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ultralytics 8.0.44 export and task fixes (#1088)

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Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2023-02-24 03:11:25 +01:00 • committed by GitHub
parent fe61018975
commit 3ea659411b
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20
ultralytics/yolo/v8/segment/train.py
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@ -32,10 +32,7 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
def get_validator(self):
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
return v8.segment.SegmentationValidator(self.test_loader,
save_dir=self.save_dir,
logger=self.console,
args=copy(self.args))
return v8.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
def criterion(self, preds, batch):
if not hasattr(self, 'compute_loss'):
@ -86,10 +83,6 @@ class SegLoss(Loss):
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
masks = batch['masks'].to(self.device).float()
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
# pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
@ -103,10 +96,15 @@ class SegLoss(Loss):
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
# bbox loss
if fg_mask.sum():
# bbox loss
loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
target_scores, target_scores_sum, fg_mask)
# masks loss
masks = batch['masks'].to(self.device).float()
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
for i in range(batch_size):
if fg_mask[i].sum():
mask_idx = target_gt_idx[i][fg_mask[i]]
@ -121,9 +119,9 @@ class SegLoss(Loss):
marea) # seg loss
# WARNING: Uncomment lines below in case of Multi-GPU DDP unused gradient errors
# else:
# loss[1] += proto.sum() * 0
# loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
# else:
# loss[1] += proto.sum() * 0
# loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.box / batch_size # seg gain