{"env_info": "sys.platform: linux\nPython: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA RTX A5000\nCUDA_HOME: /data/home/hanbo/cuda-11.6\nNVCC: Cuda compilation tools, release 11.6, V11.6.55\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nPyTorch: 1.12.1+cu116\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.6\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_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\n - CuDNN 8.3.2 (built against CUDA 11.5)\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.13.1+cu116\nOpenCV: 4.10.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.6\nMMDetection: 2.28.2+d592e33", "config": "norm_cfg = dict(\n type='BN',\n requires_grad=False,\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True)\nmodel = dict(\n type='FasterRCNNRelAfford',\n backbone=dict(\n type='mmdet.ResNet',\n depth=101,\n num_stages=3,\n strides=(1, 2, 2),\n dilations=(1, 1, 1),\n out_indices=(2, ),\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True,\n style='caffe',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='open-mmlab://detectron2/resnet101_caffe')),\n rpn_head=dict(\n type='mmdet.RPNHead',\n in_channels=1024,\n feat_channels=1024,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8, 16, 32],\n ratios=[0.33, 0.5, 1.0, 2.0, 3.0],\n strides=[16]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)),\n roi_head=None,\n child_head=dict(\n type='invigorate.PairedRoIHead',\n shared_head=dict(\n type='invigorate.PairedResLayer',\n depth=50,\n stage=3,\n stride=1,\n style='caffe',\n norm_eval=False,\n share_weights=False),\n paired_roi_extractor=dict(\n type='invigorate.VMRNPairedRoIExtractor',\n roi_layer=dict(type='RoIPool', output_size=7),\n out_channels=1024,\n featmap_strides=[16]),\n relation_head=dict(\n type='invigorate.BBoxPairHead',\n with_avg_pool=True,\n roi_feat_size=7,\n in_channels=2048,\n num_relations=1,\n loss_cls=dict(\n type='mmdet.CrossEntropyLoss',\n use_sigmoid=False,\n loss_weight=1.0))),\n leaf_head=dict(\n type='mmdet.StandardRoIHead',\n shared_head=dict(\n type='mmdet.ResLayer',\n depth=50,\n stage=3,\n stride=1,\n style='caffe',\n norm_cfg=dict(type='BN', requires_grad=False),\n norm_eval=True),\n bbox_roi_extractor=dict(\n type='mmdet.SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=1024,\n featmap_strides=[16]),\n bbox_head=dict(\n type='mmdet.BBoxHead',\n with_avg_pool=True,\n with_reg=False,\n roi_feat_size=7,\n in_channels=2048,\n num_classes=2,\n loss_cls=dict(\n type='mmdet.CrossEntropyLoss',\n use_sigmoid=False,\n loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=0,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=12000,\n max_per_img=2000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=False,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n pos_weight=-1,\n debug=False),\n child_head=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.5,\n min_pos_iou=0.7,\n match_low_quality=False,\n ignore_iof_thr=-1),\n relation_sampler=dict(\n type='RandomRelationSampler',\n num=32,\n pos_fraction=0.5,\n cls_ratio_ub=1.0,\n add_gt_as_proposals=True,\n num_relation_cls=1,\n neg_id=0),\n pos_weight=-1,\n online_data=True,\n online_start_iteration=0),\n leaf_head=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=False,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=64,\n pos_fraction=0.25,\n neg_pos_ub=3.0,\n add_gt_as_proposals=True),\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=6000,\n max_per_img=300,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.3),\n max_per_img=100),\n child_head=dict(\n bbox_score_thr=0.5, verbose_relation=False, average_scores=False),\n leaf_head=dict(score_thr=0.5, nms=None, max_per_img=100)))\ndataset_type = 'REGRADAffordDataset'\ndata_root = 'data/regrad/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='RandomCrop', crop_type='random_keep', allow_negative_crop=False),\n dict(type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves']),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n]\ndata = dict(\n train=dict(\n _delete_=True,\n type='ConcatDataset',\n datasets=[\n dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n meta_info_file='dataset_train_5k/meta_infos.json',\n ann_file='dataset_train_5k/objects.json',\n img_prefix='dataset_train_5k/RGBImages',\n seg_prefix='dataset_train_5k/SegmentationImages',\n depth_prefix='dataset_train_5k/DepthImages',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=[\n 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='RandomCrop',\n crop_type='random_keep',\n allow_negative_crop=False),\n dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n dict(\n type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ])\n ],\n min_pos_relation=1,\n class_agnostic=True),\n dict(\n type='MetaGraspNetAffordDataset',\n data_root='data/metagraspnet/sim/',\n meta_info_file='meta_infos_train.json',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=[\n 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='RandomCrop',\n crop_type='random_keep',\n allow_negative_crop=False),\n dict(\n type='Expand',\n mean=[123.675, 116.28, 103.53],\n ratio_range=(1, 2)),\n dict(\n type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ])\n ],\n min_pos_relation=1,\n class_agnostic=True),\n dict(\n type='VMRDAffordDataset',\n ann_file='data/vmrd/ImageSets/Main/trainval.txt',\n img_prefix='data/vmrd/',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=[\n 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n dict(\n type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ])\n ],\n class_agnostic=True),\n dict(\n type='VRDAffordDataset',\n data_root='data/vrd/',\n ann_file='sg_dataset/sg_train_annotations.json',\n img_prefix='sg_dataset/sg_train_images/',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=[\n 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ])\n ],\n class_agnostic=True),\n dict(\n type='VGAffordDataset',\n data_root='data/vg/downloads',\n ann_file='relationships.json',\n img_prefix='',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=[\n 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves'\n ])\n ],\n class_agnostic=True)\n ],\n separate_eval=True,\n class_agnostic=True),\n val=dict(\n _delete_=True,\n type='ConcatDataset',\n datasets=[\n dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_seen_val_1k/meta_infos.json',\n ann_file='dataset_seen_val_1k/objects.json',\n img_prefix='dataset_seen_val_1k/RGBImages',\n seg_prefix='dataset_seen_val_1k/SegmentationImages',\n depth_prefix='dataset_seen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(\n type='Collect',\n keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000),\n dict(\n type='VMRDAffordDataset',\n ann_file='data/vmrd/ImageSets/Main/test.txt',\n img_prefix='data/vmrd/',\n using_gt_proposals=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(\n type='Collect',\n keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True)\n ],\n separate_eval=True,\n class_agnostic=True),\n test=dict(\n _delete_=True,\n type='ConcatDataset',\n datasets=[\n dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_seen_val_1k/meta_infos.json',\n ann_file='dataset_seen_val_1k/objects.json',\n img_prefix='dataset_seen_val_1k/RGBImages',\n seg_prefix='dataset_seen_val_1k/SegmentationImages',\n depth_prefix='dataset_seen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(\n type='Collect',\n keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000),\n dict(\n type='VMRDAffordDataset',\n ann_file='data/vmrd/ImageSets/Main/test.txt',\n img_prefix='data/vmrd/',\n using_gt_proposals=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(\n type='Collect',\n keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True)\n ],\n separate_eval=True,\n class_agnostic=True),\n samples_per_gpu=4,\n workers_per_gpu=2)\nevaluation = dict(interval=1, metric=['mAP', 'ImgAcc'])\noptimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2))\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=4000,\n warmup_ratio=0.001,\n step=[12, 18])\nrunner = dict(type='EpochBasedRunner', max_epochs=20)\ncheckpoint_config = dict(interval=1, max_keep_ckpts=5)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\nmmdet = None\nmmdet_root = '/data/home/hanbo/projects/cloud_services/service/vmrn/vmrn_models/mmdetection/mmdet'\ntest_with_object_detector = False\ntest_crop_config = (174, 79, 462, 372)\nkinect_img_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='FixedCrop',\n crop_type='absolute',\n top_left=(174, 79),\n bottom_right=(462, 372)),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n]\nseen_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_seen_val_1k/meta_infos.json',\n ann_file='dataset_seen_val_1k/objects.json',\n img_prefix='dataset_seen_val_1k/RGBImages',\n seg_prefix='dataset_seen_val_1k/SegmentationImages',\n depth_prefix='dataset_seen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000)\nunseen_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_unseen_val_1k/meta_infos.json',\n ann_file='dataset_unseen_val_1k/objects.json',\n img_prefix='dataset_unseen_val_1k/RGBImages',\n seg_prefix='dataset_unseen_val_1k/SegmentationImages',\n depth_prefix='dataset_unseen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000)\nreal_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='real/meta_infos.json',\n ann_file='real/objects.json',\n img_prefix='real/RGBImages',\n img_suffix='png',\n depth_prefix='real/DepthImages',\n test_mode=True,\n test_gt_bbox_offset=(174, 79),\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='FixedCrop',\n crop_type='absolute',\n top_left=(174, 79),\n bottom_right=(462, 372)),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True)\nregrad_datatype = 'REGRADAffordDataset'\nregrad_root = 'data/regrad/'\nvmrd_datatype = 'VMRDAffordDataset'\nvmrd_root = 'data/vmrd/'\nvmrd_train = dict(\n type='VMRDAffordDataset',\n ann_file='data/vmrd/ImageSets/Main/trainval.txt',\n img_prefix='data/vmrd/',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ])\n ],\n class_agnostic=True)\nregrad_train = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n meta_info_file='dataset_train_5k/meta_infos.json',\n ann_file='dataset_train_5k/objects.json',\n img_prefix='dataset_train_5k/RGBImages',\n seg_prefix='dataset_train_5k/SegmentationImages',\n depth_prefix='dataset_train_5k/DepthImages',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='RandomCrop',\n crop_type='random_keep',\n allow_negative_crop=False),\n dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ])\n ],\n min_pos_relation=1,\n class_agnostic=True)\nmetagraspnet_sim_train = dict(\n type='MetaGraspNetAffordDataset',\n data_root='data/metagraspnet/sim/',\n meta_info_file='meta_infos_train.json',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='RandomCrop',\n crop_type='random_keep',\n allow_negative_crop=False),\n dict(\n type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ])\n ],\n min_pos_relation=1,\n class_agnostic=True)\nvgvrd_train_pipeline = [\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',\n 'gt_relleaves']),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'])\n]\nvrd_train = dict(\n type='VRDAffordDataset',\n data_root='data/vrd/',\n ann_file='sg_dataset/sg_train_annotations.json',\n img_prefix='sg_dataset/sg_train_images/',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ])\n ],\n class_agnostic=True)\nvg_train = dict(\n type='VGAffordDataset',\n data_root='data/vg/downloads',\n ann_file='relationships.json',\n img_prefix='',\n pipeline=[\n dict(type='LoadImageFromFile', to_float32=True),\n dict(\n type='LoadAnnotationsCustom',\n keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(\n type='DefaultFormatBundleCustom',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ]),\n dict(\n type='Collect',\n keys=[\n 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'\n ])\n ],\n class_agnostic=True)\nreal_test_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='FixedCrop',\n crop_type='absolute',\n top_left=(174, 79),\n bottom_right=(462, 372)),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n]\nregrad_seen_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_seen_val_1k/meta_infos.json',\n ann_file='dataset_seen_val_1k/objects.json',\n img_prefix='dataset_seen_val_1k/RGBImages',\n seg_prefix='dataset_seen_val_1k/SegmentationImages',\n depth_prefix='dataset_seen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000)\nregrad_unseen_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='dataset_unseen_val_1k/meta_infos.json',\n ann_file='dataset_unseen_val_1k/objects.json',\n img_prefix='dataset_unseen_val_1k/RGBImages',\n seg_prefix='dataset_unseen_val_1k/SegmentationImages',\n depth_prefix='dataset_unseen_val_1k/DepthImages',\n test_mode=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True,\n max_sample_num=1000)\nregrad_real_val_dataset = dict(\n type='REGRADAffordDataset',\n data_root='data/regrad/',\n using_depth=False,\n using_gt_proposals=True,\n meta_info_file='real/meta_infos.json',\n ann_file='real/objects.json',\n img_prefix='real/RGBImages',\n img_suffix='png',\n depth_prefix='real/DepthImages',\n test_mode=True,\n test_gt_bbox_offset=(174, 79),\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='FixedCrop',\n crop_type='absolute',\n top_left=(174, 79),\n bottom_right=(462, 372)),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True)\nvmrd_val_dataset = dict(\n type='VMRDAffordDataset',\n ann_file='data/vmrd/ImageSets/Main/test.txt',\n img_prefix='data/vmrd/',\n using_gt_proposals=True,\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadRelationProposals'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1000, 600),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[1.0, 1.0, 1.0],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img', 'relation_proposals'])\n ])\n ],\n class_agnostic=True)\ntrain_sampler = dict(\n type='DistributedWeightedSampler',\n weights=[0.1, 0.1, 0.05, 0.05, 0.7],\n sample_per_epoch=150000,\n shuffle=True)\nwork_dir = './work_dirs/relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic'\ngpu_ids = range(0, 8)\n", "seed": null, "exp_name": "relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 7e-05, "memory": 10291, "data_time": 2.48286, "loss_rpn_cls": 0.66131, "loss_rpn_bbox": 0.36403, "loss_relation": 0.68027, "acc_relation": 56.37278, "loss_leaf": 0.79301, "acc_leaf": 73.02821, "loss": 2.49862, "grad_norm": 18.80918, "time": 3.06603} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00013, "memory": 10291, "data_time": 0.02225, "loss_rpn_cls": 0.48375, "loss_rpn_bbox": 0.30694, "loss_relation": 0.60935, "acc_relation": 67.61932, "loss_leaf": 0.68858, "acc_leaf": 75.1762, "loss": 2.08861, "grad_norm": 5.65501, "time": 0.50249} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00019, "memory": 10470, "data_time": 0.02327, "loss_rpn_cls": 0.43107, "loss_rpn_bbox": 0.28568, "loss_relation": 0.57272, "acc_relation": 70.9847, "loss_leaf": 0.6442, "acc_leaf": 76.26519, "loss": 1.93367, "grad_norm": 6.08076, "time": 0.5403} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00025, "memory": 10634, "data_time": 0.02356, "loss_rpn_cls": 0.41436, "loss_rpn_bbox": 0.27743, "loss_relation": 0.54032, "acc_relation": 73.89953, "loss_leaf": 0.62211, "acc_leaf": 76.93742, "loss": 1.85421, "grad_norm": 6.44747, "time": 0.50675} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00032, "memory": 10634, "data_time": 0.02355, "loss_rpn_cls": 0.40246, "loss_rpn_bbox": 0.25669, "loss_relation": 0.51947, "acc_relation": 75.41763, "loss_leaf": 0.61325, "acc_leaf": 77.02366, "loss": 1.79186, "grad_norm": 8.21083, "time": 0.51963} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00038, "memory": 10634, "data_time": 0.02444, "loss_rpn_cls": 0.38812, "loss_rpn_bbox": 0.24572, "loss_relation": 0.5045, "acc_relation": 75.80448, "loss_leaf": 0.59991, "acc_leaf": 77.50814, "loss": 1.73824, "grad_norm": 9.53742, "time": 0.50422} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00044, "memory": 10634, "data_time": 0.02355, "loss_rpn_cls": 0.37221, "loss_rpn_bbox": 0.23004, "loss_relation": 0.49754, "acc_relation": 77.00163, "loss_leaf": 0.60082, "acc_leaf": 77.25436, "loss": 1.70061, "grad_norm": 9.77239, "time": 0.53863} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.0005, "memory": 10634, "data_time": 0.02417, "loss_rpn_cls": 0.36419, "loss_rpn_bbox": 0.22355, "loss_relation": 0.49756, "acc_relation": 76.48692, "loss_leaf": 0.58979, "acc_leaf": 77.40942, "loss": 1.67509, "grad_norm": 8.70116, "time": 0.53047} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00057, "memory": 10634, "data_time": 0.02314, "loss_rpn_cls": 0.36086, "loss_rpn_bbox": 0.21259, "loss_relation": 0.4789, "acc_relation": 77.84504, "loss_leaf": 0.59063, "acc_leaf": 77.42357, "loss": 1.64297, "grad_norm": 8.58213, "time": 0.50575} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00063, "memory": 10634, "data_time": 0.02476, "loss_rpn_cls": 0.33834, "loss_rpn_bbox": 0.20142, "loss_relation": 0.48105, "acc_relation": 77.46031, "loss_leaf": 0.58333, "acc_leaf": 77.14585, "loss": 1.60415, "grad_norm": 7.61097, "time": 0.50818} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.00069, "memory": 10634, "data_time": 0.02315, "loss_rpn_cls": 0.33828, "loss_rpn_bbox": 0.19675, "loss_relation": 0.47639, "acc_relation": 77.83461, "loss_leaf": 0.58049, "acc_leaf": 76.94355, "loss": 1.59191, "grad_norm": 6.61856, "time": 0.51441} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.00075, "memory": 10634, "data_time": 0.02307, "loss_rpn_cls": 0.32079, "loss_rpn_bbox": 0.18615, "loss_relation": 0.46564, "acc_relation": 78.52483, "loss_leaf": 0.60228, "acc_leaf": 76.36927, "loss": 1.57485, "grad_norm": 9.8383, "time": 0.51285} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.00082, "memory": 10649, "data_time": 0.02493, "loss_rpn_cls": 0.32039, "loss_rpn_bbox": 0.18652, "loss_relation": 0.47722, "acc_relation": 77.47919, "loss_leaf": 0.59653, "acc_leaf": 76.22457, "loss": 1.58066, "grad_norm": 7.209, "time": 0.52261} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.00088, "memory": 10649, "data_time": 0.02487, "loss_rpn_cls": 0.30947, "loss_rpn_bbox": 0.17921, "loss_relation": 0.47821, "acc_relation": 77.37012, "loss_leaf": 0.59668, "acc_leaf": 76.10855, "loss": 1.56357, "grad_norm": 8.12226, "time": 0.51202} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.00094, "memory": 10649, "data_time": 0.02462, "loss_rpn_cls": 0.30264, "loss_rpn_bbox": 0.17264, "loss_relation": 0.45763, "acc_relation": 79.12063, "loss_leaf": 0.59241, "acc_leaf": 76.35051, "loss": 1.52532, "grad_norm": 7.06643, "time": 0.52006} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.001, "memory": 10649, "data_time": 0.02511, "loss_rpn_cls": 0.291, "loss_rpn_bbox": 0.16467, "loss_relation": 0.46628, "acc_relation": 78.25106, "loss_leaf": 0.58659, "acc_leaf": 76.46076, "loss": 1.50854, "grad_norm": 6.29599, "time": 0.51225} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.00107, "memory": 10649, "data_time": 0.02484, "loss_rpn_cls": 0.29446, "loss_rpn_bbox": 0.16652, "loss_relation": 0.45757, "acc_relation": 78.65848, "loss_leaf": 0.58891, "acc_leaf": 76.42459, "loss": 1.50746, "grad_norm": 6.42606, "time": 0.51253} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.00113, "memory": 10649, "data_time": 0.02481, "loss_rpn_cls": 0.28421, "loss_rpn_bbox": 0.16726, "loss_relation": 0.44282, "acc_relation": 80.12064, "loss_leaf": 0.58752, "acc_leaf": 76.53568, "loss": 1.48182, "grad_norm": 6.73067, "time": 0.51298} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.00119, "memory": 10649, "data_time": 0.02483, "loss_rpn_cls": 0.28911, "loss_rpn_bbox": 0.16903, "loss_relation": 0.45212, "acc_relation": 79.35586, "loss_leaf": 0.58745, "acc_leaf": 76.63201, "loss": 1.4977, "grad_norm": 5.28868, "time": 0.51721} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 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