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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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36
sam3/eval/cgf1_eval.py
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@@ -126,9 +126,9 @@ class COCOCustom(COCO):
# MODIFICATION: faster and cached subset check
if not hasattr(self, "img_id_set"):
self.img_id_set = set(self.getImgIds())
assert set(annsImgIds).issubset(
self.img_id_set
), "Results do not correspond to current coco set"
assert set(annsImgIds).issubset(self.img_id_set), (
"Results do not correspond to current coco set"
)
# END MODIFICATION
if "caption" in anns[0]:
imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
@@ -301,9 +301,9 @@ class CGF1Eval(COCOeval):
TP = (match_scores >= thresh).sum()
FP = len(dt) - TP
FN = len(gt) - TP
assert (
FP >= 0 and FN >= 0
), f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
assert FP >= 0 and FN >= 0, (
f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
)
TPs.append(TP)
FPs.append(FP)
FNs.append(FN)
@@ -599,9 +599,9 @@ class CGF1Evaluator:
"""
assert len(self.coco_gts) > 0, "No ground truth provided for evaluation."
assert len(self.coco_gts) == len(
self.coco_evals
), "Mismatch in number of ground truths and evaluators."
assert len(self.coco_gts) == len(self.coco_evals), (
"Mismatch in number of ground truths and evaluators."
)
if self.verbose:
print(f"Loading predictions from {pred_file}")
@@ -668,17 +668,17 @@ class CGF1Evaluator:
if len(scorings) == 1:
return scorings[0]
assert (
scorings[0].ndim == 3
), f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
assert (
scorings[0].shape[0] == 1
), f"Expecting a single category, got {scorings[0].shape[0]}"
assert scorings[0].ndim == 3, (
f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
)
assert scorings[0].shape[0] == 1, (
f"Expecting a single category, got {scorings[0].shape[0]}"
)
for scoring in scorings:
assert (
scoring.shape == scorings[0].shape
), f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
assert scoring.shape == scorings[0].shape, (
f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
)
selected_imgs = []
for img_id in range(scorings[0].shape[-1]):