Converting a Pytorch Model to ONNX: YoloV8 Tutorial

Converting a PyTorch model to ONNX format can be very useful when you want to deploy your model on different platforms or frameworks that support ONNX. In this tutorial, we are going to walk through the process of converting a YOLOv8 object detection model that is implemented in PyTorch to ONNX format.

YOLOv8 is a state-of-the-art object detection algorithm that is known for its speed and accuracy. By converting our YOLOv8 model to ONNX format, we can deploy it on frameworks like TensorFlow, ONNX Runtime, or OpenVINO without losing any accuracy or performance.

Here are the steps to convert a YOLOv8 PyTorch model to ONNX format:

Step 1: Install Dependencies
First, we need to install the required dependencies for this tutorial. You can install them using pip:

pip install torch torchvision onnx onnxruntime

Step 2: Load the Pre-trained YOLOv8 Model
Next, we need to load the pre-trained YOLOv8 model in PyTorch. You can find the official implementation of YOLOv8 in PyTorch on GitHub or you can train your own model using the YOLOv8 architecture.

import torch
import torch.nn as nn
from models.yolov5 import YOLOv5

# Load the pre-trained YOLOv8 model
model = YOLOv5()
model.load_state_dict(torch.load("yolov8.pth"))
model.eval()

Step 3: Convert the PyTorch Model to ONNX
Now, we can convert the loaded PyTorch model to ONNX format using the torch.onnx.export() function.

input_sample = torch.randn(1, 3, 416, 416)  # Define an input sample
torch.onnx.export(model, input_sample, "yolov8.onnx", opset_version=11)

In the code above, we pass the PyTorch model model, an input sample input_sample, and the output file name yolov8.onnx to the torch.onnx.export() function. The opset_version parameter specifies the ONNX operator set version to use (11 is compatible with most frameworks).

Step 4: Validate the Converted ONNX Model
To validate the converted ONNX model, you can load it using the onnx package and check for any errors.

import onnx

# Load the converted ONNX model
onnx_model = onnx.load("yolov8.onnx")

# Check if the model is valid
onnx.checker.check_model(onnx_model)

If there are no errors, it means that the YOLOv8 PyTorch model has been successfully converted to ONNX format.

Step 5: Deploy the ONNX Model
Now that we have our YOLOv8 model in ONNX format, we can deploy it on different frameworks that support ONNX, such as ONNX Runtime, TensorFlow, or OpenVINO. Here is an example of how you can load and run the ONNX model using ONNX Runtime:

import onnxruntime

# Load the ONNX model using ONNX Runtime
ort_session = onnxruntime.InferenceSession("yolov8.onnx")

# Run inference on a sample input
input_sample = torch.randn(1, 3, 416, 416).numpy()
outputs = ort_session.run(None, {"input": input_sample})

print(outputs)

In this code snippet, we load the ONNX model using onnxruntime.InferenceSession() and run inference on a sample input using the run() method. The model outputs are returned as a list of NumPy arrays.

That’s it! You have successfully converted a YOLOv8 object detection model implemented in PyTorch to ONNX format. You can now deploy the ONNX model on different platforms or frameworks for inference.