{"mmdet_version": "2.28.2d592e33", "CLASSES": ["UNKNOWN", "advertisement", "advertising", "airplane", "antenna", "apple", "apples", "apron", "arch", "archway", "arm", "arms", "arrow", "ashcan", "awning", "baby", "background", "backpack", "badge", "badminton", "bag", "bags", "balcony", "ball", "balloon", "banana", "bananas", "band", "bandana", "banner", "bar", "barrel", "barrier", "bars", "base", "basket", "bat", "bathtub", "batter", "beach", "beam", "beanie", "bear", "beard", "bed", "beer", "bell", "belt", "bench", "benches", "bicycle", "bike", "bikes", "bikini", "billboard", "bin", "bird", "birdhouse", "birds", "black hair", "blanket", "blinds", "block", "blouse", "blue shirt", "board", "boat", "boats", "book", "books", "bookshelf", "boot", "boots", "bottle", "bottles", "bow", "bowl", "box", "boxes", "boy", "bracelet", "branch", "branches", "bread", "brick", "bricks", "bridge", "bridle", "brown hair", "brush", "bucket", "building", "building 2", "buildings", "bumper", "bun", "buoy", "burner", "bus", "bus stop", "bush", "bushes", "button", "buttons", "cab", "cabinet", "cabinets", "cable", "cables", "cage", "cake", "camera", "can", "candle", "candles", "candy", "canister", "canopy", "cans", "cap", "car", "card", "cargo", "carpet", "carriage", "carrot", "carrots", "cars", "cart", "case", "cat", "catcher", "ceiling", "cell phone", "cellphone", "cellular telephone", "chain", "chair", "chairs", "chandelier", "charger", "cheese", "child", "children", "chimney", "church", "circle", "city", "clock", "cloth", "clothes", "clothing", "cloud", "clouds", "coaster", "coat", "coffee", "coffee table", "collar", "column", "columns", "computer", "computer keyboard", "concrete", "cone", "cones", "container", "controller", "cooler", "cord", "cords", "corner", "couch", "counter", "countertop", "couple", "court", "cover", "cow", "cows", "crane", "crate", "cross", "crosswalk", "crowd", "cup", "cupcake", "cups", "curb", "curtain", "curtains", "cushion", "cutting board", "deck", "decoration", "design", "desk", "dirt", "dish", "dishwasher", "display", "display case", "divider", "dock", "dog", "dome", "donut", "door", "doorknob", "doors", "doorway", "drain", "drawer", "drawers", "dress", "dresser", "drink", "driver", "duck", "ear", "earphone", "earring", "earrings", "ears", "eggs", "elephant", "engine", "eye", "eyebrows", "eyes", "face", "fan", "faucet", "feet", "fence", "fender", "field", "file", "finger", "fingers", "fins", "fire hydrant", "fireplace", "flag", "flags", "flip flops", "floor", "flower", "flowers", "folder", "food", "foot", "fork", "frame", "freezer", "fridge", "frisbee", "fruit", "fur", "game", "garbage can", "gate", "giraffe", "giraffes", "girl", "girls", "glass", "glasses", "glove", "gloves", "goggles", "graffiti", "grass", "grate", "gravel", "grill", "ground", "group", "guitar", "guy", "hair", "hand", "handle", "handlebars", "handles", "hands", "hat", "hay", "head", "headband", "headboard", "headlight", "headlights", "headphones", "headset", "helmet", "hill", "hills", "holder", "hole", "hood", "hoodie", "hooves", "horns", "horse", "horses", "hose", "house", "houses", "hubcap", "hydrant", "ipod", "island", "jacket", "jar", "jeans", "jersey", "jug", "ketchup", "kettle", "keyboard", "keys", "kickstand", "kid", "kids", "kitchen", "kite", "kites", "knife", "knives", "knob", "knobs", "label", "laces", "ladder", "lady", "lamp", "lamp post", "lamp shade", "lampshade", "lanyard", "laptop", "leaf", "leash", "leaves", "ledge", "leg", "leggings", "legs", "letter", "lettering", "letters", "lettuce", "license plate", "lid", "light", "light post", "lights", "lightswitch", "line", "lines", "lips", "liquid", "lock", "logo", "loudspeaker", "luggage", "mailbox", "man", "man 1", "man 2", "man 3", "mane", "mask", "mast", "mat", "men", "menu", "meter", "microphone", "microwave", "milk", "mirror", "mitt", "mobile phone", "monitor", "motor", "motorcycle", "motorcycles", "mountain", "mountains", "mouse", "mousepad", "mouth", "mug", "mustache", "name", "napkin", "napkins", "neck", "necklace", "net", "newspaper", "nose", "notebook", "number", "numbers", "oar", "object", "ocean", "oranges", "ornament", "other man", "ottoman", "outlet", "oven", "overhang", "painting", "palm tree", "palm trees", "pan", "pants", "paper", "paper towels", "papers", "parking lot", "passengers", "path", "pattern", "pavement", "paw", "pen", "pens", "people", "pepper", "person", "phone", "photo", "picture", "pictures", "pier", "pillar", "pillars", "pillow", "pillows", "pipe", "pipes", "pistol", "pitcher", "pizza", "plane", "plant", "planter", "plants", "plate", "plates", "platform", "player", "players", "pliers", "pocket", "pole", "poles", "policeman", "ponytail", "pool", "post", "poster", "posts", "pot", "pots", "printer", "purse", "pylon", "rack", "racket", "rail", "railing", "ramp", "reflection", "refridgerator", "refrigerator", "remote", "remote control", "remote controller", "ribbon", "rim", "ring", "river", "road", "robe", "rock", "rocket", "rocks", "roof", "room", "rope", "rug", "runway", "saddle", "sail", "salad", "salt", "sand", "sandals", "sandwich", "sauce", "scarf", "scissors", "scooter", "screen", "screwdriver", "sea", "seat", "seats", "shade", "shades", "shadow", "shadows", "shaver", "sheep", "sheet", "sheets", "shelf", "shelves", "ship", "shirt", "shirt 2", "shoe", "shoes", "shore", "shorts", "shower", "shrubs", "shutters", "sidewalk", "sign", "sign 2", "signal", "signs", "sink", "skateboard", "ski", "ski lift", "ski poles", "skier", "skiis", "skirt", "skis", "skull", "sky", "sleeve", "sleeves", "smoke", "sneakers", "snow", "snowboard", "snowboarder", "soap", "sock", "socks", "soda", "sofa", "speaker", "speakers", "spectators", "spoon", "spot", "spots", "stairs", "stand", "stands", "stapler", "star", "station", "statue", "steering wheel", "stem", "steps", "stereo", "stick", "sticker", "stickers", "stone", "stool", "stop light", "stop sign", "stoplight", "store", "stove", "strap", "straps", "straw", "street", "street light", "street sign", "streetlight", "string", "stripe", "stripes", "stroller", "stuffed animal", "suit", "suitcase", "sun", "sunglasses", "surfboard", "surfer", "suv", "sweater", "sweatshirt", "symbol", "t-shirt", "table", "tablecloth", "tag", "tail", "tail light", "tail lights", "tank", "tank top", "tanktop", "tape", "tarp", "tattoo", "taxi", "teddy bear", "teeth", "telephone", "television", "tennis court", "tennis racket", "tent", "text", "the building", "the man", "the sign", "the sky", "the trees", "the wall", "tie", "tile", "tiles", "tire", "tires", "tissue", "toaster", "toilet", "toilet paper", "tomato", "tomatoes", "toothbrush", "toothpaste", "top", "towel", "towels", "tower", "toy", "track", "tracks", "traffic light", "traffic lights", "trailer", "train", "train car", "trash", "trash can", "trashcan", "tray", "tree", "tree 2", "tree trunk", "trees", "truck", "trunk", "tshirt", "tub", "tv", "umbrella", "umbrellas", "umpire", "uniform", "utensils", "van", "vase", "vegetables", "vehicle", "vent", "vessel", "vest", "visor", "walkway", "wall", "wallet", "walls", "washer", "watch", "water", "water bottle", "watermelon", "wave", "waves", "wetsuit", "wheel", "wheels", "white shirt", "window", "windows", "windshield", "wine", "wine glass", "wing", "wings", "wiper", "wipers", "wire", "wires", "woman", "woman 1", "woman 2", "women", "wood", "word", "words", "wrench", "wrist", "wrist developer", "wristband", "writing", "writting", "zebra", "zipper"], "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=[0.15, 0.15, 0.1, 0.05, 0.55],\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_2'\ngpu_ids = range(0, 8)\n", "seed": null, "exp_name": "relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic_2.py", "epoch": 2, "iter": 9376, "mmcv_version": "1.7.2", "time": "Sun Sep 22 12:31:29 2024", "hook_msgs": {"last_ckpt": "/data/home/hanbo/projects/alpha_vision_dev/work_dirs/relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic_2/epoch_1.pth"}} {"mode": "train", "epoch": 3, "iter": 50, "lr": 0.005, "memory": 10455, "data_time": 2.59355, "loss_rpn_cls": 0.17301, "loss_rpn_bbox": 0.10714, "loss_relation": 0.29083, "acc_relation": 87.68734, "loss_leaf": 0.44509, "acc_leaf": 82.77003, "loss": 1.01608, "grad_norm": 3.1046, "time": 3.16422} {"mode": "train", "epoch": 3, "iter": 100, "lr": 0.005, "memory": 10455, "data_time": 0.02217, "loss_rpn_cls": 0.17631, "loss_rpn_bbox": 0.11223, "loss_relation": 0.2866, "acc_relation": 87.83291, 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