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ultralytics 8.0.42 DDP fix and Docs updates (#1065)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
This commit is contained in:
Glenn Jocher
2023-02-20 12:56:20 +01:00
committed by GitHub
parent f6e393c1d2
commit f2a7a29e53
33 changed files with 196 additions and 93 deletions
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4
ultralytics/yolo/v8/classify/predict.py
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@ -22,7 +22,9 @@ class ClassificationPredictor(BasePredictor):
results = []
for i, pred in enumerate(preds):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
results.append(Results(probs=pred, orig_img=orig_img, names=self.model.names))
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, probs=pred))
return results

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

@ -32,7 +32,9 @@ class DetectionPredictor(BasePredictor):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
shape = orig_img.shape
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
results.append(Results(boxes=pred, orig_img=orig_img, names=self.model.names))
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))
return results
def write_results(self, idx, results, batch):

10
ultralytics/yolo/v8/segment/predict.py
View File

@ -24,9 +24,10 @@ class SegmentationPredictor(DetectionPredictor):
for i, pred in enumerate(p):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
shape = orig_img.shape
if not len(pred):
results.append(Results(boxes=pred[:, :6], orig_img=orig_img,
names=self.model.names)) # save empty boxes
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()
@ -34,7 +35,8 @@ class SegmentationPredictor(DetectionPredictor):
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()
results.append(Results(boxes=pred[:, :6], masks=masks, orig_img=orig_img, names=self.model.names))
results.append(
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results
def write_results(self, idx, results, batch):