Fixing yolov8-seg validation

src/sparseml/pytorch/utils/helpers.py

@@ -100,6 +100,7 @@
     "MEMORY_BOUNDED",
     "memory_aware_threshold",
     "download_framework_model_by_recipe_type",
+    "detach",
 ]
 
 
@@ -1162,3 +1163,14 @@ def download_framework_model_by_recipe_type(
         framework_model = zoo_model.training.default.get_file(model_name)
 
     return framework_model.path
+
+
+def detach(x: Union[torch.Tensor, List, Tuple]):
+    if isinstance(x, torch.Tensor):
+        return x.detach()
+    elif isinstance(x, List):
+        return [detach(e) for e in x]
+    elif isinstance(x, Tuple):
+        return tuple([detach(e) for e in x])
+    else:
+        raise ValueError("Unexpected type to detach")

src/sparseml/yolov8/trainers.py

@@ -593,7 +593,8 @@ def _load(self, weights: str):
                 self.model = self.ModelClass(dict(self.ckpt["model_yaml"]))
             else:
                 self.model = self.ModelClass(dict(self.ckpt["model"].yaml))
-            if "recipe" in self.ckpt:
+
+            if "recipe" in self.ckpt and self.ckpt["recipe"]:
                 manager = ScheduledModifierManager.from_yaml(self.ckpt["recipe"])
                 epoch = self.ckpt.get("epoch", -1)
                 if epoch < 0:

src/sparseml/yolov8/validators.py

@@ -17,6 +17,7 @@
 import torch
 from tqdm import tqdm
 
+from sparseml.pytorch.utils import detach
 from ultralytics.nn.autobackend import AutoBackend
 from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.yolo.engine.validator import BaseValidator
@@ -136,7 +137,7 @@ def __call__(self, trainer=None, model=None):
 
             # During QAT the resulting preds are grad required, breaking
             # the update metrics function.
-            detached_preds = [p.detach() for p in preds]
+            detached_preds = detach(preds)
             self.update_metrics(detached_preds, batch)
 
             if self.args.plots and batch_i < 3: