def liuboff(encoder: nn.Module):
sum_loss = 0
for i, batch in enumerate(LazyLoader.mafl().test_loader):
data = batch['data'].to(device)
landmarks = batch["meta"]["keypts_normalized"].cuda().type(dtype=torch.float32)
landmarks[landmarks > 1] = 0.99999
# content = heatmap_to_measure(encoder(data))[0]
pred_measure = UniformMeasure2DFactory.from_heatmap(encoder(data))
target = UniformMeasure2D01(torch.clamp(landmarks, max=1))
eye_dist = landmarks[:, 1] - landmarks[:, 0]
eye_dist = eye_dist.pow(2).sum(dim=1).sqrt()
# w1_loss = (handmadew1(pred_measure, target) / eye_dist).sum().item()
# l1_loss = ((pred_measure.coord - target.coord).pow(2).sum(dim=2).sqrt().mean(dim=1) / eye_dist).sum().item()
# print(w1_loss, l1_loss)
sum_loss += ((pred_measure.coord - target.coord).pow(2).sum(dim=2).sqrt().mean(dim=1) / eye_dist).sum().item()
return sum_loss / len(LazyLoader.mafl().test_dataset)
def liuboffMAFL(encoder: nn.Module):
sum_loss = 0
for i, batch in enumerate(LazyLoader.mafl().test_loader):
data = batch['data'].cuda()
landmarks = batch["meta"]["keypts_normalized"].cuda()
landmarks[landmarks > 1] = 0.99999
pred_measure = UniformMeasure2D01(encoder(data)["coords"])
target = UniformMeasure2D01(torch.clamp(landmarks, max=1))
eye_dist = landmarks[:, 1] - landmarks[:, 0]
eye_dist = eye_dist.pow(2).sum(dim=1).sqrt()
sum_loss += (handmadew1(pred_measure, target, 0.005) /
eye_dist).sum().item()
return sum_loss / len(LazyLoader.mafl().test_dataset)
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device, starting_model_number):
latent_size = 512
batch_size = 12
sample_z = torch.randn(8, latent_size, device=device)
MAFL.batch_size = batch_size
MAFL.test_batch_size = 64
Celeba.batch_size = batch_size
test_img = next(LazyLoader.mafl().loader_train_inf)["data"][:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
style_opt = optim.Adam(style_encoder.parameters(), lr=5e-4, betas=(0.9, 0.99))
cont_opt = optim.Adam(encoder_HG.parameters(), lr=2e-5, betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(albumentations.Compose([
ResizeMask(h=256, w=256),
albumentations.ElasticTransform(p=0.7, alpha=150, alpha_affine=1, sigma=10),
albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
ResizeMask(h=64, w=64),
NormalizeMask(dim=(0, 1, 2))
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img:
# rt_loss(encoder_HG(trans_dict['image']), trans_dict['mask'])
stariy_hm_loss(encoder_HG(trans_dict['image']), trans_dict['mask'])
)
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt:
L1("R_s")(ltnt, style_encoder(trans_dict['image']))
)
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_5").cuda().batch_repeat(batch_size)
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 4.0)
tuner = GoldTuner([0.37, 1.55, 0.9393, 0.1264, 1.7687, 0.8648, 1.8609], device=device, rule_eps=0.01 / 2,
radius=0.1, active=True)
heatmaper = ToGaussHeatMap(64, 1.0)
sparse_bc = heatmaper.forward(barycenter.coord * 63)
sparse_bc = nn.Upsample(scale_factor=4)(sparse_bc).sum(dim=1, keepdim=True).repeat(1, 3, 1, 1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
sparse_bc = (sparse_bc - sparse_bc.min()) / sparse_bc.max()
send_images_to_tensorboard(writer, sparse_bc, "BC", 0, normalize=False, range=(0, 1))
trainer_gan = gan_trainer(model, generator, decoder, encoder_HG, style_encoder, R_s, style_opt, heatmaper,
g_transforms)
content_trainer = content_trainer_with_gan(cont_opt, tuner, heatmaper, encoder_HG, R_b, R_t, model, generator,
g_transforms)
supervise_trainer = content_trainer_supervised(cont_opt, encoder_HG, LazyLoader.mafl().loader_train_inf)
for i in range(100000):
counter.update(i)
requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.mafl().loader_train_inf)["data"].to(device) \
if i % 5 == 0 else next(LazyLoader.celeba().loader).to(device)
img_content = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(img_content)
sparse_hm = heatmaper.forward(pred_measures.coord * 63).detach()
trainer_gan(i, real_img, pred_measures.detach(), sparse_hm.detach(), apply_g=False)
supervise_trainer()
if i % 4 == 0:
# real_img = next(LazyLoader.mafl().loader_train_inf)["data"].to(device)
trainer_gan(i, real_img, pred_measures.detach(), sparse_hm.detach(), apply_g=True)
content_trainer(real_img)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss.json"))
print(i, coefs)
with torch.no_grad():
# pred_measures_test, sparse_hm_test = encoder_HG(test_img)
content_test = encoder_HG(test_img)
pred_measures_test: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content_test)
heatmaper_256 = ToGaussHeatMap(256, 2.0)
sparse_hm_test = heatmaper.forward(pred_measures_test.coord * 63)
sparse_hm_test_1 = heatmaper_256.forward(pred_measures_test.coord * 255)
latent_test = style_encoder(test_img)
sparce_mask = sparse_hm_test_1.sum(dim=1, keepdim=True)
sparce_mask[sparce_mask < 0.0003] = 0
iwm = imgs_with_mask(test_img, sparce_mask)
send_images_to_tensorboard(writer, iwm, "REAL", i)
fake_img, _ = generator(sparse_hm_test, [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
restored = decoder(sparse_hm_test, latent_test)
iwm = imgs_with_mask(restored, pred_measures_test.toImage(256))
send_images_to_tensorboard(writer, iwm, "RESTORED", i)
content_test_256 = nn.Upsample(scale_factor=4)(sparse_hm_test).sum(dim=1, keepdim=True).repeat(1, 3, 1,
1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
content_test_256 = (content_test_256 - content_test_256.min()) / content_test_256.max()
send_images_to_tensorboard(writer, content_test_256, "HM", i, normalize=False, range=(0, 1))
if i % 50 == 0 and i >= 0:
test_loss = liuboff(encoder_HG)
# test_loss = nadbka(encoder_HG)
tuner.update(test_loss)
writer.add_scalar("liuboff", test_loss, i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.module.state_dict(),
'd': discriminator.module.state_dict(),
'c': encoder_HG.module.state_dict(),
"s": style_encoder.state_dict()
},
f'{Paths.default.models()}/stylegan2_MAFL_{str(i + starting_model_number).zfill(6)}.pt',
)
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device,
starting_model_number):
latent_size = 512
batch_size = 8
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
W300DatasetLoader.batch_size = batch_size
W300DatasetLoader.test_batch_size = 32
test_img = next(LazyLoader.mafl().loader_train_inf)["data"][:8].cuda()
model = CondStyleGanModel(generator, StyleGANLoss(discriminator),
(0.001 / 4, 0.0015 / 4))
style_opt = optim.Adam(style_encoder.parameters(),
lr=5e-4,
betas=(0.9, 0.99))
cont_opt = optim.Adam(encoder_HG.parameters(), lr=3e-5, betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(
albumentations.Compose([
albumentations.ElasticTransform(p=0.7,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.9, rotate_limit=15),
])),
ToTensor(device),
])
g_transforms_without_norm: albumentations.DualTransform = albumentations.Compose(
[
ToNumpy(),
NumpyBatch(
albumentations.Compose([
albumentations.ElasticTransform(p=0.3,
alpha=150,
alpha_affine=1,
sigma=10),
albumentations.ShiftScaleRotate(p=0.7, rotate_limit=15),
])),
ToTensor(device),
])
R_t = DualTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img: coord_hm_loss(
encoder_HG(trans_dict['image'])["coords"], trans_dict['mask']))
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt: WR.L1("R_s")
(ltnt, style_encoder(trans_dict['image'])))
barycenter: UniformMeasure2D01 = UniformMeasure2DFactory.load(
f"{Paths.default.models()}/face_barycenter_5").cuda().batch_repeat(
batch_size)
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 4.0)
tuner = GoldTuner([0.37, 2.78, 0.58, 1.43, 3.23],
device=device,
rule_eps=0.001,
radius=0.3,
active=False)
trainer_gan = gan_trainer(model, generator, decoder, encoder_HG,
style_encoder, R_s, style_opt, g_transforms)
content_trainer = content_trainer_with_gan(cont_opt, tuner, encoder_HG,
R_b, R_t, model, generator,
g_transforms, decoder,
style_encoder)
# supervise_trainer = content_trainer_supervised(cont_opt, encoder_HG, LazyLoader.w300().loader_train_inf)
for i in range(100000):
WR.counter.update(i)
requires_grad(encoder_HG, False)
real_img = next(LazyLoader.mafl().loader_train_inf)["data"].to(device)
encoded = encoder_HG(real_img)
internal_content = encoded["skeleton"].detach()
# trainer_gan(i, real_img, internal_content)
# content_trainer(real_img)
train_content(cont_opt, R_b, R_t, real_img, model, encoder_HG, decoder,
generator, style_encoder)
# supervise_trainer()
encoder_ema.accumulate(encoder_HG.module, i, 0.97)
if i % 50 == 0 and i > 0:
encoder_ema.write_to(encoder_HG.module)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss.json"))
print(i, coefs)
with torch.no_grad():
# pred_measures_test, sparse_hm_test = encoder_HG(test_img)
encoded_test = encoder_HG(test_img)
pred_measures_test: UniformMeasure2D01 = UniformMeasure2D01(
encoded_test["coords"])
heatmaper_256 = ToGaussHeatMap(256, 1.0)
sparse_hm_test_1 = heatmaper_256.forward(
pred_measures_test.coord)
latent_test = style_encoder(test_img)
sparce_mask = sparse_hm_test_1.sum(dim=1, keepdim=True)
sparce_mask[sparce_mask < 0.0003] = 0
iwm = imgs_with_mask(test_img, sparce_mask)
send_images_to_tensorboard(WR.writer, iwm, "REAL", i)
fake_img, _ = generator(encoded_test["skeleton"], [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures_test.toImage(256))
send_images_to_tensorboard(WR.writer, iwm, "FAKE", i)
restored = decoder(encoded_test["skeleton"], latent_test)
iwm = imgs_with_mask(restored, pred_measures_test.toImage(256))
send_images_to_tensorboard(WR.writer, iwm, "RESTORED", i)
content_test_256 = (encoded_test["skeleton"]).repeat(1, 3, 1, 1) * \
torch.tensor([1.0, 1.0, 0.0], device=device).view(1, 3, 1, 1)
content_test_256 = (content_test_256 - content_test_256.min()
) / content_test_256.max()
send_images_to_tensorboard(WR.writer,
content_test_256,
"HM",
i,
normalize=False,
range=(0, 1))
if i % 50 == 0 and i >= 0:
test_loss = liuboffMAFL(encoder_HG)
print("liuboff", test_loss)
# test_loss = nadbka(encoder_HG)
tuner.update(test_loss)
WR.writer.add_scalar("liuboff", test_loss, i)
