Update inference.py

inference.py

@@ -51,20 +51,23 @@ def sam_preprocess(
     '''
     assert img_size==1024, \
         "both SAM and Effi-SAM receive images of size 1024^2, don't change this setting unless you're sure that your employed model works well with another size."
-    x = ResizeLongestSide(img_size).apply_image(x)
-    resize_shape = x.shape[:2]
-    x = torch.from_numpy(x).permute(2,0,1).contiguous()
-
+    
     # Normalize colors
-    x = (x - pixel_mean) / pixel_std
-    if model_type=="effi" or model_type=="sam2":
-        x = F.interpolate(x.unsqueeze(0), (img_size, img_size), mode="bilinear").squeeze(0)
-    else:
+    if model_type=="ori":
+        x = ResizeLongestSide(img_size).apply_image(x)
+        h, w = resize_shape = x.shape[:2]
+        x = torch.from_numpy(x).permute(2,0,1).contiguous()
+        x = (x - pixel_mean) / pixel_std
         # Pad
-        h, w = x.shape[-2:]
         padh = img_size - h
         padw = img_size - w
         x = F.pad(x, (0, padw, 0, padh))
+    else:
+        x = torch.from_numpy(x).permute(2,0,1).contiguous()
+        x = F.interpolate(x.unsqueeze(0), (img_size, img_size), mode="bilinear", align_corners=False).squeeze(0)
+        x = (x - pixel_mean) / pixel_std
+        resize_shape = None
+    
     return x, resize_shape
 
 def beit3_preprocess(x: np.ndarray, img_size=224) -> torch.Tensor: