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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 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])
../_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: 0.6572 Acc: 0.7213
val Loss: 0.2411 Acc: 0.9085

Epoch 1/24
----------
train Loss: 0.6123 Acc: 0.7500
val Loss: 0.2118 Acc: 0.9216

Epoch 2/24
----------
train Loss: 0.3754 Acc: 0.8607
val Loss: 0.2190 Acc: 0.9150

Epoch 3/24
----------
train Loss: 0.3931 Acc: 0.8279
val Loss: 0.2091 Acc: 0.9412

Epoch 4/24
----------
train Loss: 0.6309 Acc: 0.7951
val Loss: 0.3416 Acc: 0.9085

Epoch 5/24
----------
train Loss: 0.4859 Acc: 0.8156
val Loss: 0.3627 Acc: 0.8954

Epoch 6/24
----------
train Loss: 0.4653 Acc: 0.8361
val Loss: 0.3308 Acc: 0.8889

Epoch 7/24
----------
train Loss: 0.3979 Acc: 0.8361
val Loss: 0.2934 Acc: 0.8954

Epoch 8/24
----------
train Loss: 0.3734 Acc: 0.8402
val Loss: 0.3385 Acc: 0.8889

Epoch 9/24
----------
train Loss: 0.2773 Acc: 0.8811
val Loss: 0.2766 Acc: 0.8954

Epoch 10/24
----------
train Loss: 0.3163 Acc: 0.8648
val Loss: 0.3021 Acc: 0.9020

Epoch 11/24
----------
train Loss: 0.2690 Acc: 0.8648
val Loss: 0.2581 Acc: 0.9150

Epoch 12/24
----------
train Loss: 0.3051 Acc: 0.8689
val Loss: 0.2701 Acc: 0.9085

Epoch 13/24
----------
train Loss: 0.3269 Acc: 0.8566
val Loss: 0.2690 Acc: 0.9216

Epoch 14/24
----------
train Loss: 0.2356 Acc: 0.9180
val Loss: 0.2730 Acc: 0.9216

Epoch 15/24
----------
train Loss: 0.3447 Acc: 0.8443
val Loss: 0.2767 Acc: 0.9216

Epoch 16/24
----------
train Loss: 0.2597 Acc: 0.8566
val Loss: 0.2689 Acc: 0.9216

Epoch 17/24
----------
train Loss: 0.3609 Acc: 0.8279
val Loss: 0.2719 Acc: 0.9216

Epoch 18/24
----------
train Loss: 0.3023 Acc: 0.8689
val Loss: 0.3040 Acc: 0.8889

Epoch 19/24
----------
train Loss: 0.2903 Acc: 0.8934
val Loss: 0.2681 Acc: 0.9281

Epoch 20/24
----------
train Loss: 0.3148 Acc: 0.8443
val Loss: 0.2797 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.2349 Acc: 0.9057
val Loss: 0.2756 Acc: 0.9020

Epoch 22/24
----------
train Loss: 0.2838 Acc: 0.8689
val Loss: 0.2813 Acc: 0.9085

Epoch 23/24
----------
train Loss: 0.2323 Acc: 0.9016
val Loss: 0.2749 Acc: 0.9216

Epoch 24/24
----------
train Loss: 0.2305 Acc: 0.9016
val Loss: 0.2802 Acc: 0.9150

Training complete in 1m 7s
Best val Acc: 0.941176
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: 0.6355 Acc: 0.6639
val Loss: 0.3893 Acc: 0.8105

Epoch 1/24
----------
train Loss: 0.4455 Acc: 0.8115
val Loss: 0.2201 Acc: 0.9216

Epoch 2/24
----------
train Loss: 0.7114 Acc: 0.7254
val Loss: 0.1810 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.3686 Acc: 0.8033
val Loss: 0.1867 Acc: 0.9542

Epoch 4/24
----------
train Loss: 0.6863 Acc: 0.7664
val Loss: 0.2445 Acc: 0.9150

Epoch 5/24
----------
train Loss: 0.8348 Acc: 0.7500
val Loss: 0.2605 Acc: 0.9216

Epoch 6/24
----------
train Loss: 0.5344 Acc: 0.7951
val Loss: 0.1967 Acc: 0.9412

Epoch 7/24
----------
train Loss: 0.3499 Acc: 0.8648
val Loss: 0.2180 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.3226 Acc: 0.8770
val Loss: 0.1971 Acc: 0.9412

Epoch 9/24
----------
train Loss: 0.3574 Acc: 0.8279
val Loss: 0.1825 Acc: 0.9542

Epoch 10/24
----------
train Loss: 0.3656 Acc: 0.8730
val Loss: 0.1936 Acc: 0.9542

Epoch 11/24
----------
train Loss: 0.4210 Acc: 0.8279
val Loss: 0.1881 Acc: 0.9542

Epoch 12/24
----------
train Loss: 0.3104 Acc: 0.8648
val Loss: 0.2101 Acc: 0.9346

Epoch 13/24
----------
train Loss: 0.3624 Acc: 0.8156
val Loss: 0.2077 Acc: 0.9477

Epoch 14/24
----------
train Loss: 0.3554 Acc: 0.8566
val Loss: 0.1900 Acc: 0.9477

Epoch 15/24
----------
train Loss: 0.3242 Acc: 0.8484
val Loss: 0.1746 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.4020 Acc: 0.8279
val Loss: 0.2049 Acc: 0.9346

Epoch 17/24
----------
train Loss: 0.3007 Acc: 0.8934
val Loss: 0.1946 Acc: 0.9412

Epoch 18/24
----------
train Loss: 0.3025 Acc: 0.8770
val Loss: 0.2189 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.3445 Acc: 0.8402
val Loss: 0.1971 Acc: 0.9477

Epoch 20/24
----------
train Loss: 0.3790 Acc: 0.8320
val Loss: 0.1930 Acc: 0.9412

Epoch 21/24
----------
train Loss: 0.2951 Acc: 0.8648
val Loss: 0.2073 Acc: 0.9412

Epoch 22/24
----------
train Loss: 0.3845 Acc: 0.8361
val Loss: 0.1849 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.2319 Acc: 0.9057
val Loss: 0.1968 Acc: 0.9346

Epoch 24/24
----------
train Loss: 0.3265 Acc: 0.8525
val Loss: 0.1892 Acc: 0.9412

Training complete in 0m 43s
Best val Acc: 0.954248
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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