one_side_errG = one_sided(f_enc_X.mean(0) - f_enc_Y.mean(0))
errG = torch.sqrt(mmd2_G) + lambda_rg * one_side_errG
errG.backward(one)
optimizerG.step()
gen_iterations += 1
run_time = (timeit.default_timer() - time) / 60.0
print(
'[%3d/%3d][%3d/%3d] [%5d] (%.2f m) MMD2_D %.6f hinge %.6f L2_AE_X %.6f L2_AE_Y %.6f loss_D %.6f Loss_G %.6f f_X %.6f f_Y %.6f |gD| %.4f |gG| %.4f'
% (t, args.max_iter, i, len(trn_loader), gen_iterations, run_time,
mmd2_D.data[0], one_side_errD.data[0], L2_AE_X_D.data[0],
L2_AE_Y_D.data[0], errD.data[0], errG.data[0],
f_enc_X_D.mean().data[0], f_enc_Y_D.mean().data[0],
base_module.grad_norm(netD), base_module.grad_norm(netG)))
if gen_iterations % 500 == 0:
y_fixed = netG(fixed_noise)
y_fixed.data = y_fixed.data.mul(0.5).add(0.5)
f_dec_X_D = f_dec_X_D.view(f_dec_X_D.size(0), args.nc,
args.image_size, args.image_size)
f_dec_X_D.data = f_dec_X_D.data.mul(0.5).add(0.5)
vutils.save_image(
y_fixed.data,
'{0}/fake_samples_{1}.png'.format(args.experiment,
gen_iterations))
vutils.save_image(
f_dec_X_D.data,
'{0}/decode_samples_{1}.png'.format(args.experiment,
gen_iterations))
one_side_errG = one_sided(f_enc_X.mean(0) - f_enc_Y.mean(0))
errG = torch.sqrt(mmd2_G) + lambda_rg * one_side_errG
errG.backward(one)
optimizerG.step()
gen_iterations += 1
run_time = (timeit.default_timer() - time) / 60.0
print(
'[%3d/%3d][%3d/%3d] [%5d] (%.2f m) MMD2_D %.6f hinge %.6f L2_AE_X %.6f L2_AE_Y %.6f loss_D %.6f Loss_G %.6f f_X %.6f f_Y %.6f |gD| %.4f |gG| %.4f'
% (t, args.max_iter, i, len(trn_loader), gen_iterations, run_time,
mmd2_D.data.item(), one_side_errD.data.item(),
L2_AE_X_D.data.item(), L2_AE_Y_D.data.item(), errD.data.item(),
errG.data.item(), f_enc_X_D.mean().data.item(),
f_enc_Y_D.mean().data.item(), base_module.grad_norm(netD),
base_module.grad_norm(netG)))
if gen_iterations % 500 == 0:
y_fixed = netG(fixed_noise)
y_fixed.data = y_fixed.data.mul(0.5).add(0.5)
f_dec_X_D = f_dec_X_D.view(f_dec_X_D.size(0), args.nc,
args.image_size, args.image_size)
f_dec_X_D.data = f_dec_X_D.data.mul(0.5).add(0.5)
vutils.save_image(
y_fixed.data,
'{0}/fake_samples_{1}.png'.format(args.experiment,
gen_iterations))
vutils.save_image(
f_dec_X_D.data,
'{0}/decode_samples_{1}.png'.format(args.experiment,
sys.exit('Finished diagnostics. I\'m out')
# Do various logs and print summaries.
run_time = (timeit.default_timer() - time) / 60.0
if do_log(gen_iterations):
if gen_iterations % 1000 == 0:
print(args)
# Print summary.
print(('[Epoch %3d/%3d][Batch %3d/%3d] [%5d] (%.2f m) MMD2_D %.6f '
'hinge %.6f L2_AE_X %.6f L2_AE_Y %.6f loss_D %.6f Loss_G '
'%.6f f_X %.6f f_Y %.6f |gD| %.4f |gG| %.4f') %
(global_step, args.max_iter, batch_iter, len(trn_loader),
gen_iterations, run_time, mmd2_D.data[0],
one_side_errD.data[0], L2_AE_X_D.data[0], L2_AE_Y_D.data[0],
errD.data[0], errG.data[0], f_enc_X_D.mean().data[0],
f_enc_Y_D.mean().data[0], base_module.grad_norm(netD),
base_module.grad_norm(netG)))
# Save metrics for the run.
with open(
os.path.join(save_dir, 'log_x_eval_regression_error.txt'),
'a') as f:
f.write('{:.6f}\n'.format(x_eval_error))
with open(
os.path.join(save_dir,
'log_x_eval_regression_error_1s.txt'),
'a') as f:
f.write('{:.6f}\n'.format(x_eval_error_1s))
with open(
os.path.join(save_dir,
'log_x_eval_regression_error_0s.txt'),
'a') as f: