[Other]Update python && cpp multi_thread examples (#876)

* 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

* Update python && cpp multi_thread examples

* Add cpp && python directory

* Add README.md for examples

* Delete redundant code

Co-authored-by: Jason <jiangjiajun@baidu.com>

tutorials/multi_thread/python/multi_thread_process.py

@@ -0,0 +1,156 @@
+import numpy as np
+from threading import Thread
+import fastdeploy as fd
+import cv2
+import os
+import psutil
+from multiprocessing import Pool
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image_path",
+        type=str,
+        required=True,
+        help="The directory or path or file list of the images to be predicted."
+    )
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    parser.add_argument("--thread_num", type=int, default=1, help="thread num")
+    parser.add_argument(
+        "--use_multi_process",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use multi process.")
+    parser.add_argument(
+        "--process_num", type=int, default=1, help="process num")
+    return parser.parse_args()
+
+
+def get_image_list(image_path):
+    image_list = []
+    if os.path.isfile(image_path):
+        image_list.append(image_path)
+    # load image in a directory
+    elif os.path.isdir(image_path):
+        for root, dirs, files in os.walk(image_path):
+            for f in files:
+                image_list.append(os.path.join(root, f))
+    else:
+        raise FileNotFoundError(
+            '{} is not found. it should be a path of image, or a directory including images.'.
+            format(image_path))
+
+    if len(image_list) == 0:
+        raise RuntimeError(
+            'There are not image file in `--image_path`={}'.format(image_path))
+
+    return image_list
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.device.lower() == "ipu":
+        option.use_ipu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    return option
+
+
+def predict(model, img_list, topk):
+    result_list = []
+    # predict classification result
+    for image in img_list:
+        im = cv2.imread(image)
+        result = model.predict(im, topk)
+        result_list.append(result)
+    return result_list
+
+
+def process_predict(image):
+    # predict classification result
+    im = cv2.imread(image)
+    result = model.predict(im, args.topk)
+    return result
+
+
+class WrapperThread(Thread):
+    def __init__(self, func, args):
+        super(WrapperThread, self).__init__()
+        self.func = func
+        self.args = args
+
+    def run(self):
+        self.result = self.func(*self.args)
+
+    def get_result(self):
+        return self.result
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+
+    imgs_list = get_image_list(args.image_path)
+    # configure runtime and load model
+    runtime_option = build_option(args)
+
+    model_file = os.path.join(args.model, "inference.pdmodel")
+    params_file = os.path.join(args.model, "inference.pdiparams")
+    config_file = os.path.join(args.model, "inference_cls.yaml")
+    model = fd.vision.classification.PaddleClasModel(
+        model_file, params_file, config_file, runtime_option=runtime_option)
+    if args.use_multi_process:
+        results = []
+        process_num = args.process_num
+        with Pool(process_num) as pool:
+            results = pool.map(process_predict, imgs_list)
+        for result in results:
+            print(result)
+    else:
+        threads = []
+        thread_num = args.thread_num
+        image_num_each_thread = int(len(imgs_list) / thread_num)
+        # unless you want independent model in each thread, actually model.clone()
+        # is the same as model when creating thead because of the existence of
+        # GIL(Global Interpreter Lock) in python. In addition, model.clone() will consume
+        # additional memory to store independent member variables
+        for i in range(thread_num):
+            if i == thread_num - 1:
+                t = WrapperThread(
+                    predict,
+                    args=(model.clone(), imgs_list[i * image_num_each_thread:],
+                          args.topk))
+            else:
+                t = WrapperThread(
+                    predict,
+                    args=(model.clone(), imgs_list[i * image_num_each_thread:(
+                        i + 1) * image_num_each_thread - 1], args.topk))
+            threads.append(t)
+            t.start()
+
+        for i in range(thread_num):
+            threads[i].join()
+
+        for i in range(thread_num):
+            for result in threads[i].get_result():
+                print('thread:', i, ', result: ', result)