Bug fix in the notebook's coco-evaluation functions.

tutorials/mct_model_garden/evaluation_metrics/coco_evaluation.py

@@ -21,7 +21,6 @@
 from typing import List, Dict, Tuple, Callable, Any
 import random
 from pycocotools import mask as mask_utils
-import torch
 from tqdm import tqdm
 
 from ..models_pytorch.yolov8.yolov8_postprocess import scale_boxes, scale_coords
@@ -178,7 +177,7 @@ def format_results(self, outputs: List, img_ids: List, orig_img_dims: List, outp
                             'score': scores.tolist()[ind] if isinstance(scores.tolist(), list) else scores.tolist()
                         })
 
-            return detections
+        return detections
 
 def load_and_preprocess_image(image_path: str, preprocess: Callable) -> np.ndarray:
     """
@@ -506,12 +505,13 @@ def evaluate_seg_model(annotation_file, results_file):
     coco_eval.summarize()
 
 
-def evaluate_yolov8_segmentation(model, data_dir, data_type='val2017', img_ids_limit=800, output_file='results.json',iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55):
+def evaluate_yolov8_segmentation(model, model_predict_func, data_dir, data_type='val2017', img_ids_limit=800, output_file='results.json',iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55):
     """
     Evaluate YOLOv8 model for instance segmentation on COCO dataset.
 
     Parameters:
     - model: The YOLOv8 model to be evaluated.
+    - model_predict_func: A function to execute the model preidction
     - data_dir: The directory containing the COCO dataset.
     - data_type: The type of dataset to evaluate against (default is 'val2017').
     - img_ids_limit: The maximum number of images to evaluate (default is 800).
@@ -535,11 +535,10 @@ def evaluate_yolov8_segmentation(model, data_dir, data_type='val2017', img_ids_l
 
         # Preprocess the image
         input_img = load_and_preprocess_image(image_path, yolov8_preprocess_chw_transpose).astype('float32')
-        input_tensor = torch.from_numpy(input_img).unsqueeze(0)  # Add batch dimension
 
         # Run the model
-        with torch.no_grad():
-            output = model(input_tensor)
+        output = model_predict_func(model, input_img)
+
         #run post processing (nms)
         boxes, scores, classes, masks = postprocess_yolov8_inst_seg(outputs=output, conf=conf, iou_thres=iou_thresh, max_out_dets=max_dets)
 

tutorials/mct_model_garden/models_pytorch/yolov8/yolov8.py

@@ -498,6 +498,28 @@ def keypoints_model_predict(model: Any, inputs: np.ndarray) -> List:
 
     return postprocess_yolov8_keypoints(output_np)
 
+def seg_model_predict(model: Any,
+                  inputs: np.ndarray) -> List:
+    """
+    Perform inference using the provided PyTorch model on the given inputs.
+
+    This function handles moving the inputs to the appropriate torch data type and format,
+    and returns the outputs.
+
+    Args:
+        model (Any): The PyTorch model used for inference.
+        inputs (np.ndarray): Input data to perform inference on.
+
+    Returns:
+        List: List containing tensors of predictions.
+    """
+    input_tensor = torch.from_numpy(inputs).unsqueeze(0)  # Add batch dimension
+
+    # Run the model
+    with torch.no_grad():
+        outputs = model(input_tensor)
+
+    return outputs
 
 def yolov8_pytorch(model_yaml: str) -> (nn.Module, Dict):
     """

tutorials/notebooks/imx500_notebooks/pytorch/pytorch_yolov8n_seg_for_imx500.ipynb

@@ -417,8 +417,9 @@
    },
    "outputs": [],
    "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",
-    "evaluate_yolov8_segmentation(model, 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)"
+    "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)"
    ]
   },
   {
@@ -442,7 +443,7 @@
    "outputs": [],
    "source": [
     "from tutorials.mct_model_garden.evaluation_metrics.coco_evaluation import evaluate_yolov8_segmentation\n",
-    "evaluate_yolov8_segmentation(quant_model, data_dir='coco', data_type='val2017', img_ids_limit=100, output_file='results_quant.json', iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55)"
+    "evaluate_yolov8_segmentation(quant_model, seg_model_predict, data_dir='coco', data_type='val2017', img_ids_limit=100, output_file='results_quant.json', iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55)"
    ]
   },
   {
@@ -467,7 +468,7 @@
    "outputs": [],
    "source": [
     "from tutorials.mct_model_garden.evaluation_metrics.coco_evaluation import evaluate_yolov8_segmentation\n",
-    "evaluate_yolov8_segmentation(gptq_quant_model, data_dir='coco', data_type='val2017', img_ids_limit=100, output_file='results_g_quant.json', iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55)"
+    "evaluate_yolov8_segmentation(gptq_quant_model, seg_model_predict, data_dir='coco', data_type='val2017', img_ids_limit=100, output_file='results_g_quant.json', iou_thresh=0.7, conf=0.001, max_dets=300,mask_thresh=0.55)"
    ]
   },
   {