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Introduction || Tensors || Autograd || Building Models || TensorBoard Support || Training Models || Model Understanding

Training with PyTorch

Created On: Nov 30, 2021 | Last Updated: May 31, 2023 | Last Verified: Nov 05, 2024

Follow along with the video below or on youtube.

Introduction

In past videos, we’ve discussed and demonstrated:

• Building models with the neural network layers and functions of the torch.nn module

• The mechanics of automated gradient computation, which is central to gradient-based model training

• Using TensorBoard to visualize training progress and other activities

In this video, we’ll be adding some new tools to your inventory:

• We’ll get familiar with the dataset and dataloader abstractions, and how they ease the process of feeding data to your model during a training loop

• We’ll discuss specific loss functions and when to use them

• We’ll look at PyTorch optimizers, which implement algorithms to adjust model weights based on the outcome of a loss function

Finally, we’ll pull all of these together and see a full PyTorch training loop in action.

Dataset and DataLoader

The Dataset and DataLoader classes encapsulate the process of pulling your data from storage and exposing it to your training loop in batches.

The Dataset is responsible for accessing and processing single instances of data.

The DataLoader pulls instances of data from the Dataset (either automatically or with a sampler that you define), collects them in batches, and returns them for consumption by your training loop. The DataLoader works with all kinds of datasets, regardless of the type of data they contain.

For this tutorial, we’ll be using the Fashion-MNIST dataset provided by TorchVision. We use torchvision.transforms.Normalize() to zero-center and normalize the distribution of the image tile content, and download both training and validation data splits.

import torch
import torchvision
import torchvision.transforms as transforms

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


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

# Create datasets for training & validation, download if necessary
training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
validation_set = torchvision.datasets.FashionMNIST('./data', train=False, transform=transform, download=True)

# Create data loaders for our datasets; shuffle for training, not for validation
training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False)

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

# Report split sizes
print('Training set has {} instances'.format(len(training_set)))
print('Validation set has {} instances'.format(len(validation_set)))

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Training set has 60000 instances
Validation set has 10000 instances

As always, let’s visualize the data as a sanity check:

import matplotlib.pyplot as plt
import numpy as np

# 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)))

dataiter = iter(training_loader)
images, labels = next(dataiter)

# Create a grid from the images and show them
img_grid = torchvision.utils.make_grid(images)
matplotlib_imshow(img_grid, one_channel=True)
print('  '.join(classes[labels[j]] for j in range(4)))

Bag  T-shirt/top  Trouser  Shirt

The Model

The model we’ll use in this example is a variant of LeNet-5 - it should be familiar if you’ve watched the previous videos in this series.

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

# PyTorch models inherit from torch.nn.Module
class GarmentClassifier(nn.Module):
    def __init__(self):
        super(GarmentClassifier, 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


model = GarmentClassifier()

Loss Function

For this example, we’ll be using a cross-entropy loss. For demonstration purposes, we’ll create batches of dummy output and label values, run them through the loss function, and examine the result.

loss_fn = torch.nn.CrossEntropyLoss()

# NB: Loss functions expect data in batches, so we're creating batches of 4
# Represents the model's confidence in each of the 10 classes for a given input
dummy_outputs = torch.rand(4, 10)
# Represents the correct class among the 10 being tested
dummy_labels = torch.tensor([1, 5, 3, 7])

print(dummy_outputs)
print(dummy_labels)

loss = loss_fn(dummy_outputs, dummy_labels)
print('Total loss for this batch: {}'.format(loss.item()))

tensor([[0.4588, 0.8217, 0.3731, 0.8594, 0.9620, 0.5852, 0.1047, 0.7020, 0.9868,
         0.7940],
        [0.1278, 0.5801, 0.3698, 0.4406, 0.0527, 0.8259, 0.2517, 0.1621, 0.3190,
         0.8541],
        [0.8893, 0.8885, 0.6116, 0.4784, 0.5108, 0.5029, 0.5798, 0.6625, 0.4972,
         0.0210],
        [0.0167, 0.7472, 0.9256, 0.2632, 0.5146, 0.5124, 0.2123, 0.8630, 0.6046,
         0.3053]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.120039463043213

Optimizer

For this example, we’ll be using simple stochastic gradient descent with momentum.

It can be instructive to try some variations on this optimization scheme:

• Learning rate determines the size of the steps the optimizer takes. What does a different learning rate do to the your training results, in terms of accuracy and convergence time?

• Momentum nudges the optimizer in the direction of strongest gradient over multiple steps. What does changing this value do to your results?

• Try some different optimization algorithms, such as averaged SGD, Adagrad, or Adam. How do your results differ?

# Optimizers specified in the torch.optim package
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

The Training Loop

Below, we have a function that performs one training epoch. It enumerates data from the DataLoader, and on each pass of the loop does the following:

• Gets a batch of training data from the DataLoader

• Zeros the optimizer’s gradients

• Performs an inference - that is, gets predictions from the model for an input batch

• Calculates the loss for that set of predictions vs. the labels on the dataset

• Calculates the backward gradients over the learning weights

• Tells the optimizer to perform one learning step - that is, adjust the model’s learning weights based on the observed gradients for this batch, according to the optimization algorithm we chose

• It reports on the loss for every 1000 batches.

• Finally, it reports the average per-batch loss for the last 1000 batches, for comparison with a validation run

def train_one_epoch(epoch_index, tb_writer):
    running_loss = 0.
    last_loss = 0.

    # Here, we use enumerate(training_loader) instead of
    # iter(training_loader) so that we can track the batch
    # index and do some intra-epoch reporting
    for i, data in enumerate(training_loader):
        # Every data instance is an input + label pair
        inputs, labels = data

        # Zero your gradients for every batch!
        optimizer.zero_grad()

        # Make predictions for this batch
        outputs = model(inputs)

        # Compute the loss and its gradients
        loss = loss_fn(outputs, labels)
        loss.backward()

        # Adjust learning weights
        optimizer.step()

        # Gather data and report
        running_loss += loss.item()
        if i % 1000 == 999:
            last_loss = running_loss / 1000 # loss per batch
            print('  batch {} loss: {}'.format(i + 1, last_loss))
            tb_x = epoch_index * len(training_loader) + i + 1
            tb_writer.add_scalar('Loss/train', last_loss, tb_x)
            running_loss = 0.

    return last_loss

Per-Epoch Activity

There are a couple of things we’ll want to do once per epoch:

• Perform validation by checking our relative loss on a set of data that was not used for training, and report this

• Save a copy of the model

Here, we’ll do our reporting in TensorBoard. This will require going to the command line to start TensorBoard, and opening it in another browser tab.

# Initializing in a separate cell so we can easily add more epochs to the same run
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
writer = SummaryWriter('runs/fashion_trainer_{}'.format(timestamp))
epoch_number = 0

EPOCHS = 5

best_vloss = 1_000_000.

for epoch in range(EPOCHS):
    print('EPOCH {}:'.format(epoch_number + 1))

    # Make sure gradient tracking is on, and do a pass over the data
    model.train(True)
    avg_loss = train_one_epoch(epoch_number, writer)


    running_vloss = 0.0
    # Set the model to evaluation mode, disabling dropout and using population
    # statistics for batch normalization.
    model.eval()

    # Disable gradient computation and reduce memory consumption.
    with torch.no_grad():
        for i, vdata in enumerate(validation_loader):
            vinputs, vlabels = vdata
            voutputs = model(vinputs)
            vloss = loss_fn(voutputs, vlabels)
            running_vloss += vloss

    avg_vloss = running_vloss / (i + 1)
    print('LOSS train {} valid {}'.format(avg_loss, avg_vloss))

    # Log the running loss averaged per batch
    # for both training and validation
    writer.add_scalars('Training vs. Validation Loss',
                    { 'Training' : avg_loss, 'Validation' : avg_vloss },
                    epoch_number + 1)
    writer.flush()

    # Track best performance, and save the model's state
    if avg_vloss < best_vloss:
        best_vloss = avg_vloss
        model_path = 'model_{}_{}'.format(timestamp, epoch_number)
        torch.save(model.state_dict(), model_path)

    epoch_number += 1

EPOCH 1:
  batch 1000 loss: 1.784622948050499
  batch 2000 loss: 0.8511106609553099
  batch 3000 loss: 0.7326164273638278
  batch 4000 loss: 0.6416620532548987
  batch 5000 loss: 0.614170024250634
  batch 6000 loss: 0.5754655525544659
  batch 7000 loss: 0.546334325313801
  batch 8000 loss: 0.5293236432858975
  batch 9000 loss: 0.47159357187361456
  batch 10000 loss: 0.47789190683467314
  batch 11000 loss: 0.4829156323028728
  batch 12000 loss: 0.4315095065088244
  batch 13000 loss: 0.43287891318910987
  batch 14000 loss: 0.4133518683338189
  batch 15000 loss: 0.4376176276106853
LOSS train 0.4376176276106853 valid 0.4342682957649231
EPOCH 2:
  batch 1000 loss: 0.38329241322074087
  batch 2000 loss: 0.4061445837364299
  batch 3000 loss: 0.3828190863680211
  batch 4000 loss: 0.39963046410371317
  batch 5000 loss: 0.3709598907449108
  batch 6000 loss: 0.37007126975397114
  batch 7000 loss: 0.3675668174488819
  batch 8000 loss: 0.38125942834973103
  batch 9000 loss: 0.3555297342231497
  batch 10000 loss: 0.3547279914149258
  batch 11000 loss: 0.33031073224566354
  batch 12000 loss: 0.36872647681750825
  batch 13000 loss: 0.356904874985863
  batch 14000 loss: 0.36074118577156694
  batch 15000 loss: 0.3450301461214258
LOSS train 0.3450301461214258 valid 0.3803762197494507
EPOCH 3:
  batch 1000 loss: 0.3415681545013213
  batch 2000 loss: 0.3363595938021608
  batch 3000 loss: 0.34286296825518364
  batch 4000 loss: 0.3293615269594011
  batch 5000 loss: 0.31156954377450163
  batch 6000 loss: 0.3265527327981836
  batch 7000 loss: 0.30798651921065173
  batch 8000 loss: 0.3164072989327578
  batch 9000 loss: 0.3352627821502538
  batch 10000 loss: 0.30625521660703814
  batch 11000 loss: 0.311865743274102
  batch 12000 loss: 0.31119285366447
  batch 13000 loss: 0.32160709877741467
  batch 14000 loss: 0.344822292332392
  batch 15000 loss: 0.31039012851704684
LOSS train 0.31039012851704684 valid 0.35401132702827454
EPOCH 4:
  batch 1000 loss: 0.29675389391658247
  batch 2000 loss: 0.2844620589394808
  batch 3000 loss: 0.29090076338768994
  batch 4000 loss: 0.32308929792448543
  batch 5000 loss: 0.29467708310457236
  batch 6000 loss: 0.28664144510796175
  batch 7000 loss: 0.311522031287881
  batch 8000 loss: 0.3108567202586273
  batch 9000 loss: 0.3195743517058872
  batch 10000 loss: 0.29008481238897366
  batch 11000 loss: 0.2923720023734786
  batch 12000 loss: 0.28601411641309094
  batch 13000 loss: 0.28105433511506633
  batch 14000 loss: 0.30881216267438866
  batch 15000 loss: 0.27031104325226624
LOSS train 0.27031104325226624 valid 0.3510621190071106
EPOCH 5:
  batch 1000 loss: 0.25836708917354556
  batch 2000 loss: 0.2717059097975107
  batch 3000 loss: 0.2743432261799971
  batch 4000 loss: 0.2810325593021844
  batch 5000 loss: 0.2733012254708228
  batch 6000 loss: 0.27475925677181656
  batch 7000 loss: 0.2742153012904164
  batch 8000 loss: 0.28467065418025234
  batch 9000 loss: 0.2626644345611312
  batch 10000 loss: 0.267530162991221
  batch 11000 loss: 0.2752228195667085
  batch 12000 loss: 0.29481438424371664
  batch 13000 loss: 0.2885446249743327
  batch 14000 loss: 0.27302156508385267
  batch 15000 loss: 0.2868558977507673
LOSS train 0.2868558977507673 valid 0.3054198622703552

To load a saved version of the model:

saved_model = GarmentClassifier()
saved_model.load_state_dict(torch.load(PATH))

Once you’ve loaded the model, it’s ready for whatever you need it for - more training, inference, or analysis.

Note that if your model has constructor parameters that affect model structure, you’ll need to provide them and configure the model identically to the state in which it was saved.

Other Resources

• Docs on the data utilities, including Dataset and DataLoader, at pytorch.org

• A note on the use of pinned memory for GPU training

• Documentation on the datasets available in TorchVision, TorchText, and TorchAudio

• Documentation on the loss functions available in PyTorch

• Documentation on the torch.optim package, which includes optimizers and related tools, such as learning rate scheduling

• A detailed tutorial on saving and loading models

• The Tutorials section of pytorch.org contains tutorials on a broad variety of training tasks, including classification in different domains, generative adversarial networks, reinforcement learning, and more