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- import fastdeploy as fd
- import cv2
- import os
-
-
- def parse_arguments():
- import argparse
- import ast
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--model"
- , help="Path of PaddleSeg model.",
- default='infer'
-
- )
- parser.add_argument(
- "--image", type=str, required=True, help="Path of test image file.")
- parser.add_argument(
- "--device",
- type=str,
- default='cpu',
- help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
- parser.add_argument(
- "--use_trt",
- type=ast.literal_eval,
- default=False,
- help="Wether to use tensorrt.")
- return parser.parse_args()
-
-
- def build_option(args):
- option = fd.RuntimeOption()
-
- if args.device.lower() == "gpu":
- option.use_gpu()
-
- if args.use_trt:
- option.use_trt_backend()
- # If use original Tensorrt, not Paddle-TensorRT,
- # comment the following two lines
- option.enable_paddle_to_trt()
- option.enable_paddle_trt_collect_shape()
- option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
- [1, 3, 2048, 2048])
- return option
-
-
- args = parse_arguments()
-
- # settting for runtime
- runtime_option = build_option(args)
- model_file = os.path.join(args.model, "model.pdmodel")
- params_file = os.path.join(args.model, "model.pdiparams")
- config_file = os.path.join(args.model, "deploy.yaml")
- model = fd.vision.segmentation.PaddleSegModel(
- model_file, params_file, config_file, runtime_option=runtime_option)
-
- # predict
- im = cv2.imread(args.image)
- result = model.predict(im)
- print(result)
-
- # visualize
- vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
- cv2.imwrite("vis_img.png", vis_im)
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