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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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26
sam3/eval/demo_eval.py
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@@ -13,11 +13,9 @@ from typing import Optional
import numpy as np
import pycocotools.mask as maskUtils
from pycocotools.cocoeval import COCOeval
from sam3.eval.coco_eval import CocoEvaluator
from sam3.train.masks_ops import compute_F_measure
from sam3.train.utils.distributed import is_main_process
from scipy.optimize import linear_sum_assignment
@@ -156,9 +154,9 @@ class DemoEval(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)
@@ -528,17 +526,17 @@ class DemoEvaluator(CocoEvaluator):
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]):