Fixed torch randint incoherent sampling (compatible to random.randint) (

#2401)
pytorch · Jul 7, 2020 · b572d5e · b572d5e
1 parent 86b6c3e
commit b572d5e
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
@@ -510,8 +510,8 @@ def get_params(img: Tensor, output_size: Tuple[int, int]) -> Tuple[int, int, int
         if w == tw and h == th:
             return 0, 0, h, w
 
-        i = torch.randint(0, h - th, size=(1, )).item()
-        j = torch.randint(0, w - tw, size=(1, )).item()
+        i = torch.randint(0, h - th + 1, size=(1, )).item()
+        j = torch.randint(0, w - tw + 1, size=(1, )).item()
         return i, j, th, tw
 
     def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"):
@@ -1433,8 +1433,8 @@ def get_params(
             else:
                 v = torch.tensor(value)[:, None, None]
 
-            i = torch.randint(0, img_h - h, size=(1, )).item()
-            j = torch.randint(0, img_w - w, size=(1, )).item()
+            i = torch.randint(0, img_h - h + 1, size=(1, )).item()
+            j = torch.randint(0, img_w - w + 1, size=(1, )).item()
             return i, j, h, w, v
 
         # Return original image