Update app.py

app.py

@@ -7,6 +7,7 @@ import cv2
 import torchvision.transforms as transforms
 from PIL import Image, ImageDraw
 from src.core import YAMLConfig
+from src2.core import YAMLConfig
 from pathlib import Path
 
 transformer = transforms.Compose([    
@@ -38,9 +39,12 @@ transformer = transforms.Compose([
 #  return tmodel
   
 class Model(nn.Module):
-    def __init__(self, confg=None, ckpt="") -> None:
+    def __init__(self, confg=None, ckpt="",cope=True) -> None:
         super().__init__()
-        self.cfg = YAMLConfig(confg, resume=ckpt)
+        if cope:
+            self.cfg = src.core.YAMLConfig(confg, resume=ckpt)
+        else:
+            self.cfg = src2.core.YAMLConfig(confg, resume=ckpt)
         if ckpt:
             checkpoint = torch.load(ckpt, map_location='cpu') 
             if 'ema' in checkpoint:
@@ -63,6 +67,7 @@ class Model(nn.Module):
   
 
 model = Model(confg='./configs/rtdetr/rtdetr_r101vd_6x_coco.yml',ckpt="./checkpointcope12.pth")
+model2 = Model(confg='./configs/rtdetr/rtdetr_r101vd_6x_coco.yml',ckpt="./checkpoint_init.pth",cope=False)
 
 #img = cv2.imread('./j.jpg',cv2.IMREAD_GRAYSCALE)
 #img = Image.open('./a.jpg').convert('RGB').resize((640,640))
@@ -75,6 +80,7 @@ def detect(img,thr=0.2):
   size = torch.tensor([[t_img.shape[2], t_img.shape[3]]])
   #print(t_img.shape)
   labels, boxes, scores=model(t_img,size)
+  labels2, boxes2, scores2=model2(t_img,size)
   #img_path = Path('./a.jpg')
   draw = ImageDraw.Draw(img)
   thrh = thr