def train_step(input_image, target, gen, gen_opti, discr, discr_opti, summary,
epoch):
with tf.GradientTape() as gen_tape, tf.GradientTape() as discr_tape:
input_aux = input_image
input_image = np.expand_dims(input_image, axis=0)
output_image = gen(((input_aux + 1) * 255), training=True)
input_image = np.squeeze(input_image, axis=0)
output_gen_discr = discr([input_image, output_image], training=True)
output_target_discr = discr([target, input_image], training=True)
discr_loss = discriminator.discriminator_loss(output_target_discr,
output_gen_discr)
gen_loss, gan_loss, l1_loss = generator.generator_loss(
output_gen_discr, output_image, target)
generator_grads = gen_tape.gradient(gen_loss, gen.trainable_variables)
discriminator_grads = discr_tape.gradient(discr_loss,
discr.trainable_variables)
gen_opti.apply_gradients(zip(generator_grads, gen.trainable_variables))
discr_opti.apply_gradients(
zip(discriminator_grads, discr.trainable_variables))
with summary.as_default():
tf.summary.scalar('gen_total_loss', gen_loss, step=epoch)
tf.summary.scalar('gen_gan_loss', gan_loss, step=epoch)
tf.summary.scalar('gen_l1_loss', l1_loss, step=epoch)
tf.summary.scalar('disc_loss', discr_loss, step=epoch)
def _train_vanilla_gan_on_mnist(args):
model_name = args.model_name
n_epochs = args.n_epochs
batch_size = args.batch_size
generator_model = GeneratorModelMNIST(**args)
discriminator_model = DiscriminatorModelMNIST(**args)
generator_optimizer = Adam(1e-4)
discriminator_optimizer = Adam(1e-4)
data_generator = get_mnist_dataset()
noise_dim = args.generator_noise_dim
num_examples_to_generate = args.num_examples_to_generate
seed = tf.random.normal([num_examples_to_generate, noise_dim])
plotting_callback = ml_utils.PlotAndSaveImages(test_input=seed,
model=generator_model,
model_name=model_name)
gen_ckpt = ml_utils.SimpleModelCheckPoint(model_name="mnist_generator",
model=generator_model)
disc_ckpt = ml_utils.SimpleModelCheckPoint(
model_name="mnist_discriminator", model=discriminator_model)
num_iterations = len(data_generator)
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
for epoch in range(n_epochs):
start = time()
for i, image_batch in enumerate(data_generator):
input_noise = tf.random.uniform(shape=(image_batch.shape[0],
noise_dim))
generated_images = generator_model(input_noise, training=True)
true_output = discriminator_model(image_batch, training=True)
fake_output = discriminator_model(generated_images,
training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(true_output, fake_output)
gen_gradients = gen_tape.gradient(
gen_loss, generator_model.trainable_variables)
disc_gradients = disc_tape.gradient(
disc_loss, discriminator_model.trainable_variables)
generator_optimizer.apply_gradients(
zip(gen_gradients, generator_model.trainable_variables))
discriminator_optimizer.apply_gradients(
zip(disc_gradients,
discriminator_model.trainable_variables))
logs = {"loss": gen_loss}
ckpt.on_epoch_end(epoch=epoch)
plotting_callback.on_train_end()
general_utils.smart_print(start, len(data_generator), i, epoch,
n_epochs, gen_loss, disc_loss)
def train_discriminator(r_img, r_ctg, f_noise, f_ctg):
with tf.GradientTape() as dis_tape:
r_predict = discriminator(r_img, r_ctg, training=True)
f_img_tmp = generator(f_noise[:BATCH_SIZE], f_ctg[:BATCH_SIZE])
f_predict = discriminator(f_img_tmp, f_ctg[:BATCH_SIZE], training=True)
dis_loss = discriminator_loss(r_predict, f_predict)
dis_gradients = dis_tape.gradient(dis_loss,
discriminator.trainable_variables)
optimizer.apply_gradients(
zip(dis_gradients, discriminator.trainable_variables))
train_dis_loss(dis_loss)
return
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(cross_entropy, fake_output)
disc_loss = discriminator_loss(cross_entropy, real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
def train_step(generator, discriminator, generator_optimizer,
discriminator_optimizer, real_images, real_captions,
wrong_images, wrong_captions, args):
"""
generate some noise -> pass into generator to generate fake images
get the real caption for the imaage, get a wrong caption randomly sampled from all other captions
obtain a wrong image from a different class
pass fake images and real images and wrong images through discriminator with the captions
"""
batch_size = args.batch_size
noise_dim = args.gen_input_dim
noise = tf.random.normal([batch_size, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_image_real_caption_output = discriminator(
[real_images, real_captions], training=True)
real_image_fake_wrong_output = discriminator(
[real_images, wrong_captions], training=True)
fake_image_real_caption_output = discriminator(
[generated_images, real_captions], training=True)
wrong_image_real_caption_output = discriminator(
[wrong_images, real_captions], training=True)
gen_loss = generator_loss(fake_image_real_caption_output)
disc_loss = discriminator_loss(real_image_real_caption_output,
real_image_fake_wrong_output,
fake_image_real_caption_output,
wrong_image_real_caption_output)
gen_gradients = gen_tape.gradient(gen_loss,
generator.trainable_variables)
disc_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(
zip(gen_gradients, generator.trainable_variables))
discriminator_optimizer.apply_gradients(
zip(disc_gradients, discriminator.trainable_variables))
return gen_loss, disc_loss
def train_step(images, batch_size, noise_dim):
noise = tf.random_normal([batch_size, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
generated_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(generated_output)
disc_loss = discriminator_loss(real_output, generated_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.variables)
gradients_of_discrimantor = disc_tape.gradient(disc_loss,
discriminator.variables)
generator_optimizer.apply_gradients(
zip(gradients_of_generator, generator.variables))
discriminator_optimizer.apply_gradients(
zip(gradients_of_discrimantor, discriminator.variables))
return gen_loss, disc_loss
def train(rank, args, hp, hp_str):
# if hp.train.num_gpus > 1:
# init_process_group(backend=hp.dist.dist_backend, init_method=hp.dist.dist_url,
# world_size=hp.dist.world_size * hp.train.num_gpus, rank=rank)
torch.cuda.manual_seed(hp.train.seed)
device = torch.device('cuda:{:d}'.format(rank))
generator = Generator(hp.model.in_channels,
hp.model.out_channels).to(device)
specd = SpecDiscriminator().to(device)
msd = MultiScaleDiscriminator().to(device)
stft_loss = MultiResolutionSTFTLoss()
if rank == 0:
print(generator)
os.makedirs(hp.logs.chkpt_dir, exist_ok=True)
print("checkpoints directory : ", hp.logs.chkpt_dir)
if os.path.isdir(hp.logs.chkpt_dir):
cp_g = scan_checkpoint(hp.logs.chkpt_dir, 'g_')
cp_do = scan_checkpoint(hp.logs.chkpt_dir, 'do_')
steps = 0
if cp_g is None or cp_do is None:
state_dict_do = None
last_epoch = -1
else:
state_dict_g = load_checkpoint(cp_g, device)
state_dict_do = load_checkpoint(cp_do, device)
generator.load_state_dict(state_dict_g['generator'])
specd.load_state_dict(state_dict_do['specd'])
msd.load_state_dict(state_dict_do['msd'])
steps = state_dict_do['steps'] + 1
last_epoch = state_dict_do['epoch']
if hp.train.num_gpus > 1:
generator = DistributedDataParallel(generator,
device_ids=[rank]).to(device)
specd = DistributedDataParallel(specd, device_ids=[rank]).to(device)
msd = DistributedDataParallel(msd, device_ids=[rank]).to(device)
optim_g = torch.optim.AdamW(
generator.parameters(),
hp.train.adamG.lr,
betas=[hp.train.adamG.beta1, hp.train.adamG.beta2])
optim_d = torch.optim.AdamW(
itertools.chain(msd.parameters(), specd.parameters()),
hp.train.adamD.lr,
betas=[hp.train.adamD.beta1, hp.train.adamD.beta2])
if state_dict_do is not None:
optim_g.load_state_dict(state_dict_do['optim_g'])
optim_d.load_state_dict(state_dict_do['optim_d'])
# scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hp.train.adam.lr_decay, last_epoch=last_epoch)
# scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hp.train.adam.lr_decay, last_epoch=last_epoch)
training_filelist, validation_filelist = get_dataset_filelist(args)
trainset = MelDataset(training_filelist,
hp.data.input_wavs,
hp.data.output_wavs,
hp.audio.segment_length,
hp.audio.filter_length,
hp.audio.n_mel_channels,
hp.audio.hop_length,
hp.audio.win_length,
hp.audio.sampling_rate,
hp.audio.mel_fmin,
hp.audio.mel_fmax,
n_cache_reuse=0,
shuffle=False if hp.train.num_gpus > 1 else True,
fmax_loss=None,
device=device)
train_sampler = DistributedSampler(
trainset) if hp.train.num_gpus > 1 else None
train_loader = DataLoader(trainset,
num_workers=hp.train.num_workers,
shuffle=False,
sampler=train_sampler,
batch_size=hp.train.batch_size,
pin_memory=True,
drop_last=True)
if rank == 0:
validset = MelDataset(validation_filelist,
hp.data.input_wavs,
hp.data.output_wavs,
hp.audio.segment_length,
hp.audio.filter_length,
hp.audio.n_mel_channels,
hp.audio.hop_length,
hp.audio.win_length,
hp.audio.sampling_rate,
hp.audio.mel_fmin,
hp.audio.mel_fmax,
split=False,
shuffle=False,
n_cache_reuse=0,
fmax_loss=None,
device=device)
validation_loader = DataLoader(validset,
num_workers=1,
shuffle=False,
sampler=None,
batch_size=1,
pin_memory=True,
drop_last=True)
sw = SummaryWriter(os.path.join(hp.logs.chkpt_dir, 'logs'))
generator.train()
specd.train()
msd.train()
with_postnet = False
for epoch in range(max(0, last_epoch), args.training_epochs):
if rank == 0:
start = time.time()
print("Epoch: {}".format(epoch + 1))
if hp.train.num_gpus > 1:
train_sampler.set_epoch(epoch)
for i, batch in enumerate(train_loader):
if rank == 0:
start_b = time.time()
if steps > hp.train.postnet_start_steps:
with_postnet = True
x, y, file, _, y_mel_loss = batch
x = torch.autograd.Variable(x.to(device, non_blocking=True))
y = torch.autograd.Variable(y.to(device, non_blocking=True))
y_mel_loss = torch.autograd.Variable(
y_mel_loss.to(device, non_blocking=True))
# y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
x = x.unsqueeze(1)
y = y.unsqueeze(1)
before_y_g_hat, y_g_hat = generator(x, with_postnet)
if y_g_hat is not None:
y_g_hat_mel = mel_spectrogram(
y_g_hat.squeeze(1), hp.audio.filter_length,
hp.audio.n_mel_channels, hp.audio.sampling_rate,
hp.audio.hop_length, hp.audio.win_length,
hp.audio.mel_fmin, None)
if steps > hp.train.discriminator_train_start_steps:
for _ in range(hp.train.rep_discriminator):
optim_d.zero_grad()
# SpecD
y_df_hat_r, y_df_hat_g, _, _ = specd(
y_mel_loss, y_g_hat_mel.detach())
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(
y_df_hat_r, y_df_hat_g)
# MSD
y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach())
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(
y_ds_hat_r, y_ds_hat_g)
loss_disc_all = loss_disc_s + loss_disc_f
loss_disc_all.backward()
optim_d.step()
before_y_g_hat_mel = mel_spectrogram(
before_y_g_hat.squeeze(1), hp.audio.filter_length,
hp.audio.n_mel_channels, hp.audio.sampling_rate,
hp.audio.hop_length, hp.audio.win_length, hp.audio.mel_fmin,
None)
# Generator
optim_g.zero_grad()
# L1 Mel-Spectrogram Loss
# before_loss_mel = F.l1_loss(y_mel_loss, before_y_g_hat_mel)
sc_loss, mag_loss = stft_loss(
before_y_g_hat[:, :, :y.size(2)].squeeze(1), y.squeeze(1))
before_loss_mel = sc_loss + mag_loss
# L1 Sample Loss
before_loss_sample = F.l1_loss(y, before_y_g_hat)
loss_gen_all = before_loss_mel + before_loss_sample
if y_g_hat is not None:
# L1 Mel-Spectrogram Loss
# loss_mel = F.l1_loss(y_mel_loss, y_g_hat_mel)
sc_loss_, mag_loss_ = stft_loss(
y_g_hat[:, :, :y.size(2)].squeeze(1), y.squeeze(1))
loss_mel = sc_loss_ + mag_loss_
# L1 Sample Loss
loss_sample = F.l1_loss(y, y_g_hat)
loss_gen_all += loss_mel + loss_sample
if steps > hp.train.discriminator_train_start_steps:
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = specd(
y_mel_loss, y_g_hat_mel)
y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat)
loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
loss_gen_all += hp.model.lambda_adv * (
loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f)
loss_gen_all.backward()
optim_g.step()
if rank == 0:
# STDOUT logging
if steps % args.stdout_interval == 0:
with torch.no_grad():
mel_error = F.l1_loss(y_mel_loss,
before_y_g_hat_mel).item()
sample_error = F.l1_loss(y, before_y_g_hat)
print(
'Steps : {:d}, Gen Loss Total : {:4.3f}, Sample Error: {:4.3f}, '
'Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'.format(
steps, loss_gen_all, sample_error, mel_error,
time.time() - start_b))
# checkpointing
if steps % hp.logs.save_interval == 0 and steps != 0:
checkpoint_path = "{}/g_{:08d}".format(
hp.logs.chkpt_dir, steps)
save_checkpoint(
checkpoint_path, {
'generator':
(generator.module if hp.train.num_gpus > 1 else
generator).state_dict()
})
checkpoint_path = "{}/do_{:08d}".format(
hp.logs.chkpt_dir, steps)
save_checkpoint(
checkpoint_path, {
'specd': (specd.module if hp.train.num_gpus > 1
else specd).state_dict(),
'msd': (msd.module if hp.train.num_gpus > 1 else
msd).state_dict(),
'optim_g':
optim_g.state_dict(),
'optim_d':
optim_d.state_dict(),
'steps':
steps,
'epoch':
epoch,
'hp_str':
hp_str
})
# Tensorboard summary logging
if steps % hp.logs.summary_interval == 0:
sw.add_scalar("training/gen_loss_total", loss_gen_all,
steps)
sw.add_scalar("training/mel_spec_error", mel_error, steps)
# Validation
if steps % hp.logs.validation_interval == 0: # and steps != 0:
generator.eval()
torch.cuda.empty_cache()
val_err_tot = 0
with torch.no_grad():
for j, batch in enumerate(validation_loader):
x, y, file, y_mel, y_mel_loss = batch
x = x.unsqueeze(1)
y = y.unsqueeze(1).to(device)
before_y_g_hat, y_g_hat = generator(x.to(device))
y_mel_loss = torch.autograd.Variable(
y_mel_loss.to(device, non_blocking=True))
y_g_hat_mel = mel_spectrogram(
before_y_g_hat.squeeze(1),
hp.audio.filter_length,
hp.audio.n_mel_channels,
hp.audio.sampling_rate, hp.audio.hop_length,
hp.audio.win_length, hp.audio.mel_fmin, None)
val_err_tot += F.l1_loss(y_mel_loss,
y_g_hat_mel).item()
val_err_tot += F.l1_loss(y, before_y_g_hat).item()
if y_g_hat is not None:
val_err_tot += F.l1_loss(y, y_g_hat).item()
if j <= 4:
if steps == 0:
sw.add_audio('gt_noise/y_{}'.format(j),
x[0], steps,
hp.audio.sampling_rate)
sw.add_audio('gt_clean/y_{}'.format(j),
y[0], steps,
hp.audio.sampling_rate)
sw.add_figure(
'gt/y_spec_clean_{}'.format(j),
plot_spectrogram(y_mel[0]), steps)
sw.add_audio('generated/y_hat_{}'.format(j),
before_y_g_hat[0], steps,
hp.audio.sampling_rate)
if y_g_hat is not None:
sw.add_audio(
'generated/y_hat_after_{}'.format(j),
y_g_hat[0], steps,
hp.audio.sampling_rate)
y_hat_spec = mel_spectrogram(
before_y_g_hat.squeeze(1),
hp.audio.filter_length,
hp.audio.n_mel_channels,
hp.audio.sampling_rate,
hp.audio.hop_length, hp.audio.win_length,
hp.audio.mel_fmin, None)
sw.add_figure(
'generated/y_hat_spec_{}'.format(j),
plot_spectrogram(
y_hat_spec.squeeze(0).cpu().numpy()),
steps)
val_err = val_err_tot / (j + 1)
sw.add_scalar("validation/mel_spec_error", val_err,
steps)
generator.train()
steps += 1
# scheduler_g.step()
# scheduler_d.step()
if rank == 0:
print('Time taken for epoch {} is {} sec\n'.format(
epoch + 1, int(time.time() - start)))