def example_protos_from_path(cns_path,
num_examples=10,
start_index=0,
parse_examples=True):
"""Returns a number of tf.train.Examples from the CNS path.
Args:
cns_path: A string CNS path.
num_examples: The maximum number of examples to return from the path.
start_index: The index of the first example to return.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
Returns:
A list of Example protos or serialized proto strings at the CNS path.
Raises:
InvalidUserInputError: If examples cannot be procured from cns_path.
"""
def append_examples_from_iterable(iterable, examples):
for i, value in enumerate(iterable):
if i >= start_index:
examples.append(
tf.train.Example.FromString(value
) if parse_examples else value)
if len(examples) >= num_examples:
return
filenames = filepath_to_filepath_list(cns_path)
examples = []
compression_types = [
tf.python_io.TFRecordCompressionType.NONE,
tf.python_io.TFRecordCompressionType.GZIP,
tf.python_io.TFRecordCompressionType.ZLIB,
]
current_compression_idx = 0
current_file_index = 0
while (current_file_index < len(filenames)
and current_compression_idx < len(compression_types)):
try:
record_iterator = tf.python_io.tf_record_iterator(
path=filenames[current_file_index],
options=tf.python_io.TFRecordOptions(
compression_types[current_compression_idx]))
append_examples_from_iterable(record_iterator, examples)
current_file_index += 1
if len(examples) >= num_examples:
break
except tf.errors.DataLossError:
current_compression_idx += 1
except (IOError, tf.errors.NotFoundError) as e:
raise common_utils.InvalidUserInputError(e)
if examples:
return examples
else:
raise common_utils.InvalidUserInputError(
'No tf.train.Examples found at ' + cns_path +
'. Valid formats are TFRecord files.')
def convert_pattern_to_indices(pattern):
"""Converts a printer-page-style pattern and returns a list of indices.
Args:
pattern: A printer-page-style pattern with only numeric characters,
commas, dashes, and optionally spaces.
For example, a pattern of '0,2,4-6' would yield [0, 2, 4, 5, 6].
Returns:
A list of indices represented by the pattern.
"""
pieces = [token.strip() for token in pattern.split(',')]
indices = []
for piece in pieces:
try:
if '-' in piece:
lower, upper = [
int(x.strip()) for x in piece.split('-')
]
indices.extend(range(lower, upper + 1))
else:
indices.append(int(piece.strip()))
except ValueError as e:
raise common_utils.InvalidUserInputError(e)
return sorted(indices)
def mutant_charts_for_feature(example_protos, feature_name, serving_bundles,
viz_params):
"""Returns JSON formatted for rendering all charts for a feature.
Args:
example_proto: The example protos to mutate.
feature_name: The string feature name to mutate.
serving_bundles: One `ServingBundle` object per model, that contains the
information to make the serving request.
viz_params: A `VizParams` object that contains the UI state of the request.
Raises:
InvalidUserInputError if `viz_params.feature_index_pattern` requests out of
range indices for `feature_name` within `example_proto`.
Returns:
A JSON-able dict for rendering a single mutant chart. parsed in
`tf-inference-dashboard.html`.
{
'chartType': 'numeric', # oneof('numeric', 'categorical')
'data': [A list of data] # parseable by vz-line-chart or vz-bar-chart
}
"""
def chart_for_index(index_to_mutate):
mutant_features, mutant_examples = make_mutant_tuples(
example_protos, original_feature, index_to_mutate, viz_params)
charts = []
for serving_bundle in serving_bundles:
(inference_result_proto, _) = run_inference(
mutant_examples, serving_bundle)
charts.append(make_json_formatted_for_single_chart(
mutant_features, inference_result_proto, index_to_mutate))
return charts
try:
original_feature = parse_original_feature_from_example(
example_protos[0], feature_name)
except ValueError as e:
return {
'chartType': 'categorical',
'data': []
}
indices_to_mutate = viz_params.feature_indices or range(
original_feature.length)
chart_type = ('categorical' if original_feature.feature_type == 'bytes_list'
else 'numeric')
try:
return {
'chartType': chart_type,
'data': [
chart_for_index(index_to_mutate)
for index_to_mutate in indices_to_mutate
]
}
except IndexError as e:
raise common_utils.InvalidUserInputError(e)
def example_protos_from_path(cns_path,
num_examples=10,
start_index=0,
parse_examples=True):
"""Returns a number of tf.train.Examples from the CNS path.
Args:
cns_path: A string CNS path.
num_examples: The maximum number of examples to return from the path.
start_index: The index of the first example to return.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
Returns:
A list of Example protos or serialized proto strings at the CNS path.
Raises:
InvalidUserInputError: If examples cannot be procured from cns_path.
"""
def append_examples_from_iterable(iterable, examples):
for i, value in enumerate(iterable):
if i >= start_index:
examples.append(
tf.train.Example.FromString(value) if parse_examples else value)
if len(examples) >= num_examples:
return
filenames = filepath_to_filepath_list(cns_path)
examples = []
try:
for filename in filenames:
record_iterator = tf.python_io.tf_record_iterator(path=filename)
append_examples_from_iterable(record_iterator, examples)
if len(examples) >= num_examples:
break
except (IOError, tf.errors.NotFoundError) as e:
raise common_utils.InvalidUserInputError(e)
if examples:
return examples
else:
raise common_utils.InvalidUserInputError(
'No tf.train.Examples found at ' + cns_path +
'. Valid formats are SSTable and RecordIO.')
def _parse_request_arguments(self, request):
"""Parses comma separated request arguments
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_version', 'model_signature'.
Returns:
A tuple of lists for model parameters
"""
inference_addresses = request.args.get('inference_address').split(',')
model_names = request.args.get('model_name').split(',')
model_versions = request.args.get('model_version').split(',')
model_signatures = request.args.get('model_signature').split(',')
if len(model_names) != len(inference_addresses):
raise common_utils.InvalidUserInputError('Every model should have a ' +
'name and address.')
return inference_addresses, model_names, model_versions, model_signatures
def example_protos_from_path(path,
num_examples=10,
start_index=0,
parse_examples=True,
sampling_odds=1,
example_class=tf.train.Example):
"""Returns a number of examples from the provided path.
Args:
path: A string path to the examples.
num_examples: The maximum number of examples to return from the path.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
sampling_odds: Odds of loading an example, used for sampling. When >= 1
(the default), then all examples are loaded.
example_class: tf.train.Example or tf.train.SequenceExample class to load.
Defaults to tf.train.Example.
Returns:
A list of Example protos or serialized proto strings at the path.
Raises:
InvalidUserInputError: If examples cannot be procured from the path.
"""
def append_examples_from_iterable(iterable, examples):
for value in iterable:
if sampling_odds >= 1 or random.random() < sampling_odds:
examples.append(
example_class.FromString(value
) if parse_examples else value)
if len(examples) >= num_examples:
return
examples = []
if path.endswith('.csv'):
def are_floats(values):
for value in values:
try:
float(value)
except ValueError:
return False
return True
csv.register_dialect('CsvDialect', skipinitialspace=True)
rows = csv.DictReader(open(path), dialect='CsvDialect')
for row in rows:
if sampling_odds < 1 and random.random() > sampling_odds:
continue
example = tf.train.Example()
for col in row.keys():
# Parse out individual values from vertical-bar-delimited lists
values = [val.strip() for val in row[col].split('|')]
if are_floats(values):
example.features.feature[col].float_list.value.extend(
[float(val) for val in values])
else:
example.features.feature[col].bytes_list.value.extend(
[val.encode('utf-8') for val in values])
examples.append(
example if parse_examples else example.SerializeToString())
if len(examples) >= num_examples:
break
return examples
filenames = filepath_to_filepath_list(path)
compression_types = [
'', # no compression (distinct from `None`!)
'GZIP',
'ZLIB',
]
current_compression_idx = 0
current_file_index = 0
while (current_file_index < len(filenames)
and current_compression_idx < len(compression_types)):
try:
record_iterator = tf.compat.v1.python_io.tf_record_iterator(
path=filenames[current_file_index],
options=tf.io.TFRecordOptions(
compression_types[current_compression_idx]))
append_examples_from_iterable(record_iterator, examples)
current_file_index += 1
if len(examples) >= num_examples:
break
except tf.errors.DataLossError:
current_compression_idx += 1
except (IOError, tf.errors.NotFoundError) as e:
raise common_utils.InvalidUserInputError(e)
if examples:
return examples
else:
raise common_utils.InvalidUserInputError(
'No examples found at ' + path +
'. Valid formats are TFRecord files.')
def to_int(x):
try:
return int(x)
except (ValueError, TypeError) as e:
raise common_utils.InvalidUserInputError(e)
def example_protos_from_path(path,
num_examples=10,
start_index=0,
parse_examples=True,
sampling_odds=1,
example_class=tf.train.Example):
"""Returns a number of examples from the provided path.
Args:
path: A string path to the examples.
num_examples: The maximum number of examples to return from the path.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
sampling_odds: Odds of loading an example, used for sampling. When >= 1
(the default), then all examples are loaded.
example_class: tf.train.Example or tf.train.SequenceExample class to load.
Defaults to tf.train.Example.
Returns:
A list of Example protos or serialized proto strings at the path.
Raises:
InvalidUserInputError: If examples cannot be procured from the path.
"""
def append_examples_from_iterable(iterable, examples):
for value in iterable:
if sampling_odds >= 1 or random.random() < sampling_odds:
examples.append(
example_class.FromString(value
) if parse_examples else value)
if len(examples) >= num_examples:
return
filenames = filepath_to_filepath_list(path)
examples = []
compression_types = [
tf.python_io.TFRecordCompressionType.NONE,
tf.python_io.TFRecordCompressionType.GZIP,
tf.python_io.TFRecordCompressionType.ZLIB,
]
current_compression_idx = 0
current_file_index = 0
while (current_file_index < len(filenames)
and current_compression_idx < len(compression_types)):
try:
record_iterator = tf.python_io.tf_record_iterator(
path=filenames[current_file_index],
options=tf.python_io.TFRecordOptions(
compression_types[current_compression_idx]))
append_examples_from_iterable(record_iterator, examples)
current_file_index += 1
if len(examples) >= num_examples:
break
except tf.errors.DataLossError:
current_compression_idx += 1
except (IOError, tf.errors.NotFoundError) as e:
raise common_utils.InvalidUserInputError(e)
if examples:
return examples
else:
raise common_utils.InvalidUserInputError(
'No examples found at ' + path +
'. Valid formats are TFRecord files.')
