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)))

Ankle Boot  Sandal  Bag  Sneaker

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([[0.3697, 0.1103, 0.2389, 0.2846, 0.6983, 0.8068, 0.9028, 0.4928, 0.9992,
         0.7777],
        [0.6622, 0.9821, 0.6700, 0.9801, 0.6631, 0.0178, 0.3756, 0.7354, 0.4474,
         0.0722],
        [0.3523, 0.8827, 0.5199, 0.3776, 0.9723, 0.9845, 0.7209, 0.8129, 0.5450,
         0.1174],
        [0.8556, 0.6397, 0.4264, 0.5046, 0.0734, 0.5366, 0.2868, 0.6521, 0.8654,
         0.4339]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.62253475189209

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: 2.078445885926485
  batch 2000 loss: 0.9853184329010546
  batch 3000 loss: 0.7737890719217249
  batch 4000 loss: 0.6859524527182802
  batch 5000 loss: 0.6186456295056268
  batch 6000 loss: 0.5717601936603897
  batch 7000 loss: 0.5374739476540126
  batch 8000 loss: 0.5170167652661912
  batch 9000 loss: 0.4631443543983623
  batch 10000 loss: 0.47267150053571094
  batch 11000 loss: 0.4634251589034684
  batch 12000 loss: 0.4522177288719686
  batch 13000 loss: 0.43082623432995754
  batch 14000 loss: 0.42096412543102635
  batch 15000 loss: 0.43448481692990754
LOSS train 0.43448481692990754 valid 0.4238249659538269
EPOCH 2:
  batch 1000 loss: 0.3991063394710072
  batch 2000 loss: 0.4053242630013847
  batch 3000 loss: 0.39313117962612887
  batch 4000 loss: 0.40031174979098433
  batch 5000 loss: 0.37515016218155506
  batch 6000 loss: 0.3772752701257559
  batch 7000 loss: 0.37141740097344156
  batch 8000 loss: 0.34740131870593177
  batch 9000 loss: 0.37146376077127935
  batch 10000 loss: 0.3786556771292235
  batch 11000 loss: 0.3510120540350035
  batch 12000 loss: 0.3691932062833221
  batch 13000 loss: 0.3275497554614267
  batch 14000 loss: 0.3545808146480704
  batch 15000 loss: 0.3416661732759385
LOSS train 0.3416661732759385 valid 0.36629074811935425
EPOCH 3:
  batch 1000 loss: 0.34619883446252786
  batch 2000 loss: 0.33732505791306905
  batch 3000 loss: 0.3218378667862271
  batch 4000 loss: 0.3382402218070056
  batch 5000 loss: 0.30498756019500434
  batch 6000 loss: 0.32820077318514085
  batch 7000 loss: 0.3115696356798289
  batch 8000 loss: 0.32188789568343784
  batch 9000 loss: 0.3139526225671871
  batch 10000 loss: 0.3200232334123284
  batch 11000 loss: 0.3184244549650912
  batch 12000 loss: 0.32671575834829125
  batch 13000 loss: 0.32073535430858463
  batch 14000 loss: 0.31405857127984926
  batch 15000 loss: 0.32439225951711703
LOSS train 0.32439225951711703 valid 0.3356749415397644
EPOCH 4:
  batch 1000 loss: 0.2872203590600111
  batch 2000 loss: 0.301946465279485
  batch 3000 loss: 0.29448101510899144
  batch 4000 loss: 0.3018223494642589
  batch 5000 loss: 0.30555817463664425
  batch 6000 loss: 0.3044548723964253
  batch 7000 loss: 0.29134536752272744
  batch 8000 loss: 0.29482983102928484
  batch 9000 loss: 0.2826406414659941
  batch 10000 loss: 0.29214115747269537
  batch 11000 loss: 0.29499360246781997
  batch 12000 loss: 0.29350314297291696
  batch 13000 loss: 0.2856313340542665
  batch 14000 loss: 0.3208040994987605
  batch 15000 loss: 0.3008514843018347
LOSS train 0.3008514843018347 valid 0.35053306818008423
EPOCH 5:
  batch 1000 loss: 0.273013430654104
  batch 2000 loss: 0.2721975499899418
  batch 3000 loss: 0.2700386013448442
  batch 4000 loss: 0.2823152292621453
  batch 5000 loss: 0.26805072244314215
  batch 6000 loss: 0.28127112275993565
  batch 7000 loss: 0.2902480135697697
  batch 8000 loss: 0.2787070024538261
  batch 9000 loss: 0.27521627708787855
  batch 10000 loss: 0.2811208541248252
  batch 11000 loss: 0.2936549342118406
  batch 12000 loss: 0.2634534901033694
  batch 13000 loss: 0.28787386487085315
  batch 14000 loss: 0.28128184450815386
  batch 15000 loss: 0.2877686134627966
LOSS train 0.2877686134627966 valid 0.3017216622829437

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