def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""
if not isinstance(dataset_ops.get_legacy_output_types(input_dataset),
dtypes.DType):
raise TypeError(
"DenseToSparseDataset requires an input whose elements "
"have a single component, whereas the input has %r." %
dataset_ops.get_legacy_output_types(input_dataset))
self._input_dataset = input_dataset
self._batch_size = batch_size
self._row_shape = row_shape
self._structure = structure.SparseTensorStructure(
dataset_ops.get_legacy_output_types(input_dataset),
tensor_shape.vector(None).concatenate(self._row_shape))
if compat.forward_compatible(2019, 8, 3):
variant_tensor = ged_ops.dense_to_sparse_batch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._batch_size,
row_shape=convert.partial_shape_to_tensor(self._row_shape),
**self._flat_structure)
else:
variant_tensor = ged_ops.experimental_dense_to_sparse_batch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._batch_size,
row_shape=convert.partial_shape_to_tensor(self._row_shape),
**self._flat_structure)
super(_DenseToSparseBatchDataset,
self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""
if not isinstance(
dataset_ops.get_legacy_output_types(input_dataset), dtypes.DType):
raise TypeError("`dense_to_sparse_batch` requires an input dataset whose "
"elements have a single component, but the given dataset "
"has the following component types: "
f"{dataset_ops.get_legacy_output_types(input_dataset)}.")
self._input_dataset = input_dataset
self._batch_size = batch_size
self._row_shape = row_shape
self._element_spec = sparse_tensor.SparseTensorSpec(
tensor_shape.TensorShape([None]).concatenate(self._row_shape),
dataset_ops.get_legacy_output_types(input_dataset))
variant_tensor = ged_ops.dense_to_sparse_batch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._batch_size,
row_shape=convert.partial_shape_to_tensor(self._row_shape),
**self._flat_structure)
super(_DenseToSparseBatchDataset, self).__init__(input_dataset,
variant_tensor)
