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[Turorials] Add tutorials for intel gpu (#860)
* Add tutorials for intel gpu * fix gflags dependency * Update README_CN.md * Update README.md * Update README.md
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Jason
38
tutorials/intel_gpu/python/README.md
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tutorials/intel_gpu/python/README.md
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English | [中文](README_CN.md)
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# PaddleClas Python Example
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Before deployment, confirm the following two steps
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- 1. The software and hardware environment meet the requirements. Refer to [FastDeploy Environment Requirements](../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python wheel package. Refer to [Install FastDeploy](../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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```bash
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# Get FastDeploy codes
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/tutorials/intel_gpu/python
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# Download PaddleClas model and test image
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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tar -xvf ResNet50_vd_infer.tgz
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# Inference with CPU
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python infer_resnet50.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
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# Inference with Intel GPU
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python infer_resnet50.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device intel_gpu --topk 1
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# Download PaddleDetection/PP-YOLOE model and test image
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# Inference with CPU
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python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
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# Inference with Intel GPU
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python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device intel_gpu
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```
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37
tutorials/intel_gpu/python/README_CN.md
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tutorials/intel_gpu/python/README_CN.md
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English | [中文](README_CN.md)
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# PaddleClas Python Example
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在部署前，需确认以下两个步骤
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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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```bash
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# Get FastDeploy codes
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/tutorials/intel_gpu/python
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# Download model and test image
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# Inference with CPU
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
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# Inference with Intel GPU
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python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device intel_gpu --topk 1
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# Download PaddleDetection/PP-YOLOE model and test image
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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tar xvf ppyoloe_crn_l_300e_coco.tgz
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# Inference with CPU
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python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
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# Inference with Intel GPU
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python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device intel_gpu
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```
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65
tutorials/intel_gpu/python/infer_ppyoloe.py
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tutorials/intel_gpu/python/infer_ppyoloe.py
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import fastdeploy as fd
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import cv2
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import os
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import time
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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 PP-YOLOE model.")
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parser.add_argument(
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"--image", type=str, 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 'intel_gpu'.")
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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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option.use_openvino_backend()
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assert args.device.lower(
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) in ["cpu", "intel_gpu"], "--device only support ['cpu', 'intel_gpu']"
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if args.device.lower() == "intel_gpu":
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option.set_openvino_device("HETERO:GPU,CPU")
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option.set_openvino_shape_info({
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"image": [1, 3, 640, 640],
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"scale_factor": [1, 2]
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})
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option.set_openvino_cpu_operators(["MulticlassNms"])
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return option
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args = parse_arguments()
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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, "infer_cfg.yml")
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model = fd.vision.detection.PPYOLOE(
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model_file, params_file, config_file, runtime_option=runtime_option)
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im = cv2.imread(args.image)
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print("Warmup 20 times...")
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for i in range(20):
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result = model.predict(im)
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print("Counting time...")
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start = time.time()
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for i in range(50):
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result = model.predict(im)
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end = time.time()
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print("Elapsed time: {}ms".format((end - start) * 1000))
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vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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61
tutorials/intel_gpu/python/infer_resnet50.py
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61
tutorials/intel_gpu/python/infer_resnet50.py
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import fastdeploy as fd
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import cv2
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import os
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import time
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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 PaddleClas model.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--topk", type=int, default=1, help="Return topk results.")
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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 'intel_gpu'.")
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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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option.use_openvino_backend()
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assert args.device.lower(
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) in ["cpu", "intel_gpu"], "--device only support ['cpu', 'intel_gpu']"
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if args.device.lower() == "intel_gpu":
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option.set_openvino_device("GPU")
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option.set_openvino_shape_info({"inputs": [1, 3, 224, 224]})
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return option
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args = parse_arguments()
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "inference.pdmodel")
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params_file = os.path.join(args.model, "inference.pdiparams")
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config_file = os.path.join(args.model, "inference_cls.yaml")
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model = fd.vision.classification.PaddleClasModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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im = cv2.imread(args.image)
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print("Warmup 20 times...")
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for i in range(20):
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result = model.predict(im, args.topk)
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print("Counting time...")
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start = time.time()
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for i in range(50):
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result = model.predict(im, args.topk)
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end = time.time()
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print("Elapsed time: {}ms".format((end - start) * 1000))
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print(result)
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