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model.py
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model.py
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from __future__ import division
import os
import time
from shutil import copyfile
from glob import glob
import tensorflow as tf
import numpy as np
import config
from collections import namedtuple
from module import *
from utils import *
from ops import *
from metrics import *
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ['SGE_GPU']
class cyclegan(object):
def __init__(self, sess, args):
self.sess = sess
self.batch_size = args.batch_size
self.image_size = args.fine_size # cropped size
self.time_step = args.time_step
self.pitch_range = args.pitch_range
self.input_c_dim = args.input_nc # number of input image channels
self.output_c_dim = args.output_nc # number of output image channels
self.L1_lambda = args.L1_lambda
self.gamma = args.gamma
self.sigma_d = args.sigma_d
self.dataset_dir = args.dataset_dir
self.dataset_A_dir = args.dataset_A_dir
self.dataset_B_dir = args.dataset_B_dir
self.sample_dir = args.sample_dir
self.model = args.model
self.discriminator = discriminator
self.generator = generator_resnet
self.criterionGAN = mae_criterion
OPTIONS = namedtuple('OPTIONS', 'batch_size '
'image_size '
'gf_dim '
'df_dim '
'output_c_dim '
'is_training')
self.options = OPTIONS._make((args.batch_size,
args.fine_size,
args.ngf,
args.ndf,
args.output_nc,
args.phase == 'train'))
self._build_model()
self.saver = tf.train.Saver(max_to_keep=30)
self.now_datetime = get_now_datetime()
self.pool = ImagePool(args.max_size)
def _build_model(self):
# define some placeholders
self.real_data = tf.placeholder(tf.float32, [self.batch_size, self.time_step, self.pitch_range,
self.input_c_dim + self.output_c_dim], name='real_A_and_B')
if self.model != 'base':
self.real_mixed = tf.placeholder(tf.float32, [self.batch_size, self.time_step, self.pitch_range,
self.input_c_dim], name='real_A_and_B_mixed')
self.real_A = self.real_data[:, :, :, :self.input_c_dim]
self.real_B = self.real_data[:, :, :, self.input_c_dim:self.input_c_dim + self.output_c_dim]
self.gaussian_noise = tf.placeholder(tf.float32, [self.batch_size, self.time_step, self.pitch_range,
self.input_c_dim], name='gaussian_noise')
# Generator: A - B - A
self.fake_B = self.generator(self.real_A, self.options, False, name="generatorA2B")
self.fake_A_ = self.generator(self.fake_B, self.options, False, name="generatorB2A")
# Generator: B - A - B
self.fake_A = self.generator(self.real_B, self.options, True, name="generatorB2A")
self.fake_B_ = self.generator(self.fake_A, self.options, True, name="generatorA2B")
# to binary
self.real_A_binary = to_binary(self.real_A, 0.5)
self.real_B_binary = to_binary(self.real_B, 0.5)
self.fake_A_binary = to_binary(self.fake_A, 0.5)
self.fake_B_binary = to_binary(self.fake_B, 0.5)
self.fake_A__binary = to_binary(self.fake_A_, 0.5)
self.fake_B__binary = to_binary(self.fake_B_, 0.5)
# Discriminator: Fake
self.DB_fake = self.discriminator(self.fake_B + self.gaussian_noise, self.options,
reuse=False, name="discriminatorB")
self.DA_fake = self.discriminator(self.fake_A + self.gaussian_noise, self.options,
reuse=False, name="discriminatorA")
# Discriminator: Real
self.DA_real = self.discriminator(self.real_A + self.gaussian_noise, self.options, reuse=True,
name="discriminatorA")
self.DB_real = self.discriminator(self.real_B + self.gaussian_noise, self.options, reuse=True,
name="discriminatorB")
self.fake_A_sample = tf.placeholder(tf.float32, [self.batch_size, self.time_step, self.pitch_range,
self.input_c_dim], name='fake_A_sample')
self.fake_B_sample = tf.placeholder(tf.float32, [self.batch_size, self.time_step, self.pitch_range,
self.input_c_dim], name='fake_B_sample')
self.DA_fake_sample = self.discriminator(self.fake_A_sample + self.gaussian_noise,
self.options, reuse=True, name="discriminatorA")
self.DB_fake_sample = self.discriminator(self.fake_B_sample + self.gaussian_noise,
self.options, reuse=True, name="discriminatorB")
if self.model != 'base':
# Discriminator: All
self.DA_real_all = self.discriminator(self.real_mixed + self.gaussian_noise, self.options, reuse=False,
name="discriminatorA_all")
self.DA_fake_sample_all = self.discriminator(self.fake_A_sample + self.gaussian_noise,
self.options, reuse=True, name="discriminatorA_all")
self.DB_real_all = self.discriminator(self.real_mixed + self.gaussian_noise, self.options, reuse=False,
name="discriminatorB_all")
self.DB_fake_sample_all = self.discriminator(self.fake_B_sample + self.gaussian_noise,
self.options, reuse=True, name="discriminatorB_all")
# Generator loss
self.cycle_loss = self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) + self.cycle_loss
self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) + self.cycle_loss
self.g_loss = self.g_loss_a2b + self.g_loss_b2a - self.cycle_loss
# Discriminator loss
self.db_loss_real = self.criterionGAN(self.DB_real, tf.ones_like(self.DB_real))
self.db_loss_fake = self.criterionGAN(self.DB_fake_sample, tf.zeros_like(self.DB_fake_sample))
self.db_loss = (self.db_loss_real + self.db_loss_fake) / 2
self.da_loss_real = self.criterionGAN(self.DA_real, tf.ones_like(self.DA_real))
self.da_loss_fake = self.criterionGAN(self.DA_fake_sample, tf.zeros_like(self.DA_fake_sample))
self.da_loss = (self.da_loss_real + self.da_loss_fake) / 2
self.d_loss = self.da_loss + self.db_loss
if self.model != 'base':
self.db_all_loss_real = self.criterionGAN(self.DB_real_all, tf.ones_like(self.DB_real_all))
self.db_all_loss_fake = self.criterionGAN(self.DB_fake_sample_all, tf.zeros_like(self.DB_fake_sample_all))
self.db_all_loss = (self.db_all_loss_real + self.db_all_loss_fake) / 2
self.da_all_loss_real = self.criterionGAN(self.DA_real_all, tf.ones_like(self.DA_real_all))
self.da_all_loss_fake = self.criterionGAN(self.DA_fake_sample_all, tf.zeros_like(self.DA_fake_sample_all))
self.da_all_loss = (self.da_all_loss_real + self.da_all_loss_fake) / 2
self.d_all_loss = self.da_all_loss + self.db_all_loss
self.D_loss = self.d_loss + self.gamma * self.d_all_loss
# Define all summaries
self.g_loss_a2b_sum = tf.summary.scalar("g_loss_a2b", self.g_loss_a2b)
self.g_loss_b2a_sum = tf.summary.scalar("g_loss_b2a", self.g_loss_b2a)
self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
self.cycle_loss_sum = tf.summary.scalar("cycle_loss", self.cycle_loss)
self.g_sum = tf.summary.merge([self.g_loss_a2b_sum, self.g_loss_b2a_sum, self.g_loss_sum, self.cycle_loss_sum])
self.db_loss_sum = tf.summary.scalar("db_loss", self.db_loss)
self.da_loss_sum = tf.summary.scalar("da_loss", self.da_loss)
self.d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.db_loss_real)
self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.db_loss_fake)
self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.da_loss_real)
self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.da_loss_fake)
if self.model != 'base':
self.d_all_loss_sum = tf.summary.scalar("d_all_loss", self.d_all_loss)
self.D_loss_sum = tf.summary.scalar("D_loss", self.d_loss)
self.d_sum = tf.summary.merge([self.da_loss_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.db_loss_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.d_loss_sum, self.d_all_loss_sum, self.D_loss_sum])
else:
self.d_sum = tf.summary.merge([self.da_loss_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.db_loss_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.d_loss_sum])
# Test
self.test_A = tf.placeholder(tf.float32, [None, self.time_step, self.pitch_range,
self.input_c_dim], name='test_A')
self.test_B = tf.placeholder(tf.float32, [None, self.time_step, self.pitch_range,
self.output_c_dim], name='test_B')
# A - B - A
self.testB = self.generator(self.test_A, self.options, True, name="generatorA2B")
self.testA_ = self.generator(self.testB, self.options, True, name='generatorB2A')
# B - A - B
self.testA = self.generator(self.test_B, self.options, True, name="generatorB2A")
self.testB_ = self.generator(self.testA, self.options, True, name='generatorA2B')
# to binary
self.test_A_binary = to_binary(self.test_A, 0.5)
self.test_B_binary = to_binary(self.test_B, 0.5)
self.testA_binary = to_binary(self.testA, 0.5)
self.testB_binary = to_binary(self.testB, 0.5)
self.testA__binary = to_binary(self.testA_, 0.5)
self.testB__binary = to_binary(self.testB_, 0.5)
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_vars = [var for var in t_vars if 'generator' in var.name]
for var in t_vars:
print(var.name)
def train(self, args):
# Learning rate
self.lr = tf.placeholder(tf.float32, None, name='learning_rate')
# Discriminator and Generator Optimizer
if self.model == 'base':
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1).minimize(self.d_loss, var_list=self.d_vars)
else:
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1).minimize(self.D_loss, var_list=self.d_vars)
self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1).minimize(self.g_loss, var_list=self.g_vars)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
# define the path which stores the log file, format is "{A}2{B}_{date}_{model}_{sigma}".
log_dir = './logs/{}2{}_{}_{}_{}'.format(self.dataset_A_dir, self.dataset_B_dir, self.now_datetime,
self.model, self.sigma_d)
# log_dir = './logs/{}2{}_{}_{}_{}'.format(self.dataset_A_dir, self.dataset_B_dir, '2018-06-10',
# self.model, self.sigma_d)
self.writer = tf.summary.FileWriter(log_dir, self.sess.graph)
# Data from domain A and B, and mixed dataset for partial and full models.
dataA = glob('./datasets/{}/train/*.*'.format(self.dataset_A_dir))
dataB = glob('./datasets/{}/train/*.*'.format(self.dataset_B_dir))
if self.model == 'partial':
data_mixed = dataA + dataB
if self.model == 'full':
data_mixed = glob('./datasets/JCP_mixed/*.*')
counter = 1
start_time = time.time()
if args.continue_train:
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
for epoch in range(args.epoch):
# Shuffle training data
np.random.shuffle(dataA)
np.random.shuffle(dataB)
if self.model != 'base': np.random.shuffle(data_mixed)
# Get the proper number of batches
batch_idxs = min(min(len(dataA), len(dataB)), args.train_size) // self.batch_size
# learning rate starts to decay when reaching the threshold
lr = args.lr if epoch < args.epoch_step else args.lr * (args.epoch-epoch) / (args.epoch-args.epoch_step)
for idx in range(0, batch_idxs):
# To feed real_data
batch_files = list(zip(dataA[idx * self.batch_size:(idx + 1) * self.batch_size],
dataB[idx * self.batch_size:(idx + 1) * self.batch_size]))
batch_images = [load_npy_data(batch_file) for batch_file in batch_files]
batch_images = np.array(batch_images).astype(np.float32)
# To feed gaussian noise
gaussian_noise = np.abs(np.random.normal(0, self.sigma_d, [self.batch_size, self.time_step,
self.pitch_range, self.input_c_dim]))
if self.model == 'base':
# Update G network and record fake outputs
fake_A, fake_B, _, summary_str, g_loss_a2b, g_loss_b2a, cycle_loss, g_loss = self.sess.run([self.fake_A,
self.fake_B, self.g_optim, self.g_sum, self.g_loss_a2b, self.g_loss_b2a, self.cycle_loss,
self.g_loss], feed_dict={self.real_data: batch_images, self.gaussian_noise: gaussian_noise,
self.lr: lr})
# Update D network
_, summary_str, da_loss, db_loss, d_loss = self.sess.run([
self.d_optim, self.d_sum, self.da_loss, self.db_loss, self.d_loss],
feed_dict={self.real_data: batch_images, self.fake_A_sample: fake_A, self.fake_B_sample: fake_B,
self.lr: lr, self.gaussian_noise: gaussian_noise})
print('=================================================================')
print(("Epoch: [%2d] [%4d/%4d] time: %4.4f, d_loss: %6.2f, G_loss: %6.2f" %
(epoch, idx, batch_idxs, time.time() - start_time, d_loss, g_loss)))
print(("++++++++++G_loss_A2B: %6.2f G_loss_B2A: %6.2f Cycle_loss: %6.2f DA_loss: %6.2f DB_loss: %6.2f" %
(g_loss_a2b, g_loss_b2a, cycle_loss, da_loss, db_loss)))
else:
# To feed real_mixed
batch_files_mixed = data_mixed[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_images_mixed = [np.load(batch_file) * 1. for batch_file in batch_files_mixed]
batch_images_mixed = np.array(batch_images_mixed).astype(np.float32)
# Update G network and record fake outputs
fake_A, fake_B, _, summary_str, g_loss_a2b, g_loss_b2a, cycle_loss, g_loss = self.sess.run([
self.fake_A,self.fake_B, self.g_optim, self.g_sum, self.g_loss_a2b, self.g_loss_b2a,
self.cycle_loss, self.g_loss], feed_dict={self.real_data: batch_images,
self.gaussian_noise: gaussian_noise, self.lr: lr,
self.real_mixed: batch_images_mixed})
self.writer.add_summary(summary_str, counter)
[fake_A, fake_B] = self.pool([fake_A, fake_B])
# Update D network
_, summary_str, da_loss, db_loss, d_loss, da_all_loss, db_all_loss, d_all_loss, D_loss = self.sess.run([
self.d_optim, self.d_sum, self.da_loss, self.db_loss, self.d_loss, self.da_all_loss,
self.db_all_loss, self.d_all_loss, self.D_loss],
feed_dict={self.real_data: batch_images, self.fake_A_sample: fake_A, self.fake_B_sample: fake_B,
self.lr: lr, self.gaussian_noise: gaussian_noise, self.real_mixed: batch_images_mixed})
self.writer.add_summary(summary_str, counter)
print('=================================================================')
print(("Epoch: [%2d] [%4d/%4d] time: %4.4f D_loss: %6.2f, d_loss: %6.2f, d_all_loss: %6.2f, "
"G_loss: %6.2f" %
(epoch, idx, batch_idxs, time.time() - start_time, D_loss, d_loss, d_all_loss, g_loss)))
print(("++++++++++G_loss_A2B: %6.2f G_loss_B2A: %6.2f Cycle_loss: %6.2f DA_loss: %6.2f DB_loss: %6.2f, "
"DA_all_loss: %6.2f DB_all_loss: %6.2f" %
(g_loss_a2b, g_loss_b2a, cycle_loss, da_loss, db_loss, da_all_loss, db_all_loss)))
counter += 1
if np.mod(counter, args.print_freq) == 1:
sample_dir = os.path.join(self.sample_dir, '{}2{}_{}_{}_{}'.format(self.dataset_A_dir,
self.dataset_B_dir,
self.now_datetime,
self.model,
self.sigma_d))
# sample_dir = os.path.join(self.sample_dir, '{}2{}_{}_{}_{}'.format(self.dataset_A_dir,
# self.dataset_B_dir,
# '2018-06-10',
# self.model,
# self.sigma_d))
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
self.sample_model(sample_dir, epoch, idx)
if np.mod(counter, batch_idxs) == 1:
self.save(args.checkpoint_dir, counter)
def save(self, checkpoint_dir, step):
model_name = "cyclegan.model"
model_dir = "{}2{}_{}_{}_{}".format(self.dataset_A_dir, self.dataset_B_dir, self.now_datetime, self.model,
self.sigma_d)
# model_dir = "{}2{}_{}_{}_{}".format(self.dataset_A_dir, self.dataset_B_dir, '2018-06-14', self.model,
# self.sigma_d)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess, os.path.join(checkpoint_dir, model_name), global_step=step)
def load(self, checkpoint_dir):
print(" [*] Reading checkpoint...")
model_dir = "{}2{}_{}_{}_{}".format(self.dataset_A_dir, self.dataset_B_dir, self.now_datetime, self.model,
self.sigma_d)
# model_dir = "{}2{}_{}_{}_{}".format(self.dataset_A_dir, self.dataset_B_dir, '2018-06-14', self.model,
# self.sigma_d)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
# self.saver.restore(self.sess, os.path.join(checkpoint_dir, 'cyclegan.model-7011'))
return True
else:
return False
def sample_model(self, sample_dir, epoch, idx):
print('Processing sample......')
# Testing data from 2 domains A and B and sorted in ascending order
dataA = glob('./datasets/{}/train/*.*'.format(self.dataset_A_dir))
dataB = glob('./datasets/{}/train/*.*'.format(self.dataset_B_dir))
dataA.sort(key=lambda x: int(os.path.splitext(os.path.basename(x))[0].split('_')[-1]))
dataB.sort(key=lambda x: int(os.path.splitext(os.path.basename(x))[0].split('_')[-1]))
batch_files = list(zip(dataA[:self.batch_size], dataB[:self.batch_size]))
sample_images = [load_npy_data(batch_file) for batch_file in batch_files]
sample_images = np.array(sample_images).astype(np.float32)
real_A_binary, fake_A_binary, fake_B_binary = self.sess.run([self.real_A_binary, self.fake_A_binary,
self.fake_B_binary],
feed_dict={self.real_data: sample_images})
real_B_binary, fake_A__binary, fake_B__binary = self.sess.run([self.real_B_binary, self.fake_A__binary,
self.fake_B__binary],
feed_dict={self.real_data: sample_images})
if not os.path.exists(os.path.join(sample_dir, 'B2A')):
os.makedirs(os.path.join(sample_dir, 'B2A'))
if not os.path.exists(os.path.join(sample_dir, 'A2B')):
os.makedirs(os.path.join(sample_dir, 'A2B'))
save_midis(real_A_binary, './{}/A2B/{:02d}_{:04d}_origin.mid'.format(sample_dir, epoch, idx))
save_midis(fake_B_binary, './{}/A2B/{:02d}_{:04d}_transfer.mid'.format(sample_dir, epoch, idx))
save_midis(fake_A__binary, './{}/A2B/{:02d}_{:04d}_cycle.mid'.format(sample_dir, epoch, idx))
save_midis(real_B_binary, './{}/B2A/{:02d}_{:04d}_origin.mid'.format(sample_dir, epoch, idx))
save_midis(fake_A_binary, './{}/B2A/{:02d}_{:04d}_transfer.mid'.format(sample_dir, epoch, idx))
save_midis(fake_B__binary, './{}/B2A/{:02d}_{:04d}_cycle.mid'.format(sample_dir, epoch, idx))
def test(self, args):
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
if args.which_direction == 'AtoB':
sample_files = glob('./datasets/{}/test/*.*'.format(self.dataset_A_dir))
elif args.which_direction == 'BtoA':
sample_files = glob('./datasets/{}/test/*.*'.format(self.dataset_B_dir))
else:
raise Exception('--which_direction must be AtoB or BtoA')
sample_files.sort(key=lambda x: int(os.path.splitext(os.path.basename(x))[0].split('_')[-1]))
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if args.which_direction == 'AtoB':
out_origin, out_var, out_var_cycle, in_var = (self.test_A_binary, self.testB_binary, self.testA__binary,
self.test_A)
else:
out_origin, out_var, out_var_cycle, in_var = (self.test_B_binary, self.testA_binary, self.testB__binary,
self.test_B)
test_dir_mid = os.path.join(args.test_dir, '{}2{}_{}_{}_{}/{}/mid'.format(self.dataset_A_dir,
self.dataset_B_dir,
self.now_datetime,
self.model,
self.sigma_d,
args.which_direction))
if not os.path.exists(test_dir_mid):
os.makedirs(test_dir_mid)
test_dir_npy = os.path.join(args.test_dir, '{}2{}_{}_{}_{}/{}/npy'.format(self.dataset_A_dir,
self.dataset_B_dir,
self.now_datetime,
self.model,
self.sigma_d,
args.which_direction))
if not os.path.exists(test_dir_npy):
os.makedirs(test_dir_npy)
for idx in range(len(sample_files)):
print('Processing midi: ', sample_files[idx])
sample_npy = np.load(sample_files[idx]) * 1.
sample_npy_re = sample_npy.reshape(1, sample_npy.shape[0], sample_npy.shape[1], 1)
midi_path_origin = os.path.join(test_dir_mid, '{}_origin.mid'.format(idx + 1))
midi_path_transfer = os.path.join(test_dir_mid, '{}_transfer.mid'.format(idx + 1))
midi_path_cycle = os.path.join(test_dir_mid, '{}_cycle.mid'.format(idx + 1))
origin_midi, fake_midi, fake_midi_cycle = self.sess.run([out_origin, out_var, out_var_cycle],
feed_dict={in_var: sample_npy_re})
save_midis(origin_midi, midi_path_origin)
save_midis(fake_midi, midi_path_transfer)
save_midis(fake_midi_cycle, midi_path_cycle)
npy_path_origin = os.path.join(test_dir_npy, 'origin')
npy_path_transfer = os.path.join(test_dir_npy, 'transfer')
npy_path_cycle = os.path.join(test_dir_npy, 'cycle')
if not os.path.exists(npy_path_origin):
os.makedirs(npy_path_origin)
if not os.path.exists(npy_path_transfer):
os.makedirs(npy_path_transfer)
if not os.path.exists(npy_path_cycle):
os.makedirs(npy_path_cycle)
np.save(os.path.join(npy_path_origin, '{}_origin.npy'.format(idx + 1)), origin_midi)
np.save(os.path.join(npy_path_transfer, '{}_transfer.npy'.format(idx + 1)), fake_midi)
np.save(os.path.join(npy_path_cycle, '{}_cycle.npy'.format(idx + 1)), fake_midi_cycle)
def test_famous(self, args):
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
song = np.load('./datasets/famous_songs/P2C/merged_npy/YMCA.npy')
print(song.shape)
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if args.which_direction == 'AtoB':
out_var, in_var = (self.testB_binary, self.test_A)
else:
out_var, in_var = (self.testA_binary, self.test_B)
transfer = self.sess.run(out_var, feed_dict={in_var: song * 1.})
save_midis(transfer, './datasets/famous_songs/P2C/transfer/YMCA.mid', 127)
np.save('./datasets/famous_songs/P2C/transfer/YMCA.npy', transfer)