2024/04/12 13:10:23 - 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: 2019962030 GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/share/cuda-11.3 NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 9.4.0 PyTorch: 1.11.0 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.5 - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.3.2 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.5, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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-sign-compare -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, 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: 0.12.0 OpenCV: 4.7.0 MMEngine: 0.8.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl', 'port': 29320} seed: 2019962030 Distributed launcher: slurm Distributed training: True GPU number: 8 ------------------------------------------------------------ 2024/04/12 13:10:24 - mmengine - INFO - Config: default_scope = 'embodiedscan' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1), sampler_seed=dict(type='DistSamplerSeedHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl', port=29320)) log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False n_points = 100000 point_cloud_range = [ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ] prior_generator = dict( type='AlignedAnchor3DRangeGenerator', ranges=[ [ -3.2, -3.2, -1.28, 3.2, 3.2, 1.28, ], ], rotations=[ 0.0, ]) model = dict( type='EmbodiedOccPredictor', use_valid_mask=False, use_xyz_feat=True, point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ], data_preprocessor=dict( type='Det3DDataPreprocessor', mean=[ 123.675, 116.28, 103.53, ], std=[ 58.395, 57.12, 57.375, ], bgr_to_rgb=True, pad_size_divisor=32, batchwise_inputs=True), backbone=dict( type='mmdet.ResNet', depth=50, num_stages=4, out_indices=( 0, 1, 2, 3, ), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), style='pytorch'), neck=dict( type='mmdet.FPN', in_channels=[ 256, 512, 1024, 2048, ], out_channels=256, num_outs=4), backbone_3d=dict(type='MinkResNet', in_channels=3, depth=34), neck_3d=dict( type='IndoorImVoxelNeck', in_channels=768, out_channels=128, n_blocks=[ 1, 1, 1, ]), bbox_head=dict( type='ImVoxelOccHead', volume_h=[ 20, 10, 5, ], volume_w=[ 20, 10, 5, ], volume_z=[ 8, 4, 2, ], num_classes=81, in_channels=[ 128, 128, 128, ], use_semantic=True), prior_generator=dict( type='AlignedAnchor3DRangeGenerator', ranges=[ [ -3.2, -3.2, -1.28, 3.2, 3.2, 1.28, ], ], rotations=[ 0.0, ]), n_voxels=[ 40, 40, 16, ], coord_type='DEPTH') dataset_type = 'EmbodiedScanDataset' data_root = 'data' class_names = ( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ) metainfo = dict( classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), occ_classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), box_type_3d='euler-depth') backend_args = None train_pipeline = [ dict( type='LoadAnnotations3D', with_occupancy=True, with_visible_occupancy_masks=True, with_visible_instance_masks=True), dict( type='MultiViewPipeline', n_images=10, transforms=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadDepthFromFile', backend_args=None), dict(type='ConvertRGBDToPoints', coord_type='CAMERA'), dict(type='PointSample', num_points=10000), dict(type='Resize', scale=( 480, 480, ), keep_ratio=False), ]), dict( type='AggregateMultiViewPoints', coord_type='DEPTH', save_slices=True), dict( type='PointsRangeFilter', point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ]), dict(type='ConstructMultiSweeps'), dict( type='Pack3DDetInputs', keys=[ 'img', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_occupancy', ]), ] test_pipeline = [ dict( type='LoadAnnotations3D', with_occupancy=True, with_visible_occupancy_masks=True, with_visible_instance_masks=True), dict( type='MultiViewPipeline', n_images=20, ordered=True, transforms=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadDepthFromFile', backend_args=None), dict(type='ConvertRGBDToPoints', coord_type='CAMERA'), dict(type='PointSample', num_points=10000), dict(type='Resize', scale=( 480, 480, ), keep_ratio=False), ]), dict( type='AggregateMultiViewPoints', coord_type='DEPTH', save_slices=True), dict( type='PointsRangeFilter', point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ]), dict(type='ConstructMultiSweeps'), dict( type='Pack3DDetInputs', keys=[ 'img', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_occupancy', ]), ] train_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='EmbodiedScanDataset', data_root='data', ann_file='embodiedscan_infos_train.pkl', pipeline=[ dict( type='LoadAnnotations3D', with_occupancy=True, with_visible_occupancy_masks=True, with_visible_instance_masks=True), dict( type='MultiViewPipeline', n_images=10, transforms=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadDepthFromFile', backend_args=None), dict(type='ConvertRGBDToPoints', coord_type='CAMERA'), dict(type='PointSample', num_points=10000), dict(type='Resize', scale=( 480, 480, ), keep_ratio=False), ]), dict( type='AggregateMultiViewPoints', coord_type='DEPTH', save_slices=True), dict( type='PointsRangeFilter', point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ]), dict(type='ConstructMultiSweeps'), dict( type='Pack3DDetInputs', keys=[ 'img', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_occupancy', ]), ], test_mode=False, filter_empty_gt=True, box_type_3d='Euler-Depth', metainfo=dict( classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), occ_classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), box_type_3d='euler-depth'))) val_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='EmbodiedScanDataset', data_root='data', ann_file='embodiedscan_infos_val.pkl', pipeline=[ dict( type='LoadAnnotations3D', with_occupancy=True, with_visible_occupancy_masks=True, with_visible_instance_masks=True), dict( type='MultiViewPipeline', n_images=20, ordered=True, transforms=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadDepthFromFile', backend_args=None), dict(type='ConvertRGBDToPoints', coord_type='CAMERA'), dict(type='PointSample', num_points=10000), dict(type='Resize', scale=( 480, 480, ), keep_ratio=False), ]), dict( type='AggregateMultiViewPoints', coord_type='DEPTH', save_slices=True), dict( type='PointsRangeFilter', point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ]), dict(type='ConstructMultiSweeps'), dict( type='Pack3DDetInputs', keys=[ 'img', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_occupancy', ]), ], test_mode=True, filter_empty_gt=True, box_type_3d='Euler-Depth', metainfo=dict( classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), occ_classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), box_type_3d='euler-depth'))) test_dataloader = dict( batch_size=1, num_workers=1, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='EmbodiedScanDataset', data_root='data', ann_file='embodiedscan_infos_val.pkl', pipeline=[ dict( type='LoadAnnotations3D', with_occupancy=True, with_visible_occupancy_masks=True, with_visible_instance_masks=True), dict( type='MultiViewPipeline', n_images=20, ordered=True, transforms=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='LoadDepthFromFile', backend_args=None), dict(type='ConvertRGBDToPoints', coord_type='CAMERA'), dict(type='PointSample', num_points=10000), dict(type='Resize', scale=( 480, 480, ), keep_ratio=False), ]), dict( type='AggregateMultiViewPoints', coord_type='DEPTH', save_slices=True), dict( type='PointsRangeFilter', point_cloud_range=[ -3.2, -3.2, -0.78, 3.2, 3.2, 1.78, ]), dict(type='ConstructMultiSweeps'), dict( type='Pack3DDetInputs', keys=[ 'img', 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'gt_occupancy', ]), ], test_mode=True, filter_empty_gt=True, box_type_3d='Euler-Depth', metainfo=dict( classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), occ_classes=( 'floor', 'wall', 'chair', 'cabinet', 'door', 'table', 'couch', 'shelf', 'window', 'bed', 'curtain', 'desk', 'doorframe', 'plant', 'stairs', 'pillow', 'wardrobe', 'picture', 'bathtub', 'box', 'counter', 'bench', 'stand', 'rail', 'sink', 'clothes', 'mirror', 'toilet', 'refrigerator', 'lamp', 'book', 'dresser', 'stool', 'fireplace', 'tv', 'blanket', 'commode', 'washing machine', 'monitor', 'window frame', 'radiator', 'mat', 'shower', 'rack', 'towel', 'ottoman', 'column', 'blinds', 'stove', 'bar', 'pillar', 'bin', 'heater', 'clothes dryer', 'backpack', 'blackboard', 'decoration', 'roof', 'bag', 'steps', 'windowsill', 'cushion', 'carpet', 'copier', 'board', 'countertop', 'basket', 'mailbox', 'kitchen island', 'washbasin', 'bicycle', 'drawer', 'oven', 'piano', 'excercise equipment', 'beam', 'partition', 'printer', 'microwave', 'frame', ), box_type_3d='euler-depth'))) val_evaluator = dict(type='OccupancyMetric', batchwise_anns=True) test_evaluator = dict(type='OccupancyMetric', batchwise_anns=True) train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.01), clip_grad=dict(max_norm=35.0, norm_type=2)) param_scheduler = dict( type='MultiStepLR', begin=0, end=24, by_epoch=True, milestones=[ 16, 22, ], gamma=0.1) custom_hooks = [ dict(type='EmptyCacheHook', after_iter=True), ] find_unused_parameters = True launcher = 'slurm' work_dir = '/mnt/petrelfs/wangtai/EmbodiedScan/work_dirs/cont-occupancy-benchmark' 2024/04/12 13:10:24 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "vis_backend" registry tree. As a workaround, the current "vis_backend" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:10:35 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "hook" registry tree. As a workaround, the current "hook" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:10:35 - 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 (NORMAL ) EmptyCacheHook -------------------- before_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook (LOW ) ParamSchedulerHook (VERY_LOW ) CheckpointHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) EmptyCacheHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/04/12 13:10:35 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "loop" registry tree. As a workaround, the current "loop" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:10:35 - mmengine - WARNING - euler-depth is not a meta file, simply parsed as meta information 2024/04/12 13:12:16 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "data sampler" registry tree. As a workaround, the current "data sampler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:16 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "optimizer wrapper constructor" registry tree. As a workaround, the current "optimizer wrapper constructor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:16 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "optimizer" registry tree. As a workaround, the current "optimizer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:16 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:16 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "parameter scheduler" registry tree. As a workaround, the current "parameter scheduler" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:39 - mmengine - WARNING - The prefix is not set in metric class OccupancyMetric. 2024/04/12 13:12:41 - mmengine - WARNING - Failed to search registry with scope "embodiedscan" in the "weight initializer" registry tree. As a workaround, the current "weight initializer" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "embodiedscan" is a correct scope, or whether the registry is initialized. 2024/04/12 13:12:41 - mmengine - INFO - load model from: torchvision://resnet50 2024/04/12 13:12:41 - mmengine - INFO - Loads checkpoint by torchvision backend from path: torchvision://resnet50 2024/04/12 13:12:45 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: fc.weight, fc.bias Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.0.downsample.1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.1.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn1.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.weight - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn2.bias - torch.Size([64]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer1.2.bn3.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.0.downsample.1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.1.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.2.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn1.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.weight - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn2.bias - torch.Size([128]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer2.3.bn3.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.0.downsample.1.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.1.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.2.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.3.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.4.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn1.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.weight - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn2.bias - torch.Size([256]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.weight - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer3.5.bn3.bias - torch.Size([1024]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.0.downsample.1.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.1.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn1.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.weight - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn2.bias - torch.Size([512]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.weight - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone.layer4.2.bn3.bias - torch.Size([2048]): PretrainedInit: load from torchvision://resnet50 backbone_3d.conv1.kernel - torch.Size([27, 3, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.norm1.weight - torch.Size([1, 64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.norm1.bias - torch.Size([1, 64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.conv1.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.0.norm1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.norm1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.conv2.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.0.norm2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.norm2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.0.downsample.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.0.downsample.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.1.conv1.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.1.norm1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.1.norm1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.1.conv2.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.1.norm2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.1.norm2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.2.conv1.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.2.norm1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.2.norm1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.2.conv2.kernel - torch.Size([27, 64, 64]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer1.2.norm2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer1.2.norm2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.conv1.kernel - torch.Size([27, 64, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.0.norm1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.norm1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.conv2.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.0.norm2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.norm2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.0.downsample.1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.0.downsample.1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.1.conv1.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.1.norm1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.1.norm1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.1.conv2.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.1.norm2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.1.norm2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.2.conv1.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.2.norm1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.2.norm1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.2.conv2.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.2.norm2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.2.norm2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.3.conv1.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.3.norm1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.3.norm1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.3.conv2.kernel - torch.Size([27, 128, 128]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer2.3.norm2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer2.3.norm2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.conv1.kernel - torch.Size([27, 128, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.0.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.0.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.downsample.0.kernel - torch.Size([1, 128, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.0.downsample.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.0.downsample.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.1.conv1.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.1.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.1.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.1.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.1.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.1.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.2.conv1.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.2.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.2.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.2.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.2.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.2.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.3.conv1.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.3.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.3.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.3.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.3.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.3.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.4.conv1.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.4.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.4.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.4.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.4.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.4.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.5.conv1.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.5.norm1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.5.norm1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.5.conv2.kernel - torch.Size([27, 256, 256]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer3.5.norm2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer3.5.norm2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.conv1.kernel - torch.Size([27, 256, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.0.norm1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.norm1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.conv2.kernel - torch.Size([27, 512, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.0.norm2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.norm2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.downsample.0.kernel - torch.Size([1, 256, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.0.downsample.1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.0.downsample.1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.1.conv1.kernel - torch.Size([27, 512, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.1.norm1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.1.norm1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.1.conv2.kernel - torch.Size([27, 512, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.1.norm2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.1.norm2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.2.conv1.kernel - torch.Size([27, 512, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.2.norm1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.2.norm1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.2.conv2.kernel - torch.Size([27, 512, 512]): Initialized by user-defined `init_weights` in MinkResNet backbone_3d.layer4.2.norm2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor backbone_3d.layer4.2.norm2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.lateral_convs.0.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.lateral_convs.1.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.lateral_convs.2.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): XavierInit: gain=1, distribution=uniform, bias=0 neck.lateral_convs.3.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.fpn_convs.0.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.fpn_convs.1.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.fpn_convs.2.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): XavierInit: gain=1, distribution=uniform, bias=0 neck.fpn_convs.3.conv.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.conv1.weight - torch.Size([768, 768, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.norm1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.norm1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.conv2.weight - torch.Size([768, 768, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.norm2.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_0.0.norm2.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_0.0.weight - torch.Size([128, 768, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_0.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_0.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.conv1.weight - torch.Size([1536, 768, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.norm1.weight - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.norm1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.conv2.weight - torch.Size([1536, 1536, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.norm2.weight - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.norm2.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.downsample.0.weight - torch.Size([1536, 768, 1, 1, 1]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.downsample.1.weight - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_1.0.downsample.1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.0.weight - torch.Size([1536, 768, 2, 2, 2]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.1.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.1.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.3.weight - torch.Size([768, 768, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.4.weight - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_1.4.bias - torch.Size([768]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_1.0.weight - torch.Size([128, 1536, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_1.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_1.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.conv1.weight - torch.Size([3072, 1536, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.norm1.weight - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.norm1.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.conv2.weight - torch.Size([3072, 3072, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.norm2.weight - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.norm2.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.downsample.0.weight - torch.Size([3072, 1536, 1, 1, 1]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.downsample.1.weight - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.down_layer_2.0.downsample.1.bias - torch.Size([3072]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.0.weight - torch.Size([3072, 1536, 2, 2, 2]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.1.weight - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.1.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.3.weight - torch.Size([1536, 1536, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.4.weight - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.up_block_2.4.bias - torch.Size([1536]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_2.0.weight - torch.Size([128, 3072, 3, 3, 3]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_2.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor neck_3d.out_block_2.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor bbox_head.occ.0.weight - torch.Size([81, 128, 1, 1, 1]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor bbox_head.occ.1.weight - torch.Size([81, 128, 1, 1, 1]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor bbox_head.occ.2.weight - torch.Size([81, 128, 1, 1, 1]): The value is the same before and after calling `init_weights` of EmbodiedOccPredictor 2024/04/12 13:12:45 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/04/12 13:12:45 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/04/12 13:12:45 - mmengine - INFO - Checkpoints will be saved to /mnt/petrelfs/wangtai/EmbodiedScan/work_dirs/cont-occupancy-benchmark. 2024/04/12 13:14:59 - mmengine - INFO - Epoch(train) [1][ 50/389] lr: 1.0000e-04 eta: 6:52:46 time: 2.6670 data_time: 0.2164 memory: 33470 grad_norm: 18.0487 loss: 16.5209 loss_occ_0: 9.3049 loss_occ_1: 4.7448 loss_occ_2: 2.4711 2024/04/12 13:17:05 - mmengine - INFO - Epoch(train) [1][100/389] lr: 1.0000e-04 eta: 6:40:32 time: 2.5372 data_time: 0.1316 memory: 34234 grad_norm: 9.9736 loss: 13.3587 loss_occ_0: 7.3451 loss_occ_1: 3.8398 loss_occ_2: 2.1738 2024/04/12 13:19:10 - mmengine - INFO - Epoch(train) [1][150/389] lr: 1.0000e-04 eta: 6:32:58 time: 2.4963 data_time: 0.1275 memory: 34066 grad_norm: 8.5012 loss: 12.4366 loss_occ_0: 6.7803 loss_occ_1: 3.6299 loss_occ_2: 2.0265 2024/04/12 13:21:16 - mmengine - INFO - Epoch(train) [1][200/389] lr: 1.0000e-04 eta: 6:28:53 time: 2.5157 data_time: 0.1389 memory: 35067 grad_norm: 8.1990 loss: 12.0276 loss_occ_0: 6.6505 loss_occ_1: 3.4074 loss_occ_2: 1.9697 2024/04/12 13:23:23 - mmengine - INFO - Epoch(train) [1][250/389] lr: 1.0000e-04 eta: 6:26:09 time: 2.5339 data_time: 0.1467 memory: 34287 grad_norm: 8.1231 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 13:25:28 - mmengine - INFO - Epoch(train) [1][300/389] lr: 1.0000e-04 eta: 6:23:08 time: 2.5142 data_time: 0.1314 memory: 34226 grad_norm: 7.5447 loss: 10.8227 loss_occ_0: 5.6636 loss_occ_1: 2.9705 loss_occ_2: 2.1887 2024/04/12 13:27:35 - mmengine - INFO - Epoch(train) [1][350/389] lr: 1.0000e-04 eta: 6:20:40 time: 2.5280 data_time: 0.1475 memory: 33462 grad_norm: 7.7219 loss: 10.6105 loss_occ_0: 5.4406 loss_occ_1: 2.9639 loss_occ_2: 2.2060 2024/04/12 13:29:13 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 13:29:13 - mmengine - INFO - Saving checkpoint at 1 epochs 2024/04/12 13:31:25 - mmengine - INFO - Epoch(val) [1][ 50/103] eta: 0:01:26 time: 1.6411 data_time: 1.0732 memory: 33936 2024/04/12 13:34:38 - mmengine - INFO - Epoch(val) [1][100/103] eta: 0:00:08 time: 3.8611 data_time: 3.4166 memory: 29033 2024/04/12 13:39:25 - mmengine - INFO - +--------------+---------+ | classes | IoU | +--------------+---------+ | empty | 0.76439 | | floor | 0.71541 | | wall | 0.46508 | | chair | 0.35204 | | cabinet | 0.06213 | | door | 0.14234 | | table | 0.28324 | | couch | 0.14539 | | shelf | 0.25391 | | window | 0.17642 | | bed | 0.12006 | | curtain | 0.12797 | | desk | 0.07256 | | doorframe | 0.06722 | | plant | 0.07333 | | stairs | 0.00000 | | pillow | 0.19320 | | wardrobe | 0.00000 | | picture | 0.10173 | | bathtub | 0.01899 | | box | 0.00949 | | counter | 0.11250 | | bench | 0.00000 | | stand | 0.00091 | | rail | 0.00000 | | sink | 0.14204 | | clothes | 0.00000 | | mirror | 0.01326 | | toilet | 0.03845 | | refrigerator | 0.00000 | | lamp | 0.02401 | | book | 0.00000 | | dresser | 0.00000 | | stool | 0.00000 | | tv | 0.00000 | | blanket | 0.04227 | | monitor | 0.17341 | | window frame | 0.00000 | | radiator | 0.01987 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.00000 | | column | 0.00000 | | stove | 0.00181 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.08121 | | backpack | 0.04279 | | blackboard | 0.00000 | | decoration | 0.00000 | | bag | 0.00000 | | windowsill | 0.00000 | | cushion | 0.00000 | | copier | 0.00000 | | board | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | printer | 0.00000 | | microwave | 0.00000 | +--------------+---------+ | mean | 0.08062 | +--------------+---------+ 2024/04/12 13:39:26 - mmengine - INFO - Epoch(val) [1][103/103] empty: 0.7644 floor: 0.7154 wall: 0.4651 chair: 0.3520 cabinet: 0.0621 door: 0.1423 table: 0.2832 couch: 0.1454 shelf: 0.2539 window: 0.1764 bed: 0.1201 curtain: 0.1280 desk: 0.0726 doorframe: 0.0672 plant: 0.0733 stairs: 0.0000 pillow: 0.1932 wardrobe: 0.0000 picture: 0.1017 bathtub: 0.0190 box: 0.0095 counter: 0.1125 bench: 0.0000 stand: 0.0009 rail: 0.0000 sink: 0.1420 clothes: 0.0000 mirror: 0.0133 toilet: 0.0385 refrigerator: 0.0000 lamp: 0.0240 book: 0.0000 dresser: 0.0000 stool: 0.0000 tv: 0.0000 blanket: 0.0423 monitor: 0.1734 window frame: 0.0000 radiator: 0.0199 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.0000 column: 0.0000 stove: 0.0018 bar: 0.0000 pillar: 0.0000 bin: 0.0812 backpack: 0.0428 blackboard: 0.0000 decoration: 0.0000 bag: 0.0000 windowsill: 0.0000 cushion: 0.0000 copier: 0.0000 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0000 microwave: 0.0000 data_time: 2.2883 time: 2.7936 2024/04/12 13:41:35 - mmengine - INFO - Epoch(train) [2][ 50/389] lr: 1.0000e-04 eta: 6:17:00 time: 2.5641 data_time: 0.1835 memory: 35648 grad_norm: 7.5590 loss: 9.7045 loss_occ_0: 5.2499 loss_occ_1: 2.8317 loss_occ_2: 1.6229 2024/04/12 13:43:41 - mmengine - INFO - Epoch(train) [2][100/389] lr: 1.0000e-04 eta: 6:14:44 time: 2.5337 data_time: 0.1368 memory: 35777 grad_norm: 7.4234 loss: 9.7445 loss_occ_0: 5.3486 loss_occ_1: 2.7800 loss_occ_2: 1.6159 2024/04/12 13:45:45 - mmengine - INFO - Epoch(train) [2][150/389] lr: 1.0000e-04 eta: 6:11:36 time: 2.4663 data_time: 0.1368 memory: 34012 grad_norm: 7.9216 loss: 9.4962 loss_occ_0: 5.0460 loss_occ_1: 2.8077 loss_occ_2: 1.6425 2024/04/12 13:47:51 - mmengine - INFO - Epoch(train) [2][200/389] lr: 1.0000e-04 eta: 6:09:28 time: 2.5327 data_time: 0.1424 memory: 34458 grad_norm: 7.5740 loss: 16.2060 loss_occ_0: 8.9178 loss_occ_1: 4.6965 loss_occ_2: 2.5917 2024/04/12 13:49:56 - mmengine - INFO - Epoch(train) [2][250/389] lr: 1.0000e-04 eta: 6:06:51 time: 2.4902 data_time: 0.1485 memory: 34506 grad_norm: 7.6185 loss: 9.5535 loss_occ_0: 5.1444 loss_occ_1: 2.7897 loss_occ_2: 1.6194 2024/04/12 13:52:03 - mmengine - INFO - Epoch(train) [2][300/389] lr: 1.0000e-04 eta: 6:04:50 time: 2.5390 data_time: 0.1478 memory: 34164 grad_norm: 7.4369 loss: 8.7313 loss_occ_0: 4.6752 loss_occ_1: 2.5320 loss_occ_2: 1.5240 2024/04/12 13:54:08 - mmengine - INFO - Epoch(train) [2][350/389] lr: 1.0000e-04 eta: 6:02:24 time: 2.4985 data_time: 0.1360 memory: 34035 grad_norm: 7.5989 loss: 8.9531 loss_occ_0: 4.6636 loss_occ_1: 2.7100 loss_occ_2: 1.5795 2024/04/12 13:55:46 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 13:55:46 - mmengine - INFO - Saving checkpoint at 2 epochs 2024/04/12 13:57:57 - mmengine - INFO - Epoch(val) [2][ 50/103] eta: 0:01:27 time: 1.6529 data_time: 1.0686 memory: 33800 2024/04/12 14:01:11 - mmengine - INFO - Epoch(val) [2][100/103] eta: 0:00:08 time: 3.8638 data_time: 3.4187 memory: 29030 2024/04/12 14:05:24 - mmengine - INFO - +--------------+---------+ | classes | IoU | +--------------+---------+ | empty | 0.78147 | | floor | 0.77667 | | wall | 0.46891 | | chair | 0.40885 | | cabinet | 0.09941 | | door | 0.17289 | | table | 0.28407 | | couch | 0.21410 | | shelf | 0.29235 | | window | 0.26994 | | bed | 0.34620 | | curtain | 0.31684 | | desk | 0.07304 | | doorframe | 0.05499 | | plant | 0.20897 | | stairs | 0.00000 | | pillow | 0.26112 | | wardrobe | 0.00000 | | picture | 0.12586 | | bathtub | 0.21647 | | box | 0.02247 | | counter | 0.09580 | | bench | 0.01534 | | stand | 0.01031 | | rail | 0.00000 | | sink | 0.40207 | | clothes | 0.00274 | | mirror | 0.12684 | | toilet | 0.06059 | | refrigerator | 0.00000 | | lamp | 0.14991 | | book | 0.00401 | | dresser | 0.00000 | | stool | 0.01984 | | tv | 0.00357 | | blanket | 0.01525 | | monitor | 0.25612 | | window frame | 0.00000 | | radiator | 0.11125 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.00000 | | ottoman | 0.00000 | | column | 0.00000 | | stove | 0.00722 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.13482 | | heater | 0.00000 | | backpack | 0.20112 | | blackboard | 0.04215 | | decoration | 0.00000 | | bag | 0.00000 | | steps | 0.00000 | | windowsill | 0.01172 | | cushion | 0.01164 | | copier | 0.00000 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | washbasin | 0.00000 | | printer | 0.00000 | | microwave | 0.00000 | +--------------+---------+ | mean | 0.10888 | +--------------+---------+ 2024/04/12 14:05:24 - mmengine - INFO - Epoch(val) [2][103/103] empty: 0.7815 floor: 0.7767 wall: 0.4689 chair: 0.4088 cabinet: 0.0994 door: 0.1729 table: 0.2841 couch: 0.2141 shelf: 0.2923 window: 0.2699 bed: 0.3462 curtain: 0.3168 desk: 0.0730 doorframe: 0.0550 plant: 0.2090 stairs: 0.0000 pillow: 0.2611 wardrobe: 0.0000 picture: 0.1259 bathtub: 0.2165 box: 0.0225 counter: 0.0958 bench: 0.0153 stand: 0.0103 rail: 0.0000 sink: 0.4021 clothes: 0.0027 mirror: 0.1268 toilet: 0.0606 refrigerator: 0.0000 lamp: 0.1499 book: 0.0040 dresser: 0.0000 stool: 0.0198 tv: 0.0036 blanket: 0.0152 monitor: 0.2561 window frame: 0.0000 radiator: 0.1113 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.0000 column: 0.0000 stove: 0.0072 bar: 0.0000 pillar: 0.0000 bin: 0.1348 backpack: 0.2011 blackboard: 0.0422 decoration: 0.0000 bag: 0.0000 windowsill: 0.0117 cushion: 0.0116 copier: 0.0000 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0000 microwave: 0.0000 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 data_time: 2.3033 time: 2.8165 2024/04/12 14:07:33 - mmengine - INFO - Epoch(train) [3][ 50/389] lr: 1.0000e-04 eta: 5:59:05 time: 2.5792 data_time: 0.1714 memory: 34716 grad_norm: 7.8318 loss: 8.9963 loss_occ_0: 4.8384 loss_occ_1: 2.6240 loss_occ_2: 1.5339 2024/04/12 14:09:39 - mmengine - INFO - Epoch(train) [3][100/389] lr: 1.0000e-04 eta: 5:56:52 time: 2.5190 data_time: 0.1432 memory: 34330 grad_norm: 7.3869 loss: 8.5525 loss_occ_0: 4.5034 loss_occ_1: 2.5299 loss_occ_2: 1.5192 2024/04/12 14:11:45 - mmengine - INFO - Epoch(train) [3][150/389] lr: 1.0000e-04 eta: 5:54:40 time: 2.5196 data_time: 0.1368 memory: 34213 grad_norm: 7.8500 loss: 8.3093 loss_occ_0: 4.4310 loss_occ_1: 2.3930 loss_occ_2: 1.4854 2024/04/12 14:13:50 - mmengine - INFO - Epoch(train) [3][200/389] lr: 1.0000e-04 eta: 5:52:19 time: 2.4973 data_time: 0.1457 memory: 34270 grad_norm: 7.6796 loss: 8.9391 loss_occ_0: 4.8387 loss_occ_1: 2.6005 loss_occ_2: 1.4999 2024/04/12 14:14:46 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 14:15:56 - mmengine - INFO - Epoch(train) [3][250/389] lr: 1.0000e-04 eta: 5:50:11 time: 2.5256 data_time: 0.1358 memory: 34816 grad_norm: 7.5520 loss: 9.6580 loss_occ_0: 4.8678 loss_occ_1: 2.6885 loss_occ_2: 2.1017 2024/04/12 14:18:04 - mmengine - INFO - Epoch(train) [3][300/389] lr: 1.0000e-04 eta: 5:48:16 time: 2.5586 data_time: 0.1405 memory: 34831 grad_norm: 8.2350 loss: 8.7972 loss_occ_0: 4.6820 loss_occ_1: 2.5941 loss_occ_2: 1.5210 2024/04/12 14:20:11 - mmengine - INFO - Epoch(train) [3][350/389] lr: 1.0000e-04 eta: 5:46:08 time: 2.5258 data_time: 0.1399 memory: 35542 grad_norm: 7.7134 loss: 8.4240 loss_occ_0: 4.5358 loss_occ_1: 2.4468 loss_occ_2: 1.4414 2024/04/12 14:21:50 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 14:21:50 - mmengine - INFO - Saving checkpoint at 3 epochs 2024/04/12 14:24:06 - mmengine - INFO - Epoch(val) [3][ 50/103] eta: 0:01:26 time: 1.6273 data_time: 1.0598 memory: 34160 2024/04/12 14:27:18 - mmengine - INFO - Epoch(val) [3][100/103] eta: 0:00:08 time: 3.8452 data_time: 3.3837 memory: 29031 2024/04/12 14:31:31 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.78007 | | floor | 0.79442 | | wall | 0.51419 | | chair | 0.44560 | | cabinet | 0.16364 | | door | 0.12209 | | table | 0.34813 | | couch | 0.21317 | | shelf | 0.31365 | | window | 0.25479 | | bed | 0.19298 | | curtain | 0.24826 | | desk | 0.15148 | | doorframe | 0.05617 | | plant | 0.24664 | | stairs | 0.00000 | | pillow | 0.18294 | | wardrobe | 0.00000 | | picture | 0.20539 | | bathtub | 0.43754 | | box | 0.06323 | | counter | 0.11131 | | bench | 0.13728 | | stand | 0.04130 | | rail | 0.00000 | | sink | 0.42941 | | clothes | 0.00141 | | mirror | 0.17170 | | toilet | 0.50300 | | refrigerator | 0.00228 | | lamp | 0.23066 | | book | 0.00300 | | dresser | 0.04566 | | stool | 0.07679 | | tv | 0.10414 | | blanket | 0.05434 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.30011 | | window frame | 0.00000 | | radiator | 0.21356 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.01430 | | ottoman | 0.00000 | | column | 0.00000 | | stove | 0.00931 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.17872 | | heater | 0.00000 | | backpack | 0.06127 | | blackboard | 0.06443 | | decoration | 0.00000 | | bag | 0.00317 | | steps | 0.00000 | | windowsill | 0.00000 | | cushion | 0.00745 | | copier | 0.03689 | | board | 0.00000 | | basket | 0.00149 | | mailbox | 0.00000 | | washbasin | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.00000 | | microwave | 0.01412 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.12393 | +-----------------+---------+ 2024/04/12 14:31:32 - mmengine - INFO - Epoch(val) [3][103/103] empty: 0.7801 floor: 0.7944 wall: 0.5142 chair: 0.4456 cabinet: 0.1636 door: 0.1221 table: 0.3481 couch: 0.2132 shelf: 0.3137 window: 0.2548 bed: 0.1930 curtain: 0.2483 desk: 0.1515 doorframe: 0.0562 plant: 0.2466 stairs: 0.0000 pillow: 0.1829 wardrobe: 0.0000 picture: 0.2054 bathtub: 0.4375 box: 0.0632 counter: 0.1113 bench: 0.1373 stand: 0.0413 rail: 0.0000 sink: 0.4294 clothes: 0.0014 mirror: 0.1717 toilet: 0.5030 refrigerator: 0.0023 lamp: 0.2307 book: 0.0030 dresser: 0.0457 stool: 0.0768 tv: 0.1041 blanket: 0.0543 monitor: 0.3001 window frame: 0.0000 radiator: 0.2136 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.0143 column: 0.0000 stove: 0.0093 bar: 0.0000 pillar: 0.0000 bin: 0.1787 backpack: 0.0613 blackboard: 0.0644 decoration: 0.0000 bag: 0.0032 windowsill: 0.0000 cushion: 0.0074 copier: 0.0369 board: 0.0000 basket: 0.0015 mailbox: 0.0000 printer: 0.0000 microwave: 0.0141 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 data_time: 2.2832 time: 2.7961 2024/04/12 14:33:41 - mmengine - INFO - Epoch(train) [4][ 50/389] lr: 1.0000e-04 eta: 5:42:38 time: 2.5705 data_time: 0.1433 memory: 34986 grad_norm: 7.5786 loss: 14.8796 loss_occ_0: 7.9281 loss_occ_1: 4.1798 loss_occ_2: 2.7717 2024/04/12 14:35:47 - mmengine - INFO - Epoch(train) [4][100/389] lr: 1.0000e-04 eta: 5:40:26 time: 2.5154 data_time: 0.1470 memory: 34883 grad_norm: 8.4977 loss: 8.6846 loss_occ_0: 4.3311 loss_occ_1: 2.4442 loss_occ_2: 1.9094 2024/04/12 14:37:53 - mmengine - INFO - Epoch(train) [4][150/389] lr: 1.0000e-04 eta: 5:38:21 time: 2.5370 data_time: 0.1590 memory: 34985 grad_norm: 7.0911 loss: 7.3064 loss_occ_0: 3.8617 loss_occ_1: 2.1689 loss_occ_2: 1.2758 2024/04/12 14:39:59 - mmengine - INFO - Epoch(train) [4][200/389] lr: 1.0000e-04 eta: 5:36:09 time: 2.5109 data_time: 0.1490 memory: 34421 grad_norm: 7.5112 loss: 7.2250 loss_occ_0: 3.8058 loss_occ_1: 2.1289 loss_occ_2: 1.2903 2024/04/12 14:42:05 - mmengine - INFO - Epoch(train) [4][250/389] lr: 1.0000e-04 eta: 5:34:00 time: 2.5226 data_time: 0.1344 memory: 34772 grad_norm: 7.7725 loss: 7.4986 loss_occ_0: 3.9066 loss_occ_1: 2.1949 loss_occ_2: 1.3971 2024/04/12 14:44:11 - mmengine - INFO - Epoch(train) [4][300/389] lr: 1.0000e-04 eta: 5:31:50 time: 2.5174 data_time: 0.1440 memory: 34503 grad_norm: 10.0934 loss: 7.1582 loss_occ_0: 3.7866 loss_occ_1: 2.1094 loss_occ_2: 1.2622 2024/04/12 14:46:16 - mmengine - INFO - Epoch(train) [4][350/389] lr: 1.0000e-04 eta: 5:29:36 time: 2.5020 data_time: 0.1421 memory: 34659 grad_norm: 7.6590 loss: 6.9749 loss_occ_0: 3.6646 loss_occ_1: 2.0092 loss_occ_2: 1.3010 2024/04/12 14:47:56 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 14:47:56 - mmengine - INFO - Saving checkpoint at 4 epochs 2024/04/12 14:50:09 - mmengine - INFO - Epoch(val) [4][ 50/103] eta: 0:01:26 time: 1.6294 data_time: 1.0609 memory: 34930 2024/04/12 14:53:22 - mmengine - INFO - Epoch(val) [4][100/103] eta: 0:00:08 time: 3.8698 data_time: 3.4178 memory: 29029 2024/04/12 14:57:37 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.79382 | | floor | 0.80549 | | wall | 0.49181 | | chair | 0.49047 | | cabinet | 0.17820 | | door | 0.18072 | | table | 0.36930 | | couch | 0.26013 | | shelf | 0.35418 | | window | 0.28912 | | bed | 0.36952 | | curtain | 0.46217 | | desk | 0.27450 | | doorframe | 0.09126 | | plant | 0.23470 | | stairs | 0.00000 | | pillow | 0.29021 | | wardrobe | 0.00000 | | picture | 0.21704 | | bathtub | 0.51779 | | box | 0.09899 | | counter | 0.12852 | | bench | 0.08483 | | stand | 0.09211 | | rail | 0.00000 | | sink | 0.41772 | | clothes | 0.05279 | | mirror | 0.21015 | | toilet | 0.54374 | | refrigerator | 0.00739 | | lamp | 0.34277 | | book | 0.03850 | | dresser | 0.03017 | | stool | 0.04335 | | tv | 0.18371 | | blanket | 0.07869 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.35775 | | window frame | 0.00000 | | radiator | 0.22667 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.21978 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.00000 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.20220 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.06241 | | blackboard | 0.14566 | | decoration | 0.00000 | | bag | 0.01524 | | windowsill | 0.00026 | | cushion | 0.01751 | | copier | 0.10505 | | board | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | washbasin | 0.00000 | | oven | 0.00000 | | printer | 0.00000 | | microwave | 0.05753 | +-----------------+---------+ | mean | 0.15344 | +-----------------+---------+ 2024/04/12 14:57:38 - mmengine - INFO - Epoch(val) [4][103/103] empty: 0.7938 floor: 0.8055 wall: 0.4918 chair: 0.4905 cabinet: 0.1782 door: 0.1807 table: 0.3693 couch: 0.2601 shelf: 0.3542 window: 0.2891 bed: 0.3695 curtain: 0.4622 desk: 0.2745 doorframe: 0.0913 plant: 0.2347 stairs: 0.0000 pillow: 0.2902 wardrobe: 0.0000 picture: 0.2170 bathtub: 0.5178 box: 0.0990 counter: 0.1285 bench: 0.0848 stand: 0.0921 rail: 0.0000 sink: 0.4177 clothes: 0.0528 mirror: 0.2101 toilet: 0.5437 refrigerator: 0.0074 lamp: 0.3428 book: 0.0385 dresser: 0.0302 stool: 0.0434 tv: 0.1837 blanket: 0.0787 monitor: 0.3577 window frame: 0.0000 radiator: 0.2267 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.2198 column: 0.0000 stove: 0.0000 bar: 0.0000 pillar: 0.0000 bin: 0.2022 backpack: 0.0624 blackboard: 0.1457 decoration: 0.0000 bag: 0.0152 windowsill: 0.0003 cushion: 0.0175 copier: 0.1050 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0000 microwave: 0.0575 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 data_time: 2.2994 time: 2.8084 2024/04/12 14:59:46 - mmengine - INFO - Epoch(train) [5][ 50/389] lr: 1.0000e-04 eta: 5:26:03 time: 2.5510 data_time: 0.1341 memory: 33703 grad_norm: 7.2464 loss: 7.0915 loss_occ_0: 3.6659 loss_occ_1: 2.1626 loss_occ_2: 1.2630 2024/04/12 15:01:53 - mmengine - INFO - Epoch(train) [5][100/389] lr: 1.0000e-04 eta: 5:23:59 time: 2.5419 data_time: 0.1429 memory: 35349 grad_norm: 7.6434 loss: 7.2016 loss_occ_0: 3.7699 loss_occ_1: 2.1299 loss_occ_2: 1.3017 2024/04/12 15:03:59 - mmengine - INFO - Epoch(train) [5][150/389] lr: 1.0000e-04 eta: 5:21:49 time: 2.5157 data_time: 0.1489 memory: 34627 grad_norm: 7.9040 loss: 7.2798 loss_occ_0: 3.8942 loss_occ_1: 2.1197 loss_occ_2: 1.2659 2024/04/12 15:06:05 - mmengine - INFO - Epoch(train) [5][200/389] lr: 1.0000e-04 eta: 5:19:40 time: 2.5227 data_time: 0.1454 memory: 34408 grad_norm: 7.3200 loss: 7.3357 loss_occ_0: 3.8475 loss_occ_1: 2.1739 loss_occ_2: 1.3143 2024/04/12 15:08:11 - mmengine - INFO - Epoch(train) [5][250/389] lr: 1.0000e-04 eta: 5:17:33 time: 2.5282 data_time: 0.1417 memory: 33935 grad_norm: 7.9216 loss: 7.1991 loss_occ_0: 3.7671 loss_occ_1: 2.1467 loss_occ_2: 1.2854 2024/04/12 15:10:17 - mmengine - INFO - Epoch(train) [5][300/389] lr: 1.0000e-04 eta: 5:15:26 time: 2.5254 data_time: 0.1438 memory: 34210 grad_norm: 7.7766 loss: 7.0772 loss_occ_0: 3.6441 loss_occ_1: 2.1274 loss_occ_2: 1.3056 2024/04/12 15:12:24 - mmengine - INFO - Epoch(train) [5][350/389] lr: 1.0000e-04 eta: 5:13:18 time: 2.5216 data_time: 0.1413 memory: 34664 grad_norm: 7.6497 loss: 7.0681 loss_occ_0: 3.7500 loss_occ_1: 2.0219 loss_occ_2: 1.2962 2024/04/12 15:14:02 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 15:14:02 - mmengine - INFO - Saving checkpoint at 5 epochs 2024/04/12 15:16:13 - mmengine - INFO - Epoch(val) [5][ 50/103] eta: 0:01:26 time: 1.6260 data_time: 1.0624 memory: 33410 2024/04/12 15:19:28 - mmengine - INFO - Epoch(val) [5][100/103] eta: 0:00:08 time: 3.9001 data_time: 3.4535 memory: 29032 2024/04/12 15:23:48 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.79086 | | floor | 0.79965 | | wall | 0.51863 | | chair | 0.50270 | | cabinet | 0.10206 | | door | 0.19167 | | table | 0.35840 | | couch | 0.32427 | | shelf | 0.33588 | | window | 0.29122 | | bed | 0.38526 | | curtain | 0.51403 | | desk | 0.22352 | | doorframe | 0.12415 | | plant | 0.33179 | | stairs | 0.00000 | | pillow | 0.30591 | | wardrobe | 0.00000 | | picture | 0.20961 | | bathtub | 0.56137 | | box | 0.05239 | | counter | 0.14794 | | bench | 0.14068 | | stand | 0.10238 | | rail | 0.00000 | | sink | 0.52782 | | clothes | 0.12191 | | mirror | 0.14202 | | toilet | 0.56258 | | refrigerator | 0.01194 | | lamp | 0.30171 | | book | 0.10193 | | dresser | 0.03394 | | stool | 0.09897 | | tv | 0.13513 | | blanket | 0.06825 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.43093 | | window frame | 0.00000 | | radiator | 0.34282 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.10964 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.01176 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.21857 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.09197 | | blackboard | 0.21478 | | decoration | 0.00232 | | bag | 0.02438 | | windowsill | 0.05967 | | cushion | 0.01106 | | carpet | 0.00000 | | copier | 0.09618 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | washbasin | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | printer | 0.00000 | | microwave | 0.02408 | +-----------------+---------+ | mean | 0.15576 | +-----------------+---------+ 2024/04/12 15:23:49 - mmengine - INFO - Epoch(val) [5][103/103] empty: 0.7909 floor: 0.7996 wall: 0.5186 chair: 0.5027 cabinet: 0.1021 door: 0.1917 table: 0.3584 couch: 0.3243 shelf: 0.3359 window: 0.2912 bed: 0.3853 curtain: 0.5140 desk: 0.2235 doorframe: 0.1241 plant: 0.3318 stairs: 0.0000 pillow: 0.3059 wardrobe: 0.0000 picture: 0.2096 bathtub: 0.5614 box: 0.0524 counter: 0.1479 bench: 0.1407 stand: 0.1024 rail: 0.0000 sink: 0.5278 clothes: 0.1219 mirror: 0.1420 toilet: 0.5626 refrigerator: 0.0119 lamp: 0.3017 book: 0.1019 dresser: 0.0339 stool: 0.0990 tv: 0.1351 blanket: 0.0683 monitor: 0.4309 window frame: 0.0000 radiator: 0.3428 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.1096 column: 0.0000 stove: 0.0118 bar: 0.0000 pillar: 0.0000 bin: 0.2186 backpack: 0.0920 blackboard: 0.2148 decoration: 0.0023 bag: 0.0244 windowsill: 0.0597 cushion: 0.0111 copier: 0.0962 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0000 microwave: 0.0241 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 data_time: 2.3196 time: 2.8235 2024/04/12 15:25:56 - mmengine - INFO - Epoch(train) [6][ 50/389] lr: 1.0000e-04 eta: 5:09:34 time: 2.5492 data_time: 0.1997 memory: 34205 grad_norm: 7.5054 loss: 7.3393 loss_occ_0: 3.8769 loss_occ_1: 2.1497 loss_occ_2: 1.3127 2024/04/12 15:26:09 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 15:28:04 - mmengine - INFO - Epoch(train) [6][100/389] lr: 1.0000e-04 eta: 5:07:33 time: 2.5587 data_time: 0.1527 memory: 34831 grad_norm: 7.7185 loss: 6.8091 loss_occ_0: 3.5347 loss_occ_1: 2.0511 loss_occ_2: 1.2233 2024/04/12 15:30:11 - mmengine - INFO - Epoch(train) [6][150/389] lr: 1.0000e-04 eta: 5:05:27 time: 2.5380 data_time: 0.1476 memory: 34888 grad_norm: 7.7871 loss: 6.6945 loss_occ_0: 3.5191 loss_occ_1: 1.9696 loss_occ_2: 1.2058 2024/04/12 15:32:18 - mmengine - INFO - Epoch(train) [6][200/389] lr: 1.0000e-04 eta: 5:03:20 time: 2.5262 data_time: 0.1473 memory: 35015 grad_norm: 7.8405 loss: 7.3458 loss_occ_0: 3.9170 loss_occ_1: 2.1678 loss_occ_2: 1.2609 2024/04/12 15:34:24 - mmengine - INFO - Epoch(train) [6][250/389] lr: 1.0000e-04 eta: 5:01:12 time: 2.5235 data_time: 0.1593 memory: 33280 grad_norm: 7.9936 loss: 7.3936 loss_occ_0: 3.8126 loss_occ_1: 2.2309 loss_occ_2: 1.3501 2024/04/12 15:39:27 - mmengine - INFO - Epoch(train) [6][300/389] lr: 1.0000e-04 eta: 5:08:22 time: 6.0565 data_time: 0.1489 memory: 34104 grad_norm: 7.7316 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 15:41:32 - mmengine - INFO - Epoch(train) [6][350/389] lr: 1.0000e-04 eta: 5:05:56 time: 2.5058 data_time: 0.1410 memory: 34831 grad_norm: 7.6475 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 15:43:11 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 15:43:11 - mmengine - INFO - Saving checkpoint at 6 epochs 2024/04/12 15:45:21 - mmengine - INFO - Epoch(val) [6][ 50/103] eta: 0:01:26 time: 1.6369 data_time: 1.0629 memory: 33938 2024/04/12 15:48:34 - mmengine - INFO - Epoch(val) [6][100/103] eta: 0:00:08 time: 3.8606 data_time: 3.4120 memory: 29028 2024/04/12 15:52:51 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80364 | | floor | 0.81148 | | wall | 0.53901 | | chair | 0.49340 | | cabinet | 0.14971 | | door | 0.19988 | | table | 0.41272 | | couch | 0.37191 | | shelf | 0.38518 | | window | 0.31304 | | bed | 0.43593 | | curtain | 0.52164 | | desk | 0.33749 | | doorframe | 0.12180 | | plant | 0.23394 | | stairs | 0.00000 | | pillow | 0.35862 | | wardrobe | 0.00000 | | picture | 0.32768 | | bathtub | 0.50097 | | box | 0.05616 | | counter | 0.17448 | | bench | 0.11236 | | stand | 0.17352 | | rail | 0.00000 | | sink | 0.49278 | | clothes | 0.05043 | | mirror | 0.14975 | | toilet | 0.64000 | | refrigerator | 0.15417 | | lamp | 0.31503 | | book | 0.08711 | | dresser | 0.03795 | | stool | 0.09219 | | tv | 0.18707 | | blanket | 0.04024 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.42602 | | window frame | 0.00000 | | radiator | 0.32065 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.04255 | | towel | 0.15418 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.09313 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.30126 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.14257 | | blackboard | 0.28407 | | decoration | 0.00576 | | bag | 0.03465 | | windowsill | 0.04716 | | cushion | 0.05302 | | copier | 0.11617 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | washbasin | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | printer | 0.03128 | | microwave | 0.08221 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.17206 | +-----------------+---------+ 2024/04/12 15:52:52 - mmengine - INFO - Epoch(val) [6][103/103] empty: 0.8036 floor: 0.8115 wall: 0.5390 chair: 0.4934 cabinet: 0.1497 door: 0.1999 table: 0.4127 couch: 0.3719 shelf: 0.3852 window: 0.3130 bed: 0.4359 curtain: 0.5216 desk: 0.3375 doorframe: 0.1218 plant: 0.2339 stairs: 0.0000 pillow: 0.3586 wardrobe: 0.0000 picture: 0.3277 bathtub: 0.5010 box: 0.0562 counter: 0.1745 bench: 0.1124 stand: 0.1735 rail: 0.0000 sink: 0.4928 clothes: 0.0504 mirror: 0.1497 toilet: 0.6400 refrigerator: 0.1542 lamp: 0.3150 book: 0.0871 dresser: 0.0380 stool: 0.0922 tv: 0.1871 blanket: 0.0402 monitor: 0.4260 window frame: 0.0000 radiator: 0.3207 mat: 0.0000 shower: 0.0000 rack: 0.0426 towel: 0.1542 column: 0.0000 stove: 0.0931 bar: 0.0000 pillar: 0.0000 bin: 0.3013 backpack: 0.1426 blackboard: 0.2841 decoration: 0.0058 bag: 0.0346 windowsill: 0.0472 cushion: 0.0530 copier: 0.1162 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0313 microwave: 0.0822 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 data_time: 2.2997 time: 2.8096 2024/04/12 15:54:59 - mmengine - INFO - Epoch(train) [7][ 50/389] lr: 1.0000e-04 eta: 5:01:46 time: 2.5381 data_time: 0.1898 memory: 34789 grad_norm: 7.3643 loss: 6.2917 loss_occ_0: 3.2634 loss_occ_1: 1.8577 loss_occ_2: 1.1707 2024/04/12 15:57:06 - mmengine - INFO - Epoch(train) [7][100/389] lr: 1.0000e-04 eta: 4:59:26 time: 2.5328 data_time: 0.1601 memory: 34166 grad_norm: 7.9850 loss: 6.5757 loss_occ_0: 3.4419 loss_occ_1: 1.9702 loss_occ_2: 1.1636 2024/04/12 15:59:11 - mmengine - INFO - Epoch(train) [7][150/389] lr: 1.0000e-04 eta: 4:57:03 time: 2.5093 data_time: 0.1460 memory: 33718 grad_norm: 7.8900 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 16:01:18 - mmengine - INFO - Epoch(train) [7][200/389] lr: 1.0000e-04 eta: 4:54:44 time: 2.5350 data_time: 0.1516 memory: 34206 grad_norm: 7.8535 loss: 6.2172 loss_occ_0: 3.3013 loss_occ_1: 1.8140 loss_occ_2: 1.1018 2024/04/12 16:03:25 - mmengine - INFO - Epoch(train) [7][250/389] lr: 1.0000e-04 eta: 4:52:27 time: 2.5472 data_time: 0.1378 memory: 35270 grad_norm: 7.9831 loss: 5.9910 loss_occ_0: 3.1075 loss_occ_1: 1.7808 loss_occ_2: 1.1027 2024/04/12 16:05:30 - mmengine - INFO - Epoch(train) [7][300/389] lr: 1.0000e-04 eta: 4:50:05 time: 2.5017 data_time: 0.1476 memory: 34427 grad_norm: 7.6032 loss: 6.8313 loss_occ_0: 3.6296 loss_occ_1: 1.9886 loss_occ_2: 1.2131 2024/04/12 16:07:38 - mmengine - INFO - Epoch(train) [7][350/389] lr: 1.0000e-04 eta: 4:47:50 time: 2.5572 data_time: 0.1323 memory: 33929 grad_norm: 7.7873 loss: 6.3031 loss_occ_0: 3.2867 loss_occ_1: 1.8798 loss_occ_2: 1.1367 2024/04/12 16:09:17 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 16:09:17 - mmengine - INFO - Saving checkpoint at 7 epochs 2024/04/12 16:11:32 - mmengine - INFO - Epoch(val) [7][ 50/103] eta: 0:01:26 time: 1.6365 data_time: 1.0672 memory: 35004 2024/04/12 16:14:45 - mmengine - INFO - Epoch(val) [7][100/103] eta: 0:00:08 time: 3.8734 data_time: 3.4249 memory: 29029 2024/04/12 16:19:06 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80354 | | floor | 0.78825 | | wall | 0.54904 | | chair | 0.46031 | | cabinet | 0.17800 | | door | 0.23195 | | table | 0.38456 | | couch | 0.29993 | | shelf | 0.40315 | | window | 0.34247 | | bed | 0.35724 | | curtain | 0.50025 | | desk | 0.21582 | | doorframe | 0.11113 | | plant | 0.41712 | | stairs | 0.00000 | | pillow | 0.25430 | | wardrobe | 0.00048 | | picture | 0.26509 | | bathtub | 0.69886 | | box | 0.09010 | | counter | 0.13647 | | bench | 0.17669 | | stand | 0.11861 | | rail | 0.00000 | | sink | 0.51081 | | clothes | 0.17290 | | mirror | 0.20242 | | toilet | 0.66423 | | refrigerator | 0.15689 | | lamp | 0.38055 | | book | 0.19365 | | dresser | 0.01332 | | stool | 0.11647 | | fireplace | 0.00000 | | tv | 0.19361 | | blanket | 0.03771 | | washing machine | 0.00000 | | monitor | 0.47364 | | window frame | 0.00000 | | radiator | 0.46553 | | mat | 0.00000 | | shower | 0.00287 | | rack | 0.00000 | | towel | 0.23671 | | ottoman | 0.00000 | | column | 0.00000 | | stove | 0.05826 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.28607 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.21781 | | blackboard | 0.24834 | | decoration | 0.01158 | | bag | 0.04120 | | windowsill | 0.02143 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.01741 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.02962 | | mailbox | 0.00000 | | washbasin | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.00807 | | microwave | 0.11184 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.17826 | +-----------------+---------+ 2024/04/12 16:19:07 - mmengine - INFO - Epoch(val) [7][103/103] empty: 0.8035 floor: 0.7883 wall: 0.5490 chair: 0.4603 cabinet: 0.1780 door: 0.2319 table: 0.3846 couch: 0.2999 shelf: 0.4032 window: 0.3425 bed: 0.3572 curtain: 0.5002 desk: 0.2158 doorframe: 0.1111 plant: 0.4171 stairs: 0.0000 pillow: 0.2543 wardrobe: 0.0005 picture: 0.2651 bathtub: 0.6989 box: 0.0901 counter: 0.1365 bench: 0.1767 stand: 0.1186 rail: 0.0000 sink: 0.5108 clothes: 0.1729 mirror: 0.2024 toilet: 0.6642 refrigerator: 0.1569 lamp: 0.3805 book: 0.1937 dresser: 0.0133 stool: 0.1165 tv: 0.1936 blanket: 0.0377 monitor: 0.4736 window frame: 0.0000 radiator: 0.4655 mat: 0.0000 shower: 0.0029 rack: 0.0000 towel: 0.2367 column: 0.0000 stove: 0.0583 bar: 0.0000 pillar: 0.0000 bin: 0.2861 backpack: 0.2178 blackboard: 0.2483 decoration: 0.0116 bag: 0.0412 windowsill: 0.0214 cushion: 0.0000 copier: 0.0174 board: 0.0000 basket: 0.0296 mailbox: 0.0000 printer: 0.0081 microwave: 0.1118 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 data_time: 2.3075 time: 2.8150 2024/04/12 16:21:14 - mmengine - INFO - Epoch(train) [8][ 50/389] lr: 1.0000e-04 eta: 4:43:47 time: 2.5388 data_time: 0.1529 memory: 34246 grad_norm: 7.3593 loss: 5.9247 loss_occ_0: 3.0540 loss_occ_1: 1.7415 loss_occ_2: 1.1292 2024/04/12 16:23:20 - mmengine - INFO - Epoch(train) [8][100/389] lr: 1.0000e-04 eta: 4:41:29 time: 2.5226 data_time: 0.1569 memory: 34295 grad_norm: 8.0635 loss: 5.6062 loss_occ_0: 2.9162 loss_occ_1: 1.6514 loss_occ_2: 1.0386 2024/04/12 16:25:27 - mmengine - INFO - Epoch(train) [8][150/389] lr: 1.0000e-04 eta: 4:39:14 time: 2.5422 data_time: 0.1398 memory: 34457 grad_norm: 7.5049 loss: 5.9144 loss_occ_0: 3.0694 loss_occ_1: 1.7761 loss_occ_2: 1.0688 2024/04/12 16:27:33 - mmengine - INFO - Epoch(train) [8][200/389] lr: 1.0000e-04 eta: 4:36:56 time: 2.5171 data_time: 0.1673 memory: 34125 grad_norm: 7.9982 loss: 6.4904 loss_occ_0: 3.3590 loss_occ_1: 1.8833 loss_occ_2: 1.2481 2024/04/12 16:29:39 - mmengine - INFO - Epoch(train) [8][250/389] lr: 1.0000e-04 eta: 4:34:40 time: 2.5250 data_time: 0.1359 memory: 34221 grad_norm: 7.6076 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 16:30:48 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 16:31:47 - mmengine - INFO - Epoch(train) [8][300/389] lr: 1.0000e-04 eta: 4:32:27 time: 2.5632 data_time: 0.1436 memory: 34993 grad_norm: 7.3662 loss: 6.4587 loss_occ_0: 3.4406 loss_occ_1: 1.8613 loss_occ_2: 1.1568 2024/04/12 16:33:53 - mmengine - INFO - Epoch(train) [8][350/389] lr: 1.0000e-04 eta: 4:30:10 time: 2.5150 data_time: 0.1474 memory: 35550 grad_norm: 7.7469 loss: 13.3165 loss_occ_0: 7.2710 loss_occ_1: 3.8618 loss_occ_2: 2.1837 2024/04/12 16:35:31 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 16:35:31 - mmengine - INFO - Saving checkpoint at 8 epochs 2024/04/12 16:37:43 - mmengine - INFO - Epoch(val) [8][ 50/103] eta: 0:01:28 time: 1.6609 data_time: 1.0768 memory: 34804 2024/04/12 16:40:57 - mmengine - INFO - Epoch(val) [8][100/103] eta: 0:00:08 time: 3.8851 data_time: 3.4311 memory: 29030 2024/04/12 16:45:14 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80589 | | floor | 0.80054 | | wall | 0.53947 | | chair | 0.46381 | | cabinet | 0.21457 | | door | 0.22431 | | table | 0.39708 | | couch | 0.34515 | | shelf | 0.44997 | | window | 0.34680 | | bed | 0.42633 | | curtain | 0.57624 | | desk | 0.27058 | | doorframe | 0.14830 | | plant | 0.42550 | | stairs | 0.00000 | | pillow | 0.33813 | | wardrobe | 0.00889 | | picture | 0.28649 | | bathtub | 0.65646 | | box | 0.09217 | | counter | 0.17084 | | bench | 0.14421 | | stand | 0.15684 | | rail | 0.00000 | | sink | 0.53050 | | clothes | 0.08560 | | mirror | 0.23030 | | toilet | 0.63441 | | refrigerator | 0.15187 | | lamp | 0.35578 | | book | 0.11122 | | dresser | 0.03953 | | stool | 0.11841 | | fireplace | 0.00000 | | tv | 0.13901 | | blanket | 0.03095 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.42907 | | window frame | 0.00000 | | radiator | 0.45060 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.27056 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.16411 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.33631 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.19881 | | blackboard | 0.30886 | | decoration | 0.02479 | | bag | 0.03208 | | windowsill | 0.04864 | | cushion | 0.00000 | | copier | 0.12611 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.03682 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.00572 | | microwave | 0.05684 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.17845 | +-----------------+---------+ 2024/04/12 16:45:15 - mmengine - INFO - Epoch(val) [8][103/103] empty: 0.8059 floor: 0.8005 wall: 0.5395 chair: 0.4638 cabinet: 0.2146 door: 0.2243 table: 0.3971 couch: 0.3451 shelf: 0.4500 window: 0.3468 bed: 0.4263 curtain: 0.5762 desk: 0.2706 doorframe: 0.1483 plant: 0.4255 stairs: 0.0000 pillow: 0.3381 wardrobe: 0.0089 picture: 0.2865 bathtub: 0.6565 box: 0.0922 counter: 0.1708 bench: 0.1442 stand: 0.1568 rail: 0.0000 sink: 0.5305 clothes: 0.0856 mirror: 0.2303 toilet: 0.6344 refrigerator: 0.1519 lamp: 0.3558 book: 0.1112 dresser: 0.0395 stool: 0.1184 tv: 0.1390 blanket: 0.0310 monitor: 0.4291 window frame: 0.0000 radiator: 0.4506 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.2706 column: 0.0000 stove: 0.1641 bar: 0.0000 pillar: 0.0000 bin: 0.3363 backpack: 0.1988 blackboard: 0.3089 decoration: 0.0248 bag: 0.0321 windowsill: 0.0486 cushion: 0.0000 copier: 0.1261 board: 0.0000 basket: 0.0368 mailbox: 0.0000 printer: 0.0057 microwave: 0.0568 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 data_time: 2.3156 time: 2.8328 2024/04/12 16:47:24 - mmengine - INFO - Epoch(train) [9][ 50/389] lr: 1.0000e-04 eta: 4:26:13 time: 2.5826 data_time: 0.2021 memory: 34118 grad_norm: 7.4545 loss: 5.1565 loss_occ_0: 2.6256 loss_occ_1: 1.5185 loss_occ_2: 1.0125 2024/04/12 16:49:29 - mmengine - INFO - Epoch(train) [9][100/389] lr: 1.0000e-04 eta: 4:23:56 time: 2.5069 data_time: 0.1538 memory: 34097 grad_norm: 7.3233 loss: 5.7389 loss_occ_0: 2.9611 loss_occ_1: 1.7278 loss_occ_2: 1.0500 2024/04/12 16:51:35 - mmengine - INFO - Epoch(train) [9][150/389] lr: 1.0000e-04 eta: 4:21:40 time: 2.5140 data_time: 0.1440 memory: 33970 grad_norm: 7.5752 loss: 6.8195 loss_occ_0: 3.3008 loss_occ_1: 1.8664 loss_occ_2: 1.6522 2024/04/12 16:53:42 - mmengine - INFO - Epoch(train) [9][200/389] lr: 1.0000e-04 eta: 4:19:26 time: 2.5333 data_time: 0.1367 memory: 35047 grad_norm: 7.6128 loss: 6.0394 loss_occ_0: 3.1790 loss_occ_1: 1.8306 loss_occ_2: 1.0297 2024/04/12 16:55:47 - mmengine - INFO - Epoch(train) [9][250/389] lr: 1.0000e-04 eta: 4:17:11 time: 2.5121 data_time: 0.1318 memory: 34417 grad_norm: 7.3514 loss: 12.3905 loss_occ_0: 6.7373 loss_occ_1: 3.6521 loss_occ_2: 2.0012 2024/04/12 16:57:55 - mmengine - INFO - Epoch(train) [9][300/389] lr: 1.0000e-04 eta: 4:14:59 time: 2.5543 data_time: 0.1468 memory: 33860 grad_norm: 7.7651 loss: 6.1336 loss_occ_0: 3.2271 loss_occ_1: 1.8164 loss_occ_2: 1.0901 2024/04/12 17:00:00 - mmengine - INFO - Epoch(train) [9][350/389] lr: 1.0000e-04 eta: 4:12:43 time: 2.5009 data_time: 0.1425 memory: 34309 grad_norm: 7.5468 loss: 5.8911 loss_occ_0: 3.0056 loss_occ_1: 1.7827 loss_occ_2: 1.1028 2024/04/12 17:01:40 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 17:01:40 - mmengine - INFO - Saving checkpoint at 9 epochs 2024/04/12 17:03:47 - mmengine - INFO - Epoch(val) [9][ 50/103] eta: 0:01:26 time: 1.6414 data_time: 1.0709 memory: 34467 2024/04/12 17:07:03 - mmengine - INFO - Epoch(val) [9][100/103] eta: 0:00:08 time: 3.9054 data_time: 3.4548 memory: 29031 2024/04/12 17:11:16 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80102 | | floor | 0.79080 | | wall | 0.53640 | | chair | 0.53470 | | cabinet | 0.21021 | | door | 0.24992 | | table | 0.44648 | | couch | 0.35623 | | shelf | 0.47079 | | window | 0.36763 | | bed | 0.45013 | | curtain | 0.54659 | | desk | 0.38808 | | doorframe | 0.18724 | | plant | 0.31581 | | stairs | 0.00000 | | pillow | 0.32172 | | wardrobe | 0.00000 | | picture | 0.29333 | | bathtub | 0.71258 | | box | 0.08506 | | counter | 0.19527 | | bench | 0.19664 | | stand | 0.18377 | | rail | 0.00952 | | sink | 0.48984 | | clothes | 0.16121 | | mirror | 0.21227 | | toilet | 0.65342 | | refrigerator | 0.20472 | | lamp | 0.34622 | | book | 0.23751 | | dresser | 0.01795 | | stool | 0.12593 | | fireplace | 0.00000 | | tv | 0.21448 | | blanket | 0.07116 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.46393 | | window frame | 0.00000 | | radiator | 0.48912 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.33029 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.25741 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.35461 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.16718 | | blackboard | 0.26173 | | decoration | 0.01163 | | bag | 0.03441 | | steps | 0.00000 | | windowsill | 0.04932 | | cushion | 0.00000 | | copier | 0.16409 | | board | 0.00044 | | countertop | 0.00000 | | basket | 0.02623 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.07787 | | microwave | 0.14704 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.18960 | +-----------------+---------+ 2024/04/12 17:11:17 - mmengine - INFO - Epoch(val) [9][103/103] empty: 0.8010 floor: 0.7908 wall: 0.5364 chair: 0.5347 cabinet: 0.2102 door: 0.2499 table: 0.4465 couch: 0.3562 shelf: 0.4708 window: 0.3676 bed: 0.4501 curtain: 0.5466 desk: 0.3881 doorframe: 0.1872 plant: 0.3158 stairs: 0.0000 pillow: 0.3217 wardrobe: 0.0000 picture: 0.2933 bathtub: 0.7126 box: 0.0851 counter: 0.1953 bench: 0.1966 stand: 0.1838 rail: 0.0095 sink: 0.4898 clothes: 0.1612 mirror: 0.2123 toilet: 0.6534 refrigerator: 0.2047 lamp: 0.3462 book: 0.2375 dresser: 0.0179 stool: 0.1259 tv: 0.2145 blanket: 0.0712 monitor: 0.4639 window frame: 0.0000 radiator: 0.4891 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.3303 column: 0.0000 stove: 0.2574 bar: 0.0000 pillar: 0.0000 bin: 0.3546 backpack: 0.1672 blackboard: 0.2617 decoration: 0.0116 bag: 0.0344 windowsill: 0.0493 cushion: 0.0000 copier: 0.1641 board: 0.0004 basket: 0.0262 mailbox: 0.0000 printer: 0.0779 microwave: 0.1470 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 data_time: 2.3229 time: 2.8324 2024/04/12 17:13:27 - mmengine - INFO - Epoch(train) [10][ 50/389] lr: 1.0000e-04 eta: 4:08:52 time: 2.5916 data_time: 0.1772 memory: 34675 grad_norm: 7.2942 loss: 5.3717 loss_occ_0: 2.8028 loss_occ_1: 1.5720 loss_occ_2: 0.9969 2024/04/12 17:15:33 - mmengine - INFO - Epoch(train) [10][100/389] lr: 1.0000e-04 eta: 4:06:38 time: 2.5119 data_time: 0.1534 memory: 34366 grad_norm: 7.3952 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 17:17:39 - mmengine - INFO - Epoch(train) [10][150/389] lr: 1.0000e-04 eta: 4:04:25 time: 2.5278 data_time: 0.1460 memory: 34998 grad_norm: 7.5751 loss: 5.8064 loss_occ_0: 2.7689 loss_occ_1: 1.5472 loss_occ_2: 1.4903 2024/04/12 17:19:45 - mmengine - INFO - Epoch(train) [10][200/389] lr: 1.0000e-04 eta: 4:02:11 time: 2.5243 data_time: 0.1426 memory: 34127 grad_norm: 7.7286 loss: 5.6839 loss_occ_0: 2.9734 loss_occ_1: 1.6677 loss_occ_2: 1.0428 2024/04/12 17:21:52 - mmengine - INFO - Epoch(train) [10][250/389] lr: 1.0000e-04 eta: 3:59:59 time: 2.5283 data_time: 0.1343 memory: 34987 grad_norm: 7.7257 loss: 6.1912 loss_occ_0: 3.0053 loss_occ_1: 1.6391 loss_occ_2: 1.5468 2024/04/12 17:23:57 - mmengine - INFO - Epoch(train) [10][300/389] lr: 1.0000e-04 eta: 3:57:45 time: 2.5082 data_time: 0.1423 memory: 34755 grad_norm: 7.0268 loss: 5.5776 loss_occ_0: 2.9360 loss_occ_1: 1.6160 loss_occ_2: 1.0256 2024/04/12 17:26:03 - mmengine - INFO - Epoch(train) [10][350/389] lr: 1.0000e-04 eta: 3:55:31 time: 2.5136 data_time: 0.1498 memory: 35107 grad_norm: 7.7738 loss: 5.5278 loss_occ_0: 2.8301 loss_occ_1: 1.6386 loss_occ_2: 1.0591 2024/04/12 17:27:42 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 17:27:42 - mmengine - INFO - Saving checkpoint at 10 epochs 2024/04/12 17:29:55 - mmengine - INFO - Epoch(val) [10][ 50/103] eta: 0:01:26 time: 1.6242 data_time: 1.0589 memory: 33704 2024/04/12 17:33:09 - mmengine - INFO - Epoch(val) [10][100/103] eta: 0:00:08 time: 3.8797 data_time: 3.4311 memory: 29027 2024/04/12 17:37:22 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80603 | | floor | 0.80190 | | wall | 0.56918 | | chair | 0.56722 | | cabinet | 0.26534 | | door | 0.27978 | | table | 0.44411 | | couch | 0.39077 | | shelf | 0.47416 | | window | 0.34651 | | bed | 0.44565 | | curtain | 0.55865 | | desk | 0.28764 | | doorframe | 0.12972 | | plant | 0.28195 | | stairs | 0.00000 | | pillow | 0.32498 | | wardrobe | 0.01918 | | picture | 0.31394 | | bathtub | 0.71235 | | box | 0.09607 | | counter | 0.13267 | | bench | 0.22264 | | stand | 0.17580 | | rail | 0.00000 | | sink | 0.53809 | | clothes | 0.20720 | | mirror | 0.24744 | | toilet | 0.70899 | | refrigerator | 0.17467 | | lamp | 0.36256 | | book | 0.22312 | | dresser | 0.00646 | | stool | 0.16957 | | fireplace | 0.00000 | | tv | 0.30429 | | blanket | 0.07426 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.53533 | | window frame | 0.00000 | | radiator | 0.49124 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.02285 | | towel | 0.28254 | | ottoman | 0.00000 | | column | 0.00000 | | stove | 0.24715 | | bar | 0.07843 | | pillar | 0.00000 | | bin | 0.36858 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.22899 | | blackboard | 0.29545 | | decoration | 0.00000 | | bag | 0.05692 | | windowsill | 0.09442 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.11147 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.01474 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.07831 | | microwave | 0.08317 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.19801 | +-----------------+---------+ 2024/04/12 17:37:23 - mmengine - INFO - Epoch(val) [10][103/103] empty: 0.8060 floor: 0.8019 wall: 0.5692 chair: 0.5672 cabinet: 0.2653 door: 0.2798 table: 0.4441 couch: 0.3908 shelf: 0.4742 window: 0.3465 bed: 0.4456 curtain: 0.5586 desk: 0.2876 doorframe: 0.1297 plant: 0.2820 stairs: 0.0000 pillow: 0.3250 wardrobe: 0.0192 picture: 0.3139 bathtub: 0.7123 box: 0.0961 counter: 0.1327 bench: 0.2226 stand: 0.1758 rail: 0.0000 sink: 0.5381 clothes: 0.2072 mirror: 0.2474 toilet: 0.7090 refrigerator: 0.1747 lamp: 0.3626 book: 0.2231 dresser: 0.0065 stool: 0.1696 tv: 0.3043 blanket: 0.0743 monitor: 0.5353 window frame: 0.0000 radiator: 0.4912 mat: 0.0000 shower: 0.0000 rack: 0.0229 towel: 0.2825 column: 0.0000 stove: 0.2472 bar: 0.0784 pillar: 0.0000 bin: 0.3686 backpack: 0.2290 blackboard: 0.2955 decoration: 0.0000 bag: 0.0569 windowsill: 0.0944 cushion: 0.0000 copier: 0.1115 board: 0.0000 basket: 0.0147 mailbox: 0.0000 printer: 0.0783 microwave: 0.0832 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 data_time: 2.3081 time: 2.8140 2024/04/12 17:39:30 - mmengine - INFO - Epoch(train) [11][ 50/389] lr: 1.0000e-04 eta: 3:51:38 time: 2.5390 data_time: 0.1537 memory: 35015 grad_norm: 7.5555 loss: 6.2143 loss_occ_0: 2.9846 loss_occ_1: 1.6546 loss_occ_2: 1.5751 2024/04/12 17:41:37 - mmengine - INFO - Epoch(train) [11][100/389] lr: 1.0000e-04 eta: 3:49:26 time: 2.5315 data_time: 0.1476 memory: 33997 grad_norm: 7.3594 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 17:42:02 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 17:43:43 - mmengine - INFO - Epoch(train) [11][150/389] lr: 1.0000e-04 eta: 3:47:14 time: 2.5360 data_time: 0.1413 memory: 34670 grad_norm: 6.9599 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 17:45:50 - mmengine - INFO - Epoch(train) [11][200/389] lr: 1.0000e-04 eta: 3:45:03 time: 2.5294 data_time: 0.1443 memory: 34810 grad_norm: 7.9616 loss: 5.3394 loss_occ_0: 2.7907 loss_occ_1: 1.5691 loss_occ_2: 0.9796 2024/04/12 17:47:57 - mmengine - INFO - Epoch(train) [11][250/389] lr: 1.0000e-04 eta: 3:42:52 time: 2.5335 data_time: 0.1609 memory: 33636 grad_norm: 7.8459 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 17:50:03 - mmengine - INFO - Epoch(train) [11][300/389] lr: 1.0000e-04 eta: 3:40:40 time: 2.5244 data_time: 0.1428 memory: 33642 grad_norm: 7.5915 loss: 5.4678 loss_occ_0: 2.8008 loss_occ_1: 1.6365 loss_occ_2: 1.0305 2024/04/12 17:52:10 - mmengine - INFO - Epoch(train) [11][350/389] lr: 1.0000e-04 eta: 3:38:29 time: 2.5385 data_time: 0.1490 memory: 33942 grad_norm: 7.2855 loss: 19.2913 loss_occ_0: 10.6736 loss_occ_1: 5.6037 loss_occ_2: 3.0140 2024/04/12 17:53:49 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 17:53:49 - mmengine - INFO - Saving checkpoint at 11 epochs 2024/04/12 17:55:58 - mmengine - INFO - Epoch(val) [11][ 50/103] eta: 0:01:26 time: 1.6329 data_time: 1.0656 memory: 34494 2024/04/12 17:59:14 - mmengine - INFO - Epoch(val) [11][100/103] eta: 0:00:08 time: 3.9088 data_time: 3.4594 memory: 29028 2024/04/12 18:03:30 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80249 | | floor | 0.80628 | | wall | 0.55618 | | chair | 0.55558 | | cabinet | 0.24463 | | door | 0.23003 | | table | 0.44571 | | couch | 0.35582 | | shelf | 0.45349 | | window | 0.33380 | | bed | 0.48053 | | curtain | 0.43756 | | desk | 0.23303 | | doorframe | 0.14855 | | plant | 0.34558 | | stairs | 0.00000 | | pillow | 0.35052 | | wardrobe | 0.09273 | | picture | 0.33965 | | bathtub | 0.70456 | | box | 0.07686 | | counter | 0.17669 | | bench | 0.23620 | | stand | 0.18022 | | rail | 0.00000 | | sink | 0.52970 | | clothes | 0.19544 | | mirror | 0.20912 | | toilet | 0.71790 | | refrigerator | 0.22265 | | lamp | 0.40443 | | book | 0.17063 | | dresser | 0.00224 | | stool | 0.10130 | | fireplace | 0.00000 | | tv | 0.28443 | | blanket | 0.03957 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.49055 | | window frame | 0.00000 | | radiator | 0.47707 | | mat | 0.00322 | | shower | 0.00000 | | rack | 0.00875 | | towel | 0.28179 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.27805 | | bar | 0.03643 | | pillar | 0.00000 | | bin | 0.35428 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.23716 | | blackboard | 0.21697 | | decoration | 0.03565 | | bag | 0.04655 | | steps | 0.00000 | | windowsill | 0.08428 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.10030 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.01525 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.01158 | | microwave | 0.11592 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.18760 | +-----------------+---------+ 2024/04/12 18:03:30 - mmengine - INFO - Epoch(val) [11][103/103] empty: 0.8025 floor: 0.8063 wall: 0.5562 chair: 0.5556 cabinet: 0.2446 door: 0.2300 table: 0.4457 couch: 0.3558 shelf: 0.4535 window: 0.3338 bed: 0.4805 curtain: 0.4376 desk: 0.2330 doorframe: 0.1485 plant: 0.3456 stairs: 0.0000 pillow: 0.3505 wardrobe: 0.0927 picture: 0.3397 bathtub: 0.7046 box: 0.0769 counter: 0.1767 bench: 0.2362 stand: 0.1802 rail: 0.0000 sink: 0.5297 clothes: 0.1954 mirror: 0.2091 toilet: 0.7179 refrigerator: 0.2227 lamp: 0.4044 book: 0.1706 dresser: 0.0022 stool: 0.1013 tv: 0.2844 blanket: 0.0396 monitor: 0.4906 window frame: 0.0000 radiator: 0.4771 mat: 0.0032 shower: 0.0000 rack: 0.0088 towel: 0.2818 column: 0.0000 stove: 0.2781 bar: 0.0364 pillar: 0.0000 bin: 0.3543 backpack: 0.2372 blackboard: 0.2170 decoration: 0.0357 bag: 0.0465 windowsill: 0.0843 cushion: 0.0000 copier: 0.1003 board: 0.0000 basket: 0.0153 mailbox: 0.0000 printer: 0.0116 microwave: 0.1159 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 data_time: 2.3241 time: 2.8313 2024/04/12 18:05:38 - mmengine - INFO - Epoch(train) [12][ 50/389] lr: 1.0000e-04 eta: 3:34:38 time: 2.5468 data_time: 0.1498 memory: 34920 grad_norm: 7.2654 loss: 5.1089 loss_occ_0: 2.6477 loss_occ_1: 1.4941 loss_occ_2: 0.9671 2024/04/12 18:07:44 - mmengine - INFO - Epoch(train) [12][100/389] lr: 1.0000e-04 eta: 3:32:26 time: 2.5222 data_time: 0.1334 memory: 34598 grad_norm: 7.2987 loss: 5.2339 loss_occ_0: 2.8022 loss_occ_1: 1.4951 loss_occ_2: 0.9366 2024/04/12 18:09:50 - mmengine - INFO - Epoch(train) [12][150/389] lr: 1.0000e-04 eta: 3:30:15 time: 2.5278 data_time: 0.1443 memory: 33803 grad_norm: 7.2627 loss: 5.2165 loss_occ_0: 2.7065 loss_occ_1: 1.5625 loss_occ_2: 0.9475 2024/04/12 18:11:56 - mmengine - INFO - Epoch(train) [12][200/389] lr: 1.0000e-04 eta: 3:28:04 time: 2.5107 data_time: 0.1354 memory: 34383 grad_norm: 7.0613 loss: 5.3214 loss_occ_0: 2.8189 loss_occ_1: 1.5442 loss_occ_2: 0.9583 2024/04/12 18:14:01 - mmengine - INFO - Epoch(train) [12][250/389] lr: 1.0000e-04 eta: 3:25:51 time: 2.5009 data_time: 0.1314 memory: 34258 grad_norm: 8.4106 loss: 5.7820 loss_occ_0: 3.1014 loss_occ_1: 1.6773 loss_occ_2: 1.0032 2024/04/12 18:16:08 - mmengine - INFO - Epoch(train) [12][300/389] lr: 1.0000e-04 eta: 3:23:41 time: 2.5362 data_time: 0.1343 memory: 34879 grad_norm: 7.5875 loss: 5.4808 loss_occ_0: 2.8648 loss_occ_1: 1.5935 loss_occ_2: 1.0225 2024/04/12 18:18:14 - mmengine - INFO - Epoch(train) [12][350/389] lr: 1.0000e-04 eta: 3:21:30 time: 2.5254 data_time: 0.1483 memory: 34329 grad_norm: 7.8169 loss: 5.3508 loss_occ_0: 2.8177 loss_occ_1: 1.5647 loss_occ_2: 0.9684 2024/04/12 18:19:52 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 18:19:52 - mmengine - INFO - Saving checkpoint at 12 epochs 2024/04/12 18:22:02 - mmengine - INFO - Epoch(val) [12][ 50/103] eta: 0:01:27 time: 1.6537 data_time: 1.0651 memory: 33620 2024/04/12 18:25:17 - mmengine - INFO - Epoch(val) [12][100/103] eta: 0:00:08 time: 3.8952 data_time: 3.4472 memory: 29033 2024/04/12 18:29:30 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.79834 | | floor | 0.79234 | | wall | 0.52887 | | chair | 0.51655 | | cabinet | 0.27265 | | door | 0.27921 | | table | 0.43091 | | couch | 0.35413 | | shelf | 0.38405 | | window | 0.36207 | | bed | 0.46446 | | curtain | 0.55334 | | desk | 0.26666 | | doorframe | 0.17960 | | plant | 0.33150 | | stairs | 0.00000 | | pillow | 0.31889 | | wardrobe | 0.06441 | | picture | 0.23159 | | bathtub | 0.74302 | | box | 0.09607 | | counter | 0.19494 | | bench | 0.16316 | | stand | 0.23155 | | rail | 0.00000 | | sink | 0.55737 | | clothes | 0.19475 | | mirror | 0.21784 | | toilet | 0.67654 | | refrigerator | 0.19887 | | lamp | 0.35305 | | book | 0.12060 | | dresser | 0.02547 | | stool | 0.14607 | | fireplace | 0.00000 | | tv | 0.19996 | | blanket | 0.05584 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.48624 | | window frame | 0.00000 | | radiator | 0.49747 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.21341 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.35236 | | bar | 0.00000 | | pillar | 0.00000 | | bin | 0.32244 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.20634 | | blackboard | 0.21774 | | decoration | 0.00147 | | bag | 0.06206 | | steps | 0.00000 | | windowsill | 0.05055 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.11480 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.00000 | | mailbox | 0.00000 | | washbasin | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | printer | 0.02993 | | microwave | 0.12676 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.18403 | +---------------------+---------+ 2024/04/12 18:29:31 - mmengine - INFO - Epoch(val) [12][103/103] empty: 0.7983 floor: 0.7923 wall: 0.5289 chair: 0.5165 cabinet: 0.2726 door: 0.2792 table: 0.4309 couch: 0.3541 shelf: 0.3841 window: 0.3621 bed: 0.4645 curtain: 0.5533 desk: 0.2667 doorframe: 0.1796 plant: 0.3315 stairs: 0.0000 pillow: 0.3189 wardrobe: 0.0644 picture: 0.2316 bathtub: 0.7430 box: 0.0961 counter: 0.1949 bench: 0.1632 stand: 0.2315 rail: 0.0000 sink: 0.5574 clothes: 0.1948 mirror: 0.2178 toilet: 0.6765 refrigerator: 0.1989 lamp: 0.3530 book: 0.1206 dresser: 0.0255 stool: 0.1461 tv: 0.2000 blanket: 0.0558 monitor: 0.4862 window frame: 0.0000 radiator: 0.4975 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.2134 column: 0.0000 stove: 0.3524 bar: 0.0000 pillar: 0.0000 bin: 0.3224 backpack: 0.2063 blackboard: 0.2177 decoration: 0.0015 bag: 0.0621 windowsill: 0.0506 cushion: 0.0000 copier: 0.1148 board: 0.0000 basket: 0.0000 mailbox: 0.0000 printer: 0.0299 microwave: 0.1268 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 data_time: 2.3170 time: 2.8336 2024/04/12 18:31:40 - mmengine - INFO - Epoch(train) [13][ 50/389] lr: 1.0000e-04 eta: 3:17:40 time: 2.5703 data_time: 0.1941 memory: 33884 grad_norm: 8.2343 loss: 5.7293 loss_occ_0: 3.0934 loss_occ_1: 1.6613 loss_occ_2: 0.9747 2024/04/12 18:33:47 - mmengine - INFO - Epoch(train) [13][100/389] lr: 1.0000e-04 eta: 3:15:30 time: 2.5386 data_time: 0.1421 memory: 34642 grad_norm: 8.3571 loss: 5.9418 loss_occ_0: 2.8741 loss_occ_1: 1.6162 loss_occ_2: 1.4515 2024/04/12 18:35:53 - mmengine - INFO - Epoch(train) [13][150/389] lr: 1.0000e-04 eta: 3:13:20 time: 2.5210 data_time: 0.1516 memory: 35395 grad_norm: 7.1375 loss: 6.1266 loss_occ_0: 3.0165 loss_occ_1: 1.6363 loss_occ_2: 1.4738 2024/04/12 18:38:00 - mmengine - INFO - Epoch(train) [13][200/389] lr: 1.0000e-04 eta: 3:11:10 time: 2.5481 data_time: 0.1469 memory: 33926 grad_norm: 7.5843 loss: 5.3059 loss_occ_0: 2.7865 loss_occ_1: 1.5436 loss_occ_2: 0.9758 2024/04/12 18:40:07 - mmengine - INFO - Epoch(train) [13][250/389] lr: 1.0000e-04 eta: 3:09:01 time: 2.5393 data_time: 0.1382 memory: 34887 grad_norm: 7.4556 loss: 6.0938 loss_occ_0: 3.2376 loss_occ_1: 1.7503 loss_occ_2: 1.1059 2024/04/12 18:42:14 - mmengine - INFO - Epoch(train) [13][300/389] lr: 1.0000e-04 eta: 3:06:51 time: 2.5397 data_time: 0.1385 memory: 34569 grad_norm: 6.8746 loss: 4.8764 loss_occ_0: 2.5280 loss_occ_1: 1.4582 loss_occ_2: 0.8902 2024/04/12 18:43:34 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 18:44:20 - mmengine - INFO - Epoch(train) [13][350/389] lr: 1.0000e-04 eta: 3:04:41 time: 2.5198 data_time: 0.1310 memory: 34187 grad_norm: 7.4780 loss: 6.2603 loss_occ_0: 3.0143 loss_occ_1: 1.6878 loss_occ_2: 1.5582 2024/04/12 18:45:59 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 18:45:59 - mmengine - INFO - Saving checkpoint at 13 epochs 2024/04/12 18:48:08 - mmengine - INFO - Epoch(val) [13][ 50/103] eta: 0:01:26 time: 1.6329 data_time: 1.0648 memory: 34739 2024/04/12 18:51:21 - mmengine - INFO - Epoch(val) [13][100/103] eta: 0:00:08 time: 3.8720 data_time: 3.4264 memory: 29028 2024/04/12 18:55:33 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80520 | | floor | 0.80656 | | wall | 0.56098 | | chair | 0.56094 | | cabinet | 0.23057 | | door | 0.27403 | | table | 0.41601 | | couch | 0.39480 | | shelf | 0.46247 | | window | 0.37385 | | bed | 0.45291 | | curtain | 0.52552 | | desk | 0.29505 | | doorframe | 0.14384 | | plant | 0.23044 | | stairs | 0.00000 | | pillow | 0.35404 | | wardrobe | 0.13454 | | picture | 0.31567 | | bathtub | 0.68326 | | box | 0.13094 | | counter | 0.20562 | | bench | 0.14486 | | stand | 0.19428 | | rail | 0.00000 | | sink | 0.54636 | | clothes | 0.20566 | | mirror | 0.18266 | | toilet | 0.69842 | | refrigerator | 0.29465 | | lamp | 0.39740 | | book | 0.13351 | | dresser | 0.04366 | | stool | 0.19369 | | fireplace | 0.00000 | | tv | 0.36037 | | blanket | 0.08451 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.53652 | | window frame | 0.00000 | | radiator | 0.50859 | | mat | 0.00132 | | shower | 0.00032 | | rack | 0.00000 | | towel | 0.31423 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.28125 | | bar | 0.07956 | | pillar | 0.00000 | | bin | 0.35118 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.25151 | | blackboard | 0.22741 | | decoration | 0.10021 | | bag | 0.04377 | | steps | 0.00000 | | windowsill | 0.06380 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.06928 | | board | 0.00514 | | countertop | 0.00000 | | basket | 0.01364 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | partition | 0.00000 | | printer | 0.02561 | | microwave | 0.11708 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.19257 | +-----------------+---------+ 2024/04/12 18:55:34 - mmengine - INFO - Epoch(val) [13][103/103] empty: 0.8052 floor: 0.8066 wall: 0.5610 chair: 0.5609 cabinet: 0.2306 door: 0.2740 table: 0.4160 couch: 0.3948 shelf: 0.4625 window: 0.3739 bed: 0.4529 curtain: 0.5255 desk: 0.2951 doorframe: 0.1438 plant: 0.2304 stairs: 0.0000 pillow: 0.3540 wardrobe: 0.1345 picture: 0.3157 bathtub: 0.6833 box: 0.1309 counter: 0.2056 bench: 0.1449 stand: 0.1943 rail: 0.0000 sink: 0.5464 clothes: 0.2057 mirror: 0.1827 toilet: 0.6984 refrigerator: 0.2947 lamp: 0.3974 book: 0.1335 dresser: 0.0437 stool: 0.1937 tv: 0.3604 blanket: 0.0845 monitor: 0.5365 window frame: 0.0000 radiator: 0.5086 mat: 0.0013 shower: 0.0003 rack: 0.0000 towel: 0.3142 column: 0.0000 stove: 0.2812 bar: 0.0796 pillar: 0.0000 bin: 0.3512 backpack: 0.2515 blackboard: 0.2274 decoration: 0.1002 bag: 0.0438 windowsill: 0.0638 cushion: 0.0000 copier: 0.0693 board: 0.0051 basket: 0.0136 mailbox: 0.0000 printer: 0.0256 microwave: 0.1171 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 data_time: 2.3065 time: 2.8122 2024/04/12 18:57:42 - mmengine - INFO - Epoch(train) [14][ 50/389] lr: 1.0000e-04 eta: 3:00:51 time: 2.5601 data_time: 0.1699 memory: 34742 grad_norm: 7.4999 loss: 4.7454 loss_occ_0: 2.4846 loss_occ_1: 1.3824 loss_occ_2: 0.8784 2024/04/12 18:59:48 - mmengine - INFO - Epoch(train) [14][100/389] lr: 1.0000e-04 eta: 2:58:40 time: 2.5190 data_time: 0.1394 memory: 34284 grad_norm: 7.5136 loss: 5.4605 loss_occ_0: 2.8852 loss_occ_1: 1.5883 loss_occ_2: 0.9869 2024/04/12 19:01:56 - mmengine - INFO - Epoch(train) [14][150/389] lr: 1.0000e-04 eta: 2:56:32 time: 2.5507 data_time: 0.1591 memory: 34375 grad_norm: 6.9761 loss: 5.2199 loss_occ_0: 2.7723 loss_occ_1: 1.5118 loss_occ_2: 0.9357 2024/04/12 19:04:02 - mmengine - INFO - Epoch(train) [14][200/389] lr: 1.0000e-04 eta: 2:54:22 time: 2.5302 data_time: 0.1414 memory: 34922 grad_norm: 7.3226 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 19:06:07 - mmengine - INFO - Epoch(train) [14][250/389] lr: 1.0000e-04 eta: 2:52:12 time: 2.5095 data_time: 0.1439 memory: 34189 grad_norm: 7.6248 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 19:08:15 - mmengine - INFO - Epoch(train) [14][300/389] lr: 1.0000e-04 eta: 2:50:03 time: 2.5453 data_time: 0.1382 memory: 35078 grad_norm: 7.2042 loss: 4.8994 loss_occ_0: 2.5200 loss_occ_1: 1.4602 loss_occ_2: 0.9192 2024/04/12 19:10:21 - mmengine - INFO - Epoch(train) [14][350/389] lr: 1.0000e-04 eta: 2:47:53 time: 2.5276 data_time: 0.1455 memory: 33513 grad_norm: 7.2633 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 19:12:00 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 19:12:00 - mmengine - INFO - Saving checkpoint at 14 epochs 2024/04/12 19:14:07 - mmengine - INFO - Epoch(val) [14][ 50/103] eta: 0:01:26 time: 1.6326 data_time: 1.0659 memory: 34377 2024/04/12 19:17:22 - mmengine - INFO - Epoch(val) [14][100/103] eta: 0:00:08 time: 3.9027 data_time: 3.4532 memory: 29028 2024/04/12 19:21:37 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.77445 | | floor | 0.79863 | | wall | 0.56097 | | chair | 0.52442 | | cabinet | 0.20378 | | door | 0.29769 | | table | 0.43119 | | couch | 0.42434 | | shelf | 0.46146 | | window | 0.39952 | | bed | 0.53066 | | curtain | 0.54895 | | desk | 0.30142 | | doorframe | 0.16317 | | plant | 0.35222 | | stairs | 0.00000 | | pillow | 0.35516 | | wardrobe | 0.22887 | | picture | 0.36204 | | bathtub | 0.69078 | | box | 0.10647 | | counter | 0.14879 | | bench | 0.13688 | | stand | 0.25704 | | rail | 0.00808 | | sink | 0.55002 | | clothes | 0.21748 | | mirror | 0.22870 | | toilet | 0.67160 | | refrigerator | 0.26454 | | lamp | 0.43027 | | book | 0.25713 | | dresser | 0.01755 | | stool | 0.18243 | | fireplace | 0.00000 | | tv | 0.29417 | | blanket | 0.04374 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.53329 | | window frame | 0.00000 | | radiator | 0.54610 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.01587 | | towel | 0.33880 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.22351 | | bar | 0.05604 | | pillar | 0.00000 | | bin | 0.39493 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.31840 | | blackboard | 0.25847 | | decoration | 0.06943 | | bag | 0.03046 | | steps | 0.00000 | | windowsill | 0.12811 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.15349 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.01872 | | mailbox | 0.00532 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | printer | 0.03873 | | microwave | 0.19975 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.20200 | +---------------------+---------+ 2024/04/12 19:21:38 - mmengine - INFO - Epoch(val) [14][103/103] empty: 0.7745 floor: 0.7986 wall: 0.5610 chair: 0.5244 cabinet: 0.2038 door: 0.2977 table: 0.4312 couch: 0.4243 shelf: 0.4615 window: 0.3995 bed: 0.5307 curtain: 0.5490 desk: 0.3014 doorframe: 0.1632 plant: 0.3522 stairs: 0.0000 pillow: 0.3552 wardrobe: 0.2289 picture: 0.3620 bathtub: 0.6908 box: 0.1065 counter: 0.1488 bench: 0.1369 stand: 0.2570 rail: 0.0081 sink: 0.5500 clothes: 0.2175 mirror: 0.2287 toilet: 0.6716 refrigerator: 0.2645 lamp: 0.4303 book: 0.2571 dresser: 0.0176 stool: 0.1824 tv: 0.2942 blanket: 0.0437 monitor: 0.5333 window frame: 0.0000 radiator: 0.5461 mat: 0.0000 shower: 0.0000 rack: 0.0159 towel: 0.3388 column: 0.0000 stove: 0.2235 bar: 0.0560 pillar: 0.0000 bin: 0.3949 backpack: 0.3184 blackboard: 0.2585 decoration: 0.0694 bag: 0.0305 windowsill: 0.1281 cushion: 0.0000 copier: 0.1535 board: 0.0000 basket: 0.0187 mailbox: 0.0053 printer: 0.0387 microwave: 0.1998 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 data_time: 2.3244 time: 2.8313 2024/04/12 19:23:45 - mmengine - INFO - Epoch(train) [15][ 50/389] lr: 1.0000e-04 eta: 2:44:04 time: 2.5478 data_time: 0.1613 memory: 34506 grad_norm: 6.5845 loss: 4.4674 loss_occ_0: 2.2953 loss_occ_1: 1.3552 loss_occ_2: 0.8169 2024/04/12 19:25:52 - mmengine - INFO - Epoch(train) [15][100/389] lr: 1.0000e-04 eta: 2:41:55 time: 2.5362 data_time: 0.1341 memory: 34759 grad_norm: 6.7674 loss: 4.8529 loss_occ_0: 2.5537 loss_occ_1: 1.4101 loss_occ_2: 0.8891 2024/04/12 19:27:59 - mmengine - INFO - Epoch(train) [15][150/389] lr: 1.0000e-04 eta: 2:39:46 time: 2.5410 data_time: 0.1485 memory: 34744 grad_norm: 7.0057 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 19:30:05 - mmengine - INFO - Epoch(train) [15][200/389] lr: 1.0000e-04 eta: 2:37:36 time: 2.5196 data_time: 0.1502 memory: 34239 grad_norm: 6.9919 loss: 5.6408 loss_occ_0: 3.0001 loss_occ_1: 1.6281 loss_occ_2: 1.0126 2024/04/12 19:32:12 - mmengine - INFO - Epoch(train) [15][250/389] lr: 1.0000e-04 eta: 2:35:27 time: 2.5330 data_time: 0.1328 memory: 33843 grad_norm: 8.1182 loss: 4.9720 loss_occ_0: 2.5891 loss_occ_1: 1.4661 loss_occ_2: 0.9167 2024/04/12 19:34:16 - mmengine - INFO - Epoch(train) [15][300/389] lr: 1.0000e-04 eta: 2:33:17 time: 2.4985 data_time: 0.1330 memory: 34888 grad_norm: 7.4661 loss: 4.7336 loss_occ_0: 2.4723 loss_occ_1: 1.3855 loss_occ_2: 0.8757 2024/04/12 19:36:23 - mmengine - INFO - Epoch(train) [15][350/389] lr: 1.0000e-04 eta: 2:31:08 time: 2.5265 data_time: 0.1397 memory: 34397 grad_norm: 6.9459 loss: 4.9679 loss_occ_0: 2.5723 loss_occ_1: 1.4965 loss_occ_2: 0.8990 2024/04/12 19:38:02 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 19:38:02 - mmengine - INFO - Saving checkpoint at 15 epochs 2024/04/12 19:40:10 - mmengine - INFO - Epoch(val) [15][ 50/103] eta: 0:01:26 time: 1.6393 data_time: 1.0677 memory: 34287 2024/04/12 19:43:24 - mmengine - INFO - Epoch(val) [15][100/103] eta: 0:00:08 time: 3.8855 data_time: 3.4385 memory: 29028 2024/04/12 19:47:37 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.81129 | | floor | 0.81900 | | wall | 0.58230 | | chair | 0.51799 | | cabinet | 0.31755 | | door | 0.29952 | | table | 0.41048 | | couch | 0.35247 | | shelf | 0.49005 | | window | 0.37919 | | bed | 0.49649 | | curtain | 0.58951 | | desk | 0.27616 | | doorframe | 0.18172 | | plant | 0.45223 | | stairs | 0.00000 | | pillow | 0.33936 | | wardrobe | 0.01227 | | picture | 0.34663 | | bathtub | 0.74816 | | box | 0.11177 | | counter | 0.18541 | | bench | 0.30239 | | stand | 0.22953 | | rail | 0.00000 | | sink | 0.56310 | | clothes | 0.20980 | | mirror | 0.19347 | | toilet | 0.68121 | | refrigerator | 0.41751 | | lamp | 0.40613 | | book | 0.04178 | | dresser | 0.00909 | | stool | 0.20953 | | fireplace | 0.00000 | | tv | 0.32758 | | blanket | 0.02610 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.56164 | | window frame | 0.00000 | | radiator | 0.53199 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.25673 | | ottoman | 0.00000 | | column | 0.00000 | | stove | 0.27864 | | bar | 0.01890 | | pillar | 0.00000 | | bin | 0.33173 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.22100 | | blackboard | 0.30500 | | decoration | 0.01694 | | bag | 0.01132 | | windowsill | 0.07513 | | cushion | 0.00000 | | carpet | 0.00000 | | copier | 0.06800 | | board | 0.03639 | | countertop | 0.00000 | | basket | 0.00726 | | mailbox | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.08237 | | microwave | 0.14429 | +-----------------+---------+ | mean | 0.20937 | +-----------------+---------+ 2024/04/12 19:47:38 - mmengine - INFO - Epoch(val) [15][103/103] empty: 0.8113 floor: 0.8190 wall: 0.5823 chair: 0.5180 cabinet: 0.3176 door: 0.2995 table: 0.4105 couch: 0.3525 shelf: 0.4900 window: 0.3792 bed: 0.4965 curtain: 0.5895 desk: 0.2762 doorframe: 0.1817 plant: 0.4522 stairs: 0.0000 pillow: 0.3394 wardrobe: 0.0123 picture: 0.3466 bathtub: 0.7482 box: 0.1118 counter: 0.1854 bench: 0.3024 stand: 0.2295 rail: 0.0000 sink: 0.5631 clothes: 0.2098 mirror: 0.1935 toilet: 0.6812 refrigerator: 0.4175 lamp: 0.4061 book: 0.0418 dresser: 0.0091 stool: 0.2095 tv: 0.3276 blanket: 0.0261 monitor: 0.5616 window frame: 0.0000 radiator: 0.5320 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.2567 column: 0.0000 stove: 0.2786 bar: 0.0189 pillar: 0.0000 bin: 0.3317 backpack: 0.2210 blackboard: 0.3050 decoration: 0.0169 bag: 0.0113 windowsill: 0.0751 cushion: 0.0000 copier: 0.0680 board: 0.0364 basket: 0.0073 mailbox: 0.0000 printer: 0.0824 microwave: 0.1443 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 data_time: 2.3161 time: 2.8241 2024/04/12 19:49:46 - mmengine - INFO - Epoch(train) [16][ 50/389] lr: 1.0000e-04 eta: 2:27:20 time: 2.5666 data_time: 0.1782 memory: 34354 grad_norm: 7.4552 loss: 4.6336 loss_occ_0: 2.4517 loss_occ_1: 1.3302 loss_occ_2: 0.8518 2024/04/12 19:51:53 - mmengine - INFO - Epoch(train) [16][100/389] lr: 1.0000e-04 eta: 2:25:10 time: 2.5285 data_time: 0.1396 memory: 32906 grad_norm: 7.5588 loss: 5.0964 loss_occ_0: 2.7231 loss_occ_1: 1.4753 loss_occ_2: 0.8980 2024/04/12 19:54:00 - mmengine - INFO - Epoch(train) [16][150/389] lr: 1.0000e-04 eta: 2:23:02 time: 2.5400 data_time: 0.1393 memory: 33657 grad_norm: 7.0732 loss: 4.9347 loss_occ_0: 2.6056 loss_occ_1: 1.4162 loss_occ_2: 0.9129 2024/04/12 19:54:37 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 19:56:05 - mmengine - INFO - Epoch(train) [16][200/389] lr: 1.0000e-04 eta: 2:20:52 time: 2.5097 data_time: 0.1379 memory: 34391 grad_norm: 6.8454 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 19:58:13 - mmengine - INFO - Epoch(train) [16][250/389] lr: 1.0000e-04 eta: 2:18:44 time: 2.5618 data_time: 0.1348 memory: 34893 grad_norm: 6.7700 loss: 4.8806 loss_occ_0: 2.5939 loss_occ_1: 1.3637 loss_occ_2: 0.9231 2024/04/12 20:00:19 - mmengine - INFO - Epoch(train) [16][300/389] lr: 1.0000e-04 eta: 2:16:35 time: 2.5098 data_time: 0.1547 memory: 33469 grad_norm: 7.0695 loss: 4.9052 loss_occ_0: 2.6179 loss_occ_1: 1.4106 loss_occ_2: 0.8767 2024/04/12 20:02:25 - mmengine - INFO - Epoch(train) [16][350/389] lr: 1.0000e-04 eta: 2:14:26 time: 2.5215 data_time: 0.1553 memory: 34185 grad_norm: 7.6889 loss: 5.1828 loss_occ_0: 2.4320 loss_occ_1: 1.3871 loss_occ_2: 1.3637 2024/04/12 20:04:04 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 20:04:04 - mmengine - INFO - Saving checkpoint at 16 epochs 2024/04/12 20:06:08 - mmengine - INFO - Epoch(val) [16][ 50/103] eta: 0:01:26 time: 1.6406 data_time: 1.0707 memory: 34508 2024/04/12 20:09:23 - mmengine - INFO - Epoch(val) [16][100/103] eta: 0:00:08 time: 3.8973 data_time: 3.4481 memory: 29030 2024/04/12 20:13:37 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.78059 | | floor | 0.78928 | | wall | 0.58215 | | chair | 0.54999 | | cabinet | 0.28270 | | door | 0.35643 | | table | 0.41071 | | couch | 0.44800 | | shelf | 0.47402 | | window | 0.34134 | | bed | 0.54377 | | curtain | 0.53959 | | desk | 0.35790 | | doorframe | 0.19302 | | plant | 0.34482 | | stairs | 0.00000 | | pillow | 0.37531 | | wardrobe | 0.07325 | | picture | 0.33008 | | bathtub | 0.76376 | | box | 0.15774 | | counter | 0.22894 | | bench | 0.20810 | | stand | 0.25096 | | rail | 0.00306 | | sink | 0.55702 | | clothes | 0.16804 | | mirror | 0.20165 | | toilet | 0.74745 | | refrigerator | 0.25373 | | lamp | 0.43399 | | book | 0.16619 | | dresser | 0.04084 | | stool | 0.17421 | | fireplace | 0.00000 | | tv | 0.39065 | | blanket | 0.04291 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.53309 | | window frame | 0.00000 | | radiator | 0.54829 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.02028 | | towel | 0.32851 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.27906 | | bar | 0.04509 | | pillar | 0.00000 | | bin | 0.36219 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.25565 | | blackboard | 0.35578 | | decoration | 0.06754 | | bag | 0.02261 | | windowsill | 0.12403 | | cushion | 0.02221 | | carpet | 0.00000 | | copier | 0.15243 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.03539 | | mailbox | 0.00000 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.09878 | | microwave | 0.17597 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.21038 | +-----------------+---------+ 2024/04/12 20:13:37 - mmengine - INFO - Epoch(val) [16][103/103] empty: 0.7806 floor: 0.7893 wall: 0.5822 chair: 0.5500 cabinet: 0.2827 door: 0.3564 table: 0.4107 couch: 0.4480 shelf: 0.4740 window: 0.3413 bed: 0.5438 curtain: 0.5396 desk: 0.3579 doorframe: 0.1930 plant: 0.3448 stairs: 0.0000 pillow: 0.3753 wardrobe: 0.0733 picture: 0.3301 bathtub: 0.7638 box: 0.1577 counter: 0.2289 bench: 0.2081 stand: 0.2510 rail: 0.0031 sink: 0.5570 clothes: 0.1680 mirror: 0.2016 toilet: 0.7475 refrigerator: 0.2537 lamp: 0.4340 book: 0.1662 dresser: 0.0408 stool: 0.1742 tv: 0.3907 blanket: 0.0429 monitor: 0.5331 window frame: 0.0000 radiator: 0.5483 mat: 0.0000 shower: 0.0000 rack: 0.0203 towel: 0.3285 column: 0.0000 stove: 0.2791 bar: 0.0451 pillar: 0.0000 bin: 0.3622 backpack: 0.2556 blackboard: 0.3558 decoration: 0.0675 bag: 0.0226 windowsill: 0.1240 cushion: 0.0222 copier: 0.1524 board: 0.0000 basket: 0.0354 mailbox: 0.0000 printer: 0.0988 microwave: 0.1760 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3198 time: 2.8282 2024/04/12 20:15:45 - mmengine - INFO - Epoch(train) [17][ 50/389] lr: 1.0000e-05 eta: 2:10:37 time: 2.5494 data_time: 0.1955 memory: 34361 grad_norm: 6.4243 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 20:17:52 - mmengine - INFO - Epoch(train) [17][100/389] lr: 1.0000e-05 eta: 2:08:29 time: 2.5355 data_time: 0.1664 memory: 34267 grad_norm: 6.7026 loss: 4.5975 loss_occ_0: 2.4127 loss_occ_1: 1.3601 loss_occ_2: 0.8247 2024/04/12 20:19:59 - mmengine - INFO - Epoch(train) [17][150/389] lr: 1.0000e-05 eta: 2:06:20 time: 2.5419 data_time: 0.1453 memory: 34542 grad_norm: 6.0625 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 20:22:05 - mmengine - INFO - Epoch(train) [17][200/389] lr: 1.0000e-05 eta: 2:04:11 time: 2.5201 data_time: 0.1354 memory: 34140 grad_norm: 5.9491 loss: 4.4569 loss_occ_0: 2.0474 loss_occ_1: 1.1763 loss_occ_2: 1.2331 2024/04/12 20:24:11 - mmengine - INFO - Epoch(train) [17][250/389] lr: 1.0000e-05 eta: 2:02:03 time: 2.5235 data_time: 0.1466 memory: 34311 grad_norm: 6.3476 loss: 5.0716 loss_occ_0: 2.4190 loss_occ_1: 1.3351 loss_occ_2: 1.3174 2024/04/12 20:26:17 - mmengine - INFO - Epoch(train) [17][300/389] lr: 1.0000e-05 eta: 1:59:54 time: 2.5229 data_time: 0.1447 memory: 35129 grad_norm: 6.6495 loss: 4.3662 loss_occ_0: 2.3513 loss_occ_1: 1.2172 loss_occ_2: 0.7977 2024/04/12 20:28:24 - mmengine - INFO - Epoch(train) [17][350/389] lr: 1.0000e-05 eta: 1:57:46 time: 2.5437 data_time: 0.1339 memory: 34560 grad_norm: 6.4656 loss: 4.2169 loss_occ_0: 2.1701 loss_occ_1: 1.2403 loss_occ_2: 0.8065 2024/04/12 20:30:03 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 20:30:03 - mmengine - INFO - Saving checkpoint at 17 epochs 2024/04/12 20:32:08 - mmengine - INFO - Epoch(val) [17][ 50/103] eta: 0:01:28 time: 1.6667 data_time: 1.0724 memory: 34807 2024/04/12 20:35:23 - mmengine - INFO - Epoch(val) [17][100/103] eta: 0:00:08 time: 3.8999 data_time: 3.4469 memory: 29028 2024/04/12 20:39:38 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.81051 | | floor | 0.81568 | | wall | 0.59751 | | chair | 0.55466 | | cabinet | 0.32699 | | door | 0.35223 | | table | 0.44040 | | couch | 0.42933 | | shelf | 0.50705 | | window | 0.40768 | | bed | 0.53331 | | curtain | 0.59678 | | desk | 0.36392 | | doorframe | 0.19400 | | plant | 0.41454 | | stairs | 0.00000 | | pillow | 0.38732 | | wardrobe | 0.10725 | | picture | 0.34991 | | bathtub | 0.76503 | | box | 0.15076 | | counter | 0.24796 | | bench | 0.22382 | | stand | 0.26664 | | rail | 0.00000 | | sink | 0.58592 | | clothes | 0.17880 | | mirror | 0.19606 | | toilet | 0.73375 | | refrigerator | 0.36837 | | lamp | 0.42968 | | book | 0.27354 | | dresser | 0.05000 | | stool | 0.19629 | | fireplace | 0.00000 | | tv | 0.33901 | | blanket | 0.03977 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.56030 | | window frame | 0.00000 | | radiator | 0.57657 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00136 | | towel | 0.33732 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.35051 | | bar | 0.09323 | | pillar | 0.00000 | | bin | 0.41105 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.24877 | | blackboard | 0.32274 | | decoration | 0.04233 | | bag | 0.06009 | | windowsill | 0.09337 | | cushion | 0.02981 | | carpet | 0.00000 | | copier | 0.22276 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.02086 | | mailbox | 0.00320 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.21658 | | microwave | 0.18555 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.22383 | +-----------------+---------+ 2024/04/12 20:39:39 - mmengine - INFO - Epoch(val) [17][103/103] empty: 0.8105 floor: 0.8157 wall: 0.5975 chair: 0.5547 cabinet: 0.3270 door: 0.3522 table: 0.4404 couch: 0.4293 shelf: 0.5070 window: 0.4077 bed: 0.5333 curtain: 0.5968 desk: 0.3639 doorframe: 0.1940 plant: 0.4145 stairs: 0.0000 pillow: 0.3873 wardrobe: 0.1072 picture: 0.3499 bathtub: 0.7650 box: 0.1508 counter: 0.2480 bench: 0.2238 stand: 0.2666 rail: 0.0000 sink: 0.5859 clothes: 0.1788 mirror: 0.1961 toilet: 0.7338 refrigerator: 0.3684 lamp: 0.4297 book: 0.2735 dresser: 0.0500 stool: 0.1963 tv: 0.3390 blanket: 0.0398 monitor: 0.5603 window frame: 0.0000 radiator: 0.5766 mat: 0.0000 shower: 0.0000 rack: 0.0014 towel: 0.3373 column: 0.0000 stove: 0.3505 bar: 0.0932 pillar: 0.0000 bin: 0.4111 backpack: 0.2488 blackboard: 0.3227 decoration: 0.0423 bag: 0.0601 windowsill: 0.0934 cushion: 0.0298 copier: 0.2228 board: 0.0000 basket: 0.0209 mailbox: 0.0032 printer: 0.2166 microwave: 0.1855 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3215 time: 2.8436 2024/04/12 20:41:48 - mmengine - INFO - Epoch(train) [18][ 50/389] lr: 1.0000e-05 eta: 1:53:58 time: 2.5777 data_time: 0.1584 memory: 34601 grad_norm: 6.0955 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 20:43:54 - mmengine - INFO - Epoch(train) [18][100/389] lr: 1.0000e-05 eta: 1:51:49 time: 2.5239 data_time: 0.1426 memory: 35117 grad_norm: 6.3105 loss: 4.5706 loss_occ_0: 2.4152 loss_occ_1: 1.3321 loss_occ_2: 0.8233 2024/04/12 20:46:00 - mmengine - INFO - Epoch(train) [18][150/389] lr: 1.0000e-05 eta: 1:49:41 time: 2.5284 data_time: 0.1455 memory: 34719 grad_norm: 7.0168 loss: 17.7681 loss_occ_0: 9.9959 loss_occ_1: 5.0749 loss_occ_2: 2.6972 2024/04/12 20:48:07 - mmengine - INFO - Epoch(train) [18][200/389] lr: 1.0000e-05 eta: 1:47:33 time: 2.5438 data_time: 0.1329 memory: 34023 grad_norm: 7.0946 loss: 4.4452 loss_occ_0: 2.3379 loss_occ_1: 1.2825 loss_occ_2: 0.8248 2024/04/12 20:50:13 - mmengine - INFO - Epoch(train) [18][250/389] lr: 1.0000e-05 eta: 1:45:24 time: 2.5157 data_time: 0.1392 memory: 34575 grad_norm: 6.1449 loss: 4.0389 loss_occ_0: 2.0670 loss_occ_1: 1.1785 loss_occ_2: 0.7935 2024/04/12 20:52:19 - mmengine - INFO - Epoch(train) [18][300/389] lr: 1.0000e-05 eta: 1:43:15 time: 2.5220 data_time: 0.1375 memory: 34659 grad_norm: 6.2323 loss: 4.0097 loss_occ_0: 2.1329 loss_occ_1: 1.1924 loss_occ_2: 0.6843 2024/04/12 20:54:26 - mmengine - INFO - Epoch(train) [18][350/389] lr: 1.0000e-05 eta: 1:41:07 time: 2.5304 data_time: 0.1369 memory: 34635 grad_norm: 6.2189 loss: 4.3755 loss_occ_0: 2.2861 loss_occ_1: 1.2826 loss_occ_2: 0.8068 2024/04/12 20:56:00 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 20:56:05 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 20:56:05 - mmengine - INFO - Saving checkpoint at 18 epochs 2024/04/12 20:58:10 - mmengine - INFO - Epoch(val) [18][ 50/103] eta: 0:01:26 time: 1.6333 data_time: 1.0684 memory: 34739 2024/04/12 21:01:25 - mmengine - INFO - Epoch(val) [18][100/103] eta: 0:00:08 time: 3.9011 data_time: 3.4561 memory: 29036 2024/04/12 21:05:39 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.80934 | | floor | 0.81753 | | wall | 0.59609 | | chair | 0.55074 | | cabinet | 0.32264 | | door | 0.35627 | | table | 0.45891 | | couch | 0.42812 | | shelf | 0.49364 | | window | 0.41169 | | bed | 0.54932 | | curtain | 0.59726 | | desk | 0.40494 | | doorframe | 0.20521 | | plant | 0.41650 | | stairs | 0.00000 | | pillow | 0.39182 | | wardrobe | 0.11891 | | picture | 0.34875 | | bathtub | 0.77877 | | box | 0.15959 | | counter | 0.23255 | | bench | 0.19033 | | stand | 0.27330 | | rail | 0.00000 | | sink | 0.58989 | | clothes | 0.19927 | | mirror | 0.22377 | | toilet | 0.73838 | | refrigerator | 0.41096 | | lamp | 0.42008 | | book | 0.25134 | | dresser | 0.03193 | | stool | 0.21548 | | fireplace | 0.00000 | | tv | 0.34924 | | blanket | 0.03515 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.56238 | | window frame | 0.00000 | | radiator | 0.57032 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.02465 | | towel | 0.37332 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.33311 | | bar | 0.05104 | | pillar | 0.00000 | | bin | 0.42682 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.26328 | | blackboard | 0.32780 | | decoration | 0.06279 | | bag | 0.06135 | | windowsill | 0.08013 | | cushion | 0.03226 | | carpet | 0.00000 | | copier | 0.26214 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.03152 | | mailbox | 0.00000 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.29073 | | microwave | 0.19620 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.22799 | +-----------------+---------+ 2024/04/12 21:05:40 - mmengine - INFO - Epoch(val) [18][103/103] empty: 0.8093 floor: 0.8175 wall: 0.5961 chair: 0.5507 cabinet: 0.3226 door: 0.3563 table: 0.4589 couch: 0.4281 shelf: 0.4936 window: 0.4117 bed: 0.5493 curtain: 0.5973 desk: 0.4049 doorframe: 0.2052 plant: 0.4165 stairs: 0.0000 pillow: 0.3918 wardrobe: 0.1189 picture: 0.3487 bathtub: 0.7788 box: 0.1596 counter: 0.2326 bench: 0.1903 stand: 0.2733 rail: 0.0000 sink: 0.5899 clothes: 0.1993 mirror: 0.2238 toilet: 0.7384 refrigerator: 0.4110 lamp: 0.4201 book: 0.2513 dresser: 0.0319 stool: 0.2155 tv: 0.3492 blanket: 0.0351 monitor: 0.5624 window frame: 0.0000 radiator: 0.5703 mat: 0.0000 shower: 0.0000 rack: 0.0246 towel: 0.3733 column: 0.0000 stove: 0.3331 bar: 0.0510 pillar: 0.0000 bin: 0.4268 backpack: 0.2633 blackboard: 0.3278 decoration: 0.0628 bag: 0.0613 windowsill: 0.0801 cushion: 0.0323 copier: 0.2621 board: 0.0000 basket: 0.0315 mailbox: 0.0000 printer: 0.2907 microwave: 0.1962 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3273 time: 2.8313 2024/04/12 21:07:49 - mmengine - INFO - Epoch(train) [19][ 50/389] lr: 1.0000e-05 eta: 1:37:20 time: 2.5812 data_time: 0.1668 memory: 34521 grad_norm: 6.4200 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 21:09:55 - mmengine - INFO - Epoch(train) [19][100/389] lr: 1.0000e-05 eta: 1:35:11 time: 2.5281 data_time: 0.1351 memory: 36508 grad_norm: 6.4289 loss: 3.9685 loss_occ_0: 2.0808 loss_occ_1: 1.1419 loss_occ_2: 0.7458 2024/04/12 21:12:02 - mmengine - INFO - Epoch(train) [19][150/389] lr: 1.0000e-05 eta: 1:33:03 time: 2.5299 data_time: 0.1404 memory: 33730 grad_norm: 6.4885 loss: 4.2543 loss_occ_0: 2.2769 loss_occ_1: 1.2195 loss_occ_2: 0.7579 2024/04/12 21:14:08 - mmengine - INFO - Epoch(train) [19][200/389] lr: 1.0000e-05 eta: 1:30:55 time: 2.5178 data_time: 0.1495 memory: 34099 grad_norm: 6.5108 loss: 16.7841 loss_occ_0: 9.9715 loss_occ_1: 5.0865 loss_occ_2: 1.7261 2024/04/12 21:16:13 - mmengine - INFO - Epoch(train) [19][250/389] lr: 1.0000e-05 eta: 1:28:46 time: 2.5067 data_time: 0.1442 memory: 34067 grad_norm: 6.0996 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 21:18:19 - mmengine - INFO - Epoch(train) [19][300/389] lr: 1.0000e-05 eta: 1:26:38 time: 2.5245 data_time: 0.1296 memory: 34124 grad_norm: 6.2662 loss: 3.9022 loss_occ_0: 2.0454 loss_occ_1: 1.1263 loss_occ_2: 0.7305 2024/04/12 21:20:26 - mmengine - INFO - Epoch(train) [19][350/389] lr: 1.0000e-05 eta: 1:24:30 time: 2.5291 data_time: 0.1505 memory: 34942 grad_norm: 6.3235 loss: 4.2072 loss_occ_0: 2.2208 loss_occ_1: 1.2281 loss_occ_2: 0.7583 2024/04/12 21:22:05 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 21:22:05 - mmengine - INFO - Saving checkpoint at 19 epochs 2024/04/12 21:24:15 - mmengine - INFO - Epoch(val) [19][ 50/103] eta: 0:01:26 time: 1.6342 data_time: 1.0635 memory: 34227 2024/04/12 21:27:29 - mmengine - INFO - Epoch(val) [19][100/103] eta: 0:00:08 time: 3.8858 data_time: 3.4366 memory: 29032 2024/04/12 21:31:43 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.81249 | | floor | 0.82378 | | wall | 0.60373 | | chair | 0.51716 | | cabinet | 0.32762 | | door | 0.35874 | | table | 0.44681 | | couch | 0.41053 | | shelf | 0.51205 | | window | 0.40617 | | bed | 0.54333 | | curtain | 0.61270 | | desk | 0.39186 | | doorframe | 0.21085 | | plant | 0.43024 | | stairs | 0.00000 | | pillow | 0.39108 | | wardrobe | 0.12175 | | picture | 0.36575 | | bathtub | 0.78176 | | box | 0.16541 | | counter | 0.23755 | | bench | 0.19009 | | stand | 0.29700 | | rail | 0.00000 | | sink | 0.57909 | | clothes | 0.21903 | | mirror | 0.21924 | | toilet | 0.77518 | | refrigerator | 0.37012 | | lamp | 0.41825 | | book | 0.24451 | | dresser | 0.04961 | | stool | 0.22329 | | fireplace | 0.00000 | | tv | 0.36786 | | blanket | 0.01906 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.55427 | | window frame | 0.00000 | | radiator | 0.58156 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.03067 | | towel | 0.36762 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.35628 | | bar | 0.07769 | | pillar | 0.00000 | | bin | 0.43655 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.27322 | | blackboard | 0.34386 | | decoration | 0.05441 | | bag | 0.06541 | | windowsill | 0.10327 | | cushion | 0.04040 | | carpet | 0.00000 | | copier | 0.31164 | | board | 0.00099 | | countertop | 0.00000 | | basket | 0.03646 | | mailbox | 0.00131 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | printer | 0.29386 | | microwave | 0.13872 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.23349 | +-----------------+---------+ 2024/04/12 21:31:44 - mmengine - INFO - Epoch(val) [19][103/103] empty: 0.8125 floor: 0.8238 wall: 0.6037 chair: 0.5172 cabinet: 0.3276 door: 0.3587 table: 0.4468 couch: 0.4105 shelf: 0.5121 window: 0.4062 bed: 0.5433 curtain: 0.6127 desk: 0.3919 doorframe: 0.2109 plant: 0.4302 stairs: 0.0000 pillow: 0.3911 wardrobe: 0.1217 picture: 0.3657 bathtub: 0.7818 box: 0.1654 counter: 0.2376 bench: 0.1901 stand: 0.2970 rail: 0.0000 sink: 0.5791 clothes: 0.2190 mirror: 0.2192 toilet: 0.7752 refrigerator: 0.3701 lamp: 0.4183 book: 0.2445 dresser: 0.0496 stool: 0.2233 tv: 0.3679 blanket: 0.0191 monitor: 0.5543 window frame: 0.0000 radiator: 0.5816 mat: 0.0000 shower: 0.0000 rack: 0.0307 towel: 0.3676 column: 0.0000 stove: 0.3563 bar: 0.0777 pillar: 0.0000 bin: 0.4365 backpack: 0.2732 blackboard: 0.3439 decoration: 0.0544 bag: 0.0654 windowsill: 0.1033 cushion: 0.0404 copier: 0.3116 board: 0.0010 basket: 0.0365 mailbox: 0.0013 printer: 0.2939 microwave: 0.1387 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3148 time: 2.8237 2024/04/12 21:33:51 - mmengine - INFO - Epoch(train) [20][ 50/389] lr: 1.0000e-05 eta: 1:20:42 time: 2.5443 data_time: 0.1449 memory: 33946 grad_norm: 6.9225 loss: 3.9264 loss_occ_0: 2.1059 loss_occ_1: 1.1181 loss_occ_2: 0.7024 2024/04/12 21:35:56 - mmengine - INFO - Epoch(train) [20][100/389] lr: 1.0000e-05 eta: 1:18:34 time: 2.4994 data_time: 0.1430 memory: 33692 grad_norm: 6.7123 loss: 4.5469 loss_occ_0: 2.3854 loss_occ_1: 1.3362 loss_occ_2: 0.8254 2024/04/12 21:38:02 - mmengine - INFO - Epoch(train) [20][150/389] lr: 1.0000e-05 eta: 1:16:26 time: 2.5287 data_time: 0.1452 memory: 35086 grad_norm: 6.5679 loss: 4.3344 loss_occ_0: 2.0877 loss_occ_1: 1.0971 loss_occ_2: 1.1496 2024/04/12 21:40:08 - mmengine - INFO - Epoch(train) [20][200/389] lr: 1.0000e-05 eta: 1:14:17 time: 2.5075 data_time: 0.1465 memory: 35507 grad_norm: 6.4185 loss: 4.5097 loss_occ_0: 2.3798 loss_occ_1: 1.3137 loss_occ_2: 0.8163 2024/04/12 21:42:16 - mmengine - INFO - Epoch(train) [20][250/389] lr: 1.0000e-05 eta: 1:12:10 time: 2.5537 data_time: 0.1501 memory: 34489 grad_norm: 6.5921 loss: 4.0085 loss_occ_0: 2.0678 loss_occ_1: 1.1833 loss_occ_2: 0.7574 2024/04/12 21:44:22 - mmengine - INFO - Epoch(train) [20][300/389] lr: 1.0000e-05 eta: 1:10:02 time: 2.5397 data_time: 0.1494 memory: 34161 grad_norm: 6.2782 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 21:46:29 - mmengine - INFO - Epoch(train) [20][350/389] lr: 1.0000e-05 eta: 1:07:54 time: 2.5298 data_time: 0.1322 memory: 34260 grad_norm: 6.8318 loss: 4.0023 loss_occ_0: 2.0913 loss_occ_1: 1.1695 loss_occ_2: 0.7415 2024/04/12 21:48:08 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 21:48:08 - mmengine - INFO - Saving checkpoint at 20 epochs 2024/04/12 21:50:18 - mmengine - INFO - Epoch(val) [20][ 50/103] eta: 0:01:26 time: 1.6321 data_time: 1.0639 memory: 33816 2024/04/12 21:53:33 - mmengine - INFO - Epoch(val) [20][100/103] eta: 0:00:08 time: 3.9141 data_time: 3.4507 memory: 29029 2024/04/12 21:57:49 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.80255 | | floor | 0.80060 | | wall | 0.60234 | | chair | 0.55823 | | cabinet | 0.33085 | | door | 0.36886 | | table | 0.44902 | | couch | 0.42525 | | shelf | 0.50707 | | window | 0.41229 | | bed | 0.56211 | | curtain | 0.61505 | | desk | 0.38723 | | doorframe | 0.20691 | | plant | 0.42469 | | stairs | 0.00000 | | pillow | 0.40011 | | wardrobe | 0.10062 | | picture | 0.37291 | | bathtub | 0.76100 | | box | 0.16502 | | counter | 0.25289 | | bench | 0.21182 | | stand | 0.29977 | | rail | 0.00202 | | sink | 0.58856 | | clothes | 0.23695 | | mirror | 0.23009 | | toilet | 0.75470 | | refrigerator | 0.38880 | | lamp | 0.42359 | | book | 0.20396 | | dresser | 0.04461 | | stool | 0.22710 | | fireplace | 0.00000 | | tv | 0.35289 | | blanket | 0.03384 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.57929 | | window frame | 0.00000 | | radiator | 0.58767 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.37170 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.37693 | | bar | 0.08129 | | pillar | 0.00000 | | bin | 0.43264 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.31948 | | blackboard | 0.32215 | | decoration | 0.04163 | | bag | 0.05587 | | windowsill | 0.12227 | | cushion | 0.02751 | | carpet | 0.00000 | | copier | 0.27564 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.03258 | | mailbox | 0.00151 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | printer | 0.29723 | | microwave | 0.15979 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.23144 | +---------------------+---------+ 2024/04/12 21:57:50 - mmengine - INFO - Epoch(val) [20][103/103] empty: 0.8026 floor: 0.8006 wall: 0.6023 chair: 0.5582 cabinet: 0.3308 door: 0.3689 table: 0.4490 couch: 0.4252 shelf: 0.5071 window: 0.4123 bed: 0.5621 curtain: 0.6150 desk: 0.3872 doorframe: 0.2069 plant: 0.4247 stairs: 0.0000 pillow: 0.4001 wardrobe: 0.1006 picture: 0.3729 bathtub: 0.7610 box: 0.1650 counter: 0.2529 bench: 0.2118 stand: 0.2998 rail: 0.0020 sink: 0.5886 clothes: 0.2370 mirror: 0.2301 toilet: 0.7547 refrigerator: 0.3888 lamp: 0.4236 book: 0.2040 dresser: 0.0446 stool: 0.2271 tv: 0.3529 blanket: 0.0338 monitor: 0.5793 window frame: 0.0000 radiator: 0.5877 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.3717 column: 0.0000 stove: 0.3769 bar: 0.0813 pillar: 0.0000 bin: 0.4326 backpack: 0.3195 blackboard: 0.3221 decoration: 0.0416 bag: 0.0559 windowsill: 0.1223 cushion: 0.0275 copier: 0.2756 board: 0.0000 basket: 0.0326 mailbox: 0.0015 printer: 0.2972 microwave: 0.1598 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3183 time: 2.8328 2024/04/12 21:59:58 - mmengine - INFO - Epoch(train) [21][ 50/389] lr: 1.0000e-05 eta: 1:04:06 time: 2.5533 data_time: 0.1634 memory: 34946 grad_norm: 6.1446 loss: 4.2043 loss_occ_0: 2.2724 loss_occ_1: 1.2237 loss_occ_2: 0.7082 2024/04/12 22:02:04 - mmengine - INFO - Epoch(train) [21][100/389] lr: 1.0000e-05 eta: 1:01:58 time: 2.5358 data_time: 0.1350 memory: 35022 grad_norm: 6.2872 loss: 4.0011 loss_occ_0: 2.1177 loss_occ_1: 1.1721 loss_occ_2: 0.7113 2024/04/12 22:04:11 - mmengine - INFO - Epoch(train) [21][150/389] lr: 1.0000e-05 eta: 0:59:50 time: 2.5247 data_time: 0.1395 memory: 34982 grad_norm: 6.3183 loss: 4.4350 loss_occ_0: 2.0691 loss_occ_1: 1.1411 loss_occ_2: 1.2249 2024/04/12 22:06:17 - mmengine - INFO - Epoch(train) [21][200/389] lr: 1.0000e-05 eta: 0:57:42 time: 2.5198 data_time: 0.1414 memory: 34285 grad_norm: 6.5299 loss: 3.6448 loss_occ_0: 1.8449 loss_occ_1: 1.0993 loss_occ_2: 0.7006 2024/04/12 22:07:08 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 22:08:23 - mmengine - INFO - Epoch(train) [21][250/389] lr: 1.0000e-05 eta: 0:55:35 time: 2.5234 data_time: 0.1505 memory: 34345 grad_norm: 6.8543 loss: 4.3845 loss_occ_0: 2.2974 loss_occ_1: 1.2804 loss_occ_2: 0.8068 2024/04/12 22:10:28 - mmengine - INFO - Epoch(train) [21][300/389] lr: 1.0000e-05 eta: 0:53:27 time: 2.5120 data_time: 0.1382 memory: 33694 grad_norm: 6.3962 loss: 4.2041 loss_occ_0: 2.2374 loss_occ_1: 1.1883 loss_occ_2: 0.7784 2024/04/12 22:12:36 - mmengine - INFO - Epoch(train) [21][350/389] lr: 1.0000e-05 eta: 0:51:19 time: 2.5457 data_time: 0.1356 memory: 34932 grad_norm: 6.2910 loss: 3.5943 loss_occ_0: 1.8751 loss_occ_1: 1.0512 loss_occ_2: 0.6680 2024/04/12 22:14:14 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 22:14:14 - mmengine - INFO - Saving checkpoint at 21 epochs 2024/04/12 22:16:22 - mmengine - INFO - Epoch(val) [21][ 50/103] eta: 0:01:26 time: 1.6368 data_time: 1.0668 memory: 34918 2024/04/12 22:19:36 - mmengine - INFO - Epoch(val) [21][100/103] eta: 0:00:08 time: 3.8895 data_time: 3.4382 memory: 29028 2024/04/12 22:23:52 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.80951 | | floor | 0.81346 | | wall | 0.60620 | | chair | 0.54249 | | cabinet | 0.34171 | | door | 0.37055 | | table | 0.45986 | | couch | 0.42011 | | shelf | 0.51439 | | window | 0.43149 | | bed | 0.54943 | | curtain | 0.61139 | | desk | 0.38709 | | doorframe | 0.20870 | | plant | 0.42104 | | stairs | 0.00000 | | pillow | 0.38811 | | wardrobe | 0.10482 | | picture | 0.37497 | | bathtub | 0.76796 | | box | 0.15945 | | counter | 0.25490 | | bench | 0.20604 | | stand | 0.28316 | | rail | 0.00000 | | sink | 0.58787 | | clothes | 0.19608 | | mirror | 0.24128 | | toilet | 0.75864 | | refrigerator | 0.38114 | | lamp | 0.42176 | | book | 0.24675 | | dresser | 0.05934 | | stool | 0.21797 | | fireplace | 0.00000 | | tv | 0.32188 | | blanket | 0.02696 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.58083 | | window frame | 0.00000 | | radiator | 0.58508 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00376 | | towel | 0.39177 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.35025 | | bar | 0.07303 | | pillar | 0.00000 | | bin | 0.43983 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.25943 | | blackboard | 0.32813 | | decoration | 0.03271 | | bag | 0.05348 | | windowsill | 0.10543 | | cushion | 0.03535 | | carpet | 0.00000 | | copier | 0.32209 | | board | 0.05221 | | countertop | 0.00000 | | basket | 0.03125 | | mailbox | 0.02374 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | printer | 0.30645 | | microwave | 0.15627 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.23181 | +---------------------+---------+ 2024/04/12 22:23:52 - mmengine - INFO - Epoch(val) [21][103/103] empty: 0.8095 floor: 0.8135 wall: 0.6062 chair: 0.5425 cabinet: 0.3417 door: 0.3705 table: 0.4599 couch: 0.4201 shelf: 0.5144 window: 0.4315 bed: 0.5494 curtain: 0.6114 desk: 0.3871 doorframe: 0.2087 plant: 0.4210 stairs: 0.0000 pillow: 0.3881 wardrobe: 0.1048 picture: 0.3750 bathtub: 0.7680 box: 0.1595 counter: 0.2549 bench: 0.2060 stand: 0.2832 rail: 0.0000 sink: 0.5879 clothes: 0.1961 mirror: 0.2413 toilet: 0.7586 refrigerator: 0.3811 lamp: 0.4218 book: 0.2467 dresser: 0.0593 stool: 0.2180 tv: 0.3219 blanket: 0.0270 monitor: 0.5808 window frame: 0.0000 radiator: 0.5851 mat: 0.0000 shower: 0.0000 rack: 0.0038 towel: 0.3918 column: 0.0000 stove: 0.3503 bar: 0.0730 pillar: 0.0000 bin: 0.4398 backpack: 0.2594 blackboard: 0.3281 decoration: 0.0327 bag: 0.0535 windowsill: 0.1054 cushion: 0.0354 copier: 0.3221 board: 0.0522 basket: 0.0312 mailbox: 0.0237 printer: 0.3065 microwave: 0.1563 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3149 time: 2.8244 2024/04/12 22:25:59 - mmengine - INFO - Epoch(train) [22][ 50/389] lr: 1.0000e-05 eta: 0:47:31 time: 2.5372 data_time: 0.1438 memory: 35402 grad_norm: 6.4127 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 22:28:07 - mmengine - INFO - Epoch(train) [22][100/389] lr: 1.0000e-05 eta: 0:45:24 time: 2.5445 data_time: 0.1714 memory: 33716 grad_norm: 6.1491 loss: 3.6171 loss_occ_0: 1.8282 loss_occ_1: 1.0662 loss_occ_2: 0.7227 2024/04/12 22:30:13 - mmengine - INFO - Epoch(train) [22][150/389] lr: 1.0000e-05 eta: 0:43:16 time: 2.5196 data_time: 0.1380 memory: 34105 grad_norm: 6.5621 loss: 4.1223 loss_occ_0: 2.1591 loss_occ_1: 1.2045 loss_occ_2: 0.7587 2024/04/12 22:32:20 - mmengine - INFO - Epoch(train) [22][200/389] lr: 1.0000e-05 eta: 0:41:08 time: 2.5554 data_time: 0.1395 memory: 34106 grad_norm: 6.8040 loss: 4.3147 loss_occ_0: 2.2497 loss_occ_1: 1.2512 loss_occ_2: 0.8137 2024/04/12 22:34:26 - mmengine - INFO - Epoch(train) [22][250/389] lr: 1.0000e-05 eta: 0:39:00 time: 2.5044 data_time: 0.1492 memory: 34503 grad_norm: 6.3569 loss: 3.9156 loss_occ_0: 1.9965 loss_occ_1: 1.1636 loss_occ_2: 0.7556 2024/04/12 22:36:32 - mmengine - INFO - Epoch(train) [22][300/389] lr: 1.0000e-05 eta: 0:36:52 time: 2.5211 data_time: 0.1486 memory: 34283 grad_norm: 6.3000 loss: 3.7833 loss_occ_0: 2.0019 loss_occ_1: 1.0943 loss_occ_2: 0.6870 2024/04/12 22:38:38 - mmengine - INFO - Epoch(train) [22][350/389] lr: 1.0000e-05 eta: 0:34:45 time: 2.5330 data_time: 0.1354 memory: 34079 grad_norm: 6.8854 loss: 5.0175 loss_occ_0: 2.6176 loss_occ_1: 1.5014 loss_occ_2: 0.8986 2024/04/12 22:40:18 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 22:40:18 - mmengine - INFO - Saving checkpoint at 22 epochs 2024/04/12 22:42:27 - mmengine - INFO - Epoch(val) [22][ 50/103] eta: 0:01:27 time: 1.6564 data_time: 1.0637 memory: 33992 2024/04/12 22:45:42 - mmengine - INFO - Epoch(val) [22][100/103] eta: 0:00:08 time: 3.8948 data_time: 3.4470 memory: 29029 2024/04/12 22:49:57 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.80933 | | floor | 0.80918 | | wall | 0.60400 | | chair | 0.55680 | | cabinet | 0.31882 | | door | 0.36932 | | table | 0.45282 | | couch | 0.40633 | | shelf | 0.51431 | | window | 0.41860 | | bed | 0.55936 | | curtain | 0.61976 | | desk | 0.37096 | | doorframe | 0.19690 | | plant | 0.42889 | | stairs | 0.00000 | | pillow | 0.38205 | | wardrobe | 0.11453 | | picture | 0.38091 | | bathtub | 0.75384 | | box | 0.14772 | | counter | 0.24122 | | bench | 0.20658 | | stand | 0.29197 | | rail | 0.00000 | | sink | 0.59591 | | clothes | 0.21521 | | mirror | 0.22295 | | toilet | 0.75258 | | refrigerator | 0.42529 | | lamp | 0.43742 | | book | 0.24839 | | dresser | 0.06404 | | stool | 0.23475 | | fireplace | 0.00000 | | tv | 0.37157 | | blanket | 0.02765 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.57767 | | window frame | 0.00000 | | radiator | 0.58808 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.01980 | | towel | 0.37324 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.38174 | | bar | 0.05038 | | pillar | 0.00000 | | bin | 0.42595 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.28402 | | blackboard | 0.35728 | | decoration | 0.06234 | | bag | 0.05454 | | windowsill | 0.10088 | | cushion | 0.04213 | | carpet | 0.00000 | | copier | 0.27522 | | board | 0.05134 | | countertop | 0.00000 | | basket | 0.02637 | | mailbox | 0.00318 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | partition | 0.00000 | | printer | 0.26566 | | microwave | 0.14976 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.22615 | +---------------------+---------+ 2024/04/12 22:49:58 - mmengine - INFO - Epoch(val) [22][103/103] empty: 0.8093 floor: 0.8092 wall: 0.6040 chair: 0.5568 cabinet: 0.3188 door: 0.3693 table: 0.4528 couch: 0.4063 shelf: 0.5143 window: 0.4186 bed: 0.5594 curtain: 0.6198 desk: 0.3710 doorframe: 0.1969 plant: 0.4289 stairs: 0.0000 pillow: 0.3820 wardrobe: 0.1145 picture: 0.3809 bathtub: 0.7538 box: 0.1477 counter: 0.2412 bench: 0.2066 stand: 0.2920 rail: 0.0000 sink: 0.5959 clothes: 0.2152 mirror: 0.2229 toilet: 0.7526 refrigerator: 0.4253 lamp: 0.4374 book: 0.2484 dresser: 0.0640 stool: 0.2347 tv: 0.3716 blanket: 0.0277 monitor: 0.5777 window frame: 0.0000 radiator: 0.5881 mat: 0.0000 shower: 0.0000 rack: 0.0198 towel: 0.3732 column: 0.0000 stove: 0.3817 bar: 0.0504 pillar: 0.0000 bin: 0.4260 backpack: 0.2840 blackboard: 0.3573 decoration: 0.0623 bag: 0.0545 windowsill: 0.1009 cushion: 0.0421 copier: 0.2752 board: 0.0513 basket: 0.0264 mailbox: 0.0032 printer: 0.2657 microwave: 0.1498 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3177 time: 2.8366 2024/04/12 22:52:08 - mmengine - INFO - Epoch(train) [23][ 50/389] lr: 1.0000e-06 eta: 0:30:58 time: 2.6014 data_time: 0.1345 memory: 35131 grad_norm: 6.5529 loss: 4.0370 loss_occ_0: 2.1435 loss_occ_1: 1.1244 loss_occ_2: 0.7690 2024/04/12 22:54:14 - mmengine - INFO - Epoch(train) [23][100/389] lr: 1.0000e-06 eta: 0:28:50 time: 2.5062 data_time: 0.1555 memory: 34248 grad_norm: 6.5858 loss: 4.1334 loss_occ_0: 2.2282 loss_occ_1: 1.1889 loss_occ_2: 0.7164 2024/04/12 22:56:21 - mmengine - INFO - Epoch(train) [23][150/389] lr: 1.0000e-06 eta: 0:26:42 time: 2.5444 data_time: 0.1435 memory: 34727 grad_norm: 6.4071 loss: 4.6182 loss_occ_0: 2.0932 loss_occ_1: 1.2642 loss_occ_2: 1.2609 2024/04/12 22:58:26 - mmengine - INFO - Epoch(train) [23][200/389] lr: 1.0000e-06 eta: 0:24:35 time: 2.5149 data_time: 0.1415 memory: 33892 grad_norm: 7.1317 loss: 4.0995 loss_occ_0: 2.1724 loss_occ_1: 1.1827 loss_occ_2: 0.7444 2024/04/12 23:00:33 - mmengine - INFO - Epoch(train) [23][250/389] lr: 1.0000e-06 eta: 0:22:27 time: 2.5271 data_time: 0.1461 memory: 34238 grad_norm: 6.9643 loss: 3.9588 loss_occ_0: 1.9409 loss_occ_1: 1.2370 loss_occ_2: 0.7809 2024/04/12 23:02:41 - mmengine - INFO - Epoch(train) [23][300/389] lr: 1.0000e-06 eta: 0:20:19 time: 2.5604 data_time: 0.1477 memory: 35034 grad_norm: 6.7325 loss: 3.4826 loss_occ_0: 1.7937 loss_occ_1: 1.0531 loss_occ_2: 0.6358 2024/04/12 23:04:47 - mmengine - INFO - Epoch(train) [23][350/389] lr: 1.0000e-06 eta: 0:18:12 time: 2.5323 data_time: 0.1496 memory: 34823 grad_norm: 6.2263 loss: 3.9306 loss_occ_0: 2.0474 loss_occ_1: 1.1676 loss_occ_2: 0.7156 2024/04/12 23:06:25 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 23:06:25 - mmengine - INFO - Saving checkpoint at 23 epochs 2024/04/12 23:08:34 - mmengine - INFO - Epoch(val) [23][ 50/103] eta: 0:01:27 time: 1.6463 data_time: 1.0724 memory: 35257 2024/04/12 23:11:48 - mmengine - INFO - Epoch(val) [23][100/103] eta: 0:00:08 time: 3.8845 data_time: 3.4305 memory: 29034 2024/04/12 23:16:00 - mmengine - INFO - +-----------------+---------+ | classes | IoU | +-----------------+---------+ | empty | 0.81306 | | floor | 0.81558 | | wall | 0.60800 | | chair | 0.55636 | | cabinet | 0.33834 | | door | 0.36886 | | table | 0.45879 | | couch | 0.40308 | | shelf | 0.51523 | | window | 0.42447 | | bed | 0.56286 | | curtain | 0.62083 | | desk | 0.36933 | | doorframe | 0.19651 | | plant | 0.44133 | | stairs | 0.00000 | | pillow | 0.38427 | | wardrobe | 0.12045 | | picture | 0.38831 | | bathtub | 0.77762 | | box | 0.15693 | | counter | 0.23651 | | bench | 0.20921 | | stand | 0.30319 | | rail | 0.00000 | | sink | 0.59734 | | clothes | 0.21519 | | mirror | 0.24151 | | toilet | 0.73707 | | refrigerator | 0.41318 | | lamp | 0.42342 | | book | 0.27164 | | dresser | 0.05173 | | stool | 0.23604 | | fireplace | 0.00000 | | tv | 0.35695 | | blanket | 0.03618 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.56321 | | window frame | 0.00000 | | radiator | 0.58681 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00942 | | towel | 0.35776 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.38577 | | bar | 0.06754 | | pillar | 0.00000 | | bin | 0.43068 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.30782 | | blackboard | 0.35510 | | decoration | 0.03899 | | bag | 0.06646 | | windowsill | 0.11600 | | cushion | 0.02902 | | carpet | 0.00000 | | copier | 0.28995 | | board | 0.00064 | | countertop | 0.00000 | | basket | 0.02973 | | mailbox | 0.02068 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | partition | 0.00000 | | printer | 0.33756 | | microwave | 0.13775 | | frame | 0.00000 | +-----------------+---------+ | mean | 0.23091 | +-----------------+---------+ 2024/04/12 23:16:00 - mmengine - INFO - Epoch(val) [23][103/103] empty: 0.8131 floor: 0.8156 wall: 0.6080 chair: 0.5564 cabinet: 0.3383 door: 0.3689 table: 0.4588 couch: 0.4031 shelf: 0.5152 window: 0.4245 bed: 0.5629 curtain: 0.6208 desk: 0.3693 doorframe: 0.1965 plant: 0.4413 stairs: 0.0000 pillow: 0.3843 wardrobe: 0.1205 picture: 0.3883 bathtub: 0.7776 box: 0.1569 counter: 0.2365 bench: 0.2092 stand: 0.3032 rail: 0.0000 sink: 0.5973 clothes: 0.2152 mirror: 0.2415 toilet: 0.7371 refrigerator: 0.4132 lamp: 0.4234 book: 0.2716 dresser: 0.0517 stool: 0.2360 tv: 0.3570 blanket: 0.0362 monitor: 0.5632 window frame: 0.0000 radiator: 0.5868 mat: 0.0000 shower: 0.0000 rack: 0.0094 towel: 0.3578 column: 0.0000 stove: 0.3858 bar: 0.0675 pillar: 0.0000 bin: 0.4307 backpack: 0.3078 blackboard: 0.3551 decoration: 0.0390 bag: 0.0665 windowsill: 0.1160 cushion: 0.0290 copier: 0.2900 board: 0.0006 basket: 0.0297 mailbox: 0.0207 printer: 0.3376 microwave: 0.1377 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3134 time: 2.8261 2024/04/12 23:18:10 - mmengine - INFO - Epoch(train) [24][ 50/389] lr: 1.0000e-06 eta: 0:14:25 time: 2.5813 data_time: 0.1595 memory: 34958 grad_norm: 6.1104 loss: 4.1441 loss_occ_0: 1.9612 loss_occ_1: 1.0336 loss_occ_2: 1.1494 2024/04/12 23:18:17 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 23:20:15 - mmengine - INFO - Epoch(train) [24][100/389] lr: 1.0000e-06 eta: 0:12:17 time: 2.4990 data_time: 0.1415 memory: 34290 grad_norm: 7.0002 loss: 4.7078 loss_occ_0: 2.5517 loss_occ_1: 1.3817 loss_occ_2: 0.7744 2024/04/12 23:22:21 - mmengine - INFO - Epoch(train) [24][150/389] lr: 1.0000e-06 eta: 0:10:09 time: 2.5182 data_time: 0.1373 memory: 33932 grad_norm: 6.7310 loss: 4.2722 loss_occ_0: 2.2547 loss_occ_1: 1.2620 loss_occ_2: 0.7555 2024/04/12 23:24:26 - mmengine - INFO - Epoch(train) [24][200/389] lr: 1.0000e-06 eta: 0:08:02 time: 2.5112 data_time: 0.1423 memory: 34446 grad_norm: 6.7776 loss: nan loss_occ_0: nan loss_occ_1: nan loss_occ_2: nan 2024/04/12 23:26:33 - mmengine - INFO - Epoch(train) [24][250/389] lr: 1.0000e-06 eta: 0:05:54 time: 2.5318 data_time: 0.1514 memory: 34488 grad_norm: 6.5553 loss: 4.5805 loss_occ_0: 2.1610 loss_occ_1: 1.1652 loss_occ_2: 1.2543 2024/04/12 23:28:40 - mmengine - INFO - Epoch(train) [24][300/389] lr: 1.0000e-06 eta: 0:03:47 time: 2.5510 data_time: 0.1579 memory: 34695 grad_norm: 6.3980 loss: 3.9664 loss_occ_0: 2.0530 loss_occ_1: 1.1490 loss_occ_2: 0.7644 2024/04/12 23:30:47 - mmengine - INFO - Epoch(train) [24][350/389] lr: 1.0000e-06 eta: 0:01:39 time: 2.5350 data_time: 0.1384 memory: 36043 grad_norm: 6.1799 loss: 3.6598 loss_occ_0: 1.8872 loss_occ_1: 1.1274 loss_occ_2: 0.6452 2024/04/12 23:32:26 - mmengine - INFO - Exp name: cont-occ_8xb1_embodiedscan-occ-80class_20240412_131019 2024/04/12 23:32:26 - mmengine - INFO - Saving checkpoint at 24 epochs 2024/04/12 23:34:33 - mmengine - INFO - Epoch(val) [24][ 50/103] eta: 0:01:26 time: 1.6401 data_time: 1.0679 memory: 34636 2024/04/12 23:37:48 - mmengine - INFO - Epoch(val) [24][100/103] eta: 0:00:08 time: 3.9113 data_time: 3.4636 memory: 29027 2024/04/12 23:42:00 - mmengine - INFO - +---------------------+---------+ | classes | IoU | +---------------------+---------+ | empty | 0.80234 | | floor | 0.79727 | | wall | 0.60499 | | chair | 0.55316 | | cabinet | 0.33613 | | door | 0.37962 | | table | 0.45318 | | couch | 0.41715 | | shelf | 0.51211 | | window | 0.42962 | | bed | 0.56803 | | curtain | 0.63118 | | desk | 0.36999 | | doorframe | 0.20063 | | plant | 0.43404 | | stairs | 0.00000 | | pillow | 0.38781 | | wardrobe | 0.10901 | | picture | 0.37534 | | bathtub | 0.77636 | | box | 0.15839 | | counter | 0.24630 | | bench | 0.21606 | | stand | 0.30385 | | rail | 0.00000 | | sink | 0.59056 | | clothes | 0.21850 | | mirror | 0.22754 | | toilet | 0.74815 | | refrigerator | 0.41007 | | lamp | 0.42336 | | book | 0.24466 | | dresser | 0.04433 | | stool | 0.22339 | | fireplace | 0.00000 | | tv | 0.36112 | | blanket | 0.03532 | | commode | 0.00000 | | washing machine | 0.00000 | | monitor | 0.57889 | | window frame | 0.00000 | | radiator | 0.58506 | | mat | 0.00000 | | shower | 0.00000 | | rack | 0.00000 | | towel | 0.37653 | | ottoman | 0.00000 | | column | 0.00000 | | blinds | 0.00000 | | stove | 0.39288 | | bar | 0.07843 | | pillar | 0.00000 | | bin | 0.42796 | | heater | 0.00000 | | clothes dryer | 0.00000 | | backpack | 0.28655 | | blackboard | 0.34011 | | decoration | 0.04841 | | bag | 0.04275 | | windowsill | 0.14522 | | cushion | 0.02676 | | carpet | 0.00000 | | copier | 0.32949 | | board | 0.00000 | | countertop | 0.00000 | | basket | 0.02810 | | mailbox | 0.03529 | | kitchen island | 0.00000 | | washbasin | 0.00000 | | bicycle | 0.00000 | | drawer | 0.00000 | | oven | 0.00000 | | piano | 0.00000 | | excercise equipment | 0.00000 | | partition | 0.00000 | | printer | 0.42318 | | microwave | 0.13971 | | frame | 0.00000 | +---------------------+---------+ | mean | 0.22917 | +---------------------+---------+ 2024/04/12 23:42:01 - mmengine - INFO - Epoch(val) [24][103/103] empty: 0.8023 floor: 0.7973 wall: 0.6050 chair: 0.5532 cabinet: 0.3361 door: 0.3796 table: 0.4532 couch: 0.4172 shelf: 0.5121 window: 0.4296 bed: 0.5680 curtain: 0.6312 desk: 0.3700 doorframe: 0.2006 plant: 0.4340 stairs: 0.0000 pillow: 0.3878 wardrobe: 0.1090 picture: 0.3753 bathtub: 0.7764 box: 0.1584 counter: 0.2463 bench: 0.2161 stand: 0.3038 rail: 0.0000 sink: 0.5906 clothes: 0.2185 mirror: 0.2275 toilet: 0.7482 refrigerator: 0.4101 lamp: 0.4234 book: 0.2447 dresser: 0.0443 stool: 0.2234 tv: 0.3611 blanket: 0.0353 monitor: 0.5789 window frame: 0.0000 radiator: 0.5851 mat: 0.0000 shower: 0.0000 rack: 0.0000 towel: 0.3765 column: 0.0000 stove: 0.3929 bar: 0.0784 pillar: 0.0000 bin: 0.4280 backpack: 0.2865 blackboard: 0.3401 decoration: 0.0484 bag: 0.0428 windowsill: 0.1452 cushion: 0.0268 copier: 0.3295 board: 0.0000 basket: 0.0281 mailbox: 0.0353 printer: 0.4232 microwave: 0.1397 ottoman: 0.0000 heater: 0.0000 steps: 0.0000 countertop: 0.0000 washbasin: 0.0000 commode: 0.0000 washing machine: 0.0000 oven: 0.0000 piano: 0.0000 frame: 0.0000 blinds: 0.0000 clothes dryer: 0.0000 carpet: 0.0000 drawer: 0.0000 fireplace: 0.0000 bicycle: 0.0000 excercise equipment: 0.0000 partition: 0.0000 kitchen island: 0.0000 data_time: 2.3261 time: 2.8349