def run_from_flags(melody_encoder_decoder): pipeline_instance = MelodyRNNPipeline( melody_encoder_decoder, FLAGS.eval_ratio) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)
def run_from_flags(pipeline_instance): tf.logging.set_verbosity(FLAGS.log) 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) pipeline_instance = get_pipeline( min_events=32, max_events=512, eval_ratio=FLAGS.eval_ratio, config=performance_model.default_configs[FLAGS.config]) input_dir = os.path.expanduser(FLAGS.input) output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(input_dir, pipeline_instance.input_type), output_dir)
def main(unused_argv): tf.logging.set_verbosity(FLAGS.log) pipeline_instance = polyphony_rnn_pipeline.get_pipeline( min_steps=80, # 5 measures max_steps=512, eval_ratio=FLAGS.eval_ratio, config=polyphony_model.default_configs['polyphony']) input_dir = os.path.expanduser(FLAGS.input) output_dir = os.path.expanduser(FLAGS.output_dir) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(input_dir, pipeline_instance.input_type), output_dir)
def testRunPipelineSerial(self): strings = ['abcdefg', 'helloworld!', 'qwerty'] root_dir = tempfile.mkdtemp(dir=self.get_temp_dir()) pipeline.run_pipeline_serial(MockPipeline(), iter(strings), root_dir) dataset_1_dir = os.path.join(root_dir, 'dataset_1.tfrecord') dataset_2_dir = os.path.join(root_dir, 'dataset_2.tfrecord') self.assertTrue(tf.gfile.Exists(dataset_1_dir)) self.assertTrue(tf.gfile.Exists(dataset_2_dir)) dataset_1_reader = tf.python_io.tf_record_iterator(dataset_1_dir) self.assertEqual( set([('serialized:%s_A' % s).encode('utf-8') for s in strings] + [('serialized:%s_B' % s).encode('utf-8') for s in strings]), set(dataset_1_reader)) dataset_2_reader = tf.python_io.tf_record_iterator(dataset_2_dir) self.assertEqual( set(('serialized:%s_C' % s).encode('utf-8') for s in strings), set(dataset_2_reader))
def testRunPipelineSerial(self): strings = ['abcdefg', 'helloworld!', 'qwerty'] root_dir = tempfile.mkdtemp(dir=self.get_temp_dir()) pipeline.run_pipeline_serial( MockPipeline(), iter(strings), root_dir) dataset_1_dir = os.path.join(root_dir, 'dataset_1.tfrecord') dataset_2_dir = os.path.join(root_dir, 'dataset_2.tfrecord') self.assertTrue(tf.gfile.Exists(dataset_1_dir)) self.assertTrue(tf.gfile.Exists(dataset_2_dir)) dataset_1_reader = tf.python_io.tf_record_iterator(dataset_1_dir) self.assertEqual( set([('serialized:%s_A' % s).encode('utf-8') for s in strings] + [('serialized:%s_B' % s).encode('utf-8') for s in strings]), set(dataset_1_reader)) dataset_2_reader = tf.python_io.tf_record_iterator(dataset_2_dir) self.assertEqual( set([('serialized:%s_C' % s).encode('utf-8') for s in strings]), set(dataset_2_reader))
for file_in_dir in files_in_dir: full_file_path = os.path.join(input_dir, file_in_dir) print(full_file_path) try: sequence = midi_io.midi_to_sequence_proto( tf.io.gfile.GFile(full_file_path, 'rb').read()) except midi_io.MIDIConversionError as e: tf.logging.warning('Could not parse midi file %s. Error was: %s', full_file_path, e) sequence.collection_name = os.path.basename(work_dir) sequence.filename = os.path.join(output_dir, os.path.basename(full_file_path)) sequence.id = note_sequence_io.generate_note_sequence_id( sequence.filename, sequence.collection_name, 'midi') if sequence: writer.write(sequence) filenames = [anthems_file] dataset = tf.data.TFRecordDataset(filenames) config = melody_rnn_model.default_configs['attention_rnn'] pipeline_instance = melody_rnn_pipeline.get_pipeline(config, eval_ratio=0.0) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(anthems_file, pipeline_instance.input_type), output_dir)
def run_from_flags(pipeline_instance): tf.logging.set_verbosity(tf.logging.INFO) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type), FLAGS.output_dir)