--- a
+++ b/CaraNet/test_blood.py
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+import torch
+import torch.nn.functional as F
+import numpy as np
+import os, argparse
+from scipy import misc
+#from lib.HarDMSEG import HarDMSEG
+from utils.dataloader import test_dataset
+#from CFP_Res2Net import cfpnet_res2net
+from collections import OrderedDict
+#from pranet import PraNet
+from CaraNet import caranet
+
+import cv2
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--testsize', type=int, default=352, help='testing size')
+parser.add_argument('--pth_path', type=str, default='/home/data/spleen_blood/CaraNet/snapshots/CaraNet-best.pth')
+
+for _data_name in ['test']:
+    ##### put your data_path here #####
+    data_path = '/home/data/spleen_blood/CaraNet/TestDataset/{}/'.format(_data_name)
+    ###################################
+   
+    save_path = '/home/data/spleen_blood/CaraNet/results/CaraNet/{}/'.format(_data_name)
+
+    if not os.path.exists( save_path ):
+        os.makedirs( save_path ) 
+
+    opt = parser.parse_args()
+    model = caranet()
+    weights = torch.load(opt.pth_path)
+    new_state_dict = OrderedDict()
+
+    for k, v in weights.items():
+
+    
+        if 'total_ops' not in k and 'total_params' not in k:
+            name = k
+            new_state_dict[name] = v
+        # print(new_state_dict[k])
+        
+            # # print(k)
+        # fp = open('./log3.txt','a')
+        # fp.write(str(k)+'\n')
+        # fp.close()
+    # print(new_state_dict)
+        
+    model.load_state_dict(new_state_dict)
+    model.cuda()
+    model.eval()
+
+
+    os.makedirs(save_path, exist_ok=True)
+    image_root = '{}/images/'.format(data_path)
+    gt_root = '{}/masks/'.format(data_path)
+    test_loader = test_dataset(image_root, gt_root, opt.testsize)
+
+    for i in range(test_loader.size):
+        image, gt, name = test_loader.load_data()
+        gt = np.asarray(gt, np.float32)
+        gt /= (gt.max() + 1e-8)
+        image = image.cuda()
+
+        # res = model(image)
+        res5,res4,res2,res1 = model(image)
+        res = res5
+        res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)
+        res = res.sigmoid().data.cpu().numpy().squeeze()
+        res = (res - res.min()) / (res.max() - res.min() + 1e-8)
+        
+        #misc.imsave(save_path+name, res)
+        cv2.imwrite(save_path+name, res)
+
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