mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
huangjianhui
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import fastdeploy as fd
|
|
import cv2
|
|
import os
|
|
from fastdeploy import ModelFormat
|
|
|
|
|
|
def parse_arguments():
|
|
import argparse
|
|
import ast
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--model", required=True, help="Path of yolov5 onnx model.")
|
|
parser.add_argument(
|
|
"--image", required=True, help="Path of test image file.")
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default='cpu',
|
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
|
parser.add_argument(
|
|
"--backend",
|
|
type=str,
|
|
default="default",
|
|
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
|
|
)
|
|
parser.add_argument(
|
|
"--device_id",
|
|
type=int,
|
|
default=0,
|
|
help="Define which GPU card used to run model.")
|
|
parser.add_argument(
|
|
"--cpu_thread_num",
|
|
type=int,
|
|
default=9,
|
|
help="Number of threads while inference on CPU.")
|
|
return parser.parse_args()
|
|
|
|
|
|
def build_option(args):
|
|
option = fd.RuntimeOption()
|
|
if args.device.lower() == "gpu":
|
|
option.use_gpu(0)
|
|
|
|
option.set_cpu_thread_num(args.cpu_thread_num)
|
|
|
|
if args.backend.lower() == "trt":
|
|
assert args.device.lower(
|
|
) == "gpu", "TensorRT backend require inference on device GPU."
|
|
option.use_trt_backend()
|
|
elif args.backend.lower() == "pptrt":
|
|
assert args.device.lower(
|
|
) == "gpu", "TensorRT backend require inference on device GPU."
|
|
option.use_trt_backend()
|
|
option.enable_paddle_to_trt()
|
|
elif args.backend.lower() == "ort":
|
|
option.use_ort_backend()
|
|
return option
|
|
|
|
|
|
args = parse_arguments()
|
|
|
|
model_file = os.path.join(args.model, "model.pdmodel")
|
|
params_file = os.path.join(args.model, "model.pdiparams")
|
|
# 配置runtime，加载模型
|
|
runtime_option = build_option(args)
|
|
model = fd.vision.detection.YOLOv5(
|
|
model_file,
|
|
params_file,
|
|
runtime_option=runtime_option,
|
|
model_format=ModelFormat.PADDLE)
|
|
|
|
# 预测图片检测结果
|
|
im = cv2.imread(args.image)
|
|
result = model.predict(im)
|
|
print(result)
|
|
|
|
# 预测结果可视化
|
|
vis_im = fd.vision.vis_detection(im, result)
|
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
|
print("Visualized result save in ./visualized_result.jpg")
|