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[Quantization] Update quantized model deployment examples and update readme. (#377)
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support * Add PaddleOCRv3 & PaddleOCRv2 Support * remove useless parameters * Add utils of sorting det boxes * Fix code naming convention * Fix code naming convention * Fix code naming convention * Fix bug in the Classify process * Imporve OCR Readme * Fix diff in Cls model * Update Model Download Link in Readme * Fix diff in PPOCRv2 * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Fix conflict * Add readme for OCRResult * Improve OCR readme * Add OCRResult readme * Improve OCR readme * Improve OCR readme * Add Model Quantization Demo * Fix Model Quantization Readme * Fix Model Quantization Readme * Add the function to do PTQ quantization * Improve quant tools readme * Improve quant tool readme * Improve quant tool readme * Add PaddleInference-GPU for OCR Rec model * Add QAT method to fastdeploy-quantization tool * Remove examples/slim for now * Move configs folder * Add Quantization Support for Classification Model * Imporve ways of importing preprocess * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Improve Quantization configs and readme * Add support for multi-inputs model * Add backends and params file for YOLOv7 * Add quantized model deployment support for YOLO series * Fix YOLOv5 quantize readme * Fix YOLO quantize readme * Fix YOLO quantize readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Fix bug, change Fronted to ModelFormat * Change Fronted to ModelFormat * Add examples to deploy quantized paddleclas models * Fix readme * Add quantize Readme * Add quantize Readme * Add quantize Readme * Modify readme of quantization tools * Modify readme of quantization tools * Improve quantization tools readme * Improve quantization readme * Improve PaddleClas quantized model deployment readme * Add PPYOLOE-l quantized deployment examples * Improve quantization tools readme * Improve Quantize Readme * Fix conflicts * Fix conflicts * improve readme * Improve quantization tools and readme * Improve quantization tools and readme * Add quantized deployment examples for PaddleSeg model * Fix cpp readme * Fix memory leak of reader_wrapper function * Fix model file name in PaddleClas quantization examples * Update Runtime and E2E benchmark * Update Runtime and E2E benchmark * Rename quantization tools to auto compression tools * Remove PPYOLOE data when deployed on MKLDNN * Fix readme * Support PPYOLOE with OR without NMS and update readme * Update Readme * Update configs and readme * Update configs and readme * Add Paddle-TensorRT backend in quantized model deploy examples * Support PPYOLOE+ series
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# PaddleSeg 量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成PaddleSeg量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
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## 以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/segmentation/paddleseg/quantize/python
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#下载FastDeloy提供的PP_LiteSeg_T_STDC1_cityscapes量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
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tar -xvf PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# 在CPU上使用Paddle-Inference推理量化模型
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python infer.py --model PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_QAT --image cityscapes_demo.png --device cpu --backend paddle
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```
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--backend",
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type=str,
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default="default",
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help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
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)
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parser.add_argument(
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"--device_id",
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type=int,
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default=0,
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help="Define which GPU card used to run model.")
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parser.add_argument(
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"--cpu_thread_num",
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type=int,
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default=9,
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help="Number of threads while inference on CPU.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu(0)
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option.set_cpu_thread_num(args.cpu_thread_num)
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if args.backend.lower() == "trt":
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assert args.device.lower(
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) == "gpu", "TensorRT backend require inferences on device GPU."
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option.use_trt_backend()
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option.set_trt_cache_file(os.path.join(args.model, "model.trt"))
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option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
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[1, 3, 2048, 2048])
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elif args.backend.lower() == "ort":
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option.use_ort_backend()
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elif args.backend.lower() == "paddle":
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option.use_paddle_backend()
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elif args.backend.lower() == "openvino":
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assert args.device.lower(
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) == "cpu", "OpenVINO backend require inference on device CPU."
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option.use_openvino_backend()
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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model = fd.vision.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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