mediapipe_model_maker.face_stylizer.Dataset

    Dataset library for face stylizer fine tuning.

    Inherits From: ClassificationDataset , Dataset 

      mediapipe_model_maker 
 . 
 face_stylizer 
 . 
 Dataset 
 ( 
 dataset 
 : 
 tf 
 . 
 data 
 . 
 Dataset 
 , 
 label_names 
 : 
 List 
 [ 
 str 
 ], 
 size 
 : 
 Optional 
 [ 
 int 
 ] 
 = 
 None 
 )

    tf_dataset
    A tf.data.Dataset object that contains a potentially large set of elements, where each element is a pair of (input_data, target). The input_data means the raw input data, like an image, a text etc., while the target means the ground truth of the raw input data, e.g. the classification label of the image etc.
    size
    The size of the dataset. tf.data.Dataset donesn't support a function to get the length directly since it's lazy-loaded and may be infinite.

    label_names

    num_classes

    size
    Returns the size of the dataset.

    Same functionality as calling len. See the lenmethod definition for more information.

    Methods

    from_image

    View source 

      @classmethod 
 
  from_image 
 ( 
 filename 
 : 
 str 
 ) 
 -> 
   mediapipe_model_maker 
 . 
 face_stylizer 
 . 
 dataset 
 . 
 classification_dataset 
 . 
 ClassificationDataset

    Creates a dataset from single image.

    Supported input image formats include 'jpg', 'jpeg', 'png'.

    Args

    filename
    Name of the image file.

    Returns
    Dataset containing image and label and other related info.

    gen_tf_dataset

    View source 

      gen_tf_dataset 
 ( 
 batch_size 
 : 
 int 
 = 
 1 
 , 
 is_training 
 : 
 bool 
 = 
 False 
 , 
 shuffle 
 : 
 bool 
 = 
 False 
 , 
 preprocess 
 : 
 Optional 
 [ 
 Callable 
 [ 
 ... 
 , 
 Any 
 ]] 
 = 
 None 
 , 
 drop_remainder 
 : 
 bool 
 = 
 False 
 ) 
 -> 
 tf 
 . 
 data 
 . 
 Dataset

    Generates a batched tf.data.Dataset for training/evaluation.

    Args

    batch_size
    An integer, the returned dataset will be batched by this size.
    is_training
    A boolean, when True, the returned dataset will be optionally shuffled and repeated as an endless dataset.
    shuffle
    A boolean, when True, the returned dataset will be shuffled to create randomness during model training.
    preprocess
    A function taking three arguments in order, feature, label and boolean is_training.
    drop_remainder
    boolean, whether the finally batch drops remainder.

    Returns
    A TF dataset ready to be consumed by Keras model.

    split

    View source 

      split 
 ( 
 fraction 
 : 
 float 
 ) 
 -> 
 Tuple 
 [ 
 ds 
 . 
 _DatasetT 
 , 
 ds 
 . 
 _DatasetT 
 ]

    Splits dataset into two sub-datasets with the given fraction.

    Primarily used for splitting the data set into training and testing sets.

    Args

    fraction
    float, demonstrates the fraction of the first returned subdataset in the original data.

    Returns
    The splitted two sub datasets.

    __len__

    View source 

      __len__ 
 () 
 -> 
 int

    Returns the number of element of the dataset.

    If size is not set, this method will fallback to using the lenmethod of the tf.data.Dataset in self._dataset. Calling lenon a tf.data.Dataset instance may throw a TypeError because the dataset may be lazy-loaded with an unknown size or have infinite size.

    In most cases, however, when an instance of this class is created by helper functions like 'from_folder', the size of the dataset will be preprocessed, and the _size instance variable will be already set.

    Raises
    TypeError if self._size is not set and the cardinality of self._dataset is INFINITE_CARDINALITY or UNKNOWN_CARDINALITY.
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