# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import functools
import torch.nn.functional as F
def hinge_d_loss(logits_real, logits_fake):
loss_real = torch.mean(F.relu(1.0 - logits_real))
loss_fake = torch.mean(F.relu(1.0 + 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(F.softplus(-logits_real)) + torch.mean(F.softplus(logits_fake))
)
return d_loss
def adopt_weight(weight, global_step, threshold=0, value=0.0):
if global_step < threshold:
weight = value
return weight
class ActNorm(nn.Module):
def __init__(
self, num_features, logdet=False, affine=True, allow_reverse_init=False
):
assert affine
super().__init__()
self.logdet = logdet
self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.allow_reverse_init = allow_reverse_init
self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))
def initialize(self, input):
with torch.no_grad():
flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
mean = (
flatten.mean(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
std = (
flatten.std(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
self.loc.data.copy_(-mean)
self.scale.data.copy_(1 / (std + 1e-6))
def forward(self, input, reverse=False):
if reverse:
return self.reverse(input)
if len(input.shape) == 2:
input = input[:, :, None, None]
squeeze = True
else:
squeeze = False
_, _, height, width = input.shape
if self.training and self.initialized.item() == 0:
self.initialize(input)
self.initialized.fill_(1)
h = self.scale * (input + self.loc)
if squeeze:
h = h.squeeze(-1).squeeze(-1)
if self.logdet:
log_abs = torch.log(torch.abs(self.scale))
logdet = height * width * torch.sum(log_abs)
logdet = logdet * torch.ones(input.shape[0]).to(input)
return h, logdet
return h
def reverse(self, output):
if self.training and self.initialized.item() == 0:
if not self.allow_reverse_init:
raise RuntimeError(
"Initializing ActNorm in reverse direction is "
"disabled by default. Use allow_reverse_init=True to enable."
)
else:
self.initialize(output)
self.initialized.fill_(1)
if len(output.shape) == 2:
output = output[:, :, None, None]
squeeze = True
else:
squeeze = False
h = output / self.scale - self.loc
if squeeze:
h = h.squeeze(-1).squeeze(-1)
return h
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator as in Pix2Pix
--> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
"""
def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if not use_actnorm:
norm_layer = nn.BatchNorm2d
else:
norm_layer = ActNorm
if (
type(norm_layer) == functools.partial
): # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func != nn.BatchNorm2d
else:
use_bias = norm_layer != nn.BatchNorm2d
kw = 4
padw = 1
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True),
]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
sequence += [
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=2,
padding=padw,
bias=use_bias,
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
nf_mult_prev = nf_mult
nf_mult = min(2**n_layers, 8)
sequence += [
nn.Conv2d(
ndf * nf_mult_prev,
ndf * nf_mult,
kernel_size=kw,
stride=1,
padding=padw,
bias=use_bias,
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
]
sequence += [
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
] # output 1 channel prediction map
self.main = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.main(input)
class AutoencoderLossWithDiscriminator(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.kl_weight = cfg.kl_weight
self.logvar = nn.Parameter(torch.ones(size=()) * cfg.logvar_init)
self.discriminator = NLayerDiscriminator(
input_nc=cfg.disc_in_channels,
n_layers=cfg.disc_num_layers,
use_actnorm=cfg.use_actnorm,
).apply(weights_init)
self.discriminator_iter_start = cfg.disc_start
self.discriminator_weight = cfg.disc_weight
self.disc_factor = cfg.disc_factor
self.disc_loss = hinge_d_loss
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer):
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]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(
d_weight, self.cfg.min_adapt_d_weight, self.cfg.max_adapt_d_weight
).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
def forward(
self,
inputs,
reconstructions,
posteriors,
optimizer_idx,
global_step,
last_layer,
split="train",
weights=None,
):
rec_loss = torch.abs(
inputs.contiguous() - reconstructions.contiguous()
) # l1 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]
weighted_nll_loss = torch.mean(weighted_nll_loss)
# nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
nll_loss = torch.mean(nll_loss)
kl_loss = posteriors.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
# ? kl_loss = torch.mean(kl_loss)
# now the GAN part
if optimizer_idx == 0:
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
)
total_loss = (
weighted_nll_loss
+ self.kl_weight * kl_loss
+ d_weight * disc_factor * g_loss
)
return {
"loss": total_loss,
"kl_loss": kl_loss,
"rec_loss": rec_loss.mean(),
"nll_loss": nll_loss,
"g_loss": g_loss,
"d_weight": d_weight,
"disc_factor": torch.tensor(disc_factor),
}
if optimizer_idx == 1:
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)
return {
"d_loss": d_loss,
"logits_real": logits_real.mean(),
"logits_fake": logits_fake.mean(),
}