add onnx runtime docs

index.rst

@@ -22,6 +22,7 @@
    sources/llamafactory/index.rst
    sources/accelerate/index.rst
    sources/transformers/index.rst
+   sources/onnxruntime/index.rst
 
 .. warning::
 
@@ -82,11 +83,11 @@
                 </div>
                 <div class="flex-grow"></div>
                 <div class="flex space-x-4 text-blue-600">
-                    <a href="#">官方链接</a>
+                    <a href="https://github.com/microsoft/onnxruntime">官方链接</a>
                     <span class="split">|</span>
-                    <a href="#">安装指南</a>
+                    <a href="sources/onnxruntime/install.html">安装指南</a>
                     <span class="split">|</span>
-                    <a href="#">快速上手</a>
+                    <a href="sources/onnxruntime/quick_start.html">快速上手</a>
                 </div>
             </div>
             <!-- Card 4 -->

sources/onnxruntime/index.rst

@@ -0,0 +1,8 @@
+ONNX Runtime
+============
+
+.. toctree::
+   :maxdepth: 2
+
+   install.rst
+   quick_start.rst

sources/onnxruntime/install.rst

@@ -0,0 +1,33 @@
+安装指南
+===========
+
+本教程面向使用 ONNX Runtime & Ascend NPU 的开发者，帮助完成昇腾环境下 ONNX Runtime 的安装。
+
+.. note::
+
+    阅读本篇前，请确保已按照 :doc:`安装教程 <../ascend/quick_install>` 准备好昇腾环境！
+
+ONNX Runtime 安装
+-------------------
+
+ONNX Runtime 目前提供了 源码编译 和 二进制包 两种安装方式，其中二进制包当前只支持Python。
+
+从源码安装
+^^^^^^^^^^^^
+
+.. code-block:: shell
+    :linenos:
+
+    # Default path, change it if needed.
+    source /usr/local/Ascend/ascend-toolkit/set_env.sh
+
+    ./build.sh --config <Release|Debug|RelWithDebInfo> --build_shared_lib --parallel --use_cann
+
+
+从pip安装
+^^^^^^^^^^^^
+
+.. code-block:: shell
+    :linenos:
+
+    pip3 install onnxruntime-cann

sources/onnxruntime/quick_start.rst

@@ -0,0 +1,97 @@
+快速开始
+===========
+
+.. note::
+    阅读本篇前，请确保已按照 :doc:`安装指南 <./install>` 准备好昇腾环境及 ONNX Runtime!
+
+本教程以一个简单的 resnet50 模型为例，讲述如何在 Ascend NPU上使用 ONNX Runtime 进行模型推理。
+
+环境准备
+-----------
+
+安装本教程所依赖的额外必要库。
+
+.. code-block:: shell
+  :linenos:
+
+  pip install numpy Pillow onnx
+
+模型准备
+-----------
+
+ONNX Runtime 推理需要 ONNX 格式模型作为输入，目前有以下几种主流途径获得 ONNX 模型。
+
+1. 从 `ONNX Model Zoo <https://onnx.ai/models/>`_ 中下载模型。
+2. 从 torch、TensorFlow 等框架导出 ONNX 模型。
+3. 使用转换工具，完成其他类型到 ONNX 模型的转换。
+
+本教程使用的 resnet50 模型是从 ONNX Model Zoo 中直接下载的，具体的 `下载链接 <https://github.com/onnx/models/blob/main/Computer_Vision/resnet50_Opset16_torch_hub/resnet50_Opset16.onnx>`_
+
+类别标签
+-----------
+
+类别标签用于将输出权重转换成人类可读的类别信息，具体的 `下载链接 <https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json>`_
+
+模型推理
+-----------
+
+.. code-block:: python
+  :linenos:
+
+  import onnxruntime as ort
+  import numpy as np
+  import onnx
+  from PIL import Image
+
+  def preprocess(image_path):
+      img = Image.open(image_path)
+      img = img.resize((224, 224))
+      img = np.array(img).astype(np.float32)
+
+      img = np.transpose(img, (2, 0, 1))
+      img = img / 255.0
+      mean = np.array([0.485, 0.456, 0.406]).reshape(3, 1, 1)
+      std = np.array([0.229, 0.224, 0.225]).reshape(3, 1, 1)
+      img = (img - mean) / std
+      img = np.expand_dims(img, axis=0)
+      return img
+
+  def inference(model_path, img):
+      options = ort.SessionOptions()
+      providers = [
+          (
+              "CANNExecutionProvider",
+              {
+                  "device_id": 0,
+                  "arena_extend_strategy": "kNextPowerOfTwo",
+                  "npu_mem_limit": 2 * 1024 * 1024 * 1024,
+                  "op_select_impl_mode": "high_performance",
+                  "optypelist_for_implmode": "Gelu",
+                  "enable_cann_graph": True
+              },
+          ),
+          "CPUExecutionProvider",
+      ]
+
+      session = ort.InferenceSession(model_path, sess_options=options, providers=providers)
+      input_name = session.get_inputs()[0].name
+      output_name = session.get_outputs()[0].name
+
+      result = session.run([output_name], {input_name: img})
+      return result
+
+  def display(classes_path, result):
+      with open(classes_path) as f:
+          labels = [line.strip() for line in f.readlines()]
+
+      pred_idx = np.argmax(result)
+      print(f'Predicted class: {labels[pred_idx]} ({result[0][0][pred_idx]:.4f})')
+
+  if __name__ == '__main__':
+      model_path = '~/model/resnet/resnet50.onnx'
+      image_path = '~/model/resnet/cat.jpg'
+      classes_path = '~/model/resnet/imagenet_classes.txt'
+
+      img = preprocess(image_path)
+      result = inference(model_path, img)
+      display(classes_path, result)