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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("--model", default=None, help="Path of yolov5 model.")
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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 'cpu' or 'gpu' or 'kunlunxin'.")
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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() == "kunlunxin":
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option.use_kunlunxin()
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if args.device.lower() == "ascend":
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option.use_ascend()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("images", [1, 3, 640, 640])
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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, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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model = fd.vision.detection.YOLOv5(
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model_file,
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params_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.PADDLE)
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image_file_path = "../dataset/coco/val2017"
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annotation_file_path = "../dataset/coco/annotations/instances_val2017.json"
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res = fd.vision.evaluation.eval_detection(model, image_file_path,
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annotation_file_path, 0.001, 0.65)
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print(res)
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