update

Signed-off-by: Wenqi Li <[email protected]>

monai/apps/pathology/inferers/inferer.py

@@ -178,6 +178,7 @@ def __call__(
             self.process_output,
             self.buffer_steps,
             self.buffer_dim,
+            False,
             *args,
             **kwargs,
         )

monai/inferers/inferer.py

@@ -426,6 +426,8 @@ class SlidingWindowInferer(Inferer):
             (i.e. no overlapping among the buffers) non_blocking copy may be automatically enabled for efficiency.
         buffer_dim: the spatial dimension along which the buffers are created.
             0 indicates the first spatial dimension. Default is -1, the last spatial dimension.
+        with_coord: whether to pass the window coordinates to ``network``. Defaults to False.
+            If True, the ``network``'s 2nd input argument should accept the window coordinates.
 
     Note:
         ``sw_batch_size`` denotes the max number of windows per network inference iteration,
@@ -449,6 +451,7 @@ def __init__(
         cpu_thresh: int | None = None,
         buffer_steps: int | None = None,
         buffer_dim: int = -1,
+        with_coord: bool = False,
     ) -> None:
         super().__init__()
         self.roi_size = roi_size
@@ -464,6 +467,7 @@ def __init__(
         self.cpu_thresh = cpu_thresh
         self.buffer_steps = buffer_steps
         self.buffer_dim = buffer_dim
+        self.with_coord = with_coord
 
         # compute_importance_map takes long time when computing on cpu. We thus
         # compute it once if it's static and then save it for future usage
@@ -525,6 +529,7 @@ def __call__(
             None,
             buffer_steps,
             buffer_dim,
+            self.with_coord,
             *args,
             **kwargs,
         )

monai/inferers/utils.py

@@ -57,6 +57,7 @@ def sliding_window_inference(
     process_fn: Callable | None = None,
     buffer_steps: int | None = None,
     buffer_dim: int = -1,
+    with_coord: bool = False,
     *args: Any,
     **kwargs: Any,
 ) -> torch.Tensor | tuple[torch.Tensor, ...] | dict[Any, torch.Tensor]:
@@ -125,6 +126,8 @@ def sliding_window_inference(
             (i.e. no overlapping among the buffers) non_blocking copy may be automatically enabled for efficiency.
         buffer_dim: the spatial dimension along which the buffers are created.
             0 indicates the first spatial dimension. Default is -1, the last spatial dimension.
+        with_coord: whether to pass the window coordinates to ``predictor``. Default is False.
+            If True, the signature of ``predictor`` should be ``predictor(patch_data, patch_coord, ...)``.
         args: optional args to be passed to ``predictor``.
         kwargs: optional keyword args to be passed to ``predictor``.
 
@@ -220,7 +223,10 @@ def sliding_window_inference(
             win_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(sw_device)
         else:
             win_data = inputs[unravel_slice[0]].to(sw_device)
-        seg_prob_out = predictor(win_data, *args, **kwargs)  # batched patch
+        if with_coord:
+            seg_prob_out = predictor(win_data, unravel_slice, *args, **kwargs)  # batched patch
+        else:
+            seg_prob_out = predictor(win_data, *args, **kwargs)  # batched patch
 
         # convert seg_prob_out to tuple seg_tuple, this does not allocate new memory.
         dict_keys, seg_tuple = _flatten_struct(seg_prob_out)

tests/test_sliding_window_hovernet_inference.py

@@ -237,6 +237,7 @@ def compute(data, test1, test2):
             None,
             None,
             0,
+            False,
             t1,
             test2=t2,
         )

tests/test_sliding_window_inference.py

@@ -294,6 +294,7 @@ def compute(data, test1, test2):
             None,
             None,
             0,
+            False,
             t1,
             test2=t2,
         )