如何使用pytorch的Dataset, 来定义自己的Dataset-Toy模板网

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Dataset与DataLoader的关系

如何使用pytorch的Dataset, 来定义自己的Dataset,深度学习Trick,pytorch,人工智能,python

Dataset: 构建一个数据集，其中含有所有的数据样本
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, DataLoader

class 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"""
        pass

    def __getitem__(self, index):
        """来获取数据集中样本的索引"""
        pass

    def __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, DataLoader

class 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.len

file = "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()

如何使用pytorch的Dataset, 来定义自己的Dataset,深度学习Trick,pytorch,人工智能,python

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）文章来源地址https://www.toymoban.com/news/detail-814276.html

主函数文件main.py

import os

import torch
from torchvision import transforms

from my_dataset import MyDataSet
from utils import read_split_data, plot_data_loader_image

root = "../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 = 8
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('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 Dataset


class MyDataSet(Dataset):
    """自定义数据集"""

    def __init__(self, images_path: list, images_class: list, transform=None):
        self.images_path = images_path
        self.images_class = images_class
        self.transform = transform

    def __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

    @staticmethod
    def 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.py
        images, 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 random

import matplotlib.pyplot as plt


def 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 = True
    if 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_label


def plot_data_loader_image(data_loader):
    batch_size = data_loader.batch_size
    plot_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 = data
        for 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]) * 255
            label = 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