-
Notifications
You must be signed in to change notification settings - Fork 16
/
Copy pathtf2_model.py
509 lines (413 loc) · 28.5 KB
/
tf2_model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
import os
import time
from glob import glob
import numpy as np
from collections import namedtuple
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tf2_module import build_generator, build_discriminator, abs_criterion, mae_criterion
from tf2_utils import get_now_datetime, ImagePool, to_binary, load_npy_data, save_midis
class CycleGAN(object):
def __init__(self, args):
self.batch_size = args.batch_size
self.time_step = args.time_step # number of time steps
self.pitch_range = args.pitch_range # number of pitches
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.lr = args.lr
self.L1_lambda = args.L1_lambda
self.gamma = args.gamma
self.sigma_d = args.sigma_d
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 = build_discriminator
self.generator = build_generator
self.criterionGAN = mae_criterion
OPTIONS = namedtuple('OPTIONS', 'batch_size '
'time_step '
'input_nc '
'output_nc '
'pitch_range '
'gf_dim '
'df_dim '
'is_training')
self.options = OPTIONS._make((args.batch_size,
args.time_step,
args.pitch_range,
args.input_nc,
args.output_nc,
args.ngf,
args.ndf,
args.phase == 'train'))
self.now_datetime = get_now_datetime()
self.pool = ImagePool(args.max_size)
self._build_model(args)
print("initialize model...")
def _build_model(self, args):
# Generator
self.generator_A2B = self.generator(self.options,
name='Generator_A2B')
self.generator_B2A = self.generator(self.options,
name='Generator_B2A')
# Discriminator
self.discriminator_A = self.discriminator(self.options,
name='Discriminator_A')
self.discriminator_B = self.discriminator(self.options,
name='Discriminator_B')
if self.model != 'base':
self.discriminator_A_all = self.discriminator(self.options,
name='Discriminator_A_all')
self.discriminator_B_all = self.discriminator(self.options,
name='Discriminator_B_all')
# Discriminator and Generator Optimizer
self.DA_optimizer = Adam(self.lr,
beta_1=args.beta1)
self.DB_optimizer = Adam(self.lr,
beta_1=args.beta1)
self.GA2B_optimizer = Adam(self.lr,
beta_1=args.beta1)
self.GB2A_optimizer = Adam(self.lr,
beta_1=args.beta1)
if self.model != 'base':
self.DA_all_optimizer = Adam(self.lr,
beta_1=args.beta1)
self.DB_all_optimizer = Adam(self.lr,
beta_1=args.beta1)
# Checkpoints
model_name = "cyclegan.model"
model_dir = "{}2{}_{}_{}_{}".format(self.dataset_A_dir,
self.dataset_B_dir,
self.now_datetime,
self.model,
self.sigma_d)
self.checkpoint_dir = os.path.join(args.checkpoint_dir,
model_dir,
model_name)
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
if self.model == 'base':
self.checkpoint = tf.train.Checkpoint(generator_A2B_optimizer=self.GA2B_optimizer,
generator_B2A_optimizer=self.GB2A_optimizer,
discriminator_A_optimizer=self.DA_optimizer,
discriminator_B_optimizer=self.DB_optimizer,
generator_A2B=self.generator_A2B,
generator_B2A=self.generator_B2A,
discriminator_A=self.discriminator_A,
discriminator_B=self.discriminator_B)
else:
self.checkpoint = tf.train.Checkpoint(generator_A2B_optimizer=self.GA2B_optimizer,
generator_B2A_optimizer=self.GB2A_optimizer,
discriminator_A_optimizer=self.DA_optimizer,
discriminator_B_optimizer=self.DB_optimizer,
discriminator_A_all_optimizer=self.DA_all_optimizer,
discriminator_B_all_optimizer=self.DB_all_optimizer,
generator_A2B=self.generator_A2B,
generator_B2A=self.generator_B2A,
discriminator_A=self.discriminator_A,
discriminator_B=self.discriminator_B,
discriminator_A_all=self.discriminator_A_all,
discriminator_B_all=self.discriminator_B_all)
self.checkpoint_manager = tf.train.CheckpointManager(self.checkpoint,
self.checkpoint_dir,
max_to_keep=5)
# if self.checkpoint_manager.latest_checkpoint:
# self.checkpoint.restore(self.checkpoint_manager.latest_checkpoint)
# print('Latest checkpoint restored!!')
def train(self, args):
# 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))
data_mixed = None
if self.model == 'partial':
data_mixed = dataA + dataB
if self.model == 'full':
data_mixed = glob('./datasets/JCP_mixed/*.*')
if args.continue_train:
if self.checkpoint.restore(self.checkpoint_manager.latest_checkpoint):
print(" [*] Load checkpoint succeeded!")
else:
print(" [!] Load checkpoint failed...")
counter = 1
start_time = time.time()
for epoch in range(args.epoch):
# Shuffle training data
np.random.shuffle(dataA)
np.random.shuffle(dataB)
if self.model != 'base' and data_mixed is not None:
np.random.shuffle(data_mixed)
# Get the proper number of batches
batch_idxs = min(len(dataA), len(dataB)) // self.batch_size
# learning rate starts to decay when reaching the threshold
self.lr = self.lr if epoch < args.epoch_step else self.lr * (args.epoch-epoch) / (args.epoch-args.epoch_step)
for idx in range(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_samples = [load_npy_data(batch_file) for batch_file in batch_files]
batch_samples = np.array(batch_samples).astype(np.float32) # batch_size * 64 * 84 * 2
real_A, real_B = batch_samples[:, :, :, 0], batch_samples[:, :, :, 1]
real_A = tf.expand_dims(real_A, -1)
real_B = tf.expand_dims(real_B, -1)
# generate gaussian noise for robustness improvement
gaussian_noise = np.abs(np.random.normal(0,
self.sigma_d,
[self.batch_size,
self.time_step,
self.pitch_range,
self.input_c_dim])).astype(np.float32)
if self.model == 'base':
with tf.GradientTape(persistent=True) as gen_tape, tf.GradientTape(persistent=True) as disc_tape:
fake_B = self.generator_A2B(real_A,
training=True)
cycle_A = self.generator_B2A(fake_B,
training=True)
fake_A = self.generator_B2A(real_B,
training=True)
cycle_B = self.generator_A2B(fake_A,
training=True)
[fake_A_sample, fake_B_sample] = self.pool([fake_A, fake_B])
DA_real = self.discriminator_A(real_A + gaussian_noise,
training=True)
DB_real = self.discriminator_B(real_B + gaussian_noise,
training=True)
DA_fake = self.discriminator_A(fake_A + gaussian_noise,
training=True)
DB_fake = self.discriminator_B(fake_B + gaussian_noise,
training=True)
DA_fake_sample = self.discriminator_A(fake_A_sample + gaussian_noise,
training=True)
DB_fake_sample = self.discriminator_B(fake_B_sample + gaussian_noise,
training=True)
# Generator loss
cycle_loss = self.L1_lambda * (abs_criterion(real_A, cycle_A) + abs_criterion(real_B, cycle_B))
g_A2B_loss = self.criterionGAN(DB_fake, tf.ones_like(DB_fake)) + cycle_loss
g_B2A_loss = self.criterionGAN(DA_fake, tf.ones_like(DA_fake)) + cycle_loss
g_loss = g_A2B_loss + g_B2A_loss - cycle_loss
# Discriminator loss
d_A_loss_real = self.criterionGAN(DA_real, tf.ones_like(DA_real))
d_A_loss_fake = self.criterionGAN(DA_fake_sample, tf.zeros_like(DA_fake_sample))
d_A_loss = (d_A_loss_real + d_A_loss_fake) / 2
d_B_loss_real = self.criterionGAN(DB_real, tf.ones_like(DB_real))
d_B_loss_fake = self.criterionGAN(DB_fake_sample, tf.zeros_like(DB_fake_sample))
d_B_loss = (d_B_loss_real + d_B_loss_fake) / 2
d_loss = d_A_loss + d_B_loss
# Calculate the gradients for generator and discriminator
generator_A2B_gradients = gen_tape.gradient(target=g_A2B_loss,
sources=self.generator_A2B.trainable_variables)
generator_B2A_gradients = gen_tape.gradient(target=g_B2A_loss,
sources=self.generator_B2A.trainable_variables)
discriminator_A_gradients = disc_tape.gradient(target=d_A_loss,
sources=self.discriminator_A.trainable_variables)
discriminator_B_gradients = disc_tape.gradient(target=d_B_loss,
sources=self.discriminator_B.trainable_variables)
# Apply the gradients to the optimizer
self.GA2B_optimizer.apply_gradients(zip(generator_A2B_gradients,
self.generator_A2B.trainable_variables))
self.GB2A_optimizer.apply_gradients(zip(generator_B2A_gradients,
self.generator_B2A.trainable_variables))
self.DA_optimizer.apply_gradients(zip(discriminator_A_gradients,
self.discriminator_A.trainable_variables))
self.DB_optimizer.apply_gradients(zip(discriminator_B_gradients,
self.discriminator_B.trainable_variables))
print('=================================================================')
print(("Epoch: [%2d] [%4d/%4d] time: %4.4f D_loss: %6.2f, G_loss: %6.2f, cycle_loss: %6.2f" %
(epoch, idx, batch_idxs, time.time() - start_time, d_loss, g_loss, cycle_loss)))
else:
# To feed real_mixed
batch_files_mixed = data_mixed[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_samples_mixed = [np.load(batch_file) * 1. for batch_file in batch_files_mixed]
real_mixed = np.array(batch_samples_mixed).astype(np.float32)
with tf.GradientTape(persistent=True) as gen_tape, tf.GradientTape(persistent=True) as disc_tape:
fake_B = self.generator_A2B(real_A,
training=True)
cycle_A = self.generator_B2A(fake_B,
training=True)
fake_A = self.generator_B2A(real_B,
training=True)
cycle_B = self.generator_A2B(fake_A,
training=True)
[fake_A_sample, fake_B_sample] = self.pool([fake_A, fake_B])
DA_real = self.discriminator_A(real_A + gaussian_noise,
training=True)
DB_real = self.discriminator_B(real_B + gaussian_noise,
training=True)
DA_fake = self.discriminator_A(fake_A + gaussian_noise,
training=True)
DB_fake = self.discriminator_B(fake_B + gaussian_noise,
training=True)
DA_fake_sample = self.discriminator_A(fake_A_sample + gaussian_noise,
training=True)
DB_fake_sample = self.discriminator_B(fake_B_sample + gaussian_noise,
training=True)
DA_real_all = self.discriminator_A_all(real_mixed + gaussian_noise,
training=True)
DB_real_all = self.discriminator_B_all(real_mixed + gaussian_noise,
training=True)
DA_fake_sample_all = self.discriminator_A_all(fake_A_sample + gaussian_noise,
training=True)
DB_fake_sample_all = self.discriminator_B_all(fake_B_sample + gaussian_noise,
training=True)
# Generator loss
cycle_loss = self.L1_lambda * (abs_criterion(real_A, cycle_A) + abs_criterion(real_B, cycle_B))
g_A2B_loss = self.criterionGAN(DB_fake, tf.ones_like(DB_fake)) + cycle_loss
g_B2A_loss = self.criterionGAN(DA_fake, tf.ones_like(DA_fake)) + cycle_loss
g_loss = g_A2B_loss + g_B2A_loss - cycle_loss
# Discriminator loss
d_A_loss_real = self.criterionGAN(DA_real, tf.ones_like(DA_real))
d_A_loss_fake = self.criterionGAN(DA_fake_sample, tf.zeros_like(DA_fake_sample))
d_A_loss = (d_A_loss_real + d_A_loss_fake) / 2
d_B_loss_real = self.criterionGAN(DB_real, tf.ones_like(DB_real))
d_B_loss_fake = self.criterionGAN(DB_fake_sample, tf.zeros_like(DB_fake_sample))
d_B_loss = (d_B_loss_real + d_B_loss_fake) / 2
d_loss = d_A_loss + d_B_loss
d_A_all_loss_real = self.criterionGAN(DA_real_all, tf.ones_like(DA_real_all))
d_A_all_loss_fake = self.criterionGAN(DA_fake_sample_all, tf.zeros_like(DA_fake_sample_all))
d_A_all_loss = (d_A_all_loss_real + d_A_all_loss_fake) / 2
d_B_all_loss_real = self.criterionGAN(DB_real_all, tf.ones_like(DB_real_all))
d_B_all_loss_fake = self.criterionGAN(DB_fake_sample_all, tf.zeros_like(DB_fake_sample_all))
d_B_all_loss = (d_B_all_loss_real + d_B_all_loss_fake) / 2
d_all_loss = d_A_all_loss + d_B_all_loss
D_loss = d_loss + self.gamma * d_all_loss
# Calculate the gradients for generator and discriminator
generator_A2B_gradients = gen_tape.gradient(target=g_A2B_loss,
sources=self.generator_A2B.trainable_variables)
generator_B2A_gradients = gen_tape.gradient(target=g_B2A_loss,
sources=self.generator_B2A.trainable_variables)
discriminator_A_gradients = disc_tape.gradient(target=d_A_loss,
sources=self.discriminator_A.trainable_variables)
discriminator_B_gradients = disc_tape.gradient(target=d_B_loss,
sources=self.discriminator_B.trainable_variables)
discriminator_A_all_gradients = disc_tape.gradient(target=d_A_all_loss,
sources=self.discriminator_A_all.trainable_variables)
discriminator_B_all_gradients = disc_tape.gradient(target=d_B_all_loss,
sources=self.discriminator_B_all.trainable_variables)
# Apply the gradients to the optimizer
self.GA2B_optimizer.apply_gradients(zip(generator_A2B_gradients,
self.generator_A2B.trainable_variables))
self.GB2A_optimizer.apply_gradients(zip(generator_B2A_gradients,
self.generator_B2A.trainable_variables))
self.DA_optimizer.apply_gradients(zip(discriminator_A_gradients,
self.discriminator_A.trainable_variables))
self.DB_optimizer.apply_gradients(zip(discriminator_B_gradients,
self.discriminator_B.trainable_variables))
self.DA_all_optimizer.apply_gradients(zip(discriminator_A_all_gradients,
self.discriminator_A_all.trainable_variables))
self.DB_all_optimizer.apply_gradients(zip(discriminator_B_all_gradients,
self.discriminator_B_all.trainable_variables))
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)))
counter += 1
# generate samples during training to track the learning process
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))
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
# to binary, 0 denotes note off, 1 denotes note on
samples = [to_binary(real_A, 0.5),
to_binary(fake_B, 0.5),
to_binary(cycle_A, 0.5),
to_binary(real_B, 0.5),
to_binary(fake_A, 0.5),
to_binary(cycle_B, 0.5)]
self.sample_model(samples=samples,
sample_dir=sample_dir,
epoch=epoch,
idx=idx)
if np.mod(counter, args.save_freq) == 1:
self.checkpoint_manager.save(counter)
def sample_model(self, samples, sample_dir, epoch, idx):
print('generating samples during learning......')
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(samples[0], './{}/A2B/{:02d}_{:04d}_origin.mid'.format(sample_dir, epoch, idx))
save_midis(samples[1], './{}/A2B/{:02d}_{:04d}_transfer.mid'.format(sample_dir, epoch, idx))
save_midis(samples[2], './{}/A2B/{:02d}_{:04d}_cycle.mid'.format(sample_dir, epoch, idx))
save_midis(samples[3], './{}/B2A/{:02d}_{:04d}_origin.mid'.format(sample_dir, epoch, idx))
save_midis(samples[4], './{}/B2A/{:02d}_{:04d}_transfer.mid'.format(sample_dir, epoch, idx))
save_midis(samples[5], './{}/B2A/{:02d}_{:04d}_cycle.mid'.format(sample_dir, epoch, idx))
def test(self, args):
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.checkpoint.restore(self.checkpoint_manager.latest_checkpoint):
print(" [*] Load checkpoint succeeded!")
else:
print(" [!] Load checkpoint failed...")
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.
# save midis
origin = 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))
if args.which_direction == 'AtoB':
transfer = self.generator_A2B(origin,
training=False)
cycle = self.generator_B2A(transfer,
training=False)
else:
transfer = self.generator_B2A(origin,
training=False)
cycle = self.generator_A2B(transfer,
training=False)
save_midis(origin, midi_path_origin)
save_midis(transfer, midi_path_transfer)
save_midis(cycle, midi_path_cycle)
# save npy files
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)
np.save(os.path.join(npy_path_transfer, '{}_transfer.npy'.format(idx + 1)), transfer)
np.save(os.path.join(npy_path_cycle, '{}_cycle.npy'.format(idx + 1)), cycle)
def test_famous(self, args):
song = np.load('./datasets/famous_songs/P2C/merged_npy/YMCA.npy')
if self.checkpoint.restore(self.checkpoint_manager.latest_checkpoint):
print(" [*] Load checkpoint succeeded!")
else:
print(" [!] Load checkpoint failed...")
if args.which_direction == 'AtoB':
transfer = self.generator_A2B(song,
training=False)
else:
transfer = self.generator_B2A(song,
training=False)
save_midis(transfer, './datasets/famous_songs/P2C/transfer/YMCA.mid', 127)
np.save('./datasets/famous_songs/P2C/transfer/YMCA.npy', transfer)