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 torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

cudnn.benchmark = True
plt.ion()   # interactive mode

<contextlib.ExitStack object at 0x7fcc2748bb50>

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(f'Epoch {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(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

            # 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(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
    print(f'Best val Acc: {best_acc:4f}')

    # 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(f'predicted: {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)

/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning:

The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.

/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning:

Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.

Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

  0%|          | 0.00/44.7M [00:00<?, ?B/s]
 27%|##7       | 12.2M/44.7M [00:00<00:00, 128MB/s]
 89%|########9 | 39.8M/44.7M [00:00<00:00, 223MB/s]
100%|##########| 44.7M/44.7M [00:00<00:00, 213MB/s]

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)

Epoch 0/24
----------
train Loss: 0.4966 Acc: 0.7336
val Loss: 0.1781 Acc: 0.9412

Epoch 1/24
----------
train Loss: 0.4282 Acc: 0.8361
val Loss: 0.3533 Acc: 0.8693

Epoch 2/24
----------
train Loss: 0.4928 Acc: 0.8361
val Loss: 0.2739 Acc: 0.9085

Epoch 3/24
----------
train Loss: 0.4382 Acc: 0.8279
val Loss: 0.2065 Acc: 0.9216

Epoch 4/24
----------
train Loss: 0.5388 Acc: 0.7951
val Loss: 0.2924 Acc: 0.9020

Epoch 5/24
----------
train Loss: 0.5909 Acc: 0.7992
val Loss: 0.4555 Acc: 0.8627

Epoch 6/24
----------
train Loss: 0.5437 Acc: 0.8074
val Loss: 0.5045 Acc: 0.8301

Epoch 7/24
----------
train Loss: 0.3527 Acc: 0.8566
val Loss: 0.3228 Acc: 0.9020

Epoch 8/24
----------
train Loss: 0.3443 Acc: 0.8689
val Loss: 0.3093 Acc: 0.9085

Epoch 9/24
----------
train Loss: 0.2565 Acc: 0.8893
val Loss: 0.3233 Acc: 0.8954

Epoch 10/24
----------
train Loss: 0.3668 Acc: 0.8238
val Loss: 0.2995 Acc: 0.9020

Epoch 11/24
----------
train Loss: 0.3199 Acc: 0.8730
val Loss: 0.2812 Acc: 0.9020

Epoch 12/24
----------
train Loss: 0.2686 Acc: 0.8934
val Loss: 0.2765 Acc: 0.9150

Epoch 13/24
----------
train Loss: 0.2700 Acc: 0.8730
val Loss: 0.2490 Acc: 0.9216

Epoch 14/24
----------
train Loss: 0.3138 Acc: 0.8770
val Loss: 0.2499 Acc: 0.9085

Epoch 15/24
----------
train Loss: 0.2909 Acc: 0.8811
val Loss: 0.2457 Acc: 0.9150

Epoch 16/24
----------
train Loss: 0.2272 Acc: 0.9057
val Loss: 0.2442 Acc: 0.9216

Epoch 17/24
----------
train Loss: 0.2597 Acc: 0.8852
val Loss: 0.2375 Acc: 0.9281

Epoch 18/24
----------
train Loss: 0.2831 Acc: 0.8648
val Loss: 0.2732 Acc: 0.9150

Epoch 19/24
----------
train Loss: 0.1970 Acc: 0.9180
val Loss: 0.2743 Acc: 0.9150

Epoch 20/24
----------
train Loss: 0.2134 Acc: 0.9221
val Loss: 0.2512 Acc: 0.9150

Epoch 21/24
----------
train Loss: 0.2288 Acc: 0.9098
val Loss: 0.2438 Acc: 0.9150

Epoch 22/24
----------
train Loss: 0.3445 Acc: 0.8566
val Loss: 0.2674 Acc: 0.9085

Epoch 23/24
----------
train Loss: 0.1733 Acc: 0.9303
val Loss: 0.2453 Acc: 0.9085

Epoch 24/24
----------
train Loss: 0.3039 Acc: 0.8648
val Loss: 0.2429 Acc: 0.9216

Training complete in 1m 12s
Best val Acc: 0.941176

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)

/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning:

The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.

/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning:

Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.

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)

Epoch 0/24
----------
train Loss: 0.5769 Acc: 0.6844
val Loss: 0.2187 Acc: 0.9346

Epoch 1/24
----------
train Loss: 0.5377 Acc: 0.7213
val Loss: 0.1680 Acc: 0.9542

Epoch 2/24
----------
train Loss: 0.5226 Acc: 0.7664
val Loss: 0.1993 Acc: 0.9346

Epoch 3/24
----------
train Loss: 0.6305 Acc: 0.7787
val Loss: 0.1655 Acc: 0.9477

Epoch 4/24
----------
train Loss: 0.4918 Acc: 0.7992
val Loss: 0.2700 Acc: 0.9020

Epoch 5/24
----------
train Loss: 0.5030 Acc: 0.8033
val Loss: 0.1766 Acc: 0.9477

Epoch 6/24
----------
train Loss: 0.4193 Acc: 0.8484
val Loss: 0.2362 Acc: 0.9150

Epoch 7/24
----------
train Loss: 0.5125 Acc: 0.7992
val Loss: 0.2051 Acc: 0.9216

Epoch 8/24
----------
train Loss: 0.3966 Acc: 0.8443
val Loss: 0.1604 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.3650 Acc: 0.8361
val Loss: 0.1827 Acc: 0.9216

Epoch 10/24
----------
train Loss: 0.3136 Acc: 0.8607
val Loss: 0.1725 Acc: 0.9346

Epoch 11/24
----------
train Loss: 0.4247 Acc: 0.8033
val Loss: 0.1533 Acc: 0.9477

Epoch 12/24
----------
train Loss: 0.4196 Acc: 0.8402
val Loss: 0.1813 Acc: 0.9346

Epoch 13/24
----------
train Loss: 0.4420 Acc: 0.8115
val Loss: 0.1486 Acc: 0.9477

Epoch 14/24
----------
train Loss: 0.4117 Acc: 0.8320
val Loss: 0.1669 Acc: 0.9542

Epoch 15/24
----------
train Loss: 0.4349 Acc: 0.8197
val Loss: 0.1727 Acc: 0.9281

Epoch 16/24
----------
train Loss: 0.3165 Acc: 0.8648
val Loss: 0.1613 Acc: 0.9477

Epoch 17/24
----------
train Loss: 0.3232 Acc: 0.8607
val Loss: 0.1664 Acc: 0.9477

Epoch 18/24
----------
train Loss: 0.3167 Acc: 0.8484
val Loss: 0.1607 Acc: 0.9412

Epoch 19/24
----------
train Loss: 0.3218 Acc: 0.8648
val Loss: 0.1689 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.3117 Acc: 0.8689
val Loss: 0.1755 Acc: 0.9346

Epoch 21/24
----------
train Loss: 0.3812 Acc: 0.8074
val Loss: 0.1639 Acc: 0.9346

Epoch 22/24
----------
train Loss: 0.3353 Acc: 0.8279
val Loss: 0.1626 Acc: 0.9412

Epoch 23/24
----------
train Loss: 0.2954 Acc: 0.8607
val Loss: 0.1560 Acc: 0.9542

Epoch 24/24
----------
train Loss: 0.3786 Acc: 0.8648
val Loss: 0.1676 Acc: 0.9412

Training complete in 0m 44s
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.