def main(): # load data options = get_parser().parse_args() dataset = load_standard_dataset('lodopab', ordered=True) ray_trafo = dataset.ray_trafo global_results = [] sizes = [0.001, 0.01, 0.1, 1.00] #sizes = [0.0001] #sizes = [0.001] #sizes = [0.01] #sizes = [0.1] for size_part in sizes: cached_dataset = CachedDataset(dataset, space=(ray_trafo.range, ray_trafo.domain), cache_files={ 'train': [None, None], 'validation': [None, None] }, size_part=size_part) test_data = dataset.get_data_pairs('validation', cached_dataset.validation_len) print('validation size: %d' % len(test_data)) full_size_epochs = 10 if size_part >= 0.10 else 5 lr = 0.0001 if size_part >= 0.10 else 0.001 # scale number of epochs by 1/size_part, but maximum 1000 times as many # epochs as for full size dataset epochs = min(1000 * full_size_epochs, int(1. / size_part * full_size_epochs)) reconstructor = LearnedPDReconstructor( ray_trafo, log_dir='lodopab_learnedpd_{}'.format(size_part), save_best_learned_params_path=get_weights_path( 'lodopab_learnedpd_{}'.format(size_part))) # create a Dival task table and run it task_table = TaskTable() task_table.append(reconstructor=reconstructor, measures=[PSNR, SSIM], test_data=test_data, dataset=cached_dataset, hyper_param_choices={ 'batch_size': [1], 'epochs': [epochs], 'niter': [10], 'internal_ch': [64 if size_part >= 0.10 else 32], 'lr': [lr], 'lr_min': [lr], 'init_fbp': [True], 'init_frequency_scaling': [0.7] }) results = task_table.run() # save report save_results_table(results, 'lodopab_learnedpd_{}'.format(size_part)) # select best parameters and save them best_choice, best_error = select_hyper_best_parameters(results) params = Params(best_choice) params.save('lodopab_learnedpd_{}'.format(size_part))
# create a Dival task table and run it task_table = TaskTable() task_table.append(reconstructor=reconstructor, measures=[PSNR, SSIM], test_data=test_data, dataset=cached_dataset, hyper_param_choices={ 'batch_size': [32], 'epochs': [epochs], 'niter': [10], 'lr': [0.001], 'lr_time_decay_rate': [3.2 * size_part] }) results = task_table.run() # save report save_results_table(results, 'ellipses_learnedgd_{}'.format(size_part)) # select best parameters and save them best_choice, best_error = select_hyper_best_parameters(results) params = Params(best_choice) params.save('ellipses_learnedgd_{}'.format(size_part)) # retrain the model with the optimal parameters and save the weights # reconstructor = LearnedGDReconstructor(dataset.ray_trafo, hyper_params=params.dict) # reconstructor.train(cached_dataset) save_weights(reconstructor, 'ellipses_learnedgd_{}'.format(size_part))