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# 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."""
import numpy as np
import torch
from torch_utils import training_stats
from torch_utils.ops import conv2d_gradfix
from torch_utils.ops import upfirdn2d
#----------------------------------------------------------------------------
class Loss:
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass
raise NotImplementedError()
#----------------------------------------------------------------------------
class StyleGAN2Loss(Loss):
def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0):
super().__init__()
self.device = device
self.G = G
self.D = D
self.augment_pipe = augment_pipe
self.r1_gamma = r1_gamma
self.style_mixing_prob = style_mixing_prob
self.pl_weight = pl_weight
self.pl_batch_shrink = pl_batch_shrink
self.pl_decay = pl_decay
self.pl_no_weight_grad = pl_no_weight_grad
self.pl_mean = torch.zeros([], device=device)
self.blur_init_sigma = blur_init_sigma
self.blur_fade_kimg = blur_fade_kimg
def run_G(self, z, c, update_emas=False):
ws = self.G.mapping(z, c, update_emas=update_emas)
if self.style_mixing_prob > 0:
with torch.autograd.profiler.record_function('style_mixing'):
cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1])
cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1]))
ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:]
img = self.G.synthesis(ws, update_emas=update_emas)
return img, ws
def run_D(self, img, c, blur_sigma=0, update_emas=False):
blur_size = np.floor(blur_sigma * 3)
if blur_size > 0:
with torch.autograd.profiler.record_function('blur'):
f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2()
img = upfirdn2d.filter2d(img, f / f.sum())
if self.augment_pipe is not None:
img = self.augment_pipe(img)
logits = self.D(img, c, update_emas=update_emas)
return logits
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
if self.pl_weight == 0:
phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase)
if self.r1_gamma == 0:
phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase)
blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0
# Gmain: Maximize logits for generated images.
if phase in ['Gmain', 'Gboth']:
with torch.autograd.profiler.record_function('Gmain_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c)
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
training_stats.report('Loss/scores/fake', gen_logits)
training_stats.report('Loss/signs/fake', gen_logits.sign())
loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits))
training_stats.report('Loss/G/loss', loss_Gmain)
with torch.autograd.profiler.record_function('Gmain_backward'):
loss_Gmain.mean().mul(gain).backward()
# Gpl: Apply path length regularization.
if phase in ['Greg', 'Gboth']:
with torch.autograd.profiler.record_function('Gpl_forward'):
batch_size = gen_z.shape[0] // self.pl_batch_shrink
gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size])
pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3])
with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad):
pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0]
pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
self.pl_mean.copy_(pl_mean.detach())
pl_penalty = (pl_lengths - pl_mean).square()
training_stats.report('Loss/pl_penalty', pl_penalty)
loss_Gpl = pl_penalty * self.pl_weight
training_stats.report('Loss/G/reg', loss_Gpl)
with torch.autograd.profiler.record_function('Gpl_backward'):
loss_Gpl.mean().mul(gain).backward()
# Dmain: Minimize logits for generated images.
loss_Dgen = 0
if phase in ['Dmain', 'Dboth']:
with torch.autograd.profiler.record_function('Dgen_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True)
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True)
training_stats.report('Loss/scores/fake', gen_logits)
training_stats.report('Loss/signs/fake', gen_logits.sign())
loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits))
with torch.autograd.profiler.record_function('Dgen_backward'):
loss_Dgen.mean().mul(gain).backward()
# Dmain: Maximize logits for real images.
# Dr1: Apply R1 regularization.
if phase in ['Dmain', 'Dreg', 'Dboth']:
name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1'
with torch.autograd.profiler.record_function(name + '_forward'):
real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth'])
real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma)
training_stats.report('Loss/scores/real', real_logits)
training_stats.report('Loss/signs/real', real_logits.sign())
loss_Dreal = 0
if phase in ['Dmain', 'Dboth']:
loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
loss_Dr1 = 0
if phase in ['Dreg', 'Dboth']:
with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients():
r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0]
r1_penalty = r1_grads.square().sum([1,2,3])
loss_Dr1 = r1_penalty * (self.r1_gamma / 2)
training_stats.report('Loss/r1_penalty', r1_penalty)
training_stats.report('Loss/D/reg', loss_Dr1)
with torch.autograd.profiler.record_function(name + '_backward'):
(loss_Dreal + loss_Dr1).mean().mul(gain).backward()
#----------------------------------------------------------------------------
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