ONNX Runtime技术介绍与应用

     ONNX Runtime：由微软推出，用于优化和加速机器学习推理和训练，适用于ONNX模型，是一个跨平台推理和训练机器学习加速器(ONNX Runtime is a cross-platform inference and training machine-learning accelerator)，源码地址：https://github.com/microsoft/onnxruntime，最新发布版本为v1.11.1，License为MIT：

     1.ONNX Runtime Inferencing：高性能推理引擎

     (1).可在不同的操作系统上运行，包括Windows、Linux、Mac、Android、iOS等；

     (2).可利用硬件增加性能，包括CUDA、TensorRT、DirectML、OpenVINO等；

     (3).支持PyTorch、TensorFlow等深度学习框架的模型，需先调用相应接口转换为ONNX模型；

     (4).在Python中训练，确可部署到C++/Java等应用程序中。

     2.ONNX Runtime Training：于2021年4月发布，可加快PyTorch对模型训练，可通过CUDA加速，目前多用于Linux平台。

     通过conda命令安装执行：

conda install -c conda-forge onnxruntime

     以下为测试代码：通过ResNet-50对图像进行分类

import numpy as npimport onnxruntimeimport onnxfrom onnx import numpy_helperimport urllib.requestimport osimport tarfileimport jsonimport cv2 # reference: https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/inference_demos/resnet50_modelzoo_onnxruntime_inference.ipynbdef download_onnx_model():    labels_file_name = "imagenet-simple-labels.json"    model_tar_name = "resnet50v2.tar.gz"    model_directory_name = "resnet50v2"     if os.path.exists(model_tar_name) and os.path.exists(labels_file_name):        print("files exist, don't need to download")    else:        print("files don't exist, need to download ...")         onnx_model_url = "https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz"        imagenet_labels_url = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"         # retrieve our model from the ONNX model zoo        urllib.request.urlretrieve(onnx_model_url, filename=model_tar_name)        urllib.request.urlretrieve(imagenet_labels_url, filename=labels_file_name)         print("download completed, start decompress ...")        file = tarfile.open(model_tar_name)        file.extractall("./")        file.close()     return model_directory_name, labels_file_name def load_labels(path):    with open(path) as f:        data = json.load(f)    return np.asarray(data) def images_preprocess(images_path, images_name):    input_data = []     for name in images_name:        img = cv2.imread(images_path + name)        img = cv2.resize(img, (224, 224))        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)                data = np.array(img).transpose(2, 0, 1)        #print(f"name: {name}, opencv image shape(h,w,c): {img.shape}, transpose shape(c,h,w): {data.shape}")        # convert the input data into the float32 input        data = data.astype('float32')         # normalize        mean_vec = np.array([0.485, 0.456, 0.406])        stddev_vec = np.array([0.229, 0.224, 0.225])        norm_data = np.zeros(data.shape).astype('float32')        for i in range(data.shape[0]):            norm_data[i,:,:] = (data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]         # add batch channel        norm_data = norm_data.reshape(1, 3, 224, 224).astype('float32')        input_data.append(norm_data)     return input_data def softmax(x):    x = x.reshape(-1)    e_x = np.exp(x - np.max(x))    return e_x / e_x.sum(axis=0) def postprocess(result):    return softmax(np.array(result)).tolist() def inference(onnx_model, labels, input_data, images_name, images_label):    session = onnxruntime.InferenceSession(onnx_model, None)    # get the name of the first input of the model    input_name = session.get_inputs()[0].name    count = 0    for data in input_data:        print(f"{count+1}. image name: {images_name[count]}, actual value: {images_label[count]}")        count += 1         raw_result = session.run([], {input_name: data})         res = postprocess(raw_result)         idx = np.argmax(res)        print(f" result: idx: {idx}, label: {labels[idx]}, percentage: {round(res[idx]*100, 4)}%")         sort_idx = np.flip(np.squeeze(np.argsort(res)))        print(" top 5 labels are:", labels[sort_idx[:5]]) def main():    model_directory_name, labels_file_name = download_onnx_model()     labels = load_labels(labels_file_name)    print("the number of categories is:", len(labels)) # 1000     images_path = "../../data/image/"    images_name = ["5.jpg", "6.jpg", "7.jpg", "8.jpg", "9.jpg", "10.jpg"]    images_label = ["goldfish", "hen", "ostrich", "crocodile", "goose", "sheep"]    if len(images_name) != len(images_label):        print("Error: images count and labes'length don't match")        return     input_data = images_preprocess(images_path, images_name)     onnx_model = model_directory_name + "/resnet50v2.onnx"    inference(onnx_model, labels, input_data, images_name, images_label)     print("test finish") if __name__ == "__main__":    main()

     测试图像如下所示：

     执行结果如下所示：

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