| 2024-09-21 17:32:30,446 - mmdet - INFO - Environment info: | |
| ------------------------------------------------------------ | |
| sys.platform: linux | |
| Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0] | |
| CUDA available: True | |
| GPU 0,1,2,3,4,5,6,7: NVIDIA RTX A5000 | |
| CUDA_HOME: /data/home/hanbo/cuda-11.6 | |
| NVCC: Cuda compilation tools, release 11.6, V11.6.55 | |
| GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 | |
| PyTorch: 1.12.1+cu116 | |
| PyTorch compiling details: PyTorch built with: | |
| - GCC 9.3 | |
| - C++ Version: 201402 | |
| - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications | |
| - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) | |
| - OpenMP 201511 (a.k.a. OpenMP 4.5) | |
| - LAPACK is enabled (usually provided by MKL) | |
| - NNPACK is enabled | |
| - CPU capability usage: AVX2 | |
| - CUDA Runtime 11.6 | |
| - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 | |
| - CuDNN 8.3.2 (built against CUDA 11.5) | |
| - Magma 2.6.1 | |
| - 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, | |
| TorchVision: 0.13.1+cu116 | |
| OpenCV: 4.10.0 | |
| MMCV: 1.7.2 | |
| MMCV Compiler: GCC 9.3 | |
| MMCV CUDA Compiler: 11.6 | |
| MMDetection: 2.28.2+d592e33 | |
| ------------------------------------------------------------ | |
| 2024-09-21 17:32:31,819 - mmdet - INFO - Distributed training: True | |
| 2024-09-21 17:32:33,172 - mmdet - INFO - Config: | |
| norm_cfg = dict( | |
| type='BN', | |
| requires_grad=False, | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True) | |
| model = dict( | |
| type='FasterRCNNRelAfford', | |
| backbone=dict( | |
| type='mmdet.ResNet', | |
| depth=101, | |
| num_stages=3, | |
| strides=(1, 2, 2), | |
| dilations=(1, 1, 1), | |
| out_indices=(2, ), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True, | |
| style='caffe', | |
| init_cfg=dict( | |
| type='Pretrained', | |
| checkpoint='open-mmlab://detectron2/resnet101_caffe')), | |
| rpn_head=dict( | |
| type='mmdet.RPNHead', | |
| in_channels=1024, | |
| feat_channels=1024, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8, 16, 32], | |
| ratios=[0.33, 0.5, 1.0, 2.0, 3.0], | |
| strides=[16]), | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[1.0, 1.0, 1.0, 1.0]), | |
| loss_cls=dict( | |
| type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
| loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)), | |
| roi_head=None, | |
| child_head=dict( | |
| type='invigorate.PairedRoIHead', | |
| shared_head=dict( | |
| type='invigorate.PairedResLayer', | |
| depth=50, | |
| stage=3, | |
| stride=1, | |
| style='caffe', | |
| norm_eval=False, | |
| share_weights=False), | |
| paired_roi_extractor=dict( | |
| type='invigorate.VMRNPairedRoIExtractor', | |
| roi_layer=dict(type='RoIPool', output_size=7), | |
| out_channels=1024, | |
| featmap_strides=[16]), | |
| relation_head=dict( | |
| type='invigorate.BBoxPairHead', | |
| with_avg_pool=True, | |
| roi_feat_size=7, | |
| in_channels=2048, | |
| num_relations=1, | |
| loss_cls=dict( | |
| type='mmdet.CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0))), | |
| leaf_head=dict( | |
| type='mmdet.StandardRoIHead', | |
| shared_head=dict( | |
| type='mmdet.ResLayer', | |
| depth=50, | |
| stage=3, | |
| stride=1, | |
| style='caffe', | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True), | |
| bbox_roi_extractor=dict( | |
| type='mmdet.SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
| out_channels=1024, | |
| featmap_strides=[16]), | |
| bbox_head=dict( | |
| type='mmdet.BBoxHead', | |
| with_avg_pool=True, | |
| with_reg=False, | |
| roi_feat_size=7, | |
| in_channels=2048, | |
| num_classes=2, | |
| loss_cls=dict( | |
| type='mmdet.CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=1.0))), | |
| train_cfg=dict( | |
| rpn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.3, | |
| match_low_quality=True, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.5, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=False), | |
| allowed_border=0, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=12000, | |
| max_per_img=2000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False), | |
| child_head=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.7, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| relation_sampler=dict( | |
| type='RandomRelationSampler', | |
| num=32, | |
| pos_fraction=0.5, | |
| cls_ratio_ub=1.0, | |
| add_gt_as_proposals=True, | |
| num_relation_cls=1, | |
| neg_id=0), | |
| pos_weight=-1, | |
| online_data=True, | |
| online_start_iteration=0), | |
| leaf_head=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=64, | |
| pos_fraction=0.25, | |
| neg_pos_ub=3.0, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False)), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=6000, | |
| max_per_img=300, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.3), | |
| max_per_img=100), | |
| child_head=dict( | |
| bbox_score_thr=0.5, verbose_relation=False, average_scores=False), | |
| leaf_head=dict(score_thr=0.5, nms=None, max_per_img=100))) | |
| dataset_type = 'REGRADAffordDataset' | |
| data_root = 'data/regrad/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='RandomCrop', crop_type='random_keep', allow_negative_crop=False), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ] | |
| data = dict( | |
| train=dict( | |
| _delete_=True, | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| meta_info_file='dataset_train_5k/meta_infos.json', | |
| ann_file='dataset_train_5k/objects.json', | |
| img_prefix='dataset_train_5k/RGBImages', | |
| seg_prefix='dataset_train_5k/SegmentationImages', | |
| depth_prefix='dataset_train_5k/DepthImages', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=[ | |
| 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='random_keep', | |
| allow_negative_crop=False), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict( | |
| type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]) | |
| ], | |
| min_pos_relation=1, | |
| class_agnostic=True), | |
| dict( | |
| type='MetaGraspNetAffordDataset', | |
| data_root='data/metagraspnet/sim/', | |
| meta_info_file='meta_infos_train.json', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=[ | |
| 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='random_keep', | |
| allow_negative_crop=False), | |
| dict( | |
| type='Expand', | |
| mean=[123.675, 116.28, 103.53], | |
| ratio_range=(1, 2)), | |
| dict( | |
| type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]) | |
| ], | |
| min_pos_relation=1, | |
| class_agnostic=True), | |
| dict( | |
| type='VMRDAffordDataset', | |
| ann_file='data/vmrd/ImageSets/Main/trainval.txt', | |
| img_prefix='data/vmrd/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=[ | |
| 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict( | |
| type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True), | |
| dict( | |
| type='VRDAffordDataset', | |
| data_root='data/vrd/', | |
| ann_file='sg_dataset/sg_train_annotations.json', | |
| img_prefix='sg_dataset/sg_train_images/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=[ | |
| 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict( | |
| type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True), | |
| dict( | |
| type='VGAffordDataset', | |
| data_root='data/vg/downloads', | |
| ann_file='relationships.json', | |
| img_prefix='', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=[ | |
| 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict( | |
| type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| ], | |
| separate_eval=True, | |
| class_agnostic=True), | |
| val=dict( | |
| _delete_=True, | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_seen_val_1k/meta_infos.json', | |
| ann_file='dataset_seen_val_1k/objects.json', | |
| img_prefix='dataset_seen_val_1k/RGBImages', | |
| seg_prefix='dataset_seen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_seen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000), | |
| dict( | |
| type='VMRDAffordDataset', | |
| ann_file='data/vmrd/ImageSets/Main/test.txt', | |
| img_prefix='data/vmrd/', | |
| using_gt_proposals=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| ], | |
| separate_eval=True, | |
| class_agnostic=True), | |
| test=dict( | |
| _delete_=True, | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_seen_val_1k/meta_infos.json', | |
| ann_file='dataset_seen_val_1k/objects.json', | |
| img_prefix='dataset_seen_val_1k/RGBImages', | |
| seg_prefix='dataset_seen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_seen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000), | |
| dict( | |
| type='VMRDAffordDataset', | |
| ann_file='data/vmrd/ImageSets/Main/test.txt', | |
| img_prefix='data/vmrd/', | |
| using_gt_proposals=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| ], | |
| separate_eval=True, | |
| class_agnostic=True), | |
| samples_per_gpu=4, | |
| workers_per_gpu=2) | |
| evaluation = dict(interval=1, metric=['mAP', 'ImgAcc']) | |
| optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) | |
| optimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2)) | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=4000, | |
| warmup_ratio=0.001, | |
| step=[12, 18]) | |
| runner = dict(type='EpochBasedRunner', max_epochs=20) | |
| checkpoint_config = dict(interval=1, max_keep_ckpts=5) | |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| opencv_num_threads = 0 | |
| mp_start_method = 'fork' | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| mmdet = None | |
| mmdet_root = '/data/home/hanbo/projects/cloud_services/service/vmrn/vmrn_models/mmdetection/mmdet' | |
| test_with_object_detector = False | |
| test_crop_config = (174, 79, 462, 372) | |
| kinect_img_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='FixedCrop', | |
| crop_type='absolute', | |
| top_left=(174, 79), | |
| bottom_right=(462, 372)), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ] | |
| seen_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_seen_val_1k/meta_infos.json', | |
| ann_file='dataset_seen_val_1k/objects.json', | |
| img_prefix='dataset_seen_val_1k/RGBImages', | |
| seg_prefix='dataset_seen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_seen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000) | |
| unseen_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_unseen_val_1k/meta_infos.json', | |
| ann_file='dataset_unseen_val_1k/objects.json', | |
| img_prefix='dataset_unseen_val_1k/RGBImages', | |
| seg_prefix='dataset_unseen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_unseen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000) | |
| real_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='real/meta_infos.json', | |
| ann_file='real/objects.json', | |
| img_prefix='real/RGBImages', | |
| img_suffix='png', | |
| depth_prefix='real/DepthImages', | |
| test_mode=True, | |
| test_gt_bbox_offset=(174, 79), | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='FixedCrop', | |
| crop_type='absolute', | |
| top_left=(174, 79), | |
| bottom_right=(462, 372)), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| regrad_datatype = 'REGRADAffordDataset' | |
| regrad_root = 'data/regrad/' | |
| vmrd_datatype = 'VMRDAffordDataset' | |
| vmrd_root = 'data/vmrd/' | |
| vmrd_train = dict( | |
| type='VMRDAffordDataset', | |
| ann_file='data/vmrd/ImageSets/Main/trainval.txt', | |
| img_prefix='data/vmrd/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| regrad_train = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| meta_info_file='dataset_train_5k/meta_infos.json', | |
| ann_file='dataset_train_5k/objects.json', | |
| img_prefix='dataset_train_5k/RGBImages', | |
| seg_prefix='dataset_train_5k/SegmentationImages', | |
| depth_prefix='dataset_train_5k/DepthImages', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='random_keep', | |
| allow_negative_crop=False), | |
| dict(type='Expand', mean=[123.675, 116.28, 103.53]), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]) | |
| ], | |
| min_pos_relation=1, | |
| class_agnostic=True) | |
| metagraspnet_sim_train = dict( | |
| type='MetaGraspNetAffordDataset', | |
| data_root='data/metagraspnet/sim/', | |
| meta_info_file='meta_infos_train.json', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='random_keep', | |
| allow_negative_crop=False), | |
| dict( | |
| type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]) | |
| ], | |
| min_pos_relation=1, | |
| class_agnostic=True) | |
| vgvrd_train_pipeline = [ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', | |
| 'gt_relleaves']), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']) | |
| ] | |
| vrd_train = dict( | |
| type='VRDAffordDataset', | |
| data_root='data/vrd/', | |
| ann_file='sg_dataset/sg_train_annotations.json', | |
| img_prefix='sg_dataset/sg_train_images/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| vg_train = dict( | |
| type='VGAffordDataset', | |
| data_root='data/vg/downloads', | |
| ann_file='relationships.json', | |
| img_prefix='', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', to_float32=True), | |
| dict( | |
| type='LoadAnnotationsCustom', | |
| keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict( | |
| type='DefaultFormatBundleCustom', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]), | |
| dict( | |
| type='Collect', | |
| keys=[ | |
| 'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves' | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| real_test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='FixedCrop', | |
| crop_type='absolute', | |
| top_left=(174, 79), | |
| bottom_right=(462, 372)), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ] | |
| regrad_seen_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_seen_val_1k/meta_infos.json', | |
| ann_file='dataset_seen_val_1k/objects.json', | |
| img_prefix='dataset_seen_val_1k/RGBImages', | |
| seg_prefix='dataset_seen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_seen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000) | |
| regrad_unseen_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='dataset_unseen_val_1k/meta_infos.json', | |
| ann_file='dataset_unseen_val_1k/objects.json', | |
| img_prefix='dataset_unseen_val_1k/RGBImages', | |
| seg_prefix='dataset_unseen_val_1k/SegmentationImages', | |
| depth_prefix='dataset_unseen_val_1k/DepthImages', | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True, | |
| max_sample_num=1000) | |
| regrad_real_val_dataset = dict( | |
| type='REGRADAffordDataset', | |
| data_root='data/regrad/', | |
| using_depth=False, | |
| using_gt_proposals=True, | |
| meta_info_file='real/meta_infos.json', | |
| ann_file='real/objects.json', | |
| img_prefix='real/RGBImages', | |
| img_suffix='png', | |
| depth_prefix='real/DepthImages', | |
| test_mode=True, | |
| test_gt_bbox_offset=(174, 79), | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='FixedCrop', | |
| crop_type='absolute', | |
| top_left=(174, 79), | |
| bottom_right=(462, 372)), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| vmrd_val_dataset = dict( | |
| type='VMRDAffordDataset', | |
| ann_file='data/vmrd/ImageSets/Main/test.txt', | |
| img_prefix='data/vmrd/', | |
| using_gt_proposals=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadRelationProposals'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1000, 600), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[1.0, 1.0, 1.0], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img', 'relation_proposals']) | |
| ]) | |
| ], | |
| class_agnostic=True) | |
| train_sampler = dict( | |
| type='DistributedWeightedSampler', | |
| weights=[0.1, 0.1, 0.05, 0.05, 0.7], | |
| sample_per_epoch=150000, | |
| shuffle=True) | |
| work_dir = './work_dirs/relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic' | |
| gpu_ids = range(0, 8) | |