ultralytics 8.0.35 TensorRT, ONNX and OpenVINO predict and val (#929)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Eric Pedley <ericpedley@gmail.com>

ultralytics/yolo/v8/segment/predict.py

@@ -17,10 +17,10 @@ class SegmentationPredictor(DetectionPredictor):
                                     self.args.iou,
                                     agnostic=self.args.agnostic_nms,
                                     max_det=self.args.max_det,
-                                    nm=32,
+                                    nc=len(self.model.names),
                                     classes=self.args.classes)
         results = []
-        proto = preds[1][-1]
+        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):
             shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
             if not len(pred):

ultralytics/yolo/v8/segment/val.py

@@ -28,14 +28,12 @@ class SegmentationValidator(DetectionValidator):
         return batch
 
     def init_metrics(self, model):
-        head = model.model[-1] if self.training else model.model.model[-1]
         val = self.data.get(self.args.split, '')  # validation path
         self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt')  # is COCO dataset
         self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
         self.args.save_json |= self.is_coco and not self.training  # run on final val if training COCO
-        self.nc = head.nc
-        self.nm = head.nm if hasattr(head, "nm") else 32
         self.names = model.names
+        self.nc = len(model.names)
         self.metrics.names = self.names
         self.metrics.plot = self.args.plots
         self.confusion_matrix = ConfusionMatrix(nc=self.nc)
@@ -61,8 +59,9 @@ class SegmentationValidator(DetectionValidator):
                                     multi_label=True,
                                     agnostic=self.args.single_cls,
                                     max_det=self.args.max_det,
-                                    nm=self.nm)
-        return p, preds[1][-1]
+                                    nc=self.nc)
+        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
+        return p, proto
 
     def update_metrics(self, preds, batch):
         # Metrics