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import torch
from torch import nn
import torch.nn.functional as F
from .lpips import LPIPS
from einops import rearrange
from .discriminator import NLayerDiscriminator, weights_init, NLayerDiscriminator3D
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1.0 - logits_real))
loss_fake = torch.mean(F.relu(1.0 + logits_fake))
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss
def vanilla_d_loss(logits_real, logits_fake):
d_loss = 0.5 * (
torch.mean(torch.nn.functional.softplus(-logits_real))
+ torch.mean(torch.nn.functional.softplus(logits_fake))
)
return d_loss
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
loss_real = (weights * loss_real).sum() / weights.sum()
loss_fake = (weights * loss_fake).sum() / weights.sum()
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss
def adopt_weight(weight, global_step, threshold=0, value=0.0):
if global_step < threshold:
weight = value
return weight
def measure_perplexity(predicted_indices, n_embed):
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
avg_probs = encodings.mean(0)
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
cluster_use = torch.sum(avg_probs > 0)
return perplexity, cluster_use
def l1(x, y):
return torch.abs(x - y)
def l2(x, y):
return torch.pow((x - y), 2)
class LPIPSWithDiscriminator(nn.Module):
def __init__(
self,
disc_start,
logvar_init=0.0,
kl_weight=1.0,
pixelloss_weight=1.0,
perceptual_weight=1.0,
# --- Discriminator Loss ---
disc_num_layers=3,
disc_in_channels=3,
disc_factor=1.0,
disc_weight=1.0,
use_actnorm=False,
disc_conditional=False,
disc_loss="hinge",
):
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
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
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,
split="train",
weights=None,
last_layer=None,
cond=None,
):
inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
reconstructions = rearrange(
reconstructions, "b c t h w -> (b t) c h w"
).contiguous()
rec_loss = torch.abs(inputs - reconstructions)
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs, reconstructions)
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]
# 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:
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
class LPIPSWithDiscriminator3D(nn.Module):
def __init__(
self,
disc_start,
logvar_init=0.0,
kl_weight=1.0,
pixelloss_weight=1.0,
perceptual_weight=1.0,
# --- Discriminator Loss ---
disc_num_layers=3,
disc_in_channels=3,
disc_factor=1.0,
disc_weight=1.0,
use_actnorm=False,
disc_conditional=False,
disc_loss="hinge",
):
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
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
self.discriminator = NLayerDiscriminator3D(
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
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,
split="train",
weights=None,
last_layer=None,
cond=None,
):
t = inputs.shape[2]
inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
reconstructions = rearrange(
reconstructions, "b c t h w -> (b t) c h w"
).contiguous()
rec_loss = torch.abs(inputs - reconstructions)
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs, reconstructions)
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]
inputs = rearrange(inputs, "(b t) c h w -> b c t h w", t=t).contiguous()
reconstructions = rearrange(
reconstructions, "(b t) c h w -> b c t h w", t=t
).contiguous()
# GAN Part
if optimizer_idx == 0:
# generator update
if cond is None:
assert not self.disc_conditional
logits_fake = self.discriminator(reconstructions)
else:
assert self.disc_conditional
logits_fake = self.discriminator(
torch.cat((reconstructions, 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 as e:
assert not self.training, print(e)
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:
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
class SimpleLPIPS(nn.Module):
def __init__(
self,
logvar_init=0.0,
kl_weight=1.0,
pixelloss_weight=1.0,
perceptual_weight=1.0,
disc_loss="hinge",
):
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
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
def forward(
self,
inputs,
reconstructions,
posteriors,
split="train",
weights=None,
):
inputs = rearrange(inputs, "b c t h w -> (b t) c h w").contiguous()
reconstructions = rearrange(
reconstructions, "b c t h w -> (b t) c h w"
).contiguous()
rec_loss = torch.abs(inputs - reconstructions)
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs, reconstructions)
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]
loss = weighted_nll_loss + self.kl_weight * kl_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(),
}
if self.perceptual_weight > 0:
log.update({"{}/p_loss".format(split): p_loss.detach().mean()})
return loss, log
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