def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) bundle = get_bundle() if bundle: config_id = bundle.generator_details.id config = drums_rnn_model.default_configs[config_id] config.hparams.parse(FLAGS.hparams) else: config = drums_rnn_config_flags.config_from_flags() # Having too large of a batch size will slow generation down unnecessarily. config.hparams.batch_size = min(config.hparams.batch_size, FLAGS.beam_size * FLAGS.branch_factor) generator = drums_rnn_sequence_generator.DrumsRnnSequenceGenerator( model=drums_rnn_model.DrumsRnnModel(config), details=config.details, steps_per_quarter=config.steps_per_quarter, checkpoint=get_checkpoint(), bundle=bundle) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)
def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) bundle = get_bundle() if bundle: config_id = bundle.generator_details.id config = drums_rnn_model.default_configs[config_id] config.hparams.parse(FLAGS.hparams) else: config = drums_rnn_config_flags.config_from_flags() # Having too large of a batch size will slow generation down unnecessarily. config.hparams.batch_size = min( config.hparams.batch_size, FLAGS.beam_size * FLAGS.branch_factor) generator = drums_rnn_sequence_generator.DrumsRnnSequenceGenerator( model=drums_rnn_model.DrumsRnnModel(config), details=config.details, steps_per_quarter=config.steps_per_quarter, checkpoint=get_checkpoint(), bundle=bundle) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) if not FLAGS.run_dir: tf.logging.fatal('--run_dir required') return if not FLAGS.sequence_example_file: tf.logging.fatal('--sequence_example_file required') return sequence_example_file = os.path.expanduser(FLAGS.sequence_example_file) run_dir = os.path.expanduser(FLAGS.run_dir) config = drums_rnn_config_flags.config_from_flags() mode = 'eval' if FLAGS.eval else 'train' graph = events_rnn_graph.build_graph( mode, config, sequence_example_file) train_dir = os.path.join(run_dir, 'train') if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info('Eval dir: %s', eval_dir) events_rnn_train.run_eval(graph, train_dir, eval_dir, FLAGS.num_training_steps, FLAGS.summary_frequency) else: events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps, FLAGS.summary_frequency)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() pipeline_instance = get_pipeline(config, FLAGS.eval_ratio) FLAGS.input = os.path.expanduser(FLAGS.input) FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() pipeline_instance = get_pipeline( config, FLAGS.eval_ratio) FLAGS.input = os.path.expanduser(FLAGS.input) FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) if not FLAGS.run_dir: tf.logging.fatal('--run_dir required') return if not FLAGS.sequence_example_file: tf.logging.fatal('--sequence_example_file required') return sequence_example_file_paths = tf.gfile.Glob( os.path.expanduser(FLAGS.sequence_example_file)) run_dir = os.path.expanduser(FLAGS.run_dir) config = drums_rnn_config_flags.config_from_flags() mode = 'eval' if FLAGS.eval else 'train' build_graph_fn = events_rnn_graph.get_build_graph_fn( mode, config, sequence_example_file_paths) train_dir = os.path.join(run_dir, 'train') if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info('Eval dir: %s', eval_dir) num_batches = ( (FLAGS.num_eval_examples or magenta.common.count_records(sequence_example_file_paths)) // config.hparams.batch_size) events_rnn_train.run_eval(build_graph_fn, train_dir, eval_dir, num_batches) else: events_rnn_train.run_training( build_graph_fn, train_dir, FLAGS.num_training_steps, FLAGS.summary_frequency, checkpoints_to_keep=FLAGS.num_checkpoints)
def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() generator = drums_rnn_sequence_generator.DrumsRnnSequenceGenerator( model=drums_rnn_model.DrumsRnnModel(config), details=config.details, steps_per_quarter=FLAGS.steps_per_quarter, checkpoint=get_checkpoint(), bundle=get_bundle()) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)
def main(unused_argv): """Saves bundle or runs generator based on flags.""" tf.logging.set_verbosity(FLAGS.log) config = drums_rnn_config_flags.config_from_flags() generator = drums_rnn_sequence_generator.DrumsRnnSequenceGenerator( model=drums_rnn_model.DrumsRnnModel(config), details=config.details, steps_per_quarter=config.steps_per_quarter, checkpoint=get_checkpoint(), bundle=get_bundle()) if FLAGS.save_generator_bundle: bundle_filename = os.path.expanduser(FLAGS.bundle_file) if FLAGS.bundle_description is None: tf.logging.warning('No bundle description provided.') tf.logging.info('Saving generator bundle to %s', bundle_filename) generator.create_bundle_file(bundle_filename, FLAGS.bundle_description) else: run_with_flags(generator)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) if not FLAGS.run_dir: tf.logging.fatal('--run_dir required') return if not FLAGS.sequence_example_file: tf.logging.fatal('--sequence_example_file required') return sequence_example_file_paths = tf.gfile.Glob( os.path.expanduser(FLAGS.sequence_example_file)) run_dir = os.path.expanduser(FLAGS.run_dir) config = drums_rnn_config_flags.config_from_flags() mode = 'eval' if FLAGS.eval else 'train' build_graph_fn = events_rnn_graph.get_build_graph_fn( mode, config, sequence_example_file_paths) train_dir = os.path.join(run_dir, 'train') if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info('Eval dir: %s', eval_dir) num_batches = ( (FLAGS.num_eval_examples if FLAGS.num_eval_examples else magenta.common.count_records(sequence_example_file_paths)) // config.hparams.batch_size) events_rnn_train.run_eval(build_graph_fn, train_dir, eval_dir, num_batches) else: events_rnn_train.run_training(build_graph_fn, train_dir, FLAGS.num_training_steps, FLAGS.summary_frequency, checkpoints_to_keep=FLAGS.num_checkpoints)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) if not FLAGS.run_dir: tf.logging.fatal('--run_dir required') return if not FLAGS.sequence_example_file: tf.logging.fatal('--sequence_example_file required') return sequence_example_file_paths = tf.gfile.Glob( os.path.expanduser(FLAGS.sequence_example_file)) run_dir = os.path.expanduser(FLAGS.run_dir) config = drums_rnn_config_flags.config_from_flags() mode = 'eval' if FLAGS.eval else 'train' graph = events_rnn_graph.build_graph( mode, config, sequence_example_file_paths) train_dir = os.path.join(run_dir, 'train') if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info('Eval dir: %s', eval_dir) events_rnn_train.run_eval(graph, train_dir, eval_dir, FLAGS.num_training_steps, FLAGS.summary_frequency) else: events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps, FLAGS.summary_frequency)