def train(generator, discriminator, encoder, style_encoder, device,
starting_model_number):
batch = 32
Celeba.batch_size = batch
latent_size = 512
model = CondStyleGanModel(generator, StyleGANLoss(discriminator),
(0.001, 0.0015))
style_opt = optim.Adam(style_encoder.parameters(),
lr=5e-4,
betas=(0.5, 0.97))
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(
albumentations.ElasticTransform(p=0.8,
alpha=150,
alpha_affine=1,
sigma=10)),
NumpyBatch(albumentations.ShiftScaleRotate(p=0.5, rotate_limit=10)),
ToTensor(device)
])
R_s = UnoTransformRegularizer.__call__(
g_transforms, lambda trans_dict, img, ltnt: L1("R_s")
(ltnt, style_encoder(trans_dict['image'])))
sample_z = torch.randn(batch, latent_size, device=device)
test_img = next(LazyLoader.celeba().loader).to(device)
print(test_img.shape)
test_cond = encoder(test_img)
requires_grad(encoder, False) # REMOVE BEFORE TRAINING
t_start = time.time()
for i in range(100000):
counter.update(i)
real_img = next(LazyLoader.celeba().loader).to(device)
img_content = encoder(real_img).detach()
noise = mixing_noise(batch, latent_size, 0.9, device)
fake, _ = generator(img_content, noise)
model.discriminator_train([real_img], [fake.detach()], img_content)
writable("Generator loss", model.generator_loss)([real_img], [fake], [], img_content)\
.minimize_step(model.optimizer.opt_min)
# print("gen train", time.time() - t1)
if i % 5 == 0 and i > 0:
noise = mixing_noise(batch, latent_size, 0.9, device)
img_content = encoder(real_img).detach()
fake, fake_latent = generator(img_content,
noise,
return_latents=True)
fake_latent_test = fake_latent[:, [0, 13], :].detach()
fake_latent_pred = style_encoder(fake)
fake_content_pred = encoder(fake)
restored = generator.module.decode(
img_content[:batch // 2], style_encoder(real_img[:batch // 2]))
(HMLoss("BCE content gan", 5000)(fake_content_pred, img_content) +
L1("L1 restored")(restored, real_img[:batch // 2]) * 50 +
L1("L1 style gan")(fake_latent_pred, fake_latent_test) * 30 +
R_s(fake.detach(), fake_latent_pred) * 50).minimize_step(
model.optimizer.opt_min, style_opt)
if i % 100 == 0:
t_100 = time.time()
print(i, t_100 - t_start)
t_start = time.time()
with torch.no_grad():
fake_img, _ = generator(test_cond, [sample_z])
coords, p = heatmap_to_measure(test_cond)
test_mes = ProbabilityMeasure(p, coords)
iwm = imgs_with_mask(fake_img, test_mes.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
iwm = imgs_with_mask(test_img, test_mes.toImage(256))
send_images_to_tensorboard(writer, iwm, "REAL", i)
restored = generator.module.decode(test_cond,
style_encoder(test_img))
send_images_to_tensorboard(writer, restored, "RESTORED", i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'g': generator.state_dict(),
'd': discriminator.state_dict(),
'style': style_encoder.state_dict()
# 'enc': cont_style_encoder.state_dict(),
},
f'/trinity/home/n.buzun/PycharmProjects/saved/stylegan2_w300_{str(starting_model_number + i).zfill(6)}.pt',
)
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',
)
discriminator = discriminator.cuda()
gan_model = StyleGanModel(generator, StyleGANLoss(discriminator),
(0.001, 0.0015))
gan_accumulator = Accumulator(generator, decay=0.99, write_every=100)
writer = SummaryWriter(f"{Paths.default.board()}/celeba{int(time.time())}")
print("Starting Training Loop...")
starting_model_number = 0
for i in range(300000):
print(i)
real_img = next(LazyLoader.celeba().loader).to(device)
noise: List[Tensor] = mixing_noise(batch_size, noise_size, 0.9, device)
fake, latent = generator.forward(noise, return_latents=True)
gan_model.discriminator_train([real_img], [fake.detach()])
gan_model.generator_loss_with_penalty(
[real_img], [fake], latent).minimize_step(gan_model.optimizer.opt_min)
# gan_model.generator_loss([real_img], [fake]).minimize_step(gan_model.optimizer.opt_min)
gan_accumulator.step(i)
if i % 100 == 0:
print(i)
with torch.no_grad():
fake_test, _ = generator.forward([test_sample_z])
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 = 16
test_img = next(LazyLoader.w300().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_68").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(11000):
WR.counter.update(i)
requires_grad(encoder_HG, False)
real_img = next(LazyLoader.celeba().loader).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, 0.98)
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 = liuboff(encoder_HG)
print("liuboff", test_loss)
# test_loss = nadbka(encoder_HG)
tuner.update(test_loss)
WR.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(),
"e": encoder_ema.storage_model.state_dict()
},
f'{Paths.default.models()}/stylegan2_new_{str(i + starting_model_number).zfill(6)}.pt',
)
generator.load_state_dict(weights['g'])
# style_encoder.load_state_dict(weights['s'])
encoder_HG.load_state_dict(weights['c'])
generator = generator.cuda()
# discriminator = discriminator.to(device)
encoder_HG = encoder_HG.cuda()
# style_encoder = style_encoder.cuda()
decoder = CondGenDecode(generator)
# generator = nn.DataParallel(generator, [0, 1, 3])
# discriminator = nn.DataParallel(discriminator, [0, 1, 3])
# encoder_HG = nn.DataParallel(encoder_HG, [0, 1, 3])
# decoder = nn.DataParallel(decoder, [0, 1, 3])
test_img = next(LazyLoader.celeba().loader)[:4].cuda()
heatmaper = ToGaussHeatMap(64, 1.5)
sample_z = torch.randn(4, 512, device=device)
noise = mixing_noise(4, 512, 0.9, device)
LazyLoader.w300_save = None
W300DatasetLoader.test_batch_size = 4
w300_test = next(iter(LazyLoader.w300().test_loader))
w300_test_image = w300_test['data'].to(device)[:4]
w300_test_mask = ProbabilityMeasureFabric(256).from_coord_tensor(
w300_test["meta"]["keypts_normalized"][:4].to(device))
sparse_hm = heatmaper.forward(w300_test_mask.coord * 63).detach()
g_transforms: albumentations.DualTransform = albumentations.Compose([
ToNumpy(),
NumpyBatch(
def train(generator, decoder, discriminator, encoder_HG, style_encoder, device,
starting_model_number):
latent_size = 512
batch_size = 24
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
test_img = next(LazyLoader.celeba().loader)[: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=4e-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: hm_svoego_roda_loss(
encoder_HG(trans_dict['image']), trans_dict['mask'], 1000, 0.3))
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_68").cuda().batch_repeat(
batch_size)
# plt.imshow(barycenter.toImage(256)[0][0].detach().cpu().numpy())
# plt.show()
R_b = BarycenterRegularizer.__call__(barycenter, 1.0, 2.0, 3.0)
# 4.5, 1.2, 1.12, 1.4, 0.07, 2.2
# 1.27, 3.55, 5.88, 3.83, 2.17, 0.22, 1.72
tuner = GoldTuner([2.2112, 2.3467, 3.8438, 3.2202, 2.0494, 0.0260, 5.8378],
device=device,
rule_eps=0.03,
radius=1,
active=True)
# tuner_verka = GoldTuner([3.0, 1.2, 2.0], device=device, rule_eps=0.05, radius=1, active=True)
best_igor = 100
heatmaper = ToGaussHeatMap(64, 1.5)
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)
for i in range(100000):
counter.update(i)
requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.celeba().loader).to(device)
img_content = encoder_HG(real_img).detach()
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(
img_content)
sparce_hm = heatmaper.forward(pred_measures.coord * 63).detach()
trainer_gan(i, real_img, img_content, sparce_hm)
if i % 3 == 0:
real_img = next(LazyLoader.celeba().loader).to(device)
content_trainer(real_img)
if i % 100 == 0:
coefs = json.load(open("../parameters/content_loss_sup.json"))
print(i, coefs)
with torch.no_grad():
content_test = encoder_HG(test_img)
latent_test = style_encoder(test_img)
pred_measures = UniformMeasure2DFactory.from_heatmap(
content_test)
sparce_hm = heatmaper.forward(pred_measures.coord *
63).detach()
iwm = imgs_with_mask(test_img, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "REAL", i)
fake_img, _ = generator(sparce_hm, [sample_z])
iwm = imgs_with_mask(fake_img, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "FAKE", i)
restored = decoder(sparce_hm, latent_test)
iwm = imgs_with_mask(restored, pred_measures.toImage(256))
send_images_to_tensorboard(writer, iwm, "RESTORED", i)
content_test_256 = nn.Upsample(
scale_factor=4)(content_test).sum(dim=1, keepdim=True)
content_test_256 = content_test_256 / content_test_256.max()
send_images_to_tensorboard(writer, content_test_256, "HM", i)
if i % 50 == 0 and i > 0:
test_loss = verka(encoder_HG)
tuner.update(test_loss)
writer.add_scalar("verka", 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_new_{str(i + starting_model_number).zfill(6)}.pt',
)
def train(generator, decoder, discriminator, discriminatorHG, skeleton_encoder, style_encoder, device, starting_model_number):
latent_size = 512
batch_size = 12
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
W300DatasetLoader.batch_size = batch_size
W300DatasetLoader.test_batch_size = 16
encoder_HG_supervised = IXHG(num_classes=68, heatmap_size=64)
encoder_HG_supervised.load_state_dict(torch.load(f'{Paths.default.models()}/hg2_e29.pt', map_location="cpu"))
encoder_HG_supervised = encoder_HG_supervised.cuda()
requires_grad(encoder_HG_supervised, False)
style_opt = optim.Adam(style_encoder.parameters(), lr=5e-4, betas=(0.9, 0.99))
test_img = next(LazyLoader.celeba().loader)[:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
# model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
#
loss_st2: StyleGANLoss = StyleGANLoss(discriminatorHG)
model2 = CondStyleGanModel(skeleton_encoder, loss_st2, (0.0001, 0.001))
skeletoner = CoordToGaussSkeleton(size, 4)
# tuda_trainer = gan_tuda_trainer(model, generator)
obratno_trainer = gan_obratno_trainer(model2)
# tuda_obratno_trainer = gan_tuda_obratno_trainer(generator, encoder_HG, decoder, style_encoder, style_opt)
for i in range(100000):
counter.update(i)
# requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.celeba().loader).to(device)
heatmap = encoder_HG_supervised.get_heatmaps(real_img).detach()
coords, p = heatmap_to_measure(heatmap)
skeleton = skeletoner.forward(coords).sum(dim=1, keepdim=True)
# tuda_trainer(real_img, heatmap)
obratno_trainer(real_img, skeleton)
# tuda_obratno_trainer(real_img, heatmap)
# if i % 10 == 0:
# accumulate(encoder_ema, encoder_HG.module, 0.95)
# pred_hm = encoder_HG(real_img)
# stariy_hm_loss(pred_hm, heatmap).minimize_step(model2.optimizer.opt_min)
if i % 100 == 0 and i >= 0:
with torch.no_grad():
content_test = skeleton_encoder(test_img)["skeleton"].sum(dim=1, keepdim=True)
iwm = imgs_with_mask(test_img, content_test, border=0.1)
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_hm = (content_test - content_test.min()) / content_test.max()
send_images_to_tensorboard(writer, content_test_hm, "HM", i, normalize=False, range=(0, 1))
heatmap_test = encoder_HG_supervised.get_heatmaps(test_img).detach()
coords_test, _ = heatmap_to_measure(heatmap_test)
skeleton_test = skeletoner.forward(coords_test).sum(dim=1, keepdim=True)
iwm = imgs_with_mask(test_img, skeleton_test, border=0.1)
send_images_to_tensorboard(writer, iwm, "REF", i)
if i % 50 == 0 and i >= 0:
test_loss = liuboff(skeleton_encoder)
print("liuboff", test_loss)
# 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': skeleton_encoder.module.state_dict(),
"s": style_encoder.state_dict(),
"d2": discriminatorHG.module.state_dict(),
# "ema": encoder_ema.state_dict()
},
f'{Paths.default.models()}/cyclegan_{str(i + starting_model_number).zfill(6)}.pt',
)
def train(generator, decoder, discriminator, discriminatorHG, encoder_HG, style_encoder, device, starting_model_number):
latent_size = 512
batch_size = 12
sample_z = torch.randn(8, latent_size, device=device)
Celeba.batch_size = batch_size
W300DatasetLoader.batch_size = batch_size
W300DatasetLoader.test_batch_size = 64
encoder_HG_supervised = HG_softmax2020(num_classes=68, heatmap_size=64)
encoder_HG_supervised.load_state_dict(torch.load(f'{Paths.default.models()}/hg2_e29.pt', map_location="cpu"))
encoder_HG_supervised = encoder_HG_supervised.cuda()
requires_grad(encoder_HG_supervised, False)
style_opt = optim.Adam(style_encoder.parameters(), lr=5e-4, betas=(0.9, 0.99))
test_img = next(LazyLoader.celeba().loader)[:8].cuda()
loss_st: StyleGANLoss = StyleGANLoss(discriminator)
model = CondStyleGanModel(generator, loss_st, (0.001, 0.0015))
loss_st2: StyleGANLoss = StyleGANLoss(discriminatorHG)
model2 = CondStyleGanModel(encoder_HG, loss_st2, (2e-5, 0.0005))
heatmaper = ToGaussHeatMap(64, 1.0)
# wide_heatmaper = HeatMapToGaussHeatMap(64, 5.0)
tuda_trainer = gan_tuda_trainer(model, generator)
obratno_trainer = gan_obratno_trainer(model2, encoder_HG)
tuda_obratno_trainer = gan_tuda_obratno_trainer(generator, encoder_HG, decoder, style_encoder, style_opt)
for i in range(100000):
counter.update(i)
# requires_grad(encoder_HG, False) # REMOVE BEFORE TRAINING
real_img = next(LazyLoader.celeba().loader).to(device)
heatmap = encoder_HG_supervised(real_img).detach()
# heatmap = wide_heatmaper.forward(encoder_HG_supervised(real_img)).detach()
# heatmap = encoder_HG_supervised(next(LazyLoader.celeba().loader).to(device)).detach()
# tuda_trainer(real_img, heatmap)
obratno_trainer(real_img, heatmap)
# tuda_obratno_trainer(real_img, heatmap)
# pred_hm = encoder_HG(real_img)
# stariy_hm_loss(pred_hm, heatmap).minimize_step(model2.optimizer.opt_min)
if i % 100 == 0:
with torch.no_grad():
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)
print("liuboff", test_loss)
# 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(),
"d2": discriminatorHG.state_dict()
},
f'{Paths.default.models()}/cyclegan_{str(i + starting_model_number).zfill(6)}.pt',
)
fabric = ProbabilityMeasureFabric(image_size)
barycenter: ProbabilityMeasure = fabric.random(padding).cuda()
barycenter.requires_grad_()
coord = barycenter.coord
opt = optim.Adam(iter([coord]), lr=0.0006)
encoder_HG = HG_softmax2020(num_classes=68, heatmap_size=64)
encoder_HG.load_state_dict(
torch.load(f"{Paths.default.models()}/hg2_e29.pt", map_location="cpu"))
encoder_HG = encoder_HG.cuda()
for iter in range(3000):
img = next(LazyLoader.celeba().loader).cuda()
content = encoder_HG(img)
coord, p = heatmap_to_measure(content)
mes = ProbabilityMeasure(p, coord)
barycenter_cat = fabric.cat([barycenter] * batch_size)
loss = Samples_Loss()(barycenter_cat, mes)
opt.zero_grad()
loss.to_tensor().backward()
opt.step()
barycenter.probability.data = barycenter.probability.relu().data
barycenter.probability.data /= barycenter.probability.sum(dim=1,
keepdim=True)
hg = HG_heatmap(heatmapper)
hg.load_state_dict(weights['gh'])
hg = hg.cuda()
hm_discriminator = Discriminator(image_size, input_nc=1, channel_multiplier=1)
hm_discriminator.load_state_dict(weights["dh"])
hm_discriminator = hm_discriminator.cuda()
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator, StyleGANLoss(discriminator_img), (0.001/4, 0.0015/4))
gan_model_obratno = StyleGanModel[HG_skeleton](hg, StyleGANLoss(hm_discriminator), (2e-5, 0.0015/4))
style_opt = optim.Adam(enc_dec.style_encoder.parameters(), lr=1e-5)
writer = SummaryWriter(f"{Paths.default.board()}/hm2img{int(time.time())}")
WR.writer = writer
test_img = next(LazyLoader.celeba().loader).cuda()
test_landmarks = torch.clamp(next(LazyLoader.w300_landmarks(args.data_path).loader_train_inf).cuda(), max=1)
test_landmarks = trans_w300(test_landmarks)
test_hm = heatmapper.forward(test_landmarks).sum(1, keepdim=True).detach()
test_noise = mixing_noise(batch_size, 512, 0.9, device)
psp_loss = PSPLoss().cuda()
mes_loss = MesBceWasLoss(heatmapper, bce_coef=1000000, was_coef=2000)
image_accumulator = Accumulator(enc_dec.generator, decay=0.99, write_every=100)
hm_accumulator = Accumulator(hg, decay=0.99, write_every=100)
for i in range(100000):
WR.counter.update(i)
for i in range(100000):
WR.counter.update(i)
batch = next(LazyLoader.w300().loader_train_inf)
real_img = next(LazyLoader.w300().loader_train_inf)["data"].cuda()
landmarks = torch.clamp(batch["meta"]["keypts_normalized"].cuda(),
max=1).cuda()
heatmap_sum = heatmapper.forward(landmarks).sum(1, keepdim=True).detach()
fake, fake_latent = enc_dec.generate(heatmap_sum)
fake_latent_pred = enc_dec.encode_latent(fake)
real_gan_img = real_img if i % 2 == 0 else next(
LazyLoader.celeba().loader).cuda()
gan_model_tuda.discriminator_train([real_gan_img], [fake.detach()])
(gan_model_tuda.generator_loss([real_gan_img], [fake]) +
l1_loss(fake_latent_pred, fake_latent)).minimize_step(
gan_model_tuda.optimizer.opt_min, style_opt)
latent = enc_dec.encode_latent(real_img)
restored = enc_dec.decode(heatmap_sum, latent)
WR.writable("cycle2", psp_loss.forward)(real_img, real_img, restored, latent).__mul__(20)\
.minimize_step(gan_model_tuda.optimizer.opt_min, style_opt)
image_accumulator.step(i)
# enc_accumulator.step(i)
if i % 10000 == 0 and i > 0:
