学习目标：熟练掌握mindspore.dataset

mindspore.dataset中有常用的视觉、文本、音频开源数据集供下载，点赞、关注+收藏哦

• 了解mindspore.dataset
• mindspore.dataset应用实践
• 拓展自定义数据集

昇思平台学习时间记录:

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一、关于mindspore.dataset

mindspore.dataset模块提供了加载和处理各种通用数据集的API，如MNIST、CIFAR-10、CIFAR-100、VOC、COCO、ImageNet、CelebA、CLUE等， 也支持加载业界标准格式的数据集，包括MindRecord、TFRecord、Manifest等。此外，用户还可以使用此模块定义和加载自己的数据集。

1.1 常用数据集下载资源地址

开源数据集地址url如下：
1.加载MNIST：url= "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip"
2.加载CIFAR-10："url=https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"
2.加载CIFAR-100："url=https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets cifar-100-python.tar.gz"
3.加载ImageNet：url= https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/vit_imagenet_dataset.zip
4.加载狗与牛角包分类数据集DogCroissants：url=https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/beginner/DogCroissants.zip
5. 数据集coco2017 url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/ssd_datasets.zip"

1.2 数据集地址程序下载方式

方式一：from download import download
安装依赖库download
pip install download

方式二：from mindvision.dataset import DownLoad
安装依赖库：mindvision
pip install mindvision

示例如下：

# Begin to show your code!
from download import download
from mindvision.dataset import DownLoad


def downloadData1(url="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/beginner/DogCroissants.zip"):
    dataset_url = url
    path = download(dataset_url, "./datasets", kind="zip", replace=True)  # 当前文件夹下保存DogCroissants数据集


def downloadData2(url):
    dataset_url = url
    path = "./"
    dl = DownLoad()
    # 下载并解压数据集
    dl.download_and_extract_archive(dataset_url, path)


if __name__ == "__main__":
    url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/" \
      "notebook/datasets/MNIST_Data.zip"
    downloadData1()  # 方式一，下载DogCroissants
    downloadData2(url)  # 方式二，下载MNIST

运行结果：成功下载数据集

方式三：from mindvision.dataset import Mnist

使用方法：

from mindvision.dataset import Mnist
 
download_train = Mnist(path="./mnist", split="train", batch_size=32, shuffle=True, resize=32, download=True)
download_eval = Mnist(path="./mnist", split="test", batch_size=32, resize=32, download=True)
 
dataset_train = download_train.run()
dataset_eval = download_eval.run()

1.3 常用数据集生成Mindspore格式数据集生成器接口

(1) 常用开源视觉数据集-数据集接口

	mindspore.dataset.Caltech101Dataset
    mindspore.dataset.Caltech256Dataset
    mindspore.dataset.CelebADataset
    mindspore.dataset.Cifar10Dataset
    mindspore.dataset.Cifar100Dataset
    mindspore.dataset.CityscapesDataset
    mindspore.dataset.CocoDataset
    mindspore.dataset.DIV2KDataset
    mindspore.dataset.EMnistDataset
    mindspore.dataset.FakeImageDataset
    mindspore.dataset.FashionMnistDataset
    mindspore.dataset.FlickrDataset
    mindspore.dataset.Flowers102Dataset
    mindspore.dataset.Food101Dataset
    mindspore.dataset.ImageFolderDataset
    mindspore.dataset.KITTIDataset
    mindspore.dataset.KMnistDataset
    mindspore.dataset.LFWDataset
    mindspore.dataset.LSUNDataset
    mindspore.dataset.ManifestDataset
    mindspore.dataset.MnistDataset
    mindspore.dataset.OmniglotDataset
    mindspore.dataset.PhotoTourDataset
    mindspore.dataset.Places365Dataset
    mindspore.dataset.QMnistDataset
    mindspore.dataset.RenderedSST2Dataset
    mindspore.dataset.SBDataset
    mindspore.dataset.SBUDataset
    mindspore.dataset.SemeionDataset
    mindspore.dataset.STL10Dataset
    mindspore.dataset.SUN397Dataset
    mindspore.dataset.SVHNDataset
    mindspore.dataset.USPSDataset
    mindspore.dataset.VOCDataset
    mindspore.dataset.WIDERFaceDataset

（2）标准格式数据集接口

mindspore.dataset.ImageFolderDataset
mindspore.dataset.CSVDataset
mindspore.dataset.MindDataset
mindspore.dataset.OBSMindDataset
mindspore.dataset.TFRecordDataset

（3）自定义数据集接口

mindspore.dataset.GeneratorDataset
mindspore.dataset.NumpySlicesDataset
mindspore.dataset.PaddedDataset
mindspore.dataset.RandomDataset

1.4 开源数据实践实例

利用下载的数据集，数据集标准格式

import mindspore.dataset as ds
import mindspore.dataset.vision as vision


def create_dataset(path="./datasets", batch_size=10, train=True, image_size=224):
    dataset = ds.ImageFolderDataset(path, num_parallel_workers=8, class_indexing={"croissants": 0, "dog": 1})

    # 图像增强操作
    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
    if train:
        trans = [
            vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
            vision.RandomHorizontalFlip(prob=0.5),
            #vision.Normalize(mean=mean, std=std),
            #vision.HWC2CHW()
        ]
    else:
        trans = [
            vision.Decode(),
            vision.Resize(256),
            vision.CenterCrop(image_size),
            vision.Normalize(mean=mean, std=std),
            vision.HWC2CHW()
        ]

    dataset = dataset.map(operations=trans, input_columns="image", num_parallel_workers=8)
    # 设置batch_size的大小，若最后一次抓取的样本数小于batch_size，则丢弃
    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset


if __name__ == "__main__":
    # 加载训练数据集
    train_path = "./datasets/DogCroissants/train"
    dataset_train = create_dataset(train_path, train=True)
    print(len(dataset_train))
    # 加载验证数据集
    val_path = "./datasets/DogCroissants/val"
    dataset_val = create_dataset(val_path, train=False)
    print(len(dataset_val))

执行结果：成功加载数据集

1.5 数据集图像可视化

定义可视化函数import matplotlib.pyplot as plt

import matplotlib.pyplot as plt
def visualize(dataset):
    figure = plt.figure(figsize=(4, 4))
    cols, rows = 3, 3

    plt.subplots_adjust(wspace=0.5, hspace=0.5)

    for idx, (image, label) in enumerate(dataset.create_tuple_iterator()):
        figure.add_subplot(rows, cols, idx + 1)
        # plt.title(int(label))
        plt.axis("off")
        plt.imshow(image[0].asnumpy().squeeze().squeeze())  # 默认彩色，设置灰色cmap="gray"
        if idx == cols * rows - 1:
            break
    plt.show()
if __name__ == "__main__":
    # 加载训练数据集
    train_path = "./datasets/DogCroissants/train"
    dataset_train = create_dataset(train_path, train=True)
    print(len(dataset_train))
    visualize(dataset_train)

运行结果：（成功）

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自定义数据集

import time
import numpy as np
from mindspore.dataset import MnistDataset, GeneratorDataset, transforms, vision, text


# Random-accessible object as input source
class RandomAccessDataset:
    def __init__(self):
        self._data = np.ones((5, 2))
        self._label = np.zeros((5, 1))

    def __getitem__(self, index):
        return self._data[index], self._label[index]

    def __len__(self):
        return len(self._data)


loader = RandomAccessDataset()
dataset = GeneratorDataset(source=loader, column_names=["data", "label"])
for data in dataset:
    print(data)

运行结果：成功