Python cache.cache() Examples

def transfer_values(self, image_path=None, image=None):
        """
        Calculate the transfer-values for the given image.
        These are the values of the last layer of the Inception model before
        the softmax-layer, when inputting the image to the Inception model.

        The transfer-values allow us to use the Inception model in so-called
        Transfer Learning for other data-sets and different classifications.

        It may take several hours or more to calculate the transfer-values
        for all images in a data-set. It is therefore useful to cache the
        results using the function transfer_values_cache() below.

        :param image_path:
            The input image is a jpeg-file with this file-path.

        :param image:
            The input image is a 3-dim array which is already decoded.
            The pixels MUST be values between 0 and 255 (float or int).

        :return:
            The transfer-values for those images.
        """

        # Create a feed-dict for the TensorFlow graph with the input image.
        feed_dict = self._create_feed_dict(image_path=image_path, image=image)

        # Use TensorFlow to run the graph for the Inception model.
        # This calculates the values for the last layer of the Inception model
        # prior to the softmax-classification, which we call transfer-values.
        transfer_values = self.session.run(self.transfer_layer, feed_dict=feed_dict)

        # Reduce to a 1-dim array.
        transfer_values = np.squeeze(transfer_values)

        return transfer_values


########################################################################
# Batch-processing. 

def load_cached(cache_path, in_dir):
    """
    Wrapper-function for creating a DataSet-object, which will be
    loaded from a cache-file if it already exists, otherwise a new
    object will be created and saved to the cache-file.

    This is useful if you need to ensure the ordering of the
    filenames is consistent every time you load the data-set,
    for example if you use the DataSet-object in combination
    with Transfer Values saved to another cache-file, see e.g.
    Tutorial #09 for an example of this.

    :param cache_path:
        File-path for the cache-file.

    :param in_dir:
        Root-dir for the files in the data-set.
        This is an argument for the DataSet-init function.

    :return:
        The DataSet-object.
    """

    print("Creating dataset from the files in: " + in_dir)

    # If the object-instance for DataSet(in_dir=data_dir) already
    # exists in the cache-file then reload it, otherwise create
    # an object instance and save it to the cache-file for next time.
    dataset = cache(cache_path=cache_path,
                    fn=DataSet, in_dir=in_dir)

    return dataset


######################################################################## 

def __init__(self, database):
        self._web_handlers = {}
        for c in get_all_classes(["web_handlers.py"], WebHandler):
            obj = c(database, cache)
            self._web_handlers[obj.url] = obj
        self._web_server = Flask("web_server")
        self._register(database) 

def _register(self, database):
        logger.info("Handlers register start !")
        logger.info("Handler register: ")
        for handler_name, handler_obj in self._web_handlers.items():
            self._web_server.add_url_rule(
                "/%s/<string:parameters>" % handler_name,
                view_func=handler_obj.as_view(handler_name, database, cache)
            )
            logger.info("'%s' " % handler_name, False)
        logger.info("Handlers register done !") 

def __init__(self, database):
        self._database = database
        self._articles = database.get_collection("article")
        self._database_writers = {}
        for c in get_all_classes(["database_writers.py"], DatabaseWriter):
            obj = c(database, cache)
            self._database_writers[obj.flag] = obj
        self._file_path = "" 

def transfer_values(self, image_path=None, image=None):
        """
        Calculate the transfer-values for the given image.
        These are the values of the last layer of the Inception model before
        the softmax-layer, when inputting the image to the Inception model.

        The transfer-values allow us to use the Inception model in so-called
        Transfer Learning for other data-sets and different classifications.

        It may take several hours or more to calculate the transfer-values
        for all images in a data-set. It is therefore useful to cache the
        results using the function transfer_values_cache() below.

        :param image_path:
            The input image is a jpeg-file with this file-path.

        :param image:
            The input image is a 3-dim array which is already decoded.
            The pixels MUST be values between 0 and 255 (float or int).

        :return:
            The transfer-values for those images.
        """

        # Create a feed-dict for the TensorFlow graph with the input image.
        feed_dict = self._create_feed_dict(image_path=image_path, image=image)

        # Use TensorFlow to run the graph for the Inception model.
        # This calculates the values for the last layer of the Inception model
        # prior to the softmax-classification, which we call transfer-values.
        transfer_values = self.session.run(self.transfer_layer, feed_dict=feed_dict)

        # Reduce to a 1-dim array.
        transfer_values = np.squeeze(transfer_values)

        return transfer_values


########################################################################
# Batch-processing. 

def load_cached(cache_path, in_dir):
    """
    Wrapper-function for creating a DataSet-object, which will be
    loaded from a cache-file if it already exists, otherwise a new
    object will be created and saved to the cache-file.

    This is useful if you need to ensure the ordering of the
    filenames is consistent every time you load the data-set,
    for example if you use the DataSet-object in combination
    with Transfer Values saved to another cache-file, see e.g.
    Tutorial #09 for an example of this.

    :param cache_path:
        File-path for the cache-file.

    :param in_dir:
        Root-dir for the files in the data-set.
        This is an argument for the DataSet-init function.

    :return:
        The DataSet-object.
    """

    print("Creating dataset from the files in: " + in_dir)

    # If the object-instance for DataSet(in_dir=data_dir) already
    # exists in the cache-file then reload it, otherwise create
    # an object instance and save it to the cache-file for next time.
    dataset = cache(cache_path=cache_path,
                    fn=DataSet, in_dir=in_dir)

    return dataset


######################################################################## 

def transfer_values(self, image_path=None, image=None):
        """
        Calculate the transfer-values for the given image.
        These are the values of the last layer of the Inception model before
        the softmax-layer, when inputting the image to the Inception model.

        The transfer-values allow us to use the Inception model in so-called
        Transfer Learning for other data-sets and different classifications.

        It may take several hours or more to calculate the transfer-values
        for all images in a data-set. It is therefore useful to cache the
        results using the function transfer_values_cache() below.

        :param image_path:
            The input image is a jpeg-file with this file-path.

        :param image:
            The input image is a 3-dim array which is already decoded.
            The pixels MUST be values between 0 and 255 (float or int).

        :return:
            The transfer-values for those images.
        """

        # Create a feed-dict for the TensorFlow graph with the input image.
        feed_dict = self._create_feed_dict(image_path=image_path, image=image)

        # Use TensorFlow to run the graph for the Inception model.
        # This calculates the values for the last layer of the Inception model
        # prior to the softmax-classification, which we call transfer-values.
        transfer_values = self.session.run(self.transfer_layer, feed_dict=feed_dict)

        # Reduce to a 1-dim array.
        transfer_values = np.squeeze(transfer_values)

        return transfer_values


########################################################################
# Batch-processing. 

def load_cached(cache_path, in_dir):
    """
    Wrapper-function for creating a DataSet-object, which will be
    loaded from a cache-file if it already exists, otherwise a new
    object will be created and saved to the cache-file.

    This is useful if you need to ensure the ordering of the
    filenames is consistent every time you load the data-set,
    for example if you use the DataSet-object in combination
    with Transfer Values saved to another cache-file, see e.g.
    Tutorial #09 for an example of this.

    :param cache_path:
        File-path for the cache-file.

    :param in_dir:
        Root-dir for the files in the data-set.
        This is an argument for the DataSet-init function.

    :return:
        The DataSet-object.
    """

    print("Creating dataset from the files in: " + in_dir)

    # If the object-instance for DataSet(in_dir=data_dir) already
    # exists in the cache-file then reload it, otherwise create
    # an object instance and save it to the cache-file for next time.
    dataset = cache(cache_path=cache_path,
                    fn=DataSet, in_dir=in_dir)

    return dataset


######################################################################## 

def transfer_values(self, image_path=None, image=None):
        """
        Calculate the transfer-values for the given image.
        These are the values of the last layer of the Inception model before
        the softmax-layer, when inputting the image to the Inception model.

        The transfer-values allow us to use the Inception model in so-called
        Transfer Learning for other data-sets and different classifications.

        It may take several hours or more to calculate the transfer-values
        for all images in a data-set. It is therefore useful to cache the
        results using the function transfer_values_cache() below.

        :param image_path:
            The input image is a jpeg-file with this file-path.

        :param image:
            The input image is a 3-dim array which is already decoded.
            The pixels MUST be values between 0 and 255 (float or int).

        :return:
            The transfer-values for those images.
        """

        # Create a feed-dict for the TensorFlow graph with the input image.
        feed_dict = self._create_feed_dict(image_path=image_path, image=image)

        # Use TensorFlow to run the graph for the Inception model.
        # This calculates the values for the last layer of the Inception model
        # prior to the softmax-classification, which we call transfer-values.
        transfer_values = self.session.run(self.transfer_layer, feed_dict=feed_dict)

        # Reduce to a 1-dim array.
        transfer_values = np.squeeze(transfer_values)

        return transfer_values


########################################################################
# Batch-processing. 

def transfer_values(self, image_path=None, image=None):
        """
        Calculate the transfer-values for the given image.
        These are the values of the last layer of the Inception model before
        the softmax-layer, when inputting the image to the Inception model.

        The transfer-values allow us to use the Inception model in so-called
        Transfer Learning for other data-sets and different classifications.

        It may take several hours or more to calculate the transfer-values
        for all images in a data-set. It is therefore useful to cache the
        results using the function transfer_values_cache() below.

        :param image_path:
            The input image is a jpeg-file with this file-path.

        :param image:
            The input image is a 3-dim array which is already decoded.
            The pixels MUST be values between 0 and 255 (float or int).

        :return:
            The transfer-values for those images.
        """

        # Create a feed-dict for the TensorFlow graph with the input image.
        feed_dict = self._create_feed_dict(image_path=image_path, image=image)

        # Use TensorFlow to run the graph for the Inception model.
        # This calculates the values for the last layer of the Inception model
        # prior to the softmax-classification, which we call transfer-values.
        transfer_values = self.session.run(self.transfer_layer, feed_dict=feed_dict)

        # Reduce to a 1-dim array.
        transfer_values = np.squeeze(transfer_values)

        return transfer_values


########################################################################
# Batch-processing. 

def transfer_values_cache(cache_path, model, images=None, image_paths=None):
    """
    This function either loads the transfer-values if they have
    already been calculated, otherwise it calculates the values
    and saves them to a file that can be re-loaded again later.

    Because the transfer-values can be expensive to compute, it can
    be useful to cache the values through this function instead
    of calling transfer_values() directly on the Inception model.

    See Tutorial #08 for an example on how to use this function.

    :param cache_path:
        File containing the cached transfer-values for the images.

    :param model:
        Instance of the Inception model.

    :param images:
        4-dim array with images. [image_number, height, width, colour_channel]

    :param image_paths:
        Array of file-paths for images (must be jpeg-format).

    :return:
        The transfer-values from the Inception model for those images.
    """

    # Helper-function for processing the images if the cache-file does not exist.
    # This is needed because we cannot supply both fn=process_images
    # and fn=model.transfer_values to the cache()-function.
    def fn():
        return process_images(fn=model.transfer_values, images=images, image_paths=image_paths)

    # Read the transfer-values from a cache-file, or calculate them if the file does not exist.
    transfer_values = cache(cache_path=cache_path, fn=fn)

    return transfer_values


########################################################################
# Example usage. 

def transfer_values_cache(cache_path, model, images=None, image_paths=None):
    """
    This function either loads the transfer-values if they have
    already been calculated, otherwise it calculates the values
    and saves them to a file that can be re-loaded again later.

    Because the transfer-values can be expensive to compute, it can
    be useful to cache the values through this function instead
    of calling transfer_values() directly on the Inception model.

    See Tutorial #08 for an example on how to use this function.

    :param cache_path:
        File containing the cached transfer-values for the images.

    :param model:
        Instance of the Inception model.

    :param images:
        4-dim array with images. [image_number, height, width, colour_channel]

    :param image_paths:
        Array of file-paths for images (must be jpeg-format).

    :return:
        The transfer-values from the Inception model for those images.
    """

    # Helper-function for processing the images if the cache-file does not exist.
    # This is needed because we cannot supply both fn=process_images
    # and fn=model.transfer_values to the cache()-function.
    def fn():
        return process_images(fn=model.transfer_values, images=images, image_paths=image_paths)

    # Read the transfer-values from a cache-file, or calculate them if the file does not exist.
    transfer_values = cache(cache_path=cache_path, fn=fn)

    return transfer_values


########################################################################
# Example usage. 

def transfer_values_cache(cache_path, model, images=None, image_paths=None):
    """
    This function either loads the transfer-values if they have
    already been calculated, otherwise it calculates the values
    and saves them to a file that can be re-loaded again later.

    Because the transfer-values can be expensive to compute, it can
    be useful to cache the values through this function instead
    of calling transfer_values() directly on the Inception model.

    See Tutorial #08 for an example on how to use this function.

    :param cache_path:
        File containing the cached transfer-values for the images.

    :param model:
        Instance of the Inception model.

    :param images:
        4-dim array with images. [image_number, height, width, colour_channel]

    :param image_paths:
        Array of file-paths for images (must be jpeg-format).

    :return:
        The transfer-values from the Inception model for those images.
    """

    # Helper-function for processing the images if the cache-file does not exist.
    # This is needed because we cannot supply both fn=process_images
    # and fn=model.transfer_values to the cache()-function.
    def fn():
        return process_images(fn=model.transfer_values, images=images, image_paths=image_paths)

    # Read the transfer-values from a cache-file, or calculate them if the file does not exist.
    transfer_values = cache(cache_path=cache_path, fn=fn)

    return transfer_values


########################################################################
# Example usage. 

def transfer_values_cache(cache_path, model, images=None, image_paths=None):
    """
    This function either loads the transfer-values if they have
    already been calculated, otherwise it calculates the values
    and saves them to a file that can be re-loaded again later.

    Because the transfer-values can be expensive to compute, it can
    be useful to cache the values through this function instead
    of calling transfer_values() directly on the Inception model.

    See Tutorial #08 for an example on how to use this function.

    :param cache_path:
        File containing the cached transfer-values for the images.

    :param model:
        Instance of the Inception model.

    :param images:
        4-dim array with images. [image_number, height, width, colour_channel]

    :param image_paths:
        Array of file-paths for images (must be jpeg-format).

    :return:
        The transfer-values from the Inception model for those images.
    """

    # Helper-function for processing the images if the cache-file does not exist.
    # This is needed because we cannot supply both fn=process_images
    # and fn=model.transfer_values to the cache()-function.
    def fn():
        return process_images(fn=model.transfer_values, images=images, image_paths=image_paths)

    # Read the transfer-values from a cache-file, or calculate them if the file does not exist.
    transfer_values = cache(cache_path=cache_path, fn=fn)

    return transfer_values


########################################################################
# Example usage.