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Explicit cast to uint8 when bool inputs passed to argsort in MAP #983

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merged 7 commits into from
Apr 24, 2022
Merged

Explicit cast to uint8 when bool inputs passed to argsort in MAP #983

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bf9a7a6
Explicit cast to torch.uint8 for bool types on CUDA
krshrimali Apr 24, 2022
2f202a5
Fix whitespaces
krshrimali Apr 24, 2022
235ad2b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 24, 2022
ab034df
Update tests/detection/test_map.py
krshrimali Apr 24, 2022
fed0835
Add changelog entry
krshrimali Apr 24, 2022
6f64e79
Merge branch 'fix/sorting_error/cuda' of github.com:PyTorchLightning/…
krshrimali Apr 24, 2022
98c517a
simple
Borda Apr 24, 2022
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23 changes: 22 additions & 1 deletion tests/detection/test_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,6 +133,27 @@
],
)

_inputs3 = Input(
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preds = [
[
dict(
boxes = torch.tensor([]),
scores = torch.tensor([]),
labels = torch.tensor([])
),
],
],
target=[
[
dict(
boxes=torch.tensor([[1., 2., 3., 4.]]),
scores=torch.tensor([0.8]),
labels=torch.tensor([1]),
),
],
],
)


def _compare_fn(preds, target) -> dict:
"""Comparison function for map implementation.
Expand Down Expand Up @@ -283,7 +304,7 @@ def _move_to_gpu(input):

@pytest.mark.skipif(_pytest_condition, reason="test requires that torchvision=>0.8.0 is installed")
@pytest.mark.skipif(_gpu_test_condition, reason="test requires CUDA availability")
@pytest.mark.parametrize("inputs", [_inputs, _inputs2])
@pytest.mark.parametrize("inputs", [_inputs, _inputs2, _inputs3])
def test_map_gpu(inputs):
"""Test predictions on single gpu."""
metric = MeanAveragePrecision()
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7 changes: 6 additions & 1 deletion torchmetrics/detection/mean_ap.py
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Expand Up @@ -694,7 +694,12 @@ def __calculate_recall_precision_scores(

# different sorting method generates slightly different results.
# mergesort is used to be consistent as Matlab implementation.
inds = torch.argsort(det_scores, descending=True)
# Sort in PyTorch does not support bool types on CUDA (yet, 1.11.0)
if det_scores.is_cuda and det_scores.dtype is torch.bool:
# Explicitly cast to uint8 to avoid error for bool inputs on CUDA to argsort
inds = torch.argsort(det_scores.to(torch.uint8), descending=True)
else:
inds = torch.argsort(det_scores, descending=True)
det_scores_sorted = det_scores[inds]

det_matches = torch.cat([e["dtMatches"][:, :max_det] for e in img_eval_cls_bbox], axis=1)[:, inds]
Expand Down