Release 8.0.4 fixes (#256)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: TechieG <35962141+gokulnath30@users.noreply.github.com>
Co-authored-by: Parthiban Marimuthu <66585214+partheee@users.noreply.github.com>

ultralytics/yolo/v8/classify/predict.py

@@ -39,7 +39,8 @@ class ClassificationPredictor(BasePredictor):
         self.annotator = self.get_annotator(im0)
 
         prob = preds[idx].softmax(0)
-        self.all_outputs.append(prob)
+        if self.return_outputs:
+            self.output["prob"] = prob.cpu().numpy()
         # Print results
         top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
         log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
@@ -62,7 +63,7 @@ def predict(cfg):
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
 
     predictor = ClassificationPredictor(cfg)
-    predictor()
+    predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/classify/train.py

@@ -143,6 +143,7 @@ def train(cfg):
     cfg.weight_decay = 5e-5
     cfg.label_smoothing = 0.1
     cfg.warmup_epochs = 0.0
+    cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = ClassificationTrainer(cfg)
     # trainer.train()
     from ultralytics import YOLO

ultralytics/yolo/v8/detect/predict.py

@@ -53,12 +53,15 @@ class DetectionPredictor(BasePredictor):
         self.annotator = self.get_annotator(im0)
 
         det = preds[idx]
-        self.all_outputs.append(det)
         if len(det) == 0:
             return log_string
         for c in det[:, 5].unique():
             n = (det[:, 5] == c).sum()  # detections per class
             log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
+
+        if self.return_outputs:
+            self.output["det"] = det.cpu().numpy()
+
         # write
         gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
         for *xyxy, conf, cls in reversed(det):
@@ -89,7 +92,7 @@ def predict(cfg):
     cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image size
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
     predictor = DetectionPredictor(cfg)
-    predictor()
+    predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/detect/train.py

@@ -199,6 +199,7 @@ class Loss:
 def train(cfg):
     cfg.model = cfg.model or "yolov8n.yaml"
     cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
+    cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = DetectionTrainer(cfg)
     # trainer.train()
     from ultralytics import YOLO

ultralytics/yolo/v8/segment/predict.py

@@ -58,10 +58,10 @@ class SegmentationPredictor(DetectionPredictor):
             return log_string
         # Segments
         mask = masks[idx]
-        if self.args.save_txt:
+        if self.args.save_txt or self.return_outputs:
+            shape = im0.shape if self.args.retina_masks else im.shape[2:]
             segments = [
-                ops.scale_segments(im0.shape if self.args.retina_masks else im.shape[2:], x, im0.shape, normalize=True)
-                for x in reversed(ops.masks2segments(mask))]
+                ops.scale_segments(shape, x, im0.shape, normalize=False) for x in reversed(ops.masks2segments(mask))]
 
         # Print results
         for c in det[:, 5].unique():
@@ -76,12 +76,17 @@ class SegmentationPredictor(DetectionPredictor):
             255 if self.args.retina_masks else im[idx])
 
         det = reversed(det[:, :6])
-        self.all_outputs.append([det, mask])
+        if self.return_outputs:
+            self.output["det"] = det.cpu().numpy()
+            self.output["segment"] = segments
 
         # Write results
-        for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
+        for j, (*xyxy, conf, cls) in enumerate(det):
             if self.args.save_txt:  # Write to file
-                seg = segments[j].reshape(-1)  # (n,2) to (n*2)
+                seg = segments[j].copy()
+                seg[:, 0] /= shape[1]  # width
+                seg[:, 1] /= shape[0]  # height
+                seg = seg.reshape(-1)  # (n,2) to (n*2)
                 line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg)  # label format
                 with open(f'{self.txt_path}.txt', 'a') as f:
                     f.write(('%g ' * len(line)).rstrip() % line + '\n')
@@ -106,7 +111,7 @@ def predict(cfg):
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
 
     predictor = SegmentationPredictor(cfg)
-    predictor()
+    predictor.predict_cli()
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/segment/train.py

@@ -144,6 +144,7 @@ class SegLoss(Loss):
 def train(cfg):
     cfg.model = cfg.model or "yolov8n-seg.yaml"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
+    cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = SegmentationTrainer(cfg)
     # trainer.train()
     from ultralytics import YOLO