c2/tools/dense_prediction_aggregation.py [36:51]:
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    with open(pkl_file, 'r') as fopen:
        blobs = pickle.load(fopen)
    video_id = blobs['video_id']
    label = blobs['label']
    feature = blobs[blob_name]
    n = np.max(video_id) + 1
    m = np.prod(feature.shape[1:])
    if len(label.shape) > 1:
        k = label.shape[1]
    else:
        k = 1

    # place blobs in order of video_id
    sorted_features = np.zeros((n, m), dtype=np.float)
    sorted_labels = np.zeros((n, k), dtype=np.int)
    prediction_counts = np.zeros((n, 1), dtype=np.int)
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c2/tools/dense_prediction_fusion.py [34:49]:
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    with open(pkl_file, 'r') as fopen:
        blobs = pickle.load(fopen)
    video_id = blobs['video_id']
    label = blobs['label']
    feature = blobs[blob_name]
    n = np.max(video_id) + 1
    m = np.prod(feature.shape[1:])
    if len(label.shape) > 1:
        k = label.shape[1]
    else:
        k = 1

    # place blobs in order of video_id
    sorted_features = np.zeros((n, m), dtype=np.float)
    sorted_labels = np.zeros((n, k), dtype=np.int)
    prediction_counts = np.zeros((n, 1), dtype=np.int)
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