def verka(encoder: nn.Module):
res = []
for i, (image, lm) in enumerate(LazyLoader.celeba_test(64)):
content = encoder(image.cuda())
mes = UniformMeasure2D01(lm.cuda())
pred_measures: UniformMeasure2D01 = UniformMeasure2DFactory.from_heatmap(content)
res.append(Samples_Loss(p=1)(mes, pred_measures).item() * image.shape[0])
return np.mean(res)/len(LazyLoader.celeba_test(1).dataset)
ll = (bc_net(ws).reshape(-1, padding, 2) - bs).pow(2).sum() / 32
print(ll.item())
lll = ll.item()
ll.backward()
bc_net_opt.step()
# replay_buf = replay_buf[replay_buf.__len__() - 32:]
barycenter.coord = bc_net(weights[None, :]).reshape(1, padding, 2).detach()
return barycenter, lll
# mafl_dataloader = LazyLoader.w300().loader_train_inf
# mes = UniformMeasure2D01(next(mafl_dataloader)['meta']['keypts_normalized'].type(torch.float32)).cuda()
mes = UniformMeasure2D01(next(iter(
LazyLoader.celeba_test(batch_size)))[1]).cuda()
for j in range(10000):
weights = Dirichlet(torch.ones(batch_size) / 10).sample().cuda()
barycenter, lll = compute_wbc(mes, weights, min(200, j + 10))
if j % 50 == 0:
print(j)
sced.step(lll)
if j % 50 == 0:
plt.imshow(barycenter.toImage(200)[0][0].detach().cpu().numpy())
plt.show()
starting_model_number = 0
if j % 1000 == 0 and j > 0: