'--positive_labels',
default='ESLMV',
help='Labels in CC_WEB_VIDEO datasets that '
'considered posetive. default=\'ESLMV\'')
args = vars(parser.parse_args())
print 'loading data...'
cc_dataset = pk.load(open('datasets/cc_web_video.pickle', 'rb'))
cc_features = np.load(args['evaluation_set'])
model = DNN(cc_features.shape[1],
None,
args['model_path'],
load_model=True,
trainable=False)
cc_embeddings = model.embeddings(cc_features)
print 'Evaluation set file: ', args['evaluation_set']
print 'Path to DML model: ', args['model_path']
print 'Positive labels: ', args['positive_labels']
print '\nEvaluation Results'
print '=================='
similarities = calculate_similarities(cc_dataset['queries'], cc_embeddings)
mAP, pr_curve = evaluate(cc_dataset['ground_truth'],
similarities,
positive_labels=args['positive_labels'],
all_videos=False)
print 'CC_WEB_VIDEO mAP: ', mAP
plot_pr_curve(pr_curve, 'CC_WEB_VIDEO')
mAP, pr_curve = evaluate(cc_dataset['ground_truth'],
args = vars(parser.parse_args())
print('Loading data...')
cc_dataset = pk.load(open('datasets/cc_web_video.pickle', 'rb'))
cc_features = load_features(args['evaluation_set'])
print('Loading model...')
model = DNN(cc_features.shape[1],
args['model_path'],
load_model=True,
trainable=False)
if args['fusion'].lower() == 'early':
print('Fusion type: Early')
print('Extract video embeddings...')
cc_embeddings = model.embeddings(cc_features)
else:
print('Fusion type: Late')
print('Extract video embeddings...')
assert args['evaluation_features'] is not None, \
'Argument \'--evaluation_features\' must be provided for Late fusion'
feature_files = load_feature_files(args['evaluation_features'])
cc_embeddings = np.zeros(
(len(cc_dataset['index']), model.embedding_dim))
for i, video_id in enumerate(tqdm(cc_dataset['index'])):
if video_id in feature_files:
features = load_features(feature_files[video_id])
embeddings = model.embeddings(normalize(features))
embeddings = embeddings.mean(0, keepdims=True)