Dataset与DataLoader的关系

1. Dataset: 构建一个数据集，其中含有所有的数据样本
2. DataLoader：将构建好的Dataset，通过shuffle、划分batch、多线程num_workers运行的方式，加载到可训练的迭代容器。

import torch
from torch.utils.data import Dataset, DataLoaderclass MyDataset(Dataset):"""创建自己的数据集"""def __init__(self):"""初始化构建数据集所需要的参数"""passdef __getitem__(self, index):"""来获取数据集中样本的索引"""passdef __len__(self):"""获取数据集中的样本个数"""pass# 实例化自定义的数据集
dataset = MyDataset()
# 将自定义的数据集加载到可训练的迭代容器
train_loader = DataLoader(dataset=dataset,  # 自定义的数据集batch_size=32,  # 数据集中小批量的大小shuffle=True,  # 是否要打乱数据集中样本的次序num_workers=2)  # 是否要并行

实战1：CSV数据集（结构化数据集）

import torch
import numpy as np
from torch.utils.data import Dataset, DataLoaderclass MyDataset(Dataset):"""创建自己的数据集"""def __init__(self, filepath):"""初始化构建数据集所需要的参数"""xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)self.len = xy.shape[0]  # 查看数据集中样本的个数self.x_data = torch.from_numpy(xy[:, :-1])self.y_data = torch.from_numpy(xy[:, [-1]])print("数据已准备好......")def __getitem__(self, index):"""为了支持下标操作, 即索引dataset[index]：来获取数据集中样本的索引"""return self.x_data[index], self.y_data[index]def __len__(self):"""为了使用len(dataset)：获取数据集中的样本个数"""return self.lenfile = "D:\\BaiduNetdiskDownload\\Dataset_Dataload\\diabetes1.csv"""" 1.使用 MyDataset类 构建自己的dataset """
mydataset = MyDataset(file)
""" 2.使用 DataLoader 构建train_loader """
train_loader = DataLoader(dataset=mydataset,batch_size=32,shuffle=True,num_workers=0)class MyModel(torch.nn.Module):"""定义自己的模型"""def __init__(self):super().__init__()self.linear1 = torch.nn.Linear(8, 6)self.linear2 = torch.nn.Linear(6, 4)self.linear3 = torch.nn.Linear(4, 1)self.sigmooid = torch.nn.Sigmoid()def forward(self, x):x = self.sigmooid(self.linear1(x))x = self.sigmooid(self.linear2(x))x = self.sigmooid(self.linear3(x))return x# 实例化模型
model = MyModel()# 定义损失函数
criterion = torch.nn.BCELoss(size_average=True)
# 定义优化器
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)if __name__ == "__main__":for epoch in range(10):for i, data in enumerate(train_loader, 0):# 1. 准备数据inputs, labels = data# 2. 前向传播y_pred= model(inputs)loss = criterion(y_pred, labels)print(epoch, i, loss.item())# 3. 反向传播optimizer.zero_grad()loss.backward()# 4. 梯度更新optimizer.step()

实战2：图片数据集

├── flower_data
—├── flower_photos（解压的数据集文件夹，3670个样本）
—├── train（生成的训练集，3306个样本）
—└── val（生成的验证集，364个样本）

主函数文件main.py

import osimport torch
from torchvision import transformsfrom my_dataset import MyDataSet
from utils import read_split_data, plot_data_loader_imageroot = "../data/flower_data/flower_photos"  # 数据集所在根目录def main():device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print("using {} device.".format(device))train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(root)data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),"val": transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}train_data_set = MyDataSet(images_path=train_images_path,images_class=train_images_label,transform=data_transform["train"])batch_size = 8nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workersprint('Using {} dataloader workers'.format(nw))train_loader = torch.utils.data.DataLoader(train_data_set,batch_size=batch_size,shuffle=True,num_workers=nw,collate_fn=train_data_set.collate_fn)# plot_data_loader_image(train_loader)for epoch in range(100):for step, data in enumerate(train_loader):images, labels = data# 然后在进行相应的训练操作即可if __name__ == '__main__':main()

自定义数据集文件my_dataset.py

from PIL import Image
import torch
from torch.utils.data import Datasetclass MyDataSet(Dataset):"""自定义数据集"""def __init__(self, images_path: list, images_class: list, transform=None):self.images_path = images_pathself.images_class = images_classself.transform = transformdef __len__(self):return len(self.images_path)def __getitem__(self, item):img = Image.open(self.images_path[item])# RGB为彩色图片，L为灰度图片if img.mode != 'RGB':raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item]))label = self.images_class[item]if self.transform is not None:img = self.transform(img)return img, label@staticmethoddef collate_fn(batch):# 官方实现的default_collate可以参考# https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.pyimages, labels = tuple(zip(*batch))images = torch.stack(images, dim=0)labels = torch.as_tensor(labels)return images, labels

功能文件utils.py（训练集、验证集的划分与可视化）

import os
import json
import pickle
import randomimport matplotlib.pyplot as pltdef read_split_data(root: str, val_rate: float = 0.2):random.seed(0)  # 保证随机结果可复现assert os.path.exists(root), "dataset root: {} does not exist.".format(root)  # 判断路径是否存在# 遍历文件夹，一个文件夹对应一个类别flower_class = [cla for cla in os.listdir(root) if os.path.isdir(os.path.join(root, cla))]# 排序，保证顺序一致flower_class.sort()# 生成类别名称以及对应的数字索引: 字典{’花名‘：0，’花名‘：1，···}class_indices = dict((k, v) for v, k in enumerate(flower_class))json_str = json.dumps(dict((val, key) for key, val in class_indices.items()), indent=4)  # 将花名与对应的序号分行保存with open('class_indices.json', 'w') as json_file:json_file.write(json_str)train_images_path = []  # 存储训练集的所有图片路径train_images_label = []  # 存储训练集图片对应索引信息val_images_path = []  # 存储验证集的所有图片路径val_images_label = []  # 存储验证集图片对应索引信息every_class_num = []  # 存储每个类别的样本总数supported = [".jpg", ".JPG", ".png", ".PNG"]  # 支持的文件后缀类型# 遍历每个文件夹下的文件for cla in flower_class:cla_path = os.path.join(root, cla)# 遍历获取supported支持的所有文件路径images = [os.path.join(root, cla, i) for i in os.listdir(cla_path)if os.path.splitext(i)[-1] in supported]# 获取该类别对应的索引image_class = class_indices[cla]# 记录该类别的样本数量every_class_num.append(len(images))# 按比例随机采样验证样本val_path = random.sample(images, k=int(len(images) * val_rate))for img_path in images:if img_path in val_path:  # 如果该路径在采样的验证集样本中则存入验证集val_images_path.append(img_path)val_images_label.append(image_class)else:  # 否则存入训练集train_images_path.append(img_path)train_images_label.append(image_class)print("{} images were found in the dataset.".format(sum(every_class_num)))print("{} images for training.".format(len(train_images_path)))print("{} images for validation.".format(len(val_images_path)))plot_image = Trueif plot_image:# 绘制每种类别个数柱状图plt.bar(range(len(flower_class)), every_class_num, align='center')# 将横坐标0,1,2,3,4替换为相应的类别名称plt.xticks(range(len(flower_class)), flower_class)# 在柱状图上添加数值标签for i, v in enumerate(every_class_num):plt.text(x=i, y=v + 5, s=str(v), ha='center')# 设置x坐标plt.xlabel('image class')# 设置y坐标plt.ylabel('number of images')# 设置柱状图的标题plt.title('flower class distribution')plt.show()return train_images_path, train_images_label, val_images_path, val_images_labeldef plot_data_loader_image(data_loader):batch_size = data_loader.batch_sizeplot_num = min(batch_size, 4)json_path = './class_indices.json'assert os.path.exists(json_path), json_path + " does not exist."json_file = open(json_path, 'r')class_indices = json.load(json_file)for data in data_loader:images, labels = datafor i in range(plot_num):# [C, H, W] -> [H, W, C]img = images[i].numpy().transpose(1, 2, 0)# 反Normalize操作img = (img * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]) * 255label = labels[i].item()plt.subplot(1, plot_num, i+1)plt.xlabel(class_indices[str(label)])plt.xticks([])  # 去掉x轴的刻度plt.yticks([])  # 去掉y轴的刻度plt.imshow(img.astype('uint8'))plt.show()def write_pickle(list_info: list, file_name: str):with open(file_name, 'wb') as f:pickle.dump(list_info, f)def read_pickle(file_name: str) -> list:with open(file_name, 'rb') as f:info_list = pickle.load(f)return info_list