KeyError: '001009' when i run test_net.py after i have trained my own dataset

[Dre2mspace]

voc_2007_train
voc_2007_val
voc_2007_trainval
voc_2007_test
kitti_train
kitti_val
kitti_trainval
kitti_test
nthu_71
nthu_370
Called with args:
Namespace(cfg_file='experiments/cfgs/faster_rcnn_end2end.yml', comp_mode=False, device='gpu', device_id=0, imdb_name='voc_2007_test', model='/data/Hogwarts/Faster-RCNN_TF/output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_70000.ckpt', network_name='VGGnet_test', prototxt=None, wait=True)
/data/Hogwarts/Faster-RCNN_TF/tools/../lib/fast_rcnn/config.py:294: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
yaml_cfg = edict(yaml.load(f))
Using config:
{'DATA_DIR': '/data/Hogwarts/Faster-RCNN_TF/data',
'DEDUP_BOXES': 0.0625,
'EPS': 1e-14,
'EXP_DIR': 'faster_rcnn_end2end',
'GPU_ID': 0,
'IS_MULTISCALE': False,
'MATLAB': 'matlab',
'MODELS_DIR': '/data/Hogwarts/Faster-RCNN_TF/models/pascal_voc',
'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]),
'RNG_SEED': 3,
'ROOT_DIR': '/data/Hogwarts/Faster-RCNN_TF',
'TEST': {'BBOX_REG': True,
'DEBUG_TIMELINE': False,
'HAS_RPN': True,
'MAX_SIZE': 1000,
'NMS': 0.3,
'PROPOSAL_METHOD': 'selective_search',
'RPN_MIN_SIZE': 16,
'RPN_NMS_THRESH': 0.7,
'RPN_POST_NMS_TOP_N': 300,
'RPN_PRE_NMS_TOP_N': 6000,
'SCALES': [600],
'SVM': False},
'TRAIN': {'ASPECT_GROUPING': True,
'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,
'DEBUG_TIMELINE': False,
'DISPLAY': 10,
'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': 16,
'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_INFIX': '',
'SNAPSHOT_ITERS': 5000,
'SNAPSHOT_PREFIX': 'VGGnet_fast_rcnn',
'STEPSIZE': 50000,
'USE_FLIPPED': True,
'USE_PREFETCH': False},
'USE_GPU_NMS': True}
<bound method pascal_voc.default_roidb of <datasets.pascal_voc.pascal_voc object at 0x7fa487b77f10>>
/gpu:0
Tensor("Placeholder:0", shape=(?, ?, ?, 3), dtype=float32)
Tensor("conv5_3/conv5_3:0", shape=(?, ?, ?, 512), dtype=float32)
Tensor("rpn_conv/3x3/rpn_conv/3x3:0", shape=(?, ?, ?, 512), dtype=float32)
Tensor("rpn_cls_score/rpn_cls_score:0", shape=(?, ?, ?, 18), dtype=float32)
Tensor("rpn_cls_prob:0", shape=(?, ?, ?, ?), dtype=float32)
Tensor("rpn_cls_prob_reshape:0", shape=(?, ?, ?, 18), dtype=float32)
Tensor("rpn_bbox_pred/rpn_bbox_pred:0", shape=(?, ?, ?, 36), dtype=float32)
Tensor("Placeholder_1:0", shape=(?, 3), dtype=float32)
Tensor("conv5_3/conv5_3:0", shape=(?, ?, ?, 512), dtype=float32)
Tensor("rois:0", shape=(?, 5), dtype=float32)
[<tf.Tensor 'conv5_3/conv5_3:0' shape=(?, ?, ?, 512) dtype=float32>, <tf.Tensor 'rois:0' shape=(?, 5) dtype=float32>]
Tensor("fc7/fc7:0", shape=(?, 4096), dtype=float32)
Use network VGGnet_test in training
2019-05-06 20:15:14.338942: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-05-06 20:15:14.505625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:02:00.0
totalMemory: 11.90GiB freeMemory: 11.57GiB
2019-05-06 20:15:14.505660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: TITAN Xp, pci bus id: 0000:02:00.0, compute capability: 6.1)
Loading model weights from /data/Hogwarts/Faster-RCNN_TF/output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_70000.ckpt
im_detect: 1/22 1.101s 0.000s
im_detect: 2/22 0.674s 0.000s
im_detect: 3/22 0.531s 0.000s
im_detect: 4/22 0.470s 0.000s
im_detect: 5/22 0.430s 0.000s
im_detect: 6/22 0.385s 0.000s
im_detect: 7/22 0.368s 0.000s
im_detect: 8/22 0.356s 0.000s
im_detect: 9/22 0.348s 0.000s
im_detect: 10/22 0.340s 0.000s
im_detect: 11/22 0.332s 0.000s
im_detect: 12/22 0.323s 0.000s
im_detect: 13/22 0.310s 0.000s
im_detect: 14/22 0.303s 0.000s
im_detect: 15/22 0.293s 0.000s
im_detect: 16/22 0.294s 0.000s
im_detect: 17/22 0.293s 0.000s
im_detect: 18/22 0.281s 0.000s
im_detect: 19/22 0.281s 0.000s
im_detect: 20/22 0.272s 0.000s
im_detect: 21/22 0.273s 0.000s
im_detect: 22/22 0.272s 0.000s
Evaluating detections
Writing person VOC results file
Writing traffic police VOC results file
Writing cyclist VOC results file
VOC07 metric? Yes
Traceback (most recent call last):
File "./tools/test_net.py", line 107, in
test_net(sess, network, imdb, weights_filename)
File "/data/Hogwarts/Faster-RCNN_TF/tools/../lib/fast_rcnn/test.py", line 345, in test_net
imdb.evaluate_detections(all_boxes, output_dir)
File "/data/Hogwarts/Faster-RCNN_TF/tools/../lib/datasets/pascal_voc.py", line 330, in evaluate_detections
self._do_python_eval(output_dir)
File "/data/Hogwarts/Faster-RCNN_TF/tools/../lib/datasets/pascal_voc.py", line 293, in _do_python_eval
use_07_metric=use_07_metric)
File "/data/Hogwarts/Faster-RCNN_TF/tools/../lib/datasets/voc_eval.py", line 126, in voc_eval
R = [obj for obj in recs[imagename] if obj['name'] == classname]
KeyError: '001009'

above is output and i have three classes。
how can i solve this problem？

[Dre2mspace]

i hava solve it by delete data/VOCdekit2007/annotations_cache ：）