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https://github.com/PaddlePaddle/FastDeploy.git
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1911002b90
* [FlyCV] Bump up FlyCV -> official release 1.0.0 * add valid_xpu for detection * add paddledetection model support for xpu * support all detection model in c++ and python * fix code * add python stable_diffusion support Co-authored-by: DefTruth <qiustudent_r@163.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
81 lines
2.2 KiB
Python
Executable File
81 lines
2.2 KiB
Python
Executable File
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_dir",
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default=None,
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help="Path of PaddleDetection model directory")
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parser.add_argument(
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"--image", default=None, 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 'xpu', 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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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() == "xpu":
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option.use_xpu(autotune=False, l3_workspace_size=0)
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if args.device.lower() == "gpu":
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# option.use_gpu()
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print(
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"""GPU inference with Backend::Paddle in python has not been supported yet. \
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\nWill ignore this option.""")
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if args.use_trt:
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# TODO(qiuyanjun): may remove TRT option
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# Backend::TRT has not been supported yet.
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print(
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"""Backend::TRT has not been supported yet, will ignore this option.\
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\nPaddleDetection/MaskRCNN has only support Backend::Paddle now."""
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)
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return option
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args = parse_arguments()
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if args.model_dir is None:
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model_dir = fd.download_model(name='mask_rcnn_r50_1x_coco')
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else:
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model_dir = args.model_dir
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model_file = os.path.join(model_dir, "model.pdmodel")
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params_file = os.path.join(model_dir, "model.pdiparams")
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config_file = os.path.join(model_dir, "infer_cfg.yml")
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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 = fd.vision.detection.MaskRCNN(
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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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if args.image is None:
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image = fd.utils.get_detection_test_image()
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else:
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image = args.image
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im = cv2.imread(image)
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result = model.predict(im)
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print(result)
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# 预测结果可视化
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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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