Cast points np.ndarray from float64 to float32

ia_sam_manager.py

@@ -98,6 +98,8 @@ def get_sam_mask_generator(sam_checkpoint, anime_style_chk=False):
             crop_n_layers=1,
             box_nms_thresh=0.7,
             crop_n_points_downscale_factor=2)
+        if platform.system() == "Darwin":
+            sam2_gen_kwargs.update(dict(points_per_side=32, points_per_batch=64, crop_n_points_downscale_factor=1))
 
     if os.path.isfile(sam_checkpoint):
         sam = sam_model_registry_local[model_type](checkpoint=sam_checkpoint)

sam2/automatic_mask_generator.py

@@ -284,7 +284,8 @@ class SAM2AutomaticMaskGenerator:
         orig_h, orig_w = orig_size
 
         # Run model on this batch
-        points = torch.as_tensor(points, device=self.predictor.device)
+        # points = torch.as_tensor(points, device=self.predictor.device)
+        points = torch.as_tensor(points.astype(np.float32), device=self.predictor.device)
         in_points = self.predictor._transforms.transform_coords(
             points, normalize=normalize, orig_hw=im_size
         )

sam2/utils/amg.py

@@ -103,7 +103,7 @@ def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
     ), "Batched iteration must have inputs of all the same size."
     n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
     for b in range(n_batches):
-        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+        yield [arg[b * batch_size: (b + 1) * batch_size] for arg in args]
 
 
 def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
@@ -144,7 +144,7 @@ def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
     idx = 0
     parity = False
     for count in rle["counts"]:
-        mask[idx : idx + count] = parity
+        mask[idx: idx + count] = parity
         idx += count
         parity ^= True
     mask = mask.reshape(w, h)