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import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.insert(0, '.') # nopep8
from foleycrafter.models.specvqgan.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats,
NLayerDiscriminator1dSpecs,
weights_init)
from foleycrafter.models.specvqgan.modules.losses.lpaps import LPAPS
class DummyLoss(nn.Module):
def __init__(self):
super().__init__()
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = value
return weight
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1. - logits_real))
loss_fake = torch.mean(F.relu(1. + 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
class VQLPAPSWithDiscriminator(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", min_adapt_weight=0.0, max_adapt_weight=1e4):
super().__init__()
assert disc_loss in ["hinge", "vanilla"]
self.codebook_weight = codebook_weight
self.pixel_weight = pixelloss_weight
self.perceptual_loss = LPAPS().eval()
self.perceptual_weight = perceptual_weight
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"VQLPAPSWithDiscriminator running with {disc_loss} loss.")
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
self.min_adapt_weight = min_adapt_weight
self.max_adapt_weight = max_adapt_weight
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, self.min_adapt_weight, self.max_adapt_weight).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"):
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
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(),
}
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
class VQLPAPSWithDiscriminator1dFeats(VQLPAPSWithDiscriminator):
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", min_adapt_weight=0.0, max_adapt_weight=1e4):
super().__init__(disc_start=disc_start, codebook_weight=codebook_weight,
pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers,
disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight,
perceptual_weight=perceptual_weight, use_actnorm=use_actnorm,
disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss,
min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight)
self.discriminator = NLayerDiscriminator1dFeats(input_nc=disc_in_channels, n_layers=disc_num_layers,
use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
class VQLPAPSWithDiscriminator1dSpecs(VQLPAPSWithDiscriminator):
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", min_adapt_weight=0.0, max_adapt_weight=1e4):
super().__init__(disc_start=disc_start, codebook_weight=codebook_weight,
pixelloss_weight=pixelloss_weight, disc_num_layers=disc_num_layers,
disc_in_channels=disc_in_channels, disc_factor=disc_factor, disc_weight=disc_weight,
perceptual_weight=perceptual_weight, use_actnorm=use_actnorm,
disc_conditional=disc_conditional, disc_ndf=disc_ndf, disc_loss=disc_loss,
min_adapt_weight=min_adapt_weight, max_adapt_weight=max_adapt_weight)
self.discriminator = NLayerDiscriminator1dSpecs(input_nc=disc_in_channels, n_layers=disc_num_layers,
use_actnorm=use_actnorm, ndf=disc_ndf).apply(weights_init)
if __name__ == '__main__':
from foleycrafter.models.specvqgan.modules.diffusionmodules.model import Decoder, Decoder1d
optimizer_idx = 0
loss_config = {
'disc_conditional': False,
'disc_start': 30001,
'disc_weight': 0.8,
'codebook_weight': 1.0,
}
ddconfig = {
'ch': 128,
'num_res_blocks': 2,
'dropout': 0.0,
'z_channels': 256,
'double_z': False,
}
qloss = torch.rand(1, requires_grad=True)
## AUDIO
loss_config['disc_in_channels'] = 1
ddconfig['in_channels'] = 1
ddconfig['resolution'] = 848
ddconfig['attn_resolutions'] = [53]
ddconfig['out_ch'] = 1
ddconfig['ch_mult'] = [1, 1, 2, 2, 4]
decoder = Decoder(**ddconfig)
loss = VQLPAPSWithDiscriminator(**loss_config)
x = torch.rand(16, 1, 80, 848)
# subtracting something which uses dec_conv_out so that it will be in a graph
xrec = torch.rand(16, 1, 80, 848) - decoder.conv_out(torch.rand(16, 128, 80, 848)).mean()
aeloss, log_dict_ae = loss(qloss, x, xrec, optimizer_idx, global_step=0,last_layer=decoder.conv_out.weight)
print(aeloss)
print(log_dict_ae)
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