diff options
Diffstat (limited to 'train.py')
| -rwxr-xr-x | train.py | 15 |
1 files changed, 10 insertions, 5 deletions
@@ -38,7 +38,12 @@ print('#training images = %d' % dataset_size) model = create_model(opt) visualizer = Visualizer(opt) -total_steps = (start_epoch-1) * dataset_size + epoch_iter +total_steps = (start_epoch-1) * dataset_size + epoch_iter + +display_delta = total_steps % opt.display_freq +print_delta = total_steps % opt.print_freq +save_delta = total_steps % opt.save_latest_freq + for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): epoch_start_time = time.time() if epoch != start_epoch: @@ -49,7 +54,7 @@ for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): epoch_iter += opt.batchSize # whether to collect output images - save_fake = total_steps % opt.display_freq == 0 + save_fake = total_steps % opt.display_freq == display_delta ############## Forward Pass ###################### losses, generated = model(Variable(data['label']), Variable(data['inst']), @@ -61,7 +66,7 @@ for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): # calculate final loss scalar loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 - loss_G = loss_dict['G_GAN'] + loss_dict['G_GAN_Feat'] + loss_dict['G_VGG'] + loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) ############### Backward Pass #################### # update generator weights @@ -78,7 +83,7 @@ for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): ############## Display results and errors ########## ### print out errors - if total_steps % opt.print_freq == 0: + if total_steps % opt.print_freq == print_delta: errors = {k: v.data[0] if not isinstance(v, int) else v for k, v in loss_dict.items()} t = (time.time() - iter_start_time) / opt.batchSize visualizer.print_current_errors(epoch, epoch_iter, errors, t) @@ -92,7 +97,7 @@ for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): visualizer.display_current_results(visuals, epoch, total_steps) ### save latest model - if total_steps % opt.save_latest_freq == 0: + if total_steps % opt.save_latest_freq == save_delta: print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) model.module.save('latest') np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d') |