diff --git a/testsingle.py b/testsingle.py
index 38b3a52..2bd6167 100755
--- a/testsingle.py
+++ b/testsingle.py
@@ -7,7 +7,7 @@ from torchvision import transforms
 import argparse
 from models.modeling import VisionTransformer, CONFIGS
 import time
-
+import lightrise
 
 
 #模型测试单张图片
@@ -17,11 +17,12 @@ parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
 parser.add_argument('--split', type=str, default='overlap', help="Split method")  # non-overlap
 parser.add_argument('--slide_step', type=int, default=12, help="Slide step for overlap split")
 parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value\n")
-#parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin", help="load pretrained model")
-parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model") #使用自定义VIT
+parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/new/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
+#parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model") #使用自定义VIT
 args = parser.parse_args()
 
-args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+args.device = torch.device("cpu")
 args.nprocs = torch.cuda.device_count()
 
 # 准备模型
@@ -55,7 +56,7 @@ test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEA
 
 #img = Image.open("img.jpg")
 
-img = Image.open("img.jpg")
+img = Image.open("light.jpg")
 
 
 x = test_transform(img)
@@ -74,12 +75,24 @@ for idx in top5[0, :5]:
 clas_ids = top5[0][0]
 clas_ids = 0 if 0==int(clas_ids) or 2 == int(clas_ids) or 3 == int(clas_ids) else 1
 print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item()))
+
 result={}
 result["success"] = "true"
 result["rst_cls"] = str(clas_ids)
+
+riseresult = lightrise.riseempty(Image.open("light.jpg"))
+if(int(result["rst_cls"])==1):
+    if(int(riseresult["rst_cls"])==1):
+        result = {}
+        result["success"] = "true"
+        result["rst_cls"] = 1
+    else:
+        result = {}
+        result["success"] = "true"
+        result["rst_cls"] = 0
+
 print(result)
 
 endtime = time.process_time()
 
-print("Time cost:"+ str(endtime - startime)) #评估一张图片耗时2.8秒
-
+print("Time cost:"+ str(endtime - startime)) #评估一张图片耗时2.8秒
\ No newline at end of file