split: str = "train",
时间: 2023-08-27 15:08:21 浏览: 61
split参数用于指定数据集的划分方式。在引用\[1\]中,split参数被设置为('train', 'valid', 'test'),表示将数据集划分为训练集、验证集和测试集。而在引用\[2\]和引用\[3\]中,split参数被设置为"train",表示只使用训练集进行训练。
#### 引用[.reference_title]
- *1* *2* *3* [PyTorch torchtext中带有的自然语言处理数据集](https://blog.csdn.net/Talantfuck/article/details/124557132)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]
相关问题
def get_Image_dim_len(png_dir: str,jpg_dir:str): png = Image.open(png_dir) png_w,png_h=png.width,png.height #若第十行报错,说明jpg图片没有对应的png图片 png_dim_len = len(np.array(png).shape) assert png_dim_len==2,"提示:存在三维掩码图" jpg=Image.open(jpg_dir) jpg = ImageOps.exif_transpose(jpg) jpg.save(jpg_dir) jpg_w,jpg_h=jpg.width,jpg.height print(jpg_w,jpg_h,png_w,png_h) assert png_w==jpg_w and png_h==jpg_h,print("提示:%s mask图与原图宽高参数不一致"%(png_dir)) """2.读取单个图像均值和方差""" def pixel_operation(image_path: str): img = cv.imread(image_path, cv.IMREAD_COLOR) means, dev = cv.meanStdDev(img) return means,dev """3.分割数据集,生成label文件""" # 原始数据集 ann上一级 data_root = './work/voc_data02' #图像地址 image_dir="./JPEGImages" # ann图像文件夹 ann_dir = "./SegmentationClass" # txt文件保存路径 split_dir = './ImageSets/Segmentation' mmengine.mkdir_or_exist(osp.join(data_root, split_dir)) png_filename_list = [osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, ann_dir), suffix='.png')] jpg_filename_list=[osp.splitext(filename)[0] for filename in mmengine.scandir( osp.join(data_root, image_dir), suffix='.jpg')] assert len(jpg_filename_list)==len(png_filename_list),"提示:原图与掩码图数量不统一" print("数量检查无误") for i in range(10): random.shuffle(jpg_filename_list) red_num=0 black_num=0 with open(osp.join(data_root, split_dir, 'trainval.txt'), 'w+') as f: length = int(len(jpg_filename_list)) for line in jpg_filename_list[:length]: pngpath=osp.join(data_root,ann_dir,line+'.bmp') jpgpath=osp.join(data_root,image_dir,line+'.bmp') get_Image_dim_len(pngpath,jpgpath) img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE) red_num+=len(img)*len(img[0])-len(img[img==0]) black_num+=len(img[img==0]) f.writelines(line + '\n') value=0 train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]] with open(osp.join(data_root, split_dir, 'train.txt'), 'w+') as f: train_length = int(len(jpg_filename_list) * 7/ 10) for line in jpg_filename_list[:train_length]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') with open(osp.join(data_root, split_dir, 'val.txt'), 'w+') as f: for line in jpg_filename_list[train_length:]: jpgpath=osp.join(data_root,image_dir,line+'.bmp') mean,dev=pixel_operation(jpgpath) train_mean+=mean train_dev+=dev f.writelines(line + '\n') 帮我把这段代码改成bmp图像可以制作数据集的代码
当然,我可以帮你将代码修改为支持bmp图像的数据集制作代码。请看下面的修改后的代码:
```python
from PIL import Image
import os
import random
import cv2 as cv
import numpy as np
def get_Image_dim_len(png_dir: str,jpg_dir:str):
png = Image.open(png_dir)
png_w,png_h=png.width,png.height
#若第十行报错,说明jpg图片没有对应的png图片
png_dim_len = len(np.array(png).shape)
assert png_dim_len==2,"提示:存在三维掩码图"
jpg=Image.open(jpg_dir)
jpg = jpg.transpose(Image.FLIP_LEFT_RIGHT) # 翻转图像
jpg.save(jpg_dir)
jpg_w,jpg_h=jpg.width,jpg.height
print(jpg_w,jpg_h,png_w,png_h)
assert png_w==jpg_w and png_h==jpg_h,print("提示:%s mask图与原图宽高参数不一致"%(png_dir))
def pixel_operation(image_path: str):
img = cv.imread(image_path, cv.IMREAD_COLOR)
means, dev = cv.meanStdDev(img)
return means,dev
data_root = './work/voc_data02'
image_dir="./JPEGImages"
ann_dir = "./SegmentationClass"
split_dir = './ImageSets/Segmentation'
os.makedirs(os.path.join(data_root, split_dir), exist_ok=True)
png_filename_list = [os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, ann_dir)) if filename.endswith('.bmp')]
jpg_filename_list=[os.path.splitext(filename)[0] for filename in os.listdir(os.path.join(data_root, image_dir)) if filename.endswith('.bmp')]
assert len(jpg_filename_list)==len(png_filename_list),"提示:原图与掩码图数量不统一"
print("数量检查无误")
random.shuffle(jpg_filename_list)
red_num=0
black_num=0
with open(os.path.join(data_root, split_dir, 'trainval.txt'), 'w+') as f:
length = int(len(jpg_filename_list))
for line in jpg_filename_list[:length]:
pngpath=os.path.join(data_root,ann_dir,line+'.bmp')
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
get_Image_dim_len(pngpath,jpgpath)
img=cv.imread(pngpath,cv.IMREAD_GRAYSCALE)
red_num+=len(img)*len(img[0])-len(img[img==0])
black_num+=len(img[img==0])
f.writelines(line + '\n')
train_mean,train_dev=[[0.0,0.0,0.0]],[[0.0,0.0,0.0]]
with open(os.path.join(data_root, split_dir, 'train.txt'), 'w+') as f:
train_length = int(len(jpg_filename_list) * 7/ 10)
for line in jpg_filename_list[:train_length]:
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
mean,dev=pixel_operation(jpgpath)
train_mean+=mean
train_dev+=dev
f.writelines(line + '\n')
with open(os.path.join(data_root, split_dir, 'val.txt'), 'w+') as f:
for line in jpg_filename_list[train_length:]:
jpgpath=os.path.join(data_root,image_dir,line+'.bmp')
mean,dev=pixel_operation(jpgpath)
train_mean+=mean
train_dev+=dev
f.writelines(line + '\n')
```
希望这可以帮到你!如果你还有其他问题,请随时提问。
import argparse import numpy as np import pandas as pd from sklearn import model_selection from sklearn import preprocessing from sklearn import linear_model from sklearn import metrics import joblib from config import * def train(x_train, x_test, y_train, y_test): estimator = linear_model.Ridge() estimator.fit(x_train, y_train) print('梯度下降的权重系数是:', estimator.coef_) print('梯度下降的偏置是:', estimator.intercept_) joblib.dump(estimator, model_save_path) y_predict = estimator.predict(x_test) err = metrics.mean_squared_error(y_test, y_predict) print('梯度下降的误差率为:', err) def pred(x): estimator = joblib.load(model_save_path) x= np.array(x) predict = estimator.predict(x.reshape((x.shape[0], 1))) return predict def main(): parser = argparse.ArgumentParser(description='Demo of argparse') parser.add_argument('--method', type=str, default='train') args = parser.parse_args() method = args.method if method == 'train': df = pd.read_excel(file_name, dtype={ '年份': int, '值': float }) x = df['年份'].to_numpy() x = x.reshape((x.shape[0], 1)) y = df['值'].to_numpy() x_train, x_test, y_train, y_test=model_selection.train_test_split(x, y) train(x_train, x_test, y_train, y_test) elif method == 'test': predict = pred(pred_year).round(2) df = pd.DataFrame({ '预测年份': pred_year, '预测结果(单位:万吨)': predict }) print(df) df.to_excel(save_pred_path, index=False) else: print('wrong') if __name__ == '__main__': main()
这段 Python 代码主要实现了一个基于 Ridge 回归模型的数据训练和预测程序。具体来说,代码分为以下几个部分:
1. 导入需要的库和模块,包括 argparse、numpy、pandas、scikit-learn 和 joblib。
2. 实现一个 train 函数,用于训练 Ridge 模型。该函数接收训练集和测试集的特征和标签数据,通过 scikit-learn 库中的 Ridge 类进行模型训练,输出模型的权重系数、偏置和误差率,并将训练好的模型保存到指定路径。
3. 实现一个 pred 函数,用于对给定的年份进行预测。该函数接收一个年份数组,从指定路径加载已经训练好的 Ridge 模型,对年份数组进行预测,并返回预测结果。
4. 实现一个 main 函数,用于控制整个程序的流程。该函数通过 argparse 模块解析命令行参数,从而决定程序要进行的操作类型。如果是训练操作,就从指定路径的 Excel 文件中读取数据,将年份和对应的值作为特征和标签,通过 train 函数进行训练。如果是预测操作,就调用 pred 函数进行预测,并将预测结果保存到指定路径的 Excel 文件中。如果命令行参数有误,则输出错误信息。
5. 在最后,通过 if __name__ == '__main__': 判断当前文件是否被作为模块导入,如果是,则不执行 main 函数,如果直接运行该文件,则执行 main 函数。
总的来说,这个程序使用 Ridge 回归模型对年份和对应的值进行训练,并且可以对未来的年份进行预测。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)