├── .gitignore ├── README.md ├── example.py ├── poetry.lock ├── pyproject.toml └── superdebug ├── __init__.py └── __pycache__ └── __init__.cpython-38.pyc /.gitignore: -------------------------------------------------------------------------------- 1 | dist/ 2 | .history/ 3 | tmp/ 4 | tmp.py 5 | */__pycache__ 6 | superdebug/__pycache__ 7 | super_debug -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SuperDebug 2 | 3 | ## 使用方法： 4 | 5 | `pip install superdebug` 6 | 7 | `from superdebug import debug, mail` 8 | 9 | ### 功能1: `debug(var)` 10 | 11 | 用黄色字体打印出（与普通的打印区分）： 12 | 13 | - `var`所在具体行数（command / ctrl + 单击 即可跳转到文件相应位置） 14 | - 数组形状（限list、numpy的array、torch的tensor） 15 | - `var`的具体值 16 | 17 | 进阶功能: 18 | 19 | 1. 一次性打印多个变量：`debug(var1, var2, var3, ...)` 20 | 2. 同步输出在终端（`PRINT = True`）和同目录下的debug.log文件中（`TO_FILE = True`） 21 | 3. 对于一些循环程序，你或许只想打印有限次，只需设置 `MAX_LOG = 想打印的次数` 即可 22 | 4. 如果你希望只打印变量的形状，而不打印变量的内容，可以使用 `debug(False, var)` 23 | 5. 打印完整矩阵内容，禁止pytorch、numpy自动省略：`FULL = True` 24 | 6. 对于一些套娃list / dict，可以控制详细打印至第几层，标准为0，建议用3： `PEEK_LAYER = 0` 25 | 26 | ### 功能2: `debug()` 27 | 28 | 简单标记程序已经运行到了某一位置，可用于判断程序在哪一处卡在、程序进入了哪一个if分支等，也可以查看程序运行到这一位置的时间。 29 | 30 | ### 功能3: 在出错时跳至ipdb调试界面，便捷debug 31 | 32 | 自动停在出错的那一步（需设置 `BUGGY = True`）：当出现Exception，程序不会退出，而是会暂停在出现Exception的那一步。 33 | 34 | 这时你可以使用 `var`（输出var的值）, `up`（上一步）, `down`（下一步）等控制命令进行调试。更多命令同ipdb 35 | 36 | ### 功能4: 在指定位置停下（设置断点） 37 | 38 | 在想设置断点处写 `raise Exception()`，与功能3同理，程序将会停在exception处。 39 | 40 | ### 功能5: 邮件提醒 41 | 42 | 用于在某一任务完成后发送邮件通知。 43 | 44 | 需配置环境变量 `MY_QQ_EMAIL`（QQ邮箱地址）与 `MY_QQ_EMAIL_PWD`（QQ邮箱授权码）。 45 | 46 | 使用 `mail("邮件主题", "邮件内容")`即可便捷地向你自己的邮箱发送通知，可用于提醒模型已训练完毕、某项耗时的任务已处理完成等。 47 | -------------------------------------------------------------------------------- /example.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict, defaultdict 2 | from superdebug import debug 3 | import time 4 | 5 | debug("程序开始运行了，记得看时间哦") 6 | 7 | story = ["从前有座山", "山里有座庙", "庙里有个老和尚", "老和尚在给小和尚讲故事", "讲什么呢"] * 10000 # 重复10000遍 8 | 9 | config = OrderedDict({"标题": "和尚的故事", "内容": story, "更多信息": {"作者": ("老和尚", "小和尚", "小小和尚"), "时间": {"创作时间": "很久很久以前", "timestamp": time.time()}}}) 10 | debug("你可以同时debug多个变量，没有被设置名字的变量会显示为'?'", story, 提示 = "这个故事实在太长了，我们只打印其中的一段", config = config, 另一个提示 = "我们可以清晰地看出变量的嵌套结构") 11 | 12 | from superdebug import mail 13 | debug("让我们模拟一段耗时的任务，任务结束后我会向你发送邮件") 14 | time.sleep(10) 15 | mail("任务已处理完成") 16 | debug("你收到邮件了吗？") 17 | 18 | raise Exception("哎呀，程序出错了... 去在ipdb界面中调试程序吧") -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "appnope" 3 | version = "0.1.2" 4 | description = "Disable App Nap on macOS >= 10.9" 5 | category = "main" 6 | optional = false 7 | python-versions = "*" 8 | 9 | [[package]] 10 | name = "asttokens" 11 | version = "2.0.5" 12 | description = "Annotate AST trees with source code positions" 13 | category = "main" 14 | optional = false 15 | python-versions = "*" 16 | 17 | [package.dependencies] 18 | six = "*" 19 | 20 | [package.extras] 21 | test = ["astroid", "pytest"] 22 | 23 | [[package]] 24 | name = "backcall" 25 | version = "0.2.0" 26 | description = "Specifications for callback functions passed in to an API" 27 | category = "main" 28 | optional = false 29 | python-versions = "*" 30 | 31 | [[package]] 32 | name = "certifi" 33 | version = "2021.10.8" 34 | description = "Python package for providing Mozilla's CA Bundle." 35 | category = "main" 36 | optional = false 37 | python-versions = "*" 38 | 39 | [[package]] 40 | name = "charset-normalizer" 41 | version = "2.0.12" 42 | description = "The Real First Universal Charset Detector. 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-------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # from superdebug import debug, mail 3 | import torch 4 | try: 5 | from torchvision.utils import save_image 6 | except: 7 | save_image is None 8 | tf = None 9 | if False: 10 | try: 11 | import tensorflow as tf 12 | except: 13 | pass 14 | import numpy as np 15 | import sys 16 | import os 17 | import time 18 | import re 19 | from collections import OrderedDict, defaultdict 20 | try: 21 | from PIL import Image 22 | except: 23 | Image = None 24 | 25 | # 开关 ################################# 26 | ON_DEBUG = True # debug总开关 27 | PLAIN = False # 开启则仅普通的打印（至终端或debug.log） 28 | MAX_LOG = -1 # 0: 不debug, -1: 无限输出 NOTE: 无输出？可能这里设成了0，或者数量不够高、没到需要输出的变量！ 29 | FULL = False # 是否输出完整的tensor、string内容，而不用...进行省略 30 | TO_FILE = False # 是否写入debug.log 31 | PRINT = True # 是否打印至终端 32 | BUGGY = True # 便捷地debug（出现bug则进入自动进入调试模式） 33 | PEEK_LAYER = 3 # 详细打印至第几层，不详细打印可使用0，详细打印建议用3 34 | MAX_PEEK_ITEM = 3 # 详细打印几项，标准为2 35 | MAX_STR_LEN = 540 # 最长打印的字符串长度，推荐： 540 ，无限大： 9999999999999999 36 | SAVE_IMAGE_NORM = False # 把tensor保存成图片时是否normalize 37 | # 控制是否打印细节：debug(True/False, xxx, xxx)，False则只打印形状 38 | 39 | # 教程 ################################# 40 | # 功能1: debug(xxx) : 用黄色字体打印出xxx的形状及具体值，debug(False, xxx)则只打印形状，不打印具体值。更多控制开关见上方。 41 | # 功能2: mark(xxx) : 标记运行到了某个位置，若有输入，则用黄色字体打印出xxx值，若仅用mark()无输入，则打印mark()所在的位置 42 | # 功能3: 在出错时跳至ipdb界面，便捷debug 43 | 44 | # 实现 ################################# 45 | try: 46 | MY_QQ_EMAIL = os.environ["MY_QQ_EMAIL"] # Email address 47 | MY_QQ_EMAIL_PWD = os.environ["MY_QQ_EMAIL_PWD"] # Password 48 | except: 49 | print("为了使用邮件提醒功能，请设置环境变量MY_QQ_EMAIL（QQ邮箱地址）与MY_QQ_EMAIL_PWD（QQ邮箱授权码）") 50 | 51 | debug_count = 1 52 | debug_file = None 53 | debug_path = "super_debug" 54 | if os.path.exists(debug_path): 55 | os.system("rm -r " + debug_path) 56 | os.makedirs(debug_path, exist_ok=True) 57 | log_path = os.path.join(debug_path, "debug.log") 58 | os.system("touch " + log_path) 59 | image_count = {} 60 | simple_types = [str, int, float, bool] 61 | 62 | 63 | class ExceptionHook: 64 | instance = None 65 | 66 | def __call__(self, *args, **kwargs): 67 | if not BUGGY: 68 | return 69 | if self.instance is None: 70 | from IPython.core import ultratb 71 | self.instance = ultratb.FormattedTB(mode='Plain', 72 | color_scheme='Linux', call_pdb=1) 73 | return self.instance(*args, **kwargs) 74 | 75 | 76 | sys.excepthook = ExceptionHook() 77 | 78 | 79 | def get_pos(level=1, end="\n"): 80 | position = """{}:{} {}""".format( 81 | # position = """"{}", line {}, in {}""".format( 82 | sys._getframe(level).f_code.co_filename, # 当前文件名 83 | sys._getframe(level).f_lineno, # 当前行号 84 | sys._getframe(level).f_code.co_name, # 当前函数/module名 85 | ) 86 | return position 87 | 88 | def get_time(): 89 | return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) 90 | 91 | def print_yellow(text, end="\n"): 92 | print(f"\033[1;33m{text}\033[0m", end=end) 93 | 94 | 95 | def normalize(tensor): 96 | max_value = torch.max(tensor) 97 | min_value = torch.min(tensor) 98 | tensor = (tensor - min_value) / (max_value - min_value) 99 | return tensor 100 | 101 | def print_image(tensor, name, is_np = False): 102 | if name not in image_count: 103 | image_count[name] = 0 104 | file_path = os.path.join(debug_path, f"tensor_{debug_count}_{name}_{image_count[name]}.jpg") 105 | normallized_file_path = os.path.join(debug_path, f"tensor_{debug_count}_{name}_{image_count[name]}_norm.jpg") 106 | image_count[name] += 1 107 | if Image is not None and type(tensor) == Image.Image: 108 | tensor.save(file_path) 109 | else: 110 | if is_np: 111 | tensor = torch.Tensor(tensor) 112 | normalized_tensor = normalize(tensor) 113 | try: 114 | if save_image is not None: 115 | if SAVE_IMAGE_NORM: 116 | save_image(normalized_tensor, normallized_file_path) 117 | else: 118 | save_image(tensor, file_path) 119 | except Exception: 120 | pass 121 | def mark(marker=None): 122 | print("Mark is deprecated. Use debug() instead.") 123 | 124 | 125 | def logging(*message, end="\n"): 126 | """同时输出到终端和debug.log""" 127 | message = " ".join([str(_) for _ in message]) 128 | if PRINT: 129 | print_yellow(message, end=end) 130 | if debug_file and not debug_file.closed: 131 | message = re.sub("\033\[.*?m", "", message) 132 | debug_file.write(message + end) 133 | logging("------------------\033[0m\033[1;31m", get_time(), "\033[0m\033[1;33m------------------") 134 | 135 | 136 | def info(var, name="?", detail=True, layer=0): 137 | """递归打印变量""" 138 | space = " " 139 | if type(var) == int or type(var) == float: 140 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "num val:", var) 141 | else: 142 | if var is None: 143 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "None") 144 | elif type(var) == str: 145 | length = len(var) 146 | if not FULL and len(var) >= MAX_STR_LEN: 147 | var = var[:MAX_STR_LEN - 20] + " ... " + var[-20:] 148 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "str len", str(length)+":", var) 149 | elif type(var) == bool: 150 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "bool:", var) 151 | elif type(var) == list: 152 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "list size:", len(var), end="") 153 | if layer < PEEK_LAYER and len(var) > 0 and type(var[0]) not in simple_types: # a list of complex variables 154 | logging(f" [{min(len(var), 3)*'.'}]") 155 | for no, item in enumerate(var[:MAX_PEEK_ITEM]): 156 | info(item, "item " + str(no) + ": ", detail, layer + 1) 157 | if len(var) > MAX_PEEK_ITEM: 158 | logging(space * (layer + 1), len(var) - MAX_PEEK_ITEM, "extra items") 159 | else: 160 | var_str = str(var) 161 | if len(var) > 0 and type(var[0]) in simple_types and all([type(var[i]) == type(var[0]) for i in range(len(var))]): # a list of variables of the same simple type 162 | if len(var_str) >= MAX_STR_LEN + 3: # too long 163 | # show_num = len(var_str[:MAX_STR_LEN].split(",")) 164 | logging(" val:", f"{var_str[:MAX_STR_LEN]} ... and extra items]" if detail else "*") 165 | else: 166 | logging(" val:", var_str if detail else "*") 167 | elif layer < PEEK_LAYER: # variables of different simple types 168 | logging(f" [{min(len(var), 3)*'.'}]") 169 | for no, item in enumerate(var): 170 | info(item, "item " + str(no) + ": ", detail, layer + 1) 171 | else: 172 | logging(" val:", var_str if detail else "*") 173 | elif type(var) == tuple: 174 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "tuple size:", len(var), end="") 175 | if layer < PEEK_LAYER and len(var) > 0: 176 | logging(f" ({min(len(var), 3)*'.'})") 177 | for no, item in enumerate(var): 178 | info(item, "item " + str(no) + ": ", detail, layer + 1) 179 | else: 180 | logging(" val:", var if detail else "*") 181 | elif type(var) == set: 182 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "set size:", len(var), end="") 183 | if layer < PEEK_LAYER and len(var) > 0: 184 | logging(" {" + min(len(var), 3)*'.' + "}") 185 | for no, item in enumerate(var): 186 | info(item, "item " + str(no) + ": ", detail, layer + 1) 187 | else: 188 | logging(" val:", var if detail else "*") 189 | elif type(var) == dict: 190 | dict_keys = sorted(list(var.keys())) 191 | dict_end = "" 192 | if len(dict_keys) >= 100: 193 | dict_keys = dict_keys[:100] 194 | dict_end = "... and extra items]" 195 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "dict {" + min(len(var), 3)*'.' + "} " + f"with {len(dict_keys)} keys", dict_keys, end=dict_end) 196 | if layer < PEEK_LAYER and len(var) > 0: 197 | logging("") 198 | for key in dict_keys: 199 | info(var[key], key, detail, layer + 1) 200 | else: 201 | logging(" val:", var if detail else "*") 202 | elif type(var) == OrderedDict: 203 | dict_keys = sorted(list(var.keys())) 204 | dict_end = "" 205 | if len(dict_keys) >= 100: 206 | dict_keys = dict_keys[:100] 207 | dict_end = "... and extra items]" 208 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "OrderedDict {" + min(len(var), 3)*'.' + "} " + f"with {len(dict_keys)} keys", dict_keys, end=dict_end) 209 | if layer < PEEK_LAYER and len(var) > 0: 210 | logging("") 211 | for key in dict_keys: 212 | info(var[key], key, detail, layer + 1) 213 | else: 214 | logging(" val:", var if detail else "*") 215 | elif type(var) == defaultdict: 216 | tmp_val = 12341231354124 217 | assert tmp_val not in var 218 | default_val = var[tmp_val] 219 | del var[tmp_val] 220 | 221 | dict_keys = sorted(list(var.keys())) 222 | dict_end = "" 223 | if len(dict_keys) >= 100: 224 | dict_keys = dict_keys[:100] 225 | dict_end = "... and extra items]" 226 | 227 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "defaultdict {" + min(len(var), 3)*'.' + "} with default", default_val, f"{len(dict_keys)} keys", dict_keys, end=dict_end) 228 | if layer < PEEK_LAYER and len(var) > 0: 229 | logging("") 230 | for key in dict_keys: 231 | info(var[key], key, detail, layer + 1) 232 | else: 233 | logging(" val:", var if detail else "*") 234 | elif type(var) == torch.Tensor: 235 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "Tensor size:", var.shape, "val:", var if detail else "*") 236 | print_image(var, name) 237 | elif type(var) == np.ndarray: 238 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "ndarray size:", var.shape, 239 | "val:", var if detail else "*") 240 | print_image(var, name, True) 241 | elif tf is not None and type(var) == tf.Tensor: 242 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", "Tensor size:", var.shape, "val:", var if detail else "*") 243 | elif Image is not None and type(var) == Image.Image: 244 | print_image(var, name) 245 | else: 246 | var_type = str(type(var)).split("'")[1] 247 | try: 248 | if layer >= PEEK_LAYER: 249 | raise Exception("Too many layers") 250 | props = var.__dict__ 251 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", var_type, "with props", list(props.keys()), end="") 252 | prop_valid = False 253 | for key in props: 254 | if not key.startswith("_"): 255 | if not prop_valid: 256 | prop_valid = True 257 | logging("") 258 | info(props[key], key, detail, layer + 1) 259 | if not prop_valid: 260 | logging(" val:", var) 261 | except Exception: 262 | var_type = str(type(var))[8:-2] 263 | logging(space * layer, f"\033[0m\033[1;36m{name}\033[0m\033[1;33m", var_type, "with val: ", var) 264 | 265 | 266 | def debug(*args, **kwargs): 267 | """debug打印主入口""" 268 | global ON_DEBUG 269 | global debug_count 270 | global debug_file 271 | global TO_FILE 272 | global PLAIN 273 | if not ON_DEBUG: 274 | return 275 | if TO_FILE: 276 | try: 277 | debug_file = open(log_path, "a", encoding='utf-8') 278 | except: 279 | debug_file = None 280 | logging("------------------\033[0m\033[1;31m", get_time(), "\033[0m\033[1;33m------------------") 281 | if PLAIN: 282 | logging(*args, **kwargs, end="\n") 283 | if TO_FILE and debug_file is not None: 284 | debug_file.close() 285 | return 286 | global FULL 287 | if FULL: 288 | torch.set_printoptions(profile="full") 289 | np.set_printoptions(threshold=sys.maxsize) 290 | count = 0 291 | if MAX_LOG != -1 and debug_count >= MAX_LOG: 292 | if debug_file: 293 | debug_file.close() 294 | return 295 | detail = True 296 | if args and type(args[0]) is bool: 297 | detail = args[0] 298 | args = args[1:] 299 | keys = list(kwargs.keys()) 300 | if len(args) + len(kwargs) == 0: 301 | logging(f"\033[0m\033[1;32mMARK:\033[0m\033[1;33m at \033[0m\033[1;32m{get_pos(level=2)}\033[0m\033[1;33m") 302 | elif len(args) == 1 and len(kwargs) == 0 and type(args[0]) == str: 303 | logging(f"\033[0m\033[1;32mDEBUG:\033[0m\033[1;33m at \033[0m\033[1;32m{get_pos(level=2)}\033[0m\033[1;33m") 304 | logging(f"\033[0m\033[1;36m{args[0]}\033[0m\033[1;33m") 305 | else: 306 | logging(f"\033[0m\033[1;32mDEBUG:\033[0m\033[1;33m {len(args) + len(kwargs)} vars: {['?' for _ in args] + keys}, at \033[0m\033[1;32m{get_pos(level=2)}\033[0m\033[1;33m") 307 | for var in args: 308 | logging(f"{count} / {debug_count}.", end=" ") 309 | info(var, detail=detail) 310 | debug_count += 1 311 | count += 1 312 | for key in keys: 313 | logging(f"{count} / {debug_count}.", end=" ") 314 | info(kwargs[key], key, detail=detail) 315 | count += 1 316 | debug_count += 1 317 | logging("------------------\033[0m\033[1;31m", get_time(), "\033[0m\033[1;33m------------------") 318 | if TO_FILE and debug_file is not None: 319 | debug_file.close() 320 | 321 | def mail(subject = "Progress Notification", message = ""): 322 | from email.mime.text import MIMEText 323 | subject = f"[SUPERDEBUG] {subject}" 324 | message = f"{message}\nThis email is sent at {get_pos(level=2)}" 325 | mail = MIMEText(message) 326 | mail['Subject'] = subject 327 | mail['From'] = MY_QQ_EMAIL 328 | mail['To'] = MY_QQ_EMAIL 329 | 330 | import smtplib 331 | smtp=smtplib.SMTP() 332 | smtp.connect('smtp.qq.com', 25) 333 | smtp.login(MY_QQ_EMAIL, MY_QQ_EMAIL_PWD) 334 | 335 | smtp.sendmail(MY_QQ_EMAIL, MY_QQ_EMAIL, mail.as_string()) # To是接收邮箱 336 | -------------------------------------------------------------------------------- /superdebug/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Azure-Vision/SuperDebug/e8e705eaea27c0cf7e69eaaf283cf6bab142387e/superdebug/__pycache__/__init__.cpython-38.pyc --------------------------------------------------------------------------------