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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from .lpips import LPIPS
def l2_loss(network_output, gt):
return ((network_output - gt) ** 2).mean()
def cos_loss(network_output, gt):
return 1 - F.cosine_similarity(network_output, gt, dim=1).mean()
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
# from MAGVIT, used in place hof hinge_d_loss
def sigmoid_cross_entropy_with_logits(labels, logits):
# The final formulation is: max(x, 0) - x * z + log(1 + exp(-abs(x)))
zeros = torch.zeros_like(logits, dtype=logits.dtype)
condition = logits >= zeros
relu_logits = torch.where(condition, logits, zeros)
neg_abs_logits = torch.where(condition, -logits, logits)
return relu_logits - logits * labels + torch.log1p(torch.exp(neg_abs_logits))
def lecam_reg(real_pred, fake_pred, ema_real_pred, ema_fake_pred):
assert real_pred.ndim == 0 and ema_fake_pred.ndim == 0
lecam_loss = torch.mean(torch.pow(nn.ReLU()(real_pred - ema_fake_pred), 2))
lecam_loss += torch.mean(torch.pow(nn.ReLU()(ema_real_pred - fake_pred), 2))
return lecam_loss
def gradient_penalty_fn(images, output):
gradients = torch.autograd.grad(
outputs=output,
inputs=images,
grad_outputs=torch.ones(output.size(), device=images.device),
create_graph=True,
retain_graph=True,
only_inputs=True,
)[0]
gradients = rearrange(gradients, "b ... -> b (...)")
return ((gradients.norm(2, dim=1) - 1) ** 2).mean()
class VAELoss(nn.Module):
def __init__(
self,
logvar_init=0.0,
perceptual_loss_weight=0.1,
kl_loss_weight=0.000001,
device="cpu",
dtype="bf16",
):
super().__init__()
if type(dtype) == str:
if dtype == "bf16":
dtype = torch.bfloat16
elif dtype == "fp16":
dtype = torch.float16
else:
raise NotImplementedError(f"dtype: {dtype}")
# KL Loss
self.kl_loss_weight = kl_loss_weight
# Perceptual Loss
self.perceptual_loss_fn = LPIPS().eval().to(device, dtype)
self.perceptual_loss_weight = perceptual_loss_weight
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
def forward(
self,
video,
recon_video,
posterior,
nll_weights=None,
no_perceptual=False,
):
video = rearrange(video, "b c t h w -> (b t) c h w").contiguous()
recon_video = rearrange(recon_video, "b c t h w -> (b t) c h w").contiguous()
# reconstruction loss
recon_loss = torch.abs(video - recon_video)
# perceptual loss
if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0.0 and not no_perceptual:
# handle channels
channels = video.shape[1]
assert channels in {1, 3}
if channels == 1:
input_vgg_input = repeat(video, "b 1 h w -> b c h w", c=3)
recon_vgg_input = repeat(recon_video, "b 1 h w -> b c h w", c=3)
else:
input_vgg_input = video
recon_vgg_input = recon_video
perceptual_loss = self.perceptual_loss_fn(input_vgg_input, recon_vgg_input)
recon_loss = recon_loss + self.perceptual_loss_weight * perceptual_loss
nll_loss = recon_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = nll_loss
if nll_weights is not None:
weighted_nll_loss = nll_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
weighted_kl_loss = 0
if self.kl_loss_weight is not None and self.kl_loss_weight > 0.0:
kl_loss = posterior.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
weighted_kl_loss = kl_loss * self.kl_loss_weight
return nll_loss, weighted_nll_loss, weighted_kl_loss
def adopt_weight(weight, global_step, threshold=0, value=0.0):
if global_step < threshold:
weight = value
return weight
class AdversarialLoss(nn.Module):
def __init__(
self,
discriminator_factor=1.0,
discriminator_start=50001,
generator_factor=0.5,
generator_loss_type="non-saturating",
):
super().__init__()
self.discriminator_factor = discriminator_factor
self.discriminator_start = discriminator_start
self.generator_factor = generator_factor
self.generator_loss_type = generator_loss_type
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer):
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]
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.generator_factor
return d_weight
def forward(
self,
fake_logits,
nll_loss,
last_layer,
global_step,
is_training=True,
):
# NOTE: following MAGVIT to allow non_saturating
assert self.generator_loss_type in ["hinge", "vanilla", "non-saturating"]
if self.generator_loss_type == "hinge":
gen_loss = -torch.mean(fake_logits)
elif self.generator_loss_type == "non-saturating":
gen_loss = torch.mean(
sigmoid_cross_entropy_with_logits(labels=torch.ones_like(fake_logits), logits=fake_logits)
)
else:
raise ValueError("Generator loss {} not supported".format(self.generator_loss_type))
if self.discriminator_factor is not None and self.discriminator_factor > 0.0:
try:
d_weight = self.calculate_adaptive_weight(nll_loss, gen_loss, last_layer)
except RuntimeError:
assert not is_training
d_weight = torch.tensor(0.0)
else:
d_weight = torch.tensor(0.0)
disc_factor = adopt_weight(self.discriminator_factor, global_step, threshold=self.discriminator_start)
weighted_gen_loss = d_weight * disc_factor * gen_loss
return weighted_gen_loss
class LeCamEMA:
def __init__(self, ema_real=0.0, ema_fake=0.0, decay=0.999, dtype=torch.bfloat16, device="cpu"):
self.decay = decay
self.ema_real = torch.tensor(ema_real).to(device, dtype)
self.ema_fake = torch.tensor(ema_fake).to(device, dtype)
def update(self, ema_real, ema_fake):
self.ema_real = self.ema_real * self.decay + ema_real * (1 - self.decay)
self.ema_fake = self.ema_fake * self.decay + ema_fake * (1 - self.decay)
def get(self):
return self.ema_real, self.ema_fake
class DiscriminatorLoss(nn.Module):
def __init__(
self,
discriminator_factor=1.0,
discriminator_start=50001,
discriminator_loss_type="non-saturating",
lecam_loss_weight=None,
gradient_penalty_loss_weight=None, # SCH: following MAGVIT config.vqgan.grad_penalty_cost
):
super().__init__()
assert discriminator_loss_type in ["hinge", "vanilla", "non-saturating"]
self.discriminator_factor = discriminator_factor
self.discriminator_start = discriminator_start
self.lecam_loss_weight = lecam_loss_weight
self.gradient_penalty_loss_weight = gradient_penalty_loss_weight
self.discriminator_loss_type = discriminator_loss_type
def forward(
self,
real_logits,
fake_logits,
global_step,
lecam_ema_real=None,
lecam_ema_fake=None,
real_video=None,
split="train",
):
if self.discriminator_factor is not None and self.discriminator_factor > 0.0:
disc_factor = adopt_weight(self.discriminator_factor, global_step, threshold=self.discriminator_start)
if self.discriminator_loss_type == "hinge":
disc_loss = hinge_d_loss(real_logits, fake_logits)
elif self.discriminator_loss_type == "non-saturating":
if real_logits is not None:
real_loss = sigmoid_cross_entropy_with_logits(
labels=torch.ones_like(real_logits), logits=real_logits
)
else:
real_loss = 0.0
if fake_logits is not None:
fake_loss = sigmoid_cross_entropy_with_logits(
labels=torch.zeros_like(fake_logits), logits=fake_logits
)
else:
fake_loss = 0.0
disc_loss = 0.5 * (torch.mean(real_loss) + torch.mean(fake_loss))
elif self.discriminator_loss_type == "vanilla":
disc_loss = vanilla_d_loss(real_logits, fake_logits)
else:
raise ValueError(f"Unknown GAN loss '{self.discriminator_loss_type}'.")
weighted_d_adversarial_loss = disc_factor * disc_loss
else:
weighted_d_adversarial_loss = 0
lecam_loss = torch.tensor(0.0)
if self.lecam_loss_weight is not None and self.lecam_loss_weight > 0.0:
real_pred = torch.mean(real_logits)
fake_pred = torch.mean(fake_logits)
lecam_loss = lecam_reg(real_pred, fake_pred, lecam_ema_real, lecam_ema_fake)
lecam_loss = lecam_loss * self.lecam_loss_weight
gradient_penalty = torch.tensor(0.0)
if self.gradient_penalty_loss_weight is not None and self.gradient_penalty_loss_weight > 0.0:
assert real_video is not None
gradient_penalty = gradient_penalty_fn(real_video, real_logits)
gradient_penalty *= self.gradient_penalty_loss_weight
return (weighted_d_adversarial_loss, lecam_loss, gradient_penalty)
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