Dataset library for face stylizer fine tuning.

Inherits From: ClassificationDataset, Dataset

tf_dataset A 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. donesn't support a function to get the length directly since it's lazy-loaded and may be infinite.



size Returns the size of the dataset.

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



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Creates a dataset from single image.

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

filename Name of the image file.

Dataset containing image and label and other related info.


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Generates a batched for training/evaluation.

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.

A TF dataset ready to be consumed by Keras model.


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

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

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

The splitted two sub datasets.


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Returns the number of element of the dataset.

If size is not set, this method will fallback to using the len method of the in self._dataset. Calling len on a 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.

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