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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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7
sam3/train/transforms/basic.py
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@@ -14,7 +14,6 @@ import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from sam3.model.box_ops import box_xyxy_to_cxcywh
from sam3.model.data_misc import interpolate
@@ -277,9 +276,9 @@ class RandomSizeCrop:
max(0, minY - h + 1), max(maxY - 1, max(0, minY - h + 1))
)
result_img, result_target = crop(img, target, [j, i, h, w])
assert (
len(result_target["boxes"]) == init_boxes
), f"img_w={img.width}\timg_h={img.height}\tminX={minX}\tminY={minY}\tmaxX={maxX}\tmaxY={maxY}\tminW={minW}\tminH={minH}\tmaxW={maxW}\tmaxH={maxH}\tw={w}\th={h}\ti={i}\tj={j}\tinit_boxes={init_boxes_tensor}\tresults={result_target['boxes']}"
assert len(result_target["boxes"]) == init_boxes, (
f"img_w={img.width}\timg_h={img.height}\tminX={minX}\tminY={minY}\tmaxX={maxX}\tmaxY={maxY}\tminW={minW}\tminH={minH}\tmaxW={maxW}\tmaxH={maxH}\tw={w}\th={h}\ti={i}\tj={j}\tinit_boxes={init_boxes_tensor}\tresults={result_target['boxes']}"
)
return result_img, result_target
else: