e61b7958e0576a723082f3dfa65cdb6ec3d53c0d,magenta/models/improv_rnn/improv_rnn_create_dataset.py,,get_pipeline,#Any#Any#,95

Before Change

    A pipeline.Pipeline instance.
  
  all_transpositions = config.transpose_to_key is None
  quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
  lead_sheet_extractor_train = lead_sheet_pipelines.LeadSheetExtractor(
      min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
      ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
      name="LeadSheetExtractorTrain")
  lead_sheet_extractor_eval = lead_sheet_pipelines.LeadSheetExtractor(
      min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
      ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
      name="LeadSheetExtractorEval")
  encoder_pipeline_train = EncoderPipeline(config, name="EncoderPipelineTrain")
  encoder_pipeline_eval = EncoderPipeline(config, name="EncoderPipelineEval")
  partitioner = pipelines_common.RandomPartition(
      music_pb2.NoteSequence,
      ["eval_lead_sheets", "training_lead_sheets"],
      [eval_ratio])

  dag = {quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
         partitioner: quantizer,
         lead_sheet_extractor_train: partitioner["training_lead_sheets"],
         lead_sheet_extractor_eval: partitioner["eval_lead_sheets"],
         encoder_pipeline_train: lead_sheet_extractor_train,
         encoder_pipeline_eval: lead_sheet_extractor_eval,
         dag_pipeline.DagOutput("training_lead_sheets"): encoder_pipeline_train,
         dag_pipeline.DagOutput("eval_lead_sheets"): encoder_pipeline_eval}
  return dag_pipeline.DAGPipeline(dag)


def main(unused_argv):

After Change

    A pipeline.Pipeline instance.
  
  all_transpositions = config.transpose_to_key is None
  partitioner = pipelines_common.RandomPartition(
      music_pb2.NoteSequence,
      ["eval_lead_sheets", "training_lead_sheets"],
      [eval_ratio])
  dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}

  for mode in ["eval", "training"]:
    time_change_splitter = pipelines_common.TimeChangeSplitter(
        name="TimeChangeSplitter_" + mode)
    quantizer = pipelines_common.Quantizer(
        steps_per_quarter=4, name="Quantizer_" + mode)
    lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
        min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
        ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
        name="LeadSheetExtractor_" + mode)
    encoder_pipeline = EncoderPipeline(config, name="EncoderPipeline_" + mode)

    dag[time_change_splitter] = partitioner[mode + "_lead_sheets"]
    dag[quantizer] = time_change_splitter
    dag[lead_sheet_extractor] = quantizer
    dag[encoder_pipeline] = lead_sheet_extractor
    dag[dag_pipeline.DagOutput(mode + "_lead_sheets")] = encoder_pipeline

  return dag_pipeline.DAGPipeline(dag)


def main(unused_argv):

Instances