168 lines
7.8 KiB
Python
168 lines
7.8 KiB
Python
import torch
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from torch import nn
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import torch.nn.functional as F
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from einops import repeat
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from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
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from taming.modules.losses.lpips import LPIPS
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from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
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def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
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assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
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loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
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loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
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loss_real = (weights * loss_real).sum() / weights.sum()
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loss_fake = (weights * loss_fake).sum() / weights.sum()
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d_loss = 0.5 * (loss_real + loss_fake)
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return d_loss
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def adopt_weight(weight, global_step, threshold=0, value=0.):
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if global_step < threshold:
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weight = value
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return weight
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def measure_perplexity(predicted_indices, n_embed):
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# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
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# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
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encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
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avg_probs = encodings.mean(0)
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perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
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cluster_use = torch.sum(avg_probs > 0)
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return perplexity, cluster_use
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def l1(x, y):
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return torch.abs(x-y)
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def l2(x, y):
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return torch.pow((x-y), 2)
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class VQLPIPSWithDiscriminator(nn.Module):
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def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
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disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
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perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
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disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
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pixel_loss="l1"):
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super().__init__()
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assert disc_loss in ["hinge", "vanilla"]
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assert perceptual_loss in ["lpips", "clips", "dists"]
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assert pixel_loss in ["l1", "l2"]
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self.codebook_weight = codebook_weight
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self.pixel_weight = pixelloss_weight
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if perceptual_loss == "lpips":
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print(f"{self.__class__.__name__}: Running with LPIPS.")
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self.perceptual_loss = LPIPS().eval()
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else:
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raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
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self.perceptual_weight = perceptual_weight
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if pixel_loss == "l1":
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self.pixel_loss = l1
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else:
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self.pixel_loss = l2
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self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
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n_layers=disc_num_layers,
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use_actnorm=use_actnorm,
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ndf=disc_ndf
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).apply(weights_init)
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self.discriminator_iter_start = disc_start
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if disc_loss == "hinge":
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self.disc_loss = hinge_d_loss
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elif disc_loss == "vanilla":
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self.disc_loss = vanilla_d_loss
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else:
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raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
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print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
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self.disc_factor = disc_factor
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self.discriminator_weight = disc_weight
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self.disc_conditional = disc_conditional
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self.n_classes = n_classes
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def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
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if last_layer is not None:
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nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
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g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
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else:
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nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
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g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
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d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
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d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
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d_weight = d_weight * self.discriminator_weight
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return d_weight
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def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
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global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
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if not exists(codebook_loss):
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codebook_loss = torch.tensor([0.]).to(inputs.device)
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#rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
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rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
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if self.perceptual_weight > 0:
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p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
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rec_loss = rec_loss + self.perceptual_weight * p_loss
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else:
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p_loss = torch.tensor([0.0])
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nll_loss = rec_loss
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#nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
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nll_loss = torch.mean(nll_loss)
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# now the GAN part
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if optimizer_idx == 0:
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# generator update
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if cond is None:
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assert not self.disc_conditional
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logits_fake = self.discriminator(reconstructions.contiguous())
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else:
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assert self.disc_conditional
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logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
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g_loss = -torch.mean(logits_fake)
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try:
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d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
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except RuntimeError:
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assert not self.training
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d_weight = torch.tensor(0.0)
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disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
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loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
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log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
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"{}/quant_loss".format(split): codebook_loss.detach().mean(),
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"{}/nll_loss".format(split): nll_loss.detach().mean(),
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"{}/rec_loss".format(split): rec_loss.detach().mean(),
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"{}/p_loss".format(split): p_loss.detach().mean(),
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"{}/d_weight".format(split): d_weight.detach(),
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"{}/disc_factor".format(split): torch.tensor(disc_factor),
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"{}/g_loss".format(split): g_loss.detach().mean(),
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}
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if predicted_indices is not None:
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assert self.n_classes is not None
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with torch.no_grad():
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perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
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log[f"{split}/perplexity"] = perplexity
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log[f"{split}/cluster_usage"] = cluster_usage
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return loss, log
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if optimizer_idx == 1:
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# second pass for discriminator update
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if cond is None:
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logits_real = self.discriminator(inputs.contiguous().detach())
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logits_fake = self.discriminator(reconstructions.contiguous().detach())
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else:
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logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
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logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
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disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
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d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
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log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
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"{}/logits_real".format(split): logits_real.detach().mean(),
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"{}/logits_fake".format(split): logits_fake.detach().mean()
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}
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return d_loss, log
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