def _eval_from_gen(self, eval_gen, n_test_examples):
batch_size = min(
self.batch_size, 10
) # just make batches manually, this way we can make sure we test everything
# test metrics: categorical cross-entropy and dice
cces, dice_per_label, accs, all_eval_ids = utils.eval_seg_from_gen(
self.segmenter_model,
eval_gen=eval_gen,
label_mapping=self.label_mapping,
n_eval_examples=n_test_examples,
batch_size=batch_size,
logger=self.logger)
return cces, dice_per_label, accs
n_eval_examples = min(eval_ds.params['n_validation'], eval_ds.vols_labeled_valid.shape[0])
if do_sas:
# assume single atlas
source_X = eval_ds.vols_labeled_train[[0]]
source_Y = eval_ds.segs_labeled_train[[0]]
eval_cces, eval_dice, eval_accs, eval_ids = utils.eval_seg_sas_from_gen(
sas_model=voxelmorph_model,
atlas_vol=source_X, atlas_labels=source_Y,
eval_gen=eval_gen, label_mapping=label_mapping,
n_eval_examples=n_eval_examples, batch_size=16)
else:
eval_cces, eval_dice, eval_accs, eval_ids = utils.eval_seg_from_gen(
segmenter_model=segmenter_model,
eval_gen=eval_gen, label_mapping=label_mapping,
n_eval_examples=n_eval_examples, batch_size=16)
print(eval_ids)
# save results in a .mat file
results_dir = './segmentation_test_results'
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
if do_final_test:
mode = 'test'
else:
mode = 'valid'
results_file = os.path.join(results_dir, '{}_{}_{}.mat'.format(eval_ds.display_name, mode, model_id))
print('Saved results to {}'.format(results_file))