elif evaluate_all:
logging.warning('EVALUATING ON ALL TRAINING DATA')
input_path = sys_config.data_root
output_path = os.path.join(model_path, 'predictions_alltrain')
else:
logging.warning('EVALUATING ON VALIDATION SET')
input_path = sys_config.data_root
output_path = os.path.join(model_path, 'predictions')
path_pred = os.path.join(output_path, 'prediction')
path_image = os.path.join(output_path, 'image')
utils.makefolder(path_pred)
utils.makefolder(path_image)
path_gt = os.path.join(output_path, 'ground_truth')
path_diff = os.path.join(output_path, 'difference')
path_eval = os.path.join(output_path, 'eval')
utils.makefolder(path_diff)
utils.makefolder(path_gt)
init_iteration = score_data(input_path,
output_path,
model_path,
num_classes=args.num_classes,
do_postprocessing=True,
gt_exists=(not evaluate_test_set),
evaluate_all=evaluate_all)
metrics_acdc.main(path_gt, path_pred, path_eval)
]
avg_dices = []
for s in seeds:
np.random.seed(s)
init_iteration = score_data(
input_path,
output_path,
model_path,
args,
do_postprocessing=True,
gt_exists=(not evaluate_test_set),
evaluate_all=evaluate_all,
random_center_ratio=args.random_center_ratio)
avg_dice = metrics_acdc.main(path_gt, path_pred, path_eval)
avg_dices.append(avg_dice)
avg_dices = np.asarray(avg_dices)
print(avg_dices)
print("mean: {}\tstd: {}".format(np.mean(avg_dices),
np.std(avg_dices)))
print(np.mean(avg_dices))
print(np.std(avg_dices))
else:
init_iteration = score_data(input_path,
output_path,
model_path,
args,
do_postprocessing=True,
gt_exists=(not evaluate_test_set),
evaluate_all=evaluate_all,