2023/03/21 01:17:09 - 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 01:17:09 - mmengine - INFO - Config: dataset_type = 'SemanticKittiDataset' data_root = 'data/semantickitti/' class_names = [ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ] labels_map = dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }) metainfo = dict( classes=[ 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign' ], seg_label_mapping=dict({ 0: 19, 1: 19, 10: 0, 11: 1, 13: 4, 15: 2, 16: 4, 18: 3, 20: 4, 30: 5, 31: 6, 32: 7, 40: 8, 44: 9, 48: 10, 49: 11, 50: 12, 51: 13, 52: 19, 60: 8, 70: 14, 71: 15, 72: 16, 80: 17, 81: 18, 99: 19, 252: 0, 253: 6, 254: 5, 255: 7, 256: 4, 257: 4, 258: 3, 259: 4 }), max_label=259) input_modality = dict(use_lidar=True, use_camera=False) backend_args = None train_pipeline = [ dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), dict( type='LoadAnnotations3D', with_bbox_3d=False, with_label_3d=False, with_seg_3d=True, seg_3d_dtype='np.int32', seg_offset=65536, dataset_type='semantickitti'), dict(type='PointSegClassMapping'), dict( type='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=20, encoder_channels=[20, 40, 81, 163], decoder_channels=[163, 81, 61, 61], num_stages=4, drop_ratio=0.3), decode_head=dict( type='MinkUNetHead', channels=61, num_classes=19, dropout_ratio=0, loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True), ignore_index=19), train_cfg=dict(), test_cfg=dict()) default_scope = 'mmdet3d' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='Det3DVisualizationHook')) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False lr = 0.24 optim_wrapper = dict( type='AmpOptimWrapper', loss_scale='dynamic', optimizer=dict( type='SGD', lr=0.24, weight_decay=0.0001, momentum=0.9, nesterov=True)) param_scheduler = [ dict( type='LinearLR', start_factor=0.008, by_epoch=False, begin=0, end=125), dict( type='CosineAnnealingLR', begin=0, T_max=15, by_epoch=True, eta_min=1e-05, convert_to_iter_based=True) ] train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=15, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') randomness = dict(seed=1588147245, deterministic=False, diff_rank_seed=True) launcher = 'pytorch' work_dir = './work_dirs/spvcnn_w20_8xb2-15e_semantickitti' 2023/03/21 01:17:13 - 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 01:17:14 - 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, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.conv_input.1.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.0.kernel - torch.Size([8, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.3.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.1.net.4.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.0.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.3.kernel - torch.Size([27, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.0.2.net.4.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.0.kernel - torch.Size([8, 20, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.weight - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.0.net.1.bias - torch.Size([20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.0.kernel - torch.Size([27, 20, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.3.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.net.4.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.0.kernel - torch.Size([20, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.1.downsample.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.0.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.3.kernel - torch.Size([27, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.1.2.net.4.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.0.kernel - torch.Size([8, 40, 40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.weight - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.0.net.1.bias - torch.Size([40]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.0.kernel - torch.Size([27, 40, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.0.kernel - torch.Size([40, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.1.downsample.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.0.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.2.2.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.0.kernel - torch.Size([8, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.0.kernel - torch.Size([27, 81, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.0.kernel - torch.Size([81, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.1.downsample.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.0.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.encoder.3.2.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.0.kernel - torch.Size([8, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.0.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.0.kernel - torch.Size([27, 244, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.0.kernel - torch.Size([244, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.0.downsample.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.0.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.3.kernel - torch.Size([27, 163, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.0.1.1.net.4.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.0.kernel - torch.Size([8, 163, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.0.kernel - torch.Size([27, 121, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.0.kernel - torch.Size([121, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.0.downsample.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.0.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.3.kernel - torch.Size([27, 81, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.1.1.1.net.4.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.0.kernel - torch.Size([8, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.0.kernel - torch.Size([27, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.0.kernel - torch.Size([81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.0.downsample.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.0.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.2.1.1.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.0.kernel - torch.Size([8, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.0.kernel - torch.Size([27, 81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.0.kernel - torch.Size([81, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.0.downsample.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.0.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.3.kernel - torch.Size([27, 61, 61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.decoder.3.1.1.net.4.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.weight - torch.Size([163, 20]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.0.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.weight - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.0.1.bias - torch.Size([163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.weight - torch.Size([81, 163]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.0.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.weight - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.1.1.bias - torch.Size([81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.weight - torch.Size([61, 81]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.0.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.weight - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet backbone.point_transforms.2.1.bias - torch.Size([61]): The value is the same before and after calling `init_weights` of MinkUNet decode_head.conv_seg.weight - torch.Size([19, 61]): Initialized by user-defined `init_weights` in MinkUNetHead decode_head.conv_seg.bias - torch.Size([19]): Initialized by user-defined `init_weights` in MinkUNetHead 2023/03/21 01:17:15 - 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 01:17:15 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/03/21 01:17:15 - mmengine - INFO - Checkpoints will be saved to /nvme/sunjiahao/projects/mmdetection3d/work_dirs/spvcnn_w20_8xb2-15e_semantickitti. 2023/03/21 01:18:22 - mmengine - INFO - Epoch(train) [1][ 50/1196] lr: 9.5998e-02 eta: 6:40:59 time: 1.3448 data_time: 0.0158 memory: 1668 loss: 1.5502 loss_sem_seg: 1.5502 2023/03/21 01:19:25 - mmengine - INFO - Epoch(train) [1][ 100/1196] lr: 1.9199e-01 eta: 6:25:05 time: 1.2454 data_time: 0.0050 memory: 1732 loss: 0.8919 loss_sem_seg: 0.8919 2023/03/21 01:20:24 - mmengine - INFO - Epoch(train) [1][ 150/1196] lr: 2.3996e-01 eta: 6:14:21 time: 1.1975 data_time: 0.0048 memory: 1704 loss: 0.7697 loss_sem_seg: 0.7697 2023/03/21 01:21:25 - mmengine - INFO - Epoch(train) [1][ 200/1196] lr: 2.3993e-01 eta: 6:09:27 time: 1.2106 data_time: 0.0045 memory: 1721 loss: 0.6665 loss_sem_seg: 0.6665 2023/03/21 01:22:25 - mmengine - INFO - Epoch(train) [1][ 250/1196] lr: 2.3989e-01 eta: 6:05:19 time: 1.1971 data_time: 0.0049 memory: 1698 loss: 0.5886 loss_sem_seg: 0.5886 2023/03/21 01:23:27 - mmengine - INFO - Epoch(train) [1][ 300/1196] lr: 2.3984e-01 eta: 6:04:43 time: 1.2477 data_time: 0.0048 memory: 1640 loss: 0.5566 loss_sem_seg: 0.5566 2023/03/21 01:24:29 - mmengine - INFO - Epoch(train) [1][ 350/1196] lr: 2.3978e-01 eta: 6:03:06 time: 1.2267 data_time: 0.0047 memory: 1700 loss: 0.5394 loss_sem_seg: 0.5394 2023/03/21 01:25:29 - mmengine - INFO - Epoch(train) [1][ 400/1196] lr: 2.3971e-01 eta: 6:00:47 time: 1.2033 data_time: 0.0047 memory: 1746 loss: 0.5189 loss_sem_seg: 0.5189 2023/03/21 01:26:30 - mmengine - INFO - Epoch(train) [1][ 450/1196] lr: 2.3963e-01 eta: 5:59:13 time: 1.2178 data_time: 0.0049 memory: 1682 loss: 0.4706 loss_sem_seg: 0.4706 2023/03/21 01:27:30 - mmengine - INFO - Epoch(train) [1][ 500/1196] lr: 2.3954e-01 eta: 5:57:33 time: 1.2105 data_time: 0.0048 memory: 1686 loss: 0.4619 loss_sem_seg: 0.4619 2023/03/21 01:28:32 - mmengine - INFO - Epoch(train) [1][ 550/1196] lr: 2.3945e-01 eta: 5:56:50 time: 1.2414 data_time: 0.0047 memory: 1661 loss: 0.4672 loss_sem_seg: 0.4672 2023/03/21 01:29:34 - mmengine - INFO - Epoch(train) [1][ 600/1196] lr: 2.3934e-01 eta: 5:55:45 time: 1.2287 data_time: 0.0050 memory: 1665 loss: 0.4384 loss_sem_seg: 0.4384 2023/03/21 01:30:34 - mmengine - INFO - Epoch(train) [1][ 650/1196] lr: 2.3923e-01 eta: 5:54:22 time: 1.2154 data_time: 0.0046 memory: 1774 loss: 0.4504 loss_sem_seg: 0.4504 2023/03/21 01:31:35 - mmengine - INFO - Epoch(train) [1][ 700/1196] lr: 2.3910e-01 eta: 5:53:08 time: 1.2196 data_time: 0.0048 memory: 1663 loss: 0.4287 loss_sem_seg: 0.4287 2023/03/21 01:32:36 - mmengine - INFO - Epoch(train) [1][ 750/1196] lr: 2.3897e-01 eta: 5:51:45 time: 1.2100 data_time: 0.0047 memory: 1749 loss: 0.4185 loss_sem_seg: 0.4185 2023/03/21 01:33:37 - mmengine - INFO - Epoch(train) [1][ 800/1196] lr: 2.3883e-01 eta: 5:50:37 time: 1.2214 data_time: 0.0048 memory: 1705 loss: 0.4038 loss_sem_seg: 0.4038 2023/03/21 01:34:39 - mmengine - INFO - Epoch(train) [1][ 850/1196] lr: 2.3868e-01 eta: 5:49:43 time: 1.2350 data_time: 0.0046 memory: 1632 loss: 0.3916 loss_sem_seg: 0.3916 2023/03/21 01:35:38 - mmengine - INFO - Epoch(train) [1][ 900/1196] lr: 2.3852e-01 eta: 5:48:10 time: 1.1940 data_time: 0.0048 memory: 1703 loss: 0.3785 loss_sem_seg: 0.3785 2023/03/21 01:36:38 - mmengine - INFO - Epoch(train) [1][ 950/1196] lr: 2.3835e-01 eta: 5:46:40 time: 1.1945 data_time: 0.0046 memory: 1723 loss: 0.3673 loss_sem_seg: 0.3673 2023/03/21 01:37:39 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 01:37:39 - mmengine - INFO - Epoch(train) [1][1000/1196] lr: 2.3817e-01 eta: 5:45:32 time: 1.2163 data_time: 0.0048 memory: 1748 loss: 0.3824 loss_sem_seg: 0.3824 2023/03/21 01:38:39 - mmengine - INFO - Epoch(train) [1][1050/1196] lr: 2.3798e-01 eta: 5:44:18 time: 1.2081 data_time: 0.0046 memory: 1738 loss: 0.3724 loss_sem_seg: 0.3724 2023/03/21 01:39:41 - mmengine - INFO - Epoch(train) [1][1100/1196] lr: 2.3778e-01 eta: 5:43:20 time: 1.2272 data_time: 0.0044 memory: 1687 loss: 0.3735 loss_sem_seg: 0.3735 2023/03/21 01:40:37 - mmengine - INFO - Epoch(train) [1][1150/1196] lr: 2.3758e-01 eta: 5:41:12 time: 1.1307 data_time: 0.0050 memory: 1707 loss: 0.3545 loss_sem_seg: 0.3545 2023/03/21 01:41:27 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 01:41:27 - mmengine - INFO - Saving checkpoint at 1 epochs 2023/03/21 01:41:51 - mmengine - INFO - Epoch(val) [1][ 50/509] eta: 0:03:27 time: 0.4529 data_time: 0.0084 memory: 1683 2023/03/21 01:42:13 - mmengine - INFO - Epoch(val) [1][100/509] eta: 0:03:05 time: 0.4525 data_time: 0.0072 memory: 423 2023/03/21 01:42:37 - mmengine - INFO - Epoch(val) [1][150/509] eta: 0:02:44 time: 0.4675 data_time: 0.0070 memory: 425 2023/03/21 01:43:00 - mmengine - INFO - Epoch(val) [1][200/509] eta: 0:02:21 time: 0.4545 data_time: 0.0069 memory: 417 2023/03/21 01:43:22 - mmengine - INFO - Epoch(val) [1][250/509] eta: 0:01:57 time: 0.4382 data_time: 0.0071 memory: 425 2023/03/21 01:43:44 - mmengine - INFO - Epoch(val) [1][300/509] eta: 0:01:34 time: 0.4435 data_time: 0.0071 memory: 400 2023/03/21 01:44:05 - mmengine - INFO - Epoch(val) [1][350/509] eta: 0:01:11 time: 0.4335 data_time: 0.0069 memory: 412 2023/03/21 01:44:34 - mmengine - INFO - Epoch(val) [1][400/509] eta: 0:00:50 time: 0.5703 data_time: 0.0062 memory: 414 2023/03/21 01:45:01 - mmengine - INFO - Epoch(val) [1][450/509] eta: 0:00:27 time: 0.5364 data_time: 0.0065 memory: 428 2023/03/21 01:45:28 - mmengine - INFO - Epoch(val) [1][500/509] eta: 0:00:04 time: 0.5465 data_time: 0.0066 memory: 415 2023/03/21 01:46:08 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9182 | 0.0000 | 0.0629 | 0.3210 | 0.0672 | 0.0660 | 0.0000 | 0.0000 | 0.8641 | 0.1556 | 0.6982 | 0.0004 | 0.8603 | 0.4778 | 0.8585 | 0.5652 | 0.6911 | 0.5646 | 0.1381 | 0.3847 | 0.8802 | 0.4520 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 01:46:08 - mmengine - INFO - Epoch(val) [1][509/509] car: 0.9182 bicycle: 0.0000 motorcycle: 0.0629 truck: 0.3210 bus: 0.0672 person: 0.0660 bicyclist: 0.0000 motorcyclist: 0.0000 road: 0.8641 parking: 0.1556 sidewalk: 0.6982 other-ground: 0.0004 building: 0.8603 fence: 0.4778 vegetation: 0.8585 trunck: 0.5652 terrian: 0.6911 pole: 0.5646 traffic-sign: 0.1381 miou: 0.3847 acc: 0.8802 acc_cls: 0.4520data_time: 0.0062 time: 0.5970 2023/03/21 01:47:10 - mmengine - INFO - Epoch(train) [2][ 50/1196] lr: 2.3716e-01 eta: 5:38:05 time: 1.2409 data_time: 0.0262 memory: 1722 loss: 0.3654 loss_sem_seg: 0.3654 2023/03/21 01:48:12 - mmengine - INFO - Epoch(train) [2][ 100/1196] lr: 2.3693e-01 eta: 5:37:24 time: 1.2463 data_time: 0.0049 memory: 1673 loss: 0.3249 loss_sem_seg: 0.3249 2023/03/21 01:49:12 - mmengine - INFO - Epoch(train) [2][ 150/1196] lr: 2.3669e-01 eta: 5:36:17 time: 1.2057 data_time: 0.0044 memory: 1654 loss: 0.3248 loss_sem_seg: 0.3248 2023/03/21 01:50:14 - mmengine - INFO - Epoch(train) [2][ 200/1196] lr: 2.3644e-01 eta: 5:35:26 time: 1.2335 data_time: 0.0045 memory: 1625 loss: 0.3730 loss_sem_seg: 0.3730 2023/03/21 01:51:14 - mmengine - INFO - Epoch(train) [2][ 250/1196] lr: 2.3618e-01 eta: 5:34:17 time: 1.2010 data_time: 0.0046 memory: 1723 loss: 0.3400 loss_sem_seg: 0.3400 2023/03/21 01:52:15 - mmengine - INFO - Epoch(train) [2][ 300/1196] lr: 2.3591e-01 eta: 5:33:14 time: 1.2129 data_time: 0.0047 memory: 1720 loss: 0.3267 loss_sem_seg: 0.3267 2023/03/21 01:53:13 - mmengine - INFO - Epoch(train) [2][ 350/1196] lr: 2.3563e-01 eta: 5:31:44 time: 1.1610 data_time: 0.0045 memory: 1632 loss: 0.3413 loss_sem_seg: 0.3413 2023/03/21 01:54:02 - mmengine - INFO - Epoch(train) [2][ 400/1196] lr: 2.3535e-01 eta: 5:28:44 time: 0.9806 data_time: 0.0038 memory: 1653 loss: 0.3424 loss_sem_seg: 0.3424 2023/03/21 01:54:58 - mmengine - INFO - Epoch(train) [2][ 450/1196] lr: 2.3506e-01 eta: 5:26:59 time: 1.1175 data_time: 0.0041 memory: 1660 loss: 0.3054 loss_sem_seg: 0.3054 2023/03/21 01:56:00 - mmengine - INFO - Epoch(train) [2][ 500/1196] lr: 2.3475e-01 eta: 5:26:23 time: 1.2546 data_time: 0.0046 memory: 1720 loss: 0.3193 loss_sem_seg: 0.3193 2023/03/21 01:57:02 - mmengine - INFO - Epoch(train) [2][ 550/1196] lr: 2.3444e-01 eta: 5:25:32 time: 1.2246 data_time: 0.0046 memory: 1702 loss: 0.3627 loss_sem_seg: 0.3627 2023/03/21 01:58:02 - mmengine - INFO - Epoch(train) [2][ 600/1196] lr: 2.3412e-01 eta: 5:24:33 time: 1.2103 data_time: 0.0046 memory: 1677 loss: 0.3092 loss_sem_seg: 0.3092 2023/03/21 01:59:04 - mmengine - INFO - Epoch(train) [2][ 650/1196] lr: 2.3379e-01 eta: 5:23:44 time: 1.2304 data_time: 0.0044 memory: 1658 loss: 0.3123 loss_sem_seg: 0.3123 2023/03/21 02:00:03 - mmengine - INFO - Epoch(train) [2][ 700/1196] lr: 2.3345e-01 eta: 5:22:38 time: 1.1949 data_time: 0.0045 memory: 1669 loss: 0.3277 loss_sem_seg: 0.3277 2023/03/21 02:01:05 - mmengine - INFO - Epoch(train) [2][ 750/1196] lr: 2.3311e-01 eta: 5:21:51 time: 1.2379 data_time: 0.0044 memory: 1737 loss: 0.3162 loss_sem_seg: 0.3162 2023/03/21 02:02:07 - mmengine - INFO - Epoch(train) [2][ 800/1196] lr: 2.3275e-01 eta: 5:21:01 time: 1.2334 data_time: 0.0043 memory: 1651 loss: 0.2949 loss_sem_seg: 0.2949 2023/03/21 02:02:12 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 02:03:07 - mmengine - INFO - Epoch(train) [2][ 850/1196] lr: 2.3239e-01 eta: 5:20:00 time: 1.2079 data_time: 0.0044 memory: 1687 loss: 0.2876 loss_sem_seg: 0.2876 2023/03/21 02:04:10 - mmengine - INFO - Epoch(train) [2][ 900/1196] lr: 2.3201e-01 eta: 5:19:16 time: 1.2516 data_time: 0.0044 memory: 1671 loss: 0.3149 loss_sem_seg: 0.3149 2023/03/21 02:05:10 - mmengine - INFO - Epoch(train) [2][ 950/1196] lr: 2.3163e-01 eta: 5:18:10 time: 1.1934 data_time: 0.0047 memory: 1713 loss: 0.2938 loss_sem_seg: 0.2938 2023/03/21 02:06:10 - mmengine - INFO - Epoch(train) [2][1000/1196] lr: 2.3124e-01 eta: 5:17:12 time: 1.2153 data_time: 0.0045 memory: 1723 loss: 0.3171 loss_sem_seg: 0.3171 2023/03/21 02:07:09 - mmengine - INFO - Epoch(train) [2][1050/1196] lr: 2.3085e-01 eta: 5:15:59 time: 1.1736 data_time: 0.0047 memory: 1618 loss: 0.2810 loss_sem_seg: 0.2810 2023/03/21 02:08:03 - mmengine - INFO - Epoch(train) [2][1100/1196] lr: 2.3044e-01 eta: 5:14:16 time: 1.0825 data_time: 0.0048 memory: 1660 loss: 0.3091 loss_sem_seg: 0.3091 2023/03/21 02:08:59 - mmengine - INFO - Epoch(train) [2][1150/1196] lr: 2.3002e-01 eta: 5:12:47 time: 1.1175 data_time: 0.0046 memory: 1715 loss: 0.3110 loss_sem_seg: 0.3110 2023/03/21 02:09:48 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 02:09:49 - mmengine - INFO - Saving checkpoint at 2 epochs 2023/03/21 02:10:13 - mmengine - INFO - Epoch(val) [2][ 50/509] eta: 0:03:27 time: 0.4519 data_time: 0.0118 memory: 1767 2023/03/21 02:10:35 - mmengine - INFO - Epoch(val) [2][100/509] eta: 0:03:03 time: 0.4432 data_time: 0.0068 memory: 423 2023/03/21 02:10:58 - mmengine - INFO - Epoch(val) [2][150/509] eta: 0:02:41 time: 0.4570 data_time: 0.0070 memory: 425 2023/03/21 02:11:21 - mmengine - INFO - Epoch(val) [2][200/509] eta: 0:02:19 time: 0.4587 data_time: 0.0069 memory: 417 2023/03/21 02:11:47 - mmengine - INFO - Epoch(val) [2][250/509] eta: 0:02:00 time: 0.5185 data_time: 0.0068 memory: 425 2023/03/21 02:12:14 - mmengine - INFO - Epoch(val) [2][300/509] eta: 0:01:39 time: 0.5356 data_time: 0.0076 memory: 400 2023/03/21 02:12:42 - mmengine - INFO - Epoch(val) [2][350/509] eta: 0:01:17 time: 0.5622 data_time: 0.0071 memory: 412 2023/03/21 02:13:09 - mmengine - INFO - Epoch(val) [2][400/509] eta: 0:00:54 time: 0.5452 data_time: 0.0068 memory: 414 2023/03/21 02:13:36 - mmengine - INFO - Epoch(val) [2][450/509] eta: 0:00:29 time: 0.5429 data_time: 0.0072 memory: 428 2023/03/21 02:14:08 - mmengine - INFO - Epoch(val) [2][500/509] eta: 0:00:04 time: 0.6408 data_time: 0.0067 memory: 415 2023/03/21 02:14:48 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9382 | 0.0000 | 0.2167 | 0.3091 | 0.2075 | 0.2400 | 0.2001 | 0.0000 | 0.8829 | 0.2906 | 0.7423 | 0.0042 | 0.8710 | 0.4785 | 0.8534 | 0.5909 | 0.7156 | 0.5862 | 0.2814 | 0.4426 | 0.8902 | 0.5113 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 02:14:48 - mmengine - INFO - Epoch(val) [2][509/509] car: 0.9382 bicycle: 0.0000 motorcycle: 0.2167 truck: 0.3091 bus: 0.2075 person: 0.2400 bicyclist: 0.2001 motorcyclist: 0.0000 road: 0.8829 parking: 0.2906 sidewalk: 0.7423 other-ground: 0.0042 building: 0.8710 fence: 0.4785 vegetation: 0.8534 trunck: 0.5909 terrian: 0.7156 pole: 0.5862 traffic-sign: 0.2814 miou: 0.4426 acc: 0.8902 acc_cls: 0.5113data_time: 0.0063 time: 0.6758 2023/03/21 02:15:51 - mmengine - INFO - Epoch(train) [3][ 50/1196] lr: 2.2920e-01 eta: 5:10:26 time: 1.2463 data_time: 0.0275 memory: 1807 loss: 0.3012 loss_sem_seg: 0.3012 2023/03/21 02:16:50 - mmengine - INFO - Epoch(train) [3][ 100/1196] lr: 2.2876e-01 eta: 5:09:23 time: 1.1912 data_time: 0.0047 memory: 1707 loss: 0.2814 loss_sem_seg: 0.2814 2023/03/21 02:17:53 - mmengine - INFO - Epoch(train) [3][ 150/1196] lr: 2.2832e-01 eta: 5:08:39 time: 1.2559 data_time: 0.0047 memory: 1710 loss: 0.2961 loss_sem_seg: 0.2961 2023/03/21 02:18:54 - mmengine - INFO - Epoch(train) [3][ 200/1196] lr: 2.2786e-01 eta: 5:07:43 time: 1.2172 data_time: 0.0048 memory: 1651 loss: 0.2858 loss_sem_seg: 0.2858 2023/03/21 02:19:55 - mmengine - INFO - Epoch(train) [3][ 250/1196] lr: 2.2739e-01 eta: 5:06:47 time: 1.2159 data_time: 0.0045 memory: 1692 loss: 0.2889 loss_sem_seg: 0.2889 2023/03/21 02:20:55 - mmengine - INFO - Epoch(train) [3][ 300/1196] lr: 2.2692e-01 eta: 5:05:46 time: 1.2024 data_time: 0.0047 memory: 1710 loss: 0.2754 loss_sem_seg: 0.2754 2023/03/21 02:21:57 - mmengine - INFO - Epoch(train) [3][ 350/1196] lr: 2.2644e-01 eta: 5:04:58 time: 1.2455 data_time: 0.0045 memory: 1723 loss: 0.2925 loss_sem_seg: 0.2925 2023/03/21 02:22:57 - mmengine - INFO - Epoch(train) [3][ 400/1196] lr: 2.2595e-01 eta: 5:03:58 time: 1.2052 data_time: 0.0049 memory: 1701 loss: 0.2887 loss_sem_seg: 0.2887 2023/03/21 02:24:00 - mmengine - INFO - Epoch(train) [3][ 450/1196] lr: 2.2545e-01 eta: 5:03:08 time: 1.2409 data_time: 0.0048 memory: 1633 loss: 0.2755 loss_sem_seg: 0.2755 2023/03/21 02:25:01 - mmengine - INFO - Epoch(train) [3][ 500/1196] lr: 2.2495e-01 eta: 5:02:13 time: 1.2262 data_time: 0.0046 memory: 1706 loss: 0.2800 loss_sem_seg: 0.2800 2023/03/21 02:25:56 - mmengine - INFO - Epoch(train) [3][ 550/1196] lr: 2.2443e-01 eta: 5:00:45 time: 1.0946 data_time: 0.0044 memory: 1684 loss: 0.2802 loss_sem_seg: 0.2802 2023/03/21 02:26:44 - mmengine - INFO - Epoch(train) [3][ 600/1196] lr: 2.2391e-01 eta: 4:58:44 time: 0.9609 data_time: 0.0040 memory: 1711 loss: 0.2736 loss_sem_seg: 0.2736 2023/03/21 02:26:51 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 02:27:40 - mmengine - INFO - Epoch(train) [3][ 650/1196] lr: 2.2338e-01 eta: 4:57:27 time: 1.1308 data_time: 0.0042 memory: 1687 loss: 0.2649 loss_sem_seg: 0.2649 2023/03/21 02:28:42 - mmengine - INFO - Epoch(train) [3][ 700/1196] lr: 2.2285e-01 eta: 4:56:36 time: 1.2329 data_time: 0.0047 memory: 1708 loss: 0.2776 loss_sem_seg: 0.2776 2023/03/21 02:29:44 - mmengine - INFO - Epoch(train) [3][ 750/1196] lr: 2.2230e-01 eta: 4:55:45 time: 1.2382 data_time: 0.0045 memory: 1662 loss: 0.2905 loss_sem_seg: 0.2905 2023/03/21 02:30:44 - mmengine - INFO - Epoch(train) [3][ 800/1196] lr: 2.2175e-01 eta: 4:54:45 time: 1.1992 data_time: 0.0045 memory: 1736 loss: 0.2718 loss_sem_seg: 0.2718 2023/03/21 02:31:46 - mmengine - INFO - Epoch(train) [3][ 850/1196] lr: 2.2119e-01 eta: 4:53:56 time: 1.2447 data_time: 0.0045 memory: 1702 loss: 0.2584 loss_sem_seg: 0.2584 2023/03/21 02:32:48 - mmengine - INFO - Epoch(train) [3][ 900/1196] lr: 2.2062e-01 eta: 4:53:05 time: 1.2423 data_time: 0.0045 memory: 1738 loss: 0.2717 loss_sem_seg: 0.2717 2023/03/21 02:33:51 - mmengine - INFO - Epoch(train) [3][ 950/1196] lr: 2.2004e-01 eta: 4:52:18 time: 1.2587 data_time: 0.0044 memory: 1789 loss: 0.2782 loss_sem_seg: 0.2782 2023/03/21 02:34:47 - mmengine - INFO - Epoch(train) [3][1000/1196] lr: 2.1946e-01 eta: 4:51:01 time: 1.1254 data_time: 0.0045 memory: 1665 loss: 0.2830 loss_sem_seg: 0.2830 2023/03/21 02:35:43 - mmengine - INFO - Epoch(train) [3][1050/1196] lr: 2.1887e-01 eta: 4:49:44 time: 1.1174 data_time: 0.0048 memory: 1657 loss: 0.2796 loss_sem_seg: 0.2796 2023/03/21 02:36:39 - mmengine - INFO - Epoch(train) [3][1100/1196] lr: 2.1827e-01 eta: 4:48:29 time: 1.1271 data_time: 0.0050 memory: 1675 loss: 0.2686 loss_sem_seg: 0.2686 2023/03/21 02:37:34 - mmengine - INFO - Epoch(train) [3][1150/1196] lr: 2.1766e-01 eta: 4:47:07 time: 1.0900 data_time: 0.0043 memory: 1681 loss: 0.2554 loss_sem_seg: 0.2554 2023/03/21 02:38:21 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 02:38:21 - mmengine - INFO - Saving checkpoint at 3 epochs 2023/03/21 02:38:47 - mmengine - INFO - Epoch(val) [3][ 50/509] eta: 0:03:39 time: 0.4776 data_time: 0.0116 memory: 1713 2023/03/21 02:39:12 - mmengine - INFO - Epoch(val) [3][100/509] eta: 0:03:20 time: 0.5050 data_time: 0.0071 memory: 423 2023/03/21 02:39:38 - mmengine - INFO - Epoch(val) [3][150/509] eta: 0:02:59 time: 0.5197 data_time: 0.0065 memory: 425 2023/03/21 02:40:05 - mmengine - INFO - Epoch(val) [3][200/509] eta: 0:02:37 time: 0.5428 data_time: 0.0068 memory: 417 2023/03/21 02:40:32 - mmengine - INFO - Epoch(val) [3][250/509] eta: 0:02:13 time: 0.5371 data_time: 0.0067 memory: 425 2023/03/21 02:40:58 - mmengine - INFO - Epoch(val) [3][300/509] eta: 0:01:48 time: 0.5319 data_time: 0.0070 memory: 400 2023/03/21 02:41:26 - mmengine - INFO - Epoch(val) [3][350/509] eta: 0:01:23 time: 0.5503 data_time: 0.0066 memory: 412 2023/03/21 02:41:54 - mmengine - INFO - Epoch(val) [3][400/509] eta: 0:00:57 time: 0.5550 data_time: 0.0065 memory: 414 2023/03/21 02:42:21 - mmengine - INFO - Epoch(val) [3][450/509] eta: 0:00:31 time: 0.5560 data_time: 0.0067 memory: 428 2023/03/21 02:42:54 - mmengine - INFO - Epoch(val) [3][500/509] eta: 0:00:04 time: 0.6501 data_time: 0.0064 memory: 415 2023/03/21 02:43:33 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9411 | 0.0062 | 0.3903 | 0.3072 | 0.2752 | 0.4728 | 0.5435 | 0.0000 | 0.9043 | 0.3464 | 0.7643 | 0.0001 | 0.8773 | 0.5143 | 0.8749 | 0.6540 | 0.7327 | 0.6028 | 0.3640 | 0.5038 | 0.9019 | 0.6057 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 02:43:33 - mmengine - INFO - Epoch(val) [3][509/509] car: 0.9411 bicycle: 0.0062 motorcycle: 0.3903 truck: 0.3072 bus: 0.2752 person: 0.4728 bicyclist: 0.5435 motorcyclist: 0.0000 road: 0.9043 parking: 0.3464 sidewalk: 0.7643 other-ground: 0.0001 building: 0.8773 fence: 0.5143 vegetation: 0.8749 trunck: 0.6540 terrian: 0.7327 pole: 0.6028 traffic-sign: 0.3640 miou: 0.5038 acc: 0.9019 acc_cls: 0.6057data_time: 0.0064 time: 0.6682 2023/03/21 02:44:37 - mmengine - INFO - Epoch(train) [4][ 50/1196] lr: 2.1647e-01 eta: 4:44:55 time: 1.2736 data_time: 0.0256 memory: 1737 loss: 0.2627 loss_sem_seg: 0.2627 2023/03/21 02:45:38 - mmengine - INFO - Epoch(train) [4][ 100/1196] lr: 2.1585e-01 eta: 4:44:00 time: 1.2165 data_time: 0.0045 memory: 1683 loss: 0.2641 loss_sem_seg: 0.2641 2023/03/21 02:46:40 - mmengine - INFO - Epoch(train) [4][ 150/1196] lr: 2.1521e-01 eta: 4:43:08 time: 1.2403 data_time: 0.0049 memory: 1721 loss: 0.2647 loss_sem_seg: 0.2647 2023/03/21 02:47:42 - mmengine - INFO - Epoch(train) [4][ 200/1196] lr: 2.1457e-01 eta: 4:42:17 time: 1.2413 data_time: 0.0044 memory: 1730 loss: 0.2568 loss_sem_seg: 0.2568 2023/03/21 02:48:45 - mmengine - INFO - Epoch(train) [4][ 250/1196] lr: 2.1392e-01 eta: 4:41:30 time: 1.2691 data_time: 0.0047 memory: 1675 loss: 0.2638 loss_sem_seg: 0.2638 2023/03/21 02:49:46 - mmengine - INFO - Epoch(train) [4][ 300/1196] lr: 2.1326e-01 eta: 4:40:32 time: 1.2082 data_time: 0.0046 memory: 1647 loss: 0.2600 loss_sem_seg: 0.2600 2023/03/21 02:50:46 - mmengine - INFO - Epoch(train) [4][ 350/1196] lr: 2.1259e-01 eta: 4:39:35 time: 1.2140 data_time: 0.0047 memory: 1777 loss: 0.2773 loss_sem_seg: 0.2773 2023/03/21 02:51:47 - mmengine - INFO - Epoch(train) [4][ 400/1196] lr: 2.1192e-01 eta: 4:38:38 time: 1.2149 data_time: 0.0046 memory: 1713 loss: 0.2694 loss_sem_seg: 0.2694 2023/03/21 02:52:02 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 02:52:48 - mmengine - INFO - Epoch(train) [4][ 450/1196] lr: 2.1124e-01 eta: 4:37:41 time: 1.2126 data_time: 0.0047 memory: 1767 loss: 0.2751 loss_sem_seg: 0.2751 2023/03/21 02:53:49 - mmengine - INFO - Epoch(train) [4][ 500/1196] lr: 2.1056e-01 eta: 4:36:47 time: 1.2344 data_time: 0.0049 memory: 1732 loss: 0.2564 loss_sem_seg: 0.2564 2023/03/21 02:54:51 - mmengine - INFO - Epoch(train) [4][ 550/1196] lr: 2.0986e-01 eta: 4:35:53 time: 1.2363 data_time: 0.0047 memory: 1703 loss: 0.2643 loss_sem_seg: 0.2643 2023/03/21 02:55:52 - mmengine - INFO - Epoch(train) [4][ 600/1196] lr: 2.0916e-01 eta: 4:34:57 time: 1.2237 data_time: 0.0046 memory: 1696 loss: 0.2611 loss_sem_seg: 0.2611 2023/03/21 02:56:54 - mmengine - INFO - Epoch(train) [4][ 650/1196] lr: 2.0846e-01 eta: 4:34:02 time: 1.2293 data_time: 0.0047 memory: 1749 loss: 0.2591 loss_sem_seg: 0.2591 2023/03/21 02:57:55 - mmengine - INFO - Epoch(train) [4][ 700/1196] lr: 2.0774e-01 eta: 4:33:05 time: 1.2169 data_time: 0.0046 memory: 1658 loss: 0.2507 loss_sem_seg: 0.2507 2023/03/21 02:58:56 - mmengine - INFO - Epoch(train) [4][ 750/1196] lr: 2.0702e-01 eta: 4:32:10 time: 1.2344 data_time: 0.0044 memory: 1726 loss: 0.2563 loss_sem_seg: 0.2563 2023/03/21 02:59:58 - mmengine - INFO - Epoch(train) [4][ 800/1196] lr: 2.0630e-01 eta: 4:31:15 time: 1.2292 data_time: 0.0047 memory: 1710 loss: 0.2632 loss_sem_seg: 0.2632 2023/03/21 03:01:00 - mmengine - INFO - Epoch(train) [4][ 850/1196] lr: 2.0556e-01 eta: 4:30:19 time: 1.2325 data_time: 0.0044 memory: 1632 loss: 0.2662 loss_sem_seg: 0.2662 2023/03/21 03:02:00 - mmengine - INFO - Epoch(train) [4][ 900/1196] lr: 2.0482e-01 eta: 4:29:21 time: 1.2137 data_time: 0.0046 memory: 1671 loss: 0.2559 loss_sem_seg: 0.2559 2023/03/21 03:02:55 - mmengine - INFO - Epoch(train) [4][ 950/1196] lr: 2.0408e-01 eta: 4:28:07 time: 1.1037 data_time: 0.0045 memory: 1718 loss: 0.2613 loss_sem_seg: 0.2613 2023/03/21 03:03:50 - mmengine - INFO - Epoch(train) [4][1000/1196] lr: 2.0333e-01 eta: 4:26:52 time: 1.1007 data_time: 0.0047 memory: 1692 loss: 0.2347 loss_sem_seg: 0.2347 2023/03/21 03:04:47 - mmengine - INFO - Epoch(train) [4][1050/1196] lr: 2.0257e-01 eta: 4:25:42 time: 1.1252 data_time: 0.0049 memory: 1724 loss: 0.2507 loss_sem_seg: 0.2507 2023/03/21 03:05:41 - mmengine - INFO - Epoch(train) [4][1100/1196] lr: 2.0180e-01 eta: 4:24:26 time: 1.0897 data_time: 0.0047 memory: 1663 loss: 0.2487 loss_sem_seg: 0.2487 2023/03/21 03:06:36 - mmengine - INFO - Epoch(train) [4][1150/1196] lr: 2.0103e-01 eta: 4:23:12 time: 1.0957 data_time: 0.0047 memory: 1688 loss: 0.2499 loss_sem_seg: 0.2499 2023/03/21 03:07:33 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 03:07:33 - mmengine - INFO - Saving checkpoint at 4 epochs 2023/03/21 03:08:02 - mmengine - INFO - Epoch(val) [4][ 50/509] eta: 0:04:16 time: 0.5591 data_time: 0.0114 memory: 1629 2023/03/21 03:08:29 - mmengine - INFO - Epoch(val) [4][100/509] eta: 0:03:45 time: 0.5419 data_time: 0.0069 memory: 423 2023/03/21 03:08:57 - mmengine - INFO - Epoch(val) [4][150/509] eta: 0:03:16 time: 0.5413 data_time: 0.0067 memory: 425 2023/03/21 03:09:24 - mmengine - INFO - Epoch(val) [4][200/509] eta: 0:02:48 time: 0.5437 data_time: 0.0067 memory: 417 2023/03/21 03:09:51 - mmengine - INFO - Epoch(val) [4][250/509] eta: 0:02:21 time: 0.5457 data_time: 0.0067 memory: 425 2023/03/21 03:10:18 - mmengine - INFO - Epoch(val) [4][300/509] eta: 0:01:53 time: 0.5326 data_time: 0.0067 memory: 400 2023/03/21 03:10:45 - mmengine - INFO - Epoch(val) [4][350/509] eta: 0:01:26 time: 0.5457 data_time: 0.0066 memory: 412 2023/03/21 03:11:12 - mmengine - INFO - Epoch(val) [4][400/509] eta: 0:00:59 time: 0.5469 data_time: 0.0067 memory: 414 2023/03/21 03:11:43 - mmengine - INFO - Epoch(val) [4][450/509] eta: 0:00:32 time: 0.6124 data_time: 0.0066 memory: 428 2023/03/21 03:12:15 - mmengine - INFO - Epoch(val) [4][500/509] eta: 0:00:05 time: 0.6337 data_time: 0.0064 memory: 415 2023/03/21 03:12:53 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9441 | 0.0128 | 0.4568 | 0.4610 | 0.3846 | 0.5088 | 0.6695 | 0.0000 | 0.9153 | 0.3256 | 0.7813 | 0.0023 | 0.8815 | 0.5326 | 0.8673 | 0.5678 | 0.7370 | 0.5997 | 0.3839 | 0.5280 | 0.9042 | 0.6282 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 03:12:53 - mmengine - INFO - Epoch(val) [4][509/509] car: 0.9441 bicycle: 0.0128 motorcycle: 0.4568 truck: 0.4610 bus: 0.3846 person: 0.5088 bicyclist: 0.6695 motorcyclist: 0.0000 road: 0.9153 parking: 0.3256 sidewalk: 0.7813 other-ground: 0.0023 building: 0.8815 fence: 0.5326 vegetation: 0.8673 trunck: 0.5678 terrian: 0.7370 pole: 0.5997 traffic-sign: 0.3839 miou: 0.5280 acc: 0.9042 acc_cls: 0.6282data_time: 0.0063 time: 0.6487 2023/03/21 03:13:55 - mmengine - INFO - Epoch(train) [5][ 50/1196] lr: 1.9953e-01 eta: 4:21:28 time: 1.2444 data_time: 0.0252 memory: 1673 loss: 0.2530 loss_sem_seg: 0.2530 2023/03/21 03:14:54 - mmengine - INFO - Epoch(train) [5][ 100/1196] lr: 1.9874e-01 eta: 4:20:27 time: 1.1893 data_time: 0.0047 memory: 1719 loss: 0.2442 loss_sem_seg: 0.2442 2023/03/21 03:15:57 - mmengine - INFO - Epoch(train) [5][ 150/1196] lr: 1.9794e-01 eta: 4:19:34 time: 1.2441 data_time: 0.0048 memory: 1725 loss: 0.2357 loss_sem_seg: 0.2357 2023/03/21 03:16:59 - mmengine - INFO - Epoch(train) [5][ 200/1196] lr: 1.9714e-01 eta: 4:18:40 time: 1.2443 data_time: 0.0045 memory: 1716 loss: 0.2420 loss_sem_seg: 0.2420 2023/03/21 03:17:18 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 03:17:58 - mmengine - INFO - Epoch(train) [5][ 250/1196] lr: 1.9633e-01 eta: 4:17:39 time: 1.1917 data_time: 0.0048 memory: 1683 loss: 0.2560 loss_sem_seg: 0.2560 2023/03/21 03:19:00 - mmengine - INFO - Epoch(train) [5][ 300/1196] lr: 1.9552e-01 eta: 4:16:44 time: 1.2376 data_time: 0.0045 memory: 1657 loss: 0.2460 loss_sem_seg: 0.2460 2023/03/21 03:20:02 - mmengine - INFO - Epoch(train) [5][ 350/1196] lr: 1.9470e-01 eta: 4:15:49 time: 1.2390 data_time: 0.0048 memory: 1620 loss: 0.2410 loss_sem_seg: 0.2410 2023/03/21 03:21:04 - mmengine - INFO - Epoch(train) [5][ 400/1196] lr: 1.9388e-01 eta: 4:14:53 time: 1.2288 data_time: 0.0044 memory: 1670 loss: 0.2552 loss_sem_seg: 0.2552 2023/03/21 03:22:04 - mmengine - INFO - Epoch(train) [5][ 450/1196] lr: 1.9304e-01 eta: 4:13:55 time: 1.2101 data_time: 0.0046 memory: 1722 loss: 0.2407 loss_sem_seg: 0.2407 2023/03/21 03:23:05 - mmengine - INFO - Epoch(train) [5][ 500/1196] lr: 1.9221e-01 eta: 4:12:58 time: 1.2238 data_time: 0.0046 memory: 1681 loss: 0.2418 loss_sem_seg: 0.2418 2023/03/21 03:24:08 - mmengine - INFO - Epoch(train) [5][ 550/1196] lr: 1.9137e-01 eta: 4:12:03 time: 1.2469 data_time: 0.0046 memory: 1653 loss: 0.2485 loss_sem_seg: 0.2485 2023/03/21 03:25:09 - mmengine - INFO - Epoch(train) [5][ 600/1196] lr: 1.9052e-01 eta: 4:11:06 time: 1.2237 data_time: 0.0046 memory: 1695 loss: 0.2668 loss_sem_seg: 0.2668 2023/03/21 03:26:10 - mmengine - INFO - Epoch(train) [5][ 650/1196] lr: 1.8967e-01 eta: 4:10:09 time: 1.2257 data_time: 0.0044 memory: 1683 loss: 0.2348 loss_sem_seg: 0.2348 2023/03/21 03:27:12 - mmengine - INFO - Epoch(train) [5][ 700/1196] lr: 1.8881e-01 eta: 4:09:13 time: 1.2367 data_time: 0.0044 memory: 1746 loss: 0.2374 loss_sem_seg: 0.2374 2023/03/21 03:28:13 - mmengine - INFO - Epoch(train) [5][ 750/1196] lr: 1.8794e-01 eta: 4:08:16 time: 1.2244 data_time: 0.0045 memory: 1760 loss: 0.2402 loss_sem_seg: 0.2402 2023/03/21 03:29:13 - mmengine - INFO - Epoch(train) [5][ 800/1196] lr: 1.8708e-01 eta: 4:07:16 time: 1.2014 data_time: 0.0045 memory: 1723 loss: 0.2264 loss_sem_seg: 0.2264 2023/03/21 03:30:09 - mmengine - INFO - Epoch(train) [5][ 850/1196] lr: 1.8620e-01 eta: 4:06:05 time: 1.1053 data_time: 0.0048 memory: 1769 loss: 0.2433 loss_sem_seg: 0.2433 2023/03/21 03:31:04 - mmengine - INFO - Epoch(train) [5][ 900/1196] lr: 1.8532e-01 eta: 4:04:56 time: 1.1094 data_time: 0.0046 memory: 1633 loss: 0.2455 loss_sem_seg: 0.2455 2023/03/21 03:31:58 - mmengine - INFO - Epoch(train) [5][ 950/1196] lr: 1.8444e-01 eta: 4:03:44 time: 1.0844 data_time: 0.0048 memory: 1635 loss: 0.2255 loss_sem_seg: 0.2255 2023/03/21 03:32:54 - mmengine - INFO - Epoch(train) [5][1000/1196] lr: 1.8355e-01 eta: 4:02:35 time: 1.1201 data_time: 0.0046 memory: 1717 loss: 0.2167 loss_sem_seg: 0.2167 2023/03/21 03:33:50 - mmengine - INFO - Epoch(train) [5][1050/1196] lr: 1.8266e-01 eta: 4:01:26 time: 1.1079 data_time: 0.0047 memory: 1641 loss: 0.2341 loss_sem_seg: 0.2341 2023/03/21 03:34:51 - mmengine - INFO - Epoch(train) [5][1100/1196] lr: 1.8176e-01 eta: 4:00:28 time: 1.2165 data_time: 0.0045 memory: 1649 loss: 0.2292 loss_sem_seg: 0.2292 2023/03/21 03:35:54 - mmengine - INFO - Epoch(train) [5][1150/1196] lr: 1.8086e-01 eta: 3:59:36 time: 1.2699 data_time: 0.0042 memory: 1683 loss: 0.2480 loss_sem_seg: 0.2480 2023/03/21 03:36:45 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 03:36:46 - mmengine - INFO - Saving checkpoint at 5 epochs 2023/03/21 03:37:14 - mmengine - INFO - Epoch(val) [5][ 50/509] eta: 0:04:11 time: 0.5486 data_time: 0.0112 memory: 1635 2023/03/21 03:37:42 - mmengine - INFO - Epoch(val) [5][100/509] eta: 0:03:45 time: 0.5565 data_time: 0.0066 memory: 423 2023/03/21 03:38:10 - mmengine - INFO - Epoch(val) [5][150/509] eta: 0:03:18 time: 0.5505 data_time: 0.0063 memory: 425 2023/03/21 03:38:36 - mmengine - INFO - Epoch(val) [5][200/509] eta: 0:02:48 time: 0.5297 data_time: 0.0065 memory: 417 2023/03/21 03:39:03 - mmengine - INFO - Epoch(val) [5][250/509] eta: 0:02:21 time: 0.5386 data_time: 0.0065 memory: 425 2023/03/21 03:39:31 - mmengine - INFO - Epoch(val) [5][300/509] eta: 0:01:54 time: 0.5520 data_time: 0.0066 memory: 400 2023/03/21 03:39:58 - mmengine - INFO - Epoch(val) [5][350/509] eta: 0:01:26 time: 0.5464 data_time: 0.0065 memory: 412 2023/03/21 03:40:26 - mmengine - INFO - Epoch(val) [5][400/509] eta: 0:00:59 time: 0.5498 data_time: 0.0064 memory: 414 2023/03/21 03:40:57 - mmengine - INFO - Epoch(val) [5][450/509] eta: 0:00:32 time: 0.6267 data_time: 0.0063 memory: 428 2023/03/21 03:41:29 - mmengine - INFO - Epoch(val) [5][500/509] eta: 0:00:05 time: 0.6309 data_time: 0.0061 memory: 415 2023/03/21 03:42:04 - 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 | 0.9436 | 0.0167 | 0.4428 | 0.3550 | 0.2703 | 0.4465 | 0.6378 | 0.0000 | 0.9183 | 0.3738 | 0.7830 | 0.0041 | 0.8856 | 0.5701 | 0.8795 | 0.6120 | 0.7671 | 0.5959 | 0.3948 | 0.5209 | 0.9108 | 0.6022 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 03:42:04 - mmengine - INFO - Epoch(val) [5][509/509] car: 0.9436 bicycle: 0.0167 motorcycle: 0.4428 truck: 0.3550 bus: 0.2703 person: 0.4465 bicyclist: 0.6378 motorcyclist: 0.0000 road: 0.9183 parking: 0.3738 sidewalk: 0.7830 other-ground: 0.0041 building: 0.8856 fence: 0.5701 vegetation: 0.8795 trunck: 0.6120 terrian: 0.7671 pole: 0.5959 traffic-sign: 0.3948 miou: 0.5209 acc: 0.9108 acc_cls: 0.6022data_time: 0.0061 time: 0.6399 2023/03/21 03:42:29 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 03:43:06 - mmengine - INFO - Epoch(train) [6][ 50/1196] lr: 1.7911e-01 eta: 3:57:38 time: 1.2414 data_time: 0.0278 memory: 1762 loss: 0.2362 loss_sem_seg: 0.2362 2023/03/21 03:44:07 - mmengine - INFO - Epoch(train) [6][ 100/1196] lr: 1.7819e-01 eta: 3:56:40 time: 1.2168 data_time: 0.0044 memory: 1686 loss: 0.2322 loss_sem_seg: 0.2322 2023/03/21 03:45:08 - mmengine - INFO - Epoch(train) [6][ 150/1196] lr: 1.7727e-01 eta: 3:55:43 time: 1.2280 data_time: 0.0046 memory: 1714 loss: 0.2445 loss_sem_seg: 0.2445 2023/03/21 03:46:10 - mmengine - INFO - Epoch(train) [6][ 200/1196] lr: 1.7635e-01 eta: 3:54:46 time: 1.2316 data_time: 0.0048 memory: 1724 loss: 0.2372 loss_sem_seg: 0.2372 2023/03/21 03:47:11 - mmengine - INFO - Epoch(train) [6][ 250/1196] lr: 1.7542e-01 eta: 3:53:49 time: 1.2233 data_time: 0.0046 memory: 1686 loss: 0.2216 loss_sem_seg: 0.2216 2023/03/21 03:48:12 - mmengine - INFO - Epoch(train) [6][ 300/1196] lr: 1.7448e-01 eta: 3:52:52 time: 1.2288 data_time: 0.0045 memory: 1759 loss: 0.2427 loss_sem_seg: 0.2427 2023/03/21 03:49:14 - mmengine - INFO - Epoch(train) [6][ 350/1196] lr: 1.7354e-01 eta: 3:51:55 time: 1.2328 data_time: 0.0047 memory: 1692 loss: 0.2455 loss_sem_seg: 0.2455 2023/03/21 03:50:15 - mmengine - INFO - Epoch(train) [6][ 400/1196] lr: 1.7260e-01 eta: 3:50:57 time: 1.2162 data_time: 0.0046 memory: 1711 loss: 0.2311 loss_sem_seg: 0.2311 2023/03/21 03:51:16 - mmengine - INFO - Epoch(train) [6][ 450/1196] lr: 1.7165e-01 eta: 3:50:00 time: 1.2318 data_time: 0.0045 memory: 1746 loss: 0.2271 loss_sem_seg: 0.2271 2023/03/21 03:52:20 - mmengine - INFO - Epoch(train) [6][ 500/1196] lr: 1.7070e-01 eta: 3:49:06 time: 1.2726 data_time: 0.0043 memory: 1708 loss: 0.2325 loss_sem_seg: 0.2325 2023/03/21 03:53:22 - mmengine - INFO - Epoch(train) [6][ 550/1196] lr: 1.6975e-01 eta: 3:48:09 time: 1.2343 data_time: 0.0047 memory: 1715 loss: 0.2344 loss_sem_seg: 0.2344 2023/03/21 03:54:22 - mmengine - INFO - Epoch(train) [6][ 600/1196] lr: 1.6879e-01 eta: 3:47:11 time: 1.2174 data_time: 0.0046 memory: 1680 loss: 0.2246 loss_sem_seg: 0.2246 2023/03/21 03:55:23 - mmengine - INFO - Epoch(train) [6][ 650/1196] lr: 1.6783e-01 eta: 3:46:13 time: 1.2179 data_time: 0.0048 memory: 1686 loss: 0.2441 loss_sem_seg: 0.2441 2023/03/21 03:56:26 - mmengine - INFO - Epoch(train) [6][ 700/1196] lr: 1.6686e-01 eta: 3:45:17 time: 1.2473 data_time: 0.0045 memory: 1693 loss: 0.2412 loss_sem_seg: 0.2412 2023/03/21 03:57:24 - mmengine - INFO - Epoch(train) [6][ 750/1196] lr: 1.6590e-01 eta: 3:44:13 time: 1.1629 data_time: 0.0049 memory: 1709 loss: 0.2063 loss_sem_seg: 0.2063 2023/03/21 03:58:19 - mmengine - INFO - Epoch(train) [6][ 800/1196] lr: 1.6492e-01 eta: 3:43:06 time: 1.1131 data_time: 0.0048 memory: 1700 loss: 0.2207 loss_sem_seg: 0.2207 2023/03/21 03:59:14 - mmengine - INFO - Epoch(train) [6][ 850/1196] lr: 1.6395e-01 eta: 3:41:58 time: 1.0997 data_time: 0.0045 memory: 1681 loss: 0.2256 loss_sem_seg: 0.2256 2023/03/21 04:00:10 - mmengine - INFO - Epoch(train) [6][ 900/1196] lr: 1.6297e-01 eta: 3:40:51 time: 1.1056 data_time: 0.0045 memory: 1713 loss: 0.2261 loss_sem_seg: 0.2261 2023/03/21 04:01:06 - mmengine - INFO - Epoch(train) [6][ 950/1196] lr: 1.6199e-01 eta: 3:39:45 time: 1.1264 data_time: 0.0046 memory: 1635 loss: 0.2282 loss_sem_seg: 0.2282 2023/03/21 04:02:02 - mmengine - INFO - Epoch(train) [6][1000/1196] lr: 1.6100e-01 eta: 3:38:39 time: 1.1167 data_time: 0.0047 memory: 1708 loss: 0.2134 loss_sem_seg: 0.2134 2023/03/21 04:02:31 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 04:03:08 - mmengine - INFO - Epoch(train) [6][1050/1196] lr: 1.6001e-01 eta: 3:37:49 time: 1.3260 data_time: 0.0044 memory: 1680 loss: 0.2383 loss_sem_seg: 0.2383 2023/03/21 04:04:10 - mmengine - INFO - Epoch(train) [6][1100/1196] lr: 1.5902e-01 eta: 3:36:51 time: 1.2263 data_time: 0.0047 memory: 1693 loss: 0.2224 loss_sem_seg: 0.2224 2023/03/21 04:05:12 - mmengine - INFO - Epoch(train) [6][1150/1196] lr: 1.5802e-01 eta: 3:35:55 time: 1.2400 data_time: 0.0045 memory: 1666 loss: 0.2202 loss_sem_seg: 0.2202 2023/03/21 04:06:01 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 04:06:01 - mmengine - INFO - Saving checkpoint at 6 epochs 2023/03/21 04:06:30 - mmengine - INFO - Epoch(val) [6][ 50/509] eta: 0:04:20 time: 0.5670 data_time: 0.0111 memory: 1661 2023/03/21 04:06:58 - mmengine - INFO - Epoch(val) [6][100/509] eta: 0:03:47 time: 0.5473 data_time: 0.0070 memory: 423 2023/03/21 04:07:26 - mmengine - INFO - Epoch(val) [6][150/509] eta: 0:03:20 time: 0.5575 data_time: 0.0067 memory: 425 2023/03/21 04:07:53 - mmengine - INFO - Epoch(val) [6][200/509] eta: 0:02:50 time: 0.5385 data_time: 0.0067 memory: 417 2023/03/21 04:08:20 - mmengine - INFO - Epoch(val) [6][250/509] eta: 0:02:22 time: 0.5374 data_time: 0.0065 memory: 425 2023/03/21 04:08:46 - mmengine - INFO - Epoch(val) [6][300/509] eta: 0:01:54 time: 0.5375 data_time: 0.0069 memory: 400 2023/03/21 04:09:14 - mmengine - INFO - Epoch(val) [6][350/509] eta: 0:01:26 time: 0.5440 data_time: 0.0069 memory: 412 2023/03/21 04:09:40 - mmengine - INFO - Epoch(val) [6][400/509] eta: 0:00:59 time: 0.5363 data_time: 0.0068 memory: 414 2023/03/21 04:10:12 - mmengine - INFO - Epoch(val) [6][450/509] eta: 0:00:32 time: 0.6301 data_time: 0.0066 memory: 428 2023/03/21 04:10:43 - mmengine - INFO - Epoch(val) [6][500/509] eta: 0:00:05 time: 0.6257 data_time: 0.0064 memory: 415 2023/03/21 04:11:21 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9460 | 0.0360 | 0.4566 | 0.5474 | 0.3970 | 0.5896 | 0.7075 | 0.0000 | 0.9143 | 0.3573 | 0.7825 | 0.0034 | 0.8933 | 0.5364 | 0.8747 | 0.6510 | 0.7532 | 0.6234 | 0.4087 | 0.5515 | 0.9084 | 0.6381 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 04:11:21 - mmengine - INFO - Epoch(val) [6][509/509] car: 0.9460 bicycle: 0.0360 motorcycle: 0.4566 truck: 0.5474 bus: 0.3970 person: 0.5896 bicyclist: 0.7075 motorcyclist: 0.0000 road: 0.9143 parking: 0.3573 sidewalk: 0.7825 other-ground: 0.0034 building: 0.8933 fence: 0.5364 vegetation: 0.8747 trunck: 0.6510 terrian: 0.7532 pole: 0.6234 traffic-sign: 0.4087 miou: 0.5515 acc: 0.9084 acc_cls: 0.6381data_time: 0.0064 time: 0.6434 2023/03/21 04:12:23 - mmengine - INFO - Epoch(train) [7][ 50/1196] lr: 1.5610e-01 eta: 3:33:54 time: 1.2504 data_time: 0.0259 memory: 1752 loss: 0.1998 loss_sem_seg: 0.1998 2023/03/21 04:13:23 - mmengine - INFO - Epoch(train) [7][ 100/1196] lr: 1.5510e-01 eta: 3:32:55 time: 1.1970 data_time: 0.0049 memory: 1677 loss: 0.2334 loss_sem_seg: 0.2334 2023/03/21 04:14:25 - mmengine - INFO - Epoch(train) [7][ 150/1196] lr: 1.5410e-01 eta: 3:31:57 time: 1.2315 data_time: 0.0048 memory: 1691 loss: 0.2294 loss_sem_seg: 0.2294 2023/03/21 04:15:26 - mmengine - INFO - Epoch(train) [7][ 200/1196] lr: 1.5309e-01 eta: 3:30:59 time: 1.2267 data_time: 0.0045 memory: 1704 loss: 0.2196 loss_sem_seg: 0.2196 2023/03/21 04:16:26 - mmengine - INFO - Epoch(train) [7][ 250/1196] lr: 1.5208e-01 eta: 3:30:00 time: 1.2035 data_time: 0.0044 memory: 1669 loss: 0.2184 loss_sem_seg: 0.2184 2023/03/21 04:17:27 - mmengine - INFO - Epoch(train) [7][ 300/1196] lr: 1.5106e-01 eta: 3:29:01 time: 1.2241 data_time: 0.0048 memory: 1711 loss: 0.2327 loss_sem_seg: 0.2327 2023/03/21 04:18:29 - mmengine - INFO - Epoch(train) [7][ 350/1196] lr: 1.5005e-01 eta: 3:28:04 time: 1.2372 data_time: 0.0048 memory: 1658 loss: 0.2154 loss_sem_seg: 0.2154 2023/03/21 04:19:31 - mmengine - INFO - Epoch(train) [7][ 400/1196] lr: 1.4903e-01 eta: 3:27:06 time: 1.2278 data_time: 0.0045 memory: 1670 loss: 0.2213 loss_sem_seg: 0.2213 2023/03/21 04:20:33 - mmengine - INFO - Epoch(train) [7][ 450/1196] lr: 1.4801e-01 eta: 3:26:10 time: 1.2531 data_time: 0.0046 memory: 1640 loss: 0.2287 loss_sem_seg: 0.2287 2023/03/21 04:21:33 - mmengine - INFO - Epoch(train) [7][ 500/1196] lr: 1.4698e-01 eta: 3:25:10 time: 1.2025 data_time: 0.0046 memory: 1673 loss: 0.2289 loss_sem_seg: 0.2289 2023/03/21 04:22:34 - mmengine - INFO - Epoch(train) [7][ 550/1196] lr: 1.4596e-01 eta: 3:24:11 time: 1.2075 data_time: 0.0047 memory: 1662 loss: 0.2171 loss_sem_seg: 0.2171 2023/03/21 04:23:34 - mmengine - INFO - Epoch(train) [7][ 600/1196] lr: 1.4493e-01 eta: 3:23:12 time: 1.2158 data_time: 0.0045 memory: 1652 loss: 0.2075 loss_sem_seg: 0.2075 2023/03/21 04:24:34 - mmengine - INFO - Epoch(train) [7][ 650/1196] lr: 1.4390e-01 eta: 3:22:11 time: 1.1826 data_time: 0.0046 memory: 1723 loss: 0.2269 loss_sem_seg: 0.2269 2023/03/21 04:25:29 - mmengine - INFO - Epoch(train) [7][ 700/1196] lr: 1.4287e-01 eta: 3:21:06 time: 1.1163 data_time: 0.0048 memory: 1677 loss: 0.2313 loss_sem_seg: 0.2313 2023/03/21 04:26:24 - mmengine - INFO - Epoch(train) [7][ 750/1196] lr: 1.4184e-01 eta: 3:19:59 time: 1.0941 data_time: 0.0050 memory: 1657 loss: 0.2248 loss_sem_seg: 0.2248 2023/03/21 04:27:20 - mmengine - INFO - Epoch(train) [7][ 800/1196] lr: 1.4081e-01 eta: 3:18:54 time: 1.1141 data_time: 0.0046 memory: 1638 loss: 0.2083 loss_sem_seg: 0.2083 2023/03/21 04:27:47 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 04:28:15 - mmengine - INFO - Epoch(train) [7][ 850/1196] lr: 1.3977e-01 eta: 3:17:48 time: 1.1082 data_time: 0.0048 memory: 1670 loss: 0.2118 loss_sem_seg: 0.2118 2023/03/21 04:29:10 - mmengine - INFO - Epoch(train) [7][ 900/1196] lr: 1.3873e-01 eta: 3:16:43 time: 1.1011 data_time: 0.0046 memory: 1640 loss: 0.2125 loss_sem_seg: 0.2125 2023/03/21 04:30:15 - mmengine - INFO - Epoch(train) [7][ 950/1196] lr: 1.3770e-01 eta: 3:15:48 time: 1.2866 data_time: 0.0044 memory: 1720 loss: 0.2050 loss_sem_seg: 0.2050 2023/03/21 04:31:17 - mmengine - INFO - Epoch(train) [7][1000/1196] lr: 1.3666e-01 eta: 3:14:51 time: 1.2402 data_time: 0.0046 memory: 1682 loss: 0.2148 loss_sem_seg: 0.2148 2023/03/21 04:32:18 - mmengine - INFO - Epoch(train) [7][1050/1196] lr: 1.3562e-01 eta: 3:13:52 time: 1.2198 data_time: 0.0046 memory: 1693 loss: 0.2205 loss_sem_seg: 0.2205 2023/03/21 04:33:19 - mmengine - INFO - Epoch(train) [7][1100/1196] lr: 1.3457e-01 eta: 3:12:54 time: 1.2308 data_time: 0.0048 memory: 1678 loss: 0.2366 loss_sem_seg: 0.2366 2023/03/21 04:34:20 - mmengine - INFO - Epoch(train) [7][1150/1196] lr: 1.3353e-01 eta: 3:11:56 time: 1.2241 data_time: 0.0046 memory: 1664 loss: 0.2042 loss_sem_seg: 0.2042 2023/03/21 04:35:11 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 04:35:12 - mmengine - INFO - Saving checkpoint at 7 epochs 2023/03/21 04:35:41 - mmengine - INFO - Epoch(val) [7][ 50/509] eta: 0:04:16 time: 0.5588 data_time: 0.0114 memory: 1697 2023/03/21 04:36:08 - mmengine - INFO - Epoch(val) [7][100/509] eta: 0:03:46 time: 0.5475 data_time: 0.0067 memory: 423 2023/03/21 04:36:36 - mmengine - INFO - Epoch(val) [7][150/509] eta: 0:03:18 time: 0.5489 data_time: 0.0067 memory: 425 2023/03/21 04:37:03 - mmengine - INFO - Epoch(val) [7][200/509] eta: 0:02:50 time: 0.5474 data_time: 0.0067 memory: 417 2023/03/21 04:37:31 - mmengine - INFO - Epoch(val) [7][250/509] eta: 0:02:22 time: 0.5469 data_time: 0.0066 memory: 425 2023/03/21 04:37:57 - mmengine - INFO - Epoch(val) [7][300/509] eta: 0:01:54 time: 0.5281 data_time: 0.0066 memory: 400 2023/03/21 04:38:24 - mmengine - INFO - Epoch(val) [7][350/509] eta: 0:01:26 time: 0.5413 data_time: 0.0067 memory: 412 2023/03/21 04:38:52 - mmengine - INFO - Epoch(val) [7][400/509] eta: 0:00:59 time: 0.5515 data_time: 0.0069 memory: 414 2023/03/21 04:39:22 - mmengine - INFO - Epoch(val) [7][450/509] eta: 0:00:32 time: 0.6059 data_time: 0.0066 memory: 428 2023/03/21 04:39:53 - mmengine - INFO - Epoch(val) [7][500/509] eta: 0:00:05 time: 0.6291 data_time: 0.0069 memory: 415 2023/03/21 04:40:29 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9478 | 0.1548 | 0.5747 | 0.6313 | 0.4407 | 0.5608 | 0.6773 | 0.0000 | 0.9099 | 0.3807 | 0.7717 | 0.0002 | 0.8973 | 0.5668 | 0.8779 | 0.6143 | 0.7540 | 0.6158 | 0.4478 | 0.5697 | 0.9092 | 0.6469 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 04:40:29 - mmengine - INFO - Epoch(val) [7][509/509] car: 0.9478 bicycle: 0.1548 motorcycle: 0.5747 truck: 0.6313 bus: 0.4407 person: 0.5608 bicyclist: 0.6773 motorcyclist: 0.0000 road: 0.9099 parking: 0.3807 sidewalk: 0.7717 other-ground: 0.0002 building: 0.8973 fence: 0.5668 vegetation: 0.8779 trunck: 0.6143 terrian: 0.7540 pole: 0.6158 traffic-sign: 0.4478 miou: 0.5697 acc: 0.9092 acc_cls: 0.6469data_time: 0.0069 time: 0.6528 2023/03/21 04:41:32 - mmengine - INFO - Epoch(train) [8][ 50/1196] lr: 1.3152e-01 eta: 3:10:00 time: 1.2604 data_time: 0.0253 memory: 1673 loss: 0.2072 loss_sem_seg: 0.2072 2023/03/21 04:42:33 - mmengine - INFO - Epoch(train) [8][ 100/1196] lr: 1.3048e-01 eta: 3:09:01 time: 1.2224 data_time: 0.0046 memory: 1668 loss: 0.2098 loss_sem_seg: 0.2098 2023/03/21 04:43:35 - mmengine - INFO - Epoch(train) [8][ 150/1196] lr: 1.2943e-01 eta: 3:08:04 time: 1.2467 data_time: 0.0047 memory: 1735 loss: 0.2071 loss_sem_seg: 0.2071 2023/03/21 04:44:36 - mmengine - INFO - Epoch(train) [8][ 200/1196] lr: 1.2838e-01 eta: 3:07:05 time: 1.2184 data_time: 0.0048 memory: 1660 loss: 0.2055 loss_sem_seg: 0.2055 2023/03/21 04:45:38 - mmengine - INFO - Epoch(train) [8][ 250/1196] lr: 1.2733e-01 eta: 3:06:07 time: 1.2368 data_time: 0.0046 memory: 1769 loss: 0.2015 loss_sem_seg: 0.2015 2023/03/21 04:46:29 - mmengine - INFO - Epoch(train) [8][ 300/1196] lr: 1.2629e-01 eta: 3:04:57 time: 1.0095 data_time: 0.0041 memory: 1656 loss: 0.2184 loss_sem_seg: 0.2184 2023/03/21 04:47:17 - mmengine - INFO - Epoch(train) [8][ 350/1196] lr: 1.2524e-01 eta: 3:03:45 time: 0.9587 data_time: 0.0039 memory: 1725 loss: 0.1970 loss_sem_seg: 0.1970 2023/03/21 04:48:18 - mmengine - INFO - Epoch(train) [8][ 400/1196] lr: 1.2419e-01 eta: 3:02:47 time: 1.2292 data_time: 0.0043 memory: 1711 loss: 0.2074 loss_sem_seg: 0.2074 2023/03/21 04:49:21 - mmengine - INFO - Epoch(train) [8][ 450/1196] lr: 1.2314e-01 eta: 3:01:50 time: 1.2551 data_time: 0.0048 memory: 1653 loss: 0.2045 loss_sem_seg: 0.2045 2023/03/21 04:50:22 - mmengine - INFO - Epoch(train) [8][ 500/1196] lr: 1.2209e-01 eta: 3:00:51 time: 1.2181 data_time: 0.0048 memory: 1641 loss: 0.2065 loss_sem_seg: 0.2065 2023/03/21 04:51:23 - mmengine - INFO - Epoch(train) [8][ 550/1196] lr: 1.2103e-01 eta: 2:59:53 time: 1.2276 data_time: 0.0047 memory: 1753 loss: 0.2077 loss_sem_seg: 0.2077 2023/03/21 04:52:18 - mmengine - INFO - Epoch(train) [8][ 600/1196] lr: 1.1998e-01 eta: 2:58:49 time: 1.1051 data_time: 0.0047 memory: 1693 loss: 0.1915 loss_sem_seg: 0.1915 2023/03/21 04:52:49 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 04:53:14 - mmengine - INFO - Epoch(train) [8][ 650/1196] lr: 1.1893e-01 eta: 2:57:44 time: 1.1047 data_time: 0.0048 memory: 1753 loss: 0.2222 loss_sem_seg: 0.2222 2023/03/21 04:54:09 - mmengine - INFO - Epoch(train) [8][ 700/1196] lr: 1.1788e-01 eta: 2:56:40 time: 1.1055 data_time: 0.0046 memory: 1704 loss: 0.1984 loss_sem_seg: 0.1984 2023/03/21 04:55:04 - mmengine - INFO - Epoch(train) [8][ 750/1196] lr: 1.1683e-01 eta: 2:55:36 time: 1.1087 data_time: 0.0046 memory: 1691 loss: 0.1986 loss_sem_seg: 0.1986 2023/03/21 04:56:00 - mmengine - INFO - Epoch(train) [8][ 800/1196] lr: 1.1578e-01 eta: 2:54:32 time: 1.1085 data_time: 0.0048 memory: 1680 loss: 0.1927 loss_sem_seg: 0.1927 2023/03/21 04:56:57 - mmengine - INFO - Epoch(train) [8][ 850/1196] lr: 1.1473e-01 eta: 2:53:30 time: 1.1444 data_time: 0.0048 memory: 1649 loss: 0.2049 loss_sem_seg: 0.2049 2023/03/21 04:58:02 - mmengine - INFO - Epoch(train) [8][ 900/1196] lr: 1.1368e-01 eta: 2:52:35 time: 1.2982 data_time: 0.0045 memory: 1680 loss: 0.2042 loss_sem_seg: 0.2042 2023/03/21 04:59:02 - mmengine - INFO - Epoch(train) [8][ 950/1196] lr: 1.1263e-01 eta: 2:51:36 time: 1.2069 data_time: 0.0049 memory: 1705 loss: 0.2069 loss_sem_seg: 0.2069 2023/03/21 05:00:05 - mmengine - INFO - Epoch(train) [8][1000/1196] lr: 1.1159e-01 eta: 2:50:39 time: 1.2608 data_time: 0.0046 memory: 1651 loss: 0.2002 loss_sem_seg: 0.2002 2023/03/21 05:01:06 - mmengine - INFO - Epoch(train) [8][1050/1196] lr: 1.1054e-01 eta: 2:49:40 time: 1.2119 data_time: 0.0045 memory: 1707 loss: 0.1872 loss_sem_seg: 0.1872 2023/03/21 05:02:08 - mmengine - INFO - Epoch(train) [8][1100/1196] lr: 1.0949e-01 eta: 2:48:42 time: 1.2361 data_time: 0.0048 memory: 1724 loss: 0.2161 loss_sem_seg: 0.2161 2023/03/21 05:03:10 - mmengine - INFO - Epoch(train) [8][1150/1196] lr: 1.0844e-01 eta: 2:47:45 time: 1.2415 data_time: 0.0048 memory: 1686 loss: 0.1944 loss_sem_seg: 0.1944 2023/03/21 05:04:00 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 05:04:01 - mmengine - INFO - Saving checkpoint at 8 epochs 2023/03/21 05:04:30 - mmengine - INFO - Epoch(val) [8][ 50/509] eta: 0:04:19 time: 0.5647 data_time: 0.0116 memory: 1644 2023/03/21 05:04:58 - mmengine - INFO - Epoch(val) [8][100/509] eta: 0:03:47 time: 0.5460 data_time: 0.0068 memory: 423 2023/03/21 05:05:26 - mmengine - INFO - Epoch(val) [8][150/509] eta: 0:03:20 time: 0.5621 data_time: 0.0067 memory: 425 2023/03/21 05:05:53 - mmengine - INFO - Epoch(val) [8][200/509] eta: 0:02:51 time: 0.5460 data_time: 0.0066 memory: 417 2023/03/21 05:06:20 - mmengine - INFO - Epoch(val) [8][250/509] eta: 0:02:22 time: 0.5415 data_time: 0.0068 memory: 425 2023/03/21 05:06:47 - mmengine - INFO - Epoch(val) [8][300/509] eta: 0:01:55 time: 0.5462 data_time: 0.0069 memory: 400 2023/03/21 05:07:15 - mmengine - INFO - Epoch(val) [8][350/509] eta: 0:01:27 time: 0.5465 data_time: 0.0066 memory: 412 2023/03/21 05:07:45 - mmengine - INFO - Epoch(val) [8][400/509] eta: 0:01:00 time: 0.5992 data_time: 0.0065 memory: 414 2023/03/21 05:08:15 - mmengine - INFO - Epoch(val) [8][450/509] eta: 0:00:33 time: 0.6085 data_time: 0.0068 memory: 428 2023/03/21 05:08:47 - mmengine - INFO - Epoch(val) [8][500/509] eta: 0:00:05 time: 0.6387 data_time: 0.0068 memory: 415 2023/03/21 05:09:23 - 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 | 0.9525 | 0.1298 | 0.4773 | 0.4866 | 0.3744 | 0.6410 | 0.7075 | 0.0010 | 0.9196 | 0.3890 | 0.7975 | 0.0010 | 0.8885 | 0.5507 | 0.8813 | 0.6635 | 0.7519 | 0.6231 | 0.4669 | 0.5633 | 0.9124 | 0.6478 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 05:09:23 - mmengine - INFO - Epoch(val) [8][509/509] car: 0.9525 bicycle: 0.1298 motorcycle: 0.4773 truck: 0.4866 bus: 0.3744 person: 0.6410 bicyclist: 0.7075 motorcyclist: 0.0010 road: 0.9196 parking: 0.3890 sidewalk: 0.7975 other-ground: 0.0010 building: 0.8885 fence: 0.5507 vegetation: 0.8813 trunck: 0.6635 terrian: 0.7519 pole: 0.6231 traffic-sign: 0.4669 miou: 0.5633 acc: 0.9124 acc_cls: 0.6478data_time: 0.0067 time: 0.6561 2023/03/21 05:10:26 - mmengine - INFO - Epoch(train) [9][ 50/1196] lr: 1.0644e-01 eta: 2:45:48 time: 1.2458 data_time: 0.0252 memory: 1677 loss: 0.2072 loss_sem_seg: 0.2072 2023/03/21 05:11:28 - mmengine - INFO - Epoch(train) [9][ 100/1196] lr: 1.0540e-01 eta: 2:44:51 time: 1.2481 data_time: 0.0047 memory: 1702 loss: 0.1909 loss_sem_seg: 0.1909 2023/03/21 05:12:29 - mmengine - INFO - Epoch(train) [9][ 150/1196] lr: 1.0435e-01 eta: 2:43:52 time: 1.2198 data_time: 0.0046 memory: 1690 loss: 0.1933 loss_sem_seg: 0.1933 2023/03/21 05:13:32 - mmengine - INFO - Epoch(train) [9][ 200/1196] lr: 1.0331e-01 eta: 2:42:55 time: 1.2533 data_time: 0.0044 memory: 1790 loss: 0.2019 loss_sem_seg: 0.2019 2023/03/21 05:14:32 - mmengine - INFO - Epoch(train) [9][ 250/1196] lr: 1.0227e-01 eta: 2:41:55 time: 1.2053 data_time: 0.0047 memory: 1659 loss: 0.1717 loss_sem_seg: 0.1717 2023/03/21 05:15:34 - mmengine - INFO - Epoch(train) [9][ 300/1196] lr: 1.0123e-01 eta: 2:40:57 time: 1.2300 data_time: 0.0046 memory: 1689 loss: 0.1968 loss_sem_seg: 0.1968 2023/03/21 05:16:35 - mmengine - INFO - Epoch(train) [9][ 350/1196] lr: 1.0020e-01 eta: 2:39:58 time: 1.2336 data_time: 0.0045 memory: 1744 loss: 0.2062 loss_sem_seg: 0.2062 2023/03/21 05:17:37 - mmengine - INFO - Epoch(train) [9][ 400/1196] lr: 9.9161e-02 eta: 2:39:00 time: 1.2341 data_time: 0.0048 memory: 1695 loss: 0.1934 loss_sem_seg: 0.1934 2023/03/21 05:18:17 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 05:18:38 - mmengine - INFO - Epoch(train) [9][ 450/1196] lr: 9.8127e-02 eta: 2:38:01 time: 1.2199 data_time: 0.0048 memory: 1726 loss: 0.1992 loss_sem_seg: 0.1992 2023/03/21 05:19:34 - mmengine - INFO - Epoch(train) [9][ 500/1196] lr: 9.7095e-02 eta: 2:36:58 time: 1.1206 data_time: 0.0046 memory: 1742 loss: 0.1877 loss_sem_seg: 0.1877 2023/03/21 05:20:30 - mmengine - INFO - Epoch(train) [9][ 550/1196] lr: 9.6065e-02 eta: 2:35:56 time: 1.1211 data_time: 0.0049 memory: 1687 loss: 0.1969 loss_sem_seg: 0.1969 2023/03/21 05:21:25 - mmengine - INFO - Epoch(train) [9][ 600/1196] lr: 9.5036e-02 eta: 2:34:52 time: 1.0993 data_time: 0.0047 memory: 1656 loss: 0.1848 loss_sem_seg: 0.1848 2023/03/21 05:22:20 - mmengine - INFO - Epoch(train) [9][ 650/1196] lr: 9.4009e-02 eta: 2:33:49 time: 1.0957 data_time: 0.0046 memory: 1697 loss: 0.1813 loss_sem_seg: 0.1813 2023/03/21 05:23:16 - mmengine - INFO - Epoch(train) [9][ 700/1196] lr: 9.2985e-02 eta: 2:32:46 time: 1.1260 data_time: 0.0047 memory: 1677 loss: 0.1978 loss_sem_seg: 0.1978 2023/03/21 05:24:11 - mmengine - INFO - Epoch(train) [9][ 750/1196] lr: 9.1962e-02 eta: 2:31:43 time: 1.0967 data_time: 0.0046 memory: 1609 loss: 0.1804 loss_sem_seg: 0.1804 2023/03/21 05:25:18 - mmengine - INFO - Epoch(train) [9][ 800/1196] lr: 9.0942e-02 eta: 2:30:48 time: 1.3327 data_time: 0.0050 memory: 1718 loss: 0.1843 loss_sem_seg: 0.1843 2023/03/21 05:26:14 - mmengine - INFO - Epoch(train) [9][ 850/1196] lr: 8.9923e-02 eta: 2:29:46 time: 1.1296 data_time: 0.0043 memory: 1689 loss: 0.1811 loss_sem_seg: 0.1811 2023/03/21 05:27:03 - mmengine - INFO - Epoch(train) [9][ 900/1196] lr: 8.8907e-02 eta: 2:28:39 time: 0.9874 data_time: 0.0041 memory: 1650 loss: 0.1924 loss_sem_seg: 0.1924 2023/03/21 05:28:00 - mmengine - INFO - Epoch(train) [9][ 950/1196] lr: 8.7894e-02 eta: 2:27:37 time: 1.1321 data_time: 0.0044 memory: 1710 loss: 0.1820 loss_sem_seg: 0.1820 2023/03/21 05:29:01 - mmengine - INFO - Epoch(train) [9][1000/1196] lr: 8.6883e-02 eta: 2:26:39 time: 1.2241 data_time: 0.0045 memory: 1656 loss: 0.1852 loss_sem_seg: 0.1852 2023/03/21 05:30:02 - mmengine - INFO - Epoch(train) [9][1050/1196] lr: 8.5874e-02 eta: 2:25:40 time: 1.2191 data_time: 0.0046 memory: 1682 loss: 0.1852 loss_sem_seg: 0.1852 2023/03/21 05:31:04 - mmengine - INFO - Epoch(train) [9][1100/1196] lr: 8.4868e-02 eta: 2:24:41 time: 1.2272 data_time: 0.0044 memory: 1724 loss: 0.1677 loss_sem_seg: 0.1677 2023/03/21 05:32:05 - mmengine - INFO - Epoch(train) [9][1150/1196] lr: 8.3865e-02 eta: 2:23:43 time: 1.2245 data_time: 0.0044 memory: 1694 loss: 0.1949 loss_sem_seg: 0.1949 2023/03/21 05:32:55 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 05:32:56 - mmengine - INFO - Saving checkpoint at 9 epochs 2023/03/21 05:33:25 - mmengine - INFO - Epoch(val) [9][ 50/509] eta: 0:04:14 time: 0.5536 data_time: 0.0120 memory: 1647 2023/03/21 05:33:52 - mmengine - INFO - Epoch(val) [9][100/509] eta: 0:03:43 time: 0.5407 data_time: 0.0066 memory: 423 2023/03/21 05:34:20 - mmengine - INFO - Epoch(val) [9][150/509] eta: 0:03:18 time: 0.5644 data_time: 0.0066 memory: 425 2023/03/21 05:34:47 - mmengine - INFO - Epoch(val) [9][200/509] eta: 0:02:50 time: 0.5457 data_time: 0.0067 memory: 417 2023/03/21 05:35:13 - mmengine - INFO - Epoch(val) [9][250/509] eta: 0:02:21 time: 0.5176 data_time: 0.0068 memory: 425 2023/03/21 05:35:40 - mmengine - INFO - Epoch(val) [9][300/509] eta: 0:01:53 time: 0.5313 data_time: 0.0069 memory: 400 2023/03/21 05:36:07 - mmengine - INFO - Epoch(val) [9][350/509] eta: 0:01:26 time: 0.5428 data_time: 0.0067 memory: 412 2023/03/21 05:36:35 - mmengine - INFO - Epoch(val) [9][400/509] eta: 0:00:59 time: 0.5559 data_time: 0.0068 memory: 414 2023/03/21 05:37:06 - mmengine - INFO - Epoch(val) [9][450/509] eta: 0:00:32 time: 0.6222 data_time: 0.0066 memory: 428 2023/03/21 05:37:38 - mmengine - INFO - Epoch(val) [9][500/509] eta: 0:00:05 time: 0.6431 data_time: 0.0068 memory: 415 2023/03/21 05:38:14 - 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 | 0.9574 | 0.1087 | 0.5351 | 0.6813 | 0.5258 | 0.5980 | 0.8158 | 0.0000 | 0.9308 | 0.4273 | 0.8017 | 0.0059 | 0.9009 | 0.5873 | 0.8799 | 0.6730 | 0.7436 | 0.6294 | 0.4610 | 0.5928 | 0.9159 | 0.6701 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 05:38:14 - mmengine - INFO - Epoch(val) [9][509/509] car: 0.9574 bicycle: 0.1087 motorcycle: 0.5351 truck: 0.6813 bus: 0.5258 person: 0.5980 bicyclist: 0.8158 motorcyclist: 0.0000 road: 0.9308 parking: 0.4273 sidewalk: 0.8017 other-ground: 0.0059 building: 0.9009 fence: 0.5873 vegetation: 0.8799 trunck: 0.6730 terrian: 0.7436 pole: 0.6294 traffic-sign: 0.4610 miou: 0.5928 acc: 0.9159 acc_cls: 0.6701data_time: 0.0066 time: 0.6576 2023/03/21 05:39:17 - mmengine - INFO - Epoch(train) [10][ 50/1196] lr: 8.1947e-02 eta: 2:21:47 time: 1.2609 data_time: 0.0273 memory: 1714 loss: 0.1809 loss_sem_seg: 0.1809 2023/03/21 05:40:19 - mmengine - INFO - Epoch(train) [10][ 100/1196] lr: 8.0952e-02 eta: 2:20:49 time: 1.2411 data_time: 0.0049 memory: 1711 loss: 0.1933 loss_sem_seg: 0.1933 2023/03/21 05:41:19 - mmengine - INFO - Epoch(train) [10][ 150/1196] lr: 7.9960e-02 eta: 2:19:50 time: 1.2002 data_time: 0.0047 memory: 1707 loss: 0.1902 loss_sem_seg: 0.1902 2023/03/21 05:42:22 - mmengine - INFO - Epoch(train) [10][ 200/1196] lr: 7.8971e-02 eta: 2:18:52 time: 1.2558 data_time: 0.0045 memory: 1688 loss: 0.1783 loss_sem_seg: 0.1783 2023/03/21 05:43:06 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 05:43:23 - mmengine - INFO - Epoch(train) [10][ 250/1196] lr: 7.7985e-02 eta: 2:17:53 time: 1.2312 data_time: 0.0046 memory: 1668 loss: 0.1789 loss_sem_seg: 0.1789 2023/03/21 05:44:25 - mmengine - INFO - Epoch(train) [10][ 300/1196] lr: 7.7003e-02 eta: 2:16:55 time: 1.2309 data_time: 0.0047 memory: 1621 loss: 0.1898 loss_sem_seg: 0.1898 2023/03/21 05:45:26 - mmengine - INFO - Epoch(train) [10][ 350/1196] lr: 7.6023e-02 eta: 2:15:56 time: 1.2241 data_time: 0.0047 memory: 1667 loss: 0.1822 loss_sem_seg: 0.1822 2023/03/21 05:46:24 - mmengine - INFO - Epoch(train) [10][ 400/1196] lr: 7.5048e-02 eta: 2:14:55 time: 1.1632 data_time: 0.0048 memory: 1758 loss: 0.1895 loss_sem_seg: 0.1895 2023/03/21 05:47:19 - mmengine - INFO - Epoch(train) [10][ 450/1196] lr: 7.4075e-02 eta: 2:13:52 time: 1.0894 data_time: 0.0049 memory: 1678 loss: 0.1689 loss_sem_seg: 0.1689 2023/03/21 05:48:14 - mmengine - INFO - Epoch(train) [10][ 500/1196] lr: 7.3106e-02 eta: 2:12:50 time: 1.0946 data_time: 0.0049 memory: 1787 loss: 0.1787 loss_sem_seg: 0.1787 2023/03/21 05:49:09 - mmengine - INFO - Epoch(train) [10][ 550/1196] lr: 7.2141e-02 eta: 2:11:47 time: 1.1086 data_time: 0.0048 memory: 1620 loss: 0.1821 loss_sem_seg: 0.1821 2023/03/21 05:50:04 - mmengine - INFO - Epoch(train) [10][ 600/1196] lr: 7.1179e-02 eta: 2:10:45 time: 1.1022 data_time: 0.0046 memory: 1619 loss: 0.1764 loss_sem_seg: 0.1764 2023/03/21 05:50:59 - mmengine - INFO - Epoch(train) [10][ 650/1196] lr: 7.0222e-02 eta: 2:09:42 time: 1.0874 data_time: 0.0048 memory: 1648 loss: 0.1734 loss_sem_seg: 0.1734 2023/03/21 05:52:03 - mmengine - INFO - Epoch(train) [10][ 700/1196] lr: 6.9268e-02 eta: 2:08:45 time: 1.2813 data_time: 0.0046 memory: 1629 loss: 0.1822 loss_sem_seg: 0.1822 2023/03/21 05:53:04 - mmengine - INFO - Epoch(train) [10][ 750/1196] lr: 6.8317e-02 eta: 2:07:46 time: 1.2259 data_time: 0.0048 memory: 1694 loss: 0.1854 loss_sem_seg: 0.1854 2023/03/21 05:54:05 - mmengine - INFO - Epoch(train) [10][ 800/1196] lr: 6.7371e-02 eta: 2:06:47 time: 1.2151 data_time: 0.0048 memory: 1666 loss: 0.1823 loss_sem_seg: 0.1823 2023/03/21 05:55:06 - mmengine - INFO - Epoch(train) [10][ 850/1196] lr: 6.6429e-02 eta: 2:05:49 time: 1.2215 data_time: 0.0048 memory: 1706 loss: 0.1708 loss_sem_seg: 0.1708 2023/03/21 05:56:07 - mmengine - INFO - Epoch(train) [10][ 900/1196] lr: 6.5491e-02 eta: 2:04:50 time: 1.2351 data_time: 0.0049 memory: 1668 loss: 0.1744 loss_sem_seg: 0.1744 2023/03/21 05:57:09 - mmengine - INFO - Epoch(train) [10][ 950/1196] lr: 6.4557e-02 eta: 2:03:51 time: 1.2352 data_time: 0.0046 memory: 1686 loss: 0.1694 loss_sem_seg: 0.1694 2023/03/21 05:58:11 - mmengine - INFO - Epoch(train) [10][1000/1196] lr: 6.3627e-02 eta: 2:02:53 time: 1.2442 data_time: 0.0047 memory: 1721 loss: 0.1768 loss_sem_seg: 0.1768 2023/03/21 05:59:12 - mmengine - INFO - Epoch(train) [10][1050/1196] lr: 6.2702e-02 eta: 2:01:54 time: 1.2085 data_time: 0.0045 memory: 1726 loss: 0.1847 loss_sem_seg: 0.1847 2023/03/21 06:00:13 - mmengine - INFO - Epoch(train) [10][1100/1196] lr: 6.1781e-02 eta: 2:00:55 time: 1.2301 data_time: 0.0046 memory: 1681 loss: 0.1806 loss_sem_seg: 0.1806 2023/03/21 06:01:13 - mmengine - INFO - Epoch(train) [10][1150/1196] lr: 6.0865e-02 eta: 1:59:55 time: 1.1899 data_time: 0.0047 memory: 1634 loss: 0.1709 loss_sem_seg: 0.1709 2023/03/21 06:02:02 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 06:02:02 - mmengine - INFO - Saving checkpoint at 10 epochs 2023/03/21 06:02:31 - mmengine - INFO - Epoch(val) [10][ 50/509] eta: 0:04:13 time: 0.5516 data_time: 0.0121 memory: 1664 2023/03/21 06:02:58 - mmengine - INFO - Epoch(val) [10][100/509] eta: 0:03:45 time: 0.5496 data_time: 0.0069 memory: 423 2023/03/21 06:03:26 - mmengine - INFO - Epoch(val) [10][150/509] eta: 0:03:18 time: 0.5537 data_time: 0.0069 memory: 425 2023/03/21 06:03:54 - mmengine - INFO - Epoch(val) [10][200/509] eta: 0:02:50 time: 0.5523 data_time: 0.0066 memory: 417 2023/03/21 06:04:21 - mmengine - INFO - Epoch(val) [10][250/509] eta: 0:02:22 time: 0.5394 data_time: 0.0069 memory: 425 2023/03/21 06:04:48 - mmengine - INFO - Epoch(val) [10][300/509] eta: 0:01:54 time: 0.5475 data_time: 0.0067 memory: 400 2023/03/21 06:05:16 - mmengine - INFO - Epoch(val) [10][350/509] eta: 0:01:27 time: 0.5516 data_time: 0.0067 memory: 412 2023/03/21 06:05:43 - mmengine - INFO - Epoch(val) [10][400/509] eta: 0:00:59 time: 0.5529 data_time: 0.0068 memory: 414 2023/03/21 06:06:14 - mmengine - INFO - Epoch(val) [10][450/509] eta: 0:00:32 time: 0.6220 data_time: 0.0063 memory: 428 2023/03/21 06:06:46 - mmengine - INFO - Epoch(val) [10][500/509] eta: 0:00:05 time: 0.6311 data_time: 0.0066 memory: 415 2023/03/21 06:07:23 - 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 | 0.9602 | 0.1479 | 0.5789 | 0.5947 | 0.5475 | 0.6687 | 0.8052 | 0.0001 | 0.9255 | 0.4717 | 0.7913 | 0.0046 | 0.9083 | 0.6182 | 0.8824 | 0.6200 | 0.7589 | 0.6224 | 0.4564 | 0.5980 | 0.9172 | 0.6852 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 06:07:23 - mmengine - INFO - Epoch(val) [10][509/509] car: 0.9602 bicycle: 0.1479 motorcycle: 0.5789 truck: 0.5947 bus: 0.5475 person: 0.6687 bicyclist: 0.8052 motorcyclist: 0.0001 road: 0.9255 parking: 0.4717 sidewalk: 0.7913 other-ground: 0.0046 building: 0.9083 fence: 0.6182 vegetation: 0.8824 trunck: 0.6200 terrian: 0.7589 pole: 0.6224 traffic-sign: 0.4564 miou: 0.5980 acc: 0.9172 acc_cls: 0.6852data_time: 0.0065 time: 0.6497 2023/03/21 06:08:12 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 06:08:24 - mmengine - INFO - Epoch(train) [11][ 50/1196] lr: 5.9118e-02 eta: 1:57:59 time: 1.2328 data_time: 0.0264 memory: 1705 loss: 0.1572 loss_sem_seg: 0.1572 2023/03/21 06:09:27 - mmengine - INFO - Epoch(train) [11][ 100/1196] lr: 5.8215e-02 eta: 1:57:00 time: 1.2467 data_time: 0.0045 memory: 1678 loss: 0.1745 loss_sem_seg: 0.1745 2023/03/21 06:10:28 - mmengine - INFO - Epoch(train) [11][ 150/1196] lr: 5.7317e-02 eta: 1:56:01 time: 1.2295 data_time: 0.0046 memory: 1674 loss: 0.1672 loss_sem_seg: 0.1672 2023/03/21 06:11:29 - mmengine - INFO - Epoch(train) [11][ 200/1196] lr: 5.6423e-02 eta: 1:55:02 time: 1.2191 data_time: 0.0048 memory: 1701 loss: 0.1703 loss_sem_seg: 0.1703 2023/03/21 06:12:31 - mmengine - INFO - Epoch(train) [11][ 250/1196] lr: 5.5535e-02 eta: 1:54:04 time: 1.2400 data_time: 0.0047 memory: 1724 loss: 0.1550 loss_sem_seg: 0.1550 2023/03/21 06:13:32 - mmengine - INFO - Epoch(train) [11][ 300/1196] lr: 5.4651e-02 eta: 1:53:04 time: 1.2179 data_time: 0.0046 memory: 1623 loss: 0.1679 loss_sem_seg: 0.1679 2023/03/21 06:14:27 - mmengine - INFO - Epoch(train) [11][ 350/1196] lr: 5.3772e-02 eta: 1:52:02 time: 1.0900 data_time: 0.0046 memory: 1659 loss: 0.1583 loss_sem_seg: 0.1583 2023/03/21 06:15:22 - mmengine - INFO - Epoch(train) [11][ 400/1196] lr: 5.2899e-02 eta: 1:51:00 time: 1.1044 data_time: 0.0051 memory: 1685 loss: 0.1666 loss_sem_seg: 0.1666 2023/03/21 06:16:17 - mmengine - INFO - Epoch(train) [11][ 450/1196] lr: 5.2030e-02 eta: 1:49:59 time: 1.0954 data_time: 0.0049 memory: 1656 loss: 0.1759 loss_sem_seg: 0.1759 2023/03/21 06:17:12 - mmengine - INFO - Epoch(train) [11][ 500/1196] lr: 5.1167e-02 eta: 1:48:57 time: 1.1083 data_time: 0.0048 memory: 1668 loss: 0.1836 loss_sem_seg: 0.1836 2023/03/21 06:18:08 - mmengine - INFO - Epoch(train) [11][ 550/1196] lr: 5.0309e-02 eta: 1:47:56 time: 1.1203 data_time: 0.0048 memory: 1711 loss: 0.1657 loss_sem_seg: 0.1657 2023/03/21 06:19:08 - mmengine - INFO - Epoch(train) [11][ 600/1196] lr: 4.9457e-02 eta: 1:46:56 time: 1.1960 data_time: 0.0048 memory: 1674 loss: 0.1665 loss_sem_seg: 0.1665 2023/03/21 06:20:12 - mmengine - INFO - Epoch(train) [11][ 650/1196] lr: 4.8610e-02 eta: 1:45:58 time: 1.2765 data_time: 0.0044 memory: 1669 loss: 0.1697 loss_sem_seg: 0.1697 2023/03/21 06:21:14 - mmengine - INFO - Epoch(train) [11][ 700/1196] lr: 4.7768e-02 eta: 1:45:00 time: 1.2526 data_time: 0.0046 memory: 1725 loss: 0.1654 loss_sem_seg: 0.1654 2023/03/21 06:22:16 - mmengine - INFO - Epoch(train) [11][ 750/1196] lr: 4.6932e-02 eta: 1:44:01 time: 1.2434 data_time: 0.0046 memory: 1661 loss: 0.1578 loss_sem_seg: 0.1578 2023/03/21 06:23:20 - mmengine - INFO - Epoch(train) [11][ 800/1196] lr: 4.6101e-02 eta: 1:43:03 time: 1.2709 data_time: 0.0045 memory: 1712 loss: 0.1578 loss_sem_seg: 0.1578 2023/03/21 06:24:21 - mmengine - INFO - Epoch(train) [11][ 850/1196] lr: 4.5276e-02 eta: 1:42:04 time: 1.2296 data_time: 0.0045 memory: 1652 loss: 0.1554 loss_sem_seg: 0.1554 2023/03/21 06:25:23 - mmengine - INFO - Epoch(train) [11][ 900/1196] lr: 4.4457e-02 eta: 1:41:05 time: 1.2324 data_time: 0.0045 memory: 1671 loss: 0.1697 loss_sem_seg: 0.1697 2023/03/21 06:26:26 - mmengine - INFO - Epoch(train) [11][ 950/1196] lr: 4.3644e-02 eta: 1:40:07 time: 1.2488 data_time: 0.0044 memory: 1719 loss: 0.1651 loss_sem_seg: 0.1651 2023/03/21 06:27:28 - mmengine - INFO - Epoch(train) [11][1000/1196] lr: 4.2836e-02 eta: 1:39:08 time: 1.2487 data_time: 0.0048 memory: 1702 loss: 0.1650 loss_sem_seg: 0.1650 2023/03/21 06:28:16 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 06:28:28 - mmengine - INFO - Epoch(train) [11][1050/1196] lr: 4.2035e-02 eta: 1:38:09 time: 1.2046 data_time: 0.0046 memory: 1779 loss: 0.1675 loss_sem_seg: 0.1675 2023/03/21 06:29:30 - mmengine - INFO - Epoch(train) [11][1100/1196] lr: 4.1239e-02 eta: 1:37:10 time: 1.2446 data_time: 0.0045 memory: 1670 loss: 0.1612 loss_sem_seg: 0.1612 2023/03/21 06:30:29 - mmengine - INFO - Epoch(train) [11][1150/1196] lr: 4.0449e-02 eta: 1:36:10 time: 1.1763 data_time: 0.0044 memory: 1633 loss: 0.1627 loss_sem_seg: 0.1627 2023/03/21 06:31:19 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 06:31:20 - mmengine - INFO - Saving checkpoint at 11 epochs 2023/03/21 06:31:48 - mmengine - INFO - Epoch(val) [11][ 50/509] eta: 0:04:12 time: 0.5505 data_time: 0.0116 memory: 1644 2023/03/21 06:32:16 - mmengine - INFO - Epoch(val) [11][100/509] eta: 0:03:43 time: 0.5437 data_time: 0.0067 memory: 423 2023/03/21 06:32:43 - mmengine - INFO - Epoch(val) [11][150/509] eta: 0:03:16 time: 0.5502 data_time: 0.0066 memory: 425 2023/03/21 06:33:10 - mmengine - INFO - Epoch(val) [11][200/509] eta: 0:02:48 time: 0.5342 data_time: 0.0068 memory: 417 2023/03/21 06:33:37 - mmengine - INFO - Epoch(val) [11][250/509] eta: 0:02:20 time: 0.5385 data_time: 0.0067 memory: 425 2023/03/21 06:34:03 - mmengine - INFO - Epoch(val) [11][300/509] eta: 0:01:53 time: 0.5307 data_time: 0.0065 memory: 400 2023/03/21 06:34:31 - mmengine - INFO - Epoch(val) [11][350/509] eta: 0:01:26 time: 0.5517 data_time: 0.0067 memory: 412 2023/03/21 06:34:59 - mmengine - INFO - Epoch(val) [11][400/509] eta: 0:00:59 time: 0.5550 data_time: 0.0067 memory: 414 2023/03/21 06:35:32 - mmengine - INFO - Epoch(val) [11][450/509] eta: 0:00:32 time: 0.6618 data_time: 0.0063 memory: 428 2023/03/21 06:36:04 - mmengine - INFO - Epoch(val) [11][500/509] eta: 0:00:05 time: 0.6432 data_time: 0.0067 memory: 415 2023/03/21 06:36:41 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9588 | 0.1720 | 0.6362 | 0.7711 | 0.5361 | 0.6535 | 0.8092 | 0.0000 | 0.9276 | 0.4105 | 0.7893 | 0.0023 | 0.9049 | 0.5964 | 0.8799 | 0.6648 | 0.7459 | 0.6234 | 0.4690 | 0.6079 | 0.9152 | 0.6788 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 06:36:41 - mmengine - INFO - Epoch(val) [11][509/509] car: 0.9588 bicycle: 0.1720 motorcycle: 0.6362 truck: 0.7711 bus: 0.5361 person: 0.6535 bicyclist: 0.8092 motorcyclist: 0.0000 road: 0.9276 parking: 0.4105 sidewalk: 0.7893 other-ground: 0.0023 building: 0.9049 fence: 0.5964 vegetation: 0.8799 trunck: 0.6648 terrian: 0.7459 pole: 0.6234 traffic-sign: 0.4690 miou: 0.6079 acc: 0.9152 acc_cls: 0.6788data_time: 0.0068 time: 0.6549 2023/03/21 06:37:43 - mmengine - INFO - Epoch(train) [12][ 50/1196] lr: 3.8950e-02 eta: 1:34:14 time: 1.2468 data_time: 0.0279 memory: 1684 loss: 0.1568 loss_sem_seg: 0.1568 2023/03/21 06:38:45 - mmengine - INFO - Epoch(train) [12][ 100/1196] lr: 3.8179e-02 eta: 1:33:15 time: 1.2419 data_time: 0.0048 memory: 1687 loss: 0.1657 loss_sem_seg: 0.1657 2023/03/21 06:39:48 - mmengine - INFO - Epoch(train) [12][ 150/1196] lr: 3.7414e-02 eta: 1:32:17 time: 1.2619 data_time: 0.0049 memory: 1706 loss: 0.1609 loss_sem_seg: 0.1609 2023/03/21 06:40:51 - mmengine - INFO - Epoch(train) [12][ 200/1196] lr: 3.6655e-02 eta: 1:31:18 time: 1.2454 data_time: 0.0046 memory: 1896 loss: 0.1621 loss_sem_seg: 0.1621 2023/03/21 06:41:45 - mmengine - INFO - Epoch(train) [12][ 250/1196] lr: 3.5902e-02 eta: 1:30:16 time: 1.0853 data_time: 0.0048 memory: 1660 loss: 0.1626 loss_sem_seg: 0.1626 2023/03/21 06:42:39 - mmengine - INFO - Epoch(train) [12][ 300/1196] lr: 3.5156e-02 eta: 1:29:15 time: 1.0847 data_time: 0.0048 memory: 1756 loss: 0.1570 loss_sem_seg: 0.1570 2023/03/21 06:43:35 - mmengine - INFO - Epoch(train) [12][ 350/1196] lr: 3.4416e-02 eta: 1:28:14 time: 1.1117 data_time: 0.0050 memory: 1676 loss: 0.1669 loss_sem_seg: 0.1669 2023/03/21 06:44:30 - mmengine - INFO - Epoch(train) [12][ 400/1196] lr: 3.3683e-02 eta: 1:27:12 time: 1.0941 data_time: 0.0050 memory: 1622 loss: 0.1513 loss_sem_seg: 0.1513 2023/03/21 06:45:24 - mmengine - INFO - Epoch(train) [12][ 450/1196] lr: 3.2956e-02 eta: 1:26:11 time: 1.0935 data_time: 0.0049 memory: 1716 loss: 0.1613 loss_sem_seg: 0.1613 2023/03/21 06:46:19 - mmengine - INFO - Epoch(train) [12][ 500/1196] lr: 3.2237e-02 eta: 1:25:10 time: 1.1054 data_time: 0.0045 memory: 1683 loss: 0.1500 loss_sem_seg: 0.1500 2023/03/21 06:47:27 - mmengine - INFO - Epoch(train) [12][ 550/1196] lr: 3.1524e-02 eta: 1:24:13 time: 1.3500 data_time: 0.0045 memory: 1671 loss: 0.1562 loss_sem_seg: 0.1562 2023/03/21 06:48:27 - mmengine - INFO - Epoch(train) [12][ 600/1196] lr: 3.0817e-02 eta: 1:23:13 time: 1.2014 data_time: 0.0045 memory: 1664 loss: 0.1608 loss_sem_seg: 0.1608 2023/03/21 06:49:28 - mmengine - INFO - Epoch(train) [12][ 650/1196] lr: 3.0118e-02 eta: 1:22:14 time: 1.2162 data_time: 0.0045 memory: 1675 loss: 0.1509 loss_sem_seg: 0.1509 2023/03/21 06:50:30 - mmengine - INFO - Epoch(train) [12][ 700/1196] lr: 2.9425e-02 eta: 1:21:15 time: 1.2503 data_time: 0.0045 memory: 1723 loss: 0.1558 loss_sem_seg: 0.1558 2023/03/21 06:51:31 - mmengine - INFO - Epoch(train) [12][ 750/1196] lr: 2.8740e-02 eta: 1:20:16 time: 1.2062 data_time: 0.0045 memory: 1665 loss: 0.1531 loss_sem_seg: 0.1531 2023/03/21 06:52:32 - mmengine - INFO - Epoch(train) [12][ 800/1196] lr: 2.8061e-02 eta: 1:19:16 time: 1.2227 data_time: 0.0048 memory: 1693 loss: 0.1546 loss_sem_seg: 0.1546 2023/03/21 06:53:25 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 06:53:33 - mmengine - INFO - Epoch(train) [12][ 850/1196] lr: 2.7389e-02 eta: 1:18:17 time: 1.2157 data_time: 0.0045 memory: 1754 loss: 0.1539 loss_sem_seg: 0.1539 2023/03/21 06:54:33 - mmengine - INFO - Epoch(train) [12][ 900/1196] lr: 2.6725e-02 eta: 1:17:17 time: 1.2125 data_time: 0.0046 memory: 1770 loss: 0.1381 loss_sem_seg: 0.1381 2023/03/21 06:55:34 - mmengine - INFO - Epoch(train) [12][ 950/1196] lr: 2.6068e-02 eta: 1:16:18 time: 1.2185 data_time: 0.0047 memory: 1718 loss: 0.1498 loss_sem_seg: 0.1498 2023/03/21 06:56:36 - mmengine - INFO - Epoch(train) [12][1000/1196] lr: 2.5417e-02 eta: 1:15:19 time: 1.2403 data_time: 0.0047 memory: 1665 loss: 0.1544 loss_sem_seg: 0.1544 2023/03/21 06:57:39 - mmengine - INFO - Epoch(train) [12][1050/1196] lr: 2.4775e-02 eta: 1:14:20 time: 1.2460 data_time: 0.0045 memory: 1639 loss: 0.1423 loss_sem_seg: 0.1423 2023/03/21 06:58:41 - mmengine - INFO - Epoch(train) [12][1100/1196] lr: 2.4139e-02 eta: 1:13:21 time: 1.2507 data_time: 0.0045 memory: 1657 loss: 0.1406 loss_sem_seg: 0.1406 2023/03/21 06:59:40 - mmengine - INFO - Epoch(train) [12][1150/1196] lr: 2.3511e-02 eta: 1:12:21 time: 1.1726 data_time: 0.0046 memory: 1713 loss: 0.1475 loss_sem_seg: 0.1475 2023/03/21 07:00:30 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 07:00:30 - mmengine - INFO - Saving checkpoint at 12 epochs 2023/03/21 07:00:59 - mmengine - INFO - Epoch(val) [12][ 50/509] eta: 0:04:13 time: 0.5529 data_time: 0.0131 memory: 1709 2023/03/21 07:01:27 - mmengine - INFO - Epoch(val) [12][100/509] eta: 0:03:46 time: 0.5563 data_time: 0.0066 memory: 423 2023/03/21 07:01:54 - mmengine - INFO - Epoch(val) [12][150/509] eta: 0:03:18 time: 0.5490 data_time: 0.0071 memory: 425 2023/03/21 07:02:21 - mmengine - INFO - Epoch(val) [12][200/509] eta: 0:02:49 time: 0.5404 data_time: 0.0067 memory: 417 2023/03/21 07:02:48 - mmengine - INFO - Epoch(val) [12][250/509] eta: 0:02:21 time: 0.5402 data_time: 0.0068 memory: 425 2023/03/21 07:03:16 - mmengine - INFO - Epoch(val) [12][300/509] eta: 0:01:54 time: 0.5432 data_time: 0.0068 memory: 400 2023/03/21 07:03:42 - mmengine - INFO - Epoch(val) [12][350/509] eta: 0:01:26 time: 0.5352 data_time: 0.0070 memory: 412 2023/03/21 07:04:12 - mmengine - INFO - Epoch(val) [12][400/509] eta: 0:00:59 time: 0.5865 data_time: 0.0064 memory: 414 2023/03/21 07:04:44 - mmengine - INFO - Epoch(val) [12][450/509] eta: 0:00:33 time: 0.6412 data_time: 0.0067 memory: 428 2023/03/21 07:05:16 - mmengine - INFO - Epoch(val) [12][500/509] eta: 0:00:05 time: 0.6494 data_time: 0.0064 memory: 415 2023/03/21 07:05:53 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9653 | 0.2414 | 0.6674 | 0.7268 | 0.6153 | 0.6382 | 0.8154 | 0.0002 | 0.9257 | 0.4287 | 0.7955 | 0.0047 | 0.9053 | 0.6191 | 0.8825 | 0.6618 | 0.7508 | 0.6321 | 0.4926 | 0.6194 | 0.9176 | 0.6945 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 07:05:53 - mmengine - INFO - Epoch(val) [12][509/509] car: 0.9653 bicycle: 0.2414 motorcycle: 0.6674 truck: 0.7268 bus: 0.6153 person: 0.6382 bicyclist: 0.8154 motorcyclist: 0.0002 road: 0.9257 parking: 0.4287 sidewalk: 0.7955 other-ground: 0.0047 building: 0.9053 fence: 0.6191 vegetation: 0.8825 trunck: 0.6618 terrian: 0.7508 pole: 0.6321 traffic-sign: 0.4926 miou: 0.6194 acc: 0.9176 acc_cls: 0.6945data_time: 0.0064 time: 0.6518 2023/03/21 07:06:55 - mmengine - INFO - Epoch(train) [13][ 50/1196] lr: 2.2325e-02 eta: 1:10:26 time: 1.2363 data_time: 0.0279 memory: 1736 loss: 0.1504 loss_sem_seg: 0.1504 2023/03/21 07:07:56 - mmengine - INFO - Epoch(train) [13][ 100/1196] lr: 2.1719e-02 eta: 1:09:26 time: 1.2233 data_time: 0.0048 memory: 1635 loss: 0.1491 loss_sem_seg: 0.1491 2023/03/21 07:08:53 - mmengine - INFO - Epoch(train) [13][ 150/1196] lr: 2.1120e-02 eta: 1:08:26 time: 1.1494 data_time: 0.0048 memory: 1711 loss: 0.1595 loss_sem_seg: 0.1595 2023/03/21 07:09:50 - mmengine - INFO - Epoch(train) [13][ 200/1196] lr: 2.0529e-02 eta: 1:07:25 time: 1.1281 data_time: 0.0047 memory: 1661 loss: 0.1376 loss_sem_seg: 0.1376 2023/03/21 07:10:45 - mmengine - INFO - Epoch(train) [13][ 250/1196] lr: 1.9945e-02 eta: 1:06:25 time: 1.1078 data_time: 0.0049 memory: 1691 loss: 0.1475 loss_sem_seg: 0.1475 2023/03/21 07:11:40 - mmengine - INFO - Epoch(train) [13][ 300/1196] lr: 1.9369e-02 eta: 1:05:24 time: 1.0893 data_time: 0.0049 memory: 1723 loss: 0.1387 loss_sem_seg: 0.1387 2023/03/21 07:12:35 - mmengine - INFO - Epoch(train) [13][ 350/1196] lr: 1.8800e-02 eta: 1:04:23 time: 1.1037 data_time: 0.0047 memory: 1662 loss: 0.1400 loss_sem_seg: 0.1400 2023/03/21 07:13:30 - mmengine - INFO - Epoch(train) [13][ 400/1196] lr: 1.8240e-02 eta: 1:03:23 time: 1.1115 data_time: 0.0048 memory: 1749 loss: 0.1541 loss_sem_seg: 0.1541 2023/03/21 07:14:37 - mmengine - INFO - Epoch(train) [13][ 450/1196] lr: 1.7687e-02 eta: 1:02:24 time: 1.3303 data_time: 0.0046 memory: 1706 loss: 0.1487 loss_sem_seg: 0.1487 2023/03/21 07:15:38 - mmengine - INFO - Epoch(train) [13][ 500/1196] lr: 1.7142e-02 eta: 1:01:25 time: 1.2174 data_time: 0.0043 memory: 1726 loss: 0.1555 loss_sem_seg: 0.1555 2023/03/21 07:16:40 - mmengine - INFO - Epoch(train) [13][ 550/1196] lr: 1.6605e-02 eta: 1:00:26 time: 1.2423 data_time: 0.0046 memory: 1670 loss: 0.1409 loss_sem_seg: 0.1409 2023/03/21 07:17:42 - mmengine - INFO - Epoch(train) [13][ 600/1196] lr: 1.6076e-02 eta: 0:59:27 time: 1.2377 data_time: 0.0047 memory: 1789 loss: 0.1460 loss_sem_seg: 0.1460 2023/03/21 07:18:41 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 07:18:44 - mmengine - INFO - Epoch(train) [13][ 650/1196] lr: 1.5555e-02 eta: 0:58:27 time: 1.2325 data_time: 0.0045 memory: 1660 loss: 0.1442 loss_sem_seg: 0.1442 2023/03/21 07:19:46 - mmengine - INFO - Epoch(train) [13][ 700/1196] lr: 1.5041e-02 eta: 0:57:28 time: 1.2404 data_time: 0.0046 memory: 1666 loss: 0.1461 loss_sem_seg: 0.1461 2023/03/21 07:20:47 - mmengine - INFO - Epoch(train) [13][ 750/1196] lr: 1.4536e-02 eta: 0:56:29 time: 1.2338 data_time: 0.0044 memory: 1690 loss: 0.1525 loss_sem_seg: 0.1525 2023/03/21 07:21:49 - mmengine - INFO - Epoch(train) [13][ 800/1196] lr: 1.4039e-02 eta: 0:55:29 time: 1.2368 data_time: 0.0046 memory: 1701 loss: 0.1541 loss_sem_seg: 0.1541 2023/03/21 07:22:50 - mmengine - INFO - Epoch(train) [13][ 850/1196] lr: 1.3550e-02 eta: 0:54:30 time: 1.2260 data_time: 0.0044 memory: 1753 loss: 0.1420 loss_sem_seg: 0.1420 2023/03/21 07:23:51 - mmengine - INFO - Epoch(train) [13][ 900/1196] lr: 1.3070e-02 eta: 0:53:30 time: 1.2186 data_time: 0.0046 memory: 1679 loss: 0.1476 loss_sem_seg: 0.1476 2023/03/21 07:24:54 - mmengine - INFO - Epoch(train) [13][ 950/1196] lr: 1.2597e-02 eta: 0:52:31 time: 1.2524 data_time: 0.0047 memory: 1705 loss: 0.1402 loss_sem_seg: 0.1402 2023/03/21 07:25:55 - mmengine - INFO - Epoch(train) [13][1000/1196] lr: 1.2133e-02 eta: 0:51:32 time: 1.2194 data_time: 0.0047 memory: 1737 loss: 0.1432 loss_sem_seg: 0.1432 2023/03/21 07:26:57 - mmengine - INFO - Epoch(train) [13][1050/1196] lr: 1.1677e-02 eta: 0:50:32 time: 1.2369 data_time: 0.0046 memory: 1624 loss: 0.1464 loss_sem_seg: 0.1464 2023/03/21 07:27:58 - mmengine - INFO - Epoch(train) [13][1100/1196] lr: 1.1229e-02 eta: 0:49:33 time: 1.2277 data_time: 0.0047 memory: 1700 loss: 0.1393 loss_sem_seg: 0.1393 2023/03/21 07:28:55 - mmengine - INFO - Epoch(train) [13][1150/1196] lr: 1.0790e-02 eta: 0:48:33 time: 1.1360 data_time: 0.0046 memory: 1781 loss: 0.1350 loss_sem_seg: 0.1350 2023/03/21 07:29:45 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 07:29:46 - mmengine - INFO - Saving checkpoint at 13 epochs 2023/03/21 07:30:15 - mmengine - INFO - Epoch(val) [13][ 50/509] eta: 0:04:15 time: 0.5575 data_time: 0.0115 memory: 1680 2023/03/21 07:30:42 - mmengine - INFO - Epoch(val) [13][100/509] eta: 0:03:44 time: 0.5378 data_time: 0.0068 memory: 423 2023/03/21 07:31:08 - mmengine - INFO - Epoch(val) [13][150/509] eta: 0:03:15 time: 0.5365 data_time: 0.0067 memory: 425 2023/03/21 07:31:36 - mmengine - INFO - Epoch(val) [13][200/509] eta: 0:02:49 time: 0.5578 data_time: 0.0068 memory: 417 2023/03/21 07:32:03 - mmengine - INFO - Epoch(val) [13][250/509] eta: 0:02:21 time: 0.5390 data_time: 0.0068 memory: 425 2023/03/21 07:32:31 - mmengine - INFO - Epoch(val) [13][300/509] eta: 0:01:54 time: 0.5463 data_time: 0.0068 memory: 400 2023/03/21 07:32:58 - mmengine - INFO - Epoch(val) [13][350/509] eta: 0:01:26 time: 0.5488 data_time: 0.0067 memory: 412 2023/03/21 07:33:29 - mmengine - INFO - Epoch(val) [13][400/509] eta: 0:01:00 time: 0.6130 data_time: 0.0067 memory: 414 2023/03/21 07:34:00 - mmengine - INFO - Epoch(val) [13][450/509] eta: 0:00:33 time: 0.6273 data_time: 0.0070 memory: 428 2023/03/21 07:34:32 - mmengine - INFO - Epoch(val) [13][500/509] eta: 0:00:05 time: 0.6450 data_time: 0.0068 memory: 415 2023/03/21 07:35:08 - mmengine - INFO - +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | classes | car | bicycle | motorcycle | truck | bus | person | bicyclist | motorcyclist | road | parking | sidewalk | other-ground | building | fence | vegetation | trunck | terrian | pole | traffic-sign | miou | acc | acc_cls | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ | results | 0.9636 | 0.2514 | 0.6393 | 0.8330 | 0.6148 | 0.6834 | 0.8076 | 0.0004 | 0.9312 | 0.4553 | 0.8013 | 0.0094 | 0.9100 | 0.6273 | 0.8801 | 0.6668 | 0.7415 | 0.6336 | 0.4774 | 0.6278 | 0.9182 | 0.6954 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 07:35:08 - mmengine - INFO - Epoch(val) [13][509/509] car: 0.9636 bicycle: 0.2514 motorcycle: 0.6393 truck: 0.8330 bus: 0.6148 person: 0.6834 bicyclist: 0.8076 motorcyclist: 0.0004 road: 0.9312 parking: 0.4553 sidewalk: 0.8013 other-ground: 0.0094 building: 0.9100 fence: 0.6273 vegetation: 0.8801 trunck: 0.6668 terrian: 0.7415 pole: 0.6336 traffic-sign: 0.4774 miou: 0.6278 acc: 0.9182 acc_cls: 0.6954data_time: 0.0067 time: 0.6505 2023/03/21 07:36:09 - mmengine - INFO - Epoch(train) [14][ 50/1196] lr: 9.9694e-03 eta: 0:46:37 time: 1.2161 data_time: 0.0275 memory: 1698 loss: 0.1374 loss_sem_seg: 0.1374 2023/03/21 07:37:05 - mmengine - INFO - Epoch(train) [14][ 100/1196] lr: 9.5545e-03 eta: 0:45:37 time: 1.1154 data_time: 0.0051 memory: 1635 loss: 0.1370 loss_sem_seg: 0.1370 2023/03/21 07:38:02 - mmengine - INFO - Epoch(train) [14][ 150/1196] lr: 9.1481e-03 eta: 0:44:37 time: 1.1314 data_time: 0.0048 memory: 1660 loss: 0.1415 loss_sem_seg: 0.1415 2023/03/21 07:38:47 - mmengine - INFO - Epoch(train) [14][ 200/1196] lr: 8.7502e-03 eta: 0:43:35 time: 0.8990 data_time: 0.0044 memory: 1721 loss: 0.1476 loss_sem_seg: 0.1476 2023/03/21 07:39:29 - mmengine - INFO - Epoch(train) [14][ 250/1196] lr: 8.3608e-03 eta: 0:42:33 time: 0.8558 data_time: 0.0041 memory: 1706 loss: 0.1402 loss_sem_seg: 0.1402 2023/03/21 07:40:22 - mmengine - INFO - Epoch(train) [14][ 300/1196] lr: 7.9800e-03 eta: 0:41:33 time: 1.0481 data_time: 0.0042 memory: 1629 loss: 0.1335 loss_sem_seg: 0.1335 2023/03/21 07:41:24 - mmengine - INFO - Epoch(train) [14][ 350/1196] lr: 7.6078e-03 eta: 0:40:33 time: 1.2476 data_time: 0.0046 memory: 1691 loss: 0.1436 loss_sem_seg: 0.1436 2023/03/21 07:42:27 - mmengine - INFO - Epoch(train) [14][ 400/1196] lr: 7.2442e-03 eta: 0:39:34 time: 1.2576 data_time: 0.0045 memory: 1708 loss: 0.1466 loss_sem_seg: 0.1466 2023/03/21 07:43:29 - mmengine - INFO - Epoch(train) [14][ 450/1196] lr: 6.8892e-03 eta: 0:38:35 time: 1.2339 data_time: 0.0048 memory: 1691 loss: 0.1351 loss_sem_seg: 0.1351 2023/03/21 07:43:31 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 07:44:29 - mmengine - INFO - Epoch(train) [14][ 500/1196] lr: 6.5429e-03 eta: 0:37:35 time: 1.2118 data_time: 0.0044 memory: 1689 loss: 0.1443 loss_sem_seg: 0.1443 2023/03/21 07:45:30 - mmengine - INFO - Epoch(train) [14][ 550/1196] lr: 6.2053e-03 eta: 0:36:36 time: 1.2151 data_time: 0.0044 memory: 1658 loss: 0.1360 loss_sem_seg: 0.1360 2023/03/21 07:46:31 - mmengine - INFO - Epoch(train) [14][ 600/1196] lr: 5.8765e-03 eta: 0:35:36 time: 1.2230 data_time: 0.0046 memory: 1663 loss: 0.1351 loss_sem_seg: 0.1351 2023/03/21 07:47:32 - mmengine - INFO - Epoch(train) [14][ 650/1196] lr: 5.5564e-03 eta: 0:34:37 time: 1.2155 data_time: 0.0044 memory: 1704 loss: 0.1392 loss_sem_seg: 0.1392 2023/03/21 07:48:33 - mmengine - INFO - Epoch(train) [14][ 700/1196] lr: 5.2450e-03 eta: 0:33:37 time: 1.2168 data_time: 0.0047 memory: 1678 loss: 0.1422 loss_sem_seg: 0.1422 2023/03/21 07:49:34 - mmengine - INFO - Epoch(train) [14][ 750/1196] lr: 4.9425e-03 eta: 0:32:38 time: 1.2320 data_time: 0.0047 memory: 1694 loss: 0.1383 loss_sem_seg: 0.1383 2023/03/21 07:50:35 - mmengine - INFO - Epoch(train) [14][ 800/1196] lr: 4.6488e-03 eta: 0:31:38 time: 1.2161 data_time: 0.0046 memory: 1850 loss: 0.1418 loss_sem_seg: 0.1418 2023/03/21 07:51:37 - mmengine - INFO - Epoch(train) [14][ 850/1196] lr: 4.3639e-03 eta: 0:30:39 time: 1.2340 data_time: 0.0047 memory: 1687 loss: 0.1415 loss_sem_seg: 0.1415 2023/03/21 07:52:37 - mmengine - INFO - Epoch(train) [14][ 900/1196] lr: 4.0879e-03 eta: 0:29:39 time: 1.2023 data_time: 0.0046 memory: 1764 loss: 0.1369 loss_sem_seg: 0.1369 2023/03/21 07:53:38 - mmengine - INFO - Epoch(train) [14][ 950/1196] lr: 3.8207e-03 eta: 0:28:40 time: 1.2218 data_time: 0.0047 memory: 1615 loss: 0.1423 loss_sem_seg: 0.1423 2023/03/21 07:54:38 - mmengine - INFO - Epoch(train) [14][1000/1196] lr: 3.5625e-03 eta: 0:27:40 time: 1.1982 data_time: 0.0044 memory: 1781 loss: 0.1362 loss_sem_seg: 0.1362 2023/03/21 07:55:39 - mmengine - INFO - Epoch(train) [14][1050/1196] lr: 3.3132e-03 eta: 0:26:41 time: 1.2196 data_time: 0.0046 memory: 1668 loss: 0.1413 loss_sem_seg: 0.1413 2023/03/21 07:56:39 - mmengine - INFO - Epoch(train) [14][1100/1196] lr: 3.0729e-03 eta: 0:25:41 time: 1.2059 data_time: 0.0046 memory: 1669 loss: 0.1364 loss_sem_seg: 0.1364 2023/03/21 07:57:37 - mmengine - INFO - Epoch(train) [14][1150/1196] lr: 2.8415e-03 eta: 0:24:41 time: 1.1561 data_time: 0.0048 memory: 1685 loss: 0.1353 loss_sem_seg: 0.1353 2023/03/21 07:58:26 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 07:58:27 - mmengine - INFO - Saving checkpoint at 14 epochs 2023/03/21 07:58:55 - mmengine - INFO - Epoch(val) [14][ 50/509] eta: 0:04:10 time: 0.5451 data_time: 0.0121 memory: 1635 2023/03/21 07:59:22 - mmengine - INFO - Epoch(val) [14][100/509] eta: 0:03:42 time: 0.5445 data_time: 0.0069 memory: 423 2023/03/21 07:59:50 - mmengine - INFO - Epoch(val) [14][150/509] eta: 0:03:15 time: 0.5453 data_time: 0.0067 memory: 425 2023/03/21 08:00:17 - mmengine - INFO - Epoch(val) [14][200/509] eta: 0:02:47 time: 0.5398 data_time: 0.0068 memory: 417 2023/03/21 08:00:44 - mmengine - INFO - Epoch(val) [14][250/509] eta: 0:02:20 time: 0.5406 data_time: 0.0068 memory: 425 2023/03/21 08:01:10 - mmengine - INFO - Epoch(val) [14][300/509] eta: 0:01:53 time: 0.5340 data_time: 0.0068 memory: 400 2023/03/21 08:01:38 - mmengine - INFO - Epoch(val) [14][350/509] eta: 0:01:26 time: 0.5512 data_time: 0.0068 memory: 412 2023/03/21 08:02:09 - mmengine - INFO - Epoch(val) [14][400/509] eta: 0:01:00 time: 0.6231 data_time: 0.0065 memory: 414 2023/03/21 08:02:41 - mmengine - INFO - Epoch(val) [14][450/509] eta: 0:00:33 time: 0.6305 data_time: 0.0067 memory: 428 2023/03/21 08:03:09 - mmengine - INFO - Epoch(val) [14][500/509] eta: 0:00:05 time: 0.5785 data_time: 0.0067 memory: 415 2023/03/21 08:03:44 - 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 | 0.9614 | 0.2377 | 0.6677 | 0.8083 | 0.5665 | 0.6758 | 0.8238 | 0.0011 | 0.9332 | 0.4537 | 0.8024 | 0.0027 | 0.9112 | 0.6328 | 0.8848 | 0.6554 | 0.7541 | 0.6354 | 0.4895 | 0.6262 | 0.9202 | 0.6943 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 08:03:44 - mmengine - INFO - Epoch(val) [14][509/509] car: 0.9614 bicycle: 0.2377 motorcycle: 0.6677 truck: 0.8083 bus: 0.5665 person: 0.6758 bicyclist: 0.8238 motorcyclist: 0.0011 road: 0.9332 parking: 0.4537 sidewalk: 0.8024 other-ground: 0.0027 building: 0.9112 fence: 0.6328 vegetation: 0.8848 trunck: 0.6554 terrian: 0.7541 pole: 0.6354 traffic-sign: 0.4895 miou: 0.6262 acc: 0.9202 acc_cls: 0.6943data_time: 0.0065 time: 0.5859 2023/03/21 08:04:40 - mmengine - INFO - Epoch(train) [15][ 50/1196] lr: 2.4224e-03 eta: 0:22:46 time: 1.1271 data_time: 0.0277 memory: 1736 loss: 0.1410 loss_sem_seg: 0.1410 2023/03/21 08:05:35 - mmengine - INFO - Epoch(train) [15][ 100/1196] lr: 2.2173e-03 eta: 0:21:46 time: 1.0994 data_time: 0.0048 memory: 1715 loss: 0.1443 loss_sem_seg: 0.1443 2023/03/21 08:06:31 - mmengine - INFO - Epoch(train) [15][ 150/1196] lr: 2.0212e-03 eta: 0:20:46 time: 1.1188 data_time: 0.0049 memory: 1656 loss: 0.1351 loss_sem_seg: 0.1351 2023/03/21 08:07:28 - mmengine - INFO - Epoch(train) [15][ 200/1196] lr: 1.8342e-03 eta: 0:19:47 time: 1.1279 data_time: 0.0050 memory: 1717 loss: 0.1352 loss_sem_seg: 0.1352 2023/03/21 08:08:28 - mmengine - INFO - Epoch(train) [15][ 250/1196] lr: 1.6562e-03 eta: 0:18:47 time: 1.2121 data_time: 0.0047 memory: 1678 loss: 0.1375 loss_sem_seg: 0.1375 2023/03/21 08:08:37 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 08:09:25 - mmengine - INFO - Epoch(train) [15][ 300/1196] lr: 1.4873e-03 eta: 0:17:47 time: 1.1346 data_time: 0.0044 memory: 1700 loss: 0.1464 loss_sem_seg: 0.1464 2023/03/21 08:10:17 - mmengine - INFO - Epoch(train) [15][ 350/1196] lr: 1.3275e-03 eta: 0:16:47 time: 1.0307 data_time: 0.0045 memory: 1634 loss: 0.1486 loss_sem_seg: 0.1486 2023/03/21 08:11:07 - mmengine - INFO - Epoch(train) [15][ 400/1196] lr: 1.1768e-03 eta: 0:15:47 time: 1.0184 data_time: 0.0047 memory: 1702 loss: 0.1341 loss_sem_seg: 0.1341 2023/03/21 08:11:59 - mmengine - INFO - Epoch(train) [15][ 450/1196] lr: 1.0352e-03 eta: 0:14:47 time: 1.0258 data_time: 0.0046 memory: 1687 loss: 0.1437 loss_sem_seg: 0.1437 2023/03/21 08:12:51 - mmengine - INFO - Epoch(train) [15][ 500/1196] lr: 9.0272e-04 eta: 0:13:48 time: 1.0346 data_time: 0.0044 memory: 1723 loss: 0.1294 loss_sem_seg: 0.1294 2023/03/21 08:13:41 - mmengine - INFO - Epoch(train) [15][ 550/1196] lr: 7.7936e-04 eta: 0:12:48 time: 1.0044 data_time: 0.0046 memory: 1689 loss: 0.1439 loss_sem_seg: 0.1439 2023/03/21 08:14:32 - mmengine - INFO - Epoch(train) [15][ 600/1196] lr: 6.6515e-04 eta: 0:11:48 time: 1.0269 data_time: 0.0049 memory: 1640 loss: 0.1357 loss_sem_seg: 0.1357 2023/03/21 08:15:23 - mmengine - INFO - Epoch(train) [15][ 650/1196] lr: 5.6009e-04 eta: 0:10:48 time: 1.0226 data_time: 0.0049 memory: 1705 loss: 0.1353 loss_sem_seg: 0.1353 2023/03/21 08:16:15 - mmengine - INFO - Epoch(train) [15][ 700/1196] lr: 4.6418e-04 eta: 0:09:49 time: 1.0349 data_time: 0.0045 memory: 1685 loss: 0.1428 loss_sem_seg: 0.1428 2023/03/21 08:17:06 - mmengine - INFO - Epoch(train) [15][ 750/1196] lr: 3.7744e-04 eta: 0:08:49 time: 1.0220 data_time: 0.0046 memory: 1675 loss: 0.1381 loss_sem_seg: 0.1381 2023/03/21 08:17:57 - mmengine - INFO - Epoch(train) [15][ 800/1196] lr: 2.9986e-04 eta: 0:07:50 time: 1.0228 data_time: 0.0046 memory: 1637 loss: 0.1343 loss_sem_seg: 0.1343 2023/03/21 08:18:48 - mmengine - INFO - Epoch(train) [15][ 850/1196] lr: 2.3147e-04 eta: 0:06:50 time: 1.0225 data_time: 0.0048 memory: 1638 loss: 0.1407 loss_sem_seg: 0.1407 2023/03/21 08:19:40 - mmengine - INFO - Epoch(train) [15][ 900/1196] lr: 1.7226e-04 eta: 0:05:51 time: 1.0242 data_time: 0.0045 memory: 1663 loss: 0.1286 loss_sem_seg: 0.1286 2023/03/21 08:20:30 - mmengine - INFO - Epoch(train) [15][ 950/1196] lr: 1.2223e-04 eta: 0:04:51 time: 1.0080 data_time: 0.0045 memory: 1663 loss: 0.1440 loss_sem_seg: 0.1440 2023/03/21 08:21:22 - mmengine - INFO - Epoch(train) [15][1000/1196] lr: 8.1397e-05 eta: 0:03:52 time: 1.0306 data_time: 0.0045 memory: 1729 loss: 0.1351 loss_sem_seg: 0.1351 2023/03/21 08:22:14 - mmengine - INFO - Epoch(train) [15][1050/1196] lr: 4.9756e-05 eta: 0:02:52 time: 1.0399 data_time: 0.0046 memory: 1694 loss: 0.1367 loss_sem_seg: 0.1367 2023/03/21 08:22:58 - mmengine - INFO - Epoch(train) [15][1100/1196] lr: 2.7311e-05 eta: 0:01:53 time: 0.8901 data_time: 0.0044 memory: 1687 loss: 0.1412 loss_sem_seg: 0.1412 2023/03/21 08:23:34 - mmengine - INFO - Epoch(train) [15][1150/1196] lr: 1.4064e-05 eta: 0:00:54 time: 0.7227 data_time: 0.0043 memory: 1640 loss: 0.1299 loss_sem_seg: 0.1299 2023/03/21 08:24:08 - mmengine - INFO - Exp name: spvcnn_w20_8xb2-15e_semantickitti_20230321_011649 2023/03/21 08:24:08 - mmengine - INFO - Saving checkpoint at 15 epochs 2023/03/21 08:24:27 - mmengine - INFO - Epoch(val) [15][ 50/509] eta: 0:02:38 time: 0.3456 data_time: 0.0101 memory: 1666 2023/03/21 08:24:49 - mmengine - INFO - Epoch(val) [15][100/509] eta: 0:02:40 time: 0.4369 data_time: 0.0066 memory: 423 2023/03/21 08:25:10 - mmengine - INFO - Epoch(val) [15][150/509] eta: 0:02:25 time: 0.4359 data_time: 0.0066 memory: 425 2023/03/21 08:25:31 - mmengine - INFO - Epoch(val) [15][200/509] eta: 0:02:06 time: 0.4148 data_time: 0.0070 memory: 417 2023/03/21 08:25:53 - mmengine - INFO - Epoch(val) [15][250/509] eta: 0:01:46 time: 0.4320 data_time: 0.0066 memory: 425 2023/03/21 08:26:14 - mmengine - INFO - Epoch(val) [15][300/509] eta: 0:01:26 time: 0.4294 data_time: 0.0067 memory: 400 2023/03/21 08:26:38 - mmengine - INFO - Epoch(val) [15][350/509] eta: 0:01:07 time: 0.4755 data_time: 0.0064 memory: 412 2023/03/21 08:27:03 - mmengine - INFO - Epoch(val) [15][400/509] eta: 0:00:47 time: 0.4993 data_time: 0.0063 memory: 414 2023/03/21 08:27:22 - mmengine - INFO - Epoch(val) [15][450/509] eta: 0:00:25 time: 0.3826 data_time: 0.0065 memory: 428 2023/03/21 08:27:43 - mmengine - INFO - Epoch(val) [15][500/509] eta: 0:00:03 time: 0.4250 data_time: 0.0067 memory: 415 2023/03/21 08:28:18 - 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 | 0.9630 | 0.2597 | 0.6728 | 0.7866 | 0.5866 | 0.6696 | 0.8224 | 0.0013 | 0.9345 | 0.4552 | 0.8046 | 0.0046 | 0.9104 | 0.6286 | 0.8821 | 0.6561 | 0.7488 | 0.6370 | 0.4908 | 0.6271 | 0.9195 | 0.6963 | +---------+--------+---------+------------+--------+--------+--------+-----------+--------------+--------+---------+----------+--------------+----------+--------+------------+--------+---------+--------+--------------+--------+--------+---------+ 2023/03/21 08:28:18 - mmengine - INFO - Epoch(val) [15][509/509] car: 0.9630 bicycle: 0.2597 motorcycle: 0.6728 truck: 0.7866 bus: 0.5866 person: 0.6696 bicyclist: 0.8224 motorcyclist: 0.0013 road: 0.9345 parking: 0.4552 sidewalk: 0.8046 other-ground: 0.0046 building: 0.9104 fence: 0.6286 vegetation: 0.8821 trunck: 0.6561 terrian: 0.7488 pole: 0.6370 traffic-sign: 0.4908 miou: 0.6271 acc: 0.9195 acc_cls: 0.6963data_time: 0.0066 time: 0.4406