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 = tf.gfile.Glob( os.path.expanduser(FLAGS.sequence_example_file)) run_dir = os.path.expanduser(FLAGS.run_dir) config = polyphony_model.default_configs[FLAGS.config] 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') tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') 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) 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) 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 = tf.gfile.Glob( os.path.expanduser(FLAGS.sequence_example_file)) run_dir = os.path.expanduser(FLAGS.run_dir) config = pianoroll_rnn_nade_model.default_configs[FLAGS.config] config.hparams.parse(FLAGS.hparams) mode = 'eval' if FLAGS.eval else 'train' graph = pianoroll_rnn_nade_graph.build_graph( mode, config, sequence_example_file) train_dir = os.path.join(run_dir, 'train') tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') 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(mt.FLAGS.log) data_dir = tgt.OUTPUT_DIR train_dir = os.path.join(tgt.MODEL_DIR, "logdir/train") if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) config = mt.melody_rnn_config_flags.config_from_flags() if not mt.FLAGS.eval: train_file = tf.gfile.Glob( os.path.join(data_dir, "training_melodies.tfrecord")) tf.logging.info("Train dir: %s", train_dir) with tf.gfile.Open(os.path.join(train_dir, "hparams"), mode="w") as f: f.write("\t".join([mt.FLAGS.config, mt.FLAGS.hparams])) graph = events_rnn_graph.build_graph("train", config, train_file) events_rnn_train.run_training(graph, train_dir, mt.FLAGS.num_training_steps, mt.FLAGS.summary_frequency) else: eval_file = tf.gfile.Glob( os.path.join(data_dir, "eval_melodies.tfrecord")) eval_dir = os.path.join(tgt.MODEL_DIR, "logdir/eval") if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info("Eval dir: %s", eval_dir) graph = events_rnn_graph.build_graph("eval", config, eval_file) events_rnn_train.run_eval(graph, train_dir, eval_dir, mt.FLAGS.num_training_steps, mt.FLAGS.summary_frequency)
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 = melody_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) 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(graph, train_dir, eval_dir, num_batches) else: events_rnn_train.run_training(graph, 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 = melody_rnn_config_flags.config_from_flags() if FLAGS.learn_initial_state: if not FLAGS.id_file: tf.logging.fatal('--id_file required') return # Count records for embedding id_file = os.path.expanduser(FLAGS.id_file) last_line = subprocess.check_output(['tail', '-1', id_file]) config.num_records = int(last_line.split(',')[0]) + 1 tf.logging.info('Counted %d records', config.num_records) 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) embedding_file = os.path.join(train_dir, 'embedding') 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, num_records=config.num_records, embedding_file=embedding_file)
def main(unused_argv): tf.logging.set_verbosity(mt.FLAGS.log) data_dir = tgt.OUTPUT_DIR train_dir = os.path.join(tgt.MODEL_DIR, "logdir/train") if not os.path.exists(train_dir): tf.gfile.MakeDirs(train_dir) config = mt.melody_rnn_config_flags.config_from_flags() if not mt.FLAGS.eval: train_file = tf.gfile.Glob( os.path.join(data_dir, "training_melodies.tfrecord")) tf.logging.info("Train dir: %s", train_dir) with tf.gfile.Open(os.path.join(train_dir, "hparams"), mode="w") as f: f.write("\t".join([mt.FLAGS.config, mt.FLAGS.hparams])) graph = events_rnn_graph.get_build_graph_fn("train", config, train_file) events_rnn_train.run_training( graph, train_dir, mt.FLAGS.num_training_steps, mt.FLAGS.summary_frequency, checkpoints_to_keep=mt.FLAGS.num_checkpoints) else: eval_file = tf.gfile.Glob( os.path.join(data_dir, "eval_melodies.tfrecord")) eval_dir = os.path.join(tgt.MODEL_DIR, "logdir/eval") if not os.path.exists(eval_dir): tf.gfile.MakeDirs(eval_dir) tf.logging.info("Eval dir: %s", eval_dir) examples = mt.FLAGS.num_eval_examples if mt.FLAGS.num_eval_examples else magenta.common.count_records( eval_file) if examples >= config.hparams.batch_size: num_batches = examples // config.hparams.batch_size else: config.hparams.batch_size = examples num_batches = 1 graph = events_rnn_graph.get_build_graph_fn("eval", config, eval_file) events_rnn_train.run_eval(graph, train_dir, eval_dir, num_batches)
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 = performance_model.default_configs[FLAGS.config] config.hparams.parse(FLAGS.hparams) 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') tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') 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, warm_start_bundle_file=FLAGS.warm_start_bundle_file)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) tf.logging.info('RUN FROM SOURCE') 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 = melody_rnn_config_flags.config_from_flags() mode = 'eval' if FLAGS.eval else 'train' graph = None if FLAGS.graph == 'old': tf.logging.info('Using old graph') graph = events_rnn_graph.build_graph(mode, config, sequence_example_file_paths) else: tf.logging.info('Using new graph') graph = banger_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)
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 = melody_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 = pianoroll_rnn_nade_model.default_configs[FLAGS.config] config.hparams.parse(FLAGS.hparams) mode = 'eval' if FLAGS.eval else 'train' graph = pianoroll_rnn_nade_graph.build_graph( mode, config, sequence_example_file_paths) train_dir = os.path.join(run_dir, 'train') tf.gfile.MakeDirs(train_dir) tf.logging.info('Train dir: %s', train_dir) if FLAGS.eval: eval_dir = os.path.join(run_dir, 'eval') 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(graph, train_dir, eval_dir, num_batches) else: events_rnn_train.run_training(graph, train_dir, FLAGS.num_training_steps, FLAGS.summary_frequency, checkpoints_to_keep=FLAGS.num_checkpoints)
# run_dir = os.path.join(work_dir, 'logdir') run_dir = 'logdir' print(run_dir) tf.logging.set_verbosity('INFO') sequence_example_file = 'data' + os.path.sep + 'training_melodies.tfrecord' sequence_example_file_paths = tf.io.gfile.glob( os.path.join(work_dir, sequence_example_file)) config = melody_rnn_model.default_configs['attention_rnn'] config.hparams.batch_size = 64 config.hparams.rnn_layer_sizes = [64, 64] mode = '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') eval_dir = os.path.join(run_dir, 'eval') print('Train directory: %s', train_dir) print('Evaluate directory: %s', eval_dir) print(config.hparams.batch_size) print(magenta.common.count_records(sequence_example_file_paths)) # num_batches = magenta.common.count_records(sequence_example_file_paths) // config.hparams.batch_size events_rnn_train.run_training(build_graph_fn, train_dir, 20000, 10, 3)