path = 'D:\pycharm_workspace\Test\\train_data\\*.TXT'

这是一个路径字符串，其含义是在D盘pycharm_workspace文件夹下的Test文件夹中的train_data文件夹中选择所有.TXT结尾的文件。其中"\\"用于表示转义字符"\"，以保证路径字符串的正确性。这个路径字符串可以用于Python中一些函数（如glob模块）中的参数，以获取符合条件的文件列表。

相关问题

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。

import json import os #指定原始数据集和目标路径 json_folder_path = "D:/python/pycharm/pycharm_date/Oracle/OracleCS/ce_shi_label" txt_folder_path = "D:/python/pycharm/pycharm_date/Oracle/OracleCS/labels" #获取所有json文件的路径 json_files = [os.path.join(json_folder_path, f) for f in os.listdir(json_folder_path) if f.endswith('.json')] for json_file in json_files:#打开json文件 with open(json_file, 'r', encoding='utf-8') as f:data = json.load(f) image_name = data["img_name"] + ".jpg" label_name = data["img_name"] boxes = data["ann"] #将每个矩形框转换为txt格式 txt_data = "" txt_line_1 = f"{label_name}\n" for box in boxes: x1, y1,x2, y2,class_id = str(box[0]), str(box[1]), str(box[2]),str(box[3]),str(box[4]) txt_line =f"{x1} {y1} {x2} {y2} {class_id}\n" txt_data += txt_line #写入txt文件 txt_file = os.path.join(txt_folder_path, image_name.replace(".jpg", ".txt")) with open(txt_file, 'w', encoding='utf-8') as f:f.write(txt_line_1 + txt_data)将其中json文件的名称统一放在一一个新的txt文件中

可以在循环外部先创建一个空的列表，然后在循环中每次将json文件的名称添加到列表中。循环结束后再将列表中的元素写入到一个新的txt文件中即可。

示例代码如下：

import json 
import os 

# 指定原始数据集和目标路径
json_folder_path = "D:/python/pycharm/pycharm_date/Oracle/OracleCS/ce_shi_label"
txt_folder_path = "D:/python/pycharm/pycharm_date/Oracle/OracleCS/labels"

# 获取所有json文件的路径
json_files = [os.path.join(json_folder_path, f) for f in os.listdir(json_folder_path) if f.endswith('.json')]

# 创建空的列表
json_names = []

for json_file in json_files:
    # 打开json文件
    with open(json_file, 'r', encoding='utf-8') as f:
        data = json.load(f)
        image_name = data["img_name"] + ".jpg"
        label_name = data["img_name"]
        boxes = data["ann"] 
        
        # 将json文件名称添加到列表中
        json_names.append(label_name)
        
        # 将每个矩形框转换为txt格式
        txt_data = ""
        txt_line_1 = f"{label_name}\n"
        for box in boxes:
            x1, y1,x2, y2,class_id = str(box[0]), str(box[1]), str(box[2]),str(box[3]),str(box[4])
            txt_line =f"{x1} {y1} {x2} {y2} {class_id}\n"
            txt_data += txt_line 
        
        # 写入txt文件
        txt_file = os.path.join(txt_folder_path, image_name.replace(".jpg", ".txt"))
        with open(txt_file, 'w', encoding='utf-8') as f:
            f.write(txt_line_1 + txt_data)

# 将json文件名称写入新的txt文件中
json_names_file = os.path.join(txt_folder_path, "json_names.txt")
with open(json_names_file, 'w', encoding='utf-8') as f:
    for name in json_names:
        f.write(name + "\n")

这样，就可以将所有json文件的名称统一放在一个新的txt文件中了。