Class BatchedTensorProvider

python/paddle/base/data_feeder.py:360–397

class BatchedTensorProvider:
    def __init__(self, feed_list, place, batch_size, generator, drop_last):
        self.place = place
        self.batch_size = batch_size
        self.generator = generator
        self.converters = []
        self.drop_last = drop_last

        for var in feed_list:
            if not in_pir_mode():
                assert var.lod_level == 0, "lod_level must be 0"
            self.converters.append(
                DataToDenseTensorConverter(
                    place=self.place,
                    lod_level=0,
                    shape=var.shape,
                    dtype=var.dtype,
                )
            )

    def _done(self):
        return [c.done() for c in self.converters]

    def __call__(self):
        idx = 0
        for each_sample in self.generator():
            for each_slot, each_converter in zip(each_sample, self.converters):
                each_converter.data.append(each_slot)

            idx += 1
            if idx == self.batch_size:
                idx = 0
                yield self._done()

        if not self.drop_last and idx > 0:
            yield self._done()
        else:
            [c._reset() for c in self.converters]


class DataFeeder: