Transfer Learning for Computer Vision Tutorial

Author: Sasank Chilamkurthy

In this tutorial, you will learn how to train a convolutional neural network for image classification using transfer learning. You can read more about the transfer learning at cs231n notes

Quoting these notes,

In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. ImageNet, which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.

These two major transfer learning scenarios look as follows:

• Finetuning the convnet: Instead of random initialization, we initialize the network with a pretrained network, like the one that is trained on imagenet 1000 dataset. Rest of the training looks as usual.
• ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.

# License: BSD
# Author: Sasank Chilamkurthy

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

plt.ion()   # interactive mode

Load Data

We will use torchvision and torch.utils.data packages for loading the data.

The problem we’re going to solve today is to train a model to classify ants and bees. We have about 120 training images each for ants and bees. There are 75 validation images for each class. Usually, this is a very small dataset to generalize upon, if trained from scratch. Since we are using transfer learning, we should be able to generalize reasonably well.

This dataset is a very small subset of imagenet.

Note

Download the data from here and extract it to the current directory.

# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

Visualize a few images

Let’s visualize a few training images so as to understand the data augmentations.

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated


# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs)

imshow(out, title=[class_names[x] for x in classes])

Training the model

Now, let’s write a general function to train a model. Here, we will illustrate:

• Scheduling the learning rate
• Saving the best model

In the following, parameter scheduler is an LR scheduler object from torch.optim.lr_scheduler.

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

Visualizing the model predictions

Generic function to display predictions for a few images

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

Finetuning the convnet

Load a pretrained model and reset final fully connected layer.

model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, 2)

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

Train and evaluate

It should take around 15-25 min on CPU. On GPU though, it takes less than a minute.

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                       num_epochs=25)

Out:

Epoch 0/24
----------
train Loss: 0.5813 Acc: 0.7090
val Loss: 0.2385 Acc: 0.9216

Epoch 1/24
----------
train Loss: 0.5355 Acc: 0.7541
val Loss: 0.2009 Acc: 0.9281

Epoch 2/24
----------
train Loss: 0.5543 Acc: 0.7623
val Loss: 0.2930 Acc: 0.9085

Epoch 3/24
----------
train Loss: 0.4633 Acc: 0.8033
val Loss: 0.3536 Acc: 0.8954

Epoch 4/24
----------
train Loss: 0.3731 Acc: 0.8443
val Loss: 0.2514 Acc: 0.9020

Epoch 5/24
----------
train Loss: 0.5361 Acc: 0.7951
val Loss: 0.3404 Acc: 0.9150

Epoch 6/24
----------
train Loss: 0.5117 Acc: 0.7869
val Loss: 0.1975 Acc: 0.9281

Epoch 7/24
----------
train Loss: 0.3202 Acc: 0.8607
val Loss: 0.1957 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3118 Acc: 0.8648
val Loss: 0.1896 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.2773 Acc: 0.9016
val Loss: 0.1965 Acc: 0.9412

Epoch 10/24
----------
train Loss: 0.3177 Acc: 0.8730
val Loss: 0.1820 Acc: 0.9412

Epoch 11/24
----------
train Loss: 0.3738 Acc: 0.8525
val Loss: 0.2144 Acc: 0.9412

Epoch 12/24
----------
train Loss: 0.3164 Acc: 0.8525
val Loss: 0.2027 Acc: 0.9412

Epoch 13/24
----------
train Loss: 0.3101 Acc: 0.8607
val Loss: 0.1955 Acc: 0.9412

Epoch 14/24
----------
train Loss: 0.2101 Acc: 0.9180
val Loss: 0.2030 Acc: 0.9281

Epoch 15/24
----------
train Loss: 0.2785 Acc: 0.8730
val Loss: 0.1895 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.3632 Acc: 0.8525
val Loss: 0.2162 Acc: 0.9150

Epoch 17/24
----------
train Loss: 0.2406 Acc: 0.9016
val Loss: 0.1912 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.2754 Acc: 0.8525
val Loss: 0.1866 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.2945 Acc: 0.8893
val Loss: 0.1916 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3252 Acc: 0.8443
val Loss: 0.1843 Acc: 0.9477

Epoch 21/24
----------
train Loss: 0.3343 Acc: 0.8689
val Loss: 0.1931 Acc: 0.9346

Epoch 22/24
----------
train Loss: 0.3406 Acc: 0.8607
val Loss: 0.2517 Acc: 0.9020

Epoch 23/24
----------
train Loss: 0.2705 Acc: 0.8852
val Loss: 0.1883 Acc: 0.9542

Epoch 24/24
----------
train Loss: 0.2188 Acc: 0.9303
val Loss: 0.1921 Acc: 0.9477

Training complete in 1m 7s
Best val Acc: 0.954248

visualize_model(model_ft)

ConvNet as fixed feature extractor

Here, we need to freeze all the network except the final layer. We need to set requires_grad == False to freeze the parameters so that the gradients are not computed in backward().

You can read more about this in the documentation here.

model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False

# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 2)

model_conv = model_conv.to(device)

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opposed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)

Train and evaluate

On CPU this will take about half the time compared to previous scenario. This is expected as gradients don’t need to be computed for most of the network. However, forward does need to be computed.

model_conv = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, num_epochs=25)

Out:

Epoch 0/24
----------
train Loss: 0.5209 Acc: 0.7377
val Loss: 0.2357 Acc: 0.9216

Epoch 1/24
----------
train Loss: 0.4078 Acc: 0.8361
val Loss: 0.2751 Acc: 0.8693

Epoch 2/24
----------
train Loss: 0.5337 Acc: 0.7705
val Loss: 0.1762 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.3999 Acc: 0.8197
val Loss: 0.1858 Acc: 0.9477

Epoch 4/24
----------
train Loss: 0.5100 Acc: 0.7787
val Loss: 0.2810 Acc: 0.8758

Epoch 5/24
----------
train Loss: 0.4151 Acc: 0.8115
val Loss: 0.2200 Acc: 0.9216

Epoch 6/24
----------
train Loss: 0.6330 Acc: 0.7295
val Loss: 0.2133 Acc: 0.9412

Epoch 7/24
----------
train Loss: 0.3956 Acc: 0.8238
val Loss: 0.1919 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3709 Acc: 0.8320
val Loss: 0.2262 Acc: 0.9216

Epoch 9/24
----------
train Loss: 0.2924 Acc: 0.8689
val Loss: 0.2008 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3127 Acc: 0.8689
val Loss: 0.2561 Acc: 0.9216

Epoch 11/24
----------
train Loss: 0.2789 Acc: 0.8852
val Loss: 0.2062 Acc: 0.9412

Epoch 12/24
----------
train Loss: 0.3904 Acc: 0.8320
val Loss: 0.1963 Acc: 0.9477

Epoch 13/24
----------
train Loss: 0.3272 Acc: 0.8443
val Loss: 0.2660 Acc: 0.9085

Epoch 14/24
----------
train Loss: 0.3347 Acc: 0.8525
val Loss: 0.1982 Acc: 0.9477

Epoch 15/24
----------
train Loss: 0.2351 Acc: 0.9057
val Loss: 0.2314 Acc: 0.9281

Epoch 16/24
----------
train Loss: 0.3219 Acc: 0.8648
val Loss: 0.2305 Acc: 0.9346

Epoch 17/24
----------
train Loss: 0.3631 Acc: 0.8156
val Loss: 0.1942 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3449 Acc: 0.8566
val Loss: 0.2107 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3280 Acc: 0.8525
val Loss: 0.2275 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.3265 Acc: 0.8279
val Loss: 0.2348 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.2971 Acc: 0.8852
val Loss: 0.2137 Acc: 0.9412

Epoch 22/24
----------
train Loss: 0.2441 Acc: 0.9139
val Loss: 0.2047 Acc: 0.9477

Epoch 23/24
----------
train Loss: 0.2863 Acc: 0.8934
val Loss: 0.2170 Acc: 0.9542

Epoch 24/24
----------
train Loss: 0.3384 Acc: 0.8689
val Loss: 0.2206 Acc: 0.9346

Training complete in 0m 34s
Best val Acc: 0.954248

visualize_model(model_conv)

plt.ioff()
plt.show()

Further Learning

If you would like to learn more about the applications of transfer learning, checkout our Quantized Transfer Learning for Computer Vision Tutorial.