.. _sphx_glr_intermediate_spatial_transformer_tutorial.py: Spatial Transformer Networks Tutorial ===================================== **Author**: `Ghassen HAMROUNI `_ .. figure:: /_static/img/stn/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. .. code-block:: python # 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 from torch.autograd import Variable 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. .. code-block:: python use_cuda = torch.cuda.is_available() # 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) .. rst-class:: sphx-glr-script-out Out:: Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Processing... Done! 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. .. figure:: /_static/img/stn/stn-arch.png .. Note:: We need the latest version of PyTorch that contains affine_grid and grid_sample modules. .. code-block:: python 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.fill_(0) self.fc_loc[2].bias.data = torch.FloatTensor([1, 0, 0, 0, 1, 0]) # 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() if use_cuda: model.cuda() 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. .. code-block:: python optimizer = optim.SGD(model.parameters(), lr=0.01) def train(epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): if use_cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) 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.data[0])) # # A simple test procedure to measure STN the performances on MNIST. # def test(): model.eval() test_loss = 0 correct = 0 for data, target in test_loader: if use_cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data, volatile=True), Variable(target) output = model(data) # sum up batch loss test_loss += F.nll_loss(output, target, size_average=False).data[0] # get the index of the max log-probability pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).cpu().sum() 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. .. code-block:: python 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(): # Get a batch of training data data, _ = next(iter(test_loader)) data = Variable(data, volatile=True) if use_cuda: data = data.cuda() input_tensor = data.cpu().data transformed_input_tensor = model.stn(data).cpu().data 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() .. image:: /intermediate/images/sphx_glr_spatial_transformer_tutorial_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Train Epoch: 1 [0/60000 (0%)] Loss: 2.342094 Train Epoch: 1 [32000/60000 (53%)] Loss: 0.914849 Test set: Average loss: 0.1988, Accuracy: 9407/10000 (94%) Train Epoch: 2 [0/60000 (0%)] Loss: 0.727430 Train Epoch: 2 [32000/60000 (53%)] Loss: 0.518599 Test set: Average loss: 0.1311, Accuracy: 9586/10000 (96%) Train Epoch: 3 [0/60000 (0%)] Loss: 0.286077 Train Epoch: 3 [32000/60000 (53%)] Loss: 0.178694 Test set: Average loss: 0.0941, Accuracy: 9711/10000 (97%) Train Epoch: 4 [0/60000 (0%)] Loss: 0.163664 Train Epoch: 4 [32000/60000 (53%)] Loss: 0.260930 Test set: Average loss: 0.0781, Accuracy: 9767/10000 (98%) Train Epoch: 5 [0/60000 (0%)] Loss: 0.201850 Train Epoch: 5 [32000/60000 (53%)] Loss: 0.231272 Test set: Average loss: 0.0716, Accuracy: 9789/10000 (98%) Train Epoch: 6 [0/60000 (0%)] Loss: 0.104988 Train Epoch: 6 [32000/60000 (53%)] Loss: 0.104964 Test set: Average loss: 0.0595, Accuracy: 9809/10000 (98%) Train Epoch: 7 [0/60000 (0%)] Loss: 0.047372 Train Epoch: 7 [32000/60000 (53%)] Loss: 0.083524 Test set: Average loss: 0.0587, Accuracy: 9824/10000 (98%) Train Epoch: 8 [0/60000 (0%)] Loss: 0.110441 Train Epoch: 8 [32000/60000 (53%)] Loss: 0.057427 Test set: Average loss: 0.0724, Accuracy: 9799/10000 (98%) Train Epoch: 9 [0/60000 (0%)] Loss: 0.121870 Train Epoch: 9 [32000/60000 (53%)] Loss: 0.165922 Test set: Average loss: 0.0577, Accuracy: 9815/10000 (98%) Train Epoch: 10 [0/60000 (0%)] Loss: 0.054281 Train Epoch: 10 [32000/60000 (53%)] Loss: 0.183995 Test set: Average loss: 0.0615, Accuracy: 9804/10000 (98%) Train Epoch: 11 [0/60000 (0%)] Loss: 0.112812 Train Epoch: 11 [32000/60000 (53%)] Loss: 0.041237 Test set: Average loss: 0.0619, Accuracy: 9803/10000 (98%) Train Epoch: 12 [0/60000 (0%)] Loss: 0.091255 Train Epoch: 12 [32000/60000 (53%)] Loss: 0.054673 Test set: Average loss: 0.0434, Accuracy: 9862/10000 (99%) Train Epoch: 13 [0/60000 (0%)] Loss: 0.087725 Train Epoch: 13 [32000/60000 (53%)] Loss: 0.136220 Test set: Average loss: 0.0440, Accuracy: 9853/10000 (99%) Train Epoch: 14 [0/60000 (0%)] Loss: 0.023782 Train Epoch: 14 [32000/60000 (53%)] Loss: 0.043973 Test set: Average loss: 0.0463, Accuracy: 9857/10000 (99%) Train Epoch: 15 [0/60000 (0%)] Loss: 0.187337 Train Epoch: 15 [32000/60000 (53%)] Loss: 0.204377 Test set: Average loss: 0.0496, Accuracy: 9850/10000 (98%) Train Epoch: 16 [0/60000 (0%)] Loss: 0.045294 Train Epoch: 16 [32000/60000 (53%)] Loss: 0.027516 Test set: Average loss: 0.0444, Accuracy: 9871/10000 (99%) Train Epoch: 17 [0/60000 (0%)] Loss: 0.039108 Train Epoch: 17 [32000/60000 (53%)] Loss: 0.119702 Test set: Average loss: 0.0486, Accuracy: 9845/10000 (98%) Train Epoch: 18 [0/60000 (0%)] Loss: 0.200034 Train Epoch: 18 [32000/60000 (53%)] Loss: 0.140622 Test set: Average loss: 0.0425, Accuracy: 9871/10000 (99%) Train Epoch: 19 [0/60000 (0%)] Loss: 0.091878 Train Epoch: 19 [32000/60000 (53%)] Loss: 0.087307 Test set: Average loss: 0.0369, Accuracy: 9886/10000 (99%) Train Epoch: 20 [0/60000 (0%)] Loss: 0.078879 Train Epoch: 20 [32000/60000 (53%)] Loss: 0.104612 Test set: Average loss: 0.0454, Accuracy: 9863/10000 (99%) **Total running time of the script:** ( 1 minutes 33.840 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: spatial_transformer_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: spatial_transformer_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_