Shortcuts

Spatial Transformer Networks Tutorial

Author: Ghassen HAMROUNI

../_images/FSeq.png

In this tutorial, you will learn how to augment your network using a visual attention mechanism called spatial transformer networks. You can read more about the spatial transformer networks in the DeepMind paper

Spatial transformer networks are a generalization of differentiable attention to any spatial transformation. Spatial transformer networks (STN for short) allow a neural network to learn how to perform spatial transformations on the input image in order to enhance the geometric invariance of the model. For example, it can crop a region of interest, scale and correct the orientation of an image. It can be a useful mechanism because CNNs are not invariant to rotation and scale and more general affine transformations.

One of the best things about STN is the ability to simply plug it into any existing CNN with very little modification.

# License: BSD
# Author: Ghassen Hamrouni

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np

plt.ion()   # interactive mode
<contextlib.ExitStack object at 0x7f25762af4f0>

Loading the data

In this post we experiment with the classic MNIST dataset. Using a standard convolutional network augmented with a spatial transformer network.

from six.moves import urllib
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Training dataset
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])), batch_size=64, shuffle=True, num_workers=4)
# Test dataset
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])), batch_size=64, shuffle=True, num_workers=4)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz

  0%|          | 0/9912422 [00:00<?, ?it/s]
100%|##########| 9912422/9912422 [00:00<00:00, 365266345.50it/s]
Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz

  0%|          | 0/28881 [00:00<?, ?it/s]
100%|##########| 28881/28881 [00:00<00:00, 55136865.65it/s]
Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz

  0%|          | 0/1648877 [00:00<?, ?it/s]
100%|##########| 1648877/1648877 [00:00<00:00, 253468623.66it/s]
Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz

  0%|          | 0/4542 [00:00<?, ?it/s]
100%|##########| 4542/4542 [00:00<00:00, 67795476.04it/s]
Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw

Depicting spatial transformer networks

Spatial transformer networks boils down to three main components :

  • The localization network is a regular CNN which regresses the transformation parameters. The transformation is never learned explicitly from this dataset, instead the network learns automatically the spatial transformations that enhances the global accuracy.

  • The grid generator generates a grid of coordinates in the input image corresponding to each pixel from the output image.

  • The sampler uses the parameters of the transformation and applies it to the input image.

../_images/stn-arch.png

Note

We need the latest version of PyTorch that contains affine_grid and grid_sample modules.

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

        # Spatial transformer localization-network
        self.localization = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )

        # Regressor for the 3 * 2 affine matrix
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            nn.Linear(32, 3 * 2)
        )

        # Initialize the weights/bias with identity transformation
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))

    # Spatial transformer network forward function
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 2, 3)

        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)

        return x

    def forward(self, x):
        # transform the input
        x = self.stn(x)

        # Perform the usual forward pass
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


model = Net().to(device)

Training the model

Now, let’s use the SGD algorithm to train the model. The network is learning the classification task in a supervised way. In the same time the model is learning STN automatically in an end-to-end fashion.

optimizer = optim.SGD(model.parameters(), lr=0.01)


def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)

        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 500 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
#
# A simple test procedure to measure the STN performances on MNIST.
#


def test():
    with torch.no_grad():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)

            # sum up batch loss
            test_loss += F.nll_loss(output, target, size_average=False).item()
            # get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
              .format(test_loss, correct, len(test_loader.dataset),
                      100. * correct / len(test_loader.dataset)))

Visualizing the STN results

Now, we will inspect the results of our learned visual attention mechanism.

We define a small helper function in order to visualize the transformations while training.

def convert_image_np(inp):
    """Convert a Tensor to numpy image."""
    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)
    return inp

# We want to visualize the output of the spatial transformers layer
# after the training, we visualize a batch of input images and
# the corresponding transformed batch using STN.


def visualize_stn():
    with torch.no_grad():
        # Get a batch of training data
        data = next(iter(test_loader))[0].to(device)

        input_tensor = data.cpu()
        transformed_input_tensor = model.stn(data).cpu()

        in_grid = convert_image_np(
            torchvision.utils.make_grid(input_tensor))

        out_grid = convert_image_np(
            torchvision.utils.make_grid(transformed_input_tensor))

        # Plot the results side-by-side
        f, axarr = plt.subplots(1, 2)
        axarr[0].imshow(in_grid)
        axarr[0].set_title('Dataset Images')

        axarr[1].imshow(out_grid)
        axarr[1].set_title('Transformed Images')

for epoch in range(1, 20 + 1):
    train(epoch)
    test()

# Visualize the STN transformation on some input batch
visualize_stn()

plt.ioff()
plt.show()
Dataset Images, Transformed Images
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/functional.py:4377: UserWarning:

Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/functional.py:4316: UserWarning:

Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.

Train Epoch: 1 [0/60000 (0%)]   Loss: 2.315648
Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.069561
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/_reduction.py:42: UserWarning:

size_average and reduce args will be deprecated, please use reduction='sum' instead.


Test set: Average loss: 0.2552, Accuracy: 9302/10000 (93%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.576485
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.337721

Test set: Average loss: 0.1700, Accuracy: 9503/10000 (95%)

Train Epoch: 3 [0/60000 (0%)]   Loss: 0.305803
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.242940

Test set: Average loss: 0.1638, Accuracy: 9492/10000 (95%)

Train Epoch: 4 [0/60000 (0%)]   Loss: 0.562800
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.136398

Test set: Average loss: 0.1130, Accuracy: 9662/10000 (97%)

Train Epoch: 5 [0/60000 (0%)]   Loss: 0.263275
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.186434

Test set: Average loss: 0.2288, Accuracy: 9299/10000 (93%)

Train Epoch: 6 [0/60000 (0%)]   Loss: 0.288437
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.099558

Test set: Average loss: 0.0750, Accuracy: 9761/10000 (98%)

Train Epoch: 7 [0/60000 (0%)]   Loss: 0.093766
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.154435

Test set: Average loss: 0.0783, Accuracy: 9771/10000 (98%)

Train Epoch: 8 [0/60000 (0%)]   Loss: 0.225783
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.070494

Test set: Average loss: 0.1155, Accuracy: 9661/10000 (97%)

Train Epoch: 9 [0/60000 (0%)]   Loss: 0.153917
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.060020

Test set: Average loss: 0.0928, Accuracy: 9710/10000 (97%)

Train Epoch: 10 [0/60000 (0%)]  Loss: 0.190436
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.230348

Test set: Average loss: 0.0582, Accuracy: 9808/10000 (98%)

Train Epoch: 11 [0/60000 (0%)]  Loss: 0.115070
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.112623

Test set: Average loss: 0.0571, Accuracy: 9831/10000 (98%)

Train Epoch: 12 [0/60000 (0%)]  Loss: 0.163929
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.177992

Test set: Average loss: 0.0562, Accuracy: 9823/10000 (98%)

Train Epoch: 13 [0/60000 (0%)]  Loss: 0.109893
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.146377

Test set: Average loss: 0.0573, Accuracy: 9829/10000 (98%)

Train Epoch: 14 [0/60000 (0%)]  Loss: 0.075838
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.147608

Test set: Average loss: 0.0516, Accuracy: 9850/10000 (98%)

Train Epoch: 15 [0/60000 (0%)]  Loss: 0.032759
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.102245

Test set: Average loss: 0.0522, Accuracy: 9836/10000 (98%)

Train Epoch: 16 [0/60000 (0%)]  Loss: 0.106803
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.202966

Test set: Average loss: 0.0471, Accuracy: 9863/10000 (99%)

Train Epoch: 17 [0/60000 (0%)]  Loss: 0.275279
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.173108

Test set: Average loss: 0.0453, Accuracy: 9858/10000 (99%)

Train Epoch: 18 [0/60000 (0%)]  Loss: 0.037378
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.103850

Test set: Average loss: 0.0462, Accuracy: 9861/10000 (99%)

Train Epoch: 19 [0/60000 (0%)]  Loss: 0.049184
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.087274

Test set: Average loss: 0.0435, Accuracy: 9865/10000 (99%)

Train Epoch: 20 [0/60000 (0%)]  Loss: 0.094237
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.124401

Test set: Average loss: 0.0596, Accuracy: 9824/10000 (98%)

Total running time of the script: ( 1 minutes 31.476 seconds)

Gallery generated by Sphinx-Gallery

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources