def _full_evaluation(self, model, eval_space): default_results = Evaluator.means_per_metric( Evaluator.to_model(self.val_loader, model, 'default')) eval_results = {'default': default_results} if eval_space == 'original': original_results = Evaluator.means_per_metric( Evaluator.to_model(self.val_loader, model, 'original')) eval_results['original'] = original_results for eval_space_results in eval_results.values(): Evaluator.results_to_cpu(eval_space_results) return eval_results
def _intermediate_eval(self, session, batch): if session.params['eval_space'] == 'original': batch.original_poses = self.normalizer.denormalize( batch.poses, batch.normalization_params) train_results = { 'DEFAULT': Evaluator.to_batch(batch, session.params['eval_space']) } train_mean_results = Evaluator.means_per_metric(train_results) Evaluator.results_to_cpu(train_mean_results) val_mean_results = session.test_model(self.val_loader) Evaluator.results_to_cpu(val_mean_results) return train_mean_results, val_mean_results