def main(argv):
beam_utils.BeamInit()
assert FLAGS.input_file_pattern
assert FLAGS.output_filebase
# Construct pipeline options from argv.
options = beam.options.pipeline_options.PipelineOptions(argv[1:])
reader = beam_utils.GetReader('tfrecord',
FLAGS.input_file_pattern,
value_coder=beam.coders.ProtoCoder(
dataset_pb2.Frame))
writer = beam_utils.GetWriter('tfrecord',
file_pattern=FLAGS.output_filebase,
value_coder=beam.coders.ProtoCoder(
tf.train.Example))
emitter_fn = beam_utils.GetEmitterFn('tfrecord')
with beam_utils.GetPipelineRoot(options=options) as root:
_ = (root
| 'Read' >> reader
| 'ConvertToTFExample' >> beam.ParDo(
waymo_proto_to_tfe.WaymoOpenDatasetConverter(emitter_fn))
| 'Write' >> writer)
def main(argv):
beam_utils.BeamInit()
# Construct pipeline options from argv.
options = beam.options.pipeline_options.PipelineOptions(argv[1:])
reader = beam_utils.GetReader(FLAGS.record_format,
FLAGS.input_file_pattern,
value_coder=beam.coders.BytesCoder())
with beam_utils.GetPipelineRoot(options=options) as root:
_ = (
root
| 'Read' >> reader # Read each record.
| 'EmitOne' >> beam.Map(lambda _: 1) # Emit a 1 for each record.
| 'Count' >> beam.CombineGlobally(sum) # Sum counts.
| 'WriteToText' >> beam.io.WriteToText(FLAGS.output_count_file))
def main(_):
beam_utils.BeamInit()
if not FLAGS.output_file_pattern:
raise ValueError('Must provide an output_file_pattern')
reader = beam.io.ReadFromTFRecord(FLAGS.input_file_pattern,
coder=beam.coders.ProtoCoder(
tf.train.Example))
model_name = FLAGS.model_name
split = FLAGS.split
run_preprocessors = FLAGS.run_preprocessors
with beam_utils.GetPipelineRoot() as root:
_ = (root
| 'Read' >> reader
| 'ToTFExample' >> beam.ParDo(
_ProcessShard(model_name, split, run_preprocessors))
| 'Reshuffle' >> beam.Reshuffle()
| 'Write' >> beam.io.WriteToTFRecord(FLAGS.output_file_pattern,
coder=beam.coders.ProtoCoder(
tf.train.Example)))