import os

import argparse

import torch

from networks.unet_urpc import unet_3D_dv_semi

from utils.test_patch import test_all_case

parser = argparse.ArgumentParser()

parser.add_argument('--dataset_name', type=str,  default='LA', help='dataset_name')

parser.add_argument('--root_path', type=str, default='/***/data_set/LASet/data/', help='Name of Experiment')

parser.add_argument('--exp', type=str,  default='vnet', help='exp_name')

parser.add_argument('--model', type=str,  default='URPC', 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('--labelnum', type=int, default=25, help='labeled data')

parser.add_argument('--nms', type=int, default=0, help='apply NMS post-procssing?')

FLAGS = parser.parse_args()

os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu

test_save_path = 'predictions/URPC/'

num_classes = 2

patch_size = (112, 112, 80)

FLAGS.root_path = FLAGS.root_path

with open(FLAGS.root_path + '/../test.list', 'r') as f:

    image_list = f.readlines()

image_list = [FLAGS.root_path + item.replace('\n', '') + "/mri_norm2.h5" 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 = unet_3D_dv_semi(n_classes=num_classes, in_channels=1).cuda()

    save_mode_path = 'model/LA_vnet_25_labeled/URPC/URPC_best_model.pth'

    net.load_state_dict(torch.load(save_mode_path), strict=False)  # 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=(112, 112, 80), stride_xy=18, stride_z=4,

                    save_result=False, 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)