class Model(object):
def __init__(self, opt):
super(Model, self).__init__()
# Generator
self.gen = Generator(opt).cuda(opt.gpu_id)
self.gen_params = self.gen.parameters()
num_params = 0
for p in self.gen.parameters():
num_params += p.numel()
print(self.gen)
print(num_params)
# Discriminator
self.dis = Discriminator(opt).cuda(opt.gpu_id)
self.dis_params = self.dis.parameters()
num_params = 0
for p in self.dis.parameters():
num_params += p.numel()
print(self.dis)
print(num_params)
# Regressor
if opt.mse_weight:
self.reg = torch.load('data/utils/classifier.pth').cuda(
opt.gpu_id).eval()
else:
self.reg = None
# Losses
self.criterion_gan = GANLoss(opt, self.dis)
self.criterion_mse = lambda x, y: l1_loss(x, y) * opt.mse_weight
self.loss_mse = Variable(torch.zeros(1).cuda())
self.loss_adv = Variable(torch.zeros(1).cuda())
self.loss = Variable(torch.zeros(1).cuda())
self.path = opt.experiments_dir + opt.experiment_name + '/checkpoints/'
self.gpu_id = opt.gpu_id
self.noise_channels = opt.in_channels - len(opt.input_idx.split(','))
def forward(self, inputs):
input, input_orig, target = inputs
self.input = Variable(input.cuda(self.gpu_id))
self.input_orig = Variable(input_orig.cuda(self.gpu_id))
self.target = Variable(target.cuda(self.gpu_id))
noise = Variable(
torch.randn(self.input.size(0),
self.noise_channels).cuda(self.gpu_id))
self.fake = self.gen(torch.cat([self.input, noise], 1))
def backward_G(self):
# Regressor loss
if self.reg is not None:
fake_input = self.reg(self.fake)
self.loss_mse = self.criterion_mse(fake_input, self.input_orig)
# GAN loss
loss_adv, _ = self.criterion_gan(self.fake)
loss_G = self.loss_mse + loss_adv
loss_G.backward()
def backward_D(self):
loss_adv, self.loss_adv = self.criterion_gan(self.target, self.fake)
loss_D = loss_adv
loss_D.backward()
def train(self):
self.gen.train()
self.dis.train()
def eval(self):
self.gen.eval()
self.dis.eval()
def save_checkpoint(self, epoch):
torch.save(
{
'epoch': epoch,
'gen_state_dict': self.gen.state_dict(),
'dis_state_dict': self.dis.state_dict()
}, self.path + '%d.pkl' % epoch)
def load_checkpoint(self, path, pretrained=True):
weights = torch.load(path)
self.gen.load_state_dict(weights['gen_state_dict'])
self.dis.load_state_dict(weights['dis_state_dict'])
class GanTrainer(Trainer):
def __init__(self, train_loader, test_loader, valid_loader, general_args,
trainer_args):
super(GanTrainer, self).__init__(train_loader, test_loader,
valid_loader, general_args)
# Paths
self.loadpath = trainer_args.loadpath
self.savepath = trainer_args.savepath
# Load the auto-encoder
self.use_autoencoder = False
if trainer_args.autoencoder_path and os.path.exists(
trainer_args.autoencoder_path):
self.use_autoencoder = True
self.autoencoder = AutoEncoder(general_args=general_args).to(
self.device)
self.load_pretrained_autoencoder(trainer_args.autoencoder_path)
self.autoencoder.eval()
# Load the generator
self.generator = Generator(general_args=general_args).to(self.device)
if trainer_args.generator_path and os.path.exists(
trainer_args.generator_path):
self.load_pretrained_generator(trainer_args.generator_path)
self.discriminator = Discriminator(general_args=general_args).to(
self.device)
# Optimizers and schedulers
self.generator_optimizer = torch.optim.Adam(
params=self.generator.parameters(), lr=trainer_args.generator_lr)
self.discriminator_optimizer = torch.optim.Adam(
params=self.discriminator.parameters(),
lr=trainer_args.discriminator_lr)
self.generator_scheduler = lr_scheduler.StepLR(
optimizer=self.generator_optimizer,
step_size=trainer_args.generator_scheduler_step,
gamma=trainer_args.generator_scheduler_gamma)
self.discriminator_scheduler = lr_scheduler.StepLR(
optimizer=self.discriminator_optimizer,
step_size=trainer_args.discriminator_scheduler_step,
gamma=trainer_args.discriminator_scheduler_gamma)
# Load saved states
if os.path.exists(self.loadpath):
self.load()
# Loss function and stored losses
self.adversarial_criterion = nn.BCEWithLogitsLoss()
self.generator_time_criterion = nn.MSELoss()
self.generator_frequency_criterion = nn.MSELoss()
self.generator_autoencoder_criterion = nn.MSELoss()
# Define labels
self.real_label = 1
self.generated_label = 0
# Loss scaling factors
self.lambda_adv = trainer_args.lambda_adversarial
self.lambda_freq = trainer_args.lambda_freq
self.lambda_autoencoder = trainer_args.lambda_autoencoder
# Spectrogram converter
self.spectrogram = Spectrogram(normalized=True).to(self.device)
# Boolean indicating if the model needs to be saved
self.need_saving = True
# Boolean if the generator receives the feedback from the discriminator
self.use_adversarial = trainer_args.use_adversarial
def load_pretrained_generator(self, generator_path):
"""
Loads a pre-trained generator. Can be used to stabilize the training.
:param generator_path: location of the pre-trained generator (string).
:return: None
"""
checkpoint = torch.load(generator_path, map_location=self.device)
self.generator.load_state_dict(checkpoint['generator_state_dict'])
def load_pretrained_autoencoder(self, autoencoder_path):
"""
Loads a pre-trained auto-encoder. Can be used to infer
:param autoencoder_path: location of the pre-trained auto-encoder (string).
:return: None
"""
checkpoint = torch.load(autoencoder_path, map_location=self.device)
self.autoencoder.load_state_dict(checkpoint['autoencoder_state_dict'])
def train(self, epochs):
"""
Trains the GAN for a given number of pseudo-epochs.
:param epochs: Number of time to iterate over a part of the dataset (int).
:return: None
"""
for epoch in range(epochs):
for i in range(self.train_batches_per_epoch):
self.generator.train()
self.discriminator.train()
# Transfer to GPU
local_batch = next(self.train_loader_iter)
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
batch_size = input_batch.shape[0]
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# Train the discriminator with real data
self.discriminator_optimizer.zero_grad()
label = torch.full((batch_size, ),
self.real_label,
device=self.device)
output = self.discriminator(target_batch)
# Compute and store the discriminator loss on real data
loss_discriminator_real = self.adversarial_criterion(
output, torch.unsqueeze(label, dim=1))
self.train_losses['discriminator_adversarial']['real'].append(
loss_discriminator_real.item())
loss_discriminator_real.backward()
# Train the discriminator with fake data
generated_batch = self.generator(input_batch)
label.fill_(self.generated_label)
output = self.discriminator(generated_batch.detach())
# Compute and store the discriminator loss on fake data
loss_discriminator_generated = self.adversarial_criterion(
output, torch.unsqueeze(label, dim=1))
self.train_losses['discriminator_adversarial']['fake'].append(
loss_discriminator_generated.item())
loss_discriminator_generated.backward()
# Update the discriminator weights
self.discriminator_optimizer.step()
############################
# Update G network: maximize log(D(G(z)))
###########################
self.generator_optimizer.zero_grad()
# Get the spectrogram
specgram_target_batch = self.spectrogram(target_batch)
specgram_fake_batch = self.spectrogram(generated_batch)
# Fake labels are real for the generator cost
label.fill_(self.real_label)
output = self.discriminator(generated_batch)
# Compute the generator loss on fake data
# Get the adversarial loss
loss_generator_adversarial = torch.zeros(size=[1],
device=self.device)
if self.use_adversarial:
loss_generator_adversarial = self.adversarial_criterion(
output, torch.unsqueeze(label, dim=1))
self.train_losses['generator_adversarial'].append(
loss_generator_adversarial.item())
# Get the L2 loss in time domain
loss_generator_time = self.generator_time_criterion(
generated_batch, target_batch)
self.train_losses['time_l2'].append(loss_generator_time.item())
# Get the L2 loss in frequency domain
loss_generator_frequency = self.generator_frequency_criterion(
specgram_fake_batch, specgram_target_batch)
self.train_losses['freq_l2'].append(
loss_generator_frequency.item())
# Get the L2 loss in embedding space
loss_generator_autoencoder = torch.zeros(size=[1],
device=self.device,
requires_grad=True)
if self.use_autoencoder:
# Get the embeddings
_, embedding_target_batch = self.autoencoder(target_batch)
_, embedding_generated_batch = self.autoencoder(
generated_batch)
loss_generator_autoencoder = self.generator_autoencoder_criterion(
embedding_generated_batch, embedding_target_batch)
self.train_losses['autoencoder_l2'].append(
loss_generator_autoencoder.item())
# Combine the different losses
loss_generator = self.lambda_adv * loss_generator_adversarial + loss_generator_time + \
self.lambda_freq * loss_generator_frequency + \
self.lambda_autoencoder * loss_generator_autoencoder
# Back-propagate and update the generator weights
loss_generator.backward()
self.generator_optimizer.step()
# Print message
if not (i % 10):
message = 'Batch {}: \n' \
'\t Generator: \n' \
'\t\t Time: {} \n' \
'\t\t Frequency: {} \n' \
'\t\t Autoencoder {} \n' \
'\t\t Adversarial: {} \n' \
'\t Discriminator: \n' \
'\t\t Real {} \n' \
'\t\t Fake {} \n'.format(i,
loss_generator_time.item(),
loss_generator_frequency.item(),
loss_generator_autoencoder.item(),
loss_generator_adversarial.item(),
loss_discriminator_real.item(),
loss_discriminator_generated.item())
print(message)
# Evaluate the model
with torch.no_grad():
self.eval()
# Save the trainer state
self.save()
# if self.need_saving:
# self.save()
# Increment epoch counter
self.epoch += 1
self.generator_scheduler.step()
self.discriminator_scheduler.step()
def eval(self):
self.generator.eval()
self.discriminator.eval()
batch_losses = {'time_l2': [], 'freq_l2': []}
for i in range(self.valid_batches_per_epoch):
# Transfer to GPU
local_batch = next(self.valid_loader_iter)
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
generated_batch = self.generator(input_batch)
# Get the spectrogram
specgram_target_batch = self.spectrogram(target_batch)
specgram_generated_batch = self.spectrogram(generated_batch)
loss_generator_time = self.generator_time_criterion(
generated_batch, target_batch)
batch_losses['time_l2'].append(loss_generator_time.item())
loss_generator_frequency = self.generator_frequency_criterion(
specgram_generated_batch, specgram_target_batch)
batch_losses['freq_l2'].append(loss_generator_frequency.item())
# Store the validation losses
self.valid_losses['time_l2'].append(np.mean(batch_losses['time_l2']))
self.valid_losses['freq_l2'].append(np.mean(batch_losses['freq_l2']))
# Display validation losses
message = 'Epoch {}: \n' \
'\t Time: {} \n' \
'\t Frequency: {} \n'.format(self.epoch,
np.mean(np.mean(batch_losses['time_l2'])),
np.mean(np.mean(batch_losses['freq_l2'])))
print(message)
# Check if the loss is decreasing
self.check_improvement()
def save(self):
"""
Saves the model(s), optimizer(s), scheduler(s) and losses
:return: None
"""
torch.save(
{
'epoch':
self.epoch,
'generator_state_dict':
self.generator.state_dict(),
'discriminator_state_dict':
self.discriminator.state_dict(),
'generator_optimizer_state_dict':
self.generator_optimizer.state_dict(),
'discriminator_optimizer_state_dict':
self.discriminator_optimizer.state_dict(),
'generator_scheduler_state_dict':
self.generator_scheduler.state_dict(),
'discriminator_scheduler_state_dict':
self.discriminator_scheduler.state_dict(),
'train_losses':
self.train_losses,
'test_losses':
self.test_losses,
'valid_losses':
self.valid_losses
}, self.savepath)
def load(self):
"""
Loads the model(s), optimizer(s), scheduler(s) and losses
:return: None
"""
checkpoint = torch.load(self.loadpath, map_location=self.device)
self.epoch = checkpoint['epoch']
self.generator.load_state_dict(checkpoint['generator_state_dict'])
self.discriminator.load_state_dict(
checkpoint['discriminator_state_dict'])
self.generator_optimizer.load_state_dict(
checkpoint['generator_optimizer_state_dict'])
self.discriminator_optimizer.load_state_dict(
checkpoint['discriminator_optimizer_state_dict'])
self.generator_scheduler.load_state_dict(
checkpoint['generator_scheduler_state_dict'])
self.discriminator_scheduler.load_state_dict(
checkpoint['discriminator_scheduler_state_dict'])
self.train_losses = checkpoint['train_losses']
self.test_losses = checkpoint['test_losses']
self.valid_losses = checkpoint['valid_losses']
def evaluate_metrics(self, n_batches):
"""
Evaluates the quality of the reconstruction with the SNR and LSD metrics on a specified number of batches
:param: n_batches: number of batches to process
:return: mean and std for each metric
"""
with torch.no_grad():
snrs = []
lsds = []
generator = self.generator.eval()
for k in range(n_batches):
# Transfer to GPU
local_batch = next(self.test_loader_iter)
# Transfer to GPU
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
# Generates a batch
generated_batch = generator(input_batch)
# Get the metrics
snrs.append(
snr(x=generated_batch.squeeze(),
x_ref=target_batch.squeeze()))
lsds.append(
lsd(x=generated_batch.squeeze(),
x_ref=target_batch.squeeze()))
snrs = torch.cat(snrs).cpu().numpy()
lsds = torch.cat(lsds).cpu().numpy()
# Some signals corresponding to silence will be all zeroes and cause troubles due to the logarithm
snrs[np.isinf(snrs)] = np.nan
lsds[np.isinf(lsds)] = np.nan
return np.nanmean(snrs), np.nanstd(snrs), np.nanmean(lsds), np.nanstd(
lsds)
class WGanTrainer(Trainer):
def __init__(self, train_loader, test_loader, valid_loader, general_args,
trainer_args):
super(WGanTrainer, self).__init__(train_loader, test_loader,
valid_loader, general_args)
# Paths
self.loadpath = trainer_args.loadpath
self.savepath = trainer_args.savepath
# Load the generator
self.generator = Generator(general_args=general_args).to(self.device)
if trainer_args.generator_path and os.path.exists(
trainer_args.generator_path):
self.load_pretrained_generator(trainer_args.generator_path)
self.discriminator = Discriminator(general_args=general_args).to(
self.device)
# Optimizers and schedulers
self.generator_optimizer = torch.optim.Adam(
params=self.generator.parameters(), lr=trainer_args.generator_lr)
self.discriminator_optimizer = torch.optim.Adam(
params=self.discriminator.parameters(),
lr=trainer_args.discriminator_lr)
self.generator_scheduler = lr_scheduler.StepLR(
optimizer=self.generator_optimizer,
step_size=trainer_args.generator_scheduler_step,
gamma=trainer_args.generator_scheduler_gamma)
self.discriminator_scheduler = lr_scheduler.StepLR(
optimizer=self.discriminator_optimizer,
step_size=trainer_args.discriminator_scheduler_step,
gamma=trainer_args.discriminator_scheduler_gamma)
# Load saved states
if os.path.exists(self.loadpath):
self.load()
# Loss function and stored losses
self.generator_time_criterion = nn.MSELoss()
# Loss scaling factors
self.lambda_adv = trainer_args.lambda_adversarial
self.lambda_time = trainer_args.lambda_time
# Boolean indicating if the model needs to be saved
self.need_saving = True
# Overrides losses from parent class
self.train_losses = {
'generator': {
'time_l2': [],
'adversarial': []
},
'discriminator': {
'penalty': [],
'adversarial': []
}
}
self.test_losses = {
'generator': {
'time_l2': [],
'adversarial': []
},
'discriminator': {
'penalty': [],
'adversarial': []
}
}
self.valid_losses = {
'generator': {
'time_l2': [],
'adversarial': []
},
'discriminator': {
'penalty': [],
'adversarial': []
}
}
# Select either wgan or wgan-gp method
self.use_penalty = trainer_args.use_penalty
self.gamma = trainer_args.gamma_wgan_gp
self.clipping_limit = trainer_args.clipping_limit
self.n_critic = trainer_args.n_critic
self.coupling_epoch = trainer_args.coupling_epoch
def load_pretrained_generator(self, generator_path):
"""
Loads a pre-trained generator. Can be used to stabilize the training.
:param generator_path: location of the pre-trained generator (string).
:return: None
"""
checkpoint = torch.load(generator_path, map_location=self.device)
self.generator.load_state_dict(checkpoint['generator_state_dict'])
def compute_gradient_penalty(self, input_batch, generated_batch):
"""
Compute the gradient penalty as described in the original paper
(https://papers.nips.cc/paper/7159-improved-training-of-wasserstein-gans.pdf).
:param input_batch: batch of input data (torch tensor).
:param generated_batch: batch of generated data (torch tensor).
:return: penalty as a scalar (torch tensor).
"""
batch_size = input_batch.size(0)
epsilon = torch.rand(batch_size, 1, 1)
epsilon = epsilon.expand_as(input_batch).to(self.device)
# Interpolate
interpolation = epsilon * input_batch.data + (
1 - epsilon) * generated_batch.data
interpolation = interpolation.requires_grad_(True).to(self.device)
# Computes the discriminator's prediction for the interpolated input
interpolation_logits = self.discriminator(interpolation)
# Computes a vector of outputs to make it works with 2 output classes if needed
grad_outputs = torch.ones_like(interpolation_logits).to(
self.device).requires_grad_(True)
# Get the gradients and retain the graph so that the penalty can be back-propagated
gradients = autograd.grad(outputs=interpolation_logits,
inputs=interpolation,
grad_outputs=grad_outputs,
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
gradients = gradients.view(batch_size, -1)
# Computes the norm of the gradients
gradients_norm = torch.sqrt(torch.sum(gradients**2, dim=1))
return ((gradients_norm - 1)**2).mean()
def train_discriminator_step(self, input_batch, target_batch):
"""
Trains the discriminator for a single step based on the wasserstein gan-gp framework.
:param input_batch: batch of input data (torch tensor).
:param target_batch: batch of target data (torch tensor).
:return: a batch of generated data (torch tensor).
"""
# Activate gradient tracking for the discriminator
self.change_discriminator_grad_requirement(requires_grad=True)
# Set the discriminator's gradients to zero
self.discriminator_optimizer.zero_grad()
# Generate a batch and compute the penalty
generated_batch = self.generator(input_batch)
# Compute the loss
loss_d = self.discriminator(generated_batch.detach()).mean(
) - self.discriminator(target_batch).mean()
self.train_losses['discriminator']['adversarial'].append(loss_d.item())
if self.use_penalty:
penalty = self.compute_gradient_penalty(input_batch,
generated_batch.detach())
self.train_losses['discriminator']['penalty'].append(
penalty.item())
loss_d = loss_d + self.gamma * penalty
# Update the discriminator's weights
loss_d.backward()
self.discriminator_optimizer.step()
# Apply the weight constraint if needed
if not self.use_penalty:
for p in self.discriminator.parameters():
p.data.clamp_(min=-self.clipping_limit,
max=self.clipping_limit)
# Return the generated batch to avoid redundant computation
return generated_batch
def train_generator_step(self, target_batch, generated_batch):
"""
Trains the generator for a single step based on the wasserstein gan-gp framework.
:param target_batch: batch of target data (torch tensor).
:param generated_batch: batch of generated data (torch tensor).
:return: None
"""
# Deactivate gradient tracking for the discriminator
self.change_discriminator_grad_requirement(requires_grad=False)
# Set generator's gradients to zero
self.generator_optimizer.zero_grad()
# Get the generator losses
loss_g_adversarial = -self.discriminator(generated_batch).mean()
loss_g_time = self.generator_time_criterion(generated_batch,
target_batch)
# Combine the different losses
loss_g = loss_g_time
if self.epoch >= self.coupling_epoch:
loss_g = loss_g + self.lambda_adv * loss_g_adversarial
# Back-propagate and update the generator weights
loss_g.backward()
self.generator_optimizer.step()
# Store the losses
self.train_losses['generator']['time_l2'].append(loss_g_time.item())
self.train_losses['generator']['adversarial'].append(
loss_g_adversarial.item())
def change_discriminator_grad_requirement(self, requires_grad):
"""
Changes the requires_grad flag of discriminator's parameters. This action is not absolutely needed as the
discriminator's optimizer is not called after the generators update, but it reduces the computational cost.
:param requires_grad: flag indicating if the discriminator's parameter require gradient tracking (boolean).
:return: None
"""
for p in self.discriminator.parameters():
p.requires_grad_(requires_grad)
def train(self, epochs):
"""
Trains the WGAN-GP for a given number of pseudo-epochs.
:param epochs: Number of time to iterate over a part of the dataset (int).
:return: None
"""
self.generator.train()
self.discriminator.train()
for epoch in range(epochs):
for i in range(self.train_batches_per_epoch):
# Transfer to GPU
local_batch = next(self.train_loader_iter)
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
# Train the discriminator
generated_batch = self.train_discriminator_step(
input_batch, target_batch)
# Train the generator every n_critic
if not (i % self.n_critic):
self.train_generator_step(target_batch, generated_batch)
# Print message
if not (i % 10):
message = 'Batch {}: \n' \
'\t Generator: \n' \
'\t\t Time: {} \n' \
'\t\t Adversarial: {} \n' \
'\t Discriminator: \n' \
'\t\t Penalty: {}\n' \
'\t\t Adversarial: {} \n'.format(i,
self.train_losses['generator']['time_l2'][-1],
self.train_losses['generator']['adversarial'][-1],
self.train_losses['discriminator']['penalty'][-1],
self.train_losses['discriminator']['adversarial'][-1])
print(message)
# Evaluate the model
with torch.no_grad():
self.eval()
# Save the trainer state
self.save()
# Increment epoch counter
self.epoch += 1
self.generator_scheduler.step()
self.discriminator_scheduler.step()
def eval(self):
# Set the models in evaluation mode
self.generator.eval()
self.discriminator.eval()
batch_losses = {'time_l2': []}
for i in range(self.valid_batches_per_epoch):
# Transfer to GPU
local_batch = next(self.valid_loader_iter)
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
generated_batch = self.generator(input_batch)
loss_g_time = self.generator_time_criterion(
generated_batch, target_batch)
batch_losses['time_l2'].append(loss_g_time.item())
# Store the validation losses
self.valid_losses['generator']['time_l2'].append(
np.mean(batch_losses['time_l2']))
# Display validation losses
message = 'Epoch {}: \n' \
'\t Time: {} \n'.format(self.epoch, np.mean(np.mean(batch_losses['time_l2'])))
print(message)
# Set the models in train mode
self.generator.train()
self.discriminator.eval()
def save(self):
"""
Saves the model(s), optimizer(s), scheduler(s) and losses
:return: None
"""
savepath = self.savepath.split('.')[0] + '_' + str(
self.epoch // 5) + '.' + self.savepath.split('.')[1]
torch.save(
{
'epoch':
self.epoch,
'generator_state_dict':
self.generator.state_dict(),
'discriminator_state_dict':
self.discriminator.state_dict(),
'generator_optimizer_state_dict':
self.generator_optimizer.state_dict(),
'discriminator_optimizer_state_dict':
self.discriminator_optimizer.state_dict(),
'generator_scheduler_state_dict':
self.generator_scheduler.state_dict(),
'discriminator_scheduler_state_dict':
self.discriminator_scheduler.state_dict(),
'train_losses':
self.train_losses,
'test_losses':
self.test_losses,
'valid_losses':
self.valid_losses
}, savepath)
def load(self):
"""
Loads the model(s), optimizer(s), scheduler(s) and losses
:return: None
"""
checkpoint = torch.load(self.loadpath, map_location=self.device)
self.epoch = checkpoint['epoch']
self.generator.load_state_dict(checkpoint['generator_state_dict'])
# self.discriminator.load_state_dict(checkpoint['discriminator_state_dict'])
self.generator_optimizer.load_state_dict(
checkpoint['generator_optimizer_state_dict'])
# self.discriminator_optimizer.load_state_dict(checkpoint['discriminator_optimizer_state_dict'])
self.generator_scheduler.load_state_dict(
checkpoint['generator_scheduler_state_dict'])
self.discriminator_scheduler.load_state_dict(
checkpoint['discriminator_scheduler_state_dict'])
self.train_losses = checkpoint['train_losses']
self.test_losses = checkpoint['test_losses']
self.valid_losses = checkpoint['valid_losses']
def evaluate_metrics(self, n_batches):
"""
Evaluates the quality of the reconstruction with the SNR and LSD metrics on a specified number of batches
:param: n_batches: number of batches to process
:return: mean and std for each metric
"""
with torch.no_grad():
snrs = []
lsds = []
generator = self.generator.eval()
for k in range(n_batches):
# Transfer to GPU
local_batch = next(self.test_loader_iter)
# Transfer to GPU
input_batch, target_batch = local_batch[0].to(
self.device), local_batch[1].to(self.device)
# Generates a batch
generated_batch = generator(input_batch)
# Get the metrics
snrs.append(
snr(x=generated_batch.squeeze(),
x_ref=target_batch.squeeze()))
lsds.append(
lsd(x=generated_batch.squeeze(),
x_ref=target_batch.squeeze()))
snrs = torch.cat(snrs).cpu().numpy()
lsds = torch.cat(lsds).cpu().numpy()
# Some signals corresponding to silence will be all zeroes and cause troubles due to the logarithm
snrs[np.isinf(snrs)] = np.nan
lsds[np.isinf(lsds)] = np.nan
return np.nanmean(snrs), np.nanstd(snrs), np.nanmean(lsds), np.nanstd(
lsds)