# Data¶

The data module provides APIs to 1) load and parse datasets, 2) transform data examples, and 3) sample mini-batches for the training program. This tutorial will go through these three functionalities. Let’s first import the modules needed, the key module is mxnet.gluon.data, which is imported as gdata to avoid the too commonly used name data.

In [1]:

import numpy as np
from matplotlib import pyplot as plt
import tarfile
import time

from mxnet import nd, image, io
from mxnet.gluon import utils, data as gdata


To use a dataset, we first need to parse it into individual examples with the NDArray data type. The base class is Dataset, which defines two essential methods: __getitem__ to access the i-th example and __len__ to get the number of examples.

ArrayDataset is an implementation of Dataset that combines multiple array-like objects into a dataset. In the following example, we define NDArray features X with NDArray label y, and then create the dataset.

In [2]:

features = nd.random.uniform(shape=(10, 3))
labels = nd.arange(10)
dataset = gdata.ArrayDataset(features, labels)


We can query the number examples in this dataset:

In [3]:

len(dataset)

Out[3]:

10


And access an arbitrary example with its index. The returned example is a list contains its features and label.

In [4]:

sample = dataset[1]
'features:', sample[0], 'label:', sample[1]

Out[4]:

('features:',
[0.84426576 0.60276335 0.8579456 ]
<NDArray 3 @cpu(0)>, 'label:', 1.0)


Note that the label for each example is a scalar, it is automatically converted into numpy to make using as an index easy, e.g. we don’t need to call sample[1].asscalar().

In [5]:

type(sample[1])

Out[5]:

numpy.float32


In addition, ArrayDataset can construct a dataset with any array-like objects, with an arbitrary number of arrays:

In [6]:

dataset2 = gdata.ArrayDataset(features, np.random.uniform(size=(10,1)), list(range(0,10)))
sample = dataset2[1]
type(sample[0]), type(sample[1]), type(sample[2])

Out[6]:

(mxnet.ndarray.ndarray.NDArray, numpy.ndarray, int)


### Predefined Datasets¶

This module provides several commonly used datasets that will be automatically downloaded during creation. For example, we can obtain both the training and validation set of MNIST:

In [7]:

mnist_train = gdata.vision.MNIST()
mnist_valid = gdata.vision.MNIST(train=False)
print('# of training examples =', len(mnist_train))
print('# of validation examples =', len(mnist_valid))

# of training examples = 60000
# of validation examples = 10000


Obtaining one example is as similar as before:

In [8]:

sample = mnist_train[1]
print('X shape:', sample[0].shape)
print('y:', sample[1])

X shape: (28, 28, 1)
y: 0


Besides MNIST, mxnet.gluon.data.vision provides these three datasets: FashionMNIST, CIFAR10, and CIFAR100.

In vision tasks, the examples are often stored as individual images files. If we place images within each category in separate folders, then we can use ImageFolderDataset to load both images and labels.

In [9]:

utils.download('https://github.com/dmlc/web-data/raw/master/mxnet/doc/dogcat.tar.gz')
with tarfile.open('dogcat.tar.gz') as f:
f.extractall()

Downloading dogcat.tar.gz from https://github.com/dmlc/web-data/raw/master/mxnet/doc/dogcat.tar.gz...


Then check contents within this dataset:

In [10]:

# You may need to install the tree' program, such as uncommenting the following line for Ubuntu:
# !sudo apt-get install tree
!tree dogcat

dogcat
├── cat
│   ├── cat1.jpg
│   ├── cat2.jpg
│   ├── cat3.png
│   └── cat4.jpeg
└── dog
├── dog1.jpg
├── dog2.jpg
├── dog3.jpg
└── dog4.png

2 directories, 8 files


As can be seen, it has two categories, dog and cat. Image files are placed in subfolders with categories as folder names. Now construct an ImageFolderDataset instance with specifying the dataset root folder.

In [11]:

dogcat = gdata.vision.ImageFolderDataset('./dogcat')


We can access all categories through the attribute synsets:

In [12]:

dogcat.synsets

Out[12]:

['cat', 'dog']


Next let’s print a particular sample with its label:

In [13]:

sample = dogcat[1]
plt.imshow(sample[0].asnumpy())
plt.show()
'label:', dogcat.synsets[sample[1]]

Out[13]:

('label:', 'cat')


## Transform Data Examples¶

The raw data examples often need to be transformed before feeding into a neural network. Class Dataset provides two methods transform and transform_first to allow users to specify the transformation methods.

In the following example, we define a function to resize an image into 200px height and 300px width. And then we pass it into the dataset through the transform_first method, which returns a new dataset with the transformations recorded.

In [14]:

def resize(x):
y = image.imresize(x, 300, 200)
print('resize', x.shape, 'into', y.shape)
return y
dogcat_resized = dogcat.transform_first(resize)


As can be seen, transformations are applied when accessing examples, which is necessary when these transformations contain randomness. But we can also apply all transformations during creating the dataset.

In [15]:

dogcat_cached = dogcat.transform_first(resize, lazy=False)

resize (400, 600, 3) into (200, 300, 3)
resize (428, 590, 3) into (200, 300, 3)
resize (519, 510, 3) into (200, 300, 3)
resize (380, 645, 3) into (200, 300, 3)
resize (452, 400, 3) into (200, 300, 3)
resize (486, 729, 3) into (200, 300, 3)
resize (267, 400, 3) into (200, 300, 3)
resize (486, 729, 3) into (200, 300, 3)


So no transform is needed during accessing examples.

In [16]:

dogcat_cached[0][0].shape

Out[16]:

(200, 300, 3)


Besides transform_first, we can apply transform to all entries in an example. The following examples add the label by 10 besides resizing an image:

In [17]:

dogcat_both = dogcat.transform(lambda x, y: (resize(x), y+10))
dogcat_both[0][1]

resize (400, 600, 3) into (200, 300, 3)

Out[17]:

10


The vision.transform submodule provide multiple pre-defined data transformation methods. For example, the following example chains the image resize and ToTensor, which transforms the data layout into (C x H x W) with float32 data type. Please refer to Image Augmentation for more details.

In [18]:

transforms = gdata.vision.transforms.Compose([
gdata.vision.transforms.Resize((24, 24)),
gdata.vision.transforms.ToTensor()])
mnist_transformed = mnist_train.transform_first(transforms, lazy=True)
(mnist_train[0][0].shape, '->', mnist_transformed[0][0].shape)

Out[18]:

((28, 28, 1), '->', (1, 24, 24))


## Sample Mini-batches¶

If we train a neural network with mini-batch SGD, we need to sample a mini-batch for every iteration. Class DataLoader samples a dataset into mini-batches. In the following example, we create a DataLoader instance, which is an iterator that returns a mini-batch each time.

In [19]:

data = gdata.DataLoader(dataset, batch_size=4)
for X, y in data:
print('X shape:', X.shape, '\ty:', y.asnumpy())

X shape: (4, 3)         y: [0. 1. 2. 3.]
X shape: (4, 3)         y: [4. 5. 6. 7.]
X shape: (2, 3)         y: [8. 9.]


Since the number of examples can not be divided by the batch size, the last min- batch only has two examples. We can chose to ignore the last incomplete mini- batch:

In [20]:

data = gdata.DataLoader(dataset, batch_size=4, last_batch='discard')
for X, y in data:
print('y:', y.asnumpy())

y: [0. 1. 2. 3.]
y: [4. 5. 6. 7.]


Or put it into the beginning of the next epoch:

In [21]:

data = gdata.DataLoader(dataset, batch_size=4, last_batch='rollover')
for X, y in data:
print('epoch 0, y:', y.asnumpy())
for X, y in data:
print('epoch 1, y:', y.asnumpy())

epoch 0, y: [0. 1. 2. 3.]
epoch 0, y: [4. 5. 6. 7.]
epoch 1, y: [8. 9. 0. 1.]
epoch 1, y: [2. 3. 4. 5.]
epoch 1, y: [6. 7. 8. 9.]


In mini-batch SGD, a mini-batch needs to consist of randomly sampled examples. We can set the shuffle argument to get random batches:

In [22]:

data = gdata.DataLoader(dataset, batch_size=4, shuffle=True)
for X, y in data:
print('y:', y.asnumpy())

y: [6. 9. 8. 4.]
y: [5. 7. 3. 2.]
y: [0. 1.]


### Customize Sampling¶

DataLoader reads examples either sequentially or uniformly randomly without replacement. We can change this behavior through customized samplers. An sampler is an iterator returning an sample index each time. For example, we create an sampler that first sequentially reads even indexes and then odd indexes.

In [23]:

class MySampler():
def __init__(self, length):
self.len = length
def __iter__(self):
for i in list(range(0,self.len,2))+list(range(1,self.len,2)):
yield i
for X, y in data:
print(y.asnumpy())

[0. 2. 4. 6.]
[8. 1. 3. 5.]
[7. 9.]


Similarly, we can change how a mini-batches is sampled through the batch_sampler argument.

### Multi-process¶

Reading data is often one of the major performance bottlenecks. We can accelerate it through multi-process (only Linux and Macos are supported.) Let’s first benchmark the time to read the MNIST training set:

In [24]:

tic = time.time()
for X, y in data:
pass
'%.1f sec' % (time.time() - tic)

Out[24]:

'6.6 sec'


Now let’s use 4 processes:

In [25]:

tic = time.time()
for X, y in data:
pass
'%.1f sec' % (time.time() - tic)

Out[25]:

'77.4 sec'


## Appendix: From DataIter to DataLoader¶

Before Gluon’s DataLoader, MXNet provides DataIter in the io module to read mini-batches. They are similar to each other but DataLoader often returns a tuple of (feature, label) for a mini-batches, while DataIter returns a DataBatch. The following example wraps a DataIter into a DataLoader so you can reuse the existing codes but enjoys the benefits of Gluon.

In [26]:

class DataIterLoader():
def __init__(self, data_iter):
self.data_iter = data_iter
def __iter__(self):
self.data_iter.reset()
return self
def __next__(self):
batch = self.data_iter.__next__()
assert len(batch.data) == len(batch.label) == 1
data = batch.data[0]
label = batch.label[0]
return data, label
def next(self):
return self.__next__() # for Python 2


Now create an DataIter instance, and then get the according DataLoader wrapper.

In [27]:

data_iter = io.NDArrayIter(data=features, label=labels, batch_size=4)

X shape: (4, 3)         y: [0. 1. 2. 3.]
`