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infer.py 2.74 KB
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Jason 提交于 2022-11-28 21:29 +08:00 . Rename PaddleBackend to PaddleInferBackend (#728)
import fastdeploy as fd
import cv2
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True, help="Path of PFLD model.")
parser.add_argument("--image", type=str, 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="inference backend, default, ort, ov, trt, paddle, paddle_trt.")
parser.add_argument(
"--enable_trt_fp16",
type=ast.literal_eval,
default=False,
help="whether enable fp16 in trt/paddle_trt backend")
return parser.parse_args()
def build_option(args):
option = fd.RuntimeOption()
device = args.device
backend = args.backend
enable_trt_fp16 = args.enable_trt_fp16
if device == "gpu":
option.use_gpu()
if backend == "ort":
option.use_ort_backend()
elif backend == "paddle":
option.use_paddle_infer_backend()
elif backend in ["trt", "paddle_trt"]:
option.use_trt_backend()
option.set_trt_input_shape("input", [1, 3, 112, 112])
if backend == "paddle_trt":
option.enable_paddle_to_trt()
if enable_trt_fp16:
option.enable_trt_fp16()
elif backend == "default":
return option
else:
raise Exception(
"While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.".
format(backend))
elif device == "cpu":
if backend == "ort":
option.use_ort_backend()
elif backend == "ov":
option.use_openvino_backend()
elif backend == "paddle":
option.use_paddle_infer_backend()
elif backend == "default":
return option
else:
raise Exception(
"While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.".
format(backend))
else:
raise Exception(
"Only support device CPU/GPU now, {} is not supported.".format(
device))
return option
args = parse_arguments()
# 配置runtime,加载模型
runtime_option = build_option(args)
model = fd.vision.facealign.PFLD(args.model, runtime_option=runtime_option)
# for image
im = cv2.imread(args.image)
result = model.predict(im.copy())
print(result)
# 可视化结果
vis_im = fd.vision.vis_face_alignment(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")
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