tutorials/recipes_source/recipes/loading_data_recipe.py

"""
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 <https://pytorch.org/docs/stable/data.html#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 <https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset>`__.
These datasets are currently available in:

* `torchvision <https://pytorch.org/docs/stable/torchvision/datasets.html>`__
* `torchaudio <https://pytorch.org/audio/datasets.html>`__
* `torchtext <https://pytorch.org/text/datasets.html>`__

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


"""







######################################################################
# 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 <https://www.openslr.org/1/>`__).
# 
# ``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=False,
#      transform=None,
#      target_transform=None)
# 
# 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.
# * ``transform``: Using transforms on your data allows you to take it from its source state and transform it into data that’s joined together, de-normalized, and ready for training. Each library in PyTorch supports a growing list of transformations.
# * ``target_transform``: A function/transform that takes in the target and transforms it.
# 
# 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_trainset = 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 <https://pytorch.org/tutorials/recipes/recipes/defining_a_neural_network.html>`__
# - `What is a state_dict in PyTorch <https://pytorch.org/tutorials/recipes/recipes/what_is_state_dict.html>`__