pytorch_bench.py

# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%

# %%

#null.tpl [markdown]
#
# 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.optim as optim
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms

#null.tpl [markdown]
# The output of torchvision datasets are PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1].
# <div class="alert alert-info"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting
#     the num_worker of torch.utils.data.DataLoader() to 0.</p></div>
#
#

# %%
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')

#null.tpl [markdown]
# Let us show some of the training images, for fun.
#
#

# %%

# 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()


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

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

#null.tpl [markdown]
# 2. Define a Convolutional Neural Network
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Copy the neural network from the Neural Networks section before and modify it to
# take 3-channel images (instead of 1-channel images as it was defined).
#
#

# %%


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

#null.tpl [markdown]
# 3. Define a Loss function and optimizer
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Let's use a Classification Cross-Entropy loss and SGD with momentum.
#
#

# %%

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

#null.tpl [markdown]
# 4. Train the network
# ^^^^^^^^^^^^^^^^^^^^
#
# This is when things start to get interesting.
# We simply have to loop over our data iterator, and feed the inputs to the
# network and optimize.
#
#

# %%
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

#null.tpl [markdown]
# Let's quickly save our trained model:
#
#

# %%
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

#null.tpl [markdown]
# See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_
# for more details on saving PyTorch models.
#
# 5. Test the network on the test data
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# We have trained the network for 2 passes over the training dataset.
# But we need to check if the network has learnt anything at all.
#
# We will check this by predicting the class label that the neural network
# outputs, and checking it against the ground-truth. If the prediction is
# correct, we add the sample to the list of correct predictions.
#
# Okay, first step. Let us display an image from the test set to get familiar.
#
#

# %%
dataiter = iter(testloader)
images, labels = dataiter.next()

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

#null.tpl [markdown]
# Next, let's load back in our saved model (note: saving and re-loading the model
# wasn't necessary here, we only did it to illustrate how to do so):
#
#

# %%
net = Net()
net.load_state_dict(torch.load(PATH))

#null.tpl [markdown]
# Okay, now let us see what the neural network thinks these examples above are:
#
#

# %%
outputs = net(images)

#null.tpl [markdown]
# The outputs are energies for the 10 classes.
# The higher the energy for a class, the more the network
# thinks that the image is of the particular class.
# So, let's get the index of the highest energy:
#
#

# %%
_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))

#null.tpl [markdown]
# The results seem pretty good.
#
# Let us look at how the network performs on the whole dataset.
#
#

# %%
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

#null.tpl [markdown]
# That looks way better than chance, which is 10% accuracy (randomly picking
# a class out of 10 classes).
# Seems like the network learnt something.
#
# Hmmm, what are the classes that performed well, and the classes that did
# not perform well:
#
#

# %%
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

#null.tpl [markdown]
# Okay, so what next?
#
# How do we run these neural networks on the GPU?
#
# Training on GPU
# ----------------
# Just like how you transfer a Tensor onto the GPU, you transfer the neural
# net onto the GPU.
#
# Let's first define our device as the first visible cuda device if we have
# CUDA available:
#
#

# %%
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Assuming that we are on a CUDA machine, this should print a CUDA device:

print(device)

#null.tpl [markdown]
# The rest of this section assumes that ``device`` is a CUDA device.
#
# Then these methods will recursively go over all modules and convert their
# parameters and buffers to CUDA tensors:
#
# .. code:: python
#
#     net.to(device)
#
#
# Remember that you will have to send the inputs and targets at every step
# to the GPU too:
#
# .. code:: python
#
#         inputs, labels = data[0].to(device), data[1].to(device)
#
# Why dont I notice MASSIVE speedup compared to CPU? Because your network
# is really small.
#
# **Exercise:** Try increasing the width of your network (argument 2 of
# the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –
# they need to be the same number), see what kind of speedup you get.
#
# **Goals achieved**:
#
# - Understanding PyTorch's Tensor library and neural networks at a high level.
# - Train a small neural network to classify images
#
# Training on multiple GPUs
# -------------------------
# If you want to see even more MASSIVE speedup using all of your GPUs,
# please check out :doc:`data_parallel_tutorial`.
#
# Where do I go next?
# -------------------
#
# -  :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`
# -  `Train a state-of-the-art ResNet network on imagenet`_
# -  `Train a face generator using Generative Adversarial Networks`_
# -  `Train a word-level language model using Recurrent LSTM networks`_
# -  `More examples`_
# -  `More tutorials`_
# -  `Discuss PyTorch on the Forums`_
# -  `Chat with other users on Slack`_
#
#
#

# %%