diff --git a/inference.py b/inference.py
index 88bfe01d917fbce1eff3bc2bb9d830e129a3bc24..93428d4421508f9a505e3ca94133191055a0627a 100644
--- a/inference.py
+++ b/inference.py
@@ -17,21 +17,26 @@ from datetime import datetime
 import cv2
 import torch.nn.functional as F
 
-from models.resnet import resnet50, resnet34
+from models.resnet import resnet18,resnet34,resnet50,resnet101
 from models.squeezenet import squeezenet1_1,squeezenet1_0
+from models.shufflenetv2 import ShuffleNetV2
+from models.shufflenet import ShuffleNet
+from models.mobilenetv2 import MobileNetV2
+from torchvision.models import shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0
+from models.rexnetv1 import ReXNetV1
+
 from utils.common_utils import *
 import copy
 from hand_data_iter.datasets import draw_bd_handpose
 
-
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description=' Project Hand Pose Inference')
-
-    parser.add_argument('--model_path', type=str, default = './model_exp/2021-02-21_23-25-14/model_epoch-2.pth',
+    parser.add_argument('--model_path', type=str, default = './weights/ReXNetV1-size-256-wingloss102-0.122.pth',
         help = 'model_path') # 模型路径
-    parser.add_argument('--model', type=str, default = 'resnet_50',
-        help = 'model : resnet_x,squeezenet_x') # 模型类型
+    parser.add_argument('--model', type=str, default = 'ReXNetV1',
+        help = '''model : resnet_34,resnet_50,resnet_101,squeezenet1_0,squeezenet1_1,shufflenetv2,shufflenet,mobilenetv2
+            shufflenet_v2_x1_5 ,shufflenet_v2_x1_0 , shufflenet_v2_x2_0,ReXNetV1''') # 模型类型
     parser.add_argument('--num_classes', type=int , default = 42,
         help = 'num_classes') #  手部21关键点， (x,y)*2 = 42
     parser.add_argument('--GPUS', type=str, default = '0',
@@ -63,12 +68,30 @@ if __name__ == "__main__":
 
     if ops.model == 'resnet_50':
         model_ = resnet50(num_classes = ops.num_classes,img_size=ops.img_size[0])
+    elif ops.model == 'resnet_18':
+        model_ = resnet18(num_classes = ops.num_classes,img_size=ops.img_size[0])
     elif ops.model == 'resnet_34':
         model_ = resnet34(num_classes = ops.num_classes,img_size=ops.img_size[0])
+    elif ops.model == 'resnet_101':
+        model_ = resnet101(num_classes = ops.num_classes,img_size=ops.img_size[0])
     elif ops.model == "squeezenet1_0":
         model_ = squeezenet1_0(num_classes=ops.num_classes)
     elif ops.model == "squeezenet1_1":
         model_ = squeezenet1_1(num_classes=ops.num_classes)
+    elif ops.model == "shufflenetv2":
+        model_ = ShuffleNetV2(ratio=1., num_classes=ops.num_classes)
+    elif ops.model == "shufflenet_v2_x1_5":
+        model_ = shufflenet_v2_x1_5(pretrained=False,num_classes=ops.num_classes)
+    elif ops.model == "shufflenet_v2_x1_0":
+        model_ = shufflenet_v2_x1_0(pretrained=False,num_classes=ops.num_classes)
+    elif ops.model == "shufflenet_v2_x2_0":
+        model_ = shufflenet_v2_x2_0(pretrained=False,num_classes=ops.num_classes)
+    elif ops.model == "shufflenet":
+        model_ = ShuffleNet(num_blocks = [2,4,2], num_classes=ops.num_classes, groups=3)
+    elif ops.model == "mobilenetv2":
+        model_ = MobileNetV2(num_classes=ops.num_classes)
+    elif ops.model == "ReXNetV1":
+        model_ = ReXNetV1(num_classes=ops.num_classes)
 
     use_cuda = torch.cuda.is_available()