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import os
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
from .modeling_lpips import LPIPS
from .modeling_discriminator import NLayerDiscriminator, NLayerDiscriminator3D, weights_init
from IPython import embed
class AdaptiveLossWeight:
def __init__(self, timestep_range=[0, 1], buckets=300, weight_range=[1e-7, 1e7]):
self.bucket_ranges = torch.linspace(timestep_range[0], timestep_range[1], buckets-1)
self.bucket_losses = torch.ones(buckets)
self.weight_range = weight_range
def weight(self, timestep):
indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep)
return (1/self.bucket_losses.to(timestep.device)[indices]).clamp(*self.weight_range)
def update_buckets(self, timestep, loss, beta=0.99):
indices = torch.searchsorted(self.bucket_ranges.to(timestep.device), timestep).cpu()
self.bucket_losses[indices] = self.bucket_losses[indices]*beta + loss.detach().cpu() * (1-beta)
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 adopt_weight(weight, global_step, threshold=0, value=0.0):
if global_step < threshold:
weight = value
return weight
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=4,
disc_in_channels=3,
disc_factor=1.0,
disc_weight=0.5,
disc_loss="hinge",
add_discriminator=True,
using_3d_discriminator=False,
):
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)
if add_discriminator:
disc_cls = NLayerDiscriminator3D if using_3d_discriminator else NLayerDiscriminator
self.discriminator = disc_cls(
input_nc=disc_in_channels, n_layers=disc_num_layers,
).apply(weights_init)
else:
self.discriminator = None
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.using_3d_discriminator = using_3d_discriminator
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",
last_layer=None,
):
t = reconstructions.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()
if optimizer_idx == 0:
# rec_loss = torch.mean(torch.abs(inputs - reconstructions), dim=(1,2,3), keepdim=True)
rec_loss = torch.mean(F.mse_loss(inputs, reconstructions, reduction='none'), dim=(1,2,3), keepdim=True)
if self.perceptual_weight > 0:
p_loss = self.perceptual_loss(inputs, reconstructions)
nll_loss = self.pixel_weight * rec_loss + self.perceptual_weight * p_loss
nll_loss = nll_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = 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.mean(kl_loss)
disc_factor = adopt_weight(
self.disc_factor, global_step, threshold=self.discriminator_iter_start
)
if disc_factor > 0.0:
if self.using_3d_discriminator:
reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t)
logits_fake = self.discriminator(reconstructions.contiguous())
g_loss = -torch.mean(logits_fake)
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)
g_loss = torch.tensor(0.0)
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(),
"{}/perception_loss".format(split): p_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 self.using_3d_discriminator:
inputs = rearrange(inputs, '(b t) c h w -> b c t h w', t=t)
reconstructions = rearrange(reconstructions, '(b t) c h w -> b c t h w', t=t)
logits_real = self.discriminator(inputs.contiguous().detach())
logits_fake = self.discriminator(reconstructions.contiguous().detach())
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 |