xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ultralytics 8.0.53 DDP AMP and Edge TPU fixes (#1362)

Browse Source 
Co-authored-by: Richard Aljaste <richardaljasteabramson@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Vuong Kha Sieu <75152429+hotfur@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-12 02:08:13 +01:00
committed by GitHub
parent 177a68b39f
commit f921e1ac21
46 changed files with 1045 additions and 384 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
ultralytics/yolo/v8/classify/val.py
View File

@ -43,6 +43,8 @@ class ClassificationValidator(BaseValidator):
return build_classification_dataloader(path=dataset_path,
imgsz=self.args.imgsz,
batch_size=batch_size,
augment=False,
shuffle=False,
workers=self.args.workers)
def print_results(self):

4
ultralytics/yolo/v8/detect/predict.py
View File

@ -30,8 +30,8 @@ class DetectionPredictor(BasePredictor):
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
shape = orig_img.shape
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
if not isinstance(orig_imgs, torch.Tensor):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
path, _, _, _, _ = self.batch
img_path = path[i] if isinstance(path, list) else path
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))

9
ultralytics/yolo/v8/segment/predict.py
View File

@ -23,18 +23,19 @@ class SegmentationPredictor(DetectionPredictor):
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 isinstance(orig_imgs, list) else orig_imgs
shape = orig_img.shape
path, _, _, _, _ = self.batch
img_path = path[i] if isinstance(path, list) else path
if not len(pred): # save empty boxes
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]))
continue
if self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC
if not isinstance(orig_imgs, torch.Tensor):
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
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
if not isinstance(orig_imgs, torch.Tensor):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
results.append(
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results