类
python是一门面向对象的语言,强调的是对象,当我们创建一个类时,必然要给这个类赋予对应的属性去描述它,例如一个动物的类,那么这个类应该有动物种类,颜色,年龄,体重,习性等属性,代码如下:
class Animal:
def __init__(self, species, color, age, weight, habitat):
self.species = species
self.color = color
self.age = age
self.weight = weight
self.habitat = habitat
def __str__(self):
return f"{self.species} | Color: {self.color} | Age: {self.age} years | Weight: {self.weight} kg | Habitat: {self.habitat}"
以上的代码非常易懂,但我让AI根据要求{ 自定义dataset,该类可以自义训练和测试的比例 }生成以下代码:
代码的self.images属性初始化委托给了load_images()方法,也就是说python的类初始化__init__()可以调用该类的其他方法
import torch
from PIL import Image
from torchvision import transforms
import os
class CustomImageDataset(torch.utils.data.Dataset):
def __init__(self, data_dir, train_ratio=0.8, transform=None):
super(CustomImageDataset, self).__init__()
self.data_dir = data_dir
self.train_ratio = train_ratio
self.transform = transform
self.images = self.load_images()
self.train_images, self.test_images = self.split_images()
def load_images(self):
# 加载所有图像文件
image_files = os.listdir(self.data_dir)
image_files = [os.path.join(self.data_dir, file) for file in image_files if file.endswith(('.png', '.jpg', '.jpeg', '.bmp'))]
return image_files
def split_images(self):
# 根据训练比例分割图像
train_images = self.images[:int(len(self.images) * self.train_ratio)]
test_images = self.images[int(len(self.images) * self.train_ratio):]
return train_images, test_images
def __len__(self):
return len(self.train_images)
def __getitem__(self, idx):
image_path = self.train_images[idx]
image = Image.open(image_path).convert('RGB') # 假设图像是以RGB格式打开的
if self.transform:
image = self.transform(image)
return image
# 使用自定义数据集
custom_dataset = CustomImageDataset('/path/to/your/image/data', train_ratio=0.8)
dataloader = torch.utils.data.DataLoader(custom_dataset, batch_size=64, shuffle=True)
# 创建一个数据加载器,用于迭代训练集
for images in dataloader:
# 处理图像
pass
标签:__,语言,python,train,self,理解,images,data,image
From: https://www.cnblogs.com/seekwhale13/p/17991848