4451 motorcycle: 0.2722 truck: 0.1444 bus: 0.2257 person: 0.5682 bicyclist: 0.7233 motorcyclist: 0.0254 road: 0.9237 parking: 0.3848 sidewalk: 0.7984 other-ground: 0.0071 building: 0.8737 fence: 0.4989 vegetation: 0.8917 trunck: 0.6643 terrian: 0.7777 pole: 0.6328 traffic-sign: 0.4976 miou: 0.5407 acc: 0.9129 acc_cls: 0.6218 data_time: 0.0021 time: 0.5307 2023/05/11 00:46:53 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 8.0000e-03 eta: 16:12:12 time: 1.2496 data_time: 0.0039 memory: 3330 grad_norm: 0.2430 loss: 0.3115 loss_sem_seg: 0.3115 2023/05/11 00:48:06 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 8.0000e-03 eta: 16:10:23 time: 1.4428 data_time: 0.0033 memory: 3351 grad_norm: 0.2552 loss: 0.2882 loss_sem_seg: 0.2882 2023/05/11 00:49:19 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 8.0000e-03 eta: 16:08:44 time: 1.4650 data_time: 0.0034 memory: 3236 grad_norm: 0.2370 loss: 0.3240 loss_sem_seg: 0.3240 2023/05/11 00:50:46 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 8.0000e-03 eta: 16:08:50 time: 1.7417 data_time: 0.0033 memory: 3694 grad_norm: 0.2420 loss: 0.2962 loss_sem_seg: 0.2962 2023/05/11 00:51:13 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 00:52:09 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 8.0000e-03 eta: 16:08:28 time: 1.6691 data_time: 0.0034 memory: 3343 grad_norm: 0.2678 loss: 0.3087 loss_sem_seg: 0.3087 2023/05/11 00:53:33 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 8.0000e-03 eta: 16:08:09 time: 1.6833 data_time: 0.0034 memory: 3354 grad_norm: 0.2148 loss: 0.3009 loss_sem_seg: 0.3009 2023/05/11 00:54:56 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 8.0000e-03 eta: 16:07:36 time: 1.6478 data_time: 0.0035 memory: 3321 grad_norm: 0.2061 loss: 0.2998 loss_sem_seg: 0.2998 2023/05/11 00:56:18 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 8.0000e-03 eta: 16:06:59 time: 1.6381 data_time: 0.0035 memory: 3350 grad_norm: 0.2138 loss: 0.2957 loss_sem_seg: 0.2957 2023/05/11 00:57:41 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 8.0000e-03 eta: 16:06:30 time: 1.6632 data_time: 0.0035 memory: 3364 grad_norm: 0.2450 loss: 0.3109 loss_sem_seg: 0.3109 2023/05/11 00:59:04 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 8.0000e-03 eta: 16:05:56 time: 1.6523 data_time: 0.0035 memory: 3647 grad_norm: 0.2491 loss: 0.3003 loss_sem_seg: 0.3003 2023/05/11 01:00:27 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 8.0000e-03 eta: 16:05:29 time: 1.6771 data_time: 0.0034 memory: 3368 grad_norm: 0.2375 loss: 0.2888 loss_sem_seg: 0.2888 2023/05/11 01:01:49 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 8.0000e-03 eta: 16:04:49 time: 1.6407 data_time: 0.0034 memory: 3278 grad_norm: 0.2385 loss: 0.2997 loss_sem_seg: 0.2997 2023/05/11 01:03:12 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 8.0000e-03 eta: 16:04:13 time: 1.6524 data_time: 0.0035 memory: 3329 grad_norm: 0.2352 loss: 0.3090 loss_sem_seg: 0.3090 2023/05/11 01:04:26 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 8.0000e-03 eta: 16:02:36 time: 1.4811 data_time: 0.0035 memory: 3171 grad_norm: 0.1994 loss: 0.3036 loss_sem_seg: 0.3036 2023/05/11 01:05:40 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 8.0000e-03 eta: 16:00:59 time: 1.4771 data_time: 0.0034 memory: 3246 grad_norm: 0.2360 loss: 0.2894 loss_sem_seg: 0.2894 2023/05/11 01:06:52 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 8.0000e-03 eta: 15:59:13 time: 1.4490 data_time: 0.0035 memory: 3273 grad_norm: 0.2221 loss: 0.2878 loss_sem_seg: 0.2878 2023/05/11 01:08:04 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 8.0000e-03 eta: 15:57:24 time: 1.4385 data_time: 0.0034 memory: 3333 grad_norm: 0.1904 loss: 0.2975 loss_sem_seg: 0.2975 2023/05/11 01:09:18 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 8.0000e-03 eta: 15:55:47 time: 1.4744 data_time: 0.0034 memory: 3134 grad_norm: 0.1944 loss: 0.3042 loss_sem_seg: 0.3042 2023/05/11 01:10:45 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 8.0000e-03 eta: 15:55:36 time: 1.7373 data_time: 0.0034 memory: 3403 grad_norm: 0.2412 loss: 0.2921 loss_sem_seg: 0.2921 2023/05/11 01:12:08 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 8.0000e-03 eta: 15:55:01 time: 1.6647 data_time: 0.0035 memory: 3263 grad_norm: 0.2543 loss: 0.2955 loss_sem_seg: 0.2955 2023/05/11 01:13:31 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 8.0000e-03 eta: 15:54:23 time: 1.6614 data_time: 0.0036 memory: 3409 grad_norm: 0.1899 loss: 0.2970 loss_sem_seg: 0.2970 2023/05/11 01:14:55 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 8.0000e-03 eta: 15:53:53 time: 1.6843 data_time: 0.0035 memory: 3422 grad_norm: 0.2111 loss: 0.2950 loss_sem_seg: 0.2950 2023/05/11 01:16:18 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 8.0000e-03 eta: 15:53:07 time: 1.6405 data_time: 0.0035 memory: 3635 grad_norm: 0.1858 loss: 0.3017 loss_sem_seg: 0.3017 2023/05/11 01:17:32 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 01:17:32 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/05/11 01:18:16 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:05:46 time: 0.7552 data_time: 0.0022 memory: 3378 2023/05/11 01:18:53 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:05:04 time: 0.7354 data_time: 0.0021 memory: 1105 2023/05/11 01:19:30 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:04:26 time: 0.7356 data_time: 0.0021 memory: 1110 2023/05/11 01:20:04 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:03:45 time: 0.6874 data_time: 0.0022 memory: 1100 2023/05/11 01:20:40 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:03:07 time: 0.7075 data_time: 0.0022 memory: 1111 2023/05/11 01:21:13 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:02:29 time: 0.6601 data_time: 0.0021 memory: 1075 2023/05/11 01:21:41 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:01:50 time: 0.5735 data_time: 0.0021 memory: 1091 2023/05/11 01:22:10 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:01:14 time: 0.5810 data_time: 0.0021 memory: 1090 2023/05/11 01:22:39 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:39 time: 0.5765 data_time: 0.0021 memory: 1113 2023/05/11 01:23:09 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:05 time: 0.5985 data_time: 0.0020 memory: 1098 2023/05/11 01:23:31 - 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 | 0.9128 | 0.3662 | 0.5371 | 0.3295 | 0.2003 | 0.6634 | 0.6340 | 0.0602 | 0.9210 | 0.3545 | 0.7934 | 0.0273 | 0.8870 | 0.5183 | 0.8795 | 0.6565 | 0.7739 | 0.6283 | 0.4638 | 0.5583 | 0.9104 | 0.6600 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 01:23:31 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9128 bicycle: 0.3662 motorcycle: 0.5371 truck: 0.3295 bus: 0.2003 person: 0.6634 bicyclist: 0.6340 motorcyclist: 0.0602 road: 0.9210 parking: 0.3545 sidewalk: 0.7934 other-ground: 0.0273 building: 0.8870 fence: 0.5183 vegetation: 0.8795 trunck: 0.6565 terrian: 0.7739 pole: 0.6283 traffic-sign: 0.4638 miou: 0.5583 acc: 0.9104 acc_cls: 0.6600 data_time: 0.0020 time: 0.6040 2023/05/11 01:23:55 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 01:24:33 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 8.0000e-03 eta: 15:49:35 time: 1.2516 data_time: 0.0043 memory: 3550 grad_norm: 0.2181 loss: 0.2774 loss_sem_seg: 0.2774 2023/05/11 01:25:50 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 8.0000e-03 eta: 15:48:16 time: 1.5335 data_time: 0.0034 memory: 3597 grad_norm: 0.1971 loss: 0.2725 loss_sem_seg: 0.2725 2023/05/11 01:27:07 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 8.0000e-03 eta: 15:47:02 time: 1.5465 data_time: 0.0034 memory: 3465 grad_norm: 0.2160 loss: 0.2991 loss_sem_seg: 0.2991 2023/05/11 01:28:29 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 8.0000e-03 eta: 15:46:14 time: 1.6365 data_time: 0.0035 memory: 3356 grad_norm: 0.2294 loss: 0.2795 loss_sem_seg: 0.2795 2023/05/11 01:29:51 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 8.0000e-03 eta: 15:45:26 time: 1.6359 data_time: 0.0035 memory: 3392 grad_norm: 0.1953 loss: 0.2717 loss_sem_seg: 0.2717 2023/05/11 01:31:15 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 8.0000e-03 eta: 15:44:49 time: 1.6787 data_time: 0.0035 memory: 3642 grad_norm: 0.2175 loss: 0.2851 loss_sem_seg: 0.2851 2023/05/11 01:32:38 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 8.0000e-03 eta: 15:44:06 time: 1.6587 data_time: 0.0034 memory: 3239 grad_norm: 0.1870 loss: 0.2845 loss_sem_seg: 0.2845 2023/05/11 01:34:00 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 8.0000e-03 eta: 15:43:16 time: 1.6378 data_time: 0.0035 memory: 3515 grad_norm: 0.2055 loss: 0.2850 loss_sem_seg: 0.2850 2023/05/11 01:35:22 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 8.0000e-03 eta: 15:42:28 time: 1.6431 data_time: 0.0035 memory: 3300 grad_norm: 0.2062 loss: 0.2783 loss_sem_seg: 0.2783 2023/05/11 01:36:45 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 8.0000e-03 eta: 15:41:45 time: 1.6660 data_time: 0.0035 memory: 3561 grad_norm: 0.2253 loss: 0.2844 loss_sem_seg: 0.2844 2023/05/11 01:38:08 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 8.0000e-03 eta: 15:40:58 time: 1.6551 data_time: 0.0035 memory: 3624 grad_norm: 0.2329 loss: 0.2902 loss_sem_seg: 0.2902 2023/05/11 01:39:30 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 8.0000e-03 eta: 15:40:05 time: 1.6326 data_time: 0.0035 memory: 3369 grad_norm: 0.1952 loss: 0.2847 loss_sem_seg: 0.2847 2023/05/11 01:40:53 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 8.0000e-03 eta: 15:39:19 time: 1.6584 data_time: 0.0035 memory: 3523 grad_norm: 0.1860 loss: 0.2879 loss_sem_seg: 0.2879 2023/05/11 01:42:06 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 8.0000e-03 eta: 15:37:41 time: 1.4722 data_time: 0.0035 memory: 3913 grad_norm: 0.2088 loss: 0.2924 loss_sem_seg: 0.2924 2023/05/11 01:43:20 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 8.0000e-03 eta: 15:36:05 time: 1.4775 data_time: 0.0035 memory: 3299 grad_norm: 0.2005 loss: 0.2956 loss_sem_seg: 0.2956 2023/05/11 01:44:32 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 8.0000e-03 eta: 15:34:21 time: 1.4451 data_time: 0.0035 memory: 3401 grad_norm: 0.1971 loss: 0.2968 loss_sem_seg: 0.2968 2023/05/11 01:45:44 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 8.0000e-03 eta: 15:32:36 time: 1.4410 data_time: 0.0035 memory: 3252 grad_norm: 0.1861 loss: 0.2807 loss_sem_seg: 0.2807 2023/05/11 01:46:56 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 8.0000e-03 eta: 15:30:48 time: 1.4307 data_time: 0.0034 memory: 3530 grad_norm: 0.1643 loss: 0.2773 loss_sem_seg: 0.2773 2023/05/11 01:48:23 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 8.0000e-03 eta: 15:30:23 time: 1.7436 data_time: 0.0036 memory: 3467 grad_norm: 0.1797 loss: 0.2752 loss_sem_seg: 0.2752 2023/05/11 01:49:45 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 8.0000e-03 eta: 15:29:28 time: 1.6307 data_time: 0.0035 memory: 3068 grad_norm: 0.2299 loss: 0.2695 loss_sem_seg: 0.2695 2023/05/11 01:50:17 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 01:51:07 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 8.0000e-03 eta: 15:28:37 time: 1.6489 data_time: 0.0036 memory: 3403 grad_norm: 0.1831 loss: 0.2912 loss_sem_seg: 0.2912 2023/05/11 01:52:30 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 8.0000e-03 eta: 15:27:48 time: 1.6591 data_time: 0.0035 memory: 3505 grad_norm: 0.1807 loss: 0.2825 loss_sem_seg: 0.2825 2023/05/11 01:53:54 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 8.0000e-03 eta: 15:27:02 time: 1.6701 data_time: 0.0036 memory: 3389 grad_norm: 0.1961 loss: 0.2808 loss_sem_seg: 0.2808 2023/05/11 01:55:12 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 01:55:12 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/05/11 01:55:58 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:06:04 time: 0.7935 data_time: 0.0022 memory: 3228 2023/05/11 01:56:37 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:05:24 time: 0.7938 data_time: 0.0021 memory: 1105 2023/05/11 01:57:17 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:04:44 time: 0.7864 data_time: 0.0021 memory: 1110 2023/05/11 01:57:51 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:03:56 time: 0.6828 data_time: 0.0021 memory: 1100 2023/05/11 01:58:25 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:03:13 time: 0.6838 data_time: 0.0021 memory: 1111 2023/05/11 01:58:56 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:02:31 time: 0.6145 data_time: 0.0021 memory: 1075 2023/05/11 01:59:25 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:01:52 time: 0.5810 data_time: 0.0021 memory: 1091 2023/05/11 01:59:54 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:01:15 time: 0.5789 data_time: 0.0021 memory: 1090 2023/05/11 02:00:20 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:39 time: 0.5320 data_time: 0.0021 memory: 1113 2023/05/11 02:00:47 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:05 time: 0.5355 data_time: 0.0021 memory: 1098 2023/05/11 02:01:09 - 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 | 0.9478 | 0.4863 | 0.6948 | 0.6540 | 0.4015 | 0.6687 | 0.7096 | 0.0202 | 0.9320 | 0.4599 | 0.7940 | 0.0065 | 0.9055 | 0.6361 | 0.8905 | 0.6562 | 0.7593 | 0.6472 | 0.5114 | 0.6201 | 0.9194 | 0.6981 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 02:01:09 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9478 bicycle: 0.4863 motorcycle: 0.6948 truck: 0.6540 bus: 0.4015 person: 0.6687 bicyclist: 0.7096 motorcyclist: 0.0202 road: 0.9320 parking: 0.4599 sidewalk: 0.7940 other-ground: 0.0065 building: 0.9055 fence: 0.6361 vegetation: 0.8905 trunck: 0.6562 terrian: 0.7593 pole: 0.6472 traffic-sign: 0.5114 miou: 0.6201 acc: 0.9194 acc_cls: 0.6981 data_time: 0.0021 time: 0.5484 2023/05/11 02:02:11 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 8.0000e-03 eta: 15:23:52 time: 1.2395 data_time: 0.0045 memory: 3454 grad_norm: 0.1893 loss: 0.2602 loss_sem_seg: 0.2602 2023/05/11 02:03:27 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 8.0000e-03 eta: 15:22:31 time: 1.5320 data_time: 0.0035 memory: 3337 grad_norm: 0.1971 loss: 0.2745 loss_sem_seg: 0.2745 2023/05/11 02:04:51 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 8.0000e-03 eta: 15:21:45 time: 1.6755 data_time: 0.0035 memory: 3521 grad_norm: 0.1821 loss: 0.2576 loss_sem_seg: 0.2576 2023/05/11 02:06:14 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 8.0000e-03 eta: 15:20:53 time: 1.6519 data_time: 0.0035 memory: 3507 grad_norm: 0.2045 loss: 0.2812 loss_sem_seg: 0.2812 2023/05/11 02:07:36 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 8.0000e-03 eta: 15:20:00 time: 1.6520 data_time: 0.0035 memory: 3354 grad_norm: 0.1763 loss: 0.2821 loss_sem_seg: 0.2821 2023/05/11 02:08:58 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 8.0000e-03 eta: 15:19:04 time: 1.6409 data_time: 0.0034 memory: 3226 grad_norm: 0.1730 loss: 0.2728 loss_sem_seg: 0.2728 2023/05/11 02:10:22 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 8.0000e-03 eta: 15:18:15 time: 1.6701 data_time: 0.0035 memory: 3352 grad_norm: 0.1804 loss: 0.2785 loss_sem_seg: 0.2785 2023/05/11 02:11:43 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 8.0000e-03 eta: 15:17:17 time: 1.6346 data_time: 0.0034 memory: 3337 grad_norm: 0.1873 loss: 0.2869 loss_sem_seg: 0.2869 2023/05/11 02:13:06 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 8.0000e-03 eta: 15:16:25 time: 1.6605 data_time: 0.0035 memory: 3388 grad_norm: 0.1810 loss: 0.2773 loss_sem_seg: 0.2773 2023/05/11 02:14:29 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 8.0000e-03 eta: 15:15:29 time: 1.6453 data_time: 0.0035 memory: 3314 grad_norm: 0.1581 loss: 0.2428 loss_sem_seg: 0.2428 2023/05/11 02:15:53 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 8.0000e-03 eta: 15:14:42 time: 1.6841 data_time: 0.0035 memory: 3359 grad_norm: 0.1765 loss: 0.2756 loss_sem_seg: 0.2756 2023/05/11 02:17:17 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 8.0000e-03 eta: 15:13:51 time: 1.6723 data_time: 0.0036 memory: 3390 grad_norm: 0.1607 loss: 0.2704 loss_sem_seg: 0.2704 2023/05/11 02:18:40 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 8.0000e-03 eta: 15:13:00 time: 1.6720 data_time: 0.0035 memory: 3783 grad_norm: 0.1802 loss: 0.2576 loss_sem_seg: 0.2576 2023/05/11 02:19:53 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 8.0000e-03 eta: 15:11:22 time: 1.4661 data_time: 0.0036 memory: 3431 grad_norm: 0.2056 loss: 0.2630 loss_sem_seg: 0.2630 2023/05/11 02:21:04 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 8.0000e-03 eta: 15:09:35 time: 1.4206 data_time: 0.0036 memory: 3387 grad_norm: 0.1866 loss: 0.2621 loss_sem_seg: 0.2621 2023/05/11 02:22:19 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 8.0000e-03 eta: 15:08:05 time: 1.4966 data_time: 0.0034 memory: 3293 grad_norm: 0.1567 loss: 0.2711 loss_sem_seg: 0.2711 2023/05/11 02:22:54 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 02:23:31 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 8.0000e-03 eta: 15:06:20 time: 1.4322 data_time: 0.0035 memory: 3220 grad_norm: 0.1679 loss: 0.2562 loss_sem_seg: 0.2562 2023/05/11 02:24:44 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 8.0000e-03 eta: 15:04:43 time: 1.4619 data_time: 0.0036 memory: 3490 grad_norm: inf loss: 0.2592 loss_sem_seg: 0.2592 2023/05/11 02:26:08 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 8.0000e-03 eta: 15:03:54 time: 1.6828 data_time: 0.0035 memory: 3287 grad_norm: 0.1841 loss: 0.2679 loss_sem_seg: 0.2679 2023/05/11 02:27:33 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 8.0000e-03 eta: 15:03:08 time: 1.7022 data_time: 0.0035 memory: 3375 grad_norm: 0.1653 loss: 0.2791 loss_sem_seg: 0.2791 2023/05/11 02:28:57 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 8.0000e-03 eta: 15:02:15 time: 1.6706 data_time: 0.0034 memory: 3227 grad_norm: 0.1717 loss: 0.2653 loss_sem_seg: 0.2653 2023/05/11 02:30:20 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 8.0000e-03 eta: 15:01:20 time: 1.6634 data_time: 0.0034 memory: 3240 grad_norm: 0.1788 loss: 0.2751 loss_sem_seg: 0.2751 2023/05/11 02:31:42 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 8.0000e-03 eta: 15:00:19 time: 1.6333 data_time: 0.0035 memory: 3337 grad_norm: 0.1469 loss: 0.2721 loss_sem_seg: 0.2721 2023/05/11 02:32:56 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 02:32:56 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/05/11 02:33:43 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:06:12 time: 0.8122 data_time: 0.0022 memory: 3258 2023/05/11 02:34:22 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:05:26 time: 0.7846 data_time: 0.0021 memory: 1105 2023/05/11 02:34:58 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:04:36 time: 0.7172 data_time: 0.0022 memory: 1110 2023/05/11 02:35:30 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:03:47 time: 0.6346 data_time: 0.0021 memory: 1100 2023/05/11 02:36:02 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:03:06 time: 0.6429 data_time: 0.0021 memory: 1111 2023/05/11 02:36:29 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:02:24 time: 0.5448 data_time: 0.0021 memory: 1075 2023/05/11 02:36:55 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:01:45 time: 0.5199 data_time: 0.0021 memory: 1091 2023/05/11 02:37:24 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:01:11 time: 0.5668 data_time: 0.0023 memory: 1090 2023/05/11 02:37:53 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:38 time: 0.5851 data_time: 0.0021 memory: 1113 2023/05/11 02:38:22 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:05 time: 0.5815 data_time: 0.0021 memory: 1098 2023/05/11 02:38:53 - 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 | 0.9470 | 0.4459 | 0.6453 | 0.3071 | 0.1720 | 0.6804 | 0.7345 | 0.0190 | 0.9227 | 0.3539 | 0.8081 | 0.0189 | 0.8991 | 0.6155 | 0.8940 | 0.7071 | 0.7863 | 0.6403 | 0.4813 | 0.5831 | 0.9195 | 0.6936 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 02:38:53 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9470 bicycle: 0.4459 motorcycle: 0.6453 truck: 0.3071 bus: 0.1720 person: 0.6804 bicyclist: 0.7345 motorcyclist: 0.0190 road: 0.9227 parking: 0.3539 sidewalk: 0.8081 other-ground: 0.0189 building: 0.8991 fence: 0.6155 vegetation: 0.8940 trunck: 0.7071 terrian: 0.7863 pole: 0.6403 traffic-sign: 0.4813 miou: 0.5831 acc: 0.9195 acc_cls: 0.6936 data_time: 0.0021 time: 0.5991 2023/05/11 02:39:56 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 8.0000e-03 eta: 14:57:01 time: 1.2562 data_time: 0.0043 memory: 3483 grad_norm: 0.1686 loss: 0.2765 loss_sem_seg: 0.2765 2023/05/11 02:41:13 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 8.0000e-03 eta: 14:55:41 time: 1.5435 data_time: 0.0033 memory: 3372 grad_norm: 0.1568 loss: 0.2745 loss_sem_seg: 0.2745 2023/05/11 02:42:35 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 8.0000e-03 eta: 14:54:42 time: 1.6456 data_time: 0.0034 memory: 3274 grad_norm: 0.1543 loss: 0.2688 loss_sem_seg: 0.2688 2023/05/11 02:44:00 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 8.0000e-03 eta: 14:53:51 time: 1.6841 data_time: 0.0035 memory: 3699 grad_norm: 0.1628 loss: 0.2821 loss_sem_seg: 0.2821 2023/05/11 02:45:22 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 8.0000e-03 eta: 14:52:51 time: 1.6482 data_time: 0.0035 memory: 3241 grad_norm: 0.1609 loss: 0.2707 loss_sem_seg: 0.2707 2023/05/11 02:46:46 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 8.0000e-03 eta: 14:51:57 time: 1.6727 data_time: 0.0034 memory: 3441 grad_norm: 0.1905 loss: 0.2855 loss_sem_seg: 0.2855 2023/05/11 02:48:08 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 8.0000e-03 eta: 14:50:56 time: 1.6443 data_time: 0.0034 memory: 3536 grad_norm: 0.1588 loss: 0.2589 loss_sem_seg: 0.2589 2023/05/11 02:49:32 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 8.0000e-03 eta: 14:50:05 time: 1.6909 data_time: 0.0035 memory: 3395 grad_norm: 0.1721 loss: 0.2636 loss_sem_seg: 0.2636 2023/05/11 02:50:56 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 8.0000e-03 eta: 14:49:07 time: 1.6643 data_time: 0.0033 memory: 3168 grad_norm: 0.1782 loss: 0.2774 loss_sem_seg: 0.2774 2023/05/11 02:52:18 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 8.0000e-03 eta: 14:48:06 time: 1.6453 data_time: 0.0034 memory: 3403 grad_norm: 0.1610 loss: 0.2516 loss_sem_seg: 0.2516 2023/05/11 02:53:40 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 8.0000e-03 eta: 14:47:03 time: 1.6380 data_time: 0.0035 memory: 3782 grad_norm: 0.1503 loss: 0.2714 loss_sem_seg: 0.2714 2023/05/11 02:55:03 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 8.0000e-03 eta: 14:46:04 time: 1.6540 data_time: 0.0034 memory: 3350 grad_norm: 0.1745 loss: 0.2485 loss_sem_seg: 0.2485 2023/05/11 02:55:49 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 02:56:25 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 8.0000e-03 eta: 14:45:01 time: 1.6442 data_time: 0.0035 memory: 3287 grad_norm: 0.1500 loss: 0.2649 loss_sem_seg: 0.2649 2023/05/11 02:57:37 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 8.0000e-03 eta: 14:43:23 time: 1.4528 data_time: 0.0034 memory: 3408 grad_norm: 0.1609 loss: 0.2675 loss_sem_seg: 0.2675 2023/05/11 02:58:50 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 8.0000e-03 eta: 14:41:47 time: 1.4597 data_time: 0.0034 memory: 3396 grad_norm: 0.1499 loss: 0.2588 loss_sem_seg: 0.2588 2023/05/11 03:00:03 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 8.0000e-03 eta: 14:40:09 time: 1.4537 data_time: 0.0034 memory: 3230 grad_norm: 0.1593 loss: 0.2570 loss_sem_seg: 0.2570 2023/05/11 03:01:17 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 8.0000e-03 eta: 14:38:36 time: 1.4743 data_time: 0.0034 memory: 3370 grad_norm: 0.1546 loss: 0.2755 loss_sem_seg: 0.2755 2023/05/11 03:02:30 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 8.0000e-03 eta: 14:37:00 time: 1.4609 data_time: 0.0035 memory: 3203 grad_norm: 0.1770 loss: 0.2680 loss_sem_seg: 0.2680 2023/05/11 03:03:54 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 8.0000e-03 eta: 14:36:06 time: 1.6856 data_time: 0.0034 memory: 3114 grad_norm: 0.1800 loss: 0.2643 loss_sem_seg: 0.2643 2023/05/11 03:05:16 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 8.0000e-03 eta: 14:35:01 time: 1.6322 data_time: 0.0033 memory: 3441 grad_norm: 0.1757 loss: 0.2777 loss_sem_seg: 0.2777 2023/05/11 03:06:39 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 8.0000e-03 eta: 14:34:04 time: 1.6743 data_time: 0.0035 memory: 3240 grad_norm: 0.1420 loss: 0.2405 loss_sem_seg: 0.2405 2023/05/11 03:08:03 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 8.0000e-03 eta: 14:33:05 time: 1.6702 data_time: 0.0033 memory: 3248 grad_norm: 0.1598 loss: 0.2706 loss_sem_seg: 0.2706 2023/05/11 03:09:24 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 8.0000e-03 eta: 14:31:58 time: 1.6220 data_time: 0.0035 memory: 3303 grad_norm: 0.1396 loss: 0.2625 loss_sem_seg: 0.2625 2023/05/11 03:10:40 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 03:10:40 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/05/11 03:11:25 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:05:56 time: 0.7764 data_time: 0.0021 memory: 3351 2023/05/11 03:12:05 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:05:23 time: 0.8036 data_time: 0.0021 memory: 1105 2023/05/11 03:12:42 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:04:37 time: 0.7424 data_time: 0.0022 memory: 1110 2023/05/11 03:13:16 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:03:52 time: 0.6821 data_time: 0.0021 memory: 1100 2023/05/11 03:13:50 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:03:11 time: 0.6876 data_time: 0.0022 memory: 1111 2023/05/11 03:14:18 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:02:28 time: 0.5616 data_time: 0.0021 memory: 1075 2023/05/11 03:14:47 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:01:49 time: 0.5686 data_time: 0.0021 memory: 1091 2023/05/11 03:15:16 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:01:13 time: 0.5738 data_time: 0.0020 memory: 1090 2023/05/11 03:15:43 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:38 time: 0.5529 data_time: 0.0021 memory: 1113 2023/05/11 03:16:14 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:05 time: 0.6132 data_time: 0.0021 memory: 1098 2023/05/11 03:16:36 - 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 | 0.9434 | 0.3984 | 0.5926 | 0.6310 | 0.3756 | 0.6887 | 0.8337 | 0.0301 | 0.9266 | 0.4017 | 0.8059 | 0.0010 | 0.8863 | 0.5328 | 0.8948 | 0.6424 | 0.7818 | 0.6489 | 0.5042 | 0.6063 | 0.9188 | 0.6996 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 03:16:36 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9434 bicycle: 0.3984 motorcycle: 0.5926 truck: 0.6310 bus: 0.3756 person: 0.6887 bicyclist: 0.8337 motorcyclist: 0.0301 road: 0.9266 parking: 0.4017 sidewalk: 0.8059 other-ground: 0.0010 building: 0.8863 fence: 0.5328 vegetation: 0.8948 trunck: 0.6424 terrian: 0.7818 pole: 0.6489 traffic-sign: 0.5042 miou: 0.6063 acc: 0.9188 acc_cls: 0.6996 data_time: 0.0021 time: 0.6273 2023/05/11 03:17:43 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 8.0000e-03 eta: 14:29:02 time: 1.3285 data_time: 0.0042 memory: 3539 grad_norm: 0.1435 loss: 0.2607 loss_sem_seg: 0.2607 2023/05/11 03:18:58 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 8.0000e-03 eta: 14:27:36 time: 1.5106 data_time: 0.0033 memory: 3457 grad_norm: 0.1359 loss: 0.2597 loss_sem_seg: 0.2597 2023/05/11 03:20:23 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 8.0000e-03 eta: 14:26:42 time: 1.6995 data_time: 0.0034 memory: 3476 grad_norm: 0.1430 loss: 0.2542 loss_sem_seg: 0.2542 2023/05/11 03:21:46 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 8.0000e-03 eta: 14:25:41 time: 1.6602 data_time: 0.0036 memory: 3401 grad_norm: 0.1571 loss: 0.2612 loss_sem_seg: 0.2612 2023/05/11 03:23:11 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 8.0000e-03 eta: 14:24:47 time: 1.7045 data_time: 0.0034 memory: 3341 grad_norm: 0.1715 loss: 0.2616 loss_sem_seg: 0.2616 2023/05/11 03:24:34 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 8.0000e-03 eta: 14:23:46 time: 1.6614 data_time: 0.0034 memory: 3419 grad_norm: 0.1505 loss: 0.2397 loss_sem_seg: 0.2397 2023/05/11 03:25:57 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 8.0000e-03 eta: 14:22:42 time: 1.6443 data_time: 0.0035 memory: 3389 grad_norm: 0.1523 loss: 0.2515 loss_sem_seg: 0.2515 2023/05/11 03:27:18 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 8.0000e-03 eta: 14:21:36 time: 1.6341 data_time: 0.0035 memory: 3381 grad_norm: 0.1534 loss: 0.2553 loss_sem_seg: 0.2553 2023/05/11 03:28:11 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 03:28:41 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 8.0000e-03 eta: 14:20:34 time: 1.6582 data_time: 0.0034 memory: 3436 grad_norm: 0.1363 loss: 0.2396 loss_sem_seg: 0.2396 2023/05/11 03:30:03 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 8.0000e-03 eta: 14:19:26 time: 1.6279 data_time: 0.0034 memory: 3270 grad_norm: 0.1441 loss: 0.2354 loss_sem_seg: 0.2354 2023/05/11 03:31:23 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 8.0000e-03 eta: 14:18:17 time: 1.6171 data_time: 0.0035 memory: 3565 grad_norm: 0.1443 loss: 0.2545 loss_sem_seg: 0.2545 2023/05/11 03:32:45 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 8.0000e-03 eta: 14:17:10 time: 1.6340 data_time: 0.0036 memory: 3498 grad_norm: 0.1926 loss: 0.2650 loss_sem_seg: 0.2650 2023/05/11 03:34:08 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 8.0000e-03 eta: 14:16:08 time: 1.6639 data_time: 0.0034 memory: 3272 grad_norm: 0.1414 loss: 0.2404 loss_sem_seg: 0.2404 2023/05/11 03:35:24 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 8.0000e-03 eta: 14:14:40 time: 1.5038 data_time: 0.0034 memory: 3691 grad_norm: 0.1381 loss: 0.2584 loss_sem_seg: 0.2584 2023/05/11 03:36:36 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 8.0000e-03 eta: 14:13:02 time: 1.4405 data_time: 0.0033 memory: 3278 grad_norm: 0.1427 loss: 0.2544 loss_sem_seg: 0.2544 2023/05/11 03:37:48 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 8.0000e-03 eta: 14:11:27 time: 1.4569 data_time: 0.0034 memory: 3680 grad_norm: 0.1521 loss: 0.2622 loss_sem_seg: 0.2622 2023/05/11 03:39:00 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.0000e-03 eta: 14:09:49 time: 1.4371 data_time: 0.0033 memory: 3225 grad_norm: 0.1392 loss: 0.2285 loss_sem_seg: 0.2285 2023/05/11 03:40:13 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.0000e-03 eta: 14:08:16 time: 1.4630 data_time: 0.0033 memory: 3494 grad_norm: 0.1628 loss: 0.2641 loss_sem_seg: 0.2641 2023/05/11 03:41:38 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.0000e-03 eta: 14:07:17 time: 1.6844 data_time: 0.0034 memory: 3352 grad_norm: 0.1581 loss: 0.2481 loss_sem_seg: 0.2481 2023/05/11 03:43:00 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.0000e-03 eta: 14:06:10 time: 1.6397 data_time: 0.0035 memory: 3365 grad_norm: 0.1333 loss: 0.2555 loss_sem_seg: 0.2555 2023/05/11 03:44:22 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.0000e-03 eta: 14:05:05 time: 1.6493 data_time: 0.0036 memory: 3271 grad_norm: 0.1462 loss: 0.2537 loss_sem_seg: 0.2537 2023/05/11 03:45:46 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.0000e-03 eta: 14:04:04 time: 1.6698 data_time: 0.0036 memory: 3440 grad_norm: 0.1517 loss: 0.2686 loss_sem_seg: 0.2686 2023/05/11 03:47:09 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.0000e-03 eta: 14:03:01 time: 1.6701 data_time: 0.0035 memory: 3148 grad_norm: 0.1374 loss: 0.2425 loss_sem_seg: 0.2425 2023/05/11 03:48:26 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 03:48:26 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/05/11 03:49:12 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:06:03 time: 0.7924 data_time: 0.0021 memory: 3278 2023/05/11 03:49:51 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:05:22 time: 0.7867 data_time: 0.0021 memory: 1105 2023/05/11 03:50:26 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:04:32 time: 0.6981 data_time: 0.0021 memory: 1110 2023/05/11 03:51:00 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:03:47 time: 0.6726 data_time: 0.0021 memory: 1100 2023/05/11 03:51:31 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:03:05 time: 0.6239 data_time: 0.0021 memory: 1111 2023/05/11 03:51:58 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:02:23 time: 0.5333 data_time: 0.0021 memory: 1075 2023/05/11 03:52:23 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:01:45 time: 0.5163 data_time: 0.0020 memory: 1091 2023/05/11 03:52:50 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:01:10 time: 0.5218 data_time: 0.0021 memory: 1090 2023/05/11 03:53:16 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:37 time: 0.5229 data_time: 0.0021 memory: 1113 2023/05/11 03:53:42 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:05 time: 0.5331 data_time: 0.0020 memory: 1098 2023/05/11 03:54:08 - 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 | 0.9573 | 0.5386 | 0.7621 | 0.8304 | 0.5555 | 0.6906 | 0.7792 | 0.0452 | 0.9383 | 0.4072 | 0.8061 | 0.0221 | 0.8806 | 0.5397 | 0.8900 | 0.6746 | 0.7671 | 0.6567 | 0.5011 | 0.6443 | 0.9192 | 0.7241 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 03:54:08 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9573 bicycle: 0.5386 motorcycle: 0.7621 truck: 0.8304 bus: 0.5555 person: 0.6906 bicyclist: 0.7792 motorcyclist: 0.0452 road: 0.9383 parking: 0.4072 sidewalk: 0.8061 other-ground: 0.0221 building: 0.8806 fence: 0.5397 vegetation: 0.8900 trunck: 0.6746 terrian: 0.7671 pole: 0.6567 traffic-sign: 0.5011 miou: 0.6443 acc: 0.9192 acc_cls: 0.7241 data_time: 0.0020 time: 0.5411 2023/05/11 03:55:15 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.0000e-03 eta: 14:00:12 time: 1.3318 data_time: 0.0045 memory: 3451 grad_norm: 0.1602 loss: 0.2600 loss_sem_seg: 0.2600 2023/05/11 03:56:32 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0000e-03 eta: 13:58:50 time: 1.5397 data_time: 0.0034 memory: 3447 grad_norm: 0.1320 loss: 0.2456 loss_sem_seg: 0.2456 2023/05/11 03:58:01 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 8.0000e-03 eta: 13:58:04 time: 1.7842 data_time: 0.0034 memory: 3561 grad_norm: 0.1418 loss: 0.2384 loss_sem_seg: 0.2384 2023/05/11 03:59:24 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 8.0000e-03 eta: 13:57:01 time: 1.6635 data_time: 0.0035 memory: 3356 grad_norm: 0.1324 loss: 0.2463 loss_sem_seg: 0.2463 2023/05/11 04:00:24 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 04:00:46 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 8.0000e-03 eta: 13:55:53 time: 1.6369 data_time: 0.0034 memory: 3449 grad_norm: 0.1325 loss: 0.2411 loss_sem_seg: 0.2411 2023/05/11 04:02:09 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 8.0000e-03 eta: 13:54:47 time: 1.6502 data_time: 0.0034 memory: 3416 grad_norm: inf loss: 0.2576 loss_sem_seg: 0.2576 2023/05/11 04:03:31 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 8.0000e-03 eta: 13:53:39 time: 1.6380 data_time: 0.0034 memory: 3624 grad_norm: 0.1531 loss: 0.2558 loss_sem_seg: 0.2558 2023/05/11 04:04:53 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 8.0000e-03 eta: 13:52:33 time: 1.6521 data_time: 0.0034 memory: 3176 grad_norm: 0.1435 loss: 0.2474 loss_sem_seg: 0.2474 2023/05/11 04:06:16 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 8.0000e-03 eta: 13:51:28 time: 1.6609 data_time: 0.0034 memory: 3132 grad_norm: 0.1488 loss: 0.2689 loss_sem_seg: 0.2689 2023/05/11 04:07:38 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 8.0000e-03 eta: 13:50:20 time: 1.6407 data_time: 0.0035 memory: 3346 grad_norm: 0.1669 loss: 0.2559 loss_sem_seg: 0.2559 2023/05/11 04:09:04 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 8.0000e-03 eta: 13:49:21 time: 1.7036 data_time: 0.0035 memory: 3416 grad_norm: 0.1306 loss: 0.2421 loss_sem_seg: 0.2421 2023/05/11 04:10:27 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 8.0000e-03 eta: 13:48:15 time: 1.6592 data_time: 0.0035 memory: 3322 grad_norm: 0.1392 loss: 0.2391 loss_sem_seg: 0.2391 2023/05/11 04:11:49 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 8.0000e-03 eta: 13:47:10 time: 1.6590 data_time: 0.0034 memory: 3212 grad_norm: 0.1482 loss: 0.2592 loss_sem_seg: 0.2592 2023/05/11 04:13:03 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 8.0000e-03 eta: 13:45:38 time: 1.4757 data_time: 0.0036 memory: 3925 grad_norm: 0.1336 loss: 0.2515 loss_sem_seg: 0.2515 2023/05/11 04:14:16 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 8.0000e-03 eta: 13:44:04 time: 1.4484 data_time: 0.0035 memory: 3354 grad_norm: 0.1416 loss: 0.2491 loss_sem_seg: 0.2491 2023/05/11 04:15:27 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 8.0000e-03 eta: 13:42:25 time: 1.4227 data_time: 0.0034 memory: 3456 grad_norm: 0.1461 loss: 0.2559 loss_sem_seg: 0.2559 2023/05/11 04:16:38 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 8.0000e-03 eta: 13:40:48 time: 1.4245 data_time: 0.0034 memory: 3581 grad_norm: 0.1429 loss: 0.2317 loss_sem_seg: 0.2317 2023/05/11 04:17:51 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 8.0000e-03 eta: 13:39:14 time: 1.4503 data_time: 0.0034 memory: 3339 grad_norm: 0.1393 loss: 0.2501 loss_sem_seg: 0.2501 2023/05/11 04:19:15 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 8.0000e-03 eta: 13:38:12 time: 1.6914 data_time: 0.0034 memory: 3546 grad_norm: 0.1399 loss: 0.2686 loss_sem_seg: 0.2686 2023/05/11 04:20:40 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 8.0000e-03 eta: 13:37:11 time: 1.6908 data_time: 0.0034 memory: 3606 grad_norm: 0.1418 loss: 0.2376 loss_sem_seg: 0.2376 2023/05/11 04:22:02 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 8.0000e-03 eta: 13:36:02 time: 1.6395 data_time: 0.0035 memory: 3391 grad_norm: 0.1246 loss: 0.2502 loss_sem_seg: 0.2502 2023/05/11 04:23:24 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 8.0000e-03 eta: 13:34:55 time: 1.6560 data_time: 0.0036 memory: 3381 grad_norm: 0.1386 loss: 0.2319 loss_sem_seg: 0.2319 2023/05/11 04:24:46 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 8.0000e-03 eta: 13:33:45 time: 1.6278 data_time: 0.0035 memory: 3323 grad_norm: 0.1369 loss: 0.2458 loss_sem_seg: 0.2458 2023/05/11 04:26:01 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 04:26:01 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/05/11 04:26:48 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:06:08 time: 0.8038 data_time: 0.0022 memory: 3118 2023/05/11 04:27:27 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:05:23 time: 0.7774 data_time: 0.0021 memory: 1105 2023/05/11 04:28:02 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:04:32 time: 0.6928 data_time: 0.0021 memory: 1110 2023/05/11 04:28:36 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:03:48 time: 0.6867 data_time: 0.0021 memory: 1100 2023/05/11 04:29:06 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:03:04 time: 0.6023 data_time: 0.0021 memory: 1111 2023/05/11 04:29:34 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:02:23 time: 0.5626 data_time: 0.0020 memory: 1075 2023/05/11 04:30:03 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:01:46 time: 0.5819 data_time: 0.0020 memory: 1091 2023/05/11 04:30:32 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:01:11 time: 0.5737 data_time: 0.0020 memory: 1090 2023/05/11 04:31:00 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:38 time: 0.5540 data_time: 0.0021 memory: 1113 2023/05/11 04:31:24 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:05 time: 0.4901 data_time: 0.0021 memory: 1098 2023/05/11 04:31:51 - 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 | 0.9473 | 0.4746 | 0.6989 | 0.7950 | 0.6352 | 0.7327 | 0.8585 | 0.0283 | 0.9380 | 0.3744 | 0.8062 | 0.0103 | 0.8990 | 0.6124 | 0.8976 | 0.6654 | 0.7864 | 0.6515 | 0.4914 | 0.6475 | 0.9240 | 0.7246 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 04:31:51 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9473 bicycle: 0.4746 motorcycle: 0.6989 truck: 0.7950 bus: 0.6352 person: 0.7327 bicyclist: 0.8585 motorcyclist: 0.0283 road: 0.9380 parking: 0.3744 sidewalk: 0.8062 other-ground: 0.0103 building: 0.8990 fence: 0.6124 vegetation: 0.8976 trunck: 0.6654 terrian: 0.7864 pole: 0.6515 traffic-sign: 0.4914 miou: 0.6475 acc: 0.9240 acc_cls: 0.7246 data_time: 0.0020 time: 0.4988 2023/05/11 04:32:43 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 04:32:59 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 8.0000e-03 eta: 13:30:55 time: 1.3490 data_time: 0.0042 memory: 3447 grad_norm: 0.1531 loss: 0.2455 loss_sem_seg: 0.2455 2023/05/11 04:34:15 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 8.0000e-03 eta: 13:29:31 time: 1.5204 data_time: 0.0034 memory: 3282 grad_norm: 0.1460 loss: 0.2538 loss_sem_seg: 0.2538 2023/05/11 04:35:41 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 8.0000e-03 eta: 13:28:33 time: 1.7302 data_time: 0.0036 memory: 3454 grad_norm: 0.1414 loss: 0.2401 loss_sem_seg: 0.2401 2023/05/11 04:37:02 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 8.0000e-03 eta: 13:27:20 time: 1.6115 data_time: 0.0034 memory: 3448 grad_norm: 0.1387 loss: 0.2346 loss_sem_seg: 0.2346 2023/05/11 04:38:25 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 8.0000e-03 eta: 13:26:14 time: 1.6617 data_time: 0.0034 memory: 3334 grad_norm: 0.1371 loss: 0.2396 loss_sem_seg: 0.2396 2023/05/11 04:39:48 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 8.0000e-03 eta: 13:25:07 time: 1.6608 data_time: 0.0037 memory: 3166 grad_norm: 0.1612 loss: 0.2435 loss_sem_seg: 0.2435 2023/05/11 04:41:11 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 8.0000e-03 eta: 13:23:59 time: 1.6558 data_time: 0.0034 memory: 3686 grad_norm: 0.1286 loss: 0.2390 loss_sem_seg: 0.2390 2023/05/11 04:42:34 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 8.0000e-03 eta: 13:22:53 time: 1.6656 data_time: 0.0034 memory: 3401 grad_norm: 0.1295 loss: 0.2468 loss_sem_seg: 0.2468 2023/05/11 04:43:57 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 8.0000e-03 eta: 13:21:45 time: 1.6525 data_time: 0.0033 memory: 3577 grad_norm: 0.1372 loss: 0.2413 loss_sem_seg: 0.2413 2023/05/11 04:45:19 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 8.0000e-03 eta: 13:20:34 time: 1.6357 data_time: 0.0036 memory: 3613 grad_norm: 0.1306 loss: 0.2420 loss_sem_seg: 0.2420 2023/05/11 04:46:39 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 8.0000e-03 eta: 13:19:19 time: 1.5984 data_time: 0.0036 memory: 3463 grad_norm: 0.1313 loss: 0.2376 loss_sem_seg: 0.2376 2023/05/11 04:48:00 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 8.0000e-03 eta: 13:18:07 time: 1.6204 data_time: 0.0035 memory: 3404 grad_norm: 0.1401 loss: 0.2364 loss_sem_seg: 0.2364 2023/05/11 04:49:20 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 8.0000e-03 eta: 13:16:54 time: 1.6162 data_time: 0.0035 memory: 3517 grad_norm: 0.1107 loss: 0.2329 loss_sem_seg: 0.2329 2023/05/11 04:50:33 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 8.0000e-03 eta: 13:15:21 time: 1.4497 data_time: 0.0034 memory: 3327 grad_norm: 0.1331 loss: 0.2390 loss_sem_seg: 0.2390 2023/05/11 04:51:42 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 8.0000e-03 eta: 13:13:41 time: 1.3921 data_time: 0.0034 memory: 3643 grad_norm: 0.1370 loss: 0.2379 loss_sem_seg: 0.2379 2023/05/11 04:52:53 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 8.0000e-03 eta: 13:12:03 time: 1.4066 data_time: 0.0033 memory: 3317 grad_norm: 0.1398 loss: 0.2351 loss_sem_seg: 0.2351 2023/05/11 04:54:02 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 8.0000e-03 eta: 13:10:23 time: 1.3841 data_time: 0.0033 memory: 3181 grad_norm: 0.1330 loss: 0.2406 loss_sem_seg: 0.2406 2023/05/11 04:55:12 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 8.0000e-03 eta: 13:08:46 time: 1.4086 data_time: 0.0035 memory: 3302 grad_norm: 0.1192 loss: 0.2324 loss_sem_seg: 0.2324 2023/05/11 04:56:35 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 8.0000e-03 eta: 13:07:37 time: 1.6436 data_time: 0.0033 memory: 3551 grad_norm: 0.1332 loss: 0.2598 loss_sem_seg: 0.2598 2023/05/11 04:57:54 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 8.0000e-03 eta: 13:06:20 time: 1.5847 data_time: 0.0034 memory: 3226 grad_norm: 0.1504 loss: 0.2444 loss_sem_seg: 0.2444 2023/05/11 04:58:56 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 04:59:11 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 8.0000e-03 eta: 13:04:59 time: 1.5459 data_time: 0.0033 memory: 3246 grad_norm: 0.1259 loss: 0.2428 loss_sem_seg: 0.2428 2023/05/11 05:00:29 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 8.0000e-03 eta: 13:03:39 time: 1.5516 data_time: 0.0034 memory: 3487 grad_norm: 0.1471 loss: 0.2312 loss_sem_seg: 0.2312 2023/05/11 05:01:47 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 8.0000e-03 eta: 13:02:20 time: 1.5573 data_time: 0.0035 memory: 3461 grad_norm: 0.1363 loss: 0.2407 loss_sem_seg: 0.2407 2023/05/11 05:02:58 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 05:02:58 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/05/11 05:03:39 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:05:22 time: 0.7033 data_time: 0.0021 memory: 3501 2023/05/11 05:04:14 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:04:45 time: 0.6946 data_time: 0.0021 memory: 1105 2023/05/11 05:04:43 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:03:57 time: 0.5844 data_time: 0.0021 memory: 1110 2023/05/11 05:05:12 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:03:17 time: 0.5743 data_time: 0.0021 memory: 1100 2023/05/11 05:05:37 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:02:38 time: 0.5041 data_time: 0.0020 memory: 1111 2023/05/11 05:06:00 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:02:02 time: 0.4648 data_time: 0.0021 memory: 1075 2023/05/11 05:06:23 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:01:30 time: 0.4648 data_time: 0.0020 memory: 1091 2023/05/11 05:06:46 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:01:00 time: 0.4622 data_time: 0.0020 memory: 1090 2023/05/11 05:07:10 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:32 time: 0.4675 data_time: 0.0020 memory: 1113 2023/05/11 05:07:33 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:04 time: 0.4642 data_time: 0.0021 memory: 1098 2023/05/11 05:08:32 - 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 | 0.9462 | 0.5082 | 0.7667 | 0.7439 | 0.4039 | 0.7234 | 0.8243 | 0.0350 | 0.9306 | 0.4537 | 0.8051 | 0.0057 | 0.9056 | 0.6366 | 0.8951 | 0.6568 | 0.7778 | 0.6361 | 0.4877 | 0.6391 | 0.9225 | 0.7132 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 05:08:32 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9462 bicycle: 0.5082 motorcycle: 0.7667 truck: 0.7439 bus: 0.4039 person: 0.7234 bicyclist: 0.8243 motorcyclist: 0.0350 road: 0.9306 parking: 0.4537 sidewalk: 0.8051 other-ground: 0.0057 building: 0.9056 fence: 0.6366 vegetation: 0.8951 trunck: 0.6568 terrian: 0.7778 pole: 0.6361 traffic-sign: 0.4877 miou: 0.6391 acc: 0.9225 acc_cls: 0.7132 data_time: 0.0021 time: 0.4650 2023/05/11 05:09:44 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 8.0000e-03 eta: 12:59:32 time: 1.4388 data_time: 0.0043 memory: 3226 grad_norm: 0.1274 loss: 0.2579 loss_sem_seg: 0.2579 2023/05/11 05:11:03 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 8.0000e-03 eta: 12:58:15 time: 1.5820 data_time: 0.0037 memory: 3372 grad_norm: 0.1285 loss: 0.2389 loss_sem_seg: 0.2389 2023/05/11 05:12:24 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 8.0000e-03 eta: 12:57:01 time: 1.6024 data_time: 0.0034 memory: 3269 grad_norm: 0.1429 loss: 0.2332 loss_sem_seg: 0.2332 2023/05/11 05:13:42 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 8.0000e-03 eta: 12:55:43 time: 1.5667 data_time: 0.0036 memory: 3277 grad_norm: 0.1138 loss: 0.2245 loss_sem_seg: 0.2245 2023/05/11 05:14:58 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 8.0000e-03 eta: 12:54:18 time: 1.5154 data_time: 0.0034 memory: 3349 grad_norm: 0.1279 loss: 0.2257 loss_sem_seg: 0.2257 2023/05/11 05:16:13 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 8.0000e-03 eta: 12:52:53 time: 1.5019 data_time: 0.0034 memory: 3345 grad_norm: 0.1301 loss: 0.2399 loss_sem_seg: 0.2399 2023/05/11 05:17:26 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 8.0000e-03 eta: 12:51:24 time: 1.4694 data_time: 0.0033 memory: 3279 grad_norm: 0.1342 loss: 0.2338 loss_sem_seg: 0.2338 2023/05/11 05:18:39 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 8.0000e-03 eta: 12:49:53 time: 1.4528 data_time: 0.0033 memory: 3287 grad_norm: 0.1278 loss: 0.2350 loss_sem_seg: 0.2350 2023/05/11 05:19:52 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 8.0000e-03 eta: 12:48:25 time: 1.4704 data_time: 0.0034 memory: 3350 grad_norm: 0.1408 loss: 0.2488 loss_sem_seg: 0.2488 2023/05/11 05:21:06 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 8.0000e-03 eta: 12:46:57 time: 1.4746 data_time: 0.0033 memory: 3486 grad_norm: 0.1214 loss: 0.2507 loss_sem_seg: 0.2507 2023/05/11 05:22:20 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 8.0000e-03 eta: 12:45:29 time: 1.4774 data_time: 0.0034 memory: 3448 grad_norm: 0.1205 loss: 0.2072 loss_sem_seg: 0.2072 2023/05/11 05:23:33 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 8.0000e-03 eta: 12:44:00 time: 1.4639 data_time: 0.0034 memory: 3329 grad_norm: 0.1307 loss: 0.2227 loss_sem_seg: 0.2227 2023/05/11 05:24:48 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 8.0000e-03 eta: 12:42:34 time: 1.4932 data_time: 0.0034 memory: 3610 grad_norm: 0.1356 loss: 0.2420 loss_sem_seg: 0.2420 2023/05/11 05:25:55 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 8.0000e-03 eta: 12:40:53 time: 1.3469 data_time: 0.0033 memory: 3310 grad_norm: 0.1398 loss: 0.2440 loss_sem_seg: 0.2440 2023/05/11 05:27:00 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 8.0000e-03 eta: 12:39:07 time: 1.2994 data_time: 0.0034 memory: 3399 grad_norm: 0.1112 loss: 0.2349 loss_sem_seg: 0.2349 2023/05/11 05:28:06 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 8.0000e-03 eta: 12:37:24 time: 1.3182 data_time: 0.0036 memory: 3270 grad_norm: 0.1215 loss: 0.2502 loss_sem_seg: 0.2502 2023/05/11 05:29:04 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 05:29:11 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 8.0000e-03 eta: 12:35:39 time: 1.3044 data_time: 0.0033 memory: 3195 grad_norm: 0.1252 loss: 0.2361 loss_sem_seg: 0.2361 2023/05/11 05:30:15 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 8.0000e-03 eta: 12:33:50 time: 1.2660 data_time: 0.0035 memory: 3615 grad_norm: 0.1201 loss: 0.2268 loss_sem_seg: 0.2268 2023/05/11 05:31:29 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 8.0000e-03 eta: 12:32:26 time: 1.4945 data_time: 0.0033 memory: 3159 grad_norm: 0.1232 loss: 0.2368 loss_sem_seg: 0.2368 2023/05/11 05:32:42 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 8.0000e-03 eta: 12:30:58 time: 1.4596 data_time: 0.0033 memory: 3411 grad_norm: 0.1418 loss: 0.2240 loss_sem_seg: 0.2240 2023/05/11 05:33:55 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 8.0000e-03 eta: 12:29:30 time: 1.4593 data_time: 0.0033 memory: 3288 grad_norm: 0.1388 loss: 0.2513 loss_sem_seg: 0.2513 2023/05/11 05:35:08 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 8.0000e-03 eta: 12:28:01 time: 1.4510 data_time: 0.0033 memory: 3246 grad_norm: 0.1228 loss: 0.2491 loss_sem_seg: 0.2491 2023/05/11 05:36:21 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 8.0000e-03 eta: 12:26:34 time: 1.4710 data_time: 0.0035 memory: 3295 grad_norm: 0.1191 loss: 0.2290 loss_sem_seg: 0.2290 2023/05/11 05:37:29 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 05:37:29 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/05/11 05:38:10 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:05:13 time: 0.6836 data_time: 0.0023 memory: 3181 2023/05/11 05:38:41 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:04:27 time: 0.6255 data_time: 0.0021 memory: 1105 2023/05/11 05:39:09 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:03:43 time: 0.5574 data_time: 0.0020 memory: 1110 2023/05/11 05:39:35 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:03:04 time: 0.5209 data_time: 0.0021 memory: 1100 2023/05/11 05:39:58 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:02:27 time: 0.4510 data_time: 0.0021 memory: 1111 2023/05/11 05:40:20 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:01:54 time: 0.4392 data_time: 0.0020 memory: 1075 2023/05/11 05:40:42 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:01:24 time: 0.4420 data_time: 0.0020 memory: 1091 2023/05/11 05:41:03 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:00:56 time: 0.4339 data_time: 0.0019 memory: 1090 2023/05/11 05:41:26 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:30 time: 0.4475 data_time: 0.0020 memory: 1113 2023/05/11 05:41:48 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:04 time: 0.4385 data_time: 0.0020 memory: 1098 2023/05/11 05:42:20 - 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 | 0.9605 | 0.4403 | 0.7202 | 0.6407 | 0.5249 | 0.6906 | 0.8025 | 0.0900 | 0.9301 | 0.3332 | 0.8097 | 0.0179 | 0.8981 | 0.6063 | 0.8916 | 0.6802 | 0.7750 | 0.6406 | 0.4766 | 0.6278 | 0.9210 | 0.6957 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 05:42:20 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9605 bicycle: 0.4403 motorcycle: 0.7202 truck: 0.6407 bus: 0.5249 person: 0.6906 bicyclist: 0.8025 motorcyclist: 0.0900 road: 0.9301 parking: 0.3332 sidewalk: 0.8097 other-ground: 0.0179 building: 0.8981 fence: 0.6063 vegetation: 0.8916 trunck: 0.6802 terrian: 0.7750 pole: 0.6406 traffic-sign: 0.4766 miou: 0.6278 acc: 0.9210 acc_cls: 0.6957 data_time: 0.0020 time: 0.4366 2023/05/11 05:43:25 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 8.0000e-03 eta: 12:23:31 time: 1.3000 data_time: 0.0043 memory: 3321 grad_norm: 0.1397 loss: 0.2399 loss_sem_seg: 0.2399 2023/05/11 05:44:37 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 8.0000e-03 eta: 12:22:01 time: 1.4295 data_time: 0.0034 memory: 3178 grad_norm: 0.1122 loss: 0.2088 loss_sem_seg: 0.2088 2023/05/11 05:45:50 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 8.0000e-03 eta: 12:20:33 time: 1.4577 data_time: 0.0034 memory: 3557 grad_norm: 0.1132 loss: 0.2290 loss_sem_seg: 0.2290 2023/05/11 05:47:02 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 8.0000e-03 eta: 12:19:05 time: 1.4515 data_time: 0.0034 memory: 3411 grad_norm: 0.1313 loss: 0.2273 loss_sem_seg: 0.2273 2023/05/11 05:48:16 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 8.0000e-03 eta: 12:17:39 time: 1.4749 data_time: 0.0034 memory: 3242 grad_norm: 0.1181 loss: 0.2241 loss_sem_seg: 0.2241 2023/05/11 05:49:29 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 8.0000e-03 eta: 12:16:12 time: 1.4586 data_time: 0.0035 memory: 3296 grad_norm: 0.1354 loss: 0.2203 loss_sem_seg: 0.2203 2023/05/11 05:50:42 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 8.0000e-03 eta: 12:14:46 time: 1.4642 data_time: 0.0034 memory: 3591 grad_norm: 0.1186 loss: 0.2331 loss_sem_seg: 0.2331 2023/05/11 05:51:57 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 8.0000e-03 eta: 12:13:22 time: 1.4908 data_time: 0.0034 memory: 3436 grad_norm: 0.1187 loss: 0.2318 loss_sem_seg: 0.2318 2023/05/11 05:53:12 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 8.0000e-03 eta: 12:11:59 time: 1.5024 data_time: 0.0033 memory: 3380 grad_norm: 0.1179 loss: 0.2447 loss_sem_seg: 0.2447 2023/05/11 05:54:26 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 8.0000e-03 eta: 12:10:35 time: 1.4906 data_time: 0.0034 memory: 3506 grad_norm: 0.1442 loss: 0.2424 loss_sem_seg: 0.2424 2023/05/11 05:55:41 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 8.0000e-03 eta: 12:09:12 time: 1.4963 data_time: 0.0034 memory: 3439 grad_norm: 0.1271 loss: 0.2452 loss_sem_seg: 0.2452 2023/05/11 05:56:56 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 8.0000e-03 eta: 12:07:49 time: 1.4988 data_time: 0.0033 memory: 3373 grad_norm: 0.1143 loss: 0.2228 loss_sem_seg: 0.2228 2023/05/11 05:58:07 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 05:58:09 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 8.0000e-03 eta: 12:06:23 time: 1.4653 data_time: 0.0034 memory: 3528 grad_norm: 0.1136 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/11 05:59:16 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 8.0000e-03 eta: 12:04:44 time: 1.3235 data_time: 0.0033 memory: 3347 grad_norm: 0.1336 loss: 0.2489 loss_sem_seg: 0.2489 2023/05/11 06:00:19 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 8.0000e-03 eta: 12:03:00 time: 1.2642 data_time: 0.0033 memory: 3297 grad_norm: 0.1332 loss: 0.2227 loss_sem_seg: 0.2227 2023/05/11 06:01:22 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 8.0000e-03 eta: 12:01:16 time: 1.2665 data_time: 0.0034 memory: 3261 grad_norm: 0.1156 loss: 0.2154 loss_sem_seg: 0.2154 2023/05/11 06:02:25 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 8.0000e-03 eta: 11:59:31 time: 1.2531 data_time: 0.0034 memory: 3238 grad_norm: 0.1228 loss: 0.2238 loss_sem_seg: 0.2238 2023/05/11 06:03:29 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 8.0000e-03 eta: 11:57:50 time: 1.2890 data_time: 0.0037 memory: 3363 grad_norm: 0.1186 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/11 06:04:43 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 8.0000e-03 eta: 11:56:25 time: 1.4680 data_time: 0.0034 memory: 3545 grad_norm: 0.1227 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/11 06:05:58 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 8.0000e-03 eta: 11:55:04 time: 1.5039 data_time: 0.0033 memory: 3261 grad_norm: 0.1215 loss: 0.2278 loss_sem_seg: 0.2278 2023/05/11 06:07:13 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 8.0000e-03 eta: 11:53:41 time: 1.4941 data_time: 0.0034 memory: 3315 grad_norm: 0.1262 loss: 0.2377 loss_sem_seg: 0.2377 2023/05/11 06:08:27 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 8.0000e-03 eta: 11:52:19 time: 1.4917 data_time: 0.0033 memory: 3308 grad_norm: 0.1298 loss: 0.2453 loss_sem_seg: 0.2453 2023/05/11 06:09:42 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 8.0000e-03 eta: 11:50:57 time: 1.4958 data_time: 0.0034 memory: 3521 grad_norm: 0.1343 loss: 0.2295 loss_sem_seg: 0.2295 2023/05/11 06:10:50 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 06:10:50 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/05/11 06:11:30 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:05:12 time: 0.6805 data_time: 0.0021 memory: 3367 2023/05/11 06:12:01 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:04:23 time: 0.6103 data_time: 0.0021 memory: 1105 2023/05/11 06:12:29 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:03:41 time: 0.5562 data_time: 0.0020 memory: 1110 2023/05/11 06:12:52 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:02:59 time: 0.4772 data_time: 0.0021 memory: 1100 2023/05/11 06:13:15 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:02:24 time: 0.4599 data_time: 0.0021 memory: 1111 2023/05/11 06:13:38 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:01:52 time: 0.4420 data_time: 0.0020 memory: 1075 2023/05/11 06:14:00 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:01:23 time: 0.4447 data_time: 0.0021 memory: 1091 2023/05/11 06:14:22 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:00:56 time: 0.4393 data_time: 0.0020 memory: 1090 2023/05/11 06:14:44 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:29 time: 0.4524 data_time: 0.0020 memory: 1113 2023/05/11 06:15:06 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:04 time: 0.4393 data_time: 0.0020 memory: 1098 2023/05/11 06:15:39 - 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 | 0.9629 | 0.5196 | 0.7405 | 0.7515 | 0.6296 | 0.6973 | 0.8399 | 0.0798 | 0.9321 | 0.4641 | 0.8130 | 0.0305 | 0.8892 | 0.5737 | 0.8951 | 0.6619 | 0.7774 | 0.6574 | 0.5105 | 0.6540 | 0.9228 | 0.7201 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 06:15:39 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9629 bicycle: 0.5196 motorcycle: 0.7405 truck: 0.7515 bus: 0.6296 person: 0.6973 bicyclist: 0.8399 motorcyclist: 0.0798 road: 0.9321 parking: 0.4641 sidewalk: 0.8130 other-ground: 0.0305 building: 0.8892 fence: 0.5737 vegetation: 0.8951 trunck: 0.6619 terrian: 0.7774 pole: 0.6574 traffic-sign: 0.5105 miou: 0.6540 acc: 0.9228 acc_cls: 0.7201 data_time: 0.0020 time: 0.4467 2023/05/11 06:16:43 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 8.0000e-03 eta: 11:48:00 time: 1.2938 data_time: 0.0044 memory: 3276 grad_norm: 0.1231 loss: 0.2200 loss_sem_seg: 0.2200 2023/05/11 06:17:57 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 8.0000e-03 eta: 11:46:35 time: 1.4659 data_time: 0.0034 memory: 3512 grad_norm: 0.1203 loss: 0.2293 loss_sem_seg: 0.2293 2023/05/11 06:19:10 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 8.0000e-03 eta: 11:45:11 time: 1.4720 data_time: 0.0034 memory: 3194 grad_norm: 0.1204 loss: 0.2300 loss_sem_seg: 0.2300 2023/05/11 06:20:24 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.0000e-03 eta: 11:43:48 time: 1.4731 data_time: 0.0033 memory: 3644 grad_norm: 0.1108 loss: 0.2301 loss_sem_seg: 0.2301 2023/05/11 06:21:38 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.0000e-03 eta: 11:42:24 time: 1.4775 data_time: 0.0034 memory: 3334 grad_norm: 0.1181 loss: 0.2224 loss_sem_seg: 0.2224 2023/05/11 06:22:51 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 8.0000e-03 eta: 11:41:01 time: 1.4747 data_time: 0.0034 memory: 3379 grad_norm: 0.1222 loss: 0.2314 loss_sem_seg: 0.2314 2023/05/11 06:24:05 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 8.0000e-03 eta: 11:39:36 time: 1.4623 data_time: 0.0035 memory: 3486 grad_norm: 0.1144 loss: 0.2380 loss_sem_seg: 0.2380 2023/05/11 06:25:18 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 8.0000e-03 eta: 11:38:12 time: 1.4658 data_time: 0.0034 memory: 3302 grad_norm: 0.1157 loss: 0.2172 loss_sem_seg: 0.2172 2023/05/11 06:26:31 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 8.0000e-03 eta: 11:36:49 time: 1.4731 data_time: 0.0034 memory: 3259 grad_norm: 0.1068 loss: 0.2278 loss_sem_seg: 0.2278 2023/05/11 06:26:34 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 06:27:44 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 8.0000e-03 eta: 11:35:24 time: 1.4560 data_time: 0.0034 memory: 3924 grad_norm: 0.1102 loss: 0.2325 loss_sem_seg: 0.2325 2023/05/11 06:28:58 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 8.0000e-03 eta: 11:34:00 time: 1.4662 data_time: 0.0034 memory: 3268 grad_norm: 0.1157 loss: 0.2181 loss_sem_seg: 0.2181 2023/05/11 06:30:11 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 8.0000e-03 eta: 11:32:37 time: 1.4741 data_time: 0.0034 memory: 3435 grad_norm: inf loss: 0.2189 loss_sem_seg: 0.2189 2023/05/11 06:31:25 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 8.0000e-03 eta: 11:31:14 time: 1.4723 data_time: 0.0035 memory: 3288 grad_norm: 0.1273 loss: 0.2247 loss_sem_seg: 0.2247 2023/05/11 06:32:30 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 8.0000e-03 eta: 11:29:38 time: 1.3102 data_time: 0.0035 memory: 3383 grad_norm: 0.1123 loss: 0.2210 loss_sem_seg: 0.2210 2023/05/11 06:33:34 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 8.0000e-03 eta: 11:27:58 time: 1.2661 data_time: 0.0034 memory: 3468 grad_norm: 0.1130 loss: 0.2046 loss_sem_seg: 0.2046 2023/05/11 06:34:37 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 8.0000e-03 eta: 11:26:18 time: 1.2699 data_time: 0.0033 memory: 3192 grad_norm: 0.1062 loss: 0.2212 loss_sem_seg: 0.2212 2023/05/11 06:35:40 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 8.0000e-03 eta: 11:24:38 time: 1.2514 data_time: 0.0033 memory: 3178 grad_norm: 0.1126 loss: 0.2236 loss_sem_seg: 0.2236 2023/05/11 06:36:43 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 8.0000e-03 eta: 11:22:58 time: 1.2603 data_time: 0.0034 memory: 3590 grad_norm: 0.1104 loss: 0.2280 loss_sem_seg: 0.2280 2023/05/11 06:37:53 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 8.0000e-03 eta: 11:21:30 time: 1.4075 data_time: 0.0035 memory: 3185 grad_norm: 0.1104 loss: 0.2295 loss_sem_seg: 0.2295 2023/05/11 06:39:06 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 8.0000e-03 eta: 11:20:07 time: 1.4633 data_time: 0.0033 memory: 3486 grad_norm: 0.1055 loss: 0.2253 loss_sem_seg: 0.2253 2023/05/11 06:40:19 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 8.0000e-03 eta: 11:18:44 time: 1.4587 data_time: 0.0033 memory: 3424 grad_norm: 0.1306 loss: 0.2296 loss_sem_seg: 0.2296 2023/05/11 06:41:33 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 8.0000e-03 eta: 11:17:21 time: 1.4655 data_time: 0.0034 memory: 3261 grad_norm: 0.1162 loss: 0.2172 loss_sem_seg: 0.2172 2023/05/11 06:42:46 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 8.0000e-03 eta: 11:15:58 time: 1.4648 data_time: 0.0034 memory: 3481 grad_norm: 0.1078 loss: 0.2240 loss_sem_seg: 0.2240 2023/05/11 06:43:55 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 06:43:55 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/05/11 06:44:36 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:05:18 time: 0.6937 data_time: 0.0022 memory: 3305 2023/05/11 06:45:06 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:04:24 time: 0.6020 data_time: 0.0021 memory: 1105 2023/05/11 06:45:34 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:03:42 time: 0.5645 data_time: 0.0020 memory: 1110 2023/05/11 06:45:58 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:02:59 time: 0.4659 data_time: 0.0020 memory: 1100 2023/05/11 06:46:21 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:02:24 time: 0.4683 data_time: 0.0021 memory: 1111 2023/05/11 06:46:44 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:01:53 time: 0.4507 data_time: 0.0021 memory: 1075 2023/05/11 06:47:07 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:01:24 time: 0.4605 data_time: 0.0021 memory: 1091 2023/05/11 06:47:30 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:00:56 time: 0.4562 data_time: 0.0020 memory: 1090 2023/05/11 06:47:52 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:30 time: 0.4563 data_time: 0.0021 memory: 1113 2023/05/11 06:48:14 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:04 time: 0.4355 data_time: 0.0021 memory: 1098 2023/05/11 06:49:00 - 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 | 0.9506 | 0.5148 | 0.6864 | 0.7395 | 0.4417 | 0.7583 | 0.8466 | 0.1396 | 0.9360 | 0.4133 | 0.8095 | 0.0364 | 0.8986 | 0.6074 | 0.8994 | 0.7037 | 0.7807 | 0.6493 | 0.4900 | 0.6475 | 0.9239 | 0.7305 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 06:49:00 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9506 bicycle: 0.5148 motorcycle: 0.6864 truck: 0.7395 bus: 0.4417 person: 0.7583 bicyclist: 0.8466 motorcyclist: 0.1396 road: 0.9360 parking: 0.4133 sidewalk: 0.8095 other-ground: 0.0364 building: 0.8986 fence: 0.6074 vegetation: 0.8994 trunck: 0.7037 terrian: 0.7807 pole: 0.6493 traffic-sign: 0.4900 miou: 0.6475 acc: 0.9239 acc_cls: 0.7305 data_time: 0.0020 time: 0.4435 2023/05/11 06:50:10 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 8.0000e-03 eta: 11:13:17 time: 1.4028 data_time: 0.0043 memory: 3220 grad_norm: 0.1036 loss: 0.2263 loss_sem_seg: 0.2263 2023/05/11 06:51:28 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 8.0000e-03 eta: 11:12:01 time: 1.5461 data_time: 0.0035 memory: 3334 grad_norm: 0.1091 loss: 0.2261 loss_sem_seg: 0.2261 2023/05/11 06:52:41 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 8.0000e-03 eta: 11:10:38 time: 1.4661 data_time: 0.0033 memory: 3498 grad_norm: 0.1241 loss: 0.2430 loss_sem_seg: 0.2430 2023/05/11 06:53:54 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 8.0000e-03 eta: 11:09:16 time: 1.4715 data_time: 0.0034 memory: 3683 grad_norm: 0.1055 loss: 0.2244 loss_sem_seg: 0.2244 2023/05/11 06:55:07 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 8.0000e-03 eta: 11:07:54 time: 1.4612 data_time: 0.0033 memory: 3234 grad_norm: 0.1099 loss: 0.2157 loss_sem_seg: 0.2157 2023/05/11 06:55:16 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 06:56:20 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 8.0000e-03 eta: 11:06:30 time: 1.4552 data_time: 0.0034 memory: 3365 grad_norm: 0.0942 loss: 0.2098 loss_sem_seg: 0.2098 2023/05/11 06:57:33 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 8.0000e-03 eta: 11:05:08 time: 1.4631 data_time: 0.0034 memory: 3247 grad_norm: 0.1010 loss: 0.2266 loss_sem_seg: 0.2266 2023/05/11 06:58:46 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 8.0000e-03 eta: 11:03:45 time: 1.4560 data_time: 0.0033 memory: 3360 grad_norm: 0.1201 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/11 06:59:59 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 8.0000e-03 eta: 11:02:22 time: 1.4596 data_time: 0.0033 memory: 3620 grad_norm: 0.1261 loss: 0.2110 loss_sem_seg: 0.2110 2023/05/11 07:01:12 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 8.0000e-03 eta: 11:01:00 time: 1.4656 data_time: 0.0034 memory: 3315 grad_norm: 0.1094 loss: 0.2305 loss_sem_seg: 0.2305 2023/05/11 07:02:26 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 8.0000e-03 eta: 10:59:38 time: 1.4652 data_time: 0.0034 memory: 3627 grad_norm: 0.1129 loss: 0.2288 loss_sem_seg: 0.2288 2023/05/11 07:03:38 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 8.0000e-03 eta: 10:58:15 time: 1.4544 data_time: 0.0035 memory: 3074 grad_norm: 0.1008 loss: 0.2250 loss_sem_seg: 0.2250 2023/05/11 07:04:52 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 8.0000e-03 eta: 10:56:53 time: 1.4653 data_time: 0.0034 memory: 3649 grad_norm: 0.1120 loss: 0.2325 loss_sem_seg: 0.2325 2023/05/11 07:05:57 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 8.0000e-03 eta: 10:55:19 time: 1.2959 data_time: 0.0033 memory: 3642 grad_norm: 0.1078 loss: 0.2157 loss_sem_seg: 0.2157 2023/05/11 07:06:59 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 8.0000e-03 eta: 10:53:41 time: 1.2499 data_time: 0.0033 memory: 3404 grad_norm: 0.1110 loss: 0.2269 loss_sem_seg: 0.2269 2023/05/11 07:08:03 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 8.0000e-03 eta: 10:52:06 time: 1.2756 data_time: 0.0033 memory: 3284 grad_norm: 0.1300 loss: 0.2278 loss_sem_seg: 0.2278 2023/05/11 07:09:05 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 8.0000e-03 eta: 10:50:28 time: 1.2519 data_time: 0.0033 memory: 3182 grad_norm: 0.1177 loss: 0.2317 loss_sem_seg: 0.2317 2023/05/11 07:10:08 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 8.0000e-03 eta: 10:48:52 time: 1.2616 data_time: 0.0034 memory: 3423 grad_norm: 0.1076 loss: 0.2310 loss_sem_seg: 0.2310 2023/05/11 07:11:20 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 8.0000e-03 eta: 10:47:29 time: 1.4327 data_time: 0.0035 memory: 3322 grad_norm: 0.1125 loss: 0.2226 loss_sem_seg: 0.2226 2023/05/11 07:12:33 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.0000e-03 eta: 10:46:07 time: 1.4634 data_time: 0.0034 memory: 3402 grad_norm: 0.1111 loss: 0.2235 loss_sem_seg: 0.2235 2023/05/11 07:13:47 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 8.0000e-03 eta: 10:44:46 time: 1.4724 data_time: 0.0033 memory: 3201 grad_norm: 0.1134 loss: 0.2411 loss_sem_seg: 0.2411 2023/05/11 07:15:01 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 8.0000e-03 eta: 10:43:26 time: 1.4741 data_time: 0.0033 memory: 3614 grad_norm: 0.1062 loss: 0.2227 loss_sem_seg: 0.2227 2023/05/11 07:16:14 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 8.0000e-03 eta: 10:42:04 time: 1.4646 data_time: 0.0033 memory: 3279 grad_norm: 0.1003 loss: 0.2151 loss_sem_seg: 0.2151 2023/05/11 07:17:21 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 07:17:21 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/05/11 07:18:01 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:05:13 time: 0.6830 data_time: 0.0022 memory: 3571 2023/05/11 07:18:29 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:04:12 time: 0.5532 data_time: 0.0021 memory: 1105 2023/05/11 07:18:55 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:03:29 time: 0.5118 data_time: 0.0021 memory: 1110 2023/05/11 07:19:17 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:02:49 time: 0.4519 data_time: 0.0020 memory: 1100 2023/05/11 07:19:40 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:02:17 time: 0.4517 data_time: 0.0021 memory: 1111 2023/05/11 07:20:02 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:01:47 time: 0.4380 data_time: 0.0021 memory: 1075 2023/05/11 07:20:24 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:01:20 time: 0.4370 data_time: 0.0021 memory: 1091 2023/05/11 07:20:47 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:00:54 time: 0.4616 data_time: 0.0021 memory: 1090 2023/05/11 07:21:09 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:29 time: 0.4488 data_time: 0.0021 memory: 1113 2023/05/11 07:21:32 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:04 time: 0.4613 data_time: 0.0021 memory: 1098 2023/05/11 07:22:12 - 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 | 0.9628 | 0.4650 | 0.6984 | 0.8494 | 0.6357 | 0.7381 | 0.8635 | 0.0463 | 0.9396 | 0.4078 | 0.8099 | 0.0112 | 0.9035 | 0.6316 | 0.8955 | 0.6856 | 0.7750 | 0.6411 | 0.4999 | 0.6558 | 0.9247 | 0.7351 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 07:22:12 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9628 bicycle: 0.4650 motorcycle: 0.6984 truck: 0.8494 bus: 0.6357 person: 0.7381 bicyclist: 0.8635 motorcyclist: 0.0463 road: 0.9396 parking: 0.4078 sidewalk: 0.8099 other-ground: 0.0112 building: 0.9035 fence: 0.6316 vegetation: 0.8955 trunck: 0.6856 terrian: 0.7750 pole: 0.6411 traffic-sign: 0.4999 miou: 0.6558 acc: 0.9247 acc_cls: 0.7351 data_time: 0.0021 time: 0.4685 2023/05/11 07:23:26 - mmengine - INFO - Epoch(train) [16][ 50/1196] lr: 8.0000e-03 eta: 10:39:29 time: 1.4831 data_time: 0.0044 memory: 3244 grad_norm: 0.1098 loss: 0.2268 loss_sem_seg: 0.2268 2023/05/11 07:23:43 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 07:24:47 - mmengine - INFO - Epoch(train) [16][ 100/1196] lr: 8.0000e-03 eta: 10:38:20 time: 1.6338 data_time: 0.0035 memory: 3599 grad_norm: 0.1166 loss: 0.2251 loss_sem_seg: 0.2251 2023/05/11 07:26:04 - mmengine - INFO - Epoch(train) [16][ 150/1196] lr: 8.0000e-03 eta: 10:37:03 time: 1.5308 data_time: 0.0034 memory: 3541 grad_norm: 0.1174 loss: 0.2462 loss_sem_seg: 0.2462 2023/05/11 07:27:19 - mmengine - INFO - Epoch(train) [16][ 200/1196] lr: 8.0000e-03 eta: 10:35:44 time: 1.4930 data_time: 0.0034 memory: 3406 grad_norm: 0.1067 loss: 0.2199 loss_sem_seg: 0.2199 2023/05/11 07:28:32 - mmengine - INFO - Epoch(train) [16][ 250/1196] lr: 8.0000e-03 eta: 10:34:23 time: 1.4633 data_time: 0.0034 memory: 3210 grad_norm: 0.1102 loss: 0.2198 loss_sem_seg: 0.2198 2023/05/11 07:29:44 - mmengine - INFO - Epoch(train) [16][ 300/1196] lr: 8.0000e-03 eta: 10:33:01 time: 1.4522 data_time: 0.0033 memory: 3520 grad_norm: 0.1122 loss: 0.2222 loss_sem_seg: 0.2222 2023/05/11 07:30:57 - mmengine - INFO - Epoch(train) [16][ 350/1196] lr: 8.0000e-03 eta: 10:31:40 time: 1.4591 data_time: 0.0034 memory: 3340 grad_norm: 0.1136 loss: 0.2287 loss_sem_seg: 0.2287 2023/05/11 07:32:11 - mmengine - INFO - Epoch(train) [16][ 400/1196] lr: 8.0000e-03 eta: 10:30:19 time: 1.4726 data_time: 0.0034 memory: 3278 grad_norm: 0.1213 loss: 0.2338 loss_sem_seg: 0.2338 2023/05/11 07:33:24 - mmengine - INFO - Epoch(train) [16][ 450/1196] lr: 8.0000e-03 eta: 10:28:58 time: 1.4556 data_time: 0.0033 memory: 3611 grad_norm: 0.1197 loss: 0.2353 loss_sem_seg: 0.2353 2023/05/11 07:34:37 - mmengine - INFO - Epoch(train) [16][ 500/1196] lr: 8.0000e-03 eta: 10:27:37 time: 1.4650 data_time: 0.0033 memory: 3376 grad_norm: 0.1108 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/11 07:35:50 - mmengine - INFO - Epoch(train) [16][ 550/1196] lr: 8.0000e-03 eta: 10:26:16 time: 1.4646 data_time: 0.0034 memory: 3360 grad_norm: 0.0987 loss: 0.2208 loss_sem_seg: 0.2208 2023/05/11 07:37:03 - mmengine - INFO - Epoch(train) [16][ 600/1196] lr: 8.0000e-03 eta: 10:24:55 time: 1.4567 data_time: 0.0034 memory: 3392 grad_norm: 0.1044 loss: 0.2322 loss_sem_seg: 0.2322 2023/05/11 07:38:16 - mmengine - INFO - Epoch(train) [16][ 650/1196] lr: 8.0000e-03 eta: 10:23:34 time: 1.4551 data_time: 0.0034 memory: 3338 grad_norm: 0.1220 loss: 0.2365 loss_sem_seg: 0.2365 2023/05/11 07:39:21 - mmengine - INFO - Epoch(train) [16][ 700/1196] lr: 8.0000e-03 eta: 10:22:02 time: 1.3002 data_time: 0.0033 memory: 3354 grad_norm: 0.1072 loss: 0.2143 loss_sem_seg: 0.2143 2023/05/11 07:40:24 - mmengine - INFO - Epoch(train) [16][ 750/1196] lr: 8.0000e-03 eta: 10:20:28 time: 1.2645 data_time: 0.0033 memory: 3445 grad_norm: 0.1107 loss: 0.2232 loss_sem_seg: 0.2232 2023/05/11 07:41:27 - mmengine - INFO - Epoch(train) [16][ 800/1196] lr: 8.0000e-03 eta: 10:18:55 time: 1.2651 data_time: 0.0033 memory: 3330 grad_norm: 0.1090 loss: 0.2276 loss_sem_seg: 0.2276 2023/05/11 07:42:30 - mmengine - INFO - Epoch(train) [16][ 850/1196] lr: 8.0000e-03 eta: 10:17:21 time: 1.2497 data_time: 0.0033 memory: 3268 grad_norm: inf loss: 0.2311 loss_sem_seg: 0.2311 2023/05/11 07:43:33 - mmengine - INFO - Epoch(train) [16][ 900/1196] lr: 8.0000e-03 eta: 10:15:48 time: 1.2662 data_time: 0.0034 memory: 3442 grad_norm: 0.1199 loss: 0.2376 loss_sem_seg: 0.2376 2023/05/11 07:44:46 - mmengine - INFO - Epoch(train) [16][ 950/1196] lr: 8.0000e-03 eta: 10:14:26 time: 1.4462 data_time: 0.0034 memory: 3512 grad_norm: 0.1259 loss: 0.2403 loss_sem_seg: 0.2403 2023/05/11 07:45:59 - mmengine - INFO - Epoch(train) [16][1000/1196] lr: 8.0000e-03 eta: 10:13:07 time: 1.4731 data_time: 0.0034 memory: 3222 grad_norm: 0.1125 loss: 0.2260 loss_sem_seg: 0.2260 2023/05/11 07:47:13 - mmengine - INFO - Epoch(train) [16][1050/1196] lr: 8.0000e-03 eta: 10:11:47 time: 1.4745 data_time: 0.0033 memory: 3489 grad_norm: 0.1166 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/11 07:47:28 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 07:48:26 - mmengine - INFO - Epoch(train) [16][1100/1196] lr: 8.0000e-03 eta: 10:10:27 time: 1.4609 data_time: 0.0035 memory: 3468 grad_norm: 0.1035 loss: 0.2263 loss_sem_seg: 0.2263 2023/05/11 07:49:39 - mmengine - INFO - Epoch(train) [16][1150/1196] lr: 8.0000e-03 eta: 10:09:07 time: 1.4693 data_time: 0.0034 memory: 3533 grad_norm: 0.1087 loss: 0.2321 loss_sem_seg: 0.2321 2023/05/11 07:50:47 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 07:50:47 - mmengine - INFO - Saving checkpoint at 16 epochs 2023/05/11 07:51:26 - mmengine - INFO - Epoch(val) [16][ 50/509] eta: 0:05:05 time: 0.6649 data_time: 0.0021 memory: 3381 2023/05/11 07:51:54 - mmengine - INFO - Epoch(val) [16][100/509] eta: 0:04:10 time: 0.5585 data_time: 0.0020 memory: 1105 2023/05/11 07:52:18 - mmengine - INFO - Epoch(val) [16][150/509] eta: 0:03:22 time: 0.4683 data_time: 0.0021 memory: 1110 2023/05/11 07:52:40 - mmengine - INFO - Epoch(val) [16][200/509] eta: 0:02:45 time: 0.4545 data_time: 0.0020 memory: 1100 2023/05/11 07:53:03 - mmengine - INFO - Epoch(val) [16][250/509] eta: 0:02:14 time: 0.4522 data_time: 0.0020 memory: 1111 2023/05/11 07:53:25 - mmengine - INFO - Epoch(val) [16][300/509] eta: 0:01:46 time: 0.4453 data_time: 0.0020 memory: 1075 2023/05/11 07:53:48 - mmengine - INFO - Epoch(val) [16][350/509] eta: 0:01:19 time: 0.4442 data_time: 0.0021 memory: 1091 2023/05/11 07:54:10 - mmengine - INFO - Epoch(val) [16][400/509] eta: 0:00:53 time: 0.4390 data_time: 0.0020 memory: 1090 2023/05/11 07:54:32 - mmengine - INFO - Epoch(val) [16][450/509] eta: 0:00:28 time: 0.4430 data_time: 0.0020 memory: 1113 2023/05/11 07:54:54 - mmengine - INFO - Epoch(val) [16][500/509] eta: 0:00:04 time: 0.4462 data_time: 0.0020 memory: 1098 2023/05/11 07:55:25 - 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 | 0.9587 | 0.4965 | 0.7939 | 0.8113 | 0.5695 | 0.7323 | 0.8883 | 0.1444 | 0.9415 | 0.5154 | 0.8207 | 0.0030 | 0.9025 | 0.6287 | 0.8813 | 0.6874 | 0.7360 | 0.6587 | 0.5006 | 0.6669 | 0.9211 | 0.7324 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 07:55:25 - mmengine - INFO - Epoch(val) [16][509/509] car: 0.9587 bicycle: 0.4965 motorcycle: 0.7939 truck: 0.8113 bus: 0.5695 person: 0.7323 bicyclist: 0.8883 motorcyclist: 0.1444 road: 0.9415 parking: 0.5154 sidewalk: 0.8207 other-ground: 0.0030 building: 0.9025 fence: 0.6287 vegetation: 0.8813 trunck: 0.6874 terrian: 0.7360 pole: 0.6587 traffic-sign: 0.5006 miou: 0.6669 acc: 0.9211 acc_cls: 0.7324 data_time: 0.0021 time: 0.4528 2023/05/11 07:56:37 - mmengine - INFO - Epoch(train) [17][ 50/1196] lr: 8.0000e-03 eta: 10:06:31 time: 1.4337 data_time: 0.0043 memory: 3871 grad_norm: 0.1050 loss: 0.2276 loss_sem_seg: 0.2276 2023/05/11 07:57:53 - mmengine - INFO - Epoch(train) [17][ 100/1196] lr: 8.0000e-03 eta: 10:05:14 time: 1.5114 data_time: 0.0036 memory: 3428 grad_norm: inf loss: 0.2025 loss_sem_seg: 0.2025 2023/05/11 07:59:06 - mmengine - INFO - Epoch(train) [17][ 150/1196] lr: 8.0000e-03 eta: 10:03:54 time: 1.4631 data_time: 0.0035 memory: 3108 grad_norm: 0.1096 loss: 0.2211 loss_sem_seg: 0.2211 2023/05/11 08:00:19 - mmengine - INFO - Epoch(train) [17][ 200/1196] lr: 8.0000e-03 eta: 10:02:34 time: 1.4623 data_time: 0.0033 memory: 3281 grad_norm: 0.1033 loss: 0.2147 loss_sem_seg: 0.2147 2023/05/11 08:01:33 - mmengine - INFO - Epoch(train) [17][ 250/1196] lr: 8.0000e-03 eta: 10:01:15 time: 1.4780 data_time: 0.0034 memory: 3585 grad_norm: 0.1043 loss: 0.2126 loss_sem_seg: 0.2126 2023/05/11 08:02:48 - mmengine - INFO - Epoch(train) [17][ 300/1196] lr: 8.0000e-03 eta: 9:59:58 time: 1.5089 data_time: 0.0034 memory: 3395 grad_norm: 0.1081 loss: 0.2281 loss_sem_seg: 0.2281 2023/05/11 08:04:03 - mmengine - INFO - Epoch(train) [17][ 350/1196] lr: 8.0000e-03 eta: 9:58:40 time: 1.4946 data_time: 0.0033 memory: 3316 grad_norm: 0.1082 loss: 0.2147 loss_sem_seg: 0.2147 2023/05/11 08:05:18 - mmengine - INFO - Epoch(train) [17][ 400/1196] lr: 8.0000e-03 eta: 9:57:23 time: 1.5084 data_time: 0.0034 memory: 3291 grad_norm: 0.1140 loss: 0.2216 loss_sem_seg: 0.2216 2023/05/11 08:06:33 - mmengine - INFO - Epoch(train) [17][ 450/1196] lr: 8.0000e-03 eta: 9:56:05 time: 1.4983 data_time: 0.0035 memory: 3235 grad_norm: 0.1184 loss: 0.2312 loss_sem_seg: 0.2312 2023/05/11 08:07:47 - mmengine - INFO - Epoch(train) [17][ 500/1196] lr: 8.0000e-03 eta: 9:54:46 time: 1.4698 data_time: 0.0036 memory: 3503 grad_norm: 0.0979 loss: 0.2176 loss_sem_seg: 0.2176 2023/05/11 08:09:01 - mmengine - INFO - Epoch(train) [17][ 550/1196] lr: 8.0000e-03 eta: 9:53:27 time: 1.4851 data_time: 0.0033 memory: 3394 grad_norm: 0.1026 loss: 0.2094 loss_sem_seg: 0.2094 2023/05/11 08:10:15 - mmengine - INFO - Epoch(train) [17][ 600/1196] lr: 8.0000e-03 eta: 9:52:09 time: 1.4870 data_time: 0.0034 memory: 3277 grad_norm: 0.1076 loss: 0.2282 loss_sem_seg: 0.2282 2023/05/11 08:11:30 - mmengine - INFO - Epoch(train) [17][ 650/1196] lr: 8.0000e-03 eta: 9:50:51 time: 1.4903 data_time: 0.0033 memory: 3563 grad_norm: 0.1030 loss: 0.2208 loss_sem_seg: 0.2208 2023/05/11 08:12:37 - mmengine - INFO - Epoch(train) [17][ 700/1196] lr: 8.0000e-03 eta: 9:49:24 time: 1.3462 data_time: 0.0033 memory: 3305 grad_norm: 0.1056 loss: 0.2126 loss_sem_seg: 0.2126 2023/05/11 08:13:43 - mmengine - INFO - Epoch(train) [17][ 750/1196] lr: 8.0000e-03 eta: 9:47:55 time: 1.3052 data_time: 0.0035 memory: 3298 grad_norm: 0.1031 loss: 0.2109 loss_sem_seg: 0.2109 2023/05/11 08:14:45 - mmengine - INFO - Epoch(train) [17][ 800/1196] lr: 8.0000e-03 eta: 9:46:23 time: 1.2480 data_time: 0.0037 memory: 3499 grad_norm: 0.1044 loss: 0.2078 loss_sem_seg: 0.2078 2023/05/11 08:15:47 - mmengine - INFO - Epoch(train) [17][ 850/1196] lr: 8.0000e-03 eta: 9:44:51 time: 1.2381 data_time: 0.0033 memory: 3260 grad_norm: 0.1017 loss: 0.2198 loss_sem_seg: 0.2198 2023/05/11 08:16:04 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 08:16:49 - mmengine - INFO - Epoch(train) [17][ 900/1196] lr: 8.0000e-03 eta: 9:43:19 time: 1.2426 data_time: 0.0033 memory: 3405 grad_norm: 0.1071 loss: 0.2294 loss_sem_seg: 0.2294 2023/05/11 08:17:59 - mmengine - INFO - Epoch(train) [17][ 950/1196] lr: 8.0000e-03 eta: 9:41:56 time: 1.3994 data_time: 0.0033 memory: 3319 grad_norm: 0.0976 loss: 0.2172 loss_sem_seg: 0.2172 2023/05/11 08:19:12 - mmengine - INFO - Epoch(train) [17][1000/1196] lr: 8.0000e-03 eta: 9:40:36 time: 1.4555 data_time: 0.0034 memory: 3725 grad_norm: 0.1103 loss: 0.2179 loss_sem_seg: 0.2179 2023/05/11 08:20:24 - mmengine - INFO - Epoch(train) [17][1050/1196] lr: 8.0000e-03 eta: 9:39:16 time: 1.4524 data_time: 0.0033 memory: 3422 grad_norm: 0.1070 loss: 0.2084 loss_sem_seg: 0.2084 2023/05/11 08:21:37 - mmengine - INFO - Epoch(train) [17][1100/1196] lr: 8.0000e-03 eta: 9:37:56 time: 1.4428 data_time: 0.0033 memory: 3436 grad_norm: 0.1079 loss: 0.2164 loss_sem_seg: 0.2164 2023/05/11 08:22:49 - mmengine - INFO - Epoch(train) [17][1150/1196] lr: 8.0000e-03 eta: 9:36:36 time: 1.4451 data_time: 0.0034 memory: 3777 grad_norm: 0.0964 loss: 0.2272 loss_sem_seg: 0.2272 2023/05/11 08:23:56 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 08:23:56 - mmengine - INFO - Saving checkpoint at 17 epochs 2023/05/11 08:24:34 - mmengine - INFO - Epoch(val) [17][ 50/509] eta: 0:04:53 time: 0.6405 data_time: 0.0022 memory: 3294 2023/05/11 08:25:01 - mmengine - INFO - Epoch(val) [17][100/509] eta: 0:04:04 time: 0.5562 data_time: 0.0021 memory: 1105 2023/05/11 08:25:23 - mmengine - INFO - Epoch(val) [17][150/509] eta: 0:03:15 time: 0.4349 data_time: 0.0020 memory: 1110 2023/05/11 08:25:46 - mmengine - INFO - Epoch(val) [17][200/509] eta: 0:02:40 time: 0.4493 data_time: 0.0020 memory: 1100 2023/05/11 08:26:08 - mmengine - INFO - Epoch(val) [17][250/509] eta: 0:02:11 time: 0.4498 data_time: 0.0021 memory: 1111 2023/05/11 08:26:30 - mmengine - INFO - Epoch(val) [17][300/509] eta: 0:01:43 time: 0.4410 data_time: 0.0020 memory: 1075 2023/05/11 08:26:52 - mmengine - INFO - Epoch(val) [17][350/509] eta: 0:01:17 time: 0.4387 data_time: 0.0020 memory: 1091 2023/05/11 08:27:14 - mmengine - INFO - Epoch(val) [17][400/509] eta: 0:00:52 time: 0.4387 data_time: 0.0020 memory: 1090 2023/05/11 08:27:36 - mmengine - INFO - Epoch(val) [17][450/509] eta: 0:00:28 time: 0.4435 data_time: 0.0020 memory: 1113 2023/05/11 08:27:58 - mmengine - INFO - Epoch(val) [17][500/509] eta: 0:00:04 time: 0.4436 data_time: 0.0020 memory: 1098 2023/05/11 08:28:31 - 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 | 0.9568 | 0.5049 | 0.7425 | 0.7799 | 0.5210 | 0.7209 | 0.8767 | 0.1599 | 0.9309 | 0.4889 | 0.8004 | 0.0082 | 0.9095 | 0.6515 | 0.8962 | 0.6749 | 0.7770 | 0.6580 | 0.5085 | 0.6614 | 0.9237 | 0.7417 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 08:28:31 - mmengine - INFO - Epoch(val) [17][509/509] car: 0.9568 bicycle: 0.5049 motorcycle: 0.7425 truck: 0.7799 bus: 0.5210 person: 0.7209 bicyclist: 0.8767 motorcyclist: 0.1599 road: 0.9309 parking: 0.4889 sidewalk: 0.8004 other-ground: 0.0082 building: 0.9095 fence: 0.6515 vegetation: 0.8962 trunck: 0.6749 terrian: 0.7770 pole: 0.6580 traffic-sign: 0.5085 miou: 0.6614 acc: 0.9237 acc_cls: 0.7417 data_time: 0.0020 time: 0.4477 2023/05/11 08:29:46 - mmengine - INFO - Epoch(train) [18][ 50/1196] lr: 8.0000e-03 eta: 9:34:05 time: 1.4990 data_time: 0.0045 memory: 3405 grad_norm: 0.1226 loss: 0.2227 loss_sem_seg: 0.2227 2023/05/11 08:30:58 - mmengine - INFO - Epoch(train) [18][ 100/1196] lr: 8.0000e-03 eta: 9:32:45 time: 1.4405 data_time: 0.0034 memory: 3336 grad_norm: 0.1081 loss: 0.2172 loss_sem_seg: 0.2172 2023/05/11 08:32:10 - mmengine - INFO - Epoch(train) [18][ 150/1196] lr: 8.0000e-03 eta: 9:31:25 time: 1.4382 data_time: 0.0034 memory: 3382 grad_norm: 0.1062 loss: 0.2306 loss_sem_seg: 0.2306 2023/05/11 08:33:23 - mmengine - INFO - Epoch(train) [18][ 200/1196] lr: 8.0000e-03 eta: 9:30:05 time: 1.4498 data_time: 0.0034 memory: 3501 grad_norm: 0.1001 loss: 0.2189 loss_sem_seg: 0.2189 2023/05/11 08:34:34 - mmengine - INFO - Epoch(train) [18][ 250/1196] lr: 8.0000e-03 eta: 9:28:45 time: 1.4370 data_time: 0.0034 memory: 3623 grad_norm: 0.0962 loss: 0.2035 loss_sem_seg: 0.2035 2023/05/11 08:35:47 - mmengine - INFO - Epoch(train) [18][ 300/1196] lr: 8.0000e-03 eta: 9:27:25 time: 1.4557 data_time: 0.0034 memory: 3284 grad_norm: 0.1040 loss: 0.2132 loss_sem_seg: 0.2132 2023/05/11 08:36:59 - mmengine - INFO - Epoch(train) [18][ 350/1196] lr: 8.0000e-03 eta: 9:26:05 time: 1.4423 data_time: 0.0033 memory: 3379 grad_norm: 0.1076 loss: 0.2115 loss_sem_seg: 0.2115 2023/05/11 08:38:12 - mmengine - INFO - Epoch(train) [18][ 400/1196] lr: 8.0000e-03 eta: 9:24:46 time: 1.4515 data_time: 0.0034 memory: 3383 grad_norm: 0.1031 loss: 0.2190 loss_sem_seg: 0.2190 2023/05/11 08:39:25 - mmengine - INFO - Epoch(train) [18][ 450/1196] lr: 8.0000e-03 eta: 9:23:27 time: 1.4668 data_time: 0.0035 memory: 3307 grad_norm: 0.1064 loss: 0.2337 loss_sem_seg: 0.2337 2023/05/11 08:40:38 - mmengine - INFO - Epoch(train) [18][ 500/1196] lr: 8.0000e-03 eta: 9:22:08 time: 1.4567 data_time: 0.0035 memory: 3176 grad_norm: 0.1082 loss: 0.2188 loss_sem_seg: 0.2188 2023/05/11 08:41:50 - mmengine - INFO - Epoch(train) [18][ 550/1196] lr: 8.0000e-03 eta: 9:20:48 time: 1.4448 data_time: 0.0034 memory: 3443 grad_norm: 0.0998 loss: 0.2101 loss_sem_seg: 0.2101 2023/05/11 08:43:02 - mmengine - INFO - Epoch(train) [18][ 600/1196] lr: 8.0000e-03 eta: 9:19:28 time: 1.4385 data_time: 0.0034 memory: 3449 grad_norm: 0.0944 loss: 0.2157 loss_sem_seg: 0.2157 2023/05/11 08:44:15 - mmengine - INFO - Epoch(train) [18][ 650/1196] lr: 8.0000e-03 eta: 9:18:09 time: 1.4578 data_time: 0.0034 memory: 3539 grad_norm: 0.0963 loss: 0.2034 loss_sem_seg: 0.2034 2023/05/11 08:44:39 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 08:45:19 - mmengine - INFO - Epoch(train) [18][ 700/1196] lr: 8.0000e-03 eta: 9:16:41 time: 1.2844 data_time: 0.0033 memory: 3496 grad_norm: 0.1049 loss: 0.2201 loss_sem_seg: 0.2201 2023/05/11 08:46:22 - mmengine - INFO - Epoch(train) [18][ 750/1196] lr: 8.0000e-03 eta: 9:15:11 time: 1.2467 data_time: 0.0034 memory: 3464 grad_norm: 0.1132 loss: 0.2096 loss_sem_seg: 0.2096 2023/05/11 08:47:25 - mmengine - INFO - Epoch(train) [18][ 800/1196] lr: 8.0000e-03 eta: 9:13:42 time: 1.2618 data_time: 0.0034 memory: 3273 grad_norm: 0.0939 loss: 0.2149 loss_sem_seg: 0.2149 2023/05/11 08:48:27 - mmengine - INFO - Epoch(train) [18][ 850/1196] lr: 8.0000e-03 eta: 9:12:12 time: 1.2400 data_time: 0.0033 memory: 3276 grad_norm: 0.0975 loss: 0.2072 loss_sem_seg: 0.2072 2023/05/11 08:49:29 - mmengine - INFO - Epoch(train) [18][ 900/1196] lr: 8.0000e-03 eta: 9:10:43 time: 1.2514 data_time: 0.0034 memory: 3628 grad_norm: 0.1092 loss: 0.2212 loss_sem_seg: 0.2212 2023/05/11 08:50:40 - mmengine - INFO - Epoch(train) [18][ 950/1196] lr: 8.0000e-03 eta: 9:09:22 time: 1.4161 data_time: 0.0033 memory: 3451 grad_norm: 0.0985 loss: 0.2224 loss_sem_seg: 0.2224 2023/05/11 08:51:54 - mmengine - INFO - Epoch(train) [18][1000/1196] lr: 8.0000e-03 eta: 9:08:05 time: 1.4867 data_time: 0.0034 memory: 3502 grad_norm: 0.1069 loss: 0.2262 loss_sem_seg: 0.2262 2023/05/11 08:53:09 - mmengine - INFO - Epoch(train) [18][1050/1196] lr: 8.0000e-03 eta: 9:06:48 time: 1.4873 data_time: 0.0034 memory: 3283 grad_norm: 0.1057 loss: 0.2273 loss_sem_seg: 0.2273 2023/05/11 08:54:23 - mmengine - INFO - Epoch(train) [18][1100/1196] lr: 8.0000e-03 eta: 9:05:31 time: 1.4803 data_time: 0.0035 memory: 3265 grad_norm: 0.1094 loss: 0.2167 loss_sem_seg: 0.2167 2023/05/11 08:55:37 - mmengine - INFO - Epoch(train) [18][1150/1196] lr: 8.0000e-03 eta: 9:04:13 time: 1.4792 data_time: 0.0033 memory: 3270 grad_norm: 0.1067 loss: 0.2133 loss_sem_seg: 0.2133 2023/05/11 08:56:45 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 08:56:45 - mmengine - INFO - Saving checkpoint at 18 epochs 2023/05/11 08:57:22 - mmengine - INFO - Epoch(val) [18][ 50/509] eta: 0:04:48 time: 0.6294 data_time: 0.0021 memory: 3463 2023/05/11 08:57:49 - mmengine - INFO - Epoch(val) [18][100/509] eta: 0:03:56 time: 0.5278 data_time: 0.0021 memory: 1105 2023/05/11 08:58:11 - mmengine - INFO - Epoch(val) [18][150/509] eta: 0:03:11 time: 0.4418 data_time: 0.0020 memory: 1110 2023/05/11 08:58:33 - mmengine - INFO - Epoch(val) [18][200/509] eta: 0:02:37 time: 0.4435 data_time: 0.0020 memory: 1100 2023/05/11 08:58:55 - mmengine - INFO - Epoch(val) [18][250/509] eta: 0:02:09 time: 0.4518 data_time: 0.0021 memory: 1111 2023/05/11 08:59:17 - mmengine - INFO - Epoch(val) [18][300/509] eta: 0:01:42 time: 0.4391 data_time: 0.0021 memory: 1075 2023/05/11 08:59:39 - mmengine - INFO - Epoch(val) [18][350/509] eta: 0:01:16 time: 0.4359 data_time: 0.0020 memory: 1091 2023/05/11 09:00:01 - mmengine - INFO - Epoch(val) [18][400/509] eta: 0:00:51 time: 0.4381 data_time: 0.0020 memory: 1090 2023/05/11 09:00:23 - mmengine - INFO - Epoch(val) [18][450/509] eta: 0:00:27 time: 0.4430 data_time: 0.0020 memory: 1113 2023/05/11 09:00:46 - mmengine - INFO - Epoch(val) [18][500/509] eta: 0:00:04 time: 0.4468 data_time: 0.0020 memory: 1098 2023/05/11 09:01:18 - 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 | 0.9657 | 0.3779 | 0.7982 | 0.8367 | 0.6335 | 0.7829 | 0.8309 | 0.1638 | 0.9406 | 0.4286 | 0.8150 | 0.0046 | 0.8999 | 0.6256 | 0.8823 | 0.6906 | 0.7406 | 0.6546 | 0.5173 | 0.6626 | 0.9202 | 0.7344 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 09:01:18 - mmengine - INFO - Epoch(val) [18][509/509] car: 0.9657 bicycle: 0.3779 motorcycle: 0.7982 truck: 0.8367 bus: 0.6335 person: 0.7829 bicyclist: 0.8309 motorcyclist: 0.1638 road: 0.9406 parking: 0.4286 sidewalk: 0.8150 other-ground: 0.0046 building: 0.8999 fence: 0.6256 vegetation: 0.8823 trunck: 0.6906 terrian: 0.7406 pole: 0.6546 traffic-sign: 0.5173 miou: 0.6626 acc: 0.9202 acc_cls: 0.7344 data_time: 0.0020 time: 0.4512 2023/05/11 09:02:35 - mmengine - INFO - Epoch(train) [19][ 50/1196] lr: 8.0000e-03 eta: 9:01:48 time: 1.5372 data_time: 0.0042 memory: 3342 grad_norm: 0.1121 loss: 0.2049 loss_sem_seg: 0.2049 2023/05/11 09:03:47 - mmengine - INFO - Epoch(train) [19][ 100/1196] lr: 8.0000e-03 eta: 9:00:29 time: 1.4487 data_time: 0.0034 memory: 3318 grad_norm: 0.0969 loss: 0.2148 loss_sem_seg: 0.2148 2023/05/11 09:05:00 - mmengine - INFO - Epoch(train) [19][ 150/1196] lr: 8.0000e-03 eta: 8:59:10 time: 1.4552 data_time: 0.0033 memory: 3173 grad_norm: 0.1018 loss: 0.2229 loss_sem_seg: 0.2229 2023/05/11 09:06:12 - mmengine - INFO - Epoch(train) [19][ 200/1196] lr: 8.0000e-03 eta: 8:57:52 time: 1.4508 data_time: 0.0034 memory: 3374 grad_norm: 0.1010 loss: 0.2092 loss_sem_seg: 0.2092 2023/05/11 09:07:24 - mmengine - INFO - Epoch(train) [19][ 250/1196] lr: 8.0000e-03 eta: 8:56:32 time: 1.4340 data_time: 0.0035 memory: 3461 grad_norm: 0.1001 loss: 0.2138 loss_sem_seg: 0.2138 2023/05/11 09:08:36 - mmengine - INFO - Epoch(train) [19][ 300/1196] lr: 8.0000e-03 eta: 8:55:13 time: 1.4381 data_time: 0.0034 memory: 3427 grad_norm: 0.0982 loss: 0.2202 loss_sem_seg: 0.2202 2023/05/11 09:09:49 - mmengine - INFO - Epoch(train) [19][ 350/1196] lr: 8.0000e-03 eta: 8:53:54 time: 1.4527 data_time: 0.0035 memory: 3362 grad_norm: 0.1053 loss: 0.2335 loss_sem_seg: 0.2335 2023/05/11 09:11:01 - mmengine - INFO - Epoch(train) [19][ 400/1196] lr: 8.0000e-03 eta: 8:52:36 time: 1.4473 data_time: 0.0035 memory: 3478 grad_norm: 0.1041 loss: 0.2185 loss_sem_seg: 0.2185 2023/05/11 09:12:13 - mmengine - INFO - Epoch(train) [19][ 450/1196] lr: 8.0000e-03 eta: 8:51:17 time: 1.4432 data_time: 0.0034 memory: 3228 grad_norm: 0.1083 loss: 0.2214 loss_sem_seg: 0.2214 2023/05/11 09:12:45 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 09:13:26 - mmengine - INFO - Epoch(train) [19][ 500/1196] lr: 8.0000e-03 eta: 8:49:59 time: 1.4689 data_time: 0.0034 memory: 3671 grad_norm: 0.0988 loss: 0.2041 loss_sem_seg: 0.2041 2023/05/11 09:14:39 - mmengine - INFO - Epoch(train) [19][ 550/1196] lr: 8.0000e-03 eta: 8:48:40 time: 1.4445 data_time: 0.0034 memory: 3377 grad_norm: 0.1077 loss: 0.2180 loss_sem_seg: 0.2180 2023/05/11 09:15:51 - mmengine - INFO - Epoch(train) [19][ 600/1196] lr: 8.0000e-03 eta: 8:47:22 time: 1.4545 data_time: 0.0034 memory: 3264 grad_norm: 0.0948 loss: 0.2037 loss_sem_seg: 0.2037 2023/05/11 09:17:04 - mmengine - INFO - Epoch(train) [19][ 650/1196] lr: 8.0000e-03 eta: 8:46:03 time: 1.4453 data_time: 0.0037 memory: 3584 grad_norm: 0.1068 loss: 0.2061 loss_sem_seg: 0.2061 2023/05/11 09:18:08 - mmengine - INFO - Epoch(train) [19][ 700/1196] lr: 8.0000e-03 eta: 8:44:37 time: 1.2935 data_time: 0.0034 memory: 3322 grad_norm: inf loss: 0.2249 loss_sem_seg: 0.2249 2023/05/11 09:19:11 - mmengine - INFO - Epoch(train) [19][ 750/1196] lr: 8.0000e-03 eta: 8:43:09 time: 1.2423 data_time: 0.0034 memory: 3267 grad_norm: 0.0948 loss: 0.2257 loss_sem_seg: 0.2257 2023/05/11 09:20:13 - mmengine - INFO - Epoch(train) [19][ 800/1196] lr: 8.0000e-03 eta: 8:41:41 time: 1.2494 data_time: 0.0034 memory: 3623 grad_norm: 0.1218 loss: 0.2143 loss_sem_seg: 0.2143 2023/05/11 09:21:15 - mmengine - INFO - Epoch(train) [19][ 850/1196] lr: 8.0000e-03 eta: 8:40:13 time: 1.2324 data_time: 0.0034 memory: 3538 grad_norm: 0.1044 loss: 0.2119 loss_sem_seg: 0.2119 2023/05/11 09:22:17 - mmengine - INFO - Epoch(train) [19][ 900/1196] lr: 8.0000e-03 eta: 8:38:45 time: 1.2459 data_time: 0.0035 memory: 3395 grad_norm: 0.1028 loss: 0.2197 loss_sem_seg: 0.2197 2023/05/11 09:23:28 - mmengine - INFO - Epoch(train) [19][ 950/1196] lr: 8.0000e-03 eta: 8:37:26 time: 1.4143 data_time: 0.0034 memory: 3240 grad_norm: 0.1131 loss: 0.2290 loss_sem_seg: 0.2290 2023/05/11 09:24:40 - mmengine - INFO - Epoch(train) [19][1000/1196] lr: 8.0000e-03 eta: 8:36:08 time: 1.4531 data_time: 0.0035 memory: 3817 grad_norm: 0.1149 loss: 0.2317 loss_sem_seg: 0.2317 2023/05/11 09:25:53 - mmengine - INFO - Epoch(train) [19][1050/1196] lr: 8.0000e-03 eta: 8:34:50 time: 1.4535 data_time: 0.0036 memory: 3398 grad_norm: 0.1111 loss: 0.2212 loss_sem_seg: 0.2212 2023/05/11 09:27:05 - mmengine - INFO - Epoch(train) [19][1100/1196] lr: 8.0000e-03 eta: 8:33:31 time: 1.4436 data_time: 0.0036 memory: 3476 grad_norm: 0.1096 loss: 0.2268 loss_sem_seg: 0.2268 2023/05/11 09:28:17 - mmengine - INFO - Epoch(train) [19][1150/1196] lr: 8.0000e-03 eta: 8:32:13 time: 1.4450 data_time: 0.0035 memory: 3422 grad_norm: 0.1057 loss: 0.2174 loss_sem_seg: 0.2174 2023/05/11 09:29:25 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 09:29:25 - mmengine - INFO - Saving checkpoint at 19 epochs 2023/05/11 09:30:01 - mmengine - INFO - Epoch(val) [19][ 50/509] eta: 0:04:40 time: 0.6104 data_time: 0.0022 memory: 3478 2023/05/11 09:30:25 - mmengine - INFO - Epoch(val) [19][100/509] eta: 0:03:43 time: 0.4813 data_time: 0.0021 memory: 1105 2023/05/11 09:30:47 - mmengine - INFO - Epoch(val) [19][150/509] eta: 0:03:04 time: 0.4485 data_time: 0.0021 memory: 1110 2023/05/11 09:31:09 - mmengine - INFO - Epoch(val) [19][200/509] eta: 0:02:32 time: 0.4377 data_time: 0.0020 memory: 1100 2023/05/11 09:31:32 - mmengine - INFO - Epoch(val) [19][250/509] eta: 0:02:05 time: 0.4506 data_time: 0.0020 memory: 1111 2023/05/11 09:31:54 - mmengine - INFO - Epoch(val) [19][300/509] eta: 0:01:40 time: 0.4435 data_time: 0.0021 memory: 1075 2023/05/11 09:32:16 - mmengine - INFO - Epoch(val) [19][350/509] eta: 0:01:15 time: 0.4332 data_time: 0.0020 memory: 1091 2023/05/11 09:32:37 - mmengine - INFO - Epoch(val) [19][400/509] eta: 0:00:50 time: 0.4363 data_time: 0.0021 memory: 1090 2023/05/11 09:32:59 - mmengine - INFO - Epoch(val) [19][450/509] eta: 0:00:27 time: 0.4387 data_time: 0.0021 memory: 1113 2023/05/11 09:33:22 - mmengine - INFO - Epoch(val) [19][500/509] eta: 0:00:04 time: 0.4436 data_time: 0.0021 memory: 1098 2023/05/11 09:33:54 - 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 | 0.9565 | 0.5385 | 0.7187 | 0.6644 | 0.6207 | 0.7129 | 0.8950 | 0.0213 | 0.9347 | 0.4717 | 0.8091 | 0.0097 | 0.9039 | 0.6341 | 0.8873 | 0.6534 | 0.7621 | 0.6447 | 0.5047 | 0.6496 | 0.9218 | 0.7225 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 09:33:54 - mmengine - INFO - Epoch(val) [19][509/509] car: 0.9565 bicycle: 0.5385 motorcycle: 0.7187 truck: 0.6644 bus: 0.6207 person: 0.7129 bicyclist: 0.8950 motorcyclist: 0.0213 road: 0.9347 parking: 0.4717 sidewalk: 0.8091 other-ground: 0.0097 building: 0.9039 fence: 0.6341 vegetation: 0.8873 trunck: 0.6534 terrian: 0.7621 pole: 0.6447 traffic-sign: 0.5047 miou: 0.6496 acc: 0.9218 acc_cls: 0.7225 data_time: 0.0020 time: 0.4470 2023/05/11 09:35:12 - mmengine - INFO - Epoch(train) [20][ 50/1196] lr: 8.0000e-03 eta: 8:29:48 time: 1.5528 data_time: 0.0048 memory: 3293 grad_norm: 0.1170 loss: 0.2226 loss_sem_seg: 0.2226 2023/05/11 09:36:24 - mmengine - INFO - Epoch(train) [20][ 100/1196] lr: 8.0000e-03 eta: 8:28:30 time: 1.4458 data_time: 0.0035 memory: 3797 grad_norm: 0.1051 loss: 0.2356 loss_sem_seg: 0.2356 2023/05/11 09:37:37 - mmengine - INFO - Epoch(train) [20][ 150/1196] lr: 8.0000e-03 eta: 8:27:12 time: 1.4445 data_time: 0.0035 memory: 3710 grad_norm: 0.1102 loss: 0.2218 loss_sem_seg: 0.2218 2023/05/11 09:38:49 - mmengine - INFO - Epoch(train) [20][ 200/1196] lr: 8.0000e-03 eta: 8:25:54 time: 1.4478 data_time: 0.0035 memory: 3311 grad_norm: 0.0896 loss: 0.2034 loss_sem_seg: 0.2034 2023/05/11 09:40:02 - mmengine - INFO - Epoch(train) [20][ 250/1196] lr: 8.0000e-03 eta: 8:24:37 time: 1.4591 data_time: 0.0035 memory: 3308 grad_norm: 0.0980 loss: 0.2129 loss_sem_seg: 0.2129 2023/05/11 09:40:39 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 09:41:14 - mmengine - INFO - Epoch(train) [20][ 300/1196] lr: 8.0000e-03 eta: 8:23:18 time: 1.4353 data_time: 0.0035 memory: 3182 grad_norm: 0.1008 loss: 0.2259 loss_sem_seg: 0.2259 2023/05/11 09:42:27 - mmengine - INFO - Epoch(train) [20][ 350/1196] lr: 8.0000e-03 eta: 8:22:01 time: 1.4668 data_time: 0.0035 memory: 3517 grad_norm: 0.0975 loss: 0.2251 loss_sem_seg: 0.2251 2023/05/11 09:43:39 - mmengine - INFO - Epoch(train) [20][ 400/1196] lr: 8.0000e-03 eta: 8:20:43 time: 1.4389 data_time: 0.0035 memory: 3533 grad_norm: 0.0926 loss: 0.2167 loss_sem_seg: 0.2167 2023/05/11 09:44:51 - mmengine - INFO - Epoch(train) [20][ 450/1196] lr: 8.0000e-03 eta: 8:19:24 time: 1.4396 data_time: 0.0036 memory: 3332 grad_norm: 0.0915 loss: 0.2230 loss_sem_seg: 0.2230 2023/05/11 09:46:03 - mmengine - INFO - Epoch(train) [20][ 500/1196] lr: 8.0000e-03 eta: 8:18:06 time: 1.4373 data_time: 0.0035 memory: 3193 grad_norm: 0.0990 loss: 0.1962 loss_sem_seg: 0.1962 2023/05/11 09:47:16 - mmengine - INFO - Epoch(train) [20][ 550/1196] lr: 8.0000e-03 eta: 8:16:48 time: 1.4582 data_time: 0.0035 memory: 3323 grad_norm: 0.1137 loss: 0.2447 loss_sem_seg: 0.2447 2023/05/11 09:48:28 - mmengine - INFO - Epoch(train) [20][ 600/1196] lr: 8.0000e-03 eta: 8:15:31 time: 1.4518 data_time: 0.0034 memory: 3228 grad_norm: 0.1101 loss: 0.2103 loss_sem_seg: 0.2103 2023/05/11 09:49:41 - mmengine - INFO - Epoch(train) [20][ 650/1196] lr: 8.0000e-03 eta: 8:14:13 time: 1.4538 data_time: 0.0034 memory: 3239 grad_norm: 0.1092 loss: 0.2116 loss_sem_seg: 0.2116 2023/05/11 09:50:46 - mmengine - INFO - Epoch(train) [20][ 700/1196] lr: 8.0000e-03 eta: 8:12:49 time: 1.2960 data_time: 0.0034 memory: 3247 grad_norm: 0.0941 loss: 0.2046 loss_sem_seg: 0.2046 2023/05/11 09:51:48 - mmengine - INFO - Epoch(train) [20][ 750/1196] lr: 8.0000e-03 eta: 8:11:23 time: 1.2490 data_time: 0.0034 memory: 3237 grad_norm: 0.0984 loss: 0.2066 loss_sem_seg: 0.2066 2023/05/11 09:52:51 - mmengine - INFO - Epoch(train) [20][ 800/1196] lr: 8.0000e-03 eta: 8:09:57 time: 1.2478 data_time: 0.0035 memory: 3445 grad_norm: 0.1024 loss: 0.2136 loss_sem_seg: 0.2136 2023/05/11 09:53:53 - mmengine - INFO - Epoch(train) [20][ 850/1196] lr: 8.0000e-03 eta: 8:08:31 time: 1.2350 data_time: 0.0034 memory: 3516 grad_norm: 0.1014 loss: 0.2127 loss_sem_seg: 0.2127 2023/05/11 09:54:55 - mmengine - INFO - Epoch(train) [20][ 900/1196] lr: 8.0000e-03 eta: 8:07:05 time: 1.2486 data_time: 0.0035 memory: 3175 grad_norm: 0.0910 loss: 0.2237 loss_sem_seg: 0.2237 2023/05/11 09:56:05 - mmengine - INFO - Epoch(train) [20][ 950/1196] lr: 8.0000e-03 eta: 8:05:45 time: 1.3976 data_time: 0.0033 memory: 3369 grad_norm: 0.1053 loss: 0.2107 loss_sem_seg: 0.2107 2023/05/11 09:57:17 - mmengine - INFO - Epoch(train) [20][1000/1196] lr: 8.0000e-03 eta: 8:04:27 time: 1.4344 data_time: 0.0034 memory: 3380 grad_norm: 0.1087 loss: 0.2214 loss_sem_seg: 0.2214 2023/05/11 09:58:29 - mmengine - INFO - Epoch(train) [20][1050/1196] lr: 8.0000e-03 eta: 8:03:10 time: 1.4505 data_time: 0.0036 memory: 3171 grad_norm: 0.1061 loss: 0.2332 loss_sem_seg: 0.2332 2023/05/11 09:59:41 - mmengine - INFO - Epoch(train) [20][1100/1196] lr: 8.0000e-03 eta: 8:01:52 time: 1.4442 data_time: 0.0036 memory: 3296 grad_norm: 0.1022 loss: 0.2216 loss_sem_seg: 0.2216 2023/05/11 10:00:54 - mmengine - INFO - Epoch(train) [20][1150/1196] lr: 8.0000e-03 eta: 8:00:35 time: 1.4555 data_time: 0.0035 memory: 3605 grad_norm: 0.1026 loss: 0.1939 loss_sem_seg: 0.1939 2023/05/11 10:02:01 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 10:02:01 - mmengine - INFO - Saving checkpoint at 20 epochs 2023/05/11 10:02:36 - mmengine - INFO - Epoch(val) [20][ 50/509] eta: 0:04:36 time: 0.6032 data_time: 0.0021 memory: 3195 2023/05/11 10:03:00 - mmengine - INFO - Epoch(val) [20][100/509] eta: 0:03:40 time: 0.4749 data_time: 0.0020 memory: 1105 2023/05/11 10:03:23 - mmengine - INFO - Epoch(val) [20][150/509] eta: 0:03:02 time: 0.4462 data_time: 0.0020 memory: 1110 2023/05/11 10:03:45 - mmengine - INFO - Epoch(val) [20][200/509] eta: 0:02:31 time: 0.4426 data_time: 0.0020 memory: 1100 2023/05/11 10:04:07 - mmengine - INFO - Epoch(val) [20][250/509] eta: 0:02:05 time: 0.4564 data_time: 0.0020 memory: 1111 2023/05/11 10:04:30 - mmengine - INFO - Epoch(val) [20][300/509] eta: 0:01:39 time: 0.4436 data_time: 0.0020 memory: 1075 2023/05/11 10:04:51 - mmengine - INFO - Epoch(val) [20][350/509] eta: 0:01:14 time: 0.4309 data_time: 0.0020 memory: 1091 2023/05/11 10:05:13 - mmengine - INFO - Epoch(val) [20][400/509] eta: 0:00:50 time: 0.4357 data_time: 0.0020 memory: 1090 2023/05/11 10:05:36 - mmengine - INFO - Epoch(val) [20][450/509] eta: 0:00:27 time: 0.4499 data_time: 0.0021 memory: 1113 2023/05/11 10:05:57 - mmengine - INFO - Epoch(val) [20][500/509] eta: 0:00:04 time: 0.4380 data_time: 0.0020 memory: 1098 2023/05/11 10:06:29 - 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 | 0.9562 | 0.5547 | 0.7804 | 0.8655 | 0.4949 | 0.7457 | 0.8890 | 0.0647 | 0.9413 | 0.5531 | 0.8191 | 0.0113 | 0.9014 | 0.6164 | 0.8780 | 0.6792 | 0.7286 | 0.6609 | 0.5131 | 0.6660 | 0.9193 | 0.7327 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 10:06:29 - mmengine - INFO - Epoch(val) [20][509/509] car: 0.9562 bicycle: 0.5547 motorcycle: 0.7804 truck: 0.8655 bus: 0.4949 person: 0.7457 bicyclist: 0.8890 motorcyclist: 0.0647 road: 0.9413 parking: 0.5531 sidewalk: 0.8191 other-ground: 0.0113 building: 0.9014 fence: 0.6164 vegetation: 0.8780 trunck: 0.6792 terrian: 0.7286 pole: 0.6609 traffic-sign: 0.5131 miou: 0.6660 acc: 0.9193 acc_cls: 0.7327 data_time: 0.0020 time: 0.4453 2023/05/11 10:07:47 - mmengine - INFO - Epoch(train) [21][ 50/1196] lr: 8.0000e-03 eta: 7:58:11 time: 1.5475 data_time: 0.0041 memory: 3417 grad_norm: 0.1064 loss: 0.2006 loss_sem_seg: 0.2006 2023/05/11 10:08:30 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 10:08:59 - mmengine - INFO - Epoch(train) [21][ 100/1196] lr: 8.0000e-03 eta: 7:56:54 time: 1.4484 data_time: 0.0035 memory: 3529 grad_norm: 0.1029 loss: 0.2175 loss_sem_seg: 0.2175 2023/05/11 10:10:12 - mmengine - INFO - Epoch(train) [21][ 150/1196] lr: 8.0000e-03 eta: 7:55:36 time: 1.4547 data_time: 0.0034 memory: 3326 grad_norm: 0.0954 loss: 0.2204 loss_sem_seg: 0.2204 2023/05/11 10:11:24 - mmengine - INFO - Epoch(train) [21][ 200/1196] lr: 8.0000e-03 eta: 7:54:19 time: 1.4444 data_time: 0.0035 memory: 3520 grad_norm: 0.1041 loss: 0.2114 loss_sem_seg: 0.2114 2023/05/11 10:12:37 - mmengine - INFO - Epoch(train) [21][ 250/1196] lr: 8.0000e-03 eta: 7:53:02 time: 1.4554 data_time: 0.0034 memory: 3515 grad_norm: 0.1015 loss: 0.2034 loss_sem_seg: 0.2034 2023/05/11 10:13:49 - mmengine - INFO - Epoch(train) [21][ 300/1196] lr: 8.0000e-03 eta: 7:51:44 time: 1.4380 data_time: 0.0034 memory: 3361 grad_norm: 0.0937 loss: 0.2139 loss_sem_seg: 0.2139 2023/05/11 10:15:01 - mmengine - INFO - Epoch(train) [21][ 350/1196] lr: 8.0000e-03 eta: 7:50:27 time: 1.4387 data_time: 0.0038 memory: 3380 grad_norm: 0.0937 loss: 0.2058 loss_sem_seg: 0.2058 2023/05/11 10:16:13 - mmengine - INFO - Epoch(train) [21][ 400/1196] lr: 8.0000e-03 eta: 7:49:09 time: 1.4433 data_time: 0.0036 memory: 3694 grad_norm: 0.0955 loss: 0.1983 loss_sem_seg: 0.1983 2023/05/11 10:17:26 - mmengine - INFO - Epoch(train) [21][ 450/1196] lr: 8.0000e-03 eta: 7:47:53 time: 1.4658 data_time: 0.0035 memory: 3372 grad_norm: 0.0890 loss: 0.2028 loss_sem_seg: 0.2028 2023/05/11 10:18:38 - mmengine - INFO - Epoch(train) [21][ 500/1196] lr: 8.0000e-03 eta: 7:46:35 time: 1.4436 data_time: 0.0035 memory: 3379 grad_norm: 0.0984 loss: 0.2255 loss_sem_seg: 0.2255 2023/05/11 10:19:52 - mmengine - INFO - Epoch(train) [21][ 550/1196] lr: 8.0000e-03 eta: 7:45:19 time: 1.4776 data_time: 0.0036 memory: 3224 grad_norm: inf loss: 0.2065 loss_sem_seg: 0.2065 2023/05/11 10:21:08 - mmengine - INFO - Epoch(train) [21][ 600/1196] lr: 8.0000e-03 eta: 7:44:04 time: 1.5054 data_time: 0.0035 memory: 3364 grad_norm: 0.1223 loss: 0.2468 loss_sem_seg: 0.2468 2023/05/11 10:22:22 - mmengine - INFO - Epoch(train) [21][ 650/1196] lr: 8.0000e-03 eta: 7:42:49 time: 1.4964 data_time: 0.0035 memory: 3335 grad_norm: 0.1105 loss: 0.2216 loss_sem_seg: 0.2216 2023/05/11 10:23:30 - mmengine - INFO - Epoch(train) [21][ 700/1196] lr: 8.0000e-03 eta: 7:41:29 time: 1.3588 data_time: 0.0039 memory: 3497 grad_norm: 0.0975 loss: 0.2077 loss_sem_seg: 0.2077 2023/05/11 10:24:35 - mmengine - INFO - Epoch(train) [21][ 750/1196] lr: 8.0000e-03 eta: 7:40:06 time: 1.2987 data_time: 0.0035 memory: 3429 grad_norm: 0.1065 loss: 0.2275 loss_sem_seg: 0.2275 2023/05/11 10:25:38 - mmengine - INFO - Epoch(train) [21][ 800/1196] lr: 8.0000e-03 eta: 7:38:42 time: 1.2549 data_time: 0.0035 memory: 3224 grad_norm: 0.1054 loss: 0.2109 loss_sem_seg: 0.2109 2023/05/11 10:26:40 - mmengine - INFO - Epoch(train) [21][ 850/1196] lr: 8.0000e-03 eta: 7:37:17 time: 1.2498 data_time: 0.0034 memory: 3535 grad_norm: 0.1052 loss: 0.2081 loss_sem_seg: 0.2081 2023/05/11 10:27:43 - mmengine - INFO - Epoch(train) [21][ 900/1196] lr: 8.0000e-03 eta: 7:35:53 time: 1.2480 data_time: 0.0035 memory: 3695 grad_norm: 0.1017 loss: 0.2020 loss_sem_seg: 0.2020 2023/05/11 10:28:54 - mmengine - INFO - Epoch(train) [21][ 950/1196] lr: 8.0000e-03 eta: 7:34:35 time: 1.4188 data_time: 0.0033 memory: 3278 grad_norm: 0.1106 loss: 0.2159 loss_sem_seg: 0.2159 2023/05/11 10:30:06 - mmengine - INFO - Epoch(train) [21][1000/1196] lr: 8.0000e-03 eta: 7:33:18 time: 1.4381 data_time: 0.0034 memory: 3501 grad_norm: 0.0952 loss: 0.2005 loss_sem_seg: 0.2005 2023/05/11 10:31:18 - mmengine - INFO - Epoch(train) [21][1050/1196] lr: 8.0000e-03 eta: 7:32:01 time: 1.4468 data_time: 0.0035 memory: 3458 grad_norm: 0.0922 loss: 0.2077 loss_sem_seg: 0.2077 2023/05/11 10:32:02 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 10:32:30 - mmengine - INFO - Epoch(train) [21][1100/1196] lr: 8.0000e-03 eta: 7:30:44 time: 1.4482 data_time: 0.0036 memory: 3169 grad_norm: 0.1051 loss: 0.2085 loss_sem_seg: 0.2085 2023/05/11 10:33:43 - mmengine - INFO - Epoch(train) [21][1150/1196] lr: 8.0000e-03 eta: 7:29:27 time: 1.4445 data_time: 0.0034 memory: 3443 grad_norm: 0.0910 loss: 0.2111 loss_sem_seg: 0.2111 2023/05/11 10:34:49 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 10:34:49 - mmengine - INFO - Saving checkpoint at 21 epochs 2023/05/11 10:35:25 - mmengine - INFO - Epoch(val) [21][ 50/509] eta: 0:04:32 time: 0.5932 data_time: 0.0021 memory: 3334 2023/05/11 10:35:47 - mmengine - INFO - Epoch(val) [21][100/509] eta: 0:03:32 time: 0.4453 data_time: 0.0020 memory: 1105 2023/05/11 10:36:09 - mmengine - INFO - Epoch(val) [21][150/509] eta: 0:02:57 time: 0.4456 data_time: 0.0020 memory: 1110 2023/05/11 10:36:31 - mmengine - INFO - Epoch(val) [21][200/509] eta: 0:02:29 time: 0.4454 data_time: 0.0020 memory: 1100 2023/05/11 10:36:54 - mmengine - INFO - Epoch(val) [21][250/509] eta: 0:02:03 time: 0.4546 data_time: 0.0020 memory: 1111 2023/05/11 10:37:16 - mmengine - INFO - Epoch(val) [21][300/509] eta: 0:01:38 time: 0.4428 data_time: 0.0020 memory: 1075 2023/05/11 10:37:38 - mmengine - INFO - Epoch(val) [21][350/509] eta: 0:01:13 time: 0.4297 data_time: 0.0020 memory: 1091 2023/05/11 10:38:00 - mmengine - INFO - Epoch(val) [21][400/509] eta: 0:00:50 time: 0.4404 data_time: 0.0020 memory: 1090 2023/05/11 10:38:22 - mmengine - INFO - Epoch(val) [21][450/509] eta: 0:00:27 time: 0.4496 data_time: 0.0021 memory: 1113 2023/05/11 10:38:44 - mmengine - INFO - Epoch(val) [21][500/509] eta: 0:00:04 time: 0.4365 data_time: 0.0020 memory: 1098 2023/05/11 10:39:15 - 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 | 0.9507 | 0.5637 | 0.7709 | 0.6001 | 0.4965 | 0.7462 | 0.8651 | 0.0693 | 0.9449 | 0.4740 | 0.8180 | 0.0123 | 0.9039 | 0.5922 | 0.8707 | 0.6564 | 0.7197 | 0.6463 | 0.5368 | 0.6441 | 0.9164 | 0.7129 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 10:39:15 - mmengine - INFO - Epoch(val) [21][509/509] car: 0.9507 bicycle: 0.5637 motorcycle: 0.7709 truck: 0.6001 bus: 0.4965 person: 0.7462 bicyclist: 0.8651 motorcyclist: 0.0693 road: 0.9449 parking: 0.4740 sidewalk: 0.8180 other-ground: 0.0123 building: 0.9039 fence: 0.5922 vegetation: 0.8707 trunck: 0.6564 terrian: 0.7197 pole: 0.6463 traffic-sign: 0.5368 miou: 0.6441 acc: 0.9164 acc_cls: 0.7129 data_time: 0.0020 time: 0.4427 2023/05/11 10:40:34 - mmengine - INFO - Epoch(train) [22][ 50/1196] lr: 8.0000e-03 eta: 7:27:04 time: 1.5664 data_time: 0.0043 memory: 3542 grad_norm: 0.1025 loss: 0.2052 loss_sem_seg: 0.2052 2023/05/11 10:41:46 - mmengine - INFO - Epoch(train) [22][ 100/1196] lr: 8.0000e-03 eta: 7:25:47 time: 1.4469 data_time: 0.0037 memory: 3538 grad_norm: 0.0939 loss: 0.2080 loss_sem_seg: 0.2080 2023/05/11 10:42:59 - mmengine - INFO - Epoch(train) [22][ 150/1196] lr: 8.0000e-03 eta: 7:24:30 time: 1.4525 data_time: 0.0035 memory: 3138 grad_norm: 0.0991 loss: 0.2077 loss_sem_seg: 0.2077 2023/05/11 10:44:11 - mmengine - INFO - Epoch(train) [22][ 200/1196] lr: 8.0000e-03 eta: 7:23:14 time: 1.4549 data_time: 0.0034 memory: 3216 grad_norm: 0.0947 loss: 0.2229 loss_sem_seg: 0.2229 2023/05/11 10:45:23 - mmengine - INFO - Epoch(train) [22][ 250/1196] lr: 8.0000e-03 eta: 7:21:57 time: 1.4356 data_time: 0.0034 memory: 3278 grad_norm: 0.1069 loss: 0.2161 loss_sem_seg: 0.2161 2023/05/11 10:46:36 - mmengine - INFO - Epoch(train) [22][ 300/1196] lr: 8.0000e-03 eta: 7:20:40 time: 1.4463 data_time: 0.0035 memory: 3402 grad_norm: 0.0888 loss: 0.1970 loss_sem_seg: 0.1970 2023/05/11 10:47:47 - mmengine - INFO - Epoch(train) [22][ 350/1196] lr: 8.0000e-03 eta: 7:19:23 time: 1.4372 data_time: 0.0034 memory: 3172 grad_norm: inf loss: 0.2152 loss_sem_seg: 0.2152 2023/05/11 10:49:00 - mmengine - INFO - Epoch(train) [22][ 400/1196] lr: 8.0000e-03 eta: 7:18:06 time: 1.4482 data_time: 0.0034 memory: 3366 grad_norm: 0.0868 loss: 0.2106 loss_sem_seg: 0.2106 2023/05/11 10:50:12 - mmengine - INFO - Epoch(train) [22][ 450/1196] lr: 8.0000e-03 eta: 7:16:49 time: 1.4414 data_time: 0.0034 memory: 3263 grad_norm: 0.1038 loss: 0.2119 loss_sem_seg: 0.2119 2023/05/11 10:51:24 - mmengine - INFO - Epoch(train) [22][ 500/1196] lr: 8.0000e-03 eta: 7:15:32 time: 1.4416 data_time: 0.0034 memory: 3222 grad_norm: 0.1003 loss: 0.2095 loss_sem_seg: 0.2095 2023/05/11 10:52:38 - mmengine - INFO - Epoch(train) [22][ 550/1196] lr: 8.0000e-03 eta: 7:14:17 time: 1.4866 data_time: 0.0035 memory: 3285 grad_norm: 0.0958 loss: 0.2304 loss_sem_seg: 0.2304 2023/05/11 10:53:53 - mmengine - INFO - Epoch(train) [22][ 600/1196] lr: 8.0000e-03 eta: 7:13:02 time: 1.4861 data_time: 0.0035 memory: 3387 grad_norm: 0.0964 loss: 0.2036 loss_sem_seg: 0.2036 2023/05/11 10:55:07 - mmengine - INFO - Epoch(train) [22][ 650/1196] lr: 8.0000e-03 eta: 7:11:47 time: 1.4904 data_time: 0.0034 memory: 3508 grad_norm: 0.0998 loss: 0.2022 loss_sem_seg: 0.2022 2023/05/11 10:56:16 - mmengine - INFO - Epoch(train) [22][ 700/1196] lr: 8.0000e-03 eta: 7:10:28 time: 1.3746 data_time: 0.0036 memory: 3393 grad_norm: 0.0965 loss: 0.2115 loss_sem_seg: 0.2115 2023/05/11 10:57:20 - mmengine - INFO - Epoch(train) [22][ 750/1196] lr: 8.0000e-03 eta: 7:09:05 time: 1.2771 data_time: 0.0034 memory: 3326 grad_norm: 0.1019 loss: 0.2002 loss_sem_seg: 0.2002 2023/05/11 10:58:22 - mmengine - INFO - Epoch(train) [22][ 800/1196] lr: 8.0000e-03 eta: 7:07:42 time: 1.2429 data_time: 0.0034 memory: 3255 grad_norm: 0.0991 loss: 0.2031 loss_sem_seg: 0.2031 2023/05/11 10:59:24 - mmengine - INFO - Epoch(train) [22][ 850/1196] lr: 8.0000e-03 eta: 7:06:19 time: 1.2362 data_time: 0.0034 memory: 3291 grad_norm: 0.0973 loss: 0.2079 loss_sem_seg: 0.2079 2023/05/11 11:00:06 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 11:00:26 - mmengine - INFO - Epoch(train) [22][ 900/1196] lr: 8.0000e-03 eta: 7:04:56 time: 1.2494 data_time: 0.0033 memory: 3422 grad_norm: 0.1088 loss: 0.2199 loss_sem_seg: 0.2199 2023/05/11 11:01:34 - mmengine - INFO - Epoch(train) [22][ 950/1196] lr: 8.0000e-03 eta: 7:03:36 time: 1.3568 data_time: 0.0035 memory: 3563 grad_norm: 0.1025 loss: 0.1997 loss_sem_seg: 0.1997 2023/05/11 11:02:48 - mmengine - INFO - Epoch(train) [22][1000/1196] lr: 8.0000e-03 eta: 7:02:21 time: 1.4818 data_time: 0.0035 memory: 3256 grad_norm: 0.0950 loss: 0.2024 loss_sem_seg: 0.2024 2023/05/11 11:04:01 - mmengine - INFO - Epoch(train) [22][1050/1196] lr: 8.0000e-03 eta: 7:01:05 time: 1.4479 data_time: 0.0035 memory: 3196 grad_norm: 0.0943 loss: 0.2081 loss_sem_seg: 0.2081 2023/05/11 11:05:13 - mmengine - INFO - Epoch(train) [22][1100/1196] lr: 8.0000e-03 eta: 6:59:48 time: 1.4538 data_time: 0.0035 memory: 3612 grad_norm: 0.0973 loss: 0.2108 loss_sem_seg: 0.2108 2023/05/11 11:06:25 - mmengine - INFO - Epoch(train) [22][1150/1196] lr: 8.0000e-03 eta: 6:58:32 time: 1.4372 data_time: 0.0034 memory: 3649 grad_norm: 0.0928 loss: 0.2096 loss_sem_seg: 0.2096 2023/05/11 11:07:32 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 11:07:32 - mmengine - INFO - Saving checkpoint at 22 epochs 2023/05/11 11:08:06 - mmengine - INFO - Epoch(val) [22][ 50/509] eta: 0:04:25 time: 0.5792 data_time: 0.0022 memory: 3561 2023/05/11 11:08:28 - mmengine - INFO - Epoch(val) [22][100/509] eta: 0:03:28 time: 0.4411 data_time: 0.0022 memory: 1105 2023/05/11 11:08:51 - mmengine - INFO - Epoch(val) [22][150/509] eta: 0:02:55 time: 0.4435 data_time: 0.0021 memory: 1110 2023/05/11 11:09:13 - mmengine - INFO - Epoch(val) [22][200/509] eta: 0:02:27 time: 0.4449 data_time: 0.0021 memory: 1100 2023/05/11 11:09:36 - mmengine - INFO - Epoch(val) [22][250/509] eta: 0:02:02 time: 0.4584 data_time: 0.0020 memory: 1111 2023/05/11 11:09:58 - mmengine - INFO - Epoch(val) [22][300/509] eta: 0:01:37 time: 0.4423 data_time: 0.0021 memory: 1075 2023/05/11 11:10:19 - mmengine - INFO - Epoch(val) [22][350/509] eta: 0:01:13 time: 0.4272 data_time: 0.0021 memory: 1091 2023/05/11 11:10:41 - mmengine - INFO - Epoch(val) [22][400/509] eta: 0:00:50 time: 0.4374 data_time: 0.0020 memory: 1090 2023/05/11 11:11:04 - mmengine - INFO - Epoch(val) [22][450/509] eta: 0:00:27 time: 0.4513 data_time: 0.0021 memory: 1113 2023/05/11 11:11:26 - mmengine - INFO - Epoch(val) [22][500/509] eta: 0:00:04 time: 0.4419 data_time: 0.0020 memory: 1098 2023/05/11 11:11:58 - 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 | 0.9696 | 0.4747 | 0.7749 | 0.6926 | 0.7058 | 0.7565 | 0.8732 | 0.1543 | 0.9381 | 0.4774 | 0.8220 | 0.0141 | 0.9104 | 0.6449 | 0.8831 | 0.6527 | 0.7414 | 0.6577 | 0.5481 | 0.6680 | 0.9227 | 0.7455 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 11:11:58 - mmengine - INFO - Epoch(val) [22][509/509] car: 0.9696 bicycle: 0.4747 motorcycle: 0.7749 truck: 0.6926 bus: 0.7058 person: 0.7565 bicyclist: 0.8732 motorcyclist: 0.1543 road: 0.9381 parking: 0.4774 sidewalk: 0.8220 other-ground: 0.0141 building: 0.9104 fence: 0.6449 vegetation: 0.8831 trunck: 0.6527 terrian: 0.7414 pole: 0.6577 traffic-sign: 0.5481 miou: 0.6680 acc: 0.9227 acc_cls: 0.7455 data_time: 0.0020 time: 0.4455 2023/05/11 11:13:16 - mmengine - INFO - Epoch(train) [23][ 50/1196] lr: 8.0000e-03 eta: 6:56:09 time: 1.5601 data_time: 0.0047 memory: 3490 grad_norm: 0.0971 loss: 0.2015 loss_sem_seg: 0.2015 2023/05/11 11:14:28 - mmengine - INFO - Epoch(train) [23][ 100/1196] lr: 8.0000e-03 eta: 6:54:53 time: 1.4425 data_time: 0.0035 memory: 3357 grad_norm: 0.0955 loss: 0.1996 loss_sem_seg: 0.1996 2023/05/11 11:15:40 - mmengine - INFO - Epoch(train) [23][ 150/1196] lr: 8.0000e-03 eta: 6:53:36 time: 1.4435 data_time: 0.0035 memory: 3519 grad_norm: 0.1021 loss: 0.2201 loss_sem_seg: 0.2201 2023/05/11 11:16:52 - mmengine - INFO - Epoch(train) [23][ 200/1196] lr: 8.0000e-03 eta: 6:52:20 time: 1.4425 data_time: 0.0035 memory: 3566 grad_norm: 0.0999 loss: 0.2096 loss_sem_seg: 0.2096 2023/05/11 11:18:04 - mmengine - INFO - Epoch(train) [23][ 250/1196] lr: 8.0000e-03 eta: 6:51:03 time: 1.4399 data_time: 0.0035 memory: 3547 grad_norm: 0.1173 loss: 0.2098 loss_sem_seg: 0.2098 2023/05/11 11:19:17 - mmengine - INFO - Epoch(train) [23][ 300/1196] lr: 8.0000e-03 eta: 6:49:47 time: 1.4494 data_time: 0.0035 memory: 3400 grad_norm: 0.1160 loss: 0.2122 loss_sem_seg: 0.2122 2023/05/11 11:20:29 - mmengine - INFO - Epoch(train) [23][ 350/1196] lr: 8.0000e-03 eta: 6:48:31 time: 1.4464 data_time: 0.0036 memory: 3328 grad_norm: 0.0988 loss: 0.2109 loss_sem_seg: 0.2109 2023/05/11 11:21:42 - mmengine - INFO - Epoch(train) [23][ 400/1196] lr: 8.0000e-03 eta: 6:47:15 time: 1.4487 data_time: 0.0035 memory: 3196 grad_norm: 0.0934 loss: 0.2190 loss_sem_seg: 0.2190 2023/05/11 11:22:54 - mmengine - INFO - Epoch(train) [23][ 450/1196] lr: 8.0000e-03 eta: 6:45:59 time: 1.4508 data_time: 0.0034 memory: 3312 grad_norm: 0.0899 loss: 0.2093 loss_sem_seg: 0.2093 2023/05/11 11:24:06 - mmengine - INFO - Epoch(train) [23][ 500/1196] lr: 8.0000e-03 eta: 6:44:42 time: 1.4421 data_time: 0.0034 memory: 3501 grad_norm: 0.0935 loss: 0.2217 loss_sem_seg: 0.2217 2023/05/11 11:25:18 - mmengine - INFO - Epoch(train) [23][ 550/1196] lr: 8.0000e-03 eta: 6:43:26 time: 1.4394 data_time: 0.0035 memory: 3605 grad_norm: 0.1045 loss: 0.2075 loss_sem_seg: 0.2075 2023/05/11 11:26:30 - mmengine - INFO - Epoch(train) [23][ 600/1196] lr: 8.0000e-03 eta: 6:42:09 time: 1.4357 data_time: 0.0034 memory: 3303 grad_norm: 0.1152 loss: 0.2068 loss_sem_seg: 0.2068 2023/05/11 11:27:42 - mmengine - INFO - Epoch(train) [23][ 650/1196] lr: 8.0000e-03 eta: 6:40:53 time: 1.4428 data_time: 0.0035 memory: 3349 grad_norm: 0.1070 loss: 0.2067 loss_sem_seg: 0.2067 2023/05/11 11:28:34 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 11:28:50 - mmengine - INFO - Epoch(train) [23][ 700/1196] lr: 8.0000e-03 eta: 6:39:34 time: 1.3511 data_time: 0.0035 memory: 3259 grad_norm: 0.0933 loss: 0.2082 loss_sem_seg: 0.2082 2023/05/11 11:29:52 - mmengine - INFO - Epoch(train) [23][ 750/1196] lr: 8.0000e-03 eta: 6:38:12 time: 1.2423 data_time: 0.0035 memory: 3605 grad_norm: 0.0995 loss: 0.2029 loss_sem_seg: 0.2029 2023/05/11 11:30:55 - mmengine - INFO - Epoch(train) [23][ 800/1196] lr: 8.0000e-03 eta: 6:36:50 time: 1.2556 data_time: 0.0034 memory: 3233 grad_norm: 0.0877 loss: 0.2038 loss_sem_seg: 0.2038 2023/05/11 11:31:57 - mmengine - INFO - Epoch(train) [23][ 850/1196] lr: 8.0000e-03 eta: 6:35:28 time: 1.2450 data_time: 0.0034 memory: 3242 grad_norm: 0.0827 loss: 0.2053 loss_sem_seg: 0.2053 2023/05/11 11:32:59 - mmengine - INFO - Epoch(train) [23][ 900/1196] lr: 8.0000e-03 eta: 6:34:06 time: 1.2377 data_time: 0.0038 memory: 3282 grad_norm: 0.1036 loss: 0.2322 loss_sem_seg: 0.2322 2023/05/11 11:34:04 - mmengine - INFO - Epoch(train) [23][ 950/1196] lr: 8.0000e-03 eta: 6:32:46 time: 1.3062 data_time: 0.0034 memory: 3383 grad_norm: 0.1052 loss: 0.2149 loss_sem_seg: 0.2149 2023/05/11 11:35:18 - mmengine - INFO - Epoch(train) [23][1000/1196] lr: 8.0000e-03 eta: 6:31:31 time: 1.4836 data_time: 0.0035 memory: 3190 grad_norm: 0.0961 loss: 0.2074 loss_sem_seg: 0.2074 2023/05/11 11:36:30 - mmengine - INFO - Epoch(train) [23][1050/1196] lr: 8.0000e-03 eta: 6:30:15 time: 1.4328 data_time: 0.0034 memory: 3414 grad_norm: 0.0919 loss: 0.2040 loss_sem_seg: 0.2040 2023/05/11 11:37:43 - mmengine - INFO - Epoch(train) [23][1100/1196] lr: 8.0000e-03 eta: 6:28:59 time: 1.4546 data_time: 0.0034 memory: 3482 grad_norm: 0.1037 loss: 0.2188 loss_sem_seg: 0.2188 2023/05/11 11:38:55 - mmengine - INFO - Epoch(train) [23][1150/1196] lr: 8.0000e-03 eta: 6:27:43 time: 1.4428 data_time: 0.0035 memory: 3346 grad_norm: 0.0877 loss: 0.2210 loss_sem_seg: 0.2210 2023/05/11 11:40:01 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 11:40:01 - mmengine - INFO - Saving checkpoint at 23 epochs 2023/05/11 11:40:34 - mmengine - INFO - Epoch(val) [23][ 50/509] eta: 0:04:10 time: 0.5461 data_time: 0.0021 memory: 3767 2023/05/11 11:40:57 - mmengine - INFO - Epoch(val) [23][100/509] eta: 0:03:24 time: 0.4538 data_time: 0.0021 memory: 1105 2023/05/11 11:41:19 - mmengine - INFO - Epoch(val) [23][150/509] eta: 0:02:52 time: 0.4419 data_time: 0.0020 memory: 1110 2023/05/11 11:41:41 - mmengine - INFO - Epoch(val) [23][200/509] eta: 0:02:25 time: 0.4426 data_time: 0.0020 memory: 1100 2023/05/11 11:42:03 - mmengine - INFO - Epoch(val) [23][250/509] eta: 0:02:01 time: 0.4536 data_time: 0.0021 memory: 1111 2023/05/11 11:42:26 - mmengine - INFO - Epoch(val) [23][300/509] eta: 0:01:36 time: 0.4410 data_time: 0.0021 memory: 1075 2023/05/11 11:42:47 - mmengine - INFO - Epoch(val) [23][350/509] eta: 0:01:12 time: 0.4262 data_time: 0.0020 memory: 1091 2023/05/11 11:43:09 - mmengine - INFO - Epoch(val) [23][400/509] eta: 0:00:49 time: 0.4413 data_time: 0.0021 memory: 1090 2023/05/11 11:43:31 - mmengine - INFO - Epoch(val) [23][450/509] eta: 0:00:26 time: 0.4509 data_time: 0.0020 memory: 1113 2023/05/11 11:43:53 - mmengine - INFO - Epoch(val) [23][500/509] eta: 0:00:04 time: 0.4392 data_time: 0.0020 memory: 1098 2023/05/11 11:44:25 - 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 | 0.9479 | 0.5592 | 0.7966 | 0.3551 | 0.4046 | 0.7225 | 0.8629 | 0.0911 | 0.9398 | 0.4377 | 0.8117 | 0.0172 | 0.8890 | 0.5776 | 0.8786 | 0.6470 | 0.7331 | 0.6577 | 0.5286 | 0.6241 | 0.9156 | 0.6892 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 11:44:25 - mmengine - INFO - Epoch(val) [23][509/509] car: 0.9479 bicycle: 0.5592 motorcycle: 0.7966 truck: 0.3551 bus: 0.4046 person: 0.7225 bicyclist: 0.8629 motorcyclist: 0.0911 road: 0.9398 parking: 0.4377 sidewalk: 0.8117 other-ground: 0.0172 building: 0.8890 fence: 0.5776 vegetation: 0.8786 trunck: 0.6470 terrian: 0.7331 pole: 0.6577 traffic-sign: 0.5286 miou: 0.6241 acc: 0.9156 acc_cls: 0.6892 data_time: 0.0020 time: 0.4418 2023/05/11 11:45:44 - mmengine - INFO - Epoch(train) [24][ 50/1196] lr: 8.0000e-03 eta: 6:25:21 time: 1.5719 data_time: 0.0042 memory: 3437 grad_norm: 0.0927 loss: 0.2032 loss_sem_seg: 0.2032 2023/05/11 11:46:57 - mmengine - INFO - Epoch(train) [24][ 100/1196] lr: 8.0000e-03 eta: 6:24:05 time: 1.4621 data_time: 0.0035 memory: 3266 grad_norm: 0.0925 loss: 0.2120 loss_sem_seg: 0.2120 2023/05/11 11:48:10 - mmengine - INFO - Epoch(train) [24][ 150/1196] lr: 8.0000e-03 eta: 6:22:50 time: 1.4676 data_time: 0.0035 memory: 3270 grad_norm: 0.1121 loss: 0.2162 loss_sem_seg: 0.2162 2023/05/11 11:49:25 - mmengine - INFO - Epoch(train) [24][ 200/1196] lr: 8.0000e-03 eta: 6:21:36 time: 1.4932 data_time: 0.0035 memory: 3261 grad_norm: 0.1063 loss: 0.2068 loss_sem_seg: 0.2068 2023/05/11 11:50:39 - mmengine - INFO - Epoch(train) [24][ 250/1196] lr: 8.0000e-03 eta: 6:20:21 time: 1.4836 data_time: 0.0036 memory: 3328 grad_norm: 0.0970 loss: 0.1949 loss_sem_seg: 0.1949 2023/05/11 11:51:54 - mmengine - INFO - Epoch(train) [24][ 300/1196] lr: 8.0000e-03 eta: 6:19:06 time: 1.4921 data_time: 0.0035 memory: 3329 grad_norm: 0.0962 loss: 0.2082 loss_sem_seg: 0.2082 2023/05/11 11:53:07 - mmengine - INFO - Epoch(train) [24][ 350/1196] lr: 8.0000e-03 eta: 6:17:51 time: 1.4757 data_time: 0.0036 memory: 3256 grad_norm: 0.0950 loss: 0.2048 loss_sem_seg: 0.2048 2023/05/11 11:54:20 - mmengine - INFO - Epoch(train) [24][ 400/1196] lr: 8.0000e-03 eta: 6:16:35 time: 1.4476 data_time: 0.0035 memory: 3113 grad_norm: 0.0911 loss: 0.1840 loss_sem_seg: 0.1840 2023/05/11 11:55:32 - mmengine - INFO - Epoch(train) [24][ 450/1196] lr: 8.0000e-03 eta: 6:15:20 time: 1.4466 data_time: 0.0034 memory: 3412 grad_norm: 0.0877 loss: 0.2154 loss_sem_seg: 0.2154 2023/05/11 11:56:33 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 11:56:44 - mmengine - INFO - Epoch(train) [24][ 500/1196] lr: 8.0000e-03 eta: 6:14:04 time: 1.4451 data_time: 0.0035 memory: 3450 grad_norm: 0.1137 loss: 0.2043 loss_sem_seg: 0.2043 2023/05/11 11:57:57 - mmengine - INFO - Epoch(train) [24][ 550/1196] lr: 8.0000e-03 eta: 6:12:48 time: 1.4437 data_time: 0.0034 memory: 3268 grad_norm: 0.0963 loss: 0.2019 loss_sem_seg: 0.2019 2023/05/11 11:59:08 - mmengine - INFO - Epoch(train) [24][ 600/1196] lr: 8.0000e-03 eta: 6:11:32 time: 1.4369 data_time: 0.0035 memory: 3370 grad_norm: 0.0940 loss: 0.1983 loss_sem_seg: 0.1983 2023/05/11 12:00:21 - mmengine - INFO - Epoch(train) [24][ 650/1196] lr: 8.0000e-03 eta: 6:10:16 time: 1.4556 data_time: 0.0034 memory: 3342 grad_norm: 0.0977 loss: 0.2108 loss_sem_seg: 0.2108 2023/05/11 12:01:29 - mmengine - INFO - Epoch(train) [24][ 700/1196] lr: 8.0000e-03 eta: 6:08:58 time: 1.3498 data_time: 0.0035 memory: 3348 grad_norm: 0.0912 loss: 0.1933 loss_sem_seg: 0.1933 2023/05/11 12:02:31 - mmengine - INFO - Epoch(train) [24][ 750/1196] lr: 8.0000e-03 eta: 6:07:37 time: 1.2521 data_time: 0.0035 memory: 3472 grad_norm: 0.0981 loss: 0.2047 loss_sem_seg: 0.2047 2023/05/11 12:03:33 - mmengine - INFO - Epoch(train) [24][ 800/1196] lr: 8.0000e-03 eta: 6:06:16 time: 1.2421 data_time: 0.0035 memory: 3560 grad_norm: 0.1036 loss: 0.2151 loss_sem_seg: 0.2151 2023/05/11 12:04:35 - mmengine - INFO - Epoch(train) [24][ 850/1196] lr: 8.0000e-03 eta: 6:04:55 time: 1.2319 data_time: 0.0035 memory: 3511 grad_norm: 0.0991 loss: 0.2204 loss_sem_seg: 0.2204 2023/05/11 12:05:37 - mmengine - INFO - Epoch(train) [24][ 900/1196] lr: 8.0000e-03 eta: 6:03:34 time: 1.2403 data_time: 0.0034 memory: 3509 grad_norm: 0.1003 loss: 0.2043 loss_sem_seg: 0.2043 2023/05/11 12:06:41 - mmengine - INFO - Epoch(train) [24][ 950/1196] lr: 8.0000e-03 eta: 6:02:14 time: 1.2789 data_time: 0.0034 memory: 3476 grad_norm: 0.0919 loss: 0.2069 loss_sem_seg: 0.2069 2023/05/11 12:07:56 - mmengine - INFO - Epoch(train) [24][1000/1196] lr: 8.0000e-03 eta: 6:01:00 time: 1.5049 data_time: 0.0035 memory: 3472 grad_norm: 0.0900 loss: 0.1979 loss_sem_seg: 0.1979 2023/05/11 12:09:09 - mmengine - INFO - Epoch(train) [24][1050/1196] lr: 8.0000e-03 eta: 5:59:45 time: 1.4469 data_time: 0.0035 memory: 3339 grad_norm: 0.1003 loss: 0.2025 loss_sem_seg: 0.2025 2023/05/11 12:10:21 - mmengine - INFO - Epoch(train) [24][1100/1196] lr: 8.0000e-03 eta: 5:58:29 time: 1.4413 data_time: 0.0035 memory: 3330 grad_norm: 0.0954 loss: 0.2070 loss_sem_seg: 0.2070 2023/05/11 12:11:33 - mmengine - INFO - Epoch(train) [24][1150/1196] lr: 8.0000e-03 eta: 5:57:13 time: 1.4399 data_time: 0.0035 memory: 3637 grad_norm: 0.0993 loss: 0.2015 loss_sem_seg: 0.2015 2023/05/11 12:12:39 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 12:12:39 - mmengine - INFO - Saving checkpoint at 24 epochs 2023/05/11 12:13:10 - mmengine - INFO - Epoch(val) [24][ 50/509] eta: 0:03:55 time: 0.5121 data_time: 0.0021 memory: 3388 2023/05/11 12:13:32 - mmengine - INFO - Epoch(val) [24][100/509] eta: 0:03:17 time: 0.4519 data_time: 0.0020 memory: 1105 2023/05/11 12:13:54 - mmengine - INFO - Epoch(val) [24][150/509] eta: 0:02:48 time: 0.4419 data_time: 0.0020 memory: 1110 2023/05/11 12:14:17 - mmengine - INFO - Epoch(val) [24][200/509] eta: 0:02:22 time: 0.4432 data_time: 0.0020 memory: 1100 2023/05/11 12:14:40 - mmengine - INFO - Epoch(val) [24][250/509] eta: 0:01:59 time: 0.4591 data_time: 0.0021 memory: 1111 2023/05/11 12:15:02 - mmengine - INFO - Epoch(val) [24][300/509] eta: 0:01:35 time: 0.4454 data_time: 0.0021 memory: 1075 2023/05/11 12:15:23 - mmengine - INFO - Epoch(val) [24][350/509] eta: 0:01:12 time: 0.4233 data_time: 0.0020 memory: 1091 2023/05/11 12:15:45 - mmengine - INFO - Epoch(val) [24][400/509] eta: 0:00:49 time: 0.4434 data_time: 0.0020 memory: 1090 2023/05/11 12:16:08 - mmengine - INFO - Epoch(val) [24][450/509] eta: 0:00:26 time: 0.4489 data_time: 0.0020 memory: 1113 2023/05/11 12:16:30 - mmengine - INFO - Epoch(val) [24][500/509] eta: 0:00:04 time: 0.4404 data_time: 0.0020 memory: 1098 2023/05/11 12:17:01 - 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 | 0.9613 | 0.5907 | 0.7449 | 0.3765 | 0.5091 | 0.7668 | 0.9062 | 0.1588 | 0.9407 | 0.5555 | 0.8171 | 0.1105 | 0.9083 | 0.6516 | 0.8784 | 0.6451 | 0.7389 | 0.6553 | 0.5303 | 0.6551 | 0.9197 | 0.7589 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 12:17:01 - mmengine - INFO - Epoch(val) [24][509/509] car: 0.9613 bicycle: 0.5907 motorcycle: 0.7449 truck: 0.3765 bus: 0.5091 person: 0.7668 bicyclist: 0.9062 motorcyclist: 0.1588 road: 0.9407 parking: 0.5555 sidewalk: 0.8171 other-ground: 0.1105 building: 0.9083 fence: 0.6516 vegetation: 0.8784 trunck: 0.6451 terrian: 0.7389 pole: 0.6553 traffic-sign: 0.5303 miou: 0.6551 acc: 0.9197 acc_cls: 0.7589 data_time: 0.0020 time: 0.4447 2023/05/11 12:18:20 - mmengine - INFO - Epoch(train) [25][ 50/1196] lr: 8.0000e-04 eta: 5:54:51 time: 1.5701 data_time: 0.0041 memory: 3365 grad_norm: 0.0670 loss: 0.1840 loss_sem_seg: 0.1840 2023/05/11 12:19:32 - mmengine - INFO - Epoch(train) [25][ 100/1196] lr: 8.0000e-04 eta: 5:53:36 time: 1.4454 data_time: 0.0035 memory: 3240 grad_norm: 0.0644 loss: 0.1876 loss_sem_seg: 0.1876 2023/05/11 12:20:44 - mmengine - INFO - Epoch(train) [25][ 150/1196] lr: 8.0000e-04 eta: 5:52:20 time: 1.4396 data_time: 0.0037 memory: 3613 grad_norm: 0.0649 loss: 0.1855 loss_sem_seg: 0.1855 2023/05/11 12:21:56 - mmengine - INFO - Epoch(train) [25][ 200/1196] lr: 8.0000e-04 eta: 5:51:04 time: 1.4386 data_time: 0.0035 memory: 3524 grad_norm: 0.0622 loss: 0.1805 loss_sem_seg: 0.1805 2023/05/11 12:23:08 - mmengine - INFO - Epoch(train) [25][ 250/1196] lr: 8.0000e-04 eta: 5:49:49 time: 1.4420 data_time: 0.0034 memory: 3303 grad_norm: 0.0642 loss: 0.1889 loss_sem_seg: 0.1889 2023/05/11 12:24:15 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 12:24:21 - mmengine - INFO - Epoch(train) [25][ 300/1196] lr: 8.0000e-04 eta: 5:48:34 time: 1.4501 data_time: 0.0035 memory: 3296 grad_norm: 0.0623 loss: 0.1731 loss_sem_seg: 0.1731 2023/05/11 12:25:33 - mmengine - INFO - Epoch(train) [25][ 350/1196] lr: 8.0000e-04 eta: 5:47:18 time: 1.4476 data_time: 0.0035 memory: 3765 grad_norm: 0.0679 loss: 0.1785 loss_sem_seg: 0.1785 2023/05/11 12:26:45 - mmengine - INFO - Epoch(train) [25][ 400/1196] lr: 8.0000e-04 eta: 5:46:02 time: 1.4284 data_time: 0.0035 memory: 3407 grad_norm: 0.0642 loss: 0.1776 loss_sem_seg: 0.1776 2023/05/11 12:27:57 - mmengine - INFO - Epoch(train) [25][ 450/1196] lr: 8.0000e-04 eta: 5:44:47 time: 1.4413 data_time: 0.0034 memory: 3359 grad_norm: 0.0640 loss: 0.1745 loss_sem_seg: 0.1745 2023/05/11 12:29:09 - mmengine - INFO - Epoch(train) [25][ 500/1196] lr: 8.0000e-04 eta: 5:43:31 time: 1.4420 data_time: 0.0034 memory: 3225 grad_norm: 0.0666 loss: 0.1742 loss_sem_seg: 0.1742 2023/05/11 12:30:20 - mmengine - INFO - Epoch(train) [25][ 550/1196] lr: 8.0000e-04 eta: 5:42:16 time: 1.4344 data_time: 0.0034 memory: 3151 grad_norm: 0.0611 loss: 0.1781 loss_sem_seg: 0.1781 2023/05/11 12:31:33 - mmengine - INFO - Epoch(train) [25][ 600/1196] lr: 8.0000e-04 eta: 5:41:00 time: 1.4457 data_time: 0.0034 memory: 3208 grad_norm: 0.0654 loss: 0.1684 loss_sem_seg: 0.1684 2023/05/11 12:32:44 - mmengine - INFO - Epoch(train) [25][ 650/1196] lr: 8.0000e-04 eta: 5:39:45 time: 1.4351 data_time: 0.0034 memory: 3379 grad_norm: 0.0672 loss: 0.1693 loss_sem_seg: 0.1693 2023/05/11 12:33:52 - mmengine - INFO - Epoch(train) [25][ 700/1196] lr: 8.0000e-04 eta: 5:38:27 time: 1.3569 data_time: 0.0034 memory: 3250 grad_norm: 0.0656 loss: 0.1692 loss_sem_seg: 0.1692 2023/05/11 12:34:54 - mmengine - INFO - Epoch(train) [25][ 750/1196] lr: 8.0000e-04 eta: 5:37:07 time: 1.2378 data_time: 0.0034 memory: 3332 grad_norm: 0.0614 loss: 0.1736 loss_sem_seg: 0.1736 2023/05/11 12:35:56 - mmengine - INFO - Epoch(train) [25][ 800/1196] lr: 8.0000e-04 eta: 5:35:47 time: 1.2377 data_time: 0.0034 memory: 3333 grad_norm: 0.0664 loss: 0.1766 loss_sem_seg: 0.1766 2023/05/11 12:36:57 - mmengine - INFO - Epoch(train) [25][ 850/1196] lr: 8.0000e-04 eta: 5:34:27 time: 1.2246 data_time: 0.0033 memory: 3315 grad_norm: 0.0627 loss: 0.1679 loss_sem_seg: 0.1679 2023/05/11 12:37:59 - mmengine - INFO - Epoch(train) [25][ 900/1196] lr: 8.0000e-04 eta: 5:33:07 time: 1.2368 data_time: 0.0034 memory: 3540 grad_norm: 0.0622 loss: 0.1712 loss_sem_seg: 0.1712 2023/05/11 12:39:01 - mmengine - INFO - Epoch(train) [25][ 950/1196] lr: 8.0000e-04 eta: 5:31:47 time: 1.2314 data_time: 0.0034 memory: 3461 grad_norm: 0.0663 loss: 0.1809 loss_sem_seg: 0.1809 2023/05/11 12:40:16 - mmengine - INFO - Epoch(train) [25][1000/1196] lr: 8.0000e-04 eta: 5:30:33 time: 1.5060 data_time: 0.0034 memory: 3256 grad_norm: 0.0685 loss: 0.1766 loss_sem_seg: 0.1766 2023/05/11 12:41:27 - mmengine - INFO - Epoch(train) [25][1050/1196] lr: 8.0000e-04 eta: 5:29:17 time: 1.4244 data_time: 0.0034 memory: 3183 grad_norm: 0.0618 loss: 0.1738 loss_sem_seg: 0.1738 2023/05/11 12:42:39 - mmengine - INFO - Epoch(train) [25][1100/1196] lr: 8.0000e-04 eta: 5:28:02 time: 1.4402 data_time: 0.0034 memory: 3356 grad_norm: 0.0647 loss: 0.1819 loss_sem_seg: 0.1819 2023/05/11 12:43:51 - mmengine - INFO - Epoch(train) [25][1150/1196] lr: 8.0000e-04 eta: 5:26:47 time: 1.4356 data_time: 0.0034 memory: 3303 grad_norm: 0.0618 loss: 0.1796 loss_sem_seg: 0.1796 2023/05/11 12:44:58 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 12:44:58 - mmengine - INFO - Saving checkpoint at 25 epochs 2023/05/11 12:45:28 - mmengine - INFO - Epoch(val) [25][ 50/509] eta: 0:03:48 time: 0.4976 data_time: 0.0021 memory: 3232 2023/05/11 12:45:51 - mmengine - INFO - Epoch(val) [25][100/509] eta: 0:03:15 time: 0.4575 data_time: 0.0020 memory: 1105 2023/05/11 12:46:13 - mmengine - INFO - Epoch(val) [25][150/509] eta: 0:02:46 time: 0.4389 data_time: 0.0020 memory: 1110 2023/05/11 12:46:35 - mmengine - INFO - Epoch(val) [25][200/509] eta: 0:02:22 time: 0.4451 data_time: 0.0020 memory: 1100 2023/05/11 12:46:58 - mmengine - INFO - Epoch(val) [25][250/509] eta: 0:01:58 time: 0.4539 data_time: 0.0020 memory: 1111 2023/05/11 12:47:20 - mmengine - INFO - Epoch(val) [25][300/509] eta: 0:01:35 time: 0.4405 data_time: 0.0020 memory: 1075 2023/05/11 12:47:41 - mmengine - INFO - Epoch(val) [25][350/509] eta: 0:01:11 time: 0.4245 data_time: 0.0020 memory: 1091 2023/05/11 12:48:03 - mmengine - INFO - Epoch(val) [25][400/509] eta: 0:00:48 time: 0.4372 data_time: 0.0020 memory: 1090 2023/05/11 12:48:26 - mmengine - INFO - Epoch(val) [25][450/509] eta: 0:00:26 time: 0.4517 data_time: 0.0021 memory: 1113 2023/05/11 12:48:48 - mmengine - INFO - Epoch(val) [25][500/509] eta: 0:00:04 time: 0.4403 data_time: 0.0020 memory: 1098 2023/05/11 12:49:19 - 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 | 0.9606 | 0.5943 | 0.8153 | 0.8410 | 0.5820 | 0.8004 | 0.9097 | 0.2447 | 0.9468 | 0.5361 | 0.8261 | 0.0401 | 0.9156 | 0.6711 | 0.8772 | 0.6806 | 0.7270 | 0.6611 | 0.5322 | 0.6927 | 0.9223 | 0.7615 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 12:49:19 - mmengine - INFO - Epoch(val) [25][509/509] car: 0.9606 bicycle: 0.5943 motorcycle: 0.8153 truck: 0.8410 bus: 0.5820 person: 0.8004 bicyclist: 0.9097 motorcyclist: 0.2447 road: 0.9468 parking: 0.5361 sidewalk: 0.8261 other-ground: 0.0401 building: 0.9156 fence: 0.6711 vegetation: 0.8772 trunck: 0.6806 terrian: 0.7270 pole: 0.6611 traffic-sign: 0.5322 miou: 0.6927 acc: 0.9223 acc_cls: 0.7615 data_time: 0.0020 time: 0.4442 2023/05/11 12:50:36 - mmengine - INFO - Epoch(train) [26][ 50/1196] lr: 8.0000e-04 eta: 5:24:24 time: 1.5431 data_time: 0.0041 memory: 3339 grad_norm: 0.0656 loss: 0.1802 loss_sem_seg: 0.1802 2023/05/11 12:51:48 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 12:51:48 - mmengine - INFO - Epoch(train) [26][ 100/1196] lr: 8.0000e-04 eta: 5:23:09 time: 1.4449 data_time: 0.0034 memory: 3394 grad_norm: 0.0663 loss: 0.1675 loss_sem_seg: 0.1675 2023/05/11 12:53:00 - mmengine - INFO - Epoch(train) [26][ 150/1196] lr: 8.0000e-04 eta: 5:21:54 time: 1.4319 data_time: 0.0033 memory: 3196 grad_norm: 0.0589 loss: 0.1708 loss_sem_seg: 0.1708 2023/05/11 12:54:11 - mmengine - INFO - Epoch(train) [26][ 200/1196] lr: 8.0000e-04 eta: 5:20:38 time: 1.4253 data_time: 0.0034 memory: 3504 grad_norm: 0.0610 loss: 0.1724 loss_sem_seg: 0.1724 2023/05/11 12:55:22 - mmengine - INFO - Epoch(train) [26][ 250/1196] lr: 8.0000e-04 eta: 5:19:23 time: 1.4243 data_time: 0.0035 memory: 3155 grad_norm: 0.0708 loss: 0.1797 loss_sem_seg: 0.1797 2023/05/11 12:56:34 - mmengine - INFO - Epoch(train) [26][ 300/1196] lr: 8.0000e-04 eta: 5:18:07 time: 1.4302 data_time: 0.0034 memory: 3588 grad_norm: 0.0599 loss: 0.1680 loss_sem_seg: 0.1680 2023/05/11 12:57:46 - mmengine - INFO - Epoch(train) [26][ 350/1196] lr: 8.0000e-04 eta: 5:16:52 time: 1.4364 data_time: 0.0035 memory: 3758 grad_norm: 0.0651 loss: 0.1766 loss_sem_seg: 0.1766 2023/05/11 12:58:58 - mmengine - INFO - Epoch(train) [26][ 400/1196] lr: 8.0000e-04 eta: 5:15:37 time: 1.4384 data_time: 0.0034 memory: 3615 grad_norm: 0.0635 loss: 0.1721 loss_sem_seg: 0.1721 2023/05/11 13:00:10 - mmengine - INFO - Epoch(train) [26][ 450/1196] lr: 8.0000e-04 eta: 5:14:22 time: 1.4373 data_time: 0.0034 memory: 3597 grad_norm: 0.0639 loss: 0.1568 loss_sem_seg: 0.1568 2023/05/11 13:01:21 - mmengine - INFO - Epoch(train) [26][ 500/1196] lr: 8.0000e-04 eta: 5:13:06 time: 1.4342 data_time: 0.0034 memory: 3757 grad_norm: 0.0661 loss: 0.1700 loss_sem_seg: 0.1700 2023/05/11 13:02:32 - mmengine - INFO - Epoch(train) [26][ 550/1196] lr: 8.0000e-04 eta: 5:11:51 time: 1.4145 data_time: 0.0034 memory: 3496 grad_norm: 0.0624 loss: 0.1664 loss_sem_seg: 0.1664 2023/05/11 13:03:44 - mmengine - INFO - Epoch(train) [26][ 600/1196] lr: 8.0000e-04 eta: 5:10:36 time: 1.4364 data_time: 0.0034 memory: 3283 grad_norm: 0.0613 loss: 0.1594 loss_sem_seg: 0.1594 2023/05/11 13:04:56 - mmengine - INFO - Epoch(train) [26][ 650/1196] lr: 8.0000e-04 eta: 5:09:20 time: 1.4409 data_time: 0.0035 memory: 3255 grad_norm: 0.0638 loss: 0.1685 loss_sem_seg: 0.1685 2023/05/11 13:06:05 - mmengine - INFO - Epoch(train) [26][ 700/1196] lr: 8.0000e-04 eta: 5:08:04 time: 1.3854 data_time: 0.0034 memory: 3312 grad_norm: 0.0647 loss: 0.1657 loss_sem_seg: 0.1657 2023/05/11 13:07:07 - mmengine - INFO - Epoch(train) [26][ 750/1196] lr: 8.0000e-04 eta: 5:06:45 time: 1.2393 data_time: 0.0033 memory: 3445 grad_norm: 0.0639 loss: 0.1653 loss_sem_seg: 0.1653 2023/05/11 13:08:09 - mmengine - INFO - Epoch(train) [26][ 800/1196] lr: 8.0000e-04 eta: 5:05:26 time: 1.2452 data_time: 0.0033 memory: 3211 grad_norm: 0.0638 loss: 0.1657 loss_sem_seg: 0.1657 2023/05/11 13:09:11 - mmengine - INFO - Epoch(train) [26][ 850/1196] lr: 8.0000e-04 eta: 5:04:07 time: 1.2344 data_time: 0.0034 memory: 3695 grad_norm: 0.0656 loss: 0.1633 loss_sem_seg: 0.1633 2023/05/11 13:10:13 - mmengine - INFO - Epoch(train) [26][ 900/1196] lr: 8.0000e-04 eta: 5:02:48 time: 1.2267 data_time: 0.0034 memory: 3324 grad_norm: 0.0605 loss: 0.1757 loss_sem_seg: 0.1757 2023/05/11 13:11:14 - mmengine - INFO - Epoch(train) [26][ 950/1196] lr: 8.0000e-04 eta: 5:01:29 time: 1.2319 data_time: 0.0034 memory: 3662 grad_norm: 0.0668 loss: 0.1634 loss_sem_seg: 0.1634 2023/05/11 13:12:30 - mmengine - INFO - Epoch(train) [26][1000/1196] lr: 8.0000e-04 eta: 5:00:15 time: 1.5130 data_time: 0.0034 memory: 3172 grad_norm: 0.0600 loss: 0.1652 loss_sem_seg: 0.1652 2023/05/11 13:13:41 - mmengine - INFO - Epoch(train) [26][1050/1196] lr: 8.0000e-04 eta: 4:59:00 time: 1.4330 data_time: 0.0036 memory: 3382 grad_norm: 0.0641 loss: 0.1625 loss_sem_seg: 0.1625 2023/05/11 13:14:53 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 13:14:53 - mmengine - INFO - Epoch(train) [26][1100/1196] lr: 8.0000e-04 eta: 4:57:45 time: 1.4382 data_time: 0.0035 memory: 3355 grad_norm: 0.0658 loss: 0.1730 loss_sem_seg: 0.1730 2023/05/11 13:16:05 - mmengine - INFO - Epoch(train) [26][1150/1196] lr: 8.0000e-04 eta: 4:56:30 time: 1.4283 data_time: 0.0034 memory: 3443 grad_norm: 0.0713 loss: 0.1594 loss_sem_seg: 0.1594 2023/05/11 13:17:13 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 13:17:13 - mmengine - INFO - Saving checkpoint at 26 epochs 2023/05/11 13:17:43 - mmengine - INFO - Epoch(val) [26][ 50/509] eta: 0:03:49 time: 0.4994 data_time: 0.0021 memory: 3322 2023/05/11 13:18:07 - mmengine - INFO - Epoch(val) [26][100/509] eta: 0:03:17 time: 0.4677 data_time: 0.0021 memory: 1105 2023/05/11 13:18:30 - mmengine - INFO - Epoch(val) [26][150/509] eta: 0:02:50 time: 0.4582 data_time: 0.0021 memory: 1110 2023/05/11 13:18:52 - mmengine - INFO - Epoch(val) [26][200/509] eta: 0:02:25 time: 0.4564 data_time: 0.0021 memory: 1100 2023/05/11 13:19:16 - mmengine - INFO - Epoch(val) [26][250/509] eta: 0:02:02 time: 0.4747 data_time: 0.0021 memory: 1111 2023/05/11 13:19:39 - mmengine - INFO - Epoch(val) [26][300/509] eta: 0:01:37 time: 0.4537 data_time: 0.0021 memory: 1075 2023/05/11 13:20:01 - mmengine - INFO - Epoch(val) [26][350/509] eta: 0:01:13 time: 0.4420 data_time: 0.0020 memory: 1091 2023/05/11 13:20:24 - mmengine - INFO - Epoch(val) [26][400/509] eta: 0:00:50 time: 0.4594 data_time: 0.0020 memory: 1090 2023/05/11 13:20:47 - mmengine - INFO - Epoch(val) [26][450/509] eta: 0:00:27 time: 0.4638 data_time: 0.0021 memory: 1113 2023/05/11 13:21:09 - mmengine - INFO - Epoch(val) [26][500/509] eta: 0:00:04 time: 0.4399 data_time: 0.0021 memory: 1098 2023/05/11 13:21:58 - 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 | 0.9658 | 0.5846 | 0.8210 | 0.8830 | 0.6700 | 0.7787 | 0.8968 | 0.2195 | 0.9444 | 0.5126 | 0.8231 | 0.0780 | 0.9171 | 0.6791 | 0.8739 | 0.6996 | 0.7171 | 0.6615 | 0.5303 | 0.6977 | 0.9211 | 0.7662 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 13:21:58 - mmengine - INFO - Epoch(val) [26][509/509] car: 0.9658 bicycle: 0.5846 motorcycle: 0.8210 truck: 0.8830 bus: 0.6700 person: 0.7787 bicyclist: 0.8968 motorcyclist: 0.2195 road: 0.9444 parking: 0.5126 sidewalk: 0.8231 other-ground: 0.0780 building: 0.9171 fence: 0.6791 vegetation: 0.8739 trunck: 0.6996 terrian: 0.7171 pole: 0.6615 traffic-sign: 0.5303 miou: 0.6977 acc: 0.9211 acc_cls: 0.7662 data_time: 0.0021 time: 0.4516 2023/05/11 13:23:15 - mmengine - INFO - Epoch(train) [27][ 50/1196] lr: 8.0000e-04 eta: 4:54:09 time: 1.5316 data_time: 0.0040 memory: 3352 grad_norm: 0.0632 loss: 0.1633 loss_sem_seg: 0.1633 2023/05/11 13:24:26 - mmengine - INFO - Epoch(train) [27][ 100/1196] lr: 8.0000e-04 eta: 4:52:54 time: 1.4335 data_time: 0.0035 memory: 3539 grad_norm: 0.0633 loss: 0.1561 loss_sem_seg: 0.1561 2023/05/11 13:25:38 - mmengine - INFO - Epoch(train) [27][ 150/1196] lr: 8.0000e-04 eta: 4:51:39 time: 1.4342 data_time: 0.0034 memory: 3314 grad_norm: 0.0653 loss: 0.1693 loss_sem_seg: 0.1693 2023/05/11 13:26:50 - mmengine - INFO - Epoch(train) [27][ 200/1196] lr: 8.0000e-04 eta: 4:50:24 time: 1.4400 data_time: 0.0034 memory: 3258 grad_norm: 0.0640 loss: 0.1671 loss_sem_seg: 0.1671 2023/05/11 13:28:03 - mmengine - INFO - Epoch(train) [27][ 250/1196] lr: 8.0000e-04 eta: 4:49:09 time: 1.4545 data_time: 0.0036 memory: 3413 grad_norm: 0.0619 loss: 0.1619 loss_sem_seg: 0.1619 2023/05/11 13:29:14 - mmengine - INFO - Epoch(train) [27][ 300/1196] lr: 8.0000e-04 eta: 4:47:54 time: 1.4160 data_time: 0.0035 memory: 3400 grad_norm: 0.0636 loss: 0.1648 loss_sem_seg: 0.1648 2023/05/11 13:30:25 - mmengine - INFO - Epoch(train) [27][ 350/1196] lr: 8.0000e-04 eta: 4:46:39 time: 1.4337 data_time: 0.0035 memory: 3444 grad_norm: 0.0634 loss: 0.1725 loss_sem_seg: 0.1725 2023/05/11 13:31:37 - mmengine - INFO - Epoch(train) [27][ 400/1196] lr: 8.0000e-04 eta: 4:45:24 time: 1.4352 data_time: 0.0035 memory: 3531 grad_norm: 0.0646 loss: 0.1756 loss_sem_seg: 0.1756 2023/05/11 13:32:49 - mmengine - INFO - Epoch(train) [27][ 450/1196] lr: 8.0000e-04 eta: 4:44:09 time: 1.4393 data_time: 0.0034 memory: 3384 grad_norm: 0.0669 loss: 0.1645 loss_sem_seg: 0.1645 2023/05/11 13:34:01 - mmengine - INFO - Epoch(train) [27][ 500/1196] lr: 8.0000e-04 eta: 4:42:54 time: 1.4302 data_time: 0.0036 memory: 3596 grad_norm: 0.0640 loss: 0.1597 loss_sem_seg: 0.1597 2023/05/11 13:35:12 - mmengine - INFO - Epoch(train) [27][ 550/1196] lr: 8.0000e-04 eta: 4:41:39 time: 1.4374 data_time: 0.0035 memory: 3207 grad_norm: 0.0700 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/11 13:36:24 - mmengine - INFO - Epoch(train) [27][ 600/1196] lr: 8.0000e-04 eta: 4:40:24 time: 1.4234 data_time: 0.0035 memory: 3283 grad_norm: 0.0641 loss: 0.1654 loss_sem_seg: 0.1654 2023/05/11 13:37:35 - mmengine - INFO - Epoch(train) [27][ 650/1196] lr: 8.0000e-04 eta: 4:39:09 time: 1.4278 data_time: 0.0035 memory: 3305 grad_norm: 0.0663 loss: 0.1638 loss_sem_seg: 0.1638 2023/05/11 13:38:47 - mmengine - INFO - Epoch(train) [27][ 700/1196] lr: 8.0000e-04 eta: 4:37:54 time: 1.4313 data_time: 0.0035 memory: 3319 grad_norm: 0.0645 loss: 0.1650 loss_sem_seg: 0.1650 2023/05/11 13:39:49 - mmengine - INFO - Epoch(train) [27][ 750/1196] lr: 8.0000e-04 eta: 4:36:36 time: 1.2432 data_time: 0.0036 memory: 3368 grad_norm: 0.0649 loss: 0.1743 loss_sem_seg: 0.1743 2023/05/11 13:40:50 - mmengine - INFO - Epoch(train) [27][ 800/1196] lr: 8.0000e-04 eta: 4:35:18 time: 1.2274 data_time: 0.0036 memory: 3435 grad_norm: 0.0625 loss: 0.1644 loss_sem_seg: 0.1644 2023/05/11 13:41:53 - mmengine - INFO - Epoch(train) [27][ 850/1196] lr: 8.0000e-04 eta: 4:34:00 time: 1.2497 data_time: 0.0035 memory: 3472 grad_norm: 0.0633 loss: 0.1620 loss_sem_seg: 0.1620 2023/05/11 13:42:54 - mmengine - INFO - Epoch(train) [27][ 900/1196] lr: 8.0000e-04 eta: 4:32:41 time: 1.2337 data_time: 0.0035 memory: 3315 grad_norm: 0.0678 loss: 0.1547 loss_sem_seg: 0.1547 2023/05/11 13:42:59 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 13:43:56 - mmengine - INFO - Epoch(train) [27][ 950/1196] lr: 8.0000e-04 eta: 4:31:23 time: 1.2371 data_time: 0.0035 memory: 3271 grad_norm: 0.0622 loss: 0.1517 loss_sem_seg: 0.1517 2023/05/11 13:45:09 - mmengine - INFO - Epoch(train) [27][1000/1196] lr: 8.0000e-04 eta: 4:30:09 time: 1.4637 data_time: 0.0035 memory: 3394 grad_norm: 0.0616 loss: 0.1563 loss_sem_seg: 0.1563 2023/05/11 13:46:21 - mmengine - INFO - Epoch(train) [27][1050/1196] lr: 8.0000e-04 eta: 4:28:54 time: 1.4296 data_time: 0.0036 memory: 3189 grad_norm: 0.0676 loss: 0.1662 loss_sem_seg: 0.1662 2023/05/11 13:47:33 - mmengine - INFO - Epoch(train) [27][1100/1196] lr: 8.0000e-04 eta: 4:27:40 time: 1.4431 data_time: 0.0035 memory: 3453 grad_norm: 0.0651 loss: 0.1551 loss_sem_seg: 0.1551 2023/05/11 13:48:44 - mmengine - INFO - Epoch(train) [27][1150/1196] lr: 8.0000e-04 eta: 4:26:25 time: 1.4261 data_time: 0.0035 memory: 3432 grad_norm: 0.0621 loss: 0.1625 loss_sem_seg: 0.1625 2023/05/11 13:49:50 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 13:49:50 - mmengine - INFO - Saving checkpoint at 27 epochs 2023/05/11 13:50:20 - mmengine - INFO - Epoch(val) [27][ 50/509] eta: 0:03:36 time: 0.4716 data_time: 0.0022 memory: 3524 2023/05/11 13:50:42 - mmengine - INFO - Epoch(val) [27][100/509] eta: 0:03:08 time: 0.4499 data_time: 0.0021 memory: 1105 2023/05/11 13:51:04 - mmengine - INFO - Epoch(val) [27][150/509] eta: 0:02:42 time: 0.4395 data_time: 0.0020 memory: 1110 2023/05/11 13:51:26 - mmengine - INFO - Epoch(val) [27][200/509] eta: 0:02:19 time: 0.4457 data_time: 0.0021 memory: 1100 2023/05/11 13:51:49 - mmengine - INFO - Epoch(val) [27][250/509] eta: 0:01:57 time: 0.4620 data_time: 0.0020 memory: 1111 2023/05/11 13:52:11 - mmengine - INFO - Epoch(val) [27][300/509] eta: 0:01:34 time: 0.4305 data_time: 0.0020 memory: 1075 2023/05/11 13:52:32 - mmengine - INFO - Epoch(val) [27][350/509] eta: 0:01:11 time: 0.4286 data_time: 0.0020 memory: 1091 2023/05/11 13:52:55 - mmengine - INFO - Epoch(val) [27][400/509] eta: 0:00:48 time: 0.4484 data_time: 0.0020 memory: 1090 2023/05/11 13:53:17 - mmengine - INFO - Epoch(val) [27][450/509] eta: 0:00:26 time: 0.4479 data_time: 0.0020 memory: 1113 2023/05/11 13:53:39 - mmengine - INFO - Epoch(val) [27][500/509] eta: 0:00:04 time: 0.4329 data_time: 0.0022 memory: 1098 2023/05/11 13:54:10 - 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 | 0.9657 | 0.5813 | 0.8082 | 0.8583 | 0.6430 | 0.7864 | 0.9111 | 0.2542 | 0.9477 | 0.5154 | 0.8254 | 0.0398 | 0.9181 | 0.6826 | 0.8731 | 0.6694 | 0.7149 | 0.6629 | 0.5311 | 0.6941 | 0.9211 | 0.7635 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 13:54:10 - mmengine - INFO - Epoch(val) [27][509/509] car: 0.9657 bicycle: 0.5813 motorcycle: 0.8082 truck: 0.8583 bus: 0.6430 person: 0.7864 bicyclist: 0.9111 motorcyclist: 0.2542 road: 0.9477 parking: 0.5154 sidewalk: 0.8254 other-ground: 0.0398 building: 0.9181 fence: 0.6826 vegetation: 0.8731 trunck: 0.6694 terrian: 0.7149 pole: 0.6629 traffic-sign: 0.5311 miou: 0.6941 acc: 0.9211 acc_cls: 0.7635 data_time: 0.0021 time: 0.4484 2023/05/11 13:55:26 - mmengine - INFO - Epoch(train) [28][ 50/1196] lr: 8.0000e-04 eta: 4:24:03 time: 1.5038 data_time: 0.0043 memory: 3647 grad_norm: 0.0632 loss: 0.1598 loss_sem_seg: 0.1598 2023/05/11 13:56:38 - mmengine - INFO - Epoch(train) [28][ 100/1196] lr: 8.0000e-04 eta: 4:22:48 time: 1.4427 data_time: 0.0036 memory: 3388 grad_norm: 0.0636 loss: 0.1653 loss_sem_seg: 0.1653 2023/05/11 13:57:49 - mmengine - INFO - Epoch(train) [28][ 150/1196] lr: 8.0000e-04 eta: 4:21:33 time: 1.4272 data_time: 0.0037 memory: 3195 grad_norm: 0.0672 loss: 0.1714 loss_sem_seg: 0.1714 2023/05/11 13:59:01 - mmengine - INFO - Epoch(train) [28][ 200/1196] lr: 8.0000e-04 eta: 4:20:19 time: 1.4352 data_time: 0.0034 memory: 3550 grad_norm: 0.0654 loss: 0.1670 loss_sem_seg: 0.1670 2023/05/11 14:00:12 - mmengine - INFO - Epoch(train) [28][ 250/1196] lr: 8.0000e-04 eta: 4:19:04 time: 1.4225 data_time: 0.0035 memory: 3419 grad_norm: 0.0598 loss: 0.1562 loss_sem_seg: 0.1562 2023/05/11 14:01:24 - mmengine - INFO - Epoch(train) [28][ 300/1196] lr: 8.0000e-04 eta: 4:17:49 time: 1.4359 data_time: 0.0034 memory: 3376 grad_norm: 0.0605 loss: 0.1524 loss_sem_seg: 0.1524 2023/05/11 14:02:36 - mmengine - INFO - Epoch(train) [28][ 350/1196] lr: 8.0000e-04 eta: 4:16:35 time: 1.4337 data_time: 0.0036 memory: 3474 grad_norm: 0.0639 loss: 0.1699 loss_sem_seg: 0.1699 2023/05/11 14:03:47 - mmengine - INFO - Epoch(train) [28][ 400/1196] lr: 8.0000e-04 eta: 4:15:20 time: 1.4245 data_time: 0.0036 memory: 3396 grad_norm: 0.0666 loss: 0.1673 loss_sem_seg: 0.1673 2023/05/11 14:04:59 - mmengine - INFO - Epoch(train) [28][ 450/1196] lr: 8.0000e-04 eta: 4:14:05 time: 1.4389 data_time: 0.0038 memory: 3099 grad_norm: 0.0649 loss: 0.1648 loss_sem_seg: 0.1648 2023/05/11 14:06:10 - mmengine - INFO - Epoch(train) [28][ 500/1196] lr: 8.0000e-04 eta: 4:12:51 time: 1.4293 data_time: 0.0035 memory: 3477 grad_norm: 0.0640 loss: 0.1647 loss_sem_seg: 0.1647 2023/05/11 14:07:22 - mmengine - INFO - Epoch(train) [28][ 550/1196] lr: 8.0000e-04 eta: 4:11:36 time: 1.4315 data_time: 0.0034 memory: 3151 grad_norm: 0.0637 loss: 0.1780 loss_sem_seg: 0.1780 2023/05/11 14:08:34 - mmengine - INFO - Epoch(train) [28][ 600/1196] lr: 8.0000e-04 eta: 4:10:22 time: 1.4406 data_time: 0.0034 memory: 3396 grad_norm: 0.0683 loss: 0.1605 loss_sem_seg: 0.1605 2023/05/11 14:09:46 - mmengine - INFO - Epoch(train) [28][ 650/1196] lr: 8.0000e-04 eta: 4:09:07 time: 1.4366 data_time: 0.0036 memory: 3386 grad_norm: 0.0656 loss: 0.1544 loss_sem_seg: 0.1544 2023/05/11 14:10:58 - mmengine - INFO - Epoch(train) [28][ 700/1196] lr: 8.0000e-04 eta: 4:07:53 time: 1.4414 data_time: 0.0036 memory: 3240 grad_norm: 0.0659 loss: 0.1633 loss_sem_seg: 0.1633 2023/05/11 14:11:09 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 14:12:01 - mmengine - INFO - Epoch(train) [28][ 750/1196] lr: 8.0000e-04 eta: 4:06:35 time: 1.2684 data_time: 0.0038 memory: 3371 grad_norm: 0.0629 loss: 0.1672 loss_sem_seg: 0.1672 2023/05/11 14:13:03 - mmengine - INFO - Epoch(train) [28][ 800/1196] lr: 8.0000e-04 eta: 4:05:18 time: 1.2375 data_time: 0.0037 memory: 3466 grad_norm: 0.0638 loss: 0.1641 loss_sem_seg: 0.1641 2023/05/11 14:14:05 - mmengine - INFO - Epoch(train) [28][ 850/1196] lr: 8.0000e-04 eta: 4:04:00 time: 1.2332 data_time: 0.0035 memory: 3397 grad_norm: 0.0640 loss: 0.1599 loss_sem_seg: 0.1599 2023/05/11 14:15:06 - mmengine - INFO - Epoch(train) [28][ 900/1196] lr: 8.0000e-04 eta: 4:02:43 time: 1.2237 data_time: 0.0035 memory: 3383 grad_norm: 0.0629 loss: 0.1570 loss_sem_seg: 0.1570 2023/05/11 14:16:08 - mmengine - INFO - Epoch(train) [28][ 950/1196] lr: 8.0000e-04 eta: 4:01:26 time: 1.2428 data_time: 0.0035 memory: 3599 grad_norm: 0.0655 loss: 0.1647 loss_sem_seg: 0.1647 2023/05/11 14:17:18 - mmengine - INFO - Epoch(train) [28][1000/1196] lr: 8.0000e-04 eta: 4:00:11 time: 1.4080 data_time: 0.0035 memory: 3485 grad_norm: 0.0664 loss: 0.1556 loss_sem_seg: 0.1556 2023/05/11 14:18:30 - mmengine - INFO - Epoch(train) [28][1050/1196] lr: 8.0000e-04 eta: 3:58:56 time: 1.4315 data_time: 0.0037 memory: 3226 grad_norm: 0.0662 loss: 0.1545 loss_sem_seg: 0.1545 2023/05/11 14:19:42 - mmengine - INFO - Epoch(train) [28][1100/1196] lr: 8.0000e-04 eta: 3:57:42 time: 1.4314 data_time: 0.0035 memory: 3446 grad_norm: 0.0677 loss: 0.1721 loss_sem_seg: 0.1721 2023/05/11 14:20:54 - mmengine - INFO - Epoch(train) [28][1150/1196] lr: 8.0000e-04 eta: 3:56:28 time: 1.4428 data_time: 0.0036 memory: 3169 grad_norm: 0.0650 loss: 0.1662 loss_sem_seg: 0.1662 2023/05/11 14:22:00 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 14:22:00 - mmengine - INFO - Saving checkpoint at 28 epochs 2023/05/11 14:22:29 - mmengine - INFO - Epoch(val) [28][ 50/509] eta: 0:03:33 time: 0.4654 data_time: 0.0021 memory: 3274 2023/05/11 14:22:51 - mmengine - INFO - Epoch(val) [28][100/509] eta: 0:03:07 time: 0.4517 data_time: 0.0021 memory: 1105 2023/05/11 14:23:13 - mmengine - INFO - Epoch(val) [28][150/509] eta: 0:02:42 time: 0.4367 data_time: 0.0021 memory: 1110 2023/05/11 14:23:36 - mmengine - INFO - Epoch(val) [28][200/509] eta: 0:02:19 time: 0.4474 data_time: 0.0020 memory: 1100 2023/05/11 14:23:59 - mmengine - INFO - Epoch(val) [28][250/509] eta: 0:01:57 time: 0.4588 data_time: 0.0020 memory: 1111 2023/05/11 14:24:20 - mmengine - INFO - Epoch(val) [28][300/509] eta: 0:01:33 time: 0.4283 data_time: 0.0021 memory: 1075 2023/05/11 14:24:41 - mmengine - INFO - Epoch(val) [28][350/509] eta: 0:01:10 time: 0.4245 data_time: 0.0020 memory: 1091 2023/05/11 14:25:03 - mmengine - INFO - Epoch(val) [28][400/509] eta: 0:00:48 time: 0.4444 data_time: 0.0020 memory: 1090 2023/05/11 14:25:26 - mmengine - INFO - Epoch(val) [28][450/509] eta: 0:00:26 time: 0.4496 data_time: 0.0020 memory: 1113 2023/05/11 14:25:48 - mmengine - INFO - Epoch(val) [28][500/509] eta: 0:00:03 time: 0.4376 data_time: 0.0021 memory: 1098 2023/05/11 14:26:19 - 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 | 0.9642 | 0.5680 | 0.8146 | 0.8217 | 0.6340 | 0.7880 | 0.9078 | 0.2477 | 0.9485 | 0.5477 | 0.8294 | 0.0344 | 0.9179 | 0.6813 | 0.8792 | 0.6861 | 0.7332 | 0.6637 | 0.5382 | 0.6950 | 0.9242 | 0.7663 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 14:26:19 - mmengine - INFO - Epoch(val) [28][509/509] car: 0.9642 bicycle: 0.5680 motorcycle: 0.8146 truck: 0.8217 bus: 0.6340 person: 0.7880 bicyclist: 0.9078 motorcyclist: 0.2477 road: 0.9485 parking: 0.5477 sidewalk: 0.8294 other-ground: 0.0344 building: 0.9179 fence: 0.6813 vegetation: 0.8792 trunck: 0.6861 terrian: 0.7332 pole: 0.6637 traffic-sign: 0.5382 miou: 0.6950 acc: 0.9242 acc_cls: 0.7663 data_time: 0.0021 time: 0.4484 2023/05/11 14:27:34 - mmengine - INFO - Epoch(train) [29][ 50/1196] lr: 8.0000e-04 eta: 3:54:06 time: 1.5065 data_time: 0.0047 memory: 3249 grad_norm: 0.0688 loss: 0.1619 loss_sem_seg: 0.1619 2023/05/11 14:28:46 - mmengine - INFO - Epoch(train) [29][ 100/1196] lr: 8.0000e-04 eta: 3:52:52 time: 1.4389 data_time: 0.0034 memory: 3383 grad_norm: 0.0619 loss: 0.1704 loss_sem_seg: 0.1704 2023/05/11 14:29:59 - mmengine - INFO - Epoch(train) [29][ 150/1196] lr: 8.0000e-04 eta: 3:51:37 time: 1.4521 data_time: 0.0035 memory: 3334 grad_norm: 0.0659 loss: 0.1548 loss_sem_seg: 0.1548 2023/05/11 14:31:10 - mmengine - INFO - Epoch(train) [29][ 200/1196] lr: 8.0000e-04 eta: 3:50:23 time: 1.4259 data_time: 0.0038 memory: 3316 grad_norm: 0.0694 loss: 0.1661 loss_sem_seg: 0.1661 2023/05/11 14:32:22 - mmengine - INFO - Epoch(train) [29][ 250/1196] lr: 8.0000e-04 eta: 3:49:09 time: 1.4349 data_time: 0.0035 memory: 3395 grad_norm: 0.0635 loss: 0.1597 loss_sem_seg: 0.1597 2023/05/11 14:33:34 - mmengine - INFO - Epoch(train) [29][ 300/1196] lr: 8.0000e-04 eta: 3:47:54 time: 1.4454 data_time: 0.0034 memory: 3298 grad_norm: 0.0655 loss: 0.1656 loss_sem_seg: 0.1656 2023/05/11 14:34:46 - mmengine - INFO - Epoch(train) [29][ 350/1196] lr: 8.0000e-04 eta: 3:46:40 time: 1.4329 data_time: 0.0034 memory: 3351 grad_norm: 0.0654 loss: 0.1529 loss_sem_seg: 0.1529 2023/05/11 14:35:58 - mmengine - INFO - Epoch(train) [29][ 400/1196] lr: 8.0000e-04 eta: 3:45:26 time: 1.4408 data_time: 0.0035 memory: 3450 grad_norm: 0.0633 loss: 0.1608 loss_sem_seg: 0.1608 2023/05/11 14:37:09 - mmengine - INFO - Epoch(train) [29][ 450/1196] lr: 8.0000e-04 eta: 3:44:11 time: 1.4297 data_time: 0.0037 memory: 3306 grad_norm: 0.0649 loss: 0.1619 loss_sem_seg: 0.1619 2023/05/11 14:38:21 - mmengine - INFO - Epoch(train) [29][ 500/1196] lr: 8.0000e-04 eta: 3:42:57 time: 1.4258 data_time: 0.0035 memory: 3266 grad_norm: 0.0638 loss: 0.1559 loss_sem_seg: 0.1559 2023/05/11 14:38:38 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 14:39:33 - mmengine - INFO - Epoch(train) [29][ 550/1196] lr: 8.0000e-04 eta: 3:41:43 time: 1.4383 data_time: 0.0036 memory: 3291 grad_norm: 0.0695 loss: 0.1541 loss_sem_seg: 0.1541 2023/05/11 14:40:44 - mmengine - INFO - Epoch(train) [29][ 600/1196] lr: 8.0000e-04 eta: 3:40:28 time: 1.4338 data_time: 0.0036 memory: 3417 grad_norm: 0.0666 loss: 0.1698 loss_sem_seg: 0.1698 2023/05/11 14:41:57 - mmengine - INFO - Epoch(train) [29][ 650/1196] lr: 8.0000e-04 eta: 3:39:14 time: 1.4566 data_time: 0.0035 memory: 3389 grad_norm: 0.0732 loss: 0.1721 loss_sem_seg: 0.1721 2023/05/11 14:43:09 - mmengine - INFO - Epoch(train) [29][ 700/1196] lr: 8.0000e-04 eta: 3:38:00 time: 1.4256 data_time: 0.0035 memory: 3323 grad_norm: 0.0685 loss: 0.1646 loss_sem_seg: 0.1646 2023/05/11 14:44:13 - mmengine - INFO - Epoch(train) [29][ 750/1196] lr: 8.0000e-04 eta: 3:36:44 time: 1.2927 data_time: 0.0036 memory: 3214 grad_norm: 0.0652 loss: 0.1545 loss_sem_seg: 0.1545 2023/05/11 14:45:15 - mmengine - INFO - Epoch(train) [29][ 800/1196] lr: 8.0000e-04 eta: 3:35:27 time: 1.2463 data_time: 0.0035 memory: 3334 grad_norm: 0.0649 loss: 0.1641 loss_sem_seg: 0.1641 2023/05/11 14:46:20 - mmengine - INFO - Epoch(train) [29][ 850/1196] lr: 8.0000e-04 eta: 3:34:11 time: 1.2924 data_time: 0.0034 memory: 3815 grad_norm: 0.0652 loss: 0.1690 loss_sem_seg: 0.1690 2023/05/11 14:47:23 - mmengine - INFO - Epoch(train) [29][ 900/1196] lr: 8.0000e-04 eta: 3:32:55 time: 1.2575 data_time: 0.0034 memory: 3301 grad_norm: 0.0670 loss: 0.1478 loss_sem_seg: 0.1478 2023/05/11 14:48:27 - mmengine - INFO - Epoch(train) [29][ 950/1196] lr: 8.0000e-04 eta: 3:31:39 time: 1.2792 data_time: 0.0034 memory: 3375 grad_norm: 0.0609 loss: 0.1605 loss_sem_seg: 0.1605 2023/05/11 14:49:38 - mmengine - INFO - Epoch(train) [29][1000/1196] lr: 8.0000e-04 eta: 3:30:24 time: 1.4118 data_time: 0.0034 memory: 3253 grad_norm: 0.0641 loss: 0.1739 loss_sem_seg: 0.1739 2023/05/11 14:50:52 - mmengine - INFO - Epoch(train) [29][1050/1196] lr: 8.0000e-04 eta: 3:29:11 time: 1.4848 data_time: 0.0034 memory: 3558 grad_norm: 0.0663 loss: 0.1557 loss_sem_seg: 0.1557 2023/05/11 14:52:03 - mmengine - INFO - Epoch(train) [29][1100/1196] lr: 8.0000e-04 eta: 3:27:56 time: 1.4174 data_time: 0.0035 memory: 3281 grad_norm: 0.0660 loss: 0.1488 loss_sem_seg: 0.1488 2023/05/11 14:53:14 - mmengine - INFO - Epoch(train) [29][1150/1196] lr: 8.0000e-04 eta: 3:26:42 time: 1.4189 data_time: 0.0034 memory: 3522 grad_norm: 0.0669 loss: 0.1566 loss_sem_seg: 0.1566 2023/05/11 14:54:19 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 14:54:19 - mmengine - INFO - Saving checkpoint at 29 epochs 2023/05/11 14:54:47 - mmengine - INFO - Epoch(val) [29][ 50/509] eta: 0:03:27 time: 0.4512 data_time: 0.0021 memory: 3585 2023/05/11 14:55:09 - mmengine - INFO - Epoch(val) [29][100/509] eta: 0:03:03 time: 0.4451 data_time: 0.0021 memory: 1105 2023/05/11 14:55:31 - mmengine - INFO - Epoch(val) [29][150/509] eta: 0:02:39 time: 0.4364 data_time: 0.0020 memory: 1110 2023/05/11 14:55:53 - mmengine - INFO - Epoch(val) [29][200/509] eta: 0:02:16 time: 0.4373 data_time: 0.0020 memory: 1100 2023/05/11 14:56:16 - mmengine - INFO - Epoch(val) [29][250/509] eta: 0:01:55 time: 0.4584 data_time: 0.0020 memory: 1111 2023/05/11 14:56:37 - mmengine - INFO - Epoch(val) [29][300/509] eta: 0:01:32 time: 0.4229 data_time: 0.0020 memory: 1075 2023/05/11 14:56:58 - mmengine - INFO - Epoch(val) [29][350/509] eta: 0:01:09 time: 0.4228 data_time: 0.0021 memory: 1091 2023/05/11 14:57:20 - mmengine - INFO - Epoch(val) [29][400/509] eta: 0:00:47 time: 0.4426 data_time: 0.0020 memory: 1090 2023/05/11 14:57:43 - mmengine - INFO - Epoch(val) [29][450/509] eta: 0:00:25 time: 0.4494 data_time: 0.0020 memory: 1113 2023/05/11 14:58:04 - mmengine - INFO - Epoch(val) [29][500/509] eta: 0:00:03 time: 0.4322 data_time: 0.0020 memory: 1098 2023/05/11 14:58:35 - 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 | 0.9750 | 0.5789 | 0.8143 | 0.8410 | 0.7787 | 0.7906 | 0.9132 | 0.2341 | 0.9462 | 0.5270 | 0.8249 | 0.0737 | 0.9162 | 0.6740 | 0.8767 | 0.6797 | 0.7232 | 0.6635 | 0.5317 | 0.7033 | 0.9228 | 0.7730 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 14:58:35 - mmengine - INFO - Epoch(val) [29][509/509] car: 0.9750 bicycle: 0.5789 motorcycle: 0.8143 truck: 0.8410 bus: 0.7787 person: 0.7906 bicyclist: 0.9132 motorcyclist: 0.2341 road: 0.9462 parking: 0.5270 sidewalk: 0.8249 other-ground: 0.0737 building: 0.9162 fence: 0.6740 vegetation: 0.8767 trunck: 0.6797 terrian: 0.7232 pole: 0.6635 traffic-sign: 0.5317 miou: 0.7033 acc: 0.9228 acc_cls: 0.7730 data_time: 0.0020 time: 0.4451 2023/05/11 14:59:49 - mmengine - INFO - Epoch(train) [30][ 50/1196] lr: 8.0000e-04 eta: 3:24:20 time: 1.4783 data_time: 0.0042 memory: 3300 grad_norm: 0.0638 loss: 0.1621 loss_sem_seg: 0.1621 2023/05/11 15:01:00 - mmengine - INFO - Epoch(train) [30][ 100/1196] lr: 8.0000e-04 eta: 3:23:06 time: 1.4073 data_time: 0.0035 memory: 3318 grad_norm: 0.0669 loss: 0.1514 loss_sem_seg: 0.1514 2023/05/11 15:02:10 - mmengine - INFO - Epoch(train) [30][ 150/1196] lr: 8.0000e-04 eta: 3:21:51 time: 1.4137 data_time: 0.0035 memory: 3436 grad_norm: 0.0680 loss: 0.1527 loss_sem_seg: 0.1527 2023/05/11 15:03:21 - mmengine - INFO - Epoch(train) [30][ 200/1196] lr: 8.0000e-04 eta: 3:20:37 time: 1.4214 data_time: 0.0035 memory: 3225 grad_norm: 0.0669 loss: 0.1530 loss_sem_seg: 0.1530 2023/05/11 15:04:33 - mmengine - INFO - Epoch(train) [30][ 250/1196] lr: 8.0000e-04 eta: 3:19:23 time: 1.4274 data_time: 0.0034 memory: 3404 grad_norm: 0.0625 loss: 0.1691 loss_sem_seg: 0.1691 2023/05/11 15:05:44 - mmengine - INFO - Epoch(train) [30][ 300/1196] lr: 8.0000e-04 eta: 3:18:08 time: 1.4152 data_time: 0.0035 memory: 3457 grad_norm: 0.0619 loss: 0.1578 loss_sem_seg: 0.1578 2023/05/11 15:06:06 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 15:06:55 - mmengine - INFO - Epoch(train) [30][ 350/1196] lr: 8.0000e-04 eta: 3:16:54 time: 1.4297 data_time: 0.0035 memory: 3154 grad_norm: 0.0683 loss: 0.1615 loss_sem_seg: 0.1615 2023/05/11 15:08:06 - mmengine - INFO - Epoch(train) [30][ 400/1196] lr: 8.0000e-04 eta: 3:15:40 time: 1.4254 data_time: 0.0035 memory: 3586 grad_norm: 0.0647 loss: 0.1676 loss_sem_seg: 0.1676 2023/05/11 15:09:17 - mmengine - INFO - Epoch(train) [30][ 450/1196] lr: 8.0000e-04 eta: 3:14:26 time: 1.4207 data_time: 0.0034 memory: 3315 grad_norm: 0.0682 loss: 0.1466 loss_sem_seg: 0.1466 2023/05/11 15:10:27 - mmengine - INFO - Epoch(train) [30][ 500/1196] lr: 8.0000e-04 eta: 3:13:11 time: 1.3927 data_time: 0.0036 memory: 3388 grad_norm: 0.0630 loss: 0.1418 loss_sem_seg: 0.1418 2023/05/11 15:11:38 - mmengine - INFO - Epoch(train) [30][ 550/1196] lr: 8.0000e-04 eta: 3:11:57 time: 1.4154 data_time: 0.0035 memory: 3297 grad_norm: 0.0634 loss: 0.1512 loss_sem_seg: 0.1512 2023/05/11 15:12:49 - mmengine - INFO - Epoch(train) [30][ 600/1196] lr: 8.0000e-04 eta: 3:10:43 time: 1.4170 data_time: 0.0034 memory: 3412 grad_norm: 0.0669 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/11 15:14:00 - mmengine - INFO - Epoch(train) [30][ 650/1196] lr: 8.0000e-04 eta: 3:09:29 time: 1.4184 data_time: 0.0035 memory: 3259 grad_norm: 0.0691 loss: 0.1611 loss_sem_seg: 0.1611 2023/05/11 15:15:10 - mmengine - INFO - Epoch(train) [30][ 700/1196] lr: 8.0000e-04 eta: 3:08:14 time: 1.4146 data_time: 0.0035 memory: 3335 grad_norm: 0.0650 loss: 0.1524 loss_sem_seg: 0.1524 2023/05/11 15:16:16 - mmengine - INFO - Epoch(train) [30][ 750/1196] lr: 8.0000e-04 eta: 3:06:59 time: 1.3102 data_time: 0.0034 memory: 3189 grad_norm: 0.0640 loss: 0.1661 loss_sem_seg: 0.1661 2023/05/11 15:17:17 - mmengine - INFO - Epoch(train) [30][ 800/1196] lr: 8.0000e-04 eta: 3:05:43 time: 1.2300 data_time: 0.0034 memory: 3335 grad_norm: 0.0675 loss: 0.1492 loss_sem_seg: 0.1492 2023/05/11 15:18:19 - mmengine - INFO - Epoch(train) [30][ 850/1196] lr: 8.0000e-04 eta: 3:04:27 time: 1.2241 data_time: 0.0033 memory: 3401 grad_norm: 0.0668 loss: 0.1646 loss_sem_seg: 0.1646 2023/05/11 15:19:19 - mmengine - INFO - Epoch(train) [30][ 900/1196] lr: 8.0000e-04 eta: 3:03:10 time: 1.2119 data_time: 0.0034 memory: 3357 grad_norm: inf loss: 0.1643 loss_sem_seg: 0.1643 2023/05/11 15:20:20 - mmengine - INFO - Epoch(train) [30][ 950/1196] lr: 8.0000e-04 eta: 3:01:54 time: 1.2269 data_time: 0.0034 memory: 3513 grad_norm: 0.0620 loss: 0.1415 loss_sem_seg: 0.1415 2023/05/11 15:21:22 - mmengine - INFO - Epoch(train) [30][1000/1196] lr: 8.0000e-04 eta: 3:00:38 time: 1.2377 data_time: 0.0035 memory: 3727 grad_norm: 0.0692 loss: 0.1635 loss_sem_seg: 0.1635 2023/05/11 15:22:36 - mmengine - INFO - Epoch(train) [30][1050/1196] lr: 8.0000e-04 eta: 2:59:25 time: 1.4781 data_time: 0.0034 memory: 3426 grad_norm: 0.0682 loss: 0.1618 loss_sem_seg: 0.1618 2023/05/11 15:23:47 - mmengine - INFO - Epoch(train) [30][1100/1196] lr: 8.0000e-04 eta: 2:58:11 time: 1.4170 data_time: 0.0035 memory: 3359 grad_norm: 0.0630 loss: 0.1595 loss_sem_seg: 0.1595 2023/05/11 15:24:58 - mmengine - INFO - Epoch(train) [30][1150/1196] lr: 8.0000e-04 eta: 2:56:57 time: 1.4143 data_time: 0.0036 memory: 3436 grad_norm: 0.0654 loss: 0.1649 loss_sem_seg: 0.1649 2023/05/11 15:26:03 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 15:26:03 - mmengine - INFO - Saving checkpoint at 30 epochs 2023/05/11 15:26:32 - mmengine - INFO - Epoch(val) [30][ 50/509] eta: 0:03:35 time: 0.4687 data_time: 0.0021 memory: 3889 2023/05/11 15:26:55 - mmengine - INFO - Epoch(val) [30][100/509] eta: 0:03:07 time: 0.4468 data_time: 0.0021 memory: 1105 2023/05/11 15:27:17 - mmengine - INFO - Epoch(val) [30][150/509] eta: 0:02:41 time: 0.4370 data_time: 0.0020 memory: 1110 2023/05/11 15:27:39 - mmengine - INFO - Epoch(val) [30][200/509] eta: 0:02:18 time: 0.4399 data_time: 0.0020 memory: 1100 2023/05/11 15:28:01 - mmengine - INFO - Epoch(val) [30][250/509] eta: 0:01:56 time: 0.4568 data_time: 0.0020 memory: 1111 2023/05/11 15:28:23 - mmengine - INFO - Epoch(val) [30][300/509] eta: 0:01:33 time: 0.4226 data_time: 0.0020 memory: 1075 2023/05/11 15:28:44 - mmengine - INFO - Epoch(val) [30][350/509] eta: 0:01:10 time: 0.4286 data_time: 0.0020 memory: 1091 2023/05/11 15:29:06 - mmengine - INFO - Epoch(val) [30][400/509] eta: 0:00:48 time: 0.4412 data_time: 0.0020 memory: 1090 2023/05/11 15:29:28 - mmengine - INFO - Epoch(val) [30][450/509] eta: 0:00:26 time: 0.4455 data_time: 0.0020 memory: 1113 2023/05/11 15:29:51 - mmengine - INFO - Epoch(val) [30][500/509] eta: 0:00:03 time: 0.4455 data_time: 0.0021 memory: 1098 2023/05/11 15:30:22 - 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 | 0.9681 | 0.5847 | 0.8229 | 0.8473 | 0.6695 | 0.7871 | 0.9152 | 0.1819 | 0.9471 | 0.5125 | 0.8257 | 0.0470 | 0.9160 | 0.6681 | 0.8757 | 0.6892 | 0.7205 | 0.6647 | 0.5304 | 0.6933 | 0.9220 | 0.7618 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 15:30:22 - mmengine - INFO - Epoch(val) [30][509/509] car: 0.9681 bicycle: 0.5847 motorcycle: 0.8229 truck: 0.8473 bus: 0.6695 person: 0.7871 bicyclist: 0.9152 motorcyclist: 0.1819 road: 0.9471 parking: 0.5125 sidewalk: 0.8257 other-ground: 0.0470 building: 0.9160 fence: 0.6681 vegetation: 0.8757 trunck: 0.6892 terrian: 0.7205 pole: 0.6647 traffic-sign: 0.5304 miou: 0.6933 acc: 0.9220 acc_cls: 0.7618 data_time: 0.0021 time: 0.4622 2023/05/11 15:31:35 - mmengine - INFO - Epoch(train) [31][ 50/1196] lr: 8.0000e-04 eta: 2:54:35 time: 1.4758 data_time: 0.0040 memory: 3351 grad_norm: 0.0687 loss: 0.1599 loss_sem_seg: 0.1599 2023/05/11 15:32:47 - mmengine - INFO - Epoch(train) [31][ 100/1196] lr: 8.0000e-04 eta: 2:53:21 time: 1.4245 data_time: 0.0036 memory: 3398 grad_norm: 0.0677 loss: 0.1584 loss_sem_seg: 0.1584 2023/05/11 15:33:15 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 15:33:57 - mmengine - INFO - Epoch(train) [31][ 150/1196] lr: 8.0000e-04 eta: 2:52:07 time: 1.4163 data_time: 0.0035 memory: 3214 grad_norm: 0.0685 loss: 0.1580 loss_sem_seg: 0.1580 2023/05/11 15:35:08 - mmengine - INFO - Epoch(train) [31][ 200/1196] lr: 8.0000e-04 eta: 2:50:53 time: 1.4186 data_time: 0.0034 memory: 3581 grad_norm: 0.0742 loss: 0.1663 loss_sem_seg: 0.1663 2023/05/11 15:36:20 - mmengine - INFO - Epoch(train) [31][ 250/1196] lr: 8.0000e-04 eta: 2:49:39 time: 1.4245 data_time: 0.0034 memory: 3383 grad_norm: 0.0614 loss: 0.1522 loss_sem_seg: 0.1522 2023/05/11 15:37:31 - mmengine - INFO - Epoch(train) [31][ 300/1196] lr: 8.0000e-04 eta: 2:48:25 time: 1.4219 data_time: 0.0035 memory: 3291 grad_norm: 0.0655 loss: 0.1553 loss_sem_seg: 0.1553 2023/05/11 15:38:41 - mmengine - INFO - Epoch(train) [31][ 350/1196] lr: 8.0000e-04 eta: 2:47:11 time: 1.4146 data_time: 0.0034 memory: 3341 grad_norm: 0.0640 loss: 0.1600 loss_sem_seg: 0.1600 2023/05/11 15:39:52 - mmengine - INFO - Epoch(train) [31][ 400/1196] lr: 8.0000e-04 eta: 2:45:57 time: 1.4129 data_time: 0.0036 memory: 3443 grad_norm: 0.0610 loss: 0.1548 loss_sem_seg: 0.1548 2023/05/11 15:41:03 - mmengine - INFO - Epoch(train) [31][ 450/1196] lr: 8.0000e-04 eta: 2:44:43 time: 1.4171 data_time: 0.0035 memory: 3760 grad_norm: 0.0672 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/11 15:42:14 - mmengine - INFO - Epoch(train) [31][ 500/1196] lr: 8.0000e-04 eta: 2:43:30 time: 1.4216 data_time: 0.0036 memory: 3361 grad_norm: 0.0661 loss: 0.1613 loss_sem_seg: 0.1613 2023/05/11 15:43:24 - mmengine - INFO - Epoch(train) [31][ 550/1196] lr: 8.0000e-04 eta: 2:42:15 time: 1.4062 data_time: 0.0035 memory: 3583 grad_norm: 0.0677 loss: 0.1566 loss_sem_seg: 0.1566 2023/05/11 15:44:36 - mmengine - INFO - Epoch(train) [31][ 600/1196] lr: 8.0000e-04 eta: 2:41:02 time: 1.4429 data_time: 0.0035 memory: 3607 grad_norm: 0.0680 loss: 0.1550 loss_sem_seg: 0.1550 2023/05/11 15:45:47 - mmengine - INFO - Epoch(train) [31][ 650/1196] lr: 8.0000e-04 eta: 2:39:48 time: 1.4057 data_time: 0.0034 memory: 3444 grad_norm: 0.0634 loss: 0.1582 loss_sem_seg: 0.1582 2023/05/11 15:46:57 - mmengine - INFO - Epoch(train) [31][ 700/1196] lr: 8.0000e-04 eta: 2:38:34 time: 1.4142 data_time: 0.0034 memory: 3313 grad_norm: 0.0644 loss: 0.1709 loss_sem_seg: 0.1709 2023/05/11 15:48:05 - mmengine - INFO - Epoch(train) [31][ 750/1196] lr: 8.0000e-04 eta: 2:37:19 time: 1.3498 data_time: 0.0035 memory: 3374 grad_norm: 0.0652 loss: 0.1624 loss_sem_seg: 0.1624 2023/05/11 15:49:06 - mmengine - INFO - Epoch(train) [31][ 800/1196] lr: 8.0000e-04 eta: 2:36:04 time: 1.2279 data_time: 0.0034 memory: 3435 grad_norm: 0.0651 loss: 0.1659 loss_sem_seg: 0.1659 2023/05/11 15:50:07 - mmengine - INFO - Epoch(train) [31][ 850/1196] lr: 8.0000e-04 eta: 2:34:48 time: 1.2225 data_time: 0.0034 memory: 3645 grad_norm: 0.0682 loss: 0.1658 loss_sem_seg: 0.1658 2023/05/11 15:51:08 - mmengine - INFO - Epoch(train) [31][ 900/1196] lr: 8.0000e-04 eta: 2:33:33 time: 1.2155 data_time: 0.0034 memory: 3351 grad_norm: 0.0676 loss: 0.1618 loss_sem_seg: 0.1618 2023/05/11 15:52:09 - mmengine - INFO - Epoch(train) [31][ 950/1196] lr: 8.0000e-04 eta: 2:32:17 time: 1.2151 data_time: 0.0034 memory: 3346 grad_norm: 0.0651 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/11 15:53:10 - mmengine - INFO - Epoch(train) [31][1000/1196] lr: 8.0000e-04 eta: 2:31:02 time: 1.2172 data_time: 0.0035 memory: 3278 grad_norm: 0.0632 loss: 0.1622 loss_sem_seg: 0.1622 2023/05/11 15:54:24 - mmengine - INFO - Epoch(train) [31][1050/1196] lr: 8.0000e-04 eta: 2:29:48 time: 1.4908 data_time: 0.0035 memory: 3503 grad_norm: 0.0700 loss: 0.1527 loss_sem_seg: 0.1527 2023/05/11 15:55:35 - mmengine - INFO - Epoch(train) [31][1100/1196] lr: 8.0000e-04 eta: 2:28:35 time: 1.4121 data_time: 0.0035 memory: 3693 grad_norm: 0.0682 loss: 0.1563 loss_sem_seg: 0.1563 2023/05/11 15:56:03 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 15:56:45 - mmengine - INFO - Epoch(train) [31][1150/1196] lr: 8.0000e-04 eta: 2:27:21 time: 1.4075 data_time: 0.0034 memory: 3385 grad_norm: 0.0717 loss: 0.1603 loss_sem_seg: 0.1603 2023/05/11 15:57:51 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 15:57:51 - mmengine - INFO - Saving checkpoint at 31 epochs 2023/05/11 15:58:20 - mmengine - INFO - Epoch(val) [31][ 50/509] eta: 0:03:31 time: 0.4617 data_time: 0.0021 memory: 3429 2023/05/11 15:58:42 - mmengine - INFO - Epoch(val) [31][100/509] eta: 0:03:06 time: 0.4489 data_time: 0.0020 memory: 1105 2023/05/11 15:59:04 - mmengine - INFO - Epoch(val) [31][150/509] eta: 0:02:41 time: 0.4366 data_time: 0.0020 memory: 1110 2023/05/11 15:59:26 - mmengine - INFO - Epoch(val) [31][200/509] eta: 0:02:18 time: 0.4429 data_time: 0.0021 memory: 1100 2023/05/11 15:59:49 - mmengine - INFO - Epoch(val) [31][250/509] eta: 0:01:56 time: 0.4559 data_time: 0.0020 memory: 1111 2023/05/11 16:00:10 - mmengine - INFO - Epoch(val) [31][300/509] eta: 0:01:33 time: 0.4244 data_time: 0.0021 memory: 1075 2023/05/11 16:00:32 - mmengine - INFO - Epoch(val) [31][350/509] eta: 0:01:10 time: 0.4247 data_time: 0.0020 memory: 1091 2023/05/11 16:00:54 - mmengine - INFO - Epoch(val) [31][400/509] eta: 0:00:48 time: 0.4411 data_time: 0.0020 memory: 1090 2023/05/11 16:01:16 - mmengine - INFO - Epoch(val) [31][450/509] eta: 0:00:26 time: 0.4452 data_time: 0.0020 memory: 1113 2023/05/11 16:01:38 - mmengine - INFO - Epoch(val) [31][500/509] eta: 0:00:03 time: 0.4303 data_time: 0.0020 memory: 1098 2023/05/11 16:02:08 - 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 | 0.9639 | 0.5813 | 0.8058 | 0.8707 | 0.6475 | 0.7827 | 0.9121 | 0.1671 | 0.9461 | 0.5389 | 0.8254 | 0.0484 | 0.9159 | 0.6732 | 0.8755 | 0.6818 | 0.7195 | 0.6643 | 0.5302 | 0.6921 | 0.9217 | 0.7599 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 16:02:08 - mmengine - INFO - Epoch(val) [31][509/509] car: 0.9639 bicycle: 0.5813 motorcycle: 0.8058 truck: 0.8707 bus: 0.6475 person: 0.7827 bicyclist: 0.9121 motorcyclist: 0.1671 road: 0.9461 parking: 0.5389 sidewalk: 0.8254 other-ground: 0.0484 building: 0.9159 fence: 0.6732 vegetation: 0.8755 trunck: 0.6818 terrian: 0.7195 pole: 0.6643 traffic-sign: 0.5302 miou: 0.6921 acc: 0.9217 acc_cls: 0.7599 data_time: 0.0020 time: 0.4484 2023/05/11 16:03:23 - mmengine - INFO - Epoch(train) [32][ 50/1196] lr: 8.0000e-04 eta: 2:25:00 time: 1.4881 data_time: 0.0045 memory: 3360 grad_norm: 0.0667 loss: 0.1504 loss_sem_seg: 0.1504 2023/05/11 16:04:34 - mmengine - INFO - Epoch(train) [32][ 100/1196] lr: 8.0000e-04 eta: 2:23:46 time: 1.4250 data_time: 0.0035 memory: 3521 grad_norm: 0.0643 loss: 0.1549 loss_sem_seg: 0.1549 2023/05/11 16:05:45 - mmengine - INFO - Epoch(train) [32][ 150/1196] lr: 8.0000e-04 eta: 2:22:32 time: 1.4177 data_time: 0.0035 memory: 3324 grad_norm: 0.0699 loss: 0.1468 loss_sem_seg: 0.1468 2023/05/11 16:06:56 - mmengine - INFO - Epoch(train) [32][ 200/1196] lr: 8.0000e-04 eta: 2:21:19 time: 1.4136 data_time: 0.0035 memory: 3231 grad_norm: 0.0650 loss: 0.1485 loss_sem_seg: 0.1485 2023/05/11 16:08:07 - mmengine - INFO - Epoch(train) [32][ 250/1196] lr: 8.0000e-04 eta: 2:20:05 time: 1.4183 data_time: 0.0034 memory: 3300 grad_norm: 0.0642 loss: 0.1564 loss_sem_seg: 0.1564 2023/05/11 16:09:18 - mmengine - INFO - Epoch(train) [32][ 300/1196] lr: 8.0000e-04 eta: 2:18:51 time: 1.4217 data_time: 0.0035 memory: 3482 grad_norm: 0.0682 loss: 0.1636 loss_sem_seg: 0.1636 2023/05/11 16:10:29 - mmengine - INFO - Epoch(train) [32][ 350/1196] lr: 8.0000e-04 eta: 2:17:38 time: 1.4272 data_time: 0.0034 memory: 3442 grad_norm: 0.0630 loss: 0.1580 loss_sem_seg: 0.1580 2023/05/11 16:11:41 - mmengine - INFO - Epoch(train) [32][ 400/1196] lr: 8.0000e-04 eta: 2:16:24 time: 1.4425 data_time: 0.0035 memory: 3460 grad_norm: 0.0642 loss: 0.1555 loss_sem_seg: 0.1555 2023/05/11 16:12:54 - mmengine - INFO - Epoch(train) [32][ 450/1196] lr: 8.0000e-04 eta: 2:15:11 time: 1.4629 data_time: 0.0035 memory: 3407 grad_norm: 0.0615 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/11 16:14:07 - mmengine - INFO - Epoch(train) [32][ 500/1196] lr: 8.0000e-04 eta: 2:13:57 time: 1.4619 data_time: 0.0035 memory: 3642 grad_norm: inf loss: 0.1561 loss_sem_seg: 0.1561 2023/05/11 16:15:20 - mmengine - INFO - Epoch(train) [32][ 550/1196] lr: 8.0000e-04 eta: 2:12:44 time: 1.4520 data_time: 0.0035 memory: 3378 grad_norm: 0.0718 loss: 0.1548 loss_sem_seg: 0.1548 2023/05/11 16:16:33 - mmengine - INFO - Epoch(train) [32][ 600/1196] lr: 8.0000e-04 eta: 2:11:30 time: 1.4674 data_time: 0.0035 memory: 3489 grad_norm: 0.0706 loss: 0.1644 loss_sem_seg: 0.1644 2023/05/11 16:17:46 - mmengine - INFO - Epoch(train) [32][ 650/1196] lr: 8.0000e-04 eta: 2:10:17 time: 1.4459 data_time: 0.0035 memory: 3166 grad_norm: 0.0646 loss: 0.1497 loss_sem_seg: 0.1497 2023/05/11 16:18:56 - mmengine - INFO - Epoch(train) [32][ 700/1196] lr: 8.0000e-04 eta: 2:09:03 time: 1.4134 data_time: 0.0035 memory: 3296 grad_norm: 0.0664 loss: 0.1440 loss_sem_seg: 0.1440 2023/05/11 16:20:07 - mmengine - INFO - Epoch(train) [32][ 750/1196] lr: 8.0000e-04 eta: 2:07:50 time: 1.4021 data_time: 0.0034 memory: 3448 grad_norm: 0.0653 loss: 0.1546 loss_sem_seg: 0.1546 2023/05/11 16:21:07 - mmengine - INFO - Epoch(train) [32][ 800/1196] lr: 8.0000e-04 eta: 2:06:35 time: 1.2194 data_time: 0.0034 memory: 3281 grad_norm: 0.0678 loss: 0.1611 loss_sem_seg: 0.1611 2023/05/11 16:22:08 - mmengine - INFO - Epoch(train) [32][ 850/1196] lr: 8.0000e-04 eta: 2:05:19 time: 1.2111 data_time: 0.0034 memory: 3349 grad_norm: 0.0693 loss: 0.1479 loss_sem_seg: 0.1479 2023/05/11 16:23:09 - mmengine - INFO - Epoch(train) [32][ 900/1196] lr: 8.0000e-04 eta: 2:04:04 time: 1.2139 data_time: 0.0035 memory: 3472 grad_norm: 0.0674 loss: 0.1597 loss_sem_seg: 0.1597 2023/05/11 16:23:38 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 16:24:09 - mmengine - INFO - Epoch(train) [32][ 950/1196] lr: 8.0000e-04 eta: 2:02:50 time: 1.2143 data_time: 0.0034 memory: 3575 grad_norm: 0.0693 loss: 0.1600 loss_sem_seg: 0.1600 2023/05/11 16:25:10 - mmengine - INFO - Epoch(train) [32][1000/1196] lr: 8.0000e-04 eta: 2:01:35 time: 1.2077 data_time: 0.0034 memory: 3340 grad_norm: 0.0655 loss: 0.1529 loss_sem_seg: 0.1529 2023/05/11 16:26:22 - mmengine - INFO - Epoch(train) [32][1050/1196] lr: 8.0000e-04 eta: 2:00:21 time: 1.4526 data_time: 0.0034 memory: 3326 grad_norm: 0.0687 loss: 0.1540 loss_sem_seg: 0.1540 2023/05/11 16:27:33 - mmengine - INFO - Epoch(train) [32][1100/1196] lr: 8.0000e-04 eta: 1:59:08 time: 1.4077 data_time: 0.0037 memory: 3354 grad_norm: 0.0660 loss: 0.1564 loss_sem_seg: 0.1564 2023/05/11 16:28:44 - mmengine - INFO - Epoch(train) [32][1150/1196] lr: 8.0000e-04 eta: 1:57:54 time: 1.4175 data_time: 0.0034 memory: 3347 grad_norm: 0.0684 loss: 0.1654 loss_sem_seg: 0.1654 2023/05/11 16:29:48 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 16:29:48 - mmengine - INFO - Saving checkpoint at 32 epochs 2023/05/11 16:30:17 - mmengine - INFO - Epoch(val) [32][ 50/509] eta: 0:03:29 time: 0.4575 data_time: 0.0022 memory: 3481 2023/05/11 16:30:39 - mmengine - INFO - Epoch(val) [32][100/509] eta: 0:03:04 time: 0.4438 data_time: 0.0021 memory: 1105 2023/05/11 16:31:01 - mmengine - INFO - Epoch(val) [32][150/509] eta: 0:02:39 time: 0.4353 data_time: 0.0020 memory: 1110 2023/05/11 16:31:23 - mmengine - INFO - Epoch(val) [32][200/509] eta: 0:02:17 time: 0.4417 data_time: 0.0021 memory: 1100 2023/05/11 16:31:46 - mmengine - INFO - Epoch(val) [32][250/509] eta: 0:01:55 time: 0.4566 data_time: 0.0020 memory: 1111 2023/05/11 16:32:07 - mmengine - INFO - Epoch(val) [32][300/509] eta: 0:01:32 time: 0.4185 data_time: 0.0020 memory: 1075 2023/05/11 16:32:28 - mmengine - INFO - Epoch(val) [32][350/509] eta: 0:01:09 time: 0.4228 data_time: 0.0020 memory: 1091 2023/05/11 16:32:50 - mmengine - INFO - Epoch(val) [32][400/509] eta: 0:00:47 time: 0.4428 data_time: 0.0020 memory: 1090 2023/05/11 16:33:12 - mmengine - INFO - Epoch(val) [32][450/509] eta: 0:00:26 time: 0.4487 data_time: 0.0020 memory: 1113 2023/05/11 16:33:34 - mmengine - INFO - Epoch(val) [32][500/509] eta: 0:00:03 time: 0.4363 data_time: 0.0020 memory: 1098 2023/05/11 16:34:05 - 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 | 0.9649 | 0.5804 | 0.8198 | 0.8855 | 0.6538 | 0.7872 | 0.9199 | 0.1595 | 0.9463 | 0.5429 | 0.8264 | 0.0431 | 0.9198 | 0.6908 | 0.8728 | 0.6882 | 0.7155 | 0.6677 | 0.5212 | 0.6950 | 0.9216 | 0.7606 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 16:34:06 - mmengine - INFO - Epoch(val) [32][509/509] car: 0.9649 bicycle: 0.5804 motorcycle: 0.8198 truck: 0.8855 bus: 0.6538 person: 0.7872 bicyclist: 0.9199 motorcyclist: 0.1595 road: 0.9463 parking: 0.5429 sidewalk: 0.8264 other-ground: 0.0431 building: 0.9198 fence: 0.6908 vegetation: 0.8728 trunck: 0.6882 terrian: 0.7155 pole: 0.6677 traffic-sign: 0.5212 miou: 0.6950 acc: 0.9216 acc_cls: 0.7606 data_time: 0.0021 time: 0.4426 2023/05/11 16:35:20 - mmengine - INFO - Epoch(train) [33][ 50/1196] lr: 8.0000e-05 eta: 1:55:33 time: 1.4845 data_time: 0.0048 memory: 3353 grad_norm: 0.0675 loss: 0.1623 loss_sem_seg: 0.1623 2023/05/11 16:36:31 - mmengine - INFO - Epoch(train) [33][ 100/1196] lr: 8.0000e-05 eta: 1:54:20 time: 1.4208 data_time: 0.0034 memory: 3416 grad_norm: 0.0657 loss: 0.1638 loss_sem_seg: 0.1638 2023/05/11 16:37:42 - mmengine - INFO - Epoch(train) [33][ 150/1196] lr: 8.0000e-05 eta: 1:53:06 time: 1.4145 data_time: 0.0034 memory: 3553 grad_norm: 0.0637 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/11 16:38:52 - mmengine - INFO - Epoch(train) [33][ 200/1196] lr: 8.0000e-05 eta: 1:51:53 time: 1.4137 data_time: 0.0035 memory: 3400 grad_norm: 0.0634 loss: 0.1535 loss_sem_seg: 0.1535 2023/05/11 16:40:03 - mmengine - INFO - Epoch(train) [33][ 250/1196] lr: 8.0000e-05 eta: 1:50:39 time: 1.4128 data_time: 0.0035 memory: 3160 grad_norm: 0.0650 loss: 0.1480 loss_sem_seg: 0.1480 2023/05/11 16:41:14 - mmengine - INFO - Epoch(train) [33][ 300/1196] lr: 8.0000e-05 eta: 1:49:26 time: 1.4203 data_time: 0.0036 memory: 3439 grad_norm: 0.0691 loss: 0.1459 loss_sem_seg: 0.1459 2023/05/11 16:42:24 - mmengine - INFO - Epoch(train) [33][ 350/1196] lr: 8.0000e-05 eta: 1:48:12 time: 1.4032 data_time: 0.0035 memory: 3193 grad_norm: 0.0657 loss: 0.1626 loss_sem_seg: 0.1626 2023/05/11 16:43:34 - mmengine - INFO - Epoch(train) [33][ 400/1196] lr: 8.0000e-05 eta: 1:46:59 time: 1.4081 data_time: 0.0035 memory: 3401 grad_norm: 0.0610 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/11 16:44:45 - mmengine - INFO - Epoch(train) [33][ 450/1196] lr: 8.0000e-05 eta: 1:45:45 time: 1.4072 data_time: 0.0035 memory: 3333 grad_norm: 0.0642 loss: 0.1594 loss_sem_seg: 0.1594 2023/05/11 16:45:56 - mmengine - INFO - Epoch(train) [33][ 500/1196] lr: 8.0000e-05 eta: 1:44:32 time: 1.4165 data_time: 0.0035 memory: 3695 grad_norm: 0.0666 loss: 0.1614 loss_sem_seg: 0.1614 2023/05/11 16:47:06 - mmengine - INFO - Epoch(train) [33][ 550/1196] lr: 8.0000e-05 eta: 1:43:18 time: 1.4084 data_time: 0.0036 memory: 3307 grad_norm: 0.0635 loss: 0.1414 loss_sem_seg: 0.1414 2023/05/11 16:48:17 - mmengine - INFO - Epoch(train) [33][ 600/1196] lr: 8.0000e-05 eta: 1:42:05 time: 1.4201 data_time: 0.0034 memory: 3173 grad_norm: 0.0622 loss: 0.1458 loss_sem_seg: 0.1458 2023/05/11 16:49:28 - mmengine - INFO - Epoch(train) [33][ 650/1196] lr: 8.0000e-05 eta: 1:40:51 time: 1.4088 data_time: 0.0035 memory: 3644 grad_norm: 0.0597 loss: 0.1466 loss_sem_seg: 0.1466 2023/05/11 16:50:38 - mmengine - INFO - Epoch(train) [33][ 700/1196] lr: 8.0000e-05 eta: 1:39:38 time: 1.4091 data_time: 0.0034 memory: 3350 grad_norm: 0.0643 loss: 0.1441 loss_sem_seg: 0.1441 2023/05/11 16:51:18 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 16:51:48 - mmengine - INFO - Epoch(train) [33][ 750/1196] lr: 8.0000e-05 eta: 1:38:24 time: 1.4084 data_time: 0.0035 memory: 3292 grad_norm: 0.0611 loss: 0.1501 loss_sem_seg: 0.1501 2023/05/11 16:52:50 - mmengine - INFO - Epoch(train) [33][ 800/1196] lr: 8.0000e-05 eta: 1:37:10 time: 1.2371 data_time: 0.0034 memory: 3745 grad_norm: 0.0660 loss: 0.1613 loss_sem_seg: 0.1613 2023/05/11 16:53:51 - mmengine - INFO - Epoch(train) [33][ 850/1196] lr: 8.0000e-05 eta: 1:35:56 time: 1.2223 data_time: 0.0034 memory: 3548 grad_norm: 0.0659 loss: 0.1440 loss_sem_seg: 0.1440 2023/05/11 16:54:52 - mmengine - INFO - Epoch(train) [33][ 900/1196] lr: 8.0000e-05 eta: 1:34:41 time: 1.2195 data_time: 0.0035 memory: 3415 grad_norm: 0.0607 loss: 0.1482 loss_sem_seg: 0.1482 2023/05/11 16:55:53 - mmengine - INFO - Epoch(train) [33][ 950/1196] lr: 8.0000e-05 eta: 1:33:27 time: 1.2084 data_time: 0.0034 memory: 3426 grad_norm: 0.0619 loss: 0.1546 loss_sem_seg: 0.1546 2023/05/11 16:56:53 - mmengine - INFO - Epoch(train) [33][1000/1196] lr: 8.0000e-05 eta: 1:32:12 time: 1.2123 data_time: 0.0034 memory: 3456 grad_norm: 0.0631 loss: 0.1579 loss_sem_seg: 0.1579 2023/05/11 16:58:04 - mmengine - INFO - Epoch(train) [33][1050/1196] lr: 8.0000e-05 eta: 1:30:59 time: 1.4162 data_time: 0.0034 memory: 3307 grad_norm: 0.0627 loss: 0.1589 loss_sem_seg: 0.1589 2023/05/11 16:59:15 - mmengine - INFO - Epoch(train) [33][1100/1196] lr: 8.0000e-05 eta: 1:29:46 time: 1.4056 data_time: 0.0035 memory: 3551 grad_norm: 0.0645 loss: 0.1458 loss_sem_seg: 0.1458 2023/05/11 17:00:27 - mmengine - INFO - Epoch(train) [33][1150/1196] lr: 8.0000e-05 eta: 1:28:33 time: 1.4411 data_time: 0.0034 memory: 3265 grad_norm: 0.0661 loss: 0.1523 loss_sem_seg: 0.1523 2023/05/11 17:01:31 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 17:01:31 - mmengine - INFO - Saving checkpoint at 33 epochs 2023/05/11 17:01:59 - mmengine - INFO - Epoch(val) [33][ 50/509] eta: 0:03:26 time: 0.4490 data_time: 0.0021 memory: 3265 2023/05/11 17:02:21 - mmengine - INFO - Epoch(val) [33][100/509] eta: 0:03:03 time: 0.4471 data_time: 0.0020 memory: 1105 2023/05/11 17:02:43 - mmengine - INFO - Epoch(val) [33][150/509] eta: 0:02:39 time: 0.4350 data_time: 0.0020 memory: 1110 2023/05/11 17:03:05 - mmengine - INFO - Epoch(val) [33][200/509] eta: 0:02:16 time: 0.4387 data_time: 0.0020 memory: 1100 2023/05/11 17:03:28 - mmengine - INFO - Epoch(val) [33][250/509] eta: 0:01:55 time: 0.4574 data_time: 0.0020 memory: 1111 2023/05/11 17:03:49 - mmengine - INFO - Epoch(val) [33][300/509] eta: 0:01:32 time: 0.4198 data_time: 0.0020 memory: 1075 2023/05/11 17:04:10 - mmengine - INFO - Epoch(val) [33][350/509] eta: 0:01:09 time: 0.4214 data_time: 0.0020 memory: 1091 2023/05/11 17:04:32 - mmengine - INFO - Epoch(val) [33][400/509] eta: 0:00:47 time: 0.4402 data_time: 0.0020 memory: 1090 2023/05/11 17:04:54 - mmengine - INFO - Epoch(val) [33][450/509] eta: 0:00:25 time: 0.4472 data_time: 0.0020 memory: 1113 2023/05/11 17:05:16 - mmengine - INFO - Epoch(val) [33][500/509] eta: 0:00:03 time: 0.4293 data_time: 0.0020 memory: 1098 2023/05/11 17:05:48 - 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 | 0.9667 | 0.5719 | 0.8159 | 0.8662 | 0.6653 | 0.7873 | 0.9150 | 0.1721 | 0.9467 | 0.5268 | 0.8263 | 0.0549 | 0.9195 | 0.6874 | 0.8768 | 0.6837 | 0.7253 | 0.6646 | 0.5234 | 0.6945 | 0.9230 | 0.7618 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 17:05:48 - mmengine - INFO - Epoch(val) [33][509/509] car: 0.9667 bicycle: 0.5719 motorcycle: 0.8159 truck: 0.8662 bus: 0.6653 person: 0.7873 bicyclist: 0.9150 motorcyclist: 0.1721 road: 0.9467 parking: 0.5268 sidewalk: 0.8263 other-ground: 0.0549 building: 0.9195 fence: 0.6874 vegetation: 0.8768 trunck: 0.6837 terrian: 0.7253 pole: 0.6646 traffic-sign: 0.5234 miou: 0.6945 acc: 0.9230 acc_cls: 0.7618 data_time: 0.0021 time: 0.4448 2023/05/11 17:07:01 - mmengine - INFO - Epoch(train) [34][ 50/1196] lr: 8.0000e-05 eta: 1:26:12 time: 1.4662 data_time: 0.0047 memory: 3492 grad_norm: 0.0613 loss: 0.1515 loss_sem_seg: 0.1515 2023/05/11 17:08:12 - mmengine - INFO - Epoch(train) [34][ 100/1196] lr: 8.0000e-05 eta: 1:24:59 time: 1.4101 data_time: 0.0035 memory: 3347 grad_norm: 0.0706 loss: 0.1508 loss_sem_seg: 0.1508 2023/05/11 17:09:22 - mmengine - INFO - Epoch(train) [34][ 150/1196] lr: 8.0000e-05 eta: 1:23:45 time: 1.4094 data_time: 0.0034 memory: 3186 grad_norm: 0.0632 loss: 0.1473 loss_sem_seg: 0.1473 2023/05/11 17:10:32 - mmengine - INFO - Epoch(train) [34][ 200/1196] lr: 8.0000e-05 eta: 1:22:32 time: 1.4049 data_time: 0.0035 memory: 3211 grad_norm: 0.0588 loss: 0.1518 loss_sem_seg: 0.1518 2023/05/11 17:11:43 - mmengine - INFO - Epoch(train) [34][ 250/1196] lr: 8.0000e-05 eta: 1:21:19 time: 1.4185 data_time: 0.0035 memory: 3162 grad_norm: 0.0619 loss: 0.1451 loss_sem_seg: 0.1451 2023/05/11 17:12:55 - mmengine - INFO - Epoch(train) [34][ 300/1196] lr: 8.0000e-05 eta: 1:20:05 time: 1.4250 data_time: 0.0034 memory: 3295 grad_norm: 0.0666 loss: 0.1573 loss_sem_seg: 0.1573 2023/05/11 17:14:05 - mmengine - INFO - Epoch(train) [34][ 350/1196] lr: 8.0000e-05 eta: 1:18:52 time: 1.4144 data_time: 0.0038 memory: 3427 grad_norm: inf loss: 0.1521 loss_sem_seg: 0.1521 2023/05/11 17:15:15 - mmengine - INFO - Epoch(train) [34][ 400/1196] lr: 8.0000e-05 eta: 1:17:39 time: 1.4034 data_time: 0.0035 memory: 3263 grad_norm: 0.0694 loss: 0.1621 loss_sem_seg: 0.1621 2023/05/11 17:16:26 - mmengine - INFO - Epoch(train) [34][ 450/1196] lr: 8.0000e-05 eta: 1:16:26 time: 1.4079 data_time: 0.0036 memory: 3338 grad_norm: 0.0650 loss: 0.1444 loss_sem_seg: 0.1444 2023/05/11 17:17:36 - mmengine - INFO - Epoch(train) [34][ 500/1196] lr: 8.0000e-05 eta: 1:15:12 time: 1.4096 data_time: 0.0034 memory: 3222 grad_norm: 0.0619 loss: 0.1606 loss_sem_seg: 0.1606 2023/05/11 17:18:22 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 17:18:48 - mmengine - INFO - Epoch(train) [34][ 550/1196] lr: 8.0000e-05 eta: 1:13:59 time: 1.4234 data_time: 0.0034 memory: 3379 grad_norm: 0.0673 loss: 0.1557 loss_sem_seg: 0.1557 2023/05/11 17:19:59 - mmengine - INFO - Epoch(train) [34][ 600/1196] lr: 8.0000e-05 eta: 1:12:46 time: 1.4245 data_time: 0.0034 memory: 3331 grad_norm: 0.0606 loss: 0.1472 loss_sem_seg: 0.1472 2023/05/11 17:21:09 - mmengine - INFO - Epoch(train) [34][ 650/1196] lr: 8.0000e-05 eta: 1:11:33 time: 1.4139 data_time: 0.0035 memory: 3842 grad_norm: 0.0665 loss: 0.1557 loss_sem_seg: 0.1557 2023/05/11 17:22:20 - mmengine - INFO - Epoch(train) [34][ 700/1196] lr: 8.0000e-05 eta: 1:10:19 time: 1.4116 data_time: 0.0035 memory: 3312 grad_norm: 0.0650 loss: 0.1651 loss_sem_seg: 0.1651 2023/05/11 17:23:31 - mmengine - INFO - Epoch(train) [34][ 750/1196] lr: 8.0000e-05 eta: 1:09:06 time: 1.4161 data_time: 0.0034 memory: 3348 grad_norm: 0.0611 loss: 0.1525 loss_sem_seg: 0.1525 2023/05/11 17:24:33 - mmengine - INFO - Epoch(train) [34][ 800/1196] lr: 8.0000e-05 eta: 1:07:52 time: 1.2453 data_time: 0.0034 memory: 3219 grad_norm: 0.0597 loss: 0.1572 loss_sem_seg: 0.1572 2023/05/11 17:25:34 - mmengine - INFO - Epoch(train) [34][ 850/1196] lr: 8.0000e-05 eta: 1:06:39 time: 1.2177 data_time: 0.0035 memory: 3255 grad_norm: 0.0588 loss: 0.1542 loss_sem_seg: 0.1542 2023/05/11 17:26:35 - mmengine - INFO - Epoch(train) [34][ 900/1196] lr: 8.0000e-05 eta: 1:05:25 time: 1.2248 data_time: 0.0035 memory: 3363 grad_norm: 0.0614 loss: 0.1442 loss_sem_seg: 0.1442 2023/05/11 17:27:35 - mmengine - INFO - Epoch(train) [34][ 950/1196] lr: 8.0000e-05 eta: 1:04:11 time: 1.2020 data_time: 0.0035 memory: 3185 grad_norm: 0.0607 loss: 0.1612 loss_sem_seg: 0.1612 2023/05/11 17:28:36 - mmengine - INFO - Epoch(train) [34][1000/1196] lr: 8.0000e-05 eta: 1:02:57 time: 1.2183 data_time: 0.0034 memory: 3167 grad_norm: 0.0601 loss: 0.1501 loss_sem_seg: 0.1501 2023/05/11 17:29:45 - mmengine - INFO - Epoch(train) [34][1050/1196] lr: 8.0000e-05 eta: 1:01:44 time: 1.3795 data_time: 0.0035 memory: 3102 grad_norm: 0.0645 loss: 0.1529 loss_sem_seg: 0.1529 2023/05/11 17:30:56 - mmengine - INFO - Epoch(train) [34][1100/1196] lr: 8.0000e-05 eta: 1:00:31 time: 1.4067 data_time: 0.0035 memory: 3657 grad_norm: 0.0674 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/11 17:32:06 - mmengine - INFO - Epoch(train) [34][1150/1196] lr: 8.0000e-05 eta: 0:59:18 time: 1.4174 data_time: 0.0035 memory: 3346 grad_norm: 0.0644 loss: 0.1463 loss_sem_seg: 0.1463 2023/05/11 17:33:12 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 17:33:12 - mmengine - INFO - Saving checkpoint at 34 epochs 2023/05/11 17:33:40 - mmengine - INFO - Epoch(val) [34][ 50/509] eta: 0:03:33 time: 0.4643 data_time: 0.0021 memory: 3355 2023/05/11 17:34:03 - mmengine - INFO - Epoch(val) [34][100/509] eta: 0:03:06 time: 0.4478 data_time: 0.0020 memory: 1105 2023/05/11 17:34:24 - mmengine - INFO - Epoch(val) [34][150/509] eta: 0:02:41 time: 0.4344 data_time: 0.0020 memory: 1110 2023/05/11 17:34:46 - mmengine - INFO - Epoch(val) [34][200/509] eta: 0:02:17 time: 0.4388 data_time: 0.0020 memory: 1100 2023/05/11 17:35:09 - mmengine - INFO - Epoch(val) [34][250/509] eta: 0:01:56 time: 0.4568 data_time: 0.0020 memory: 1111 2023/05/11 17:35:30 - mmengine - INFO - Epoch(val) [34][300/509] eta: 0:01:32 time: 0.4233 data_time: 0.0020 memory: 1075 2023/05/11 17:35:52 - mmengine - INFO - Epoch(val) [34][350/509] eta: 0:01:10 time: 0.4231 data_time: 0.0020 memory: 1091 2023/05/11 17:36:14 - mmengine - INFO - Epoch(val) [34][400/509] eta: 0:00:48 time: 0.4410 data_time: 0.0020 memory: 1090 2023/05/11 17:36:36 - mmengine - INFO - Epoch(val) [34][450/509] eta: 0:00:26 time: 0.4455 data_time: 0.0021 memory: 1113 2023/05/11 17:36:57 - mmengine - INFO - Epoch(val) [34][500/509] eta: 0:00:03 time: 0.4284 data_time: 0.0020 memory: 1098 2023/05/11 17:37:29 - 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 | 0.9680 | 0.5767 | 0.8227 | 0.8786 | 0.6929 | 0.7893 | 0.9190 | 0.1689 | 0.9462 | 0.5277 | 0.8263 | 0.0569 | 0.9178 | 0.6807 | 0.8747 | 0.6850 | 0.7186 | 0.6650 | 0.5234 | 0.6968 | 0.9220 | 0.7636 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 17:37:29 - mmengine - INFO - Epoch(val) [34][509/509] car: 0.9680 bicycle: 0.5767 motorcycle: 0.8227 truck: 0.8786 bus: 0.6929 person: 0.7893 bicyclist: 0.9190 motorcyclist: 0.1689 road: 0.9462 parking: 0.5277 sidewalk: 0.8263 other-ground: 0.0569 building: 0.9178 fence: 0.6807 vegetation: 0.8747 trunck: 0.6850 terrian: 0.7186 pole: 0.6650 traffic-sign: 0.5234 miou: 0.6968 acc: 0.9220 acc_cls: 0.7636 data_time: 0.0020 time: 0.4457 2023/05/11 17:38:45 - mmengine - INFO - Epoch(train) [35][ 50/1196] lr: 8.0000e-05 eta: 0:56:58 time: 1.5157 data_time: 0.0044 memory: 3313 grad_norm: 0.0600 loss: 0.1486 loss_sem_seg: 0.1486 2023/05/11 17:39:57 - mmengine - INFO - Epoch(train) [35][ 100/1196] lr: 8.0000e-05 eta: 0:55:45 time: 1.4579 data_time: 0.0036 memory: 3289 grad_norm: 0.0667 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/11 17:41:10 - mmengine - INFO - Epoch(train) [35][ 150/1196] lr: 8.0000e-05 eta: 0:54:32 time: 1.4479 data_time: 0.0035 memory: 3470 grad_norm: 0.0602 loss: 0.1571 loss_sem_seg: 0.1571 2023/05/11 17:42:23 - mmengine - INFO - Epoch(train) [35][ 200/1196] lr: 8.0000e-05 eta: 0:53:19 time: 1.4580 data_time: 0.0035 memory: 3229 grad_norm: 0.0645 loss: 0.1551 loss_sem_seg: 0.1551 2023/05/11 17:43:35 - mmengine - INFO - Epoch(train) [35][ 250/1196] lr: 8.0000e-05 eta: 0:52:06 time: 1.4448 data_time: 0.0035 memory: 3277 grad_norm: 0.0639 loss: 0.1492 loss_sem_seg: 0.1492 2023/05/11 17:44:45 - mmengine - INFO - Epoch(train) [35][ 300/1196] lr: 8.0000e-05 eta: 0:50:52 time: 1.4053 data_time: 0.0034 memory: 3325 grad_norm: 0.0671 loss: 0.1615 loss_sem_seg: 0.1615 2023/05/11 17:45:36 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 17:45:55 - mmengine - INFO - Epoch(train) [35][ 350/1196] lr: 8.0000e-05 eta: 0:49:39 time: 1.4011 data_time: 0.0035 memory: 3268 grad_norm: 0.0644 loss: 0.1498 loss_sem_seg: 0.1498 2023/05/11 17:47:07 - mmengine - INFO - Epoch(train) [35][ 400/1196] lr: 8.0000e-05 eta: 0:48:26 time: 1.4249 data_time: 0.0035 memory: 3232 grad_norm: 0.0597 loss: 0.1528 loss_sem_seg: 0.1528 2023/05/11 17:48:17 - mmengine - INFO - Epoch(train) [35][ 450/1196] lr: 8.0000e-05 eta: 0:47:13 time: 1.4029 data_time: 0.0035 memory: 3241 grad_norm: 0.0691 loss: 0.1519 loss_sem_seg: 0.1519 2023/05/11 17:49:27 - mmengine - INFO - Epoch(train) [35][ 500/1196] lr: 8.0000e-05 eta: 0:46:00 time: 1.4082 data_time: 0.0035 memory: 3367 grad_norm: 0.0646 loss: 0.1639 loss_sem_seg: 0.1639 2023/05/11 17:50:38 - mmengine - INFO - Epoch(train) [35][ 550/1196] lr: 8.0000e-05 eta: 0:44:47 time: 1.4092 data_time: 0.0035 memory: 3256 grad_norm: 0.0623 loss: 0.1615 loss_sem_seg: 0.1615 2023/05/11 17:51:48 - mmengine - INFO - Epoch(train) [35][ 600/1196] lr: 8.0000e-05 eta: 0:43:34 time: 1.4145 data_time: 0.0034 memory: 3241 grad_norm: 0.0619 loss: 0.1545 loss_sem_seg: 0.1545 2023/05/11 17:52:59 - mmengine - INFO - Epoch(train) [35][ 650/1196] lr: 8.0000e-05 eta: 0:42:21 time: 1.4134 data_time: 0.0035 memory: 3249 grad_norm: 0.0617 loss: 0.1442 loss_sem_seg: 0.1442 2023/05/11 17:54:09 - mmengine - INFO - Epoch(train) [35][ 700/1196] lr: 8.0000e-05 eta: 0:41:08 time: 1.4071 data_time: 0.0037 memory: 3416 grad_norm: 0.0660 loss: 0.1585 loss_sem_seg: 0.1585 2023/05/11 17:55:20 - mmengine - INFO - Epoch(train) [35][ 750/1196] lr: 8.0000e-05 eta: 0:39:55 time: 1.4069 data_time: 0.0035 memory: 3521 grad_norm: 0.0616 loss: 0.1521 loss_sem_seg: 0.1521 2023/05/11 17:56:23 - mmengine - INFO - Epoch(train) [35][ 800/1196] lr: 8.0000e-05 eta: 0:38:42 time: 1.2652 data_time: 0.0035 memory: 3552 grad_norm: 0.0615 loss: 0.1461 loss_sem_seg: 0.1461 2023/05/11 17:57:23 - mmengine - INFO - Epoch(train) [35][ 850/1196] lr: 8.0000e-05 eta: 0:37:28 time: 1.2073 data_time: 0.0035 memory: 3461 grad_norm: 0.0596 loss: 0.1516 loss_sem_seg: 0.1516 2023/05/11 17:58:24 - mmengine - INFO - Epoch(train) [35][ 900/1196] lr: 8.0000e-05 eta: 0:36:15 time: 1.2200 data_time: 0.0035 memory: 3118 grad_norm: 0.0659 loss: 0.1607 loss_sem_seg: 0.1607 2023/05/11 17:59:25 - mmengine - INFO - Epoch(train) [35][ 950/1196] lr: 8.0000e-05 eta: 0:35:01 time: 1.2039 data_time: 0.0035 memory: 3324 grad_norm: 0.0624 loss: 0.1509 loss_sem_seg: 0.1509 2023/05/11 18:00:25 - mmengine - INFO - Epoch(train) [35][1000/1196] lr: 8.0000e-05 eta: 0:33:48 time: 1.2166 data_time: 0.0035 memory: 3349 grad_norm: 0.0651 loss: 0.1531 loss_sem_seg: 0.1531 2023/05/11 18:01:34 - mmengine - INFO - Epoch(train) [35][1050/1196] lr: 8.0000e-05 eta: 0:32:35 time: 1.3710 data_time: 0.0034 memory: 3116 grad_norm: 0.0615 loss: 0.1491 loss_sem_seg: 0.1491 2023/05/11 18:02:45 - mmengine - INFO - Epoch(train) [35][1100/1196] lr: 8.0000e-05 eta: 0:31:22 time: 1.4173 data_time: 0.0035 memory: 3325 grad_norm: 0.0624 loss: 0.1450 loss_sem_seg: 0.1450 2023/05/11 18:03:55 - mmengine - INFO - Epoch(train) [35][1150/1196] lr: 8.0000e-05 eta: 0:30:09 time: 1.4075 data_time: 0.0035 memory: 3462 grad_norm: 0.0622 loss: 0.1559 loss_sem_seg: 0.1559 2023/05/11 18:05:00 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 18:05:00 - mmengine - INFO - Saving checkpoint at 35 epochs 2023/05/11 18:05:29 - mmengine - INFO - Epoch(val) [35][ 50/509] eta: 0:03:35 time: 0.4698 data_time: 0.0021 memory: 3281 2023/05/11 18:05:51 - mmengine - INFO - Epoch(val) [35][100/509] eta: 0:03:06 time: 0.4420 data_time: 0.0020 memory: 1105 2023/05/11 18:06:13 - mmengine - INFO - Epoch(val) [35][150/509] eta: 0:02:41 time: 0.4370 data_time: 0.0020 memory: 1110 2023/05/11 18:06:35 - mmengine - INFO - Epoch(val) [35][200/509] eta: 0:02:18 time: 0.4396 data_time: 0.0020 memory: 1100 2023/05/11 18:06:58 - mmengine - INFO - Epoch(val) [35][250/509] eta: 0:01:56 time: 0.4591 data_time: 0.0020 memory: 1111 2023/05/11 18:07:19 - mmengine - INFO - Epoch(val) [35][300/509] eta: 0:01:33 time: 0.4235 data_time: 0.0020 memory: 1075 2023/05/11 18:07:41 - mmengine - INFO - Epoch(val) [35][350/509] eta: 0:01:10 time: 0.4230 data_time: 0.0020 memory: 1091 2023/05/11 18:08:03 - mmengine - INFO - Epoch(val) [35][400/509] eta: 0:00:48 time: 0.4398 data_time: 0.0020 memory: 1090 2023/05/11 18:08:25 - mmengine - INFO - Epoch(val) [35][450/509] eta: 0:00:26 time: 0.4482 data_time: 0.0020 memory: 1113 2023/05/11 18:08:46 - mmengine - INFO - Epoch(val) [35][500/509] eta: 0:00:03 time: 0.4281 data_time: 0.0020 memory: 1098 2023/05/11 18:09:18 - 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 | 0.9677 | 0.5684 | 0.8207 | 0.8704 | 0.6782 | 0.7914 | 0.9184 | 0.1954 | 0.9472 | 0.5269 | 0.8278 | 0.0551 | 0.9193 | 0.6870 | 0.8729 | 0.6905 | 0.7155 | 0.6652 | 0.5247 | 0.6970 | 0.9218 | 0.7638 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 18:09:18 - mmengine - INFO - Epoch(val) [35][509/509] car: 0.9677 bicycle: 0.5684 motorcycle: 0.8207 truck: 0.8704 bus: 0.6782 person: 0.7914 bicyclist: 0.9184 motorcyclist: 0.1954 road: 0.9472 parking: 0.5269 sidewalk: 0.8278 other-ground: 0.0551 building: 0.9193 fence: 0.6870 vegetation: 0.8729 trunck: 0.6905 terrian: 0.7155 pole: 0.6652 traffic-sign: 0.5247 miou: 0.6970 acc: 0.9218 acc_cls: 0.7638 data_time: 0.0020 time: 0.4425 2023/05/11 18:10:32 - mmengine - INFO - Epoch(train) [36][ 50/1196] lr: 8.0000e-05 eta: 0:27:49 time: 1.4834 data_time: 0.0041 memory: 3449 grad_norm: 0.0625 loss: 0.1494 loss_sem_seg: 0.1494 2023/05/11 18:11:43 - mmengine - INFO - Epoch(train) [36][ 100/1196] lr: 8.0000e-05 eta: 0:26:36 time: 1.4104 data_time: 0.0034 memory: 3429 grad_norm: 0.0611 loss: 0.1517 loss_sem_seg: 0.1517 2023/05/11 18:12:40 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 18:12:54 - mmengine - INFO - Epoch(train) [36][ 150/1196] lr: 8.0000e-05 eta: 0:25:24 time: 1.4188 data_time: 0.0035 memory: 3392 grad_norm: 0.0601 loss: 0.1470 loss_sem_seg: 0.1470 2023/05/11 18:14:05 - mmengine - INFO - Epoch(train) [36][ 200/1196] lr: 8.0000e-05 eta: 0:24:11 time: 1.4196 data_time: 0.0035 memory: 4037 grad_norm: 0.0621 loss: 0.1368 loss_sem_seg: 0.1368 2023/05/11 18:15:16 - mmengine - INFO - Epoch(train) [36][ 250/1196] lr: 8.0000e-05 eta: 0:22:58 time: 1.4162 data_time: 0.0034 memory: 3225 grad_norm: 0.0577 loss: 0.1474 loss_sem_seg: 0.1474 2023/05/11 18:16:26 - mmengine - INFO - Epoch(train) [36][ 300/1196] lr: 8.0000e-05 eta: 0:21:45 time: 1.4063 data_time: 0.0035 memory: 3317 grad_norm: 0.0601 loss: 0.1480 loss_sem_seg: 0.1480 2023/05/11 18:17:37 - mmengine - INFO - Epoch(train) [36][ 350/1196] lr: 8.0000e-05 eta: 0:20:32 time: 1.4128 data_time: 0.0035 memory: 3550 grad_norm: 0.0641 loss: 0.1537 loss_sem_seg: 0.1537 2023/05/11 18:18:47 - mmengine - INFO - Epoch(train) [36][ 400/1196] lr: 8.0000e-05 eta: 0:19:19 time: 1.4068 data_time: 0.0036 memory: 3355 grad_norm: 0.0636 loss: 0.1388 loss_sem_seg: 0.1388 2023/05/11 18:19:58 - mmengine - INFO - Epoch(train) [36][ 450/1196] lr: 8.0000e-05 eta: 0:18:06 time: 1.4149 data_time: 0.0035 memory: 3314 grad_norm: 0.0612 loss: 0.1504 loss_sem_seg: 0.1504 2023/05/11 18:21:08 - mmengine - INFO - Epoch(train) [36][ 500/1196] lr: 8.0000e-05 eta: 0:16:53 time: 1.4057 data_time: 0.0037 memory: 3385 grad_norm: 0.0645 loss: 0.1467 loss_sem_seg: 0.1467 2023/05/11 18:22:19 - mmengine - INFO - Epoch(train) [36][ 550/1196] lr: 8.0000e-05 eta: 0:15:40 time: 1.4151 data_time: 0.0035 memory: 3368 grad_norm: 0.0656 loss: 0.1496 loss_sem_seg: 0.1496 2023/05/11 18:23:29 - mmengine - INFO - Epoch(train) [36][ 600/1196] lr: 8.0000e-05 eta: 0:14:28 time: 1.4058 data_time: 0.0034 memory: 3452 grad_norm: 0.0594 loss: 0.1479 loss_sem_seg: 0.1479 2023/05/11 18:24:40 - mmengine - INFO - Epoch(train) [36][ 650/1196] lr: 8.0000e-05 eta: 0:13:15 time: 1.4121 data_time: 0.0035 memory: 3408 grad_norm: 0.0623 loss: 0.1394 loss_sem_seg: 0.1394 2023/05/11 18:25:50 - mmengine - INFO - Epoch(train) [36][ 700/1196] lr: 8.0000e-05 eta: 0:12:02 time: 1.4069 data_time: 0.0036 memory: 3279 grad_norm: 0.0633 loss: 0.1610 loss_sem_seg: 0.1610 2023/05/11 18:27:01 - mmengine - INFO - Epoch(train) [36][ 750/1196] lr: 8.0000e-05 eta: 0:10:49 time: 1.4099 data_time: 0.0035 memory: 3245 grad_norm: 0.0628 loss: 0.1465 loss_sem_seg: 0.1465 2023/05/11 18:28:05 - mmengine - INFO - Epoch(train) [36][ 800/1196] lr: 8.0000e-05 eta: 0:09:36 time: 1.2869 data_time: 0.0035 memory: 3496 grad_norm: 0.0629 loss: 0.1488 loss_sem_seg: 0.1488 2023/05/11 18:29:06 - mmengine - INFO - Epoch(train) [36][ 850/1196] lr: 8.0000e-05 eta: 0:08:23 time: 1.2191 data_time: 0.0034 memory: 3292 grad_norm: 0.0641 loss: 0.1416 loss_sem_seg: 0.1416 2023/05/11 18:30:07 - mmengine - INFO - Epoch(train) [36][ 900/1196] lr: 8.0000e-05 eta: 0:07:10 time: 1.2230 data_time: 0.0035 memory: 3316 grad_norm: 0.0581 loss: 0.1546 loss_sem_seg: 0.1546 2023/05/11 18:31:07 - mmengine - INFO - Epoch(train) [36][ 950/1196] lr: 8.0000e-05 eta: 0:05:58 time: 1.1984 data_time: 0.0034 memory: 3556 grad_norm: 0.0626 loss: 0.1498 loss_sem_seg: 0.1498 2023/05/11 18:32:08 - mmengine - INFO - Epoch(train) [36][1000/1196] lr: 8.0000e-05 eta: 0:04:45 time: 1.2187 data_time: 0.0035 memory: 3391 grad_norm: 0.0640 loss: 0.1485 loss_sem_seg: 0.1485 2023/05/11 18:33:14 - mmengine - INFO - Epoch(train) [36][1050/1196] lr: 8.0000e-05 eta: 0:03:32 time: 1.3116 data_time: 0.0037 memory: 3361 grad_norm: 0.0650 loss: 0.1491 loss_sem_seg: 0.1491 2023/05/11 18:34:26 - mmengine - INFO - Epoch(train) [36][1100/1196] lr: 8.0000e-05 eta: 0:02:19 time: 1.4463 data_time: 0.0035 memory: 3273 grad_norm: 0.0594 loss: 0.1550 loss_sem_seg: 0.1550 2023/05/11 18:35:22 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 18:35:37 - mmengine - INFO - Epoch(train) [36][1150/1196] lr: 8.0000e-05 eta: 0:01:06 time: 1.4129 data_time: 0.0035 memory: 3348 grad_norm: 0.0647 loss: 0.1620 loss_sem_seg: 0.1620 2023/05/11 18:36:42 - mmengine - INFO - Exp name: minkunet34v2_w32_8xb2-amp-3x_noseed_lpmix_semantickitti_20230510_221853 2023/05/11 18:36:42 - mmengine - INFO - Saving checkpoint at 36 epochs 2023/05/11 18:37:11 - mmengine - INFO - Epoch(val) [36][ 50/509] eta: 0:03:33 time: 0.4649 data_time: 0.0021 memory: 3456 2023/05/11 18:37:33 - mmengine - INFO - Epoch(val) [36][100/509] eta: 0:03:06 time: 0.4484 data_time: 0.0020 memory: 1105 2023/05/11 18:37:55 - mmengine - INFO - Epoch(val) [36][150/509] eta: 0:02:40 time: 0.4308 data_time: 0.0020 memory: 1110 2023/05/11 18:38:17 - mmengine - INFO - Epoch(val) [36][200/509] eta: 0:02:17 time: 0.4419 data_time: 0.0020 memory: 1100 2023/05/11 18:38:40 - mmengine - INFO - Epoch(val) [36][250/509] eta: 0:01:56 time: 0.4551 data_time: 0.0020 memory: 1111 2023/05/11 18:39:01 - mmengine - INFO - Epoch(val) [36][300/509] eta: 0:01:32 time: 0.4252 data_time: 0.0021 memory: 1075 2023/05/11 18:39:22 - mmengine - INFO - Epoch(val) [36][350/509] eta: 0:01:10 time: 0.4226 data_time: 0.0020 memory: 1091 2023/05/11 18:39:44 - mmengine - INFO - Epoch(val) [36][400/509] eta: 0:00:48 time: 0.4414 data_time: 0.0020 memory: 1090 2023/05/11 18:40:07 - mmengine - INFO - Epoch(val) [36][450/509] eta: 0:00:26 time: 0.4463 data_time: 0.0020 memory: 1113 2023/05/11 18:40:28 - mmengine - INFO - Epoch(val) [36][500/509] eta: 0:00:03 time: 0.4316 data_time: 0.0021 memory: 1098 2023/05/11 18:40:59 - 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 | 0.9666 | 0.5692 | 0.8185 | 0.8724 | 0.6713 | 0.7872 | 0.9144 | 0.2002 | 0.9476 | 0.5361 | 0.8278 | 0.0624 | 0.9198 | 0.6874 | 0.8750 | 0.6904 | 0.7201 | 0.6634 | 0.5244 | 0.6976 | 0.9227 | 0.7654 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/05/11 18:41:00 - mmengine - INFO - Epoch(val) [36][509/509] car: 0.9666 bicycle: 0.5692 motorcycle: 0.8185 truck: 0.8724 bus: 0.6713 person: 0.7872 bicyclist: 0.9144 motorcyclist: 0.2002 road: 0.9476 parking: 0.5361 sidewalk: 0.8278 other-ground: 0.0624 building: 0.9198 fence: 0.6874 vegetation: 0.8750 trunck: 0.6904 terrian: 0.7201 pole: 0.6634 traffic-sign: 0.5244 miou: 0.6976 acc: 0.9227 acc_cls: 0.7654 data_time: 0.0020 time: 0.4509