[Docs] Pick paddleclas fastdeploy docs from PaddleClas (#1654)

* Adjust folders structures in paddleclas * remove useless files * Update sophgo * improve readme
2025-10-06 17:17:14 +08:00 · 2023-03-23 13:06:09 +08:00
parent ab65557121
commit c91e99b5f5
90 changed files with 2005 additions and 2584 deletions
--- a/examples/vision/classification/paddleclas/cpu-gpu/python/README.md
+++ b/examples/vision/classification/paddleclas/cpu-gpu/python/README.md
@@ -0,0 +1,93 @@
+# PaddleClas CPU-GPU Python部署示例
+本目录下提供`infer.py`快速完成PaddleClas在CPU/GPU上部署的示例.
+
+## 1. 说明  
+PaddleClas支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署图像分类模型
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
+
+## 3. 部署模型准备
+在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleClas模型列表](../README.md)中下载所需模型.
+
+## 4. 运行部署示例
+```bash
+# 安装FastDpeloy python包（详细文档请参考`部署环境准备`）
+pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
+conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
+
+# 下载部署示例代码
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/classification/paddleclas/cpu-gpu/python
+
+# 如果您希望从PaddleClas下载示例代码，请运行
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+git checkout develop
+cd PaddleClas/deploy/fastdeploy/cpu-gpu/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# 在CPU上使用Paddle Inference推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend paddle --topk 1
+# 在CPU上使用OenVINO推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend openvino --topk 1
+# 在CPU上使用ONNX Runtime推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend ort --topk 1
+# 在CPU上使用Paddle Lite推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --backend pplite --topk 1
+# 在GPU上使用Paddle Inference推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend paddle --topk 1
+# 在GPU上使用Paddle TensorRT推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend pptrt --topk 1
+# 在GPU上使用ONNX Runtime推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend ort --topk 1
+# 在GPU上使用Nvidia TensorRT推理
+python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --backend trt --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
+
+## 5. 部署示例选项说明  
+
+|参数|含义|默认值
+|---|---|---|  
+|--model|指定模型文件夹所在的路径|None|
+|--image|指定测试图片所在的路径|None|  
+|--device|指定即将运行的硬件类型，支持的值为`[cpu, gpu]`，当设置为cpu时，可运行在x86 cpu/arm cpu等cpu上|cpu|
+|--device_id|使用gpu时, 指定设备号|0|
+|--backend|部署模型时使用的后端, 支持的值为`[paddle,pptrt,pplite,ort,openvino,trt]` |openvino|
+|--topk|返回的前topk准确率, 支持的为`1,5` |1|
+
+关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+## 6. 更多指南
+- [PaddleClas系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/image_classification.html)
+- [PaddleClas C++ 部署](../cpp)
+- [PaddleClas C 部署](../c)
+- [PaddleClas C# 部署](../csharp)
+
+## 7. 常见问题
+- PaddleClas能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
+
+|硬件类型|支持的后端|
+|:---:|:---:|
+|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
+|ARM CPU| Paddle Lite |
+|飞腾 CPU| ONNX Runtime |
+|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
+
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
--- a/examples/vision/classification/paddleclas/cpu-gpu/python/infer.py
+++ b/examples/vision/classification/paddleclas/cpu-gpu/python/infer.py
@@ -0,0 +1,87 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
+    )
+    parser.add_argument(
+        "--device_id",
+        type=int,
+        default=0,
+        help="Define which GPU card used to run model.")
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="default",
+        help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
+    )
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu(args.device_id)
+
+    if args.backend.lower() == "trt":
+        assert args.device.lower(
+        ) == "gpu", "TensorRT backend require inference on device GPU."
+        option.use_trt_backend()
+
+    elif args.backend.lower() == "pptrt":
+        assert args.device.lower(
+        ) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
+        option.use_paddle_infer_backend()
+        option.paddle_infer_option.enable_trt = True
+
+    elif args.backend.lower() == "ort":
+        option.use_ort_backend()
+
+    elif args.backend.lower() == "paddle":
+        option.use_paddle_infer_backend()
+
+    elif args.backend.lower() == "openvino":
+        assert args.device.lower(
+        ) == "cpu", "OpenVINO backend require inference on device CPU."
+        option.use_openvino_backend()
+
+    elif args.backend.lower() == "pplite":
+        assert args.device.lower(
+        ) == "cpu", "Paddle Lite backend require inference on device CPU."
+        option.use_lite_backend()
+
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+model = fd.vision.classification.PaddleClasModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)