def load_vidor_dataset():
if not os.path.exists('dataset/vidor-dataset.pkl'):
dataset = VidOR(anno_path, video_path, ['training', 'validation'], low_memory=True)
with open('dataset/vidor-dataset.pkl','wb') as file:
pickle.dump(dataset,file)
else:
with open('dataset/vidor-dataset.pkl','rb') as file:
dataset = pickle.load(file)
return dataset
def load_vidor_dataset():
dataset_path = '/home/wluo/vidor-dataset'
anno_path = os.path.join(dataset_path, 'annotation')
video_path = os.path.join(dataset_path, 'video')
frame_path = os.path.join(dataset_path, 'frame')
if not os.path.exists('dataset/vidor-dataset.pkl'):
dataset = VidOR(anno_path,
video_path, ['training', 'validation'],
low_memory=True)
with open('dataset/vidor-dataset.pkl', 'wb') as file:
pickle.dump(dataset, file)
else:
with open('dataset/vidor-dataset.pkl', 'rb') as file:
dataset = pickle.load(file)
return dataset
args = parser.parse_args()
if args.dataset=='imagenet-vidvrd':
if args.split=='testing':
args.split = 'test'
else:
print('[warning] there is no validation set in ImageNet-VidVRD dataset')
if args.task=='relation':
# load train set for zero-shot evaluation
dataset = ImagenetVidVRD('../vidvrd-dataset', '../vidvrd-dataset/videos', ['train', args.split])
else:
dataset = ImagenetVidVRD('../vidvrd-dataset', '../vidvrd-dataset/videos', [args.split])
elif args.dataset=='vidor':
if args.task=='relation':
# load train set for zero-shot evaluation
dataset = VidOR('../vidor-dataset/annotation', '../vidor-dataset/video', ['training', args.split], low_memory=True)
else:
dataset = VidOR('../vidor-dataset/annotation', '../vidor-dataset/video', [args.split], low_memory=True)
else:
raise Exception('Unknown dataset {}'.format(args.dataset))
print('Loading prediction from {}'.format(args.prediction))
with lzma.open(args.prediction, 'rt') as fin:
pred = json.load(fin)
print('Number of videos in prediction: {}'.format(len(pred['results'])))
if args.task=='object':
evaluate_object(dataset, args.split, pred['results'])
elif args.task=='relation':
evaluate_relation(dataset, args.split, pred['results'])
parser.add_argument('--dataset',
choices=['vidvrd', 'vidor'],
help='the dataset name')
parser.add_argument('--train',
action="store_true",
default=False,
help='Train model')
parser.add_argument('--detect',
action="store_true",
default=False,
help='Detect video visual relation')
args = parser.parse_args()
if args.dataset == 'vidvrd':
dataset = ImagenetVidVRD('./vidvrd-dataset', './vidvrd-dataset/videos',
['train', 'test'])
elif args.dataset == 'vidor':
dataset = VidOR('./vidor-dataset/annotation', './vidor-dataset/video',
['training', 'validation'])
if args.load_feature or args.train or args.detect:
if args.load_feature:
load_object_trajectory_proposal()
load_relation_feature()
if args.train:
train()
if args.detect:
detect()
else:
parser.print_help()
parser.add_argument('prediction', type=str, help='Prediction json file')
parser.add_argument("--object",
action="store_true",
help="whether to evaluate video objects")
parser.add_argument("--action",
action="store_true",
help="whether to evaluate actions")
parser.add_argument("--relation",
action="store_true",
help="whether to evaluate visual relations")
args = parser.parse_args()
if args.dataset == 'vidvrd':
dataset = VidVRD('../vidvrd-dataset', '../vidvrd-dataset/videos',
[args.split])
elif args.dataset == 'vidor':
dataset = VidOR('../vidor/annotation',
'../vidor/vidor', [args.split],
low_memory=True)
else:
raise Exception('Unknown dataset {}'.format(args.dataset))
with open(args.prediction, 'r') as fin:
prediction_json = json.load(fin)
if args.object:
evaluate_object(dataset, args.split, prediction_json)
if args.action:
evaluate_action(dataset, args.split, prediction_json)
if args.relation:
evaluate_relation(dataset, args.split, prediction_json)