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https://github.com/PaddlePaddle/FastDeploy.git
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87bcb5df21
* add style transfer model * add examples for generation model * add unit test * add speed comparison * add speed comparison * add variable for constant * add preprocessor and postprocessor * add preprocessor and postprocessor * fix * fix according to review Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import cv2
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import os
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import fastdeploy as fd
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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("--model", required=True, help="Name of the 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 '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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if args.device.lower() == "gpu":
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option.use_gpu()
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else:
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option.set_paddle_mkldnn(False)
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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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fd.download_model(name=args.model, path='./', format='paddle')
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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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model = fd.vision.generation.AnimeGAN(
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model_file, params_file, runtime_option=runtime_option)
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# 预测图片并保存结果
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im = cv2.imread(args.image)
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result = model.predict(im)
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cv2.imwrite('style_transfer_result.png', result)
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