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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()
/__w/tutorials/tutorials/pytorch/tutorials/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())
/__w/tutorials/tutorials/pytorch/tutorials/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: 2.332559
Train Epoch: 1 [32000/60000 (53%)] Loss: 0.940983
/usr/local/lib/python3.12/dist-packages/torch/nn/functional.py:3236: 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: 0.2582, Accuracy: 9151/10000 (92%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.587569
Train Epoch: 2 [32000/60000 (53%)] Loss: 0.508844
Test set: Average loss: 0.1567, Accuracy: 9525/10000 (95%)
Train Epoch: 3 [0/60000 (0%)] Loss: 0.248818
Train Epoch: 3 [32000/60000 (53%)] Loss: 0.316511
Test set: Average loss: 0.0920, Accuracy: 9720/10000 (97%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.137640
Train Epoch: 4 [32000/60000 (53%)] Loss: 0.253229
Test set: Average loss: 0.0668, Accuracy: 9788/10000 (98%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.065571
Train Epoch: 5 [32000/60000 (53%)] Loss: 0.174838
Test set: Average loss: 0.0660, Accuracy: 9791/10000 (98%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.110490
Train Epoch: 6 [32000/60000 (53%)] Loss: 0.251692
Test set: Average loss: 0.0740, Accuracy: 9782/10000 (98%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.154854
Train Epoch: 7 [32000/60000 (53%)] Loss: 0.195541
Test set: Average loss: 0.0556, Accuracy: 9815/10000 (98%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.099088
Train Epoch: 8 [32000/60000 (53%)] Loss: 0.255493
Test set: Average loss: 0.0474, Accuracy: 9845/10000 (98%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.119628
Train Epoch: 9 [32000/60000 (53%)] Loss: 0.066614
Test set: Average loss: 0.0576, Accuracy: 9828/10000 (98%)
Train Epoch: 10 [0/60000 (0%)] Loss: 0.166568
Train Epoch: 10 [32000/60000 (53%)] Loss: 0.070234
Test set: Average loss: 0.0423, Accuracy: 9865/10000 (99%)
Train Epoch: 11 [0/60000 (0%)] Loss: 0.026872
Train Epoch: 11 [32000/60000 (53%)] Loss: 0.351948
Test set: Average loss: 0.0507, Accuracy: 9843/10000 (98%)
Train Epoch: 12 [0/60000 (0%)] Loss: 0.156402
Train Epoch: 12 [32000/60000 (53%)] Loss: 0.083423
Test set: Average loss: 0.0440, Accuracy: 9866/10000 (99%)
Train Epoch: 13 [0/60000 (0%)] Loss: 0.204176
Train Epoch: 13 [32000/60000 (53%)] Loss: 0.077210
Test set: Average loss: 0.0431, Accuracy: 9862/10000 (99%)
Train Epoch: 14 [0/60000 (0%)] Loss: 0.066927
Train Epoch: 14 [32000/60000 (53%)] Loss: 0.102251
Test set: Average loss: 0.0474, Accuracy: 9858/10000 (99%)
Train Epoch: 15 [0/60000 (0%)] Loss: 0.185688
Train Epoch: 15 [32000/60000 (53%)] Loss: 0.103873
Test set: Average loss: 0.0380, Accuracy: 9882/10000 (99%)
Train Epoch: 16 [0/60000 (0%)] Loss: 0.066640
Train Epoch: 16 [32000/60000 (53%)] Loss: 0.048075
Test set: Average loss: 0.0392, Accuracy: 9880/10000 (99%)
Train Epoch: 17 [0/60000 (0%)] Loss: 0.013197
Train Epoch: 17 [32000/60000 (53%)] Loss: 0.109586
Test set: Average loss: 0.0386, Accuracy: 9888/10000 (99%)
Train Epoch: 18 [0/60000 (0%)] Loss: 0.122926
Train Epoch: 18 [32000/60000 (53%)] Loss: 0.113722
Test set: Average loss: 0.0362, Accuracy: 9887/10000 (99%)
Train Epoch: 19 [0/60000 (0%)] Loss: 0.105665
Train Epoch: 19 [32000/60000 (53%)] Loss: 0.080806
Test set: Average loss: 0.0373, Accuracy: 9886/10000 (99%)
Train Epoch: 20 [0/60000 (0%)] Loss: 0.326196
Train Epoch: 20 [32000/60000 (53%)] Loss: 0.066830
Test set: Average loss: 0.0370, Accuracy: 9888/10000 (99%)
Total running time of the script: (1 minutes 24.837 seconds)