HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.170 apply is_list fixes for torch.Tensor inputs (#4713)

Browse source 
Co-authored-by: Gezhi Zhang <765724965@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-09-04 02:22:05 +02:00 • committed by GitHub
parent a1c1d6b483
commit aa9133bb88
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
15 changed files with 74 additions and 36 deletions
Download patch file Download diff file Expand all files Collapse all files

13
ultralytics/models/fastsam/predict.py
View file

@ -30,21 +30,22 @@ class FastSAMPredictor(DetectionPredictor):
full_box[0][4] = p[0][critical_iou_index][:, 4]
full_box[0][6:] = p[0][critical_iou_index][:, 6:]
p[0][critical_iou_index] = full_box
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
results = []
is_list = isinstance(orig_imgs, list) # input images are a list, not a torch.Tensor
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
for i, pred in enumerate(p):
orig_img = orig_imgs[i] if is_list else orig_imgs
orig_img = orig_imgs[i]
img_path = self.batch[0][i]
if not len(pred): # save empty boxes
masks = None
elif self.args.retina_masks:
if is_list:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
else:
masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
if is_list:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results