def test_infer_feature_schema_bad_rank(self):
with tf.Graph().as_default() as graph:
tensors = {
'a': tf.placeholder(tf.float32, ()),
}
with self.assertRaises(ValueError):
schema_inference.infer_feature_schema(tensors, graph)
def _get_schema(self,
preprocessing_fn,
use_compat_v1,
inputs=None,
input_signature=None,
create_session=False):
if inputs is None:
inputs = {}
if input_signature is None:
input_signature = {}
if use_compat_v1:
with tf.compat.v1.Graph().as_default() as graph:
# Convert eager tensors to graph tensors.
inputs_copy = {
k: tf.constant(v, input_signature[k].dtype)
for k, v in inputs.items()
}
tensors = preprocessing_fn(inputs_copy)
if create_session:
# Create a session to actually evaluate the annotations and extract
# the output schema with annotations applied.
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
tensors, graph, session)
else:
schema = schema_inference.infer_feature_schema(
tensors, graph)
else:
tf_func = tf.function(preprocessing_fn,
input_signature=[input_signature
]).get_concrete_function()
tensors = tf.nest.pack_sequence_as(
structure=tf_func.structured_outputs,
flat_sequence=tf_func.outputs,
expand_composites=True)
structured_inputs = tf2_utils.get_structured_inputs_from_func_graph(
tf_func.graph)
tf_graph_context = graph_context.TFGraphContext(
module_to_export=tf.Module(),
temp_dir=os.path.join(self.get_temp_dir(),
self._testMethodName),
evaluated_replacements={})
concrete_metadata_fn = schema_inference.get_traced_metadata_fn(
preprocessing_fn=preprocessing_fn,
structured_inputs=structured_inputs,
tf_graph_context=tf_graph_context,
evaluate_schema_overrides=create_session)
schema = schema_inference.infer_feature_schema_v2(
tensors,
concrete_metadata_fn,
evaluate_schema_overrides=create_session)
return schema
def test_infer_feature_schema(self, make_tensors_fn, feature_spec,
domains=None, create_session=False):
with tf.Graph().as_default() as graph:
tensors = make_tensors_fn()
if create_session:
with tf.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(tensors, graph, session)
else:
schema = schema_inference.infer_feature_schema(tensors, graph)
expected_schema = dataset_schema.from_feature_spec(feature_spec, domains)
self.assertEqual(schema, expected_schema)
def test_global_annotation(self):
# TODO(b/132098015): Schema annotations aren't yet supported in OSS builds.
# pylint: disable=g-import-not-at-top
try:
from tensorflow_transform import annotations_pb2
except ImportError:
return
# pylint: enable=g-import-not-at-top
with tf.Graph().as_default() as graph:
outputs = {
'foo': tf.convert_to_tensor([0, 1, 2, 3], dtype=tf.int64),
'bar': tf.convert_to_tensor([0, 2, 0, 2], dtype=tf.int64),
}
# Annotate an arbitrary proto at the schema level (not sure what global
# schema boundaries would mean, but hey I'm just a test).
boundaries = tf.constant([[1.0]])
message_type = annotations_pb2.BucketBoundaries.DESCRIPTOR.full_name
sizes = tf.expand_dims([tf.size(boundaries)], axis=0)
message_proto = encode_proto_op.encode_proto(
sizes, [tf.cast(boundaries, tf.float32)], ['boundaries'],
message_type)[0]
type_url = os.path.join('type.googleapis.com', message_type)
schema_inference.annotate(type_url, message_proto)
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
outputs, graph, session)
self.assertLen(schema._schema_proto.annotation.extra_metadata,
1)
for annotation in schema._schema_proto.annotation.extra_metadata:
# Extract the annotated message and validate its contents
message = annotations_pb2.BucketBoundaries()
annotation.Unpack(message)
self.assertAllClose(list(message.boundaries), [1])
def test_infer_feature_schema_with_ragged_tensor(self):
with tf.compat.v1.Graph().as_default() as graph:
outputs = {
'foo':
tf.RaggedTensor.from_row_splits(values=tf.constant(
[3, 1, 4, 1, 5, 9, 2, 6], tf.int64),
row_splits=[0, 4, 4, 7, 8, 8]),
}
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
outputs, graph, session)
expected_schema_ascii = """feature {
name: "foo"
type: INT
annotation {
tag: "ragged_tensor"
}
}
"""
expected_schema = text_format.Parse(expected_schema_ascii,
schema_pb2.Schema())
schema_utils_legacy.set_generate_legacy_feature_spec(
expected_schema, False)
self.assertProtoEquals(expected_schema, schema)
with self.assertRaisesRegexp(
ValueError, 'Feature "foo" had tag "ragged_tensor"'):
schema_utils.schema_as_feature_spec(schema)
def testInferFeatureSchemaWithSession(self):
with tf.Graph().as_default() as graph:
tensors = {
'a': tf.placeholder(tf.float32, (None, )),
'b': tf.placeholder(tf.string, (1, 2, 3)),
'c': tf.placeholder(tf.int64, (None, ))
}
schema_inference.set_tensor_schema_override(
tensors['c'], tf.constant(5), tf.constant(6))
with tf.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
tensors, graph, session)
expected_schema = dataset_schema.Schema(
column_schemas={
'a':
dataset_schema.ColumnSchema(
tf.float32, [],
dataset_schema.FixedColumnRepresentation()),
'b':
dataset_schema.ColumnSchema(
tf.string, [2, 3],
dataset_schema.FixedColumnRepresentation()),
'c':
dataset_schema.ColumnSchema(
dataset_schema.IntDomain(
tf.int64, 5, 6, is_categorical=True), [],
dataset_schema.FixedColumnRepresentation())
})
self.assertEqual(schema, expected_schema)
def test_global_annotation(self):
# pylint: enable=g-import-not-at-top
with tf.compat.v1.Graph().as_default() as graph:
outputs = {
'foo': tf.convert_to_tensor([0, 1, 2, 3], dtype=tf.int64),
'bar': tf.convert_to_tensor([0, 2, 0, 2], dtype=tf.int64),
}
# Annotate an arbitrary proto at the schema level (not sure what global
# schema boundaries would mean, but hey I'm just a test).
boundaries = tf.constant([[1.0]])
message_type = annotations_pb2.BucketBoundaries.DESCRIPTOR.full_name
sizes = tf.expand_dims([tf.size(boundaries)], axis=0)
message_proto = tf.raw_ops.EncodeProto(
sizes=sizes,
values=[tf.cast(boundaries, tf.float32)],
field_names=['boundaries'],
message_type=message_type)[0]
type_url = os.path.join('type.googleapis.com', message_type)
schema_inference.annotate(type_url, message_proto)
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
outputs, graph, session)
self.assertLen(schema.annotation.extra_metadata, 1)
for annotation in schema.annotation.extra_metadata:
# Extract the annotated message and validate its contents
message = annotations_pb2.BucketBoundaries()
annotation.Unpack(message)
self.assertAllClose(list(message.boundaries), [1])
def analyze_in_place(preprocessing_fn, force_tf_compat_v1, feature_specs,
type_specs, transform_output_path):
"""Analyzes the `preprocessing_fn` in-place without looking at the data.
This should only be used if the `preprocessing_fn` contains no TFT
analyzers or TFT mappers that use analyzers.
Writes out a transform function and transformed metadata to subdirs under
`transform_output_path`.
Args:
preprocessing_fn: The tf.Transform preprocessing_fn.
force_tf_compat_v1: If True, call Transform's API to use Tensorflow in
tf.compat.v1 mode.
feature_specs: a Dict from input feature key to its feature spec.
type_specs: a Dict from input feature key to its type spec.
transform_output_path: An absolute path to write the output to.
Raises:
RuntimeError if `preprocessing_fn` contains TFT analyzers.
"""
use_tf_compat_v1 = tf2_utils.use_tf_compat_v1(force_tf_compat_v1)
transform_fn_path = os.path.join(transform_output_path,
TFTransformOutput.TRANSFORM_FN_DIR)
if use_tf_compat_v1:
graph, structured_inputs, structured_outputs = (
trace_preprocessing_function(preprocessing_fn,
feature_specs,
use_tf_compat_v1=use_tf_compat_v1))
_assert_no_analyzers_in_graph(graph)
with tf.compat.v1.Session(graph=graph) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
saved_transform_io.write_saved_transform_from_session(
sess, structured_inputs, structured_outputs, transform_fn_path)
transformed_metadata = dataset_metadata.DatasetMetadata(
schema=schema_inference.infer_feature_schema(
structured_outputs, graph, sess))
else:
concrete_transform_fn = _trace_and_write_transform_fn(
saved_model_dir=transform_fn_path,
preprocessing_fn=preprocessing_fn,
input_signature=type_specs,
base_temp_dir=None,
tensor_replacement_map=None,
output_keys_to_name_map=None)
_assert_no_analyzers_in_graph(concrete_transform_fn.graph)
structured_inputs = tf2_utils.get_structured_inputs_from_func_graph(
concrete_transform_fn.graph)
transformed_metadata = _trace_and_get_metadata(
concrete_transform_fn=concrete_transform_fn,
structured_inputs=structured_inputs,
preprocessing_fn=preprocessing_fn,
base_temp_dir=None,
tensor_replacement_map=None)
transformed_metadata_dir = os.path.join(
transform_output_path, TFTransformOutput.TRANSFORMED_METADATA_DIR)
metadata_io.write_metadata(transformed_metadata, transformed_metadata_dir)
def _infer_metadata_from_saved_model(saved_model_dir):
"""Infers a DatasetMetadata for outputs of a SavedModel."""
with tf.Graph().as_default() as graph:
with tf.Session(graph=graph) as session:
_, outputs = (
saved_transform_io.partially_apply_saved_transform_internal(
saved_model_dir, {}))
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
return dataset_metadata.DatasetMetadata(
schema=schema_inference.infer_feature_schema(outputs, graph, session))
def test_bucketization_annotation(self):
# TODO(b/132098015): Schema annotations aren't yet supported in OSS builds.
# pylint: disable=g-import-not-at-top
try:
from tensorflow_transform import annotations_pb2
except ImportError:
return
# pylint: enable=g-import-not-at-top
with tf.Graph().as_default() as graph:
inputs = {
'foo': tf.convert_to_tensor([0, 1, 2, 3]),
'bar': tf.convert_to_tensor([0, 2, 0, 2]),
}
boundaries_foo = tf.expand_dims(tf.convert_to_tensor([.5, 1.5]),
axis=0)
boundaries_bar = tf.expand_dims(tf.convert_to_tensor([.1, .2]),
axis=0)
outputs = {}
# tft.apply_buckets will annotate the feature in the output schema to
# indicate the bucket boundaries that were applied.
outputs['Bucketized_foo'] = mappers.apply_buckets(
inputs['foo'], boundaries_foo)
outputs['Bucketized_bar'] = mappers.apply_buckets(
inputs['bar'], boundaries_bar)
# Create a session to actually evaluate the annotations and extract the
# the output schema with annotations applied.
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
outputs, graph, session)
self.assertLen(schema.feature, 2)
for feature in schema.feature:
self.assertLen(feature.annotation.extra_metadata, 1)
for annotation in feature.annotation.extra_metadata:
# Extract the annotated message and validate its contents
message = annotations_pb2.BucketBoundaries()
annotation.Unpack(message)
if feature.name == 'Bucketized_foo':
self.assertAllClose(list(message.boundaries),
[.5, 1.5])
elif feature.name == 'Bucketized_bar':
self.assertAllClose(list(message.boundaries),
[.1, .2])
else:
raise RuntimeError('Unexpected features in schema')
def test_vocab_annotation(self):
with tf.compat.v1.Graph().as_default() as graph:
tensors = {
'foo': tf.convert_to_tensor([0, 1, 2, 3], dtype=tf.int64),
}
analyzers._maybe_annotate_vocab_metadata(
'file1', tf.constant(100, dtype=tf.int64))
analyzers._maybe_annotate_vocab_metadata(
'file2', tf.constant(200, dtype=tf.int64))
# Create a session to actually evaluate the annotations and extract the
# the output schema with annotations applied.
with tf.compat.v1.Session(graph=graph) as session:
schema = schema_inference.infer_feature_schema(
tensors, graph, session)
self.assertLen(schema.annotation.extra_metadata, 2)
sizes = {}
for annotation in schema.annotation.extra_metadata:
message = annotations_pb2.VocabularyMetadata()
annotation.Unpack(message)
sizes[
message.file_name] = message.unfiltered_vocabulary_size
self.assertDictEqual(sizes, {'file1': 100, 'file2': 200})
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
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, dataset_cache_dict,
input_metadata) = dataset
if self._use_tfxio:
input_schema = None
input_tensor_adapter_config = input_metadata
else:
input_schema = input_metadata.schema
input_tensor_adapter_config = None
input_values_pcoll_dict = input_values_pcoll_dict or dict()
with tf.compat.v1.Graph().as_default() as graph:
with tf.compat.v1.name_scope('inputs'):
if self._use_tfxio:
specs = TensorAdapter(input_tensor_adapter_config).OriginalTypeSpecs()
else:
specs = schema_utils.schema_as_feature_spec(input_schema).feature_spec
input_signature = impl_helper.batched_placeholders_from_specs(specs)
# 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.compat.v1.GraphKeys.TRAINABLE_VARIABLES):
raise ValueError(
'The preprocessing function contained trainable variables '
'{}'.format(
graph.get_collection_ref(
tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES)))
pipeline = self.pipeline or (flattened_pcoll or next(
v for v in input_values_pcoll_dict.values() if v is not None)).pipeline
# Add a stage that inspects graph collections for API use counts and logs
# them as a beam metric.
_ = (pipeline | 'InstrumentAPI' >> _InstrumentAPI(graph))
tf_config = _DEFAULT_TENSORFLOW_CONFIG_BY_BEAM_RUNNER_TYPE.get(
type(pipeline.runner))
extra_args = beam_common.ConstructBeamPipelineVisitor.ExtraArgs(
base_temp_dir=Context.create_base_temp_dir(),
tf_config=tf_config,
pipeline=pipeline,
flat_pcollection=flattened_pcoll,
pcollection_dict=input_values_pcoll_dict,
graph=graph,
input_signature=input_signature,
input_schema=input_schema,
input_tensor_adapter_config=input_tensor_adapter_config,
use_tfxio=self._use_tfxio,
cache_pcoll_dict=dataset_cache_dict)
transform_fn_future, cache_value_nodes = analysis_graph_builder.build(
graph,
input_signature,
output_signature,
input_values_pcoll_dict.keys(),
cache_dict=dataset_cache_dict)
traverser = nodes.Traverser(
beam_common.ConstructBeamPipelineVisitor(extra_args))
transform_fn_pcoll = traverser.visit_value_node(transform_fn_future)
if cache_value_nodes is not None:
output_cache_pcoll_dict = {}
for (dataset_key,
cache_key), value_node in six.iteritems(cache_value_nodes):
if dataset_key not in output_cache_pcoll_dict:
output_cache_pcoll_dict[dataset_key] = {}
output_cache_pcoll_dict[dataset_key][cache_key] = (
traverser.visit_value_node(value_node))
else:
output_cache_pcoll_dict = None
# 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. _infer_metadata_from_saved_model 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), output_cache_pcoll_dict
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.
"""
input_values, input_metadata = dataset
input_schema = input_metadata.schema
base_temp_dir = Context.create_base_temp_dir()
with tf.Graph().as_default() as graph:
with tf.name_scope('inputs'):
feature_spec = input_schema.as_feature_spec()
inputs = 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.
outputs = self._preprocessing_fn(impl_helper.copy_tensors(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 outputs:
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)))
# NOTE: it's important that create_phases is called directly after
# preprocessing_fn, because we later mutate the graph's TABLE_INITIALIZERS
# collection which would break the logic in create_phases.
phases = impl_helper.create_phases(inputs)
# Iterate through levels. tensor_pcoll_mapping is a mapping from tensor
# names to singleton PCollections containing a _TensorValue. We compute
# tensor_pcoll_mapping in phases, where at each phase we compute the
# analyzers that are ready to run and update tensor_pcoll_mapping.
tensor_pcoll_mapping = {}
table_initializers = graph.get_collection_ref(
tf.GraphKeys.TABLE_INITIALIZERS)
original_table_initializers = list(table_initializers)
del table_initializers[:]
serialized_tf_config = (
common._DEFAULT_TENSORFLOW_CONFIG_BY_RUNNER.get( # pylint: disable=protected-access
input_values.pipeline.runner))
for level, phase in enumerate(phases):
# Create a SavedModel that describes the mapping from the input data
# to the inputs of the analyzers at this level. The colum names of the
# outputs are the tensor names of the analyzer inputs in the graph.
# This graph has the anaylzer outputs computed so far replaced with
# constants.
analyzer_inputs = {}
for analyzer in phase.analyzer_infos:
for input_tensor_name in analyzer.input_tensor_names:
analyzer_inputs[
input_tensor_name] = graph.get_tensor_by_name(
input_tensor_name)
table_initializers.extend(phase.table_initializers)
unbound_saved_model_dir = common.make_unique_temp_dir(
base_temp_dir)
_write_saved_transform(graph, inputs, analyzer_inputs,
unbound_saved_model_dir)
tensor_pcoll_mapping_update = (
(input_values, tensor_pcoll_mapping)
| 'RunPhase[{}]'.format(level) >> _RunPhase(
phase.analyzer_infos, unbound_saved_model_dir,
base_temp_dir, input_schema, serialized_tf_config,
level))
# Update the mapping for all analyzers.
tensor_pcoll_mapping.update(tensor_pcoll_mapping_update)
del table_initializers[:]
table_initializers.extend(original_table_initializers)
saved_model_dir = common.make_unique_temp_dir(base_temp_dir)
_write_saved_transform(graph, inputs, outputs, saved_model_dir)
transform_fn = (
tensor_pcoll_mapping
|
'ReplaceTensorsWithConstants' >> _ReplaceTensorsWithConstants(
saved_model_dir, base_temp_dir, input_values.pipeline))
# 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(outputs, graph))
deferred_metadata = (transform_fn
| 'ComputeDeferredMetadata' >>
beam.Map(_infer_metadata_from_saved_model))
full_metadata = beam_metadata_io.BeamDatasetMetadata(
metadata, deferred_metadata)
_clear_shared_state_after_barrier(input_values.pipeline,
transform_fn)
return transform_fn, full_metadata
