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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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21
sam3/train/loss/loss_fns.py
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@@ -9,11 +9,8 @@ import torch
import torch.distributed
import torch.nn.functional as F
import torchmetrics
from sam3.model import box_ops
from sam3.model.data_misc import interpolate
from sam3.train.loss.sigmoid_focal_loss import (
triton_sigmoid_focal_loss,
triton_sigmoid_focal_loss_reduce,
@@ -327,7 +324,9 @@ class IABCEMdetr(LossWithWeights):
if num_det_queries is not None:
logging.warning("note: it's not needed to set num_det_queries anymore")
if self.use_separate_loss_for_det_and_trk:
assert not self.weak_loss, "Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead"
assert not self.weak_loss, (
"Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead"
)
self.det_exhaustive_loss_scale_pos = det_exhaustive_loss_scale_pos
self.det_exhaustive_loss_scale_neg = det_exhaustive_loss_scale_neg
self.det_non_exhaustive_loss_scale_pos = det_non_exhaustive_loss_scale_pos
@@ -342,7 +341,9 @@ class IABCEMdetr(LossWithWeights):
and det_non_exhaustive_loss_scale_neg == 1.0
and trk_loss_scale_pos == 1.0
and trk_loss_scale_neg == 1.0
), "If not using separate loss for detection and tracking queries, separate detection and tracking loss scales should all be 1.0"
), (
"If not using separate loss for detection and tracking queries, separate detection and tracking loss scales should all be 1.0"
)
def get_loss(self, outputs, targets, indices, num_boxes):
assert len(outputs["pred_logits"].shape) > 2, "Incorrect predicted logits shape"
@@ -443,7 +444,9 @@ class IABCEMdetr(LossWithWeights):
pass
if self.weak_loss:
assert not self.use_separate_loss_for_det_and_trk, "Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead"
assert not self.use_separate_loss_for_det_and_trk, (
"Do not use weak_loss in this case -- set separate loss for detection and tracking queries instead"
)
# nullify the negative loss for the non-exhaustive classes
assert loss_bce.shape[0] == targets["is_exhaustive"].shape[0]
@@ -497,9 +500,9 @@ class IABCEMdetr(LossWithWeights):
loss_bce = loss_bce.mean()
else:
assert isinstance(self.pad_n_queries, int)
assert (
loss_bce.size(1) < self.pad_n_queries
), f"The number of predictions is more than the expected total after padding. Got {loss_bce.size(1)} predictions."
assert loss_bce.size(1) < self.pad_n_queries, (
f"The number of predictions is more than the expected total after padding. Got {loss_bce.size(1)} predictions."
)
loss_bce = loss_bce.sum() / (self.pad_n_queries * loss_bce.size(0))
bce_f1 = torchmetrics.functional.f1_score(