{"env_info": "sys.platform: linux\nPython: 3.7.10 (default, Jun  4 2021, 14:48:32) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1,2,3: Tesla V100-SXM2-32GB\nCUDA_HOME: /mnt/lustre/share/cuda-10.1\nNVCC: Cuda compilation tools, release 10.1, V10.1.168\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.5.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.1\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\n  - CuDNN 7.6.3\n  - Magma 2.5.2\n  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, \n\nTorchVision: 0.6.0a0+82fd1c8\nOpenCV: 4.5.2\nMMCV: 1.3.8\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 10.1\nMMDetection: 2.14.0\nMMSegmentation: 0.14.1\nMMDetection3D: 0.14.0+7fec1d5", "config": "dataset_type = 'ScanNetDataset'\ndata_root = './data/scannet/'\nclass_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',\n               'bookshelf', 'picture', 'counter', 'desk', 'curtain',\n               'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',\n               'garbagebin')\ntrain_pipeline = [\n    dict(\n        type='LoadPointsFromFile',\n        coord_type='DEPTH',\n        load_dim=6,\n        use_dim=[0, 1, 2]),\n    dict(\n        type='LoadAnnotations3D',\n        with_bbox_3d=True,\n        with_label_3d=True,\n        with_mask_3d=True,\n        with_seg_3d=True),\n    dict(type='GlobalAlignment', rotation_axis=2),\n    dict(\n        type='PointSegClassMapping',\n        valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,\n                       36, 39)),\n    dict(type='IndoorPointSample', num_points=50000),\n    dict(\n        type='RandomFlip3D',\n        sync_2d=False,\n        flip_ratio_bev_horizontal=0.5,\n        flip_ratio_bev_vertical=0.5),\n    dict(\n        type='GlobalRotScaleTrans',\n        rot_range=[-0.087266, 0.087266],\n        scale_ratio_range=[1.0, 1.0]),\n    dict(\n        type='DefaultFormatBundle3D',\n        class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',\n                     'window', 'bookshelf', 'picture', 'counter', 'desk',\n                     'curtain', 'refrigerator', 'showercurtrain', 'toilet',\n                     'sink', 'bathtub', 'garbagebin')),\n    dict(\n        type='Collect3D',\n        keys=[\n            'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',\n            'pts_instance_mask'\n        ])\n]\ntest_pipeline = [\n    dict(\n        type='LoadPointsFromFile',\n        coord_type='DEPTH',\n        load_dim=6,\n        use_dim=[0, 1, 2]),\n    dict(type='GlobalAlignment', rotation_axis=2),\n    dict(\n        type='MultiScaleFlipAug3D',\n        img_scale=(1333, 800),\n        pts_scale_ratio=1,\n        flip=False,\n        transforms=[\n            dict(\n                type='GlobalRotScaleTrans',\n                rot_range=[0, 0],\n                scale_ratio_range=[1.0, 1.0],\n                translation_std=[0, 0, 0]),\n            dict(\n                type='RandomFlip3D',\n                sync_2d=False,\n                flip_ratio_bev_horizontal=0.5,\n                flip_ratio_bev_vertical=0.5),\n            dict(type='IndoorPointSample', num_points=50000),\n            dict(\n                type='DefaultFormatBundle3D',\n                class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',\n                             'door', 'window', 'bookshelf', 'picture',\n                             'counter', 'desk', 'curtain', 'refrigerator',\n                             'showercurtrain', 'toilet', 'sink', 'bathtub',\n                             'garbagebin'),\n                with_label=False),\n            dict(type='Collect3D', keys=['points'])\n        ])\n]\neval_pipeline = [\n    dict(\n        type='LoadPointsFromFile',\n        coord_type='DEPTH',\n        shift_height=False,\n        load_dim=6,\n        use_dim=[0, 1, 2]),\n    dict(type='GlobalAlignment', rotation_axis=2),\n    dict(\n        type='DefaultFormatBundle3D',\n        class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',\n                     'window', 'bookshelf', 'picture', 'counter', 'desk',\n                     'curtain', 'refrigerator', 'showercurtrain', 'toilet',\n                     'sink', 'bathtub', 'garbagebin'),\n        with_label=False),\n    dict(type='Collect3D', keys=['points'])\n]\ndata = dict(\n    samples_per_gpu=8,\n    workers_per_gpu=4,\n    train=dict(\n        type='RepeatDataset',\n        times=1,\n        dataset=dict(\n            type='ScanNetDataset',\n            data_root='./data/scannet/',\n            ann_file='./data/scannet/scannet_infos_train.pkl',\n            pipeline=[\n                dict(\n                    type='LoadPointsFromFile',\n                    coord_type='DEPTH',\n                    load_dim=6,\n                    use_dim=[0, 1, 2]),\n                dict(\n                    type='LoadAnnotations3D',\n                    with_bbox_3d=True,\n                    with_label_3d=True,\n                    with_mask_3d=True,\n                    with_seg_3d=True),\n                dict(type='GlobalAlignment', rotation_axis=2),\n                dict(\n                    type='PointSegClassMapping',\n                    valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,\n                                   28, 33, 34, 36, 39)),\n                dict(type='IndoorPointSample', num_points=50000),\n                dict(\n                    type='RandomFlip3D',\n                    sync_2d=False,\n                    flip_ratio_bev_horizontal=0.5,\n                    flip_ratio_bev_vertical=0.5),\n                dict(\n                    type='GlobalRotScaleTrans',\n                    rot_range=[-0.087266, 0.087266],\n                    scale_ratio_range=[1.0, 1.0]),\n                dict(\n                    type='DefaultFormatBundle3D',\n                    class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',\n                                 'door', 'window', 'bookshelf', 'picture',\n                                 'counter', 'desk', 'curtain', 'refrigerator',\n                                 'showercurtrain', 'toilet', 'sink', 'bathtub',\n                                 'garbagebin')),\n                dict(\n                    type='Collect3D',\n                    keys=[\n                        'points', 'gt_bboxes_3d', 'gt_labels_3d',\n                        'pts_semantic_mask', 'pts_instance_mask'\n                    ])\n            ],\n            filter_empty_gt=False,\n            classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',\n                     'window', 'bookshelf', 'picture', 'counter', 'desk',\n                     'curtain', 'refrigerator', 'showercurtrain', 'toilet',\n                     'sink', 'bathtub', 'garbagebin'),\n            box_type_3d='Depth')),\n    val=dict(\n        type='ScanNetDataset',\n        data_root='./data/scannet/',\n        ann_file='./data/scannet/scannet_infos_val.pkl',\n        pipeline=[\n            dict(\n                type='LoadPointsFromFile',\n                coord_type='DEPTH',\n                load_dim=6,\n                use_dim=[0, 1, 2]),\n            dict(type='GlobalAlignment', rotation_axis=2),\n            dict(\n                type='MultiScaleFlipAug3D',\n                img_scale=(1333, 800),\n                pts_scale_ratio=1,\n                flip=False,\n                transforms=[\n                    dict(\n                        type='GlobalRotScaleTrans',\n                        rot_range=[0, 0],\n                        scale_ratio_range=[1.0, 1.0],\n                        translation_std=[0, 0, 0]),\n                    dict(\n                        type='RandomFlip3D',\n                        sync_2d=False,\n                        flip_ratio_bev_horizontal=0.5,\n                        flip_ratio_bev_vertical=0.5),\n                    dict(type='IndoorPointSample', num_points=50000),\n                    dict(\n                        type='DefaultFormatBundle3D',\n                        class_names=('cabinet', 'bed', 'chair', 'sofa',\n                                     'table', 'door', 'window', 'bookshelf',\n                                     'picture', 'counter', 'desk', 'curtain',\n                                     'refrigerator', 'showercurtrain',\n                                     'toilet', 'sink', 'bathtub',\n                                     'garbagebin'),\n                        with_label=False),\n                    dict(type='Collect3D', keys=['points'])\n                ])\n        ],\n        classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',\n                 'bookshelf', 'picture', 'counter', 'desk', 'curtain',\n                 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',\n                 'garbagebin'),\n        test_mode=True,\n        box_type_3d='Depth'),\n    test=dict(\n        type='ScanNetDataset',\n        data_root='./data/scannet/',\n        ann_file='./data/scannet/scannet_infos_val.pkl',\n        pipeline=[\n            dict(\n                type='LoadPointsFromFile',\n                coord_type='DEPTH',\n                load_dim=6,\n                use_dim=[0, 1, 2]),\n            dict(type='GlobalAlignment', rotation_axis=2),\n            dict(\n                type='MultiScaleFlipAug3D',\n                img_scale=(1333, 800),\n                pts_scale_ratio=1,\n                flip=False,\n                transforms=[\n                    dict(\n                        type='GlobalRotScaleTrans',\n                        rot_range=[0, 0],\n                        scale_ratio_range=[1.0, 1.0],\n                        translation_std=[0, 0, 0]),\n                    dict(\n                        type='RandomFlip3D',\n                        sync_2d=False,\n                        flip_ratio_bev_horizontal=0.5,\n                        flip_ratio_bev_vertical=0.5),\n                    dict(type='IndoorPointSample', num_points=50000),\n                    dict(\n                        type='DefaultFormatBundle3D',\n                        class_names=('cabinet', 'bed', 'chair', 'sofa',\n                                     'table', 'door', 'window', 'bookshelf',\n                                     'picture', 'counter', 'desk', 'curtain',\n                                     'refrigerator', 'showercurtrain',\n                                     'toilet', 'sink', 'bathtub',\n                                     'garbagebin'),\n                        with_label=False),\n                    dict(type='Collect3D', keys=['points'])\n                ])\n        ],\n        classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',\n                 'bookshelf', 'picture', 'counter', 'desk', 'curtain',\n                 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',\n                 'garbagebin'),\n        test_mode=True,\n        box_type_3d='Depth'))\nevaluation = dict(pipeline=[\n    dict(\n        type='LoadPointsFromFile',\n        coord_type='DEPTH',\n        shift_height=False,\n        load_dim=6,\n        use_dim=[0, 1, 2]),\n    dict(type='GlobalAlignment', rotation_axis=2),\n    dict(\n        type='DefaultFormatBundle3D',\n        class_names=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',\n                     'window', 'bookshelf', 'picture', 'counter', 'desk',\n                     'curtain', 'refrigerator', 'showercurtrain', 'toilet',\n                     'sink', 'bathtub', 'garbagebin'),\n        with_label=False),\n    dict(type='Collect3D', keys=['points'])\n])\nmodel = dict(\n    type='GroupFree3DNet',\n    backbone=dict(\n        type='PointNet2SASSG',\n        in_channels=3,\n        num_points=(2048, 1024, 512, 256),\n        radius=(0.2, 0.4, 0.8, 1.2),\n        num_samples=(64, 32, 16, 16),\n        sa_channels=((128, 128, 256), (256, 256, 512), (256, 256, 512),\n                     (256, 256, 512)),\n        fp_channels=((512, 512), (512, 288)),\n        norm_cfg=dict(type='BN2d'),\n        sa_cfg=dict(\n            type='PointSAModule',\n            pool_mod='max',\n            use_xyz=True,\n            normalize_xyz=True)),\n    bbox_head=dict(\n        type='GroupFree3DHead',\n        in_channels=288,\n        num_decoder_layers=12,\n        num_proposal=256,\n        transformerlayers=dict(\n            type='BaseTransformerLayer',\n            attn_cfgs=dict(\n                type='GroupFree3DMHA',\n                embed_dims=288,\n                num_heads=8,\n                attn_drop=0.1,\n                dropout_layer=dict(type='Dropout', drop_prob=0.1)),\n            ffn_cfgs=dict(\n                embed_dims=288,\n                feedforward_channels=2048,\n                ffn_drop=0.1,\n                act_cfg=dict(type='ReLU', inplace=True)),\n            operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn',\n                             'norm')),\n        pred_layer_cfg=dict(\n            in_channels=288, shared_conv_channels=(288, 288), bias=True),\n        sampling_objectness_loss=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=8.0),\n        objectness_loss=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=1.0),\n        center_loss=dict(\n            type='SmoothL1Loss', reduction='sum', loss_weight=10.0, beta=0.04),\n        dir_class_loss=dict(\n            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),\n        dir_res_loss=dict(\n            type='SmoothL1Loss', reduction='sum', loss_weight=10.0),\n        size_class_loss=dict(\n            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),\n        size_res_loss=dict(\n            type='SmoothL1Loss',\n            beta=0.1111111111111111,\n            reduction='sum',\n            loss_weight=1.1111111111111112),\n        semantic_loss=dict(\n            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),\n        num_classes=18,\n        size_cls_agnostic=False,\n        bbox_coder=dict(\n            type='GroupFree3DBBoxCoder',\n            num_sizes=18,\n            num_dir_bins=1,\n            with_rot=False,\n            size_cls_agnostic=False,\n            mean_sizes=[[0.76966727, 0.8116021, 0.92573744],\n                        [1.876858, 1.8425595, 1.1931566],\n                        [0.61328, 0.6148609, 0.7182701],\n                        [1.3955007, 1.5121545, 0.83443564],\n                        [0.97949594, 1.0675149, 0.6329687],\n                        [0.531663, 0.5955577, 1.7500148],\n                        [0.9624706, 0.72462326, 1.1481868],\n                        [0.83221924, 1.0490936, 1.6875663],\n                        [0.21132214, 0.4206159, 0.5372846],\n                        [1.4440073, 1.8970833, 0.26985747],\n                        [1.0294262, 1.4040797, 0.87554324],\n                        [1.3766412, 0.65521795, 1.6813129],\n                        [0.6650819, 0.71111923, 1.298853],\n                        [0.41999173, 0.37906948, 1.7513971],\n                        [0.59359556, 0.5912492, 0.73919016],\n                        [0.50867593, 0.50656086, 0.30136237],\n                        [1.1511526, 1.0546296, 0.49706793],\n                        [0.47535285, 0.49249494, 0.5802117]])),\n    train_cfg=dict(sample_mod='kps'),\n    test_cfg=dict(\n        sample_mod='kps',\n        nms_thr=0.25,\n        score_thr=0.0,\n        per_class_proposal=True,\n        prediction_stages='last_three'))\nlr = 0.006\noptimizer = dict(\n    type='AdamW',\n    lr=0.006,\n    weight_decay=0.0005,\n    paramwise_cfg=dict(\n        custom_keys=dict({\n            'bbox_head.decoder_layers':\n            dict(lr_mult=0.1, decay_mult=1.0),\n            'bbox_head.decoder_self_posembeds':\n            dict(lr_mult=0.1, decay_mult=1.0),\n            'bbox_head.decoder_cross_posembeds':\n            dict(lr_mult=0.1, decay_mult=1.0),\n            'bbox_head.decoder_query_proj':\n            dict(lr_mult=0.1, decay_mult=1.0),\n            'bbox_head.decoder_key_proj':\n            dict(lr_mult=0.1, decay_mult=1.0)\n        })))\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nlr_config = dict(policy='step', warmup=None, step=[280, 340])\nrunner = dict(type='EpochBasedRunner', max_epochs=400)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(\n    interval=30,\n    hooks=[dict(type='TextLoggerHook'),\n           dict(type='TensorboardLoggerHook')])\ndist_params = dict(backend='nccl', port=29525)\nlog_level = 'INFO'\nwork_dir = '/mnt/lustre/jinhui/outputs/groupfree3d_models/scannet/w2xl12o256/04'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ngpu_ids = range(0, 4)\n", "seed": 0, "exp_name": 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