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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 initializaion, 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])
../_images/sphx_glr_transfer_learning_tutorial_001.png

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: 0.5877 Acc: 0.7336
val Loss: 0.2338 Acc: 0.8824

Epoch 1/24
----------
train Loss: 0.5560 Acc: 0.7951
val Loss: 1.5479 Acc: 0.7124

Epoch 2/24
----------
train Loss: 0.6227 Acc: 0.7869
val Loss: 0.7957 Acc: 0.8039

Epoch 3/24
----------
train Loss: 0.7734 Acc: 0.7336
val Loss: 0.4263 Acc: 0.8627

Epoch 4/24
----------
train Loss: 0.5302 Acc: 0.8238
val Loss: 0.3171 Acc: 0.8824

Epoch 5/24
----------
train Loss: 0.3418 Acc: 0.8525
val Loss: 0.2746 Acc: 0.8954

Epoch 6/24
----------
train Loss: 0.4866 Acc: 0.8402
val Loss: 0.3198 Acc: 0.9020

Epoch 7/24
----------
train Loss: 0.3215 Acc: 0.8607
val Loss: 0.2713 Acc: 0.8889

Epoch 8/24
----------
train Loss: 0.2979 Acc: 0.8811
val Loss: 0.2670 Acc: 0.9216

Epoch 9/24
----------
train Loss: 0.3270 Acc: 0.8648
val Loss: 0.2459 Acc: 0.9216

Epoch 10/24
----------
train Loss: 0.3746 Acc: 0.8361
val Loss: 0.2363 Acc: 0.9216

Epoch 11/24
----------
train Loss: 0.2525 Acc: 0.8852
val Loss: 0.2329 Acc: 0.9150

Epoch 12/24
----------
train Loss: 0.3245 Acc: 0.8238
val Loss: 0.2469 Acc: 0.9216

Epoch 13/24
----------
train Loss: 0.3200 Acc: 0.8525
val Loss: 0.2088 Acc: 0.9281

Epoch 14/24
----------
train Loss: 0.3081 Acc: 0.8730
val Loss: 0.2240 Acc: 0.9346

Epoch 15/24
----------
train Loss: 0.2369 Acc: 0.9098
val Loss: 0.2449 Acc: 0.9346

Epoch 16/24
----------
train Loss: 0.2430 Acc: 0.9098
val Loss: 0.2315 Acc: 0.9150

Epoch 17/24
----------
train Loss: 0.2640 Acc: 0.8934
val Loss: 0.2445 Acc: 0.9216

Epoch 18/24
----------
train Loss: 0.2987 Acc: 0.8648
val Loss: 0.2353 Acc: 0.9346

Epoch 19/24
----------
train Loss: 0.2760 Acc: 0.8648
val Loss: 0.2326 Acc: 0.9281

Epoch 20/24
----------
train Loss: 0.3100 Acc: 0.8525
val Loss: 0.2235 Acc: 0.9346

Epoch 21/24
----------
train Loss: 0.2670 Acc: 0.8975
val Loss: 0.2200 Acc: 0.9216

Epoch 22/24
----------
train Loss: 0.2289 Acc: 0.9098
val Loss: 0.2149 Acc: 0.9346

Epoch 23/24
----------
train Loss: 0.2850 Acc: 0.8525
val Loss: 0.2384 Acc: 0.9150

Epoch 24/24
----------
train Loss: 0.2746 Acc: 0.8893
val Loss: 0.2148 Acc: 0.9346

Training complete in 1m 7s
Best val Acc: 0.934641
visualize_model(model_ft)
../_images/sphx_glr_transfer_learning_tutorial_002.png

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: 0.8235 Acc: 0.6025
val Loss: 0.4955 Acc: 0.7451

Epoch 1/24
----------
train Loss: 0.5782 Acc: 0.7254
val Loss: 0.3971 Acc: 0.8431

Epoch 2/24
----------
train Loss: 0.5007 Acc: 0.7664
val Loss: 0.1858 Acc: 0.9542

Epoch 3/24
----------
train Loss: 0.5555 Acc: 0.7418
val Loss: 0.1966 Acc: 0.9477

Epoch 4/24
----------
train Loss: 0.4745 Acc: 0.8074
val Loss: 0.1764 Acc: 0.9477

Epoch 5/24
----------
train Loss: 0.4644 Acc: 0.7951
val Loss: 0.1707 Acc: 0.9477

Epoch 6/24
----------
train Loss: 0.4698 Acc: 0.8033
val Loss: 0.1918 Acc: 0.9477

Epoch 7/24
----------
train Loss: 0.3555 Acc: 0.8566
val Loss: 0.1615 Acc: 0.9608

Epoch 8/24
----------
train Loss: 0.2972 Acc: 0.8811
val Loss: 0.1758 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.3659 Acc: 0.8279
val Loss: 0.1698 Acc: 0.9608

Epoch 10/24
----------
train Loss: 0.2799 Acc: 0.8852
val Loss: 0.2063 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.3695 Acc: 0.8197
val Loss: 0.1723 Acc: 0.9542

Epoch 12/24
----------
train Loss: 0.2779 Acc: 0.8934
val Loss: 0.1977 Acc: 0.9281

Epoch 13/24
----------
train Loss: 0.4370 Acc: 0.8279
val Loss: 0.1775 Acc: 0.9608

Epoch 14/24
----------
train Loss: 0.2787 Acc: 0.8770
val Loss: 0.1731 Acc: 0.9477

Epoch 15/24
----------
train Loss: 0.2794 Acc: 0.8689
val Loss: 0.1764 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.3601 Acc: 0.8279
val Loss: 0.1762 Acc: 0.9477

Epoch 17/24
----------
train Loss: 0.3371 Acc: 0.8279
val Loss: 0.1745 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3374 Acc: 0.8525
val Loss: 0.1720 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3768 Acc: 0.8320
val Loss: 0.1773 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3414 Acc: 0.8279
val Loss: 0.1722 Acc: 0.9477

Epoch 21/24
----------
train Loss: 0.3316 Acc: 0.8525
val Loss: 0.1810 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.3603 Acc: 0.8402
val Loss: 0.1744 Acc: 0.9477

Epoch 23/24
----------
train Loss: 0.3227 Acc: 0.8525
val Loss: 0.1691 Acc: 0.9542

Epoch 24/24
----------
train Loss: 0.3453 Acc: 0.8566
val Loss: 0.1725 Acc: 0.9542

Training complete in 0m 34s
Best val Acc: 0.960784
visualize_model(model_conv)

plt.ioff()
plt.show()
../_images/sphx_glr_transfer_learning_tutorial_003.png

Further Learning

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

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