+ echo Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_.txt.2016-03-26_18-46-04
Logging output to experiments/logs/faster_rcnn_end2end_VGG_CNN_M_1024_.txt.2016-03-26_18-46-04
+ ./tools/train_net.py --gpu 0 --solver models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/solver.prototxt --weights
data/imagenet_models/VGG_CNN_M_1024.v2.caffemodel --imdb voc_2007_trainval --iters 70000 --cfg experiments/cfgs/faster_rcnn_end2end.yml
Called with args:
Namespace(cfg_file='experiments/cfgs/faster_rcnn_end2end.yml', gpu_id=0, imdb_name='voc_2007_trainval', max_iters=70000,
pretrained_model='data/imagenet_models/VGG_CNN_M_1024.v2.caffemodel', randomize=False, set_cfgs=None,
solver='models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/solver.prototxt')
Using config:
{'DATA_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn/data',
 'DEDUP_BOXES': 0.0625,
 'EPS': 1e-14,
 'EXP_DIR': 'faster_rcnn_end2end',
 'GPU_ID': 0,
 'MATLAB': 'matlab',
 'MODELS_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn/models/pascal_voc',
 'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]),
 'RNG_SEED': 3,
 'ROOT_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn',
 'TEST': {'BBOX_REG': True,
          '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,
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'HAS_RPN': True,
           'IMS_PER_BATCH': 1,
           'MAX_SIZE': 1000,
           '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': 10000,
           'USE_FLIPPED': True,
           'USE_PREFETCH': False},
 'USE_GPU_NMS': True}
Loaded dataset `voc_2007_trainval` for training
{'DATA_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn/data',
 'DEDUP_BOXES': 0.0625,
 'EPS': 1e-14,
 'EXP_DIR': 'faster_rcnn_end2end',
 'GPU_ID': 0,
 'MATLAB': 'matlab',
 'MODELS_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn/models/pascal_voc',
 'PIXEL_MEANS': array([[[ 102.9801,  115.9465,  122.7717]]]),
 'RNG_SEED': 3,
 'ROOT_DIR': '/home/ghegde/workspace/caffe-ws/py-faster-rcnn',
 'TEST': {'BBOX_REG': True,
          '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,
           'FG_FRACTION': 0.25,
           'FG_THRESH': 0.5,
           'HAS_RPN': True,
           'IMS_PER_BATCH': 1,
           'MAX_SIZE': 1000,
           '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': 10000,
           'USE_FLIPPED': True,
           'USE_PREFETCH': False},
 'USE_GPU_NMS': True}
Set proposal method: selective_search
Appending horizontally-flipped training examples...
voc_2007_trainval gt roidb loaded from /home/ghegde/workspace/caffe-ws/py-faster-rcnn/data/cache/voc_2007_trainval_gt_roidb.pkl
Traceback (most recent call last):
  File "./tools/train_net.py", line 106, in <module>
    imdb, roidb = combined_roidb(args.imdb_name)
  File "./tools/train_net.py", line 70, in combined_roidb
    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
  File "./tools/train_net.py", line 67, in get_roidb
    roidb = get_training_roidb(imdb)
  File "/home/ghegde/workspace/caffe-ws/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 118, in get_training_roidb
    imdb.append_flipped_images()
  File "/home/ghegde/workspace/caffe-ws/py-faster-rcnn/tools/../lib/datasets/imdb.py", line 106, in append_flipped_images
    boxes = self.roidb[i]['boxes'].copy()
  File "/home/ghegde/workspace/caffe-ws/py-faster-rcnn/tools/../lib/datasets/imdb.py", line 67, in roidb
    self._roidb = self.roidb_handler()
  File "/home/ghegde/workspace/caffe-ws/py-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 132, in selective_search_roidb
    ss_roidb = self._load_selective_search_roidb(gt_roidb)
  File "/home/ghegde/workspace/caffe-ws/py-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 166, in _load_selective_search_roidb
    'Selective search data not found at: {}'.format(filename)
AssertionError: Selective search data not found at:
/home/ghegde/workspace/caffe-ws/py-faster-rcnn/data/selective_search_data/voc_2007_trainval.mat