本文整理匯總了Python中datasets.factory.get_imdb方法的典型用法代碼示例。如果您正苦於以下問題：Python factory.get_imdb方法的具體用法？Python factory.get_imdb怎麽用？Python factory.get_imdb使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類datasets.factory的用法示例。
在下文中一共展示了factory.get_imdb方法的10個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: combined_roidb
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
"""
Combine multiple roidbs
"""
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print('Loaded dataset `{:s}` for training'.format(imdb.name))
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
tmp = get_imdb(imdb_names.split('+')[1])
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:25，代碼來源:trainval_net.py
示例2: combined_roidb
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
"""
Combine multiple roidbs
"""
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print('Loaded dataset `{:s}` for training'.format(imdb.name))
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
tmp = get_imdb(imdb_names.split('+')[1])
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb 示例3: combined_roidb
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
imdb = datasets.imdb.imdb(imdb_names)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb 示例4: combined_roidb
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def combined_roidb(imdb_names):
"""
Combine multiple roidbs
"""
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print('Loaded dataset `{:s}` for training'.format(imdb.name))
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
tmp = get_imdb(imdb_names.split('+')[1])
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb 示例5: from_mats
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def from_mats(imdb_name, output_dir):
import scipy.io as sio
imdb = get_imdb(imdb_name)
aps = []
for i, cls in enumerate(imdb.classes[1:]):
mat = sio.loadmat(os.path.join(output_dir, cls + '_pr.mat'))
ap = mat['ap'][0, 0] * 100
apAuC = mat['ap_auc'][0, 0] * 100
print '!!! {} : {:.1f} {:.1f}'.format(cls, ap, apAuC)
aps.append(ap)
print '~~~~~~~~~~~~~~~~~~~'
print 'Results (from mat files):'
for ap in aps:
print '{:.1f}'.format(ap)
print '{:.1f}'.format(np.array(aps).mean())
print '~~~~~~~~~~~~~~~~~~~' 示例6: from_dets
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def from_dets(imdb_name, output_dir, args):
imdb = get_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
imdb.config['matlab_eval'] = args.matlab_eval
with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
dets = pickle.load(f)
if args.apply_nms:
print('Applying NMS to all detections')
nms_dets = apply_nms(dets, cfg.TEST.NMS)
else:
nms_dets = dets
print('Evaluating detections')
imdb.evaluate_detections(nms_dets, output_dir) 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:17，代碼來源:reval.py
示例7: from_dets
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def from_dets(imdb_name, output_dir, args):
imdb = get_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
with open(os.path.join(output_dir, 'discovery.pkl'), 'rb') as f:
dets = pickle.load(f)
print('Evaluating detections')
imdb.evaluate_discovery(dets, output_dir) 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:11，代碼來源:reval_discovery.py
示例8: get_roidb
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def get_roidb(imdb_name, rpn_file=None):
imdb = get_imdb(imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
if rpn_file is not None:
imdb.config['rpn_file'] = rpn_file
roidb = get_training_roidb(imdb)
return roidb, imdb 示例9: rpn_generate
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def rpn_generate(queue=None, imdb_name=None, rpn_model_path=None, cfg=None,
rpn_test_prototxt=None):
"""Use a trained RPN to generate proposals.
"""
cfg.TEST.RPN_PRE_NMS_TOP_N = -1 # no pre NMS filtering
cfg.TEST.RPN_POST_NMS_TOP_N = 2000 # limit top boxes after NMS
print 'RPN model: {}'.format(rpn_model_path)
print('Using config:')
pprint.pprint(cfg)
import caffe
_init_caffe(cfg)
# NOTE: the matlab implementation computes proposals on flipped images, too.
# We compute them on the image once and then flip the already computed
# proposals. This might cause a minor loss in mAP (less proposal jittering).
imdb = get_imdb(imdb_name)
print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name)
# Load RPN and configure output directory
rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST)
output_dir = get_output_dir(imdb)
print 'Output will be saved to `{:s}`'.format(output_dir)
# Generate proposals on the imdb
rpn_proposals = imdb_proposals(rpn_net, imdb)
# Write proposals to disk and send the proposal file path through the
# multiprocessing queue
rpn_net_name = os.path.splitext(os.path.basename(rpn_model_path))[0]
rpn_proposals_path = os.path.join(
output_dir, rpn_net_name + '_proposals.pkl')
with open(rpn_proposals_path, 'wb') as f:
cPickle.dump(rpn_proposals, f, cPickle.HIGHEST_PROTOCOL)
print 'Wrote RPN proposals to {}'.format(rpn_proposals_path)
queue.put({'proposal_path': rpn_proposals_path}) 示例10: from_dets
# 需要導入模塊: from datasets import factory [as 別名]
# 或者: from datasets.factory import get_imdb [as 別名]
def from_dets(imdb_name, output_dir, args):
imdb = get_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
imdb.config['matlab_eval'] = args.matlab_eval
with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
dets = cPickle.load(f)
if args.apply_nms:
print 'Applying NMS to all detections'
nms_dets = apply_nms(dets, cfg.TEST.NMS)
else:
nms_dets = dets
print 'Evaluating detections'
imdb.evaluate_detections(nms_dets, output_dir)