yolov8pose关键点检测onnx

### YOLOv8 Pose Estimation ONNX Conversion and Usage For converting a YOLOv8 model used for pose estimation into an ONNX format, one can follow these steps with Python code snippets to ensure compatibility across different platforms or frameworks. #### Preparation of Environment Before proceeding with the conversion process, it is essential that the environment supports both PyTorch (for running YOLOv8 models) and ONNX. This includes having `torch`, `onnx`, and `onnxruntime` installed within the working virtual environment: ```bash pip install torch torchvision torchaudio onnx onnxruntime ``` #### Exporting Model from PyTorch to ONNX Format Once all necessary libraries are set up, exporting the trained YOLOv8 pose estimation model involves specifying input dimensions expected by this network during inference time as well as setting dynamic axes if variable-sized inputs need support. Here's how such exportation might look like in practice: ```python import torch.onnx from ultralytics import YOLO model = YOLO('path/to/yolov8_pose_model.pt') # Load pre-trained yolov8 pose estimation model. dummy_input = torch.randn(1, 3, 640, 640) input_names = ["image"] output_names = ['boxes', 'keypoints'] dynamic_axes = { "image": {0: "batch_size"}, } torch.onnx.export( model, dummy_input, "yolov8_pose_estimation.onnx", verbose=True, opset_version=12, do_constant_folding=True, input_names=input_names, output_names=output_names, dynamic_axes=dynamic_axes ) ``` This script saves the converted model file named `"yolov8_pose_estimation.onnx"` which now adheres to ONNX standards[^1]. #### Using Converted ONNX Model for Inference After successfully transforming the original `.pt` checkpoint into an `.onnx` file, performing predictions using this new representation requires loading said artifact through appropriate APIs provided either directly via `onnxruntime` sessions or indirectly when integrating inside applications built upon higher-level abstractions over deep learning runtimes supporting ONNX natively. Below demonstrates initializing session settings along with executing forward passes against sample data points fed into our optimized pipeline: ```python import numpy as np import onnxruntime as ort session_options = ort.SessionOptions() session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED ort_session = ort.InferenceSession("yolov8_pose_estimation.onnx", sess_options=session_options) def predict(image_data): """Predict function.""" outputs = ort_session.run(None, {"image": image_data}) boxes = outputs[0] keypoints = outputs[1] return boxes, keypoints # Assuming we have some preprocessing step here... preprocessed_image = ... predicted_boxes, predicted_keypoints = predict(preprocessed_image) print(predicted_boxes.shape, predicted_keypoints.shape) ``` The above example assumes there exists prior knowledge about preparing images correctly before feeding them into prediction functions—this typically entails resizing, normalizing pixel values according to what was done while training, etc.