def test_validate_dataset_keys(self):
analyzer_cache.validate_dataset_keys(
{'foo', 'Foo', 'A1', 'A_1', 'A.1', 'A-1'})
for key in {'foo 1', 'foo@1', 'foo*', 'foo[]', 'foo/goo'}:
with self.assertRaisesRegexp(
ValueError, 'Dataset key .* does not match allowed pattern:'):
analyzer_cache.validate_dataset_keys({key})
def expand(self, dataset):
"""Analyze the dataset.
Args:
dataset: A dataset.
Returns:
A TransformFn containing the deferred transform function.
Raises:
ValueError: If preprocessing_fn has no outputs.
"""
flattened_pcoll, input_values_pcoll_dict, input_metadata = dataset
input_schema = input_metadata.schema
input_values_pcoll_dict = input_values_pcoll_dict or dict()
analyzer_cache.validate_dataset_keys(input_values_pcoll_dict.keys())
with tf.Graph().as_default() as graph:
with tf.name_scope('inputs'):
feature_spec = input_schema.as_feature_spec()
input_signature = impl_helper.feature_spec_as_batched_placeholders(
feature_spec)
# In order to avoid a bug where import_graph_def fails when the
# input_map and return_elements of an imported graph are the same
# (b/34288791), we avoid using the placeholder of an input column as an
# output of a graph. We do this by applying tf.identity to all inputs of
# the preprocessing_fn. Note this applies at the level of raw tensors.
# TODO(b/34288791): Remove this workaround and use a shallow copy of
# inputs instead. A shallow copy is needed in case
# self._preprocessing_fn mutates its input.
copied_inputs = impl_helper.copy_tensors(input_signature)
output_signature = self._preprocessing_fn(copied_inputs)
# At this point we check that the preprocessing_fn has at least one
# output. This is because if we allowed the output of preprocessing_fn to
# be empty, we wouldn't be able to determine how many instances to
# "unbatch" the output into.
if not output_signature:
raise ValueError('The preprocessing function returned an empty dict')
if graph.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
raise ValueError(
'The preprocessing function contained trainable variables '
'{}'.format(
graph.get_collection_ref(tf.GraphKeys.TRAINABLE_VARIABLES)))
pipeline = flattened_pcoll.pipeline
serialized_tf_config = common._DEFAULT_TENSORFLOW_CONFIG_BY_RUNNER.get( # pylint: disable=protected-access
pipeline.runner)
extra_args = common.ConstructBeamPipelineVisitor.ExtraArgs(
base_temp_dir=Context.create_base_temp_dir(),
serialized_tf_config=serialized_tf_config,
pipeline=pipeline,
flat_pcollection=flattened_pcoll,
pcollection_dict=input_values_pcoll_dict,
graph=graph,
input_signature=input_signature,
input_schema=input_schema,
cache_location=self._cache_location)
transform_fn_future = analysis_graph_builder.build(
graph, input_signature, output_signature,
input_values_pcoll_dict.keys(), self._cache_location)
transform_fn_pcoll = nodes.Traverser(
common.ConstructBeamPipelineVisitor(extra_args)).visit_value_node(
transform_fn_future)
# Infer metadata. We take the inferred metadata and apply overrides that
# refer to values of tensors in the graph. The override tensors must
# be "constant" in that they don't depend on input data. The tensors can
# depend on analyzer outputs though. This allows us to set metadata that
# depends on analyzer outputs. _augment_metadata will use the analyzer
# outputs stored in `transform_fn` to compute the metadata in a
# deferred manner, once the analyzer outputs are known.
metadata = dataset_metadata.DatasetMetadata(
schema=schema_inference.infer_feature_schema(output_signature, graph))
deferred_metadata = (
transform_fn_pcoll
|
'ComputeDeferredMetadata' >> beam.Map(_infer_metadata_from_saved_model))
full_metadata = beam_metadata_io.BeamDatasetMetadata(
metadata, deferred_metadata)
_clear_shared_state_after_barrier(pipeline, transform_fn_pcoll)
return transform_fn_pcoll, full_metadata