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reproduce.txt
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INFO test_net_rel.py: 86: Called with args:
INFO test_net_rel.py: 87: Namespace(cfg_file='configs/vrd/e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml', dataset='vrd', do_special=False, do_val=True, do_vis=False, load_ckpt='Outputs/e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained/Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3/ckpt/model_step7559.pth', load_detectron=None, multi_gpu_testing=False, output_dir='Outputs/vrd_VGG16_COCO_pretrained', range=None, set_cfgs=[], topk=100, use_gt_boxes=False, use_gt_labels=False)
INFO test_net_rel.py: 262: Testing with config:
INFO test_net_rel.py: 263: {'BBOX_XFORM_CLIP': 4.135166556742356,
'CROP_RESIZE_WITH_MAX_POOL': True,
'CUDA': False,
'DATA_DIR': '/content/gdrive/My Drive/ctreexreldn/rel/data',
'DATA_LOADER': {'NUM_THREADS': 4},
'DEBUG': False,
'DEDUP_BOXES': 0.0625,
'EPS': 1e-14,
'EXPECTED_RESULTS': [],
'EXPECTED_RESULTS_ATOL': 0.005,
'EXPECTED_RESULTS_EMAIL': '',
'EXPECTED_RESULTS_RTOL': 0.1,
'FAST_RCNN': {'CONV_HEAD_DIM': 256,
'MLP_HEAD_DIM': 1024,
'NUM_STACKED_CONVS': 4,
'PRD_HEAD': '',
'ROI_BOX_HEAD': 'VGG16.VGG16_roi_conv5_head',
'ROI_XFORM_METHOD': 'RoIAlign',
'ROI_XFORM_RESOLUTION': 14,
'ROI_XFORM_SAMPLING_RATIO': 0},
'FPN': {'COARSEST_STRIDE': 32,
'DIM': 256,
'EXTRA_CONV_LEVELS': False,
'FPN_ON': False,
'MULTILEVEL_ROIS': False,
'MULTILEVEL_RPN': False,
'ROI_CANONICAL_LEVEL': 4,
'ROI_CANONICAL_SCALE': 224,
'ROI_MAX_LEVEL': 5,
'ROI_MIN_LEVEL': 2,
'RPN_ANCHOR_START_SIZE': 32,
'RPN_ASPECT_RATIOS': (0.5, 1, 2),
'RPN_COLLECT_SCALE': 1,
'RPN_MAX_LEVEL': 6,
'RPN_MIN_LEVEL': 2,
'USE_GN': False,
'ZERO_INIT_LATERAL': False},
'GROUP_NORM': {'DIM_PER_GP': -1, 'EPSILON': 1e-05, 'NUM_GROUPS': 32},
'KRCNN': {'CONV_HEAD_DIM': 256,
'CONV_HEAD_KERNEL': 3,
'CONV_INIT': 'GaussianFill',
'DECONV_DIM': 256,
'DECONV_KERNEL': 4,
'DILATION': 1,
'HEATMAP_SIZE': -1,
'INFERENCE_MIN_SIZE': 0,
'KEYPOINT_CONFIDENCE': 'bbox',
'LOSS_WEIGHT': 1.0,
'MIN_KEYPOINT_COUNT_FOR_VALID_MINIBATCH': 20,
'NMS_OKS': False,
'NORMALIZE_BY_VISIBLE_KEYPOINTS': True,
'NUM_KEYPOINTS': -1,
'NUM_STACKED_CONVS': 8,
'ROI_KEYPOINTS_HEAD': '',
'ROI_XFORM_METHOD': 'RoIAlign',
'ROI_XFORM_RESOLUTION': 7,
'ROI_XFORM_SAMPLING_RATIO': 0,
'UP_SCALE': -1,
'USE_DECONV': False,
'USE_DECONV_OUTPUT': False},
'MATLAB': 'matlab',
'MODEL': {'ADD_SCORES_ALL': True,
'ADD_SO_SCORES': True,
'BBOX_REG_WEIGHTS': (10.0, 10.0, 5.0, 5.0),
'CLS_AGNOSTIC_BBOX_REG': False,
'CONV_BODY': 'VGG16.VGG16_conv_body',
'FASTER_RCNN': True,
'FEAT_LEVEL': 7,
'KEYPOINTS_ON': False,
'LOAD_COCO_PRETRAINED_WEIGHTS': False,
'LOAD_IMAGENET_PRETRAINED_WEIGHTS': False,
'LOAD_VRD_PRETRAINED_WEIGHTS': False,
'MASK_ON': False,
'NODE_CONTRASTIVE_MARGIN': 0.2,
'NODE_CONTRASTIVE_P_AWARE_MARGIN': 0.2,
'NODE_CONTRASTIVE_P_AWARE_WEIGHT': 0.1,
'NODE_CONTRASTIVE_SO_AWARE_MARGIN': 0.2,
'NODE_CONTRASTIVE_SO_AWARE_WEIGHT': 0.5,
'NODE_CONTRASTIVE_WEIGHT': 1.0,
'NODE_SAMPLE_SIZE': 128,
'NO_FC7_RELU': True,
'NUM_ATT_CLASSES': -1,
'NUM_CLASSES': 101,
'NUM_PRD_CLASSES': 70,
'RPN_ONLY': False,
'RUN_BASELINE': False,
'SHARE_RES5': False,
'SUBTYPE': 3,
'TYPE': 'generalized_rcnn',
'UNFREEZE_DET': False,
'UNSUPERVISED_POSE': False,
'USE_BG': True,
'USE_FREQ_BIAS': True,
'USE_NODE_CONTRASTIVE_LOSS': True,
'USE_NODE_CONTRASTIVE_P_AWARE_LOSS': True,
'USE_NODE_CONTRASTIVE_SO_AWARE_LOSS': True,
'USE_OVLP_FILTER': True,
'USE_REL_PYRAMID': False,
'USE_SEM_FEAT': False,
'USE_SIMPLE_P': False,
'USE_SPATIAL_FEAT': True,
'USE_SPO_AGNOSTIC_COMPENSATION': False},
'MRCNN': {'CLS_SPECIFIC_MASK': True,
'CONV_INIT': 'GaussianFill',
'DILATION': 2,
'DIM_REDUCED': 256,
'MEMORY_EFFICIENT_LOSS': True,
'RESOLUTION': 14,
'ROI_MASK_HEAD': '',
'ROI_XFORM_METHOD': 'RoIAlign',
'ROI_XFORM_RESOLUTION': 7,
'ROI_XFORM_SAMPLING_RATIO': 0,
'THRESH_BINARIZE': 0.5,
'UPSAMPLE_RATIO': 1,
'USE_FC_OUTPUT': False,
'WEIGHT_LOSS_MASK': 1.0},
'NUM_GPUS': 8,
'OUTPUT_DIR': 'Outputs',
'PIXEL_MEANS': array([[[102.98 , 115.947, 122.772]]]),
'POOLING_MODE': 'crop',
'POOLING_SIZE': 7,
'PYTORCH_VERSION_LESS_THAN_040': False,
'RESNETS': {'ATT_RCNN_PRETRAINED_WEIGHTS': '',
'COCO_PRETRAINED_WEIGHTS': '',
'FREEZE_AT': 2,
'IMAGENET_PRETRAINED_WEIGHTS': '',
'NUM_GROUPS': 1,
'OI_ATT_PRETRAINED_WEIGHTS': '',
'OI_PRETRAINED_WEIGHTS': '',
'OI_REL_PRD_PRETRAINED_WEIGHTS': '',
'OI_REL_PRETRAINED_WEIGHTS': '',
'REL_PRETRAINED_WEIGHTS': '',
'REL_RCNN_PRETRAINED_WEIGHTS': '',
'RES5_DILATION': 1,
'SHORTCUT_FUNC': 'basic_bn_shortcut',
'STEM_FUNC': 'basic_bn_stem',
'STRIDE_1X1': True,
'TO_BE_FINETUNED_WEIGHTS': '',
'TRANS_FUNC': 'bottleneck_transformation',
'USE_GN': False,
'VG_PRD_PRETRAINED_WEIGHTS': '',
'VG_PRETRAINED_WEIGHTS': '',
'VRD_PRD_PRETRAINED_WEIGHTS': '',
'VRD_PRETRAINED_WEIGHTS': '',
'WIDTH_PER_GROUP': 64},
'RETINANET': {'ANCHOR_SCALE': 4,
'ASPECT_RATIOS': (0.5, 1.0, 2.0),
'BBOX_REG_BETA': 0.11,
'BBOX_REG_WEIGHT': 1.0,
'CLASS_SPECIFIC_BBOX': False,
'INFERENCE_TH': 0.05,
'LOSS_ALPHA': 0.25,
'LOSS_GAMMA': 2.0,
'NEGATIVE_OVERLAP': 0.4,
'NUM_CONVS': 4,
'POSITIVE_OVERLAP': 0.5,
'PRE_NMS_TOP_N': 1000,
'PRIOR_PROB': 0.01,
'RETINANET_ON': False,
'SCALES_PER_OCTAVE': 3,
'SHARE_CLS_BBOX_TOWER': False,
'SOFTMAX': False},
'RFCN': {'PS_GRID_SIZE': 3},
'RNG_SEED': 3,
'ROOT_DIR': '/content/gdrive/My Drive/ctreexreldn/rel',
'RPN': {'ASPECT_RATIOS': (0.5, 1, 2),
'CLS_ACTIVATION': 'sigmoid',
'OUT_DIM': 512,
'OUT_DIM_AS_IN_DIM': True,
'RPN_ON': True,
'SIZES': (32, 64, 128, 256, 512),
'STRIDE': 16},
'SOLVER': {'BACKBONE_LR_SCALAR': 0.1,
'BASE_LR': 0.01,
'BIAS_DOUBLE_LR': True,
'BIAS_WEIGHT_DECAY': False,
'COSINE_LR': False,
'COSINE_MULTI': 2,
'COSINE_NUM_EPOCH': 13,
'COSINE_T0': 3,
'GAMMA': 0.1,
'LOG_LR_CHANGE_THRESHOLD': 1.1,
'LRS': [],
'LR_POLICY': 'steps_with_decay',
'MAX_ITER': 7560,
'MOMENTUM': 0.9,
'SCALE_MOMENTUM': True,
'SCALE_MOMENTUM_THRESHOLD': 1.1,
'STEPS': [0, 5040, 6720],
'STEP_SIZE': 30000,
'TYPE': 'SGD',
'WARM_UP_FACTOR': 0.3333333333333333,
'WARM_UP_ITERS': 500,
'WARM_UP_METHOD': 'linear',
'WEIGHT_DECAY': 0.0001,
'WEIGHT_DECAY_GN': 0.0},
'TEST': {'BBOX_AUG': {'AREA_TH_HI': 32400,
'AREA_TH_LO': 2500,
'ASPECT_RATIOS': (),
'ASPECT_RATIO_H_FLIP': False,
'COORD_HEUR': 'UNION',
'ENABLED': False,
'H_FLIP': False,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': False,
'SCALE_SIZE_DEP': False,
'SCORE_HEUR': 'UNION'},
'BBOX_REG': True,
'BBOX_VOTE': {'ENABLED': False,
'SCORING_METHOD': 'ID',
'SCORING_METHOD_BETA': 1.0,
'VOTE_TH': 0.8},
'COMPETITION_MODE': True,
'DATASETS': ('vrd_val',),
'DETECTIONS_PER_IM': 100,
'FORCE_JSON_DATASET_EVAL': True,
'KPS_AUG': {'AREA_TH': 32400,
'ASPECT_RATIOS': (),
'ASPECT_RATIO_H_FLIP': False,
'ENABLED': False,
'HEUR': 'HM_AVG',
'H_FLIP': False,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': False,
'SCALE_SIZE_DEP': False},
'MASK_AUG': {'AREA_TH': 32400,
'ASPECT_RATIOS': (),
'ASPECT_RATIO_H_FLIP': False,
'ENABLED': False,
'HEUR': 'SOFT_AVG',
'H_FLIP': False,
'MAX_SIZE': 4000,
'SCALES': (),
'SCALE_H_FLIP': False,
'SCALE_SIZE_DEP': False},
'MAX_SIZE': 1333,
'NMS': 0.5,
'PRD_Ks': (1, 10, 70),
'PRECOMPUTED_PROPOSALS': False,
'PROPOSAL_FILES': (),
'PROPOSAL_LIMIT': 2000,
'RPN_MIN_SIZE': 0,
'RPN_NMS_THRESH': 0.7,
'RPN_POST_NMS_TOP_N': 1000,
'RPN_PRE_NMS_TOP_N': 6000,
'SCALE': 800,
'SCORE_THRESH': 0.05,
'SOFT_NMS': {'ENABLED': False, 'METHOD': 'linear', 'SIGMA': 0.5},
'SPO_SCORE_THRESH': 1e-05,
'USE_GT_BOXES': False},
'TRAIN': {'ASPECT_CROPPING': False,
'ASPECT_GROUPING': True,
'ASPECT_HI': 2,
'ASPECT_LO': 0.5,
'BATCH_SIZE_PER_IM': 512,
'BBOX_INSIDE_WEIGHTS': (1.0, 1.0, 1.0, 1.0),
'BBOX_NORMALIZE_MEANS': (0.0, 0.0, 0.0, 0.0),
'BBOX_NORMALIZE_STDS': (0.1, 0.1, 0.2, 0.2),
'BBOX_NORMALIZE_TARGETS': True,
'BBOX_NORMALIZE_TARGETS_PRECOMPUTED': False,
'BBOX_THRESH': 0.5,
'BG_THRESH_HI': 0.5,
'BG_THRESH_LO': 0.0,
'CROWD_FILTER_THRESH': 0.7,
'DATASETS': (),
'FG_ATT_FRACTION': 0.25,
'FG_ATT_SIZE_PER_IM': 512,
'FG_FRACTION': 0.25,
'FG_REL_FRACTION': 0.25,
'FG_REL_SIZE_PER_IM': 512,
'FG_THRESH': 0.5,
'FREEZE_CONV_BODY': False,
'FREEZE_PRD_BOX_HEAD': False,
'FREEZE_PRD_CONV_BODY': False,
'GT_MIN_AREA': -1,
'IMS_PER_BATCH': 1,
'MAX_SIZE': 1333,
'PROPOSAL_FILES': (),
'RPN_BATCH_SIZE_PER_IM': 256,
'RPN_FG_FRACTION': 0.5,
'RPN_MIN_SIZE': 0,
'RPN_NEGATIVE_OVERLAP': 0.3,
'RPN_NMS_THRESH': 0.7,
'RPN_POSITIVE_OVERLAP': 0.7,
'RPN_POST_NMS_TOP_N': 2000,
'RPN_PRE_NMS_TOP_N': 12000,
'RPN_STRADDLE_THRESH': 0,
'SCALES': (800,),
'SNAPSHOT_FREQ': 1,
'SNAPSHOT_ITERS': 20000,
'USE_FLIPPED': True},
'VGG16': {'COCO_PRETRAINED_WEIGHTS': '',
'IMAGENET_PRETRAINED_WEIGHTS': '',
'OI_PRETRAINED_WEIGHTS': '',
'OI_REL_PRD_PRETRAINED_WEIGHTS': '',
'OI_REL_PRETRAINED_WEIGHTS': '',
'TO_BE_FINETUNED_WEIGHTS': '',
'VG_PRD_PRETRAINED_WEIGHTS': '',
'VG_PRETRAINED_WEIGHTS': '',
'VRD_PRD_PRETRAINED_WEIGHTS': 'detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth',
'VRD_PRETRAINED_WEIGHTS': 'detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth'},
'VIS': False,
'VIS_TH': 0.9}
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
INFO json_dataset_rel.py: 395: Loading cached gt_roidb from /content/gdrive/My Drive/ctreexreldn/rel/data/cache/vrd_val_rel_gt_roidb.pkl
INFO sparse_targets_rel.py: 59: Frequency bias tables loaded.
INFO model_builder_rel.py: 261: loading pretrained weights from detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth
INFO model_builder_rel.py: 170: loading prd pretrained weights from detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth
INFO test_engine_rel.py: 290: loading checkpoint Outputs/e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained/Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3/ckpt/model_step7559.pth
/content/gdrive/My Drive/ctreexreldn/rel/lib/modeling_rel/reldn_heads.py:118: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
ttl_cls_scores = torch.tensor(prd_vis_scores)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 1/1000 0.736s (eta: 0:12:15)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 11/1000 0.340s (eta: 0:05:35)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 21/1000 0.314s (eta: 0:05:07)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 31/1000 0.305s (eta: 0:04:55)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 41/1000 0.301s (eta: 0:04:48)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 51/1000 0.302s (eta: 0:04:46)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 61/1000 0.301s (eta: 0:04:43)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 71/1000 0.298s (eta: 0:04:37)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 81/1000 0.298s (eta: 0:04:33)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 91/1000 0.300s (eta: 0:04:32)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 101/1000 0.300s (eta: 0:04:29)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 111/1000 0.299s (eta: 0:04:25)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 121/1000 0.298s (eta: 0:04:22)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 131/1000 0.298s (eta: 0:04:18)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 141/1000 0.299s (eta: 0:04:16)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 151/1000 0.300s (eta: 0:04:14)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 161/1000 0.301s (eta: 0:04:12)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 191/1000 0.300s (eta: 0:04:02)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 231/1000 0.299s (eta: 0:03:50)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 241/1000 0.299s (eta: 0:03:46)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 251/1000 0.300s (eta: 0:03:44)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 261/1000 0.300s (eta: 0:03:42)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 271/1000 0.300s (eta: 0:03:38)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 281/1000 0.300s (eta: 0:03:35)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 291/1000 0.301s (eta: 0:03:33)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 301/1000 0.301s (eta: 0:03:30)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 311/1000 0.301s (eta: 0:03:27)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 321/1000 0.301s (eta: 0:03:24)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 331/1000 0.301s (eta: 0:03:21)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 341/1000 0.301s (eta: 0:03:18)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 351/1000 0.301s (eta: 0:03:15)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 361/1000 0.301s (eta: 0:03:12)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 371/1000 0.302s (eta: 0:03:09)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 381/1000 0.302s (eta: 0:03:06)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 391/1000 0.302s (eta: 0:03:03)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 401/1000 0.302s (eta: 0:03:01)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 411/1000 0.302s (eta: 0:02:57)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 511/1000 0.304s (eta: 0:02:28)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 521/1000 0.304s (eta: 0:02:25)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 531/1000 0.305s (eta: 0:02:22)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 541/1000 0.304s (eta: 0:02:19)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 561/1000 0.305s (eta: 0:02:14)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 601/1000 0.305s (eta: 0:02:01)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 621/1000 0.305s (eta: 0:01:55)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 671/1000 0.306s (eta: 0:01:40)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 681/1000 0.306s (eta: 0:01:37)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 701/1000 0.306s (eta: 0:01:31)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 711/1000 0.306s (eta: 0:01:28)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 721/1000 0.307s (eta: 0:01:25)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 731/1000 0.306s (eta: 0:01:22)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 781/1000 0.306s (eta: 0:01:07)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 851/1000 0.307s (eta: 0:00:45)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 871/1000 0.307s (eta: 0:00:39)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 881/1000 0.307s (eta: 0:00:36)
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INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 901/1000 0.307s (eta: 0:00:30)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 911/1000 0.307s (eta: 0:00:27)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 921/1000 0.307s (eta: 0:00:24)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 931/1000 0.307s (eta: 0:00:21)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 941/1000 0.307s (eta: 0:00:18)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 951/1000 0.307s (eta: 0:00:15)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 961/1000 0.307s (eta: 0:00:11)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 971/1000 0.307s (eta: 0:00:08)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 981/1000 0.307s (eta: 0:00:05)
INFO test_engine_rel.py: 259: im_detect: range [1, 1000] of 1000: 991/1000 0.307s (eta: 0:00:02)
INFO test_engine_rel.py: 274: Wrote rel_detections to: /content/gdrive/My Drive/ctreexreldn/rel/Outputs/vrd_VGG16_COCO_pretrained/rel_detections.pkl
INFO test_engine_rel.py: 147: Total inference time: 1008.389s
INFO test_engine_rel.py: 149: Starting evaluation now...
================== reldet ==================
prd_k = 1:
100% 1000/1000 [00:04<00:00, 209.16it/s]
R@20: 18.34
R@50: 23.97
R@100: 27.60
prd_k = 2:
100% 1000/1000 [00:06<00:00, 165.85it/s]
R@20: 19.08
R@50: 26.62
R@100: 31.95
prd_k = 3:
100% 1000/1000 [00:05<00:00, 175.27it/s]
R@20: 19.08
R@50: 26.81
R@100: 32.59
prd_k = 4:
100% 1000/1000 [00:04<00:00, 203.68it/s]
R@20: 19.08
R@50: 26.85
R@100: 32.82
prd_k = 5:
100% 1000/1000 [00:05<00:00, 199.53it/s]
R@20: 19.08
R@50: 26.88
R@100: 32.94
prd_k = 6:
100% 1000/1000 [00:04<00:00, 201.31it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.98
prd_k = 7:
100% 1000/1000 [00:05<00:00, 192.29it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 8:
100% 1000/1000 [00:04<00:00, 203.80it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 9:
100% 1000/1000 [00:04<00:00, 201.78it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 10:
100% 1000/1000 [00:05<00:00, 193.56it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 15:
100% 1000/1000 [00:05<00:00, 191.49it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 20:
100% 1000/1000 [00:05<00:00, 183.32it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
prd_k = 70:
100% 1000/1000 [00:08<00:00, 118.67it/s]
R@20: 19.08
R@50: 26.89
R@100: 32.99
Saving topk dets...
INFO task_evaluation_vg_and_vrd.py: 149: topk_dets size: 1000
Done.
================== phrdet ==================
prd_k = 1:
100% 1000/1000 [00:04<00:00, 218.63it/s]
R@20: 22.22
R@50: 29.52
R@100: 34.64
prd_k = 2:
100% 1000/1000 [00:04<00:00, 221.17it/s]
R@20: 23.00
R@50: 32.40
R@100: 39.25
prd_k = 3:
100% 1000/1000 [00:04<00:00, 204.62it/s]
R@20: 23.00
R@50: 32.64
R@100: 40.01
prd_k = 4:
100% 1000/1000 [00:04<00:00, 209.62it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.18
prd_k = 5:
100% 1000/1000 [00:04<00:00, 211.19it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.30
prd_k = 6:
100% 1000/1000 [00:04<00:00, 201.80it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.34
prd_k = 7:
100% 1000/1000 [00:04<00:00, 203.68it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 8:
100% 1000/1000 [00:04<00:00, 206.38it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 9:
100% 1000/1000 [00:04<00:00, 204.14it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 10:
100% 1000/1000 [00:04<00:00, 203.21it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 15:
100% 1000/1000 [00:05<00:00, 189.73it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 20:
100% 1000/1000 [00:05<00:00, 180.89it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
prd_k = 70:
100% 1000/1000 [00:08<00:00, 119.20it/s]
R@20: 23.02
R@50: 32.74
R@100: 40.35
Saving topk dets...
INFO task_evaluation_vg_and_vrd.py: 149: topk_dets size: 1000
Done.
Called with args:
Namespace(batch_size=None, cfg_file='configs/vrd/e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml', cuda=True, dataset='vrd', disp_interval=20, iter_size=1, load_ckpt=None, load_detectron=None, lr=0.0001, lr_decay_gamma=None, no_save=False, num_workers=8, optimizer=None, resume=False, set_cfgs=[], start_step=0, use_tfboard=True)
effective_batch_size = batch_size * iter_size = 1 * 1
Adaptive config changes:
effective_batch_size: 1 --> 1
NUM_GPUS: 1 --> 1
IMS_PER_BATCH: 1 --> 1
Adjust BASE_LR linearly according to batch_size change:
BASE_LR: 0.01 --> 0.01
Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:
SOLVER.STEPS: [0, 5040, 6720] --> [0, 5040, 6720]
SOLVER.MAX_ITER: 7560 --> 7560
Number of data loading threads: 8
loading annotations into memory...
Done (t=0.10s)
creating index...
index created!
INFO json_dataset_rel.py: 395: Loading cached gt_roidb from /content/gdrive/My Drive/ctreexreldn/rel/data/cache/vrd_train_rel_gt_roidb.pkl
INFO roidb_rel.py: 52: Appending horizontally-flipped training examples...
INFO roidb_rel.py: 54: Loaded dataset: vrd_train
INFO roidb_rel.py: 156: Filtered 440 roidb entries: 8000 -> 7560
INFO roidb_rel.py: 71: Computing image aspect ratios and ordering the ratios...
INFO roidb_rel.py: 73: done
INFO roidb_rel.py: 77: Computing bounding-box regression targets...
INFO roidb_rel.py: 79: done
INFO train_net_step_rel.py: 268: 7560 roidb entries
INFO train_net_step_rel.py: 269: Takes 15.76 sec(s) to construct roidb
INFO sparse_targets_rel.py: 59: Frequency bias tables loaded.
INFO model_builder_rel.py: 261: loading pretrained weights from detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth
INFO model_builder_rel.py: 170: loading prd pretrained weights from detection_models/vrd/VGG16/COCO_pretrained/model_step4499.pth
INFO train_net_step_rel.py: 421: Training starts !
INFO net_rel.py: 46: Changing learning rate 0.000000 -> 0.000033
/content/gdrive/My Drive/ctreexreldn/rel/lib/modeling_rel/reldn_heads.py:118: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
ttl_cls_scores = torch.tensor(prd_vis_scores)
[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1 / 7560]
loss: 3.446287, lr: 0.000033 backbone_lr: 0.000003 time: 1.164195, eta: 2:26:41
accuracy_cls: 0.863281, accuracy_cls_ttl: 0.354980
loss_rpn_cls: 0.073695, loss_rpn_bbox: 0.086236, loss_cls: 0.327498, loss_bbox: 0.139622, loss_cls_ttl: 1.976545, loss_contrastive_sbj: 0.283794, loss_contrastive_obj: 0.318330, loss_so_contrastive_sbj: 0.075547, loss_so_contrastive_obj: 0.119047, loss_p_contrastive_sbj: 0.020979, loss_p_contrastive_obj: 0.024993
[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 21 / 7560]
loss: 3.104680, lr: 0.000036 backbone_lr: 0.000004 time: 1.149343, eta: 2:24:26
accuracy_cls: 0.897461, accuracy_cls_ttl: 0.641583
loss_rpn_cls: 0.073454, loss_rpn_bbox: 0.072336, loss_cls: 0.305175, loss_bbox: 0.152891, loss_cls_ttl: 1.604321, loss_contrastive_sbj: 0.314266, loss_contrastive_obj: 0.328398, loss_so_contrastive_sbj: 0.096796, loss_so_contrastive_obj: 0.099199, loss_p_contrastive_sbj: 0.022346, loss_p_contrastive_obj: 0.020317
[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 41 / 7560]
loss: 2.838495, lr: 0.000039 backbone_lr: 0.000004 time: 1.143482, eta: 2:23:18
accuracy_cls: 0.907227, accuracy_cls_ttl: 0.750000
loss_rpn_cls: 0.078926, loss_rpn_bbox: 0.109059, loss_cls: 0.280878, loss_bbox: 0.138117, loss_cls_ttl: 1.407178, loss_contrastive_sbj: 0.235357, loss_contrastive_obj: 0.246725, loss_so_contrastive_sbj: 0.074392, loss_so_contrastive_obj: 0.082247, loss_p_contrastive_sbj: 0.017079, loss_p_contrastive_obj: 0.012715
[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 61 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 81 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 101 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 121 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 141 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 161 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 181 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 201 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 221 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 241 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 261 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 281 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 301 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 321 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 341 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 361 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 381 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 401 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 421 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 441 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 461 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 481 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 501 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 521 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 541 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 561 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 581 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 601 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 621 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 641 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 661 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 681 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 701 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 721 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 741 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 761 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 781 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 801 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 821 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 841 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 861 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 881 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 901 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 921 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 941 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 981 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1001 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1021 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1041 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1061 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1081 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1101 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1121 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1181 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1201 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1221 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1241 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1261 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1281 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1301 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1321 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1341 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1361 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1381 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1401 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1421 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1441 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1581 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1601 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1621 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1641 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1661 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1681 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1701 / 7560]
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1721 / 7560]
loss: 1.881716, lr: 0.000100 backbone_lr: 0.000010 time: 1.146059, eta: 1:51:32
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1741 / 7560]
loss: 2.035793, lr: 0.000100 backbone_lr: 0.000010 time: 1.146208, eta: 1:51:10
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1761 / 7560]
loss: 2.151290, lr: 0.000100 backbone_lr: 0.000010 time: 1.145870, eta: 1:50:46
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1781 / 7560]
loss: 1.988540, lr: 0.000100 backbone_lr: 0.000010 time: 1.146815, eta: 1:50:28
accuracy_cls: 0.901367, accuracy_cls_ttl: 0.768799
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[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1801 / 7560]
loss: 1.907027, lr: 0.000100 backbone_lr: 0.000010 time: 1.147120, eta: 1:50:07
accuracy_cls: 0.913086, accuracy_cls_ttl: 0.779053
loss_rpn_cls: 0.064632, loss_rpn_bbox: 0.058446, loss_cls: 0.243054, loss_bbox: 0.102200, loss_cls_ttl: 0.804813, loss_contrastive_sbj: 0.189707, loss_contrastive_obj: 0.162608, loss_so_contrastive_sbj: 0.071992, loss_so_contrastive_obj: 0.040687, loss_p_contrastive_sbj: 0.008511, loss_p_contrastive_obj: 0.016079
[Jul17-05-19-41_7ba9b3c97626_step_with_prd_cls_v3][e2e_faster_rcnn_VGG16_16_epochs_vrd_v3_default_node_contrastive_loss_w_so_p_aware_margin_point2_so_weight_point5_COCO_pretrained.yaml][Step 1821 / 7560]