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apply Black 25.11.0 style in fbcode/deeplearning/projects (21/92)

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Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476315

fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f
This commit is contained in:
Bowie Chen
2026-01-11 23:16:49 -08:00
committed by meta-codesync[bot]
parent 7b89b8fc3f
commit 11dec2936d
69 changed files with 445 additions and 522 deletions
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34
sam3/eval/ytvis_eval.py
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@@ -109,9 +109,7 @@ class YTVISevalMixin:
) # Num preds x Num GTS x Num frames
inter = inter.sum(-1)
union = union.sum(-1)
assert (
union > 0
).all(), (
assert (union > 0).all(), (
"There exists a tracklet with zero GTs across time. This is suspicious"
)
return inter / union
@@ -136,9 +134,9 @@ class YTVISevalMixin:
iou = inter / union
assert iou >= 0 and iou <= 1, "Encountered an error in IoU computation"
else:
assert np.isclose(inter, 0) and np.isclose(
union, 0
), "Encountered an error in IoU computation"
assert np.isclose(inter, 0) and np.isclose(union, 0), (
"Encountered an error in IoU computation"
)
iou = 1
return iou
@@ -206,16 +204,16 @@ class YTVISResultsWriter:
if len(prediction) == 0:
continue
for k in ["boxes", "scores", "labels"]:
assert (
k in prediction
), f"Expected predictions to have `{k}` key, available keys are {prediction.keys()}"
assert k in prediction, (
f"Expected predictions to have `{k}` key, available keys are {prediction.keys()}"
)
if self.save_per_frame_scores:
assert (
"per_frame_scores" in prediction
), f"Expected predictions to have `per_frame_scores` key, available keys are {prediction.keys()}"
assert xor(
"masks" in prediction, "masks_rle" in prediction
), f"Expected predictions to have either `masks` key or `masks_rle` key, available keys are {prediction.keys()}"
assert "per_frame_scores" in prediction, (
f"Expected predictions to have `per_frame_scores` key, available keys are {prediction.keys()}"
)
assert xor("masks" in prediction, "masks_rle" in prediction), (
f"Expected predictions to have either `masks` key or `masks_rle` key, available keys are {prediction.keys()}"
)
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
@@ -223,9 +221,9 @@ class YTVISResultsWriter:
labels = prediction["labels"].tolist()
if "masks" in prediction:
masks = prediction["masks"].squeeze(2)
assert (
masks.ndim == 4
), "Expected masks to be of shape(N_preds,T_frames,H,W)"
assert masks.ndim == 4, (
"Expected masks to be of shape(N_preds,T_frames,H,W)"
)
areas = [mask.flatten(1).sum(1).tolist() for mask in masks]
rles = [rle_encode(masklet) for masklet in masks]