Spatial Transformer Networks Tutorial

Author: Ghassen HAMROUNI

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

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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
Failed to download (trying next):
HTTP Error 503: Service Unavailable

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 503: Service Unavailable

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Failed to download (trying next):
HTTP Error 503: Service Unavailable

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Failed to download (trying next):
HTTP Error 503: Service Unavailable

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz
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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.

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()

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/functional.py:4404: 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:4343: 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/autograd/graph.py:744: UserWarning:

Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)

Train Epoch: 1 [0/60000 (0%)]   Loss: 2.315648
Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.042431
/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: 0.2510, Accuracy: 9305/10000 (93%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.507192
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.387233

Test set: Average loss: 0.1992, Accuracy: 9382/10000 (94%)

Train Epoch: 3 [0/60000 (0%)]   Loss: 0.481253
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.215451

Test set: Average loss: 0.1066, Accuracy: 9692/10000 (97%)

Train Epoch: 4 [0/60000 (0%)]   Loss: 0.361152
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.115190

Test set: Average loss: 0.1238, Accuracy: 9625/10000 (96%)

Train Epoch: 5 [0/60000 (0%)]   Loss: 0.231215
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.175230

Test set: Average loss: 0.0827, Accuracy: 9742/10000 (97%)

Train Epoch: 6 [0/60000 (0%)]   Loss: 0.109081
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.109808

Test set: Average loss: 0.0727, Accuracy: 9775/10000 (98%)

Train Epoch: 7 [0/60000 (0%)]   Loss: 0.134205
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.159504

Test set: Average loss: 0.0635, Accuracy: 9793/10000 (98%)

Train Epoch: 8 [0/60000 (0%)]   Loss: 0.257681
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.064861

Test set: Average loss: 0.0728, Accuracy: 9786/10000 (98%)

Train Epoch: 9 [0/60000 (0%)]   Loss: 0.099144
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.089610

Test set: Average loss: 0.0882, Accuracy: 9730/10000 (97%)

Train Epoch: 10 [0/60000 (0%)]  Loss: 0.067610
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.224519

Test set: Average loss: 0.0673, Accuracy: 9789/10000 (98%)

Train Epoch: 11 [0/60000 (0%)]  Loss: 0.197018
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.091700

Test set: Average loss: 0.0557, Accuracy: 9832/10000 (98%)

Train Epoch: 12 [0/60000 (0%)]  Loss: 0.161317
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.167143

Test set: Average loss: 0.0526, Accuracy: 9847/10000 (98%)

Train Epoch: 13 [0/60000 (0%)]  Loss: 0.123520
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.212684

Test set: Average loss: 0.0462, Accuracy: 9857/10000 (99%)

Train Epoch: 14 [0/60000 (0%)]  Loss: 0.094894
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.142882

Test set: Average loss: 0.0544, Accuracy: 9835/10000 (98%)

Train Epoch: 15 [0/60000 (0%)]  Loss: 0.047924
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.082701

Test set: Average loss: 0.0459, Accuracy: 9857/10000 (99%)

Train Epoch: 16 [0/60000 (0%)]  Loss: 0.062099
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.219063

Test set: Average loss: 0.0522, Accuracy: 9846/10000 (98%)

Train Epoch: 17 [0/60000 (0%)]  Loss: 0.294385
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.270039

Test set: Average loss: 0.0541, Accuracy: 9826/10000 (98%)

Train Epoch: 18 [0/60000 (0%)]  Loss: 0.040320
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.133602

Test set: Average loss: 0.0501, Accuracy: 9857/10000 (99%)

Train Epoch: 19 [0/60000 (0%)]  Loss: 0.067084
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.111051

Test set: Average loss: 0.0547, Accuracy: 9846/10000 (98%)

Train Epoch: 20 [0/60000 (0%)]  Loss: 0.077483
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.022697

Test set: Average loss: 0.0543, Accuracy: 9839/10000 (98%)