Remove normalization from bounding box coordinates in yolov8n pytorch

tutorials/mct_model_garden/models_pytorch/yolov8/yolov8.py

@@ -1,16 +1,27 @@
 # The following code was mostly duplicated from https://github.com/ultralytics/ultralytics
 # and changed to generate an equivalent PyTorch model suitable for quantization.
 # Main changes:
-#   * Modify layers to make them more suitable for quantization.
+#   * Modify layers to make them more suitable for quantization
+#       * torch.fx compatibility
+#       * Detect head (mainly the box decoding part that was optimized for model quantization)
 #   * Inheritance class from HuggingFace
 #   * Implement box decoding into Detect Layer
 # ==============================================================================
 """
 Yolov8n Object Detection Model - PyTorch implementation
 
 This code contains a PyTorch implementation of Yolov8n object detection model, following
-https://github.com/ultralytics/ultralytics. This implementation includes a slightly modified version of yolov8
-detection-head (mainly the box decoding part) that was optimized for model quantization.
+https://github.com/ultralytics/ultralytics.
+
+Usage:
+  model, cfg_dict = yolov8_pytorch("yolov8n.yaml")
+  pretrained_weights = torch.load('/path/to/pretrained/yolov8n.pt')['model'].state_dict()
+  model.load_state_dict(pretrained_weights, strict=False)
+  model.eval()
+
+Notes and Limitations:
+- The model has been tested only with the default settings from Ultralytics, specifically using a 640x640 input resolution and 80 object classes.
+- Anchors and strides are hardcoded as constants within the model, meaning they are not included in the weights file from Ultralytics.
 
 The code is organized as follows:
 - Classes definitions of Yolov8n building blocks: Conv, Bottleneck, C2f, SPPF, Upsample, Concaat, DFL and Detect
@@ -242,12 +253,7 @@ def __init__(self, nc: int = 80,
         self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
         anchors, strides = (x.transpose(0, 1) for x in make_anchors(self.feat_sizes,
                                                                     self.stride, 0.5))
-        strides = strides / self.img_size
         anchors = anchors * strides
-        self.relu1 = nn.ReLU()
-        self.relu2 = nn.ReLU()
-        self.relu3 = nn.ReLU()
-        self.relu4 = nn.ReLU()
 
         self.register_buffer('anchors', anchors)
         self.register_buffer('strides', strides)

tutorials/notebooks/imx500_notebooks/pytorch/pytorch_yolov8n_for_imx500.ipynb

@@ -132,7 +132,7 @@
    "source": [
     "from tutorials.mct_model_garden.models_pytorch.yolov8.yolov8 import ModelPyTorch, yaml_load, model_predict\n",
     "cfg_dict = yaml_load(\"tutorials/mct_model_garden/models_pytorch/yolov8/yolov8n.yaml\", append_filename=True)  # model dict\n",
-    "model = ModelPyTorch.from_pretrained(\"SSI-DNN/pytorch_yolov8n_640x640_bb_decoding\", cfg=cfg_dict)"
+    "model = ModelPyTorch.from_pretrained(\"SSI-DNN/pytorch_yolov8n_detection_640x640\", cfg=cfg_dict)"
    ],
    "metadata": {
     "collapsed": false
@@ -445,7 +445,7 @@
     "INPUT_RESOLUTION = 640\n",
     "\n",
     "# Define resizing information to map between the model's output and the original image dimensions\n",
-    "output_resize = {'shape': (INPUT_RESOLUTION, INPUT_RESOLUTION), 'aspect_ratio_preservation': True}\n",
+    "output_resize = {'shape': (INPUT_RESOLUTION, INPUT_RESOLUTION), 'aspect_ratio_preservation': True, 'normalized_coords': False}\n",
     "\n",
     "# Wrapped the model with PostProcess NMS.\n",
     "# Define PostProcess params\n",