def test_decode_example_empty_feature(self):
example_proto_text = """
features {
feature { key: "int_feature" value { int64_list { value: [ 0 ] } } }
feature { key: "int_feature_empty" value { } }
feature { key: "float_feature" value { float_list { value: [ 4.0 ] } } }
feature { key: "float_feature_empty" value { } }
feature { key: "str_feature" value { bytes_list { value: [ 'male' ] } } }
feature { key: "str_feature_empty" value { } }
}
"""
expected_decoded = {
'int_feature': np.array([0], dtype=np.integer),
'int_feature_empty': np.array([], dtype=np.object),
'float_feature': np.array([4.0], dtype=np.floating),
'float_feature_empty': np.array([], dtype=np.object),
'str_feature': np.array([b'male'], dtype=np.object),
'str_feature_empty': np.array([], dtype=np.object),
}
example = tf.train.Example()
text_format.Merge(example_proto_text, example)
decoder = tf_example_decoder.TFExampleDecoder()
self._check_decoding_results(
decoder.decode(example.SerializeToString()), expected_decoded)
def generate_statistics_from_tfrecord(
data_location,
output_path=None,
stats_options=options.StatsOptions(),
pipeline_options=None,
):
"""Compute data statistics from TFRecord files containing TFExamples.
Runs a Beam pipeline to compute the data statistics and return the result
data statistics proto.
This is a convenience method for users with data in TFRecord format.
Users with data in unsupported file/data formats, or users who wish
to create their own Beam pipelines need to use the 'GenerateStatistics'
PTransform API directly instead.
Args:
data_location: The location of the input data files.
output_path: The file path to output data statistics result to. If None, we
use a temporary directory. It will be a TFRecord file containing a single
data statistics proto, and can be read with the 'load_statistics' API.
stats_options: Options for generating data statistics.
pipeline_options: Optional beam pipeline options. This allows users to
specify various beam pipeline execution parameters like pipeline runner
(DirectRunner or DataflowRunner), cloud dataflow service project id, etc.
See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for
more details.
Returns:
A DatasetFeatureStatisticsList proto.
"""
if output_path is None:
output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
output_dir_path = os.path.dirname(output_path)
if not tf.gfile.Exists(output_dir_path):
tf.gfile.MakeDirs(output_dir_path)
# PyLint doesn't understand Beam PTransforms.
# pylint: disable=no-value-for-parameter
with beam.Pipeline(options=pipeline_options) as p:
# Auto detect tfrecord file compression format based on input data
# path suffix.
_ = (
p
|
'ReadData' >> beam.io.ReadFromTFRecord(file_pattern=data_location)
| 'DecodeData' >> beam.Map(
tf_example_decoder.TFExampleDecoder().decode)
|
'GenerateStatistics' >> stats_api.GenerateStatistics(stats_options)
| 'WriteStatsOutput' >> beam.io.WriteToTFRecord(
output_path,
shard_name_template='',
coder=beam.coders.ProtoCoder(
statistics_pb2.DatasetFeatureStatisticsList)))
return load_statistics(output_path)
def test_decode_example_none_ref_count(self):
example = text_format.Parse(
'''
features {
feature {
key: 'x'
value { }
}
}
''', tf.train.Example())
before_refcount = sys.getrefcount(None)
_ = tf_example_decoder.TFExampleDecoder().decode(
example.SerializeToString())
after_refcount = sys.getrefcount(None)
self.assertEqual(before_refcount + 1, after_refcount)
def test_decode_example(self, example_proto_text, decoded_example):
example = tf.train.Example()
text_format.Merge(example_proto_text, example)
decoder = tf_example_decoder.TFExampleDecoder()
self._check_decoding_results(
decoder.decode(example.SerializeToString()), decoded_example)
def test_decode_example_empty_input(self):
example = tf.train.Example()
decoder = tf_example_decoder.TFExampleDecoder()
self._check_decoding_results(
decoder.decode(example.SerializeToString()), {})