[Model] Support PP-ShiTuV2 models for PaddleClas (#1900)

* [cmake] add faiss.cmake -> pp-shituv2 * [PP-ShiTuV2] Support PP-ShituV2-Det model * [PP-ShiTuV2] Support PP-ShiTuV2-Det model * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support * [Bug Fix] fix ppshitu_pybind error * [benchmark] Add ppshituv2-det c++ benchmark * [examples] Add PP-ShiTuV2 det & rec examples * [vision] Update vision classification result * [Bug Fix] fix trt shapes setting errors
2025-10-06 17:17:14 +08:00 · 2023-05-08 14:04:09 +08:00
parent df8dd3e3ac
commit 77cb9db6da
31 changed files with 1835 additions and 51 deletions
--- a/examples/vision/classification/ppshitu/cpu-gpu/python/README.md
+++ b/examples/vision/classification/ppshitu/cpu-gpu/python/README.md
@@ -0,0 +1,75 @@
+# PaddleClas CPU-GPU Python部署示例
+本目录下提供`infer_ppshituv2_det.py`快速完成PP-ShiTuV2在CPU/GPU上部署的示例.
+
+## 1. 说明  
+PP-ShiTuV2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. 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
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/classification/ppshitu/cpu-gpu/python
+
+# 下载模型文件和测试图片
+wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar && tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
+wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0_infer.tar && tar -xf general_PPLCNetV2_base_pretrained_v1.0_infer.tar
+wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v2.0.tar && tar -xf drink_dataset_v2.0.tar
+
+# 在CPU上使用Paddle Inference推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend paddle
+# 在CPU上使用OenVINO推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend openvino
+# 在CPU上使用ONNX Runtime推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend ort
+# 在CPU上使用Paddle Lite推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device cpu --backend pplite
+# 在GPU上使用Paddle Inference推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend paddle
+# 在GPU上使用Paddle TensorRT推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend pptrt
+# 在GPU上使用ONNX Runtime推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend ort
+# 在GPU上使用Nvidia TensorRT推理
+python infer_ppshituv2_det.py --model picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer --image drink_dataset_v2.0/test_images/100.jpeg --device gpu --backend trt
+```
+
+## 4. 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)
+
+## 5. 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)
+
+## 6. 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/ppshitu/cpu-gpu/python/infer_ppshituv2_det.py
+++ b/examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_det.py
@@ -0,0 +1,96 @@
+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 PP-ShiTuV2 detector model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    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
+        option.paddle_infer_option.collect_trt_shape = True
+        option.trt_option.set_shape("image", [1, 3, 640, 640],
+                                    [1, 3, 640, 640], [1, 3, 640, 640])
+        option.trt_option.set_shape("scale_factor", [1, 2], [1, 2], [1, 2])
+        option.trt_option.set_shape("im_shape", [1, 2], [1, 2], [1, 2])
+
+    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, "infer_cfg.yml")
+model = fd.vision.classification.PPShiTuV2Detector(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测主体检测结果
+im = cv2.imread(args.image)
+result = model.predict(im)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")
+
+print(result)
--- a/examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_rec.py
+++ b/examples/vision/classification/ppshitu/cpu-gpu/python/infer_ppshituv2_rec.py
@@ -0,0 +1,85 @@
+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 PP-ShiTuV2 recognizer model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    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.PPShiTuV2Recognizer(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测特征抽取结果
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)