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ultralytics 8.2.38 official YOLOv10 support (#13113)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Burhan 2024-06-20 14:31:48 -04:00 • committed by GitHub
parent 821e5fa477
commit ffb46fd7fb
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23 changed files with 785 additions and 32 deletions
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ultralytics/utils/loss.py
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@ -148,7 +148,7 @@ class KeypointLoss(nn.Module):
class v8DetectionLoss:
"""Criterion class for computing training losses."""
def __init__(self, model): # model must be de-paralleled
def __init__(self, model, tal_topk=10): # model must be de-paralleled
"""Initializes v8DetectionLoss with the model, defining model-related properties and BCE loss function."""
device = next(model.parameters()).device # get model device
h = model.args # hyperparameters
@ -164,7 +164,7 @@ class v8DetectionLoss:
self.use_dfl = m.reg_max > 1
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
self.assigner = TaskAlignedAssigner(topk=tal_topk, num_classes=self.nc, alpha=0.5, beta=6.0)
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
@ -714,3 +714,21 @@ class v8OBBLoss(v8DetectionLoss):
b, a, c = pred_dist.shape # batch, anchors, channels
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)
class E2EDetectLoss:
"""Criterion class for computing training losses."""
def __init__(self, model):
"""Initialize E2EDetectLoss with one-to-many and one-to-one detection losses using the provided model."""
self.one2many = v8DetectionLoss(model, tal_topk=10)
self.one2one = v8DetectionLoss(model, tal_topk=1)
def __call__(self, preds, batch):
"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
preds = preds[1] if isinstance(preds, tuple) else preds
one2many = preds["one2many"]
loss_one2many = self.one2many(one2many, batch)
one2one = preds["one2one"]
loss_one2one = self.one2one(one2one, batch)
return loss_one2many[0] + loss_one2one[0], loss_one2many[1] + loss_one2one[1]