Method decorate_sample_generator

python/paddle/base/reader.py:1412–1493

Set the data source of the PyReader object. The provided :code:`sample_generator` should be a Python generator, which yields list(numpy.ndarray)-typed data of each sample. :code:`places` must be set when the PyReader object is iterable. If all inputs have

(
        self, sample_generator, batch_size, drop_last=True, places=None
    )

        self._loader.reset()

    def decorate_sample_generator(
        self, sample_generator, batch_size, drop_last=True, places=None
    ):
        '''
        Set the data source of the PyReader object.

        The provided :code:`sample_generator` should be a Python generator,
        which yields list(numpy.ndarray)-typed data of each sample.

        :code:`places` must be set when the PyReader object is iterable.

        If all inputs have no lods, this method is faster than
        :code:`decorate_sample_list_generator(paddle.batch(sample_generator, ...))` .

        Args:
            sample_generator (generator): Python generator that yields
                list(numpy.ndarray)-typed sample data.
            batch_size (int): batch size. Must be larger than 0.
            drop_last (bool): Whether to drop the last batch when sample number
                is less than batch_size.
            places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
                be provided when PyReader is iterable.

        Example:
            .. code-block:: pycon

                >>> import paddle
                >>> import paddle.base as base
                >>> import numpy as np

                >>> paddle.enable_static()

                >>> EPOCH_NUM = 3
                >>> ITER_NUM = 15
                >>> BATCH_SIZE = 3

                >>> def network(image, label):
                ...     # User-defined network, here is an example of softmax regression.
                ...     predict = paddle.static.nn.fc(x=image, size=10, activation='softmax')
                ...     return paddle.nn.functional.cross_entropy(
                ...         input=predict,
                ...         label=label,
                ...         reduction='none',
                ...         use_softmax=False,
                ...     )

                >>> def random_image_and_label_generator(height, width):
                ...     def generator():
                ...         for i in range(ITER_NUM):
                ...             fake_image = np.random.uniform(
                ...                 low=0,
                ...                 high=255,
                ...                 size=[height, width],
                ...             )
                ...             fake_label = np.array([1])
                ...             yield fake_image, fake_label
                ...
                ...     return generator