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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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 79%|███████▊  | 3.47M/4.42M [00:00<00:00, 6.85MB/s]
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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)))

T-shirt/top  Ankle Boot  T-shirt/top  Ankle Boot

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([[0.4136, 0.5780, 0.6322, 0.5602, 0.9690, 0.6201, 0.1045, 0.2775, 0.3988,
         0.2362],
        [0.4783, 0.0956, 0.0695, 0.2914, 0.5784, 0.6135, 0.2122, 0.4925, 0.8731,
         0.5130],
        [0.1394, 0.5479, 0.4386, 0.3213, 0.0195, 0.1422, 0.4464, 0.0376, 0.3112,
         0.7655],
        [0.4853, 0.8517, 0.1313, 0.8095, 0.1210, 0.6882, 0.2715, 0.6303, 0.7685,
         0.7147]])
tensor([1, 5, 3, 7])
Total loss for this batch: 2.2371749877929688

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: 1.7758210811205208
  batch 2000 loss: 0.8684514756985009
  batch 3000 loss: 0.7269933776920662
  batch 4000 loss: 0.6264328583646566
  batch 5000 loss: 0.5713831033515744
  batch 6000 loss: 0.5716565410976764
  batch 7000 loss: 0.523069411519682
  batch 8000 loss: 0.5059755043592304
  batch 9000 loss: 0.46764329217243356
  batch 10000 loss: 0.4756787621000549
  batch 11000 loss: 0.45533774535809063
  batch 12000 loss: 0.434188809020794
  batch 13000 loss: 0.4432140271391254
  batch 14000 loss: 0.433044406996778
  batch 15000 loss: 0.4146991293230094
LOSS train 0.4146991293230094 valid 0.4503612220287323
EPOCH 2:
  batch 1000 loss: 0.39955700305808567
  batch 2000 loss: 0.41123968731809873
  batch 3000 loss: 0.3859535540444776
  batch 4000 loss: 0.3470500040081679
  batch 5000 loss: 0.3668477054270334
  batch 6000 loss: 0.36335744826548033
  batch 7000 loss: 0.38008951136260294
  batch 8000 loss: 0.3750837282325665
  batch 9000 loss: 0.36501676612862505
  batch 10000 loss: 0.35058384872134774
  batch 11000 loss: 0.36770042545941395
  batch 12000 loss: 0.3568097979603335
  batch 13000 loss: 0.3509265405783517
  batch 14000 loss: 0.3462942300561117
  batch 15000 loss: 0.3561768907784135
LOSS train 0.3561768907784135 valid 0.3701411485671997
EPOCH 3:
  batch 1000 loss: 0.3243762938613363
  batch 2000 loss: 0.3167930108278233
  batch 3000 loss: 0.3454286119421013
  batch 4000 loss: 0.3484262980411295
  batch 5000 loss: 0.32934922833560265
  batch 6000 loss: 0.3102569465649849
  batch 7000 loss: 0.32708222679484605
  batch 8000 loss: 0.3257536272257348
  batch 9000 loss: 0.3355292390795657
  batch 10000 loss: 0.30939457686892274
  batch 11000 loss: 0.3203176606516936
  batch 12000 loss: 0.31400518341270073
  batch 13000 loss: 0.32281278402548197
  batch 14000 loss: 0.31659204009777747
  batch 15000 loss: 0.3119617188770499
LOSS train 0.3119617188770499 valid 0.34070858359336853
EPOCH 4:
  batch 1000 loss: 0.29988487648974843
  batch 2000 loss: 0.29513267787147196
  batch 3000 loss: 0.29591830945741093
  batch 4000 loss: 0.29502063494139114
  batch 5000 loss: 0.2923672856846097
  batch 6000 loss: 0.29842070014849015
  batch 7000 loss: 0.32958982204912174
  batch 8000 loss: 0.29485688532363563
  batch 9000 loss: 0.2765466377943376
  batch 10000 loss: 0.31751291519972435
  batch 11000 loss: 0.29047842672202384
  batch 12000 loss: 0.30218106864616856
  batch 13000 loss: 0.28686269072255893
  batch 14000 loss: 0.30787017451071735
  batch 15000 loss: 0.2951680972841132
LOSS train 0.2951680972841132 valid 0.3243757486343384
EPOCH 5:
  batch 1000 loss: 0.28259117489257185
  batch 2000 loss: 0.2710777925652437
  batch 3000 loss: 0.2820857833838163
  batch 4000 loss: 0.28647030706787335
  batch 5000 loss: 0.2911521529511374
  batch 6000 loss: 0.28676370393694744
  batch 7000 loss: 0.3009271204024071
  batch 8000 loss: 0.27809979932467105
  batch 9000 loss: 0.2641369462262956
  batch 10000 loss: 0.281442152015843
  batch 11000 loss: 0.2740388377367817
  batch 12000 loss: 0.2635333917985927
  batch 13000 loss: 0.28544877841679406
  batch 14000 loss: 0.28253569301004244
  batch 15000 loss: 0.2722428723026424
LOSS train 0.2722428723026424 valid 0.3119978904724121

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