Loading data in PyTorch

PyTorch features extensive neural network building blocks with a simple, intuitive, and stable API. PyTorch includes packages to prepare and load common datasets for your model.

Introduction

At the heart of PyTorch data loading utility is the torch.utils.data.DataLoader class. It represents a Python iterable over a dataset. Libraries in PyTorch offer built-in high-quality datasets for you to use in torch.utils.data.Dataset. These datasets are currently available in:

• torchvision

• torchaudio

• torchtext

with more to come. Using the yesno dataset from torchaudio.datasets.YESNO, we will demonstrate how to effectively and efficiently load data from a PyTorch Dataset into a PyTorch DataLoader.

Setup

Before we begin, we need to install torchaudio to have access to the dataset.

# pip install torchaudio

To run in Google Colab, uncomment the following line:

# !pip install torchaudio

Steps

1. Import all necessary libraries for loading our data

2. Access the data in the dataset

3. Loading the data

4. Iterate over the data

5. [Optional] Visualize the data

1. Import necessary libraries for loading our data

For this recipe, we will use torch and torchaudio. Depending on what built-in datasets you use, you can also install and import torchvision or torchtext.

import torch
import torchaudio

2. Access the data in the dataset

The yesno dataset in torchaudio features sixty recordings of one individual saying yes or no in Hebrew; with each recording being eight words long (read more here).

torchaudio.datasets.YESNO creates a dataset for yesno.

torchaudio.datasets.YESNO(
     root='./',
     url='http://www.openslr.org/resources/1/waves_yesno.tar.gz',
     folder_in_archive='waves_yesno',
     download=True)

Each item in the dataset is a tuple of the form: (waveform, sample_rate, labels).

You must set a root for the yesno dataset, which is where the training and testing dataset will exist. The other parameters are optional, with their default values shown. Here is some additional useful info on the other parameters:

# * ``download``: If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.
#
# Let’s access our ``yesno`` data:
#

# A data point in ``yesno`` is a tuple (waveform, sample_rate, labels) where labels
# is a list of integers with 1 for yes and 0 for no.
yesno_data = torchaudio.datasets.YESNO('./', download=True)

# Pick data point number 3 to see an example of the the ``yesno_data``:
n = 3
waveform, sample_rate, labels = yesno_data[n]
print("Waveform: {}\nSample rate: {}\nLabels: {}".format(waveform, sample_rate, labels))

When using this data in practice, it is best practice to provision the data into a “training” dataset and a “testing” dataset. This ensures that you have out-of-sample data to test the performance of your model.

3. Loading the data

Now that we have access to the dataset, we must pass it through torch.utils.data.DataLoader. The DataLoader combines the dataset and a sampler, returning an iterable over the dataset.

data_loader = torch.utils.data.DataLoader(yesno_data,
                                          batch_size=1,
                                          shuffle=True)

4. Iterate over the data

Our data is now iterable using the data_loader. This will be necessary when we begin training our model! You will notice that now each data entry in the data_loader object is converted to a tensor containing tensors representing our waveform, sample rate, and labels.

for data in data_loader:
  print("Data: ", data)
  print("Waveform: {}\nSample rate: {}\nLabels: {}".format(data[0], data[1], data[2]))
  break

5. [Optional] Visualize the data

You can optionally visualize your data to further understand the output from your DataLoader.

import matplotlib.pyplot as plt

print(data[0][0].numpy())

plt.figure()
plt.plot(waveform.t().numpy())

Congratulations! You have successfully loaded data in PyTorch.

Learn More

Take a look at these other recipes to continue your learning:

• Defining a Neural Network

• What is a state_dict in PyTorch