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# 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 imaginaire.evaluation import compute_fid
from imaginaire.losses import (GANLoss, GaussianKLLoss,
PerceptualLoss)
from imaginaire.trainers.base import BaseTrainer
from imaginaire.utils.misc import random_shift
from imaginaire.utils.distributed import master_only_print as print
from imaginaire.utils.diff_aug import apply_diff_aug
class Trainer(BaseTrainer):
r"""Reimplementation of the MUNIT (https://arxiv.org/abs/1804.04732)
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.gan_recon = getattr(cfg.trainer, 'gan_recon', False)
self.best_fid_a = None
self.best_fid_b = None
def _init_loss(self, cfg):
r"""Initialize loss terms. In MUNIT, we have several loss terms
including the GAN loss, the image reconstruction loss, the content
reconstruction loss, the style reconstruction loss, the cycle
reconstruction loss. We also have an optional perceptual loss. A user
can choose to have gradient penalty or consistency regularization too.
Args:
cfg (obj): Global configuration.
"""
self.criteria['gan'] = GANLoss(cfg.trainer.gan_mode)
self.criteria['kl'] = GaussianKLLoss()
self.criteria['image_recon'] = torch.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
def gen_forward(self, data):
r"""Compute the loss for MUNIT generator.
Args:
data (dict): Training data at the current iteration.
"""
cycle_recon = 'cycle_recon' in self.weights
image_recon = 'image_recon' in self.weights
perceptual = 'perceptual' in self.weights
within_latent_recon = 'style_recon_within' in self.weights or \
'content_recon_within' in self.weights
net_G_output = self.net_G(data,
image_recon=image_recon,
cycle_recon=cycle_recon,
within_latent_recon=within_latent_recon)
# Differentiable augmentation.
keys = ['images_ab', 'images_ba']
if self.gan_recon:
keys += ['images_aa', 'images_bb']
net_D_output = self.net_D(data,
apply_diff_aug(
net_G_output, keys, self.aug_policy),
real=False,
gan_recon=self.gan_recon)
self._time_before_loss()
# GAN loss
if self.gan_recon:
self.gen_losses['gan_a'] = \
0.5 * (self.criteria['gan'](net_D_output['out_ba'],
True, dis_update=False) +
self.criteria['gan'](net_D_output['out_aa'],
True, dis_update=False))
self.gen_losses['gan_b'] = \
0.5 * (self.criteria['gan'](net_D_output['out_ab'],
True, dis_update=False) +
self.criteria['gan'](net_D_output['out_bb'],
True, dis_update=False))
else:
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
if image_recon:
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'])
# Style reconstruction loss
self.gen_losses['style_recon_a'] = torch.abs(
net_G_output['style_ba'] -
net_G_output['style_a_rand']).mean()
self.gen_losses['style_recon_b'] = torch.abs(
net_G_output['style_ab'] -
net_G_output['style_b_rand']).mean()
self.gen_losses['style_recon'] = \
self.gen_losses['style_recon_a'] + self.gen_losses['style_recon_b']
if within_latent_recon:
self.gen_losses['style_recon_aa'] = torch.abs(
net_G_output['style_aa'] -
net_G_output['style_a'].detach()).mean()
self.gen_losses['style_recon_bb'] = torch.abs(
net_G_output['style_bb'] -
net_G_output['style_b'].detach()).mean()
self.gen_losses['style_recon_within'] = \
self.gen_losses['style_recon_aa'] + \
self.gen_losses['style_recon_bb']
# Content reconstruction loss
self.gen_losses['content_recon_a'] = torch.abs(
net_G_output['content_ab'] -
net_G_output['content_a'].detach()).mean()
self.gen_losses['content_recon_b'] = torch.abs(
net_G_output['content_ba'] -
net_G_output['content_b'].detach()).mean()
self.gen_losses['content_recon'] = \
self.gen_losses['content_recon_a'] + \
self.gen_losses['content_recon_b']
if within_latent_recon:
self.gen_losses['content_recon_aa'] = torch.abs(
net_G_output['content_aa'] -
net_G_output['content_a'].detach()).mean()
self.gen_losses['content_recon_bb'] = torch.abs(
net_G_output['content_bb'] -
net_G_output['content_b'].detach()).mean()
self.gen_losses['content_recon_within'] = \
self.gen_losses['content_recon_aa'] + \
self.gen_losses['content_recon_bb']
# KL loss
self.gen_losses['kl'] = \
self.criteria['kl'](net_G_output['style_a']) + \
self.criteria['kl'](net_G_output['style_b'])
# Cycle reconstruction loss
if cycle_recon:
self.gen_losses['cycle_recon'] = \
torch.abs(net_G_output['images_aba'] -
data['images_a']).mean() + \
torch.abs(net_G_output['images_bab'] -
data['images_b']).mean()
# Compute total loss
total_loss = self._get_total_loss(gen_forward=True)
return total_loss
def dis_forward(self, data):
r"""Compute the loss for MUNIT discriminator.
Args:
data (dict): Training data at the current iteration.
"""
with torch.no_grad():
net_G_output = self.net_G(data,
image_recon=self.gan_recon,
latent_recon=False,
cycle_recon=False,
within_latent_recon=False)
net_G_output['images_ba'].requires_grad = True
net_G_output['images_ab'].requires_grad = True
# Differentiable augmentation.
keys_fake = ['images_ab', 'images_ba']
if self.gan_recon:
keys_fake += ['images_aa', 'images_bb']
keys_real = ['images_a', 'images_b']
net_D_output = self.net_D(
apply_diff_aug(data, keys_real, self.aug_policy),
apply_diff_aug(net_G_output, keys_fake, self.aug_policy),
gan_recon=self.gan_recon)
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']
# Consistency regularization.
self.dis_losses['consistency_reg'] = \
torch.tensor(0., device=torch.device('cuda'))
if 'consistency_reg' in self.weights:
data_aug, net_G_output_aug = {}, {}
data_aug['images_a'] = random_shift(data['images_a'].flip(-1))
data_aug['images_b'] = random_shift(data['images_b'].flip(-1))
net_G_output_aug['images_ab'] = \
random_shift(net_G_output['images_ab'].flip(-1))
net_G_output_aug['images_ba'] = \
random_shift(net_G_output['images_ba'].flip(-1))
net_D_output_aug = self.net_D(data_aug, net_G_output_aug)
feature_names = ['fea_ba', 'fea_ab',
'fea_a', 'fea_b']
for feature_name in feature_names:
self.dis_losses['consistency_reg'] += \
torch.pow(net_D_output_aug[feature_name] -
net_D_output[feature_name], 2).mean()
# 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, random_style=False)
net_G_output_random = 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_random['images_ab'],
net_G_output['images_ba'],
net_G_output_random['images_ba'],
net_G_output['images_aba'],
net_G_output['images_bab']]
return vis_images
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
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