tun3d_structured3d.log

2025/09/13 12:49:49 - mmengine - INFO - 
------------------------------------------------------------
System environment:
    sys.platform: linux
    Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
    CUDA available: True
    numpy_random_seed: 1829955487
    GPU 0: NVIDIA H100 80GB HBM3
    CUDA_HOME: /usr/local/cuda
    NVCC: Cuda compilation tools, release 12.1, V12.1.105
    GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    PyTorch: 2.1.2+cu121
    PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

    TorchVision: 0.16.2+cu121
    OpenCV: 4.11.0
    MMEngine: 0.9.0

Runtime environment:
    cudnn_benchmark: False
    mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
    dist_cfg: {'backend': 'nccl'}
    seed: 1829955487
    Distributed launcher: none
    Distributed training: False
    GPU number: 1
------------------------------------------------------------

2025/09/13 12:49:50 - mmengine - INFO - Config:
EPOCHS = 12
backend_args = None
class_agnostic_eval = True
classes_stru3d = [
    'door',
    'window',
]
custom_hooks = [
    dict(after_iter=True, type='EmptyCacheHook'),
]
custom_imports = dict(imports=[
    'tr3d',
])
data_root = '/home/jovyan/users/koodiazhnyi/msu-masters/tr3d/data/structured3d'
dataset_type = 'Stru3DDataset'
default_hooks = dict(
    checkpoint=dict(
        _scope_='mmdet3d', interval=1, max_keep_ckpts=2,
        type='CheckpointHook'),
    logger=dict(_scope_='mmdet3d', interval=50, type='LoggerHook'),
    param_scheduler=dict(_scope_='mmdet3d', type='ParamSchedulerHook'),
    sampler_seed=dict(_scope_='mmdet3d', type='DistSamplerSeedHook'),
    timer=dict(_scope_='mmdet3d', type='IterTimerHook'),
    visualization=dict(_scope_='mmdet3d', type='Det3DVisualizationHook'))
default_scope = 'mmdet3d'
env_cfg = dict(
    cudnn_benchmark=False,
    dist_cfg=dict(backend='nccl'),
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(
    _scope_='mmdet3d', by_epoch=True, type='LogProcessor', window_size=50)
metainfo = dict(classes=[
    'door',
    'window',
])
model = dict(
    backbone=dict(
        depth=34,
        in_channels=3,
        norm='batch',
        num_planes=(
            64,
            128,
            128,
            128,
        ),
        type='TR3DMinkResNet'),
    bbox_head=dict(
        add_z_feat=True,
        angles=[
            False,
        ],
        datasets=[
            'structured3d',
        ],
        datasets_classes=[
            [
                'door',
                'window',
            ],
        ],
        datasets_weights=[
            1.0,
        ],
        in_channels=128,
        label2level=[
            [
                0,
                0,
            ],
        ],
        layout_level=1,
        loss_weights=[
            [
                0.75,
                0.25,
            ],
        ],
        n_q_feats=10,
        n_spconv2d=3,
        num_layout_reg_outs=5,
        num_reg_outs=6,
        pts_center_threshold=6,
        pts_center_threshold_layout=6,
        type='TR3DHead',
        voxel_size=0.01),
    data_preprocessor=dict(type='Det3DDataPreprocessor'),
    neck=dict(
        in_channels=(
            64,
            128,
            128,
            128,
        ), out_channels=128, type='TR3DNeck'),
    test_cfg=dict(
        class_agnostic_eval=True,
        enable_double_layout_nms=True,
        iou_thr=0.15,
        iou_thr_layout=0.1,
        nms_pre=1000,
        nms_pre_layout=150,
        nms_radius=0.1,
        score_thr=0.35,
        score_thr_layout=0.5),
    train_cfg=dict(),
    type='MinkSingleStage3DDetector')
optim_wrapper = dict(
    clip_grad=dict(max_norm=10, norm_type=2),
    optimizer=dict(lr=0.001, type='AdamW', weight_decay=0.0001),
    type='OptimWrapper')
param_scheduler = dict(
    begin=0,
    by_epoch=True,
    end=12,
    gamma=0.1,
    milestones=[
        8,
        11,
    ],
    type='MultiStepLR')
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='structured3d_infos_val_spatial_lm_v3.pkl',
        backend_args=None,
        box_type_3d='Depth',
        data_root=
        '/home/jovyan/users/koodiazhnyi/msu-masters/tr3d/data/structured3d',
        metainfo=dict(classes=[
            'door',
            'window',
        ]),
        pipeline=[
            dict(
                backend_args=None,
                coord_type='DEPTH',
                load_dim=6,
                shift_height=False,
                type='LoadPointsFromFile',
                use_color=True,
                use_dim=[
                    0,
                    1,
                    2,
                    3,
                    4,
                    5,
                ]),
            dict(type='AddLayoutLabels'),
            dict(
                flip=False,
                img_scale=(
                    1333,
                    800,
                ),
                pts_scale_ratio=1,
                transforms=[
                    dict(
                        color_mean=[
                            127.5,
                            127.5,
                            127.5,
                        ],
                        type='NormalizePointsColor'),
                ],
                type='MultiScaleFlipAug3D'),
            dict(type='LayoutOrientation'),
            dict(keys=[
                'points',
            ], load_meta=True, type='Pack3DDetInputs_'),
        ],
        test_mode=True,
        type='Stru3DDataset'),
    num_workers=1,
    sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
    datasets=[
        'structured3d',
    ],
    datasets_classes=[
        [
            'door',
            'window',
        ],
    ],
    dist_thr=[
        0.4,
        0.6,
    ],
    floor_and_ceiling=False,
    type='IndoorLayoutMetric_')
test_pipeline = [
    dict(
        backend_args=None,
        coord_type='DEPTH',
        load_dim=6,
        shift_height=False,
        type='LoadPointsFromFile',
        use_color=True,
        use_dim=[
            0,
            1,
            2,
            3,
            4,
            5,
        ]),
    dict(type='AddLayoutLabels'),
    dict(
        flip=False,
        img_scale=(
            1333,
            800,
        ),
        pts_scale_ratio=1,
        transforms=[
            dict(
                color_mean=[
                    127.5,
                    127.5,
                    127.5,
                ],
                type='NormalizePointsColor'),
        ],
        type='MultiScaleFlipAug3D'),
    dict(type='LayoutOrientation'),
    dict(keys=[
        'points',
    ], load_meta=True, type='Pack3DDetInputs_'),
]
train_cfg = dict(max_epochs=12, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
    batch_size=16,
    dataset=dict(
        dataset=dict(
            ann_file='structured3d_infos_train_spatial_lm_v3.pkl',
            backend_args=None,
            box_type_3d='Depth',
            data_root=
            '/home/jovyan/users/koodiazhnyi/msu-masters/tr3d/data/structured3d',
            filter_empty_gt=False,
            metainfo=dict(classes=[
                'door',
                'window',
            ]),
            pipeline=[
                dict(
                    backend_args=None,
                    coord_type='DEPTH',
                    load_dim=6,
                    shift_height=False,
                    type='LoadPointsFromFile',
                    use_color=True,
                    use_dim=[
                        0,
                        1,
                        2,
                        3,
                        4,
                        5,
                    ]),
                dict(
                    backend_args=None,
                    type='LoadAnnotations3D',
                    with_bbox_3d=True,
                    with_label_3d=True,
                    with_mask_3d=False,
                    with_seg_3d=False),
                dict(type='AddLayoutLabels'),
                dict(num_points=0.33, type='TR3DPointSample'),
                dict(
                    flip_ratio_bev_horizontal=0.5,
                    flip_ratio_bev_vertical=0.5,
                    sync_2d=False,
                    type='RandomFlip3DLayout'),
                dict(
                    rot_range=[
                        0,
                        0,
                    ],
                    scale_ratio_range=[
                        0.85,
                        1.15,
                    ],
                    shift_height=False,
                    translation_std=[
                        0.1,
                        0.1,
                        0.1,
                    ],
                    type='GlobalRotScaleTransLayout'),
                dict(
                    color_mean=[
                        127.5,
                        127.5,
                        127.5,
                    ],
                    type='NormalizePointsColor'),
                dict(type='LayoutOrientation'),
                dict(
                    keys=[
                        'points',
                        'gt_bboxes_3d',
                        'gt_labels_3d',
                    ],
                    type='Pack3DDetInputs_'),
            ],
            type='Stru3DDataset'),
        times=7,
        type='RepeatDataset'),
    num_workers=8,
    sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
    dict(
        backend_args=None,
        coord_type='DEPTH',
        load_dim=6,
        shift_height=False,
        type='LoadPointsFromFile',
        use_color=True,
        use_dim=[
            0,
            1,
            2,
            3,
            4,
            5,
        ]),
    dict(
        backend_args=None,
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        with_mask_3d=False,
        with_seg_3d=False),
    dict(type='AddLayoutLabels'),
    dict(num_points=0.33, type='TR3DPointSample'),
    dict(
        flip_ratio_bev_horizontal=0.5,
        flip_ratio_bev_vertical=0.5,
        sync_2d=False,
        type='RandomFlip3DLayout'),
    dict(
        rot_range=[
            0,
            0,
        ],
        scale_ratio_range=[
            0.85,
            1.15,
        ],
        shift_height=False,
        translation_std=[
            0.1,
            0.1,
            0.1,
        ],
        type='GlobalRotScaleTransLayout'),
    dict(color_mean=[
        127.5,
        127.5,
        127.5,
    ], type='NormalizePointsColor'),
    dict(type='LayoutOrientation'),
    dict(
        keys=[
            'points',
            'gt_bboxes_3d',
            'gt_labels_3d',
        ],
        type='Pack3DDetInputs_'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
    batch_size=1,
    dataset=dict(
        ann_file='structured3d_infos_val_spatial_lm_v3.pkl',
        backend_args=None,
        box_type_3d='Depth',
        data_root=
        '/home/jovyan/users/koodiazhnyi/msu-masters/tr3d/data/structured3d',
        metainfo=dict(classes=[
            'door',
            'window',
        ]),
        pipeline=[
            dict(
                backend_args=None,
                coord_type='DEPTH',
                load_dim=6,
                shift_height=False,
                type='LoadPointsFromFile',
                use_color=True,
                use_dim=[
                    0,
                    1,
                    2,
                    3,
                    4,
                    5,
                ]),
            dict(type='AddLayoutLabels'),
            dict(
                flip=False,
                img_scale=(
                    1333,
                    800,
                ),
                pts_scale_ratio=1,
                transforms=[
                    dict(
                        color_mean=[
                            127.5,
                            127.5,
                            127.5,
                        ],
                        type='NormalizePointsColor'),
                ],
                type='MultiScaleFlipAug3D'),
            dict(type='LayoutOrientation'),
            dict(keys=[
                'points',
            ], load_meta=True, type='Pack3DDetInputs_'),
        ],
        test_mode=True,
        type='Stru3DDataset'),
    num_workers=1,
    sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
    datasets=[
        'structured3d',
    ],
    datasets_classes=[
        [
            'door',
            'window',
        ],
    ],
    dist_thr=[
        0.4,
        0.6,
    ],
    floor_and_ceiling=False,
    type='IndoorLayoutMetric_')
vis_backends = [
    dict(type='LocalVisBackend'),
]
visualizer = dict(
    name='visualizer',
    type='Det3DLocalVisualizer',
    vis_backends=[
        dict(type='LocalVisBackend'),
    ])
work_dir = './work_dirs/tr3d_1xb16_structured3d_v51'

2025/09/13 12:49:52 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
2025/09/13 12:49:52 - 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:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_val_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) EmptyCacheHook                     
 -------------------- 
before_val_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_val_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) Det3DVisualizationHook             
(NORMAL      ) EmptyCacheHook                     
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_val_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) EmptyCacheHook                     
(BELOW_NORMAL) LoggerHook                         
(LOW         ) ParamSchedulerHook                 
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
after_val:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_train:
(VERY_HIGH   ) RuntimeInfoHook                    
(VERY_LOW    ) CheckpointHook                     
 -------------------- 
before_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
before_test_epoch:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) EmptyCacheHook                     
 -------------------- 
before_test_iter:
(NORMAL      ) IterTimerHook                      
 -------------------- 
after_test_iter:
(NORMAL      ) IterTimerHook                      
(NORMAL      ) Det3DVisualizationHook             
(NORMAL      ) EmptyCacheHook                     
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test_epoch:
(VERY_HIGH   ) RuntimeInfoHook                    
(NORMAL      ) IterTimerHook                      
(NORMAL      ) EmptyCacheHook                     
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
after_test:
(VERY_HIGH   ) RuntimeInfoHook                    
 -------------------- 
after_run:
(BELOW_NORMAL) LoggerHook                         
 -------------------- 
2025/09/13 12:49:53 - mmengine - INFO - ------------------------------
2025/09/13 12:49:53 - mmengine - INFO - The length of training dataset: 2510
2025/09/13 12:49:53 - mmengine - INFO - The number of instances per category in the dataset:
+----------+--------+
| category | number |
+----------+--------+
| door     | 30101  |
| window   | 16551  |
+----------+--------+
2025/09/13 12:49:54 - mmengine - INFO - ------------------------------
2025/09/13 12:49:54 - mmengine - INFO - The length of test dataset: 241
2025/09/13 12:49:54 - mmengine - INFO - The number of instances per category in the dataset:
+----------+--------+
| category | number |
+----------+--------+
| door     | 3222   |
| window   | 1650   |
+----------+--------+
2025/09/13 12:49:54 - mmengine - WARNING - The prefix is not set in metric class IndoorLayoutMetric_.
Name of parameter - Initialization information

backbone.conv1.kernel - torch.Size([27, 3, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.norm1.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.norm1.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.conv1.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.0.norm1.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.norm1.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.conv2.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.0.norm2.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.norm2.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.0.downsample.1.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.0.downsample.1.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.1.conv1.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.1.norm1.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.1.norm1.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.1.conv2.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.1.norm2.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.1.norm2.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.2.conv1.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.2.norm1.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.2.norm1.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.2.conv2.kernel - torch.Size([27, 64, 64]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer1.2.norm2.bn.weight - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer1.2.norm2.bn.bias - torch.Size([64]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.conv1.kernel - torch.Size([27, 64, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.0.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.0.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.0.downsample.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.0.downsample.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.1.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.1.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.1.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.1.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.1.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.1.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.2.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.2.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.2.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.2.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.2.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.2.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.3.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.3.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.3.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.3.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer2.3.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer2.3.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.0.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.0.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.downsample.0.kernel - torch.Size([1, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.0.downsample.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.0.downsample.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.1.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.1.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.1.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.1.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.1.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.1.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.2.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.2.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.2.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.2.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.2.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.2.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.3.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.3.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.3.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.3.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.3.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.3.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.4.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.4.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.4.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.4.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.4.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.4.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.5.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.5.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.5.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.5.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer3.5.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer3.5.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.0.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.0.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.downsample.0.kernel - torch.Size([1, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.0.downsample.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.0.downsample.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.1.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.1.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.1.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.1.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.1.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.1.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.2.conv1.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.2.norm1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.2.norm1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.2.conv2.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DMinkResNet  

backbone.layer4.2.norm2.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

backbone.layer4.2.norm2.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.lateral_block_0.0.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DNeck  

neck.lateral_block_0.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.lateral_block_0.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.out_block_0.0.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DNeck  

neck.out_block_0.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.out_block_0.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_1.0.kernel - torch.Size([27, 128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_1.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_1.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.lateral_block_1.0.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DNeck  

neck.lateral_block_1.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.lateral_block_1.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.out_block_1.0.kernel - torch.Size([27, 128, 128]): 
Initialized by user-defined `init_weights` in TR3DNeck  

neck.out_block_1.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.out_block_1.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_2.0.kernel - torch.Size([27, 128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_2.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

neck.up_block_2.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.conv_reg.kernel - torch.Size([128, 6]): 
Initialized by user-defined `init_weights` in TR3DHead  

bbox_head.conv_reg.bias - torch.Size([1, 6]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.conv_cls.kernel - torch.Size([128, 2]): 
Initialized by user-defined `init_weights` in TR3DHead  

bbox_head.conv_cls.bias - torch.Size([1, 2]): 
Initialized by user-defined `init_weights` in TR3DHead  

bbox_head.layout_head.add_feats_encoder.0.weight - torch.Size([32, 10]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.0.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.2.weight - torch.Size([32, 32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.2.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.4.weight - torch.Size([32, 32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.4.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.5.weight - torch.Size([32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.add_feats_encoder.5.bias - torch.Size([32]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_reg_conv.0.kernel - torch.Size([160, 160]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_reg_conv.0.bias - torch.Size([1, 160]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_reg_conv.2.kernel - torch.Size([160, 5]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_reg_conv.2.bias - torch.Size([1, 5]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_cls_conv.kernel - torch.Size([128, 1]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.out_cls_conv.bias - torch.Size([1, 1]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.0.0.kernel - torch.Size([9, 128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.0.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.0.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.1.0.kernel - torch.Size([9, 128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.1.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.1.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.2.0.kernel - torch.Size([9, 128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.2.1.bn.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.conv_blocks.2.1.bn.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.0.weight - torch.Size([128, 1]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.0.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.2.weight - torch.Size([128, 128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.2.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.4.weight - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  

bbox_head.layout_head.z_fusion_block.4.bias - torch.Size([128]): 
The value is the same before and after calling `init_weights` of MinkSingleStage3DDetector  
2025/09/13 12:49:54 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
2025/09/13 12:49:54 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
2025/09/13 12:49:54 - mmengine - INFO - Checkpoints will be saved to /home/jovyan/users/koodiazhnyi/msu-masters/tr3d/work_dirs/tr3d_1xb16_structured3d_v51.
2025/09/13 12:52:35 - mmengine - INFO - Epoch(train)  [1][  50/1099]  lr: 1.0000e-03  eta: 11:45:55  time: 3.2239  data_time: 0.2323  memory: 29192  grad_norm: 9.3184  loss: 2.1326  bbox_loss: 0.5978  cls_loss: 0.5300  layout_loss: 0.8683  cls_layout_loss: 0.1365
2025/09/13 12:55:11 - mmengine - INFO - Epoch(train)  [1][ 100/1099]  lr: 1.0000e-03  eta: 11:30:48  time: 3.1100  data_time: 0.1371  memory: 25749  grad_norm: 15.4519  loss: 1.7446  bbox_loss: 0.5438  cls_loss: 0.3832  layout_loss: 0.7605  cls_layout_loss: 0.0571
2025/09/13 12:57:50 - mmengine - INFO - Epoch(train)  [1][ 150/1099]  lr: 1.0000e-03  eta: 11:29:31  time: 3.1856  data_time: 0.1648  memory: 33202  grad_norm: 18.9536  loss: 1.6705  bbox_loss: 0.5309  cls_loss: 0.3413  layout_loss: 0.7430  cls_layout_loss: 0.0553
2025/09/13 13:00:23 - mmengine - INFO - Epoch(train)  [1][ 200/1099]  lr: 1.0000e-03  eta: 11:21:18  time: 3.0702  data_time: 0.1491  memory: 25550  grad_norm: 15.8186  loss: 1.4874  bbox_loss: 0.4973  cls_loss: 0.3163  layout_loss: 0.6197  cls_layout_loss: 0.0541
2025/09/13 13:02:59 - mmengine - INFO - Epoch(train)  [1][ 250/1099]  lr: 1.0000e-03  eta: 11:17:23  time: 3.1173  data_time: 0.1603  memory: 26623  grad_norm: 20.0858  loss: 1.3611  bbox_loss: 0.4896  cls_loss: 0.2907  layout_loss: 0.5309  cls_layout_loss: 0.0500
2025/09/13 13:05:36 - mmengine - INFO - Epoch(train)  [1][ 300/1099]  lr: 1.0000e-03  eta: 11:14:16  time: 3.1277  data_time: 0.1312  memory: 33776  grad_norm: 19.0561  loss: 1.2665  bbox_loss: 0.4688  cls_loss: 0.2689  layout_loss: 0.4811  cls_layout_loss: 0.0478
2025/09/13 13:08:13 - mmengine - INFO - Epoch(train)  [1][ 350/1099]  lr: 1.0000e-03  eta: 11:11:47  time: 3.1433  data_time: 0.1621  memory: 26211  grad_norm: 19.2816  loss: 1.1945  bbox_loss: 0.4614  cls_loss: 0.2559  layout_loss: 0.4328  cls_layout_loss: 0.0444
2025/09/13 13:10:49 - mmengine - INFO - Epoch(train)  [1][ 400/1099]  lr: 1.0000e-03  eta: 11:08:48  time: 3.1261  data_time: 0.1578  memory: 26777  grad_norm: 19.2562  loss: 1.1358  bbox_loss: 0.4483  cls_loss: 0.2493  layout_loss: 0.3962  cls_layout_loss: 0.0420
2025/09/13 13:13:31 - mmengine - INFO - Epoch(train)  [1][ 450/1099]  lr: 1.0000e-03  eta: 11:08:21  time: 3.2296  data_time: 0.2030  memory: 26155  grad_norm: 18.7840  loss: 1.1183  bbox_loss: 0.4369  cls_loss: 0.2409  layout_loss: 0.3982  cls_layout_loss: 0.0422
2025/09/13 13:16:11 - mmengine - INFO - Epoch(train)  [1][ 500/1099]  lr: 1.0000e-03  eta: 11:06:51  time: 3.2012  data_time: 0.1603  memory: 27328  grad_norm: 19.6828  loss: 1.1131  bbox_loss: 0.4297  cls_loss: 0.2427  layout_loss: 0.4005  cls_layout_loss: 0.0402
2025/09/13 13:18:51 - mmengine - INFO - Epoch(train)  [1][ 550/1099]  lr: 1.0000e-03  eta: 11:05:14  time: 3.2064  data_time: 0.1487  memory: 27534  grad_norm: 19.9812  loss: 1.0364  bbox_loss: 0.4195  cls_loss: 0.2184  layout_loss: 0.3604  cls_layout_loss: 0.0381
2025/09/13 13:21:29 - mmengine - INFO - Epoch(train)  [1][ 600/1099]  lr: 1.0000e-03  eta: 11:02:39  time: 3.1613  data_time: 0.1485  memory: 26207  grad_norm: 18.4832  loss: 1.0224  bbox_loss: 0.4161  cls_loss: 0.2210  layout_loss: 0.3486  cls_layout_loss: 0.0367
2025/09/13 13:24:12 - mmengine - INFO - Epoch(train)  [1][ 650/1099]  lr: 1.0000e-03  eta: 11:01:36  time: 3.2564  data_time: 0.1599  memory: 25803  grad_norm: 18.2279  loss: 0.9966  bbox_loss: 0.4021  cls_loss: 0.2140  layout_loss: 0.3434  cls_layout_loss: 0.0370
2025/09/13 13:26:49 - mmengine - INFO - Epoch(train)  [1][ 700/1099]  lr: 1.0000e-03  eta: 10:58:32  time: 3.1376  data_time: 0.1412  memory: 25730  grad_norm: 20.4529  loss: 0.9695  bbox_loss: 0.4025  cls_loss: 0.2067  layout_loss: 0.3257  cls_layout_loss: 0.0346
2025/09/13 13:29:28 - mmengine - INFO - Epoch(train)  [1][ 750/1099]  lr: 1.0000e-03  eta: 10:56:19  time: 3.1941  data_time: 0.1504  memory: 33947  grad_norm: 16.7543  loss: 1.0094  bbox_loss: 0.4000  cls_loss: 0.2111  layout_loss: 0.3589  cls_layout_loss: 0.0395
2025/09/13 13:32:07 - mmengine - INFO - Epoch(train)  [1][ 800/1099]  lr: 1.0000e-03  eta: 10:53:53  time: 3.1819  data_time: 0.1517  memory: 29815  grad_norm: 20.2903  loss: 0.9732  bbox_loss: 0.4027  cls_loss: 0.2052  layout_loss: 0.3304  cls_layout_loss: 0.0350
2025/09/13 13:34:44 - mmengine - INFO - Epoch(train)  [1][ 850/1099]  lr: 1.0000e-03  eta: 10:50:48  time: 3.1307  data_time: 0.1542  memory: 24681  grad_norm: 18.9074  loss: 0.9553  bbox_loss: 0.3920  cls_loss: 0.2032  layout_loss: 0.3258  cls_layout_loss: 0.0343
2025/09/13 13:37:27 - mmengine - INFO - Epoch(train)  [1][ 900/1099]  lr: 1.0000e-03  eta: 10:49:07  time: 3.2491  data_time: 0.1810  memory: 26765  grad_norm: 11.3360  loss: 0.9649  bbox_loss: 0.3858  cls_loss: 0.2045  layout_loss: 0.3392  cls_layout_loss: 0.0355
2025/09/13 13:40:09 - mmengine - INFO - Epoch(train)  [1][ 950/1099]  lr: 1.0000e-03  eta: 10:47:22  time: 3.2527  data_time: 0.1839  memory: 34914  grad_norm: 21.0052  loss: 0.9846  bbox_loss: 0.4026  cls_loss: 0.2004  layout_loss: 0.3468  cls_layout_loss: 0.0347
2025/09/13 13:42:44 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 13:42:44 - mmengine - INFO - Epoch(train)  [1][1000/1099]  lr: 1.0000e-03  eta: 10:43:58  time: 3.0989  data_time: 0.1791  memory: 26806  grad_norm: 23.3966  loss: 0.9196  bbox_loss: 0.3985  cls_loss: 0.1904  layout_loss: 0.2984  cls_layout_loss: 0.0324
2025/09/13 13:45:24 - mmengine - INFO - Epoch(train)  [1][1050/1099]  lr: 1.0000e-03  eta: 10:41:37  time: 3.2015  data_time: 0.1768  memory: 25043  grad_norm: 15.5485  loss: 0.9071  bbox_loss: 0.3754  cls_loss: 0.1936  layout_loss: 0.3057  cls_layout_loss: 0.0323
2025/09/13 13:47:58 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 13:47:58 - mmengine - INFO - Saving checkpoint at 1 epochs
2025/09/13 13:48:40 - mmengine - INFO - Epoch(val)  [1][ 50/241]    eta: 0:02:39  time: 0.8371  data_time: 0.0977  memory: 26286  
2025/09/13 13:49:17 - mmengine - INFO - Epoch(val)  [1][100/241]    eta: 0:01:50  time: 0.7241  data_time: 0.0770  memory: 1369  
2025/09/13 13:49:58 - mmengine - INFO - Epoch(val)  [1][150/241]    eta: 0:01:12  time: 0.8324  data_time: 0.0807  memory: 1230  
2025/09/13 13:50:37 - mmengine - INFO - Epoch(val)  [1][200/241]    eta: 0:00:32  time: 0.7671  data_time: 0.0830  memory: 1081  
2025/09/13 13:52:14 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8017      | 0.7587      |
| door    | 0.8517      | 0.8432      |
| window  | 0.7778      | 0.7628      |
+---------+-------------+-------------+
| Overall | 0.8104      | 0.7882      |
+---------+-------------+-------------+
2025/09/13 13:52:14 - mmengine - INFO - Epoch(val) [1][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8016858904256758, 'wall_f1_50': 0.8016858904256758, 'door_f1_25': 0.8517388530806652, 'door_f1_50': 0.8517388530806652, 'window_f1_25': 0.7778354591812932, 'window_f1_50': 0.7778354591812932, 'f1_25': 0.8104200675625447, 'f1_50': 0.7882330456312149}}  data_time: 0.0836  time: 0.8073
2025/09/13 13:54:52 - mmengine - INFO - Epoch(train)  [2][  50/1099]  lr: 1.0000e-03  eta: 10:36:04  time: 3.1713  data_time: 0.2631  memory: 27430  grad_norm: 14.9634  loss: 0.8698  bbox_loss: 0.3701  cls_loss: 0.1816  layout_loss: 0.2868  cls_layout_loss: 0.0314
2025/09/13 13:57:33 - mmengine - INFO - Epoch(train)  [2][ 100/1099]  lr: 1.0000e-03  eta: 10:33:51  time: 3.2202  data_time: 0.2075  memory: 26628  grad_norm: 15.2659  loss: 0.8886  bbox_loss: 0.3743  cls_loss: 0.1843  layout_loss: 0.2982  cls_layout_loss: 0.0318
2025/09/13 14:00:16 - mmengine - INFO - Epoch(train)  [2][ 150/1099]  lr: 1.0000e-03  eta: 10:31:47  time: 3.2444  data_time: 0.1800  memory: 29871  grad_norm: 14.6373  loss: 0.8841  bbox_loss: 0.3695  cls_loss: 0.1853  layout_loss: 0.2980  cls_layout_loss: 0.0313
2025/09/13 14:02:50 - mmengine - INFO - Epoch(train)  [2][ 200/1099]  lr: 1.0000e-03  eta: 10:28:31  time: 3.0954  data_time: 0.1357  memory: 27881  grad_norm: 14.6564  loss: 0.8575  bbox_loss: 0.3690  cls_loss: 0.1829  layout_loss: 0.2756  cls_layout_loss: 0.0300
2025/09/13 14:05:26 - mmengine - INFO - Epoch(train)  [2][ 250/1099]  lr: 1.0000e-03  eta: 10:25:31  time: 3.1230  data_time: 0.1686  memory: 26169  grad_norm: 13.0837  loss: 0.8534  bbox_loss: 0.3583  cls_loss: 0.1757  layout_loss: 0.2891  cls_layout_loss: 0.0303
2025/09/13 14:08:03 - mmengine - INFO - Epoch(train)  [2][ 300/1099]  lr: 1.0000e-03  eta: 10:22:38  time: 3.1348  data_time: 0.1507  memory: 36044  grad_norm: 13.7084  loss: 0.8719  bbox_loss: 0.3631  cls_loss: 0.1849  layout_loss: 0.2933  cls_layout_loss: 0.0307
2025/09/13 14:10:42 - mmengine - INFO - Epoch(train)  [2][ 350/1099]  lr: 1.0000e-03  eta: 10:20:01  time: 3.1719  data_time: 0.1567  memory: 29802  grad_norm: 15.5127  loss: 0.8344  bbox_loss: 0.3590  cls_loss: 0.1715  layout_loss: 0.2741  cls_layout_loss: 0.0298
2025/09/13 14:13:21 - mmengine - INFO - Epoch(train)  [2][ 400/1099]  lr: 1.0000e-03  eta: 10:17:30  time: 3.1884  data_time: 0.1578  memory: 27716  grad_norm: 11.3239  loss: 0.8369  bbox_loss: 0.3557  cls_loss: 0.1737  layout_loss: 0.2775  cls_layout_loss: 0.0300
2025/09/13 14:15:57 - mmengine - INFO - Epoch(train)  [2][ 450/1099]  lr: 1.0000e-03  eta: 10:14:30  time: 3.1131  data_time: 0.1652  memory: 27806  grad_norm: 14.1903  loss: 0.8433  bbox_loss: 0.3578  cls_loss: 0.1774  layout_loss: 0.2781  cls_layout_loss: 0.0300
2025/09/13 14:18:33 - mmengine - INFO - Epoch(train)  [2][ 500/1099]  lr: 1.0000e-03  eta: 10:11:38  time: 3.1302  data_time: 0.1939  memory: 26894  grad_norm: 19.3871  loss: 0.8109  bbox_loss: 0.3612  cls_loss: 0.1641  layout_loss: 0.2572  cls_layout_loss: 0.0284
2025/09/13 14:21:10 - mmengine - INFO - Epoch(train)  [2][ 550/1099]  lr: 1.0000e-03  eta: 10:08:45  time: 3.1238  data_time: 0.1662  memory: 25521  grad_norm: 11.0723  loss: 0.8446  bbox_loss: 0.3597  cls_loss: 0.1807  layout_loss: 0.2750  cls_layout_loss: 0.0293
2025/09/13 14:23:50 - mmengine - INFO - Epoch(train)  [2][ 600/1099]  lr: 1.0000e-03  eta: 10:06:19  time: 3.2028  data_time: 0.1799  memory: 28920  grad_norm: 12.0865  loss: 0.8226  bbox_loss: 0.3504  cls_loss: 0.1727  layout_loss: 0.2707  cls_layout_loss: 0.0288
2025/09/13 14:26:28 - mmengine - INFO - Epoch(train)  [2][ 650/1099]  lr: 1.0000e-03  eta: 10:03:40  time: 3.1630  data_time: 0.1884  memory: 29793  grad_norm: 15.8633  loss: 0.8167  bbox_loss: 0.3529  cls_loss: 0.1700  layout_loss: 0.2657  cls_layout_loss: 0.0281
2025/09/13 14:29:04 - mmengine - INFO - Epoch(train)  [2][ 700/1099]  lr: 1.0000e-03  eta: 10:00:44  time: 3.1106  data_time: 0.1728  memory: 28310  grad_norm: 16.1020  loss: 0.7943  bbox_loss: 0.3510  cls_loss: 0.1640  layout_loss: 0.2521  cls_layout_loss: 0.0272
2025/09/13 14:31:39 - mmengine - INFO - Epoch(train)  [2][ 750/1099]  lr: 1.0000e-03  eta: 9:57:48  time: 3.1070  data_time: 0.1481  memory: 29672  grad_norm: 13.2598  loss: 0.8128  bbox_loss: 0.3532  cls_loss: 0.1714  layout_loss: 0.2596  cls_layout_loss: 0.0286
2025/09/13 14:34:16 - mmengine - INFO - Epoch(train)  [2][ 800/1099]  lr: 1.0000e-03  eta: 9:55:02  time: 3.1391  data_time: 0.1515  memory: 27498  grad_norm: 12.3919  loss: 0.8170  bbox_loss: 0.3489  cls_loss: 0.1673  layout_loss: 0.2720  cls_layout_loss: 0.0287
2025/09/13 14:36:51 - mmengine - INFO - Epoch(train)  [2][ 850/1099]  lr: 1.0000e-03  eta: 9:52:09  time: 3.1091  data_time: 0.1592  memory: 31462  grad_norm: 12.3094  loss: 0.8003  bbox_loss: 0.3430  cls_loss: 0.1702  layout_loss: 0.2595  cls_layout_loss: 0.0277
2025/09/13 14:39:27 - mmengine - INFO - Epoch(train)  [2][ 900/1099]  lr: 1.0000e-03  eta: 9:49:21  time: 3.1255  data_time: 0.1652  memory: 29827  grad_norm: 12.8247  loss: 0.8095  bbox_loss: 0.3466  cls_loss: 0.1673  layout_loss: 0.2673  cls_layout_loss: 0.0284
2025/09/13 14:39:30 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 14:42:08 - mmengine - INFO - Epoch(train)  [2][ 950/1099]  lr: 1.0000e-03  eta: 9:46:57  time: 3.2118  data_time: 0.1558  memory: 27809  grad_norm: 10.9599  loss: 0.7900  bbox_loss: 0.3386  cls_loss: 0.1648  layout_loss: 0.2591  cls_layout_loss: 0.0274
2025/09/13 14:44:52 - mmengine - INFO - Epoch(train)  [2][1000/1099]  lr: 1.0000e-03  eta: 9:44:47  time: 3.2703  data_time: 0.1554  memory: 33632  grad_norm: 12.4292  loss: 0.7835  bbox_loss: 0.3377  cls_loss: 0.1589  layout_loss: 0.2601  cls_layout_loss: 0.0268
2025/09/13 14:47:29 - mmengine - INFO - Epoch(train)  [2][1050/1099]  lr: 1.0000e-03  eta: 9:42:04  time: 3.1453  data_time: 0.1923  memory: 27538  grad_norm: 12.3213  loss: 0.8025  bbox_loss: 0.3414  cls_loss: 0.1662  layout_loss: 0.2675  cls_layout_loss: 0.0274
2025/09/13 14:49:59 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 14:49:59 - mmengine - INFO - Saving checkpoint at 2 epochs
2025/09/13 14:50:41 - mmengine - INFO - Epoch(val)  [2][ 50/241]    eta: 0:02:36  time: 0.8192  data_time: 0.0691  memory: 30380  
2025/09/13 14:51:15 - mmengine - INFO - Epoch(val)  [2][100/241]    eta: 0:01:44  time: 0.6686  data_time: 0.0978  memory: 1369  
2025/09/13 14:51:54 - mmengine - INFO - Epoch(val)  [2][150/241]    eta: 0:01:08  time: 0.7812  data_time: 0.0803  memory: 1230  
2025/09/13 14:52:31 - mmengine - INFO - Epoch(val)  [2][200/241]    eta: 0:00:30  time: 0.7529  data_time: 0.0986  memory: 1081  
2025/09/13 14:54:07 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8248      | 0.7920      |
| door    | 0.8874      | 0.8784      |
| window  | 0.8189      | 0.7978      |
+---------+-------------+-------------+
| Overall | 0.8437      | 0.8227      |
+---------+-------------+-------------+
2025/09/13 14:54:07 - mmengine - INFO - Epoch(val) [2][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8247947830014796, 'wall_f1_50': 0.8247947830014796, 'door_f1_25': 0.8874413840752182, 'door_f1_50': 0.8874413840752182, 'window_f1_25': 0.8189078864986725, 'window_f1_50': 0.8189078864986725, 'f1_25': 0.8437146845251234, 'f1_50': 0.822717278745543}}  data_time: 0.0825  time: 0.7659
2025/09/13 14:56:51 - mmengine - INFO - Epoch(train)  [3][  50/1099]  lr: 1.0000e-03  eta: 9:36:54  time: 3.2799  data_time: 0.2795  memory: 28956  grad_norm: 11.7121  loss: 0.7695  bbox_loss: 0.3386  cls_loss: 0.1563  layout_loss: 0.2478  cls_layout_loss: 0.0268
2025/09/13 14:59:22 - mmengine - INFO - Epoch(train)  [3][ 100/1099]  lr: 1.0000e-03  eta: 9:33:44  time: 3.0284  data_time: 0.1268  memory: 27584  grad_norm: 10.4452  loss: 0.7595  bbox_loss: 0.3359  cls_loss: 0.1588  layout_loss: 0.2386  cls_layout_loss: 0.0262
2025/09/13 15:01:59 - mmengine - INFO - Epoch(train)  [3][ 150/1099]  lr: 1.0000e-03  eta: 9:31:01  time: 3.1397  data_time: 0.1756  memory: 26712  grad_norm: 13.0512  loss: 0.7818  bbox_loss: 0.3360  cls_loss: 0.1650  layout_loss: 0.2542  cls_layout_loss: 0.0266
2025/09/13 15:04:40 - mmengine - INFO - Epoch(train)  [3][ 200/1099]  lr: 1.0000e-03  eta: 9:28:36  time: 3.2173  data_time: 0.1589  memory: 32135  grad_norm: 10.5807  loss: 0.7753  bbox_loss: 0.3346  cls_loss: 0.1590  layout_loss: 0.2554  cls_layout_loss: 0.0263
2025/09/13 15:07:25 - mmengine - INFO - Epoch(train)  [3][ 250/1099]  lr: 1.0000e-03  eta: 9:26:26  time: 3.2900  data_time: 0.1776  memory: 26889  grad_norm: 14.1275  loss: 0.7560  bbox_loss: 0.3371  cls_loss: 0.1545  layout_loss: 0.2385  cls_layout_loss: 0.0259
2025/09/13 15:10:06 - mmengine - INFO - Epoch(train)  [3][ 300/1099]  lr: 1.0000e-03  eta: 9:24:03  time: 3.2359  data_time: 0.1563  memory: 33934  grad_norm: 10.0496  loss: 0.7705  bbox_loss: 0.3322  cls_loss: 0.1541  layout_loss: 0.2578  cls_layout_loss: 0.0264
2025/09/13 15:12:50 - mmengine - INFO - Epoch(train)  [3][ 350/1099]  lr: 1.0000e-03  eta: 9:21:46  time: 3.2683  data_time: 0.1900  memory: 32598  grad_norm: 12.4778  loss: 0.7493  bbox_loss: 0.3330  cls_loss: 0.1532  layout_loss: 0.2373  cls_layout_loss: 0.0258
2025/09/13 15:15:34 - mmengine - INFO - Epoch(train)  [3][ 400/1099]  lr: 1.0000e-03  eta: 9:19:30  time: 3.2779  data_time: 0.1770  memory: 28900  grad_norm: 11.5219  loss: 0.7444  bbox_loss: 0.3280  cls_loss: 0.1495  layout_loss: 0.2417  cls_layout_loss: 0.0251
2025/09/13 15:18:15 - mmengine - INFO - Epoch(train)  [3][ 450/1099]  lr: 1.0000e-03  eta: 9:17:03  time: 3.2284  data_time: 0.1766  memory: 27066  grad_norm: 11.6814  loss: 0.7328  bbox_loss: 0.3309  cls_loss: 0.1511  layout_loss: 0.2258  cls_layout_loss: 0.0251
2025/09/13 15:20:57 - mmengine - INFO - Epoch(train)  [3][ 500/1099]  lr: 1.0000e-03  eta: 9:14:37  time: 3.2347  data_time: 0.1524  memory: 28346  grad_norm: 10.5252  loss: 0.7590  bbox_loss: 0.3306  cls_loss: 0.1532  layout_loss: 0.2496  cls_layout_loss: 0.0257
2025/09/13 15:23:38 - mmengine - INFO - Epoch(train)  [3][ 550/1099]  lr: 1.0000e-03  eta: 9:12:05  time: 3.2080  data_time: 0.1527  memory: 26013  grad_norm: 13.2540  loss: 0.7418  bbox_loss: 0.3282  cls_loss: 0.1484  layout_loss: 0.2399  cls_layout_loss: 0.0253
2025/09/13 15:26:19 - mmengine - INFO - Epoch(train)  [3][ 600/1099]  lr: 1.0000e-03  eta: 9:09:38  time: 3.2338  data_time: 0.1815  memory: 28684  grad_norm: 9.1707  loss: 0.7516  bbox_loss: 0.3228  cls_loss: 0.1479  layout_loss: 0.2554  cls_layout_loss: 0.0254
2025/09/13 15:28:54 - mmengine - INFO - Epoch(train)  [3][ 650/1099]  lr: 1.0000e-03  eta: 9:06:46  time: 3.1058  data_time: 0.1322  memory: 27856  grad_norm: 9.9702  loss: 0.7387  bbox_loss: 0.3219  cls_loss: 0.1503  layout_loss: 0.2415  cls_layout_loss: 0.0250
2025/09/13 15:31:35 - mmengine - INFO - Epoch(train)  [3][ 700/1099]  lr: 1.0000e-03  eta: 9:04:13  time: 3.2016  data_time: 0.1474  memory: 28139  grad_norm: 10.1922  loss: 0.7315  bbox_loss: 0.3256  cls_loss: 0.1497  layout_loss: 0.2314  cls_layout_loss: 0.0247
2025/09/13 15:34:21 - mmengine - INFO - Epoch(train)  [3][ 750/1099]  lr: 1.0000e-03  eta: 9:02:01  time: 3.3271  data_time: 0.1509  memory: 30625  grad_norm: 10.4234  loss: 0.7564  bbox_loss: 0.3303  cls_loss: 0.1564  layout_loss: 0.2442  cls_layout_loss: 0.0255
2025/09/13 15:36:57 - mmengine - INFO - Epoch(train)  [3][ 800/1099]  lr: 1.0000e-03  eta: 8:59:12  time: 3.1167  data_time: 0.1500  memory: 26731  grad_norm: 10.6396  loss: 0.7178  bbox_loss: 0.3241  cls_loss: 0.1464  layout_loss: 0.2231  cls_layout_loss: 0.0242
2025/09/13 15:37:03 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 15:39:35 - mmengine - INFO - Epoch(train)  [3][ 850/1099]  lr: 1.0000e-03  eta: 8:56:30  time: 3.1566  data_time: 0.1816  memory: 26593  grad_norm: 10.3285  loss: 0.7261  bbox_loss: 0.3247  cls_loss: 0.1479  layout_loss: 0.2291  cls_layout_loss: 0.0244
2025/09/13 15:42:18 - mmengine - INFO - Epoch(train)  [3][ 900/1099]  lr: 1.0000e-03  eta: 8:54:07  time: 3.2687  data_time: 0.1844  memory: 35031  grad_norm: 9.5217  loss: 0.7382  bbox_loss: 0.3208  cls_loss: 0.1503  layout_loss: 0.2421  cls_layout_loss: 0.0250
2025/09/13 15:44:55 - mmengine - INFO - Epoch(train)  [3][ 950/1099]  lr: 1.0000e-03  eta: 8:51:23  time: 3.1429  data_time: 0.1447  memory: 26394  grad_norm: 9.9423  loss: 0.7300  bbox_loss: 0.3237  cls_loss: 0.1547  layout_loss: 0.2266  cls_layout_loss: 0.0250
2025/09/13 15:47:32 - mmengine - INFO - Epoch(train)  [3][1000/1099]  lr: 1.0000e-03  eta: 8:48:37  time: 3.1335  data_time: 0.1487  memory: 27577  grad_norm: 8.3437  loss: 0.7152  bbox_loss: 0.3217  cls_loss: 0.1460  layout_loss: 0.2229  cls_layout_loss: 0.0245
2025/09/13 15:50:12 - mmengine - INFO - Epoch(train)  [3][1050/1099]  lr: 1.0000e-03  eta: 8:46:03  time: 3.2035  data_time: 0.1223  memory: 31434  grad_norm: 9.6703  loss: 0.7159  bbox_loss: 0.3214  cls_loss: 0.1465  layout_loss: 0.2235  cls_layout_loss: 0.0244
2025/09/13 15:52:47 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 15:52:47 - mmengine - INFO - Saving checkpoint at 3 epochs
2025/09/13 15:53:30 - mmengine - INFO - Epoch(val)  [3][ 50/241]    eta: 0:02:38  time: 0.8314  data_time: 0.0641  memory: 26593  
2025/09/13 15:54:03 - mmengine - INFO - Epoch(val)  [3][100/241]    eta: 0:01:45  time: 0.6714  data_time: 0.0290  memory: 1369  
2025/09/13 15:54:43 - mmengine - INFO - Epoch(val)  [3][150/241]    eta: 0:01:09  time: 0.7897  data_time: 0.0406  memory: 1230  
2025/09/13 15:55:20 - mmengine - INFO - Epoch(val)  [3][200/241]    eta: 0:00:31  time: 0.7424  data_time: 0.0663  memory: 1081  
2025/09/13 15:56:54 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8370      | 0.8085      |
| door    | 0.9082      | 0.9032      |
| window  | 0.8277      | 0.8134      |
+---------+-------------+-------------+
| Overall | 0.8577      | 0.8417      |
+---------+-------------+-------------+
2025/09/13 15:56:54 - mmengine - INFO - Epoch(val) [3][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8370050194970318, 'wall_f1_50': 0.8370050194970318, 'door_f1_25': 0.9082381395668033, 'door_f1_50': 0.9082381395668033, 'window_f1_25': 0.8277157733439822, 'window_f1_50': 0.8277157733439822, 'f1_25': 0.8576529774692725, 'f1_50': 0.8416883973680691}}  data_time: 0.0543  time: 0.7698
2025/09/13 15:59:31 - mmengine - INFO - Epoch(train)  [4][  50/1099]  lr: 1.0000e-03  eta: 8:40:40  time: 3.1362  data_time: 0.2099  memory: 29074  grad_norm: 9.3336  loss: 0.6909  bbox_loss: 0.3163  cls_loss: 0.1391  layout_loss: 0.2115  cls_layout_loss: 0.0240
2025/09/13 16:02:15 - mmengine - INFO - Epoch(train)  [4][ 100/1099]  lr: 1.0000e-03  eta: 8:38:15  time: 3.2748  data_time: 0.1616  memory: 26766  grad_norm: 9.9993  loss: 0.7084  bbox_loss: 0.3174  cls_loss: 0.1413  layout_loss: 0.2259  cls_layout_loss: 0.0238
2025/09/13 16:04:54 - mmengine - INFO - Epoch(train)  [4][ 150/1099]  lr: 1.0000e-03  eta: 8:35:36  time: 3.1735  data_time: 0.1549  memory: 27890  grad_norm: 10.1389  loss: 0.7295  bbox_loss: 0.3207  cls_loss: 0.1490  layout_loss: 0.2355  cls_layout_loss: 0.0243
2025/09/13 16:07:35 - mmengine - INFO - Epoch(train)  [4][ 200/1099]  lr: 1.0000e-03  eta: 8:33:04  time: 3.2268  data_time: 0.1609  memory: 32135  grad_norm: 9.1830  loss: 0.7270  bbox_loss: 0.3197  cls_loss: 0.1489  layout_loss: 0.2338  cls_layout_loss: 0.0245
2025/09/13 16:10:16 - mmengine - INFO - Epoch(train)  [4][ 250/1099]  lr: 1.0000e-03  eta: 8:30:31  time: 3.2171  data_time: 0.1554  memory: 29855  grad_norm: 10.5340  loss: 0.6820  bbox_loss: 0.3151  cls_loss: 0.1387  layout_loss: 0.2046  cls_layout_loss: 0.0237
2025/09/13 16:12:55 - mmengine - INFO - Epoch(train)  [4][ 300/1099]  lr: 1.0000e-03  eta: 8:27:53  time: 3.1812  data_time: 0.1656  memory: 30636  grad_norm: 9.8181  loss: 0.7085  bbox_loss: 0.3171  cls_loss: 0.1413  layout_loss: 0.2266  cls_layout_loss: 0.0234
2025/09/13 16:15:37 - mmengine - INFO - Epoch(train)  [4][ 350/1099]  lr: 1.0000e-03  eta: 8:25:21  time: 3.2327  data_time: 0.1937  memory: 33110  grad_norm: 7.7999  loss: 0.6953  bbox_loss: 0.3126  cls_loss: 0.1402  layout_loss: 0.2193  cls_layout_loss: 0.0232
2025/09/13 16:18:21 - mmengine - INFO - Epoch(train)  [4][ 400/1099]  lr: 1.0000e-03  eta: 8:22:56  time: 3.2832  data_time: 0.1922  memory: 30274  grad_norm: 9.0177  loss: 0.7247  bbox_loss: 0.3146  cls_loss: 0.1490  layout_loss: 0.2364  cls_layout_loss: 0.0246
2025/09/13 16:21:00 - mmengine - INFO - Epoch(train)  [4][ 450/1099]  lr: 1.0000e-03  eta: 8:20:17  time: 3.1855  data_time: 0.1525  memory: 27957  grad_norm: 9.4082  loss: 0.6876  bbox_loss: 0.3121  cls_loss: 0.1373  layout_loss: 0.2145  cls_layout_loss: 0.0237
2025/09/13 16:23:43 - mmengine - INFO - Epoch(train)  [4][ 500/1099]  lr: 1.0000e-03  eta: 8:17:49  time: 3.2632  data_time: 0.2048  memory: 26878  grad_norm: 8.7611  loss: 0.6969  bbox_loss: 0.3157  cls_loss: 0.1419  layout_loss: 0.2162  cls_layout_loss: 0.0230
2025/09/13 16:26:22 - mmengine - INFO - Epoch(train)  [4][ 550/1099]  lr: 1.0000e-03  eta: 8:15:09  time: 3.1764  data_time: 0.1483  memory: 27969  grad_norm: 9.8938  loss: 0.6919  bbox_loss: 0.3155  cls_loss: 0.1370  layout_loss: 0.2163  cls_layout_loss: 0.0232
2025/09/13 16:29:02 - mmengine - INFO - Epoch(train)  [4][ 600/1099]  lr: 1.0000e-03  eta: 8:12:33  time: 3.2016  data_time: 0.1669  memory: 28888  grad_norm: 8.2858  loss: 0.6939  bbox_loss: 0.3153  cls_loss: 0.1432  layout_loss: 0.2118  cls_layout_loss: 0.0236
2025/09/13 16:31:41 - mmengine - INFO - Epoch(train)  [4][ 650/1099]  lr: 1.0000e-03  eta: 8:09:53  time: 3.1721  data_time: 0.1879  memory: 28203  grad_norm: 7.6550  loss: 0.7096  bbox_loss: 0.3141  cls_loss: 0.1416  layout_loss: 0.2302  cls_layout_loss: 0.0237
2025/09/13 16:34:24 - mmengine - INFO - Epoch(train)  [4][ 700/1099]  lr: 1.0000e-03  eta: 8:07:23  time: 3.2646  data_time: 0.1651  memory: 31374  grad_norm: 8.7400  loss: 0.7197  bbox_loss: 0.3161  cls_loss: 0.1491  layout_loss: 0.2309  cls_layout_loss: 0.0236
2025/09/13 16:34:34 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 16:37:02 - mmengine - INFO - Epoch(train)  [4][ 750/1099]  lr: 1.0000e-03  eta: 8:04:42  time: 3.1650  data_time: 0.1357  memory: 29845  grad_norm: 9.3675  loss: 0.6859  bbox_loss: 0.3109  cls_loss: 0.1402  layout_loss: 0.2115  cls_layout_loss: 0.0232
2025/09/13 16:39:45 - mmengine - INFO - Epoch(train)  [4][ 800/1099]  lr: 1.0000e-03  eta: 8:02:11  time: 3.2529  data_time: 0.1659  memory: 28980  grad_norm: 8.5838  loss: 0.6686  bbox_loss: 0.3108  cls_loss: 0.1352  layout_loss: 0.2000  cls_layout_loss: 0.0226
2025/09/13 16:42:20 - mmengine - INFO - Epoch(train)  [4][ 850/1099]  lr: 1.0000e-03  eta: 7:59:23  time: 3.1025  data_time: 0.1543  memory: 30674  grad_norm: 10.7037  loss: 0.7034  bbox_loss: 0.3157  cls_loss: 0.1449  layout_loss: 0.2192  cls_layout_loss: 0.0235
2025/09/13 16:45:00 - mmengine - INFO - Epoch(train)  [4][ 900/1099]  lr: 1.0000e-03  eta: 7:56:46  time: 3.2009  data_time: 0.1726  memory: 27565  grad_norm: 8.5167  loss: 0.6827  bbox_loss: 0.3090  cls_loss: 0.1365  layout_loss: 0.2147  cls_layout_loss: 0.0225
2025/09/13 16:47:41 - mmengine - INFO - Epoch(train)  [4][ 950/1099]  lr: 1.0000e-03  eta: 7:54:11  time: 3.2138  data_time: 0.1724  memory: 31564  grad_norm: 7.8274  loss: 0.6706  bbox_loss: 0.3102  cls_loss: 0.1360  layout_loss: 0.2016  cls_layout_loss: 0.0227
2025/09/13 16:50:20 - mmengine - INFO - Epoch(train)  [4][1000/1099]  lr: 1.0000e-03  eta: 7:51:33  time: 3.1943  data_time: 0.1585  memory: 30819  grad_norm: 9.2864  loss: 0.6723  bbox_loss: 0.3080  cls_loss: 0.1342  layout_loss: 0.2074  cls_layout_loss: 0.0227
2025/09/13 16:53:02 - mmengine - INFO - Epoch(train)  [4][1050/1099]  lr: 1.0000e-03  eta: 7:48:58  time: 3.2294  data_time: 0.1451  memory: 27379  grad_norm: 7.5440  loss: 0.6779  bbox_loss: 0.3095  cls_loss: 0.1398  layout_loss: 0.2060  cls_layout_loss: 0.0226
2025/09/13 16:55:30 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 16:55:30 - mmengine - INFO - Saving checkpoint at 4 epochs
2025/09/13 16:56:12 - mmengine - INFO - Epoch(val)  [4][ 50/241]    eta: 0:02:36  time: 0.8195  data_time: 0.0591  memory: 28143  
2025/09/13 16:56:45 - mmengine - INFO - Epoch(val)  [4][100/241]    eta: 0:01:43  time: 0.6487  data_time: 0.0666  memory: 1369  
2025/09/13 16:57:23 - mmengine - INFO - Epoch(val)  [4][150/241]    eta: 0:01:07  time: 0.7617  data_time: 0.0559  memory: 1230  
2025/09/13 16:57:58 - mmengine - INFO - Epoch(val)  [4][200/241]    eta: 0:00:30  time: 0.7127  data_time: 0.0815  memory: 1081  
2025/09/13 16:59:34 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8520      | 0.8316      |
| door    | 0.9158      | 0.9113      |
| window  | 0.8513      | 0.8403      |
+---------+-------------+-------------+
| Overall | 0.8731      | 0.8611      |
+---------+-------------+-------------+
2025/09/13 16:59:34 - mmengine - INFO - Epoch(val) [4][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8519955832524444, 'wall_f1_50': 0.8519955832524444, 'door_f1_25': 0.9158427468410787, 'door_f1_50': 0.9158427468410787, 'window_f1_25': 0.8513290629773265, 'window_f1_50': 0.8513290629773265, 'f1_25': 0.8730557976902832, 'f1_50': 0.8610649718667661}}  data_time: 0.0661  time: 0.7538
2025/09/13 17:02:21 - mmengine - INFO - Epoch(train)  [5][  50/1099]  lr: 1.0000e-03  eta: 7:43:43  time: 3.3271  data_time: 0.2737  memory: 29676  grad_norm: 7.3469  loss: 0.6929  bbox_loss: 0.3072  cls_loss: 0.1426  layout_loss: 0.2205  cls_layout_loss: 0.0226
2025/09/13 17:04:59 - mmengine - INFO - Epoch(train)  [5][ 100/1099]  lr: 1.0000e-03  eta: 7:41:03  time: 3.1789  data_time: 0.1746  memory: 27363  grad_norm: 8.1686  loss: 0.6643  bbox_loss: 0.3065  cls_loss: 0.1359  layout_loss: 0.1997  cls_layout_loss: 0.0222
2025/09/13 17:07:42 - mmengine - INFO - Epoch(train)  [5][ 150/1099]  lr: 1.0000e-03  eta: 7:38:30  time: 3.2499  data_time: 0.1887  memory: 30927  grad_norm: 7.8508  loss: 0.6610  bbox_loss: 0.3041  cls_loss: 0.1314  layout_loss: 0.2032  cls_layout_loss: 0.0222
2025/09/13 17:10:15 - mmengine - INFO - Epoch(train)  [5][ 200/1099]  lr: 1.0000e-03  eta: 7:35:40  time: 3.0687  data_time: 0.1590  memory: 23976  grad_norm: 8.3836  loss: 0.6539  bbox_loss: 0.3044  cls_loss: 0.1301  layout_loss: 0.1975  cls_layout_loss: 0.0219
2025/09/13 17:12:51 - mmengine - INFO - Epoch(train)  [5][ 250/1099]  lr: 1.0000e-03  eta: 7:32:55  time: 3.1088  data_time: 0.1433  memory: 29848  grad_norm: 9.1917  loss: 0.6729  bbox_loss: 0.3115  cls_loss: 0.1362  layout_loss: 0.2026  cls_layout_loss: 0.0226
2025/09/13 17:15:37 - mmengine - INFO - Epoch(train)  [5][ 300/1099]  lr: 1.0000e-03  eta: 7:30:30  time: 3.3378  data_time: 0.1744  memory: 26734  grad_norm: 7.3828  loss: 0.6577  bbox_loss: 0.3026  cls_loss: 0.1335  layout_loss: 0.1997  cls_layout_loss: 0.0219
2025/09/13 17:18:13 - mmengine - INFO - Epoch(train)  [5][ 350/1099]  lr: 1.0000e-03  eta: 7:27:44  time: 3.1147  data_time: 0.1502  memory: 26392  grad_norm: 6.9180  loss: 0.6563  bbox_loss: 0.3018  cls_loss: 0.1335  layout_loss: 0.1989  cls_layout_loss: 0.0221
2025/09/13 17:20:53 - mmengine - INFO - Epoch(train)  [5][ 400/1099]  lr: 1.0000e-03  eta: 7:25:06  time: 3.1940  data_time: 0.2013  memory: 29503  grad_norm: 8.7732  loss: 0.6650  bbox_loss: 0.3064  cls_loss: 0.1331  layout_loss: 0.2033  cls_layout_loss: 0.0221
2025/09/13 17:23:36 - mmengine - INFO - Epoch(train)  [5][ 450/1099]  lr: 1.0000e-03  eta: 7:22:34  time: 3.2569  data_time: 0.1768  memory: 27665  grad_norm: 6.8143  loss: 0.6585  bbox_loss: 0.3026  cls_loss: 0.1346  layout_loss: 0.1988  cls_layout_loss: 0.0225
2025/09/13 17:26:17 - mmengine - INFO - Epoch(train)  [5][ 500/1099]  lr: 1.0000e-03  eta: 7:19:58  time: 3.2300  data_time: 0.1867  memory: 30168  grad_norm: 7.8502  loss: 0.6783  bbox_loss: 0.3108  cls_loss: 0.1367  layout_loss: 0.2082  cls_layout_loss: 0.0226
2025/09/13 17:29:00 - mmengine - INFO - Epoch(train)  [5][ 550/1099]  lr: 1.0000e-03  eta: 7:17:26  time: 3.2587  data_time: 0.1704  memory: 28526  grad_norm: 8.0814  loss: 0.6668  bbox_loss: 0.3012  cls_loss: 0.1360  layout_loss: 0.2075  cls_layout_loss: 0.0222
2025/09/13 17:31:39 - mmengine - INFO - Epoch(train)  [5][ 600/1099]  lr: 1.0000e-03  eta: 7:14:45  time: 3.1679  data_time: 0.1683  memory: 27560  grad_norm: 8.2348  loss: 0.6531  bbox_loss: 0.3018  cls_loss: 0.1287  layout_loss: 0.2006  cls_layout_loss: 0.0220
2025/09/13 17:31:51 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 17:34:20 - mmengine - INFO - Epoch(train)  [5][ 650/1099]  lr: 1.0000e-03  eta: 7:12:10  time: 3.2382  data_time: 0.1531  memory: 28162  grad_norm: 7.1332  loss: 0.6654  bbox_loss: 0.3038  cls_loss: 0.1336  layout_loss: 0.2058  cls_layout_loss: 0.0222
2025/09/13 17:36:58 - mmengine - INFO - Epoch(train)  [5][ 700/1099]  lr: 1.0000e-03  eta: 7:09:28  time: 3.1538  data_time: 0.1663  memory: 33758  grad_norm: 8.2948  loss: 0.6556  bbox_loss: 0.3044  cls_loss: 0.1305  layout_loss: 0.1985  cls_layout_loss: 0.0223
2025/09/13 17:39:40 - mmengine - INFO - Epoch(train)  [5][ 750/1099]  lr: 1.0000e-03  eta: 7:06:52  time: 3.2208  data_time: 0.1584  memory: 27618  grad_norm: 10.1578  loss: 0.6689  bbox_loss: 0.3094  cls_loss: 0.1324  layout_loss: 0.2046  cls_layout_loss: 0.0224
2025/09/13 17:42:18 - mmengine - INFO - Epoch(train)  [5][ 800/1099]  lr: 1.0000e-03  eta: 7:04:12  time: 3.1761  data_time: 0.1560  memory: 26116  grad_norm: 8.3738  loss: 0.6292  bbox_loss: 0.2996  cls_loss: 0.1240  layout_loss: 0.1844  cls_layout_loss: 0.0211
2025/09/13 17:44:58 - mmengine - INFO - Epoch(train)  [5][ 850/1099]  lr: 1.0000e-03  eta: 7:01:34  time: 3.1947  data_time: 0.1645  memory: 29065  grad_norm: 9.3925  loss: 0.6484  bbox_loss: 0.3024  cls_loss: 0.1278  layout_loss: 0.1965  cls_layout_loss: 0.0217
2025/09/13 17:47:39 - mmengine - INFO - Epoch(train)  [5][ 900/1099]  lr: 1.0000e-03  eta: 6:58:57  time: 3.2257  data_time: 0.1710  memory: 28619  grad_norm: 7.6833  loss: 0.6609  bbox_loss: 0.3038  cls_loss: 0.1344  layout_loss: 0.2009  cls_layout_loss: 0.0218
2025/09/13 17:50:17 - mmengine - INFO - Epoch(train)  [5][ 950/1099]  lr: 1.0000e-03  eta: 6:56:16  time: 3.1545  data_time: 0.1599  memory: 33319  grad_norm: 6.7463  loss: 0.6455  bbox_loss: 0.2985  cls_loss: 0.1297  layout_loss: 0.1959  cls_layout_loss: 0.0213
2025/09/13 17:52:57 - mmengine - INFO - Epoch(train)  [5][1000/1099]  lr: 1.0000e-03  eta: 6:53:38  time: 3.2052  data_time: 0.1853  memory: 42833  grad_norm: 7.1353  loss: 0.6492  bbox_loss: 0.2990  cls_loss: 0.1297  layout_loss: 0.1985  cls_layout_loss: 0.0220
2025/09/13 17:55:37 - mmengine - INFO - Epoch(train)  [5][1050/1099]  lr: 1.0000e-03  eta: 6:51:00  time: 3.1952  data_time: 0.1779  memory: 26245  grad_norm: 7.8461  loss: 0.6525  bbox_loss: 0.3032  cls_loss: 0.1317  layout_loss: 0.1962  cls_layout_loss: 0.0213
2025/09/13 17:58:08 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 17:58:09 - mmengine - INFO - Saving checkpoint at 5 epochs
2025/09/13 17:58:50 - mmengine - INFO - Epoch(val)  [5][ 50/241]    eta: 0:02:34  time: 0.8095  data_time: 0.0789  memory: 28048  
2025/09/13 17:59:26 - mmengine - INFO - Epoch(val)  [5][100/241]    eta: 0:01:47  time: 0.7219  data_time: 0.0746  memory: 1369  
2025/09/13 18:00:04 - mmengine - INFO - Epoch(val)  [5][150/241]    eta: 0:01:09  time: 0.7668  data_time: 0.0692  memory: 1230  
2025/09/13 18:00:42 - mmengine - INFO - Epoch(val)  [5][200/241]    eta: 0:00:31  time: 0.7489  data_time: 0.0938  memory: 1081  
2025/09/13 18:02:18 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8557      | 0.8359      |
| door    | 0.9176      | 0.9093      |
| window  | 0.8523      | 0.8362      |
+---------+-------------+-------------+
| Overall | 0.8752      | 0.8605      |
+---------+-------------+-------------+
2025/09/13 18:02:18 - mmengine - INFO - Epoch(val) [5][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8557155169996679, 'wall_f1_50': 0.8557155169996679, 'door_f1_25': 0.917635081256629, 'door_f1_50': 0.917635081256629, 'window_f1_25': 0.852285343163436, 'window_f1_50': 0.852285343163436, 'f1_25': 0.8752119804732442, 'f1_50': 0.8604501909411288}}  data_time: 0.0762  time: 0.7751
2025/09/13 18:04:57 - mmengine - INFO - Epoch(train)  [6][  50/1099]  lr: 1.0000e-03  eta: 6:45:37  time: 3.1777  data_time: 0.2887  memory: 27621  grad_norm: 7.5313  loss: 0.6427  bbox_loss: 0.2990  cls_loss: 0.1283  layout_loss: 0.1938  cls_layout_loss: 0.0216
2025/09/13 18:07:36 - mmengine - INFO - Epoch(train)  [6][ 100/1099]  lr: 1.0000e-03  eta: 6:42:58  time: 3.1824  data_time: 0.1936  memory: 29651  grad_norm: 8.1896  loss: 0.6483  bbox_loss: 0.3023  cls_loss: 0.1298  layout_loss: 0.1947  cls_layout_loss: 0.0215
2025/09/13 18:10:18 - mmengine - INFO - Epoch(train)  [6][ 150/1099]  lr: 1.0000e-03  eta: 6:40:22  time: 3.2319  data_time: 0.1343  memory: 31071  grad_norm: 8.7005  loss: 0.6570  bbox_loss: 0.3044  cls_loss: 0.1328  layout_loss: 0.1981  cls_layout_loss: 0.0217
2025/09/13 18:12:52 - mmengine - INFO - Epoch(train)  [6][ 200/1099]  lr: 1.0000e-03  eta: 6:37:35  time: 3.0792  data_time: 0.1512  memory: 25821  grad_norm: 6.8937  loss: 0.6109  bbox_loss: 0.2933  cls_loss: 0.1227  layout_loss: 0.1746  cls_layout_loss: 0.0203
2025/09/13 18:15:37 - mmengine - INFO - Epoch(train)  [6][ 250/1099]  lr: 1.0000e-03  eta: 6:35:03  time: 3.2951  data_time: 0.1850  memory: 36986  grad_norm: 6.7653  loss: 0.6556  bbox_loss: 0.2985  cls_loss: 0.1279  layout_loss: 0.2076  cls_layout_loss: 0.0216
2025/09/13 18:18:14 - mmengine - INFO - Epoch(train)  [6][ 300/1099]  lr: 1.0000e-03  eta: 6:32:22  time: 3.1567  data_time: 0.1700  memory: 26462  grad_norm: 8.4172  loss: 0.6366  bbox_loss: 0.3002  cls_loss: 0.1257  layout_loss: 0.1896  cls_layout_loss: 0.0211
2025/09/13 18:20:53 - mmengine - INFO - Epoch(train)  [6][ 350/1099]  lr: 1.0000e-03  eta: 6:29:42  time: 3.1725  data_time: 0.1517  memory: 30735  grad_norm: 6.9265  loss: 0.6481  bbox_loss: 0.2998  cls_loss: 0.1312  layout_loss: 0.1956  cls_layout_loss: 0.0214
2025/09/13 18:23:34 - mmengine - INFO - Epoch(train)  [6][ 400/1099]  lr: 1.0000e-03  eta: 6:27:05  time: 3.2165  data_time: 0.1531  memory: 31044  grad_norm: 7.0833  loss: 0.6555  bbox_loss: 0.3000  cls_loss: 0.1307  layout_loss: 0.2038  cls_layout_loss: 0.0211
2025/09/13 18:26:13 - mmengine - INFO - Epoch(train)  [6][ 450/1099]  lr: 1.0000e-03  eta: 6:24:26  time: 3.1835  data_time: 0.1431  memory: 31062  grad_norm: 7.8782  loss: 0.6496  bbox_loss: 0.3011  cls_loss: 0.1265  layout_loss: 0.2002  cls_layout_loss: 0.0219
2025/09/13 18:28:52 - mmengine - INFO - Epoch(train)  [6][ 500/1099]  lr: 1.0000e-03  eta: 6:21:46  time: 3.1743  data_time: 0.1394  memory: 27192  grad_norm: 6.2651  loss: 0.6265  bbox_loss: 0.2951  cls_loss: 0.1227  layout_loss: 0.1878  cls_layout_loss: 0.0209
2025/09/13 18:29:08 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 18:31:28 - mmengine - INFO - Epoch(train)  [6][ 550/1099]  lr: 1.0000e-03  eta: 6:19:03  time: 3.1239  data_time: 0.2019  memory: 26171  grad_norm: 8.6159  loss: 0.6309  bbox_loss: 0.2969  cls_loss: 0.1268  layout_loss: 0.1862  cls_layout_loss: 0.0211
2025/09/13 18:34:09 - mmengine - INFO - Epoch(train)  [6][ 600/1099]  lr: 1.0000e-03  eta: 6:16:26  time: 3.2228  data_time: 0.1394  memory: 29268  grad_norm: 6.7264  loss: 0.6387  bbox_loss: 0.2991  cls_loss: 0.1300  layout_loss: 0.1881  cls_layout_loss: 0.0215
2025/09/13 18:36:48 - mmengine - INFO - Epoch(train)  [6][ 650/1099]  lr: 1.0000e-03  eta: 6:13:47  time: 3.1781  data_time: 0.1379  memory: 29006  grad_norm: 6.4940  loss: 0.6189  bbox_loss: 0.2937  cls_loss: 0.1249  layout_loss: 0.1794  cls_layout_loss: 0.0209
2025/09/13 18:39:22 - mmengine - INFO - Epoch(train)  [6][ 700/1099]  lr: 1.0000e-03  eta: 6:11:02  time: 3.0922  data_time: 0.1547  memory: 29975  grad_norm: 6.3581  loss: 0.6232  bbox_loss: 0.2940  cls_loss: 0.1227  layout_loss: 0.1858  cls_layout_loss: 0.0207
2025/09/13 18:41:58 - mmengine - INFO - Epoch(train)  [6][ 750/1099]  lr: 1.0000e-03  eta: 6:08:19  time: 3.1160  data_time: 0.1509  memory: 33724  grad_norm: 6.6837  loss: 0.6346  bbox_loss: 0.2943  cls_loss: 0.1242  layout_loss: 0.1951  cls_layout_loss: 0.0210
2025/09/13 18:44:36 - mmengine - INFO - Epoch(train)  [6][ 800/1099]  lr: 1.0000e-03  eta: 6:05:39  time: 3.1532  data_time: 0.1739  memory: 26178  grad_norm: 7.5438  loss: 0.6240  bbox_loss: 0.2968  cls_loss: 0.1274  layout_loss: 0.1794  cls_layout_loss: 0.0205
2025/09/13 18:47:10 - mmengine - INFO - Epoch(train)  [6][ 850/1099]  lr: 1.0000e-03  eta: 6:02:54  time: 3.0858  data_time: 0.1492  memory: 25479  grad_norm: 6.4790  loss: 0.6226  bbox_loss: 0.2934  cls_loss: 0.1264  layout_loss: 0.1820  cls_layout_loss: 0.0208
2025/09/13 18:49:52 - mmengine - INFO - Epoch(train)  [6][ 900/1099]  lr: 1.0000e-03  eta: 6:00:18  time: 3.2446  data_time: 0.1995  memory: 28078  grad_norm: 7.1846  loss: 0.6280  bbox_loss: 0.2980  cls_loss: 0.1282  layout_loss: 0.1810  cls_layout_loss: 0.0208
2025/09/13 18:52:33 - mmengine - INFO - Epoch(train)  [6][ 950/1099]  lr: 1.0000e-03  eta: 5:57:41  time: 3.2185  data_time: 0.1600  memory: 34057  grad_norm: 5.8847  loss: 0.6383  bbox_loss: 0.2970  cls_loss: 0.1271  layout_loss: 0.1927  cls_layout_loss: 0.0214
2025/09/13 18:55:13 - mmengine - INFO - Epoch(train)  [6][1000/1099]  lr: 1.0000e-03  eta: 5:55:02  time: 3.1824  data_time: 0.1858  memory: 28759  grad_norm: 8.1307  loss: 0.6265  bbox_loss: 0.2968  cls_loss: 0.1236  layout_loss: 0.1849  cls_layout_loss: 0.0211
2025/09/13 18:57:53 - mmengine - INFO - Epoch(train)  [6][1050/1099]  lr: 1.0000e-03  eta: 5:52:24  time: 3.2048  data_time: 0.1571  memory: 28997  grad_norm: 6.7060  loss: 0.6179  bbox_loss: 0.2930  cls_loss: 0.1237  layout_loss: 0.1810  cls_layout_loss: 0.0202
2025/09/13 19:00:29 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 19:00:29 - mmengine - INFO - Saving checkpoint at 6 epochs
2025/09/13 19:01:09 - mmengine - INFO - Epoch(val)  [6][ 50/241]    eta: 0:02:26  time: 0.7673  data_time: 0.0825  memory: 29003  
2025/09/13 19:01:40 - mmengine - INFO - Epoch(val)  [6][100/241]    eta: 0:01:37  time: 0.6213  data_time: 0.0885  memory: 1369  
2025/09/13 19:02:15 - mmengine - INFO - Epoch(val)  [6][150/241]    eta: 0:01:03  time: 0.7099  data_time: 0.0712  memory: 1230  
2025/09/13 19:02:49 - mmengine - INFO - Epoch(val)  [6][200/241]    eta: 0:00:28  time: 0.6828  data_time: 0.0807  memory: 1081  
2025/09/13 19:04:24 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8606      | 0.8422      |
| door    | 0.9274      | 0.9232      |
| window  | 0.8722      | 0.8544      |
+---------+-------------+-------------+
| Overall | 0.8867      | 0.8732      |
+---------+-------------+-------------+
2025/09/13 19:04:24 - mmengine - INFO - Epoch(val) [6][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8606251303168886, 'wall_f1_50': 0.8606251303168886, 'door_f1_25': 0.9273867099453994, 'door_f1_50': 0.9273867099453994, 'window_f1_25': 0.8721593995488645, 'window_f1_50': 0.8721593995488645, 'f1_25': 0.8867237466037174, 'f1_50': 0.8732411124598274}}  data_time: 0.0764  time: 0.7138
2025/09/13 19:07:10 - mmengine - INFO - Epoch(train)  [7][  50/1099]  lr: 1.0000e-03  eta: 5:47:15  time: 3.3187  data_time: 0.2941  memory: 25199  grad_norm: 6.8318  loss: 0.6223  bbox_loss: 0.2933  cls_loss: 0.1224  layout_loss: 0.1860  cls_layout_loss: 0.0206
2025/09/13 19:09:48 - mmengine - INFO - Epoch(train)  [7][ 100/1099]  lr: 1.0000e-03  eta: 5:44:35  time: 3.1486  data_time: 0.1328  memory: 29365  grad_norm: 7.8828  loss: 0.6243  bbox_loss: 0.2970  cls_loss: 0.1270  layout_loss: 0.1798  cls_layout_loss: 0.0205
2025/09/13 19:12:25 - mmengine - INFO - Epoch(train)  [7][ 150/1099]  lr: 1.0000e-03  eta: 5:41:54  time: 3.1460  data_time: 0.1685  memory: 28273  grad_norm: 6.3674  loss: 0.6186  bbox_loss: 0.2906  cls_loss: 0.1215  layout_loss: 0.1860  cls_layout_loss: 0.0205
2025/09/13 19:15:06 - mmengine - INFO - Epoch(train)  [7][ 200/1099]  lr: 1.0000e-03  eta: 5:39:16  time: 3.2270  data_time: 0.1796  memory: 25015  grad_norm: 6.2255  loss: 0.6257  bbox_loss: 0.2930  cls_loss: 0.1234  layout_loss: 0.1886  cls_layout_loss: 0.0208
2025/09/13 19:17:45 - mmengine - INFO - Epoch(train)  [7][ 250/1099]  lr: 1.0000e-03  eta: 5:36:37  time: 3.1805  data_time: 0.1710  memory: 27452  grad_norm: 6.6723  loss: 0.6123  bbox_loss: 0.2926  cls_loss: 0.1204  layout_loss: 0.1787  cls_layout_loss: 0.0206
2025/09/13 19:20:21 - mmengine - INFO - Epoch(train)  [7][ 300/1099]  lr: 1.0000e-03  eta: 5:33:55  time: 3.1220  data_time: 0.1585  memory: 27545  grad_norm: 6.5987  loss: 0.6248  bbox_loss: 0.2945  cls_loss: 0.1300  layout_loss: 0.1799  cls_layout_loss: 0.0205
2025/09/13 19:22:59 - mmengine - INFO - Epoch(train)  [7][ 350/1099]  lr: 1.0000e-03  eta: 5:31:15  time: 3.1633  data_time: 0.1765  memory: 27564  grad_norm: 7.1988  loss: 0.6216  bbox_loss: 0.2942  cls_loss: 0.1239  layout_loss: 0.1830  cls_layout_loss: 0.0204
2025/09/13 19:25:37 - mmengine - INFO - Epoch(train)  [7][ 400/1099]  lr: 1.0000e-03  eta: 5:28:35  time: 3.1552  data_time: 0.1597  memory: 26373  grad_norm: 6.6615  loss: 0.6212  bbox_loss: 0.2942  cls_loss: 0.1229  layout_loss: 0.1839  cls_layout_loss: 0.0202
2025/09/13 19:25:56 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 19:28:09 - mmengine - INFO - Epoch(train)  [7][ 450/1099]  lr: 1.0000e-03  eta: 5:25:50  time: 3.0480  data_time: 0.1459  memory: 25504  grad_norm: 6.5436  loss: 0.5983  bbox_loss: 0.2897  cls_loss: 0.1163  layout_loss: 0.1719  cls_layout_loss: 0.0203
2025/09/13 19:30:48 - mmengine - INFO - Epoch(train)  [7][ 500/1099]  lr: 1.0000e-03  eta: 5:23:10  time: 3.1612  data_time: 0.1554  memory: 30774  grad_norm: 7.8974  loss: 0.6168  bbox_loss: 0.2927  cls_loss: 0.1246  layout_loss: 0.1798  cls_layout_loss: 0.0197
2025/09/13 19:33:20 - mmengine - INFO - Epoch(train)  [7][ 550/1099]  lr: 1.0000e-03  eta: 5:20:24  time: 3.0398  data_time: 0.1460  memory: 28247  grad_norm: 6.4011  loss: 0.6116  bbox_loss: 0.2898  cls_loss: 0.1217  layout_loss: 0.1800  cls_layout_loss: 0.0200
2025/09/13 19:35:58 - mmengine - INFO - Epoch(train)  [7][ 600/1099]  lr: 1.0000e-03  eta: 5:17:45  time: 3.1782  data_time: 0.1598  memory: 29232  grad_norm: 6.3764  loss: 0.6375  bbox_loss: 0.2966  cls_loss: 0.1284  layout_loss: 0.1919  cls_layout_loss: 0.0206
2025/09/13 19:38:39 - mmengine - INFO - Epoch(train)  [7][ 650/1099]  lr: 1.0000e-03  eta: 5:15:07  time: 3.2056  data_time: 0.1563  memory: 32673  grad_norm: 6.1819  loss: 0.6085  bbox_loss: 0.2893  cls_loss: 0.1188  layout_loss: 0.1801  cls_layout_loss: 0.0203
2025/09/13 19:41:17 - mmengine - INFO - Epoch(train)  [7][ 700/1099]  lr: 1.0000e-03  eta: 5:12:28  time: 3.1702  data_time: 0.1562  memory: 26304  grad_norm: 6.4609  loss: 0.6127  bbox_loss: 0.2909  cls_loss: 0.1221  layout_loss: 0.1791  cls_layout_loss: 0.0205
2025/09/13 19:43:54 - mmengine - INFO - Epoch(train)  [7][ 750/1099]  lr: 1.0000e-03  eta: 5:09:47  time: 3.1328  data_time: 0.1696  memory: 27958  grad_norm: 5.7427  loss: 0.6117  bbox_loss: 0.2895  cls_loss: 0.1216  layout_loss: 0.1805  cls_layout_loss: 0.0202
2025/09/13 19:46:28 - mmengine - INFO - Epoch(train)  [7][ 800/1099]  lr: 1.0000e-03  eta: 5:07:04  time: 3.0856  data_time: 0.1657  memory: 29163  grad_norm: 6.6596  loss: 0.6076  bbox_loss: 0.2920  cls_loss: 0.1177  layout_loss: 0.1775  cls_layout_loss: 0.0204
2025/09/13 19:49:08 - mmengine - INFO - Epoch(train)  [7][ 850/1099]  lr: 1.0000e-03  eta: 5:04:26  time: 3.1979  data_time: 0.1797  memory: 27236  grad_norm: 7.2534  loss: 0.6111  bbox_loss: 0.2915  cls_loss: 0.1234  layout_loss: 0.1761  cls_layout_loss: 0.0202
2025/09/13 19:51:45 - mmengine - INFO - Epoch(train)  [7][ 900/1099]  lr: 1.0000e-03  eta: 5:01:45  time: 3.1348  data_time: 0.1513  memory: 30234  grad_norm: 6.3193  loss: 0.6173  bbox_loss: 0.2922  cls_loss: 0.1202  layout_loss: 0.1846  cls_layout_loss: 0.0204
2025/09/13 19:54:22 - mmengine - INFO - Epoch(train)  [7][ 950/1099]  lr: 1.0000e-03  eta: 4:59:05  time: 3.1381  data_time: 0.1647  memory: 26501  grad_norm: 6.4649  loss: 0.5938  bbox_loss: 0.2882  cls_loss: 0.1163  layout_loss: 0.1699  cls_layout_loss: 0.0194
2025/09/13 19:57:03 - mmengine - INFO - Epoch(train)  [7][1000/1099]  lr: 1.0000e-03  eta: 4:56:27  time: 3.2288  data_time: 0.1932  memory: 33740  grad_norm: 6.1691  loss: 0.6011  bbox_loss: 0.2857  cls_loss: 0.1136  layout_loss: 0.1816  cls_layout_loss: 0.0202
2025/09/13 19:59:39 - mmengine - INFO - Epoch(train)  [7][1050/1099]  lr: 1.0000e-03  eta: 4:53:46  time: 3.1152  data_time: 0.1444  memory: 30791  grad_norm: 6.1823  loss: 0.6142  bbox_loss: 0.2905  cls_loss: 0.1239  layout_loss: 0.1797  cls_layout_loss: 0.0200
2025/09/13 20:02:14 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 20:02:15 - mmengine - INFO - Saving checkpoint at 7 epochs
2025/09/13 20:02:50 - mmengine - INFO - Epoch(val)  [7][ 50/241]    eta: 0:02:13  time: 0.7011  data_time: 0.0807  memory: 28842  
2025/09/13 20:03:23 - mmengine - INFO - Epoch(val)  [7][100/241]    eta: 0:01:35  time: 0.6509  data_time: 0.0563  memory: 1369  
2025/09/13 20:03:55 - mmengine - INFO - Epoch(val)  [7][150/241]    eta: 0:01:00  time: 0.6476  data_time: 0.0675  memory: 1230  
2025/09/13 20:04:29 - mmengine - INFO - Epoch(val)  [7][200/241]    eta: 0:00:27  time: 0.6770  data_time: 0.0651  memory: 1081  
2025/09/13 20:06:00 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8614      | 0.8476      |
| door    | 0.9270      | 0.9221      |
| window  | 0.8696      | 0.8486      |
+---------+-------------+-------------+
| Overall | 0.8860      | 0.8728      |
+---------+-------------+-------------+
2025/09/13 20:06:00 - mmengine - INFO - Epoch(val) [7][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8613869912848221, 'wall_f1_50': 0.8613869912848221, 'door_f1_25': 0.927018507340305, 'door_f1_50': 0.927018507340305, 'window_f1_25': 0.8695505509586754, 'window_f1_50': 0.8695505509586754, 'f1_25': 0.8859853498612674, 'f1_50': 0.8727628776779014}}  data_time: 0.0686  time: 0.6839
2025/09/13 20:08:45 - mmengine - INFO - Epoch(train)  [8][  50/1099]  lr: 1.0000e-03  eta: 4:48:35  time: 3.3096  data_time: 0.2481  memory: 28440  grad_norm: 6.1856  loss: 0.6102  bbox_loss: 0.2922  cls_loss: 0.1217  layout_loss: 0.1762  cls_layout_loss: 0.0201
2025/09/13 20:11:25 - mmengine - INFO - Epoch(train)  [8][ 100/1099]  lr: 1.0000e-03  eta: 4:45:57  time: 3.1899  data_time: 0.1569  memory: 28992  grad_norm: 5.7781  loss: 0.6144  bbox_loss: 0.2925  cls_loss: 0.1211  layout_loss: 0.1807  cls_layout_loss: 0.0201
2025/09/13 20:14:04 - mmengine - INFO - Epoch(train)  [8][ 150/1099]  lr: 1.0000e-03  eta: 4:43:17  time: 3.1789  data_time: 0.1572  memory: 27743  grad_norm: 6.9183  loss: 0.5936  bbox_loss: 0.2869  cls_loss: 0.1158  layout_loss: 0.1709  cls_layout_loss: 0.0200
2025/09/13 20:16:39 - mmengine - INFO - Epoch(train)  [8][ 200/1099]  lr: 1.0000e-03  eta: 4:40:36  time: 3.0966  data_time: 0.1794  memory: 29725  grad_norm: 5.6513  loss: 0.5977  bbox_loss: 0.2838  cls_loss: 0.1189  layout_loss: 0.1752  cls_layout_loss: 0.0198
2025/09/13 20:19:19 - mmengine - INFO - Epoch(train)  [8][ 250/1099]  lr: 1.0000e-03  eta: 4:37:58  time: 3.2090  data_time: 0.1694  memory: 27959  grad_norm: 5.3682  loss: 0.6077  bbox_loss: 0.2900  cls_loss: 0.1225  layout_loss: 0.1751  cls_layout_loss: 0.0201
2025/09/13 20:22:02 - mmengine - INFO - Epoch(train)  [8][ 300/1099]  lr: 1.0000e-03  eta: 4:35:21  time: 3.2610  data_time: 0.1800  memory: 25575  grad_norm: 6.4331  loss: 0.6027  bbox_loss: 0.2895  cls_loss: 0.1226  layout_loss: 0.1710  cls_layout_loss: 0.0197
2025/09/13 20:22:23 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 20:24:42 - mmengine - INFO - Epoch(train)  [8][ 350/1099]  lr: 1.0000e-03  eta: 4:32:43  time: 3.1999  data_time: 0.1463  memory: 25639  grad_norm: 5.7575  loss: 0.6059  bbox_loss: 0.2871  cls_loss: 0.1188  layout_loss: 0.1799  cls_layout_loss: 0.0200
2025/09/13 20:27:22 - mmengine - INFO - Epoch(train)  [8][ 400/1099]  lr: 1.0000e-03  eta: 4:30:04  time: 3.1991  data_time: 0.1889  memory: 27725  grad_norm: 6.5223  loss: 0.6057  bbox_loss: 0.2908  cls_loss: 0.1163  layout_loss: 0.1789  cls_layout_loss: 0.0198
2025/09/13 20:30:02 - mmengine - INFO - Epoch(train)  [8][ 450/1099]  lr: 1.0000e-03  eta: 4:27:26  time: 3.2064  data_time: 0.1365  memory: 32109  grad_norm: 7.4987  loss: 0.5934  bbox_loss: 0.2879  cls_loss: 0.1148  layout_loss: 0.1713  cls_layout_loss: 0.0194
2025/09/13 20:32:42 - mmengine - INFO - Epoch(train)  [8][ 500/1099]  lr: 1.0000e-03  eta: 4:24:47  time: 3.1896  data_time: 0.1619  memory: 29044  grad_norm: 5.7442  loss: 0.5970  bbox_loss: 0.2848  cls_loss: 0.1186  layout_loss: 0.1739  cls_layout_loss: 0.0198
2025/09/13 20:35:22 - mmengine - INFO - Epoch(train)  [8][ 550/1099]  lr: 1.0000e-03  eta: 4:22:09  time: 3.2100  data_time: 0.1645  memory: 33111  grad_norm: 6.0075  loss: 0.6098  bbox_loss: 0.2885  cls_loss: 0.1207  layout_loss: 0.1806  cls_layout_loss: 0.0200
2025/09/13 20:38:02 - mmengine - INFO - Epoch(train)  [8][ 600/1099]  lr: 1.0000e-03  eta: 4:19:30  time: 3.1860  data_time: 0.1816  memory: 28677  grad_norm: 5.7074  loss: 0.5836  bbox_loss: 0.2833  cls_loss: 0.1169  layout_loss: 0.1643  cls_layout_loss: 0.0191
2025/09/13 20:40:44 - mmengine - INFO - Epoch(train)  [8][ 650/1099]  lr: 1.0000e-03  eta: 4:16:53  time: 3.2426  data_time: 0.1524  memory: 31735  grad_norm: 5.4369  loss: 0.5965  bbox_loss: 0.2843  cls_loss: 0.1181  layout_loss: 0.1745  cls_layout_loss: 0.0196
2025/09/13 20:43:23 - mmengine - INFO - Epoch(train)  [8][ 700/1099]  lr: 1.0000e-03  eta: 4:14:14  time: 3.1860  data_time: 0.1683  memory: 25671  grad_norm: 6.5646  loss: 0.6034  bbox_loss: 0.2894  cls_loss: 0.1199  layout_loss: 0.1742  cls_layout_loss: 0.0199
2025/09/13 20:46:05 - mmengine - INFO - Epoch(train)  [8][ 750/1099]  lr: 1.0000e-03  eta: 4:11:37  time: 3.2312  data_time: 0.1647  memory: 30446  grad_norm: 5.8300  loss: 0.6020  bbox_loss: 0.2892  cls_loss: 0.1205  layout_loss: 0.1728  cls_layout_loss: 0.0195
2025/09/13 20:48:39 - mmengine - INFO - Epoch(train)  [8][ 800/1099]  lr: 1.0000e-03  eta: 4:08:55  time: 3.0929  data_time: 0.1555  memory: 25750  grad_norm: 7.0731  loss: 0.5753  bbox_loss: 0.2876  cls_loss: 0.1088  layout_loss: 0.1598  cls_layout_loss: 0.0192
2025/09/13 20:51:16 - mmengine - INFO - Epoch(train)  [8][ 850/1099]  lr: 1.0000e-03  eta: 4:06:15  time: 3.1404  data_time: 0.1697  memory: 30071  grad_norm: 5.6928  loss: 0.5890  bbox_loss: 0.2881  cls_loss: 0.1141  layout_loss: 0.1674  cls_layout_loss: 0.0193
2025/09/13 20:53:53 - mmengine - INFO - Epoch(train)  [8][ 900/1099]  lr: 1.0000e-03  eta: 4:03:35  time: 3.1378  data_time: 0.1511  memory: 27659  grad_norm: 5.8219  loss: 0.5850  bbox_loss: 0.2865  cls_loss: 0.1125  layout_loss: 0.1670  cls_layout_loss: 0.0189
2025/09/13 20:56:35 - mmengine - INFO - Epoch(train)  [8][ 950/1099]  lr: 1.0000e-03  eta: 4:00:57  time: 3.2307  data_time: 0.1673  memory: 32695  grad_norm: 6.2038  loss: 0.5989  bbox_loss: 0.2868  cls_loss: 0.1168  layout_loss: 0.1759  cls_layout_loss: 0.0193
2025/09/13 20:59:13 - mmengine - INFO - Epoch(train)  [8][1000/1099]  lr: 1.0000e-03  eta: 3:58:18  time: 3.1661  data_time: 0.1566  memory: 26740  grad_norm: 6.1501  loss: 0.5868  bbox_loss: 0.2841  cls_loss: 0.1125  layout_loss: 0.1704  cls_layout_loss: 0.0198
2025/09/13 21:01:54 - mmengine - INFO - Epoch(train)  [8][1050/1099]  lr: 1.0000e-03  eta: 3:55:40  time: 3.2283  data_time: 0.1751  memory: 28241  grad_norm: 6.1625  loss: 0.5766  bbox_loss: 0.2850  cls_loss: 0.1105  layout_loss: 0.1622  cls_layout_loss: 0.0189
2025/09/13 21:04:26 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 21:04:27 - mmengine - INFO - Saving checkpoint at 8 epochs
2025/09/13 21:05:06 - mmengine - INFO - Epoch(val)  [8][ 50/241]    eta: 0:02:26  time: 0.7665  data_time: 0.0586  memory: 24688  
2025/09/13 21:05:39 - mmengine - INFO - Epoch(val)  [8][100/241]    eta: 0:01:39  time: 0.6416  data_time: 0.0556  memory: 1369  
2025/09/13 21:06:11 - mmengine - INFO - Epoch(val)  [8][150/241]    eta: 0:01:02  time: 0.6490  data_time: 0.0456  memory: 1230  
2025/09/13 21:06:44 - mmengine - INFO - Epoch(val)  [8][200/241]    eta: 0:00:27  time: 0.6556  data_time: 0.0809  memory: 1081  
2025/09/13 21:08:13 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8702      | 0.8582      |
| door    | 0.9326      | 0.9306      |
| window  | 0.8831      | 0.8712      |
+---------+-------------+-------------+
| Overall | 0.8953      | 0.8867      |
+---------+-------------+-------------+
2025/09/13 21:08:13 - mmengine - INFO - Epoch(val) [8][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8701736125015925, 'wall_f1_50': 0.8701736125015925, 'door_f1_25': 0.9326243153350507, 'door_f1_50': 0.9326243153350507, 'window_f1_25': 0.8830856147632826, 'window_f1_50': 0.8830856147632826, 'f1_25': 0.8952945141999753, 'f1_50': 0.8866601100554897}}  data_time: 0.0592  time: 0.6866
2025/09/13 21:11:00 - mmengine - INFO - Epoch(train)  [9][  50/1099]  lr: 1.0000e-04  eta: 3:50:27  time: 3.3391  data_time: 0.2609  memory: 26291  grad_norm: 4.3532  loss: 0.5704  bbox_loss: 0.2757  cls_loss: 0.1144  layout_loss: 0.1614  cls_layout_loss: 0.0188
2025/09/13 21:13:38 - mmengine - INFO - Epoch(train)  [9][ 100/1099]  lr: 1.0000e-04  eta: 3:47:47  time: 3.1632  data_time: 0.1619  memory: 35112  grad_norm: 4.1512  loss: 0.5719  bbox_loss: 0.2761  cls_loss: 0.1124  layout_loss: 0.1640  cls_layout_loss: 0.0194
2025/09/13 21:16:17 - mmengine - INFO - Epoch(train)  [9][ 150/1099]  lr: 1.0000e-04  eta: 3:45:08  time: 3.1861  data_time: 0.1735  memory: 27487  grad_norm: 4.8169  loss: 0.5507  bbox_loss: 0.2725  cls_loss: 0.1099  layout_loss: 0.1499  cls_layout_loss: 0.0184
2025/09/13 21:18:59 - mmengine - INFO - Epoch(train)  [9][ 200/1099]  lr: 1.0000e-04  eta: 3:42:30  time: 3.2354  data_time: 0.1324  memory: 28128  grad_norm: 4.3515  loss: 0.5519  bbox_loss: 0.2734  cls_loss: 0.1094  layout_loss: 0.1508  cls_layout_loss: 0.0183
2025/09/13 21:19:25 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 21:21:37 - mmengine - INFO - Epoch(train)  [9][ 250/1099]  lr: 1.0000e-04  eta: 3:39:51  time: 3.1664  data_time: 0.1812  memory: 27299  grad_norm: 4.0580  loss: 0.5436  bbox_loss: 0.2715  cls_loss: 0.1075  layout_loss: 0.1460  cls_layout_loss: 0.0186
2025/09/13 21:24:15 - mmengine - INFO - Epoch(train)  [9][ 300/1099]  lr: 1.0000e-04  eta: 3:37:11  time: 3.1506  data_time: 0.1583  memory: 30055  grad_norm: 4.4423  loss: 0.5539  bbox_loss: 0.2739  cls_loss: 0.1126  layout_loss: 0.1491  cls_layout_loss: 0.0183
2025/09/13 21:26:51 - mmengine - INFO - Epoch(train)  [9][ 350/1099]  lr: 1.0000e-04  eta: 3:34:31  time: 3.1313  data_time: 0.1717  memory: 26104  grad_norm: 4.0762  loss: 0.5348  bbox_loss: 0.2680  cls_loss: 0.1076  layout_loss: 0.1407  cls_layout_loss: 0.0186
2025/09/13 21:29:29 - mmengine - INFO - Epoch(train)  [9][ 400/1099]  lr: 1.0000e-04  eta: 3:31:51  time: 3.1496  data_time: 0.1551  memory: 26193  grad_norm: 3.9925  loss: 0.5333  bbox_loss: 0.2687  cls_loss: 0.1061  layout_loss: 0.1399  cls_layout_loss: 0.0186
2025/09/13 21:32:06 - mmengine - INFO - Epoch(train)  [9][ 450/1099]  lr: 1.0000e-04  eta: 3:29:11  time: 3.1323  data_time: 0.1400  memory: 35424  grad_norm: 4.0377  loss: 0.5365  bbox_loss: 0.2662  cls_loss: 0.1046  layout_loss: 0.1476  cls_layout_loss: 0.0182
2025/09/13 21:34:42 - mmengine - INFO - Epoch(train)  [9][ 500/1099]  lr: 1.0000e-04  eta: 3:26:31  time: 3.1154  data_time: 0.1431  memory: 31733  grad_norm: 4.4425  loss: 0.5488  bbox_loss: 0.2689  cls_loss: 0.1081  layout_loss: 0.1533  cls_layout_loss: 0.0185
2025/09/13 21:37:21 - mmengine - INFO - Epoch(train)  [9][ 550/1099]  lr: 1.0000e-04  eta: 3:23:52  time: 3.1852  data_time: 0.1539  memory: 26202  grad_norm: 3.6083  loss: 0.5242  bbox_loss: 0.2664  cls_loss: 0.1034  layout_loss: 0.1365  cls_layout_loss: 0.0179
2025/09/13 21:40:02 - mmengine - INFO - Epoch(train)  [9][ 600/1099]  lr: 1.0000e-04  eta: 3:21:14  time: 3.2236  data_time: 0.1662  memory: 29507  grad_norm: 4.1237  loss: 0.5274  bbox_loss: 0.2661  cls_loss: 0.1058  layout_loss: 0.1377  cls_layout_loss: 0.0179
2025/09/13 21:42:40 - mmengine - INFO - Epoch(train)  [9][ 650/1099]  lr: 1.0000e-04  eta: 3:18:34  time: 3.1726  data_time: 0.1549  memory: 28074  grad_norm: 3.9647  loss: 0.5499  bbox_loss: 0.2698  cls_loss: 0.1098  layout_loss: 0.1521  cls_layout_loss: 0.0182
2025/09/13 21:45:20 - mmengine - INFO - Epoch(train)  [9][ 700/1099]  lr: 1.0000e-04  eta: 3:15:55  time: 3.1842  data_time: 0.1553  memory: 31795  grad_norm: 4.1700  loss: 0.5427  bbox_loss: 0.2662  cls_loss: 0.1046  layout_loss: 0.1538  cls_layout_loss: 0.0181
2025/09/13 21:47:57 - mmengine - INFO - Epoch(train)  [9][ 750/1099]  lr: 1.0000e-04  eta: 3:13:16  time: 3.1467  data_time: 0.1789  memory: 25173  grad_norm: 4.2684  loss: 0.5261  bbox_loss: 0.2663  cls_loss: 0.1059  layout_loss: 0.1361  cls_layout_loss: 0.0178
2025/09/13 21:50:39 - mmengine - INFO - Epoch(train)  [9][ 800/1099]  lr: 1.0000e-04  eta: 3:10:38  time: 3.2335  data_time: 0.2008  memory: 26242  grad_norm: 4.4069  loss: 0.5329  bbox_loss: 0.2680  cls_loss: 0.1047  layout_loss: 0.1421  cls_layout_loss: 0.0181
2025/09/13 21:53:18 - mmengine - INFO - Epoch(train)  [9][ 850/1099]  lr: 1.0000e-04  eta: 3:07:59  time: 3.1938  data_time: 0.1733  memory: 27726  grad_norm: 4.0786  loss: 0.5285  bbox_loss: 0.2641  cls_loss: 0.1045  layout_loss: 0.1423  cls_layout_loss: 0.0176
2025/09/13 21:55:52 - mmengine - INFO - Epoch(train)  [9][ 900/1099]  lr: 1.0000e-04  eta: 3:05:18  time: 3.0696  data_time: 0.1742  memory: 26221  grad_norm: 4.5411  loss: 0.5388  bbox_loss: 0.2700  cls_loss: 0.1079  layout_loss: 0.1423  cls_layout_loss: 0.0186
2025/09/13 21:58:25 - mmengine - INFO - Epoch(train)  [9][ 950/1099]  lr: 1.0000e-04  eta: 3:02:37  time: 3.0616  data_time: 0.1310  memory: 27491  grad_norm: 3.9199  loss: 0.5315  bbox_loss: 0.2672  cls_loss: 0.1067  layout_loss: 0.1398  cls_layout_loss: 0.0178
2025/09/13 22:01:00 - mmengine - INFO - Epoch(train)  [9][1000/1099]  lr: 1.0000e-04  eta: 2:59:56  time: 3.0959  data_time: 0.1774  memory: 32149  grad_norm: 4.1351  loss: 0.5355  bbox_loss: 0.2670  cls_loss: 0.1054  layout_loss: 0.1451  cls_layout_loss: 0.0180
2025/09/13 22:03:37 - mmengine - INFO - Epoch(train)  [9][1050/1099]  lr: 1.0000e-04  eta: 2:57:17  time: 3.1354  data_time: 0.1694  memory: 35163  grad_norm: 3.8675  loss: 0.5165  bbox_loss: 0.2621  cls_loss: 0.0998  layout_loss: 0.1373  cls_layout_loss: 0.0173
2025/09/13 22:06:09 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 22:06:09 - mmengine - INFO - Saving checkpoint at 9 epochs
2025/09/13 22:06:47 - mmengine - INFO - Epoch(val)  [9][ 50/241]    eta: 0:02:20  time: 0.7335  data_time: 0.0479  memory: 30089  
2025/09/13 22:07:21 - mmengine - INFO - Epoch(val)  [9][100/241]    eta: 0:01:39  time: 0.6791  data_time: 0.0645  memory: 1369  
2025/09/13 22:07:56 - mmengine - INFO - Epoch(val)  [9][150/241]    eta: 0:01:03  time: 0.6941  data_time: 0.0855  memory: 1230  
2025/09/13 22:08:29 - mmengine - INFO - Epoch(val)  [9][200/241]    eta: 0:00:28  time: 0.6656  data_time: 0.0568  memory: 1081  
2025/09/13 22:10:02 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8790      | 0.8657      |
| door    | 0.9362      | 0.9336      |
| window  | 0.8967      | 0.8833      |
+---------+-------------+-------------+
| Overall | 0.9039      | 0.8942      |
+---------+-------------+-------------+
2025/09/13 22:10:02 - mmengine - INFO - Epoch(val) [9][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8789501644206176, 'wall_f1_50': 0.8789501644206176, 'door_f1_25': 0.9361946663292635, 'door_f1_50': 0.9361946663292635, 'window_f1_25': 0.8966795289205506, 'window_f1_50': 0.8966795289205506, 'f1_25': 0.9039414532234772, 'f1_50': 0.8941689079561351}}  data_time: 0.0631  time: 0.6979
2025/09/13 22:12:54 - mmengine - INFO - Epoch(train) [10][  50/1099]  lr: 1.0000e-04  eta: 2:52:05  time: 3.4321  data_time: 0.3218  memory: 35991  grad_norm: 4.8028  loss: 0.5323  bbox_loss: 0.2663  cls_loss: 0.1043  layout_loss: 0.1439  cls_layout_loss: 0.0179
2025/09/13 22:15:28 - mmengine - INFO - Epoch(train) [10][ 100/1099]  lr: 1.0000e-04  eta: 2:49:24  time: 3.0916  data_time: 0.1789  memory: 31724  grad_norm: 3.9967  loss: 0.5258  bbox_loss: 0.2639  cls_loss: 0.1027  layout_loss: 0.1413  cls_layout_loss: 0.0179
2025/09/13 22:15:59 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 22:18:05 - mmengine - INFO - Epoch(train) [10][ 150/1099]  lr: 1.0000e-04  eta: 2:46:45  time: 3.1291  data_time: 0.1582  memory: 26883  grad_norm: 3.8833  loss: 0.5284  bbox_loss: 0.2657  cls_loss: 0.1053  layout_loss: 0.1394  cls_layout_loss: 0.0180
2025/09/13 22:20:44 - mmengine - INFO - Epoch(train) [10][ 200/1099]  lr: 1.0000e-04  eta: 2:44:06  time: 3.1868  data_time: 0.1451  memory: 27716  grad_norm: 4.2531  loss: 0.5300  bbox_loss: 0.2654  cls_loss: 0.1048  layout_loss: 0.1419  cls_layout_loss: 0.0180
2025/09/13 22:23:23 - mmengine - INFO - Epoch(train) [10][ 250/1099]  lr: 1.0000e-04  eta: 2:41:27  time: 3.1676  data_time: 0.1685  memory: 27468  grad_norm: 4.0689  loss: 0.5296  bbox_loss: 0.2662  cls_loss: 0.1050  layout_loss: 0.1405  cls_layout_loss: 0.0179
2025/09/13 22:25:57 - mmengine - INFO - Epoch(train) [10][ 300/1099]  lr: 1.0000e-04  eta: 2:38:46  time: 3.0975  data_time: 0.1580  memory: 28414  grad_norm: 4.3693  loss: 0.5216  bbox_loss: 0.2660  cls_loss: 0.1026  layout_loss: 0.1354  cls_layout_loss: 0.0176
2025/09/13 22:28:33 - mmengine - INFO - Epoch(train) [10][ 350/1099]  lr: 1.0000e-04  eta: 2:36:06  time: 3.1055  data_time: 0.1653  memory: 27584  grad_norm: 3.9711  loss: 0.5324  bbox_loss: 0.2656  cls_loss: 0.1082  layout_loss: 0.1405  cls_layout_loss: 0.0181
2025/09/13 22:31:15 - mmengine - INFO - Epoch(train) [10][ 400/1099]  lr: 1.0000e-04  eta: 2:33:29  time: 3.2544  data_time: 0.1666  memory: 26478  grad_norm: 4.6938  loss: 0.5336  bbox_loss: 0.2674  cls_loss: 0.1092  layout_loss: 0.1389  cls_layout_loss: 0.0180
2025/09/13 22:33:51 - mmengine - INFO - Epoch(train) [10][ 450/1099]  lr: 1.0000e-04  eta: 2:30:49  time: 3.1031  data_time: 0.1320  memory: 30074  grad_norm: 4.4155  loss: 0.5176  bbox_loss: 0.2644  cls_loss: 0.1022  layout_loss: 0.1334  cls_layout_loss: 0.0176
2025/09/13 22:36:29 - mmengine - INFO - Epoch(train) [10][ 500/1099]  lr: 1.0000e-04  eta: 2:28:09  time: 3.1614  data_time: 0.1442  memory: 31207  grad_norm: 3.6790  loss: 0.5212  bbox_loss: 0.2637  cls_loss: 0.1040  layout_loss: 0.1361  cls_layout_loss: 0.0174
2025/09/13 22:39:07 - mmengine - INFO - Epoch(train) [10][ 550/1099]  lr: 1.0000e-04  eta: 2:25:30  time: 3.1598  data_time: 0.1412  memory: 30829  grad_norm: 4.0609  loss: 0.5236  bbox_loss: 0.2625  cls_loss: 0.1047  layout_loss: 0.1391  cls_layout_loss: 0.0172
2025/09/13 22:41:48 - mmengine - INFO - Epoch(train) [10][ 600/1099]  lr: 1.0000e-04  eta: 2:22:52  time: 3.2284  data_time: 0.1567  memory: 30694  grad_norm: 4.4562  loss: 0.5427  bbox_loss: 0.2683  cls_loss: 0.1078  layout_loss: 0.1484  cls_layout_loss: 0.0183
2025/09/13 22:44:23 - mmengine - INFO - Epoch(train) [10][ 650/1099]  lr: 1.0000e-04  eta: 2:20:12  time: 3.0985  data_time: 0.1494  memory: 31280  grad_norm: 4.2675  loss: 0.5260  bbox_loss: 0.2661  cls_loss: 0.1030  layout_loss: 0.1390  cls_layout_loss: 0.0179
2025/09/13 22:47:00 - mmengine - INFO - Epoch(train) [10][ 700/1099]  lr: 1.0000e-04  eta: 2:17:32  time: 3.1242  data_time: 0.1461  memory: 34370  grad_norm: 4.8503  loss: 0.5318  bbox_loss: 0.2654  cls_loss: 0.1047  layout_loss: 0.1435  cls_layout_loss: 0.0182
2025/09/13 22:49:41 - mmengine - INFO - Epoch(train) [10][ 750/1099]  lr: 1.0000e-04  eta: 2:14:54  time: 3.2250  data_time: 0.1688  memory: 26529  grad_norm: 4.4920  loss: 0.5271  bbox_loss: 0.2645  cls_loss: 0.1048  layout_loss: 0.1400  cls_layout_loss: 0.0178
2025/09/13 22:52:19 - mmengine - INFO - Epoch(train) [10][ 800/1099]  lr: 1.0000e-04  eta: 2:12:15  time: 3.1733  data_time: 0.1900  memory: 26465  grad_norm: 4.2188  loss: 0.5233  bbox_loss: 0.2657  cls_loss: 0.1014  layout_loss: 0.1386  cls_layout_loss: 0.0177
2025/09/13 22:54:55 - mmengine - INFO - Epoch(train) [10][ 850/1099]  lr: 1.0000e-04  eta: 2:09:36  time: 3.1177  data_time: 0.1507  memory: 27147  grad_norm: 3.9009  loss: 0.5240  bbox_loss: 0.2633  cls_loss: 0.1050  layout_loss: 0.1380  cls_layout_loss: 0.0177
2025/09/13 22:57:34 - mmengine - INFO - Epoch(train) [10][ 900/1099]  lr: 1.0000e-04  eta: 2:06:57  time: 3.1849  data_time: 0.1560  memory: 29676  grad_norm: 4.0135  loss: 0.5203  bbox_loss: 0.2650  cls_loss: 0.1030  layout_loss: 0.1347  cls_layout_loss: 0.0177
2025/09/13 23:00:14 - mmengine - INFO - Epoch(train) [10][ 950/1099]  lr: 1.0000e-04  eta: 2:04:18  time: 3.1842  data_time: 0.1721  memory: 27896  grad_norm: 4.1126  loss: 0.5278  bbox_loss: 0.2647  cls_loss: 0.1010  layout_loss: 0.1442  cls_layout_loss: 0.0179
2025/09/13 23:02:53 - mmengine - INFO - Epoch(train) [10][1000/1099]  lr: 1.0000e-04  eta: 2:01:39  time: 3.1959  data_time: 0.1434  memory: 27804  grad_norm: 3.9994  loss: 0.5279  bbox_loss: 0.2661  cls_loss: 0.1040  layout_loss: 0.1397  cls_layout_loss: 0.0180
2025/09/13 23:05:32 - mmengine - INFO - Epoch(train) [10][1050/1099]  lr: 1.0000e-04  eta: 1:59:00  time: 3.1631  data_time: 0.1489  memory: 36464  grad_norm: 4.2432  loss: 0.5174  bbox_loss: 0.2639  cls_loss: 0.1006  layout_loss: 0.1356  cls_layout_loss: 0.0173
2025/09/13 23:08:06 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 23:08:07 - mmengine - INFO - Saving checkpoint at 10 epochs
2025/09/13 23:08:45 - mmengine - INFO - Epoch(val) [10][ 50/241]    eta: 0:02:22  time: 0.7448  data_time: 0.0866  memory: 28814  
2025/09/13 23:09:17 - mmengine - INFO - Epoch(val) [10][100/241]    eta: 0:01:38  time: 0.6511  data_time: 0.0628  memory: 1369  
2025/09/13 23:09:49 - mmengine - INFO - Epoch(val) [10][150/241]    eta: 0:01:01  time: 0.6454  data_time: 0.0582  memory: 1230  
2025/09/13 23:10:24 - mmengine - INFO - Epoch(val) [10][200/241]    eta: 0:00:27  time: 0.6867  data_time: 0.0611  memory: 1081  
2025/09/13 23:11:56 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8799      | 0.8680      |
| door    | 0.9388      | 0.9378      |
| window  | 0.8980      | 0.8847      |
+---------+-------------+-------------+
| Overall | 0.9056      | 0.8968      |
+---------+-------------+-------------+
2025/09/13 23:11:56 - mmengine - INFO - Epoch(val) [10][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8799432064620621, 'wall_f1_50': 0.8799432064620621, 'door_f1_25': 0.9388397554712523, 'door_f1_50': 0.9388397554712523, 'window_f1_25': 0.8980157565282779, 'window_f1_50': 0.8980157565282779, 'f1_25': 0.9055995728205307, 'f1_50': 0.8968283311816623}}  data_time: 0.0665  time: 0.6891
2025/09/13 23:12:32 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/13 23:14:41 - mmengine - INFO - Epoch(train) [11][  50/1099]  lr: 1.0000e-04  eta: 1:53:46  time: 3.2959  data_time: 0.2654  memory: 30974  grad_norm: 4.4031  loss: 0.5349  bbox_loss: 0.2668  cls_loss: 0.1059  layout_loss: 0.1443  cls_layout_loss: 0.0179
2025/09/13 23:17:17 - mmengine - INFO - Epoch(train) [11][ 100/1099]  lr: 1.0000e-04  eta: 1:51:07  time: 3.1142  data_time: 0.1805  memory: 31187  grad_norm: 4.0806  loss: 0.5284  bbox_loss: 0.2622  cls_loss: 0.0974  layout_loss: 0.1512  cls_layout_loss: 0.0176
2025/09/13 23:19:55 - mmengine - INFO - Epoch(train) [11][ 150/1099]  lr: 1.0000e-04  eta: 1:48:28  time: 3.1605  data_time: 0.1597  memory: 34765  grad_norm: 4.2669  loss: 0.5391  bbox_loss: 0.2637  cls_loss: 0.1064  layout_loss: 0.1510  cls_layout_loss: 0.0180
2025/09/13 23:22:28 - mmengine - INFO - Epoch(train) [11][ 200/1099]  lr: 1.0000e-04  eta: 1:45:48  time: 3.0658  data_time: 0.1251  memory: 27382  grad_norm: 4.3479  loss: 0.5119  bbox_loss: 0.2632  cls_loss: 0.1013  layout_loss: 0.1298  cls_layout_loss: 0.0177
2025/09/13 23:25:04 - mmengine - INFO - Epoch(train) [11][ 250/1099]  lr: 1.0000e-04  eta: 1:43:09  time: 3.1116  data_time: 0.1504  memory: 31062  grad_norm: 4.0474  loss: 0.5144  bbox_loss: 0.2643  cls_loss: 0.1005  layout_loss: 0.1320  cls_layout_loss: 0.0176
2025/09/13 23:27:47 - mmengine - INFO - Epoch(train) [11][ 300/1099]  lr: 1.0000e-04  eta: 1:40:30  time: 3.2637  data_time: 0.1572  memory: 32483  grad_norm: 4.0985  loss: 0.5207  bbox_loss: 0.2638  cls_loss: 0.0989  layout_loss: 0.1400  cls_layout_loss: 0.0180
2025/09/13 23:30:25 - mmengine - INFO - Epoch(train) [11][ 350/1099]  lr: 1.0000e-04  eta: 1:37:51  time: 3.1663  data_time: 0.1636  memory: 26464  grad_norm: 4.1367  loss: 0.5165  bbox_loss: 0.2625  cls_loss: 0.1023  layout_loss: 0.1340  cls_layout_loss: 0.0177
2025/09/13 23:33:00 - mmengine - INFO - Epoch(train) [11][ 400/1099]  lr: 1.0000e-04  eta: 1:35:12  time: 3.1002  data_time: 0.1281  memory: 29042  grad_norm: 4.1852  loss: 0.5408  bbox_loss: 0.2685  cls_loss: 0.1105  layout_loss: 0.1435  cls_layout_loss: 0.0184
2025/09/13 23:35:38 - mmengine - INFO - Epoch(train) [11][ 450/1099]  lr: 1.0000e-04  eta: 1:32:33  time: 3.1568  data_time: 0.1439  memory: 28157  grad_norm: 4.8580  loss: 0.5170  bbox_loss: 0.2622  cls_loss: 0.1045  layout_loss: 0.1327  cls_layout_loss: 0.0176
2025/09/13 23:38:14 - mmengine - INFO - Epoch(train) [11][ 500/1099]  lr: 1.0000e-04  eta: 1:29:54  time: 3.1072  data_time: 0.1544  memory: 31639  grad_norm: 4.6542  loss: 0.5255  bbox_loss: 0.2618  cls_loss: 0.1056  layout_loss: 0.1406  cls_layout_loss: 0.0175
2025/09/13 23:40:49 - mmengine - INFO - Epoch(train) [11][ 550/1099]  lr: 1.0000e-04  eta: 1:27:14  time: 3.1083  data_time: 0.1582  memory: 26991  grad_norm: 4.3425  loss: 0.5080  bbox_loss: 0.2627  cls_loss: 0.0996  layout_loss: 0.1287  cls_layout_loss: 0.0170
2025/09/13 23:43:29 - mmengine - INFO - Epoch(train) [11][ 600/1099]  lr: 1.0000e-04  eta: 1:24:36  time: 3.2057  data_time: 0.1368  memory: 26642  grad_norm: 4.6768  loss: 0.5126  bbox_loss: 0.2617  cls_loss: 0.1006  layout_loss: 0.1332  cls_layout_loss: 0.0171
2025/09/13 23:46:07 - mmengine - INFO - Epoch(train) [11][ 650/1099]  lr: 1.0000e-04  eta: 1:21:57  time: 3.1494  data_time: 0.1370  memory: 25260  grad_norm: 4.1259  loss: 0.5230  bbox_loss: 0.2650  cls_loss: 0.1058  layout_loss: 0.1345  cls_layout_loss: 0.0177
2025/09/13 23:48:46 - mmengine - INFO - Epoch(train) [11][ 700/1099]  lr: 1.0000e-04  eta: 1:19:18  time: 3.1792  data_time: 0.1751  memory: 27457  grad_norm: 3.9879  loss: 0.5312  bbox_loss: 0.2662  cls_loss: 0.1051  layout_loss: 0.1419  cls_layout_loss: 0.0179
2025/09/13 23:51:24 - mmengine - INFO - Epoch(train) [11][ 750/1099]  lr: 1.0000e-04  eta: 1:16:39  time: 3.1671  data_time: 0.1915  memory: 30775  grad_norm: 4.4231  loss: 0.5213  bbox_loss: 0.2625  cls_loss: 0.1046  layout_loss: 0.1364  cls_layout_loss: 0.0177
2025/09/13 23:54:00 - mmengine - INFO - Epoch(train) [11][ 800/1099]  lr: 1.0000e-04  eta: 1:14:00  time: 3.1084  data_time: 0.1613  memory: 27438  grad_norm: 4.0974  loss: 0.5176  bbox_loss: 0.2630  cls_loss: 0.1019  layout_loss: 0.1348  cls_layout_loss: 0.0178
2025/09/13 23:56:36 - mmengine - INFO - Epoch(train) [11][ 850/1099]  lr: 1.0000e-04  eta: 1:11:21  time: 3.1363  data_time: 0.1515  memory: 29269  grad_norm: 4.3977  loss: 0.5262  bbox_loss: 0.2669  cls_loss: 0.1062  layout_loss: 0.1358  cls_layout_loss: 0.0173
2025/09/13 23:59:15 - mmengine - INFO - Epoch(train) [11][ 900/1099]  lr: 1.0000e-04  eta: 1:08:42  time: 3.1686  data_time: 0.1432  memory: 29576  grad_norm: 3.8403  loss: 0.5086  bbox_loss: 0.2603  cls_loss: 0.0983  layout_loss: 0.1331  cls_layout_loss: 0.0170
2025/09/14 00:01:54 - mmengine - INFO - Epoch(train) [11][ 950/1099]  lr: 1.0000e-04  eta: 1:06:03  time: 3.1868  data_time: 0.1856  memory: 29191  grad_norm: 4.2565  loss: 0.5211  bbox_loss: 0.2631  cls_loss: 0.1039  layout_loss: 0.1369  cls_layout_loss: 0.0172
2025/09/14 00:04:34 - mmengine - INFO - Epoch(train) [11][1000/1099]  lr: 1.0000e-04  eta: 1:03:24  time: 3.1916  data_time: 0.2177  memory: 24668  grad_norm: 4.2230  loss: 0.5002  bbox_loss: 0.2585  cls_loss: 0.1000  layout_loss: 0.1244  cls_layout_loss: 0.0173
2025/09/14 00:05:06 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/14 00:07:16 - mmengine - INFO - Epoch(train) [11][1050/1099]  lr: 1.0000e-04  eta: 1:00:46  time: 3.2428  data_time: 0.1817  memory: 29108  grad_norm: 4.1869  loss: 0.5162  bbox_loss: 0.2619  cls_loss: 0.1028  layout_loss: 0.1344  cls_layout_loss: 0.0171
2025/09/14 00:09:50 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/14 00:09:51 - mmengine - INFO - Saving checkpoint at 11 epochs
2025/09/14 00:10:29 - mmengine - INFO - Epoch(val) [11][ 50/241]    eta: 0:02:20  time: 0.7333  data_time: 0.0618  memory: 28709  
2025/09/14 00:11:02 - mmengine - INFO - Epoch(val) [11][100/241]    eta: 0:01:38  time: 0.6575  data_time: 0.0795  memory: 1369  
2025/09/14 00:11:35 - mmengine - INFO - Epoch(val) [11][150/241]    eta: 0:01:02  time: 0.6642  data_time: 0.0563  memory: 1230  
2025/09/14 00:12:09 - mmengine - INFO - Epoch(val) [11][200/241]    eta: 0:00:28  time: 0.6815  data_time: 0.0778  memory: 1081  
2025/09/14 00:13:41 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8817      | 0.8672      |
| door    | 0.9409      | 0.9387      |
| window  | 0.8938      | 0.8816      |
+---------+-------------+-------------+
| Overall | 0.9055      | 0.8958      |
+---------+-------------+-------------+
2025/09/14 00:13:41 - mmengine - INFO - Epoch(val) [11][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8817000472540413, 'wall_f1_50': 0.8817000472540413, 'door_f1_25': 0.9408538614795029, 'door_f1_50': 0.9408538614795029, 'window_f1_25': 0.8938483506781523, 'window_f1_50': 0.8938483506781523, 'f1_25': 0.9054674198038987, 'f1_50': 0.8958324347292853}}  data_time: 0.0684  time: 0.6894
2025/09/14 00:16:24 - mmengine - INFO - Epoch(train) [12][  50/1099]  lr: 1.0000e-05  eta: 0:55:32  time: 3.2561  data_time: 0.2547  memory: 27287  grad_norm: 3.8779  loss: 0.5167  bbox_loss: 0.2609  cls_loss: 0.1028  layout_loss: 0.1355  cls_layout_loss: 0.0175
2025/09/14 00:19:09 - mmengine - INFO - Epoch(train) [12][ 100/1099]  lr: 1.0000e-05  eta: 0:52:53  time: 3.2942  data_time: 0.1935  memory: 30391  grad_norm: 3.5448  loss: 0.5037  bbox_loss: 0.2611  cls_loss: 0.0986  layout_loss: 0.1271  cls_layout_loss: 0.0169
2025/09/14 00:21:42 - mmengine - INFO - Epoch(train) [12][ 150/1099]  lr: 1.0000e-05  eta: 0:50:14  time: 3.0559  data_time: 0.1328  memory: 27860  grad_norm: 3.6955  loss: 0.5056  bbox_loss: 0.2586  cls_loss: 0.0991  layout_loss: 0.1305  cls_layout_loss: 0.0174
2025/09/14 00:24:19 - mmengine - INFO - Epoch(train) [12][ 200/1099]  lr: 1.0000e-05  eta: 0:47:35  time: 3.1594  data_time: 0.1415  memory: 29310  grad_norm: 3.9266  loss: 0.5268  bbox_loss: 0.2644  cls_loss: 0.1070  layout_loss: 0.1378  cls_layout_loss: 0.0176
2025/09/14 00:27:01 - mmengine - INFO - Epoch(train) [12][ 250/1099]  lr: 1.0000e-05  eta: 0:44:56  time: 3.2185  data_time: 0.1654  memory: 27101  grad_norm: 3.6185  loss: 0.5179  bbox_loss: 0.2630  cls_loss: 0.1049  layout_loss: 0.1325  cls_layout_loss: 0.0174
2025/09/14 00:29:42 - mmengine - INFO - Epoch(train) [12][ 300/1099]  lr: 1.0000e-05  eta: 0:42:18  time: 3.2284  data_time: 0.1851  memory: 30320  grad_norm: 4.1170  loss: 0.5137  bbox_loss: 0.2612  cls_loss: 0.1007  layout_loss: 0.1343  cls_layout_loss: 0.0176
2025/09/14 00:32:19 - mmengine - INFO - Epoch(train) [12][ 350/1099]  lr: 1.0000e-05  eta: 0:39:39  time: 3.1507  data_time: 0.1659  memory: 27364  grad_norm: 4.0937  loss: 0.5141  bbox_loss: 0.2610  cls_loss: 0.1001  layout_loss: 0.1355  cls_layout_loss: 0.0175
2025/09/14 00:34:58 - mmengine - INFO - Epoch(train) [12][ 400/1099]  lr: 1.0000e-05  eta: 0:37:00  time: 3.1811  data_time: 0.1634  memory: 26170  grad_norm: 3.4857  loss: 0.5126  bbox_loss: 0.2620  cls_loss: 0.1005  layout_loss: 0.1320  cls_layout_loss: 0.0181
2025/09/14 00:37:31 - mmengine - INFO - Epoch(train) [12][ 450/1099]  lr: 1.0000e-05  eta: 0:34:21  time: 3.0520  data_time: 0.1524  memory: 31143  grad_norm: 3.8839  loss: 0.5163  bbox_loss: 0.2579  cls_loss: 0.1012  layout_loss: 0.1397  cls_layout_loss: 0.0175
2025/09/14 00:40:12 - mmengine - INFO - Epoch(train) [12][ 500/1099]  lr: 1.0000e-05  eta: 0:31:42  time: 3.2279  data_time: 0.1601  memory: 29912  grad_norm: 3.9856  loss: 0.5130  bbox_loss: 0.2622  cls_loss: 0.1006  layout_loss: 0.1328  cls_layout_loss: 0.0175
2025/09/14 00:42:51 - mmengine - INFO - Epoch(train) [12][ 550/1099]  lr: 1.0000e-05  eta: 0:29:03  time: 3.1722  data_time: 0.1299  memory: 35418  grad_norm: 3.5797  loss: 0.5177  bbox_loss: 0.2591  cls_loss: 0.1032  layout_loss: 0.1381  cls_layout_loss: 0.0174
2025/09/14 00:45:33 - mmengine - INFO - Epoch(train) [12][ 600/1099]  lr: 1.0000e-05  eta: 0:26:25  time: 3.2439  data_time: 0.1699  memory: 30213  grad_norm: 3.7907  loss: 0.5182  bbox_loss: 0.2637  cls_loss: 0.1027  layout_loss: 0.1341  cls_layout_loss: 0.0178
2025/09/14 00:48:12 - mmengine - INFO - Epoch(train) [12][ 650/1099]  lr: 1.0000e-05  eta: 0:23:46  time: 3.1834  data_time: 0.1411  memory: 27237  grad_norm: 3.7748  loss: 0.5164  bbox_loss: 0.2621  cls_loss: 0.1028  layout_loss: 0.1341  cls_layout_loss: 0.0175
2025/09/14 00:50:47 - mmengine - INFO - Epoch(train) [12][ 700/1099]  lr: 1.0000e-05  eta: 0:21:07  time: 3.0888  data_time: 0.1263  memory: 26371  grad_norm: 3.8791  loss: 0.5028  bbox_loss: 0.2563  cls_loss: 0.1010  layout_loss: 0.1285  cls_layout_loss: 0.0170
2025/09/14 00:53:23 - mmengine - INFO - Epoch(train) [12][ 750/1099]  lr: 1.0000e-05  eta: 0:18:28  time: 3.1124  data_time: 0.1606  memory: 25702  grad_norm: 3.9926  loss: 0.4995  bbox_loss: 0.2584  cls_loss: 0.0984  layout_loss: 0.1253  cls_layout_loss: 0.0173
2025/09/14 00:56:04 - mmengine - INFO - Epoch(train) [12][ 800/1099]  lr: 1.0000e-05  eta: 0:15:49  time: 3.2354  data_time: 0.1767  memory: 34163  grad_norm: 3.9406  loss: 0.5222  bbox_loss: 0.2612  cls_loss: 0.1027  layout_loss: 0.1406  cls_layout_loss: 0.0177
2025/09/14 00:58:42 - mmengine - INFO - Epoch(train) [12][ 850/1099]  lr: 1.0000e-05  eta: 0:13:10  time: 3.1526  data_time: 0.1498  memory: 31730  grad_norm: 4.0290  loss: 0.5156  bbox_loss: 0.2591  cls_loss: 0.1009  layout_loss: 0.1385  cls_layout_loss: 0.0171
2025/09/14 01:01:20 - mmengine - INFO - Epoch(train) [12][ 900/1099]  lr: 1.0000e-05  eta: 0:10:32  time: 3.1663  data_time: 0.1558  memory: 28044  grad_norm: 3.8966  loss: 0.4952  bbox_loss: 0.2585  cls_loss: 0.0966  layout_loss: 0.1229  cls_layout_loss: 0.0172
2025/09/14 01:01:56 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/14 01:03:54 - mmengine - INFO - Epoch(train) [12][ 950/1099]  lr: 1.0000e-05  eta: 0:07:53  time: 3.0727  data_time: 0.1509  memory: 28430  grad_norm: 3.6550  loss: 0.5115  bbox_loss: 0.2593  cls_loss: 0.1023  layout_loss: 0.1323  cls_layout_loss: 0.0175
2025/09/14 01:06:37 - mmengine - INFO - Epoch(train) [12][1000/1099]  lr: 1.0000e-05  eta: 0:05:14  time: 3.2546  data_time: 0.1218  memory: 28344  grad_norm: 3.6517  loss: 0.5199  bbox_loss: 0.2624  cls_loss: 0.1022  layout_loss: 0.1377  cls_layout_loss: 0.0176
2025/09/14 01:09:22 - mmengine - INFO - Epoch(train) [12][1050/1099]  lr: 1.0000e-05  eta: 0:02:35  time: 3.2998  data_time: 0.1664  memory: 33331  grad_norm: 3.6780  loss: 0.5153  bbox_loss: 0.2628  cls_loss: 0.1046  layout_loss: 0.1306  cls_layout_loss: 0.0173
2025/09/14 01:11:55 - mmengine - INFO - Exp name: tr3d_1xb16_structured3d_v51_20250913_124949
2025/09/14 01:11:56 - mmengine - INFO - Saving checkpoint at 12 epochs
2025/09/14 01:12:36 - mmengine - INFO - Epoch(val) [12][ 50/241]    eta: 0:02:29  time: 0.7825  data_time: 0.0855  memory: 36540  
2025/09/14 01:13:10 - mmengine - INFO - Epoch(val) [12][100/241]    eta: 0:01:43  time: 0.6829  data_time: 0.0777  memory: 1369  
2025/09/14 01:13:44 - mmengine - INFO - Epoch(val) [12][150/241]    eta: 0:01:05  time: 0.6947  data_time: 0.0928  memory: 1230  
2025/09/14 01:14:18 - mmengine - INFO - Epoch(val) [12][200/241]    eta: 0:00:28  time: 0.6686  data_time: 0.0812  memory: 1081  
2025/09/14 01:15:50 - mmengine - INFO - 
+---------+-------------+-------------+
| Layouts | F1 @.25 IoU | F1 @.50 IoU |
+---------+-------------+-------------+
| wall    | 0.8815      | 0.8690      |
| door    | 0.9388      | 0.9366      |
| window  | 0.8955      | 0.8816      |
+---------+-------------+-------------+
| Overall | 0.9053      | 0.8957      |
+---------+-------------+-------------+
2025/09/14 01:15:50 - mmengine - INFO - Epoch(val) [12][241/241]    structured3d: {'layout': {'wall_f1_25': 0.8815168409881027, 'wall_f1_50': 0.8815168409881027, 'door_f1_25': 0.9387956729237671, 'door_f1_50': 0.9387956729237671, 'window_f1_25': 0.8955387510911067, 'window_f1_50': 0.8955387510911067, 'f1_25': 0.9052837550009922, 'f1_50': 0.8957268870155275}}  data_time: 0.0814  time: 0.7114