2023/03/08 11:33:31 - 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: 1588147245 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: 1588147245 deterministic: False diff_rank_seed: True Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2023/03/08 11:33:32 - 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='SPVCNNBackbone', in_channels=4, base_channels=32, enc_channels=[32, 64, 128, 256], dec_channels=[256, 128, 96, 96], num_stages=4, drop_ratio=0.3), decode_head=dict( type='MinkUNetHead', channels=96, num_classes=19, dropout_ratio=0, loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True), ignore_index=19), train_cfg=dict(), test_cfg=dict()) 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=1588147245, deterministic=False, diff_rank_seed=True) launcher = 'pytorch' work_dir = './work_dirs/spvcnn_w32_8xb2-15e_semantickitti' 2023/03/08 11:33:33 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. 2023/03/08 11:33:34 - 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, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.0.kernel - torch.Size([8, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.0.kernel - torch.Size([8, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.0.kernel - torch.Size([27, 32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.0.kernel - torch.Size([32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.0.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.0.kernel - torch.Size([8, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.0.kernel - torch.Size([27, 64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.0.kernel - torch.Size([64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.0.kernel - torch.Size([8, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.0.kernel - torch.Size([27, 128, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.0.kernel - torch.Size([128, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.0.kernel - torch.Size([8, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.0.kernel - torch.Size([27, 384, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.0.kernel - torch.Size([384, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.0.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.3.kernel - torch.Size([27, 256, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.0.kernel - torch.Size([8, 256, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.0.kernel - torch.Size([27, 192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.0.kernel - torch.Size([192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.0.kernel - torch.Size([8, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.0.kernel - torch.Size([27, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.0.kernel - torch.Size([128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.0.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.0.kernel - torch.Size([8, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.0.kernel - torch.Size([27, 128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.0.kernel - torch.Size([128, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.0.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.3.kernel - torch.Size([27, 96, 96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.weight - torch.Size([256, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.weight - torch.Size([128, 256]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.weight - torch.Size([96, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.weight - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.bias - torch.Size([96]): The value is the same before and after calling `init_weights` of MinkUNet decode_head.conv_seg.weight - torch.Size([19, 96]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/03/08 11:33:37 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/work_dirs/spvcnn_w32_8xb2-15e_semantickitti. 2023/03/08 11:34:31 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 9.5998e-02 eta: 5:21:46 time: 1.0792 data_time: 0.0091 memory: 2612 loss: 1.4896 loss_sem_seg: 1.4896 2023/03/08 11:35:23 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 1.9199e-01 eta: 5:15:33 time: 1.0434 data_time: 0.0032 memory: 2708 loss: 0.8619 loss_sem_seg: 0.8619 2023/03/08 11:36:13 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 2.3996e-01 eta: 5:08:02 time: 0.9942 data_time: 0.0032 memory: 2658 loss: 0.7168 loss_sem_seg: 0.7168 2023/03/08 11:37:03 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 2.3993e-01 eta: 5:05:38 time: 1.0182 data_time: 0.0031 memory: 2703 loss: 0.6384 loss_sem_seg: 0.6384 2023/03/08 11:37:53 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 2.3989e-01 eta: 5:02:23 time: 0.9931 data_time: 0.0033 memory: 2638 loss: 0.5664 loss_sem_seg: 0.5664 2023/03/08 11:38:45 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 2.3984e-01 eta: 5:01:45 time: 1.0301 data_time: 0.0031 memory: 2564 loss: 0.5341 loss_sem_seg: 0.5341 2023/03/08 11:39:36 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 2.3978e-01 eta: 5:01:17 time: 1.0358 data_time: 0.0032 memory: 2648 loss: 0.5204 loss_sem_seg: 0.5204 2023/03/08 11:40:27 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 2.3971e-01 eta: 5:00:04 time: 1.0181 data_time: 0.0031 memory: 2746 loss: 0.4816 loss_sem_seg: 0.4816 2023/03/08 11:41:19 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 2.3963e-01 eta: 4:59:08 time: 1.0241 data_time: 0.0032 memory: 2625 loss: 0.4398 loss_sem_seg: 0.4398 2023/03/08 11:42:08 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 2.3954e-01 eta: 4:57:09 time: 0.9871 data_time: 0.0031 memory: 2630 loss: 0.4273 loss_sem_seg: 0.4273 2023/03/08 11:42:59 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 2.3945e-01 eta: 4:56:19 time: 1.0230 data_time: 0.0033 memory: 2596 loss: 0.4422 loss_sem_seg: 0.4422 2023/03/08 11:43:49 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 2.3934e-01 eta: 4:54:51 time: 0.9972 data_time: 0.0033 memory: 2600 loss: 0.4188 loss_sem_seg: 0.4188 2023/03/08 11:44:41 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 2.3923e-01 eta: 4:54:28 time: 1.0410 data_time: 0.0032 memory: 2774 loss: 0.4179 loss_sem_seg: 0.4179 2023/03/08 11:45:32 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 2.3910e-01 eta: 4:53:44 time: 1.0277 data_time: 0.0032 memory: 2601 loss: 0.4010 loss_sem_seg: 0.4010 2023/03/08 11:46:24 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 2.3897e-01 eta: 4:53:02 time: 1.0301 data_time: 0.0032 memory: 2745 loss: 0.3937 loss_sem_seg: 0.3937 2023/03/08 11:47:16 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 2.3883e-01 eta: 4:52:24 time: 1.0356 data_time: 0.0031 memory: 2651 loss: 0.3771 loss_sem_seg: 0.3771 2023/03/08 11:48:07 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 2.3868e-01 eta: 4:51:31 time: 1.0218 data_time: 0.0031 memory: 2539 loss: 0.3609 loss_sem_seg: 0.3609 2023/03/08 11:48:57 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 2.3852e-01 eta: 4:50:26 time: 1.0086 data_time: 0.0031 memory: 2660 loss: 0.3557 loss_sem_seg: 0.3557 2023/03/08 11:49:48 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 2.3835e-01 eta: 4:49:29 time: 1.0167 data_time: 0.0032 memory: 2697 loss: 0.3441 loss_sem_seg: 0.3441 2023/03/08 11:50:37 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 11:50:37 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 2.3817e-01 eta: 4:48:02 time: 0.9794 data_time: 0.0031 memory: 2736 loss: 0.3535 loss_sem_seg: 0.3535 2023/03/08 11:51:17 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 2.3798e-01 eta: 4:44:20 time: 0.8082 data_time: 0.0030 memory: 2694 loss: 0.3486 loss_sem_seg: 0.3486 2023/03/08 11:51:49 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 2.3778e-01 eta: 4:38:39 time: 0.6303 data_time: 0.0030 memory: 2636 loss: 0.3511 loss_sem_seg: 0.3511 2023/03/08 11:52:18 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 2.3758e-01 eta: 4:32:44 time: 0.5737 data_time: 0.0029 memory: 2666 loss: 0.3352 loss_sem_seg: 0.3352 2023/03/08 11:52:44 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 11:52:44 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/03/08 11:52:59 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:02:02 time: 0.2664 data_time: 0.0052 memory: 2620 2023/03/08 11:53:12 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:01:48 time: 0.2622 data_time: 0.0040 memory: 744 2023/03/08 11:53:25 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:01:34 time: 0.2604 data_time: 0.0040 memory: 747 2023/03/08 11:53:35 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:01:17 time: 0.2116 data_time: 0.0043 memory: 737 2023/03/08 11:53:54 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:01:11 time: 0.3737 data_time: 0.0047 memory: 752 2023/03/08 11:54:17 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:01:03 time: 0.4545 data_time: 0.0045 memory: 713 2023/03/08 11:54:45 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:00:54 time: 0.5622 data_time: 0.0046 memory: 729 2023/03/08 11:55:11 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:00:39 time: 0.5190 data_time: 0.0044 memory: 731 2023/03/08 11:55:37 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:22 time: 0.5240 data_time: 0.0042 memory: 747 2023/03/08 11:56:03 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:03 time: 0.5268 data_time: 0.0042 memory: 735 2023/03/08 11:56:50 - 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 | 0.9199 | 0.0000 | 0.0982 | 0.3890 | 0.0784 | 0.1659 | 0.0589 | 0.0000 | 0.8653 | 0.1682 | 0.7127 | 0.0007 | 0.8707 | 0.5234 | 0.8614 | 0.5919 | 0.7319 | 0.5730 | 0.2064 | 0.4114 | 0.8873 | 0.4786 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 11:56:50 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.9199 bicycle: 0.0000 motorcycle: 0.0982 truck: 0.3890 bus: 0.0784 person: 0.1659 bicyclist: 0.0589 motorcyclist: 0.0000 road: 0.8653 parking: 0.1682 sidewalk: 0.7127 other-ground: 0.0007 building: 0.8707 fence: 0.5234 vegetation: 0.8614 trunck: 0.5919 terrian: 0.7319 pole: 0.5730 traffic-sign: 0.2064 miou: 0.4114 acc: 0.8873 acc_cls: 0.4786 2023/03/08 11:57:41 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 2.3716e-01 eta: 4:27:35 time: 1.0138 data_time: 0.0163 memory: 2701 loss: 0.3370 loss_sem_seg: 0.3370 2023/03/08 11:58:31 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 2.3693e-01 eta: 4:27:16 time: 1.0077 data_time: 0.0033 memory: 2614 loss: 0.3083 loss_sem_seg: 0.3083 2023/03/08 11:59:22 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 2.3669e-01 eta: 4:26:53 time: 1.0048 data_time: 0.0032 memory: 2585 loss: 0.3030 loss_sem_seg: 0.3030 2023/03/08 12:00:13 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 2.3644e-01 eta: 4:26:40 time: 1.0241 data_time: 0.0032 memory: 2544 loss: 0.3477 loss_sem_seg: 0.3477 2023/03/08 12:01:03 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 2.3618e-01 eta: 4:26:11 time: 1.0016 data_time: 0.0033 memory: 2699 loss: 0.3194 loss_sem_seg: 0.3194 2023/03/08 12:01:54 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 2.3591e-01 eta: 4:25:49 time: 1.0163 data_time: 0.0032 memory: 2681 loss: 0.3094 loss_sem_seg: 0.3094 2023/03/08 12:02:45 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 2.3563e-01 eta: 4:25:32 time: 1.0289 data_time: 0.0034 memory: 2538 loss: 0.3209 loss_sem_seg: 0.3209 2023/03/08 12:03:36 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 2.3535e-01 eta: 4:25:09 time: 1.0208 data_time: 0.0033 memory: 2581 loss: 0.3305 loss_sem_seg: 0.3305 2023/03/08 12:04:28 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 2.3506e-01 eta: 4:24:47 time: 1.0287 data_time: 0.0033 memory: 2591 loss: 0.2873 loss_sem_seg: 0.2873 2023/03/08 12:05:19 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 2.3475e-01 eta: 4:24:20 time: 1.0210 data_time: 0.0032 memory: 2690 loss: 0.2993 loss_sem_seg: 0.2993 2023/03/08 12:06:10 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 2.3444e-01 eta: 4:23:54 time: 1.0243 data_time: 0.0034 memory: 2661 loss: 0.3416 loss_sem_seg: 0.3416 2023/03/08 12:07:01 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 2.3412e-01 eta: 4:23:21 time: 1.0126 data_time: 0.0032 memory: 2618 loss: 0.2858 loss_sem_seg: 0.2858 2023/03/08 12:07:52 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 2.3379e-01 eta: 4:22:53 time: 1.0288 data_time: 0.0031 memory: 2585 loss: 0.2918 loss_sem_seg: 0.2918 2023/03/08 12:08:42 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 2.3345e-01 eta: 4:22:13 time: 0.9996 data_time: 0.0031 memory: 2609 loss: 0.3066 loss_sem_seg: 0.3066 2023/03/08 12:09:33 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 2.3311e-01 eta: 4:21:43 time: 1.0269 data_time: 0.0031 memory: 2693 loss: 0.3149 loss_sem_seg: 0.3149 2023/03/08 12:10:25 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 2.3275e-01 eta: 4:21:10 time: 1.0226 data_time: 0.0032 memory: 2590 loss: 0.2783 loss_sem_seg: 0.2783 2023/03/08 12:10:29 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 12:11:15 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 2.3239e-01 eta: 4:20:27 time: 0.9990 data_time: 0.0034 memory: 2638 loss: 0.2735 loss_sem_seg: 0.2735 2023/03/08 12:12:06 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 2.3201e-01 eta: 4:19:56 time: 1.0316 data_time: 0.0033 memory: 2616 loss: 0.3079 loss_sem_seg: 0.3079 2023/03/08 12:12:53 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 2.3163e-01 eta: 4:18:52 time: 0.9442 data_time: 0.0034 memory: 2675 loss: 0.2743 loss_sem_seg: 0.2743 2023/03/08 12:13:38 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 2.3124e-01 eta: 4:17:29 time: 0.8886 data_time: 0.0033 memory: 2698 loss: 0.2982 loss_sem_seg: 0.2982 2023/03/08 12:14:20 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 2.3085e-01 eta: 4:15:55 time: 0.8514 data_time: 0.0032 memory: 2533 loss: 0.2623 loss_sem_seg: 0.2623 2023/03/08 12:14:53 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 2.3044e-01 eta: 4:13:16 time: 0.6576 data_time: 0.0031 memory: 2597 loss: 0.2892 loss_sem_seg: 0.2892 2023/03/08 12:15:23 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 2.3002e-01 eta: 4:10:21 time: 0.5894 data_time: 0.0031 memory: 2674 loss: 0.2887 loss_sem_seg: 0.2887 2023/03/08 12:15:50 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 12:15:50 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/03/08 12:16:05 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:02:02 time: 0.2672 data_time: 0.0068 memory: 2769 2023/03/08 12:16:15 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:01:36 time: 0.2048 data_time: 0.0044 memory: 744 2023/03/08 12:16:29 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:01:30 time: 0.2866 data_time: 0.0046 memory: 747 2023/03/08 12:16:46 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:01:24 time: 0.3371 data_time: 0.0041 memory: 737 2023/03/08 12:17:05 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:01:16 time: 0.3863 data_time: 0.0040 memory: 752 2023/03/08 12:17:28 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:01:07 time: 0.4508 data_time: 0.0044 memory: 713 2023/03/08 12:17:57 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:00:57 time: 0.5895 data_time: 0.0045 memory: 729 2023/03/08 12:18:24 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:00:41 time: 0.5297 data_time: 0.0044 memory: 731 2023/03/08 12:18:50 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:23 time: 0.5262 data_time: 0.0044 memory: 747 2023/03/08 12:19:16 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:03 time: 0.5230 data_time: 0.0043 memory: 735 2023/03/08 12:20:02 - 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 | 0.9415 | 0.0014 | 0.2524 | 0.4785 | 0.2331 | 0.4056 | 0.2819 | 0.0000 | 0.8974 | 0.3040 | 0.7601 | 0.0011 | 0.8696 | 0.5128 | 0.8550 | 0.6275 | 0.7224 | 0.5990 | 0.3476 | 0.4785 | 0.8960 | 0.5427 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 12:20:02 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.9415 bicycle: 0.0014 motorcycle: 0.2524 truck: 0.4785 bus: 0.2331 person: 0.4056 bicyclist: 0.2819 motorcyclist: 0.0000 road: 0.8974 parking: 0.3040 sidewalk: 0.7601 other-ground: 0.0011 building: 0.8696 fence: 0.5128 vegetation: 0.8550 trunck: 0.6275 terrian: 0.7224 pole: 0.5990 traffic-sign: 0.3476 miou: 0.4785 acc: 0.8960 acc_cls: 0.5427 2023/03/08 12:20:54 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 2.2920e-01 eta: 4:07:20 time: 1.0329 data_time: 0.0174 memory: 2825 loss: 0.2811 loss_sem_seg: 0.2811 2023/03/08 12:21:44 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 2.2876e-01 eta: 4:06:44 time: 0.9951 data_time: 0.0037 memory: 2677 loss: 0.2622 loss_sem_seg: 0.2622 2023/03/08 12:22:35 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 2.2832e-01 eta: 4:06:18 time: 1.0314 data_time: 0.0041 memory: 2663 loss: 0.2753 loss_sem_seg: 0.2753 2023/03/08 12:23:26 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 2.2786e-01 eta: 4:05:47 time: 1.0163 data_time: 0.0038 memory: 2576 loss: 0.2631 loss_sem_seg: 0.2631 2023/03/08 12:24:17 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 2.2739e-01 eta: 4:05:12 time: 1.0057 data_time: 0.0039 memory: 2651 loss: 0.2699 loss_sem_seg: 0.2699 2023/03/08 12:25:07 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 2.2692e-01 eta: 4:04:35 time: 1.0023 data_time: 0.0038 memory: 2668 loss: 0.2578 loss_sem_seg: 0.2578 2023/03/08 12:25:58 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 2.2644e-01 eta: 4:04:05 time: 1.0258 data_time: 0.0036 memory: 2695 loss: 0.2781 loss_sem_seg: 0.2781 2023/03/08 12:26:48 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 2.2595e-01 eta: 4:03:27 time: 1.0035 data_time: 0.0037 memory: 2656 loss: 0.2628 loss_sem_seg: 0.2628 2023/03/08 12:27:40 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 2.2545e-01 eta: 4:02:59 time: 1.0394 data_time: 0.0038 memory: 2553 loss: 0.2547 loss_sem_seg: 0.2547 2023/03/08 12:28:30 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 2.2495e-01 eta: 4:02:21 time: 1.0039 data_time: 0.0037 memory: 2671 loss: 0.2590 loss_sem_seg: 0.2590 2023/03/08 12:29:22 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 2.2443e-01 eta: 4:01:52 time: 1.0431 data_time: 0.0037 memory: 2635 loss: 0.2622 loss_sem_seg: 0.2622 2023/03/08 12:30:13 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 2.2391e-01 eta: 4:01:13 time: 1.0055 data_time: 0.0038 memory: 2669 loss: 0.2512 loss_sem_seg: 0.2512 2023/03/08 12:30:21 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 12:31:04 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 2.2338e-01 eta: 4:00:37 time: 1.0207 data_time: 0.0040 memory: 2646 loss: 0.2447 loss_sem_seg: 0.2447 2023/03/08 12:31:56 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 2.2285e-01 eta: 4:00:05 time: 1.0348 data_time: 0.0036 memory: 2670 loss: 0.2608 loss_sem_seg: 0.2608 2023/03/08 12:32:47 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 2.2230e-01 eta: 3:59:31 time: 1.0322 data_time: 0.0037 memory: 2593 loss: 0.2783 loss_sem_seg: 0.2783 2023/03/08 12:33:39 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 2.2175e-01 eta: 3:58:57 time: 1.0357 data_time: 0.0038 memory: 2718 loss: 0.2555 loss_sem_seg: 0.2555 2023/03/08 12:34:31 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 2.2119e-01 eta: 3:58:23 time: 1.0358 data_time: 0.0038 memory: 2665 loss: 0.2406 loss_sem_seg: 0.2406 2023/03/08 12:35:18 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 2.2062e-01 eta: 3:57:27 time: 0.9382 data_time: 0.0038 memory: 2723 loss: 0.2483 loss_sem_seg: 0.2483 2023/03/08 12:36:03 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 2.2004e-01 eta: 3:56:25 time: 0.9155 data_time: 0.0038 memory: 2799 loss: 0.2570 loss_sem_seg: 0.2570 2023/03/08 12:36:49 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 2.1946e-01 eta: 3:55:26 time: 0.9195 data_time: 0.0036 memory: 2598 loss: 0.2577 loss_sem_seg: 0.2577 2023/03/08 12:37:34 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 2.1887e-01 eta: 3:54:18 time: 0.8823 data_time: 0.0036 memory: 2585 loss: 0.2599 loss_sem_seg: 0.2599 2023/03/08 12:38:09 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 2.1827e-01 eta: 3:52:37 time: 0.7137 data_time: 0.0035 memory: 2633 loss: 0.2511 loss_sem_seg: 0.2511 2023/03/08 12:38:39 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 2.1766e-01 eta: 3:50:33 time: 0.5954 data_time: 0.0035 memory: 2614 loss: 0.2370 loss_sem_seg: 0.2370 2023/03/08 12:39:15 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 12:39:15 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/08 12:39:34 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:02:39 time: 0.3475 data_time: 0.0079 memory: 2683 2023/03/08 12:39:51 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:02:19 time: 0.3358 data_time: 0.0048 memory: 744 2023/03/08 12:40:08 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:02:02 time: 0.3375 data_time: 0.0048 memory: 747 2023/03/08 12:40:25 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:01:45 time: 0.3384 data_time: 0.0046 memory: 737 2023/03/08 12:40:45 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:01:30 time: 0.3953 data_time: 0.0051 memory: 752 2023/03/08 12:41:03 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:01:13 time: 0.3669 data_time: 0.0053 memory: 713 2023/03/08 12:41:31 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:01:00 time: 0.5515 data_time: 0.0055 memory: 729 2023/03/08 12:41:56 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:00:43 time: 0.5184 data_time: 0.0051 memory: 731 2023/03/08 12:42:23 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:24 time: 0.5363 data_time: 0.0057 memory: 747 2023/03/08 12:42:49 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:03 time: 0.5178 data_time: 0.0057 memory: 735 2023/03/08 12:43:24 - 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 | 0.9443 | 0.0127 | 0.4224 | 0.3663 | 0.3400 | 0.5064 | 0.6923 | 0.0000 | 0.9049 | 0.3141 | 0.7789 | 0.0009 | 0.8921 | 0.5689 | 0.8674 | 0.6445 | 0.7129 | 0.6167 | 0.3837 | 0.5247 | 0.9030 | 0.6162 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 12:43:24 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9443 bicycle: 0.0127 motorcycle: 0.4224 truck: 0.3663 bus: 0.3400 person: 0.5064 bicyclist: 0.6923 motorcyclist: 0.0000 road: 0.9049 parking: 0.3141 sidewalk: 0.7789 other-ground: 0.0009 building: 0.8921 fence: 0.5689 vegetation: 0.8674 trunck: 0.6445 terrian: 0.7129 pole: 0.6167 traffic-sign: 0.3837 miou: 0.5247 acc: 0.9030 acc_cls: 0.6162 2023/03/08 12:44:16 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 2.1647e-01 eta: 3:48:44 time: 1.0378 data_time: 0.0183 memory: 2727 loss: 0.2411 loss_sem_seg: 0.2411 2023/03/08 12:45:07 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 2.1585e-01 eta: 3:48:09 time: 1.0249 data_time: 0.0041 memory: 2618 loss: 0.2539 loss_sem_seg: 0.2539 2023/03/08 12:45:59 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 2.1521e-01 eta: 3:47:35 time: 1.0319 data_time: 0.0041 memory: 2693 loss: 0.2543 loss_sem_seg: 0.2543 2023/03/08 12:46:50 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 2.1457e-01 eta: 3:46:59 time: 1.0259 data_time: 0.0042 memory: 2699 loss: 0.2504 loss_sem_seg: 0.2504 2023/03/08 12:47:41 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 2.1392e-01 eta: 3:46:20 time: 1.0151 data_time: 0.0041 memory: 2615 loss: 0.2428 loss_sem_seg: 0.2428 2023/03/08 12:48:31 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 2.1326e-01 eta: 3:45:39 time: 1.0000 data_time: 0.0042 memory: 2579 loss: 0.2430 loss_sem_seg: 0.2430 2023/03/08 12:49:21 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 2.1259e-01 eta: 3:44:56 time: 0.9949 data_time: 0.0040 memory: 2784 loss: 0.2566 loss_sem_seg: 0.2566 2023/03/08 12:50:11 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 2.1192e-01 eta: 3:44:17 time: 1.0139 data_time: 0.0039 memory: 2693 loss: 0.2493 loss_sem_seg: 0.2493 2023/03/08 12:50:23 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 12:51:02 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 2.1124e-01 eta: 3:43:35 time: 1.0031 data_time: 0.0040 memory: 2778 loss: 0.2544 loss_sem_seg: 0.2544 2023/03/08 12:51:53 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 2.1056e-01 eta: 3:42:56 time: 1.0195 data_time: 0.0042 memory: 2711 loss: 0.2449 loss_sem_seg: 0.2449 2023/03/08 12:52:43 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 2.0986e-01 eta: 3:42:16 time: 1.0154 data_time: 0.0039 memory: 2682 loss: 0.2562 loss_sem_seg: 0.2562 2023/03/08 12:53:34 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 2.0916e-01 eta: 3:41:34 time: 1.0041 data_time: 0.0040 memory: 2641 loss: 0.2404 loss_sem_seg: 0.2404 2023/03/08 12:54:24 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 2.0846e-01 eta: 3:40:53 time: 1.0097 data_time: 0.0041 memory: 2738 loss: 0.2365 loss_sem_seg: 0.2365 2023/03/08 12:55:15 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 2.0774e-01 eta: 3:40:11 time: 1.0114 data_time: 0.0040 memory: 2588 loss: 0.2311 loss_sem_seg: 0.2311 2023/03/08 12:56:05 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 2.0702e-01 eta: 3:39:30 time: 1.0151 data_time: 0.0040 memory: 2694 loss: 0.2450 loss_sem_seg: 0.2450 2023/03/08 12:56:55 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 2.0630e-01 eta: 3:38:46 time: 0.9971 data_time: 0.0039 memory: 2684 loss: 0.2405 loss_sem_seg: 0.2405 2023/03/08 12:57:40 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 2.0556e-01 eta: 3:37:46 time: 0.8916 data_time: 0.0039 memory: 2543 loss: 0.2424 loss_sem_seg: 0.2424 2023/03/08 12:58:25 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 2.0482e-01 eta: 3:36:48 time: 0.9058 data_time: 0.0039 memory: 2611 loss: 0.2351 loss_sem_seg: 0.2351 2023/03/08 12:59:09 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 2.0408e-01 eta: 3:35:47 time: 0.8777 data_time: 0.0039 memory: 2697 loss: 0.2437 loss_sem_seg: 0.2437 2023/03/08 12:59:53 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 2.0333e-01 eta: 3:34:47 time: 0.8881 data_time: 0.0035 memory: 2648 loss: 0.2255 loss_sem_seg: 0.2255 2023/03/08 13:00:37 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 2.0257e-01 eta: 3:33:46 time: 0.8727 data_time: 0.0036 memory: 2689 loss: 0.2292 loss_sem_seg: 0.2292 2023/03/08 13:01:17 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 2.0180e-01 eta: 3:32:33 time: 0.7940 data_time: 0.0039 memory: 2600 loss: 0.2331 loss_sem_seg: 0.2331 2023/03/08 13:01:57 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 2.0103e-01 eta: 3:31:23 time: 0.8005 data_time: 0.0042 memory: 2632 loss: 0.2348 loss_sem_seg: 0.2348 2023/03/08 13:02:29 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:02:29 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/03/08 13:02:48 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:02:34 time: 0.3356 data_time: 0.0077 memory: 2550 2023/03/08 13:03:04 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:02:16 time: 0.3337 data_time: 0.0049 memory: 744 2023/03/08 13:03:21 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:02:00 time: 0.3344 data_time: 0.0048 memory: 747 2023/03/08 13:03:38 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:01:43 time: 0.3387 data_time: 0.0048 memory: 737 2023/03/08 13:03:58 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:01:29 time: 0.3912 data_time: 0.0048 memory: 752 2023/03/08 13:04:17 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:01:13 time: 0.3778 data_time: 0.0049 memory: 713 2023/03/08 13:04:43 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:01:00 time: 0.5359 data_time: 0.0046 memory: 729 2023/03/08 13:05:09 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:00:43 time: 0.5156 data_time: 0.0048 memory: 731 2023/03/08 13:05:36 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:24 time: 0.5346 data_time: 0.0044 memory: 747 2023/03/08 13:06:01 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:03 time: 0.5123 data_time: 0.0044 memory: 735 2023/03/08 13:06:32 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9398 | 0.0377 | 0.4533 | 0.5404 | 0.2730 | 0.5091 | 0.4673 | 0.0000 | 0.9217 | 0.3627 | 0.7928 | 0.0036 | 0.8930 | 0.5715 | 0.8696 | 0.5728 | 0.7342 | 0.6150 | 0.4598 | 0.5272 | 0.9077 | 0.6129 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 13:06:32 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9398 bicycle: 0.0377 motorcycle: 0.4533 truck: 0.5404 bus: 0.2730 person: 0.5091 bicyclist: 0.4673 motorcyclist: 0.0000 road: 0.9217 parking: 0.3627 sidewalk: 0.7928 other-ground: 0.0036 building: 0.8930 fence: 0.5715 vegetation: 0.8696 trunck: 0.5728 terrian: 0.7342 pole: 0.6150 traffic-sign: 0.4598 miou: 0.5272 acc: 0.9077 acc_cls: 0.6129 2023/03/08 13:07:23 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 1.9953e-01 eta: 3:29:29 time: 1.0381 data_time: 0.0180 memory: 2613 loss: 0.2350 loss_sem_seg: 0.2350 2023/03/08 13:08:14 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 1.9874e-01 eta: 3:28:48 time: 1.0094 data_time: 0.0039 memory: 2680 loss: 0.2276 loss_sem_seg: 0.2276 2023/03/08 13:09:05 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 1.9794e-01 eta: 3:28:08 time: 1.0202 data_time: 0.0038 memory: 2712 loss: 0.2183 loss_sem_seg: 0.2183 2023/03/08 13:09:56 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 1.9714e-01 eta: 3:27:28 time: 1.0220 data_time: 0.0042 memory: 2689 loss: 0.2255 loss_sem_seg: 0.2255 2023/03/08 13:10:12 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:10:46 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 1.9633e-01 eta: 3:26:44 time: 0.9968 data_time: 0.0040 memory: 2623 loss: 0.2306 loss_sem_seg: 0.2306 2023/03/08 13:11:38 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 1.9552e-01 eta: 3:26:06 time: 1.0407 data_time: 0.0041 memory: 2590 loss: 0.2289 loss_sem_seg: 0.2289 2023/03/08 13:12:29 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 1.9470e-01 eta: 3:25:26 time: 1.0262 data_time: 0.0039 memory: 2551 loss: 0.2194 loss_sem_seg: 0.2194 2023/03/08 13:13:21 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 1.9388e-01 eta: 3:24:47 time: 1.0372 data_time: 0.0040 memory: 2615 loss: 0.2442 loss_sem_seg: 0.2442 2023/03/08 13:14:12 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 1.9304e-01 eta: 3:24:05 time: 1.0129 data_time: 0.0042 memory: 2702 loss: 0.2304 loss_sem_seg: 0.2304 2023/03/08 13:15:03 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 1.9221e-01 eta: 3:23:24 time: 1.0209 data_time: 0.0041 memory: 2630 loss: 0.2242 loss_sem_seg: 0.2242 2023/03/08 13:15:54 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 1.9137e-01 eta: 3:22:43 time: 1.0225 data_time: 0.0041 memory: 2581 loss: 0.2306 loss_sem_seg: 0.2306 2023/03/08 13:16:44 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 1.9052e-01 eta: 3:21:59 time: 1.0080 data_time: 0.0039 memory: 2640 loss: 0.2387 loss_sem_seg: 0.2387 2023/03/08 13:17:35 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 1.8967e-01 eta: 3:21:16 time: 1.0037 data_time: 0.0041 memory: 2629 loss: 0.2160 loss_sem_seg: 0.2160 2023/03/08 13:18:26 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 1.8881e-01 eta: 3:20:35 time: 1.0346 data_time: 0.0041 memory: 2734 loss: 0.2169 loss_sem_seg: 0.2169 2023/03/08 13:19:14 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 1.8794e-01 eta: 3:19:45 time: 0.9467 data_time: 0.0038 memory: 2772 loss: 0.2153 loss_sem_seg: 0.2153 2023/03/08 13:19:58 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 1.8708e-01 eta: 3:18:47 time: 0.8863 data_time: 0.0039 memory: 2697 loss: 0.2089 loss_sem_seg: 0.2089 2023/03/08 13:20:42 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 1.8620e-01 eta: 3:17:50 time: 0.8853 data_time: 0.0041 memory: 2773 loss: 0.2242 loss_sem_seg: 0.2242 2023/03/08 13:21:27 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 1.8532e-01 eta: 3:16:55 time: 0.8933 data_time: 0.0040 memory: 2562 loss: 0.2283 loss_sem_seg: 0.2283 2023/03/08 13:22:11 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 1.8444e-01 eta: 3:15:57 time: 0.8798 data_time: 0.0036 memory: 2559 loss: 0.2088 loss_sem_seg: 0.2088 2023/03/08 13:22:56 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 1.8355e-01 eta: 3:15:02 time: 0.8952 data_time: 0.0038 memory: 2691 loss: 0.2012 loss_sem_seg: 0.2012 2023/03/08 13:23:49 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 1.8266e-01 eta: 3:14:24 time: 1.0636 data_time: 0.0039 memory: 2567 loss: 0.2149 loss_sem_seg: 0.2149 2023/03/08 13:24:34 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 1.8176e-01 eta: 3:13:29 time: 0.8979 data_time: 0.0040 memory: 2579 loss: 0.2089 loss_sem_seg: 0.2089 2023/03/08 13:25:10 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 1.8086e-01 eta: 3:12:18 time: 0.7362 data_time: 0.0041 memory: 2623 loss: 0.2316 loss_sem_seg: 0.2316 2023/03/08 13:25:43 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:25:43 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/03/08 13:26:02 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:02:36 time: 0.3412 data_time: 0.0085 memory: 2569 2023/03/08 13:26:19 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:02:17 time: 0.3308 data_time: 0.0054 memory: 744 2023/03/08 13:26:35 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:01:59 time: 0.3250 data_time: 0.0053 memory: 747 2023/03/08 13:26:52 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:01:43 time: 0.3401 data_time: 0.0052 memory: 737 2023/03/08 13:27:09 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:01:26 time: 0.3323 data_time: 0.0048 memory: 752 2023/03/08 13:27:26 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:01:10 time: 0.3499 data_time: 0.0048 memory: 713 2023/03/08 13:27:45 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:00:54 time: 0.3876 data_time: 0.0051 memory: 729 2023/03/08 13:28:12 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:00:40 time: 0.5397 data_time: 0.0050 memory: 731 2023/03/08 13:28:38 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:22 time: 0.5201 data_time: 0.0056 memory: 747 2023/03/08 13:29:04 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:03 time: 0.5192 data_time: 0.0050 memory: 735 2023/03/08 13:29:41 - 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 | 0.9493 | 0.0354 | 0.4208 | 0.4791 | 0.3398 | 0.4874 | 0.6872 | 0.0000 | 0.9209 | 0.4010 | 0.7895 | 0.0066 | 0.8908 | 0.5703 | 0.8865 | 0.6176 | 0.7738 | 0.6182 | 0.4249 | 0.5421 | 0.9147 | 0.6125 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 13:29:41 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9493 bicycle: 0.0354 motorcycle: 0.4208 truck: 0.4791 bus: 0.3398 person: 0.4874 bicyclist: 0.6872 motorcyclist: 0.0000 road: 0.9209 parking: 0.4010 sidewalk: 0.7895 other-ground: 0.0066 building: 0.8908 fence: 0.5703 vegetation: 0.8865 trunck: 0.6176 terrian: 0.7738 pole: 0.6182 traffic-sign: 0.4249 miou: 0.5421 acc: 0.9147 acc_cls: 0.6125 2023/03/08 13:30:03 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:30:34 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 1.7911e-01 eta: 3:10:31 time: 1.0420 data_time: 0.0186 memory: 2777 loss: 0.2126 loss_sem_seg: 0.2126 2023/03/08 13:31:25 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 1.7819e-01 eta: 3:09:49 time: 1.0202 data_time: 0.0041 memory: 2634 loss: 0.2083 loss_sem_seg: 0.2083 2023/03/08 13:32:16 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 1.7727e-01 eta: 3:09:08 time: 1.0338 data_time: 0.0042 memory: 2673 loss: 0.2245 loss_sem_seg: 0.2245 2023/03/08 13:33:07 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 1.7635e-01 eta: 3:08:26 time: 1.0198 data_time: 0.0040 memory: 2680 loss: 0.2253 loss_sem_seg: 0.2253 2023/03/08 13:33:58 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 1.7542e-01 eta: 3:07:43 time: 1.0139 data_time: 0.0040 memory: 2637 loss: 0.1976 loss_sem_seg: 0.1976 2023/03/08 13:34:49 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 1.7448e-01 eta: 3:07:01 time: 1.0289 data_time: 0.0042 memory: 2771 loss: 0.2193 loss_sem_seg: 0.2193 2023/03/08 13:35:40 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 1.7354e-01 eta: 3:06:17 time: 1.0146 data_time: 0.0039 memory: 2641 loss: 0.2215 loss_sem_seg: 0.2215 2023/03/08 13:36:31 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 1.7260e-01 eta: 3:05:35 time: 1.0250 data_time: 0.0041 memory: 2670 loss: 0.2130 loss_sem_seg: 0.2130 2023/03/08 13:37:23 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 1.7165e-01 eta: 3:04:52 time: 1.0256 data_time: 0.0041 memory: 2713 loss: 0.2179 loss_sem_seg: 0.2179 2023/03/08 13:38:14 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 1.7070e-01 eta: 3:04:10 time: 1.0340 data_time: 0.0041 memory: 2651 loss: 0.2082 loss_sem_seg: 0.2082 2023/03/08 13:39:06 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 1.6975e-01 eta: 3:03:27 time: 1.0251 data_time: 0.0042 memory: 2676 loss: 0.2150 loss_sem_seg: 0.2150 2023/03/08 13:39:57 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 1.6879e-01 eta: 3:02:45 time: 1.0273 data_time: 0.0039 memory: 2630 loss: 0.2039 loss_sem_seg: 0.2039 2023/03/08 13:40:49 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 1.6783e-01 eta: 3:02:03 time: 1.0444 data_time: 0.0041 memory: 2630 loss: 0.2153 loss_sem_seg: 0.2153 2023/03/08 13:41:34 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 1.6686e-01 eta: 3:01:09 time: 0.9026 data_time: 0.0041 memory: 2642 loss: 0.2146 loss_sem_seg: 0.2146 2023/03/08 13:42:19 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 1.6590e-01 eta: 3:00:15 time: 0.8886 data_time: 0.0038 memory: 2666 loss: 0.1933 loss_sem_seg: 0.1933 2023/03/08 13:43:03 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 1.6492e-01 eta: 2:59:21 time: 0.8938 data_time: 0.0040 memory: 2645 loss: 0.2035 loss_sem_seg: 0.2035 2023/03/08 13:43:48 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 1.6395e-01 eta: 2:58:27 time: 0.8948 data_time: 0.0039 memory: 2633 loss: 0.2153 loss_sem_seg: 0.2153 2023/03/08 13:44:33 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 1.6297e-01 eta: 2:57:34 time: 0.9007 data_time: 0.0036 memory: 2685 loss: 0.2187 loss_sem_seg: 0.2187 2023/03/08 13:45:19 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 1.6199e-01 eta: 2:56:41 time: 0.9080 data_time: 0.0034 memory: 2548 loss: 0.2094 loss_sem_seg: 0.2094 2023/03/08 13:46:15 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 1.6100e-01 eta: 2:56:05 time: 1.1221 data_time: 0.0038 memory: 2664 loss: 0.1944 loss_sem_seg: 0.1944 2023/03/08 13:46:35 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:47:06 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 1.6001e-01 eta: 2:55:21 time: 1.0185 data_time: 0.0040 memory: 2625 loss: 0.2141 loss_sem_seg: 0.2141 2023/03/08 13:47:51 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 1.5902e-01 eta: 2:54:28 time: 0.9018 data_time: 0.0039 memory: 2657 loss: 0.2097 loss_sem_seg: 0.2097 2023/03/08 13:48:29 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 1.5802e-01 eta: 2:53:25 time: 0.7596 data_time: 0.0038 memory: 2606 loss: 0.2039 loss_sem_seg: 0.2039 2023/03/08 13:49:00 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 13:49:00 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/08 13:49:19 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:02:40 time: 0.3489 data_time: 0.0075 memory: 2595 2023/03/08 13:49:36 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:02:20 time: 0.3393 data_time: 0.0050 memory: 744 2023/03/08 13:49:53 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:02:02 time: 0.3331 data_time: 0.0048 memory: 747 2023/03/08 13:50:10 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:01:45 time: 0.3410 data_time: 0.0048 memory: 737 2023/03/08 13:50:27 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:01:28 time: 0.3432 data_time: 0.0046 memory: 752 2023/03/08 13:50:44 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:01:11 time: 0.3380 data_time: 0.0047 memory: 713 2023/03/08 13:51:02 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:00:54 time: 0.3679 data_time: 0.0049 memory: 729 2023/03/08 13:51:26 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:00:39 time: 0.4822 data_time: 0.0046 memory: 731 2023/03/08 13:51:52 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:22 time: 0.5167 data_time: 0.0048 memory: 747 2023/03/08 13:52:19 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:03 time: 0.5278 data_time: 0.0046 memory: 735 2023/03/08 13:52:54 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9534 | 0.1587 | 0.5085 | 0.5401 | 0.3881 | 0.5910 | 0.6979 | 0.0000 | 0.9173 | 0.4540 | 0.7853 | 0.0097 | 0.8938 | 0.5770 | 0.8780 | 0.6420 | 0.7374 | 0.6230 | 0.4413 | 0.5682 | 0.9107 | 0.6521 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 13:52:54 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9534 bicycle: 0.1587 motorcycle: 0.5085 truck: 0.5401 bus: 0.3881 person: 0.5910 bicyclist: 0.6979 motorcyclist: 0.0000 road: 0.9173 parking: 0.4540 sidewalk: 0.7853 other-ground: 0.0097 building: 0.8938 fence: 0.5770 vegetation: 0.8780 trunck: 0.6420 terrian: 0.7374 pole: 0.6230 traffic-sign: 0.4413 miou: 0.5682 acc: 0.9107 acc_cls: 0.6521 2023/03/08 13:53:45 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 1.5610e-01 eta: 2:51:37 time: 1.0224 data_time: 0.0186 memory: 2755 loss: 0.1957 loss_sem_seg: 0.1957 2023/03/08 13:54:34 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 1.5510e-01 eta: 2:50:51 time: 0.9826 data_time: 0.0037 memory: 2619 loss: 0.2078 loss_sem_seg: 0.2078 2023/03/08 13:55:25 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 1.5410e-01 eta: 2:50:07 time: 1.0130 data_time: 0.0037 memory: 2643 loss: 0.2145 loss_sem_seg: 0.2145 2023/03/08 13:56:16 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 1.5309e-01 eta: 2:49:23 time: 1.0194 data_time: 0.0039 memory: 2678 loss: 0.2018 loss_sem_seg: 0.2018 2023/03/08 13:57:06 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 1.5208e-01 eta: 2:48:38 time: 1.0163 data_time: 0.0041 memory: 2602 loss: 0.2133 loss_sem_seg: 0.2133 2023/03/08 13:57:57 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 1.5106e-01 eta: 2:47:54 time: 1.0198 data_time: 0.0039 memory: 2676 loss: 0.2191 loss_sem_seg: 0.2191 2023/03/08 13:58:48 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 1.5005e-01 eta: 2:47:09 time: 1.0100 data_time: 0.0038 memory: 2593 loss: 0.2050 loss_sem_seg: 0.2050 2023/03/08 13:59:40 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 1.4903e-01 eta: 2:46:26 time: 1.0324 data_time: 0.0039 memory: 2606 loss: 0.2091 loss_sem_seg: 0.2091 2023/03/08 14:00:31 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 1.4801e-01 eta: 2:45:42 time: 1.0320 data_time: 0.0039 memory: 2564 loss: 0.2052 loss_sem_seg: 0.2052 2023/03/08 14:01:22 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 1.4698e-01 eta: 2:44:57 time: 1.0126 data_time: 0.0039 memory: 2621 loss: 0.1984 loss_sem_seg: 0.1984 2023/03/08 14:02:13 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 1.4596e-01 eta: 2:44:13 time: 1.0161 data_time: 0.0039 memory: 2596 loss: 0.1950 loss_sem_seg: 0.1950 2023/03/08 14:03:00 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 1.4493e-01 eta: 2:43:24 time: 0.9545 data_time: 0.0042 memory: 2587 loss: 0.1851 loss_sem_seg: 0.1851 2023/03/08 14:03:45 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 1.4390e-01 eta: 2:42:31 time: 0.8969 data_time: 0.0040 memory: 2683 loss: 0.1987 loss_sem_seg: 0.1987 2023/03/08 14:04:30 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 1.4287e-01 eta: 2:41:38 time: 0.8945 data_time: 0.0038 memory: 2621 loss: 0.2021 loss_sem_seg: 0.2021 2023/03/08 14:05:14 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 1.4184e-01 eta: 2:40:46 time: 0.8883 data_time: 0.0039 memory: 2575 loss: 0.2025 loss_sem_seg: 0.2025 2023/03/08 14:05:59 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 1.4081e-01 eta: 2:39:53 time: 0.8885 data_time: 0.0036 memory: 2558 loss: 0.1864 loss_sem_seg: 0.1864 2023/03/08 14:06:20 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:06:43 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 1.3977e-01 eta: 2:39:00 time: 0.8872 data_time: 0.0038 memory: 2609 loss: 0.1873 loss_sem_seg: 0.1873 2023/03/08 14:07:35 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 1.3873e-01 eta: 2:38:16 time: 1.0378 data_time: 0.0036 memory: 2565 loss: 0.1884 loss_sem_seg: 0.1884 2023/03/08 14:08:26 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 1.3770e-01 eta: 2:37:32 time: 1.0246 data_time: 0.0038 memory: 2700 loss: 0.1852 loss_sem_seg: 0.1852 2023/03/08 14:09:18 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 1.3666e-01 eta: 2:36:48 time: 1.0407 data_time: 0.0041 memory: 2629 loss: 0.1983 loss_sem_seg: 0.1983 2023/03/08 14:10:10 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 1.3562e-01 eta: 2:36:04 time: 1.0307 data_time: 0.0040 memory: 2642 loss: 0.2010 loss_sem_seg: 0.2010 2023/03/08 14:10:58 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 1.3457e-01 eta: 2:35:16 time: 0.9566 data_time: 0.0039 memory: 2624 loss: 0.2140 loss_sem_seg: 0.2140 2023/03/08 14:11:38 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 1.3353e-01 eta: 2:34:18 time: 0.8002 data_time: 0.0039 memory: 2601 loss: 0.1848 loss_sem_seg: 0.1848 2023/03/08 14:12:11 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:12:11 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/03/08 14:12:30 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:02:39 time: 0.3478 data_time: 0.0081 memory: 2643 2023/03/08 14:12:46 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:02:19 time: 0.3365 data_time: 0.0048 memory: 744 2023/03/08 14:13:03 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:02:01 time: 0.3339 data_time: 0.0053 memory: 747 2023/03/08 14:13:20 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:01:44 time: 0.3375 data_time: 0.0052 memory: 737 2023/03/08 14:13:37 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:01:27 time: 0.3426 data_time: 0.0050 memory: 752 2023/03/08 14:13:54 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:01:10 time: 0.3314 data_time: 0.0049 memory: 713 2023/03/08 14:14:12 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:00:54 time: 0.3692 data_time: 0.0049 memory: 729 2023/03/08 14:14:33 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:00:38 time: 0.4066 data_time: 0.0048 memory: 731 2023/03/08 14:15:00 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:21 time: 0.5420 data_time: 0.0052 memory: 747 2023/03/08 14:15:26 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:03 time: 0.5177 data_time: 0.0050 memory: 735 2023/03/08 14:16:02 - 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 | 0.9554 | 0.0932 | 0.5527 | 0.6769 | 0.5152 | 0.6527 | 0.7715 | 0.0000 | 0.9015 | 0.3864 | 0.7662 | 0.0003 | 0.9004 | 0.6021 | 0.8718 | 0.6330 | 0.7238 | 0.6302 | 0.4554 | 0.5836 | 0.9067 | 0.6596 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 14:16:02 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9554 bicycle: 0.0932 motorcycle: 0.5527 truck: 0.6769 bus: 0.5152 person: 0.6527 bicyclist: 0.7715 motorcyclist: 0.0000 road: 0.9015 parking: 0.3864 sidewalk: 0.7662 other-ground: 0.0003 building: 0.9004 fence: 0.6021 vegetation: 0.8718 trunck: 0.6330 terrian: 0.7238 pole: 0.6302 traffic-sign: 0.4554 miou: 0.5836 acc: 0.9067 acc_cls: 0.6596 2023/03/08 14:16:54 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 1.3152e-01 eta: 2:32:37 time: 1.0425 data_time: 0.0193 memory: 2624 loss: 0.1823 loss_sem_seg: 0.1823 2023/03/08 14:17:46 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 1.3048e-01 eta: 2:31:53 time: 1.0264 data_time: 0.0042 memory: 2609 loss: 0.1850 loss_sem_seg: 0.1850 2023/03/08 14:18:37 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 1.2943e-01 eta: 2:31:08 time: 1.0277 data_time: 0.0040 memory: 2716 loss: 0.1861 loss_sem_seg: 0.1861 2023/03/08 14:19:29 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 1.2838e-01 eta: 2:30:24 time: 1.0396 data_time: 0.0045 memory: 2591 loss: 0.1891 loss_sem_seg: 0.1891 2023/03/08 14:20:20 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 1.2733e-01 eta: 2:29:39 time: 1.0159 data_time: 0.0040 memory: 2773 loss: 0.1806 loss_sem_seg: 0.1806 2023/03/08 14:21:11 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 1.2629e-01 eta: 2:28:53 time: 1.0161 data_time: 0.0040 memory: 2587 loss: 0.1902 loss_sem_seg: 0.1902 2023/03/08 14:22:00 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 1.2524e-01 eta: 2:28:07 time: 0.9951 data_time: 0.0040 memory: 2690 loss: 0.1788 loss_sem_seg: 0.1788 2023/03/08 14:22:52 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 1.2419e-01 eta: 2:27:22 time: 1.0297 data_time: 0.0041 memory: 2670 loss: 0.1960 loss_sem_seg: 0.1960 2023/03/08 14:23:43 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 1.2314e-01 eta: 2:26:37 time: 1.0274 data_time: 0.0044 memory: 2584 loss: 0.1791 loss_sem_seg: 0.1791 2023/03/08 14:24:33 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 1.2209e-01 eta: 2:25:50 time: 0.9973 data_time: 0.0040 memory: 2570 loss: 0.1797 loss_sem_seg: 0.1797 2023/03/08 14:25:18 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 1.2103e-01 eta: 2:24:59 time: 0.9020 data_time: 0.0041 memory: 2753 loss: 0.1887 loss_sem_seg: 0.1887 2023/03/08 14:26:02 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 1.1998e-01 eta: 2:24:07 time: 0.8793 data_time: 0.0042 memory: 2649 loss: 0.1660 loss_sem_seg: 0.1660 2023/03/08 14:26:27 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:26:47 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 1.1893e-01 eta: 2:23:15 time: 0.8905 data_time: 0.0040 memory: 2740 loss: 0.1986 loss_sem_seg: 0.1986 2023/03/08 14:27:31 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 1.1788e-01 eta: 2:22:22 time: 0.8794 data_time: 0.0042 memory: 2662 loss: 0.1874 loss_sem_seg: 0.1874 2023/03/08 14:28:15 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 1.1683e-01 eta: 2:21:30 time: 0.8834 data_time: 0.0041 memory: 2654 loss: 0.1778 loss_sem_seg: 0.1778 2023/03/08 14:29:01 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 1.1578e-01 eta: 2:20:40 time: 0.9194 data_time: 0.0037 memory: 2628 loss: 0.1714 loss_sem_seg: 0.1714 2023/03/08 14:29:55 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 1.1473e-01 eta: 2:19:58 time: 1.0882 data_time: 0.0041 memory: 2584 loss: 0.1801 loss_sem_seg: 0.1801 2023/03/08 14:30:46 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 1.1368e-01 eta: 2:19:12 time: 1.0218 data_time: 0.0038 memory: 2630 loss: 0.1803 loss_sem_seg: 0.1803 2023/03/08 14:31:36 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 1.1263e-01 eta: 2:18:26 time: 0.9944 data_time: 0.0040 memory: 2660 loss: 0.1854 loss_sem_seg: 0.1854 2023/03/08 14:32:28 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 1.1159e-01 eta: 2:17:41 time: 1.0360 data_time: 0.0040 memory: 2582 loss: 0.1804 loss_sem_seg: 0.1804 2023/03/08 14:33:18 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 1.1054e-01 eta: 2:16:54 time: 1.0001 data_time: 0.0040 memory: 2662 loss: 0.1709 loss_sem_seg: 0.1709 2023/03/08 14:34:08 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 1.0949e-01 eta: 2:16:07 time: 0.9936 data_time: 0.0041 memory: 2702 loss: 0.1883 loss_sem_seg: 0.1883 2023/03/08 14:34:51 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 1.0844e-01 eta: 2:15:15 time: 0.8748 data_time: 0.0041 memory: 2641 loss: 0.1779 loss_sem_seg: 0.1779 2023/03/08 14:35:24 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:35:24 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/03/08 14:35:43 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:02:43 time: 0.3573 data_time: 0.0080 memory: 2573 2023/03/08 14:36:00 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:02:22 time: 0.3398 data_time: 0.0054 memory: 744 2023/03/08 14:36:17 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:02:04 time: 0.3411 data_time: 0.0054 memory: 747 2023/03/08 14:36:35 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:01:46 time: 0.3424 data_time: 0.0051 memory: 737 2023/03/08 14:36:51 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:01:28 time: 0.3348 data_time: 0.0049 memory: 752 2023/03/08 14:37:08 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:01:11 time: 0.3313 data_time: 0.0049 memory: 713 2023/03/08 14:37:26 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:00:54 time: 0.3578 data_time: 0.0049 memory: 729 2023/03/08 14:37:45 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:00:38 time: 0.3887 data_time: 0.0050 memory: 731 2023/03/08 14:38:10 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:21 time: 0.4953 data_time: 0.0049 memory: 747 2023/03/08 14:38:35 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:03 time: 0.5038 data_time: 0.0052 memory: 735 2023/03/08 14:39:10 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9552 | 0.2031 | 0.5663 | 0.6062 | 0.4456 | 0.6262 | 0.7725 | 0.0000 | 0.9239 | 0.4060 | 0.7989 | 0.0012 | 0.8961 | 0.5912 | 0.8770 | 0.6439 | 0.7400 | 0.6344 | 0.4666 | 0.5871 | 0.9134 | 0.6534 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 14:39:10 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9552 bicycle: 0.2031 motorcycle: 0.5663 truck: 0.6062 bus: 0.4456 person: 0.6262 bicyclist: 0.7725 motorcyclist: 0.0000 road: 0.9239 parking: 0.4060 sidewalk: 0.7989 other-ground: 0.0012 building: 0.8961 fence: 0.5912 vegetation: 0.8770 trunck: 0.6439 terrian: 0.7400 pole: 0.6344 traffic-sign: 0.4666 miou: 0.5871 acc: 0.9134 acc_cls: 0.6534 2023/03/08 14:40:02 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 1.0644e-01 eta: 2:13:35 time: 1.0324 data_time: 0.0194 memory: 2614 loss: 0.1886 loss_sem_seg: 0.1886 2023/03/08 14:40:54 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 1.0540e-01 eta: 2:12:50 time: 1.0405 data_time: 0.0042 memory: 2655 loss: 0.1747 loss_sem_seg: 0.1747 2023/03/08 14:41:45 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 1.0435e-01 eta: 2:12:04 time: 1.0176 data_time: 0.0041 memory: 2632 loss: 0.1735 loss_sem_seg: 0.1735 2023/03/08 14:42:36 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 1.0331e-01 eta: 2:11:19 time: 1.0265 data_time: 0.0039 memory: 2791 loss: 0.1808 loss_sem_seg: 0.1808 2023/03/08 14:43:27 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 1.0227e-01 eta: 2:10:33 time: 1.0193 data_time: 0.0041 memory: 2593 loss: 0.1520 loss_sem_seg: 0.1520 2023/03/08 14:44:19 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 1.0123e-01 eta: 2:09:47 time: 1.0251 data_time: 0.0042 memory: 2627 loss: 0.1735 loss_sem_seg: 0.1735 2023/03/08 14:45:10 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 1.0020e-01 eta: 2:09:01 time: 1.0230 data_time: 0.0042 memory: 2728 loss: 0.1836 loss_sem_seg: 0.1836 2023/03/08 14:46:02 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 9.9161e-02 eta: 2:08:16 time: 1.0368 data_time: 0.0040 memory: 2640 loss: 0.1743 loss_sem_seg: 0.1743 2023/03/08 14:46:33 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:46:49 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 9.8127e-02 eta: 2:07:27 time: 0.9413 data_time: 0.0041 memory: 2706 loss: 0.1787 loss_sem_seg: 0.1787 2023/03/08 14:47:33 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 9.7095e-02 eta: 2:06:36 time: 0.8915 data_time: 0.0040 memory: 2724 loss: 0.1635 loss_sem_seg: 0.1635 2023/03/08 14:48:18 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 9.6065e-02 eta: 2:05:44 time: 0.8917 data_time: 0.0042 memory: 2640 loss: 0.1706 loss_sem_seg: 0.1706 2023/03/08 14:49:02 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 9.5036e-02 eta: 2:04:53 time: 0.8776 data_time: 0.0041 memory: 2590 loss: 0.1591 loss_sem_seg: 0.1591 2023/03/08 14:49:46 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 9.4009e-02 eta: 2:04:02 time: 0.8879 data_time: 0.0039 memory: 2640 loss: 0.1612 loss_sem_seg: 0.1612 2023/03/08 14:50:31 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 9.2985e-02 eta: 2:03:11 time: 0.8958 data_time: 0.0038 memory: 2621 loss: 0.1754 loss_sem_seg: 0.1754 2023/03/08 14:51:25 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 9.1962e-02 eta: 2:02:28 time: 1.0878 data_time: 0.0039 memory: 2506 loss: 0.1578 loss_sem_seg: 0.1578 2023/03/08 14:52:16 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 9.0942e-02 eta: 2:01:41 time: 1.0107 data_time: 0.0042 memory: 2695 loss: 0.1644 loss_sem_seg: 0.1644 2023/03/08 14:53:07 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.9923e-02 eta: 2:00:55 time: 1.0189 data_time: 0.0039 memory: 2637 loss: 0.1575 loss_sem_seg: 0.1575 2023/03/08 14:53:57 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.8907e-02 eta: 2:00:08 time: 1.0038 data_time: 0.0041 memory: 2579 loss: 0.1624 loss_sem_seg: 0.1624 2023/03/08 14:54:47 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.7894e-02 eta: 1:59:21 time: 1.0085 data_time: 0.0038 memory: 2671 loss: 0.1606 loss_sem_seg: 0.1606 2023/03/08 14:55:38 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.6883e-02 eta: 1:58:34 time: 1.0056 data_time: 0.0039 memory: 2583 loss: 0.1597 loss_sem_seg: 0.1597 2023/03/08 14:56:29 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.5874e-02 eta: 1:57:48 time: 1.0248 data_time: 0.0038 memory: 2628 loss: 0.1616 loss_sem_seg: 0.1616 2023/03/08 14:57:20 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.4868e-02 eta: 1:57:02 time: 1.0285 data_time: 0.0042 memory: 2680 loss: 0.1460 loss_sem_seg: 0.1460 2023/03/08 14:58:04 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.3865e-02 eta: 1:56:11 time: 0.8846 data_time: 0.0040 memory: 2655 loss: 0.1723 loss_sem_seg: 0.1723 2023/03/08 14:58:38 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 14:58:39 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/08 14:58:58 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:02:42 time: 0.3551 data_time: 0.0092 memory: 2568 2023/03/08 14:59:15 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:02:21 time: 0.3387 data_time: 0.0056 memory: 744 2023/03/08 14:59:32 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:02:03 time: 0.3349 data_time: 0.0056 memory: 747 2023/03/08 14:59:49 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:01:45 time: 0.3360 data_time: 0.0055 memory: 737 2023/03/08 15:00:05 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:01:27 time: 0.3329 data_time: 0.0053 memory: 752 2023/03/08 15:00:22 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:01:10 time: 0.3313 data_time: 0.0055 memory: 713 2023/03/08 15:00:40 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:00:54 time: 0.3564 data_time: 0.0052 memory: 729 2023/03/08 15:00:58 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:00:37 time: 0.3768 data_time: 0.0051 memory: 731 2023/03/08 15:01:20 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:20 time: 0.4309 data_time: 0.0055 memory: 747 2023/03/08 15:01:48 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:03 time: 0.5656 data_time: 0.0054 memory: 735 2023/03/08 15:02:27 - 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 | 0.9633 | 0.1944 | 0.5686 | 0.6815 | 0.6007 | 0.6454 | 0.7728 | 0.0000 | 0.9307 | 0.4312 | 0.8005 | 0.0034 | 0.9089 | 0.5954 | 0.8779 | 0.6753 | 0.7445 | 0.6269 | 0.4625 | 0.6044 | 0.9163 | 0.6738 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 15:02:27 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9633 bicycle: 0.1944 motorcycle: 0.5686 truck: 0.6815 bus: 0.6007 person: 0.6454 bicyclist: 0.7728 motorcyclist: 0.0000 road: 0.9307 parking: 0.4312 sidewalk: 0.8005 other-ground: 0.0034 building: 0.9089 fence: 0.5954 vegetation: 0.8779 trunck: 0.6753 terrian: 0.7445 pole: 0.6269 traffic-sign: 0.4625 miou: 0.6044 acc: 0.9163 acc_cls: 0.6738 2023/03/08 15:03:20 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.1947e-02 eta: 1:54:34 time: 1.0450 data_time: 0.0207 memory: 2675 loss: 0.1600 loss_sem_seg: 0.1600 2023/03/08 15:04:11 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0952e-02 eta: 1:53:48 time: 1.0215 data_time: 0.0041 memory: 2670 loss: 0.1654 loss_sem_seg: 0.1654 2023/03/08 15:05:01 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 7.9960e-02 eta: 1:53:01 time: 1.0069 data_time: 0.0042 memory: 2658 loss: 0.1645 loss_sem_seg: 0.1645 2023/03/08 15:05:52 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 7.8971e-02 eta: 1:52:15 time: 1.0291 data_time: 0.0039 memory: 2642 loss: 0.1576 loss_sem_seg: 0.1576 2023/03/08 15:06:29 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 15:06:43 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 7.7985e-02 eta: 1:51:28 time: 1.0053 data_time: 0.0041 memory: 2600 loss: 0.1554 loss_sem_seg: 0.1554 2023/03/08 15:07:34 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 7.7003e-02 eta: 1:50:41 time: 1.0290 data_time: 0.0040 memory: 2550 loss: 0.1597 loss_sem_seg: 0.1597 2023/03/08 15:08:23 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 7.6023e-02 eta: 1:49:53 time: 0.9823 data_time: 0.0040 memory: 2603 loss: 0.1555 loss_sem_seg: 0.1555 2023/03/08 15:09:09 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 7.5048e-02 eta: 1:49:03 time: 0.9067 data_time: 0.0041 memory: 2740 loss: 0.1633 loss_sem_seg: 0.1633 2023/03/08 15:09:53 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 7.4075e-02 eta: 1:48:13 time: 0.8902 data_time: 0.0043 memory: 2628 loss: 0.1457 loss_sem_seg: 0.1457 2023/03/08 15:10:38 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 7.3106e-02 eta: 1:47:22 time: 0.8875 data_time: 0.0039 memory: 2793 loss: 0.1556 loss_sem_seg: 0.1556 2023/03/08 15:11:22 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 7.2141e-02 eta: 1:46:32 time: 0.8874 data_time: 0.0039 memory: 2548 loss: 0.1604 loss_sem_seg: 0.1604 2023/03/08 15:12:07 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 7.1179e-02 eta: 1:45:42 time: 0.9108 data_time: 0.0038 memory: 2523 loss: 0.1555 loss_sem_seg: 0.1555 2023/03/08 15:12:52 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 7.0222e-02 eta: 1:44:52 time: 0.8958 data_time: 0.0040 memory: 2577 loss: 0.1509 loss_sem_seg: 0.1509 2023/03/08 15:13:48 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 6.9268e-02 eta: 1:44:08 time: 1.1198 data_time: 0.0040 memory: 2555 loss: 0.1576 loss_sem_seg: 0.1576 2023/03/08 15:14:39 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 6.8317e-02 eta: 1:43:21 time: 1.0167 data_time: 0.0039 memory: 2642 loss: 0.1589 loss_sem_seg: 0.1589 2023/03/08 15:15:30 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 6.7371e-02 eta: 1:42:34 time: 1.0130 data_time: 0.0039 memory: 2607 loss: 0.1607 loss_sem_seg: 0.1607 2023/03/08 15:16:20 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 6.6429e-02 eta: 1:41:47 time: 0.9978 data_time: 0.0036 memory: 2671 loss: 0.1469 loss_sem_seg: 0.1469 2023/03/08 15:17:11 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 6.5491e-02 eta: 1:41:00 time: 1.0275 data_time: 0.0039 memory: 2605 loss: 0.1497 loss_sem_seg: 0.1497 2023/03/08 15:18:01 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 6.4557e-02 eta: 1:40:13 time: 1.0070 data_time: 0.0040 memory: 2636 loss: 0.1428 loss_sem_seg: 0.1428 2023/03/08 15:18:53 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 6.3627e-02 eta: 1:39:26 time: 1.0271 data_time: 0.0037 memory: 2695 loss: 0.1545 loss_sem_seg: 0.1545 2023/03/08 15:19:43 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 6.2702e-02 eta: 1:38:39 time: 1.0069 data_time: 0.0037 memory: 2701 loss: 0.1581 loss_sem_seg: 0.1581 2023/03/08 15:20:34 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 6.1781e-02 eta: 1:37:52 time: 1.0197 data_time: 0.0038 memory: 2624 loss: 0.1583 loss_sem_seg: 0.1583 2023/03/08 15:21:18 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 6.0865e-02 eta: 1:37:02 time: 0.8865 data_time: 0.0037 memory: 2566 loss: 0.1481 loss_sem_seg: 0.1481 2023/03/08 15:21:53 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 15:21:53 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/03/08 15:22:12 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:02:42 time: 0.3539 data_time: 0.0081 memory: 2610 2023/03/08 15:22:29 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:02:21 time: 0.3402 data_time: 0.0055 memory: 744 2023/03/08 15:22:46 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:02:03 time: 0.3391 data_time: 0.0049 memory: 747 2023/03/08 15:23:03 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:01:45 time: 0.3388 data_time: 0.0048 memory: 737 2023/03/08 15:23:20 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:01:28 time: 0.3406 data_time: 0.0046 memory: 752 2023/03/08 15:23:37 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:01:11 time: 0.3373 data_time: 0.0046 memory: 713 2023/03/08 15:23:54 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:00:54 time: 0.3340 data_time: 0.0046 memory: 729 2023/03/08 15:24:12 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:00:37 time: 0.3670 data_time: 0.0047 memory: 731 2023/03/08 15:24:33 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:20 time: 0.4130 data_time: 0.0049 memory: 747 2023/03/08 15:24:58 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:03 time: 0.5091 data_time: 0.0050 memory: 735 2023/03/08 15:25:32 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9612 | 0.2835 | 0.5820 | 0.6409 | 0.4856 | 0.6843 | 0.8353 | 0.0000 | 0.9263 | 0.4550 | 0.7934 | 0.0002 | 0.9133 | 0.6285 | 0.8935 | 0.6346 | 0.7787 | 0.6331 | 0.4901 | 0.6115 | 0.9215 | 0.6954 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 15:25:32 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9612 bicycle: 0.2835 motorcycle: 0.5820 truck: 0.6409 bus: 0.4856 person: 0.6843 bicyclist: 0.8353 motorcyclist: 0.0000 road: 0.9263 parking: 0.4550 sidewalk: 0.7934 other-ground: 0.0002 building: 0.9133 fence: 0.6285 vegetation: 0.8935 trunck: 0.6346 terrian: 0.7787 pole: 0.6331 traffic-sign: 0.4901 miou: 0.6115 acc: 0.9215 acc_cls: 0.6954 2023/03/08 15:26:13 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 15:26:24 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 5.9118e-02 eta: 1:35:26 time: 1.0337 data_time: 0.0191 memory: 2663 loss: 0.1383 loss_sem_seg: 0.1383 2023/03/08 15:27:14 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 5.8215e-02 eta: 1:34:39 time: 1.0128 data_time: 0.0039 memory: 2617 loss: 0.1476 loss_sem_seg: 0.1476 2023/03/08 15:28:04 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 5.7317e-02 eta: 1:33:51 time: 0.9915 data_time: 0.0041 memory: 2617 loss: 0.1442 loss_sem_seg: 0.1442 2023/03/08 15:28:55 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 5.6423e-02 eta: 1:33:04 time: 1.0135 data_time: 0.0038 memory: 2651 loss: 0.1447 loss_sem_seg: 0.1447 2023/03/08 15:29:46 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 5.5535e-02 eta: 1:32:17 time: 1.0190 data_time: 0.0039 memory: 2702 loss: 0.1337 loss_sem_seg: 0.1337 2023/03/08 15:30:31 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 5.4651e-02 eta: 1:31:27 time: 0.9171 data_time: 0.0040 memory: 2536 loss: 0.1455 loss_sem_seg: 0.1455 2023/03/08 15:31:15 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 5.3772e-02 eta: 1:30:37 time: 0.8792 data_time: 0.0041 memory: 2587 loss: 0.1346 loss_sem_seg: 0.1346 2023/03/08 15:31:59 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 5.2899e-02 eta: 1:29:47 time: 0.8798 data_time: 0.0042 memory: 2632 loss: 0.1442 loss_sem_seg: 0.1442 2023/03/08 15:32:44 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 5.2030e-02 eta: 1:28:57 time: 0.8880 data_time: 0.0041 memory: 2591 loss: 0.1493 loss_sem_seg: 0.1493 2023/03/08 15:33:28 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 5.1167e-02 eta: 1:28:07 time: 0.8779 data_time: 0.0040 memory: 2610 loss: 0.1525 loss_sem_seg: 0.1525 2023/03/08 15:34:12 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 5.0309e-02 eta: 1:27:16 time: 0.8800 data_time: 0.0037 memory: 2689 loss: 0.1414 loss_sem_seg: 0.1414 2023/03/08 15:35:05 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 4.9457e-02 eta: 1:26:30 time: 1.0662 data_time: 0.0037 memory: 2613 loss: 0.1442 loss_sem_seg: 0.1442 2023/03/08 15:35:56 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 4.8610e-02 eta: 1:25:43 time: 1.0210 data_time: 0.0039 memory: 2606 loss: 0.1467 loss_sem_seg: 0.1467 2023/03/08 15:36:49 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 4.7768e-02 eta: 1:24:57 time: 1.0608 data_time: 0.0037 memory: 2703 loss: 0.1410 loss_sem_seg: 0.1410 2023/03/08 15:37:40 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 4.6932e-02 eta: 1:24:10 time: 1.0252 data_time: 0.0036 memory: 2597 loss: 0.1377 loss_sem_seg: 0.1377 2023/03/08 15:38:33 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 4.6101e-02 eta: 1:23:24 time: 1.0568 data_time: 0.0036 memory: 2670 loss: 0.1386 loss_sem_seg: 0.1386 2023/03/08 15:39:24 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 4.5276e-02 eta: 1:22:36 time: 1.0239 data_time: 0.0038 memory: 2590 loss: 0.1358 loss_sem_seg: 0.1358 2023/03/08 15:40:15 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 4.4457e-02 eta: 1:21:49 time: 1.0204 data_time: 0.0038 memory: 2615 loss: 0.1482 loss_sem_seg: 0.1482 2023/03/08 15:41:06 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 4.3644e-02 eta: 1:21:02 time: 1.0104 data_time: 0.0037 memory: 2696 loss: 0.1407 loss_sem_seg: 0.1407 2023/03/08 15:41:57 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 4.2836e-02 eta: 1:20:14 time: 1.0207 data_time: 0.0037 memory: 2655 loss: 0.1441 loss_sem_seg: 0.1441 2023/03/08 15:42:37 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 15:42:47 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 4.2035e-02 eta: 1:19:27 time: 0.9982 data_time: 0.0037 memory: 2785 loss: 0.1439 loss_sem_seg: 0.1439 2023/03/08 15:43:38 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 4.1239e-02 eta: 1:18:39 time: 1.0236 data_time: 0.0036 memory: 2607 loss: 0.1365 loss_sem_seg: 0.1365 2023/03/08 15:44:24 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 4.0449e-02 eta: 1:17:50 time: 0.9251 data_time: 0.0037 memory: 2558 loss: 0.1359 loss_sem_seg: 0.1359 2023/03/08 15:45:02 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 15:45:03 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/03/08 15:45:21 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:02:38 time: 0.3462 data_time: 0.0081 memory: 2571 2023/03/08 15:45:39 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:02:20 time: 0.3432 data_time: 0.0056 memory: 744 2023/03/08 15:45:55 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:02:02 time: 0.3343 data_time: 0.0056 memory: 747 2023/03/08 15:46:12 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:01:44 time: 0.3338 data_time: 0.0051 memory: 737 2023/03/08 15:46:29 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:01:27 time: 0.3392 data_time: 0.0051 memory: 752 2023/03/08 15:46:46 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:01:10 time: 0.3367 data_time: 0.0050 memory: 713 2023/03/08 15:47:03 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:00:53 time: 0.3336 data_time: 0.0048 memory: 729 2023/03/08 15:47:20 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:00:37 time: 0.3557 data_time: 0.0049 memory: 731 2023/03/08 15:47:39 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:20 time: 0.3790 data_time: 0.0051 memory: 747 2023/03/08 15:48:02 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:03 time: 0.4613 data_time: 0.0050 memory: 735 2023/03/08 15:48:35 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9638 | 0.2696 | 0.6376 | 0.8385 | 0.6038 | 0.7028 | 0.8304 | 0.0000 | 0.9255 | 0.4874 | 0.7929 | 0.0009 | 0.9081 | 0.6064 | 0.8858 | 0.6759 | 0.7559 | 0.6425 | 0.5022 | 0.6332 | 0.9189 | 0.7012 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 15:48:35 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9638 bicycle: 0.2696 motorcycle: 0.6376 truck: 0.8385 bus: 0.6038 person: 0.7028 bicyclist: 0.8304 motorcyclist: 0.0000 road: 0.9255 parking: 0.4874 sidewalk: 0.7929 other-ground: 0.0009 building: 0.9081 fence: 0.6064 vegetation: 0.8858 trunck: 0.6759 terrian: 0.7559 pole: 0.6425 traffic-sign: 0.5022 miou: 0.6332 acc: 0.9189 acc_cls: 0.7012 2023/03/08 15:49:28 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 3.8950e-02 eta: 1:16:17 time: 1.0470 data_time: 0.0182 memory: 2632 loss: 0.1342 loss_sem_seg: 0.1342 2023/03/08 15:50:20 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 3.8179e-02 eta: 1:15:30 time: 1.0394 data_time: 0.0036 memory: 2636 loss: 0.1388 loss_sem_seg: 0.1388 2023/03/08 15:51:11 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 3.7414e-02 eta: 1:14:42 time: 1.0372 data_time: 0.0038 memory: 2672 loss: 0.1393 loss_sem_seg: 0.1393 2023/03/08 15:52:00 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 3.6655e-02 eta: 1:13:54 time: 0.9823 data_time: 0.0041 memory: 2965 loss: 0.1367 loss_sem_seg: 0.1367 2023/03/08 15:52:45 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 3.5902e-02 eta: 1:13:04 time: 0.8803 data_time: 0.0039 memory: 2590 loss: 0.1390 loss_sem_seg: 0.1390 2023/03/08 15:53:28 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 3.5156e-02 eta: 1:12:15 time: 0.8776 data_time: 0.0041 memory: 2746 loss: 0.1361 loss_sem_seg: 0.1361 2023/03/08 15:54:12 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 3.4416e-02 eta: 1:11:25 time: 0.8803 data_time: 0.0041 memory: 2615 loss: 0.1410 loss_sem_seg: 0.1410 2023/03/08 15:54:57 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 3.3683e-02 eta: 1:10:35 time: 0.8823 data_time: 0.0039 memory: 2534 loss: 0.1295 loss_sem_seg: 0.1295 2023/03/08 15:55:40 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 3.2956e-02 eta: 1:09:45 time: 0.8711 data_time: 0.0039 memory: 2695 loss: 0.1369 loss_sem_seg: 0.1369 2023/03/08 15:56:33 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 3.2237e-02 eta: 1:08:59 time: 1.0672 data_time: 0.0038 memory: 2634 loss: 0.1271 loss_sem_seg: 0.1271 2023/03/08 15:57:24 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 3.1524e-02 eta: 1:08:11 time: 1.0201 data_time: 0.0040 memory: 2602 loss: 0.1324 loss_sem_seg: 0.1324 2023/03/08 15:58:14 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 3.0817e-02 eta: 1:07:23 time: 0.9881 data_time: 0.0037 memory: 2602 loss: 0.1381 loss_sem_seg: 0.1381 2023/03/08 15:59:05 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 3.0118e-02 eta: 1:06:36 time: 1.0224 data_time: 0.0037 memory: 2622 loss: 0.1288 loss_sem_seg: 0.1288 2023/03/08 15:59:56 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 2.9425e-02 eta: 1:05:48 time: 1.0205 data_time: 0.0037 memory: 2698 loss: 0.1334 loss_sem_seg: 0.1334 2023/03/08 16:00:46 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 2.8740e-02 eta: 1:05:00 time: 0.9968 data_time: 0.0037 memory: 2602 loss: 0.1297 loss_sem_seg: 0.1297 2023/03/08 16:01:36 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 2.8061e-02 eta: 1:04:12 time: 0.9961 data_time: 0.0038 memory: 2642 loss: 0.1287 loss_sem_seg: 0.1287 2023/03/08 16:02:20 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:02:27 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 2.7389e-02 eta: 1:03:25 time: 1.0215 data_time: 0.0040 memory: 2753 loss: 0.1296 loss_sem_seg: 0.1296 2023/03/08 16:03:18 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 2.6725e-02 eta: 1:02:37 time: 1.0206 data_time: 0.0041 memory: 2773 loss: 0.1189 loss_sem_seg: 0.1189 2023/03/08 16:04:07 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 2.6068e-02 eta: 1:01:49 time: 0.9915 data_time: 0.0040 memory: 2679 loss: 0.1287 loss_sem_seg: 0.1287 2023/03/08 16:04:59 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 2.5417e-02 eta: 1:01:02 time: 1.0348 data_time: 0.0036 memory: 2599 loss: 0.1288 loss_sem_seg: 0.1288 2023/03/08 16:05:51 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 2.4775e-02 eta: 1:00:14 time: 1.0423 data_time: 0.0038 memory: 2555 loss: 0.1216 loss_sem_seg: 0.1216 2023/03/08 16:06:44 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 2.4139e-02 eta: 0:59:27 time: 1.0493 data_time: 0.0037 memory: 2587 loss: 0.1189 loss_sem_seg: 0.1189 2023/03/08 16:07:32 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 2.3511e-02 eta: 0:58:38 time: 0.9750 data_time: 0.0037 memory: 2671 loss: 0.1258 loss_sem_seg: 0.1258 2023/03/08 16:08:12 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:08:13 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/08 16:08:32 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:02:41 time: 0.3521 data_time: 0.0082 memory: 2685 2023/03/08 16:08:49 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:02:21 time: 0.3400 data_time: 0.0054 memory: 744 2023/03/08 16:09:06 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:02:03 time: 0.3385 data_time: 0.0053 memory: 747 2023/03/08 16:09:22 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:01:45 time: 0.3360 data_time: 0.0052 memory: 737 2023/03/08 16:09:40 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:01:28 time: 0.3406 data_time: 0.0047 memory: 752 2023/03/08 16:09:56 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:01:10 time: 0.3282 data_time: 0.0048 memory: 713 2023/03/08 16:10:13 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:00:53 time: 0.3329 data_time: 0.0047 memory: 729 2023/03/08 16:10:30 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:00:36 time: 0.3468 data_time: 0.0045 memory: 731 2023/03/08 16:10:48 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:20 time: 0.3592 data_time: 0.0048 memory: 747 2023/03/08 16:11:08 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:03 time: 0.4070 data_time: 0.0049 memory: 735 2023/03/08 16:11:39 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9677 | 0.2734 | 0.6421 | 0.8583 | 0.6390 | 0.7103 | 0.8452 | 0.0000 | 0.9313 | 0.4690 | 0.8081 | 0.0051 | 0.9140 | 0.6466 | 0.8802 | 0.6697 | 0.7441 | 0.6348 | 0.4953 | 0.6386 | 0.9200 | 0.7056 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 16:11:39 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9677 bicycle: 0.2734 motorcycle: 0.6421 truck: 0.8583 bus: 0.6390 person: 0.7103 bicyclist: 0.8452 motorcyclist: 0.0000 road: 0.9313 parking: 0.4690 sidewalk: 0.8081 other-ground: 0.0051 building: 0.9140 fence: 0.6466 vegetation: 0.8802 trunck: 0.6697 terrian: 0.7441 pole: 0.6348 traffic-sign: 0.4953 miou: 0.6386 acc: 0.9200 acc_cls: 0.7056 2023/03/08 16:12:30 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 2.2325e-02 eta: 0:57:05 time: 1.0330 data_time: 0.0184 memory: 2717 loss: 0.1276 loss_sem_seg: 0.1276 2023/03/08 16:13:20 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 2.1719e-02 eta: 0:56:17 time: 0.9987 data_time: 0.0040 memory: 2563 loss: 0.1263 loss_sem_seg: 0.1263 2023/03/08 16:14:05 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 2.1120e-02 eta: 0:55:28 time: 0.8865 data_time: 0.0033 memory: 2656 loss: 0.1364 loss_sem_seg: 0.1364 2023/03/08 16:14:50 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 2.0529e-02 eta: 0:54:39 time: 0.9045 data_time: 0.0032 memory: 2590 loss: 0.1179 loss_sem_seg: 0.1179 2023/03/08 16:15:35 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 1.9945e-02 eta: 0:53:49 time: 0.8944 data_time: 0.0032 memory: 2644 loss: 0.1273 loss_sem_seg: 0.1273 2023/03/08 16:16:19 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 1.9369e-02 eta: 0:53:00 time: 0.8861 data_time: 0.0033 memory: 2694 loss: 0.1204 loss_sem_seg: 0.1204 2023/03/08 16:17:03 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 1.8800e-02 eta: 0:52:11 time: 0.8873 data_time: 0.0032 memory: 2594 loss: 0.1209 loss_sem_seg: 0.1209 2023/03/08 16:17:49 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 1.8240e-02 eta: 0:51:22 time: 0.9098 data_time: 0.0033 memory: 2729 loss: 0.1302 loss_sem_seg: 0.1302 2023/03/08 16:18:44 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 1.7687e-02 eta: 0:50:35 time: 1.1074 data_time: 0.0032 memory: 2655 loss: 0.1280 loss_sem_seg: 0.1280 2023/03/08 16:19:34 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 1.7142e-02 eta: 0:49:47 time: 1.0051 data_time: 0.0032 memory: 2713 loss: 0.1313 loss_sem_seg: 0.1313 2023/03/08 16:20:26 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 1.6605e-02 eta: 0:48:59 time: 1.0222 data_time: 0.0032 memory: 2624 loss: 0.1196 loss_sem_seg: 0.1196 2023/03/08 16:21:16 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 1.6076e-02 eta: 0:48:11 time: 1.0063 data_time: 0.0033 memory: 2805 loss: 0.1239 loss_sem_seg: 0.1239 2023/03/08 16:22:05 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:22:07 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 1.5555e-02 eta: 0:47:23 time: 1.0210 data_time: 0.0033 memory: 2585 loss: 0.1210 loss_sem_seg: 0.1210 2023/03/08 16:22:57 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 1.5041e-02 eta: 0:46:35 time: 1.0092 data_time: 0.0033 memory: 2603 loss: 0.1242 loss_sem_seg: 0.1242 2023/03/08 16:23:49 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 1.4536e-02 eta: 0:45:47 time: 1.0229 data_time: 0.0033 memory: 2637 loss: 0.1276 loss_sem_seg: 0.1276 2023/03/08 16:24:40 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 1.4039e-02 eta: 0:45:00 time: 1.0235 data_time: 0.0033 memory: 2653 loss: 0.1306 loss_sem_seg: 0.1306 2023/03/08 16:25:30 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 1.3550e-02 eta: 0:44:11 time: 1.0016 data_time: 0.0033 memory: 2736 loss: 0.1211 loss_sem_seg: 0.1211 2023/03/08 16:26:20 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 1.3070e-02 eta: 0:43:23 time: 0.9962 data_time: 0.0032 memory: 2624 loss: 0.1269 loss_sem_seg: 0.1269 2023/03/08 16:27:11 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 1.2597e-02 eta: 0:42:35 time: 1.0265 data_time: 0.0034 memory: 2649 loss: 0.1184 loss_sem_seg: 0.1184 2023/03/08 16:28:01 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 1.2133e-02 eta: 0:41:47 time: 1.0096 data_time: 0.0034 memory: 2728 loss: 0.1195 loss_sem_seg: 0.1195 2023/03/08 16:28:51 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 1.1677e-02 eta: 0:40:59 time: 0.9945 data_time: 0.0034 memory: 2530 loss: 0.1236 loss_sem_seg: 0.1236 2023/03/08 16:29:41 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 1.1229e-02 eta: 0:40:11 time: 0.9982 data_time: 0.0034 memory: 2660 loss: 0.1197 loss_sem_seg: 0.1197 2023/03/08 16:30:31 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 1.0790e-02 eta: 0:39:23 time: 1.0043 data_time: 0.0033 memory: 2782 loss: 0.1154 loss_sem_seg: 0.1154 2023/03/08 16:31:11 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:31:12 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/03/08 16:31:31 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:02:40 time: 0.3505 data_time: 0.0079 memory: 2621 2023/03/08 16:31:47 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:02:20 time: 0.3374 data_time: 0.0047 memory: 744 2023/03/08 16:32:04 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:02:03 time: 0.3401 data_time: 0.0049 memory: 747 2023/03/08 16:32:21 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:01:44 time: 0.3274 data_time: 0.0043 memory: 737 2023/03/08 16:32:38 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:01:27 time: 0.3368 data_time: 0.0045 memory: 752 2023/03/08 16:32:54 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:01:10 time: 0.3288 data_time: 0.0047 memory: 713 2023/03/08 16:33:11 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:00:53 time: 0.3371 data_time: 0.0046 memory: 729 2023/03/08 16:33:28 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:00:36 time: 0.3386 data_time: 0.0045 memory: 731 2023/03/08 16:33:45 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:19 time: 0.3500 data_time: 0.0048 memory: 747 2023/03/08 16:34:04 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:03 time: 0.3803 data_time: 0.0048 memory: 735 2023/03/08 16:34:34 - 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 | 0.9668 | 0.3213 | 0.6167 | 0.8605 | 0.6438 | 0.7154 | 0.8449 | 0.0000 | 0.9333 | 0.4838 | 0.8090 | 0.0010 | 0.9175 | 0.6642 | 0.8828 | 0.6568 | 0.7482 | 0.6429 | 0.4934 | 0.6422 | 0.9216 | 0.7052 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 16:34:34 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9668 bicycle: 0.3213 motorcycle: 0.6167 truck: 0.8605 bus: 0.6438 person: 0.7154 bicyclist: 0.8449 motorcyclist: 0.0000 road: 0.9333 parking: 0.4838 sidewalk: 0.8090 other-ground: 0.0010 building: 0.9175 fence: 0.6642 vegetation: 0.8828 trunck: 0.6568 terrian: 0.7482 pole: 0.6429 traffic-sign: 0.4934 miou: 0.6422 acc: 0.9216 acc_cls: 0.7052 2023/03/08 16:35:22 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 9.9694e-03 eta: 0:37:49 time: 0.9613 data_time: 0.0181 memory: 2650 loss: 0.1154 loss_sem_seg: 0.1154 2023/03/08 16:36:07 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 9.5545e-03 eta: 0:37:00 time: 0.9016 data_time: 0.0033 memory: 2563 loss: 0.1166 loss_sem_seg: 0.1166 2023/03/08 16:36:52 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 9.1481e-03 eta: 0:36:11 time: 0.8990 data_time: 0.0032 memory: 2592 loss: 0.1202 loss_sem_seg: 0.1202 2023/03/08 16:37:36 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.7502e-03 eta: 0:35:22 time: 0.8758 data_time: 0.0033 memory: 2702 loss: 0.1250 loss_sem_seg: 0.1250 2023/03/08 16:38:21 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.3608e-03 eta: 0:34:33 time: 0.8970 data_time: 0.0033 memory: 2662 loss: 0.1181 loss_sem_seg: 0.1181 2023/03/08 16:39:06 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 7.9800e-03 eta: 0:33:44 time: 0.8949 data_time: 0.0034 memory: 2555 loss: 0.1129 loss_sem_seg: 0.1129 2023/03/08 16:39:59 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 7.6078e-03 eta: 0:32:56 time: 1.0668 data_time: 0.0034 memory: 2638 loss: 0.1205 loss_sem_seg: 0.1205 2023/03/08 16:40:51 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 7.2442e-03 eta: 0:32:08 time: 1.0420 data_time: 0.0034 memory: 2663 loss: 0.1234 loss_sem_seg: 0.1234 2023/03/08 16:41:41 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 6.8892e-03 eta: 0:31:20 time: 0.9924 data_time: 0.0034 memory: 2638 loss: 0.1165 loss_sem_seg: 0.1165 2023/03/08 16:41:43 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:42:30 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 6.5429e-03 eta: 0:30:32 time: 0.9935 data_time: 0.0034 memory: 2635 loss: 0.1240 loss_sem_seg: 0.1240 2023/03/08 16:43:20 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 6.2053e-03 eta: 0:29:44 time: 0.9968 data_time: 0.0033 memory: 2588 loss: 0.1158 loss_sem_seg: 0.1158 2023/03/08 16:44:11 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 5.8765e-03 eta: 0:28:55 time: 1.0203 data_time: 0.0032 memory: 2599 loss: 0.1157 loss_sem_seg: 0.1157 2023/03/08 16:45:02 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 5.5564e-03 eta: 0:28:07 time: 1.0125 data_time: 0.0033 memory: 2652 loss: 0.1177 loss_sem_seg: 0.1177 2023/03/08 16:45:51 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 5.2450e-03 eta: 0:27:19 time: 0.9816 data_time: 0.0032 memory: 2613 loss: 0.1195 loss_sem_seg: 0.1195 2023/03/08 16:46:40 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 4.9425e-03 eta: 0:26:30 time: 0.9902 data_time: 0.0032 memory: 2642 loss: 0.1180 loss_sem_seg: 0.1180 2023/03/08 16:47:32 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 4.6488e-03 eta: 0:25:42 time: 1.0207 data_time: 0.0032 memory: 2889 loss: 0.1198 loss_sem_seg: 0.1198 2023/03/08 16:48:22 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 4.3639e-03 eta: 0:24:54 time: 1.0057 data_time: 0.0032 memory: 2643 loss: 0.1216 loss_sem_seg: 0.1216 2023/03/08 16:49:13 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 4.0879e-03 eta: 0:24:06 time: 1.0221 data_time: 0.0033 memory: 2773 loss: 0.1150 loss_sem_seg: 0.1150 2023/03/08 16:50:03 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 3.8207e-03 eta: 0:23:17 time: 1.0097 data_time: 0.0033 memory: 2518 loss: 0.1208 loss_sem_seg: 0.1208 2023/03/08 16:50:53 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 3.5625e-03 eta: 0:22:29 time: 0.9911 data_time: 0.0032 memory: 2787 loss: 0.1163 loss_sem_seg: 0.1163 2023/03/08 16:51:44 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 3.3132e-03 eta: 0:21:41 time: 1.0138 data_time: 0.0033 memory: 2601 loss: 0.1206 loss_sem_seg: 0.1206 2023/03/08 16:52:34 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 3.0729e-03 eta: 0:20:52 time: 1.0106 data_time: 0.0034 memory: 2616 loss: 0.1147 loss_sem_seg: 0.1147 2023/03/08 16:53:26 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 2.8415e-03 eta: 0:20:04 time: 1.0341 data_time: 0.0033 memory: 2653 loss: 0.1137 loss_sem_seg: 0.1137 2023/03/08 16:54:08 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 16:54:08 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/03/08 16:54:28 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:02:52 time: 0.3756 data_time: 0.0074 memory: 2560 2023/03/08 16:54:45 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:02:25 time: 0.3374 data_time: 0.0044 memory: 744 2023/03/08 16:55:02 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:02:05 time: 0.3345 data_time: 0.0047 memory: 747 2023/03/08 16:55:19 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:01:47 time: 0.3388 data_time: 0.0045 memory: 737 2023/03/08 16:55:36 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:01:29 time: 0.3377 data_time: 0.0046 memory: 752 2023/03/08 16:55:52 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:01:11 time: 0.3265 data_time: 0.0047 memory: 713 2023/03/08 16:56:08 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:00:54 time: 0.3278 data_time: 0.0046 memory: 729 2023/03/08 16:56:25 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:00:36 time: 0.3362 data_time: 0.0044 memory: 731 2023/03/08 16:56:41 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:19 time: 0.3106 data_time: 0.0047 memory: 747 2023/03/08 16:56:51 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:02 time: 0.2046 data_time: 0.0049 memory: 735 2023/03/08 16:57:24 - 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 | 0.2941 | 0.6413 | 0.8246 | 0.6439 | 0.7117 | 0.8544 | 0.0000 | 0.9354 | 0.4684 | 0.8109 | 0.0007 | 0.9159 | 0.6567 | 0.8847 | 0.6538 | 0.7530 | 0.6412 | 0.4944 | 0.6396 | 0.9224 | 0.7012 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 16:57:24 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9678 bicycle: 0.2941 motorcycle: 0.6413 truck: 0.8246 bus: 0.6439 person: 0.7117 bicyclist: 0.8544 motorcyclist: 0.0000 road: 0.9354 parking: 0.4684 sidewalk: 0.8109 other-ground: 0.0007 building: 0.9159 fence: 0.6567 vegetation: 0.8847 trunck: 0.6538 terrian: 0.7530 pole: 0.6412 traffic-sign: 0.4944 miou: 0.6396 acc: 0.9224 acc_cls: 0.7012 2023/03/08 16:58:12 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 2.4224e-03 eta: 0:18:31 time: 0.9710 data_time: 0.0186 memory: 2738 loss: 0.1202 loss_sem_seg: 0.1202 2023/03/08 16:58:56 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 2.2173e-03 eta: 0:17:42 time: 0.8741 data_time: 0.0035 memory: 2678 loss: 0.1235 loss_sem_seg: 0.1235 2023/03/08 16:59:40 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 2.0212e-03 eta: 0:16:53 time: 0.8787 data_time: 0.0036 memory: 2585 loss: 0.1141 loss_sem_seg: 0.1141 2023/03/08 17:00:26 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 1.8342e-03 eta: 0:16:05 time: 0.9114 data_time: 0.0035 memory: 2676 loss: 0.1144 loss_sem_seg: 0.1144 2023/03/08 17:01:21 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 1.6562e-03 eta: 0:15:17 time: 1.1065 data_time: 0.0034 memory: 2622 loss: 0.1149 loss_sem_seg: 0.1149 2023/03/08 17:01:29 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 17:02:04 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 1.4873e-03 eta: 0:14:28 time: 0.8676 data_time: 0.0034 memory: 2654 loss: 0.1231 loss_sem_seg: 0.1231 2023/03/08 17:02:45 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 1.3275e-03 eta: 0:13:39 time: 0.8104 data_time: 0.0035 memory: 2547 loss: 0.1276 loss_sem_seg: 0.1276 2023/03/08 17:03:24 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 1.1768e-03 eta: 0:12:50 time: 0.7769 data_time: 0.0033 memory: 2657 loss: 0.1137 loss_sem_seg: 0.1137 2023/03/08 17:04:04 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 1.0352e-03 eta: 0:12:01 time: 0.7977 data_time: 0.0035 memory: 2634 loss: 0.1213 loss_sem_seg: 0.1213 2023/03/08 17:04:44 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 9.0272e-04 eta: 0:11:13 time: 0.8097 data_time: 0.0033 memory: 2683 loss: 0.1100 loss_sem_seg: 0.1100 2023/03/08 17:05:22 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 7.7936e-04 eta: 0:10:24 time: 0.7645 data_time: 0.0033 memory: 2630 loss: 0.1227 loss_sem_seg: 0.1227 2023/03/08 17:06:02 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 6.6515e-04 eta: 0:09:35 time: 0.7918 data_time: 0.0035 memory: 2558 loss: 0.1161 loss_sem_seg: 0.1161 2023/03/08 17:06:42 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 5.6009e-04 eta: 0:08:47 time: 0.7921 data_time: 0.0034 memory: 2659 loss: 0.1159 loss_sem_seg: 0.1159 2023/03/08 17:07:21 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 4.6418e-04 eta: 0:07:58 time: 0.7987 data_time: 0.0034 memory: 2626 loss: 0.1198 loss_sem_seg: 0.1198 2023/03/08 17:08:02 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 3.7744e-04 eta: 0:07:10 time: 0.8024 data_time: 0.0033 memory: 2618 loss: 0.1175 loss_sem_seg: 0.1175 2023/03/08 17:08:41 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 2.9986e-04 eta: 0:06:21 time: 0.7923 data_time: 0.0034 memory: 2563 loss: 0.1149 loss_sem_seg: 0.1149 2023/03/08 17:09:21 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 2.3147e-04 eta: 0:05:33 time: 0.7907 data_time: 0.0033 memory: 2567 loss: 0.1177 loss_sem_seg: 0.1177 2023/03/08 17:10:02 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 1.7226e-04 eta: 0:04:45 time: 0.8177 data_time: 0.0033 memory: 2599 loss: 0.1098 loss_sem_seg: 0.1098 2023/03/08 17:10:40 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 1.2223e-04 eta: 0:03:56 time: 0.7743 data_time: 0.0034 memory: 2591 loss: 0.1239 loss_sem_seg: 0.1239 2023/03/08 17:11:19 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.1397e-05 eta: 0:03:08 time: 0.7727 data_time: 0.0033 memory: 2704 loss: 0.1140 loss_sem_seg: 0.1140 2023/03/08 17:11:59 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 4.9756e-05 eta: 0:02:20 time: 0.7965 data_time: 0.0033 memory: 2649 loss: 0.1170 loss_sem_seg: 0.1170 2023/03/08 17:12:38 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 2.7311e-05 eta: 0:01:32 time: 0.7852 data_time: 0.0034 memory: 2631 loss: 0.1170 loss_sem_seg: 0.1170 2023/03/08 17:13:17 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 1.4064e-05 eta: 0:00:44 time: 0.7767 data_time: 0.0034 memory: 2568 loss: 0.1083 loss_sem_seg: 0.1083 2023/03/08 17:13:50 - mmengine - INFO - Exp name: spvcnn_w32_8xb2-15e_semantickitti_20230308_113324 2023/03/08 17:13:50 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/08 17:14:04 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:01:52 time: 0.2458 data_time: 0.0082 memory: 2602 2023/03/08 17:14:15 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:01:33 time: 0.2129 data_time: 0.0052 memory: 744 2023/03/08 17:14:25 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:01:19 time: 0.2071 data_time: 0.0052 memory: 747 2023/03/08 17:14:36 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:01:07 time: 0.2123 data_time: 0.0045 memory: 737 2023/03/08 17:14:46 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:00:56 time: 0.2102 data_time: 0.0044 memory: 752 2023/03/08 17:14:56 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:00:44 time: 0.2003 data_time: 0.0043 memory: 713 2023/03/08 17:15:06 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:00:33 time: 0.2023 data_time: 0.0043 memory: 729 2023/03/08 17:15:17 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:00:23 time: 0.2117 data_time: 0.0042 memory: 731 2023/03/08 17:15:27 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:12 time: 0.2079 data_time: 0.0053 memory: 747 2023/03/08 17:15:36 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:01 time: 0.1767 data_time: 0.0050 memory: 735 2023/03/08 17:16:00 - 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 | 0.9686 | 0.3161 | 0.6492 | 0.8338 | 0.6588 | 0.7114 | 0.8516 | 0.0000 | 0.9367 | 0.4815 | 0.8117 | 0.0008 | 0.9154 | 0.6538 | 0.8834 | 0.6542 | 0.7494 | 0.6441 | 0.4948 | 0.6429 | 0.9222 | 0.7054 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/08 17:16:00 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9686 bicycle: 0.3161 motorcycle: 0.6492 truck: 0.8338 bus: 0.6588 person: 0.7114 bicyclist: 0.8516 motorcyclist: 0.0000 road: 0.9367 parking: 0.4815 sidewalk: 0.8117 other-ground: 0.0008 building: 0.9154 fence: 0.6538 vegetation: 0.8834 trunck: 0.6542 terrian: 0.7494 pole: 0.6441 traffic-sign: 0.4948 miou: 0.6429 acc: 0.9222 acc_cls: 0.7054