create_task(crawl_page(url)) for url in urls] # *tasks 解包列表,将列表变成了函数的参数 await asyncio.gather(*tasks) asyncio.run(main(['u_1', 'u_2', 'u_3', 'u_4'])) asyncio.create_task,asyncio.run 这些函数都是 Python 3.7 以上的版本才提供的。 解密协程运行时 # 代码1 import ...
())print(platform.release())#10print(platform.system())#windows#获取操作系统的版本 10.0.17763print(platform.version())#包含上面所有的信息汇总print("\n\n\n")forkeyinplatform.uname():print(key)'''WindowsDESKTOP-90JE76G1010.0.17763AMD64Intel64 Family 6 Model 158 Stepping 10, GenuineIntel'...
importplatformdefTestPlatform():print("---SYSTEM INFO---")#获取Python版本 3.7.1print(platform.python_version())#获取Python版本 v3.7.1print(platform.python_branch())#获取操作系统可执行程序的结构 (’32bit’, ‘WindowsPE’)print(platform.architecture())#计算机的名称 DESKTOP-90JE76Gprint(platform...
Last but not least, DeepDiff gives you the exact path of the item(s) that were changed in Python syntax. Example in Json Patch for replacing: { "op": "replace", "path": "/a/b/c", "value": 42 } Example in DeepDiff for the same operation: >>> item1 = {'a':{'b':{'c':...
= '': if os.path.isfile(img_path+img_dic[_key][img_dic_ind]): _her_cnt = 1 _ctl_top_left = None for _meth in _methods: _method = eval(_meth) _hers = cv2.imread(img_path+img_dic[_key][img_dic_ind], 1) _hers= cv2.cvtColor(np.array(_hers), cv2.COLOR_RGB2BGR) _...
I do not understand why does it always look for incorrect path i.e. /Users/aamit/datasets/dataset_config.yaml/train. Why is it appending yaml file in the path? This is the error message that I get: Ultralytics YOLOv8.0.77 🚀 Python-3.9.16 torch-2.0.0 CPU yolo/engine/trainer: ta...
executor_context: Context which will be passed to each resolver (as a dict). Very useful for passing handlers to services, functions or data that you want to use in your resolvers. req: Request object fromaiohttp app: Application object fromaiohttp ...
for i, data in enumerate(val_loader): image_features, audio_features, feature_masks, labels, idx = data image_features = image_features.cuda() audio_features = audio_features.cuda() feature_masks = feature_masks.cuda() labels = labels.cuda() batch_size = image_features.shape[0] inferenc...
key=lambda x: x[0]) ] self.data_files = data_files log.info("package--> self.data_files {0}".format(self.data_files)) log.info("package.py--> self.data_files {0}".format(self.data_files)) _install_data.run(self) # Specific packaging for Unix platforms if os.name != 'nt'...
__iter__create an iterator; default behavior is to iterate ininordertraversal __reversed__create a reversed iterator; default behavior is to iterate in reverseinordertraversal indexprovide the selection statistic for that matches the provided key;a[a.index(value)] = value ...