def make_sequence_example_parse_fn(
feature_config: FeatureConfig,
preprocessing_map: PreprocessingMap,
max_sequence_size: int = 25,
required_fields_only: bool = False,
pad_sequence: bool = True,
) -> tf.function:
"""
Create a parse function using the SequenceExample features spec
Parameters
----------
feature_config : `FeatureConfig`
FeatureConfig object defining context and sequence feature information
preprocessing_map : int
map of preprocessing feature functions
max_sequence_size : int
Maximum number of sequence per query. Used for padding
required_fields_only : bool, optional
Whether to only use required fields from the feature_config
pad_sequence : bool
Whether to pad sequence
Returns
-------
`tf.function`
Parsing function that takes in a serialized SequenceExample message
and extracts a feature dictionary for context and sequence features
"""
context_features_spec = dict()
sequence_features_spec = dict()
for feature_info in feature_config.get_all_features():
serving_info = feature_info["serving_info"]
if not required_fields_only or serving_info.get("required", feature_info["trainable"]):
feature_name = feature_info["name"]
dtype = feature_info["dtype"]
default_value = feature_config.get_default_value(feature_info)
if feature_info["tfrecord_type"] == SequenceExampleTypeKey.CONTEXT:
context_features_spec[feature_name] = io.FixedLenFeature(
[], dtype, default_value=default_value
)
elif feature_info["tfrecord_type"] == SequenceExampleTypeKey.SEQUENCE:
sequence_features_spec[feature_name] = io.VarLenFeature(dtype=dtype)
@tf.function
def _parse_sequence_example_fn(sequence_example_proto):
"""
Parse the input `tf.SequenceExample` proto using the features_spec
Parameters
----------
sequence_example_proto : string
serialized tfrecord SequenceExample protobuf message
Returns
-------
features : dict
parsed features as `tf.Tensor` objects extracted from the protobuf
labels : `tf.Tensor`
parsed label as a `tf.Tensor` object extracted from the protobuf
"""
context_features, sequence_features = io.parse_single_sequence_example(
serialized=sequence_example_proto,
context_features=context_features_spec,
sequence_features=sequence_features_spec,
)
features_dict = dict()
# Handle context features
for feature_info in feature_config.get_context_features():
feature_node_name = feature_info.get("node_name", feature_info["name"])
default_tensor = tf.constant(
value=feature_config.get_default_value(feature_info), dtype=feature_info["dtype"],
)
feature_tensor = context_features.get(feature_info["name"], default_tensor)
feature_tensor = tf.expand_dims(feature_tensor, axis=0)
# Preprocess features
feature_tensor = preprocess_feature(feature_tensor, feature_info, preprocessing_map)
features_dict[feature_node_name] = feature_tensor
# Define mask to identify padded sequence
if required_fields_only and not feature_config.get_rank("serving_info")["required"]:
"""
Define dummy mask if the rank field is not a required field for serving
NOTE:
This masks all max_sequence_size as 1 as there is no real way to know
the number of sequence in the query. There is no predefined required field,
and hence we would need to do a full pass of all features to find the record shape.
This approach might be unstable if different features have different shapes.
Hence we just mask all sequence
"""
features_dict["mask"] = tf.constant(
value=1, shape=[max_sequence_size], dtype=feature_config.get_rank("dtype")
)
sequence_size = tf.constant(max_sequence_size, dtype=tf.int64)
else:
# Typically used at training time, to pad/clip to a fixed number of sequence per query
# Use rank as a reference tensor to infer shape/sequence_size in query
reference_tensor = sequence_features.get(feature_config.get_rank(key="node_name"))
# Add mask for identifying padded sequence
mask = tf.ones_like(sparse.to_dense(sparse.reset_shape(reference_tensor)))
sequence_size = tf.cast(tf.reduce_sum(mask), tf.int64)
if pad_sequence:
mask = tf.expand_dims(mask, axis=-1)
def crop_fn():
tf.print("\n[WARN] Bad query found. Number of sequence : ", tf.shape(mask)[1])
return image.crop_to_bounding_box(
mask,
offset_height=0,
offset_width=0,
target_height=1,
target_width=max_sequence_size,
)
mask = tf.cond(
tf.shape(mask)[1] <= max_sequence_size,
# Pad if there are missing sequence
lambda: image.pad_to_bounding_box(
mask,
offset_height=0,
offset_width=0,
target_height=1,
target_width=max_sequence_size,
),
# Crop if there are extra sequence
crop_fn,
)
mask = tf.squeeze(mask)
else:
mask = tf.squeeze(mask, axis=0)
# Check validity of mask
tf.debugging.assert_greater(sequence_size, tf.constant(0, dtype=tf.int64))
features_dict["mask"] = mask
sequence_size = max_sequence_size if pad_sequence else sequence_size
# Pad sequence features to max_sequence_size
for feature_info in feature_config.get_sequence_features():
feature_node_name = feature_info.get("node_name", feature_info["name"])
default_tensor = tf.fill(
value=tf.constant(
value=feature_config.get_default_value(feature_info),
dtype=feature_info["dtype"],
),
dims=[max_sequence_size if pad_sequence else sequence_size],
)
feature_tensor = sequence_features.get(feature_info["name"], default_tensor)
if isinstance(feature_tensor, sparse.SparseTensor):
feature_tensor = sparse.reset_shape(
feature_tensor,
new_shape=[1, max_sequence_size if pad_sequence else sequence_size],
)
feature_tensor = sparse.to_dense(feature_tensor)
feature_tensor = tf.squeeze(feature_tensor, axis=0)
# Preprocess features
feature_tensor = preprocess_feature(feature_tensor, feature_info, preprocessing_map)
features_dict[feature_node_name] = feature_tensor
labels = features_dict.pop(feature_config.get_label(key="name"))
return features_dict, labels
return _parse_sequence_example_fn
def make_example_parse_fn(
feature_config: FeatureConfig,
preprocessing_map: PreprocessingMap,
required_fields_only: bool = False,
) -> tf.function:
"""
Create a parse function using the Example features spec
Parameters
----------
feature_config : `FeatureConfig`
FeatureConfig object defining context and sequence feature information
preprocessing_map : `PreprocessingMap` object
map of preprocessing feature functions
required_fields_only : bool, optional
Whether to only use required fields from the feature_config
Returns
-------
`tf.function`
Parsing function that takes in a serialized Example message and extracts a feature dictionary
"""
features_spec = dict()
for feature_info in feature_config.get_all_features():
serving_info = feature_info["serving_info"]
if not required_fields_only or serving_info.get("required", feature_info["trainable"]):
feature_name = feature_info["name"]
dtype = feature_info["dtype"]
default_value = feature_config.get_default_value(feature_info)
features_spec[feature_name] = io.FixedLenFeature(
[], dtype, default_value=default_value
)
print(features_spec)
@tf.function
def _parse_example_fn(example_proto):
"""
Parse the input `tf.Example` proto using the features_spec
Parameters
----------
example_proto : string
serialized tfrecord Example protobuf message
Returns
-------
features : dict
parsed features as `tf.Tensor` objects extracted from the protobuf
labels : `tf.Tensor`
parsed label as a `tf.Tensor` object extracted from the protobuf
"""
features = io.parse_single_example(serialized=example_proto, features=features_spec)
features_dict = dict()
# Process all features, including label.
for feature_info in feature_config.get_all_features():
feature_node_name = feature_info.get("node_name", feature_info["name"])
default_tensor = tf.constant(
value=feature_config.get_default_value(feature_info), dtype=feature_info["dtype"],
)
feature_tensor = features.get(feature_info["name"], default_tensor)
feature_tensor = tf.expand_dims(feature_tensor, axis=0)
feature_tensor = preprocess_feature(feature_tensor, feature_info, preprocessing_map)
features_dict[feature_node_name] = feature_tensor
labels = features_dict.pop(feature_config.get_label(key="name"))
return features_dict, labels
return _parse_example_fn
def make_example_parse_fn(
feature_config: FeatureConfig,
preprocessing_map: PreprocessingMap,
required_fields_only: bool = False,
) -> tf.function:
"""
Create a parse function using the Example features spec
Args:
feature_config: FeatureConfig object defining context and sequence features
max_sequence_size: Maximum number of sequence per query. Used for padding.
required_fields_only: Whether to only use required fields from the feature_config
pad_sequence: Whether to pad sequence
"""
features_spec = dict()
for feature_info in feature_config.get_all_features():
serving_info = feature_info["serving_info"]
if not required_fields_only or serving_info.get(
"required", feature_info["trainable"]):
feature_name = feature_info["name"]
dtype = feature_info["dtype"]
default_value = feature_config.get_default_value(feature_info)
features_spec[feature_name] = io.FixedLenFeature(
[], dtype, default_value=default_value)
print(features_spec)
@tf.function
def _parse_example_fn(example_proto):
"""
Parse the input `tf.Example` proto using the features_spec
Args:
example_proto: tfrecord Example protobuf data
Returns:
features: parsed features extracted from the protobuf
labels: parsed label extracted from the protobuf
"""
features = io.parse_single_example(serialized=example_proto,
features=features_spec)
features_dict = dict()
# Process all features, including label.
for feature_info in feature_config.get_all_features():
feature_node_name = feature_info.get("node_name",
feature_info["name"])
default_tensor = tf.constant(
value=feature_config.get_default_value(feature_info),
dtype=feature_info["dtype"],
)
feature_tensor = features.get(feature_info["name"], default_tensor)
feature_tensor = tf.expand_dims(feature_tensor, axis=0)
feature_tensor = preprocess_feature(feature_tensor, feature_info,
preprocessing_map)
features_dict[feature_node_name] = feature_tensor
labels = features_dict.pop(feature_config.get_label(key="name"))
return features_dict, labels
return _parse_example_fn