文档反馈使用数据Pipeline加载 & 处理数据集
此指南展示了mindspore.dataset 模块中的各种用法。
环境准备
[1]:
from download import download
import matplotlib.pyplot as plt
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
# Download opensource datasets
mnist_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip"
download(mnist_url, "./", kind="zip", replace=True)
cifar10_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"
download(cifar10_url, "./", kind="tar.gz", replace=True)
# Env set for randomness and prepare plot function
ds.config.set_seed(0)
def plot(imgs, first_origin=None):
num_rows = 1
num_cols = len(imgs)
_, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
for idx, img in enumerate(imgs):
ax = axs[0, idx]
ax.imshow(img.asnumpy())
ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
if first_origin:
axs[0, 0].set(title='Original image')
axs[0, 0].title.set_size(8)
plt.tight_layout()
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)
file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 10.5MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz (162.2 MB)
file_sizes: 100%|████████████████████████████| 170M/170M [00:12<00:00, 14.0MB/s]
Extracting tar.gz file...
Successfully downloaded / unzipped to ./
加载开源数据集
使用 mindspore.dataset.MnistDataset 和 mindspore.dataset.Cifar10Dataset 加载MNIST/Cifar10数据集。
示例展示了如何加载数据集文件并显示数据集的内容。
加载 MNIST 数据集
[2]:
import os
# Show the directory
print(os.listdir())
# Load MNIST dataset
mnist_dataset = ds.MnistDataset("MNIST_Data/train")
# Iter the dataset to collect 5 samples
images = []
for image, label in mnist_dataset:
print("image shape", image.shape, "label shape", label.shape)
images.append(image)
if len(images) > 5:
break
plot(images)
['vision_gallery.ipynb', 'MNIST_Data', 'text_gallery.ipynb', 'imageset', 'cifar-10-batches-bin', 'audio_gallery.ipynb', 'dataset_gallery.ipynb']
image shape (28, 28, 1) label shape ()
image shape (28, 28, 1) label shape ()
image shape (28, 28, 1) label shape ()
image shape (28, 28, 1) label shape ()
image shape (28, 28, 1) label shape ()
image shape (28, 28, 1) label shape ()
加载 CIFAR 数据集
[3]:
import os
# Show the directory
print(os.listdir())
# Load Cifar10 dataset
cifar_dataset = ds.Cifar10Dataset("cifar-10-batches-bin")
# Iter the dataset to collect 5 samples
images = []
for image in cifar_dataset:
print("image shape", image[0].shape, "label shape", image[1].shape)
images.append(image[0])
if len(images) > 5:
break
plot(images)
['vision_gallery.ipynb', 'MNIST_Data', 'text_gallery.ipynb', 'imageset', 'cifar-10-batches-bin', 'audio_gallery.ipynb', 'dataset_gallery.ipynb']
image shape (32, 32, 3) label shape ()
image shape (32, 32, 3) label shape ()
image shape (32, 32, 3) label shape ()
image shape (32, 32, 3) label shape ()
image shape (32, 32, 3) label shape ()
image shape (32, 32, 3) label shape ()
加载文件目录结构的数据集
对于ImageNet数据集或其他具有类似结构的数据集，建议使用 mindspore.dataset.ImageFolderDataset 将数据集文件加载到数据Pipeline中。
Structure of ImageNet dataset:
/path/to/ImageNet2012/
├── train
│ ├── n01440764
| | ├── 000000000001.jpg
| | ├── 000000000002.jpg
| | ├── ...
│ ├── n01484850
| | ├── 000000000001.jpg
| | ├── 000000000002.jpg
| | ├── ...
│ ├── n01494475
│ └── ...
└── val
├── n11879895
└── ...
这个示例展示了如何加载具有树状文件结构的数据集文件，代码将下载具有以下结构的文件夹并加载它。
imageset/
├── cat
│ ├── cat_0.jpg
│ ├── cat_1.jpg
│ └── cat_2.jpg
├── fish
│ ├── fish_0.jpg
│ ├── fish_1.jpg
│ ├── fish_2.jpg
│ └── fish_3.jpg
├── fruits
│ ├── fruits_0.jpg
│ ├── fruits_1.jpg
│ └── fruits_2.jpg
├── plane
│ ├── plane_0.jpg
│ ├── plane_1.jpg
│ └── plane_2.jpg
└── tree
├── tree_0.jpg
├── tree_1.jpg
└── tree_2.jpg
[4]:
# Download a small image set as example
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/imageset.zip"
download(url, "./", kind="zip", replace=True)
# There are 5 classes in the image folder.
os.listdir("./imageset")
# Pass the image folder path to ImageFolderDataset, like "/path/to/ImageNet2012/train"
imagenet_dataset = ds.ImageFolderDataset("./imageset", decode=True)
# Iter the dataset to get outputs
images = []
for image, label in imagenet_dataset:
images.append(image)
print("image shape", image.shape, "label", label)
plot(images[:5], False)
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/imageset.zip (45 kB)
file_sizes: 100%|██████████████████████████| 45.7k/45.7k [00:00<00:00, 1.04MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
image shape (64, 64, 3) label 0
image shape (64, 64, 3) label 4
image shape (64, 64, 3) label 0
image shape (64, 64, 3) label 1
image shape (64, 64, 3) label 2
image shape (64, 64, 3) label 3
image shape (64, 64, 3) label 1
image shape (64, 64, 3) label 3
image shape (64, 64, 3) label 1
image shape (64, 64, 3) label 3
image shape (64, 64, 3) label 1
image shape (64, 64, 3) label 4
image shape (64, 64, 3) label 4
image shape (64, 64, 3) label 0
image shape (64, 64, 3) label 2
image shape (64, 64, 3) label 2
加载自定义数据集
mindspore.dataset模块提供了一些常用的公开数据集和标准格式数据集的加载API。
对于MindSpore暂不支持直接加载的数据集，mindspore.dataset.GeneratorDataset 提供了一种自定义的方式加载和处理数据。
GeneratorDataset支持通过可随机访问数据集对象、可迭代数据集对象和生成器(generator)构造自定义数据集。
可随机访问数据集
可随机访问数据集是实现了__getitem__和__len__方法的数据集，表示可以通过索引/键直接访问对应位置的数据样本。
例如，当使用dataset[idx]访问这样的数据集时，可以读取dataset内容中第idx个样本或标签。
[5]:
# Define randomaccessable class to load and process data
class RandomAccessDataset():
def __init__(self):
'''init the class object to hold the data'''
self.data = [i for i in range(5)]
def __getitem__(self, id):
'''overrode the getitem method to support random access'''
return self.data[id]
def __len__(self):
'''specify the length of data'''
return len(self.data)
dataset = RandomAccessDataset()
print("Access with dataset[0]", dataset[0])
# Create a dataloader
dataloader1 = ds.GeneratorDataset(RandomAccessDataset(), column_names=["data"])
# Iter the dataset and check if the data is created successful
for data in dataloader1:
print("RandomAccess dataset:", data)
Access with dataset[0] 0
RandomAccess dataset: [Tensor(shape=[], dtype=Int64, value= 2)]
RandomAccess dataset: [Tensor(shape=[], dtype=Int64, value= 4)]
RandomAccess dataset: [Tensor(shape=[], dtype=Int64, value= 3)]
RandomAccess dataset: [Tensor(shape=[], dtype=Int64, value= 0)]
RandomAccess dataset: [Tensor(shape=[], dtype=Int64, value= 1)]
可迭代数据集
可迭代的数据集是实现了__iter__和__next__方法的数据集，表示可以通过迭代的方式逐步获取数据样本。这种类型的数据集特别适用于随机访问成本太高或者不可行的情况。
例如，当使用iter(dataset)的形式访问数据集时，可以读取从数据库、远程服务器返回的数据流。
[6]:
# Define iterable class to load and process data
class IterableDataset():
def __init__(self, start, end):
'''init the class object to hold the data'''
self.start = start
self.end = end
def __next__(self):
'''iter one data and return'''
return next(self.data)
def __iter__(self):
'''reset the iter'''
self.data = iter(range(self.start, self.end))
return self
dataset = IterableDataset(0, 5)
print("Iter dataset with next(iter(dataset))", next(iter(dataset)))
# Create a dataloader
dataloader2 = ds.GeneratorDataset(IterableDataset(0, 5), column_names=["data"])
# Iter the dataset and check if the data is created successful
for data in dataloader2:
print("Iterable dataset:", data)
Iter dataset with next(iter(dataset)) 0
Iterable dataset: [Tensor(shape=[], dtype=Int64, value= 0)]
Iterable dataset: [Tensor(shape=[], dtype=Int64, value= 1)]
Iterable dataset: [Tensor(shape=[], dtype=Int64, value= 2)]
Iterable dataset: [Tensor(shape=[], dtype=Int64, value= 3)]
Iterable dataset: [Tensor(shape=[], dtype=Int64, value= 4)]
生成器
生成器也属于可迭代的数据集类型，其直接依赖Python的生成器类型generator返回数据，直至生成器抛出StopIteration异常。
[7]:
# Define a generator
def my_generator(start, end):
for i in range(start, end):
yield i
# Since a generator instance can be only iterated once, we need to wrap it by lambda to generate multiple instances
dataloader3 = ds.GeneratorDataset(source=lambda: my_generator(3, 6), column_names=["data"])
for data in dataloader3:
print("Generator", data)
Generator [Tensor(shape=[], dtype=Int64, value= 3)]
Generator [Tensor(shape=[], dtype=Int64, value= 4)]
Generator [Tensor(shape=[], dtype=Int64, value= 5)]
获取数据集的属性信息
数据集被定义完成后，我们可以方便地通过预定义的“getter”的方式获取数据集的属性。
示例展示了如何获取数据集的基本属性，例如数据的类型、数据的形状(shape)、数据集的长度大小等。
[8]:
# Take Cifar dataset as example
cifar_dataset = ds.Cifar10Dataset("cifar-10-batches-bin")
# Get how many samples in the dataset
print("length of cifar10 dataset:", len(cifar_dataset))
print("length of cifar10 dataset:", cifar_dataset.get_dataset_size())
# Get the data columns in dataset
print("data columns of cifar10 dataset:", cifar_dataset.get_col_names())
# Get the shapes of first sample, shown in data column order
print("shapes of cifar10 dataset sample:", cifar_dataset.output_shapes())
# Get the types of first sample, shown in data column order
print("types of cifar10 dataset sample:", cifar_dataset.output_types())
length of cifar10 dataset: 60000
length of cifar10 dataset: 60000
data columns of cifar10 dataset: ['image', 'label']
shapes of cifar10 dataset sample: [[32, 32, 3], []]
types of cifar10 dataset sample: [dtype('uint8'), dtype('uint32')]
在数据集上应用变换
源数据集对象只表示数据集的原始状态，这意味着它没有经过任何变换处理。
一般来说，我们需要对数据集进行一些扩充和增强，使其适合训练。
[9]:
# Take Cifar dataset as example
cifar_dataset = ds.Cifar10Dataset("cifar-10-batches-bin")
# Apply batch on dataset, then we got a new sample with 5 image batched together
cifar_dataset = cifar_dataset.batch(5)
batched_image, batched_label = next(iter(cifar_dataset))
print("Apply batch operation...")
print("batched_image", batched_image.shape, "batched_label", batched_label.shape)
# Take 3 batches from dataset
print("Apply take operation...")
cifar_dataset = cifar_dataset.take(3)
for i, (image, label) in enumerate(cifar_dataset):
print(f"Take 3 batches, {i+1}/3 batch:", image.shape, label.shape)
# Map augmentations on each images in batch
print("Apply map operation...")
## option 1. use transform as function call, input_columns means apply transform on "image" column
def augment(imgs):
resize = vision.Resize(size=(16, 16))
return resize(imgs)
cifar_dataset = cifar_dataset.map(operations=augment, input_columns=["image"])
## option 2. embed transform into dataset pipeline, input_columns means apply transform on "image" column
cifar_dataset = cifar_dataset.map(operations=vision.Resize(size=(16, 16)), input_columns=["image"])
for i, (image, label) in enumerate(cifar_dataset):
print(f"Map transforms on 3 batches, {i+1}/3 batch:", image.shape, label.shape)
Apply batch operation...
batched_image (5, 32, 32, 3) batched_label (5,)
Apply take operation...
Take 3 batches, 1/3 batch: (5, 32, 32, 3) (5,)
Take 3 batches, 2/3 batch: (5, 32, 32, 3) (5,)
Take 3 batches, 3/3 batch: (5, 32, 32, 3) (5,)
Apply map operation...
Map transforms on 3 batches, 1/3 batch: (5, 16, 16, 3) (5,)
Map transforms on 3 batches, 2/3 batch: (5, 16, 16, 3) (5,)
Map transforms on 3 batches, 3/3 batch: (5, 16, 16, 3) (5,)
