training/loss.py

# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Loss functions."""

from torch_utils import training_stats
from R3GAN.Trainer import AdversarialTraining
import torch

#----------------------------------------------------------------------------

class R3GANLoss:
    def __init__(self, G, D, augment_pipe=None):
        self.trainer = AdversarialTraining(G, D)
        if augment_pipe is not None:
            self.preprocessor = lambda x: augment_pipe(x.to(torch.float32)).to(x.dtype)
        else:
            self.preprocessor = lambda x: x
        
    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gamma, gain):
        # G
        if phase == 'G':
            AdversarialLoss, RelativisticLogits = self.trainer.AccumulateGeneratorGradients(gen_z, real_img, real_c, gain, self.preprocessor)
            
            training_stats.report('Loss/scores/fake', RelativisticLogits)
            training_stats.report('Loss/signs/fake', RelativisticLogits.sign())
            training_stats.report('Loss/G/loss', AdversarialLoss)
            
        # D
        if phase == 'D':
            AdversarialLoss, RelativisticLogits, R1Penalty, R2Penalty = self.trainer.AccumulateDiscriminatorGradients(gen_z, real_img, real_c, gamma, gain, self.preprocessor)
            
            training_stats.report('Loss/scores/real', RelativisticLogits)
            training_stats.report('Loss/signs/real', RelativisticLogits.sign())
            training_stats.report('Loss/D/loss', AdversarialLoss)
            training_stats.report('Loss/r1_penalty', R1Penalty)
            training_stats.report('Loss/r2_penalty', R2Penalty)
#----------------------------------------------------------------------------