Transfer Learning tutorial

Author: Sasank Chilamkurthy

In this tutorial, you will learn how to train your network using transfer learning. You can read more about the transfer learning at cs231n notes

Quoting this 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 looks 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
from torch.autograd import Variable
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os

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.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Scale(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = '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

use_gpu = torch.cuda.is_available()

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 = 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':
                scheduler.step()
                model.train(True)  # Set model to training mode
            else:
                model.train(False)  # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for data in dataloaders[phase]:
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                if use_gpu:
                    inputs = Variable(inputs.cuda())
                    labels = Variable(labels.cuda())
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                loss = criterion(outputs, labels)

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

                # statistics
                running_loss += loss.data[0]
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects / 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 = 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):
    images_so_far = 0
    fig = plt.figure()

    for i, data in enumerate(dataloaders['val']):
        inputs, labels = data
        if use_gpu:
            inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
        else:
            inputs, labels = Variable(inputs), Variable(labels)

        outputs = model(inputs)
        _, preds = torch.max(outputs.data, 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:
                return

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
model_ft.fc = nn.Linear(num_ftrs, 2)

if use_gpu:
    model_ft = model_ft.cuda()

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: 0.1660 Acc: 0.6762
val Loss: 0.0445 Acc: 0.9542

Epoch 1/24
----------
train Loss: 0.1141 Acc: 0.8033
val Loss: 0.0877 Acc: 0.8693

Epoch 2/24
----------
train Loss: 0.1440 Acc: 0.7623
val Loss: 0.0484 Acc: 0.9346

Epoch 3/24
----------
train Loss: 0.1082 Acc: 0.8074
val Loss: 0.0787 Acc: 0.8824

Epoch 4/24
----------
train Loss: 0.1751 Acc: 0.7500
val Loss: 0.2313 Acc: 0.7647

Epoch 5/24
----------
train Loss: 0.1367 Acc: 0.8074
val Loss: 0.1766 Acc: 0.7908

Epoch 6/24
----------
train Loss: 0.1456 Acc: 0.8156
val Loss: 0.1116 Acc: 0.7908

Epoch 7/24
----------
train Loss: 0.1259 Acc: 0.8033
val Loss: 0.0793 Acc: 0.8627

Epoch 8/24
----------
train Loss: 0.0807 Acc: 0.8607
val Loss: 0.0781 Acc: 0.8758

Epoch 9/24
----------
train Loss: 0.0618 Acc: 0.8730
val Loss: 0.0778 Acc: 0.8824

Epoch 10/24
----------
train Loss: 0.0804 Acc: 0.8566
val Loss: 0.0876 Acc: 0.8758

Epoch 11/24
----------
train Loss: 0.0751 Acc: 0.8607
val Loss: 0.0945 Acc: 0.8693

Epoch 12/24
----------
train Loss: 0.0695 Acc: 0.8770
val Loss: 0.0950 Acc: 0.8824

Epoch 13/24
----------
train Loss: 0.0596 Acc: 0.8852
val Loss: 0.0907 Acc: 0.8889

Epoch 14/24
----------
train Loss: 0.0624 Acc: 0.9016
val Loss: 0.0785 Acc: 0.8824

Epoch 15/24
----------
train Loss: 0.0546 Acc: 0.9139
val Loss: 0.0810 Acc: 0.8824

Epoch 16/24
----------
train Loss: 0.0982 Acc: 0.8484
val Loss: 0.1054 Acc: 0.8824

Epoch 17/24
----------
train Loss: 0.0659 Acc: 0.8893
val Loss: 0.0839 Acc: 0.8889

Epoch 18/24
----------
train Loss: 0.0645 Acc: 0.8893
val Loss: 0.0760 Acc: 0.8824

Epoch 19/24
----------
train Loss: 0.0723 Acc: 0.8934
val Loss: 0.0699 Acc: 0.8758

Epoch 20/24
----------
train Loss: 0.0689 Acc: 0.8852
val Loss: 0.0733 Acc: 0.8627

Epoch 21/24
----------
train Loss: 0.0656 Acc: 0.8893
val Loss: 0.0915 Acc: 0.8954

Epoch 22/24
----------
train Loss: 0.0756 Acc: 0.8770
val Loss: 0.0772 Acc: 0.8889

Epoch 23/24
----------
train Loss: 0.0695 Acc: 0.8934
val Loss: 0.0724 Acc: 0.8627

Epoch 24/24
----------
train Loss: 0.0556 Acc: 0.9139
val Loss: 0.0821 Acc: 0.8889

Training complete in 1m 26s
Best val Acc: 0.954248
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)

if use_gpu:
    model_conv = model_conv.cuda()

criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opoosed 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: 0.1913 Acc: 0.5738
val Loss: 0.1566 Acc: 0.6732

Epoch 1/24
----------
train Loss: 0.1469 Acc: 0.7295
val Loss: 0.0659 Acc: 0.9085

Epoch 2/24
----------
train Loss: 0.1189 Acc: 0.7623
val Loss: 0.0458 Acc: 0.9477

Epoch 3/24
----------
train Loss: 0.1191 Acc: 0.8033
val Loss: 0.0463 Acc: 0.9281

Epoch 4/24
----------
train Loss: 0.1470 Acc: 0.7582
val Loss: 0.1730 Acc: 0.7255

Epoch 5/24
----------
train Loss: 0.1590 Acc: 0.7746
val Loss: 0.0451 Acc: 0.9346

Epoch 6/24
----------
train Loss: 0.0950 Acc: 0.8361
val Loss: 0.0486 Acc: 0.9412

Epoch 7/24
----------
train Loss: 0.0734 Acc: 0.8975
val Loss: 0.0502 Acc: 0.9412

Epoch 8/24
----------
train Loss: 0.0821 Acc: 0.8689
val Loss: 0.0417 Acc: 0.9477

Epoch 9/24
----------
train Loss: 0.1085 Acc: 0.7910
val Loss: 0.0513 Acc: 0.9346

Epoch 10/24
----------
train Loss: 0.0908 Acc: 0.8443
val Loss: 0.0468 Acc: 0.9477

Epoch 11/24
----------
train Loss: 0.0803 Acc: 0.8484
val Loss: 0.0416 Acc: 0.9477

Epoch 12/24
----------
train Loss: 0.0907 Acc: 0.8525
val Loss: 0.0425 Acc: 0.9477

Epoch 13/24
----------
train Loss: 0.0811 Acc: 0.8443
val Loss: 0.0433 Acc: 0.9542

Epoch 14/24
----------
train Loss: 0.1185 Acc: 0.8115
val Loss: 0.0460 Acc: 0.9542

Epoch 15/24
----------
train Loss: 0.0851 Acc: 0.8361
val Loss: 0.0434 Acc: 0.9542

Epoch 16/24
----------
train Loss: 0.0975 Acc: 0.8361
val Loss: 0.0431 Acc: 0.9542

Epoch 17/24
----------
train Loss: 0.0756 Acc: 0.8730
val Loss: 0.0518 Acc: 0.9346

Epoch 18/24
----------
train Loss: 0.0938 Acc: 0.8361
val Loss: 0.0448 Acc: 0.9477

Epoch 19/24
----------
train Loss: 0.0837 Acc: 0.8402
val Loss: 0.0462 Acc: 0.9412

Epoch 20/24
----------
train Loss: 0.0849 Acc: 0.8443
val Loss: 0.0448 Acc: 0.9477

Epoch 21/24
----------
train Loss: 0.0701 Acc: 0.8934
val Loss: 0.0470 Acc: 0.9477

Epoch 22/24
----------
train Loss: 0.0822 Acc: 0.8525
val Loss: 0.0403 Acc: 0.9477

Epoch 23/24
----------
train Loss: 0.0934 Acc: 0.8525
val Loss: 0.0433 Acc: 0.9412

Epoch 24/24
----------
train Loss: 0.0872 Acc: 0.8361
val Loss: 0.0393 Acc: 0.9477

Training complete in 0m 51s
Best val Acc: 0.954248
visualize_model(model_conv)

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

Total running time of the script: ( 2 minutes 29.239 seconds)

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