def materialize(self, shared_name=None, container=None):
"""Materialize creates a MaterializedIndexedDataset.
IndexedDatasets can be combined through operations such as TBD. Therefore,
they are only materialized when absolutely required.
Args:
shared_name: a string for the shared name to use for the resource.
container: a string for the container to store the resource.
Returns:
A MaterializedIndexedDataset.
"""
if container is None:
container = ""
if shared_name is None:
shared_name = ""
materialized_resource = (
ged_ops.experimental_materialized_index_dataset_handle(
container=container,
shared_name=shared_name,
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_types(self.output_shapes,
self.output_classes))))
with ops.colocate_with(materialized_resource):
materializer = ged_ops.experimental_indexed_dataset_materialize(
self._as_variant_tensor(), materialized_resource)
return MaterializedIndexedDataset(materialized_resource, materializer,
self.output_classes, self.output_types,
self.output_shapes)
def materialize(self, shared_name=None, container=None):
"""Materialize creates a MaterializedIndexedDataset.
IndexedDatasets can be combined through operations such as TBD. Therefore,
they are only materialized when absolutely required.
Args:
shared_name: a string for the shared name to use for the resource.
container: a string for the container to store the resource.
Returns:
A MaterializedIndexedDataset.
"""
if container is None:
container = ""
if shared_name is None:
shared_name = ""
materialized_resource = (
ged_ops.experimental_materialized_index_dataset_handle(
container=container,
shared_name=shared_name,
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_types(self.output_shapes,
self.output_classes))))
with ops.colocate_with(materialized_resource):
materializer = ged_ops.experimental_indexed_dataset_materialize(
self._as_variant_tensor(), materialized_resource)
return MaterializedIndexedDataset(materialized_resource, materializer,
self.output_classes, self.output_types,
self.output_shapes)
def testLowLevelIndexedDatasetOps(self):
identity = ged_ops.experimental_identity_indexed_dataset(
ops.convert_to_tensor(16, dtype=dtypes.uint64))
handle = ged_ops.experimental_materialized_index_dataset_handle(
container="",
shared_name="",
output_types=[dtypes.uint64],
output_shapes=[[]])
materialize = ged_ops.experimental_indexed_dataset_materialize(
identity, handle)
get_op = ged_ops.experimental_indexed_dataset_get(
handle, 3, output_types=[dtypes.uint64], output_shapes=[[]])
self.evaluate(materialize)
self.assertEqual([3], self.evaluate(get_op))
def testLowLevelIndexedDatasetOps(self):
identity = ged_ops.experimental_identity_indexed_dataset(
ops.convert_to_tensor(16, dtype=dtypes.uint64))
handle = ged_ops.experimental_materialized_index_dataset_handle(
container="",
shared_name="",
output_types=[dtypes.uint64],
output_shapes=[[]])
materialize = ged_ops.experimental_indexed_dataset_materialize(
identity, handle)
get_op = ged_ops.experimental_indexed_dataset_get(
handle, 3, output_types=[dtypes.uint64], output_shapes=[[]])
self.evaluate(materialize)
self.assertEqual([3], self.evaluate(get_op))
def testLowLevelIndexedDatasetOps(self):
identity = ged_ops.experimental_identity_indexed_dataset(
ops.convert_to_tensor(16, dtype=dtypes.uint64))
handle = ged_ops.experimental_materialized_index_dataset_handle(
container="",
shared_name="",
output_types=[dtypes.uint64],
output_shapes=[[]])
materialize = ged_ops.experimental_indexed_dataset_materialize(
identity, handle)
index = array_ops.placeholder(dtypes.uint64)
get_op = ged_ops.experimental_indexed_dataset_get(
handle, index, output_types=[dtypes.uint64], output_shapes=[[]])
with self.cached_session() as sess:
sess.run(materialize)
self.assertEqual([3], sess.run(get_op, feed_dict={index: 3}))