.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _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 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 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) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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.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. .. 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): 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 STN the 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. .. 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(): 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() .. image:: /intermediate/images/sphx_glr_spatial_transformer_tutorial_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Train Epoch: 1 [0/60000 (0%)] Loss: 2.313799 Train Epoch: 1 [32000/60000 (53%)] Loss: 0.819115 Test set: Average loss: 0.2063, Accuracy: 9431/10000 (94%) Train Epoch: 2 [0/60000 (0%)] Loss: 0.381877 Train Epoch: 2 [32000/60000 (53%)] Loss: 0.342607 Test set: Average loss: 0.1208, Accuracy: 9642/10000 (96%) Train Epoch: 3 [0/60000 (0%)] Loss: 0.247812 Train Epoch: 3 [32000/60000 (53%)] Loss: 0.249339 Test set: Average loss: 0.0961, Accuracy: 9700/10000 (97%) Train Epoch: 4 [0/60000 (0%)] Loss: 0.080917 Train Epoch: 4 [32000/60000 (53%)] Loss: 0.224414 Test set: Average loss: 0.0832, Accuracy: 9754/10000 (98%) Train Epoch: 5 [0/60000 (0%)] Loss: 0.204265 Train Epoch: 5 [32000/60000 (53%)] Loss: 0.142722 Test set: Average loss: 0.0733, Accuracy: 9762/10000 (98%) Train Epoch: 6 [0/60000 (0%)] Loss: 0.195066 Train Epoch: 6 [32000/60000 (53%)] Loss: 0.127173 Test set: Average loss: 0.2049, Accuracy: 9377/10000 (94%) Train Epoch: 7 [0/60000 (0%)] Loss: 0.395596 Train Epoch: 7 [32000/60000 (53%)] Loss: 0.257278 Test set: Average loss: 0.0741, Accuracy: 9772/10000 (98%) Train Epoch: 8 [0/60000 (0%)] Loss: 0.051441 Train Epoch: 8 [32000/60000 (53%)] Loss: 0.182687 Test set: Average loss: 0.0533, Accuracy: 9842/10000 (98%) Train Epoch: 9 [0/60000 (0%)] Loss: 0.048953 Train Epoch: 9 [32000/60000 (53%)] Loss: 0.075130 Test set: Average loss: 0.0634, Accuracy: 9795/10000 (98%) Train Epoch: 10 [0/60000 (0%)] Loss: 0.134152 Train Epoch: 10 [32000/60000 (53%)] Loss: 0.087218 Test set: Average loss: 0.0547, Accuracy: 9833/10000 (98%) Train Epoch: 11 [0/60000 (0%)] Loss: 0.099078 Train Epoch: 11 [32000/60000 (53%)] Loss: 0.169374 Test set: Average loss: 0.0498, Accuracy: 9848/10000 (98%) Train Epoch: 12 [0/60000 (0%)] Loss: 0.103957 Train Epoch: 12 [32000/60000 (53%)] Loss: 0.254779 Test set: Average loss: 0.2090, Accuracy: 9412/10000 (94%) Train Epoch: 13 [0/60000 (0%)] Loss: 0.226625 Train Epoch: 13 [32000/60000 (53%)] Loss: 0.043412 Test set: Average loss: 0.0728, Accuracy: 9784/10000 (98%) Train Epoch: 14 [0/60000 (0%)] Loss: 0.273524 Train Epoch: 14 [32000/60000 (53%)] Loss: 0.200305 Test set: Average loss: 0.0606, Accuracy: 9832/10000 (98%) Train Epoch: 15 [0/60000 (0%)] Loss: 0.064689 Train Epoch: 15 [32000/60000 (53%)] Loss: 0.098237 Test set: Average loss: 0.0502, Accuracy: 9852/10000 (99%) Train Epoch: 16 [0/60000 (0%)] Loss: 0.052908 Train Epoch: 16 [32000/60000 (53%)] Loss: 0.120961 Test set: Average loss: 0.0437, Accuracy: 9863/10000 (99%) Train Epoch: 17 [0/60000 (0%)] Loss: 0.078355 Train Epoch: 17 [32000/60000 (53%)] Loss: 0.093149 Test set: Average loss: 0.0464, Accuracy: 9859/10000 (99%) Train Epoch: 18 [0/60000 (0%)] Loss: 0.038080 Train Epoch: 18 [32000/60000 (53%)] Loss: 0.044862 Test set: Average loss: 0.0519, Accuracy: 9844/10000 (98%) Train Epoch: 19 [0/60000 (0%)] Loss: 0.034135 Train Epoch: 19 [32000/60000 (53%)] Loss: 0.116658 Test set: Average loss: 0.0462, Accuracy: 9866/10000 (99%) Train Epoch: 20 [0/60000 (0%)] Loss: 0.048453 Train Epoch: 20 [32000/60000 (53%)] Loss: 0.114311 Test set: Average loss: 0.0392, Accuracy: 9892/10000 (99%) **Total running time of the script:** ( 20 minutes 37.855 seconds) .. _sphx_glr_download_intermediate_spatial_transformer_tutorial.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. 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 `_