# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
import torch
from torch import nn
from imaginaire.evaluation import compute_fid
from imaginaire.losses import GANLoss, PerceptualLoss # GaussianKLLoss
from imaginaire.trainers.base import BaseTrainer
[docs]class Trainer(BaseTrainer):
r"""Reimplementation of the UNIT (https://arxiv.org/abs/1703.00848)
algorithm.
Args:
cfg (obj): Global configuration.
net_G (obj): Generator network.
net_D (obj): Discriminator network.
opt_G (obj): Optimizer for the generator network.
opt_D (obj): Optimizer for the discriminator network.
sch_G (obj): Scheduler for the generator optimizer.
sch_D (obj): Scheduler for the discriminator optimizer.
train_data_loader (obj): Train data loader.
val_data_loader (obj): Validation data loader.
"""
def __init__(self, cfg, net_G, net_D, opt_G, opt_D, sch_G, sch_D,
train_data_loader, val_data_loader):
super().__init__(cfg, net_G, net_D, opt_G, opt_D, sch_G, sch_D,
train_data_loader, val_data_loader)
self.best_fid_a = None
self.best_fid_b = None
def _init_loss(self, cfg):
r"""Initialize loss terms. In UNIT, we have several loss terms
including the GAN loss, the image reconstruction loss, the cycle
reconstruction loss, and the gaussian kl loss. We also have an
optional perceptual loss. A user can choose to have the gradient
penalty loss too.
Args:
cfg (obj): Global configuration.
"""
self.criteria['gan'] = GANLoss(cfg.trainer.gan_mode)
# self.criteria['gaussian_kl'] = GaussianKLLoss()
self.criteria['image_recon'] = nn.L1Loss()
self.criteria['cycle_recon'] = nn.L1Loss()
if getattr(cfg.trainer.loss_weight, 'perceptual', 0) > 0:
self.criteria['perceptual'] = \
PerceptualLoss(network=cfg.trainer.perceptual_mode,
layers=cfg.trainer.perceptual_layers)
for loss_name, loss_weight in cfg.trainer.loss_weight.__dict__.items():
if loss_weight > 0:
self.weights[loss_name] = loss_weight
[docs] def gen_forward(self, data):
r"""Compute the loss for UNIT generator.
Args:
data (dict): Training data at the current iteration.
"""
cycle_recon = 'cycle_recon' in self.weights
perceptual = 'perceptual' in self.weights
net_G_output = self.net_G(data, cycle_recon=cycle_recon)
net_D_output = self.net_D(data, net_G_output, real=False)
self._time_before_loss()
# GAN loss
self.gen_losses['gan_a'] = self.criteria['gan'](
net_D_output['out_ba'], True, dis_update=False)
self.gen_losses['gan_b'] = self.criteria['gan'](
net_D_output['out_ab'], True, dis_update=False)
self.gen_losses['gan'] = \
self.gen_losses['gan_a'] + self.gen_losses['gan_b']
# Perceptual loss
if perceptual:
self.gen_losses['perceptual_a'] = \
self.criteria['perceptual'](net_G_output['images_ab'],
data['images_a'])
self.gen_losses['perceptual_b'] = \
self.criteria['perceptual'](net_G_output['images_ba'],
data['images_b'])
self.gen_losses['perceptual'] = \
self.gen_losses['perceptual_a'] + \
self.gen_losses['perceptual_b']
# Image reconstruction loss
self.gen_losses['image_recon'] = \
self.criteria['image_recon'](net_G_output['images_aa'],
data['images_a']) + \
self.criteria['image_recon'](net_G_output['images_bb'],
data['images_b'])
"""
# KL loss
self.gen_losses['gaussian_kl'] = \
self.criteria['gaussian_kl'](net_G_output['content_mu_a']) + \
self.criteria['gaussian_kl'](net_G_output['content_mu_b']) + \
self.criteria['gaussian_kl'](net_G_output['content_mu_a_recon']) + \
self.criteria['gaussian_kl'](net_G_output['content_mu_b_recon'])
"""
# Cycle reconstruction loss
if cycle_recon:
self.gen_losses['cycle_recon_aba'] = \
self.criteria['cycle_recon'](net_G_output['images_aba'],
data['images_a'])
self.gen_losses['cycle_recon_bab'] = \
self.criteria['cycle_recon'](net_G_output['images_bab'],
data['images_b'])
self.gen_losses['cycle_recon'] = \
self.gen_losses['cycle_recon_aba'] + \
self.gen_losses['cycle_recon_bab']
# Compute total loss
total_loss = self._get_total_loss(gen_forward=True)
return total_loss
[docs] def dis_forward(self, data):
r"""Compute the loss for UNIT discriminator.
Args:
data (dict): Training data at the current iteration.
"""
with torch.no_grad():
net_G_output = self.net_G(data, image_recon=False,
cycle_recon=False)
net_G_output['images_ba'].requires_grad = True
net_G_output['images_ab'].requires_grad = True
net_D_output = self.net_D(data, net_G_output)
self._time_before_loss()
# GAN loss.
self.dis_losses['gan_a'] = \
self.criteria['gan'](net_D_output['out_a'], True) + \
self.criteria['gan'](net_D_output['out_ba'], False)
self.dis_losses['gan_b'] = \
self.criteria['gan'](net_D_output['out_b'], True) + \
self.criteria['gan'](net_D_output['out_ab'], False)
self.dis_losses['gan'] = \
self.dis_losses['gan_a'] + self.dis_losses['gan_b']
# Compute total loss
total_loss = self._get_total_loss(gen_forward=False)
return total_loss
def _get_visualizations(self, data):
r"""Compute visualization image.
Args:
data (dict): The current batch.
"""
if self.cfg.trainer.model_average_config.enabled:
net_G_for_evaluation = self.net_G.module.averaged_model
else:
net_G_for_evaluation = self.net_G
with torch.no_grad():
net_G_output = net_G_for_evaluation(data)
vis_images = [data['images_a'],
data['images_b'],
net_G_output['images_aa'],
net_G_output['images_bb'],
net_G_output['images_ab'],
net_G_output['images_ba'],
net_G_output['images_aba'],
net_G_output['images_bab']]
return vis_images
[docs] def write_metrics(self):
r"""Compute metrics and save them to tensorboard"""
cur_fid_a, cur_fid_b = self._compute_fid()
if self.best_fid_a is not None:
self.best_fid_a = min(self.best_fid_a, cur_fid_a)
else:
self.best_fid_a = cur_fid_a
if self.best_fid_b is not None:
self.best_fid_b = min(self.best_fid_b, cur_fid_b)
else:
self.best_fid_b = cur_fid_b
self._write_to_meters({'FID_a': cur_fid_a,
'best_FID_a': self.best_fid_a,
'FID_b': cur_fid_b,
'best_FID_b': self.best_fid_b},
self.metric_meters)
self._flush_meters(self.metric_meters)
def _compute_fid(self):
r"""Compute FID for both domains.
"""
self.net_G.eval()
if self.cfg.trainer.model_average_config.enabled:
net_G_for_evaluation = self.net_G.module.averaged_model
else:
net_G_for_evaluation = self.net_G
fid_a_path = self._get_save_path('fid_a', 'npy')
fid_b_path = self._get_save_path('fid_b', 'npy')
fid_value_a = compute_fid(fid_a_path, self.val_data_loader,
net_G_for_evaluation, 'images_a', 'images_ba')
fid_value_b = compute_fid(fid_b_path, self.val_data_loader,
net_G_for_evaluation, 'images_b', 'images_ab')
print('Epoch {:05}, Iteration {:09}, FID a {}, FID b {}'.format(
self.current_epoch, self.current_iteration,
fid_value_a, fid_value_b))
return fid_value_a, fid_value_b