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Rename sgm/__init__.py to sgm/modules/autoencoding/losses/__init__.py
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from typing import Any, Union
import torch
import torch.nn as nn
from einops import rearrange
from ....util import default, instantiate_from_config
from ..lpips.loss.lpips import LPIPS
from ..lpips.model.model import NLayerDiscriminator, weights_init
from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
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