cifar10_predict.py

from random import shuffle
import os
import sys


def patch_path(path):
    return os.path.join(os.path.dirname(__file__), path)


def load_audio_path_label_pairs(max_allowed_pairs=None):
    from mxnet_audio.library.utility.gtzan_loader import download_gtzan_genres_if_not_found
    download_gtzan_genres_if_not_found(patch_path('very_large_data/gtzan'))
    audio_paths = []
    with open(patch_path('data/lists/test_songs_gtzan_list.txt'), 'rt') as file:
        for line in file:
            audio_path = patch_path('very_large_data/' + line.strip())
            audio_paths.append(audio_path)
    pairs = []
    with open(patch_path('data/lists/test_gt_gtzan_list.txt'), 'rt') as file:
        for line in file:
            label = int(line)
            if max_allowed_pairs is None or len(pairs) < max_allowed_pairs:
                pairs.append((audio_paths[len(pairs)], label))
            else:
                break
    return pairs


def main():
    sys.path.append(patch_path('..'))

    audio_path_label_pairs = load_audio_path_label_pairs()
    shuffle(audio_path_label_pairs)
    print('loaded: ', len(audio_path_label_pairs))

    from mxnet_audio.library.cifar10 import Cifar10AudioClassifier
    from mxnet_audio.library.utility.gtzan_loader import gtzan_labels

    classifier = Cifar10AudioClassifier()
    classifier.load_model(model_dir_path=patch_path('models'))

    for i in range(0, 20):
        audio_path, actual_label_id = audio_path_label_pairs[i]
        predicted_label_id = classifier.predict_class(audio_path)
        print(audio_path)
        predicted_label = gtzan_labels[predicted_label_id]
        actual_label = gtzan_labels[actual_label_id]

        print('predicted: ', predicted_label, 'actual: ', actual_label)


if __name__ == '__main__':
    main()