def test(epoch):
if epoch % args.epochs == 0 or epoch % args.test_freq == 0:
output = model(fixed_latent)
output = to_numpy(output)
if args.animate:
i_plot = epoch // args.test_freq
plot_prediction_det_animate2(run_dir,
output_arr,
output[0],
epoch,
args.idx,
i_plot,
plot_fn='imshow',
cmap=args.cmap,
same_scale=args.same_scale)
else:
plot_prediction_det(run_dir,
output_arr,
output[0],
epoch,
args.idx,
plot_fn='imshow',
cmap=args.cmap,
same_scale=args.same_scale)
np.save(run_dir + f'/epoch{epoch}.npy', output[0])
def test(epoch):
if epoch % args.epochs == 0 or epoch % args.test_freq == 0:
# plot the solution
xx, yy = np.meshgrid(np.arange(ngrids[0]), np.arange(ngrids[1]))
x_test = xx.flatten()[:, None] / ngrids[1]
y_test = yy.flatten()[:, None] / ngrids[0]
x_test, y_test = to_tensor_gpu(x_test, y_test)
net_u.eval()
x_test.requires_grad = True
y_test.requires_grad = True
xy_test = torch.cat((y_test, x_test), 1)
y_pred = net_u(xy_test)
target = output_arr
# three output of net_u from 0-3 channel: u, flux_y, flux_x
u_pred = y_pred[:, 0].detach().cpu().numpy().reshape(*ngrids)
u_y = y_pred[:, 1].detach().cpu().numpy().reshape(*ngrids)
u_x = y_pred[:, 2].detach().cpu().numpy().reshape(*ngrids)
prediction = np.stack((u_pred, u_x, u_y))
# prediction = y_pred.view(*ngrids, -1).transpose(0, 1).permute(2, 1, 0).detach().cpu().numpy()
if args.animate:
i_plot = epoch // args.test_freq
plot_prediction_det_animate2(run_dir, target, prediction, epoch, args.idx, i_plot,
plot_fn='imshow', cmap=args.cmap, same_scale=args.same_scale)
else:
plot_prediction_det(run_dir, target, prediction, epoch, args.idx,
plot_fn='imshow', cmap=args.cmap, same_scale=args.same_scale)
np.save(run_dir + f'/epoch{epoch}.npy', prediction)
def test(epoch):
model.eval()
mse = 0.
for batch_idx, (input, target) in enumerate(test_loader):
input, target = input.to(device), target.to(device)
output = model(input)
mse += F.mse_loss(output, target, size_average=False).item()
# plot predictions
if epoch % args.plot_freq == 0 and batch_idx == 0:
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)
rmse_test = np.sqrt(mse / n_out_pixels_test)
r2_score = 1 - mse / test_stats['y_var']
print("epoch: {}, test r2-score: {:.6f}".format(epoch, r2_score))
if epoch % args.log_freq == 0:
logger['r2_test'].append(r2_score)
logger['rmse_test'].append(rmse_test)
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.
relative_l2, err2 = [], []
for batch_idx, (input, target) in enumerate(test_loader):
input, target = input.to(device), target.to(device)
output = model(input)
loss = F.mse_loss(output, target)
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}, relative-l2: {relative_l2}"
)
if epoch % args.log_freq == 0:
logger['loss_test'].append(loss_test)
logger['r2_test'].append(r2_score)
logger['nrmse_test'].append(relative_l2)