mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
9937b6c325
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST * Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg * Add multi thread demo * Add python model clone function * Add multi thread python && C++ example * Fix bug Co-authored-by: Jason <jiangjiajun@baidu.com>
129 lines
3.6 KiB
Python
129 lines
3.6 KiB
Python
import numpy as np
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from threading import Thread
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import fastdeploy as fd
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import cv2
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import os
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import psutil
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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(
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"--model", required=True, help="Path of PaddleClas model.")
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parser.add_argument(
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"--image_path",
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type=str,
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required=True,
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help="The directory or path or file list of the images to be predicted."
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)
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parser.add_argument(
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"--topk", type=int, default=1, help="Return topk results.")
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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 'ipu'.")
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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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parser.add_argument("--thread_num", type=int, default=1, help="thread num")
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return parser.parse_args()
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def get_image_list(image_path):
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image_list = []
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if os.path.isfile(image_path):
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image_list.append(image_path)
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# load image in a directory
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elif os.path.isdir(image_path):
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for root, dirs, files in os.walk(image_path):
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for f in files:
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image_list.append(os.path.join(root, f))
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else:
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raise FileNotFoundError(
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'{} is not found. it should be a path of image, or a directory including images.'.
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format(image_path))
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if len(image_list) == 0:
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raise RuntimeError(
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'There are not image file in `--image_path`={}'.format(image_path))
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return image_list
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.device.lower() == "ipu":
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option.use_ipu()
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if args.use_trt:
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option.use_trt_backend()
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return option
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def predict(model, img_list, topk):
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result_list = []
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# 预测图片分类结果
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for image in img_list:
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im = cv2.imread(image)
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result = model.predict(im, topk)
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result_list.append(result)
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return result_list
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class WrapperThread(Thread):
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def __init__(self, func, args):
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super(WrapperThread, self).__init__()
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self.func = func
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self.args = args
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def run(self):
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self.result = self.func(*self.args)
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def get_result(self):
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return self.result
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if __name__ == '__main__':
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args = parse_arguments()
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thread_num = args.thread_num
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imgs_list = get_image_list(args.image_path)
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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, "inference.pdmodel")
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params_file = os.path.join(args.model, "inference.pdiparams")
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config_file = os.path.join(args.model, "inference_cls.yaml")
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model = fd.vision.classification.PaddleClasModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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threads = []
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image_num_each_thread = int(len(imgs_list) / thread_num)
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for i in range(thread_num):
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if i == thread_num - 1:
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t = WrapperThread(
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predict,
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args=(model, imgs_list[i * image_num_each_thread:], i))
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else:
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t = WrapperThread(
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predict,
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args=(model.clone(), imgs_list[i * image_num_each_thread:(
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i + 1) * image_num_each_thread - 1], i))
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threads.append(t)
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t.start()
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for i in range(thread_num):
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threads[i].join()
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for i in range(thread_num):
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for result in threads[i].get_result():
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print('thread:', i, ', result: ', result)
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