def closure():
optimizer.zero_grad()
output = model(fixed_latent)
if args.nonlinear:
energy = constitutive_constraint(perm_tensor, output,
sobel_filter, args.alpha1, args.alpha2) \
+ continuity_constraint(output, sobel_filter)
else:
energy = constitutive_constraint(
perm_tensor, output, sobel_filter) + continuity_constraint(
output, sobel_filter)
loss_dirichlet, loss_neumann = boundary_condition(output)
loss_boundary = loss_dirichlet + loss_neumann
loss = energy + loss_boundary * args.weight_bound
loss.backward()
if args.verbose:
print(f'epoch {epoch}: loss {loss.item():6f}, '\
f'energy {energy.item():.6f}, diri {loss_dirichlet.item():.6f}, '\
f'neum {loss_neumann.item():.6f}')
return loss
def test(epoch):
model.eval()
loss_test = 0.
relative_l2, err2 = [], []
for batch_idx, (input, target) in enumerate(test_loader):
input, target = input.to(device), target.to(device)
output = model(input)
loss_pde = constitutive_constraint(input, output, sobel_filter) \
+ continuity_constraint(output, sobel_filter)
loss_dirichlet, loss_neumann = boundary_condition(output)
loss_boundary = loss_dirichlet + loss_neumann
loss = loss_pde + loss_boundary * args.weight_bound
loss_test += loss.item()
# sum over H, W --> (B, C)
err2_sum = torch.sum((output - target)**2, [-1, -2])
relative_l2.append(torch.sqrt(err2_sum /
(target**2).sum([-1, -2])))
err2.append(err2_sum)
# plot predictions
if (epoch % args.plot_freq == 0 or epoch == args.epochs) and \
batch_idx == len(test_loader) - 1:
n_samples = 6 if epoch == args.epochs else 2
idx = torch.randperm(input.size(0))[:n_samples]
samples_output = output.data.cpu()[idx].numpy()
samples_target = target.data.cpu()[idx].numpy()
for i in range(n_samples):
print('epoch {}: plotting prediction {}'.format(epoch, i))
plot_prediction_det(args.pred_dir,
samples_target[i],
samples_output[i],
epoch,
i,
plot_fn=args.plot_fn)
loss_test /= (batch_idx + 1)
relative_l2 = to_numpy(torch.cat(relative_l2, 0).mean(0))
r2_score = 1 - to_numpy(torch.cat(err2, 0).sum(0)) / y_test_variation
print(f"Epoch: {epoch}, test r2-score: {r2_score}")
print(f"Epoch: {epoch}, test relative-l2: {relative_l2}")
print(f'Epoch {epoch}: test loss: {loss_train:.6f}, loss_pde: {loss_pde.item():.6f}, '\
f'dirichlet {loss_dirichlet:.6f}, nuemann {loss_neumann.item():.6f}')
if epoch % args.log_freq == 0:
logger['loss_test'].append(loss_test)
logger['r2_test'].append(r2_score)
logger['nrmse_test'].append(relative_l2)
def test(epoch):
model.eval()
loss_test = 0.
# mse = 0.
relative_l2, err2 = [], []
for batch_idx, (input, target) in enumerate(test_loader):
input, target = input.to(device), target.to(device)
# every 10 epochs evaluate the mean accurately
if epoch % 10 == 0:
output_samples = model.sample(input,
n_samples=20,
temperature=1.0)
output = output_samples.mean(0)
else:
# evaluate with one output sample
output, _ = model.generate(input)
residual_norm = constitutive_constraint(input, output, sobel_filter) \
+ continuity_constraint(output, sobel_filter)
loss_dirichlet, loss_neumann = boundary_condition(output)
loss_boundary = loss_dirichlet + loss_neumann
loss_pde = residual_norm + loss_boundary * args.weight_bound
# evaluate predictive entropy: E_p(y|x) [log p(y|x)]
neg_entropy = log_likeihood.mean() / math.log(2.) / n_out_pixels
loss = loss_pde * args.beta + neg_entropy
loss_test += loss.item()
err2_sum = torch.sum((output - target)**2, [-1, -2])
# print(err2_sum)
relative_l2.append(torch.sqrt(err2_sum /
(target**2).sum([-1, -2])))
err2.append(err2_sum)
# plot predictions
if (epoch % args.plot_freq == 0
or epoch % args.epochs == 0) and batch_idx == 0:
n_samples = 6 if epoch == args.epochs else 2
idx = np.random.permutation(input.size(0))[:n_samples]
samples_target = target.data.cpu()[idx].numpy()
for i in range(n_samples):
print('epoch {}: plotting prediction {}'.format(epoch, i))
pred_mean, pred_var = model.predict(input[[idx[i]]])
plot_prediction_bayes2(args.pred_dir,
samples_target[i],
pred_mean[0],
pred_var[0],
epoch,
idx[i],
plot_fn='imshow',
cmap='jet',
same_scale=False)
# plot samples p(y|x)
print(idx[i])
print(input[[idx[i]]].shape)
samples_pred = model.sample(input[[idx[i]]],
n_samples=15)[:, 0]
samples = torch.cat((target[[idx[i]]], samples_pred), 0)
# print(samples.shape)
save_samples(args.pred_dir,
samples,
epoch,
idx[i],
'samples',
nrow=4,
heatmap=True,
cmap='jet')
loss_test /= (batch_idx + 1)
relative_l2 = to_numpy(torch.cat(relative_l2, 0).mean(0))
r2_score = 1 - to_numpy(torch.cat(err2, 0).sum(0)) / y_test_variation
print(f"Epoch {epoch}: test r2-score: {r2_score}")
print(f"Epoch {epoch}: test relative l2: {relative_l2}")
print(f'Epoch {epoch}: test loss: {loss_test:.6f}, residual: {residual_norm.item():.6f}, '\
f'boundary {loss_boundary.item():.6f}, neg entropy {neg_entropy.item():.6f}')
if epoch % args.log_freq == 0:
logger['loss_test'].append(loss_test)
logger['r2_test'].append(r2_score)
logger['nrmse_test'].append(relative_l2)
logger['entropy_test'].append(-neg_entropy.item())
print(f'total steps: {total_steps}')
for epoch in range(start_epoch, args.epochs + 1):
model.train()
# if epoch == 30:
# print('begin finding lr')
# logs,losses = find_lr(model, train_loader, optimizer, loss_fn,
# args.weight_bound, init_value=1e-8, final_value=10., beta=0.98)
# plt.plot(logs[10:-5], losses[10:-5])
# plt.savefig(args.train_dir + '/find_lr.png')
# sys.exit(0)
loss_train, mse = 0., 0.
for batch_idx, (input, ) in enumerate(train_loader, start=1):
input = input.to(device)
model.zero_grad()
output = model(input)
loss_pde = constitutive_constraint(input, output, sobel_filter) \
+ continuity_constraint(output, sobel_filter)
loss_dirichlet, loss_neumann = boundary_condition(output)
loss_boundary = loss_dirichlet + loss_neumann
loss = loss_pde + loss_boundary * args.weight_bound
loss.backward()
# lr scheduling
step = (epoch - 1) * len(train_loader) + batch_idx
pct = step / total_steps
lr = scheduler.step(pct)
adjust_learning_rate(optimizer, lr)
optimizer.step()
loss_train += loss.item()
loss_train /= batch_idx
for batch_idx, (input, target) in enumerate(test_loader):
input, target = input.to(device), target.to(device)
model.zero_grad()
latent, logp, eps = model(target, input)
break
initialized = True
print('Finished data initialization of Actnorm')
for batch_idx, (input, ) in enumerate(train_loader):
input = input.to(device)
model.zero_grad()
# sample output for each input
with autograd.detect_anomaly():
output, log_likeihood = model.generate(input)
# evaluate energy functional/residual norm: E_x E_p(y|x) [\beta V(y; x)]
residual_norm = constitutive_constraint(input, output, sobel_filter) \
+ continuity_constraint(output, sobel_filter)
loss_dirichlet, loss_neumann = boundary_condition(output)
loss_boundary = loss_dirichlet + loss_neumann
loss_pde = residual_norm + loss_boundary * args.weight_bound
# evaluate predictive entropy: E_p(y|x) [log p(y|x)], bits per pixel
neg_entropy = log_likeihood.mean() / math.log(
2.) / n_out_pixels
loss = loss_pde * args.beta + neg_entropy
loss.backward()
step = (epoch - 1) * len(train_loader) + batch_idx
pct = step / total_steps
lr = scheduler.step(pct)
adjust_learning_rate(optimizer, lr)