Method decorate_batch_generator

python/paddle/base/reader.py:1568–1637

Set the data source of the PyReader object. The provided :code:`reader` should be a Python generator, which yields numpy.ndarray-typed or DenseTensor-typed batched data. :code:`places` must be set when the PyReader object is iterable. Args: rea

(self, reader, places=None)

        self._loader.set_sample_list_generator(reader, places)

    def decorate_batch_generator(self, reader, places=None):
        '''
        Set the data source of the PyReader object.

        The provided :code:`reader` should be a Python generator,
        which yields numpy.ndarray-typed or DenseTensor-typed batched data.

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

        Args:
            reader (generator): Python generator that yields DenseTensor-typed
                batched data.
            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):
                ...             batch_image = np.random.uniform(low=0, high=255, size=[BATCH_SIZE, height, width])
                ...             batch_label = np.ones([BATCH_SIZE, 1])
                ...             batch_image = batch_image.astype('float32')
                ...             batch_label = batch_label.astype('int64')
                ...             yield batch_image, batch_label
                ...
                ...     return generator

                >>> image = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
                >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
                >>> reader = base.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)

                >>> user_defined_generator = random_image_and_label_generator(784, 784)
                >>> reader.decorate_batch_generator(
                ...     user_defined_generator,
                ...     paddle.CPUPlace(),
                ... )