import torch
import swapae.util as util
from swapae.models import BaseModel
import swapae.models.networks as networks
import swapae.models.networks.loss as loss
class SwappingAutoencoderModel(BaseModel):
@staticmethod
def modify_commandline_options(parser, is_train):
BaseModel.modify_commandline_options(parser, is_train)
parser.add_argument("--spatial_code_ch", default=8, type=int)
parser.add_argument("--global_code_ch", default=2048, type=int)
parser.add_argument("--lambda_R1", default=10.0, type=float)
parser.add_argument("--lambda_patch_R1", default=1.0, type=float)
parser.add_argument("--lambda_L1", default=1.0, type=float)
parser.add_argument("--lambda_GAN", default=1.0, type=float)
parser.add_argument("--lambda_PatchGAN", default=1.0, type=float)
parser.add_argument("--patch_min_scale", default=1 / 8, type=float)
parser.add_argument("--patch_max_scale", default=1 / 4, type=float)
parser.add_argument("--patch_num_crops", default=8, type=int)
parser.add_argument("--patch_use_aggregation",
type=util.str2bool, default=True)
return parser
def initialize(self):
self.E = networks.create_network(self.opt, self.opt.netE, "encoder")
self.G = networks.create_network(self.opt, self.opt.netG, "generator")
if self.opt.lambda_GAN > 0.0:
self.D = networks.create_network(
self.opt, self.opt.netD, "discriminator")
if self.opt.lambda_PatchGAN > 0.0:
self.Dpatch = networks.create_network(
self.opt, self.opt.netPatchD, "patch_discriminator"
)
# Count the iteration count of the discriminator
# Used for lazy R1 regularization (c.f. Appendix B of StyleGAN2)
self.register_buffer(
"num_discriminator_iters", torch.zeros(1, dtype=torch.long)
)
self.l1_loss = torch.nn.L1Loss()
if (not self.opt.isTrain) or self.opt.continue_train:
self.load()
if self.opt.num_gpus > 0:
self.to("cuda:0")
def per_gpu_initialize(self):
pass
def swap(self, x):
""" Swaps (or mixes) the ordering of the minibatch """
shape = x.shape
assert shape[0] % 2 == 0, "Minibatch size must be a multiple of 2"
new_shape = [shape[0] // 2, 2] + list(shape[1:])
x = x.view(*new_shape)
x = torch.flip(x, [1])
return x.view(*shape)
def compute_image_discriminator_losses(self, real, rec, mix):
if self.opt.lambda_GAN == 0.0:
return {}
pred_real = self.D(real)
pred_rec = self.D(rec)
pred_mix = self.D(mix)
losses = {}
losses["D_real"] = loss.gan_loss(
pred_real, should_be_classified_as_real=True
) * self.opt.lambda_GAN
losses["D_rec"] = loss.gan_loss(
pred_rec, should_be_classified_as_real=False
) * (0.5 * self.opt.lambda_GAN)
losses["D_mix"] = loss.gan_loss(
pred_mix, should_be_classified_as_real=False
) * (0.5 * self.opt.lambda_GAN)
return losses
def get_random_crops(self, x, crop_window=None):
""" Make random crops.
Corresponds to the yellow and blue random crops of Figure 2.
"""
crops = util.apply_random_crop(
x, self.opt.patch_size,
(self.opt.patch_min_scale, self.opt.patch_max_scale),
num_crops=self.opt.patch_num_crops
)
return crops
def compute_patch_discriminator_losses(self, real, mix):
losses = {}
real_feat = self.Dpatch.extract_features(
self.get_random_crops(real),
aggregate=self.opt.patch_use_aggregation
)
target_feat = self.Dpatch.extract_features(self.get_random_crops(real))
mix_feat = self.Dpatch.extract_features(self.get_random_crops(mix))
losses["PatchD_real"] = loss.gan_loss(
self.Dpatch.discriminate_features(real_feat, target_feat),
should_be_classified_as_real=True,
) * self.opt.lambda_PatchGAN
losses["PatchD_mix"] = loss.gan_loss(
self.Dpatch.discriminate_features(real_feat, mix_feat),
should_be_classified_as_real=False,
) * self.opt.lambda_PatchGAN
return losses
def compute_discriminator_losses(self, real):
self.num_discriminator_iters.add_(1)
sp, gl = self.E(real)
B = real.size(0)
assert B % 2 == 0, "Batch size must be even on each GPU."
# To save memory, compute the GAN loss on only
# half of the reconstructed images
rec = self.G(sp[:B // 2], gl[:B // 2])
mix = self.G(self.swap(sp), gl)
losses = self.compute_image_discriminator_losses(real, rec, mix)
if self.opt.lambda_PatchGAN > 0.0:
patch_losses = self.compute_patch_discriminator_losses(real, mix)
losses.update(patch_losses)
metrics = {} # no metrics to report for the Discriminator iteration
return losses, metrics, sp.detach(), gl.detach()
def compute_R1_loss(self, real):
losses = {}
if self.opt.lambda_R1 > 0.0:
real.requires_grad_()
pred_real = self.D(real).sum()
grad_real, = torch.autograd.grad(
outputs=pred_real,
inputs=[real],
create_graph=True,
retain_graph=True,
)
grad_real2 = grad_real.pow(2)
dims = list(range(1, grad_real2.ndim))
grad_penalty = grad_real2.sum(dims) * (self.opt.lambda_R1 * 0.5)
else:
grad_penalty = 0.0
if self.opt.lambda_patch_R1 > 0.0:
real_crop = self.get_random_crops(real).detach()
real_crop.requires_grad_()
target_crop = self.get_random_crops(real).detach()
target_crop.requires_grad_()
real_feat = self.Dpatch.extract_features(
real_crop,
aggregate=self.opt.patch_use_aggregation)
target_feat = self.Dpatch.extract_features(target_crop)
pred_real_patch = self.Dpatch.discriminate_features(
real_feat, target_feat
).sum()
grad_real, grad_target = torch.autograd.grad(
outputs=pred_real_patch,
inputs=[real_crop, target_crop],
create_graph=True,
retain_graph=True,
)
dims = list(range(1, grad_real.ndim))
grad_crop_penalty = grad_real.pow(2).sum(dims) + \
grad_target.pow(2).sum(dims)
grad_crop_penalty *= (0.5 * self.opt.lambda_patch_R1 * 0.5)
else:
grad_crop_penalty = 0.0
losses["D_R1"] = grad_penalty + grad_crop_penalty
return losses
def compute_generator_losses(self, real, sp_ma, gl_ma):
losses, metrics = {}, {}
B = real.size(0)
sp, gl = self.E(real)
rec = self.G(sp[:B // 2], gl[:B // 2]) # only on B//2 to save memory
sp_mix = self.swap(sp)
if self.opt.crop_size >= 1024:
# another momery-saving trick: reduce #outputs to save memory
real = real[B // 2:]
gl = gl[B // 2:]
sp_mix = sp_mix[B // 2:]
mix = self.G(sp_mix, gl)
# record the error of the reconstructed images for monitoring purposes
metrics["L1_dist"] = self.l1_loss(rec, real[:B // 2])
if self.opt.lambda_L1 > 0.0:
losses["G_L1"] = metrics["L1_dist"] * self.opt.lambda_L1
if self.opt.lambda_GAN > 0.0:
losses["G_GAN_rec"] = loss.gan_loss(
self.D(rec),
should_be_classified_as_real=True
) * (self.opt.lambda_GAN * 0.5)
losses["G_GAN_mix"] = loss.gan_loss(
self.D(mix),
should_be_classified_as_real=True
) * (self.opt.lambda_GAN * 1.0)
if self.opt.lambda_PatchGAN > 0.0:
real_feat = self.Dpatch.extract_features(
self.get_random_crops(real),
aggregate=self.opt.patch_use_aggregation).detach()
mix_feat = self.Dpatch.extract_features(self.get_random_crops(mix))
losses["G_mix"] = loss.gan_loss(
self.Dpatch.discriminate_features(real_feat, mix_feat),
should_be_classified_as_real=True,
) * self.opt.lambda_PatchGAN
return losses, metrics
def get_visuals_for_snapshot(self, real):
if self.opt.isTrain:
# avoid the overhead of generating too many visuals during training
real = real[:2] if self.opt.num_gpus > 1 else real[:4]
sp, gl = self.E(real)
layout = util.resize2d_tensor(util.visualize_spatial_code(sp), real)
rec = self.G(sp, gl)
mix = self.G(sp, self.swap(gl))
visuals = {"real": real, "layout": layout, "rec": rec, "mix": mix}
return visuals
def fix_noise(self, sample_image=None):
""" The generator architecture is stochastic because of the noise
input at each layer (StyleGAN2 architecture). It could lead to
flickering of the outputs even when identical inputs are given.
Prevent flickering by fixing the noise injection of the generator.
"""
if sample_image is not None:
# The generator should be run at least once,
# so that the noise dimensions could be computed
sp, gl = self.E(sample_image)
self.G(sp, gl)
noise_var = self.G.fix_and_gather_noise_parameters()
return noise_var
def encode(self, image, extract_features=False):
return self.E(image, extract_features=extract_features)
def decode(self, spatial_code, global_code):
return self.G(spatial_code, global_code)
def get_parameters_for_mode(self, mode):
if mode == "generator":
return list(self.G.parameters()) + list(self.E.parameters())
elif mode == "discriminator":
Dparams = []
if self.opt.lambda_GAN > 0.0:
Dparams += list(self.D.parameters())
if self.opt.lambda_PatchGAN > 0.0:
Dparams += list(self.Dpatch.parameters())
return Dparams