Add transform to handle empty box as training data (Project-MONAI#6170)

Fixes Project-MONAI#5990 .

### Description

Add transforms to convert empty box with shape (0,M) or (0,) into (0,4)
or (0,6).
Provide format checking inside detector so users can know how to solve
the format issue with empty box input.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Can Zhao <canz@nvidia.com>

monai/apps/detection/networks/retinanet_detector.py

@@ -485,7 +485,9 @@ def forward(
         """
         # 1. Check if input arguments are valid
         if self.training:
-            check_training_targets(input_images, targets, self.spatial_dims, self.target_label_key, self.target_box_key)
+            targets = check_training_targets(
+                input_images, targets, self.spatial_dims, self.target_label_key, self.target_box_key
+            )
             self._check_detector_training_components()
 
         # 2. Pad list of images to a single Tensor `images` with spatial size divisible by self.size_divisible.
@@ -877,7 +879,7 @@ def get_cls_train_sample_per_image(
 
         foreground_idxs_per_image = matched_idxs_per_image >= 0
 
-        num_foreground = foreground_idxs_per_image.sum()
+        num_foreground = int(foreground_idxs_per_image.sum())
         num_gt_box = targets_per_image[self.target_box_key].shape[0]
 
         if self.debug:

monai/apps/detection/transforms/array.py

@@ -28,6 +28,7 @@
     convert_box_to_standard_mode,
     get_spatial_dims,
     spatial_crop_boxes,
+    standardize_empty_box,
 )
 from monai.transforms import Rotate90, SpatialCrop
 from monai.transforms.transform import Transform
@@ -46,6 +47,7 @@
 )
 
 __all__ = [
+    "StandardizeEmptyBox",
     "ConvertBoxToStandardMode",
     "ConvertBoxMode",
     "AffineBox",
@@ -60,6 +62,27 @@
 ]
 
 
+class StandardizeEmptyBox(Transform):
+    """
+    When boxes are empty, this transform standardize it to shape of (0,4) or (0,6).
+
+    Args:
+        spatial_dims: number of spatial dimensions of the bounding boxes.
+    """
+
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
+    def __init__(self, spatial_dims: int) -> None:
+        self.spatial_dims = spatial_dims
+
+    def __call__(self, boxes: NdarrayOrTensor) -> NdarrayOrTensor:
+        """
+        Args:
+            boxes: source bounding boxes, Nx4 or Nx6 or 0xM torch tensor or ndarray.
+        """
+        return standardize_empty_box(boxes, spatial_dims=self.spatial_dims)
+
+
 class ConvertBoxMode(Transform):
     """
     This transform converts the boxes in src_mode to the dst_mode.

monai/apps/detection/transforms/dictionary.py

@@ -34,6 +34,7 @@
     MaskToBox,
     RotateBox90,
     SpatialCropBox,
+    StandardizeEmptyBox,
     ZoomBox,
 )
 from monai.apps.detection.transforms.box_ops import convert_box_to_mask
@@ -51,6 +52,9 @@
 from monai.utils.type_conversion import convert_data_type, convert_to_tensor
 
 __all__ = [
+    "StandardizeEmptyBoxd",
+    "StandardizeEmptyBoxD",
+    "StandardizeEmptyBoxDict",
     "ConvertBoxModed",
     "ConvertBoxModeD",
     "ConvertBoxModeDict",
@@ -95,6 +99,50 @@
 DEFAULT_POST_FIX = PostFix.meta()
 
 
+class StandardizeEmptyBoxd(MapTransform, InvertibleTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.apps.detection.transforms.array.StandardizeEmptyBox`.
+
+    When boxes are empty, this transform standardize it to shape of (0,4) or (0,6).
+
+    Example:
+        .. code-block:: python
+
+            data = {"boxes": torch.ones(0,), "image": torch.ones(1, 128, 128, 128)}
+            box_converter = StandardizeEmptyBoxd(box_keys=["boxes"], box_ref_image_keys="image")
+            box_converter(data)
+    """
+
+    def __init__(self, box_keys: KeysCollection, box_ref_image_keys: str, allow_missing_keys: bool = False) -> None:
+        """
+        Args:
+            box_keys: Keys to pick data for transformation.
+            box_ref_image_keys: The single key that represents the reference image to which ``box_keys`` are attached.
+            allow_missing_keys: don't raise exception if key is missing.
+
+        See also :py:class:`monai.apps.detection,transforms.array.ConvertBoxToStandardMode`
+        """
+        super().__init__(box_keys, allow_missing_keys)
+        box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
+        if len(box_ref_image_keys_tuple) > 1:
+            raise ValueError(
+                "Please provide a single key for box_ref_image_keys.\
+                All boxes of box_keys are attached to box_ref_image_keys."
+            )
+        self.box_ref_image_keys = box_ref_image_keys
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        spatial_dims = len(d[self.box_ref_image_keys].shape) - 1
+        self.converter = StandardizeEmptyBox(spatial_dims=spatial_dims)
+        for key in self.key_iterator(d):
+            d[key] = self.converter(d[key])
+        return d
+
+    def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
+        return dict(data)
+
+
 class ConvertBoxModed(MapTransform, InvertibleTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.apps.detection.transforms.array.ConvertBoxMode`.
@@ -1353,3 +1401,4 @@ def inverse(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torch
 RandCropBoxByPosNegLabelD = RandCropBoxByPosNegLabelDict = RandCropBoxByPosNegLabeld
 RotateBox90D = RotateBox90Dict = RotateBox90d
 RandRotateBox90D = RandRotateBox90Dict = RandRotateBox90d
+StandardizeEmptyBoxD = StandardizeEmptyBoxDict = StandardizeEmptyBoxd

monai/apps/detection/utils/detector_utils.py

@@ -11,13 +11,15 @@
 
 from __future__ import annotations
 
+import warnings
 from collections.abc import Sequence
 from typing import Any
 
 import torch
 import torch.nn.functional as F
 from torch import Tensor
 
+from monai.data.box_utils import standardize_empty_box
 from monai.transforms.croppad.array import SpatialPad
 from monai.transforms.utils import compute_divisible_spatial_size, convert_pad_mode
 from monai.utils import PytorchPadMode, ensure_tuple_rep
@@ -56,7 +58,7 @@ def check_training_targets(
     spatial_dims: int,
     target_label_key: str,
     target_box_key: str,
-) -> None:
+) -> list[dict[str, Tensor]]:
     """
     Validate the input images/targets during training (raise a `ValueError` if invalid).
 
@@ -75,7 +77,8 @@ def check_training_targets(
     if len(input_images) != len(targets):
         raise ValueError(f"len(input_images) should equal to len(targets), got {len(input_images)}, {len(targets)}.")
 
-    for target in targets:
+    for i in range(len(targets)):
+        target = targets[i]
         if (target_label_key not in target.keys()) or (target_box_key not in target.keys()):
             raise ValueError(
                 f"{target_label_key} and {target_box_key} are expected keys in targets. Got {target.keys()}."
@@ -85,10 +88,24 @@ def check_training_targets(
         if not isinstance(boxes, torch.Tensor):
             raise ValueError(f"Expected target boxes to be of type Tensor, got {type(boxes)}.")
         if len(boxes.shape) != 2 or boxes.shape[-1] != 2 * spatial_dims:
-            raise ValueError(
-                f"Expected target boxes to be a tensor " f"of shape [N, {2* spatial_dims}], got {boxes.shape}."
-            )
-    return
+            if boxes.numel() == 0:
+                warnings.warn(
+                    f"Warning: Given target boxes has shape of {boxes.shape}. "
+                    f"The detector reshaped it with boxes = torch.reshape(boxes, [0, {2* spatial_dims}])."
+                )
+            else:
+                raise ValueError(
+                    f"Expected target boxes to be a tensor of shape [N, {2* spatial_dims}], got {boxes.shape}.)."
+                )
+        if not torch.is_floating_point(boxes):
+            raise ValueError(f"Expected target boxes to be a float tensor, got {boxes.dtype}.")
+        targets[i][target_box_key] = standardize_empty_box(boxes, spatial_dims=spatial_dims)  # type: ignore
+
+        labels = target[target_label_key]
+        if torch.is_floating_point(labels):
+            warnings.warn(f"Warning: Given target labels is {labels.dtype}. The detector converted it to torch.long.")
+            targets[i][target_label_key] = labels.long()
+    return targets
 
 
 def pad_images(

monai/data/box_utils.py

@@ -395,19 +395,41 @@ def get_spatial_dims(
 
     # Check the validity of each input and add its corresponding spatial_dims to spatial_dims_set
     if boxes is not None:
-        if int(boxes.shape[1]) not in [4, 6]:
+        if len(boxes.shape) != 2:
+            if boxes.shape[0] == 0:
+                raise ValueError(
+                    f"Currently we support only boxes with shape [N,4] or [N,6], "
+                    f"got boxes with shape {boxes.shape}. "
+                    f"Please reshape it with boxes = torch.reshape(boxes, [0, 4]) or torch.reshape(boxes, [0, 6])."
+                )
+            else:
+                raise ValueError(
+                    f"Currently we support only boxes with shape [N,4] or [N,6], got boxes with shape {boxes.shape}."
+                )
+        if int(boxes.shape[1] / 2) not in SUPPORTED_SPATIAL_DIMS:
             raise ValueError(
                 f"Currently we support only boxes with shape [N,4] or [N,6], got boxes with shape {boxes.shape}."
             )
         spatial_dims_set.add(int(boxes.shape[1] / 2))
     if points is not None:
+        if len(points.shape) != 2:
+            if points.shape[0] == 0:
+                raise ValueError(
+                    f"Currently we support only points with shape [N,2] or [N,3], "
+                    f"got points with shape {points.shape}. "
+                    f"Please reshape it with points = torch.reshape(points, [0, 2]) or torch.reshape(points, [0, 3])."
+                )
+            else:
+                raise ValueError(
+                    f"Currently we support only points with shape [N,2] or [N,3], got points with shape {points.shape}."
+                )
         if int(points.shape[1]) not in SUPPORTED_SPATIAL_DIMS:
             raise ValueError(
-                f"Currently we support only points with shape [N,2] or [N,3], got boxes with shape {points.shape}."
+                f"Currently we support only points with shape [N,2] or [N,3], got points with shape {points.shape}."
             )
         spatial_dims_set.add(int(points.shape[1]))
     if corners is not None:
-        if len(corners) not in [4, 6]:
+        if len(corners) // 2 not in SUPPORTED_SPATIAL_DIMS:
             raise ValueError(
                 f"Currently we support only boxes with shape [N,4] or [N,6], got box corner tuple with length {len(corners)}."
             )
@@ -494,6 +516,33 @@ def get_boxmode(mode: str | BoxMode | type[BoxMode] | None = None, *args, **kwar
     return StandardMode(*args, **kwargs)
 
 
+def standardize_empty_box(boxes: NdarrayOrTensor, spatial_dims: int) -> NdarrayOrTensor:
+    """
+    When boxes are empty, this function standardize it to shape of (0,4) or (0,6).
+
+    Args:
+        boxes: bounding boxes, Nx4 or Nx6 or empty torch tensor or ndarray
+        spatial_dims: number of spatial dimensions of the bounding boxes.
+
+    Returns:
+        bounding boxes with shape (N,4) or (N,6), N can be 0.
+
+    Example:
+        .. code-block:: python
+
+            boxes = torch.ones(0,)
+            standardize_empty_box(boxes, 3)
+    """
+    # convert numpy to tensor if needed
+    boxes_t, *_ = convert_data_type(boxes, torch.Tensor)
+    # handle empty box
+    if boxes_t.shape[0] == 0:
+        boxes_t = torch.reshape(boxes_t, [0, spatial_dims * 2])
+    # convert tensor back to numpy if needed
+    boxes_dst, *_ = convert_to_dst_type(src=boxes_t, dst=boxes)
+    return boxes_dst
+
+
 def convert_box_mode(
     boxes: NdarrayOrTensor,
     src_mode: str | BoxMode | type[BoxMode] | None = None,
@@ -522,6 +571,10 @@ def convert_box_mode(
             convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode)
             convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode())
     """
+    # handle empty box
+    if boxes.shape[0] == 0:
+        return boxes
+
     src_boxmode = get_boxmode(src_mode)
     dst_boxmode = get_boxmode(dst_mode)
 

tests/test_retinanet_detector.py

@@ -134,20 +134,21 @@ def test_retina_detector_resnet_backbone_shape(self, input_param, input_shape):
 
         detector.set_atss_matcher()
         detector.set_hard_negative_sampler(10, 0.5)
-        gt_box_start = torch.randint(2, (3, input_param["spatial_dims"])).to(torch.float16)
-        gt_box_end = gt_box_start + torch.randint(1, 10, (3, input_param["spatial_dims"]))
-        one_target = {
-            "boxes": torch.cat((gt_box_start, gt_box_end), dim=1),
-            "labels": torch.randint(input_param["num_classes"], (3,)),
-        }
-        with train_mode(detector):
-            input_data = torch.randn(input_shape)
-            targets = [one_target] * len(input_data)
-            result = detector.forward(input_data, targets)
-
-            input_data = [torch.randn(input_shape[1:]) for _ in range(random.randint(1, 9))]
-            targets = [one_target] * len(input_data)
-            result = detector.forward(input_data, targets)
+        for num_gt_box in [0, 3]:  # test for both empty and non-empty boxes
+            gt_box_start = torch.randint(2, (num_gt_box, input_param["spatial_dims"])).to(torch.float16)
+            gt_box_end = gt_box_start + torch.randint(1, 10, (num_gt_box, input_param["spatial_dims"]))
+            one_target = {
+                "boxes": torch.cat((gt_box_start, gt_box_end), dim=1),
+                "labels": torch.randint(input_param["num_classes"], (num_gt_box,)),
+            }
+            with train_mode(detector):
+                input_data = torch.randn(input_shape)
+                targets = [one_target] * len(input_data)
+                result = detector.forward(input_data, targets)
+
+                input_data = [torch.randn(input_shape[1:]) for _ in range(random.randint(1, 9))]
+                targets = [one_target] * len(input_data)
+                result = detector.forward(input_data, targets)
 
     @parameterized.expand(TEST_CASES)
     def test_naive_retina_detector_shape(self, input_param, input_shape):