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https://github.com/PaddlePaddle/FastDeploy.git
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534d5b8c8b
* Add tutorials for intel gpu * fix gflags dependency * Update README_CN.md * Update README.md * Update README.md
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
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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