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# Copyright (c) 2021, NVIDIA CORPORATION. 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.
import numpy as np
import torch
from torch_utils import training_stats
from torch_utils import misc
from torch_utils.ops import conv2d_gradfix
from training.adaaug import AdaAugment
#----------------------------------------------------------------------------
class Loss:
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain): # to be overridden by subclass
raise NotImplementedError()
#----------------------------------------------------------------------------
class StyleGAN2LossCL(Loss):
def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=None, style_mixing_prob=0.9, r1_gamma=10, pl_batch_shrink=2, pl_decay=0.01, pl_weight=2):
super().__init__()
self.device = device
self.G_mapping = G_mapping
self.G_synthesis = G_synthesis
self.D = D
self.augment_pipe = augment_pipe
self.style_mixing_prob = style_mixing_prob
self.r1_gamma = r1_gamma
self.pl_batch_shrink = pl_batch_shrink
self.pl_decay = pl_decay
self.pl_weight = pl_weight
self.pl_mean = torch.zeros([], device=device)
self.image_disturb = AdaAugment(p=0.2).to(device)
def run_G(self, z, c, sync):
with misc.ddp_sync(self.G_mapping, sync):
ws = self.G_mapping(z, c)
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, skip_w_avg_update=True)[:, cutoff:]
with misc.ddp_sync(self.G_synthesis, sync):
img = self.G_synthesis(ws)
return img, ws
def run_D(self, img, c, sync):
if self.augment_pipe is not None:
img, t = self.augment_pipe(img)
with misc.ddp_sync(self.D, sync):
logits = self.D(img, c, t)
return logits
def run_cl(self, img, c, sync, contrastive_head, D_ema, loss_name='', loss_only=False, img1=None, update_q=False):
# contrastive loss fwd
# augmentation first via ada-aug
# assert(self.augment_pipe is not None)
img0 = self.image_disturb(img)
img1 = self.image_disturb(img) if img1 is None else self.image_disturb(img1)
batch_size, device = img.shape[0], img.device
# img0 = img
# img1 = img.clone() + torch.randn_like(img) * 0.02 if img1 is None else img1
# extract features for two views via D and momentum D
_, logits0 = self.D(img0, c, torch.zeros((batch_size, 1)).long().to(device), return_feats=True)
with torch.no_grad():
_, logits1 = D_ema(img1, c, torch.zeros((batch_size, 1)).long().to(device), return_feats=True)
# project features into the unit sphere and calculate contrastive loss
loss = contrastive_head(logits0, logits1, loss_only=loss_only, update_q=update_q)
training_stats.report('Loss/'+loss_name, loss)
return loss
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain, cl_phases=None, D_ema=None, lw_real_cl=1.0, lw_fake_cl=1.0, lw_fake_cl_on_g=1.0, g_fake_cl=False):
assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
do_Gmain = (phase in ['Gmain', 'Gboth'])
do_Dmain = (phase in ['Dmain', 'Dboth'])
do_Gpl = (phase in ['Greg', 'Gboth']) and (self.pl_weight != 0)
do_Dr1 = (phase in ['Dreg', 'Dboth']) and (self.r1_gamma != 0)
# Gmain: Maximize logits for generated images.
if do_Gmain:
with torch.autograd.profiler.record_function('Gmain_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=(sync and not do_Gpl)) # May get synced by Gpl.
gen_logits = self.run_D(gen_img, gen_c, sync=False)
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)
# Diversity generation loss from fake instance discrimination
if cl_phases.get('GHeadmain', None) is not None and g_fake_cl:
# when fake cl on g, no params in D encoder and head would be updated, including feature queue.
Gphase = cl_phases['GHeadmain']
Gphase.module.requires_grad_(False)
# fake_cl on g: gradients bp to generator
loss_Gmain = loss_Gmain + lw_fake_cl_on_g * self.run_cl(gen_img, gen_c, False, Gphase.module, D_ema, loss_name='G_cl_on_g', loss_only=True)
with torch.autograd.profiler.record_function('Gmain_backward'):
loss_Gmain.mean().mul(gain).backward()
if cl_phases.get('GHeadmain', None) is not None and g_fake_cl:
Gphase = cl_phases['GHeadmain']
Gphase.module.requires_grad_(True)
# Gpl: Apply path length regularization.
if do_Gpl:
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], sync=sync)
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():
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'):
(gen_img[:, 0, 0, 0] * 0 + loss_Gpl).mean().mul(gain).backward()
# Dmain: Minimize logits for generated images.
loss_Dgen = 0
if do_Dmain:
with torch.autograd.profiler.record_function('Dgen_forward'):
gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=False)
gen_logits = self.run_D(gen_img, gen_c, sync=False) # Gets synced by loss_Dreal.
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 do_Dmain or do_Dr1:
name = 'Dreal_Dr1' if do_Dmain and do_Dr1 else 'Dreal' if do_Dmain else 'Dr1'
with torch.autograd.profiler.record_function(name + '_forward'):
real_img_tmp = real_img.detach().requires_grad_(do_Dr1)
real_logits = self.run_D(real_img_tmp, real_c, sync=sync)
training_stats.report('Loss/scores/real', real_logits)
training_stats.report('Loss/signs/real', real_logits.sign())
loss_Dreal = 0
if do_Dmain:
loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
# Contrastive loss would be added to the normal binary cls loss of D
# real instance discrimination
if cl_phases.get('DHeadmain', None) is not None:
Dphase = cl_phases['DHeadmain']
Dphase.opt.zero_grad(set_to_none=True)
loss_Dreal = loss_Dreal + lw_real_cl * self.run_cl(real_img_tmp, real_c, sync, Dphase.module, D_ema, loss_name='D_cl')
# fake instance discrimination
if cl_phases.get('GHeadmain', None) is not None:
Gphase = cl_phases['GHeadmain']
Gphase.opt.zero_grad(set_to_none=True)
# noisy perturbation
with torch.no_grad():
delta_z = torch.randn(gen_z.shape, device=gen_z.device) * 0.15
noisy_gen_img, _ = self.run_G(gen_z + delta_z, gen_c, sync=False)
loss_Dreal = loss_Dreal + lw_fake_cl * self.run_cl(gen_img, gen_c, False, Gphase.module, D_ema, loss_name='G_cl', img1=noisy_gen_img, update_q=True)
loss_Dr1 = 0
if do_Dr1:
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'):
(real_logits * 0 + loss_Dreal + loss_Dr1).mean().mul(gain).backward()
# after backward of contrastive loss together with the original loss of D,
# manually call optim.step() to update the parameters of contrative head
if cl_phases.get('DHeadmain', None) is not None and do_Dmain:
Dphase.opt.step()
if cl_phases.get('GHeadmain', None) is not None and do_Dmain:
Gphase.opt.step()
#----------------------------------------------------------------------------
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