def verka_human(enc):
sum_loss = 0
n = len(LazyLoader.human36().test_dataset)
for i, batch in enumerate(LazyLoader.human36().test_loader):
data = batch['A'].cuda()
landmarks = batch["paired_B"].cuda()
pred = enc(data)["mes"].coord
# eye_dist = landmarks[:, 45] - landmarks[:, 36]
# eye_dist = eye_dist.pow(2).sum(dim=1).sqrt()
sum_loss += ((pred - landmarks).pow(2).sum(dim=2).sqrt().mean(
dim=1)).sum().item()
print("test brule_loss: ", sum_loss / n)
return sum_loss / n
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()}/human{int(time.time())}")
WR.writer = writer
batch = next(LazyLoader.human36().loader_train_inf)
test_img = batch["A"].cuda()
test_landmarks = torch.clamp(next(
LazyLoader.human_landmarks(args.data_path).loader_train_inf).cuda(),
max=1)
test_hm = heatmapper.forward(test_landmarks).detach()
test_noise = mixing_noise(batch_size, 512, 0.9, device)
psp_loss = PSPLoss(id_lambda=0).cuda()
mes_loss = MesBceL2Loss(heatmapper, bce_coef=10000 / 2, l2_coef=100)
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(10000):
enc_dec = StyleGanAutoEncoder(
hm_nc=measure_size, image_size=image_size).load_state_dict(weights).cuda()
discriminator_img = Discriminator(image_size)
discriminator_img.load_state_dict(weights['di'])
discriminator_img = discriminator_img.cuda()
gan_model_tuda = StyleGanModel[HeatmapToImage](enc_dec.generator,
StyleGANLoss(discriminator_img),
(0.001, 0.0015))
writer = SummaryWriter(f"{Paths.default.board()}/human_gan{int(time.time())}")
WR.writer = writer
batch = next(LazyLoader.human36(use_mask=True).loader_train_inf)
test_img = batch["A"].cuda()
test_landmarks = torch.clamp(batch["paired_B"].cuda(), max=1)
test_hm = heatmapper.forward(test_landmarks).detach()
test_noise = mixing_noise(batch_size, 512, 0.9, device)
image_accumulator = Accumulator(enc_dec.generator, decay=0.99, write_every=100)
mes_loss = MesBceL2Loss(heatmapper, bce_coef=10000 / 2, l2_coef=100)
for i in range(100000):
WR.counter.update(i)
batch = next(LazyLoader.human36(use_mask=True).loader_train_inf)
real_img = batch["A"].cuda()
