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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# 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
# ----------------------------------------------------------------------------
class Loss:
def accumulate_gradients(
self, phase, real_img, real_c, real_h, gen_z, gen_c, gen_h, sync, gain
): # to be overridden by subclass
raise NotImplementedError()
# ----------------------------------------------------------------------------
class StyleGAN2Loss(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)
def run_G(self, z, c, h, sync):
with misc.ddp_sync(self.G_mapping, sync):
ws = self.G_mapping(z, c, h)
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, h, 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, h, sync):
if self.augment_pipe is not None:
img = self.augment_pipe(img)
with misc.ddp_sync(self.D, sync):
logits = self.D(img, c, h)
return logits
def accumulate_gradients(
self, phase, real_img, real_c, real_h, gen_z, gen_c, gen_h, sync, gain
):
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, gen_h, sync=(sync and not do_Gpl)
) # May get synced by Gpl.
gen_logits = self.run_D(gen_img, gen_c, gen_h, 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)
with torch.autograd.profiler.record_function("Gmain_backward"):
loss_Gmain.mean().mul(gain).backward()
# 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],
gen_h[: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, gen_h, sync=False)
gen_logits = self.run_D(
gen_img, gen_c, gen_h, 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, real_h, 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)
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()
# ----------------------------------------------------------------------------
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