def get_cnn_model(params,
x,
processing_specgan=True,
return_last_activations=False):
batch_size = params['batch_size']
layers_transferred = params['layers_transferred']
layer_1_size = params['layers'][1]
layer_2_size = params['layers'][2]
with tf.variable_scope('D'):
D_x_training = SpecGANDiscriminator(
x) if processing_specgan else WaveGANDiscriminator(
x) # leave the other parameters default
last_conv_op_name = 'D_x/D/Maximum' + ("_" + str(layers_transferred) if
layers_transferred != 0 else "")
last_conv_tensor = tf.get_default_graph().get_operation_by_name(
last_conv_op_name).values()[0]
last_conv_tensor = tf.reshape(
last_conv_tensor, [batch_size, -1],
name="reshape_of_last_transferred_layer") # Flatten
with tf.variable_scope('decision_layers'):
if layer_1_size == 0:
last_activations = last_conv_tensor
output = last_conv_tensor
else:
with tf.variable_scope('decision_layer_1'):
last_activations = tf.layers.dense(last_conv_tensor,
layer_1_size,
activation=tf.nn.sigmoid)
output = last_activations
if layer_2_size != 0:
with tf.variable_scope('decision_layer_2'):
last_activations = tf.layers.dense(
output, layer_2_size, activation=tf.nn.sigmoid)
output = last_activations
with tf.variable_scope('cnn_output_layer'):
output = tf.layers.dense(output, 10)
if return_last_activations:
return output, last_activations
return output
def train(fps, args):
with tf.name_scope('loader'):
x, y = loader.get_batch(fps,
args.train_batch_size,
_WINDOW_LEN,
args.data_first_window,
labels=True)
# Make inputs
y_fill = tf.expand_dims(y, axis=2)
z = tf.random_uniform([args.train_batch_size, _D_Z],
-1.,
1.,
dtype=tf.float32)
# concatenate
x = tf.concat([x, y_fill], 1)
z = tf.concat([z, y], 1)
# Make generator
with tf.variable_scope('G'):
G_z = WaveGANGenerator(z, train=True, **args.wavegan_g_kwargs)
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z,
1,
args.wavegan_genr_pp_len,
use_bias=False,
padding='same')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
# Print G summary
print('-' * 80)
print('Generator vars')
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
# Summarize
tf.summary.audio('x', x, _FS)
tf.summary.audio('G_z', G_z, _FS)
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
# Print D summary
print('-' * 80)
print('Discriminator vars')
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
print('-' * 80)
# Make fake discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size], dtype=tf.float32)
real = tf.ones([args.train_batch_size], dtype=tf.float32)
G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=real))
D_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=fake))
D_loss += tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x, labels=real))
D_loss /= 2.
elif args.wavegan_loss == 'lsgan':
G_loss = tf.reduce_mean((D_G_z - 1.)**2)
D_loss = tf.reduce_mean((D_x - 1.)**2)
D_loss += tf.reduce_mean(D_G_z**2)
D_loss /= 2.
elif args.wavegan_loss == 'wgan':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])))
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
differences = G_z - x
interpolates = x + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(interpolates,
**args.wavegan_d_kwargs)
LAMBDA = 10
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
D_loss += LAMBDA * gradient_penalty
else:
raise NotImplementedError()
tf.summary.scalar('G_loss', G_loss)
tf.summary.scalar('D_loss', D_loss)
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
G_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
else:
raise NotImplementedError()
# Create training ops
G_train_op = G_opt.minimize(
G_loss,
var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
# Run training
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
while True:
# Train discriminator
for i in xrange(args.wavegan_disc_nupdates):
sess.run(D_train_op)
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights)
# Train generator
sess.run(G_train_op)
def initialize(self, opt):
BaseModel.initialize(self, opt)
# load/define networks
# The naming conversion is different from those used in the paper
# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self.netG_A = WaveGANGenerator(
model_size=opt.model_size,
ngpus=opt.ngpus,
latent_dim=opt.latent_dim,
alpha=opt.alpha,
post_proc_filt_len=opt.post_proc_filt_len)
self.netG_B = WaveGANGenerator(
model_size=opt.model_size,
ngpus=opt.ngpus,
latent_dim=opt.latent_dim,
alpha=opt.alpha,
post_proc_filt_len=opt.post_proc_filt_len)
if self.isTrain:
use_sigmoid = opt.gan_loss != 'lsgan'
self.netD_A = WaveGANDiscriminator(model_size=opt.model_size,
ngpus=opt.ngpus,
shift_factor=opt.shift_factor,
alpha=opt.alpha,
batch_shuffle=opt.batch_shuffle)
self.netD_B = WaveGANDiscriminator(model_size=opt.model_size,
ngpus=opt.ngpus,
shift_factor=opt.shift_factor,
alpha=opt.alpha,
batch_shuffle=opt.batch_shuffle)
if self.isTrain:
self.fake_A_pool = AudioPool(opt.pool_size)
self.fake_B_pool = AudioPool(opt.pool_size)
# define loss functions
self.criterionGAN = GANLoss(loss_type=opt.gan_loss,
tensor=self.Tensor)
self.criterionCycle = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers
self.optimizer_G = torch.optim.Adam(itertools.chain(
self.netG_A.parameters(), self.netG_B.parameters()),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizer_D_A = torch.optim.Adam(self.netD_A.parameters(),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizer_D_B = torch.optim.Adam(self.netD_B.parameters(),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizers = []
self.schedulers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D_A)
self.optimizers.append(self.optimizer_D_B)
for optimizer in self.optimizers:
self.schedulers.append(get_scheduler(optimizer, opt))
LOGGER.info('---------- Networks initialized -------------')
print_network(self.netG_A)
print_network(self.netG_B)
if self.isTrain:
print_network(self.netD_A)
print_network(self.netD_B)
LOGGER.info('-----------------------------------------------')
class CycleGANModel(BaseModel):
def name(self):
return 'CycleGANModel'
def initialize(self, opt):
BaseModel.initialize(self, opt)
# load/define networks
# The naming conversion is different from those used in the paper
# Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
self.netG_A = WaveGANGenerator(
model_size=opt.model_size,
ngpus=opt.ngpus,
latent_dim=opt.latent_dim,
alpha=opt.alpha,
post_proc_filt_len=opt.post_proc_filt_len)
self.netG_B = WaveGANGenerator(
model_size=opt.model_size,
ngpus=opt.ngpus,
latent_dim=opt.latent_dim,
alpha=opt.alpha,
post_proc_filt_len=opt.post_proc_filt_len)
if self.isTrain:
use_sigmoid = opt.gan_loss != 'lsgan'
self.netD_A = WaveGANDiscriminator(model_size=opt.model_size,
ngpus=opt.ngpus,
shift_factor=opt.shift_factor,
alpha=opt.alpha,
batch_shuffle=opt.batch_shuffle)
self.netD_B = WaveGANDiscriminator(model_size=opt.model_size,
ngpus=opt.ngpus,
shift_factor=opt.shift_factor,
alpha=opt.alpha,
batch_shuffle=opt.batch_shuffle)
if self.isTrain:
self.fake_A_pool = AudioPool(opt.pool_size)
self.fake_B_pool = AudioPool(opt.pool_size)
# define loss functions
self.criterionGAN = GANLoss(loss_type=opt.gan_loss,
tensor=self.Tensor)
self.criterionCycle = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers
self.optimizer_G = torch.optim.Adam(itertools.chain(
self.netG_A.parameters(), self.netG_B.parameters()),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizer_D_A = torch.optim.Adam(self.netD_A.parameters(),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizer_D_B = torch.optim.Adam(self.netD_B.parameters(),
lr=opt.lr,
betas=(opt.beta1, opt.beta2))
self.optimizers = []
self.schedulers = []
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D_A)
self.optimizers.append(self.optimizer_D_B)
for optimizer in self.optimizers:
self.schedulers.append(get_scheduler(optimizer, opt))
LOGGER.info('---------- Networks initialized -------------')
print_network(self.netG_A)
print_network(self.netG_B)
if self.isTrain:
print_network(self.netD_A)
print_network(self.netD_B)
LOGGER.info('-----------------------------------------------')
def set_input(self, input):
AtoB = self.opt.which_direction == 'AtoB'
input_A = input['A' if AtoB else 'B']
input_B = input['B' if AtoB else 'A']
if len(self.gpu_ids) > 0:
input_A = input_A.cuda(self.gpu_ids[0], async=True)
input_B = input_B.cuda(self.gpu_ids[0], async=True)
self.input_A = input_A
self.input_B = input_B
self.audio_paths = input['A_paths' if AtoB else 'B_paths']
def forward(self):
self.real_A = Variable(self.input_A)
self.real_B = Variable(self.input_B)
def test(self):
real_A = Variable(self.input_A, volatile=True)
fake_B = self.netG_A(real_A)
self.rec_A = self.netG_B(fake_B).data
self.fake_B = fake_B.data
real_B = Variable(self.input_B, volatile=True)
fake_A = self.netG_B(real_B)
self.rec_B = self.netG_A(fake_A).data
self.fake_A = fake_A.data
# get audio paths
def get_audio_paths(self):
return self.audio_paths
def backward_D_basic(self, netD, real, fake):
# Real
pred_real = netD(real)
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD(fake.detach())
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss
loss_D = (loss_D_real + loss_D_fake) * 0.5
# backward
loss_D.backward()
return loss_D
def backward_D_wp(self, netD, real, fake):
# Gradient penalty loss for WGAN-WP
loss_D_wp = calc_gradient_penalty(netD,
real,
fake,
self.opt.batchSize,
self.opt.lambda_wp,
use_cuda=len(self.gpu_ids) > 0)
loss_D_wp.backward()
return loss_D_wp
def backward_D_A(self):
fake_B = self.fake_B_pool.query(self.fake_B)
loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
self.loss_D_A = loss_D_A.data[0]
if self.opt.gan_loss == 'wgan-wp':
loss_D_wp_A = self.backward_D_wp(self.netD_A, self.real_B, fake_B)
self.loss_D_wp_A = loss_D_wp_A.data[0]
else:
self.loss_D_wp_A = 0
def backward_D_B(self):
fake_A = self.fake_A_pool.query(self.fake_A)
loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
self.loss_D_B = loss_D_B.data[0]
if self.opt.gan_loss == 'wgan-wp':
loss_D_wp_B = self.backward_D_wp(self.netD_B, self.real_A, fake_A)
self.loss_D_wp_B = loss_D_wp_B.data[0]
else:
self.loss_D_wp_B = 0
def backward_G(self):
lambda_idt = self.opt.lambda_identity
lambda_A = self.opt.lambda_A
lambda_B = self.opt.lambda_B
# Identity loss
if lambda_idt > 0:
# G_A should be identity if real_B is fed.
idt_A = self.netG_A(self.real_B)
loss_idt_A = self.criterionIdt(idt_A,
self.real_B) * lambda_B * lambda_idt
# G_B should be identity if real_A is fed.
idt_B = self.netG_B(self.real_A)
loss_idt_B = self.criterionIdt(idt_B,
self.real_A) * lambda_A * lambda_idt
self.idt_A = idt_A.data
self.idt_B = idt_B.data
self.loss_idt_A = loss_idt_A.data[0]
self.loss_idt_B = loss_idt_B.data[0]
else:
loss_idt_A = 0
loss_idt_B = 0
self.loss_idt_A = 0
self.loss_idt_B = 0
# GAN loss D_A(G_A(A))
fake_B = self.netG_A(self.real_A)
pred_fake = self.netD_A(fake_B)
loss_G_A = self.criterionGAN(pred_fake, True)
# GAN loss D_B(G_B(B))
fake_A = self.netG_B(self.real_B)
pred_fake = self.netD_B(fake_A)
loss_G_B = self.criterionGAN(pred_fake, True)
# Forward cycle loss
rec_A = self.netG_B(fake_B)
loss_cycle_A = self.criterionCycle(rec_A, self.real_A) * lambda_A
# Backward cycle loss
rec_B = self.netG_A(fake_A)
loss_cycle_B = self.criterionCycle(rec_B, self.real_B) * lambda_B
# combined loss
loss_G = loss_G_A + loss_G_B + loss_cycle_A + loss_cycle_B + loss_idt_A + loss_idt_B
loss_G.backward()
self.fake_B = fake_B.data
self.fake_A = fake_A.data
self.rec_A = rec_A.data
self.rec_B = rec_B.data
self.loss_G_A = loss_G_A.data[0]
self.loss_G_B = loss_G_B.data[0]
self.loss_cycle_A = loss_cycle_A.data[0]
self.loss_cycle_B = loss_cycle_B.data[0]
def optimize_parameters(self):
# forward
self.forward()
# G_A and G_B
self.optimizer_G.zero_grad()
self.backward_G()
self.optimizer_G.step()
# D_A
self.optimizer_D_A.zero_grad()
self.backward_D_A()
self.optimizer_D_A.step()
# D_B
self.optimizer_D_B.zero_grad()
self.backward_D_B()
self.optimizer_D_B.step()
def get_current_errors(self):
ret_errors = OrderedDict([('D_A', self.loss_D_A),
('G_A', self.loss_G_A),
('Cyc_A', self.loss_cycle_A),
('D_B', self.loss_D_B),
('G_B', self.loss_G_B),
('Cyc_B', self.loss_cycle_B)])
if self.opt.lambda_identity > 0.0:
ret_errors['idt_A'] = self.loss_idt_A
ret_errors['idt_B'] = self.loss_idt_B
if self.opt.gan_loss == 'wgan-wp':
ret_errors['WP_A'] = self.loss_D_wp_A
ret_errors['WP_B'] = self.loss_D_wp_B
return ret_errors
def get_current_audibles(self):
real_A = tensor2audio(self.input_A)
fake_B = tensor2audio(self.fake_B)
rec_A = tensor2audio(self.rec_A)
real_B = tensor2audio(self.input_B)
fake_A = tensor2audio(self.fake_A)
rec_B = tensor2audio(self.rec_B)
ret_visuals = OrderedDict([('real_A', real_A), ('fake_B', fake_B),
('rec_A', rec_A), ('real_B', real_B),
('fake_A', fake_A), ('rec_B', rec_B)])
if self.opt.isTrain and self.opt.lambda_identity > 0.0:
ret_visuals['idt_A'] = tensor2audio(self.idt_A)
ret_visuals['idt_B'] = tensor2audio(self.idt_B)
return ret_visuals
def save(self, label):
self.save_network(self.netG_A, 'G_A', label, self.gpu_ids)
self.save_network(self.netD_A, 'D_A', label, self.gpu_ids)
self.save_network(self.netG_B, 'G_B', label, self.gpu_ids)
self.save_network(self.netD_B, 'D_B', label, self.gpu_ids)
def train(fps, args):
with tf.name_scope('loader'):
x = loader.decode_extract_and_batch(
fps,
batch_size=args.train_batch_size,
slice_len=args.data_slice_len,
decode_fs=args.data_sample_rate,
decode_num_channels=args.data_num_channels,
decode_fast_wav=args.data_fast_wav,
decode_parallel_calls=4,
slice_randomize_offset=False if args.data_first_slice else True,
slice_first_only=args.data_first_slice,
slice_overlap_ratio=0.
if args.data_first_slice else args.data_overlap_ratio,
slice_pad_end=True if args.data_first_slice else args.data_pad_end,
repeat=True,
shuffle=True,
shuffle_buffer_size=4096,
prefetch_size=args.train_batch_size * 4,
prefetch_gpu_num=args.data_prefetch_gpu_num)[:, :, 0]
# Make z vector
z = tf.random_uniform([args.train_batch_size, args.wavegan_latent_dim],
-1.,
1.,
dtype=tf.float32)
# Make generator
with tf.variable_scope('G'):
G_z = WaveGANGenerator(z, train=True, **args.wavegan_g_kwargs)
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z,
1,
args.wavegan_genr_pp_len,
use_bias=False,
padding='same')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
# Print G summary
print('-' * 80)
print('Generator vars')
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
# Summarize
tf.summary.audio('x', x, args.data_sample_rate)
tf.summary.audio('G_z', G_z, args.data_sample_rate)
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
# Print D summary
print('-' * 80)
print('Discriminator vars')
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
print('-' * 80)
# Make fake discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size], dtype=tf.float32)
real = tf.ones([args.train_batch_size], dtype=tf.float32)
G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=real))
D_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=fake))
D_loss += tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x, labels=real))
D_loss /= 2.
elif args.wavegan_loss == 'lsgan':
G_loss = tf.reduce_mean((D_G_z - 1.)**2)
D_loss = tf.reduce_mean((D_x - 1.)**2)
D_loss += tf.reduce_mean(D_G_z**2)
D_loss /= 2.
elif args.wavegan_loss == 'wgan':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])))
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
differences = G_z - x
interpolates = x + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(interpolates,
**args.wavegan_d_kwargs)
LAMBDA = 10
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
D_loss += LAMBDA * gradient_penalty
else:
raise NotImplementedError()
# Load adversarial input
fs, audio = wavread(args.adv_input)
assert fs == args.data_sample_rate
assert audio.dtype == np.float32
assert len(audio.shape) == 1
# Synthesis
if audio.shape[0] < args.data_slice_len:
audio = np.pad(audio, (0, args.data_slice_len - audio.shape[0]),
'constant')
adv_input = tf.constant(
audio[:args.data_slice_len], dtype=np.float32
) + args.adv_magnitude * tf.reshape(G_z,
G_z.get_shape().as_list()[:-1])
# Calculate MFCCs
spectrograms = tf.abs(
tf.signal.stft(adv_input, frame_length=320, frame_step=160))
linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
40, spectrograms.shape[-1].value, fs, 20, 4000)
mel_spectrograms = tf.tensordot(spectrograms, linear_to_mel_weight_matrix,
1)
mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_spectrograms = tf.math.log(mel_spectrograms + 1e-6)
mfccs = tf.expand_dims(
tf.signal.mfccs_from_log_mel_spectrograms(log_mel_spectrograms)[
-1, :99, :40], -1)
# Load a model for speech command classification
with tf.gfile.FastGFile(args.adv_model, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.variable_scope('Speech'):
adv_logits, = tf.import_graph_def(graph_def,
input_map={'Mfcc:0': mfccs},
return_elements=['add_2:0'])
# Load labels for speech command classification
adv_labels = [line.rstrip() for line in tf.gfile.GFile(args.adv_label)]
adv_index = adv_labels.index(args.adv_target)
# Make adversarial loss
# Came from: https://github.com/carlini/nn_robust_attacks/blob/master/l2_attack.py
adv_targets = tf.one_hot(
tf.constant([adv_index] * args.train_batch_size, dtype=tf.int32),
len(adv_labels))
adv_target_logit = tf.reduce_sum(adv_targets * adv_logits, 1)
adv_others_logit = tf.reduce_max(
(1 - adv_targets) * adv_logits - (adv_targets * 10000), 1)
adv_loss = tf.reduce_mean(
tf.maximum(0.0,
adv_others_logit - adv_target_logit + args.adv_confidence))
# Summarize audios
tf.summary.audio('adv_input',
adv_input,
fs,
max_outputs=args.adv_max_outputs)
tf.summary.scalar('adv_loss', adv_loss)
tf.summary.histogram('adv_classes', tf.argmax(adv_logits, axis=1))
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
G_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
else:
raise NotImplementedError()
# Create training ops
G_train_op = G_opt.minimize(
G_loss + args.adv_lambda * adv_loss,
var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Run training
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.train_dir,
config=config,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
print('-' * 80)
print(
'Training has started. Please use \'tensorboard --logdir={}\' to monitor.'
.format(args.train_dir))
while True:
# Train discriminator
for i in xrange(args.wavegan_disc_nupdates):
sess.run(D_train_op)
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights)
# Train generator
sess.run(G_train_op)
# Try on some training data
print('Loading audio data...')
audio_filepaths = get_all_audio_filepaths(args['audio_dir'])
train_gen, valid_data, test_data \
= create_data_split(audio_filepaths, args['valid_ratio'], args['test_ratio'],
batch_size, batch_size, batch_size)
print('Creating models...')
model_gen = WaveGANGenerator(model_size=model_size,
ngpus=ngpus,
latent_dim=latent_dim,
post_proc_filt_len=args['post_proc_filt_len'],
upsample=True)
model_dis = WaveGANDiscriminator(model_size=model_size,
ngpus=ngpus,
alpha=args['alpha'],
shift_factor=args['shift_factor'],
batch_shuffle=args['batch_shuffle'])
print('Starting training...')
model_gen, model_dis, history, final_discr_metrics, samples = train_wgan(
model_gen=model_gen,
model_dis=model_dis,
train_gen=train_gen,
valid_data=valid_data,
test_data=test_data,
num_epochs=args['num_epochs'],
batches_per_epoch=args['batches_per_epoch'],
batch_size=batch_size,
output_dir=model_dir,
lr=args['learning_rate'],
def train(fps, args):
with tf.name_scope('loader'):
x = loader.get_batch(fps, args.train_batch_size, _WINDOW_LEN, args.data_first_window)
# Make z vector
if args.use_sequence:
z = tf.random_uniform([args.train_batch_size, 16, args.d_z], -1., 1., dtype=tf.float32)
else:
z = tf.random_uniform([args.train_batch_size, args.d_z], -1., 1., dtype=tf.float32)#tf.random_normal([args.train_batch_size, _D_Z])
# Make generator
with tf.variable_scope('G'):
gru_layer = tf.keras.layers.CuDNNGRU(args.d_z, return_sequences=True)
G_z, gru = WaveGANGenerator(z, gru_layer=gru_layer, train=True, return_gru=True, reuse=False,
use_sequence=args.use_sequence, **args.wavegan_g_kwargs)
print('G_z.shape:',G_z.get_shape().as_list())
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z, 1, args.wavegan_genr_pp_len, use_bias=False, padding='same')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
G_var_names = [g_var.name for g_var in G_vars]
# Print G summary
print('-' * 80)
print('Generator vars')
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) / (1024 * 1024)))
extra_secs = 1
if not args.use_sequence:
z_feed_long = z
else:
added_noise = tf.random_uniform([args.train_batch_size, 16*extra_secs, args.d_z], -1., 1., dtype=tf.float32)
z_feed_long = tf.concat([z, added_noise], axis=1)
with tf.variable_scope('G', reuse=True):
#gru_layer.reset_states()
G_z_long, gru_long = WaveGANGenerator(z_feed_long, gru_layer=gru_layer, train=False, length=16*extra_secs,
return_gru=True,
reuse=True, use_sequence=args.use_sequence, **args.wavegan_g_kwargs)
print('G_z_long.shape:',G_z_long.get_shape().as_list())
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt', reuse=True):
G_z_long = tf.layers.conv1d(G_z_long, 1, args.wavegan_genr_pp_len, use_bias=False, padding='same')
# Summarize
tf.summary.audio('x', x, _FS)
tf.summary.audio('G_z', G_z, _FS)
tf.summary.audio('G_z_long', G_z_long, _FS)
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
print('D_vars:', D_vars)
# Print D summary
print('-' * 80)
print('Discriminator vars')
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) / (1024 * 1024)))
print('-' * 80)
# Make fake discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size], dtype=tf.float32)
real = tf.ones([args.train_batch_size], dtype=tf.float32)
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_G_z,
labels=real
))
D_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_G_z,
labels=fake
))
D_loss += tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_x,
labels=real
))
D_loss /= 2.
elif args.wavegan_loss == 'lsgan':
G_loss = tf.reduce_mean((D_G_z - 1.) ** 2)
D_loss = tf.reduce_mean((D_x - 1.) ** 2)
D_loss += tf.reduce_mean(D_G_z ** 2)
D_loss /= 2.
elif args.wavegan_loss == 'wgan':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])
)
)
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
G_loss = -tf.reduce_mean(D_G_z)# - D_x)#-tf.reduce_mean(D_G_z) + tf.reduce_mean(D_x)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)# - tf.reduce_mean()
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1], minval=0., maxval=1.)
differences = G_z - x
interpolates = x + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True): #
#stft = tf.log1p(tf.abs(tf.contrib.signal.stft(interpolates[:,:,0], 512,128,fft_length=512)[:,:,:,tf.newaxis]))
#D_interp = WaveGANDiscriminator(interpolates, x_cqt=stft, **args.wavegan_d_kwargs)
#D_interp = tf.reduce_sum(tf.log1p(tf.abs(tf.contrib.signal.stft(interpolates[:,:,0], 2048,512,fft_length=2048)[:,:,:,tf.newaxis])))
D_interp = WaveGANDiscriminator(interpolates, **args.wavegan_d_kwargs)
LAMBDA = 10
gradients = tf.gradients(D_interp, [interpolates])[0]
print('gradients:', gradients)
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2.)
D_loss += LAMBDA * gradient_penalty
else:
raise NotImplementedError()
tf.summary.scalar('G_loss', G_loss)
tf.summary.scalar('D_loss', D_loss)
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(
learning_rate=2e-4,
beta1=0.5)
D_opt = tf.train.AdamOptimizer(
learning_rate=2e-4,
beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(
learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(
learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(
learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(
learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
my_learning_rate = tf.train.exponential_decay(1e-4,
tf.get_collection(tf.GraphKeys.GLOBAL_STEP),
decay_steps=100000,
decay_rate=0.5)
G_opt = tf.train.AdamOptimizer(
learning_rate=my_learning_rate,
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(
learning_rate=my_learning_rate,
beta1=0.5,
beta2=0.9)
else:
raise NotImplementedError()
# Create training ops
G_train_op = G_opt.minimize(G_loss, var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
saver = tf.train.Saver(max_to_keep=10)
#tf_max, tf_min = tf.reduce_max(x[:,:,0], axis=-1), tf.reduce_min(x[:,:,0], axis=-1)
global_step = tf.get_collection(tf.GraphKeys.GLOBAL_STEP)
# Run training
with tf.train.MonitoredTrainingSession(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
#saver.restore(sess, tf.train.latest_checkpoint(args.train_dir))
iterator_count = 0
while True:
# Train discriminator
for i in xrange(args.wavegan_disc_nupdates):
sess.run(D_train_op)
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights)
# Train generator
#_, g_losses, d_losses, gru_, gru_long_ = sess.run([G_train_op, G_loss, D_loss, gru, gru_long])
_, g_losses, d_losses, global_step_ = sess.run([G_train_op, G_loss, D_loss, global_step])
print('i:', global_step_[0], 'G_loss:', g_losses, 'D_loss:', d_losses)
if iterator_count == 0:
G_var_dict = {}
G_vars_np = sess.run(G_vars)
for g_var_name, g_var in zip(G_var_names, G_vars_np):
G_var_dict[g_var_name] = g_var
with open('saved_G_vars_iteration-{}.pkl'.format(global_step_[0]), 'wb') as f:
pickle.dump(G_var_dict, f)
#print('maxs:', maxs)
#print('mins:', mins)
#print(gru_[0])
#print(gru_long_[0])
iterator_count += 1
def train(fps, args):
with tf.name_scope('loader'):
x = loader.decode_extract_and_batch(
fps,
batch_size=args.train_batch_size,
slice_len=args.data_slice_len,
decode_fs=args.data_sample_rate,
decode_num_channels=args.data_num_channels,
decode_fast_wav=args.data_fast_wav,
decode_parallel_calls=4,
slice_randomize_offset=False if args.data_first_slice else True,
slice_first_only=args.data_first_slice,
slice_overlap_ratio=0.
if args.data_first_slice else args.data_overlap_ratio,
slice_pad_end=True if args.data_first_slice else args.data_pad_end,
repeat=True,
shuffle=True,
shuffle_buffer_size=4096,
prefetch_size=args.train_batch_size * 4,
prefetch_gpu_num=args.data_prefetch_gpu_num)[:, :, 0]
# Make z vector
z = tf.random_uniform([args.train_batch_size, args.wavegan_latent_dim],
-1.,
1.,
dtype=tf.float32)
# Make generator
with tf.variable_scope('G'):
G_z = WaveGANGenerator(z, train=True, **args.wavegan_g_kwargs)
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z,
1,
args.wavegan_genr_pp_len,
use_bias=False,
padding='same')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
# Print G summary
print('-' * 80)
print('Generator vars')
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
# Summarize
tf.summary.audio('x', x, args.data_sample_rate)
tf.summary.audio('G_z', G_z, args.data_sample_rate)
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
# Print D summary
print('-' * 80)
print('Discriminator vars')
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
print('-' * 80)
# Make fake discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size], dtype=tf.float32)
real = tf.ones([args.train_batch_size], dtype=tf.float32)
G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=real))
D_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=fake))
D_loss += tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x, labels=real))
D_loss /= 2.
elif args.wavegan_loss == 'lsgan':
G_loss = tf.reduce_mean((D_G_z - 1.)**2)
D_loss = tf.reduce_mean((D_x - 1.)**2)
D_loss += tf.reduce_mean(D_G_z**2)
D_loss /= 2.
elif args.wavegan_loss == 'wgan':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])))
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
differences = G_z - x
interpolates = x + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(interpolates,
**args.wavegan_d_kwargs)
LAMBDA = 10
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
D_loss += LAMBDA * gradient_penalty
else:
raise NotImplementedError()
tf.summary.scalar('G_loss', G_loss)
tf.summary.scalar('D_loss', D_loss)
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
G_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
else:
raise NotImplementedError()
# Create training ops
G_train_op = G_opt.minimize(
G_loss,
var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
# Run training
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
print('-' * 80)
print(
'Training has started. Please use \'tensorboard --logdir={}\' to monitor.'
.format(args.train_dir))
counter = 0
while True:
# Train discriminator
for i in xrange(args.wavegan_disc_nupdates):
sess.run(D_train_op)
print("Ran D_train_op")
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights)
print("Ran D_clip_weights")
# Train generator
sess.run(G_train_op)
print("Ran G_train_op")
counter += 1
print("Iteration: " + str(counter))
print("DONE TRAINING")
def train(fps, args):
# global train_dataset_size
# global train_data_percentage
train_data_percentage = args.train_data_percentage
with tf.name_scope('loader'):
# This was actually not necessarily good. However, we can keep it as a point for 115 tfrecords
# train_fps, _ = loader.split_files_test_val(fps, train_data_percentage, 0)
# fps = train_fps
# fps = fps[:gan_train_data_size]
logging.info("Full training datasize = " +
str(find_data_size(fps, None)))
length = len(fps)
fps = fps[:(int(train_data_percentage / 100.0 * length))]
logging.info("GAN training datasize (before exclude) = " +
str(find_data_size(fps, None)))
if args.exclude_class is None:
pass
elif args.exclude_class != -1:
train_dataset_size = find_data_size(fps, args.exclude_class)
logging.info("GAN training datasize (after exclude) = " +
str(train_dataset_size))
elif args.exclude_class == -1:
fps, _ = loader.split_files_test_val(fps, 0.9, 0)
train_dataset_size = find_data_size(fps, args.exclude_class)
logging.info(
"GAN training datasize (after exclude - random sampling) = " +
str(train_dataset_size))
else: # LOL :P
raise ValueError(
"args.exclude_class should be either [0, num_class), None, or -1 for random sampling 90%"
)
training_iterator = loader.get_batch(fps,
args.train_batch_size,
_WINDOW_LEN,
args.data_first_window,
repeat=True,
initializable=True,
labels=True,
exclude_class=args.exclude_class)
x, _ = training_iterator.get_next() # Important: ignore the labels
print("x_wav.shape = %s" % str(x.shape))
logging.info("train_dataset_size = " + str(train_dataset_size))
# Make z vector
z = tf.random_uniform([args.train_batch_size, _D_Z],
-1.,
1.,
dtype=tf.float32)
# Make generator
with tf.variable_scope('G'):
G_z = WaveGANGenerator(z, train=True, **args.wavegan_g_kwargs)
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z,
1,
args.wavegan_genr_pp_len,
use_bias=False,
padding='same')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
# Print G summary
print '-' * 80
print 'Generator vars'
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print '{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name)
print 'Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024))
# Summarize
tf.summary.audio('x', x, _FS)
tf.summary.audio('G_z', G_z, _FS)
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
# Print D summary
print '-' * 80
print 'Discriminator vars'
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print '{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name)
print 'Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024))
print '-' * 80
# Make fake discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size], dtype=tf.float32)
real = tf.ones([args.train_batch_size], dtype=tf.float32)
G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=real))
D_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z, labels=fake))
D_loss += tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x, labels=real))
D_loss /= 2.
elif args.wavegan_loss == 'lsgan':
G_loss = tf.reduce_mean((D_G_z - 1.)**2)
D_loss = tf.reduce_mean((D_x - 1.)**2)
D_loss += tf.reduce_mean(D_G_z**2)
D_loss /= 2.
elif args.wavegan_loss == 'wgan':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])))
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
G_loss = -tf.reduce_mean(D_G_z)
D_loss = tf.reduce_mean(D_G_z) - tf.reduce_mean(D_x)
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
differences = G_z - x
interpolates = x + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(interpolates,
**args.wavegan_d_kwargs)
LAMBDA = 10
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
D_loss += LAMBDA * gradient_penalty
else:
raise NotImplementedError()
tf.summary.scalar('G_loss', G_loss)
tf.summary.scalar('D_loss', D_loss)
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
G_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=1e-4,
beta1=0.5,
beta2=0.9)
else:
raise NotImplementedError()
# Create training ops
G_train_op = G_opt.minimize(
G_loss,
var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
# Run training
current_step = -1
scaffold = tf.train.Scaffold(local_init_op=tf.group(
tf.local_variables_initializer(), training_iterator.initializer),
saver=tf.train.Saver(max_to_keep=5))
with tf.train.MonitoredTrainingSession(
hooks=[SaveAtEnd(os.path.join(args.train_dir, 'model'))],
scaffold=scaffold,
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
while True:
global_step = sess.run(tf.train.get_or_create_global_step())
logging.info("Global step: " + str(global_step))
if args.stop_at_global_step != 0 and global_step >= args.stop_at_global_step:
logging.info(
"Stopping because args.stop_at_global_step is set to " +
str(args.stop_at_global_step))
break
# Train discriminator
# for i in range(args.wavegan_disc_nupdates):
# try:
# sess.run(D_train_op)
# current_step += 1
#
# # Stop training after x% of training data seen
# if current_step * args.train_batch_size > math.ceil(train_dataset_size * train_data_percentage / 100.0):
# print("Stopping at batch: " + str(current_step))
# current_step = -1
# sess.run(training_iterator.initializer)
#
# except tf.errors.OutOfRangeError:
# # End of training dataset
# if train_data_percentage != 100:
# print(
# "ERROR: end of dataset for only part of data! Achieved end of training dataset with train_data_percentage = " + str(
# train_data_percentage))
# else:
# current_step = -1
# sess.run(training_iterator.initializer)
try:
for i in range(args.wavegan_disc_nupdates):
sess.run(D_train_op)
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights)
except tf.errors.OutOfRangeError:
sess.run(training_iterator.initializer)
# Train generator
sess.run(G_train_op)
args['model_dir'] = model_dir
# save samples for every N epochs.
epochs_per_sample = args['epochs_per_sample']
# gradient penalty regularization factor.
lmbda = args['lmbda']
# Dir
audio_dir = args['audio_dir']
output_dir = args['output_dir']
# =============Network===============
netG = WaveGANGenerator(model_size=model_size, ngpus=ngpus, latent_dim=latent_dim, upsample=True)
netD = WaveGANDiscriminator(model_size=model_size, ngpus=ngpus)
if cuda:
netG = torch.nn.DataParallel(netG).cuda()
netD = torch.nn.DataParallel(netD).cuda()
# "Two time-scale update rule"(TTUR) to update netD 4x faster than netG.
optimizerG = optim.Adam(netG.parameters(), lr=args['learning_rate'], betas=(args['beta1'], args['beta2']))
optimizerD = optim.Adam(netD.parameters(), lr=args['learning_rate'], betas=(args['beta1'], args['beta2']))
# Sample noise used for generated output.
sample_noise = torch.randn(args['sample_size'], latent_dim)
if cuda:
sample_noise = sample_noise.cuda()
sample_noise_Var = autograd.Variable(sample_noise, requires_grad=False)
def train(fps, args):
with tf.name_scope('loader'):
x, cond_text, _ = loader.get_batch(fps,
args.train_batch_size,
_WINDOW_LEN,
args.data_first_window,
conditionals=True,
name='batch')
wrong_audio = loader.get_batch(fps,
args.train_batch_size,
_WINDOW_LEN,
args.data_first_window,
conditionals=False,
name='wrong_batch')
# wrong_cond_text, wrong_cond_text_embed = loader.get_batch(fps, args.train_batch_size, _WINDOW_LEN, args.data_first_window, wavs=False, conditionals=True, name='batch')
# Make z vector
z = tf.random_normal([args.train_batch_size, _D_Z])
embed = hub.Module('https://tfhub.dev/google/elmo/2',
trainable=False,
name='embed')
cond_text_embed = embed(cond_text)
# Add conditioning input to the model
args.wavegan_g_kwargs['context_embedding'] = cond_text_embed
args.wavegan_d_kwargs['context_embedding'] = args.wavegan_g_kwargs[
'context_embedding']
lod = tf.placeholder(tf.float32, shape=[])
with tf.variable_scope('G'):
# Make generator
G_z, c_kl_loss = WaveGANGenerator(z,
lod,
train=True,
**args.wavegan_g_kwargs)
if args.wavegan_genr_pp:
with tf.variable_scope('pp_filt'):
G_z = tf.layers.conv1d(G_z,
1,
args.wavegan_genr_pp_len,
use_bias=False,
padding='same')
# Summarize
G_z_rms = tf.sqrt(tf.reduce_mean(tf.square(G_z[:, :, 0]), axis=1))
x_rms = tf.sqrt(tf.reduce_mean(tf.square(x[:, :, 0]), axis=1))
x_rms_lod_4 = tf.sqrt(
tf.reduce_mean(tf.square(avg_downsample(x)[:, :, 0]), axis=1))
x_rms_lod_3 = tf.sqrt(
tf.reduce_mean(tf.square(avg_downsample(avg_downsample(x))[:, :, 0]),
axis=1))
x_rms_lod_2 = tf.sqrt(
tf.reduce_mean(tf.square(
avg_downsample(avg_downsample(avg_downsample(x)))[:, :, 0]),
axis=1))
x_rms_lod_1 = tf.sqrt(
tf.reduce_mean(tf.square(
avg_downsample(avg_downsample(avg_downsample(
avg_downsample(x))))[:, :, 0]),
axis=1))
x_rms_lod_0 = tf.sqrt(
tf.reduce_mean(tf.square(
avg_downsample(
avg_downsample(
avg_downsample(avg_downsample(avg_downsample(x)))))[:, :,
0]),
axis=1))
tf.summary.histogram('x_rms_batch', x_rms)
tf.summary.histogram('G_z_rms_batch', G_z_rms)
tf.summary.scalar('x_rms', tf.reduce_mean(x_rms))
tf.summary.scalar('x_rms_lod_4', tf.reduce_mean(x_rms_lod_4))
tf.summary.scalar('x_rms_lod_3', tf.reduce_mean(x_rms_lod_3))
tf.summary.scalar('x_rms_lod_2', tf.reduce_mean(x_rms_lod_2))
tf.summary.scalar('x_rms_lod_1', tf.reduce_mean(x_rms_lod_1))
tf.summary.scalar('x_rms_lod_0', tf.reduce_mean(x_rms_lod_0))
tf.summary.scalar('G_z_rms', tf.reduce_mean(G_z_rms))
tf.summary.audio('x', x, _FS, max_outputs=10)
tf.summary.audio('G_z', G_z, _FS, max_outputs=10)
tf.summary.text('Conditioning Text', cond_text[:10])
# with tf.variable_scope('G'):
# # Make history buffer
# history_buffer = HistoryBuffer(_WINDOW_LEN, args.train_batch_size * 100, args.train_batch_size)
# # Select half of batch from history buffer
# g_from_history, r_from_history, embeds_from_history = history_buffer.get_from_history_buffer()
# new_fake_batch = tf.concat([G_z[:tf.shape(G_z)[0] - tf.shape(g_from_history)[0]], g_from_history], 0) # Use tf.shape to handle case when g_from_history is empty
# new_cond_embeds = tf.concat([cond_text_embed[:tf.shape(cond_text_embed)[0] - tf.shape(embeds_from_history)[0]], embeds_from_history], 0)
# new_real_batch = tf.concat([x[:tf.shape(x)[0] - tf.shape(r_from_history)[0]], r_from_history], 0)
# with tf.control_dependencies([new_fake_batch, new_real_batch, new_cond_embeds]):
# with tf.control_dependencies([history_buffer.add_to_history_buffer(G_z, x, cond_text_embed)]):
# G_z = tf.identity(new_fake_batch)
# x = tf.identity(new_real_batch)
# args.wavegan_g_kwargs['context_embedding'] = tf.identity(new_cond_embeds)
# args.wavegan_d_kwargs['context_embedding'] = args.wavegan_g_kwargs['context_embedding']
# G_z.set_shape([args.train_batch_size, _WINDOW_LEN, 1])
# x.set_shape([args.train_batch_size, _WINDOW_LEN, 1])
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='G')
# Print G summary
print('-' * 80)
print('Generator vars')
nparams = 0
for v in G_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
# Summarize
# tf.summary.scalar('history_buffer_size', history_buffer.current_size)
# tf.summary.scalar('g_from_history_size', tf.shape(g_from_history)[0])
# tf.summary.scalar('r_from_history_size', tf.shape(r_from_history)[0])
# tf.summary.scalar('embeds_from_history_size', tf.shape(embeds_from_history)[0])
# tf.summary.audio('G_z_history', g_from_history, _FS, max_outputs=10)
# tf.summary.audio('x_history', r_from_history, _FS, max_outputs=10)
tf.summary.audio('wrong_audio', wrong_audio, _FS, max_outputs=10)
tf.summary.scalar('Conditional Resample - KL-Loss', c_kl_loss)
# tf.summary.scalar('embed_error_cosine', tf.reduce_sum(tf.multiply(cond_text_embed, expected_embed)) / (tf.norm(cond_text_embed) * tf.norm(expected_embed)))
# tf.summary.scalar('embed_error_cosine_wrong', tf.reduce_sum(tf.multiply(wrong_cond_text_embed, expected_embed)) / (tf.norm(wrong_cond_text_embed) * tf.norm(expected_embed)))
# Make real discriminator
with tf.name_scope('D_x'), tf.variable_scope('D'):
D_x = WaveGANDiscriminator(x, lod, **args.wavegan_d_kwargs)
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D')
# Print D summary
print('-' * 80)
print('Discriminator vars')
nparams = 0
for v in D_vars:
v_shape = v.get_shape().as_list()
v_n = reduce(lambda x, y: x * y, v_shape)
nparams += v_n
print('{} ({}): {}'.format(v.get_shape().as_list(), v_n, v.name))
print('Total params: {} ({:.2f} MB)'.format(nparams, (float(nparams) * 4) /
(1024 * 1024)))
print('-' * 80)
# Make fake / wrong discriminator
with tf.name_scope('D_G_z'), tf.variable_scope('D', reuse=True):
D_G_z = WaveGANDiscriminator(G_z, lod, **args.wavegan_d_kwargs)
with tf.name_scope('D_w'), tf.variable_scope('D', reuse=True):
D_w = WaveGANDiscriminator(wrong_audio, lod, **args.wavegan_d_kwargs)
# Create loss
D_clip_weights = None
if args.wavegan_loss == 'dcgan':
fake = tf.zeros([args.train_batch_size, 1], dtype=tf.float32)
real = tf.ones([args.train_batch_size, 1], dtype=tf.float32)
# Conditional G Loss
G_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z[0],
labels=real))
G_loss += c_kl_loss
# Unconditional G Loss
if args.use_extra_uncond_loss:
G_loss += tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z[1],
labels=real))
G_loss /= 2
# Conditional D Losses
D_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z[0],
labels=fake))
D_loss_wrong = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_w[0],
labels=fake))
D_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x[0],
labels=real))
# Unconditional D Losses
if args.use_extra_uncond_loss:
D_loss_fake_uncond = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_G_z[1],
labels=fake))
D_loss_wrong_uncond = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_w[1],
labels=real))
D_loss_real_uncond = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=D_x[1],
labels=real))
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond
D_loss /= 2
else:
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake)
# Warmup Conditional Loss
# D_warmup_loss = D_loss_real + D_loss_wrong
elif args.wavegan_loss == 'lsgan':
# Conditional G Loss
G_loss = tf.reduce_mean((D_G_z[0] - 1.)**2)
G_loss += c_kl_loss
# Unconditional G Loss
if args.use_extra_uncond_loss:
G_loss += tf.reduce_mean((D_G_z[1] - 1.)**2)
G_loss /= 2
# Conditional D Loss
D_loss_real = tf.reduce_mean((D_x[0] - 1.)**2)
D_loss_wrong = tf.reduce_mean(D_w[0]**2)
D_loss_fake = tf.reduce_mean(D_G_z[0]**2)
# Unconditional D Loss
if args.use_extra_uncond_loss:
D_loss_real_uncond = tf.reduce_mean((D_x[1] - 1.)**2)
D_loss_wrong_uncond = tf.reduce_mean((D_w[1] - 1.)**2)
D_loss_fake_uncond = tf.reduce_mean(D_G_z[1]**2)
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond
D_loss /= 2
else:
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake)
# Warmup Conditional Loss
# D_warmup_loss = D_loss_real + D_loss_wrong
elif args.wavegan_loss == 'wgan':
# Conditional G Loss
G_loss = -tf.reduce_mean(D_G_z[0])
G_loss += c_kl_loss
# Unconditional G Loss
if args.use_extra_uncond_loss:
G_loss += -tf.reduce_mean(D_G_z[1])
G_loss /= 2
# Conditional D Loss
D_loss_real = -tf.reduce_mean(D_x[0])
D_loss_wrong = tf.reduce_mean(D_w[0])
D_loss_fake = tf.reduce_mean(D_G_z[0])
# Unconditional D Loss
if args.use_extra_uncond_loss:
D_loss_real_uncond = -tf.reduce_mean(D_x[1])
D_loss_wrong_uncond = -tf.reduce_mean(D_w[1])
D_loss_fake_uncond = tf.reduce_mean(D_G_z[1])
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond
D_loss /= 2
else:
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake)
# Warmup Conditional Loss
# D_warmup_loss = D_loss_real + D_loss_wrong
with tf.name_scope('D_clip_weights'):
clip_ops = []
for var in D_vars:
clip_bounds = [-.01, .01]
clip_ops.append(
tf.assign(
var,
tf.clip_by_value(var, clip_bounds[0], clip_bounds[1])))
D_clip_weights = tf.group(*clip_ops)
elif args.wavegan_loss == 'wgan-gp':
# Conditional G Loss
G_loss = -tf.reduce_mean(D_G_z[0])
G_loss += c_kl_loss
# Unconditional G Loss
if args.use_extra_uncond_loss:
G_loss += -tf.reduce_mean(D_G_z[1])
G_loss /= 2
# Conditional D Loss
D_loss_real = -tf.reduce_mean(D_x[0])
D_loss_wrong = tf.reduce_mean(D_w[0])
D_loss_fake = tf.reduce_mean(D_G_z[0])
# Unconditional D Loss
if args.use_extra_uncond_loss:
D_loss_real_uncond = -tf.reduce_mean(D_x[1])
D_loss_wrong_uncond = -tf.reduce_mean(D_w[1])
D_loss_fake_uncond = tf.reduce_mean(D_G_z[1])
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond
D_loss /= 2
else:
D_loss = D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake)
# Warmup Conditional Loss
# D_warmup_loss = D_loss_real + D_loss_wrong
# Conditional Gradient Penalty
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
real = x
fake = tf.concat([
G_z[:args.train_batch_size // 2],
wrong_audio[:args.train_batch_size // 2]
], 0)
differences = fake - real
interpolates = real + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(
interpolates, lod,
**args.wavegan_d_kwargs)[0] # Only want conditional output
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
cond_gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
# Unconditional Gradient Penalty
alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1],
minval=0.,
maxval=1.)
real = tf.concat([
x[:args.train_batch_size // 2],
wrong_audio[:args.train_batch_size // 2]
], 0)
fake = G_z
differences = fake - real
interpolates = real + (alpha * differences)
with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
D_interp = WaveGANDiscriminator(
interpolates, lod,
**args.wavegan_d_kwargs)[1] # Only want unconditional output
gradients = tf.gradients(D_interp, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
uncond_gradient_penalty = tf.reduce_mean((slopes - 1.)**2.)
# Warmup Gradient Penalty
# alpha = tf.random_uniform(shape=[args.train_batch_size, 1, 1], minval=0., maxval=1.)
# real = x
# fake = wrong_audio
# differences = fake - real
# interpolates = real + (alpha * differences)
# with tf.name_scope('D_interp'), tf.variable_scope('D', reuse=True):
# D_interp = WaveGANDiscriminator(interpolates, lod, **args.wavegan_d_kwargs)[0] # Only want conditional output
# gradients = tf.gradients(D_interp, [interpolates])[0]
# slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
# warmup_gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2.)
gradient_penalty = (cond_gradient_penalty +
uncond_gradient_penalty) / 2
LAMBDA = 10
D_loss += LAMBDA * gradient_penalty
# D_warmup_loss += LAMBDA * warmup_gradient_penalty
else:
raise NotImplementedError()
tf.summary.scalar('G_loss', G_loss)
if (args.wavegan_loss == 'wgan-gp'):
tf.summary.scalar('Gradient Penalty', LAMBDA * gradient_penalty)
if (args.wavegan_loss == 'wgan' or args.wavegan_loss == 'wgan-gp'):
if args.use_extra_uncond_loss:
tf.summary.scalar('Critic Score - Real Data - Condition Match',
-D_loss_real)
tf.summary.scalar('Critic Score - Fake Data - Condition Match',
D_loss_fake)
tf.summary.scalar('Critic Score - Wrong Data - Condition Match',
D_loss_wrong)
tf.summary.scalar('Critic Score - Real Data', -D_loss_real_uncond)
tf.summary.scalar('Critic Score - Wrong Data',
-D_loss_wrong_uncond)
tf.summary.scalar('Critic Score - Fake Data', D_loss_fake_uncond)
tf.summary.scalar('Wasserstein Distance - No Regularization Term',
-((D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond) / 2))
tf.summary.scalar('Wasserstein Distance - Real-Wrong Only',
-(D_loss_real + D_loss_wrong))
tf.summary.scalar('Wasserstein Distance - Real-Fake Only',
-((D_loss_real + D_loss_fake \
+ D_loss_real_uncond + D_loss_fake_uncond) / 2))
else:
tf.summary.scalar('Critic Score - Real Data', -D_loss_real)
tf.summary.scalar('Critic Score - Wrong Data', D_loss_wrong)
tf.summary.scalar('Critic Score - Fake Data', D_loss_fake)
tf.summary.scalar(
'Wasserstein Distance - No Regularization Term',
-(D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake)))
tf.summary.scalar('Wasserstein Distance - With Regularization Term',
-D_loss)
else:
if args.use_extra_uncond_loss:
tf.summary.scalar('D_acc_uncond', 0.5 * ((0.5 * (tf.reduce_mean(tf.sigmoid(D_x[1])) + tf.reduce_mean(tf.sigmoid(D_w[1])))) \
+ tf.reduce_mean(1 - tf.sigmoid(D_G_z[1]))))
tf.summary.scalar('D_acc', 0.5 * (tf.reduce_mean(tf.sigmoid(D_x[0])) \
+ 0.5 * (tf.reduce_mean(1 - tf.sigmoid(D_w[0])) + tf.reduce_mean(1 - tf.sigmoid(D_G_z[0])))))
tf.summary.scalar('D_acc_real_wrong_only', 0.5 * (tf.reduce_mean(tf.sigmoid(D_x[0])) \
+ tf.reduce_mean(1 - tf.sigmoid(D_w[0]))))
tf.summary.scalar('D_loss_cond_real', D_loss_real)
tf.summary.scalar('D_loss_uncond_real', D_loss_real_uncond)
tf.summary.scalar('D_loss_cond_wrong', D_loss_wrong)
tf.summary.scalar('D_loss_uncond_wrong', D_loss_wrong_uncond)
tf.summary.scalar('D_loss_cond_fake', D_loss_fake)
tf.summary.scalar('D_loss_uncond_fake', D_loss_fake_uncond)
tf.summary.scalar('D_loss_unregularized',
(D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake) \
+ 0.5 * (D_loss_real_uncond + D_loss_wrong_uncond) + D_loss_fake_uncond) / 2)
else:
tf.summary.scalar('D_acc', 0.5 * (tf.reduce_mean(tf.sigmoid(D_x[0])) \
+ 0.5 * (tf.reduce_mean(1 - tf.sigmoid(D_w[0])) + tf.reduce_mean(1 - tf.sigmoid(D_G_z[0])))))
tf.summary.scalar('D_loss_real', D_loss_real)
tf.summary.scalar('D_loss_wrong', D_loss_wrong)
tf.summary.scalar('D_loss_fake', D_loss_fake)
tf.summary.scalar('D_loss_unregularized',
D_loss_real + 0.5 * (D_loss_wrong + D_loss_fake))
tf.summary.scalar('D_loss', D_loss)
# Create (recommended) optimizer
if args.wavegan_loss == 'dcgan':
G_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
D_opt = tf.train.AdamOptimizer(learning_rate=2e-4, beta1=0.5)
elif args.wavegan_loss == 'lsgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
D_opt = tf.train.RMSPropOptimizer(learning_rate=1e-4)
elif args.wavegan_loss == 'wgan':
G_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
D_opt = tf.train.RMSPropOptimizer(learning_rate=5e-5)
elif args.wavegan_loss == 'wgan-gp':
G_opt = tf.train.AdamOptimizer(learning_rate=4e-4,
beta1=0.0,
beta2=0.9)
D_opt = tf.train.AdamOptimizer(learning_rate=4e-4,
beta1=0.0,
beta2=0.9)
else:
raise NotImplementedError()
# Optimizer internal state reset ops
reset_G_opt_op = tf.variables_initializer(G_opt.variables())
reset_D_opt_op = tf.variables_initializer(D_opt.variables())
# Create training ops
G_train_op = G_opt.minimize(
G_loss,
var_list=G_vars,
global_step=tf.train.get_or_create_global_step())
D_train_op = D_opt.minimize(D_loss, var_list=D_vars)
def smoothstep(x, mi, mx):
return mi + (mx - mi) * (lambda t: np.where(
t < 0, 0, np.where(t <= 1, 3 * t**2 - 2 * t**3, 1)))(x)
def np_lerp_clip(t, a, b):
return a + (b - a) * np.clip(t, 0.0, 1.0)
def get_lod_at_step(step):
return np.piecewise(float(step), [
step < 10000, 10000 <= step < 20000, 20000 <= step < 30000,
30000 <= step < 40000, 40000 <= step < 50000,
50000 <= step < 60000, 60000 <= step < 70000,
70000 <= step < 80000, 80000 <= step < 90000,
90000 <= step < 100000
], [
0, lambda x: np_lerp_clip((x - 10000) / 10000, 0, 1), 1,
lambda x: np_lerp_clip(
(x - 30000) / 10000, 1, 2), 2, lambda x: np_lerp_clip(
(x - 50000) / 10000, 2, 3), 3, lambda x: np_lerp_clip(
(x - 70000) / 10000, 3, 4), 4, lambda x: np_lerp_clip(
(x - 90000) / 10000, 4, 5), 5
])
def my_filter_callable(datum, tensor):
if (not isinstance(tensor, debug_data.InconvertibleTensorProto)) and (
tensor.dtype == np.float32 or tensor.dtype == np.float64):
return np.any([
np.any(np.greater_equal(tensor, 50.0)),
np.any(np.less_equal(tensor, -50.0))
])
else:
return False
# Create a LocalCLIDebugHook and use it as a monitor
# debug_hook = tf_debug.LocalCLIDebugHook(dump_root='C:/d/t/')
# debug_hook.add_tensor_filter('large_values', my_filter_callable)
# hooks = [debug_hook]
# Run training
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.train_dir,
save_checkpoint_secs=args.train_save_secs,
save_summaries_secs=args.train_summary_secs) as sess:
# Get the summary writer for writing extra summary statistics
summary_writer = SummaryWriterCache.get(args.train_dir)
cur_lod = 0
while True:
# Calculate Maximum LOD to train
step = sess.run(tf.train.get_or_create_global_step(),
feed_dict={lod: cur_lod})
cur_lod = get_lod_at_step(step)
prev_lod = get_lod_at_step(step - 1)
# Reset optimizer internal state when new layers are introduced
if np.floor(cur_lod) != np.floor(prev_lod) or np.ceil(
cur_lod) != np.ceil(prev_lod):
print(
"Resetting optimizers' internal states at step {}".format(
step))
sess.run([reset_G_opt_op, reset_D_opt_op],
feed_dict={lod: cur_lod})
# Output current LOD and 'steps at currrent LOD' to tensorboard
step = float(
sess.run(tf.train.get_or_create_global_step(),
feed_dict={lod: cur_lod}))
lod_summary = tf.Summary(value=[
tf.Summary.Value(tag="current_lod",
simple_value=float(cur_lod)),
])
summary_writer.add_summary(lod_summary, step)
# Train discriminator
for i in xrange(args.wavegan_disc_nupdates):
sess.run(D_train_op, feed_dict={lod: cur_lod})
# Enforce Lipschitz constraint for WGAN
if D_clip_weights is not None:
sess.run(D_clip_weights, feed_dict={lod: cur_lod})
# Train generator
sess.run(G_train_op, feed_dict={lod: cur_lod})
config_path = os.path.join(model_dir, 'config.json')
with open(config_path, 'w') as f:
json.dump(args, f)
# Try on some training data
LOGGER.info('Loading audio data...')
audio_filepaths = get_all_audio_filepaths(args['audio_dir'])
train_gen, valid_data, test_data \
= create_data_split(audio_filepaths, args['valid_ratio'], args['test_ratio'],
batch_size, batch_size, batch_size)
LOGGER.info('Creating models...')
model_gen = WaveGANGenerator(model_size=model_size, ngpus=ngpus, latent_dim=latent_dim,
post_proc_filt_len=args['post_proc_filt_len'], upsample=True, num_class=10)
model_dis = WaveGANDiscriminator(model_size=model_size, ngpus=ngpus,
alpha=args['alpha'], shift_factor=args['shift_factor'],
batch_shuffle=args['batch_shuffle'], num_class=10)
LOGGER.info('Starting training...')
model_gen, model_dis, history, final_discr_metrics, samples = train_wgan(
model_gen=model_gen,
model_dis=model_dis,
train_gen=train_gen,
valid_data=valid_data,
test_data=test_data,
num_epochs=args['num_epochs'],
batches_per_epoch=args['batches_per_epoch'],
batch_size=batch_size,
output_dir=model_dir,
lr=args['learning_rate'],
beta_1=args['beta1'],
LOGGER.info('Saving configurations...')
config_path = os.path.join(model_dir, 'config.json')
with open(config_path, 'w') as f:
json.dump(args, f)
# Try on some training data
LOGGER.info('Loading audio data...')
audio_filepaths = get_all_audio_filepaths(args['audio_dir'])
train_gen, valid_data, test_data \
= create_data_split(audio_filepaths, args['valid_ratio'], args['test_ratio'],
batch_size, batch_size, batch_size)
LOGGER.info('Creating models...')
model_gen = WaveGANGenerator(model_size=model_size, ngpus=ngpus, latent_dim=latent_dim,
post_proc_filt_len=args['post_proc_filt_len'])
model_dis = WaveGANDiscriminator(model_size=model_size, ngpus=ngpus,
alpha=args['alpha'])
LOGGER.info('Starting training...')
model_gen, model_dis, history, final_discr_metrics, samples = train_wgan(
model_gen=model_gen,
model_dis=model_dis,
train_gen=train_gen,
valid_data=valid_data,
test_data=test_data,
num_epochs=args['num_epochs'],
batches_per_epoch=args['batches_per_epoch'],
batch_size=batch_size,
output_dir=model_dir,
lmbda=args['lmbda'],
use_cuda=ngpus>=1,
discriminator_updates=args['discriminator_updates'],