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 testBuildGraphCnn(self): self.config.hparams.dilated_cnn = True self.config.hparams.block_num = 1 self.config.hparams.block_size = 7 self.config.hparams.residual_cnl = 32 self.config.hparams.dilation_cnl = 16 self.config.hparams.output_cnl = 64 self.config.hparams.use_gate = True self.config.hparams.use_step = True g = events_rnn_graph.build_graph( 'train', self.config, sequence_example_file_paths=['test']) self.assertTrue(isinstance(g, tf.Graph))
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 = polyphony_model.default_configs[FLAGS.config] config.hparams.parse(FLAGS.hparams) 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') 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)
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) 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 = polyphony_model.default_configs[FLAGS.config] config.hparams.parse(FLAGS.hparams) 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 _build_graph_for_generation(self): return events_rnn_graph.build_graph('generate', self._config)
def testBuildGenerateGraph(self): g = events_rnn_graph.build_graph('generate', self.config) self.assertTrue(isinstance(g, tf.Graph))
def testBuildEvalGraph(self): g = events_rnn_graph.build_graph( 'eval', self.config, sequence_example_file_paths=[self._sequence_file.name]) self.assertTrue(isinstance(g, tf.Graph))
def testBuildGraphWithAttention(self): self.config.hparams.attn_length = 10 g = events_rnn_graph.build_graph( 'train', self.config, sequence_example_file_paths=['test']) self.assertTrue(isinstance(g, tf.Graph))
def testBuildTrainGraph(self): g = events_rnn_graph.build_graph( 'train', self.config, sequence_example_file_paths=['test']) self.assertTrue(isinstance(g, tf.Graph))
def testBuildEvalGraph(self): g = events_rnn_graph.build_graph( 'eval', self.config, sequence_example_file_paths=['test']) self.assertTrue(isinstance(g, tf.Graph))
def testBuildTrainGraph(self): g = events_rnn_graph.build_graph( 'train', self.config, sequence_example_file_paths=[self._sequence_file.name]) self.assertTrue(isinstance(g, tf.Graph))
from magenta.models.shared.events_rnn_graph import build_graph from magenta.models.melody_rnn.melody_rnn_sequence_generator import MelodyRnnSequenceGenerator from magenta.models.melody_rnn.melody_rnn_model import MelodyRnnModel logging.basicConfig(level=logging.DEBUG) BUNDLE_NAME = 'attention_rnn' config = magenta.models.melody_rnn.melody_rnn_model.default_configs[BUNDLE_NAME] bundle_file = magenta.music.read_bundle_file(os.path.abspath(BUNDLE_NAME+'.mag')) steps_per_quarter = 4 generator = MelodyRnnSequenceGenerator( model=MelodyRnnModel(config), details=config.details, steps_per_quarter=steps_per_quarter, bundle=bundle_file) generator.create_bundle_file('/Users/mdpicket/Documents/tmp/bundle') exit() with tf.Session() as sess: graph = build_graph('generate', config) writer = tf.summary.FileWriter(logdir='/Users/mdpicket/Documents/tmp', graph=graph) writer.flush() with tf.Graph() as graph: saver = tf.train.Saver(tf.global_variables()) saver.save(sess, '/Users/mdpicket/Documents/tmp/model.saver')