Python tensorflow.keras.utils.get_file() Examples

The following are 5 code examples of tensorflow.keras.utils.get_file(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.keras.utils , or try the search function .
Example #1
Source File: gender_detection.py    From cvlib with MIT License 6 votes vote down vote up 
def __init__(self):

        proto_url = 'https://download.cvlib.net/config/gender_detection/gender_deploy.prototxt'
        model_url = 'https://github.com/arunponnusamy/cvlib-files/releases/download/v0.1/gender_net.caffemodel'
        save_dir = os.path.expanduser('~') + os.path.sep + '.cvlib' + os.path.sep + 'pre-trained'

        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        self.proto = get_file('gender_deploy.prototxt', proto_url,
                                cache_subdir=save_dir) 
        self.model = get_file('gender_net.caffemodel', model_url,
                              cache_subdir=save_dir)

        self.labels = ['male', 'female']
        self.mean = (78.4263377603, 87.7689143744, 114.895847746)

        print('[INFO] Initializing gender detection model ..')
        self.net = cv2.dnn.readNetFromCaffe(self.proto, self.model)
Example #2
Source File: gender_detection.py    From cvlib with MIT License 6 votes vote down vote up 
def __init__(self):

        proto_url = 'https://download.cvlib.net/config/gender_detection/gender_deploy.prototxt'
        model_url = 'https://github.com/arunponnusamy/cvlib-files/releases/download/v0.1/gender_net.caffemodel'
        save_dir = os.path.expanduser('~') + os.path.sep + '.cvlib' + os.path.sep + 'pre-trained'

        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        self.proto = get_file('gender_deploy.prototxt', proto_url,
                                cache_subdir=save_dir) 
        self.model = get_file('gender_net.caffemodel', model_url,
                              cache_subdir=save_dir)

        self.labels = ['male', 'female']
        self.mean = (78.4263377603, 87.7689143744, 114.895847746)

        print('[INFO] Initializing gender detection model ..')
        self.net = cv2.dnn.readNetFromCaffe(self.proto, self.model)
Example #3
Source File: qm9.py    From spektral with MIT License 5 votes vote down vote up 
def _download_data():
    _ = get_file(
        'qm9.tar.gz', DATASET_URL,
        extract=True, cache_dir=DATA_PATH, cache_subdir=DATA_PATH
    )
    os.rename(DATA_PATH + 'gdb9.sdf', DATA_PATH + 'qm9.sdf')
    os.rename(DATA_PATH + 'gdb9.sdf.csv', DATA_PATH + 'qm9.sdf.csv')
    os.remove(DATA_PATH + 'qm9.tar.gz')
Example #4
Source File: __init__.py    From platypush with MIT License 5 votes vote down vote up 
def _get_data(cls, data: Union[str, np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]], model: Model) \
            -> Union[np.ndarray, Iterable, Dict[str, Union[Iterable, np.ndarray]]]:
        if not isinstance(data, str):
            return data

        if data.startswith('http://') or data.startswith('https://'):
            filename = '{timestamp}_{filename}'.format(
                timestamp=datetime.now().timestamp(), filename=data.split('/')[-1])
            data_file = utils.get_file(filename, data)
        else:
            data_file = os.path.abspath(os.path.expanduser(data))

        extensions = [ext for ext in cls._supported_data_file_extensions if data_file.endswith('.' + ext)]

        if os.path.isfile(data_file):
            assert extensions, 'Unsupported type for file {}. Supported extensions: {}'.format(
                data_file, cls._supported_data_file_extensions
            )

            extension = extensions.pop()
            if extension in cls._csv_extensions:
                return cls._get_csv_data(data_file)
            if extension == 'npy':
                return cls._get_numpy_data(data_file)
            if extension == 'npz':
                return cls._get_numpy_compressed_data(data_file)
            if extension in cls._image_extensions:
                return cls._get_image(data_file, model)

            raise AssertionError('Unsupported file type: {}'.format(data_file))
        elif os.path.isdir(data_file):
            return cls._get_dir(data_file, model)
Example #5
Source File: pbt_memnn_example.py    From ray with Apache License 2.0 5 votes vote down vote up 
def read_data():
    # Get the file
    try:
        path = get_file(
            "babi-tasks-v1-2.tar.gz",
            origin="https://s3.amazonaws.com/text-datasets/"
            "babi_tasks_1-20_v1-2.tar.gz")
    except Exception:
        print(
            "Error downloading dataset, please download it manually:\n"
            "$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2"  # noqa: E501
            ".tar.gz\n"
            "$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz"  # noqa: E501
        )
        raise

    # Choose challenge
    challenges = {
        # QA1 with 10,000 samples
        "single_supporting_fact_10k": "tasks_1-20_v1-2/en-10k/qa1_"
        "single-supporting-fact_{}.txt",
        # QA2 with 10,000 samples
        "two_supporting_facts_10k": "tasks_1-20_v1-2/en-10k/qa2_"
        "two-supporting-facts_{}.txt",
    }
    challenge_type = "single_supporting_fact_10k"
    challenge = challenges[challenge_type]

    with tarfile.open(path) as tar:
        train_stories = get_stories(tar.extractfile(challenge.format("train")))
        test_stories = get_stories(tar.extractfile(challenge.format("test")))

    return train_stories, test_stories