def compute_loss_GSWD(self,
discriminator,
optimizer,
minibatch,
rand_dist,
g,
r,
num_projection,
p=2):
label = torch.full((minibatch.shape[0], ), 1, device=self.device)
criterion = nn.BCELoss()
data = minibatch.to(self.device)
z_prior = rand_dist((data.shape[0], self.latent_size)).to(self.device)
data_fake = self.decoder(z_prior)
y_data, data = discriminator(data)
errD_real = criterion(y_data, label)
optimizer.zero_grad()
errD_real.backward(retain_graph=True)
optimizer.step()
y_fake, data_fake = discriminator(data_fake)
label.fill_(0)
errD_fake = criterion(y_fake, label)
optimizer.zero_grad()
errD_fake.backward(retain_graph=True)
optimizer.step()
_gswd = generalized_sliced_wasserstein_distance(
data.view(data.shape[0], -1), data_fake.view(data.shape[0], -1), g,
r, num_projection, p, self.device)
return _gswd
def compute_loss_GSWD(self,minibatch,rand_dist,g_function,r,num_projection,p=2):
data = minibatch.to(self.device)
z_prior = rand_dist((data.shape[0],self.latent_size)).to(self.device)
data_fake= self.decoder(z_prior)
_gswd = generalized_sliced_wasserstein_distance(data.view(data.shape[0],-1),data_fake.view(data.shape[0],-1) ,g_function,r,
num_projection, p,
self.device)
return _gswd