def testCompositionStats(self):
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir,
output_every_nth=30)
stat_dict = rlt.evaluate_music_theory_metrics(num_compositions=10)
self.assertTrue(stat_dict['num_repeated_notes'] >= 0)
self.assertTrue(len(stat_dict['autocorrelation1']) > 1)
def testAction(self):
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
initial_note = rlt.prime_internal_models()
action = rlt.action(initial_note, 100, enable_random=False)
self.assertTrue(action is not None)
def testRewardNetwork(self):
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
zero_state = rlt.q_network.get_zero_state()
priming_note = rlt.get_random_note()
reward_scores = rlt.get_reward_rnn_scores(priming_note, zero_state)
self.assertTrue(reward_scores is not None)
def testInitialGeneration(self):
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
plot_name = 'test_initial_plot.png'
rlt.generate_music_sequence(visualize_probs=True,
prob_image_name=plot_name)
output_path = os.path.join(self.output_dir, plot_name)
self.assertTrue(os.path.exists(output_path))
def main(_):
hparams = rl_tuner_ops.default_hparams()
if FLAGS.note_rnn_type == 'basic_rnn':
hparams = rl_tuner_ops.basic_rnn_hparams()
elif FLAGS.note_rnn_type == 'attention_rnn':
hparams = rl_tuner_ops.attention_rnn_hparams()
dqn_hparams = tf.contrib.training.HParams(random_action_probability=0.1,
store_every_nth=1,
train_every_nth=5,
minibatch_size=32,
discount_rate=0.5,
max_experience=100000,
target_network_update_rate=0.01)
output_dir = os.path.join(FLAGS.output_dir, FLAGS.algorithm)
output_ckpt = FLAGS.algorithm + '.ckpt'
backup_checkpoint_file = os.path.join(FLAGS.note_rnn_checkpoint_dir,
FLAGS.note_rnn_checkpoint_name)
rlt = rl_tuner.RLTuner(output_dir,
midi_primer=FLAGS.midi_primer,
dqn_hparams=dqn_hparams,
reward_scaler=FLAGS.reward_scaler,
save_name=output_ckpt,
output_every_nth=FLAGS.output_every_nth,
note_rnn_checkpoint_dir=FLAGS.note_rnn_checkpoint_dir,
note_rnn_checkpoint_file=backup_checkpoint_file,
note_rnn_type=FLAGS.note_rnn_type,
note_rnn_hparams=hparams,
num_notes_in_melody=FLAGS.num_notes_in_melody,
exploration_mode=FLAGS.exploration_mode,
algorithm=FLAGS.algorithm)
tf.logging.info('Saving images and melodies to: %s', rlt.output_dir)
tf.logging.info('Training...')
rlt.train(num_steps=FLAGS.training_steps,
exploration_period=FLAGS.exploration_steps)
tf.logging.info('Finished training. Saving output figures and composition.')
rlt.plot_rewards(image_name='Rewards-' + FLAGS.algorithm + '.eps')
rlt.generate_music_sequence(visualize_probs=True, title=FLAGS.algorithm,
prob_image_name=FLAGS.algorithm + '.png')
rlt.save_model_and_figs(FLAGS.algorithm)
tf.logging.info('Calculating music theory metric stats for 1000 '
'compositions.')
statistics = rlt.evaluate_music_theory_metrics(num_compositions=1000)
print("music theory evaluation statistic: {0}".format(statistics))
def testTraining(self):
rlt = rl_tuner.RLTuner(
self.output_dir, note_rnn_checkpoint_dir=self.checkpoint_dir,
output_every_nth=30)
rlt.train(num_steps=31, exploration_period=3)
checkpoint_dir = os.path.dirname(rlt.save_path)
checkpoint_files = [
f for f in os.listdir(checkpoint_dir)
if os.path.isfile(os.path.join(checkpoint_dir, f))]
checkpoint_step_30 = [
f for f in checkpoint_files
if os.path.basename(rlt.save_path) + '-30' in f]
self.assertTrue(len(checkpoint_step_30))
self.assertTrue(len(rlt.rewards_batched) >= 1)
self.assertTrue(len(rlt.eval_avg_reward) >= 1)
def run_algorithm(alg, dqn_hparams, hparams, num_compositions):
output_dir = os.path.join(FLAGS.output_dir, alg)
output_ckpt = alg + '.ckpt'
backup_checkpoint_file = os.path.join(FLAGS.note_rnn_checkpoint_dir,
FLAGS.note_rnn_checkpoint_name)
rlt = rl_tuner.RLTuner(output_dir,
midi_primer=FLAGS.midi_primer,
dqn_hparams=dqn_hparams,
reward_scaler=FLAGS.reward_scaler,
save_name=output_ckpt,
output_every_nth=FLAGS.output_every_nth,
note_rnn_checkpoint_dir=FLAGS.note_rnn_checkpoint_dir,
note_rnn_checkpoint_file=backup_checkpoint_file,
note_rnn_type=FLAGS.note_rnn_type,
note_rnn_hparams=hparams,
num_notes_in_melody=FLAGS.num_notes_in_melody,
exploration_mode=FLAGS.exploration_mode,
algorithm=alg)
tf.logging.info('Saving images and melodies to: %s', rlt.output_dir)
if (alg != "none_rl"):
tf.logging.info('Training...')
rlt.train(num_steps=FLAGS.training_steps,
exploration_period=FLAGS.exploration_steps)
tf.logging.info('Finished training. Saving output figures and composition.')
rlt.plot_rewards(image_name='Rewards-' + alg + '.eps')
rlt.generate_music_sequence(visualize_probs=True, title=alg,
prob_image_name=alg + '.png')
rlt.save_model_and_figs(alg)
tf.logging.info('Calculating music theory metric stats for %d '
'compositions.', num_compositions)
rlt.evaluate_music_theory_metrics(num_compositions=num_compositions)
return rlt
def testInitializationAndPriming(self):
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
initial_note = rlt.prime_internal_models()
self.assertTrue(initial_note is not None)
# Model parameter settings
SAVE_PATH = "/tmp/rl_tuner/"
ALGORITHM = 'q'
REWARD_SCALER = 1
OUTPUT_EVERY_NTH = 50000
NUM_NOTES_IN_COMPOSITION = 32
PRIME_WITH_MIDI = True
NOTE_RNN_CHECKPOINT_FILE = 'note_rnn.ckpt'
MIDI_PRIMER = 'bach_846.mid'
PRIMING_MODE = 'single_midi'
NOTE_RNN_TYPE = 'default'
# Creates RLTuner object with above specified parameters.
rl_net = rl_tuner.RLTuner(
SAVE_PATH,
# Hyperparameters
reward_scaler=REWARD_SCALER,
priming_mode=PRIMING_MODE,
algorithm=ALGORITHM,
note_rnn_checkpoint_file=NOTE_RNN_CHECKPOINT_FILE,
note_rnn_type=NOTE_RNN_TYPE,
# Other music related settings.
num_notes_in_melody=NUM_NOTES_IN_COMPOSITION,
input_size=rl_tuner_ops.NUM_CLASSES,
num_actions=rl_tuner_ops.NUM_CLASSES,
midi_primer=MIDI_PRIMER,
# Logistics.
output_every_nth=OUTPUT_EVERY_NTH)
# Generate and display music sequence
rl_net.generate_music_sequence(visualize_probs=True, title='post_rl')
class IndividualDrum(Individual):
_count = 0
vae = CVae()
# reward = reward.RLTunerTest()
# reward.setUp()
graph = tf.Graph()
session = tf.Session(graph=graph)
note_rnn = note_rnn_loader.NoteRNNLoader(
graph, scope='test', checkpoint_dir=os.getcwd() +
"/src/") # , midi_primer='/tmp/RL/nice.mid')
# note_rnn = note_rnn_loader.NoteRNNLoader(graph, scope='test',
# checkpoint_dir=None) # , midi_primer='/tmp/RL/nice.mid')
note_rnn.initialize_new(session)
with graph.as_default():
saver = tf.train.Saver(var_list=note_rnn.get_variable_name_dict())
saver.save(session, "/tmp/RL/")
rlt = rl_tuner.RLTuner(os.getcwd() + "/output/",
note_rnn_checkpoint_dir=os.getcwd() + "/src/")
# rlt = rl_tuner.RLTuner("/Users/Cyril_Musique/Documents/Cours/M2/MuGen/output",
# note_rnn_checkpoint_dir=None)
def overlapped_keys(self, key_to_check, bars):
overlapped = []
for key in bars:
if key_to_check.pitch != key.pitch:
if key_to_check.timestamp <= key.timestamp <= (
key_to_check.timestamp + key_to_check.duration):
overlapped.append(key)
# print("key ", key_to_check, " overlapped by ", key )
return overlapped
def check_collision(self, key_to_check, changed_pitch, bars):
for key in bars:
if (key_to_check.bit.pitch + changed_pitch) == key.bit.pitch:
if key_to_check.bit.timestamp <= key.bit.timestamp <= (
key_to_check.bit.timestamp +
key_to_check.bit.duration):
return False
return True
def mutate(self):
"""
for key in self.sequence:
RL = True
if not RL:
#print("key",key)
# probability to switch a key
#if random.random() > 1 / len(self.sequence):
# change_pitch = random.randint(-1, 1)
# if self.check_collision(key, change_pitch, self.sequence) and 49 <= key.bit.pitch + change_pitch <= 58:
# key.bit.pitch += change_pitch
# #print("MUTATE KEY")
# self.sequence.remove(key)
if random.random()>0.1:
self.sequence.remove(key)
self.generate_note()
if random.random() > 1 / len(self.sequence):
if random.random()>0.5:
if key.bit.timestamp>0.5:
key.bit.timestamp -= 0.01
else:
if key.bit.timestamp< 7.5:
key.bit.timestamp += 0.01
if RL:
#random_note = random.randrange(0, self.number_of_notes - 1)
#change_pitch = random.randint(-3, 3)
if key.bit.pitch + change_pitch < 24 and key.bit.pitch + change_pitch > 0:
key.bit.pitch += change_pitch
if random.random() > 1 / len(self.sequence):
change_pitch = random.randint(-3,3)
if key.bit.pitch + change_pitch<24 and key.bit.pitch + change_pitch>0:
key.bit.pitch += change_pitch
#if random.random()>0.9:
# key.bit.duration/=2
# new_note = Note(key.bit.pitch, key.bit.timestamp+key.bit.duration, key.bit.duration)
# self.sequence.append(GeneDrum(new_note))
#if random.random() > 0.5:
# index = self.sequence.index(key)
# self.sequence[index-1].bit.duration*=2
# self.sequence.remove(key)
"""
if len(self.sequence) > 10:
random_note = random.randrange(0, len(self.sequence))
change_pitch = random.randint(-7, 7)
key = self.sequence[random_note]
if key.bit.pitch + change_pitch < 24 and key.bit.pitch + change_pitch > 0:
key.bit.pitch += change_pitch
# if random.random() > 0.9:
# index = self.sequence.index(key)
# if index >0:
# self.sequence[index-1].bit.duration*=2
# self.sequence.remove(key)
else:
print("CANNOT")
def crossover(self, other):
fc = IndividualDrum(self.parameters, empty=True)
sc = IndividualDrum(self.parameters, empty=True)
fc.sequence = deepcopy(self.sequence)
sc.sequence = deepcopy(other.sequence)
return fc, sc
def __init__(self, parameters, empty=False):
super().__init__(parameters)
IndividualDrum._count += 1
self.ind = IndividualDrum._count
max_number_of_notes = 8 # 16 POUR 2 MESURES
self.length = 4 # *4 = 8 MESURES
self.number_of_notes = max_number_of_notes * self.length # randint(0, max_number_of_notes)
if not empty:
self.generate_seq()
def create_midi_file(self, file_name=None):
track = 0
channel = 1
tempo = 120 # In BPM
volume = 100 # 0-127, as per the MIDI standard
my_midi = MIDIFile(
1
) # One track, defaults to format 1 (tempo track is created automatically)
my_midi.addTempo(track, 0, tempo)
# my_midi.addProgramChange(0, 10, 0, 0)
my_midi.tracks[0].addChannelPressure(0, 4, 0)
repertory = "output/"
if file_name is not None:
file = file_name + ".mid"
else:
file = str(self.ind) + ".mid"
for note in self.sequence:
# print(note)
my_midi.addNote(track, channel, note.bit.pitch + 36,
note.bit.timestamp, note.bit.duration, volume)
with open(repertory + file, "wb") as output_file:
my_midi.writeFile(output_file)
def generate_note(self):
# allowed_pitch = [36, 38, 42, 46, 41, 45, 48, 51, 49]
new_note = Note(random.randrange(0, 24),
round_down(round(uniform(0, 7.75), 2), 0.25),
0.25) # QUANTIZED MELO
# new_note = Note(sample(allowed_pitch, 1)[0], round_down(round(uniform(0, 7.75), 2), 0.25), 0.25) #QUANTIZED
# new_note = Note(random.sample(allowed_pitch, 1)[0], round(random.uniform(0, 7.75), 2), 0.25) #UNQUANTIZED
if new_note not in self.sequence:
self.sequence.append(GeneDrum(new_note))
def generate_seq(self):
for i in range(16):
automate = _automate.create_automate()
while not automate.has_finished():
self.sequence.append(
GeneDrum(automate.next_state(position=i * 4)))
# Create a PrettyMIDI object
# pm = pretty_midi.PrettyMIDI()
"""
RL = True
if not RL:
max_number_of_notes = 100
#self.number_of_notes = randint(20, max_number_of_notes)
for x in range(self.number_of_notes):
self.generate_note()
if RL:
divide = 8/self.number_of_notes*self.length
#print(divide)
for i in range(self.number_of_notes):
#self.sequence.append(GeneDrum(Note( random.randrange(0,24),i*divide, divide )))
self.sequence.append(GeneDrum(Note(12, i * divide, divide)))
#print(self.sequence)
#self.create_midi_file()
"""
'''
midi_data = PrettyMIDI(repertory + file)
a = None
for instrument in midi_data.instruments:
if instrument.is_drum:
instrument.is_drum = False
a = instrument.get_piano_roll()[36:48]
a[a > 0] = 1
a = np.pad(a, [(0, 0), (0, 400 - a.shape[1])], 'constant')
a = a.astype(dtype=bool)
# a = a.transpose()
break
for i in range(a.shape[0]):
self.sequence.append(GeneDrum( ))
# print(len(self.sequence))
# b = np.array(self.sequence)
# print(b.shape)
'''
def fitness(self, should_print=False):
RL = True
if not RL:
# self.create_midi_file()
repertory = "output/"
file = repertory + str(self.ind) + ".mid"
return -abs(self.vae.get_distance(file, self.ind))
else:
'''
self.rlt.train(num_steps=100000, exploration_period=500000)
stat_dict = self.rlt.evaluate_music_theory_metrics(num_compositions=100)
self.rlt.plot_rewards()
stat3 = self.rlt.generate_music_sequence(visualize_probs=True, title='post_rl')
print(stat3)
exit(0)
'''
# print("NOTE COMPO: ")
self.rlt.num_notes_in_melody = self.number_of_notes
self.rlt.reset_composition()
to_mean_note_reward = []
to_mean_rnn = []
for note in self.sequence:
one_hot = np.array(
rl_tuner_ops.make_onehot([note.bit.pitch], 38)).flatten()
note_reward = self.rlt.reward_music_theory(one_hot)
# if should_print:
# print(one_hot,note_reward )
self.rlt.composition.append(np.argmax(one_hot))
self.rlt.beat += 1
to_mean_note_reward.append(note_reward)
a, b, c = self.rlt.action(one_hot)
reward_scores = np.reshape(c, (38))
# print(to_mean_rnn)
note_rnn_reward = self.rlt.reward_from_reward_rnn_scores(
one_hot, reward_scores)
to_mean_rnn.append(note_rnn_reward)
# print(self.rlt.composition)
mean_note_reward = np.mean(to_mean_note_reward)
to_mean_rnn = np.mean(to_mean_rnn)
return to_mean_rnn + mean_note_reward
# # print(to_mean_note_reward)
# if len(to_mean_note_reward) > 8:
# worst_score = np.min(to_mean_note_reward)
#
# # print("mean", mean_note_reward)
# if len(to_mean_note_reward) > 8:
# # if should_print:
# # print("Worst : ",worst_score, " mean :", mean_note_reward, " ALL ", worst_score * 0.8 + 0.2 * mean_note_reward)
# return worst_score * 0.8 + 0.2 * mean_note_reward
# else:
# return -100
def __eq__(self, other):
if type(other) != type(self):
return False
for a, b in zip(self.sequence, other.sequence):
if a.bit != b.bit:
return False
return True
def __repr__(self):
# r = f"I: {self.fitness()}"
# for g in self.sequence:
# r += f'\n\t{g.bit}'
r = str(self.ind)
return r
def __hash__(self):
r = 0
for _ in self.sequence:
r += randint(1, 100)
return r
def testRewardNetwork(self):
graph = tf.Graph()
self.session = tf.Session(graph=graph)
note_rnn = note_rnn_loader.NoteRNNLoader(
graph, scope='test',
checkpoint_dir=None) #, midi_primer='/tmp/RL/nice.mid')
note_rnn.initialize_new(self.session)
#print("NOTESEQ", note_rnn.primer)
#print("AFER")
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
initial_note = rlt.prime_internal_models()
print("INITIAL NOTE", initial_note)
print("FIRST SCORE", rlt.reward_key(initial_note))
action = rlt.action(initial_note, 100, enable_random=True)
print("ACTION CHOSEN ", action[0])
print("ACTION REWARD ", action[1])
print("ACTION NEXT OBS ", action[2])
print("FINAL", rlt.reward_key(action[2]))
print("ONE HOT CREATED")
x = np.array(rl_tuner_ops.make_onehot([10], 24)).flatten()
print(x)
print("FINAL", rlt.reward_key(x))
last_observation = rlt.prime_internal_models()
rlt.num_notes_in_melody = 12
rlt.reset_composition()
for _ in range(rlt.num_notes_in_melody):
_, new_observation, reward_scores = rlt.action(
last_observation,
0,
enable_random=False,
sample_next_obs=False)
music_theory_reward = rlt.reward_music_theory(new_observation)
#music_theory_rewards = music_theory_reward * rlt.reward_scaler
print(music_theory_reward)
print(new_observation)
rlt.composition.append(np.argmax(new_observation))
rlt.beat += 1
last_observation = new_observation
print("num note", rlt.num_notes_in_melody)
rlt.reset_composition()
final = []
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
final.append(test)
avg = []
print("BEGIN")
for x in final:
reward = rlt.reward_music_theory(x)
print(x, reward)
rlt.composition.append(np.argmax(x))
rlt.beat += 1
avg.append(reward)
rlt.music_theory_reward_last_n += reward * rlt.reward_scaler
print("AVG", np.mean(avg))
rlt.reset_composition()
final = []
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
final.append(test)
test = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
print("SECOND")
avg = []
for x in final:
reward = rlt.reward_music_theory(x)
print(x, reward)
rlt.composition.append(np.argmax(x))
rlt.beat += 1
avg.append(reward)
rlt.music_theory_reward_last_n += reward * rlt.reward_scaler
print("AVG", np.mean(avg))