segmentation tutorial fix (sony#1228)

resolve issue sony#1222 fix gpu execusion on tutorial
lapid92 · Sep 24, 2024 · c90c5f2 · c90c5f2
1 parent 3eed10b
commit c90c5f2
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diff --git a/tutorials/mct_model_garden/models_pytorch/yolov8/yolov8.py b/tutorials/mct_model_garden/models_pytorch/yolov8/yolov8.py
@@ -536,11 +536,12 @@ def seg_model_predict(model: Any,
         List: List containing tensors of predictions.
     """
     input_tensor = torch.from_numpy(inputs).unsqueeze(0)  # Add batch dimension
-
+    device = get_working_device()
+    input_tensor = input_tensor.to(device)
     # Run the model
     with torch.no_grad():
         outputs = model(input_tensor)
-
+    outputs = [output.cpu() for output in outputs]
     return outputs
 
 def yolov8_pytorch(model_yaml: str) -> (nn.Module, Dict):

diff --git a/tutorials/notebooks/imx500_notebooks/pytorch/pytorch_yolov8n_seg_for_imx500.ipynb b/tutorials/notebooks/imx500_notebooks/pytorch/pytorch_yolov8n_seg_for_imx500.ipynb
@@ -428,6 +428,8 @@
    "source": [
     "from tutorials.mct_model_garden.models_pytorch.yolov8.yolov8 import seg_model_predict\n",
     "from tutorials.mct_model_garden.evaluation_metrics.coco_evaluation import evaluate_yolov8_segmentation\n",
+    "device = get_working_device()\n",
+    "model = model.to(device)\n",
     "evaluate_yolov8_segmentation(model, seg_model_predict, data_dir='coco', data_type='val2017', img_ids_limit=100, output_file='results.json', iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55)"
    ]
   },