# import datasets.MSRII
import numpy as np
DATA_LIST = ['UCF-Sports', 'JHMDB', 'UCF101', 'MSRII']
MOD_LIST = ['RGB', 'FLOW']
LEN_LIST = ['1', '5', '10']
SPLIT_LIST = ['0', '1', '2']
for dataset in DATA_LIST:
for mod in MOD_LIST:
for lens in LEN_LIST:
for split in SPLIT_LIST:
name = '{}_{}_{}_split_{}'.format(dataset, mod, lens, split)
image_set = name
if dataset is 'UCF-Sports':
__sets[name] = (lambda split=image_set, phase='TRAIN':
ucfsports(split, phase))
elif dataset is 'JHMDB':
__sets[name] = (lambda split=image_set, phase='TRAIN':
JHMDB(split, phase))
elif dataset is 'UCF101':
__sets[name] = (lambda split=image_set, phase='TRAIN':
UCF101(split, phase))
# elif dataset is 'MSRII':
# __sets[name] = (lambda split=split, datapath='/home/lear/xpeng/data/MSR_II/pweinzaeMSR2/frames': #frames #features/fat2/motion_cnn_proposals/jpeg0
# datasets.MSRII(split, datapath))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
if __name__ == '__main__':
args = parse_args()
if not os.path.isfile(args.net):
raise IOError(('{:s} not found.').format(args.net))
cfg.TEST.HAS_RPN = True
cfg.TEST.SCALES = [600]
MOD = args.imdb.split('_')[1]
LEN = int(args.imdb.split('_')[2])
if MOD == 'FLOW' and LEN == 1:
cfg.PIXEL_MEANS = np.array([[[128., 128., 128.]]])
if MOD == 'FLOW' and LEN == 5:
cfg.PIXEL_MEANS = np.array([[[128., 128., 128.] * 5]])
ucfsports_test = ucfsports(args.imdb, 'TEST')
roidb = ucfsports_test.roidb
if not os.path.exists(args.savepath):
if args.cpu_mode:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
caffe_net = caffe.Net(args.proto, args.net, caffe.TEST)
pred_all_dets = {}
n_fr = len(ucfsports_test.image_index)
for i in range(n_fr):
image_name = ucfsports_test.image_index[i]
image_path = os.path.join(ucfsports_test._data_path, image_name)