2023/03/21 01:17:03 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0] CUDA available: True numpy_random_seed: 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) 7.5.0 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 8.2.1 - Built with CuDNN 8.3.2 - 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: 0.7.0 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 ------------------------------------------------------------ 2023/03/21 01:17:03 - mmengine - INFO - Config: dataset_type = 'SemanticKittiDataset' data_root = 'data/semantickitti/' class_names = [ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ] labels_map = dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }) metainfo = dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259) input_modality = dict(use_lidar=True, use_camera=False) backend_args = None train_pipeline = [ dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti'), dict(type='PointSegClassMapping'), dict( type='GlobalRotScaleTrans', rot_range=[0.0, 6.28318531], scale_ratio_range=[0.95, 1.05], translation_std=[0, 0, 0]), dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ] test_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) ] eval_pipeline = [ dict( type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict(type='Pack3DDetInputs', keys=['points', '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), 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, backend_args=None))) 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, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict( type='Pack3DDetInputs', keys=['points', '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, backend_args=None))) 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, backend_args=None), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti', backend_args=None), dict(type='PointSegClassMapping'), dict( type='Pack3DDetInputs', keys=['points', '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, backend_args=None))) val_evaluator = dict(type='SegMetric') test_evaluator = dict(type='SegMetric') vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='Det3DLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') 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=16, encoder_channels=[16, 32, 64, 128], decoder_channels=[128, 64, 48, 48], num_stages=4, drop_ratio=0.3), decode_head=dict( type='MinkUNetHead', channels=48, 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_w16_8xb2-15e_semantickitti' 2023/03/21 01:17:08 - 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 -------------------- 2023/03/21 01:17:09 - mmengine - WARNING - The prefix is not set in metric class SegMetric. Name of parameter - Initialization information backbone.conv_input.0.net.0.kernel - torch.Size([27, 4, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.0.kernel - torch.Size([27, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.0.kernel - torch.Size([8, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.0.kernel - torch.Size([27, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.3.kernel - torch.Size([27, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.0.kernel - torch.Size([27, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.3.kernel - torch.Size([27, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.0.kernel - torch.Size([8, 16, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.weight - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.bias - torch.Size([16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.0.kernel - torch.Size([27, 16, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.0.kernel - torch.Size([16, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.0.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.3.kernel - torch.Size([27, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.0.kernel - torch.Size([8, 32, 32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.weight - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.bias - torch.Size([32]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.0.kernel - torch.Size([27, 32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.0.kernel - torch.Size([32, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.0.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.0.kernel - torch.Size([8, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.0.kernel - torch.Size([27, 64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.0.kernel - torch.Size([64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.0.kernel - torch.Size([8, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.0.kernel - torch.Size([27, 192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.0.kernel - torch.Size([192, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.0.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.3.kernel - torch.Size([27, 128, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.0.kernel - torch.Size([8, 128, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.0.kernel - torch.Size([27, 96, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.0.kernel - torch.Size([96, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.0.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.3.kernel - torch.Size([27, 64, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.0.kernel - torch.Size([8, 64, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.0.kernel - torch.Size([27, 64, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.3.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.0.kernel - torch.Size([64, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.0.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.3.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.0.kernel - torch.Size([8, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.0.kernel - torch.Size([27, 64, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.3.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.0.kernel - torch.Size([64, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.0.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.3.kernel - torch.Size([27, 48, 48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.weight - torch.Size([128, 16]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.weight - torch.Size([64, 128]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.weight - torch.Size([48, 64]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.weight - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.bias - torch.Size([48]): The value is the same before and after calling `init_weights` of MinkUNet decode_head.conv_seg.weight - torch.Size([19, 48]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/03/21 01:17:12 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/03/21 01:17:12 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/03/21 01:17:12 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/work_dirs/spvcnn_w16_8xb2-15e_semantickitti. 2023/03/21 01:18:14 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 9.5998e-02 eta: 6:09:46 time: 1.2402 data_time: 0.0068 memory: 1354 loss: 1.6023 loss_sem_seg: 1.6023 2023/03/21 01:19:11 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 1.9199e-01 eta: 5:56:09 time: 1.1556 data_time: 0.0037 memory: 1408 loss: 0.9524 loss_sem_seg: 0.9524 2023/03/21 01:20:07 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 2.3996e-01 eta: 5:46:22 time: 1.1090 data_time: 0.0036 memory: 1387 loss: 0.7683 loss_sem_seg: 0.7683 2023/03/21 01:21:04 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 2.3993e-01 eta: 5:44:03 time: 1.1501 data_time: 0.0035 memory: 1394 loss: 0.6760 loss_sem_seg: 0.6760 2023/03/21 01:22:01 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 2.3989e-01 eta: 5:41:23 time: 1.1349 data_time: 0.0036 memory: 1376 loss: 0.5934 loss_sem_seg: 0.5934 2023/03/21 01:22:59 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 2.3984e-01 eta: 5:40:17 time: 1.1550 data_time: 0.0035 memory: 1332 loss: 0.5622 loss_sem_seg: 0.5622 2023/03/21 01:23:57 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 2.3978e-01 eta: 5:39:11 time: 1.1544 data_time: 0.0035 memory: 1382 loss: 0.5476 loss_sem_seg: 0.5476 2023/03/21 01:24:52 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 2.3971e-01 eta: 5:36:50 time: 1.1188 data_time: 0.0036 memory: 1422 loss: 0.5111 loss_sem_seg: 0.5111 2023/03/21 01:25:50 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 2.3963e-01 eta: 5:35:45 time: 1.1489 data_time: 0.0037 memory: 1370 loss: 0.4668 loss_sem_seg: 0.4668 2023/03/21 01:26:45 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 2.3954e-01 eta: 5:33:29 time: 1.1069 data_time: 0.0037 memory: 1373 loss: 0.4617 loss_sem_seg: 0.4617 2023/03/21 01:27:42 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 2.3945e-01 eta: 5:32:25 time: 1.1426 data_time: 0.0034 memory: 1351 loss: 0.4726 loss_sem_seg: 0.4726 2023/03/21 01:28:40 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 2.3934e-01 eta: 5:31:27 time: 1.1468 data_time: 0.0038 memory: 1357 loss: 0.4521 loss_sem_seg: 0.4521 2023/03/21 01:29:37 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 2.3923e-01 eta: 5:30:34 time: 1.1499 data_time: 0.0037 memory: 1436 loss: 0.4636 loss_sem_seg: 0.4636 2023/03/21 01:30:34 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 2.3910e-01 eta: 5:29:17 time: 1.1316 data_time: 0.0034 memory: 1351 loss: 0.4329 loss_sem_seg: 0.4329 2023/03/21 01:31:30 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 2.3897e-01 eta: 5:27:49 time: 1.1193 data_time: 0.0035 memory: 1418 loss: 0.4306 loss_sem_seg: 0.4306 2023/03/21 01:32:27 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 2.3883e-01 eta: 5:26:47 time: 1.1391 data_time: 0.0036 memory: 1382 loss: 0.4139 loss_sem_seg: 0.4139 2023/03/21 01:33:25 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 2.3868e-01 eta: 5:26:14 time: 1.1687 data_time: 0.0036 memory: 1322 loss: 0.3904 loss_sem_seg: 0.3904 2023/03/21 01:34:23 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 2.3852e-01 eta: 5:25:26 time: 1.1549 data_time: 0.0034 memory: 1385 loss: 0.3828 loss_sem_seg: 0.3828 2023/03/21 01:35:19 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 2.3835e-01 eta: 5:24:11 time: 1.1264 data_time: 0.0036 memory: 1397 loss: 0.3724 loss_sem_seg: 0.3724 2023/03/21 01:36:15 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 01:36:15 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 2.3817e-01 eta: 5:22:52 time: 1.1191 data_time: 0.0035 memory: 1425 loss: 0.3936 loss_sem_seg: 0.3936 2023/03/21 01:37:11 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 2.3798e-01 eta: 5:21:29 time: 1.1117 data_time: 0.0037 memory: 1408 loss: 0.3812 loss_sem_seg: 0.3812 2023/03/21 01:38:08 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 2.3778e-01 eta: 5:20:35 time: 1.1463 data_time: 0.0036 memory: 1375 loss: 0.3884 loss_sem_seg: 0.3884 2023/03/21 01:39:05 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 2.3758e-01 eta: 5:19:34 time: 1.1369 data_time: 0.0036 memory: 1393 loss: 0.3635 loss_sem_seg: 0.3635 2023/03/21 01:39:58 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 01:39:58 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/03/21 01:40:30 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:04:52 time: 0.6369 data_time: 0.0061 memory: 1368 2023/03/21 01:41:02 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:04:18 time: 0.6294 data_time: 0.0046 memory: 328 2023/03/21 01:41:31 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:03:42 time: 0.5896 data_time: 0.0046 memory: 330 2023/03/21 01:41:53 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:02:56 time: 0.4335 data_time: 0.0044 memory: 324 2023/03/21 01:42:15 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:02:21 time: 0.4376 data_time: 0.0043 memory: 333 2023/03/21 01:42:38 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:01:51 time: 0.4718 data_time: 0.0043 memory: 312 2023/03/21 01:43:00 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:01:22 time: 0.4363 data_time: 0.0042 memory: 319 2023/03/21 01:43:22 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:00:55 time: 0.4313 data_time: 0.0042 memory: 322 2023/03/21 01:43:43 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:29 time: 0.4317 data_time: 0.0044 memory: 333 2023/03/21 01:44:05 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:04 time: 0.4271 data_time: 0.0044 memory: 322 2023/03/21 01:44:36 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9145 | 0.0000 | 0.0432 | 0.3091 | 0.0908 | 0.0288 | 0.0000 | 0.0000 | 0.8662 | 0.1708 | 0.7177 | 0.0000 | 0.8602 | 0.5014 | 0.8563 | 0.5507 | 0.7147 | 0.5450 | 0.1919 | 0.3874 | 0.8839 | 0.4572 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 01:44:36 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.9145 bicycle: 0.0000 motorcycle: 0.0432 truck: 0.3091 bus: 0.0908 person: 0.0288 bicyclist: 0.0000 motorcyclist: 0.0000 road: 0.8662 parking: 0.1708 sidewalk: 0.7177 other-ground: 0.0000 building: 0.8602 fence: 0.5014 vegetation: 0.8563 trunck: 0.5507 terrian: 0.7147 pole: 0.5450 traffic-sign: 0.1919 miou: 0.3874 acc: 0.8839 acc_cls: 0.4572data_time: 0.0043 time: 0.4339 2023/03/21 01:45:20 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 2.3716e-01 eta: 5:14:54 time: 0.8836 data_time: 0.0186 memory: 1401 loss: 0.3748 loss_sem_seg: 0.3748 2023/03/21 01:46:26 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 2.3693e-01 eta: 5:15:54 time: 1.3127 data_time: 0.0034 memory: 1363 loss: 0.3413 loss_sem_seg: 0.3413 2023/03/21 01:47:23 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 2.3669e-01 eta: 5:15:00 time: 1.1451 data_time: 0.0036 memory: 1351 loss: 0.3399 loss_sem_seg: 0.3399 2023/03/21 01:48:21 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 2.3644e-01 eta: 5:14:08 time: 1.1467 data_time: 0.0038 memory: 1320 loss: 0.3817 loss_sem_seg: 0.3817 2023/03/21 01:49:17 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 2.3618e-01 eta: 5:12:59 time: 1.1181 data_time: 0.0038 memory: 1399 loss: 0.3487 loss_sem_seg: 0.3487 2023/03/21 01:50:13 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 2.3591e-01 eta: 5:11:59 time: 1.1324 data_time: 0.0036 memory: 1392 loss: 0.3461 loss_sem_seg: 0.3461 2023/03/21 01:51:09 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 2.3563e-01 eta: 5:10:49 time: 1.1145 data_time: 0.0036 memory: 1326 loss: 0.3573 loss_sem_seg: 0.3573 2023/03/21 01:52:06 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 2.3535e-01 eta: 5:09:52 time: 1.1379 data_time: 0.0037 memory: 1345 loss: 0.3551 loss_sem_seg: 0.3551 2023/03/21 01:53:01 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 2.3506e-01 eta: 5:08:39 time: 1.1040 data_time: 0.0034 memory: 1353 loss: 0.3225 loss_sem_seg: 0.3225 2023/03/21 01:53:46 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 2.3475e-01 eta: 5:05:54 time: 0.9109 data_time: 0.0035 memory: 1402 loss: 0.3331 loss_sem_seg: 0.3331 2023/03/21 01:54:33 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 2.3444e-01 eta: 5:03:25 time: 0.9299 data_time: 0.0035 memory: 1382 loss: 0.3674 loss_sem_seg: 0.3674 2023/03/21 01:55:31 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 2.3412e-01 eta: 5:02:44 time: 1.1580 data_time: 0.0034 memory: 1362 loss: 0.3244 loss_sem_seg: 0.3244 2023/03/21 01:56:28 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 2.3379e-01 eta: 5:01:55 time: 1.1427 data_time: 0.0034 memory: 1350 loss: 0.3219 loss_sem_seg: 0.3219 2023/03/21 01:57:25 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 2.3345e-01 eta: 5:01:05 time: 1.1400 data_time: 0.0034 memory: 1361 loss: 0.3335 loss_sem_seg: 0.3335 2023/03/21 01:58:23 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 2.3311e-01 eta: 5:00:19 time: 1.1501 data_time: 0.0033 memory: 1403 loss: 0.3335 loss_sem_seg: 0.3335 2023/03/21 01:59:20 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 2.3275e-01 eta: 4:59:32 time: 1.1511 data_time: 0.0033 memory: 1341 loss: 0.3073 loss_sem_seg: 0.3073 2023/03/21 01:59:24 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 02:00:16 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 2.3239e-01 eta: 4:58:32 time: 1.1188 data_time: 0.0035 memory: 1366 loss: 0.3049 loss_sem_seg: 0.3049 2023/03/21 02:01:13 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 2.3201e-01 eta: 4:57:44 time: 1.1475 data_time: 0.0038 memory: 1364 loss: 0.3384 loss_sem_seg: 0.3384 2023/03/21 02:02:10 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 2.3163e-01 eta: 4:56:46 time: 1.1249 data_time: 0.0037 memory: 1388 loss: 0.3085 loss_sem_seg: 0.3085 2023/03/21 02:03:06 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 2.3124e-01 eta: 4:55:47 time: 1.1190 data_time: 0.0034 memory: 1394 loss: 0.3340 loss_sem_seg: 0.3340 2023/03/21 02:04:03 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 2.3085e-01 eta: 4:54:55 time: 1.1391 data_time: 0.0035 memory: 1314 loss: 0.2924 loss_sem_seg: 0.2924 2023/03/21 02:04:58 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 2.3044e-01 eta: 4:53:55 time: 1.1172 data_time: 0.0035 memory: 1348 loss: 0.3183 loss_sem_seg: 0.3183 2023/03/21 02:05:55 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 2.3002e-01 eta: 4:53:00 time: 1.1304 data_time: 0.0033 memory: 1387 loss: 0.3230 loss_sem_seg: 0.3230 2023/03/21 02:06:47 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 02:06:47 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/03/21 02:07:21 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:04:56 time: 0.6464 data_time: 0.0077 memory: 1434 2023/03/21 02:07:52 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:04:21 time: 0.6323 data_time: 0.0049 memory: 328 2023/03/21 02:08:25 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:03:50 time: 0.6456 data_time: 0.0046 memory: 330 2023/03/21 02:08:57 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:03:18 time: 0.6468 data_time: 0.0046 memory: 324 2023/03/21 02:09:29 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:02:46 time: 0.6406 data_time: 0.0046 memory: 333 2023/03/21 02:09:56 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:02:10 time: 0.5295 data_time: 0.0051 memory: 312 2023/03/21 02:10:17 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:01:34 time: 0.4333 data_time: 0.0047 memory: 319 2023/03/21 02:10:39 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:01:02 time: 0.4326 data_time: 0.0045 memory: 322 2023/03/21 02:11:02 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:33 time: 0.4565 data_time: 0.0048 memory: 333 2023/03/21 02:11:24 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:04 time: 0.4561 data_time: 0.0045 memory: 322 2023/03/21 02:11:54 - 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 | 0.9310 | 0.0000 | 0.2372 | 0.5179 | 0.3107 | 0.2980 | 0.0431 | 0.0000 | 0.8876 | 0.2666 | 0.7495 | 0.0036 | 0.8649 | 0.5007 | 0.8590 | 0.5698 | 0.7181 | 0.5780 | 0.3151 | 0.4553 | 0.8925 | 0.5299 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 02:11:54 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.9310 bicycle: 0.0000 motorcycle: 0.2372 truck: 0.5179 bus: 0.3107 person: 0.2980 bicyclist: 0.0431 motorcyclist: 0.0000 road: 0.8876 parking: 0.2666 sidewalk: 0.7495 other-ground: 0.0036 building: 0.8649 fence: 0.5007 vegetation: 0.8590 trunck: 0.5698 terrian: 0.7181 pole: 0.5780 traffic-sign: 0.3151 miou: 0.4553 acc: 0.8925 acc_cls: 0.5299data_time: 0.0045 time: 0.4525 2023/03/21 02:12:38 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 2.2920e-01 eta: 4:49:55 time: 0.8800 data_time: 0.0204 memory: 1457 loss: 0.3179 loss_sem_seg: 0.3179 2023/03/21 02:13:21 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 2.2876e-01 eta: 4:47:34 time: 0.8514 data_time: 0.0036 memory: 1388 loss: 0.2935 loss_sem_seg: 0.2935 2023/03/21 02:14:12 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 2.2832e-01 eta: 4:46:09 time: 1.0210 data_time: 0.0036 memory: 1383 loss: 0.3066 loss_sem_seg: 0.3066 2023/03/21 02:15:17 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 2.2786e-01 eta: 4:46:10 time: 1.3059 data_time: 0.0039 memory: 1342 loss: 0.3001 loss_sem_seg: 0.3001 2023/03/21 02:16:13 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 2.2739e-01 eta: 4:45:12 time: 1.1101 data_time: 0.0037 memory: 1364 loss: 0.2982 loss_sem_seg: 0.2982 2023/03/21 02:17:08 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 2.2692e-01 eta: 4:44:15 time: 1.1140 data_time: 0.0037 memory: 1386 loss: 0.2927 loss_sem_seg: 0.2927 2023/03/21 02:18:07 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 2.2644e-01 eta: 4:43:32 time: 1.1662 data_time: 0.0036 memory: 1399 loss: 0.3035 loss_sem_seg: 0.3035 2023/03/21 02:19:03 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 2.2595e-01 eta: 4:42:37 time: 1.1235 data_time: 0.0037 memory: 1371 loss: 0.2949 loss_sem_seg: 0.2949 2023/03/21 02:20:00 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 2.2545e-01 eta: 4:41:46 time: 1.1400 data_time: 0.0037 memory: 1326 loss: 0.2844 loss_sem_seg: 0.2844 2023/03/21 02:20:56 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 2.2495e-01 eta: 4:40:53 time: 1.1306 data_time: 0.0038 memory: 1379 loss: 0.2842 loss_sem_seg: 0.2842 2023/03/21 02:21:53 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 2.2443e-01 eta: 4:40:03 time: 1.1426 data_time: 0.0035 memory: 1363 loss: 0.2952 loss_sem_seg: 0.2952 2023/03/21 02:22:50 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 2.2391e-01 eta: 4:39:09 time: 1.1275 data_time: 0.0038 memory: 1389 loss: 0.2848 loss_sem_seg: 0.2848 2023/03/21 02:22:59 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 02:23:47 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 2.2338e-01 eta: 4:38:17 time: 1.1396 data_time: 0.0035 memory: 1365 loss: 0.2720 loss_sem_seg: 0.2720 2023/03/21 02:24:45 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 2.2285e-01 eta: 4:37:34 time: 1.1730 data_time: 0.0036 memory: 1383 loss: 0.2954 loss_sem_seg: 0.2954 2023/03/21 02:25:40 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 2.2230e-01 eta: 4:36:30 time: 1.0873 data_time: 0.0035 memory: 1352 loss: 0.2990 loss_sem_seg: 0.2990 2023/03/21 02:26:26 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 2.2175e-01 eta: 4:34:49 time: 0.9282 data_time: 0.0035 memory: 1413 loss: 0.2886 loss_sem_seg: 0.2886 2023/03/21 02:27:15 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 2.2119e-01 eta: 4:33:22 time: 0.9810 data_time: 0.0035 memory: 1381 loss: 0.2719 loss_sem_seg: 0.2719 2023/03/21 02:28:14 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 2.2062e-01 eta: 4:32:38 time: 1.1673 data_time: 0.0036 memory: 1416 loss: 0.2810 loss_sem_seg: 0.2810 2023/03/21 02:29:11 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 2.2004e-01 eta: 4:31:49 time: 1.1500 data_time: 0.0035 memory: 1441 loss: 0.2859 loss_sem_seg: 0.2859 2023/03/21 02:30:10 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 2.1946e-01 eta: 4:31:05 time: 1.1706 data_time: 0.0033 memory: 1349 loss: 0.2913 loss_sem_seg: 0.2913 2023/03/21 02:31:08 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 2.1887e-01 eta: 4:30:19 time: 1.1652 data_time: 0.0033 memory: 1339 loss: 0.2871 loss_sem_seg: 0.2871 2023/03/21 02:32:05 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 2.1827e-01 eta: 4:29:28 time: 1.1432 data_time: 0.0035 memory: 1358 loss: 0.2804 loss_sem_seg: 0.2804 2023/03/21 02:33:03 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 2.1766e-01 eta: 4:28:41 time: 1.1632 data_time: 0.0034 memory: 1361 loss: 0.2644 loss_sem_seg: 0.2644 2023/03/21 02:33:55 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 02:33:56 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/21 02:34:29 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:04:55 time: 0.6434 data_time: 0.0080 memory: 1390 2023/03/21 02:35:02 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:04:25 time: 0.6564 data_time: 0.0047 memory: 328 2023/03/21 02:35:34 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:03:53 time: 0.6483 data_time: 0.0047 memory: 330 2023/03/21 02:36:06 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:03:20 time: 0.6457 data_time: 0.0046 memory: 324 2023/03/21 02:36:39 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:02:47 time: 0.6473 data_time: 0.0046 memory: 333 2023/03/21 02:37:11 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:02:15 time: 0.6421 data_time: 0.0047 memory: 312 2023/03/21 02:37:43 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:01:42 time: 0.6415 data_time: 0.0047 memory: 319 2023/03/21 02:38:12 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:01:09 time: 0.5861 data_time: 0.0047 memory: 322 2023/03/21 02:38:36 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:36 time: 0.4802 data_time: 0.0047 memory: 333 2023/03/21 02:38:59 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:05 time: 0.4537 data_time: 0.0046 memory: 322 2023/03/21 02:39:28 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9338 | 0.0056 | 0.3522 | 0.3869 | 0.2513 | 0.3957 | 0.4938 | 0.0000 | 0.9056 | 0.3098 | 0.7627 | 0.0003 | 0.8809 | 0.5396 | 0.8723 | 0.6334 | 0.7326 | 0.5963 | 0.3381 | 0.4943 | 0.9013 | 0.5839 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 02:39:28 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9338 bicycle: 0.0056 motorcycle: 0.3522 truck: 0.3869 bus: 0.2513 person: 0.3957 bicyclist: 0.4938 motorcyclist: 0.0000 road: 0.9056 parking: 0.3098 sidewalk: 0.7627 other-ground: 0.0003 building: 0.8809 fence: 0.5396 vegetation: 0.8723 trunck: 0.6334 terrian: 0.7326 pole: 0.5963 traffic-sign: 0.3381 miou: 0.4943 acc: 0.9013 acc_cls: 0.5839data_time: 0.0046 time: 0.4549 2023/03/21 02:40:12 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 2.1647e-01 eta: 4:26:10 time: 0.8849 data_time: 0.0228 memory: 1414 loss: 0.2715 loss_sem_seg: 0.2715 2023/03/21 02:40:55 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 2.1585e-01 eta: 4:24:24 time: 0.8604 data_time: 0.0033 memory: 1363 loss: 0.2832 loss_sem_seg: 0.2832 2023/03/21 02:41:39 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 2.1521e-01 eta: 4:22:43 time: 0.8709 data_time: 0.0034 memory: 1396 loss: 0.2779 loss_sem_seg: 0.2779 2023/03/21 02:42:22 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 2.1457e-01 eta: 4:21:02 time: 0.8667 data_time: 0.0033 memory: 1406 loss: 0.2733 loss_sem_seg: 0.2733 2023/03/21 02:43:27 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 2.1392e-01 eta: 4:20:42 time: 1.3009 data_time: 0.0036 memory: 1362 loss: 0.2764 loss_sem_seg: 0.2764 2023/03/21 02:44:26 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 2.1326e-01 eta: 4:19:59 time: 1.1780 data_time: 0.0034 memory: 1340 loss: 0.2714 loss_sem_seg: 0.2714 2023/03/21 02:45:22 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 2.1259e-01 eta: 4:19:05 time: 1.1218 data_time: 0.0034 memory: 1435 loss: 0.2857 loss_sem_seg: 0.2857 2023/03/21 02:46:18 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 2.1192e-01 eta: 4:18:13 time: 1.1269 data_time: 0.0032 memory: 1387 loss: 0.2783 loss_sem_seg: 0.2783 2023/03/21 02:46:32 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 02:47:15 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 2.1124e-01 eta: 4:17:19 time: 1.1226 data_time: 0.0032 memory: 1434 loss: 0.2858 loss_sem_seg: 0.2858 2023/03/21 02:48:12 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 2.1056e-01 eta: 4:16:32 time: 1.1555 data_time: 0.0033 memory: 1415 loss: 0.2730 loss_sem_seg: 0.2730 2023/03/21 02:49:10 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 2.0986e-01 eta: 4:15:44 time: 1.1583 data_time: 0.0035 memory: 1379 loss: 0.2814 loss_sem_seg: 0.2814 2023/03/21 02:50:06 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 2.0916e-01 eta: 4:14:48 time: 1.1105 data_time: 0.0035 memory: 1375 loss: 0.2689 loss_sem_seg: 0.2689 2023/03/21 02:51:02 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 2.0846e-01 eta: 4:13:54 time: 1.1214 data_time: 0.0034 memory: 1422 loss: 0.2672 loss_sem_seg: 0.2672 2023/03/21 02:51:59 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 2.0774e-01 eta: 4:13:03 time: 1.1424 data_time: 0.0034 memory: 1346 loss: 0.2670 loss_sem_seg: 0.2670 2023/03/21 02:52:56 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 2.0702e-01 eta: 4:12:12 time: 1.1404 data_time: 0.0036 memory: 1401 loss: 0.2713 loss_sem_seg: 0.2713 2023/03/21 02:53:53 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 2.0630e-01 eta: 4:11:22 time: 1.1472 data_time: 0.0035 memory: 1392 loss: 0.2731 loss_sem_seg: 0.2731 2023/03/21 02:54:51 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 2.0556e-01 eta: 4:10:33 time: 1.1594 data_time: 0.0034 memory: 1326 loss: 0.2829 loss_sem_seg: 0.2829 2023/03/21 02:55:50 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 2.0482e-01 eta: 4:09:45 time: 1.1638 data_time: 0.0033 memory: 1354 loss: 0.2658 loss_sem_seg: 0.2658 2023/03/21 02:56:46 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 2.0408e-01 eta: 4:08:52 time: 1.1345 data_time: 0.0034 memory: 1387 loss: 0.2810 loss_sem_seg: 0.2810 2023/03/21 02:57:44 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 2.0333e-01 eta: 4:08:04 time: 1.1612 data_time: 0.0034 memory: 1373 loss: 0.2466 loss_sem_seg: 0.2466 2023/03/21 02:58:41 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 2.0257e-01 eta: 4:07:12 time: 1.1413 data_time: 0.0034 memory: 1396 loss: 0.2715 loss_sem_seg: 0.2715 2023/03/21 02:59:38 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 2.0180e-01 eta: 4:06:18 time: 1.1272 data_time: 0.0033 memory: 1353 loss: 0.2652 loss_sem_seg: 0.2652 2023/03/21 03:00:35 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 2.0103e-01 eta: 4:05:25 time: 1.1366 data_time: 0.0033 memory: 1371 loss: 0.2655 loss_sem_seg: 0.2655 2023/03/21 03:01:28 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:01:28 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/03/21 03:02:02 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:05:07 time: 0.6689 data_time: 0.0080 memory: 1325 2023/03/21 03:02:34 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:04:25 time: 0.6288 data_time: 0.0048 memory: 328 2023/03/21 03:03:07 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:03:53 time: 0.6550 data_time: 0.0049 memory: 330 2023/03/21 03:03:38 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:03:19 time: 0.6361 data_time: 0.0046 memory: 324 2023/03/21 03:04:10 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:02:47 time: 0.6382 data_time: 0.0050 memory: 333 2023/03/21 03:04:42 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:02:14 time: 0.6343 data_time: 0.0046 memory: 312 2023/03/21 03:05:14 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:01:42 time: 0.6339 data_time: 0.0047 memory: 319 2023/03/21 03:05:45 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:01:09 time: 0.6354 data_time: 0.0047 memory: 322 2023/03/21 03:06:17 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:37 time: 0.6355 data_time: 0.0048 memory: 333 2023/03/21 03:06:48 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:05 time: 0.6166 data_time: 0.0048 memory: 322 2023/03/21 03:07:23 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9365 | 0.0160 | 0.3529 | 0.6235 | 0.3316 | 0.5121 | 0.6133 | 0.0000 | 0.9053 | 0.3165 | 0.7660 | 0.0083 | 0.8824 | 0.5469 | 0.8676 | 0.5554 | 0.7365 | 0.5977 | 0.4055 | 0.5249 | 0.9016 | 0.6160 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 03:07:23 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9365 bicycle: 0.0160 motorcycle: 0.3529 truck: 0.6235 bus: 0.3316 person: 0.5121 bicyclist: 0.6133 motorcyclist: 0.0000 road: 0.9053 parking: 0.3165 sidewalk: 0.7660 other-ground: 0.0083 building: 0.8824 fence: 0.5469 vegetation: 0.8676 trunck: 0.5554 terrian: 0.7365 pole: 0.5977 traffic-sign: 0.4055 miou: 0.5249 acc: 0.9016 acc_cls: 0.6160data_time: 0.0047 time: 0.6059 2023/03/21 03:08:10 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 1.9953e-01 eta: 4:03:17 time: 0.9271 data_time: 0.0201 memory: 1361 loss: 0.2656 loss_sem_seg: 0.2656 2023/03/21 03:08:54 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 1.9874e-01 eta: 4:01:50 time: 0.8793 data_time: 0.0033 memory: 1397 loss: 0.2591 loss_sem_seg: 0.2591 2023/03/21 03:09:36 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 1.9794e-01 eta: 4:00:20 time: 0.8509 data_time: 0.0034 memory: 1404 loss: 0.2483 loss_sem_seg: 0.2483 2023/03/21 03:10:20 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 1.9714e-01 eta: 3:58:54 time: 0.8707 data_time: 0.0033 memory: 1394 loss: 0.2619 loss_sem_seg: 0.2619 2023/03/21 03:10:34 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:11:03 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 1.9633e-01 eta: 3:57:28 time: 0.8657 data_time: 0.0034 memory: 1373 loss: 0.2630 loss_sem_seg: 0.2630 2023/03/21 03:11:54 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 1.9552e-01 eta: 3:56:22 time: 1.0208 data_time: 0.0034 memory: 1350 loss: 0.2593 loss_sem_seg: 0.2593 2023/03/21 03:12:57 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 1.9470e-01 eta: 3:55:45 time: 1.2511 data_time: 0.0035 memory: 1315 loss: 0.2482 loss_sem_seg: 0.2482 2023/03/21 03:13:55 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 1.9388e-01 eta: 3:54:58 time: 1.1690 data_time: 0.0037 memory: 1357 loss: 0.2635 loss_sem_seg: 0.2635 2023/03/21 03:14:51 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 1.9304e-01 eta: 3:54:05 time: 1.1225 data_time: 0.0036 memory: 1408 loss: 0.2510 loss_sem_seg: 0.2510 2023/03/21 03:15:49 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 1.9221e-01 eta: 3:53:16 time: 1.1582 data_time: 0.0036 memory: 1365 loss: 0.2538 loss_sem_seg: 0.2538 2023/03/21 03:16:45 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 1.9137e-01 eta: 3:52:23 time: 1.1234 data_time: 0.0034 memory: 1337 loss: 0.2694 loss_sem_seg: 0.2694 2023/03/21 03:17:44 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 1.9052e-01 eta: 3:51:34 time: 1.1642 data_time: 0.0034 memory: 1380 loss: 0.2769 loss_sem_seg: 0.2769 2023/03/21 03:18:40 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 1.8967e-01 eta: 3:50:42 time: 1.1300 data_time: 0.0034 memory: 1369 loss: 0.2511 loss_sem_seg: 0.2511 2023/03/21 03:19:39 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 1.8881e-01 eta: 3:49:54 time: 1.1716 data_time: 0.0034 memory: 1416 loss: 0.2541 loss_sem_seg: 0.2541 2023/03/21 03:20:35 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 1.8794e-01 eta: 3:49:00 time: 1.1217 data_time: 0.0034 memory: 1418 loss: 0.2510 loss_sem_seg: 0.2510 2023/03/21 03:21:31 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 1.8708e-01 eta: 3:48:06 time: 1.1268 data_time: 0.0034 memory: 1407 loss: 0.2376 loss_sem_seg: 0.2376 2023/03/21 03:22:28 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 1.8620e-01 eta: 3:47:14 time: 1.1324 data_time: 0.0033 memory: 1436 loss: 0.2563 loss_sem_seg: 0.2563 2023/03/21 03:23:25 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 1.8532e-01 eta: 3:46:23 time: 1.1474 data_time: 0.0033 memory: 1330 loss: 0.2591 loss_sem_seg: 0.2591 2023/03/21 03:24:21 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 1.8444e-01 eta: 3:45:28 time: 1.1129 data_time: 0.0035 memory: 1329 loss: 0.2344 loss_sem_seg: 0.2344 2023/03/21 03:25:17 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 1.8355e-01 eta: 3:44:34 time: 1.1255 data_time: 0.0034 memory: 1394 loss: 0.2312 loss_sem_seg: 0.2312 2023/03/21 03:26:13 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 1.8266e-01 eta: 3:43:41 time: 1.1281 data_time: 0.0034 memory: 1337 loss: 0.2416 loss_sem_seg: 0.2416 2023/03/21 03:27:11 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 1.8176e-01 eta: 3:42:49 time: 1.1443 data_time: 0.0033 memory: 1339 loss: 0.2473 loss_sem_seg: 0.2473 2023/03/21 03:28:08 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 1.8086e-01 eta: 3:41:59 time: 1.1587 data_time: 0.0034 memory: 1370 loss: 0.2620 loss_sem_seg: 0.2620 2023/03/21 03:29:02 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:29:02 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/03/21 03:29:35 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:04:51 time: 0.6350 data_time: 0.0087 memory: 1328 2023/03/21 03:30:07 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:04:21 time: 0.6461 data_time: 0.0048 memory: 328 2023/03/21 03:30:40 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:03:51 time: 0.6544 data_time: 0.0047 memory: 330 2023/03/21 03:31:12 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:03:19 time: 0.6414 data_time: 0.0048 memory: 324 2023/03/21 03:31:44 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:02:47 time: 0.6472 data_time: 0.0046 memory: 333 2023/03/21 03:32:16 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:02:14 time: 0.6323 data_time: 0.0047 memory: 312 2023/03/21 03:32:48 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:01:42 time: 0.6449 data_time: 0.0046 memory: 319 2023/03/21 03:33:20 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:01:10 time: 0.6422 data_time: 0.0046 memory: 322 2023/03/21 03:33:52 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:37 time: 0.6403 data_time: 0.0047 memory: 333 2023/03/21 03:34:25 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:05 time: 0.6549 data_time: 0.0047 memory: 322 2023/03/21 03:34:58 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9407 | 0.0073 | 0.2988 | 0.4574 | 0.3213 | 0.4157 | 0.5650 | 0.0000 | 0.9121 | 0.3504 | 0.7725 | 0.0053 | 0.8855 | 0.5595 | 0.8860 | 0.6057 | 0.7700 | 0.5919 | 0.4078 | 0.5133 | 0.9096 | 0.5944 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 03:34:58 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9407 bicycle: 0.0073 motorcycle: 0.2988 truck: 0.4574 bus: 0.3213 person: 0.4157 bicyclist: 0.5650 motorcyclist: 0.0000 road: 0.9121 parking: 0.3504 sidewalk: 0.7725 other-ground: 0.0053 building: 0.8855 fence: 0.5595 vegetation: 0.8860 trunck: 0.6057 terrian: 0.7700 pole: 0.5919 traffic-sign: 0.4078 miou: 0.5133 acc: 0.9096 acc_cls: 0.5944data_time: 0.0046 time: 0.6486 2023/03/21 03:35:22 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:35:54 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 1.7911e-01 eta: 3:40:18 time: 1.1213 data_time: 0.0204 memory: 1425 loss: 0.2408 loss_sem_seg: 0.2408 2023/03/21 03:36:46 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 1.7819e-01 eta: 3:39:14 time: 1.0223 data_time: 0.0034 memory: 1371 loss: 0.2334 loss_sem_seg: 0.2334 2023/03/21 03:37:29 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 1.7727e-01 eta: 3:37:55 time: 0.8708 data_time: 0.0035 memory: 1390 loss: 0.2522 loss_sem_seg: 0.2522 2023/03/21 03:38:13 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 1.7635e-01 eta: 3:36:38 time: 0.8767 data_time: 0.0036 memory: 1390 loss: 0.2568 loss_sem_seg: 0.2568 2023/03/21 03:38:56 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 1.7542e-01 eta: 3:35:20 time: 0.8647 data_time: 0.0033 memory: 1371 loss: 0.2261 loss_sem_seg: 0.2261 2023/03/21 03:39:40 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 1.7448e-01 eta: 3:34:03 time: 0.8740 data_time: 0.0033 memory: 1426 loss: 0.2533 loss_sem_seg: 0.2533 2023/03/21 03:40:24 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 1.7354e-01 eta: 3:32:48 time: 0.8786 data_time: 0.0033 memory: 1374 loss: 0.2638 loss_sem_seg: 0.2638 2023/03/21 03:41:20 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 1.7260e-01 eta: 3:31:54 time: 1.1134 data_time: 0.0036 memory: 1386 loss: 0.2512 loss_sem_seg: 0.2512 2023/03/21 03:42:22 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 1.7165e-01 eta: 3:31:13 time: 1.2529 data_time: 0.0034 memory: 1418 loss: 0.2444 loss_sem_seg: 0.2444 2023/03/21 03:43:20 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 1.7070e-01 eta: 3:30:23 time: 1.1589 data_time: 0.0036 memory: 1385 loss: 0.2438 loss_sem_seg: 0.2438 2023/03/21 03:44:18 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 1.6975e-01 eta: 3:29:32 time: 1.1518 data_time: 0.0035 memory: 1394 loss: 0.2444 loss_sem_seg: 0.2444 2023/03/21 03:45:14 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 1.6879e-01 eta: 3:28:40 time: 1.1348 data_time: 0.0034 memory: 1361 loss: 0.2382 loss_sem_seg: 0.2382 2023/03/21 03:46:11 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 1.6783e-01 eta: 3:27:48 time: 1.1395 data_time: 0.0034 memory: 1374 loss: 0.2458 loss_sem_seg: 0.2458 2023/03/21 03:47:08 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 1.6686e-01 eta: 3:26:56 time: 1.1396 data_time: 0.0033 memory: 1374 loss: 0.2573 loss_sem_seg: 0.2573 2023/03/21 03:48:06 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 1.6590e-01 eta: 3:26:05 time: 1.1516 data_time: 0.0036 memory: 1390 loss: 0.2226 loss_sem_seg: 0.2226 2023/03/21 03:49:03 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 1.6492e-01 eta: 3:25:14 time: 1.1470 data_time: 0.0034 memory: 1381 loss: 0.2286 loss_sem_seg: 0.2286 2023/03/21 03:50:01 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 1.6395e-01 eta: 3:24:23 time: 1.1599 data_time: 0.0034 memory: 1368 loss: 0.2322 loss_sem_seg: 0.2322 2023/03/21 03:50:58 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 1.6297e-01 eta: 3:23:30 time: 1.1321 data_time: 0.0034 memory: 1398 loss: 0.2400 loss_sem_seg: 0.2400 2023/03/21 03:51:56 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 1.6199e-01 eta: 3:22:40 time: 1.1680 data_time: 0.0034 memory: 1329 loss: 0.2447 loss_sem_seg: 0.2447 2023/03/21 03:52:54 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 1.6100e-01 eta: 3:21:48 time: 1.1517 data_time: 0.0034 memory: 1392 loss: 0.2276 loss_sem_seg: 0.2276 2023/03/21 03:53:17 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:53:51 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 1.6001e-01 eta: 3:20:57 time: 1.1508 data_time: 0.0034 memory: 1365 loss: 0.2464 loss_sem_seg: 0.2464 2023/03/21 03:54:47 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 1.5902e-01 eta: 3:20:02 time: 1.1174 data_time: 0.0034 memory: 1370 loss: 0.2325 loss_sem_seg: 0.2325 2023/03/21 03:55:46 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 1.5802e-01 eta: 3:19:12 time: 1.1731 data_time: 0.0033 memory: 1354 loss: 0.2373 loss_sem_seg: 0.2373 2023/03/21 03:56:39 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 03:56:39 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/21 03:57:13 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:05:03 time: 0.6608 data_time: 0.0079 memory: 1344 2023/03/21 03:57:45 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:04:27 time: 0.6451 data_time: 0.0047 memory: 328 2023/03/21 03:58:17 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:03:52 time: 0.6349 data_time: 0.0046 memory: 330 2023/03/21 03:58:49 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:03:19 time: 0.6396 data_time: 0.0046 memory: 324 2023/03/21 03:59:21 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:02:46 time: 0.6345 data_time: 0.0046 memory: 333 2023/03/21 03:59:52 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:02:14 time: 0.6347 data_time: 0.0047 memory: 312 2023/03/21 04:00:25 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:01:42 time: 0.6547 data_time: 0.0047 memory: 319 2023/03/21 04:00:57 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:01:10 time: 0.6367 data_time: 0.0045 memory: 322 2023/03/21 04:01:29 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:37 time: 0.6489 data_time: 0.0047 memory: 333 2023/03/21 04:02:01 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:05 time: 0.6397 data_time: 0.0046 memory: 322 2023/03/21 04:02:36 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9451 | 0.0120 | 0.3655 | 0.5170 | 0.3594 | 0.5245 | 0.5897 | 0.0000 | 0.9050 | 0.3808 | 0.7714 | 0.0036 | 0.8913 | 0.5845 | 0.8815 | 0.6132 | 0.7566 | 0.6219 | 0.3969 | 0.5326 | 0.9086 | 0.6114 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 04:02:36 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9451 bicycle: 0.0120 motorcycle: 0.3655 truck: 0.5170 bus: 0.3594 person: 0.5245 bicyclist: 0.5897 motorcyclist: 0.0000 road: 0.9050 parking: 0.3808 sidewalk: 0.7714 other-ground: 0.0036 building: 0.8913 fence: 0.5845 vegetation: 0.8815 trunck: 0.6132 terrian: 0.7566 pole: 0.6219 traffic-sign: 0.3969 miou: 0.5326 acc: 0.9086 acc_cls: 0.6114data_time: 0.0045 time: 0.6452 2023/03/21 04:03:34 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 1.5610e-01 eta: 3:17:33 time: 1.1685 data_time: 0.0203 memory: 1419 loss: 0.2180 loss_sem_seg: 0.2180 2023/03/21 04:04:30 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 1.5510e-01 eta: 3:16:38 time: 1.1039 data_time: 0.0035 memory: 1368 loss: 0.2448 loss_sem_seg: 0.2448 2023/03/21 04:05:26 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 1.5410e-01 eta: 3:15:44 time: 1.1209 data_time: 0.0036 memory: 1374 loss: 0.2398 loss_sem_seg: 0.2398 2023/03/21 04:06:15 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 1.5309e-01 eta: 3:14:39 time: 0.9774 data_time: 0.0035 memory: 1377 loss: 0.2356 loss_sem_seg: 0.2356 2023/03/21 04:06:58 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 1.5208e-01 eta: 3:13:27 time: 0.8657 data_time: 0.0034 memory: 1353 loss: 0.2319 loss_sem_seg: 0.2319 2023/03/21 04:07:41 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 1.5106e-01 eta: 3:12:15 time: 0.8628 data_time: 0.0033 memory: 1388 loss: 0.2462 loss_sem_seg: 0.2462 2023/03/21 04:08:24 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 1.5005e-01 eta: 3:11:02 time: 0.8517 data_time: 0.0032 memory: 1349 loss: 0.2315 loss_sem_seg: 0.2315 2023/03/21 04:09:07 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 1.4903e-01 eta: 3:09:51 time: 0.8620 data_time: 0.0032 memory: 1357 loss: 0.2365 loss_sem_seg: 0.2365 2023/03/21 04:09:51 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 1.4801e-01 eta: 3:08:42 time: 0.8879 data_time: 0.0032 memory: 1328 loss: 0.2292 loss_sem_seg: 0.2292 2023/03/21 04:10:49 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 1.4698e-01 eta: 3:07:51 time: 1.1667 data_time: 0.0034 memory: 1357 loss: 0.2310 loss_sem_seg: 0.2310 2023/03/21 04:11:51 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 1.4596e-01 eta: 3:07:06 time: 1.2375 data_time: 0.0034 memory: 1353 loss: 0.2319 loss_sem_seg: 0.2319 2023/03/21 04:12:49 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 1.4493e-01 eta: 3:06:14 time: 1.1584 data_time: 0.0033 memory: 1345 loss: 0.2166 loss_sem_seg: 0.2166 2023/03/21 04:13:46 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 1.4390e-01 eta: 3:05:21 time: 1.1274 data_time: 0.0034 memory: 1398 loss: 0.2370 loss_sem_seg: 0.2370 2023/03/21 04:14:43 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 1.4287e-01 eta: 3:04:29 time: 1.1444 data_time: 0.0032 memory: 1362 loss: 0.2453 loss_sem_seg: 0.2453 2023/03/21 04:15:39 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 1.4184e-01 eta: 3:03:35 time: 1.1177 data_time: 0.0032 memory: 1343 loss: 0.2392 loss_sem_seg: 0.2392 2023/03/21 04:16:35 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 1.4081e-01 eta: 3:02:42 time: 1.1338 data_time: 0.0033 memory: 1330 loss: 0.2205 loss_sem_seg: 0.2205 2023/03/21 04:17:03 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 04:17:32 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 1.3977e-01 eta: 3:01:50 time: 1.1345 data_time: 0.0033 memory: 1355 loss: 0.2252 loss_sem_seg: 0.2252 2023/03/21 04:18:29 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 1.3873e-01 eta: 3:00:57 time: 1.1340 data_time: 0.0033 memory: 1329 loss: 0.2251 loss_sem_seg: 0.2251 2023/03/21 04:19:25 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 1.3770e-01 eta: 3:00:03 time: 1.1248 data_time: 0.0034 memory: 1397 loss: 0.2105 loss_sem_seg: 0.2105 2023/03/21 04:20:23 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 1.3666e-01 eta: 2:59:12 time: 1.1693 data_time: 0.0032 memory: 1368 loss: 0.2358 loss_sem_seg: 0.2358 2023/03/21 04:21:21 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 1.3562e-01 eta: 2:58:20 time: 1.1465 data_time: 0.0034 memory: 1376 loss: 0.2346 loss_sem_seg: 0.2346 2023/03/21 04:22:17 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 1.3457e-01 eta: 2:57:26 time: 1.1328 data_time: 0.0032 memory: 1361 loss: 0.2481 loss_sem_seg: 0.2481 2023/03/21 04:23:14 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 1.3353e-01 eta: 2:56:33 time: 1.1337 data_time: 0.0033 memory: 1350 loss: 0.2166 loss_sem_seg: 0.2166 2023/03/21 04:24:07 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 04:24:08 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/03/21 04:24:42 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:05:01 time: 0.6577 data_time: 0.0082 memory: 1376 2023/03/21 04:25:14 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:04:24 time: 0.6374 data_time: 0.0047 memory: 328 2023/03/21 04:25:46 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:03:51 time: 0.6398 data_time: 0.0046 memory: 330 2023/03/21 04:26:18 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:03:19 time: 0.6483 data_time: 0.0047 memory: 324 2023/03/21 04:26:50 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:02:47 time: 0.6451 data_time: 0.0047 memory: 333 2023/03/21 04:27:22 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:02:14 time: 0.6358 data_time: 0.0047 memory: 312 2023/03/21 04:27:55 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:01:42 time: 0.6499 data_time: 0.0046 memory: 319 2023/03/21 04:28:26 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:01:10 time: 0.6349 data_time: 0.0046 memory: 322 2023/03/21 04:28:58 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:37 time: 0.6335 data_time: 0.0048 memory: 333 2023/03/21 04:29:30 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:05 time: 0.6458 data_time: 0.0046 memory: 322 2023/03/21 04:30:04 - 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 | 0.9442 | 0.0348 | 0.4730 | 0.7076 | 0.4319 | 0.5139 | 0.7250 | 0.0000 | 0.9037 | 0.3410 | 0.7644 | 0.0013 | 0.8913 | 0.5844 | 0.8815 | 0.6106 | 0.7690 | 0.5959 | 0.4295 | 0.5581 | 0.9090 | 0.6269 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 04:30:04 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9442 bicycle: 0.0348 motorcycle: 0.4730 truck: 0.7076 bus: 0.4319 person: 0.5139 bicyclist: 0.7250 motorcyclist: 0.0000 road: 0.9037 parking: 0.3410 sidewalk: 0.7644 other-ground: 0.0013 building: 0.8913 fence: 0.5844 vegetation: 0.8815 trunck: 0.6106 terrian: 0.7690 pole: 0.5959 traffic-sign: 0.4295 miou: 0.5581 acc: 0.9090 acc_cls: 0.6269data_time: 0.0046 time: 0.6429 2023/03/21 04:31:03 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 1.3152e-01 eta: 2:54:54 time: 1.1674 data_time: 0.0221 memory: 1360 loss: 0.2166 loss_sem_seg: 0.2166 2023/03/21 04:32:00 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 1.3048e-01 eta: 2:54:01 time: 1.1488 data_time: 0.0035 memory: 1357 loss: 0.2182 loss_sem_seg: 0.2182 2023/03/21 04:32:57 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 1.2943e-01 eta: 2:53:09 time: 1.1446 data_time: 0.0035 memory: 1410 loss: 0.2231 loss_sem_seg: 0.2231 2023/03/21 04:33:55 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 1.2838e-01 eta: 2:52:16 time: 1.1576 data_time: 0.0038 memory: 1352 loss: 0.2205 loss_sem_seg: 0.2205 2023/03/21 04:34:49 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 1.2733e-01 eta: 2:51:20 time: 1.0802 data_time: 0.0038 memory: 1440 loss: 0.2173 loss_sem_seg: 0.2173 2023/03/21 04:35:37 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 1.2629e-01 eta: 2:50:17 time: 0.9488 data_time: 0.0039 memory: 1345 loss: 0.2269 loss_sem_seg: 0.2269 2023/03/21 04:36:20 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 1.2524e-01 eta: 2:49:09 time: 0.8575 data_time: 0.0038 memory: 1402 loss: 0.2085 loss_sem_seg: 0.2085 2023/03/21 04:37:04 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 1.2419e-01 eta: 2:48:02 time: 0.8790 data_time: 0.0037 memory: 1382 loss: 0.2225 loss_sem_seg: 0.2225 2023/03/21 04:37:47 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 1.2314e-01 eta: 2:46:55 time: 0.8751 data_time: 0.0036 memory: 1346 loss: 0.2168 loss_sem_seg: 0.2168 2023/03/21 04:38:30 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 1.2209e-01 eta: 2:45:48 time: 0.8604 data_time: 0.0034 memory: 1333 loss: 0.2240 loss_sem_seg: 0.2240 2023/03/21 04:39:18 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 1.2103e-01 eta: 2:44:46 time: 0.9443 data_time: 0.0035 memory: 1420 loss: 0.2164 loss_sem_seg: 0.2164 2023/03/21 04:40:18 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 1.1998e-01 eta: 2:43:56 time: 1.2031 data_time: 0.0035 memory: 1369 loss: 0.2048 loss_sem_seg: 0.2048 2023/03/21 04:40:51 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 04:41:17 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 1.1893e-01 eta: 2:43:06 time: 1.1932 data_time: 0.0034 memory: 1422 loss: 0.2351 loss_sem_seg: 0.2351 2023/03/21 04:42:14 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 1.1788e-01 eta: 2:42:12 time: 1.1219 data_time: 0.0034 memory: 1381 loss: 0.2088 loss_sem_seg: 0.2088 2023/03/21 04:43:10 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 1.1683e-01 eta: 2:41:19 time: 1.1292 data_time: 0.0033 memory: 1367 loss: 0.2062 loss_sem_seg: 0.2062 2023/03/21 04:44:07 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 1.1578e-01 eta: 2:40:26 time: 1.1379 data_time: 0.0033 memory: 1368 loss: 0.2028 loss_sem_seg: 0.2028 2023/03/21 04:45:04 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 1.1473e-01 eta: 2:39:33 time: 1.1378 data_time: 0.0034 memory: 1344 loss: 0.2146 loss_sem_seg: 0.2146 2023/03/21 04:45:59 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 1.1368e-01 eta: 2:38:39 time: 1.1061 data_time: 0.0036 memory: 1370 loss: 0.2190 loss_sem_seg: 0.2190 2023/03/21 04:46:44 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 1.1263e-01 eta: 2:37:35 time: 0.9007 data_time: 0.0033 memory: 1386 loss: 0.2212 loss_sem_seg: 0.2212 2023/03/21 04:47:33 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 1.1159e-01 eta: 2:36:34 time: 0.9763 data_time: 0.0033 memory: 1348 loss: 0.2086 loss_sem_seg: 0.2086 2023/03/21 04:48:30 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 1.1054e-01 eta: 2:35:41 time: 1.1351 data_time: 0.0033 memory: 1381 loss: 0.1970 loss_sem_seg: 0.1970 2023/03/21 04:49:27 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 1.0949e-01 eta: 2:34:48 time: 1.1443 data_time: 0.0033 memory: 1412 loss: 0.2238 loss_sem_seg: 0.2238 2023/03/21 04:50:23 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 1.0844e-01 eta: 2:33:55 time: 1.1238 data_time: 0.0034 memory: 1372 loss: 0.2047 loss_sem_seg: 0.2047 2023/03/21 04:51:17 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 04:51:17 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/03/21 04:51:50 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:04:56 time: 0.6468 data_time: 0.0080 memory: 1337 2023/03/21 04:52:23 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:04:24 time: 0.6480 data_time: 0.0047 memory: 328 2023/03/21 04:52:55 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:03:51 time: 0.6410 data_time: 0.0048 memory: 330 2023/03/21 04:53:28 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:03:20 time: 0.6588 data_time: 0.0047 memory: 324 2023/03/21 04:54:00 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:02:47 time: 0.6400 data_time: 0.0046 memory: 333 2023/03/21 04:54:32 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:02:15 time: 0.6513 data_time: 0.0046 memory: 312 2023/03/21 04:55:04 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:01:42 time: 0.6383 data_time: 0.0047 memory: 319 2023/03/21 04:55:36 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:01:10 time: 0.6384 data_time: 0.0046 memory: 322 2023/03/21 04:56:08 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:38 time: 0.6407 data_time: 0.0047 memory: 333 2023/03/21 04:56:40 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:05 time: 0.6328 data_time: 0.0047 memory: 322 2023/03/21 04:57:11 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9479 | 0.0231 | 0.4795 | 0.5989 | 0.4078 | 0.6118 | 0.6992 | 0.0000 | 0.9231 | 0.4028 | 0.7960 | 0.0012 | 0.8979 | 0.5863 | 0.8855 | 0.6223 | 0.7642 | 0.6290 | 0.4421 | 0.5641 | 0.9156 | 0.6481 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 04:57:11 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9479 bicycle: 0.0231 motorcycle: 0.4795 truck: 0.5989 bus: 0.4078 person: 0.6118 bicyclist: 0.6992 motorcyclist: 0.0000 road: 0.9231 parking: 0.4028 sidewalk: 0.7960 other-ground: 0.0012 building: 0.8979 fence: 0.5863 vegetation: 0.8855 trunck: 0.6223 terrian: 0.7642 pole: 0.6290 traffic-sign: 0.4421 miou: 0.5641 acc: 0.9156 acc_cls: 0.6481data_time: 0.0046 time: 0.6335 2023/03/21 04:58:09 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 1.0644e-01 eta: 2:32:15 time: 1.1570 data_time: 0.0230 memory: 1362 loss: 0.2222 loss_sem_seg: 0.2222 2023/03/21 04:59:06 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 1.0540e-01 eta: 2:31:22 time: 1.1380 data_time: 0.0035 memory: 1382 loss: 0.2068 loss_sem_seg: 0.2068 2023/03/21 05:00:03 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 1.0435e-01 eta: 2:30:29 time: 1.1422 data_time: 0.0035 memory: 1369 loss: 0.2038 loss_sem_seg: 0.2038 2023/03/21 05:01:01 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 1.0331e-01 eta: 2:29:36 time: 1.1437 data_time: 0.0036 memory: 1451 loss: 0.2192 loss_sem_seg: 0.2192 2023/03/21 05:01:57 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 1.0227e-01 eta: 2:28:42 time: 1.1346 data_time: 0.0034 memory: 1346 loss: 0.1885 loss_sem_seg: 0.1885 2023/03/21 05:02:54 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 1.0123e-01 eta: 2:27:49 time: 1.1314 data_time: 0.0032 memory: 1372 loss: 0.2094 loss_sem_seg: 0.2094 2023/03/21 05:03:46 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 1.0020e-01 eta: 2:26:52 time: 1.0527 data_time: 0.0033 memory: 1421 loss: 0.2258 loss_sem_seg: 0.2258 2023/03/21 05:04:33 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 9.9161e-02 eta: 2:25:50 time: 0.9268 data_time: 0.0034 memory: 1374 loss: 0.2091 loss_sem_seg: 0.2091 2023/03/21 05:05:01 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 05:05:17 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 9.8127e-02 eta: 2:24:47 time: 0.8826 data_time: 0.0032 memory: 1407 loss: 0.2120 loss_sem_seg: 0.2120 2023/03/21 05:06:00 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 9.7095e-02 eta: 2:23:43 time: 0.8639 data_time: 0.0032 memory: 1420 loss: 0.1985 loss_sem_seg: 0.1985 2023/03/21 05:06:44 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 9.6065e-02 eta: 2:22:39 time: 0.8706 data_time: 0.0031 memory: 1376 loss: 0.2120 loss_sem_seg: 0.2120 2023/03/21 05:07:29 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 9.5036e-02 eta: 2:21:37 time: 0.9120 data_time: 0.0032 memory: 1345 loss: 0.1944 loss_sem_seg: 0.1944 2023/03/21 05:08:20 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 9.4009e-02 eta: 2:20:40 time: 1.0074 data_time: 0.0032 memory: 1371 loss: 0.1917 loss_sem_seg: 0.1917 2023/03/21 05:09:22 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 9.2985e-02 eta: 2:19:50 time: 1.2377 data_time: 0.0033 memory: 1363 loss: 0.2127 loss_sem_seg: 0.2127 2023/03/21 05:10:19 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 9.1962e-02 eta: 2:18:58 time: 1.1452 data_time: 0.0033 memory: 1303 loss: 0.1917 loss_sem_seg: 0.1917 2023/03/21 05:11:15 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 9.0942e-02 eta: 2:18:04 time: 1.1194 data_time: 0.0033 memory: 1400 loss: 0.2013 loss_sem_seg: 0.2013 2023/03/21 05:12:13 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.9923e-02 eta: 2:17:12 time: 1.1632 data_time: 0.0033 memory: 1376 loss: 0.1941 loss_sem_seg: 0.1941 2023/03/21 05:13:11 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.8907e-02 eta: 2:16:19 time: 1.1559 data_time: 0.0033 memory: 1347 loss: 0.2059 loss_sem_seg: 0.2059 2023/03/21 05:14:07 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.7894e-02 eta: 2:15:26 time: 1.1335 data_time: 0.0034 memory: 1392 loss: 0.2111 loss_sem_seg: 0.2111 2023/03/21 05:15:04 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.6883e-02 eta: 2:14:32 time: 1.1325 data_time: 0.0034 memory: 1343 loss: 0.2033 loss_sem_seg: 0.2033 2023/03/21 05:16:01 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.5874e-02 eta: 2:13:39 time: 1.1336 data_time: 0.0033 memory: 1373 loss: 0.2019 loss_sem_seg: 0.2019 2023/03/21 05:16:59 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.4868e-02 eta: 2:12:46 time: 1.1617 data_time: 0.0033 memory: 1396 loss: 0.1797 loss_sem_seg: 0.1797 2023/03/21 05:17:56 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.3865e-02 eta: 2:11:53 time: 1.1415 data_time: 0.0033 memory: 1372 loss: 0.2120 loss_sem_seg: 0.2120 2023/03/21 05:18:49 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 05:18:49 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/21 05:19:22 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:04:52 time: 0.6372 data_time: 0.0079 memory: 1338 2023/03/21 05:19:54 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:04:19 time: 0.6337 data_time: 0.0048 memory: 328 2023/03/21 05:20:26 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:03:49 time: 0.6479 data_time: 0.0048 memory: 330 2023/03/21 05:20:58 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:03:18 time: 0.6498 data_time: 0.0046 memory: 324 2023/03/21 05:21:30 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:02:45 time: 0.6318 data_time: 0.0046 memory: 333 2023/03/21 05:22:02 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:02:14 time: 0.6482 data_time: 0.0046 memory: 312 2023/03/21 05:22:34 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:01:41 time: 0.6322 data_time: 0.0046 memory: 319 2023/03/21 05:23:06 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:01:09 time: 0.6474 data_time: 0.0047 memory: 322 2023/03/21 05:23:39 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:37 time: 0.6529 data_time: 0.0047 memory: 333 2023/03/21 05:24:11 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:05 time: 0.6426 data_time: 0.0047 memory: 322 2023/03/21 05:24:42 - 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 | 0.9533 | 0.0322 | 0.5254 | 0.6826 | 0.4450 | 0.5431 | 0.7392 | 0.0005 | 0.9297 | 0.4195 | 0.7972 | 0.0044 | 0.8924 | 0.5448 | 0.8877 | 0.6699 | 0.7687 | 0.6304 | 0.4204 | 0.5730 | 0.9167 | 0.6437 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 05:24:42 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9533 bicycle: 0.0322 motorcycle: 0.5254 truck: 0.6826 bus: 0.4450 person: 0.5431 bicyclist: 0.7392 motorcyclist: 0.0005 road: 0.9297 parking: 0.4195 sidewalk: 0.7972 other-ground: 0.0044 building: 0.8924 fence: 0.5448 vegetation: 0.8877 trunck: 0.6699 terrian: 0.7687 pole: 0.6304 traffic-sign: 0.4204 miou: 0.5730 acc: 0.9167 acc_cls: 0.6437data_time: 0.0046 time: 0.6472 2023/03/21 05:25:41 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.1947e-02 eta: 2:10:12 time: 1.1716 data_time: 0.0209 memory: 1392 loss: 0.1948 loss_sem_seg: 0.1948 2023/03/21 05:26:29 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0952e-02 eta: 2:09:13 time: 0.9664 data_time: 0.0037 memory: 1391 loss: 0.2040 loss_sem_seg: 0.2040 2023/03/21 05:27:15 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 7.9960e-02 eta: 2:08:13 time: 0.9227 data_time: 0.0036 memory: 1388 loss: 0.2055 loss_sem_seg: 0.2055 2023/03/21 05:28:11 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 7.8971e-02 eta: 2:07:19 time: 1.1206 data_time: 0.0035 memory: 1370 loss: 0.2002 loss_sem_seg: 0.2002 2023/03/21 05:28:52 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 05:29:08 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 7.7985e-02 eta: 2:06:25 time: 1.1300 data_time: 0.0036 memory: 1356 loss: 0.1943 loss_sem_seg: 0.1943 2023/03/21 05:30:04 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 7.7003e-02 eta: 2:05:31 time: 1.1264 data_time: 0.0038 memory: 1318 loss: 0.2036 loss_sem_seg: 0.2036 2023/03/21 05:31:01 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 7.6023e-02 eta: 2:04:38 time: 1.1351 data_time: 0.0037 memory: 1347 loss: 0.1918 loss_sem_seg: 0.1918 2023/03/21 05:31:59 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 7.5048e-02 eta: 2:03:45 time: 1.1646 data_time: 0.0039 memory: 1431 loss: 0.2072 loss_sem_seg: 0.2072 2023/03/21 05:32:51 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 7.4075e-02 eta: 2:02:48 time: 1.0288 data_time: 0.0040 memory: 1362 loss: 0.1793 loss_sem_seg: 0.1793 2023/03/21 05:33:34 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 7.3106e-02 eta: 2:01:47 time: 0.8772 data_time: 0.0040 memory: 1443 loss: 0.1896 loss_sem_seg: 0.1896 2023/03/21 05:34:19 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 7.2141e-02 eta: 2:00:47 time: 0.8994 data_time: 0.0038 memory: 1317 loss: 0.1955 loss_sem_seg: 0.1955 2023/03/21 05:35:03 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 7.1179e-02 eta: 1:59:45 time: 0.8716 data_time: 0.0038 memory: 1312 loss: 0.1911 loss_sem_seg: 0.1911 2023/03/21 05:35:45 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 7.0222e-02 eta: 1:58:43 time: 0.8357 data_time: 0.0036 memory: 1341 loss: 0.1840 loss_sem_seg: 0.1840 2023/03/21 05:36:29 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 6.9268e-02 eta: 1:57:43 time: 0.8861 data_time: 0.0035 memory: 1325 loss: 0.1943 loss_sem_seg: 0.1943 2023/03/21 05:37:23 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 6.8317e-02 eta: 1:56:48 time: 1.0808 data_time: 0.0042 memory: 1375 loss: 0.1951 loss_sem_seg: 0.1951 2023/03/21 05:38:24 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 6.7371e-02 eta: 1:55:57 time: 1.2203 data_time: 0.0036 memory: 1347 loss: 0.1967 loss_sem_seg: 0.1967 2023/03/21 05:39:20 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 6.6429e-02 eta: 1:55:04 time: 1.1260 data_time: 0.0034 memory: 1389 loss: 0.1826 loss_sem_seg: 0.1826 2023/03/21 05:40:18 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 6.5491e-02 eta: 1:54:11 time: 1.1546 data_time: 0.0034 memory: 1360 loss: 0.1845 loss_sem_seg: 0.1845 2023/03/21 05:41:15 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 6.4557e-02 eta: 1:53:17 time: 1.1294 data_time: 0.0034 memory: 1374 loss: 0.1809 loss_sem_seg: 0.1809 2023/03/21 05:42:13 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 6.3627e-02 eta: 1:52:25 time: 1.1715 data_time: 0.0035 memory: 1391 loss: 0.1939 loss_sem_seg: 0.1939 2023/03/21 05:43:09 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 6.2702e-02 eta: 1:51:31 time: 1.1222 data_time: 0.0035 memory: 1402 loss: 0.1947 loss_sem_seg: 0.1947 2023/03/21 05:44:06 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 6.1781e-02 eta: 1:50:37 time: 1.1385 data_time: 0.0034 memory: 1362 loss: 0.2013 loss_sem_seg: 0.2013 2023/03/21 05:45:03 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 6.0865e-02 eta: 1:49:44 time: 1.1368 data_time: 0.0034 memory: 1335 loss: 0.1910 loss_sem_seg: 0.1910 2023/03/21 05:45:54 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 05:45:54 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/03/21 05:46:27 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:04:56 time: 0.6468 data_time: 0.0083 memory: 1357 2023/03/21 05:46:59 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:04:22 time: 0.6350 data_time: 0.0047 memory: 328 2023/03/21 05:47:31 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:03:49 time: 0.6385 data_time: 0.0048 memory: 330 2023/03/21 05:48:03 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:03:17 time: 0.6422 data_time: 0.0047 memory: 324 2023/03/21 05:48:35 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:02:46 time: 0.6427 data_time: 0.0047 memory: 333 2023/03/21 05:49:07 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:02:13 time: 0.6366 data_time: 0.0048 memory: 312 2023/03/21 05:49:39 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:01:41 time: 0.6395 data_time: 0.0048 memory: 319 2023/03/21 05:50:11 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:01:09 time: 0.6355 data_time: 0.0046 memory: 322 2023/03/21 05:50:42 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:37 time: 0.6269 data_time: 0.0048 memory: 333 2023/03/21 05:51:14 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:05 time: 0.6375 data_time: 0.0046 memory: 322 2023/03/21 05:51:49 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9521 | 0.0280 | 0.5284 | 0.6527 | 0.4199 | 0.5796 | 0.7369 | 0.0002 | 0.9223 | 0.4574 | 0.7875 | 0.0056 | 0.9033 | 0.6083 | 0.8902 | 0.6352 | 0.7768 | 0.6331 | 0.4517 | 0.5773 | 0.9181 | 0.6529 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 05:51:49 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9521 bicycle: 0.0280 motorcycle: 0.5284 truck: 0.6527 bus: 0.4199 person: 0.5796 bicyclist: 0.7369 motorcyclist: 0.0002 road: 0.9223 parking: 0.4574 sidewalk: 0.7875 other-ground: 0.0056 building: 0.9033 fence: 0.6083 vegetation: 0.8902 trunck: 0.6352 terrian: 0.7768 pole: 0.6331 traffic-sign: 0.4517 miou: 0.5773 acc: 0.9181 acc_cls: 0.6529data_time: 0.0046 time: 0.6347 2023/03/21 05:52:34 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 05:52:46 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 5.9118e-02 eta: 1:48:00 time: 1.1410 data_time: 0.0200 memory: 1383 loss: 0.1709 loss_sem_seg: 0.1709 2023/03/21 05:53:43 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 5.8215e-02 eta: 1:47:07 time: 1.1419 data_time: 0.0035 memory: 1362 loss: 0.1881 loss_sem_seg: 0.1881 2023/03/21 05:54:39 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 5.7317e-02 eta: 1:46:13 time: 1.1141 data_time: 0.0034 memory: 1366 loss: 0.1799 loss_sem_seg: 0.1799 2023/03/21 05:55:35 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 5.6423e-02 eta: 1:45:19 time: 1.1258 data_time: 0.0034 memory: 1378 loss: 0.1834 loss_sem_seg: 0.1834 2023/03/21 05:56:32 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 5.5535e-02 eta: 1:44:26 time: 1.1518 data_time: 0.0034 memory: 1401 loss: 0.1674 loss_sem_seg: 0.1674 2023/03/21 05:57:30 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 5.4651e-02 eta: 1:43:32 time: 1.1568 data_time: 0.0034 memory: 1316 loss: 0.1949 loss_sem_seg: 0.1949 2023/03/21 05:58:26 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 5.3772e-02 eta: 1:42:38 time: 1.1209 data_time: 0.0034 memory: 1347 loss: 0.1685 loss_sem_seg: 0.1685 2023/03/21 05:59:22 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 5.2899e-02 eta: 1:41:44 time: 1.1237 data_time: 0.0034 memory: 1369 loss: 0.1772 loss_sem_seg: 0.1772 2023/03/21 06:00:19 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 5.2030e-02 eta: 1:40:50 time: 1.1233 data_time: 0.0035 memory: 1344 loss: 0.1885 loss_sem_seg: 0.1885 2023/03/21 06:01:14 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 5.1167e-02 eta: 1:39:56 time: 1.1058 data_time: 0.0033 memory: 1354 loss: 0.1966 loss_sem_seg: 0.1966 2023/03/21 06:02:04 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 5.0309e-02 eta: 1:38:59 time: 1.0011 data_time: 0.0034 memory: 1384 loss: 0.1799 loss_sem_seg: 0.1799 2023/03/21 06:02:47 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 4.9457e-02 eta: 1:38:00 time: 0.8644 data_time: 0.0033 memory: 1362 loss: 0.1814 loss_sem_seg: 0.1814 2023/03/21 06:03:32 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 4.8610e-02 eta: 1:37:01 time: 0.8855 data_time: 0.0036 memory: 1360 loss: 0.1822 loss_sem_seg: 0.1822 2023/03/21 06:04:15 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 4.7768e-02 eta: 1:36:01 time: 0.8709 data_time: 0.0035 memory: 1404 loss: 0.1784 loss_sem_seg: 0.1784 2023/03/21 06:04:59 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 4.6932e-02 eta: 1:35:02 time: 0.8689 data_time: 0.0035 memory: 1350 loss: 0.1732 loss_sem_seg: 0.1732 2023/03/21 06:05:43 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 4.6101e-02 eta: 1:34:04 time: 0.8872 data_time: 0.0034 memory: 1387 loss: 0.1714 loss_sem_seg: 0.1714 2023/03/21 06:06:39 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 4.5276e-02 eta: 1:33:10 time: 1.1144 data_time: 0.0033 memory: 1345 loss: 0.1704 loss_sem_seg: 0.1704 2023/03/21 06:07:41 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 4.4457e-02 eta: 1:32:18 time: 1.2448 data_time: 0.0036 memory: 1356 loss: 0.1803 loss_sem_seg: 0.1803 2023/03/21 06:08:39 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 4.3644e-02 eta: 1:31:25 time: 1.1554 data_time: 0.0037 memory: 1392 loss: 0.1766 loss_sem_seg: 0.1766 2023/03/21 06:09:38 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 4.2836e-02 eta: 1:30:32 time: 1.1782 data_time: 0.0036 memory: 1385 loss: 0.1811 loss_sem_seg: 0.1811 2023/03/21 06:10:22 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 06:10:34 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 4.2035e-02 eta: 1:29:38 time: 1.1202 data_time: 0.0038 memory: 1437 loss: 0.1822 loss_sem_seg: 0.1822 2023/03/21 06:11:30 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 4.1239e-02 eta: 1:28:44 time: 1.1379 data_time: 0.0039 memory: 1348 loss: 0.1749 loss_sem_seg: 0.1749 2023/03/21 06:12:28 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 4.0449e-02 eta: 1:27:51 time: 1.1493 data_time: 0.0035 memory: 1327 loss: 0.1760 loss_sem_seg: 0.1760 2023/03/21 06:13:20 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 06:13:21 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/03/21 06:13:54 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:04:58 time: 0.6494 data_time: 0.0084 memory: 1333 2023/03/21 06:14:25 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:04:21 time: 0.6289 data_time: 0.0048 memory: 328 2023/03/21 06:14:58 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:03:50 time: 0.6493 data_time: 0.0048 memory: 330 2023/03/21 06:15:30 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:03:18 time: 0.6391 data_time: 0.0047 memory: 324 2023/03/21 06:16:02 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:02:46 time: 0.6416 data_time: 0.0048 memory: 333 2023/03/21 06:16:34 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:02:14 time: 0.6479 data_time: 0.0048 memory: 312 2023/03/21 06:17:06 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:01:42 time: 0.6415 data_time: 0.0048 memory: 319 2023/03/21 06:17:38 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:01:09 time: 0.6348 data_time: 0.0047 memory: 322 2023/03/21 06:18:10 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:37 time: 0.6456 data_time: 0.0048 memory: 333 2023/03/21 06:18:43 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:05 time: 0.6425 data_time: 0.0049 memory: 322 2023/03/21 06:19:16 - 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 | 0.1393 | 0.5610 | 0.6728 | 0.5127 | 0.6363 | 0.7804 | 0.0001 | 0.9279 | 0.4090 | 0.7890 | 0.0083 | 0.9013 | 0.5947 | 0.8841 | 0.6800 | 0.7541 | 0.6395 | 0.4749 | 0.5958 | 0.9160 | 0.6686 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 06:19:16 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9552 bicycle: 0.1393 motorcycle: 0.5610 truck: 0.6728 bus: 0.5127 person: 0.6363 bicyclist: 0.7804 motorcyclist: 0.0001 road: 0.9279 parking: 0.4090 sidewalk: 0.7890 other-ground: 0.0083 building: 0.9013 fence: 0.5947 vegetation: 0.8841 trunck: 0.6800 terrian: 0.7541 pole: 0.6395 traffic-sign: 0.4749 miou: 0.5958 acc: 0.9160 acc_cls: 0.6686data_time: 0.0047 time: 0.6403 2023/03/21 06:20:14 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 3.8950e-02 eta: 1:26:08 time: 1.1621 data_time: 0.0209 memory: 1374 loss: 0.1705 loss_sem_seg: 0.1705 2023/03/21 06:21:11 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 3.8179e-02 eta: 1:25:15 time: 1.1472 data_time: 0.0036 memory: 1376 loss: 0.1769 loss_sem_seg: 0.1769 2023/03/21 06:22:09 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 3.7414e-02 eta: 1:24:21 time: 1.1647 data_time: 0.0035 memory: 1388 loss: 0.1751 loss_sem_seg: 0.1751 2023/03/21 06:23:08 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 3.6655e-02 eta: 1:23:28 time: 1.1770 data_time: 0.0034 memory: 1529 loss: 0.1755 loss_sem_seg: 0.1755 2023/03/21 06:24:05 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 3.5902e-02 eta: 1:22:34 time: 1.1380 data_time: 0.0035 memory: 1348 loss: 0.1799 loss_sem_seg: 0.1799 2023/03/21 06:25:02 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 3.5156e-02 eta: 1:21:40 time: 1.1403 data_time: 0.0034 memory: 1432 loss: 0.1676 loss_sem_seg: 0.1676 2023/03/21 06:26:00 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 3.4416e-02 eta: 1:20:47 time: 1.1476 data_time: 0.0036 memory: 1363 loss: 0.1787 loss_sem_seg: 0.1787 2023/03/21 06:26:56 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 3.3683e-02 eta: 1:19:52 time: 1.1276 data_time: 0.0037 memory: 1319 loss: 0.1651 loss_sem_seg: 0.1651 2023/03/21 06:27:51 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 3.2956e-02 eta: 1:18:58 time: 1.1086 data_time: 0.0036 memory: 1390 loss: 0.1729 loss_sem_seg: 0.1729 2023/03/21 06:28:48 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 3.2237e-02 eta: 1:18:04 time: 1.1350 data_time: 0.0034 memory: 1364 loss: 0.1626 loss_sem_seg: 0.1626 2023/03/21 06:29:47 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 3.1524e-02 eta: 1:17:11 time: 1.1708 data_time: 0.0034 memory: 1355 loss: 0.1679 loss_sem_seg: 0.1679 2023/03/21 06:30:38 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 3.0817e-02 eta: 1:16:15 time: 1.0348 data_time: 0.0036 memory: 1354 loss: 0.1740 loss_sem_seg: 0.1740 2023/03/21 06:31:28 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 3.0118e-02 eta: 1:15:19 time: 0.9994 data_time: 0.0037 memory: 1359 loss: 0.1611 loss_sem_seg: 0.1611 2023/03/21 06:32:12 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 2.9425e-02 eta: 1:14:21 time: 0.8808 data_time: 0.0036 memory: 1403 loss: 0.1721 loss_sem_seg: 0.1721 2023/03/21 06:32:56 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 2.8740e-02 eta: 1:13:23 time: 0.8815 data_time: 0.0034 memory: 1358 loss: 0.1704 loss_sem_seg: 0.1704 2023/03/21 06:33:39 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 2.8061e-02 eta: 1:12:25 time: 0.8525 data_time: 0.0032 memory: 1372 loss: 0.1672 loss_sem_seg: 0.1672 2023/03/21 06:34:17 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 06:34:22 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 2.7389e-02 eta: 1:11:28 time: 0.8651 data_time: 0.0034 memory: 1418 loss: 0.1687 loss_sem_seg: 0.1687 2023/03/21 06:35:11 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 2.6725e-02 eta: 1:10:32 time: 0.9749 data_time: 0.0033 memory: 1436 loss: 0.1522 loss_sem_seg: 0.1522 2023/03/21 06:36:03 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 2.6068e-02 eta: 1:09:36 time: 1.0362 data_time: 0.0034 memory: 1391 loss: 0.1654 loss_sem_seg: 0.1654 2023/03/21 06:37:07 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 2.5417e-02 eta: 1:08:44 time: 1.2781 data_time: 0.0035 memory: 1351 loss: 0.1658 loss_sem_seg: 0.1658 2023/03/21 06:38:03 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 2.4775e-02 eta: 1:07:51 time: 1.1328 data_time: 0.0034 memory: 1330 loss: 0.1556 loss_sem_seg: 0.1556 2023/03/21 06:39:02 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 2.4139e-02 eta: 1:06:57 time: 1.1668 data_time: 0.0033 memory: 1346 loss: 0.1520 loss_sem_seg: 0.1520 2023/03/21 06:39:59 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 2.3511e-02 eta: 1:06:03 time: 1.1524 data_time: 0.0032 memory: 1385 loss: 0.1610 loss_sem_seg: 0.1610 2023/03/21 06:40:53 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 06:40:53 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/21 06:41:26 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:04:52 time: 0.6368 data_time: 0.0080 memory: 1392 2023/03/21 06:41:57 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:04:18 time: 0.6287 data_time: 0.0047 memory: 328 2023/03/21 06:42:29 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:03:48 time: 0.6455 data_time: 0.0047 memory: 330 2023/03/21 06:43:01 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:03:16 time: 0.6325 data_time: 0.0046 memory: 324 2023/03/21 06:43:33 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:02:44 time: 0.6371 data_time: 0.0046 memory: 333 2023/03/21 06:44:05 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:02:12 time: 0.6371 data_time: 0.0046 memory: 312 2023/03/21 06:44:36 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:01:41 time: 0.6297 data_time: 0.0048 memory: 319 2023/03/21 06:45:08 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:01:09 time: 0.6381 data_time: 0.0045 memory: 322 2023/03/21 06:45:40 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:37 time: 0.6453 data_time: 0.0046 memory: 333 2023/03/21 06:46:12 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:05 time: 0.6440 data_time: 0.0047 memory: 322 2023/03/21 06:46:47 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9595 | 0.1896 | 0.6068 | 0.7980 | 0.5664 | 0.6217 | 0.7446 | 0.0000 | 0.9236 | 0.4396 | 0.7931 | 0.0135 | 0.9029 | 0.6164 | 0.8835 | 0.6573 | 0.7561 | 0.6308 | 0.4810 | 0.6097 | 0.9171 | 0.6820 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 06:46:47 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9595 bicycle: 0.1896 motorcycle: 0.6068 truck: 0.7980 bus: 0.5664 person: 0.6217 bicyclist: 0.7446 motorcyclist: 0.0000 road: 0.9236 parking: 0.4396 sidewalk: 0.7931 other-ground: 0.0135 building: 0.9029 fence: 0.6164 vegetation: 0.8835 trunck: 0.6573 terrian: 0.7561 pole: 0.6308 traffic-sign: 0.4810 miou: 0.6097 acc: 0.9171 acc_cls: 0.6820data_time: 0.0046 time: 0.6426 2023/03/21 06:47:44 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 2.2325e-02 eta: 1:04:20 time: 1.1456 data_time: 0.0203 memory: 1415 loss: 0.1648 loss_sem_seg: 0.1648 2023/03/21 06:48:42 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 2.1719e-02 eta: 1:03:26 time: 1.1461 data_time: 0.0038 memory: 1332 loss: 0.1606 loss_sem_seg: 0.1606 2023/03/21 06:49:38 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 2.1120e-02 eta: 1:02:32 time: 1.1182 data_time: 0.0038 memory: 1384 loss: 0.1773 loss_sem_seg: 0.1773 2023/03/21 06:50:36 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 2.0529e-02 eta: 1:01:38 time: 1.1608 data_time: 0.0036 memory: 1351 loss: 0.1499 loss_sem_seg: 0.1499 2023/03/21 06:51:33 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 1.9945e-02 eta: 1:00:44 time: 1.1538 data_time: 0.0040 memory: 1375 loss: 0.1590 loss_sem_seg: 0.1590 2023/03/21 06:52:30 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 1.9369e-02 eta: 0:59:50 time: 1.1338 data_time: 0.0037 memory: 1403 loss: 0.1513 loss_sem_seg: 0.1513 2023/03/21 06:53:27 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 1.8800e-02 eta: 0:58:56 time: 1.1341 data_time: 0.0035 memory: 1347 loss: 0.1505 loss_sem_seg: 0.1505 2023/03/21 06:54:23 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 1.8240e-02 eta: 0:58:02 time: 1.1261 data_time: 0.0036 memory: 1424 loss: 0.1651 loss_sem_seg: 0.1651 2023/03/21 06:55:20 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 1.7687e-02 eta: 0:57:08 time: 1.1437 data_time: 0.0037 memory: 1383 loss: 0.1640 loss_sem_seg: 0.1640 2023/03/21 06:56:16 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 1.7142e-02 eta: 0:56:13 time: 1.1214 data_time: 0.0038 memory: 1404 loss: 0.1675 loss_sem_seg: 0.1675 2023/03/21 06:57:14 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 1.6605e-02 eta: 0:55:19 time: 1.1448 data_time: 0.0037 memory: 1355 loss: 0.1530 loss_sem_seg: 0.1530 2023/03/21 06:58:09 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 1.6076e-02 eta: 0:54:25 time: 1.1164 data_time: 0.0039 memory: 1445 loss: 0.1616 loss_sem_seg: 0.1616 2023/03/21 06:59:05 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 06:59:07 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 1.5555e-02 eta: 0:53:31 time: 1.1563 data_time: 0.0039 memory: 1349 loss: 0.1567 loss_sem_seg: 0.1567 2023/03/21 07:00:00 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 1.5041e-02 eta: 0:52:36 time: 1.0506 data_time: 0.0037 memory: 1356 loss: 0.1595 loss_sem_seg: 0.1595 2023/03/21 07:00:47 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 1.4536e-02 eta: 0:51:40 time: 0.9508 data_time: 0.0039 memory: 1373 loss: 0.1683 loss_sem_seg: 0.1683 2023/03/21 07:01:31 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 1.4039e-02 eta: 0:50:43 time: 0.8774 data_time: 0.0036 memory: 1383 loss: 0.1690 loss_sem_seg: 0.1690 2023/03/21 07:02:15 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 1.3550e-02 eta: 0:49:47 time: 0.8703 data_time: 0.0034 memory: 1419 loss: 0.1585 loss_sem_seg: 0.1585 2023/03/21 07:02:57 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 1.3070e-02 eta: 0:48:50 time: 0.8512 data_time: 0.0034 memory: 1368 loss: 0.1621 loss_sem_seg: 0.1621 2023/03/21 07:03:40 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 1.2597e-02 eta: 0:47:53 time: 0.8631 data_time: 0.0036 memory: 1375 loss: 0.1545 loss_sem_seg: 0.1545 2023/03/21 07:04:34 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 1.2133e-02 eta: 0:46:59 time: 1.0619 data_time: 0.0035 memory: 1409 loss: 0.1547 loss_sem_seg: 0.1547 2023/03/21 07:05:27 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 1.1677e-02 eta: 0:46:04 time: 1.0710 data_time: 0.0036 memory: 1318 loss: 0.1594 loss_sem_seg: 0.1594 2023/03/21 07:06:29 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 1.1229e-02 eta: 0:45:11 time: 1.2337 data_time: 0.0037 memory: 1381 loss: 0.1553 loss_sem_seg: 0.1553 2023/03/21 07:07:25 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 1.0790e-02 eta: 0:44:17 time: 1.1331 data_time: 0.0033 memory: 1441 loss: 0.1462 loss_sem_seg: 0.1462 2023/03/21 07:08:18 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 07:08:18 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/03/21 07:08:51 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:04:53 time: 0.6390 data_time: 0.0083 memory: 1364 2023/03/21 07:09:23 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:04:24 time: 0.6534 data_time: 0.0049 memory: 328 2023/03/21 07:09:56 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:03:51 time: 0.6412 data_time: 0.0047 memory: 330 2023/03/21 07:10:27 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:03:18 time: 0.6339 data_time: 0.0046 memory: 324 2023/03/21 07:10:59 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:02:46 time: 0.6446 data_time: 0.0047 memory: 333 2023/03/21 07:11:31 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:02:13 time: 0.6319 data_time: 0.0047 memory: 312 2023/03/21 07:12:02 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:01:41 time: 0.6285 data_time: 0.0047 memory: 319 2023/03/21 07:12:35 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:01:09 time: 0.6437 data_time: 0.0048 memory: 322 2023/03/21 07:13:07 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:37 time: 0.6488 data_time: 0.0048 memory: 333 2023/03/21 07:13:39 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:05 time: 0.6358 data_time: 0.0049 memory: 322 2023/03/21 07:14:14 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9620 | 0.1607 | 0.5763 | 0.7741 | 0.5896 | 0.6462 | 0.8007 | 0.0001 | 0.9299 | 0.4504 | 0.7992 | 0.0063 | 0.9045 | 0.6228 | 0.8807 | 0.6584 | 0.7434 | 0.6391 | 0.4696 | 0.6113 | 0.9172 | 0.6806 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 07:14:14 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9620 bicycle: 0.1607 motorcycle: 0.5763 truck: 0.7741 bus: 0.5896 person: 0.6462 bicyclist: 0.8007 motorcyclist: 0.0001 road: 0.9299 parking: 0.4504 sidewalk: 0.7992 other-ground: 0.0063 building: 0.9045 fence: 0.6228 vegetation: 0.8807 trunck: 0.6584 terrian: 0.7434 pole: 0.6391 traffic-sign: 0.4696 miou: 0.6113 acc: 0.9172 acc_cls: 0.6806data_time: 0.0048 time: 0.6264 2023/03/21 07:15:11 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 9.9694e-03 eta: 0:42:33 time: 1.1405 data_time: 0.0207 memory: 1376 loss: 0.1493 loss_sem_seg: 0.1493 2023/03/21 07:16:08 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 9.5545e-03 eta: 0:41:39 time: 1.1434 data_time: 0.0037 memory: 1325 loss: 0.1517 loss_sem_seg: 0.1517 2023/03/21 07:17:06 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 9.1481e-03 eta: 0:40:45 time: 1.1626 data_time: 0.0040 memory: 1342 loss: 0.1566 loss_sem_seg: 0.1566 2023/03/21 07:18:04 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.7502e-03 eta: 0:39:51 time: 1.1618 data_time: 0.0038 memory: 1395 loss: 0.1630 loss_sem_seg: 0.1630 2023/03/21 07:19:02 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.3608e-03 eta: 0:38:56 time: 1.1579 data_time: 0.0038 memory: 1388 loss: 0.1515 loss_sem_seg: 0.1515 2023/03/21 07:19:59 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 7.9800e-03 eta: 0:38:02 time: 1.1430 data_time: 0.0037 memory: 1326 loss: 0.1442 loss_sem_seg: 0.1442 2023/03/21 07:20:56 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 7.6078e-03 eta: 0:37:08 time: 1.1381 data_time: 0.0035 memory: 1371 loss: 0.1587 loss_sem_seg: 0.1587 2023/03/21 07:21:54 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 7.2442e-03 eta: 0:36:14 time: 1.1656 data_time: 0.0036 memory: 1390 loss: 0.1595 loss_sem_seg: 0.1595 2023/03/21 07:22:52 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 6.8892e-03 eta: 0:35:20 time: 1.1638 data_time: 0.0036 memory: 1373 loss: 0.1471 loss_sem_seg: 0.1471 2023/03/21 07:22:55 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 07:23:48 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 6.5429e-03 eta: 0:34:25 time: 1.1114 data_time: 0.0037 memory: 1375 loss: 0.1572 loss_sem_seg: 0.1572 2023/03/21 07:24:44 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 6.2053e-03 eta: 0:33:31 time: 1.1294 data_time: 0.0037 memory: 1344 loss: 0.1485 loss_sem_seg: 0.1485 2023/03/21 07:25:41 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 5.8765e-03 eta: 0:32:37 time: 1.1333 data_time: 0.0038 memory: 1352 loss: 0.1503 loss_sem_seg: 0.1503 2023/03/21 07:26:39 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 5.5564e-03 eta: 0:31:42 time: 1.1586 data_time: 0.0037 memory: 1383 loss: 0.1513 loss_sem_seg: 0.1513 2023/03/21 07:27:34 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 5.2450e-03 eta: 0:30:48 time: 1.0966 data_time: 0.0037 memory: 1364 loss: 0.1560 loss_sem_seg: 0.1560 2023/03/21 07:28:29 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 4.9425e-03 eta: 0:29:53 time: 1.1104 data_time: 0.0035 memory: 1369 loss: 0.1533 loss_sem_seg: 0.1533 2023/03/21 07:29:21 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 4.6488e-03 eta: 0:28:58 time: 1.0332 data_time: 0.0036 memory: 1493 loss: 0.1541 loss_sem_seg: 0.1541 2023/03/21 07:30:08 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 4.3639e-03 eta: 0:28:03 time: 0.9461 data_time: 0.0037 memory: 1372 loss: 0.1540 loss_sem_seg: 0.1540 2023/03/21 07:30:52 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 4.0879e-03 eta: 0:27:07 time: 0.8717 data_time: 0.0036 memory: 1427 loss: 0.1503 loss_sem_seg: 0.1503 2023/03/21 07:31:36 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 3.8207e-03 eta: 0:26:12 time: 0.8789 data_time: 0.0035 memory: 1316 loss: 0.1548 loss_sem_seg: 0.1548 2023/03/21 07:32:19 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 3.5625e-03 eta: 0:25:16 time: 0.8534 data_time: 0.0036 memory: 1440 loss: 0.1491 loss_sem_seg: 0.1491 2023/03/21 07:33:02 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 3.3132e-03 eta: 0:24:21 time: 0.8761 data_time: 0.0035 memory: 1354 loss: 0.1553 loss_sem_seg: 0.1553 2023/03/21 07:33:58 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 3.0729e-03 eta: 0:23:27 time: 1.1030 data_time: 0.0034 memory: 1356 loss: 0.1511 loss_sem_seg: 0.1511 2023/03/21 07:34:54 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 2.8415e-03 eta: 0:22:32 time: 1.1263 data_time: 0.0039 memory: 1367 loss: 0.1498 loss_sem_seg: 0.1498 2023/03/21 07:35:49 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 07:35:50 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/03/21 07:36:23 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:04:57 time: 0.6488 data_time: 0.0080 memory: 1330 2023/03/21 07:36:55 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:04:25 time: 0.6500 data_time: 0.0047 memory: 328 2023/03/21 07:37:27 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:03:51 time: 0.6370 data_time: 0.0047 memory: 330 2023/03/21 07:38:00 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:03:20 time: 0.6618 data_time: 0.0047 memory: 324 2023/03/21 07:38:28 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:02:42 time: 0.5433 data_time: 0.0047 memory: 333 2023/03/21 07:38:53 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:02:07 time: 0.5112 data_time: 0.0047 memory: 312 2023/03/21 07:39:18 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:01:34 time: 0.4964 data_time: 0.0048 memory: 319 2023/03/21 07:39:43 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:01:03 time: 0.4953 data_time: 0.0046 memory: 322 2023/03/21 07:40:14 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:34 time: 0.6347 data_time: 0.0047 memory: 333 2023/03/21 07:40:47 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:05 time: 0.6403 data_time: 0.0048 memory: 322 2023/03/21 07:41:18 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9614 | 0.1678 | 0.5915 | 0.8048 | 0.5818 | 0.6471 | 0.8028 | 0.0021 | 0.9328 | 0.4756 | 0.8032 | 0.0048 | 0.9062 | 0.6259 | 0.8828 | 0.6575 | 0.7494 | 0.6399 | 0.4783 | 0.6166 | 0.9191 | 0.6837 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 07:41:18 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9614 bicycle: 0.1678 motorcycle: 0.5915 truck: 0.8048 bus: 0.5818 person: 0.6471 bicyclist: 0.8028 motorcyclist: 0.0021 road: 0.9328 parking: 0.4756 sidewalk: 0.8032 other-ground: 0.0048 building: 0.9062 fence: 0.6259 vegetation: 0.8828 trunck: 0.6575 terrian: 0.7494 pole: 0.6399 traffic-sign: 0.4783 miou: 0.6166 acc: 0.9191 acc_cls: 0.6837data_time: 0.0048 time: 0.6411 2023/03/21 07:42:17 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 2.4224e-03 eta: 0:20:48 time: 1.1804 data_time: 0.0196 memory: 1404 loss: 0.1533 loss_sem_seg: 0.1533 2023/03/21 07:43:14 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 2.2173e-03 eta: 0:19:54 time: 1.1379 data_time: 0.0034 memory: 1396 loss: 0.1580 loss_sem_seg: 0.1580 2023/03/21 07:44:10 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 2.0212e-03 eta: 0:19:00 time: 1.1232 data_time: 0.0034 memory: 1347 loss: 0.1475 loss_sem_seg: 0.1475 2023/03/21 07:45:07 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 1.8342e-03 eta: 0:18:05 time: 1.1449 data_time: 0.0034 memory: 1392 loss: 0.1492 loss_sem_seg: 0.1492 2023/03/21 07:46:04 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 1.6562e-03 eta: 0:17:11 time: 1.1363 data_time: 0.0034 memory: 1363 loss: 0.1506 loss_sem_seg: 0.1506 2023/03/21 07:46:10 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 07:47:00 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 1.4873e-03 eta: 0:16:17 time: 1.1285 data_time: 0.0035 memory: 1380 loss: 0.1583 loss_sem_seg: 0.1583 2023/03/21 07:47:58 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 1.3275e-03 eta: 0:15:22 time: 1.1574 data_time: 0.0036 memory: 1328 loss: 0.1618 loss_sem_seg: 0.1618 2023/03/21 07:48:55 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 1.1768e-03 eta: 0:14:28 time: 1.1269 data_time: 0.0035 memory: 1375 loss: 0.1473 loss_sem_seg: 0.1473 2023/03/21 07:49:52 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 1.0352e-03 eta: 0:13:33 time: 1.1488 data_time: 0.0037 memory: 1369 loss: 0.1585 loss_sem_seg: 0.1585 2023/03/21 07:50:49 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 9.0272e-04 eta: 0:12:39 time: 1.1511 data_time: 0.0036 memory: 1396 loss: 0.1395 loss_sem_seg: 0.1395 2023/03/21 07:51:47 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 7.7936e-04 eta: 0:11:44 time: 1.1408 data_time: 0.0036 memory: 1372 loss: 0.1588 loss_sem_seg: 0.1588 2023/03/21 07:52:43 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 6.6515e-04 eta: 0:10:50 time: 1.1310 data_time: 0.0034 memory: 1334 loss: 0.1465 loss_sem_seg: 0.1465 2023/03/21 07:53:39 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 5.6009e-04 eta: 0:09:55 time: 1.1269 data_time: 0.0034 memory: 1380 loss: 0.1487 loss_sem_seg: 0.1487 2023/03/21 07:54:36 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 4.6418e-04 eta: 0:09:01 time: 1.1234 data_time: 0.0034 memory: 1377 loss: 0.1547 loss_sem_seg: 0.1547 2023/03/21 07:55:32 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 3.7744e-04 eta: 0:08:06 time: 1.1349 data_time: 0.0036 memory: 1363 loss: 0.1503 loss_sem_seg: 0.1503 2023/03/21 07:56:28 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 2.9986e-04 eta: 0:07:12 time: 1.1226 data_time: 0.0034 memory: 1335 loss: 0.1474 loss_sem_seg: 0.1474 2023/03/21 07:57:24 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 2.3147e-04 eta: 0:06:17 time: 1.1016 data_time: 0.0035 memory: 1324 loss: 0.1519 loss_sem_seg: 0.1519 2023/03/21 07:58:14 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 1.7226e-04 eta: 0:05:23 time: 1.0042 data_time: 0.0034 memory: 1354 loss: 0.1399 loss_sem_seg: 0.1399 2023/03/21 07:58:58 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 1.2223e-04 eta: 0:04:28 time: 0.8831 data_time: 0.0037 memory: 1349 loss: 0.1570 loss_sem_seg: 0.1570 2023/03/21 07:59:42 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.1397e-05 eta: 0:03:33 time: 0.8823 data_time: 0.0034 memory: 1407 loss: 0.1462 loss_sem_seg: 0.1462 2023/03/21 08:00:26 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 4.9756e-05 eta: 0:02:39 time: 0.8696 data_time: 0.0033 memory: 1379 loss: 0.1498 loss_sem_seg: 0.1498 2023/03/21 08:01:08 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 2.7311e-05 eta: 0:01:44 time: 0.8557 data_time: 0.0034 memory: 1372 loss: 0.1545 loss_sem_seg: 0.1545 2023/03/21 08:01:54 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 1.4064e-05 eta: 0:00:50 time: 0.9123 data_time: 0.0033 memory: 1338 loss: 0.1415 loss_sem_seg: 0.1415 2023/03/21 08:02:45 - mmengine - INFO - Exp name: spvcnn_w16_8xb2-15e_semantickitti_20230321_011645 2023/03/21 08:02:45 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/21 08:03:15 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:04:21 time: 0.5697 data_time: 0.0081 memory: 1350 2023/03/21 08:03:47 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:04:08 time: 0.6475 data_time: 0.0048 memory: 328 2023/03/21 08:04:19 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:03:42 time: 0.6402 data_time: 0.0049 memory: 330 2023/03/21 08:04:51 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:03:13 time: 0.6456 data_time: 0.0047 memory: 324 2023/03/21 08:05:23 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:02:42 time: 0.6403 data_time: 0.0047 memory: 333 2023/03/21 08:05:56 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:02:11 time: 0.6454 data_time: 0.0048 memory: 312 2023/03/21 08:06:28 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:01:40 time: 0.6421 data_time: 0.0046 memory: 319 2023/03/21 08:07:00 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:01:09 time: 0.6533 data_time: 0.0048 memory: 322 2023/03/21 08:07:33 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:37 time: 0.6475 data_time: 0.0047 memory: 333 2023/03/21 08:08:05 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:05 time: 0.6442 data_time: 0.0047 memory: 322 2023/03/21 08:08:44 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9623 | 0.2015 | 0.6036 | 0.7977 | 0.5899 | 0.6535 | 0.8057 | 0.0007 | 0.9335 | 0.4691 | 0.8027 | 0.0050 | 0.9057 | 0.6229 | 0.8811 | 0.6608 | 0.7457 | 0.6389 | 0.4829 | 0.6191 | 0.9185 | 0.6883 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 08:08:44 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9623 bicycle: 0.2015 motorcycle: 0.6036 truck: 0.7977 bus: 0.5899 person: 0.6535 bicyclist: 0.8057 motorcyclist: 0.0007 road: 0.9335 parking: 0.4691 sidewalk: 0.8027 other-ground: 0.0050 building: 0.9057 fence: 0.6229 vegetation: 0.8811 trunck: 0.6608 terrian: 0.7457 pole: 0.6389 traffic-sign: 0.4829 miou: 0.6191 acc: 0.9185 acc_cls: 0.6883data_time: 0.0047 time: 0.6344