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.. _sphx_glr_beginner_transfer_learning_tutorial.py:
Transfer Learning for Computer Vision Tutorial
==============================================
**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_
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 <https://cs231n.github.io/transfer-learning/>`__
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.
.. GENERATED FROM PYTHON SOURCE LINES 33-53
.. code-block:: default
# License: BSD
# Author: Sasank Chilamkurthy
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
from PIL import Image
from tempfile import TemporaryDirectory
cudnn.benchmark = True
plt.ion() # interactive mode
.. GENERATED FROM PYTHON SOURCE LINES 54-73
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 <https://download.pytorch.org/tutorial/hymenoptera_data.zip>`_
and extract it to the current directory.
.. GENERATED FROM PYTHON SOURCE LINES 73-103
.. code-block:: default
# 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")
.. GENERATED FROM PYTHON SOURCE LINES 104-108
Visualize a few images
^^^^^^^^^^^^^^^^^^^^^^
Let's visualize a few training images so as to understand the data
augmentations.
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.. code-block:: default
def imshow(inp, title=None):
"""Display image 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])
.. GENERATED FROM PYTHON SOURCE LINES 132-143
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``.
.. GENERATED FROM PYTHON SOURCE LINES 143-216
.. code-block:: default
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
# Create a temporary directory to save training checkpoints
with TemporaryDirectory() as tempdir:
best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
torch.save(model.state_dict(), best_model_params_path)
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
torch.save(model.state_dict(), best_model_params_path)
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(torch.load(best_model_params_path))
return model
.. GENERATED FROM PYTHON SOURCE LINES 217-222
Visualizing the model predictions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Generic function to display predictions for a few images
.. GENERATED FROM PYTHON SOURCE LINES 222-249
.. code-block:: default
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)
.. GENERATED FROM PYTHON SOURCE LINES 250-255
Finetuning the ConvNet
----------------------
Load a pretrained model and reset final fully connected layer.
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.. code-block:: default
model_ft = models.resnet18(weights='IMAGENET1K_V1')
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)
.. GENERATED FROM PYTHON SOURCE LINES 273-279
Train and evaluate
^^^^^^^^^^^^^^^^^^
It should take around 15-25 min on CPU. On GPU though, it takes less than a
minute.
.. GENERATED FROM PYTHON SOURCE LINES 279-283
.. code-block:: default
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=25)
.. GENERATED FROM PYTHON SOURCE LINES 285-289
.. code-block:: default
visualize_model(model_ft)
.. GENERATED FROM PYTHON SOURCE LINES 290-300
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 <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.
.. GENERATED FROM PYTHON SOURCE LINES 300-321
.. code-block:: default
model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1')
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)
.. GENERATED FROM PYTHON SOURCE LINES 322-329
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.
.. GENERATED FROM PYTHON SOURCE LINES 329-333
.. code-block:: default
model_conv = train_model(model_conv, criterion, optimizer_conv,
exp_lr_scheduler, num_epochs=25)
.. GENERATED FROM PYTHON SOURCE LINES 335-342
.. code-block:: default
visualize_model(model_conv)
plt.ioff()
plt.show()
.. GENERATED FROM PYTHON SOURCE LINES 343-349
Inference on custom images
--------------------------
Use the trained model to make predictions on custom images and visualize
the predicted class labels along with the images.
.. GENERATED FROM PYTHON SOURCE LINES 349-370
.. code-block:: default
def visualize_model_predictions(model,img_path):
was_training = model.training
model.eval()
img = Image.open(img_path)
img = data_transforms['val'](img)
img = img.unsqueeze(0)
img = img.to(device)
with torch.no_grad():
outputs = model(img)
_, preds = torch.max(outputs, 1)
ax = plt.subplot(2,2,1)
ax.axis('off')
ax.set_title(f'Predicted: {class_names[preds[0]]}')
imshow(img.cpu().data[0])
model.train(mode=was_training)
.. GENERATED FROM PYTHON SOURCE LINES 372-382
.. code-block:: default
visualize_model_predictions(
model_conv,
img_path='data/hymenoptera_data/val/bees/72100438_73de9f17af.jpg'
)
plt.ioff()
plt.show()
.. GENERATED FROM PYTHON SOURCE LINES 383-390
Further Learning
-----------------
If you would like to learn more about the applications of transfer learning,
checkout our `Quantized Transfer Learning for Computer Vision Tutorial <https://pytorch.org/tutorials/intermediate/quantized_transfer_learning_tutorial.html>`_.
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