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

from __future__ import print_function
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

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)

Out:

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
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw

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
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw

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
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw

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
Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
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()
../_images/sphx_glr_spatial_transformer_tutorial_001.png

Out:

Train Epoch: 1 [0/60000 (0%)]   Loss: 2.332633
Train Epoch: 1 [32000/60000 (53%)]      Loss: 0.994687

Test set: Average loss: 0.2272, Accuracy: 9356/10000 (94%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.617683
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.365682

Test set: Average loss: 0.1571, Accuracy: 9502/10000 (95%)

Train Epoch: 3 [0/60000 (0%)]   Loss: 0.390435
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.128925

Test set: Average loss: 0.1006, Accuracy: 9668/10000 (97%)

Train Epoch: 4 [0/60000 (0%)]   Loss: 0.381457
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.122249

Test set: Average loss: 0.0735, Accuracy: 9768/10000 (98%)

Train Epoch: 5 [0/60000 (0%)]   Loss: 0.102412
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.270365

Test set: Average loss: 0.0693, Accuracy: 9780/10000 (98%)

Train Epoch: 6 [0/60000 (0%)]   Loss: 0.205418
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.261906

Test set: Average loss: 0.0756, Accuracy: 9751/10000 (98%)

Train Epoch: 7 [0/60000 (0%)]   Loss: 0.126013
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.199387

Test set: Average loss: 0.1080, Accuracy: 9675/10000 (97%)

Train Epoch: 8 [0/60000 (0%)]   Loss: 0.136268
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.085391

Test set: Average loss: 0.0769, Accuracy: 9757/10000 (98%)

Train Epoch: 9 [0/60000 (0%)]   Loss: 0.312037
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.090655

Test set: Average loss: 0.0430, Accuracy: 9858/10000 (99%)

Train Epoch: 10 [0/60000 (0%)]  Loss: 0.333043
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.114096

Test set: Average loss: 0.0388, Accuracy: 9874/10000 (99%)

Train Epoch: 11 [0/60000 (0%)]  Loss: 0.052727
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.138484

Test set: Average loss: 0.0569, Accuracy: 9823/10000 (98%)

Train Epoch: 12 [0/60000 (0%)]  Loss: 0.121934
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.228902

Test set: Average loss: 0.0456, Accuracy: 9857/10000 (99%)

Train Epoch: 13 [0/60000 (0%)]  Loss: 0.122824
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.166901

Test set: Average loss: 0.0540, Accuracy: 9829/10000 (98%)

Train Epoch: 14 [0/60000 (0%)]  Loss: 0.139658
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.253410

Test set: Average loss: 0.0355, Accuracy: 9891/10000 (99%)

Train Epoch: 15 [0/60000 (0%)]  Loss: 0.040901
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.096400

Test set: Average loss: 0.0370, Accuracy: 9895/10000 (99%)

Train Epoch: 16 [0/60000 (0%)]  Loss: 0.041369
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.174068

Test set: Average loss: 0.0406, Accuracy: 9875/10000 (99%)

Train Epoch: 17 [0/60000 (0%)]  Loss: 0.051754
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.087030

Test set: Average loss: 0.0410, Accuracy: 9877/10000 (99%)

Train Epoch: 18 [0/60000 (0%)]  Loss: 0.094429
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.485432

Test set: Average loss: 0.0355, Accuracy: 9893/10000 (99%)

Train Epoch: 19 [0/60000 (0%)]  Loss: 0.129513
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.069283

Test set: Average loss: 0.0368, Accuracy: 9892/10000 (99%)

Train Epoch: 20 [0/60000 (0%)]  Loss: 0.131115
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.052767

Test set: Average loss: 0.0338, Accuracy: 9895/10000 (99%)

Total running time of the script: ( 1 minutes 46.257 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