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7951 anchors are not centered on grid cells #8475

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7951 anchors are not centered on grid cells #8475

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e2c45c4
fixed centering of anchors on grid cells
hachoj Jun 8, 2025
34d73e5
updated unit tests to match new functionality
hachoj Jun 8, 2025
a17ce8b
further update to unit tests to model test new functionlaity
hachoj Jun 9, 2025
8f6e2c7
Merge branch 'dev' into 7951-anchors-are-not-centered-on-grid-cells
ericspod Jun 17, 2025
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2 changes: 1 addition & 1 deletion monai/apps/detection/utils/anchor_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -253,7 +253,7 @@ def grid_anchors(self, grid_sizes: list[list[int]], strides: list[list[Tensor]])
# compute anchor centers regarding to the image.
# shifts_centers is [x_center, y_center] or [x_center, y_center, z_center]
shifts_centers = [
torch.arange(0, size[axis], dtype=torch.int32, device=device) * stride[axis]
torch.arange(0, size[axis], dtype=torch.int32, device=device) * stride[axis] + stride[axis] // 2
for axis in range(self.spatial_dims)
]

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23 changes: 13 additions & 10 deletions tests/apps/detection/utils/test_anchor_box.py
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Expand Up @@ -45,9 +45,9 @@ class TestAnchorGenerator(unittest.TestCase):
@parameterized.expand(TEST_CASES_2D)
def test_anchor_2d(self, input_param, image_shape, feature_maps_shapes):
torch_anchor_utils, _ = optional_import("torchvision.models.detection.anchor_utils")
image_list, _ = optional_import("torchvision.models.detection.image_list")

# test it behaves the same with torchvision for 2d
# test it behaves for new functionality of centered anchors
# pytorch does not follow this functionality
anchor = AnchorGenerator(**input_param, indexing="xy")
anchor_ref = torch_anchor_utils.AnchorGenerator(**input_param)
for a, a_f in zip(anchor.cell_anchors, anchor_ref.cell_anchors):
Expand All @@ -57,15 +57,18 @@ def test_anchor_2d(self, input_param, image_shape, feature_maps_shapes):

grid_sizes = [[2, 2], [1, 1]]
strides = [[torch.tensor(1), torch.tensor(2)], [torch.tensor(2), torch.tensor(4)]]
for a, a_f in zip(anchor.grid_anchors(grid_sizes, strides), anchor_ref.grid_anchors(grid_sizes, strides)):
assert_allclose(a, a_f, type_test=True, device_test=False, atol=1e-3)

images = torch.rand(image_shape)
feature_maps = tuple(torch.rand(fs) for fs in feature_maps_shapes)
result = anchor(images, feature_maps)
result_ref = anchor_ref(image_list.ImageList(images, ([123, 122],)), feature_maps)
for a, a_f in zip(result, result_ref):
assert_allclose(a, a_f, type_test=True, device_test=False, atol=0.1)
monai_anchors = anchor.grid_anchors(grid_sizes, strides)
torchvision_anchors = anchor_ref.grid_anchors(grid_sizes, strides)

for a, a_f, s in zip(monai_anchors, torchvision_anchors, strides):
stride_y, stride_x = s

offset_x = stride_x // 2
offset_y = stride_y // 2
offset = torch.tensor([offset_x, offset_y, offset_x, offset_y], dtype=a_f.dtype, device=a_f.device)

assert_allclose(a, a_f + offset, type_test=True, device_test=False, atol=1e-3)

@parameterized.expand(TEST_CASES_2D)
def test_script_2d(self, input_param, image_shape, feature_maps_shapes):
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