dvc_test/download.py

# -*- coding: utf-8 -*-
"""
Training a Classifier
=====================

This is it. You have seen how to define neural networks, compute loss and make
updates to the weights of the network.

Now you might be thinking,

What about data?
----------------

Generally, when you have to deal with image, text, audio or video data,
you can use standard python packages that load data into a numpy array.
Then you can convert this array into a ``torch.*Tensor``.

-  For images, packages such as Pillow, OpenCV are useful
-  For audio, packages such as scipy and librosa
-  For text, either raw Python or Cython based loading, or NLTK and
   SpaCy are useful

Specifically for vision, we have created a package called
``torchvision``, that has data loaders for common datasets such as
Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,
``torchvision.datasets`` and ``torch.utils.data.DataLoader``.

This provides a huge convenience and avoids writing boilerplate code.

For this tutorial, we will use the CIFAR10 dataset.
It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,
‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of
size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.

.. figure:: /_static/img/cifar10.png
   :alt: cifar10

   cifar10


Training an image classifier
----------------------------

We will do the following steps in order:

1. Load and normalizing the CIFAR10 training and test datasets using
   ``torchvision``
2. Define a Convolutional Neural Network
3. Define a loss function
4. Train the network on the training data
5. Test the network on the test data

1. Loading and normalizing CIFAR10
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Using ``torchvision``, it’s extremely easy to load CIFAR10.
"""
import torch
import torchvision
import torchvision.transforms as transforms

########################################################################
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

########################################################################
# Let us show some of the training images, for fun.
# 
# import matplotlib.pyplot as plt
# import numpy as np
#
# # functions to show an image
#
#
# def imshow(img):
#     img = img / 2 + 0.5     # unnormalize
#     npimg = img.numpy()
#     plt.imshow(np.transpose(npimg, (1, 2, 0)))
#     plt.show()

# show images
# imshow(torchvision.utils.make_grid(images))
# print labels
# print(' '.join('%5s' % classes[labels[j]] for j in range(4)))