'--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)