def get_roidb(imdb_name):
imdb = get_repo_imdb(imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
imdb.set_proposal_method(cfg.TRAIN.OBJ_DET.PROPOSAL_METHOD)
print 'Set proposal method: {:s}'.format(cfg.TRAIN.OBJ_DET.PROPOSAL_METHOD)
roidb = get_training_roidb(imdb)
return imdb, roidb
def from_dets(imdb_name, output_dir, args):
imdb = get_repo_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
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)
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.GPU_ID = args.gpu_id
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(args.caffemodel) and args.wait:
print('Waiting for {} to exist...'.format(args.caffemodel))
time.sleep(10)
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]
print(args.imdb_name)
imdb = get_repo_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
if not cfg.TEST.OBJ_DET.HAS_RPN:
imdb.set_proposal_method(cfg.TEST.PROPOSAL_METHOD)
test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis)
