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import torch
import torch.nn as nn
from einops import rearrange
from taming.modules.losses.vqperceptual import *
class LPIPSWithDiscriminator(nn.Module):
def __init__(
self,
disc_start,
logvar_init=0.0,
kl_weight=1.0,
pixelloss_weight=1.0,
disc_num_layers=3,
disc_in_channels=3,
disc_factor=1.0,
disc_weight=1.0,
perceptual_weight=1.0,
use_actnorm=False,
disc_conditional=False,
disc_loss="hinge",
max_bs=None,
):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
self.kl_weight = kl_weight
self.pixel_weight = pixelloss_weight
self.perceptual_loss = LPIPS().eval()
self.perceptual_weight = perceptual_weight
# output log variance
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
self.discriminator = NLayerDiscriminator(
input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm
).apply(weights_init)
self.discriminator_iter_start = disc_start
self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
self.max_bs = max_bs
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
else:
nll_grads = torch.autograd.grad(
nll_loss, self.last_layer[0], retain_graph=True
)[0]
g_grads = torch.autograd.grad(
g_loss, self.last_layer[0], retain_graph=True
)[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(
self,
inputs,
reconstructions,
posteriors,
optimizer_idx,
global_step,
last_layer=None,
cond=None,
split="train",
weights=None,
):
if inputs.dim() == 5:
inputs = rearrange(inputs, "b c t h w -> (b t) c h w")
if reconstructions.dim() == 5:
reconstructions = rearrange(reconstructions, "b c t h w -> (b t) c h w")
rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
if self.perceptual_weight > 0:
if self.max_bs is not None and self.max_bs < inputs.shape[0]:
input_list = torch.split(inputs, self.max_bs, dim=0)
reconstruction_list = torch.split(reconstructions, self.max_bs, dim=0)
p_losses = [
self.perceptual_loss(
inputs.contiguous(), reconstructions.contiguous()
)
for inputs, reconstructions in zip(input_list, reconstruction_list)
]
p_loss = torch.cat(p_losses, dim=0)
else:
p_loss = self.perceptual_loss(
inputs.contiguous(), reconstructions.contiguous()
)
rec_loss = rec_loss + self.perceptual_weight * p_loss
nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = nll_loss
if weights is not None:
weighted_nll_loss = weights * nll_loss
weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
kl_loss = posteriors.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
# now the GAN part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
logits_fake = self.discriminator(
torch.cat((reconstructions.contiguous(), cond), dim=1)
)
g_loss = -torch.mean(logits_fake)
if self.disc_factor > 0.0:
try:
d_weight = self.calculate_adaptive_weight(
nll_loss, g_loss, last_layer=last_layer
)
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
else:
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(
self.disc_factor, global_step, threshold=self.discriminator_iter_start
)
loss = (
weighted_nll_loss
+ self.kl_weight * kl_loss
+ d_weight * disc_factor * g_loss
)
log = {
"{}/total_loss".format(split): loss.clone().detach().mean(),
"{}/logvar".format(split): self.logvar.detach(),
"{}/kl_loss".format(split): kl_loss.detach().mean(),
"{}/nll_loss".format(split): nll_loss.detach().mean(),
"{}/rec_loss".format(split): rec_loss.detach().mean(),
"{}/d_weight".format(split): d_weight.detach(),
"{}/disc_factor".format(split): torch.tensor(disc_factor),
"{}/g_loss".format(split): g_loss.detach().mean(),
}
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
else:
logits_real = self.discriminator(
torch.cat((inputs.contiguous().detach(), cond), dim=1)
)
logits_fake = self.discriminator(
torch.cat((reconstructions.contiguous().detach(), cond), dim=1)
)
disc_factor = adopt_weight(
self.disc_factor, global_step, threshold=self.discriminator_iter_start
)
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
log = {
"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
"{}/logits_real".format(split): logits_real.detach().mean(),
"{}/logits_fake".format(split): logits_fake.detach().mean(),
}
return d_loss, log
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