tensorboardyt_tutorial.py

"""
`Introduction <introyt1_tutorial.html>`_ ||
`Tensors <tensors_deeper_tutorial.html>`_ ||
`Autograd <autogradyt_tutorial.html>`_ ||
`Building Models <modelsyt_tutorial.html>`_ ||
**TensorBoard Support** ||
`Training Models <trainingyt.html>`_ ||
`Model Understanding <captumyt.html>`_

PyTorch TensorBoard Support
===========================

Follow along with the video below or on `youtube <https://www.youtube.com/watch?v=6CEld3hZgqc>`__.

.. raw:: html

   <div style="margin-top:10px; margin-bottom:10px;">
     <iframe width="560" height="315" src="https://www.youtube.com/embed/6CEld3hZgqc" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
   </div>

Before You Start
----------------

To run this tutorial, you’ll need to install PyTorch, TorchVision,
Matplotlib, and TensorBoard.

With ``conda``:

``conda install pytorch torchvision -c pytorch``
``conda install matplotlib tensorboard``

With ``pip``:

``pip install torch torchvision matplotlib tensorboard``

Once the dependencies are installed, restart this notebook in the Python
environment where you installed them.


Introduction
------------
 
In this notebook, we’ll be training a variant of LeNet-5 against the
Fashion-MNIST dataset. Fashion-MNIST is a set of image tiles depicting
various garments, with ten class labels indicating the type of garment
depicted. 

"""

# PyTorch model and training necessities
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

# Image datasets and image manipulation
import torchvision
import torchvision.transforms as transforms

# Image display
import matplotlib.pyplot as plt
import numpy as np

# PyTorch TensorBoard support
from torch.utils.tensorboard import SummaryWriter


######################################################################
# Showing Images in TensorBoard
# -----------------------------
# 
# Let’s start by adding sample images from our dataset to TensorBoard:
# 

# Gather datasets and prepare them for consumption
transform = transforms.Compose(
    [transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))])

# Store separate training and validations splits in ./data
training_set = torchvision.datasets.FashionMNIST('./data',
    download=True,
    train=True,
    transform=transform)
validation_set = torchvision.datasets.FashionMNIST('./data',
    download=True,
    train=False,
    transform=transform)

training_loader = torch.utils.data.DataLoader(training_set,
                                              batch_size=4,
                                              shuffle=True,
                                              num_workers=2)


validation_loader = torch.utils.data.DataLoader(validation_set,
                                                batch_size=4,
                                                shuffle=False,
                                                num_workers=2)

# Class labels
classes = ('T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
        'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot')

# Helper function for inline image display
def matplotlib_imshow(img, one_channel=False):
    if one_channel:
        img = img.mean(dim=0)
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    if one_channel:
        plt.imshow(npimg, cmap="Greys")
    else:
        plt.imshow(np.transpose(npimg, (1, 2, 0)))

# Extract a batch of 4 images
dataiter = iter(training_loader)
images, labels = dataiter.next()

# Create a grid from the images and show them
img_grid = torchvision.utils.make_grid(images)
matplotlib_imshow(img_grid, one_channel=True)


########################################################################
# Above, we used TorchVision and Matplotlib to create a visual grid of a
# minibatch of our input data. Below, we use the ``add_image()`` call on
# ``SummaryWriter`` to log the image for consumption by TensorBoard, and
# we also call ``flush()`` to make sure it’s written to disk right away.
# 

# Default log_dir argument is "runs" - but it's good to be specific
# torch.utils.tensorboard.SummaryWriter is imported above
writer = SummaryWriter('runs/fashion_mnist_experiment_1')

# Write image data to TensorBoard log dir
writer.add_image('Four Fashion-MNIST Images', img_grid)
writer.flush()

# To view, start TensorBoard on the command line with:
#   tensorboard --logdir=runs
# ...and open a browser tab to http://localhost:6006/


##########################################################################
# If you start TensorBoard at the command line and open it in a new
# browser tab (usually at `localhost:6006 <localhost:6006>`__), you should
# see the image grid under the IMAGES tab.
# 
# Graphing Scalars to Visualize Training
# --------------------------------------
# 
# TensorBoard is useful for tracking the progress and efficacy of your
# training. Below, we’ll run a training loop, track some metrics, and save
# the data for TensorBoard’s consumption.
# 
# Let’s define a model to categorize our image tiles, and an optimizer and
# loss function for training:
# 

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 4 * 4, 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 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    

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


##########################################################################
# Now let’s train a single epoch, and evaluate the training vs. validation
# set losses every 1000 batches:
# 

print(len(validation_loader))
for epoch in range(1):  # loop over the dataset multiple times
    running_loss = 0.0

    for i, data in enumerate(training_loader, 0):
        # basic training loop
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 1000 == 999:    # Every 1000 mini-batches...
            print('Batch {}'.format(i + 1))
            # Check against the validation set
            running_vloss = 0.0
            
            net.train(False) # Don't need to track gradents for validation
            for j, vdata in enumerate(validation_loader, 0):
                vinputs, vlabels = vdata
                voutputs = net(vinputs)
                vloss = criterion(voutputs, vlabels)
                running_vloss += vloss.item()
            net.train(True) # Turn gradients back on for training
            
            avg_loss = running_loss / 1000
            avg_vloss = running_vloss / len(validation_loader)
            
            # Log the running loss averaged per batch
            writer.add_scalars('Training vs. Validation Loss',
                            { 'Training' : avg_loss, 'Validation' : avg_vloss },
                            epoch * len(training_loader) + i)

            running_loss = 0.0
print('Finished Training')

writer.flush()


#########################################################################
# Switch to your open TensorBoard and have a look at the SCALARS tab.
# 
# Visualizing Your Model
# ----------------------
# 
# TensorBoard can also be used to examine the data flow within your model.
# To do this, call the ``add_graph()`` method with a model and sample
# input. When you open
# 

# Again, grab a single mini-batch of images
dataiter = iter(training_loader)
images, labels = dataiter.next()

# add_graph() will trace the sample input through your model,
# and render it as a graph.
writer.add_graph(net, images)
writer.flush()


#########################################################################
# When you switch over to TensorBoard, you should see a GRAPHS tab.
# Double-click the “NET” node to see the layers and data flow within your
# model.
# 
# Visualizing Your Dataset with Embeddings
# ----------------------------------------
# 
# The 28-by-28 image tiles we’re using can be modeled as 784-dimensional
# vectors (28 \* 28 = 784). It can be instructive to project this to a
# lower-dimensional representation. The ``add_embedding()`` method will
# project a set of data onto the three dimensions with highest variance,
# and display them as an interactive 3D chart. The ``add_embedding()``
# method does this automatically by projecting to the three dimensions
# with highest variance.
# 
# Below, we’ll take a sample of our data, and generate such an embedding:
# 

# Select a random subset of data and corresponding labels
def select_n_random(data, labels, n=100):
    assert len(data) == len(labels)

    perm = torch.randperm(len(data))
    return data[perm][:n], labels[perm][:n]

# Extract a random subset of data
images, labels = select_n_random(training_set.data, training_set.targets)

# get the class labels for each image
class_labels = [classes[label] for label in labels]

# log embeddings
features = images.view(-1, 28 * 28)
writer.add_embedding(features,
                    metadata=class_labels,
                    label_img=images.unsqueeze(1))
writer.flush()
writer.close()


#######################################################################
# Now if you switch to TensorBoard and select the PROJECTOR tab, you
# should see a 3D representation of the projection. You can rotate and
# zoom the model. Examine it at large and small scales, and see whether
# you can spot patterns in the projected data and the clustering of
# labels.
# 
# For better visibility, it’s recommended to:
# 
# - Select “label” from the “Color by” drop-down on the left.
# - Toggle the Night Mode icon along the top to place the
#   light-colored images on a dark background.
# 
# Other Resources
# ---------------
# 
# For more information, have a look at:
# 
# - PyTorch documentation on `torch.utils.tensorboard.SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html?highlight=summarywriter>`__
# - Tensorboard tutorial content in the `PyTorch.org Tutorials <https://pytorch.org/tutorials/>`__ 
# - For more information about TensorBoard, see the `TensorBoard
#   documentation <https://www.tensorflow.org/tensorboard>`__