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: 0.6859 Acc: 0.6926
val Loss: 0.2377 Acc: 0.9150

Epoch 1/24
----------
train Loss: 0.5622 Acc: 0.7746
val Loss: 0.5810 Acc: 0.7451

Epoch 2/24
----------
train Loss: 0.4721 Acc: 0.7910
val Loss: 0.2630 Acc: 0.9085

Epoch 3/24
----------
train Loss: 0.3883 Acc: 0.8115
val Loss: 0.3083 Acc: 0.8889

Epoch 4/24
----------
train Loss: 0.4784 Acc: 0.8361
val Loss: 0.4422 Acc: 0.8366

Epoch 5/24
----------
train Loss: 0.3851 Acc: 0.8525
val Loss: 0.4703 Acc: 0.8301

Epoch 6/24
----------
train Loss: 0.3585 Acc: 0.8361
val Loss: 0.5883 Acc: 0.8562

Epoch 7/24
----------
train Loss: 0.4253 Acc: 0.8770
val Loss: 0.3080 Acc: 0.8889

Epoch 8/24
----------
train Loss: 0.3607 Acc: 0.8648
val Loss: 0.3494 Acc: 0.9020

Epoch 9/24
----------
train Loss: 0.3674 Acc: 0.8566
val Loss: 0.2692 Acc: 0.9020

Epoch 10/24
----------
train Loss: 0.3726 Acc: 0.8402
val Loss: 0.2406 Acc: 0.9281

Epoch 11/24
----------
train Loss: 0.2516 Acc: 0.9139
val Loss: 0.2855 Acc: 0.9020

Epoch 12/24
----------
train Loss: 0.3844 Acc: 0.8279
val Loss: 0.2753 Acc: 0.9085

Epoch 13/24
----------
train Loss: 0.2933 Acc: 0.8730
val Loss: 0.2206 Acc: 0.9150

Epoch 14/24
----------
train Loss: 0.2629 Acc: 0.8730
val Loss: 0.2150 Acc: 0.9150

Epoch 15/24
----------
train Loss: 0.2778 Acc: 0.8893
val Loss: 0.2299 Acc: 0.9085

Epoch 16/24
----------
train Loss: 0.2367 Acc: 0.8893
val Loss: 0.2356 Acc: 0.9150

Epoch 17/24
----------
train Loss: 0.2920 Acc: 0.8770
val Loss: 0.2259 Acc: 0.9281

Epoch 18/24
----------
train Loss: 0.2970 Acc: 0.8689
val Loss: 0.2280 Acc: 0.9216

Epoch 19/24
----------
train Loss: 0.3153 Acc: 0.8648
val Loss: 0.2553 Acc: 0.9085

Epoch 20/24
----------
train Loss: 0.3375 Acc: 0.8525
val Loss: 0.2258 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.3279 Acc: 0.8361
val Loss: 0.2121 Acc: 0.9281

Epoch 22/24
----------
train Loss: 0.2656 Acc: 0.8975
val Loss: 0.2736 Acc: 0.9150

Epoch 23/24
----------
train Loss: 0.2755 Acc: 0.8811
val Loss: 0.2277 Acc: 0.9216

Epoch 24/24
----------
train Loss: 0.3343 Acc: 0.8525
val Loss: 0.2198 Acc: 0.9216

Training complete in 1m 7s
Best val Acc: 0.928105

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: 0.5907 Acc: 0.6639
val Loss: 0.2982 Acc: 0.8889

Epoch 1/24
----------
train Loss: 0.4397 Acc: 0.8115
val Loss: 0.3887 Acc: 0.8431

Epoch 2/24
----------
train Loss: 0.5147 Acc: 0.7582
val Loss: 0.1947 Acc: 0.9346

Epoch 3/24
----------
train Loss: 0.5502 Acc: 0.7582
val Loss: 0.1750 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.5128 Acc: 0.7705
val Loss: 0.2007 Acc: 0.9281

Epoch 5/24
----------
train Loss: 0.4390 Acc: 0.8074
val Loss: 0.2465 Acc: 0.9085

Epoch 6/24
----------
train Loss: 0.4498 Acc: 0.7992
val Loss: 0.2978 Acc: 0.8889

Epoch 7/24
----------
train Loss: 0.3750 Acc: 0.8484
val Loss: 0.1962 Acc: 0.9281

Epoch 8/24
----------
train Loss: 0.3553 Acc: 0.8361
val Loss: 0.1805 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.2956 Acc: 0.8730
val Loss: 0.1603 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3901 Acc: 0.8074
val Loss: 0.1811 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3705 Acc: 0.8238
val Loss: 0.2072 Acc: 0.9412

Epoch 12/24
----------
train Loss: 0.2885 Acc: 0.8730
val Loss: 0.1828 Acc: 0.9542

Epoch 13/24
----------
train Loss: 0.3789 Acc: 0.8279
val Loss: 0.1981 Acc: 0.9542

Epoch 14/24
----------
train Loss: 0.3195 Acc: 0.8443
val Loss: 0.1803 Acc: 0.9412

Epoch 15/24
----------
train Loss: 0.3245 Acc: 0.8811
val Loss: 0.1704 Acc: 0.9477

Epoch 16/24
----------
train Loss: 0.3205 Acc: 0.8730
val Loss: 0.1995 Acc: 0.9346

Epoch 17/24
----------
train Loss: 0.3600 Acc: 0.8402
val Loss: 0.1734 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.2972 Acc: 0.8770
val Loss: 0.1954 Acc: 0.9346

Epoch 19/24
----------
train Loss: 0.3046 Acc: 0.8566
val Loss: 0.1881 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.3576 Acc: 0.8443
val Loss: 0.1854 Acc: 0.9542

Epoch 21/24
----------
train Loss: 0.2448 Acc: 0.8893
val Loss: 0.1850 Acc: 0.9412

Epoch 22/24
----------
train Loss: 0.3478 Acc: 0.8320
val Loss: 0.1907 Acc: 0.9346

Epoch 23/24
----------
train Loss: 0.3051 Acc: 0.8648
val Loss: 0.2067 Acc: 0.9346

Epoch 24/24
----------
train Loss: 0.3236 Acc: 0.8361
val Loss: 0.1582 Acc: 0.9542

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.