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loss.py
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loss.py
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from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class GANLossGenerator(nn.Module):
"""
This class implements the standard generator GAN loss proposed in:
https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(GANLossGenerator, self).__init__()
def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Standard generator GAN loss
"""
# Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log
return - F.softplus(discriminator_prediction_fake).mean()
class GANLossDiscriminator(nn.Module):
"""
This class implements the standard discriminator GAN loss proposed in:
https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(GANLossDiscriminator, self).__init__()
def forward(self, discriminator_prediction_real: torch.Tensor,
discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Standard discriminator GAN loss
"""
# Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log
return F.softplus(- discriminator_prediction_real).mean() \
+ F.softplus(discriminator_prediction_fake).mean()
class NSGANLossGenerator(nn.Module):
"""
This class implements the non-saturating generator GAN loss proposed in:
https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(NSGANLossGenerator, self).__init__()
def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Non-saturating generator GAN loss
"""
# Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log
return F.softplus(- discriminator_prediction_fake).mean()
class NSGANLossDiscriminator(GANLossDiscriminator):
"""
This class implements the non-saturating discriminator GAN loss proposed in:
https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(NSGANLossDiscriminator, self).__init__()
class WassersteinGANLossGenerator(nn.Module):
"""
This class implements the Wasserstein generator GAN loss proposed in:
http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf
"""
def __index__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(WassersteinGANLossGenerator, self).__index__()
def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Wasserstein Generator GAN loss with gradient
"""
return - discriminator_prediction_fake.mean()
class WassersteinGANLossDiscriminator(nn.Module):
"""
This class implements the Wasserstein generator GAN loss proposed in:
http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(WassersteinGANLossDiscriminator, self).__init__()
def forward(self, discriminator_prediction_real: torch.Tensor,
discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Wasserstein generator GAN loss with gradient penalty
"""
return - discriminator_prediction_real.mean() \
+ discriminator_prediction_fake.mean()
class WassersteinGANLossGPGenerator(WassersteinGANLossGenerator):
"""
This class implements the Wasserstein generator GAN loss proposed in:
https://proceedings.neurips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf
"""
def __index__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(WassersteinGANLossGPGenerator, self).__index__()
class WassersteinGANLossGPDiscriminator(nn.Module):
"""
This class implements the Wasserstein generator GAN loss proposed in:
https://proceedings.neurips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(WassersteinGANLossGPDiscriminator, self).__init__()
def forward(self, discriminator_prediction_real: torch.Tensor,
discriminator_prediction_fake: torch.Tensor,
discriminator: nn.Module,
real_samples: torch.Tensor,
fake_samples: torch.Tensor,
lambda_gradient_penalty: Optional[float] = 2., **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Wasserstein discriminator GAN loss with gradient penalty
"""
# Generate random alpha for interpolation
alpha = torch.rand((real_samples.shape[0], 1), device=real_samples.device)
# Make interpolated samples
samples_interpolated = (alpha * real_samples + (1. - alpha) * fake_samples)
samples_interpolated.requires_grad = True
# Make discriminator prediction
discriminator_prediction_interpolated = discriminator(samples_interpolated)
# Calc gradients
gradients = torch.autograd.grad(outputs=discriminator_prediction_interpolated.sum(),
inputs=samples_interpolated,
create_graph=True,
retain_graph=True)[0]
# Calc gradient penalty
gradient_penalty = (gradients.view(gradients.shape[0], -1).norm(dim=1) - 1.).pow(2).mean()
return - discriminator_prediction_real.mean() \
+ discriminator_prediction_fake.mean() \
+ lambda_gradient_penalty * gradient_penalty
class LSGANLossGenerator(nn.Module):
"""
This class implements the least squares generator GAN loss proposed in:
https://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(LSGANLossGenerator, self).__init__()
def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Generator LSGAN loss
"""
return - 0.5 * (discriminator_prediction_fake - 1.).pow(2).mean()
class LSGANLossDiscriminator(nn.Module):
"""
This class implements the least squares discriminator GAN loss proposed in:
https://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(LSGANLossDiscriminator, self).__init__()
def forward(self, discriminator_prediction_real: torch.Tensor,
discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Discriminator LSGAN loss
"""
return 0.5 * ((- discriminator_prediction_real - 1.).pow(2).mean()
+ discriminator_prediction_fake.pow(2).mean())
class HingeGANLossGenerator(WassersteinGANLossGenerator):
"""
This class implements the Hinge generator GAN loss proposed in:
https://arxiv.org/pdf/1705.02894.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(HingeGANLossGenerator, self).__init__()
class HingeGANLossDiscriminator(nn.Module):
"""
This class implements the Hinge discriminator GAN loss proposed in:
https://arxiv.org/pdf/1705.02894.pdf
"""
def __init__(self) -> None:
"""
Constructor method.
"""
# Call super constructor
super(HingeGANLossDiscriminator, self).__init__()
def forward(self, discriminator_prediction_real: torch.Tensor,
discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor:
"""
Forward pass.
:param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples
:param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples
:return: (torch.Tensor) Hinge discriminator GAN loss
"""
return - torch.minimum(torch.tensor(0., dtype=torch.float, device=discriminator_prediction_real.device),
discriminator_prediction_real - 1.).mean() \
- torch.minimum(torch.tensor(0., dtype=torch.float, device=discriminator_prediction_fake.device),
- discriminator_prediction_fake - 1.).mean()