作业2 if _name_==‘_main_‘:的含义,why?

if __name__ == '__main__': 这一行在 Python 中的含义是用来判断当前脚本是被直接运行还是被作为模块导入。解释:

1、__name__:这是一个 Python 内置的特殊变量,表示当前模块的名称。如果脚本是直接运行的,__name__ 的值会是 '__main__'。如果脚本作为模块被导入到其他脚本中,__name__ 的值则是该模块的名称(即文件名,不带 .py 扩展名)。

2、'__main__':这是一个字符串,表示当前脚本是作为主程序直接执行的,而不是作为模块被导入。

 

为什么要使用这种结构?

答:这种结构让脚本既可以作为独立的程序运行,也可以作为模块被其他程序导入,增加了代码的复用性。也避免不必要的代码执行,当脚本作为模块导入时,你不希望脚本中的一些代码被自动执行。使用 if __name__ == '__main__': 可以确保只有函数和类会被导入,其他代码则不会自动执行。

"""run_train.py Main HoVer-Net training script. Usage: run_train.py [--gpu=<id>] [--view=<dset>] run_train.py (-h | --help) run_train.py --version Options: -h --help Show this string. --version Show version. --gpu=<id> Comma separated GPU list. [default: 0,1,2,3] --view=<dset> Visualise images after augmentation. Choose 'train' or 'valid'. """ import cv2 cv2.setNumThreads(0) import argparse import glob import importlib import inspect import json import os import shutil import matplotlib import numpy as np import torch from docopt import docopt from tensorboardX import SummaryWriter from torch.nn import DataParallel # TODO: switch to DistributedDataParallel from torch.utils.data import DataLoader from config import Config from dataloader.train_loader import FileLoader from misc.utils import rm_n_mkdir from run_utils.engine import RunEngine from run_utils.utils import ( check_log_dir, check_manual_seed, colored, convert_pytorch_checkpoint, ) #### have to move outside because of spawn # * must initialize augmentor per worker, else duplicated rng generators may happen def worker_init_fn(worker_id): # ! to make the seed chain reproducible, must use the torch random, not numpy # the torch rng from main thread will regenerate a base seed, which is then # copied into the dataloader each time it created (i.e start of each epoch) # then dataloader with this seed will spawn worker, now we reseed the worker worker_info = torch.utils.data.get_worker_info() # to make it more random, simply switch torch.randint to np.randint worker_seed = torch.randint(0, 2 ** 32, (1,))[0].cpu().item() + worker_id # print('Loader Worker %d Uses RNG Seed: %d' % (worker_id, worker_seed)) # retrieve the dataset copied into this worker process # then set the random seed for each augmentation worker_info.dataset.setup_augmentor(worker_id, worker_seed) return #### class TrainManager(Config): """Either used to view the dataset or to initialise the main training loop.""" def __init__(self): super().__init__() return #### def view_dataset(self, mode="train"): """ Manually change to plt.savefig or plt.show if using on headless machine or not """ self.nr_gpus = 1 import matplotlib.pyplot as plt check_manual_seed(self.seed) # TODO: what if each phase want diff annotation ? phase_list = self.model_config["phase_list"][0] target_info = phase_list["target_info"] prep_func, prep_kwargs = target_info["viz"] dataloader = self._get_datagen(2, mode, target_info["gen"]) for batch_data in dataloader: # convert from Tensor to Numpy batch_data = {k: v.numpy() for k, v in batch_data.items()} viz = prep_func(batch_data, is_batch=True, **prep_kwargs) plt.imshow(viz) plt.show() self.nr_gpus = -1 return #### def _get_datagen(self, batch_size, run_mode, target_gen, nr_procs=0, fold_idx=0): nr_procs = nr_procs if not self.debug else 0 # ! Hard assumption on file type file_list = [] if run_mode == "train": data_dir_list = self.train_dir_list else: data_dir_list = self.valid_dir_list for dir_path in data_dir_list: file_list.extend(glob.glob("%s/*.npy" % dir_path)) file_list.sort() # to always ensure same input ordering assert len(file_list) > 0, ( "No .npy found for `%s`, please check `%s` in `config.py`" % (run_mode, "%s_dir_list" % run_mode) ) print("Dataset %s: %d" % (run_mode, len(file_list))) input_dataset = FileLoader( file_list, mode=run_mode, with_type=self.type_classification, setup_augmentor=nr_procs == 0, target_gen=target_gen, **self.shape_info[run_mode] ) dataloader = DataLoader( input_dataset, num_workers=nr_procs, batch_size=batch_size * self.nr_gpus, shuffle=run_mode == "train", drop_last=run_mode == "train", worker_init_fn=worker_init_fn, ) return dataloader #### def run_once(self, opt, run_engine_opt, log_dir, prev_log_dir=None, fold_idx=0): """Simply run the defined run_step of the related method once.""" check_manual_seed(self.seed) log_info = {} if self.logging: # check_log_dir(log_dir) rm_n_mkdir(log_dir) tfwriter = SummaryWriter(log_dir=log_dir) json_log_file = log_dir + "/stats.json" with open(json_log_file, "w") as json_file: json.dump({}, json_file) # create empty file log_info = { "json_file": json_log_file, "tfwriter": tfwriter, } #### loader_dict = {} for runner_name, runner_opt in run_engine_opt.items(): loader_dict[runner_name] = self._get_datagen( opt["batch_size"][runner_name], runner_name, opt["target_info"]["gen"], nr_procs=runner_opt["nr_procs"], fold_idx=fold_idx, ) #### def get_last_chkpt_path(prev_phase_dir, net_name): stat_file_path = prev_phase_dir + "/stats.json" with open(stat_file_path) as stat_file: info = json.load(stat_file) epoch_list = [int(v) for v in info.keys()] last_chkpts_path = "%s/%s_epoch=%d.tar" % ( prev_phase_dir, net_name, max(epoch_list), ) return last_chkpts_path # TODO: adding way to load pretrained weight or resume the training # parsing the network and optimizer information net_run_info = {} net_info_opt = opt["run_info"] for net_name, net_info in net_info_opt.items(): assert inspect.isclass(net_info["desc"]) or inspect.isfunction( net_info["desc"] ), "`desc` must be a Class or Function which instantiate NEW objects !!!" net_desc = net_info["desc"]() # TODO: customize print-out for each run ? # summary_string(net_desc, (3, 270, 270), device='cpu') pretrained_path = net_info["pretrained"] if pretrained_path is not None: if pretrained_path == -1: # * depend on logging format so may be broken if logging format has been changed pretrained_path = get_last_chkpt_path(prev_log_dir, net_name) net_state_dict = torch.load(pretrained_path)["desc"] else: chkpt_ext = os.path.basename(pretrained_path).split(".")[-1] if chkpt_ext == "npz": net_state_dict = dict(np.load(pretrained_path)) net_state_dict = { k: torch.from_numpy(v) for k, v in net_state_dict.items() } elif chkpt_ext == "tar": # ! assume same saving format we desire net_state_dict = torch.load(pretrained_path)["desc"] colored_word = colored(net_name, color="red", attrs=["bold"]) print( "Model `%s` pretrained path: %s" % (colored_word, pretrained_path) ) # load_state_dict returns (missing keys, unexpected keys) net_state_dict = convert_pytorch_checkpoint(net_state_dict) load_feedback = net_desc.load_state_dict(net_state_dict, strict=False) # * uncomment for your convenience print("Missing Variables: \n", load_feedback[0]) print("Detected Unknown Variables: \n", load_feedback[1]) # * extremely slow to pass this on DGX with 1 GPU, why (?) net_desc = DataParallel(net_desc) net_desc = net_desc.to("cuda") # print(net_desc) # * dump network definition or not? optimizer, optimizer_args = net_info["optimizer"] optimizer = optimizer(net_desc.parameters(), **optimizer_args) # TODO: expand for external aug for scheduler nr_iter = opt["nr_epochs"] * len(loader_dict["train"]) scheduler = net_info["lr_scheduler"](optimizer) net_run_info[net_name] = { "desc": net_desc, "optimizer": optimizer, "lr_scheduler": scheduler, # TODO: standardize API for external hooks "extra_info": net_info["extra_info"], } # parsing the running engine configuration assert ( "train" in run_engine_opt ), "No engine for training detected in description file" # initialize runner and attach callback afterward # * all engine shared the same network info declaration runner_dict = {} for runner_name, runner_opt in run_engine_opt.items(): runner_dict[runner_name] = RunEngine( dataloader=loader_dict[runner_name], engine_name=runner_name, run_step=runner_opt["run_step"], run_info=net_run_info, log_info=log_info, ) for runner_name, runner in runner_dict.items(): callback_info = run_engine_opt[runner_name]["callbacks"] for event, callback_list, in callback_info.items(): for callback in callback_list: if callback.engine_trigger: triggered_runner_name = callback.triggered_engine_name callback.triggered_engine = runner_dict[triggered_runner_name] runner.add_event_handler(event, callback) # retrieve main runner main_runner = runner_dict["train"] main_runner.state.logging = self.logging main_runner.state.log_dir = log_dir # start the run loop main_runner.run(opt["nr_epochs"]) print("\n") print("########################################################") print("########################################################") print("\n") return #### def run(self): """Define multi-stage run or cross-validation or whatever in here.""" self.nr_gpus = torch.cuda.device_count() print('Detect #GPUS: %d' % self.nr_gpus) phase_list = self.model_config["phase_list"] engine_opt = self.model_config["run_engine"] prev_save_path = None for phase_idx, phase_info in enumerate(phase_list): if len(phase_list) == 1: save_path = self.log_dir else: save_path = self.log_dir + "/%02d/" % (phase_idx) self.run_once( phase_info, engine_opt, save_path, prev_log_dir=prev_save_path ) prev_save_path = save_path #### if __name__ == "__main__": args = docopt(__doc__, version="HoVer-Net v1.0") trainer = TrainManager() if args["--view"]: if args["--view"] != "train" and args["--view"] != "valid": raise Exception('Use "train" or "valid" for --view.') trainer.view_dataset(args["--view"]) else: os.environ["CUDA_VISIBLE_DEVICES"] = args["--gpu"] trainer. Run() 一句一句解释这段代码的意思
08-13
以下是run_train.py的全部代码,结合上述问题,做出诊断: import matplotlib matplotlib.use("TkAgg", force=True) # force参数确保覆盖其他设置 import cv2 cv2.setNumThreads(0) import argparse import glob import importlib import inspect import json import os import shutil import matplotlib import numpy as np import torch from docopt import docopt from tensorboardX import SummaryWriter from torch.nn import DataParallel # TODO: switch to DistributedDataParallel from torch.utils.data import DataLoader from config import Config from dataloader.train_loader import FileLoader from misc.utils import rm_n_mkdir from run_utils.engine import RunEngine from run_utils.utils import ( check_log_dir, check_manual_seed, colored, convert_pytorch_checkpoint, ) #### have to move outside because of spawn # * must initialize augmentor per worker, else duplicated rng generators may happen def worker_init_fn(worker_id): # ! to make the seed chain reproducible, must use the torch random, not numpy # the torch rng from main thread will regenerate a base seed, which is then # copied into the dataloader each time it created (i.e start of each epoch) # then dataloader with this seed will spawn worker, now we reseed the worker worker_info = torch.utils.data.get_worker_info() # to make it more random, simply switch torch.randint to np.randint worker_seed = torch.randint(0, 2 ** 32, (1,))[0].cpu().item() + worker_id # print('Loader Worker %d Uses RNG Seed: %d' % (worker_id, worker_seed)) # retrieve the dataset copied into this worker process # then set the random seed for each augmentation worker_info.dataset.setup_augmentor(worker_id, worker_seed) return #### class TrainManager(Config): """Either used to view the dataset or to initialise the main training loop.""" def __init__(self): super().__init__() return #### def view_dataset(self, mode="train"): """ Manually change to plt.savefig or plt.show if using on headless machine or not """ self.nr_gpus = 1 import matplotlib.pyplot as plt check_manual_seed(self.seed) # TODO: what if each phase want diff annotation ? phase_list = self.model_config["phase_list"][0] target_info = phase_list["target_info"] prep_func, prep_kwargs = target_info["viz"] dataloader = self._get_datagen(2, mode, target_info["gen"]) for batch_data in dataloader: # convert from Tensor to Numpy batch_data = {k: v.numpy() for k, v in batch_data.items()} viz = prep_func(batch_data, is_batch=True, **prep_kwargs) plt.imshow(viz) print("训练脚本实际后端:", matplotlib.get_backend()) plt.show() self.nr_gpus = -1 return #### def _get_datagen(self, batch_size, run_mode, target_gen, nr_procs=0, fold_idx=0): nr_procs = nr_procs if not self.debug else 0 # ! Hard assumption on file type file_list = [] if run_mode == "train": data_dir_list = self.train_dir_list else: data_dir_list = self.valid_dir_list #print(data_dir_list,'#####') for dir_path in data_dir_list: file_list.extend(glob.glob("%s/*.npy" % dir_path)) file_list.sort() # to always ensure same input ordering assert len(file_list) > 0, ( "No .npy found for `%s`, please check `%s` in `config.py`" % (run_mode, "%s_dir_list" % run_mode) ) print("Dataset %s: %d" % (run_mode, len(file_list))) input_dataset = FileLoader( file_list, mode=run_mode, with_type=self.type_classification, setup_augmentor=nr_procs == 0, target_gen=target_gen, **self.shape_info[run_mode] ) dataloader = DataLoader( input_dataset, num_workers=nr_procs, batch_size=batch_size * self.nr_gpus, shuffle=run_mode == "train", drop_last=run_mode == "train", worker_init_fn=worker_init_fn, ) return dataloader #### def run_once(self, opt, run_engine_opt, log_dir, prev_log_dir=None, fold_idx=0): """Simply run the defined run_step of the related method once.""" check_manual_seed(self.seed) log_info = {} if self.logging: # check_log_dir(log_dir) rm_n_mkdir(log_dir) tfwriter = SummaryWriter(log_dir=log_dir) json_log_file = log_dir + "/stats.json" with open(json_log_file, "w") as json_file: json.dump({}, json_file) # create empty file log_info = { "json_file": json_log_file, "tfwriter": tfwriter, } #### loader_dict = {} for runner_name, runner_opt in run_engine_opt.items(): loader_dict[runner_name] = self._get_datagen( opt["batch_size"][runner_name], runner_name, opt["target_info"]["gen"], nr_procs=runner_opt["nr_procs"], fold_idx=fold_idx, ) #### def get_last_chkpt_path(prev_phase_dir, net_name): stat_file_path = prev_phase_dir + "/stats.json" with open(stat_file_path) as stat_file: info = json.load(stat_file) epoch_list = [int(v) for v in info.keys()] last_chkpts_path = "%s/%s_epoch=%d.tar" % ( prev_phase_dir, net_name, max(epoch_list), ) return last_chkpts_path # TODO: adding way to load pretrained weight or resume the training # parsing the network and optimizer information net_run_info = {} net_info_opt = opt["run_info"] for net_name, net_info in net_info_opt.items(): assert inspect.isclass(net_info["desc"]) or inspect.isfunction( net_info["desc"] ), "`desc` must be a Class or Function which instantiate NEW objects !!!" net_desc = net_info["desc"]() # TODO: customize print-out for each run ? # summary_string(net_desc, (3, 270, 270), device='cpu') pretrained_path = net_info["pretrained"] if pretrained_path is not None: if pretrained_path == -1: # * depend on logging format so may be broken if logging format has been changed pretrained_path = get_last_chkpt_path(prev_log_dir, net_name) net_state_dict = torch.load(pretrained_path)["desc"] else: chkpt_ext = os.path.basename(pretrained_path).split(".")[-1] if chkpt_ext == "npz": net_state_dict = dict(np.load(pretrained_path)) net_state_dict = { k: torch.from_numpy(v) for k, v in net_state_dict.items() } elif chkpt_ext == "tar": # ! assume same saving format we desire net_state_dict = torch.load(pretrained_path)["desc"] colored_word = colored(net_name, color="red", attrs=["bold"]) print( "Model `%s` pretrained path: %s" % (colored_word, pretrained_path) ) # load_state_dict returns (missing keys, unexpected keys) net_state_dict = convert_pytorch_checkpoint(net_state_dict) load_feedback = net_desc.load_state_dict(net_state_dict, strict=False) # * uncomment for your convenience print("Missing Variables: \n", load_feedback[0]) print("Detected Unknown Variables: \n", load_feedback[1]) # * extremely slow to pass this on DGX with 1 GPU, why (?) net_desc = DataParallel(net_desc) net_desc = net_desc.to("cuda") # print(net_desc) # * dump network definition or not? optimizer, optimizer_args = net_info["optimizer"] optimizer = optimizer(net_desc.parameters(), **optimizer_args) # TODO: expand for external aug for scheduler nr_iter = opt["nr_epochs"] * len(loader_dict["train"]) scheduler = net_info["lr_scheduler"](optimizer) net_run_info[net_name] = { "desc": net_desc, "optimizer": optimizer, "lr_scheduler": scheduler, # TODO: standardize API for external hooks "extra_info": net_info["extra_info"], } # parsing the running engine configuration assert ( "train" in run_engine_opt ), "No engine for training detected in description file" # initialize runner and attach callback afterward # * all engine shared the same network info declaration runner_dict = {} for runner_name, runner_opt in run_engine_opt.items(): runner_dict[runner_name] = RunEngine( dataloader=loader_dict[runner_name], engine_name=runner_name, run_step=runner_opt["run_step"], run_info=net_run_info, log_info=log_info, ) for runner_name, runner in runner_dict.items(): callback_info = run_engine_opt[runner_name]["callbacks"] for event, callback_list, in callback_info.items(): for callback in callback_list: if callback.engine_trigger: triggered_runner_name = callback.triggered_engine_name callback.triggered_engine = runner_dict[triggered_runner_name] runner.add_event_handler(event, callback) # retrieve main runner main_runner = runner_dict["train"] main_runner.state.logging = self.logging main_runner.state.log_dir = log_dir # start the run loop main_runner.run(opt["nr_epochs"]) print("\n") print("########################################################") print("########################################################") print("\n") return #### def run(self): """Define multi-stage run or cross-validation or whatever in here.""" self.nr_gpus = torch.cuda.device_count() print('Detect #GPUS: %d' % self.nr_gpus) phase_list = self.model_config["phase_list"] engine_opt = self.model_config["run_engine"] prev_save_path = None for phase_idx, phase_info in enumerate(phase_list): if len(phase_list) == 1: save_path = self.log_dir else: save_path = self.log_dir + "/%02d/" % (phase_idx) self.run_once( phase_info, engine_opt, save_path, prev_log_dir=prev_save_path ) prev_save_path = save_path #### if __name__ == "__main__": args = docopt(__doc__, version="HoVer-Net v1.0") trainer = TrainManager() if args["--view"]: if args["--view"] != "train" and args["--view"] != "valid": raise Exception('Use "train" or "valid" for --view.') trainer.view_dataset(args["--view"]) else: os.environ["CUDA_VISIBLE_DEVICES"] = args["--gpu"] trainer.run()
08-17
=== 函数签名 === 函数名: Diag21_PID_C9 参数: U1 u1_a_num === 所有变量分析 === 类型 | 名称 | 作用域 | 行号 -------------------------------------------------- 参数 | u1_a_num | 参数 | 1 变量 | u1_t_cmplt | 全局 | 3 变量 | u1_t_cnt | 全局 | 4 变量 | sensor | 全局 | 5 变量 | u1_t_swrstcnt | 全局 | 7 变量 | u4_g_cmd | 全局 | 11 变量 | u4_g_data | 全局 | 11 变量 | u2_g_buf | 全局 | 13 变量 | u1_g_data | 全局 | 13 函数调用 | u1_g_InspSoftwareVersion | 调用 | 11 函数调用 | vd_s_Diag21_U2ToU1 | 调用 | 13 未找到局部变量 === 使用的全局变量 === u1_t_cmplt (行号: 3) u1_t_cnt (行号: 4) sensor (行号: 5) u1_t_swrstcnt (行号: 7) u4_g_cmd (行号: 11) u4_g_data (行号: 11) u2_g_buf (行号: 13) u1_g_data (行号: 13) === 函数调用 === 函数名: u1_g_InspSoftwareVersion (行号: 11) 返回类型: U1 参数: u4_g_cmd, &u4_g_data, (U1)TRUE -------------------------------------------------- 函数名: vd_s_Diag21_U2ToU1 (行号: 13) 返回类型: void 参数: u2_g_buf, u1_g_data, (U1)DIAG21_PIDC9_FLAG -------------------------------------------------- === 解析统计 === 参数数量: 1 局部变量数量: 0 全局变量数量: 8 函数调用数量: 2 总变量数量: 11 1、没有识别出来局部变量 2、结构体要单独识别出来,不要与变量混为一谈 3、结构体不一定会在函数体内被定义,可能在函数体外被声明,函数体内只是SensorData sensor; 代码如下: import tkinter as tk from tkinter import scrolledtext, ttk, messagebox import logging from datetime import datetime import traceback class SimpleCLexer: def __init__(self): self.tokens = [] def tokenize(self, input_str): tokens = [] pos = 0 line = 1 column = 0 length = len(input_str) # 定义C语言的关键词和类型 keywords = { 'void', 'int', 'char', 'float', 'double', 'short', 'long', 'signed', 'unsigned', 'struct', 'union', 'enum', 'typedef', 'static', 'extern', 'auto', 'register', 'const', 'volatile', 'return', 'if', 'else', 'switch', 'case', 'default', 'for', 'while', 'do', 'break', 'continue', 'goto', 'sizeof' } # 扩展类型别名识别 types = {'U1', 'U2', 'U4', 'S1', 'S2', 'S4', 'BOOL', 'BYTE', 'WORD', 'DWORD'} while pos < length: char = input_str[pos] # 跳过空白字符 if char in ' \t': pos += 1 column += 1 continue # 处理换行 if char == '\n': line += 1 column = 0 pos += 1 continue # 处理单行注释 if pos + 1 < length and input_str[pos:pos+2] == '//': end = input_str.find('\n', pos) if end == -1: end = length pos = end continue # 处理多行注释 if pos + 1 < length and input_str[pos:pos+2] == '/*': end = input_str.find('*/', pos + 2) if end == -1: end = length else: end += 2 pos = end continue # 处理标识符 if char.isalpha() or char == '_': start = pos pos += 1 while pos < length and (input_str[pos].isalnum() or input_str[pos] == '_'): pos += 1 token_text = input_str[start:pos] token_type = 'IDENTIFIER' # 检查是否为关键字或类型 if token_text in keywords: token_type = 'KEYWORD' elif token_text in types: token_type = 'TYPE' tokens.append({ 'type': token_type, 'text': token_text, 'line': line, 'column': column }) column += (pos - start) continue # 处理数字 if char.isdigit(): start = pos pos += 1 while pos < length and (input_str[pos].isdigit() or input_str[pos] in '.xXabcdefABCDEF'): pos += 1 tokens.append({ 'type': 'NUMBER', 'text': input_str[start:pos], 'line': line, 'column': column }) column += (pos - start) continue # 处理字符串 if char == '"': start = pos pos += 1 while pos < length and input_str[pos] != '"': if input_str[pos] == '\\' and pos + 1 < length: pos += 2 else: pos += 1 if pos < length and input_str[pos] == '"': pos += 1 tokens.append({ 'type': 'STRING', 'text': input_str[start:pos], 'line': line, 'column': column }) column += (pos - start) continue # 处理字符 if char == "'": start = pos pos += 1 while pos < length and input_str[pos] != "'": if input_str[pos] == '\\' and pos + 1 < length: pos += 2 else: pos += 1 if pos < length and input_str[pos] == "'": pos += 1 tokens.append({ 'type': 'CHAR', 'text': input_str[start:pos], 'line': line, 'column': column }) column += (pos - start) continue # 处理运算符和标点符号 operators = { '(', ')', '{', '}', '[', ']', ';', ',', '.', '->', '++', '--', '&', '*', '+', '-', '~', '!', '/', '%', '<<', '>>', '<', '>', '<=', '>=', '==', '!=', '^', '|', '&&', '||', '?', ':', '=', '+=', '-=', '*=', '/=', '%=', '<<=', '>>=', '&=', '^=', '|=', ',' } # 尝试匹配最长的运算符 matched = False for op_len in range(3, 0, -1): if pos + op_len <= length and input_str[pos:pos+op_len] in operators: tokens.append({ 'type': 'OPERATOR', 'text': input_str[pos:pos+op_len], 'line': line, 'column': column }) pos += op_len column += op_len matched = True break if matched: continue # 无法识别的字符 tokens.append({ 'type': 'UNKNOWN', 'text': char, 'line': line, 'column': column }) pos += 1 column += 1 return tokens class FunctionAnalyzer: def __init__(self): self.function_name = "" self.parameters = [] self.global_vars = [] self.function_calls = [] self.in_function = False self.in_function_body = False self.brace_depth = 0 self.variable_declarations = {} self.macro_definitions = set() self.storage_classes = {"static", "extern", "auto", "register"} self.local_vars = [] # 局部变量列表 self.struct_tags = set() # 存储识别的结构体标签 self.recorded_locals = set() # 新增:跟踪已记录的局部变量 self.recorded_globals = set() self.recorded_params = set() # 定义允许的类型 self.basic_types = {'void', 'int', 'char', 'float', 'double', 'short', 'long', 'signed', 'unsigned'} self.type_aliases = {"U1", "U2", "U4", "S1", "S2", "S4"} self.allowed_types = self.basic_types | self.type_aliases self.allowed_types.add('struct') # 添加结构体支持 self.debug_level = 2 # 1=基本, 2=详细 def analyze(self, tokens): self.tokens = tokens self.pos = 0 self.current_line = 0 self.brace_depth = 0 # 识别宏定义 self._identify_macros() while self.pos < len(self.tokens): token = self.tokens[self.pos] self.current_line = token['line'] # 检测结构体类型声明 if token['text'] == 'struct' and self.pos + 1 < len(self.tokens): next_token = self.tokens[self.pos + 1] if next_token['type'] == 'IDENTIFIER': self.struct_tags.add(next_token['text']) self.allowed_types.add(next_token['text']) # 添加为有效类型 self.pos += 2 # 跳过 'struct' 和标识符 continue # 跟踪大括号深度 if token['text'] == '{': self.brace_depth += 1 if self.in_function and self.brace_depth == 1: self.in_function_body = True elif token['text'] == '}': self.brace_depth -= 1 if self.brace_depth == 0 and self.in_function: self.in_function = False self.in_function_body = False # 检测函数定义 if token['text'] in self.storage_classes or token['text'] in self.allowed_types: if self._is_function_definition(): self._handle_function_definition() continue # 检测变量声明 - 只在函数体内处理 if self.in_function_body and token['text'] in self.allowed_types: # 检查下一个token是否是标识符(变量名) if self.pos + 1 < len(self.tokens) and \ self.tokens[self.pos + 1]['type'] == 'IDENTIFIER': # 确保不是函数返回类型 if self.pos + 2 < len(self.tokens) and self.tokens[self.pos + 2]['text'] != '(': self._handle_variable_declaration() continue # 检测函数调用 if token['type'] == 'IDENTIFIER' and self.pos + 1 < len(self.tokens): next_token = self.tokens[self.pos + 1] if next_token['text'] == '(': self._handle_function_call() continue # 检测变量使用 if token['type'] == 'IDENTIFIER': self._handle_identifier_use(token) self.pos += 1 return self def _identify_macros(self): """识别宏定义(全大写标识符)""" for token in self.tokens: if token['type'] == 'IDENTIFIER' and token['text'].isupper(): self.macro_definitions.add(token['text']) def _is_function_definition(self): pos = self.pos storage_class = None # 检测存储类说明符 if self.tokens[pos]['text'] in self.storage_classes: storage_class = self.tokens[pos]['text'] pos += 1 # 检测返回类型 if pos >= len(self.tokens) or self.tokens[pos]['text'] not in self.allowed_types: return False return_type = self.tokens[pos]['text'] pos += 1 # 处理指针声明 if pos < len(self.tokens) and self.tokens[pos]['text'] == '*': return_type += '*' pos += 1 # 检测函数名 if pos < len(self.tokens) and self.tokens[pos]['type'] == 'IDENTIFIER': func_name = self.tokens[pos]['text'] pos += 1 else: return False # 检测参数列表开头的'(' if pos < len(self.tokens) and self.tokens[pos]['text'] == '(': pos += 1 else: return False # 检测函数体开头的'{' (允许最多5个token的间隔) found_brace = False for i in range(min(5, len(self.tokens) - pos)): if self.tokens[pos + i]['text'] == '{': found_brace = True break return found_brace def _handle_function_definition(self): start_pos = self.pos storage_class = None # 处理存储类说明符 if self.tokens[self.pos]['text'] in self.storage_classes: storage_class = self.tokens[self.pos]['text'] self.pos += 1 # 获取返回类型 return_type = self.tokens[self.pos]['text'] self.pos += 1 # 处理指针声明 if self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == '*': return_type += '*' self.pos += 1 # 获取函数名 if self.pos < len(self.tokens) and self.tokens[self.pos]['type'] == 'IDENTIFIER': func_name = self.tokens[self.pos]['text'] self.pos += 1 else: self.pos = start_pos return # 记录函数名 self.function_name = func_name self.variable_declarations[func_name] = True # 跳过 '(' if self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == '(': self.pos += 1 # 提取参数 params = [] current_param = [] depth = 1 param_line = self.current_line while self.pos < len(self.tokens) and depth > 0: token = self.tokens[self.pos] if token['text'] == '(': depth += 1 elif token['text'] == ')': depth -= 1 if depth == 0: break elif token['text'] == ',' and depth == 1: # 提取参数类型和名称 param_type, param_name = self._extract_param_info(current_param) if param_type and param_name: params.append({ 'type': param_type, 'name': param_name, 'line': param_line }) self.variable_declarations[param_name] = True current_param = [] param_line = token['line'] self.pos += 1 continue current_param.append(token) self.pos += 1 # 处理最后一个参数 if current_param: param_type, param_name = self._extract_param_info(current_param) if param_type and param_name: params.append({ 'type': param_type, 'name': param_name, 'line': param_line }) self.variable_declarations[param_name] = True # 记录参数 self.parameters = params # 记录参数为参数类型 for param in params: self.recorded_params.add(param['name']) self.variable_declarations[param['name']] = True # 查找函数体开头的'{' while self.pos < len(self.tokens) and self.tokens[self.pos]['text'] != '{': self.pos += 1 if self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == '{': self.in_function = True self.brace_depth = 0 self.pos += 1 return [p['name'] for p in params] if params else [] def _extract_param_info(self, tokens): """从参数token列表中提取类型和名称""" param_type = [] param_name = None for token in tokens: if token['type'] in ('KEYWORD', 'TYPE') or token['text'] in self.allowed_types: param_type.append(token['text']) elif token['type'] == 'IDENTIFIER' and not token['text'].isupper(): param_name = token['text'] return ' '.join(param_type), param_name def _handle_variable_declaration(self): start_pos = self.pos current_line = self.current_line # 获取变量类型 var_type = self.tokens[self.pos]['text'] # 处理结构体声明 if var_type == 'struct': # 收集结构体类型名 struct_type = [] self.pos += 1 while self.pos < len(self.tokens) and self.tokens[self.pos]['text'] != ';': token = self.tokens[self.pos] if token['text'] == '{': # 跳过结构体定义 depth = 1 self.pos += 1 while self.pos < len(self.tokens) and depth > 0: if self.tokens[self.pos]['text'] == '{': depth += 1 elif self.tokens[self.pos]['text'] == '}': depth -= 1 self.pos += 1 continue elif token['type'] == 'IDENTIFIER': struct_type.append(token['text']) self.pos += 1 if struct_type: var_type = 'struct ' + ' '.join(struct_type) else: var_type = 'struct' else: self.pos += 1 # 处理指针声明 while self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == '*': var_type += '*' self.pos += 1 var_names = [] # 处理变量名和声明 while self.pos < len(self.tokens): token = self.tokens[self.pos] # 结束声明 if token['text'] == ';': self.pos += 1 break # 标识符 - 变量名 if token['type'] == 'IDENTIFIER' and not token['text'].isupper(): var_name = token['text'] # 跳过宏定义 if var_name not in self.macro_definitions: var_names.append(var_name) self.variable_declarations[var_name] = True self.pos += 1 continue # 逗号 - 多个变量声明 elif token['text'] == ',': self.pos += 1 # 处理指针声明 if self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == '*': var_type += '*' self.pos += 1 continue # 数组声明 - 跳过数组大小 elif token['text'] == '[': self.pos += 1 depth = 1 while self.pos < len(self.tokens) and depth > 0: t = self.tokens[self.pos] if t['text'] == '[': depth += 1 elif t['text'] == ']': depth -= 1 self.pos += 1 continue # 初始化 - 跳过初始化表达式 elif token['text'] == '=': self.pos += 1 depth = 0 while self.pos < len(self.tokens): t = self.tokens[self.pos] if t['text'] in {'(', '['}: depth += 1 elif t['text'] in {')', ']'}: depth -= 1 elif t['text'] in {',', ';'} and depth == 0: break self.pos += 1 continue self.pos += 1 # 添加到局部变量 (确保在函数体内) if self.in_function_body: for var_name in var_names: # 检查是否在参数列表中 is_param = any(param['name'] == var_name for param in self.parameters) if not is_param and var_name not in self.macro_definitions: # 添加到局部变量 self.local_vars.append({ 'type': var_type, 'name': var_name, 'line': current_line, 'scope': 'local' }) # 记录为局部变量 self.recorded_locals.add(var_name) self.variable_declarations[var_name] = True else: # 全局变量处理 for var_name in var_names: if var_name not in self.recorded_globals: self.global_vars.append({ 'name': var_name, 'line': current_line, 'scope': 'global' }) self.recorded_globals.add(var_name) self.variable_declarations[var_name] = True def _handle_identifier_use(self, token): var_name = token['text'] line = token['line'] # 跳过已处理的标识符 skip_conditions = ( var_name in self.variable_declarations, var_name in self.macro_definitions, var_name == self.function_name, var_name in self.struct_tags # 跳过结构体标签 ) if any(skip_conditions): return # 函数体内使用的标识符 if self.in_function_body: # 如果未声明,则视为全局变量 if var_name not in self.recorded_locals and var_name not in self.recorded_globals: if var_name not in self.recorded_globals: self.global_vars.append({ 'name': var_name, 'line': line, 'scope': 'global' }) self.variable_declarations[var_name] = True self.recorded_globals.add(var_name) else: # 函数体外使用的标识符 if var_name not in self.recorded_globals: self.global_vars.append({ 'name': var_name, 'line': line, 'scope': 'global' }) self.variable_declarations[var_name] = True self.recorded_globals.add(var_name) def _handle_function_call(self): # 提取函数名 func_name = self.tokens[self.pos]['text'] line = self.current_line self.pos += 2 # 跳过函数名和 '(' # 提取参数 params = [] depth = 1 current_param = [] # 保存所有参数token用于后续分析 param_tokens = [] while self.pos < len(self.tokens) and depth > 0: token = self.tokens[self.pos] if token['text'] == '(': depth += 1 elif token['text'] == ')': depth -= 1 if depth == 0: break elif token['text'] == ',' and depth == 1: params.append(''.join([t['text'] for t in current_param]).strip()) param_tokens.extend(current_param) current_param = [] self.pos += 1 continue current_param.append(token) self.pos += 1 if current_param: params.append(''.join([t['text'] for t in current_param]).strip()) param_tokens.extend(current_param) # 跳过 ')' 如果还在范围内 if self.pos < len(self.tokens) and self.tokens[self.pos]['text'] == ')': self.pos += 1 # 处理参数中的标识符 for token in param_tokens: if token['type'] == 'IDENTIFIER' and not token['text'].isupper(): self._handle_identifier_use(token) # 确定返回类型 return_type = "unknown" if func_name.startswith("vd_"): return_type = "void" elif func_name.startswith(("u1_", "u2_", "u4_", "s1_", "s2_", "s4_")): prefix = func_name.split("_")[0] return_type = prefix.upper() # 添加到函数调用列表 self.function_calls.append({ 'name': func_name, 'return_type': return_type, 'params': ", ".join(params), 'line': line }) class FunctionParserApp: def __init__(self, root): self.root = root self.root.title("C语言函数解析器") self.root.geometry("1000x800") self.root.configure(bg="#f0f0f0") self.setup_logging() # 创建样式 style = ttk.Style() style.configure("TFrame", background="#f0f0f0") style.configure("TLabelFrame", background="#f0f0f0", font=("Arial", 10, "bold")) style.configure("TButton", font=("Arial", 10), padding=5) style.configure("TCombobox", padding=5) style.configure("TProgressbar", thickness=10) # 主框架 main_frame = ttk.Frame(root) main_frame.pack(fill="both", expand=True, padx=15, pady=15) # 创建输入区域 input_frame = ttk.LabelFrame(main_frame, text="输入C语言函数体") input_frame.pack(fill="both", expand=True, padx=5, pady=5) self.input_text = scrolledtext.ScrolledText(input_frame, width=100, height=15, font=("Consolas", 11), bg="#ffffff") self.input_text.pack(fill="both", expand=True, padx=10, pady=10) # 按钮区域 btn_frame = ttk.Frame(main_frame) btn_frame.pack(fill="x", padx=5, pady=5) # 解析按钮 parse_btn = ttk.Button(btn_frame, text="解析函数", command=self.parse_function) parse_btn.pack(side="left", padx=5) # 保存日志按钮 save_log_btn = ttk.Button(btn_frame, text="保存日志", command=self.save_logs) save_log_btn.pack(side="right", padx=5) # 进度条 self.progress = ttk.Progressbar(btn_frame, orient="horizontal", length=300, mode="determinate") self.progress.pack(side="left", padx=10, fill="x", expand=True) # 调试级别控制 debug_frame = ttk.Frame(btn_frame) debug_frame.pack(side="left", padx=10) ttk.Label(debug_frame, text="调试级别:").pack(side="left") self.debug_level = tk.IntVar(value=1) ttk.Combobox(debug_frame, textvariable=self.debug_level, values=[1, 2], width=3).pack(side="left") # 示例按钮 example_btn = ttk.Button(btn_frame, text="加载示例", command=self.load_example) example_btn.pack(side="right", padx=5) # 创建输出区域 output_frame = ttk.LabelFrame(main_frame, text="解析结果") output_frame.pack(fill="both", expand=True, padx=5, pady=5) self.output_text = scrolledtext.ScrolledText(output_frame, width=100, height=15, font=("Consolas", 11), bg="#ffffff") self.output_text.pack(fill="both", expand=True, padx=10, pady=10) self.output_text.config(state=tk.DISABLED) # 日志区域 log_frame = ttk.LabelFrame(main_frame, text="日志信息") log_frame.pack(fill="both", expand=True, padx=5, pady=5) self.log_text = scrolledtext.ScrolledText(log_frame, width=100, height=6, font=("Consolas", 10), bg="#f8f8f8") self.log_text.pack(fill="both", expand=True, padx=10, pady=10) self.log_text.config(state=tk.DISABLED) # 示例函数体 self.example_code = """static void Diag21_PID_C9(U1 u1_a_num) { U1 u1_t_cmplt; U1 u1_t_cnt; struct SensorData sensor; if((U1)DIAG_CNT_ZERO == u1_t_swrstcnt) /* Determine if a software reset is in progress */ { for(u1_t_cnt = (U1)DIAG21_ZERO; u1_t_cnt < (U1)DIAG21_PIDC9_FLAG; u1_t_cnt ++) { u1_t_cmplt = u1_g_InspSoftwareVersion(u4_g_cmd, &u4_g_data, (U1)TRUE); } vd_s_Diag21_U2ToU1(u2_g_buf, u1_g_data, (U1)DIAG21_PIDC9_FLAG); } else { /* Do Nothing */ } }""" # 加载示例 self.load_example() def setup_logging(self): """配置日志系统""" self.log_filename = f"parser_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log" # 创建文件处理器 file_handler = logging.FileHandler(self.log_filename, encoding='utf-8') file_handler.setLevel(logging.INFO) file_handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")) # 配置根日志器 root_logger = logging.getLogger() root_logger.setLevel(logging.INFO) root_logger.addHandler(file_handler) def log_to_gui(self, message, level="info"): """将日志信息显示在GUI中""" try: self.log_text.config(state=tk.NORMAL) timestamp = datetime.now().strftime("%H:%M:%S") self.log_text.insert(tk.END, f"[{timestamp}] {message}\n") self.log_text.see(tk.END) self.log_text.config(state=tk.DISABLED) if level == "info": logging.info(message) elif level == "warning": logging.warning(message) elif level == "error": logging.error(message) except Exception as e: logging.error(f"GUI日志错误: {str(e)}") def save_logs(self): """保存日志到文件""" try: log_content = self.log_text.get("1.0", tk.END) filename = f"saved_log_{datetime.now().strftime('%H%M%S')}.txt" with open(filename, "w", encoding='utf-8') as f: f.write(log_content) self.log_to_gui(f"日志已保存到: {filename}", "info") messagebox.showinfo("保存成功", f"日志已保存到:\n{filename}") except Exception as e: self.log_to_gui(f"保存日志失败: {str(e)}", "error") messagebox.showerror("保存失败", f"无法保存日志:\n{str(e)}") def update_progress(self, value): """更新进度条""" self.progress['value'] = value self.root.update_idletasks() def load_example(self): """加载示例函数体""" self.input_text.delete(1.0, tk.END) self.input_text.insert(tk.END, self.example_code) self.log_to_gui("已加载示例函数体") def parse_function(self): """使用内置解析器解析C语言函数体""" try: code = self.input_text.get(1.0, tk.END) if not code.strip(): self.log_to_gui("错误: 没有输入函数体", "error") messagebox.showerror("错误", "请输入要解析的C语言函数体") return self.log_to_gui("开始解析函数体...") self.output_text.config(state=tk.NORMAL) self.output_text.delete(1.0, tk.END) self.update_progress(0) # 使用内置词法分析器 self.log_to_gui("执行词法分析...") lexer = SimpleCLexer() tokens = lexer.tokenize(code) self.update_progress(30) # 使用内置语法分析器 self.log_to_gui("执行语法分析...") analyzer = FunctionAnalyzer() analyzer.debug_level = self.debug_level.get() analyzer.analyze(tokens) # 显示结果 self.log_to_gui("生成解析报告...") self.display_results( analyzer.local_vars, analyzer.global_vars, analyzer.function_calls, analyzer.function_name, analyzer.parameters ) self.update_progress(100) self.output_text.config(state=tk.DISABLED) self.log_to_gui("解析完成!") messagebox.showinfo("完成", "函数体解析成功完成!") except Exception as e: self.log_to_gui(f"解析错误: {str(e)}", "error") self.log_to_gui(f"错误详情: {traceback.format_exc()}", "error") messagebox.showerror("解析错误", f"发生错误:\n{str(e)}") self.update_progress(0) def display_results(self, local_vars, global_vars, function_calls, func_name, func_params): """增强版结果显示,包含所有变量信息""" # 显示函数签名 self.output_text.insert(tk.END, "=== 函数签名 ===\n", "header") if func_name: self.output_text.insert(tk.END, f"函数名: {func_name}\n") if func_params: param_list = [] for param in func_params: param_list.append(f"{param['type']} {param['name']}") self.output_text.insert(tk.END, f"参数: {', '.join(param_list)}\n\n") else: self.output_text.insert(tk.END, "参数: 无\n\n") else: self.output_text.insert(tk.END, "警告: 无法识别函数签名\n\n") # 显示所有找到的变量 self.output_text.insert(tk.END, "=== 所有变量分析 ===\n", "header") self.output_text.insert(tk.END, "类型 | 名称 | 作用域 | 行号\n", "subheader") self.output_text.insert(tk.END, "-" * 50 + "\n") # 显示参数 for param in func_params: self.output_text.insert(tk.END, f"参数 | {param['name']} | 参数 | {param['line']}\n") # 显示局部变量 for var in local_vars: self.output_text.insert(tk.END, f"变量 | {var['name']} | 局部 | {var['line']}\n") # 显示全局变量 for var in global_vars: self.output_text.insert(tk.END, f"变量 | {var['name']} | 全局 | {var['line']}\n") # 显示函数调用 for func in function_calls: self.output_text.insert(tk.END, f"函数调用 | {func['name']} | 调用 | {func['line']}\n") self.output_text.insert(tk.END, "\n") # 显示局部变量 if local_vars: self.output_text.insert(tk.END, "=== 局部变量 ===\n", "header") for var in local_vars: self.output_text.insert(tk.END, f"{var['type']} {var['name']} (行号: {var['line']})\n") else: self.output_text.insert(tk.END, "未找到局部变量\n\n") # 显示使用的全局变量 if global_vars: self.output_text.insert(tk.END, "=== 使用的全局变量 ===\n", "header") for var in global_vars: self.output_text.insert(tk.END, f"{var['name']} (行号: {var['line']})\n") self.output_text.insert(tk.END, "\n") else: self.output_text.insert(tk.END, "未使用全局变量\n\n") # 显示函数调用 if function_calls: self.output_text.insert(tk.END, "=== 函数调用 ===\n", "header") for func in function_calls: self.output_text.insert(tk.END, f"函数名: {func['name']} (行号: {func['line']})\n") self.output_text.insert(tk.END, f"返回类型: {func['return_type']}\n") self.output_text.insert(tk.END, f"参数: {func['params']}\n") self.output_text.insert(tk.END, "-" * 50 + "\n") else: self.output_text.insert(tk.END, "未调用任何函数\n\n") # 添加变量统计 self.output_text.insert(tk.END, "=== 解析统计 ===\n", "header") self.output_text.insert(tk.END, f"参数数量: {len(func_params)}\n") self.output_text.insert(tk.END, f"局部变量数量: {len(local_vars)}\n") self.output_text.insert(tk.END, f"全局变量数量: {len(global_vars)}\n") self.output_text.insert(tk.END, f"函数调用数量: {len(function_calls)}\n") self.output_text.insert(tk.END, f"总变量数量: {len(func_params) + len(local_vars) + len(global_vars) + len(function_calls)}\n") # 配置标签样式 self.output_text.tag_config("header", font=("Arial", 12, "bold"), foreground="#2c3e50") self.output_text.tag_config("subheader", font=("Arial", 10, "bold"), foreground="#34495e") if __name__ == "__main__": root = tk.Tk() app = FunctionParserApp(root) root.mainloop()
07-19
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