168 lines
7.8 KiB
Python
168 lines
7.8 KiB
Python
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
from einops import repeat
|
||
|
|
|
||
|
|
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
|
||
|
|
from taming.modules.losses.lpips import LPIPS
|
||
|
|
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_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. - logits_real), dim=[1,2,3])
|
||
|
|
loss_fake = torch.mean(F.relu(1. + 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.):
|
||
|
|
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 VQLPIPSWithDiscriminator(nn.Module):
|
||
|
|
def __init__(self, disc_start, codebook_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_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
|
||
|
|
pixel_loss="l1"):
|
||
|
|
super().__init__()
|
||
|
|
assert disc_loss in ["hinge", "vanilla"]
|
||
|
|
assert perceptual_loss in ["lpips", "clips", "dists"]
|
||
|
|
assert pixel_loss in ["l1", "l2"]
|
||
|
|
self.codebook_weight = codebook_weight
|
||
|
|
self.pixel_weight = pixelloss_weight
|
||
|
|
if perceptual_loss == "lpips":
|
||
|
|
print(f"{self.__class__.__name__}: Running with LPIPS.")
|
||
|
|
self.perceptual_loss = LPIPS().eval()
|
||
|
|
else:
|
||
|
|
raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
|
||
|
|
self.perceptual_weight = perceptual_weight
|
||
|
|
|
||
|
|
if pixel_loss == "l1":
|
||
|
|
self.pixel_loss = l1
|
||
|
|
else:
|
||
|
|
self.pixel_loss = l2
|
||
|
|
|
||
|
|
self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
|
||
|
|
n_layers=disc_num_layers,
|
||
|
|
use_actnorm=use_actnorm,
|
||
|
|
ndf=disc_ndf
|
||
|
|
).apply(weights_init)
|
||
|
|
self.discriminator_iter_start = disc_start
|
||
|
|
if disc_loss == "hinge":
|
||
|
|
self.disc_loss = hinge_d_loss
|
||
|
|
elif disc_loss == "vanilla":
|
||
|
|
self.disc_loss = vanilla_d_loss
|
||
|
|
else:
|
||
|
|
raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
|
||
|
|
print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
|
||
|
|
self.disc_factor = disc_factor
|
||
|
|
self.discriminator_weight = disc_weight
|
||
|
|
self.disc_conditional = disc_conditional
|
||
|
|
self.n_classes = n_classes
|
||
|
|
|
||
|
|
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, codebook_loss, inputs, reconstructions, optimizer_idx,
|
||
|
|
global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
|
||
|
|
if not exists(codebook_loss):
|
||
|
|
codebook_loss = torch.tensor([0.]).to(inputs.device)
|
||
|
|
#rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
|
||
|
|
rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
|
||
|
|
if self.perceptual_weight > 0:
|
||
|
|
p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
|
||
|
|
rec_loss = rec_loss + self.perceptual_weight * p_loss
|
||
|
|
else:
|
||
|
|
p_loss = torch.tensor([0.0])
|
||
|
|
|
||
|
|
nll_loss = rec_loss
|
||
|
|
#nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
|
||
|
|
nll_loss = torch.mean(nll_loss)
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
|
||
|
|
loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
|
||
|
|
|
||
|
|
log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
|
||
|
|
"{}/quant_loss".format(split): codebook_loss.detach().mean(),
|
||
|
|
"{}/nll_loss".format(split): nll_loss.detach().mean(),
|
||
|
|
"{}/rec_loss".format(split): rec_loss.detach().mean(),
|
||
|
|
"{}/p_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(),
|
||
|
|
}
|
||
|
|
if predicted_indices is not None:
|
||
|
|
assert self.n_classes is not None
|
||
|
|
with torch.no_grad():
|
||
|
|
perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
|
||
|
|
log[f"{split}/perplexity"] = perplexity
|
||
|
|
log[f"{split}/cluster_usage"] = cluster_usage
|
||
|
|
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
|