Spaces:
Paused
Paused
from typing import Any, Union | |
import torch | |
import torch.nn as nn | |
from einops import rearrange | |
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 | |
from ....util import default, instantiate_from_config | |
def adopt_weight(weight, global_step, threshold=0, value=0.0): | |
if global_step < threshold: | |
weight = value | |
return weight | |
class LatentLPIPS(nn.Module): | |
def __init__( | |
self, | |
decoder_config, | |
perceptual_weight=1.0, | |
latent_weight=1.0, | |
scale_input_to_tgt_size=False, | |
scale_tgt_to_input_size=False, | |
perceptual_weight_on_inputs=0.0, | |
): | |
super().__init__() | |
self.scale_input_to_tgt_size = scale_input_to_tgt_size | |
self.scale_tgt_to_input_size = scale_tgt_to_input_size | |
self.init_decoder(decoder_config) | |
self.perceptual_loss = LPIPS().eval() | |
self.perceptual_weight = perceptual_weight | |
self.latent_weight = latent_weight | |
self.perceptual_weight_on_inputs = perceptual_weight_on_inputs | |
def init_decoder(self, config): | |
self.decoder = instantiate_from_config(config) | |
if hasattr(self.decoder, "encoder"): | |
del self.decoder.encoder | |
def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"): | |
log = dict() | |
loss = (latent_inputs - latent_predictions) ** 2 | |
log[f"{split}/latent_l2_loss"] = loss.mean().detach() | |
image_reconstructions = None | |
if self.perceptual_weight > 0.0: | |
image_reconstructions = self.decoder.decode(latent_predictions) | |
image_targets = self.decoder.decode(latent_inputs) | |
perceptual_loss = self.perceptual_loss( | |
image_targets.contiguous(), image_reconstructions.contiguous() | |
) | |
loss = ( | |
self.latent_weight * loss.mean() | |
+ self.perceptual_weight * perceptual_loss.mean() | |
) | |
log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach() | |
if self.perceptual_weight_on_inputs > 0.0: | |
image_reconstructions = default( | |
image_reconstructions, self.decoder.decode(latent_predictions) | |
) | |
if self.scale_input_to_tgt_size: | |
image_inputs = torch.nn.functional.interpolate( | |
image_inputs, | |
image_reconstructions.shape[2:], | |
mode="bicubic", | |
antialias=True, | |
) | |
elif self.scale_tgt_to_input_size: | |
image_reconstructions = torch.nn.functional.interpolate( | |
image_reconstructions, | |
image_inputs.shape[2:], | |
mode="bicubic", | |
antialias=True, | |
) | |
perceptual_loss2 = self.perceptual_loss( | |
image_inputs.contiguous(), image_reconstructions.contiguous() | |
) | |
loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean() | |
log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach() | |
return loss, log | |
class GeneralLPIPSWithDiscriminator(nn.Module): | |
def __init__( | |
self, | |
disc_start: int, | |
logvar_init: float = 0.0, | |
pixelloss_weight=1.0, | |
disc_num_layers: int = 3, | |
disc_in_channels: int = 3, | |
disc_factor: float = 1.0, | |
disc_weight: float = 1.0, | |
perceptual_weight: float = 1.0, | |
disc_loss: str = "hinge", | |
scale_input_to_tgt_size: bool = False, | |
dims: int = 2, | |
learn_logvar: bool = False, | |
regularization_weights: Union[None, dict] = None, | |
): | |
super().__init__() | |
self.dims = dims | |
if self.dims > 2: | |
print( | |
f"running with dims={dims}. This means that for perceptual loss calculation, " | |
f"the LPIPS loss will be applied to each frame independently. " | |
) | |
self.scale_input_to_tgt_size = scale_input_to_tgt_size | |
assert disc_loss in ["hinge", "vanilla"] | |
self.pixel_weight = pixelloss_weight | |
self.perceptual_loss = LPIPS().eval() | |
self.perceptual_weight = perceptual_weight | |
# output log variance | |
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) | |
self.learn_logvar = learn_logvar | |
self.discriminator = NLayerDiscriminator( | |
input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False | |
).apply(weights_init) | |
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.regularization_weights = default(regularization_weights, {}) | |
def get_trainable_parameters(self) -> Any: | |
return self.discriminator.parameters() | |
def get_trainable_autoencoder_parameters(self) -> Any: | |
if self.learn_logvar: | |
yield self.logvar | |
yield from () | |
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, | |
regularization_log, | |
inputs, | |
reconstructions, | |
optimizer_idx, | |
global_step, | |
last_layer=None, | |
split="train", | |
weights=None, | |
): | |
if self.scale_input_to_tgt_size: | |
inputs = torch.nn.functional.interpolate( | |
inputs, reconstructions.shape[2:], mode="bicubic", antialias=True | |
) | |
if self.dims > 2: | |
inputs, reconstructions = map( | |
lambda x: rearrange(x, "b c t h w -> (b t) c h w"), | |
(inputs, reconstructions), | |
) | |
rec_loss = torch.abs(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 | |
nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar | |
weighted_nll_loss = nll_loss | |
if weights is not None: | |
weighted_nll_loss = 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] | |
# now the GAN part | |
if optimizer_idx == 0: | |
# generator update | |
logits_fake = self.discriminator(reconstructions.contiguous()) | |
g_loss = -torch.mean(logits_fake) | |
if self.disc_factor > 0.0: | |
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) | |
disc_factor = adopt_weight( | |
self.disc_factor, global_step, threshold=self.discriminator_iter_start | |
) | |
loss = weighted_nll_loss + d_weight * disc_factor * g_loss | |
log = dict() | |
for k in regularization_log: | |
if k in self.regularization_weights: | |
loss = loss + self.regularization_weights[k] * regularization_log[k] | |
log[f"{split}/{k}"] = regularization_log[k].detach().mean() | |
log.update( | |
{ | |
"{}/total_loss".format(split): loss.clone().detach().mean(), | |
"{}/logvar".format(split): self.logvar.detach(), | |
"{}/nll_loss".format(split): nll_loss.detach().mean(), | |
"{}/rec_loss".format(split): rec_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: | |
# second pass for discriminator update | |
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 | |