2023/03/09 16:07:26 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0] CUDA available: True numpy_random_seed: 0 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /nvme/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 5.4.0 PyTorch: 1.12.1+cu113 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - 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 8.3.2 (built against CUDA 11.5) - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -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_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.13.1+cu113 OpenCV: 4.7.0 MMEngine: 0.5.0 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 0 deterministic: False diff_rank_seed: True Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/03/09 16:07:26 - 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) file_client_args = dict(backend='disk') train_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), 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='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, file_client_args=dict(backend='disk')), 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='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ] eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, file_client_args=dict(backend='disk')), 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='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ] train_dataloader = dict( batch_size=2, num_workers=4, sampler=dict(type='DefaultSampler', shuffle=True, seed=0), dataset=dict( type='RepeatDataset', times=1, 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, file_client_args=dict(backend='disk')), 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='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))) test_dataloader = dict( batch_size=1, num_workers=1, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='RepeatDataset', times=1, 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, file_client_args=dict(backend='disk')), 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='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, test_mode=True))) val_dataloader = dict( batch_size=1, num_workers=1, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='RepeatDataset', times=1, 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, file_client_args=dict(backend='disk')), 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='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, test_mode=True))) 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') model = dict( type='MinkUNet', data_preprocessor=dict( type='Det3DDataPreprocessor', voxel=True, voxel_type='minkunet', voxel_layer=dict( max_num_points=-1, point_cloud_range=[-100, -100, -20, 100, 100, 20], voxel_size=[0.05, 0.05, 0.05], max_voxels=(-1, -1))), backbone=dict( type='MinkUNetBackbone', in_channels=4, base_channels=20, encoder_channels=[20, 40, 81, 163], decoder_channels=[163, 81, 61, 61], num_stages=4, init_cfg=None), decode_head=dict( type='MinkUNetHead', channels=61, 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 = 0.24 optim_wrapper = dict( type='AmpOptimWrapper', loss_scale='dynamic', optimizer=dict( type='SGD', lr=0.24, weight_decay=0.0001, momentum=0.9, nesterov=True)) param_scheduler = [ dict( type='LinearLR', start_factor=0.008, by_epoch=False, begin=0, end=125), dict( type='CosineAnnealingLR', begin=0, T_max=15, by_epoch=True, eta_min=1e-05, convert_to_iter_based=True) ] train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=15, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') randomness = dict(seed=0, deterministic=False, diff_rank_seed=True) launcher = 'pytorch' work_dir = './work_dirs/minkunet_w20_8xb2-15e_semantickitti' 2023/03/09 16:07:28 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. 2023/03/09 16:07:28 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (BELOW_NORMAL) LoggerHook -------------------- before_train: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) Det3DVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- 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 -------------------- Name of parameter - Initialization information backbone.conv_input.0.net.0.kernel - torch.Size([27, 4, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.0.kernel - torch.Size([8, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.3.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.3.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.0.kernel - torch.Size([8, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.0.kernel - torch.Size([27, 20, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.3.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.0.kernel - torch.Size([20, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.0.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.3.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.0.kernel - torch.Size([8, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.0.kernel - torch.Size([27, 40, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.0.kernel - torch.Size([40, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.0.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.0.kernel - torch.Size([8, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.0.kernel - torch.Size([27, 81, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.0.kernel - torch.Size([81, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.0.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.0.kernel - torch.Size([8, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.0.kernel - torch.Size([27, 244, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.0.kernel - torch.Size([244, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.0.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.0.kernel - torch.Size([8, 163, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.0.kernel - torch.Size([27, 121, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.0.kernel - torch.Size([121, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.0.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.0.kernel - torch.Size([8, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.0.kernel - torch.Size([27, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.0.kernel - torch.Size([81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.0.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.0.kernel - torch.Size([8, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.0.kernel - torch.Size([27, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.0.kernel - torch.Size([81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.0.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet decode_head.conv_seg.weight - torch.Size([19, 61]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/03/09 16:07:30 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/work_dirs/minkunet_w20_8xb2-15e_semantickitti. 2023/03/09 16:07:50 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 9.5998e-02 eta: 1:59:27 time: 0.4007 data_time: 0.0104 memory: 1274 loss: 1.7531 loss_sem_seg: 1.7531 2023/03/09 16:08:13 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 1.9199e-01 eta: 2:08:44 time: 0.4653 data_time: 0.0030 memory: 1317 loss: 1.1637 loss_sem_seg: 1.1637 2023/03/09 16:08:36 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 2.3996e-01 eta: 2:11:21 time: 0.4631 data_time: 0.0030 memory: 1314 loss: 0.9480 loss_sem_seg: 0.9480 2023/03/09 16:08:59 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 2.3993e-01 eta: 2:12:28 time: 0.4631 data_time: 0.0030 memory: 1324 loss: 0.8212 loss_sem_seg: 0.8212 2023/03/09 16:09:22 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 2.3989e-01 eta: 2:12:58 time: 0.4630 data_time: 0.0030 memory: 1293 loss: 0.7137 loss_sem_seg: 0.7137 2023/03/09 16:09:45 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 2.3984e-01 eta: 2:13:03 time: 0.4602 data_time: 0.0030 memory: 1244 loss: 0.6373 loss_sem_seg: 0.6373 2023/03/09 16:10:09 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 2.3978e-01 eta: 2:13:24 time: 0.4701 data_time: 0.0030 memory: 1270 loss: 0.6161 loss_sem_seg: 0.6161 2023/03/09 16:10:32 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 2.3971e-01 eta: 2:13:19 time: 0.4632 data_time: 0.0032 memory: 1303 loss: 0.5700 loss_sem_seg: 0.5700 2023/03/09 16:10:55 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 2.3963e-01 eta: 2:13:13 time: 0.4645 data_time: 0.0033 memory: 1284 loss: 0.5099 loss_sem_seg: 0.5099 2023/03/09 16:11:19 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 2.3954e-01 eta: 2:13:11 time: 0.4689 data_time: 0.0030 memory: 1259 loss: 0.5085 loss_sem_seg: 0.5085 2023/03/09 16:11:42 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 2.3945e-01 eta: 2:12:53 time: 0.4616 data_time: 0.0031 memory: 1253 loss: 0.5251 loss_sem_seg: 0.5251 2023/03/09 16:12:05 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 2.3934e-01 eta: 2:12:35 time: 0.4618 data_time: 0.0031 memory: 1255 loss: 0.4795 loss_sem_seg: 0.4795 2023/03/09 16:12:28 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 2.3923e-01 eta: 2:12:22 time: 0.4661 data_time: 0.0032 memory: 1360 loss: 0.4939 loss_sem_seg: 0.4939 2023/03/09 16:12:51 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 2.3910e-01 eta: 2:12:03 time: 0.4625 data_time: 0.0032 memory: 1259 loss: 0.4544 loss_sem_seg: 0.4544 2023/03/09 16:13:14 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 2.3897e-01 eta: 2:11:43 time: 0.4621 data_time: 0.0032 memory: 1352 loss: 0.4511 loss_sem_seg: 0.4511 2023/03/09 16:13:38 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 2.3883e-01 eta: 2:11:23 time: 0.4625 data_time: 0.0032 memory: 1299 loss: 0.4305 loss_sem_seg: 0.4305 2023/03/09 16:14:01 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 2.3868e-01 eta: 2:11:02 time: 0.4619 data_time: 0.0032 memory: 1218 loss: 0.4183 loss_sem_seg: 0.4183 2023/03/09 16:14:24 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 2.3852e-01 eta: 2:10:39 time: 0.4609 data_time: 0.0032 memory: 1281 loss: 0.4122 loss_sem_seg: 0.4122 2023/03/09 16:14:47 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 2.3835e-01 eta: 2:10:16 time: 0.4595 data_time: 0.0032 memory: 1308 loss: 0.3971 loss_sem_seg: 0.3971 2023/03/09 16:15:10 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:15:10 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 2.3817e-01 eta: 2:09:56 time: 0.4634 data_time: 0.0032 memory: 1332 loss: 0.4152 loss_sem_seg: 0.4152 2023/03/09 16:15:33 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 2.3798e-01 eta: 2:09:37 time: 0.4661 data_time: 0.0032 memory: 1311 loss: 0.3924 loss_sem_seg: 0.3924 2023/03/09 16:15:56 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 2.3778e-01 eta: 2:09:16 time: 0.4632 data_time: 0.0032 memory: 1288 loss: 0.4063 loss_sem_seg: 0.4063 2023/03/09 16:16:19 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 2.3758e-01 eta: 2:08:54 time: 0.4614 data_time: 0.0032 memory: 1298 loss: 0.3865 loss_sem_seg: 0.3865 2023/03/09 16:16:36 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:16:37 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/03/09 16:16:46 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:01:15 time: 0.1636 data_time: 0.0057 memory: 1271 2023/03/09 16:16:54 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:01:06 time: 0.1622 data_time: 0.0042 memory: 348 2023/03/09 16:17:02 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:00:58 time: 0.1654 data_time: 0.0042 memory: 349 2023/03/09 16:17:10 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:00:50 time: 0.1613 data_time: 0.0042 memory: 344 2023/03/09 16:17:18 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:00:41 time: 0.1558 data_time: 0.0042 memory: 354 2023/03/09 16:17:26 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:00:33 time: 0.1577 data_time: 0.0042 memory: 326 2023/03/09 16:17:34 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:00:25 time: 0.1619 data_time: 0.0041 memory: 339 2023/03/09 16:17:42 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:00:17 time: 0.1618 data_time: 0.0041 memory: 340 2023/03/09 16:17:49 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:09 time: 0.1375 data_time: 0.0042 memory: 349 2023/03/09 16:17:53 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:01 time: 0.0799 data_time: 0.0044 memory: 341 2023/03/09 16:18:28 - 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 | 0.9103 | 0.0000 | 0.0000 | 0.0825 | 0.1920 | 0.0000 | 0.0000 | 0.0000 | 0.8777 | 0.1991 | 0.7354 | 0.0002 | 0.8517 | 0.4912 | 0.8499 | 0.5201 | 0.6903 | 0.5272 | 0.0351 | 0.3665 | 0.8827 | 0.4160 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 16:18:28 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.9103 bicycle: 0.0000 motorcycle: 0.0000 truck: 0.0825 bus: 0.1920 person: 0.0000 bicyclist: 0.0000 motorcyclist: 0.0000 road: 0.8777 parking: 0.1991 sidewalk: 0.7354 other-ground: 0.0002 building: 0.8517 fence: 0.4912 vegetation: 0.8499 trunck: 0.5201 terrian: 0.6903 pole: 0.5272 traffic-sign: 0.0351 miou: 0.3665 acc: 0.8827 acc_cls: 0.4160 2023/03/09 16:18:53 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 2.3716e-01 eta: 2:07:35 time: 0.4895 data_time: 0.0191 memory: 1305 loss: 0.3848 loss_sem_seg: 0.3848 2023/03/09 16:19:16 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 2.3693e-01 eta: 2:07:17 time: 0.4674 data_time: 0.0031 memory: 1286 loss: 0.3560 loss_sem_seg: 0.3560 2023/03/09 16:19:39 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 2.3669e-01 eta: 2:06:59 time: 0.4664 data_time: 0.0031 memory: 1264 loss: 0.3394 loss_sem_seg: 0.3394 2023/03/09 16:20:03 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 2.3644e-01 eta: 2:06:41 time: 0.4673 data_time: 0.0030 memory: 1243 loss: 0.4029 loss_sem_seg: 0.4029 2023/03/09 16:20:26 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 2.3618e-01 eta: 2:06:22 time: 0.4666 data_time: 0.0032 memory: 1326 loss: 0.3760 loss_sem_seg: 0.3760 2023/03/09 16:20:50 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 2.3591e-01 eta: 2:06:04 time: 0.4696 data_time: 0.0033 memory: 1295 loss: 0.3523 loss_sem_seg: 0.3523 2023/03/09 16:21:13 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 2.3563e-01 eta: 2:05:45 time: 0.4661 data_time: 0.0032 memory: 1240 loss: 0.3760 loss_sem_seg: 0.3760 2023/03/09 16:21:36 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 2.3535e-01 eta: 2:05:27 time: 0.4709 data_time: 0.0033 memory: 1259 loss: 0.3643 loss_sem_seg: 0.3643 2023/03/09 16:22:00 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 2.3506e-01 eta: 2:05:07 time: 0.4671 data_time: 0.0031 memory: 1248 loss: 0.3305 loss_sem_seg: 0.3305 2023/03/09 16:22:23 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 2.3475e-01 eta: 2:04:48 time: 0.4688 data_time: 0.0033 memory: 1307 loss: 0.3440 loss_sem_seg: 0.3440 2023/03/09 16:22:47 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 2.3444e-01 eta: 2:04:30 time: 0.4727 data_time: 0.0034 memory: 1289 loss: 0.3776 loss_sem_seg: 0.3776 2023/03/09 16:23:10 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 2.3412e-01 eta: 2:04:11 time: 0.4703 data_time: 0.0033 memory: 1289 loss: 0.3346 loss_sem_seg: 0.3346 2023/03/09 16:23:34 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 2.3379e-01 eta: 2:03:51 time: 0.4666 data_time: 0.0033 memory: 1254 loss: 0.3313 loss_sem_seg: 0.3313 2023/03/09 16:23:57 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 2.3345e-01 eta: 2:03:29 time: 0.4654 data_time: 0.0032 memory: 1264 loss: 0.3364 loss_sem_seg: 0.3364 2023/03/09 16:24:20 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 2.3311e-01 eta: 2:03:08 time: 0.4667 data_time: 0.0032 memory: 1335 loss: 0.3454 loss_sem_seg: 0.3454 2023/03/09 16:24:44 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 2.3275e-01 eta: 2:02:47 time: 0.4667 data_time: 0.0033 memory: 1257 loss: 0.3163 loss_sem_seg: 0.3163 2023/03/09 16:24:45 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:25:07 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 2.3239e-01 eta: 2:02:26 time: 0.4679 data_time: 0.0034 memory: 1268 loss: 0.3055 loss_sem_seg: 0.3055 2023/03/09 16:25:30 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 2.3201e-01 eta: 2:02:05 time: 0.4687 data_time: 0.0033 memory: 1289 loss: 0.3317 loss_sem_seg: 0.3317 2023/03/09 16:25:54 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 2.3163e-01 eta: 2:01:44 time: 0.4663 data_time: 0.0032 memory: 1293 loss: 0.3185 loss_sem_seg: 0.3185 2023/03/09 16:26:17 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 2.3124e-01 eta: 2:01:22 time: 0.4658 data_time: 0.0032 memory: 1313 loss: 0.3379 loss_sem_seg: 0.3379 2023/03/09 16:26:40 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 2.3085e-01 eta: 2:01:00 time: 0.4663 data_time: 0.0033 memory: 1209 loss: 0.3069 loss_sem_seg: 0.3069 2023/03/09 16:27:03 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 2.3044e-01 eta: 2:00:37 time: 0.4624 data_time: 0.0035 memory: 1245 loss: 0.3357 loss_sem_seg: 0.3357 2023/03/09 16:27:24 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 2.3002e-01 eta: 1:59:58 time: 0.4152 data_time: 0.0037 memory: 1306 loss: 0.3233 loss_sem_seg: 0.3233 2023/03/09 16:27:42 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:27:42 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/03/09 16:27:51 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:01:16 time: 0.1676 data_time: 0.0077 memory: 1326 2023/03/09 16:27:59 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:01:07 time: 0.1606 data_time: 0.0049 memory: 348 2023/03/09 16:28:07 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:00:58 time: 0.1608 data_time: 0.0049 memory: 349 2023/03/09 16:28:16 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:00:50 time: 0.1640 data_time: 0.0048 memory: 344 2023/03/09 16:28:24 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:00:42 time: 0.1587 data_time: 0.0048 memory: 354 2023/03/09 16:28:31 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:00:33 time: 0.1566 data_time: 0.0047 memory: 326 2023/03/09 16:28:39 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:00:25 time: 0.1542 data_time: 0.0050 memory: 339 2023/03/09 16:28:44 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:00:16 time: 0.1050 data_time: 0.0045 memory: 340 2023/03/09 16:28:50 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:08 time: 0.1026 data_time: 0.0047 memory: 349 2023/03/09 16:28:58 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:01 time: 0.1637 data_time: 0.0046 memory: 341 2023/03/09 16:29:50 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9301 | 0.0000 | 0.1478 | 0.2497 | 0.3478 | 0.1046 | 0.0002 | 0.0000 | 0.8848 | 0.2866 | 0.7324 | 0.0027 | 0.8511 | 0.4040 | 0.8490 | 0.5176 | 0.6865 | 0.5685 | 0.2607 | 0.4118 | 0.8821 | 0.4734 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 16:29:50 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.9301 bicycle: 0.0000 motorcycle: 0.1478 truck: 0.2497 bus: 0.3478 person: 0.1046 bicyclist: 0.0002 motorcyclist: 0.0000 road: 0.8848 parking: 0.2866 sidewalk: 0.7324 other-ground: 0.0027 building: 0.8511 fence: 0.4040 vegetation: 0.8490 trunck: 0.5176 terrian: 0.6865 pole: 0.5685 traffic-sign: 0.2607 miou: 0.4118 acc: 0.8821 acc_cls: 0.4734 2023/03/09 16:30:15 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 2.2920e-01 eta: 1:58:57 time: 0.4890 data_time: 0.0194 memory: 1373 loss: 0.3208 loss_sem_seg: 0.3208 2023/03/09 16:30:38 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 2.2876e-01 eta: 1:58:36 time: 0.4663 data_time: 0.0033 memory: 1295 loss: 0.3020 loss_sem_seg: 0.3020 2023/03/09 16:31:01 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 2.2832e-01 eta: 1:58:14 time: 0.4658 data_time: 0.0032 memory: 1287 loss: 0.3047 loss_sem_seg: 0.3047 2023/03/09 16:31:25 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 2.2786e-01 eta: 1:57:53 time: 0.4672 data_time: 0.0034 memory: 1238 loss: 0.3024 loss_sem_seg: 0.3024 2023/03/09 16:31:48 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 2.2739e-01 eta: 1:57:32 time: 0.4676 data_time: 0.0033 memory: 1287 loss: 0.3061 loss_sem_seg: 0.3061 2023/03/09 16:32:12 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 2.2692e-01 eta: 1:57:11 time: 0.4688 data_time: 0.0033 memory: 1300 loss: 0.2929 loss_sem_seg: 0.2929 2023/03/09 16:32:35 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 2.2644e-01 eta: 1:56:50 time: 0.4677 data_time: 0.0035 memory: 1291 loss: 0.3055 loss_sem_seg: 0.3055 2023/03/09 16:32:58 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 2.2595e-01 eta: 1:56:29 time: 0.4695 data_time: 0.0035 memory: 1299 loss: 0.2991 loss_sem_seg: 0.2991 2023/03/09 16:33:22 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 2.2545e-01 eta: 1:56:08 time: 0.4672 data_time: 0.0035 memory: 1230 loss: 0.2806 loss_sem_seg: 0.2806 2023/03/09 16:33:45 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 2.2495e-01 eta: 1:55:46 time: 0.4684 data_time: 0.0032 memory: 1291 loss: 0.3035 loss_sem_seg: 0.3035 2023/03/09 16:34:09 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 2.2443e-01 eta: 1:55:25 time: 0.4679 data_time: 0.0034 memory: 1268 loss: 0.2952 loss_sem_seg: 0.2952 2023/03/09 16:34:32 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 2.2391e-01 eta: 1:55:03 time: 0.4653 data_time: 0.0033 memory: 1292 loss: 0.2988 loss_sem_seg: 0.2988 2023/03/09 16:34:36 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:34:55 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 2.2338e-01 eta: 1:54:41 time: 0.4679 data_time: 0.0034 memory: 1281 loss: 0.2831 loss_sem_seg: 0.2831 2023/03/09 16:35:19 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 2.2285e-01 eta: 1:54:19 time: 0.4666 data_time: 0.0033 memory: 1310 loss: 0.2936 loss_sem_seg: 0.2936 2023/03/09 16:35:42 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 2.2230e-01 eta: 1:53:57 time: 0.4659 data_time: 0.0033 memory: 1259 loss: 0.3127 loss_sem_seg: 0.3127 2023/03/09 16:36:05 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 2.2175e-01 eta: 1:53:35 time: 0.4680 data_time: 0.0033 memory: 1332 loss: 0.2920 loss_sem_seg: 0.2920 2023/03/09 16:36:29 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 2.2119e-01 eta: 1:53:13 time: 0.4644 data_time: 0.0032 memory: 1307 loss: 0.2748 loss_sem_seg: 0.2748 2023/03/09 16:36:52 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 2.2062e-01 eta: 1:52:50 time: 0.4664 data_time: 0.0033 memory: 1323 loss: 0.2799 loss_sem_seg: 0.2799 2023/03/09 16:37:15 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 2.2004e-01 eta: 1:52:28 time: 0.4661 data_time: 0.0033 memory: 1382 loss: 0.2840 loss_sem_seg: 0.2840 2023/03/09 16:37:38 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 2.1946e-01 eta: 1:52:06 time: 0.4671 data_time: 0.0032 memory: 1269 loss: 0.2931 loss_sem_seg: 0.2931 2023/03/09 16:38:01 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 2.1887e-01 eta: 1:51:42 time: 0.4586 data_time: 0.0033 memory: 1251 loss: 0.2997 loss_sem_seg: 0.2997 2023/03/09 16:38:22 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 2.1827e-01 eta: 1:51:09 time: 0.4145 data_time: 0.0033 memory: 1269 loss: 0.2912 loss_sem_seg: 0.2912 2023/03/09 16:38:43 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 2.1766e-01 eta: 1:50:36 time: 0.4125 data_time: 0.0032 memory: 1294 loss: 0.2805 loss_sem_seg: 0.2805 2023/03/09 16:39:01 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:39:01 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/09 16:39:10 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:01:16 time: 0.1661 data_time: 0.0079 memory: 1323 2023/03/09 16:39:18 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:01:06 time: 0.1613 data_time: 0.0044 memory: 348 2023/03/09 16:39:27 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:00:58 time: 0.1616 data_time: 0.0043 memory: 349 2023/03/09 16:39:35 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:00:50 time: 0.1648 data_time: 0.0047 memory: 344 2023/03/09 16:39:42 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:00:41 time: 0.1503 data_time: 0.0049 memory: 354 2023/03/09 16:39:48 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:00:32 time: 0.1185 data_time: 0.0049 memory: 326 2023/03/09 16:39:53 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:00:23 time: 0.1035 data_time: 0.0044 memory: 339 2023/03/09 16:39:59 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:00:15 time: 0.1057 data_time: 0.0044 memory: 340 2023/03/09 16:40:04 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:08 time: 0.1143 data_time: 0.0049 memory: 349 2023/03/09 16:40:20 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:01 time: 0.3088 data_time: 0.0050 memory: 341 2023/03/09 16:41:18 - 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 | 0.9184 | 0.0000 | 0.1646 | 0.4996 | 0.1479 | 0.2099 | 0.2753 | 0.0000 | 0.8827 | 0.1970 | 0.7421 | 0.0001 | 0.8892 | 0.4795 | 0.8447 | 0.6300 | 0.6804 | 0.5934 | 0.2999 | 0.4450 | 0.8846 | 0.5201 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 16:41:18 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9184 bicycle: 0.0000 motorcycle: 0.1646 truck: 0.4996 bus: 0.1479 person: 0.2099 bicyclist: 0.2753 motorcyclist: 0.0000 road: 0.8827 parking: 0.1970 sidewalk: 0.7421 other-ground: 0.0001 building: 0.8892 fence: 0.4795 vegetation: 0.8447 trunck: 0.6300 terrian: 0.6804 pole: 0.5934 traffic-sign: 0.2999 miou: 0.4450 acc: 0.8846 acc_cls: 0.5201 2023/03/09 16:41:42 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 2.1647e-01 eta: 1:49:44 time: 0.4919 data_time: 0.0218 memory: 1322 loss: 0.2683 loss_sem_seg: 0.2683 2023/03/09 16:42:06 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 2.1585e-01 eta: 1:49:23 time: 0.4696 data_time: 0.0032 memory: 1273 loss: 0.2854 loss_sem_seg: 0.2854 2023/03/09 16:42:29 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 2.1521e-01 eta: 1:49:02 time: 0.4707 data_time: 0.0033 memory: 1295 loss: 0.2907 loss_sem_seg: 0.2907 2023/03/09 16:42:52 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 2.1457e-01 eta: 1:48:39 time: 0.4625 data_time: 0.0033 memory: 1304 loss: 0.2845 loss_sem_seg: 0.2845 2023/03/09 16:43:16 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 2.1392e-01 eta: 1:48:18 time: 0.4681 data_time: 0.0033 memory: 1260 loss: 0.2873 loss_sem_seg: 0.2873 2023/03/09 16:43:39 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 2.1326e-01 eta: 1:47:55 time: 0.4649 data_time: 0.0033 memory: 1273 loss: 0.2846 loss_sem_seg: 0.2846 2023/03/09 16:44:02 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 2.1259e-01 eta: 1:47:33 time: 0.4665 data_time: 0.0033 memory: 1360 loss: 0.2875 loss_sem_seg: 0.2875 2023/03/09 16:44:26 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 2.1192e-01 eta: 1:47:11 time: 0.4629 data_time: 0.0031 memory: 1312 loss: 0.2798 loss_sem_seg: 0.2798 2023/03/09 16:44:31 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:44:49 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 2.1124e-01 eta: 1:46:49 time: 0.4690 data_time: 0.0032 memory: 1339 loss: 0.2912 loss_sem_seg: 0.2912 2023/03/09 16:45:12 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 2.1056e-01 eta: 1:46:27 time: 0.4673 data_time: 0.0032 memory: 1328 loss: 0.2760 loss_sem_seg: 0.2760 2023/03/09 16:45:36 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 2.0986e-01 eta: 1:46:05 time: 0.4652 data_time: 0.0032 memory: 1330 loss: 0.2809 loss_sem_seg: 0.2809 2023/03/09 16:45:59 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 2.0916e-01 eta: 1:45:42 time: 0.4662 data_time: 0.0032 memory: 1305 loss: 0.2858 loss_sem_seg: 0.2858 2023/03/09 16:46:22 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 2.0846e-01 eta: 1:45:20 time: 0.4668 data_time: 0.0031 memory: 1328 loss: 0.2657 loss_sem_seg: 0.2657 2023/03/09 16:46:46 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 2.0774e-01 eta: 1:44:58 time: 0.4652 data_time: 0.0031 memory: 1260 loss: 0.2608 loss_sem_seg: 0.2608 2023/03/09 16:47:09 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 2.0702e-01 eta: 1:44:35 time: 0.4644 data_time: 0.0031 memory: 1331 loss: 0.2698 loss_sem_seg: 0.2698 2023/03/09 16:47:32 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 2.0630e-01 eta: 1:44:12 time: 0.4616 data_time: 0.0031 memory: 1310 loss: 0.2747 loss_sem_seg: 0.2747 2023/03/09 16:47:55 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 2.0556e-01 eta: 1:43:49 time: 0.4637 data_time: 0.0032 memory: 1233 loss: 0.2768 loss_sem_seg: 0.2768 2023/03/09 16:48:18 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 2.0482e-01 eta: 1:43:26 time: 0.4612 data_time: 0.0031 memory: 1264 loss: 0.2672 loss_sem_seg: 0.2672 2023/03/09 16:48:41 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 2.0408e-01 eta: 1:43:03 time: 0.4606 data_time: 0.0031 memory: 1305 loss: 0.2795 loss_sem_seg: 0.2795 2023/03/09 16:49:04 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 2.0333e-01 eta: 1:42:41 time: 0.4663 data_time: 0.0032 memory: 1264 loss: 0.2474 loss_sem_seg: 0.2474 2023/03/09 16:49:25 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 2.0257e-01 eta: 1:42:11 time: 0.4177 data_time: 0.0032 memory: 1301 loss: 0.2615 loss_sem_seg: 0.2615 2023/03/09 16:49:46 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 2.0180e-01 eta: 1:41:42 time: 0.4126 data_time: 0.0031 memory: 1280 loss: 0.2728 loss_sem_seg: 0.2728 2023/03/09 16:50:07 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 2.0103e-01 eta: 1:41:12 time: 0.4139 data_time: 0.0030 memory: 1298 loss: 0.2778 loss_sem_seg: 0.2778 2023/03/09 16:50:24 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:50:25 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/03/09 16:50:34 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:01:17 time: 0.1687 data_time: 0.0077 memory: 1235 2023/03/09 16:50:42 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:01:07 time: 0.1631 data_time: 0.0042 memory: 348 2023/03/09 16:50:50 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:00:59 time: 0.1613 data_time: 0.0046 memory: 349 2023/03/09 16:50:58 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:00:49 time: 0.1447 data_time: 0.0048 memory: 344 2023/03/09 16:51:03 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:00:38 time: 0.1130 data_time: 0.0044 memory: 354 2023/03/09 16:51:08 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:00:29 time: 0.0987 data_time: 0.0043 memory: 326 2023/03/09 16:51:13 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:00:21 time: 0.1033 data_time: 0.0043 memory: 339 2023/03/09 16:51:21 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:00:15 time: 0.1545 data_time: 0.0047 memory: 340 2023/03/09 16:51:31 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:08 time: 0.1961 data_time: 0.0045 memory: 349 2023/03/09 16:51:46 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:01 time: 0.3043 data_time: 0.0048 memory: 341 2023/03/09 16:52:43 - 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 | 0.9384 | 0.0008 | 0.1952 | 0.5160 | 0.3576 | 0.3856 | 0.5097 | 0.0000 | 0.9155 | 0.3416 | 0.7754 | 0.0045 | 0.8644 | 0.4930 | 0.8652 | 0.5787 | 0.7294 | 0.6123 | 0.3394 | 0.4959 | 0.9003 | 0.5772 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 16:52:43 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9384 bicycle: 0.0008 motorcycle: 0.1952 truck: 0.5160 bus: 0.3576 person: 0.3856 bicyclist: 0.5097 motorcyclist: 0.0000 road: 0.9155 parking: 0.3416 sidewalk: 0.7754 other-ground: 0.0045 building: 0.8644 fence: 0.4930 vegetation: 0.8652 trunck: 0.5787 terrian: 0.7294 pole: 0.6123 traffic-sign: 0.3394 miou: 0.4959 acc: 0.9003 acc_cls: 0.5772 2023/03/09 16:53:07 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 1.9953e-01 eta: 1:40:22 time: 0.4895 data_time: 0.0211 memory: 1260 loss: 0.2497 loss_sem_seg: 0.2497 2023/03/09 16:53:31 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 1.9874e-01 eta: 1:40:01 time: 0.4724 data_time: 0.0032 memory: 1313 loss: 0.2596 loss_sem_seg: 0.2596 2023/03/09 16:53:54 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 1.9794e-01 eta: 1:39:39 time: 0.4666 data_time: 0.0033 memory: 1311 loss: 0.2505 loss_sem_seg: 0.2505 2023/03/09 16:54:18 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 1.9714e-01 eta: 1:39:17 time: 0.4702 data_time: 0.0032 memory: 1317 loss: 0.2692 loss_sem_seg: 0.2692 2023/03/09 16:54:25 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 16:54:41 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 1.9633e-01 eta: 1:38:55 time: 0.4667 data_time: 0.0033 memory: 1290 loss: 0.2679 loss_sem_seg: 0.2679 2023/03/09 16:55:04 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 1.9552e-01 eta: 1:38:33 time: 0.4662 data_time: 0.0032 memory: 1263 loss: 0.2604 loss_sem_seg: 0.2604 2023/03/09 16:55:28 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 1.9470e-01 eta: 1:38:11 time: 0.4664 data_time: 0.0031 memory: 1225 loss: 0.2533 loss_sem_seg: 0.2533 2023/03/09 16:55:51 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 1.9388e-01 eta: 1:37:49 time: 0.4671 data_time: 0.0031 memory: 1278 loss: 0.2778 loss_sem_seg: 0.2778 2023/03/09 16:56:14 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 1.9304e-01 eta: 1:37:26 time: 0.4667 data_time: 0.0032 memory: 1324 loss: 0.2613 loss_sem_seg: 0.2613 2023/03/09 16:56:38 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 1.9221e-01 eta: 1:37:04 time: 0.4635 data_time: 0.0031 memory: 1293 loss: 0.2583 loss_sem_seg: 0.2583 2023/03/09 16:57:01 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 1.9137e-01 eta: 1:36:41 time: 0.4595 data_time: 0.0032 memory: 1245 loss: 0.2751 loss_sem_seg: 0.2751 2023/03/09 16:57:24 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 1.9052e-01 eta: 1:36:18 time: 0.4607 data_time: 0.0032 memory: 1317 loss: 0.2903 loss_sem_seg: 0.2903 2023/03/09 16:57:47 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 1.8967e-01 eta: 1:35:56 time: 0.4673 data_time: 0.0032 memory: 1278 loss: 0.2501 loss_sem_seg: 0.2501 2023/03/09 16:58:10 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 1.8881e-01 eta: 1:35:33 time: 0.4647 data_time: 0.0031 memory: 1350 loss: 0.2524 loss_sem_seg: 0.2524 2023/03/09 16:58:33 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 1.8794e-01 eta: 1:35:10 time: 0.4618 data_time: 0.0032 memory: 1349 loss: 0.2553 loss_sem_seg: 0.2553 2023/03/09 16:58:56 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 1.8708e-01 eta: 1:34:47 time: 0.4613 data_time: 0.0032 memory: 1314 loss: 0.2432 loss_sem_seg: 0.2432 2023/03/09 16:59:20 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 1.8620e-01 eta: 1:34:25 time: 0.4663 data_time: 0.0032 memory: 1340 loss: 0.2651 loss_sem_seg: 0.2651 2023/03/09 16:59:43 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 1.8532e-01 eta: 1:34:02 time: 0.4635 data_time: 0.0032 memory: 1228 loss: 0.2604 loss_sem_seg: 0.2604 2023/03/09 17:00:06 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 1.8444e-01 eta: 1:33:40 time: 0.4646 data_time: 0.0033 memory: 1225 loss: 0.2381 loss_sem_seg: 0.2381 2023/03/09 17:00:27 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 1.8355e-01 eta: 1:33:12 time: 0.4185 data_time: 0.0033 memory: 1316 loss: 0.2375 loss_sem_seg: 0.2375 2023/03/09 17:00:47 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 1.8266e-01 eta: 1:32:44 time: 0.4092 data_time: 0.0034 memory: 1231 loss: 0.2369 loss_sem_seg: 0.2369 2023/03/09 17:01:08 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 1.8176e-01 eta: 1:32:16 time: 0.4130 data_time: 0.0033 memory: 1248 loss: 0.2446 loss_sem_seg: 0.2446 2023/03/09 17:01:28 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 1.8086e-01 eta: 1:31:48 time: 0.4076 data_time: 0.0033 memory: 1285 loss: 0.2640 loss_sem_seg: 0.2640 2023/03/09 17:01:46 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:01:47 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/03/09 17:01:56 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:01:16 time: 0.1666 data_time: 0.0088 memory: 1223 2023/03/09 17:02:04 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:01:06 time: 0.1568 data_time: 0.0045 memory: 348 2023/03/09 17:02:10 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:00:53 time: 0.1196 data_time: 0.0047 memory: 349 2023/03/09 17:02:15 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:00:42 time: 0.1073 data_time: 0.0046 memory: 344 2023/03/09 17:02:21 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:00:33 time: 0.1012 data_time: 0.0047 memory: 354 2023/03/09 17:02:28 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:00:28 time: 0.1531 data_time: 0.0051 memory: 326 2023/03/09 17:02:38 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:00:22 time: 0.1944 data_time: 0.0045 memory: 339 2023/03/09 17:02:48 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:00:16 time: 0.1949 data_time: 0.0045 memory: 340 2023/03/09 17:02:57 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:09 time: 0.1950 data_time: 0.0044 memory: 349 2023/03/09 17:03:12 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:01 time: 0.2957 data_time: 0.0045 memory: 341 2023/03/09 17:04:05 - 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 | 0.9522 | 0.0104 | 0.3016 | 0.3469 | 0.3686 | 0.4152 | 0.5069 | 0.0000 | 0.9101 | 0.3027 | 0.7817 | 0.0068 | 0.8844 | 0.5824 | 0.8827 | 0.5734 | 0.7555 | 0.6014 | 0.3949 | 0.5041 | 0.9095 | 0.5852 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 17:04:05 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9522 bicycle: 0.0104 motorcycle: 0.3016 truck: 0.3469 bus: 0.3686 person: 0.4152 bicyclist: 0.5069 motorcyclist: 0.0000 road: 0.9101 parking: 0.3027 sidewalk: 0.7817 other-ground: 0.0068 building: 0.8844 fence: 0.5824 vegetation: 0.8827 trunck: 0.5734 terrian: 0.7555 pole: 0.6014 traffic-sign: 0.3949 miou: 0.5041 acc: 0.9095 acc_cls: 0.5852 2023/03/09 17:04:15 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:04:29 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 1.7911e-01 eta: 1:31:00 time: 0.4884 data_time: 0.0208 memory: 1344 loss: 0.2460 loss_sem_seg: 0.2460 2023/03/09 17:04:53 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 1.7819e-01 eta: 1:30:38 time: 0.4695 data_time: 0.0033 memory: 1275 loss: 0.2347 loss_sem_seg: 0.2347 2023/03/09 17:05:16 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 1.7727e-01 eta: 1:30:16 time: 0.4623 data_time: 0.0033 memory: 1283 loss: 0.2543 loss_sem_seg: 0.2543 2023/03/09 17:05:39 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 1.7635e-01 eta: 1:29:54 time: 0.4670 data_time: 0.0033 memory: 1321 loss: 0.2639 loss_sem_seg: 0.2639 2023/03/09 17:06:02 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 1.7542e-01 eta: 1:29:31 time: 0.4645 data_time: 0.0033 memory: 1298 loss: 0.2360 loss_sem_seg: 0.2360 2023/03/09 17:06:26 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 1.7448e-01 eta: 1:29:09 time: 0.4639 data_time: 0.0033 memory: 1373 loss: 0.2627 loss_sem_seg: 0.2627 2023/03/09 17:06:49 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 1.7354e-01 eta: 1:28:46 time: 0.4611 data_time: 0.0034 memory: 1276 loss: 0.2620 loss_sem_seg: 0.2620 2023/03/09 17:07:12 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 1.7260e-01 eta: 1:28:23 time: 0.4622 data_time: 0.0033 memory: 1305 loss: 0.2491 loss_sem_seg: 0.2491 2023/03/09 17:07:35 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 1.7165e-01 eta: 1:28:01 time: 0.4673 data_time: 0.0033 memory: 1332 loss: 0.2539 loss_sem_seg: 0.2539 2023/03/09 17:07:58 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 1.7070e-01 eta: 1:27:39 time: 0.4666 data_time: 0.0033 memory: 1285 loss: 0.2387 loss_sem_seg: 0.2387 2023/03/09 17:08:22 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 1.6975e-01 eta: 1:27:17 time: 0.4645 data_time: 0.0034 memory: 1324 loss: 0.2496 loss_sem_seg: 0.2496 2023/03/09 17:08:45 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 1.6879e-01 eta: 1:26:54 time: 0.4634 data_time: 0.0034 memory: 1251 loss: 0.2681 loss_sem_seg: 0.2681 2023/03/09 17:09:08 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 1.6783e-01 eta: 1:26:31 time: 0.4638 data_time: 0.0033 memory: 1278 loss: 0.2602 loss_sem_seg: 0.2602 2023/03/09 17:09:31 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 1.6686e-01 eta: 1:26:09 time: 0.4645 data_time: 0.0033 memory: 1269 loss: 0.2603 loss_sem_seg: 0.2603 2023/03/09 17:09:54 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 1.6590e-01 eta: 1:25:46 time: 0.4637 data_time: 0.0033 memory: 1307 loss: 0.2273 loss_sem_seg: 0.2273 2023/03/09 17:10:18 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 1.6492e-01 eta: 1:25:24 time: 0.4618 data_time: 0.0033 memory: 1270 loss: 0.2380 loss_sem_seg: 0.2380 2023/03/09 17:10:41 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 1.6395e-01 eta: 1:25:01 time: 0.4614 data_time: 0.0033 memory: 1264 loss: 0.2339 loss_sem_seg: 0.2339 2023/03/09 17:11:04 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 1.6297e-01 eta: 1:24:38 time: 0.4609 data_time: 0.0032 memory: 1320 loss: 0.2404 loss_sem_seg: 0.2404 2023/03/09 17:11:25 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 1.6199e-01 eta: 1:24:12 time: 0.4176 data_time: 0.0033 memory: 1240 loss: 0.2405 loss_sem_seg: 0.2405 2023/03/09 17:11:45 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 1.6100e-01 eta: 1:23:45 time: 0.4126 data_time: 0.0033 memory: 1286 loss: 0.2260 loss_sem_seg: 0.2260 2023/03/09 17:11:53 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:12:06 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 1.6001e-01 eta: 1:23:19 time: 0.4124 data_time: 0.0032 memory: 1287 loss: 0.2425 loss_sem_seg: 0.2425 2023/03/09 17:12:26 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 1.5902e-01 eta: 1:22:52 time: 0.4055 data_time: 0.0032 memory: 1299 loss: 0.2343 loss_sem_seg: 0.2343 2023/03/09 17:12:47 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 1.5802e-01 eta: 1:22:25 time: 0.4086 data_time: 0.0034 memory: 1274 loss: 0.2273 loss_sem_seg: 0.2273 2023/03/09 17:13:04 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:13:05 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/09 17:13:13 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:01:02 time: 0.1368 data_time: 0.0084 memory: 1274 2023/03/09 17:13:18 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:00:49 time: 0.1051 data_time: 0.0047 memory: 348 2023/03/09 17:13:23 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:00:41 time: 0.1034 data_time: 0.0046 memory: 349 2023/03/09 17:13:30 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:00:37 time: 0.1377 data_time: 0.0047 memory: 344 2023/03/09 17:13:40 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:00:34 time: 0.1921 data_time: 0.0047 memory: 354 2023/03/09 17:13:49 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:00:30 time: 0.1907 data_time: 0.0045 memory: 326 2023/03/09 17:13:59 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:00:24 time: 0.1955 data_time: 0.0044 memory: 339 2023/03/09 17:14:09 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:00:17 time: 0.1945 data_time: 0.0043 memory: 340 2023/03/09 17:14:18 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:09 time: 0.1956 data_time: 0.0044 memory: 349 2023/03/09 17:14:32 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:01 time: 0.2665 data_time: 0.0042 memory: 341 2023/03/09 17:15:26 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9500 | 0.0094 | 0.3927 | 0.5037 | 0.4017 | 0.4409 | 0.5292 | 0.0000 | 0.9286 | 0.4053 | 0.7919 | 0.0039 | 0.8848 | 0.5292 | 0.8764 | 0.6475 | 0.7525 | 0.6006 | 0.3690 | 0.5272 | 0.9110 | 0.6053 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 17:15:26 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9500 bicycle: 0.0094 motorcycle: 0.3927 truck: 0.5037 bus: 0.4017 person: 0.4409 bicyclist: 0.5292 motorcyclist: 0.0000 road: 0.9286 parking: 0.4053 sidewalk: 0.7919 other-ground: 0.0039 building: 0.8848 fence: 0.5292 vegetation: 0.8764 trunck: 0.6475 terrian: 0.7525 pole: 0.6006 traffic-sign: 0.3690 miou: 0.5272 acc: 0.9110 acc_cls: 0.6053 2023/03/09 17:15:51 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 1.5610e-01 eta: 1:21:39 time: 0.4983 data_time: 0.0205 memory: 1358 loss: 0.2167 loss_sem_seg: 0.2167 2023/03/09 17:16:15 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 1.5510e-01 eta: 1:21:17 time: 0.4677 data_time: 0.0033 memory: 1284 loss: 0.2485 loss_sem_seg: 0.2485 2023/03/09 17:16:38 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 1.5410e-01 eta: 1:20:55 time: 0.4655 data_time: 0.0033 memory: 1292 loss: 0.2414 loss_sem_seg: 0.2414 2023/03/09 17:17:01 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 1.5309e-01 eta: 1:20:33 time: 0.4651 data_time: 0.0033 memory: 1291 loss: 0.2383 loss_sem_seg: 0.2383 2023/03/09 17:17:24 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 1.5208e-01 eta: 1:20:10 time: 0.4680 data_time: 0.0033 memory: 1248 loss: 0.2232 loss_sem_seg: 0.2232 2023/03/09 17:17:48 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 1.5106e-01 eta: 1:19:48 time: 0.4685 data_time: 0.0035 memory: 1311 loss: 0.2454 loss_sem_seg: 0.2454 2023/03/09 17:18:11 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 1.5005e-01 eta: 1:19:26 time: 0.4678 data_time: 0.0033 memory: 1260 loss: 0.2296 loss_sem_seg: 0.2296 2023/03/09 17:18:35 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 1.4903e-01 eta: 1:19:04 time: 0.4650 data_time: 0.0034 memory: 1281 loss: 0.2303 loss_sem_seg: 0.2303 2023/03/09 17:18:58 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 1.4801e-01 eta: 1:18:41 time: 0.4654 data_time: 0.0034 memory: 1248 loss: 0.2226 loss_sem_seg: 0.2226 2023/03/09 17:19:21 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 1.4698e-01 eta: 1:18:19 time: 0.4662 data_time: 0.0033 memory: 1269 loss: 0.2312 loss_sem_seg: 0.2312 2023/03/09 17:19:44 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 1.4596e-01 eta: 1:17:57 time: 0.4655 data_time: 0.0033 memory: 1288 loss: 0.2286 loss_sem_seg: 0.2286 2023/03/09 17:20:08 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 1.4493e-01 eta: 1:17:34 time: 0.4663 data_time: 0.0033 memory: 1258 loss: 0.2264 loss_sem_seg: 0.2264 2023/03/09 17:20:31 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 1.4390e-01 eta: 1:17:12 time: 0.4665 data_time: 0.0034 memory: 1295 loss: 0.2433 loss_sem_seg: 0.2433 2023/03/09 17:20:54 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 1.4287e-01 eta: 1:16:50 time: 0.4658 data_time: 0.0033 memory: 1306 loss: 0.2407 loss_sem_seg: 0.2407 2023/03/09 17:21:18 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 1.4184e-01 eta: 1:16:27 time: 0.4643 data_time: 0.0033 memory: 1253 loss: 0.2390 loss_sem_seg: 0.2390 2023/03/09 17:21:41 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 1.4081e-01 eta: 1:16:05 time: 0.4670 data_time: 0.0035 memory: 1243 loss: 0.2232 loss_sem_seg: 0.2232 2023/03/09 17:21:52 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:22:04 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 1.3977e-01 eta: 1:15:42 time: 0.4683 data_time: 0.0035 memory: 1244 loss: 0.2285 loss_sem_seg: 0.2285 2023/03/09 17:22:26 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 1.3873e-01 eta: 1:15:17 time: 0.4251 data_time: 0.0035 memory: 1241 loss: 0.2227 loss_sem_seg: 0.2227 2023/03/09 17:22:46 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 1.3770e-01 eta: 1:14:52 time: 0.4103 data_time: 0.0033 memory: 1298 loss: 0.2187 loss_sem_seg: 0.2187 2023/03/09 17:23:07 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 1.3666e-01 eta: 1:14:26 time: 0.4117 data_time: 0.0032 memory: 1294 loss: 0.2291 loss_sem_seg: 0.2291 2023/03/09 17:23:27 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 1.3562e-01 eta: 1:14:00 time: 0.4076 data_time: 0.0031 memory: 1298 loss: 0.2418 loss_sem_seg: 0.2418 2023/03/09 17:23:48 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 1.3457e-01 eta: 1:13:35 time: 0.4109 data_time: 0.0031 memory: 1274 loss: 0.2499 loss_sem_seg: 0.2499 2023/03/09 17:24:08 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 1.3353e-01 eta: 1:13:09 time: 0.4099 data_time: 0.0035 memory: 1276 loss: 0.2184 loss_sem_seg: 0.2184 2023/03/09 17:24:34 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:24:34 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/03/09 17:24:44 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:01:20 time: 0.1747 data_time: 0.0090 memory: 1279 2023/03/09 17:24:54 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:01:15 time: 0.1958 data_time: 0.0048 memory: 348 2023/03/09 17:25:04 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:01:07 time: 0.1952 data_time: 0.0046 memory: 349 2023/03/09 17:25:14 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:00:59 time: 0.1992 data_time: 0.0053 memory: 344 2023/03/09 17:25:23 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:00:49 time: 0.1910 data_time: 0.0054 memory: 354 2023/03/09 17:25:33 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:00:40 time: 0.1926 data_time: 0.0054 memory: 326 2023/03/09 17:25:42 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:00:30 time: 0.1930 data_time: 0.0052 memory: 339 2023/03/09 17:25:52 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:00:20 time: 0.1956 data_time: 0.0050 memory: 340 2023/03/09 17:26:02 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:11 time: 0.1939 data_time: 0.0052 memory: 349 2023/03/09 17:26:19 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:01 time: 0.3351 data_time: 0.0051 memory: 341 2023/03/09 17:26:44 - 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 | 0.9511 | 0.0201 | 0.3867 | 0.3750 | 0.4909 | 0.5559 | 0.5836 | 0.0000 | 0.9182 | 0.3439 | 0.7794 | 0.0017 | 0.8863 | 0.4982 | 0.8798 | 0.6401 | 0.7632 | 0.6145 | 0.3887 | 0.5304 | 0.9090 | 0.6026 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 17:26:44 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9511 bicycle: 0.0201 motorcycle: 0.3867 truck: 0.3750 bus: 0.4909 person: 0.5559 bicyclist: 0.5836 motorcyclist: 0.0000 road: 0.9182 parking: 0.3439 sidewalk: 0.7794 other-ground: 0.0017 building: 0.8863 fence: 0.4982 vegetation: 0.8798 trunck: 0.6401 terrian: 0.7632 pole: 0.6145 traffic-sign: 0.3887 miou: 0.5304 acc: 0.9090 acc_cls: 0.6026 2023/03/09 17:27:09 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 1.3152e-01 eta: 1:12:33 time: 0.4971 data_time: 0.0217 memory: 1242 loss: 0.2188 loss_sem_seg: 0.2188 2023/03/09 17:27:32 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 1.3048e-01 eta: 1:12:11 time: 0.4701 data_time: 0.0035 memory: 1242 loss: 0.2136 loss_sem_seg: 0.2136 2023/03/09 17:27:56 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 1.2943e-01 eta: 1:11:49 time: 0.4713 data_time: 0.0035 memory: 1312 loss: 0.2231 loss_sem_seg: 0.2231 2023/03/09 17:28:19 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 1.2838e-01 eta: 1:11:26 time: 0.4692 data_time: 0.0034 memory: 1279 loss: 0.2267 loss_sem_seg: 0.2267 2023/03/09 17:28:43 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 1.2733e-01 eta: 1:11:04 time: 0.4684 data_time: 0.0035 memory: 1356 loss: 0.2117 loss_sem_seg: 0.2117 2023/03/09 17:29:06 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 1.2629e-01 eta: 1:10:42 time: 0.4756 data_time: 0.0036 memory: 1261 loss: 0.2245 loss_sem_seg: 0.2245 2023/03/09 17:29:31 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 1.2524e-01 eta: 1:10:21 time: 0.4936 data_time: 0.0035 memory: 1310 loss: 0.2008 loss_sem_seg: 0.2008 2023/03/09 17:29:56 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 1.2419e-01 eta: 1:10:00 time: 0.4898 data_time: 0.0034 memory: 1307 loss: 0.2216 loss_sem_seg: 0.2216 2023/03/09 17:30:20 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 1.2314e-01 eta: 1:09:38 time: 0.4779 data_time: 0.0037 memory: 1238 loss: 0.2215 loss_sem_seg: 0.2215 2023/03/09 17:30:43 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 1.2209e-01 eta: 1:09:16 time: 0.4674 data_time: 0.0035 memory: 1244 loss: 0.2135 loss_sem_seg: 0.2135 2023/03/09 17:31:07 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 1.2103e-01 eta: 1:08:53 time: 0.4735 data_time: 0.0035 memory: 1352 loss: 0.2234 loss_sem_seg: 0.2234 2023/03/09 17:31:30 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 1.1998e-01 eta: 1:08:31 time: 0.4664 data_time: 0.0036 memory: 1296 loss: 0.1971 loss_sem_seg: 0.1971 2023/03/09 17:31:43 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:31:53 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 1.1893e-01 eta: 1:08:09 time: 0.4710 data_time: 0.0036 memory: 1338 loss: 0.2297 loss_sem_seg: 0.2297 2023/03/09 17:32:17 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 1.1788e-01 eta: 1:07:46 time: 0.4713 data_time: 0.0036 memory: 1303 loss: 0.2156 loss_sem_seg: 0.2156 2023/03/09 17:32:40 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 1.1683e-01 eta: 1:07:24 time: 0.4668 data_time: 0.0035 memory: 1304 loss: 0.2059 loss_sem_seg: 0.2059 2023/03/09 17:33:03 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 1.1578e-01 eta: 1:07:01 time: 0.4625 data_time: 0.0034 memory: 1275 loss: 0.2085 loss_sem_seg: 0.2085 2023/03/09 17:33:24 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 1.1473e-01 eta: 1:06:36 time: 0.4154 data_time: 0.0035 memory: 1269 loss: 0.2139 loss_sem_seg: 0.2139 2023/03/09 17:33:45 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 1.1368e-01 eta: 1:06:11 time: 0.4119 data_time: 0.0033 memory: 1287 loss: 0.2144 loss_sem_seg: 0.2144 2023/03/09 17:34:06 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 1.1263e-01 eta: 1:05:46 time: 0.4133 data_time: 0.0034 memory: 1292 loss: 0.2106 loss_sem_seg: 0.2106 2023/03/09 17:34:26 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 1.1159e-01 eta: 1:05:21 time: 0.4077 data_time: 0.0034 memory: 1245 loss: 0.2029 loss_sem_seg: 0.2029 2023/03/09 17:34:47 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 1.1054e-01 eta: 1:04:56 time: 0.4119 data_time: 0.0034 memory: 1309 loss: 0.2044 loss_sem_seg: 0.2044 2023/03/09 17:35:07 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 1.0949e-01 eta: 1:04:31 time: 0.4114 data_time: 0.0034 memory: 1302 loss: 0.2259 loss_sem_seg: 0.2259 2023/03/09 17:35:38 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 1.0844e-01 eta: 1:04:15 time: 0.6197 data_time: 0.0035 memory: 1292 loss: 0.2010 loss_sem_seg: 0.2010 2023/03/09 17:35:59 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:36:00 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/03/09 17:36:11 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:01:32 time: 0.2013 data_time: 0.0083 memory: 1237 2023/03/09 17:36:21 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:01:20 time: 0.1942 data_time: 0.0046 memory: 348 2023/03/09 17:36:30 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:01:11 time: 0.1982 data_time: 0.0048 memory: 349 2023/03/09 17:36:41 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:01:01 time: 0.2023 data_time: 0.0054 memory: 344 2023/03/09 17:36:51 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:00:51 time: 0.1994 data_time: 0.0054 memory: 354 2023/03/09 17:37:00 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:00:41 time: 0.1858 data_time: 0.0051 memory: 326 2023/03/09 17:37:10 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:00:31 time: 0.1943 data_time: 0.0052 memory: 339 2023/03/09 17:37:19 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:00:21 time: 0.1968 data_time: 0.0052 memory: 340 2023/03/09 17:37:29 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:11 time: 0.1976 data_time: 0.0050 memory: 349 2023/03/09 17:37:40 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:01 time: 0.2064 data_time: 0.0051 memory: 341 2023/03/09 17:38:00 - 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 | 0.9561 | 0.0388 | 0.4572 | 0.6238 | 0.4827 | 0.5454 | 0.6598 | 0.0000 | 0.9165 | 0.3078 | 0.7858 | 0.0026 | 0.8947 | 0.5691 | 0.8739 | 0.6430 | 0.7375 | 0.6325 | 0.4195 | 0.5551 | 0.9093 | 0.6221 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 17:38:00 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9561 bicycle: 0.0388 motorcycle: 0.4572 truck: 0.6238 bus: 0.4827 person: 0.5454 bicyclist: 0.6598 motorcyclist: 0.0000 road: 0.9165 parking: 0.3078 sidewalk: 0.7858 other-ground: 0.0026 building: 0.8947 fence: 0.5691 vegetation: 0.8739 trunck: 0.6430 terrian: 0.7375 pole: 0.6325 traffic-sign: 0.4195 miou: 0.5551 acc: 0.9093 acc_cls: 0.6221 2023/03/09 17:38:25 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 1.0644e-01 eta: 1:03:33 time: 0.4923 data_time: 0.0259 memory: 1264 loss: 0.2229 loss_sem_seg: 0.2229 2023/03/09 17:38:48 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 1.0540e-01 eta: 1:03:10 time: 0.4666 data_time: 0.0033 memory: 1285 loss: 0.2023 loss_sem_seg: 0.2023 2023/03/09 17:39:12 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 1.0435e-01 eta: 1:02:48 time: 0.4670 data_time: 0.0034 memory: 1280 loss: 0.1980 loss_sem_seg: 0.1980 2023/03/09 17:39:35 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 1.0331e-01 eta: 1:02:25 time: 0.4653 data_time: 0.0036 memory: 1371 loss: 0.2136 loss_sem_seg: 0.2136 2023/03/09 17:39:58 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 1.0227e-01 eta: 1:02:03 time: 0.4658 data_time: 0.0035 memory: 1259 loss: 0.1866 loss_sem_seg: 0.1866 2023/03/09 17:40:21 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 1.0123e-01 eta: 1:01:40 time: 0.4668 data_time: 0.0034 memory: 1288 loss: 0.2079 loss_sem_seg: 0.2079 2023/03/09 17:40:45 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 1.0020e-01 eta: 1:01:17 time: 0.4687 data_time: 0.0035 memory: 1350 loss: 0.2209 loss_sem_seg: 0.2209 2023/03/09 17:41:08 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 9.9161e-02 eta: 1:00:55 time: 0.4669 data_time: 0.0034 memory: 1299 loss: 0.2074 loss_sem_seg: 0.2074 2023/03/09 17:41:23 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:41:32 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 9.8127e-02 eta: 1:00:32 time: 0.4661 data_time: 0.0034 memory: 1314 loss: 0.2102 loss_sem_seg: 0.2102 2023/03/09 17:41:55 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 9.7095e-02 eta: 1:00:10 time: 0.4692 data_time: 0.0035 memory: 1333 loss: 0.1905 loss_sem_seg: 0.1905 2023/03/09 17:42:18 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 9.6065e-02 eta: 0:59:47 time: 0.4693 data_time: 0.0036 memory: 1278 loss: 0.2082 loss_sem_seg: 0.2082 2023/03/09 17:42:42 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 9.5036e-02 eta: 0:59:25 time: 0.4687 data_time: 0.0034 memory: 1242 loss: 0.1957 loss_sem_seg: 0.1957 2023/03/09 17:43:05 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 9.4009e-02 eta: 0:59:02 time: 0.4673 data_time: 0.0035 memory: 1291 loss: 0.1977 loss_sem_seg: 0.1977 2023/03/09 17:43:29 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 9.2985e-02 eta: 0:58:39 time: 0.4666 data_time: 0.0035 memory: 1253 loss: 0.2099 loss_sem_seg: 0.2099 2023/03/09 17:43:51 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 9.1962e-02 eta: 0:58:16 time: 0.4473 data_time: 0.0040 memory: 1223 loss: 0.2019 loss_sem_seg: 0.2019 2023/03/09 17:44:12 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 9.0942e-02 eta: 0:57:51 time: 0.4132 data_time: 0.0035 memory: 1300 loss: 0.2101 loss_sem_seg: 0.2101 2023/03/09 17:44:32 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.9923e-02 eta: 0:57:27 time: 0.4120 data_time: 0.0034 memory: 1307 loss: 0.1952 loss_sem_seg: 0.1952 2023/03/09 17:44:53 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.8907e-02 eta: 0:57:02 time: 0.4115 data_time: 0.0035 memory: 1254 loss: 0.2036 loss_sem_seg: 0.2036 2023/03/09 17:45:13 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.7894e-02 eta: 0:56:38 time: 0.4102 data_time: 0.0033 memory: 1306 loss: 0.1932 loss_sem_seg: 0.1932 2023/03/09 17:45:34 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.6883e-02 eta: 0:56:13 time: 0.4112 data_time: 0.0034 memory: 1237 loss: 0.1913 loss_sem_seg: 0.1913 2023/03/09 17:45:58 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.5874e-02 eta: 0:55:51 time: 0.4756 data_time: 0.0035 memory: 1261 loss: 0.1921 loss_sem_seg: 0.1921 2023/03/09 17:46:28 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.4868e-02 eta: 0:55:33 time: 0.6069 data_time: 0.0036 memory: 1316 loss: 0.1719 loss_sem_seg: 0.1719 2023/03/09 17:46:51 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.3865e-02 eta: 0:55:10 time: 0.4627 data_time: 0.0033 memory: 1271 loss: 0.2033 loss_sem_seg: 0.2033 2023/03/09 17:47:13 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:47:13 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/09 17:47:24 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:01:32 time: 0.2025 data_time: 0.0091 memory: 1237 2023/03/09 17:47:34 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:01:22 time: 0.1987 data_time: 0.0049 memory: 348 2023/03/09 17:47:44 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:01:11 time: 0.1941 data_time: 0.0049 memory: 349 2023/03/09 17:47:54 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:01:00 time: 0.1921 data_time: 0.0047 memory: 344 2023/03/09 17:48:04 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:00:51 time: 0.1996 data_time: 0.0046 memory: 354 2023/03/09 17:48:13 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:00:40 time: 0.1874 data_time: 0.0048 memory: 326 2023/03/09 17:48:22 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:00:30 time: 0.1892 data_time: 0.0054 memory: 339 2023/03/09 17:48:32 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:00:21 time: 0.2001 data_time: 0.0050 memory: 340 2023/03/09 17:48:42 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:11 time: 0.2007 data_time: 0.0050 memory: 349 2023/03/09 17:48:52 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:01 time: 0.1919 data_time: 0.0049 memory: 341 2023/03/09 17:49:13 - 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 | 0.9614 | 0.0458 | 0.5435 | 0.6854 | 0.5776 | 0.5932 | 0.7898 | 0.0000 | 0.9284 | 0.3983 | 0.7959 | 0.0154 | 0.9018 | 0.5957 | 0.8709 | 0.6541 | 0.7280 | 0.6350 | 0.4555 | 0.5882 | 0.9125 | 0.6683 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 17:49:13 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9614 bicycle: 0.0458 motorcycle: 0.5435 truck: 0.6854 bus: 0.5776 person: 0.5932 bicyclist: 0.7898 motorcyclist: 0.0000 road: 0.9284 parking: 0.3983 sidewalk: 0.7959 other-ground: 0.0154 building: 0.9018 fence: 0.5957 vegetation: 0.8709 trunck: 0.6541 terrian: 0.7280 pole: 0.6350 traffic-sign: 0.4555 miou: 0.5882 acc: 0.9125 acc_cls: 0.6683 2023/03/09 17:49:39 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.1947e-02 eta: 0:54:29 time: 0.5274 data_time: 0.0210 memory: 1309 loss: 0.1901 loss_sem_seg: 0.1901 2023/03/09 17:50:03 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0952e-02 eta: 0:54:06 time: 0.4688 data_time: 0.0034 memory: 1283 loss: 0.1985 loss_sem_seg: 0.1985 2023/03/09 17:50:26 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 7.9960e-02 eta: 0:53:44 time: 0.4692 data_time: 0.0033 memory: 1301 loss: 0.2085 loss_sem_seg: 0.2085 2023/03/09 17:50:50 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 7.8971e-02 eta: 0:53:21 time: 0.4694 data_time: 0.0034 memory: 1278 loss: 0.1929 loss_sem_seg: 0.1929 2023/03/09 17:51:07 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:51:13 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 7.7985e-02 eta: 0:52:58 time: 0.4683 data_time: 0.0034 memory: 1248 loss: 0.1849 loss_sem_seg: 0.1849 2023/03/09 17:51:37 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 7.7003e-02 eta: 0:52:36 time: 0.4683 data_time: 0.0033 memory: 1256 loss: 0.1952 loss_sem_seg: 0.1952 2023/03/09 17:52:00 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 7.6023e-02 eta: 0:52:13 time: 0.4675 data_time: 0.0034 memory: 1259 loss: 0.1931 loss_sem_seg: 0.1931 2023/03/09 17:52:24 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 7.5048e-02 eta: 0:51:50 time: 0.4708 data_time: 0.0034 memory: 1338 loss: 0.1967 loss_sem_seg: 0.1967 2023/03/09 17:52:47 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 7.4075e-02 eta: 0:51:28 time: 0.4646 data_time: 0.0034 memory: 1267 loss: 0.1789 loss_sem_seg: 0.1789 2023/03/09 17:53:10 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 7.3106e-02 eta: 0:51:05 time: 0.4650 data_time: 0.0034 memory: 1360 loss: 0.1839 loss_sem_seg: 0.1839 2023/03/09 17:53:33 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 7.2141e-02 eta: 0:50:42 time: 0.4662 data_time: 0.0033 memory: 1240 loss: 0.1955 loss_sem_seg: 0.1955 2023/03/09 17:53:57 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 7.1179e-02 eta: 0:50:19 time: 0.4674 data_time: 0.0032 memory: 1238 loss: 0.1919 loss_sem_seg: 0.1919 2023/03/09 17:54:20 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 7.0222e-02 eta: 0:49:57 time: 0.4703 data_time: 0.0033 memory: 1252 loss: 0.1788 loss_sem_seg: 0.1788 2023/03/09 17:54:42 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 6.9268e-02 eta: 0:49:33 time: 0.4261 data_time: 0.0033 memory: 1234 loss: 0.1898 loss_sem_seg: 0.1898 2023/03/09 17:55:02 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 6.8317e-02 eta: 0:49:08 time: 0.4111 data_time: 0.0035 memory: 1251 loss: 0.1911 loss_sem_seg: 0.1911 2023/03/09 17:55:23 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 6.7371e-02 eta: 0:48:44 time: 0.4129 data_time: 0.0033 memory: 1266 loss: 0.1896 loss_sem_seg: 0.1896 2023/03/09 17:55:43 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 6.6429e-02 eta: 0:48:20 time: 0.4082 data_time: 0.0033 memory: 1302 loss: 0.1771 loss_sem_seg: 0.1771 2023/03/09 17:56:04 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 6.5491e-02 eta: 0:47:56 time: 0.4103 data_time: 0.0036 memory: 1248 loss: 0.1822 loss_sem_seg: 0.1822 2023/03/09 17:56:24 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 6.4557e-02 eta: 0:47:32 time: 0.4113 data_time: 0.0033 memory: 1285 loss: 0.1717 loss_sem_seg: 0.1717 2023/03/09 17:56:55 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 6.3627e-02 eta: 0:47:13 time: 0.6123 data_time: 0.0033 memory: 1301 loss: 0.1844 loss_sem_seg: 0.1844 2023/03/09 17:57:19 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 6.2702e-02 eta: 0:46:50 time: 0.4787 data_time: 0.0035 memory: 1306 loss: 0.1899 loss_sem_seg: 0.1899 2023/03/09 17:57:42 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 6.1781e-02 eta: 0:46:28 time: 0.4662 data_time: 0.0034 memory: 1311 loss: 0.1934 loss_sem_seg: 0.1934 2023/03/09 17:58:06 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 6.0865e-02 eta: 0:46:05 time: 0.4734 data_time: 0.0035 memory: 1242 loss: 0.1855 loss_sem_seg: 0.1855 2023/03/09 17:58:27 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 17:58:28 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/03/09 17:58:39 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:01:36 time: 0.2093 data_time: 0.0088 memory: 1276 2023/03/09 17:58:49 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:01:23 time: 0.1975 data_time: 0.0049 memory: 348 2023/03/09 17:58:59 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:01:11 time: 0.1947 data_time: 0.0054 memory: 349 2023/03/09 17:59:09 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:01:01 time: 0.1936 data_time: 0.0052 memory: 344 2023/03/09 17:59:19 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:00:51 time: 0.1983 data_time: 0.0055 memory: 354 2023/03/09 17:59:28 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:00:41 time: 0.1863 data_time: 0.0052 memory: 326 2023/03/09 17:59:37 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:00:31 time: 0.1868 data_time: 0.0051 memory: 339 2023/03/09 17:59:47 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:00:21 time: 0.1954 data_time: 0.0051 memory: 340 2023/03/09 17:59:57 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:11 time: 0.2013 data_time: 0.0054 memory: 349 2023/03/09 18:00:07 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:01 time: 0.1967 data_time: 0.0050 memory: 341 2023/03/09 18:00:29 - 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 | 0.9649 | 0.0448 | 0.5543 | 0.8283 | 0.5695 | 0.6274 | 0.7403 | 0.0000 | 0.9220 | 0.4465 | 0.7937 | 0.0107 | 0.9078 | 0.5942 | 0.8788 | 0.6575 | 0.7527 | 0.6427 | 0.4551 | 0.5995 | 0.9153 | 0.6682 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:00:29 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9649 bicycle: 0.0448 motorcycle: 0.5543 truck: 0.8283 bus: 0.5695 person: 0.6274 bicyclist: 0.7403 motorcyclist: 0.0000 road: 0.9220 parking: 0.4465 sidewalk: 0.7937 other-ground: 0.0107 building: 0.9078 fence: 0.5942 vegetation: 0.8788 trunck: 0.6575 terrian: 0.7527 pole: 0.6427 traffic-sign: 0.4551 miou: 0.5995 acc: 0.9153 acc_cls: 0.6682 2023/03/09 18:00:52 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:00:56 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 5.9118e-02 eta: 0:45:23 time: 0.5376 data_time: 0.0236 memory: 1326 loss: 0.1641 loss_sem_seg: 0.1641 2023/03/09 18:01:20 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 5.8215e-02 eta: 0:45:00 time: 0.4709 data_time: 0.0035 memory: 1252 loss: 0.1843 loss_sem_seg: 0.1843 2023/03/09 18:01:43 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 5.7317e-02 eta: 0:44:38 time: 0.4692 data_time: 0.0036 memory: 1270 loss: 0.1720 loss_sem_seg: 0.1720 2023/03/09 18:02:07 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 5.6423e-02 eta: 0:44:15 time: 0.4656 data_time: 0.0035 memory: 1277 loss: 0.1735 loss_sem_seg: 0.1735 2023/03/09 18:02:30 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 5.5535e-02 eta: 0:43:52 time: 0.4686 data_time: 0.0035 memory: 1299 loss: 0.1617 loss_sem_seg: 0.1617 2023/03/09 18:02:53 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 5.4651e-02 eta: 0:43:29 time: 0.4666 data_time: 0.0034 memory: 1208 loss: 0.1785 loss_sem_seg: 0.1785 2023/03/09 18:03:17 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 5.3772e-02 eta: 0:43:06 time: 0.4658 data_time: 0.0034 memory: 1262 loss: 0.1601 loss_sem_seg: 0.1601 2023/03/09 18:03:40 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 5.2899e-02 eta: 0:42:44 time: 0.4645 data_time: 0.0034 memory: 1298 loss: 0.1669 loss_sem_seg: 0.1669 2023/03/09 18:04:03 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 5.2030e-02 eta: 0:42:21 time: 0.4657 data_time: 0.0034 memory: 1253 loss: 0.1813 loss_sem_seg: 0.1813 2023/03/09 18:04:27 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 5.1167e-02 eta: 0:41:58 time: 0.4680 data_time: 0.0034 memory: 1268 loss: 0.1915 loss_sem_seg: 0.1915 2023/03/09 18:04:50 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 5.0309e-02 eta: 0:41:35 time: 0.4675 data_time: 0.0035 memory: 1297 loss: 0.1772 loss_sem_seg: 0.1772 2023/03/09 18:05:13 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 4.9457e-02 eta: 0:41:12 time: 0.4553 data_time: 0.0034 memory: 1280 loss: 0.1698 loss_sem_seg: 0.1698 2023/03/09 18:05:33 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 4.8610e-02 eta: 0:40:48 time: 0.4153 data_time: 0.0035 memory: 1262 loss: 0.1778 loss_sem_seg: 0.1778 2023/03/09 18:05:54 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 4.7768e-02 eta: 0:40:24 time: 0.4113 data_time: 0.0034 memory: 1308 loss: 0.1692 loss_sem_seg: 0.1692 2023/03/09 18:06:15 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 4.6932e-02 eta: 0:40:00 time: 0.4115 data_time: 0.0034 memory: 1278 loss: 0.1648 loss_sem_seg: 0.1648 2023/03/09 18:06:35 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 4.6101e-02 eta: 0:39:36 time: 0.4091 data_time: 0.0035 memory: 1303 loss: 0.1651 loss_sem_seg: 0.1651 2023/03/09 18:06:56 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 4.5276e-02 eta: 0:39:12 time: 0.4128 data_time: 0.0035 memory: 1250 loss: 0.1640 loss_sem_seg: 0.1640 2023/03/09 18:07:17 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 4.4457e-02 eta: 0:38:49 time: 0.4333 data_time: 0.0034 memory: 1293 loss: 0.1786 loss_sem_seg: 0.1786 2023/03/09 18:07:48 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 4.3644e-02 eta: 0:38:29 time: 0.6180 data_time: 0.0037 memory: 1331 loss: 0.1687 loss_sem_seg: 0.1687 2023/03/09 18:08:12 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 4.2836e-02 eta: 0:38:06 time: 0.4656 data_time: 0.0035 memory: 1302 loss: 0.1728 loss_sem_seg: 0.1728 2023/03/09 18:08:30 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:08:35 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 4.2035e-02 eta: 0:37:43 time: 0.4680 data_time: 0.0034 memory: 1384 loss: 0.1793 loss_sem_seg: 0.1793 2023/03/09 18:08:58 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 4.1239e-02 eta: 0:37:21 time: 0.4662 data_time: 0.0033 memory: 1271 loss: 0.1671 loss_sem_seg: 0.1671 2023/03/09 18:09:22 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 4.0449e-02 eta: 0:36:58 time: 0.4685 data_time: 0.0035 memory: 1248 loss: 0.1687 loss_sem_seg: 0.1687 2023/03/09 18:09:43 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:09:43 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/03/09 18:09:55 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:01:35 time: 0.2078 data_time: 0.0086 memory: 1232 2023/03/09 18:10:05 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:01:23 time: 0.2002 data_time: 0.0047 memory: 348 2023/03/09 18:10:14 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:01:11 time: 0.1896 data_time: 0.0047 memory: 349 2023/03/09 18:10:24 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:01:01 time: 0.1968 data_time: 0.0050 memory: 344 2023/03/09 18:10:34 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:00:51 time: 0.2027 data_time: 0.0054 memory: 354 2023/03/09 18:10:44 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:00:41 time: 0.1956 data_time: 0.0052 memory: 326 2023/03/09 18:10:54 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:00:31 time: 0.1906 data_time: 0.0052 memory: 339 2023/03/09 18:11:03 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:00:21 time: 0.1921 data_time: 0.0051 memory: 340 2023/03/09 18:11:13 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:11 time: 0.1997 data_time: 0.0053 memory: 349 2023/03/09 18:11:23 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:01 time: 0.1944 data_time: 0.0051 memory: 341 2023/03/09 18:11:45 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9545 | 0.0778 | 0.5806 | 0.7049 | 0.4109 | 0.5833 | 0.7703 | 0.0000 | 0.9229 | 0.3911 | 0.7888 | 0.0069 | 0.9043 | 0.5910 | 0.8762 | 0.6715 | 0.7383 | 0.6459 | 0.4770 | 0.5840 | 0.9126 | 0.6502 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:11:45 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9545 bicycle: 0.0778 motorcycle: 0.5806 truck: 0.7049 bus: 0.4109 person: 0.5833 bicyclist: 0.7703 motorcyclist: 0.0000 road: 0.9229 parking: 0.3911 sidewalk: 0.7888 other-ground: 0.0069 building: 0.9043 fence: 0.5910 vegetation: 0.8762 trunck: 0.6715 terrian: 0.7383 pole: 0.6459 traffic-sign: 0.4770 miou: 0.5840 acc: 0.9126 acc_cls: 0.6502 2023/03/09 18:12:13 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 3.8950e-02 eta: 0:36:16 time: 0.5575 data_time: 0.0215 memory: 1285 loss: 0.1638 loss_sem_seg: 0.1638 2023/03/09 18:12:36 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 3.8179e-02 eta: 0:35:53 time: 0.4667 data_time: 0.0035 memory: 1280 loss: 0.1681 loss_sem_seg: 0.1681 2023/03/09 18:13:00 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 3.7414e-02 eta: 0:35:30 time: 0.4668 data_time: 0.0033 memory: 1286 loss: 0.1688 loss_sem_seg: 0.1688 2023/03/09 18:13:23 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 3.6655e-02 eta: 0:35:07 time: 0.4673 data_time: 0.0035 memory: 1485 loss: 0.1661 loss_sem_seg: 0.1661 2023/03/09 18:13:46 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 3.5902e-02 eta: 0:34:44 time: 0.4663 data_time: 0.0033 memory: 1248 loss: 0.1673 loss_sem_seg: 0.1673 2023/03/09 18:14:09 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 3.5156e-02 eta: 0:34:21 time: 0.4647 data_time: 0.0034 memory: 1331 loss: 0.1602 loss_sem_seg: 0.1602 2023/03/09 18:14:33 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 3.4416e-02 eta: 0:33:58 time: 0.4682 data_time: 0.0034 memory: 1267 loss: 0.1705 loss_sem_seg: 0.1705 2023/03/09 18:14:56 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 3.3683e-02 eta: 0:33:36 time: 0.4695 data_time: 0.0033 memory: 1217 loss: 0.1564 loss_sem_seg: 0.1564 2023/03/09 18:15:20 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 3.2956e-02 eta: 0:33:13 time: 0.4682 data_time: 0.0033 memory: 1308 loss: 0.1678 loss_sem_seg: 0.1678 2023/03/09 18:15:43 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 3.2237e-02 eta: 0:32:50 time: 0.4666 data_time: 0.0033 memory: 1268 loss: 0.1584 loss_sem_seg: 0.1584 2023/03/09 18:16:05 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 3.1524e-02 eta: 0:32:26 time: 0.4343 data_time: 0.0036 memory: 1282 loss: 0.1590 loss_sem_seg: 0.1590 2023/03/09 18:16:25 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 3.0817e-02 eta: 0:32:03 time: 0.4137 data_time: 0.0034 memory: 1286 loss: 0.1666 loss_sem_seg: 0.1666 2023/03/09 18:16:46 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 3.0118e-02 eta: 0:31:39 time: 0.4122 data_time: 0.0034 memory: 1268 loss: 0.1534 loss_sem_seg: 0.1534 2023/03/09 18:17:07 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 2.9425e-02 eta: 0:31:15 time: 0.4105 data_time: 0.0034 memory: 1310 loss: 0.1655 loss_sem_seg: 0.1655 2023/03/09 18:17:27 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 2.8740e-02 eta: 0:30:52 time: 0.4095 data_time: 0.0034 memory: 1262 loss: 0.1593 loss_sem_seg: 0.1593 2023/03/09 18:17:48 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 2.8061e-02 eta: 0:30:28 time: 0.4121 data_time: 0.0035 memory: 1277 loss: 0.1587 loss_sem_seg: 0.1587 2023/03/09 18:18:10 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:18:15 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 2.7389e-02 eta: 0:30:06 time: 0.5440 data_time: 0.0034 memory: 1338 loss: 0.1628 loss_sem_seg: 0.1628 2023/03/09 18:18:42 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 2.6725e-02 eta: 0:29:44 time: 0.5348 data_time: 0.0036 memory: 1359 loss: 0.1436 loss_sem_seg: 0.1436 2023/03/09 18:19:05 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 2.6068e-02 eta: 0:29:22 time: 0.4710 data_time: 0.0033 memory: 1302 loss: 0.1550 loss_sem_seg: 0.1550 2023/03/09 18:19:28 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 2.5417e-02 eta: 0:28:59 time: 0.4658 data_time: 0.0034 memory: 1271 loss: 0.1551 loss_sem_seg: 0.1551 2023/03/09 18:19:52 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 2.4775e-02 eta: 0:28:36 time: 0.4688 data_time: 0.0033 memory: 1230 loss: 0.1461 loss_sem_seg: 0.1461 2023/03/09 18:20:15 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 2.4139e-02 eta: 0:28:13 time: 0.4674 data_time: 0.0034 memory: 1269 loss: 0.1453 loss_sem_seg: 0.1453 2023/03/09 18:20:39 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 2.3511e-02 eta: 0:27:50 time: 0.4681 data_time: 0.0034 memory: 1323 loss: 0.1524 loss_sem_seg: 0.1524 2023/03/09 18:21:00 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:21:01 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/09 18:21:12 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:01:34 time: 0.2068 data_time: 0.0093 memory: 1329 2023/03/09 18:21:22 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:01:23 time: 0.1992 data_time: 0.0048 memory: 348 2023/03/09 18:21:32 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:01:11 time: 0.1907 data_time: 0.0057 memory: 349 2023/03/09 18:21:41 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:01:01 time: 0.1958 data_time: 0.0052 memory: 344 2023/03/09 18:21:51 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:00:51 time: 0.1994 data_time: 0.0050 memory: 354 2023/03/09 18:22:01 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:00:41 time: 0.1896 data_time: 0.0055 memory: 327 2023/03/09 18:22:10 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:00:31 time: 0.1885 data_time: 0.0055 memory: 339 2023/03/09 18:22:20 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:00:21 time: 0.1943 data_time: 0.0054 memory: 341 2023/03/09 18:22:30 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:11 time: 0.1988 data_time: 0.0048 memory: 349 2023/03/09 18:22:40 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:01 time: 0.1959 data_time: 0.0048 memory: 341 2023/03/09 18:23:02 - 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 | 0.9678 | 0.1264 | 0.5764 | 0.8413 | 0.6128 | 0.5976 | 0.7863 | 0.0002 | 0.9274 | 0.4168 | 0.8051 | 0.0040 | 0.9041 | 0.6023 | 0.8793 | 0.6760 | 0.7440 | 0.6488 | 0.4861 | 0.6107 | 0.9173 | 0.6744 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:23:02 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9678 bicycle: 0.1264 motorcycle: 0.5764 truck: 0.8413 bus: 0.6128 person: 0.5976 bicyclist: 0.7863 motorcyclist: 0.0002 road: 0.9274 parking: 0.4168 sidewalk: 0.8051 other-ground: 0.0040 building: 0.9041 fence: 0.6023 vegetation: 0.8793 trunck: 0.6760 terrian: 0.7440 pole: 0.6488 traffic-sign: 0.4861 miou: 0.6107 acc: 0.9173 acc_cls: 0.6744 2023/03/09 18:23:31 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 2.2325e-02 eta: 0:27:07 time: 0.5640 data_time: 0.0223 memory: 1323 loss: 0.1564 loss_sem_seg: 0.1564 2023/03/09 18:23:54 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 2.1719e-02 eta: 0:26:44 time: 0.4673 data_time: 0.0035 memory: 1245 loss: 0.1555 loss_sem_seg: 0.1555 2023/03/09 18:24:17 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 2.1120e-02 eta: 0:26:21 time: 0.4655 data_time: 0.0035 memory: 1311 loss: 0.1648 loss_sem_seg: 0.1648 2023/03/09 18:24:41 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 2.0529e-02 eta: 0:25:58 time: 0.4668 data_time: 0.0034 memory: 1244 loss: 0.1425 loss_sem_seg: 0.1425 2023/03/09 18:25:04 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 1.9945e-02 eta: 0:25:36 time: 0.4641 data_time: 0.0034 memory: 1288 loss: 0.1521 loss_sem_seg: 0.1521 2023/03/09 18:25:27 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 1.9369e-02 eta: 0:25:13 time: 0.4655 data_time: 0.0034 memory: 1310 loss: 0.1441 loss_sem_seg: 0.1441 2023/03/09 18:25:50 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 1.8800e-02 eta: 0:24:50 time: 0.4673 data_time: 0.0034 memory: 1279 loss: 0.1434 loss_sem_seg: 0.1434 2023/03/09 18:26:14 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 1.8240e-02 eta: 0:24:27 time: 0.4653 data_time: 0.0035 memory: 1345 loss: 0.1588 loss_sem_seg: 0.1588 2023/03/09 18:26:37 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 1.7687e-02 eta: 0:24:04 time: 0.4661 data_time: 0.0037 memory: 1291 loss: 0.1552 loss_sem_seg: 0.1552 2023/03/09 18:26:58 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 1.7142e-02 eta: 0:23:40 time: 0.4144 data_time: 0.0034 memory: 1326 loss: 0.1600 loss_sem_seg: 0.1600 2023/03/09 18:27:18 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 1.6605e-02 eta: 0:23:17 time: 0.4097 data_time: 0.0034 memory: 1274 loss: 0.1467 loss_sem_seg: 0.1467 2023/03/09 18:27:39 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 1.6076e-02 eta: 0:22:53 time: 0.4137 data_time: 0.0033 memory: 1363 loss: 0.1536 loss_sem_seg: 0.1536 2023/03/09 18:27:58 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:27:59 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 1.5555e-02 eta: 0:22:30 time: 0.4071 data_time: 0.0034 memory: 1272 loss: 0.1481 loss_sem_seg: 0.1481 2023/03/09 18:28:20 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 1.5041e-02 eta: 0:22:06 time: 0.4116 data_time: 0.0035 memory: 1263 loss: 0.1525 loss_sem_seg: 0.1525 2023/03/09 18:28:40 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 1.4536e-02 eta: 0:21:43 time: 0.4107 data_time: 0.0034 memory: 1298 loss: 0.1571 loss_sem_seg: 0.1571 2023/03/09 18:29:12 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 1.4039e-02 eta: 0:21:21 time: 0.6284 data_time: 0.0034 memory: 1289 loss: 0.1611 loss_sem_seg: 0.1611 2023/03/09 18:29:35 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 1.3550e-02 eta: 0:20:59 time: 0.4684 data_time: 0.0035 memory: 1343 loss: 0.1470 loss_sem_seg: 0.1470 2023/03/09 18:29:59 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 1.3070e-02 eta: 0:20:36 time: 0.4682 data_time: 0.0035 memory: 1255 loss: 0.1513 loss_sem_seg: 0.1513 2023/03/09 18:30:22 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 1.2597e-02 eta: 0:20:13 time: 0.4659 data_time: 0.0034 memory: 1265 loss: 0.1445 loss_sem_seg: 0.1445 2023/03/09 18:30:45 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 1.2133e-02 eta: 0:19:50 time: 0.4664 data_time: 0.0035 memory: 1335 loss: 0.1474 loss_sem_seg: 0.1474 2023/03/09 18:31:09 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 1.1677e-02 eta: 0:19:27 time: 0.4691 data_time: 0.0035 memory: 1224 loss: 0.1516 loss_sem_seg: 0.1516 2023/03/09 18:31:32 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 1.1229e-02 eta: 0:19:04 time: 0.4661 data_time: 0.0036 memory: 1309 loss: 0.1453 loss_sem_seg: 0.1453 2023/03/09 18:31:56 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 1.0790e-02 eta: 0:18:41 time: 0.4720 data_time: 0.0034 memory: 1383 loss: 0.1402 loss_sem_seg: 0.1402 2023/03/09 18:32:17 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:32:17 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/03/09 18:32:29 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:01:36 time: 0.2105 data_time: 0.0087 memory: 1280 2023/03/09 18:32:39 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:01:23 time: 0.1988 data_time: 0.0053 memory: 348 2023/03/09 18:32:49 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:01:11 time: 0.1924 data_time: 0.0055 memory: 349 2023/03/09 18:32:59 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:01:01 time: 0.1975 data_time: 0.0054 memory: 344 2023/03/09 18:33:09 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:00:51 time: 0.2044 data_time: 0.0052 memory: 354 2023/03/09 18:33:19 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:00:41 time: 0.1916 data_time: 0.0051 memory: 327 2023/03/09 18:33:28 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:00:31 time: 0.1897 data_time: 0.0055 memory: 339 2023/03/09 18:33:38 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:00:21 time: 0.1931 data_time: 0.0051 memory: 341 2023/03/09 18:33:48 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:11 time: 0.1980 data_time: 0.0051 memory: 349 2023/03/09 18:33:57 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:01 time: 0.1947 data_time: 0.0051 memory: 341 2023/03/09 18:34:20 - 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 | 0.9677 | 0.1512 | 0.5675 | 0.7877 | 0.5989 | 0.6511 | 0.8264 | 0.0000 | 0.9302 | 0.4382 | 0.8047 | 0.0024 | 0.9078 | 0.6171 | 0.8811 | 0.6625 | 0.7461 | 0.6491 | 0.4975 | 0.6151 | 0.9185 | 0.6838 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:34:20 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9677 bicycle: 0.1512 motorcycle: 0.5675 truck: 0.7877 bus: 0.5989 person: 0.6511 bicyclist: 0.8264 motorcyclist: 0.0000 road: 0.9302 parking: 0.4382 sidewalk: 0.8047 other-ground: 0.0024 building: 0.9078 fence: 0.6171 vegetation: 0.8811 trunck: 0.6625 terrian: 0.7461 pole: 0.6491 traffic-sign: 0.4975 miou: 0.6151 acc: 0.9185 acc_cls: 0.6838 2023/03/09 18:34:48 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 9.9694e-03 eta: 0:17:58 time: 0.5612 data_time: 0.0211 memory: 1287 loss: 0.1429 loss_sem_seg: 0.1429 2023/03/09 18:35:12 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 9.5545e-03 eta: 0:17:35 time: 0.4690 data_time: 0.0035 memory: 1228 loss: 0.1421 loss_sem_seg: 0.1421 2023/03/09 18:35:35 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 9.1481e-03 eta: 0:17:12 time: 0.4662 data_time: 0.0037 memory: 1263 loss: 0.1467 loss_sem_seg: 0.1467 2023/03/09 18:35:58 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.7502e-03 eta: 0:16:49 time: 0.4697 data_time: 0.0037 memory: 1320 loss: 0.1518 loss_sem_seg: 0.1518 2023/03/09 18:36:22 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.3608e-03 eta: 0:16:26 time: 0.4705 data_time: 0.0036 memory: 1284 loss: 0.1463 loss_sem_seg: 0.1463 2023/03/09 18:36:45 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 7.9800e-03 eta: 0:16:03 time: 0.4644 data_time: 0.0036 memory: 1224 loss: 0.1360 loss_sem_seg: 0.1360 2023/03/09 18:37:09 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 7.6078e-03 eta: 0:15:40 time: 0.4709 data_time: 0.0038 memory: 1282 loss: 0.1476 loss_sem_seg: 0.1476 2023/03/09 18:37:31 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 7.2442e-03 eta: 0:15:17 time: 0.4418 data_time: 0.0037 memory: 1304 loss: 0.1497 loss_sem_seg: 0.1497 2023/03/09 18:37:51 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 6.8892e-03 eta: 0:14:53 time: 0.4130 data_time: 0.0033 memory: 1289 loss: 0.1396 loss_sem_seg: 0.1396 2023/03/09 18:37:52 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:38:12 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 6.5429e-03 eta: 0:14:30 time: 0.4130 data_time: 0.0035 memory: 1277 loss: 0.1512 loss_sem_seg: 0.1512 2023/03/09 18:38:33 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 6.2053e-03 eta: 0:14:07 time: 0.4129 data_time: 0.0035 memory: 1272 loss: 0.1390 loss_sem_seg: 0.1390 2023/03/09 18:38:53 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 5.8765e-03 eta: 0:13:44 time: 0.4100 data_time: 0.0035 memory: 1251 loss: 0.1424 loss_sem_seg: 0.1424 2023/03/09 18:39:14 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 5.5564e-03 eta: 0:13:20 time: 0.4134 data_time: 0.0034 memory: 1276 loss: 0.1446 loss_sem_seg: 0.1446 2023/03/09 18:39:39 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 5.2450e-03 eta: 0:12:58 time: 0.5013 data_time: 0.0036 memory: 1267 loss: 0.1475 loss_sem_seg: 0.1475 2023/03/09 18:40:07 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 4.9425e-03 eta: 0:12:35 time: 0.5543 data_time: 0.0038 memory: 1286 loss: 0.1432 loss_sem_seg: 0.1432 2023/03/09 18:40:30 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 4.6488e-03 eta: 0:12:12 time: 0.4690 data_time: 0.0034 memory: 1446 loss: 0.1450 loss_sem_seg: 0.1450 2023/03/09 18:40:54 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 4.3639e-03 eta: 0:11:49 time: 0.4693 data_time: 0.0032 memory: 1272 loss: 0.1473 loss_sem_seg: 0.1473 2023/03/09 18:41:17 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 4.0879e-03 eta: 0:11:26 time: 0.4681 data_time: 0.0034 memory: 1346 loss: 0.1405 loss_sem_seg: 0.1405 2023/03/09 18:41:40 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 3.8207e-03 eta: 0:11:03 time: 0.4681 data_time: 0.0035 memory: 1220 loss: 0.1457 loss_sem_seg: 0.1457 2023/03/09 18:42:04 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 3.5625e-03 eta: 0:10:40 time: 0.4707 data_time: 0.0036 memory: 1363 loss: 0.1413 loss_sem_seg: 0.1413 2023/03/09 18:42:27 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 3.3132e-03 eta: 0:10:17 time: 0.4667 data_time: 0.0034 memory: 1257 loss: 0.1471 loss_sem_seg: 0.1471 2023/03/09 18:42:51 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 3.0729e-03 eta: 0:09:54 time: 0.4698 data_time: 0.0033 memory: 1297 loss: 0.1407 loss_sem_seg: 0.1407 2023/03/09 18:43:14 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 2.8415e-03 eta: 0:09:31 time: 0.4658 data_time: 0.0033 memory: 1316 loss: 0.1394 loss_sem_seg: 0.1394 2023/03/09 18:43:36 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:43:36 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/03/09 18:43:48 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:01:37 time: 0.2116 data_time: 0.0088 memory: 1253 2023/03/09 18:43:58 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:01:24 time: 0.1997 data_time: 0.0051 memory: 348 2023/03/09 18:44:07 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:01:12 time: 0.1929 data_time: 0.0058 memory: 349 2023/03/09 18:44:17 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:01:01 time: 0.1942 data_time: 0.0054 memory: 344 2023/03/09 18:44:27 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:00:51 time: 0.2009 data_time: 0.0055 memory: 354 2023/03/09 18:44:37 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:00:41 time: 0.1931 data_time: 0.0052 memory: 327 2023/03/09 18:44:46 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:00:31 time: 0.1868 data_time: 0.0052 memory: 339 2023/03/09 18:44:56 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:00:21 time: 0.1953 data_time: 0.0048 memory: 341 2023/03/09 18:45:06 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:11 time: 0.1982 data_time: 0.0050 memory: 349 2023/03/09 18:45:16 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:01 time: 0.1964 data_time: 0.0049 memory: 341 2023/03/09 18:45:38 - 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 | 0.9654 | 0.1200 | 0.5830 | 0.8507 | 0.5745 | 0.6436 | 0.8283 | 0.0000 | 0.9331 | 0.4643 | 0.8071 | 0.0027 | 0.9080 | 0.6210 | 0.8829 | 0.6656 | 0.7493 | 0.6485 | 0.4955 | 0.6181 | 0.9198 | 0.6814 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:45:38 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9654 bicycle: 0.1200 motorcycle: 0.5830 truck: 0.8507 bus: 0.5745 person: 0.6436 bicyclist: 0.8283 motorcyclist: 0.0000 road: 0.9331 parking: 0.4643 sidewalk: 0.8071 other-ground: 0.0027 building: 0.9080 fence: 0.6210 vegetation: 0.8829 trunck: 0.6656 terrian: 0.7493 pole: 0.6485 traffic-sign: 0.4955 miou: 0.6181 acc: 0.9198 acc_cls: 0.6814 2023/03/09 18:46:07 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 2.4224e-03 eta: 0:08:47 time: 0.5745 data_time: 0.0210 memory: 1326 loss: 0.1463 loss_sem_seg: 0.1463 2023/03/09 18:46:30 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 2.2173e-03 eta: 0:08:24 time: 0.4674 data_time: 0.0037 memory: 1315 loss: 0.1505 loss_sem_seg: 0.1505 2023/03/09 18:46:53 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 2.0212e-03 eta: 0:08:01 time: 0.4634 data_time: 0.0033 memory: 1251 loss: 0.1404 loss_sem_seg: 0.1404 2023/03/09 18:47:17 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 1.8342e-03 eta: 0:07:38 time: 0.4660 data_time: 0.0033 memory: 1311 loss: 0.1402 loss_sem_seg: 0.1402 2023/03/09 18:47:40 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 1.6562e-03 eta: 0:07:15 time: 0.4683 data_time: 0.0034 memory: 1255 loss: 0.1406 loss_sem_seg: 0.1406 2023/03/09 18:47:43 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:48:03 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 1.4873e-03 eta: 0:06:52 time: 0.4691 data_time: 0.0034 memory: 1296 loss: 0.1505 loss_sem_seg: 0.1505 2023/03/09 18:48:24 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 1.3275e-03 eta: 0:06:29 time: 0.4150 data_time: 0.0039 memory: 1231 loss: 0.1552 loss_sem_seg: 0.1552 2023/03/09 18:48:45 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 1.1768e-03 eta: 0:06:06 time: 0.4122 data_time: 0.0037 memory: 1294 loss: 0.1388 loss_sem_seg: 0.1388 2023/03/09 18:49:05 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 1.0352e-03 eta: 0:05:43 time: 0.4130 data_time: 0.0035 memory: 1289 loss: 0.1459 loss_sem_seg: 0.1459 2023/03/09 18:49:26 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 9.0272e-04 eta: 0:05:20 time: 0.4070 data_time: 0.0035 memory: 1324 loss: 0.1317 loss_sem_seg: 0.1317 2023/03/09 18:49:46 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 7.7936e-04 eta: 0:04:57 time: 0.4113 data_time: 0.0035 memory: 1280 loss: 0.1502 loss_sem_seg: 0.1502 2023/03/09 18:50:07 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 6.6515e-04 eta: 0:04:34 time: 0.4102 data_time: 0.0034 memory: 1246 loss: 0.1391 loss_sem_seg: 0.1391 2023/03/09 18:50:36 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 5.6009e-04 eta: 0:04:11 time: 0.5880 data_time: 0.0034 memory: 1283 loss: 0.1400 loss_sem_seg: 0.1400 2023/03/09 18:50:52 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 4.6418e-04 eta: 0:03:48 time: 0.3163 data_time: 0.0032 memory: 1275 loss: 0.1461 loss_sem_seg: 0.1461 2023/03/09 18:51:08 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 3.7744e-04 eta: 0:03:24 time: 0.3169 data_time: 0.0032 memory: 1247 loss: 0.1408 loss_sem_seg: 0.1408 2023/03/09 18:51:24 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 2.9986e-04 eta: 0:03:01 time: 0.3160 data_time: 0.0032 memory: 1260 loss: 0.1362 loss_sem_seg: 0.1362 2023/03/09 18:51:40 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 2.3147e-04 eta: 0:02:38 time: 0.3163 data_time: 0.0031 memory: 1240 loss: 0.1420 loss_sem_seg: 0.1420 2023/03/09 18:51:55 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 1.7226e-04 eta: 0:02:15 time: 0.3151 data_time: 0.0031 memory: 1275 loss: 0.1330 loss_sem_seg: 0.1330 2023/03/09 18:52:11 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 1.2223e-04 eta: 0:01:52 time: 0.3170 data_time: 0.0031 memory: 1290 loss: 0.1480 loss_sem_seg: 0.1480 2023/03/09 18:52:27 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.1397e-05 eta: 0:01:29 time: 0.3144 data_time: 0.0033 memory: 1313 loss: 0.1384 loss_sem_seg: 0.1384 2023/03/09 18:52:43 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 4.9756e-05 eta: 0:01:06 time: 0.3177 data_time: 0.0031 memory: 1283 loss: 0.1413 loss_sem_seg: 0.1413 2023/03/09 18:52:59 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 2.7311e-05 eta: 0:00:43 time: 0.3162 data_time: 0.0032 memory: 1261 loss: 0.1452 loss_sem_seg: 0.1452 2023/03/09 18:53:14 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 1.4064e-05 eta: 0:00:20 time: 0.3165 data_time: 0.0033 memory: 1219 loss: 0.1322 loss_sem_seg: 0.1322 2023/03/09 18:53:29 - mmengine - INFO - Exp name: minkunet_w20_8xb2-15e_semantickitti_20230309_160718 2023/03/09 18:53:29 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/09 18:53:35 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:00:45 time: 0.0995 data_time: 0.0085 memory: 1254 2023/03/09 18:53:41 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:00:42 time: 0.1090 data_time: 0.0047 memory: 348 2023/03/09 18:53:46 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:00:37 time: 0.1057 data_time: 0.0047 memory: 349 2023/03/09 18:53:51 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:00:32 time: 0.1047 data_time: 0.0053 memory: 344 2023/03/09 18:53:57 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:00:27 time: 0.1054 data_time: 0.0053 memory: 354 2023/03/09 18:54:02 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:00:21 time: 0.1055 data_time: 0.0049 memory: 327 2023/03/09 18:54:07 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:00:16 time: 0.0996 data_time: 0.0044 memory: 339 2023/03/09 18:54:12 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:00:11 time: 0.1025 data_time: 0.0047 memory: 341 2023/03/09 18:54:17 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:06 time: 0.1051 data_time: 0.0052 memory: 349 2023/03/09 18:54:23 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:00 time: 0.1047 data_time: 0.0043 memory: 341 2023/03/09 18:54:43 - 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 | 0.9665 | 0.1277 | 0.5836 | 0.8621 | 0.5904 | 0.6377 | 0.8285 | 0.0000 | 0.9316 | 0.4455 | 0.8057 | 0.0053 | 0.9054 | 0.6064 | 0.8814 | 0.6625 | 0.7466 | 0.6486 | 0.4980 | 0.6175 | 0.9185 | 0.6805 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/09 18:54:43 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9665 bicycle: 0.1277 motorcycle: 0.5836 truck: 0.8621 bus: 0.5904 person: 0.6377 bicyclist: 0.8285 motorcyclist: 0.0000 road: 0.9316 parking: 0.4455 sidewalk: 0.8057 other-ground: 0.0053 building: 0.9054 fence: 0.6064 vegetation: 0.8814 trunck: 0.6625 terrian: 0.7466 pole: 0.6486 traffic-sign: 0.4980 miou: 0.6175 acc: 0.9185 acc_cls: 0.6805