mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
huangjianhui
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
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("--model", required=True, help="Path of FSANet model.")
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parser.add_argument("--image", type=str, 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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parser.add_argument(
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"--backend",
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type=str,
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default="default",
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help="inference backend, default, ort, ov, trt, paddle, paddle_trt.")
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parser.add_argument(
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"--enable_trt_fp16",
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type=ast.literal_eval,
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default=False,
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help="whether enable fp16 in trt/paddle_trt backend")
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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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device = args.device
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backend = args.backend
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enable_trt_fp16 = args.enable_trt_fp16
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if device == "gpu":
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option.use_gpu()
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if backend == "ort":
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option.use_ort_backend()
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elif backend == "paddle":
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option.use_paddle_infer_backend()
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elif backend in ["trt", "paddle_trt"]:
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option.use_trt_backend()
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option.set_trt_input_shape("input", [1, 3, 64, 64])
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if backend == "paddle_trt":
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option.enable_paddle_to_trt()
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if enable_trt_fp16:
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option.enable_trt_fp16()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.".
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format(backend))
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elif device == "cpu":
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if backend == "ort":
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option.use_ort_backend()
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elif backend == "ov":
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option.use_openvino_backend()
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elif backend == "paddle":
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option.use_paddle_infer_backend()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.".
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format(backend))
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else:
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raise Exception(
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"Only support device CPU/GPU now, {} is not supported.".format(
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device))
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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 = fd.vision.headpose.FSANet(args.model, runtime_option=runtime_option)
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# for image
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im = cv2.imread(args.image)
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result = model.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_headpose(im, result)
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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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