Error in `python': corrupted size vs. prev_size: 0x0000000000ec3240

[zdm123]

I only do the 25000 iteration, and test the model, but have the following problem. Do you have any ideal what is the problem? Writing aeroplane VOC results file Writing bicycle VOC results file Writing bird VOC results file Writing boat VOC results file Writing bottle VOC results file Writing bus VOC results file Writing car VOC results file Writing cat VOC results file Writing chair VOC results file Writing cow VOC results file Writing diningtable VOC results file Writing dog VOC results file Writing horse VOC results file Writing motorbike VOC results file Writing person VOC results file Writing pottedplant VOC results file Writing sheep VOC results file Writing sofa VOC results file Writing train VOC results file Writing tvmonitor VOC results file VOC07 metric? Yes Traceback (most recent call last): File "./tools/test_net.py", line 117, in test_net(sess, net, imdb, filename, max_per_image=args.max_per_image) File "/home2/sdin7632/shanshan/tf-faster-rcnn-r1.0/tools/../lib/model/test.py", line 196, in test_net imdb.evaluate_detections(all_boxes, output_dir) File "/home2/sdin7632/shanshan/tf-faster-rcnn-r1.0/tools/../lib/datasets/pascal_voc.py", line 281, in evaluate_detections self._do_python_eval(output_dir) File "/home2/sdin7632/shanshan/tf-faster-rcnn-r1.0/tools/../lib/datasets/pascal_voc.py", line 244, in _do_python_eval use_07_metric=use_07_metric) File "/home2/sdin7632/shanshan/tf-faster-rcnn-r1.0/tools/../lib/datasets/voc_eval.py", line 134, in voc_eval R = [obj for obj in recs[imagename] if obj['name'] == classname] KeyError: '1' Error in `python': corrupted size vs. prev_size: 0x0000000000ec3240 Command terminated by signal 6 198.54user 187.98system 8:45.24elapsed 73%CPU (0avgtext+0avgdata 1548152maxresident)k 406352inputs+1688outputs (0major+1913505minor)pagefaults 0swaps

[Hsy1996]

@zdm123 I have the same problem. Did you solve it ?

[AliceLcz]

I also have the same problem. Following is my logs: li@li-RESCUER-Y720-15IKB:~/tf-faster-rcnn$ ./experiments/scripts/test_faster_rcnn.sh 0 pascal_voc vgg16

• set -e
• export PYTHONUNBUFFERED=True
• PYTHONUNBUFFERED=True
• GPU_ID=0
• DATASET=pascal_voc
• NET=vgg16
• array=($@)
• len=3
• EXTRA_ARGS=
• EXTRA_ARGS_SLUG=
• case ${DATASET} in
• TRAIN_IMDB=voc_2007_trainval
• TEST_IMDB=voc_2007_test
• ITERS=10000
• ANCHORS='[8,16,32]'
• RATIOS='[0.5,1,2]' ++ date +%Y-%m-%d_%H-%M-%S
• LOG=experiments/logs/test_vgg16_voc_2007trainval.txt.2018-03-09_09-06-01
• exec ++ tee -a experiments/logs/test_vgg16_voc_2007trainval.txt.2018-03-09_09-06-01
• echo Logging output to experiments/logs/test_vgg16_voc_2007trainval.txt.2018-03-09_09-06-01 Logging output to experiments/logs/test_vgg16_voc_2007trainval.txt.2018-03-09_09-06-01
• set +x
• [[ ! -z '' ]]
• CUDA_VISIBLE_DEVICES=0
• time python ./tools/test_net.py --imdb voc_2007_test --model output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_10000.ckpt --cfg experiments/cfgs/vgg16.yml --net vgg16 --set ANCHOR_SCALES '[8,16,32]' ANCHOR_RATIOS '[0.5,1,2]' /home/li/anaconda3/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float to np.floating is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type. from ._conv import register_converters as _register_converters Called with args: Namespace(cfg_file='experiments/cfgs/vgg16.yml', comp_mode=False, imdb_name='voc_2007_test', max_per_image=100, model='output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_10000.ckpt', net='vgg16', set_cfgs=['ANCHOR_SCALES', '[8,16,32]', 'ANCHOR_RATIOS', '[0.5,1,2]'], tag='') Using config: {'ANCHOR_RATIOS': [0.5, 1, 2], 'ANCHOR_SCALES': [8, 16, 32], 'DATA_DIR': '/home/li/tf-faster-rcnn/data', 'EXP_DIR': 'vgg16', 'MATLAB': 'matlab', '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': 'crop', 'POOLING_SIZE': 7, 'RESNET': {'FIXED_BLOCKS': 1, 'MAX_POOL': False}, 'RNG_SEED': 3, 'ROOT_DIR': '/home/li/tf-faster-rcnn', 'RPN_CHANNELS': 512, 'TEST': {'BBOX_REG': True, 'HAS_RPN': True, 'MAX_SIZE': 1000, 'MODE': 'nms', 'NMS': 0.3, 'PROPOSAL_METHOD': 'gt', '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': 256, '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, 'DISPLAY': 20, 'DOUBLE_BIAS': True, '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_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': 'vgg16_faster_rcnn', 'STEPSIZE': [30000], 'SUMMARY_INTERVAL': 180, 'TRUNCATED': False, 'USE_ALL_GT': True, 'USE_FLIPPED': True, 'USE_GT': False, 'WEIGHT_DECAY': 0.0001}, 'USE_GPU_NMS': True} 2018-03-09 09:06:08.997773: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2018-03-09 09:06:08.997840: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2018-03-09 09:06:08.997886: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2018-03-09 09:06:08.997915: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2018-03-09 09:06:08.997939: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 2018-03-09 09:06:09.150783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2018-03-09 09:06:09.151227: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate (GHz) 1.6705 pciBusID 0000:01:00.0 Total memory: 5.94GiB Free memory: 5.48GiB 2018-03-09 09:06:09.151241: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 2018-03-09 09:06:09.151246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y 2018-03-09 09:06:09.151256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0) Loading model check point from output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_10000.ckpt Loaded. im_detect: 1/10 2.103s 0.001s im_detect: 2/10 1.964s 0.001s im_detect: 3/10 1.944s 0.001s im_detect: 4/10 2.057s 0.001s im_detect: 5/10 2.067s 0.001s im_detect: 6/10 2.094s 0.002s im_detect: 7/10 2.008s 0.002s im_detect: 8/10 2.046s 0.001s im_detect: 9/10 2.026s 0.001s im_detect: 10/10 1.956s 0.001s Evaluating detections Writing aeroplane VOC results file VOC07 metric? Yes Traceback (most recent call last): File "./tools/test_net.py", line 120, in test_net(sess, net, imdb, filename, max_per_image=args.max_per_image) File "/home/li/tf-faster-rcnn/tools/../lib/model/test.py", line 192, in test_net imdb.evaluate_detections(all_boxes, output_dir) File "/home/li/tf-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 285, in evaluate_detections self._do_python_eval(output_dir) File "/home/li/tf-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 248, in _do_python_eval use_07_metric=use_07_metric, use_diff=self.config['use_diff']) File "/home/li/tf-faster-rcnn/tools/../lib/datasets/voc_eval.py", line 135, in voc_eval R = [obj for obj in recs[imagename] if obj['name'] == classname] KeyError: '000017' Command exited with non-zero status 1 22.08user 6.63system 0:30.15elapsed 95%CPU (0avgtext+0avgdata 1857444maxresident)k 1296inputs+88outputs (6major+475117minor)pagefaults 0swaps

000017 is a image name of my test set, did you solve it?

[dnd88]

Hi, I also had the similar problem. In my case, there were two data-sets(directory) in the same directory. (/tf-faster-rcnn/data/VOCdevkit2007/) One was face data, the other one was license plate data. When I wanted to test about license plate detection, I faced the similar problem.

When I printed "recs" by "print(recs)" at (File "/tf-faster-rcnn/tools/../lib/datasets/voc_eval.py", line 134, in voc_eval), it didn't read license plate data, but read face data. So I just removed the directory for face data, it worked after that.