def foward_d(self, source, output, target):
fake = losses.conditional_input(source, output, self.conditional)
real = losses.conditional_input(source, target, self.conditional)
d_fake = self.adv_loss(self.dnet(fake), False)
d_real = self.adv_loss(self.dnet(real), True)
d_terms = {'d/real': d_real, 'd/fake': d_fake, 'd/loss': d_real + d_fake}
d_loss = d_terms['d/loss']
return d_loss, d_terms
def foward_d(self, source, output, target):
fake = losses.conditional_input(source, output, self.conditional)
real = losses.conditional_input(source, target, self.conditional)
d_fake = self.adv_loss(self.dnet(fake), False)
d_real = self.adv_loss(self.dnet(real), True)
d_gp = losses.gradient_panelty(self.dnet, real, fake)
d_terms = {'d/real': d_real, 'd/fake': d_fake, 'd/gp': d_gp, 'd/loss': d_real + d_fake + self.gp_weight * d_gp}
d_loss = d_terms['d/loss']
return d_loss, d_terms
def test_loss_terms():
source = torch.randn(10, 3, 128, 128, device=device())
target = torch.randn(10, 3, 128, 128, device=device())
output = g(source)
real = L.conditional_input(source, target, conditional)
fake = L.conditional_input(source, output, conditional)
L.adversarial_ce_loss(F.sigmoid(d(fake)), 1)
L.adversarial_ls_loss(d(fake), 1)
L.adversarial_w_loss(d(fake), True)
L.gradient_penalty(d, real, fake)
def forward_g(self, source, output, target):
recon = self.recon_loss(output, target)
adv = self.adv_loss(
self.dnet(
losses.conditional_input(source, output, self.conditional)),
True)
g_terms = {
'g/recon': recon,
'g/adv': adv,
'g/loss': recon * self.recon_weight + adv
}
g_loss = g_terms['g/loss']
return g_loss, g_terms