introyt1_tutorial.py

"""
**Introduction** ||
`Tensors <tensors_deeper_tutorial.html>`_ ||
`Autograd <autogradyt_tutorial.html>`_ ||
`Building Models <modelsyt_tutorial.html>`_ ||
`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
`Training Models <trainingyt.html>`_ ||
`Model Understanding <captumyt.html>`_

Introduction to PyTorch
=======================

Follow along with the video below or on `youtube <https://www.youtube.com/watch?v=IC0_FRiX-sw>`__.

.. raw:: html

   <div style="margin-top:10px; margin-bottom:10px;">
     <iframe width="560" height="315" src="https://www.youtube.com/embed/IC0_FRiX-sw" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
   </div>

PyTorch Tensors
---------------

Follow along with the video beginning at `03:50 <https://www.youtube.com/watch?v=IC0_FRiX-sw&t=230s>`__.

First, we’ll import pytorch.

"""

import torch

######################################################################
# Let’s see a few basic tensor manipulations. First, just a few of the
# ways to create tensors:
# 

z = torch.zeros(5, 3)
print(z)
print(z.dtype)


#########################################################################
# Above, we create a 5x3 matrix filled with zeros, and query its datatype
# to find out that the zeros are 32-bit floating point numbers, which is
# the default PyTorch.
# 
# What if you wanted integers instead? You can always override the
# default:
# 

i = torch.ones((5, 3), dtype=torch.int16)
print(i)


######################################################################
# You can see that when we do change the default, the tensor helpfully
# reports this when printed.
# 
# It’s common to initialize learning weights randomly, often with a
# specific seed for the PRNG for reproducibility of results:
# 

torch.manual_seed(1729)
r1 = torch.rand(2, 2)
print('A random tensor:')
print(r1)

r2 = torch.rand(2, 2)
print('\nA different random tensor:')
print(r2) # new values

torch.manual_seed(1729)
r3 = torch.rand(2, 2)
print('\nShould match r1:')
print(r3) # repeats values of r1 because of re-seed


#######################################################################
# PyTorch tensors perform arithmetic operations intuitively. Tensors of
# similar shapes may be added, multiplied, etc. Operations with scalars
# are distributed over the tensor:
# 

ones = torch.ones(2, 3)
print(ones)

twos = torch.ones(2, 3) * 2 # every element is multiplied by 2
print(twos)

threes = ones + twos       # addition allowed because shapes are similar
print(threes)              # tensors are added element-wise
print(threes.shape)        # this has the same dimensions as input tensors

r1 = torch.rand(2, 3)
r2 = torch.rand(3, 2)
# uncomment this line to get a runtime error
# r3 = r1 + r2


######################################################################
# Here’s a small sample of the mathematical operations available:
# 

r = (torch.rand(2, 2) - 0.5) * 2 # values between -1 and 1
print('A random matrix, r:')
print(r)

# Common mathematical operations are supported:
print('\nAbsolute value of r:')
print(torch.abs(r))

# ...as are trigonometric functions:
print('\nInverse sine of r:')
print(torch.asin(r))

# ...and linear algebra operations like determinant and singular value decomposition
print('\nDeterminant of r:')
print(torch.det(r))
print('\nSingular value decomposition of r:')
print(torch.svd(r))

# ...and statistical and aggregate operations:
print('\nAverage and standard deviation of r:')
print(torch.std_mean(r))
print('\nMaximum value of r:')
print(torch.max(r))


##########################################################################
# There’s a good deal more to know about the power of PyTorch tensors,
# including how to set them up for parallel computations on GPU - we’ll be
# going into more depth in another video.
# 
# PyTorch Models
# --------------
#
# Follow along with the video beginning at `10:00 <https://www.youtube.com/watch?v=IC0_FRiX-sw&t=600s>`__.
#
# Let’s talk about how we can express models in PyTorch
#

import torch                     # for all things PyTorch
import torch.nn as nn            # for torch.nn.Module, the parent object for PyTorch models
import torch.nn.functional as F  # for the activation function


#########################################################################
# .. figure:: /_static/img/mnist.png
#    :alt: le-net-5 diagram
#
# *Figure: LeNet-5*
# 
# Above is a diagram of LeNet-5, one of the earliest convolutional neural
# nets, and one of the drivers of the explosion in Deep Learning. It was
# built to read small images of handwritten numbers (the MNIST dataset),
# and correctly classify which digit was represented in the image.
# 
# Here’s the abridged version of how it works:
# 
# -  Layer C1 is a convolutional layer, meaning that it scans the input
#    image for features it learned during training. It outputs a map of
#    where it saw each of its learned features in the image. This
#    “activation map” is downsampled in layer S2.
# -  Layer C3 is another convolutional layer, this time scanning C1’s
#    activation map for *combinations* of features. It also puts out an
#    activation map describing the spatial locations of these feature
#    combinations, which is downsampled in layer S4.
# -  Finally, the fully-connected layers at the end, F5, F6, and OUTPUT,
#    are a *classifier* that takes the final activation map, and
#    classifies it into one of ten bins representing the 10 digits.
# 
# How do we express this simple neural network in code?
# 

class LeNet(nn.Module):

    def __init__(self):
        super(LeNet, self).__init__()
        # 3 input image channel (RGB image), 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5*5 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


############################################################################
# Looking over this code, you should be able to spot some structural
# similarities with the diagram above.
# 
# This demonstrates the structure of a typical PyTorch model: 
#
# -  It inherits from ``torch.nn.Module`` - modules may be nested - in fact,
#    even the ``Conv2d`` and ``Linear`` layer classes inherit from
#    ``torch.nn.Module``.
# -  A model will have an ``__init__()`` function, where it instantiates
#    its layers, and loads any data artifacts it might
#    need (e.g., an NLP model might load a vocabulary).
# -  A model will have a ``forward()`` function. This is where the actual
#    computation happens: An input is passed through the network layers
#    and various functions to generate an output.
# -  Other than that, you can build out your model class like any other
#    Python class, adding whatever properties and methods you need to
#    support your model’s computation.
# 
# Let’s instantiate this object and run a sample input through it.
# 

net = LeNet()
print(net)                         # what does the object tell us about itself?

input = torch.rand(1, 1, 32, 32)   # stand-in for a 32x32 black & white image
print('\nImage batch shape:')
print(input.shape)

output = net(input)                # we don't call forward() directly
print('\nRaw output:')
print(output)
print(output.shape)


##########################################################################
# There are a few important things happening above:
# 
# First, we instantiate the ``LeNet`` class, and we print the ``net``
# object. A subclass of ``torch.nn.Module`` will report the layers it has
# created and their shapes and parameters. This can provide a handy
# overview of a model if you want to get the gist of its processing.
# 
# Below that, we create a dummy input representing a 32x32 image with 1
# color channel. Normally, you would load an image tile and convert it to
# a tensor of this shape.
# 
# You may have noticed an extra dimension to our tensor - the *batch
# dimension.* PyTorch models assume they are working on *batches* of data
# - for example, a batch of 16 of our image tiles would have the shape
# ``(16, 1, 32, 32)``. Since we’re only using one image, we create a batch
# of 1 with shape ``(1, 1, 32, 32)``.
# 
# We ask the model for an inference by calling it like a function:
# ``net(input)``. The output of this call represents the model’s
# confidence that the input represents a particular digit. (Since this
# instance of the model hasn’t learned anything yet, we shouldn’t expect
# to see any signal in the output.) Looking at the shape of ``output``, we
# can see that it also has a batch dimension, the size of which should
# always match the input batch dimension. If we had passed in an input
# batch of 16 instances, ``output`` would have a shape of ``(16, 10)``.
# 
# Datasets and Dataloaders
# ------------------------
#
# Follow along with the video beginning at `14:00 <https://www.youtube.com/watch?v=IC0_FRiX-sw&t=840s>`__.
#
# Below, we’re going to demonstrate using one of the ready-to-download,
# open-access datasets from TorchVision, how to transform the images for
# consumption by your model, and how to use the DataLoader to feed batches
# of data to your model.
#
# The first thing we need to do is transform our incoming images into a
# PyTorch tensor.
#

#%matplotlib inline

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))])


##########################################################################
# Here, we specify two transformations for our input:
#
# -  ``transforms.ToTensor()`` converts images loaded by Pillow into 
#    PyTorch tensors.
# -  ``transforms.Normalize()`` adjusts the values of the tensor so
#    that their average is zero and their standard deviation is 1.0. Most
#    activation functions have their strongest gradients around x = 0, so
#    centering our data there can speed learning.
#    The values passed to the transform are the means (first tuple) and the
#    standard deviations (second tuple) of the rgb values of the images in
#    the dataset. You can calculate these values yourself by running these
#    few lines of code:
#          ```
#           from torch.utils.data import ConcatDataset
#           transform = transforms.Compose([transforms.ToTensor()])
#           trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
#                                        download=True, transform=transform)
#
#           #stack all train images together into a tensor of shape 
#           #(50000, 3, 32, 32)
#           x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
#           
#           #get the mean of each channel            
#           mean = torch.mean(x, dim=(0,2,3)) #tensor([0.4914, 0.4822, 0.4465])
#           std = torch.std(x, dim=(0,2,3)) #tensor([0.2470, 0.2435, 0.2616])  
# 
#          ```   
# 
# There are many more transforms available, including cropping, centering,
# rotation, and reflection.
# 
# Next, we’ll create an instance of the CIFAR10 dataset. This is a set of
# 32x32 color image tiles representing 10 classes of objects: 6 of animals
# (bird, cat, deer, dog, frog, horse) and 4 of vehicles (airplane,
# automobile, ship, truck):
# 

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)


##########################################################################
# .. note::
#      When you run the cell above, it may take a little time for the 
#      dataset to download.
# 
# This is an example of creating a dataset object in PyTorch. Downloadable
# datasets (like CIFAR-10 above) are subclasses of
# ``torch.utils.data.Dataset``. ``Dataset`` classes in PyTorch include the
# downloadable datasets in TorchVision, Torchtext, and TorchAudio, as well
# as utility dataset classes such as ``torchvision.datasets.ImageFolder``,
# which will read a folder of labeled images. You can also create your own
# subclasses of ``Dataset``.
# 
# When we instantiate our dataset, we need to tell it a few things:
#
# -  The filesystem path to where we want the data to go. 
# -  Whether or not we are using this set for training; most datasets
#    will be split into training and test subsets.
# -  Whether we would like to download the dataset if we haven’t already.
# -  The transformations we want to apply to the data.
# 
# Once your dataset is ready, you can give it to the ``DataLoader``:
# 

trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)


##########################################################################
# A ``Dataset`` subclass wraps access to the data, and is specialized to
# the type of data it’s serving. The ``DataLoader`` knows *nothing* about
# the data, but organizes the input tensors served by the ``Dataset`` into
# batches with the parameters you specify.
# 
# In the example above, we’ve asked a ``DataLoader`` to give us batches of
# 4 images from ``trainset``, randomizing their order (``shuffle=True``),
# and we told it to spin up two workers to load data from disk.
# 
# It’s good practice to visualize the batches your ``DataLoader`` serves:
# 

import matplotlib.pyplot as plt
import numpy as np

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

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


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

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


########################################################################
# Running the above cell should show you a strip of four images, and the
# correct label for each.
# 
# Training Your PyTorch Model
# ---------------------------
#
# Follow along with the video beginning at `17:10 <https://www.youtube.com/watch?v=IC0_FRiX-sw&t=1030s>`__.
#
# Let’s put all the pieces together, and train a model:
#

#%matplotlib inline

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

import matplotlib
import matplotlib.pyplot as plt
import numpy as np


#########################################################################
# First, we’ll need training and test datasets. If you haven’t already,
# run the cell below to make sure the dataset is downloaded. (It may take
# a minute.)
# 

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')


######################################################################
# We’ll run our check on the output from ``DataLoader``:
# 

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)))


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

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


##########################################################################
# This is the model we’ll train. If it looks familiar, that’s because it’s
# a variant of LeNet - discussed earlier in this video - adapted for
# 3-color images.
# 

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()


######################################################################
# The last ingredients we need are a loss function and an optimizer:
# 

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


##########################################################################
# The loss function, as discussed earlier in this video, is a measure of
# how far from our ideal output the model’s prediction was. Cross-entropy
# loss is a typical loss function for classification models like ours.
# 
# The **optimizer** is what drives the learning. Here we have created an
# optimizer that implements *stochastic gradient descent,* one of the more
# straightforward optimization algorithms. Besides parameters of the
# algorithm, like the learning rate (``lr``) and momentum, we also pass in
# ``net.parameters()``, which is a collection of all the learning weights
# in the model - which is what the optimizer adjusts.
# 
# Finally, all of this is assembled into the training loop. Go ahead and
# run this cell, as it will likely take a few minutes to execute:
# 

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
        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')


########################################################################
# Here, we are doing only **2 training epochs** (line 1) - that is, two
# passes over the training dataset. Each pass has an inner loop that
# **iterates over the training data** (line 4), serving batches of
# transformed input images and their correct labels.
# 
# **Zeroing the gradients** (line 9) is an important step. Gradients are
# accumulated over a batch; if we do not reset them for every batch, they
# will keep accumulating, which will provide incorrect gradient values,
# making learning impossible.
# 
# In line 12, we **ask the model for its predictions** on this batch. In
# the following line (13), we compute the loss - the difference between
# ``outputs`` (the model prediction) and ``labels`` (the correct output).
# 
# In line 14, we do the ``backward()`` pass, and calculate the gradients
# that will direct the learning.
# 
# In line 15, the optimizer performs one learning step - it uses the
# gradients from the ``backward()`` call to nudge the learning weights in
# the direction it thinks will reduce the loss.
# 
# The remainder of the loop does some light reporting on the epoch number,
# how many training instances have been completed, and what the collected
# loss is over the training loop.
# 
# **When you run the cell above,** you should see something like this:
# 
# .. code-block:: sh
# 
#    [1,  2000] loss: 2.235
#    [1,  4000] loss: 1.940
#    [1,  6000] loss: 1.713
#    [1,  8000] loss: 1.573
#    [1, 10000] loss: 1.507
#    [1, 12000] loss: 1.442
#    [2,  2000] loss: 1.378
#    [2,  4000] loss: 1.364
#    [2,  6000] loss: 1.349
#    [2,  8000] loss: 1.319
#    [2, 10000] loss: 1.284
#    [2, 12000] loss: 1.267
#    Finished Training
# 
# Note that the loss is monotonically descending, indicating that our
# model is continuing to improve its performance on the training dataset.
# 
# As a final step, we should check that the model is actually doing
# *general* learning, and not simply “memorizing” the dataset. This is
# called **overfitting,** and usually indicates that the dataset is too
# small (not enough examples for general learning), or that the model has
# more learning parameters than it needs to correctly model the dataset.
# 
# This is the reason datasets are split into training and test subsets -
# to test the generality of the model, we ask it to make predictions on
# data it hasn’t trained on:
# 

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))


#########################################################################
# If you followed along, you should see that the model is roughly 50%
# accurate at this point. That’s not exactly state-of-the-art, but it’s
# far better than the 10% accuracy we’d expect from a random output. This
# demonstrates that some general learning did happen in the model.
#