import os,time import numpy as np from pathlib import Path class ch_cwd_to_this_file: def __init__(self, _code_file_path): # _code_file_path typically receives __file__ self._code_file_path = _code_file_path def __enter__(self): self._old_dir = os.getcwd() cwd=os.path.dirname(os.path.abspath(self._code_file_path)) os.chdir(cwd) def __exit__(self, exc_type, exc_val, exc_tb): os.chdir(self._old_dir) # def img_2_img_full_path(img,format='jpg',original_name_or_path=''): # """ # thread safe # """ # assert isinstance(img,np.ndarray) # assert img.shape[2]==3 or img.shape[2]==4 # original_img_name_without_dir=os.path.basename(original_name_or_path) # full_path = os.path.join(root_config.path_root, f'./tmp_images/[{root_config.DATASET}][{tmp_cate_or_obj}][{sequence_name}]{img_name_without_suffix}.jpg') # if not os.path.exists(os.path.dirname(full_path)): # os.makedirs(os.path.dirname(full_path)) # print("get_data path:", full_path) # img.save(full_path) # return img_full_path import datetime import pytz def beijing_datetime()->datetime.datetime: """ Example: print(f'Current Beijing time = {beijing_time:%Y.%m.%d %H:%M:%S}') """ # get the local timezone local_tz = datetime.datetime.now(datetime.timezone.utc).astimezone().tzinfo # get Beijing timezone beijing_tz = pytz.timezone('Asia/Shanghai') # get the current time now = datetime.datetime.now() # convert current time to local timezone local_time = now.astimezone(local_tz) # convert local time to Beijing timezone beijing_time:datetime.datetime = local_time.astimezone(beijing_tz) return beijing_time def beijing_str_A( os_is_windows=False)->str: """ print( beijing_str_A() ) """ ret= f"{beijing_datetime():%m.%d-%H:%M:%S}" if os_is_windows: ret=ret.replace(':','：') return ret # convert numpy or tensor to json/dict import json import numpy import PIL import torch from torch import Tensor def to_list_to_primitive(obj): if isinstance(obj, numpy.ndarray): return obj.tolist() if isinstance(obj, torch.Tensor): return obj.cpu().data.numpy().tolist() if isinstance(obj, list): return [to_list_to_primitive(i) for i in obj] # if isinstance(obj, DataFrame): # return obj.values.tolist() elif (isinstance(obj, numpy.int32) or isinstance(obj, numpy.int64) or isinstance(obj, numpy.float32) or isinstance(obj, numpy.float64)): return obj.item() elif (isinstance(obj, int) or isinstance(obj, float) ): return obj else: raise TypeError("got {}".format(type(obj))) def to_ndarray(x): if isinstance(x, numpy.ndarray): return x if isinstance(x, torch.Tensor): return x.cpu().data.numpy() if isinstance(x, list): return numpy.array(x) if isinstance(x, PIL.Image.Image): return numpy.array(x) # if isinstance(x, int) or isinstance(x, float): # return numpy.array([x]) raise TypeError("got {}".format(type(x))) def to_tensor(x): if isinstance(x, numpy.ndarray): return torch.from_numpy(x) if isinstance(x, torch.Tensor): return x if isinstance(x, PIL.Image.Image): return torch.from_numpy(numpy.array(x)) if isinstance(x, list): return torch.tensor(x) # if isinstance(x, int) or isinstance(x, float): # return torch.tensor([x]) raise TypeError("got {}".format(type(x))) def to_pil(x): import torch if isinstance(x, PIL.Image.Image): return x if isinstance(x, numpy.ndarray): return PIL.Image.fromarray(x) if isinstance(x, torch.Tensor): return PIL.Image.fromarray(x.cpu().data.numpy()) raise TypeError("got {}".format(type(x))) class myJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, numpy.ndarray): return obj.tolist() if isinstance(obj, Tensor): return obj.cpu().data.numpy().tolist() elif (isinstance(obj, numpy.int32) or isinstance(obj, numpy.int64) or isinstance(obj, numpy.float32) or isinstance(obj, numpy.float64)): return obj.item() elif isinstance(obj,Path): return str(obj) return json.JSONEncoder.default(self, obj) if(__name__=="__main__"): import torch dic = {'x': torch.randn(2, 3), 'rec': numpy.array([[11, 22, 33], [44, 55, 66], [77, 88, 99]])} s_dic=json.dumps(dic , cls=myJSONEncoder, sort_keys=True, indent=2, separators=(',', ': '), ensure_ascii=False) with open('test.json', 'w', encoding='utf8') as f: json.dump(dic,f, # sort_keys=True, sort_keys=False, indent=2, separators=(',', ': '), ensure_ascii=False) def truncate_str(string:str,MAX_LEN:int,suffix_if_truncate="......")->str: assert isinstance(string,str) if len(string)> MAX_LEN: string=string[:MAX_LEN]+suffix_if_truncate return string def map_string_to_int(string,MIN,MAX): """ Map strings evenly into [MIN, MAX] """ assert isinstance(MIN,int) assert isinstance(MAX,int) assert MAX-MIN>=2 # compute ASCII sum sum = 0 for char in string: sum += ord(char) # print("sum", sum) ret=2**sum ret += sum # avoid producing only powers of two ret=ret%(MAX-MIN) ret+=MIN return ret if 0: import pprint def print_optimizer(optimizer): state_dict=optimizer.state_dict() param_groups=state_dict['param_groups'] # for i,param_group in enumerate(param_groups): pprint.pprint(param_groups) def dic_key_str_2_int(dic: dict) -> dict: ret = {} for k, v in dic.items(): if isinstance(k, str) and k.isdigit(): k = int(k) ret[k] = v return ret def dic_key_str_2_int__nested(dic: dict) -> dict: ret = {} for k, v in dic.items(): if isinstance(k, str) and k.isdigit(): k = int(k) if isinstance(v, dict): v = dic_key_str_2_int__nested(v) ret[k] = v return ret def dic_list_2_tuple_nested(dic: dict) -> dict:#if k,v is list, to tuple ret = {} for k, v in dic.items(): if isinstance(k, list): k = tuple(k) if isinstance(v, list): v = tuple(v) if isinstance(v, dict): v = dic_list_2_tuple_nested(v) ret[k] = v return ret import re def inverse_fstring(string:str,fmt:str,): """ Inverse of string format in python from https://stackoverflow.com/questions/48536295/inverse-of-string-format-in-python """ reg_keys = '{([^{}:]+)[^{}]*}' reg_fmts = '{[^{}:]+[^{}]*}' pat_keys = re.compile(reg_keys) pat_fmts = re.compile(reg_fmts) keys = pat_keys.findall(fmt) lmts = pat_fmts.split(fmt) temp = string values = [] for lmt in lmts: if not len(lmt)==0: value,temp = temp.split(lmt,1) if len(value)>0: values.append(value) if len(temp)>0: values.append(temp) return dict(zip(keys,values)) def sort_strings_asc_A(l:list,fmt:str)->list: """ fmt: eg. 'home/frame{d}.png' """ ret=sorted(l, key= lambda s:int( inverse_fstring(s, fmt )['d']) ) return ret from natsort import natsorted def ls_natsort(folder,re_="*"): folder = Path(folder) files = list(folder.glob(re_)) return natsorted(files ) return natsorted(files, key=lambda x: x.name) if __name__=='__main__': print( beijing_str_A() ) if 1: fmt = '{k1:}+{k2:}={k:3}' res = '1+1=2' print (inverse_fstring(res,fmt)) fmt = '{name:} {age:} {gender}' res = 'Alice 10 F' print (inverse_fstring(res,fmt)) fmt = 'Hi, {k1:}, this is {k2:}' res = 'Hi, Alice, this is Bob' print (inverse_fstring(res,fmt))