fix(auto3dseg): handle precomputed crops and safe no-grad cleanup

Signed-off-by: Jun Hyeok Lee <bluehyena123@naver.com>

Author: bluehyena

monai/auto3dseg/analyzer.py

@@ -216,50 +216,58 @@ def __init__(self, image_key: str, stats_name: str = DataStatsKeys.IMAGE_STATS)
         super().__init__(stats_name, report_format)
         self.update_ops(ImageStatsKeys.INTENSITY, SampleOperations())
 
+    @torch.no_grad()
     def __call__(self, data):
-        # Input Validation Addition
-        if not isinstance(data, dict):
-            raise TypeError(f"Input data must be a dict, but got {type(data).__name__}.")
-        if self.image_key not in data:
-            raise KeyError(f"Key '{self.image_key}' not found in input data.")
-        image = data[self.image_key]
-        if not isinstance(image, (np.ndarray, torch.Tensor, MetaTensor)):
-            raise TypeError(
-                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
-                f"but got {type(image).__name__}."
-            )
-        if image.ndim < 3:
-            raise ValueError(
-                f"Image data under '{self.image_key}' must have at least 3 dimensions, but got shape {image.shape}."
-            )
-            # --- End of validation ---
         """
-        Callable to execute the pre-defined functions
+        Callable to execute the pre-defined functions.
 
         Returns:
             A dictionary. The dict has the key in self.report_format. The value of
             ImageStatsKeys.INTENSITY is in a list format. Each element of the value list
             has stats pre-defined by SampleOperations (max, min, ....).
 
         Raises:
-            RuntimeError if the stats report generated is not consistent with the pre-
+            KeyError: if ``self.image_key`` is not present in the input data.
+            TypeError: if the input data is not a dictionary, or if the image value is
+                not a numpy array, torch.Tensor, or MetaTensor.
+            ValueError: if the image has fewer than 3 dimensions, or if pre-computed
+                ``nda_croppeds`` is not a list/tuple with one entry per image channel.
+            RuntimeError: if the stats report generated is not consistent with the pre-
                 defined report_format.
 
         Note:
             The stats operation uses numpy and torch to compute max, min, and other
             functions. If the input has nan/inf, the stats results will be nan/inf.
 
         """
+        if not isinstance(data, dict):
+            raise TypeError(f"Input data must be a dict, but got {type(data).__name__}.")
+        if self.image_key not in data:
+            raise KeyError(f"Key '{self.image_key}' not found in input data.")
+        image = data[self.image_key]
+        if not isinstance(image, (np.ndarray, torch.Tensor, MetaTensor)):
+            raise TypeError(
+                f"Value for '{self.image_key}' must be a numpy array, torch.Tensor, or MetaTensor, "
+                f"but got {type(image).__name__}."
+            )
+        if image.ndim < 3:
+            raise ValueError(
+                f"Image data under '{self.image_key}' must have at least 3 dimensions, but got shape {image.shape}."
+            )
+
         d = dict(data)
         start = time.time()
-        restore_grad_state = torch.is_grad_enabled()
-        torch.set_grad_enabled(False)
-
         ndas = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])]
-        if "nda_croppeds" not in d:
+        if "nda_croppeds" in d:
+            nda_croppeds = d["nda_croppeds"]
+            if not isinstance(nda_croppeds, (list, tuple)) or len(nda_croppeds) != len(ndas):
+                raise ValueError(
+                    "Pre-computed 'nda_croppeds' must be a list or tuple with one entry per image channel "
+                    f"(expected {len(ndas)})."
+                )
+        else:
             nda_croppeds = [get_foreground_image(nda) for nda in ndas]
 
-        # perform calculation
         report = deepcopy(self.get_report_format())
 
         report[ImageStatsKeys.SHAPE] = [list(nda.shape) for nda in ndas]
@@ -275,16 +283,13 @@ def __call__(self, data):
             a * b for a, b in zip(report[ImageStatsKeys.SHAPE][0], report[ImageStatsKeys.SPACING])
         ]
 
-        report[ImageStatsKeys.INTENSITY] = [
-            self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_c) for nda_c in nda_croppeds
-        ]
+        report[ImageStatsKeys.INTENSITY] = [self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_c) for nda_c in nda_croppeds]
 
         if not verify_report_format(report, self.get_report_format()):
             raise RuntimeError(f"report generated by {self.__class__} differs from the report format.")
 
         d[self.stats_name] = report
 
-        torch.set_grad_enabled(restore_grad_state)
         logger.debug(f"Get image stats spent {time.time() - start}")
         return d
 
@@ -321,6 +326,7 @@ def __init__(self, image_key: str, label_key: str, stats_name: str = DataStatsKe
         super().__init__(stats_name, report_format)
         self.update_ops(ImageStatsKeys.INTENSITY, SampleOperations())
 
+    @torch.no_grad()
     def __call__(self, data: Mapping) -> dict:
         """
         Callable to execute the pre-defined functions
@@ -341,9 +347,6 @@ def __call__(self, data: Mapping) -> dict:
 
         d = dict(data)
         start = time.time()
-        restore_grad_state = torch.is_grad_enabled()
-        torch.set_grad_enabled(False)
-
         ndas = [d[self.image_key][i] for i in range(d[self.image_key].shape[0])]
         ndas_label = d[self.label_key]  # (H,W,D)
 
@@ -353,19 +356,15 @@ def __call__(self, data: Mapping) -> dict:
         nda_foregrounds = [get_foreground_label(nda, ndas_label) for nda in ndas]
         nda_foregrounds = [nda if nda.numel() > 0 else MetaTensor([0.0]) for nda in nda_foregrounds]
 
-        # perform calculation
         report = deepcopy(self.get_report_format())
 
-        report[ImageStatsKeys.INTENSITY] = [
-            self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_f) for nda_f in nda_foregrounds
-        ]
+        report[ImageStatsKeys.INTENSITY] = [self.ops[ImageStatsKeys.INTENSITY].evaluate(nda_f) for nda_f in nda_foregrounds]
 
         if not verify_report_format(report, self.get_report_format()):
             raise RuntimeError(f"report generated by {self.__class__} differs from the report format.")
 
         d[self.stats_name] = report
 
-        torch.set_grad_enabled(restore_grad_state)
         logger.debug(f"Get foreground image stats spent {time.time() - start}")
         return d
 
@@ -418,6 +417,7 @@ def __init__(
         id_seq = ID_SEP_KEY.join([LabelStatsKeys.LABEL, "0", LabelStatsKeys.IMAGE_INTST])
         self.update_ops_nested_label(id_seq, SampleOperations())
 
+    @torch.no_grad()
     def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTensor | dict]:
         """
         Callable to execute the pre-defined functions.
@@ -470,19 +470,13 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
         start = time.time()
         image_tensor = d[self.image_key]
         label_tensor = d[self.label_key]
-        # Check if either tensor is on CUDA to determine if we should move both to CUDA for processing
         using_cuda = any(
             isinstance(t, (torch.Tensor, MetaTensor)) and t.device.type == "cuda" for t in (image_tensor, label_tensor)
         )
-        restore_grad_state = torch.is_grad_enabled()
-        torch.set_grad_enabled(False)
 
-        if isinstance(image_tensor, (MetaTensor, torch.Tensor)) and isinstance(
-            label_tensor, (MetaTensor, torch.Tensor)
-        ):
+        if isinstance(image_tensor, (MetaTensor, torch.Tensor)) and isinstance(label_tensor, (MetaTensor, torch.Tensor)):
             if label_tensor.device != image_tensor.device:
                 if using_cuda:
-                    # Move both tensors to CUDA when mixing devices
                     cuda_device = image_tensor.device if image_tensor.device.type == "cuda" else label_tensor.device
                     image_tensor = cast(MetaTensor, image_tensor.to(cuda_device))
                     label_tensor = cast(MetaTensor, label_tensor.to(cuda_device))
@@ -504,7 +498,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
 
         unique_label = unique_label.astype(np.int16).tolist()
 
-        label_substats = []  # each element is one label
+        label_substats = []
         pixel_sum = 0
         pixel_arr = []
         for index in unique_label:
@@ -513,17 +507,13 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
             mask_index = ndas_label == index
 
             nda_masks = [nda[mask_index] for nda in ndas]
-            label_dict[LabelStatsKeys.IMAGE_INTST] = [
-                self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_m) for nda_m in nda_masks
-            ]
+            label_dict[LabelStatsKeys.IMAGE_INTST] = [self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_m) for nda_m in nda_masks]
 
             pixel_count = sum(mask_index)
             pixel_arr.append(pixel_count)
             pixel_sum += pixel_count
-            if self.do_ccp:  # apply connected component
+            if self.do_ccp:
                 if using_cuda:
-                    # The back end of get_label_ccp is CuPy
-                    # which is unable to automatically release CUDA GPU memory held by PyTorch
                     del nda_masks
                     torch.cuda.empty_cache()
                 shape_list, ncomponents = get_label_ccp(mask_index)
@@ -538,17 +528,14 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
 
         report = deepcopy(self.get_report_format())
         report[LabelStatsKeys.LABEL_UID] = unique_label
-        report[LabelStatsKeys.IMAGE_INTST] = [
-            self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_f) for nda_f in nda_foregrounds
-        ]
+        report[LabelStatsKeys.IMAGE_INTST] = [self.ops[LabelStatsKeys.IMAGE_INTST].evaluate(nda_f) for nda_f in nda_foregrounds]
         report[LabelStatsKeys.LABEL] = label_substats
 
         if not verify_report_format(report, self.get_report_format()):
             raise RuntimeError(f"report generated by {self.__class__} differs from the report format.")
 
         d[self.stats_name] = report  # type: ignore[assignment]
 
-        torch.set_grad_enabled(restore_grad_state)
         logger.debug(f"Get label stats spent {time.time() - start}")
         return d  # type: ignore[return-value]
 

tests/apps/test_auto3dseg.py

@@ -53,7 +53,7 @@
     SqueezeDimd,
     ToDeviced,
 )
-from monai.utils.enums import DataStatsKeys, LabelStatsKeys
+from monai.utils.enums import DataStatsKeys, ImageStatsKeys, LabelStatsKeys
 from tests.test_utils import skip_if_no_cuda
 
 device = "cpu"
@@ -322,6 +322,18 @@ def test_image_stats_case_analyzer(self):
             report_format = analyzer.get_report_format()
             assert verify_report_format(d["image_stats"], report_format)
 
+    def test_image_stats_uses_precomputed_nda_croppeds(self):
+        analyzer = ImageStats(image_key="image")
+        image = torch.arange(64.0, dtype=torch.float32).reshape(1, 4, 4, 4)
+        nda_croppeds = [torch.ones((2, 2, 2), dtype=torch.float32)]
+
+        result = analyzer({"image": image, "nda_croppeds": nda_croppeds})
+        report = result["image_stats"]
+
+        assert verify_report_format(report, analyzer.get_report_format())
+        assert report[ImageStatsKeys.CROPPED_SHAPE] == [[2, 2, 2]]
+        self.assertAlmostEqual(report[ImageStatsKeys.INTENSITY][0]["mean"], 1.0)
+
     def test_foreground_image_stats_cases_analyzer(self):
         analyzer = FgImageStats(image_key="image", label_key="label")
         transform_list = [
@@ -412,6 +424,39 @@ def test_label_stats_mixed_device_analyzer(self, input_params):
         self.assertAlmostEqual(foreground_stats[0]["mean"], 4.75)
         self.assertAlmostEqual(foreground_stats[1]["mean"], 14.75)
 
+    def test_case_analyzers_restore_grad_state_on_exception(self):
+        cases = [
+            (
+                "image_stats",
+                ImageStats(image_key="image"),
+                {"image": torch.randn(1, 4, 4, 4), "nda_croppeds": [None]},
+                AttributeError,
+            ),
+            (
+                "fg_image_stats",
+                FgImageStats(image_key="image", label_key="label"),
+                {"image": torch.randn(1, 4, 4, 4), "label": torch.ones(3, 4, 4)},
+                ValueError,
+            ),
+            (
+                "label_stats",
+                LabelStats(image_key="image", label_key="label"),
+                {"image": MetaTensor(torch.randn(1, 4, 4, 4)), "label": MetaTensor(torch.ones(3, 4, 4))},
+                ValueError,
+            ),
+        ]
+
+        original_grad_state = torch.is_grad_enabled()
+        try:
+            for name, analyzer, data, error in cases:
+                with self.subTest(analyzer=name):
+                    torch.set_grad_enabled(True)
+                    with self.assertRaises(error):
+                        analyzer(data)
+                    self.assertTrue(torch.is_grad_enabled())
+        finally:
+            torch.set_grad_enabled(original_grad_state)
+
     def test_filename_case_analyzer(self):
         analyzer_image = FilenameStats("image", DataStatsKeys.BY_CASE_IMAGE_PATH)
         analyzer_label = FilenameStats("label", DataStatsKeys.BY_CASE_IMAGE_PATH)