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Spatial Transformer Networks Tutorial#
Created On: Nov 08, 2017 | Last Updated: Jan 19, 2024 | Last Verified: Nov 05, 2024
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)
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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()
/var/lib/workspace/intermediate_source/spatial_transformer_tutorial.py:130: 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.
grid = F.affine_grid(theta, x.size())
/var/lib/workspace/intermediate_source/spatial_transformer_tutorial.py:131: 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.
x = F.grid_sample(x, grid)
Train Epoch: 1 [0/60000 (0%)] Loss: 2.319373
Train Epoch: 1 [32000/60000 (53%)] Loss: 0.640663
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:3230: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.
reduction = _Reduction.legacy_get_string(size_average, reduce)
Test set: Average loss: 0.2829, Accuracy: 9181/10000 (92%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.597811
Train Epoch: 2 [32000/60000 (53%)] Loss: 0.225014
Test set: Average loss: 0.1119, Accuracy: 9671/10000 (97%)
Train Epoch: 3 [0/60000 (0%)] Loss: 0.248640
Train Epoch: 3 [32000/60000 (53%)] Loss: 0.150967
Test set: Average loss: 0.0901, Accuracy: 9705/10000 (97%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.211731
Train Epoch: 4 [32000/60000 (53%)] Loss: 0.220436
Test set: Average loss: 0.1162, Accuracy: 9644/10000 (96%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.259199
Train Epoch: 5 [32000/60000 (53%)] Loss: 0.159384
Test set: Average loss: 0.1034, Accuracy: 9694/10000 (97%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.133721
Train Epoch: 6 [32000/60000 (53%)] Loss: 0.154104
Test set: Average loss: 0.0712, Accuracy: 9773/10000 (98%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.057991
Train Epoch: 7 [32000/60000 (53%)] Loss: 0.189073
Test set: Average loss: 0.0596, Accuracy: 9813/10000 (98%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.192279
Train Epoch: 8 [32000/60000 (53%)] Loss: 0.327895
Test set: Average loss: 0.0555, Accuracy: 9822/10000 (98%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.167730
Train Epoch: 9 [32000/60000 (53%)] Loss: 0.140442
Test set: Average loss: 0.0508, Accuracy: 9840/10000 (98%)
Train Epoch: 10 [0/60000 (0%)] Loss: 0.086255
Train Epoch: 10 [32000/60000 (53%)] Loss: 0.150249
Test set: Average loss: 0.0551, Accuracy: 9821/10000 (98%)
Train Epoch: 11 [0/60000 (0%)] Loss: 0.027344
Train Epoch: 11 [32000/60000 (53%)] Loss: 0.120082
Test set: Average loss: 0.0501, Accuracy: 9843/10000 (98%)
Train Epoch: 12 [0/60000 (0%)] Loss: 0.079203
Train Epoch: 12 [32000/60000 (53%)] Loss: 0.057663
Test set: Average loss: 0.0438, Accuracy: 9863/10000 (99%)
Train Epoch: 13 [0/60000 (0%)] Loss: 0.267768
Train Epoch: 13 [32000/60000 (53%)] Loss: 0.035480
Test set: Average loss: 0.0504, Accuracy: 9837/10000 (98%)
Train Epoch: 14 [0/60000 (0%)] Loss: 0.110553
Train Epoch: 14 [32000/60000 (53%)] Loss: 0.055174
Test set: Average loss: 0.0471, Accuracy: 9859/10000 (99%)
Train Epoch: 15 [0/60000 (0%)] Loss: 0.069420
Train Epoch: 15 [32000/60000 (53%)] Loss: 0.035747
Test set: Average loss: 0.0456, Accuracy: 9855/10000 (99%)
Train Epoch: 16 [0/60000 (0%)] Loss: 0.046540
Train Epoch: 16 [32000/60000 (53%)] Loss: 0.032023
Test set: Average loss: 0.0506, Accuracy: 9850/10000 (98%)
Train Epoch: 17 [0/60000 (0%)] Loss: 0.061237
Train Epoch: 17 [32000/60000 (53%)] Loss: 0.036754
Test set: Average loss: 0.0495, Accuracy: 9851/10000 (99%)
Train Epoch: 18 [0/60000 (0%)] Loss: 0.030266
Train Epoch: 18 [32000/60000 (53%)] Loss: 0.088448
Test set: Average loss: 0.0469, Accuracy: 9853/10000 (99%)
Train Epoch: 19 [0/60000 (0%)] Loss: 0.043927
Train Epoch: 19 [32000/60000 (53%)] Loss: 0.059978
Test set: Average loss: 0.0414, Accuracy: 9875/10000 (99%)
Train Epoch: 20 [0/60000 (0%)] Loss: 0.042281
Train Epoch: 20 [32000/60000 (53%)] Loss: 0.055533
Test set: Average loss: 0.0413, Accuracy: 9873/10000 (99%)
Total running time of the script: (1 minutes 36.325 seconds)