video_vae/modeling_loss.py

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