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