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* add xpu support * fix docs * update code * update doc * update code * update yolov5 * update cmake * add int64_t data support * fix * update download links * add en doc * update code * update xpu options * update doc * update doc * update doc * update lib links * update doc * update code * update lite xpu link * update xpu lib * update doc * update en doc
61 lines
1.6 KiB
Python
Executable File
61 lines
1.6 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", 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 'gpu' or 'ipu'.")
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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() == "gpu":
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option.use_gpu()
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if args.device.lower() == "ipu":
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option.use_ipu()
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if args.device.lower() == "xpu":
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option.use_xpu()
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if args.use_trt:
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option.use_trt_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, "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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# 预测图片分类结果
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im = cv2.imread(args.image)
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result = model.predict(im, args.topk)
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print(result)
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