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evaluate the model on COCO dataset

See original GitHub issue

python test_net.py --dataset coco --net res101 --checksession 1 --checkepoch 10 --checkpoint 14657 --cuda Called with args: Namespace(cfg_file=‘cfgs/vgg16.yml’, checkepoch=10, checkpoint=14657, checksession=1, class_agnostic=False, cuda=True, dataset=‘coco’, large_scale=False, load_dir=‘/home/hs/hs/pytorch/faster-rcnn.pytorch-master/models’, mGPUs=False, net=‘res101’, parallel_type=0, set_cfgs=None, vis=False) Using config: {‘ANCHOR_RATIOS’: [0.5, 1, 2], ‘ANCHOR_SCALES’: [4, 8, 16, 32], ‘CROP_RESIZE_WITH_MAX_POOL’: False, ‘CUDA’: False, ‘DATA_DIR’: ‘/home/hs/data’, ‘DEDUP_BOXES’: 0.0625, ‘EPS’: 1e-14, ‘EXP_DIR’: ‘res101’, ‘FEAT_STRIDE’: [16], ‘GPU_ID’: 0, ‘MATLAB’: ‘matlab’, ‘MAX_NUM_GT_BOXES’: 20, ‘MOBILENET’: {‘DEPTH_MULTIPLIER’: 1.0, ‘FIXED_LAYERS’: 5, ‘REGU_DEPTH’: False, ‘WEIGHT_DECAY’: 4e-05}, ‘PIXEL_MEANS’: array([[[102.9801, 115.9465, 122.7717]]]), ‘POOLING_MODE’: ‘align’, ‘POOLING_SIZE’: 7, ‘RESNET’: {‘FIXED_BLOCKS’: 1, ‘MAX_POOL’: False}, ‘RNG_SEED’: 3, ‘ROOT_DIR’: ‘/home/hs/hs/pytorch/faster-rcnn.pytorch-master’, ‘TEST’: {‘BBOX_REG’: True, ‘HAS_RPN’: True, ‘MAX_SIZE’: 1000, ‘MODE’: ‘nms’, ‘NMS’: 0.3, ‘PROPOSAL_METHOD’: ‘gt’, ‘RPN_MIN_SIZE’: 16, ‘RPN_NMS_THRESH’: 0.7, ‘RPN_POST_NMS_TOP_N’: 300, ‘RPN_PRE_NMS_TOP_N’: 6000, ‘RPN_TOP_N’: 5000, ‘SCALES’: [600], ‘SVM’: False}, ‘TRAIN’: {‘ASPECT_GROUPING’: False, ‘BATCH_SIZE’: 128, ‘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’: True, ‘BBOX_REG’: True, ‘BBOX_THRESH’: 0.5, ‘BG_THRESH_HI’: 0.5, ‘BG_THRESH_LO’: 0.0, ‘BIAS_DECAY’: False, ‘BN_TRAIN’: False, ‘DISPLAY’: 20, ‘DOUBLE_BIAS’: False, ‘FG_FRACTION’: 0.25, ‘FG_THRESH’: 0.5, ‘GAMMA’: 0.1, ‘HAS_RPN’: True, ‘IMS_PER_BATCH’: 1, ‘LEARNING_RATE’: 0.001, ‘MAX_SIZE’: 1000, ‘MOMENTUM’: 0.9, ‘PROPOSAL_METHOD’: ‘gt’, ‘RPN_BATCHSIZE’: 256, ‘RPN_BBOX_INSIDE_WEIGHTS’: [1.0, 1.0, 1.0, 1.0], ‘RPN_CLOBBER_POSITIVES’: False, ‘RPN_FG_FRACTION’: 0.5, ‘RPN_MIN_SIZE’: 8, ‘RPN_NEGATIVE_OVERLAP’: 0.3, ‘RPN_NMS_THRESH’: 0.7, ‘RPN_POSITIVE_OVERLAP’: 0.7, ‘RPN_POSITIVE_WEIGHT’: -1.0, ‘RPN_POST_NMS_TOP_N’: 2000, ‘RPN_PRE_NMS_TOP_N’: 12000, ‘SCALES’: [600], ‘SNAPSHOT_ITERS’: 5000, ‘SNAPSHOT_KEPT’: 3, ‘SNAPSHOT_PREFIX’: ‘res101_faster_rcnn’, ‘STEPSIZE’: [30000], ‘SUMMARY_INTERVAL’: 180, ‘TRIM_HEIGHT’: 600, ‘TRIM_WIDTH’: 600, ‘TRUNCATED’: False, ‘USE_ALL_GT’: True, ‘USE_FLIPPED’: True, ‘USE_GT’: False, ‘WEIGHT_DECAY’: 0.0001}, ‘USE_GPU_NMS’: True} loading annotations into memory… Done (t=0.33s) creating index… index created! Loaded dataset coco_2014_minival for training Set proposal method: gt Preparing training data… coco_2014_minival gt roidb loaded from /home/hs/data/cache/coco_2014_minival_gt_roidb.pkl done loading annotations into memory… Done (t=0.46s) creating index… index created! 5000 roidb entries load checkpoint /home/hs/hs/pytorch/faster-rcnn.pytorch-master/models/res101/coco/faster_rcnn_1_10_14657.pth load model successfully! /home/hs/hs/pytorch/faster-rcnn.pytorch-master/lib/model/rpn/rpn.py:68: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape) /home/hs/hs/pytorch/faster-rcnn.pytorch-master/lib/model/faster_rcnn/faster_rcnn.py:99: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. cls_prob = F.softmax(cls_score) Evaluating detections0.115s 0.044s
Collecting person results Writing results json to /home/hs/hs/pytorch/faster-rcnn.pytorch-master/output/res101/coco_2014_minival/faster_rcnn_10/detections_minival2014_results.json Loading and preparing results…
DONE (t=2.79s) creating index… index created! Running per image evaluation…
DONE (t=35.32s). Accumulating evaluation results…
Traceback (most recent call last): File “test_net.py”, line 324, in <module> imdb.evaluate_detections(all_boxes, output_dir) File “/home/hs/hs/pytorch/faster-rcnn.pytorch-master/lib/datasets/coco.py”, line 314, in evaluate_detections self._do_detection_eval(res_file, output_dir) File “/home/hs/hs/pytorch/faster-rcnn.pytorch-master/lib/datasets/coco.py”, line 260, in _do_detection_eval coco_eval.accumulate() File “/home/hs/hs/pytorch/faster-rcnn.pytorch-master/lib/pycocotools/cocoeval.py”, line 327, in accumulate if len(E) == 0: TypeError: object of type ‘filter’ has no len()

Issue Analytics

State:
Created 5 years ago
Comments:5 (1 by maintainers)

Top GitHub Comments

2reactions

AndOneDaycommented, Oct 24, 2018

您好，我用您通过coco训练好的模型来评估，总是报这个错误，但是我不知道该怎么解决，麻烦您有时间解答一下，非常感激

if you use python3, you can change code E = filter(None, E) to E = list(filter(None, E)) in cocoeval.py

0reactions

237014845commented, Oct 24, 2018

@AndOneDay Thank you for your reply!

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