| import os |
| import argparse |
| import torch |
| from networks.net_factory import net_factory |
| from utils.test_patch import test_all_case |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--name', type=str, default='CoactSeg', help='name') |
| parser.add_argument('--root_path', type=str, default='./', help='Name of Experiment') |
| parser.add_argument('--exp', type=str, default='reg', help='exp_name') |
| parser.add_argument('--model', type=str, default='vnet', help='model_name') |
| parser.add_argument('--gpu', type=str, default='0', help='GPU to use') |
| parser.add_argument('--detail', type=int, default=1, help='print metrics for every samples?') |
| parser.add_argument('--nms', type=int, default=0, help='apply NMS post-procssing?') |
|
|
| FLAGS = parser.parse_args() |
| os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu |
| snapshot_path = FLAGS.root_path + "model/{}_{}/{}".format(FLAGS.name, FLAGS.exp, FLAGS.model) |
| test_save_path = FLAGS.root_path + "model/{}_{}/{}_predictions/".format(FLAGS.name, FLAGS.exp, FLAGS.model) |
|
|
| num_classes = 2 |
|
|
| patch_size = (80, 80, 80) |
| FLAGS.root_path = FLAGS.root_path + 'data/' |
| with open(FLAGS.root_path + '/val.list', 'r') as f: |
| image_list = f.readlines() |
| image_list = [item.replace('\n','') for item in image_list] |
| if not os.path.exists(test_save_path): |
| os.makedirs(test_save_path) |
| print(test_save_path) |
|
|
| def test_calculate_metric(): |
| |
| net = net_factory(net_type=FLAGS.model, in_chns=3, class_num=num_classes, mode="test") |
| save_mode_path = os.path.join(snapshot_path, '{}_best_model.pth'.format(FLAGS.model)) |
| net.load_state_dict(torch.load(save_mode_path), strict=False) |
| print("init weight from {}".format(save_mode_path)) |
| net.eval() |
|
|
| avg_metric = test_all_case(FLAGS.model, 1, net, image_list, num_classes=num_classes, |
| patch_size=(80, 80, 80), stride_xy=20, stride_z=20, |
| save_result=True, test_save_path=test_save_path, |
| metric_detail=FLAGS.detail, nms=FLAGS.nms) |
| return avg_metric |
|
|
|
|
| if __name__ == '__main__': |
| metric = test_calculate_metric() |
| print(metric) |
|
|
