def gan_train(real_img, skeleton):
B = real_img.shape[0]
C = 512
requires_grad(generator, True)
requires_grad(decoder, True)
condition = skeleton.detach().requires_grad_(True)
noise = mixing_noise(B, C, 0.9, real_img.device)
fake, fake_latent = generator(condition, noise, return_latents=True)
model.discriminator_train([real_img], [fake], [condition])
WR.writable("Generator brule_loss", model.generator_loss)([real_img], [fake], [condition]) \
.minimize_step(model.optimizer.opt_min)
fake = fake.detach()
fake_latent_pred = style_encoder(fake)
restored = decoder(condition, style_encoder(real_img))
fake_latent = torch.cat([f[:, None, :] for f in fake_latent],
dim=1).detach()
coefs = json.load(open("../parameters/gan_loss.json"))
(WR.L1("L1 restored")(restored, real_img) * coefs["L1 restored"] +
WR.L1("L1 style gan")(fake_latent_pred, fake_latent) *
coefs["L1 style gan"]).minimize_step(model.optimizer.opt_min,
style_opt)
def do_train(real_img):
B = real_img.shape[0]
coefs = json.load(open("../parameters/content_loss.json"))
requires_grad(encoder_HG, True)
requires_grad(decoder, False)
requires_grad(generator, False)
encoded = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = encoded["mes"]
heatmap_content = heatmapper.forward(pred_measures.coord).detach()
restored = decoder(encoded["skeleton"], style_encoder(real_img))
noise = mixing_noise(B, C, 0.9, real_img.device)
fake, _ = generator(encoded["skeleton"], noise)
fake_content = encoder_HG(fake.detach())["mes"]
ll = (WR.L1("L1 image")(restored, real_img) * coefs["L1 image"] +
WR.writable("fake_content brule_loss", coord_hm_loss)
(fake_content, heatmap_content) *
coefs["fake_content brule_loss"] +
WR.writable("Fake-content D", model.loss.generator_loss)
(real=None, fake=[fake, encoded["skeleton"].detach()]) *
coefs["Fake-content D"])
ll.minimize_step(model.optimizer.opt_min)
def train_content(cont_opt, R_b, R_t, real_img, encoder_HG):
# heatmapper = ToGaussHeatMap(256, 4)
requires_grad(encoder_HG, True)
coefs = json.load(open(os.path.join(sys.path[0], "../parameters/content_loss.json")))
encoded = encoder_HG(real_img)
pred_measures: UniformMeasure2D01 = encoded["mes"]
heatmap_content = encoded["hm"]
ll = (
WR.writable("R_b", R_b.__call__)(real_img, pred_measures) * coefs["R_b"] +
WR.writable("R_t", R_t.__call__)(real_img, heatmap_content) * coefs["R_t"]
)
ll.minimize_step(cont_opt)
fake_latent_pred = enc_dec.encode_latent(fake)
gan_model_tuda.discriminator_train([real_img], [fake.detach()])
(
gan_model_tuda.generator_loss([real_img], [fake]) +
l1_loss(fake_latent_pred, fake_latent) * coefs["style"]
).minimize_step(gan_model_tuda.optimizer.opt_min, style_opt)
hm_pred = hg.forward(real_img)["hm_sum"]
hm_ref = heatmapper.forward(landmarks).detach().sum(1, keepdim=True)
gan_model_obratno.discriminator_train([hm_ref], [hm_pred.detach()])
gan_model_obratno.generator_loss([hm_ref], [hm_pred]).__mul__(coefs["obratno"]) \
.minimize_step(gan_model_obratno.optimizer.opt_min)
fake2, _ = enc_dec.generate(heatmap_sum)
WR.writable("cycle", mes_loss.forward)(hg.forward(fake2)["mes"], UniformMeasure2D01(landmarks)).__mul__(coefs["hm"]) \
.minimize_step(gan_model_tuda.optimizer.opt_min, gan_model_obratno.optimizer.opt_min)
latent = enc_dec.encode_latent(g_transforms(image=real_img)["image"])
restored = enc_dec.decode(hg.forward(real_img)["hm_sum"], latent)
WR.writable("cycle2", psp_loss.forward)(real_img, real_img, restored, latent).__mul__(coefs["img"]) \
.minimize_step(gan_model_tuda.optimizer.opt_min, gan_model_obratno.optimizer.opt_min, style_opt)
image_accumulator.step(i)
hm_accumulator.step(i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'gi': enc_dec.generator.state_dict(),
'gh': hg.state_dict(),
'di': discriminator_img.state_dict(),
pred = hg.forward(real_img)
hm_pred = pred["hm_sum"].detach()
mes_pred = pred["mes"].detach()
fake, fake_latent = enc_dec.generate(hm_pred)
fake_latent_pred = enc_dec.encode_latent(fake)
gan_model_tuda.discriminator_train([real_img], [fake.detach()])
(gan_model_tuda.generator_loss([real_img], [fake]) +
l1_loss(fake_latent_pred, fake_latent) * coefs["style"]).minimize_step(
gan_model_tuda.optimizer.opt_min, style_opt)
train_content(cont_opt, R_b, R_t, real_img, hg)
fake2, _ = enc_dec.generate(hm_pred)
WR.writable("cycle", mes_loss.forward)(hg.forward(fake2)["mes"], mes_pred).__mul__(coefs["hm"]) \
.minimize_step(gan_model_tuda.optimizer.opt_min, cont_opt)
latent = enc_dec.encode_latent(g_transforms(image=real_img)["image"])
restored = enc_dec.decode(hg.forward(real_img)["hm_sum"], latent)
WR.writable("cycle2", psp_loss.forward)(real_img, real_img, restored, latent).__mul__(coefs["img"])\
.minimize_step(gan_model_tuda.optimizer.opt_min, cont_opt, style_opt)
# requires_grad(discriminator_img, False)
# requires_grad(enc_dec.generator, False)
# fake3, _ = enc_dec.generate(hg.forward(real_img)["hm_sum"])
# gan_model_tuda.generator_loss([real_img], [fake3]).__mul__(coefs["ganhg"]).minimize_step(cont_opt)
# requires_grad(discriminator_img, True)
# requires_grad(enc_dec.generator, True)
image_accumulator.step(i)
hm_accumulator.step(i)
fake, fake_latent = enc_dec.generate(real_seg)
fake_latent_pred = enc_dec.encode_latent(fake)
gan_model_tuda.discriminator_train([real_img], [fake.detach()])
(
gan_model_tuda.generator_loss([real_img], [fake]) +
l1_loss(fake_latent_pred, fake_latent) * coefs["style"]
).minimize_step(gan_model_tuda.optimizer.opt_min, style_opt)
seg_pred = hg.forward(real_img)
gan_model_obratno.discriminator_train([real_seg], [seg_pred.detach()])
gan_model_obratno.generator_loss([real_seg], [seg_pred]).__mul__(coefs["obratno"])\
.minimize_step(gan_model_obratno.optimizer.opt_min)
fake2, _ = enc_dec.generate(real_seg)
WR.writable("cycle", our_loss.forward)(hg.forward(fake2), real_seg).__mul__(coefs["hm"])\
.minimize_step(gan_model_tuda.optimizer.opt_min, gan_model_obratno.optimizer.opt_min)
latent = enc_dec.encode_latent(real_img)
# latent = enc_dec.encode_latent(g_transforms(image=real_img)["image"])
restored = enc_dec.decode(hg.forward(real_img), latent)
WR.writable("cycle2", psp_loss.forward)(real_img, real_img, restored, latent).__mul__(coefs["img"])\
.minimize_step(gan_model_tuda.optimizer.opt_min, gan_model_obratno.optimizer.opt_min, style_opt)
image_accumulator.step(i)
hm_accumulator.step(i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'gi': enc_dec.generator.state_dict(),
'gh': hg.state_dict(),
def hm_svoego_roda_loss(pred, target):
pred_xy, _ = heatmap_to_measure(pred)
t_xy, _ = heatmap_to_measure(target)
return Loss(nn.BCELoss()(pred, target) * 10 +
nn.MSELoss()(pred_xy, t_xy) * 0.005 +
(pred - target).abs().mean() * 3)
for i in range(100000):
WR.counter.update(i)
batch = next(LazyLoader.w300().loader_train_inf)
real_img = batch["data"].cuda()
landmarks = torch.clamp(batch["meta"]['keypts_normalized'].cuda(), max=1)
WR.writable("cycle", hm_svoego_roda_loss)(hg.forward(real_img)["hm"], heatmapper.forward(landmarks))\
.minimize_step(hg_opt)
if i % 100 == 0:
print(i)
with torch.no_grad():
tl2 = verka_300w(hg)
writer.add_scalar("verka", tl2, i)
# sk_pred = hg.forward(test_img)["hm_sum"]
# send_images_to_tensorboard(writer, test_img + sk_pred, "REAL", i)
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:
torch.save(
{
'gi': enc_dec.generator.state_dict(),
'di': discriminator_img.state_dict(),
's': enc_dec.style_encoder.state_dict()
},
f'{Paths.default.models()}/300w_encoder_{str(i + starting_model_number).zfill(6)}.pt',
)
if i % 100 == 0:
# 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)
batch = next(LazyLoader.cardio().loader_train_inf)
real_img = batch["image"].cuda()
train_landmarks = batch["keypoints"].cuda()
coefs = json.load(open(os.path.join(sys.path[0], "../parameters/cycle_loss_2.json")))
WR.writable("sup", mes_loss.forward)(hg.forward(real_img)["mes"], UniformMeasure2D01(train_landmarks)).__mul__(coefs["sup"]) \
.minimize_step(cont_opt)
hm_accumulator.step(i)
if i % 1000 == 0 and i > 0:
torch.save(
{
'gh': hg.state_dict(),
},
f'{Paths.default.models()}/cardio_brule_sup_{str(i + starting_model_number).zfill(6)}.pt',
)
if i % 100 == 0:
print(i)
with torch.no_grad():
batch = next(LazyLoader.human36(use_mask=True).loader_train_inf)
real_img = batch["A"].cuda()
landmarks = torch.clamp(batch["paired_B"].cuda(), min=0, max=1)
heatmap = heatmapper.forward(landmarks).detach()
coefs = json.load(
open(os.path.join(sys.path[0], "../parameters/cycle_loss.json")))
fake, fake_latent = enc_dec.generate(heatmap)
gan_model_tuda.discriminator_train([real_img], [fake.detach()])
(gan_model_tuda.generator_loss([real_img], [fake])).minimize_step(
gan_model_tuda.optimizer.opt_min)
fake2, _ = enc_dec.generate(heatmap)
WR.writable("cycle", mes_loss.forward)(hg.forward(fake2)["mes"], UniformMeasure2D01(landmarks)).__mul__(coefs["hm"]) \
.minimize_step(gan_model_tuda.optimizer.opt_min)
image_accumulator.step(i)
if i % 10000 == 0 and i > 0:
torch.save(
{
'gi': enc_dec.generator.state_dict(),
'di': discriminator_img.state_dict(),
's': enc_dec.style_encoder.state_dict()
},
f'{Paths.default.models()}/human_gan_{str(i + starting_model_number).zfill(6)}.pt',
)
if i % 100 == 0:
print(i)
