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import torch
from models import MultiGPUModelWrapper
from swapae.optimizers.base_optimizer import BaseOptimizer
import swapae.util
class PatchDAutoencoderOptimizer(BaseOptimizer):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument("--lr", default=0.002, type=float)
parser.add_argument("--beta1", default=0.0, type=float)
parser.add_argument("--beta2", default=0.99, type=float)
parser.add_argument("--R1_once_every", default=16, type=int,
help="lazy R1 regularization. R1 loss is computed once in 1/R1_freq times")
return parser
def __init__(self, model: MultiGPUModelWrapper):
self.opt = model.opt
opt = self.opt
self.model = model
self.training_mode_index = 0
self.Gparams = self.model.get_parameters_for_mode("generator")
self.Dparams = self.model.get_parameters_for_mode("discriminator")
self.num_discriminator_iters = 0
self.optimizer_G = torch.optim.Adam(self.Gparams, lr=opt.lr,
betas=(opt.beta1, opt.beta2))
# StyleGAN2 Appendix B
c = opt.R1_once_every / (1 + opt.R1_once_every)
self.optimizer_D = torch.optim.Adam(self.Dparams,
lr=opt.lr * c,
betas=(opt.beta1 ** c,
opt.beta2 ** c))
def set_requires_grad(self, params, requires_grad):
for p in params:
p.requires_grad_(requires_grad)
def prepare_images(self, data_i):
A = data_i["real_A"]
if "real_B" in data_i:
B = data_i["real_B"]
A = torch.cat([A, B], dim=0)
A = A[torch.randperm(A.size(0))]
return A
def toggle_training_mode(self):
all_modes = ["generator", "discriminator"]
self.training_mode_index = (self.training_mode_index + 1) % len(all_modes)
return all_modes[self.training_mode_index]
def train_one_step(self, data_i, total_steps_so_far):
images_minibatch = self.prepare_images(data_i)
if self.toggle_training_mode() == "generator":
losses = self.train_discriminator_one_step(images_minibatch)
else:
losses = self.train_generator_one_step(images_minibatch)
return util.to_numpy(losses)
def train_generator_one_step(self, images):
self.set_requires_grad(self.Dparams, False)
self.set_requires_grad(self.Gparams, True)
_, gl_ma = self.model(images, command="encode",
use_momentum_encoder=True)
self.optimizer_G.zero_grad()
g_losses, g_metrics = self.model(images, gl_ma,
command="compute_generator_losses")
g_loss = sum([v.mean() for v in g_losses.values()])
g_loss.backward()
self.optimizer_G.step()
g_losses.update(g_metrics)
return g_losses
def train_discriminator_one_step(self, images):
if self.opt.lambda_GAN == 0.0 and self.opt.lambda_PatchGAN == 0.0:
return {}
self.set_requires_grad(self.Dparams, True)
self.set_requires_grad(self.Gparams, False)
self.num_discriminator_iters += 1
self.optimizer_D.zero_grad()
d_losses, d_metrics, features = self.model(images,
command="compute_discriminator_losses")
nce_losses, nce_metrics = self.model.singlegpu_model(*features,
command="compute_discriminator_nce_losses")
d_losses.update(nce_losses)
d_metrics.update(nce_metrics)
d_loss = sum([v.mean() for v in d_losses.values()])
d_loss.backward()
self.optimizer_D.step()
needs_R1 = (self.opt.lambda_R1 > 0.0 or self.opt.lambda_patch_R1) and \
(self.num_discriminator_iters % self.opt.R1_once_every == 0)
if needs_R1:
self.optimizer_D.zero_grad()
r1_losses = self.model(images,
command="compute_R1_loss")
d_losses.update(r1_losses)
r1_loss = sum([v.mean() for v in r1_losses.values()])
r1_loss = r1_loss * self.opt.R1_once_every
r1_loss.backward()
self.optimizer_D.step()
d_losses.update(d_metrics)
return d_losses
def get_visuals_for_snapshot(self, data_i):
images = self.prepare_images(data_i)
with torch.no_grad():
return self.model(images, command="get_visuals_for_snapshot")
def save(self, total_steps_so_far):
self.model.save(total_steps_so_far)
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