Fix lint.

segment_anything/modeling/image_encoder.py

@@ -144,8 +144,8 @@ class Block(nn.Module):
             rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
             window_size (int): Window size for window attention blocks. If it equals 0, then
                 use global attention.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative positional
-                parameter size.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
         """
         super().__init__()
         self.norm1 = norm_layer(dim)
@@ -201,8 +201,8 @@ class Attention(nn.Module):
             qkv_bias (bool):  If True, add a learnable bias to query, key, value.
             rel_pos (bool): If True, add relative positional embeddings to the attention map.
             rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (tuple(int, int) or None): Input resolution for calculating the relative positional
-                parameter size.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
         """
         super().__init__()
         self.num_heads = num_heads

segment_anything/utils/onnx.py

@@ -82,7 +82,7 @@ class SamOnnxModel(nn.Module):
         )
 
         prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)
-        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]
+        masks = masks[..., : prepadded_size[0], : prepadded_size[1]]  # type: ignore
 
         orig_im_size = orig_im_size.to(torch.int64)
         h, w = orig_im_size[0], orig_im_size[1]