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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
<matplotlib.pyplot._IonContext object at 0x7f1aab6d0390>

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
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Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
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Downloading http://yann.lecun.com/exdb/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.

../_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/lib/python3.7/site-packages/torch/nn/functional.py:4278: 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/lib/python3.7/site-packages/torch/nn/functional.py:4216: 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: 2.301124
Train Epoch: 1 [32000/60000 (53%)]      Loss: 0.744129
/opt/conda/lib/python3.7/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: 0.1923, Accuracy: 9424/10000 (94%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.410787
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.304414

Test set: Average loss: 0.1670, Accuracy: 9493/10000 (95%)

Train Epoch: 3 [0/60000 (0%)]   Loss: 0.452477
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.158786

Test set: Average loss: 0.0942, Accuracy: 9710/10000 (97%)

Train Epoch: 4 [0/60000 (0%)]   Loss: 0.354082
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.289561

Test set: Average loss: 0.0845, Accuracy: 9731/10000 (97%)

Train Epoch: 5 [0/60000 (0%)]   Loss: 0.141156
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.126742

Test set: Average loss: 0.0737, Accuracy: 9775/10000 (98%)

Train Epoch: 6 [0/60000 (0%)]   Loss: 0.310810
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.146613

Test set: Average loss: 0.0623, Accuracy: 9796/10000 (98%)

Train Epoch: 7 [0/60000 (0%)]   Loss: 0.225018
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.057507

Test set: Average loss: 0.0633, Accuracy: 9799/10000 (98%)

Train Epoch: 8 [0/60000 (0%)]   Loss: 0.058816
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.286849

Test set: Average loss: 0.0586, Accuracy: 9820/10000 (98%)

Train Epoch: 9 [0/60000 (0%)]   Loss: 0.037404
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.102292

Test set: Average loss: 0.0534, Accuracy: 9840/10000 (98%)

Train Epoch: 10 [0/60000 (0%)]  Loss: 0.196225
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.197092

Test set: Average loss: 0.0571, Accuracy: 9818/10000 (98%)

Train Epoch: 11 [0/60000 (0%)]  Loss: 0.192402
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.062712

Test set: Average loss: 0.0567, Accuracy: 9824/10000 (98%)

Train Epoch: 12 [0/60000 (0%)]  Loss: 0.200749
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.106185

Test set: Average loss: 0.0563, Accuracy: 9832/10000 (98%)

Train Epoch: 13 [0/60000 (0%)]  Loss: 0.056352
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.070339

Test set: Average loss: 0.0500, Accuracy: 9846/10000 (98%)

Train Epoch: 14 [0/60000 (0%)]  Loss: 0.232691
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.098345

Test set: Average loss: 0.0458, Accuracy: 9857/10000 (99%)

Train Epoch: 15 [0/60000 (0%)]  Loss: 0.134162
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.176878

Test set: Average loss: 0.0454, Accuracy: 9872/10000 (99%)

Train Epoch: 16 [0/60000 (0%)]  Loss: 0.078800
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.080257

Test set: Average loss: 0.0494, Accuracy: 9855/10000 (99%)

Train Epoch: 17 [0/60000 (0%)]  Loss: 0.034558
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.066345

Test set: Average loss: 0.0547, Accuracy: 9853/10000 (99%)

Train Epoch: 18 [0/60000 (0%)]  Loss: 0.112314
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.132258

Test set: Average loss: 0.0491, Accuracy: 9876/10000 (99%)

Train Epoch: 19 [0/60000 (0%)]  Loss: 0.145988
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.094771

Test set: Average loss: 0.0418, Accuracy: 9882/10000 (99%)

Train Epoch: 20 [0/60000 (0%)]  Loss: 0.053701
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.162641

Test set: Average loss: 0.0452, Accuracy: 9864/10000 (99%)

Total running time of the script: ( 3 minutes 1.483 seconds)

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