.. 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 to ./MNIST/raw/train-images-idx3-ubyte.gz Extracting ./MNIST/raw/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz Extracting ./MNIST/raw/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: 2.345713 Train Epoch: 1 [32000/60000 (53%)] Loss: 0.800387 Test set: Average loss: 0.2062, Accuracy: 9431/10000 (94%) Train Epoch: 2 [0/60000 (0%)] Loss: 0.353793 Train Epoch: 2 [32000/60000 (53%)] Loss: 0.311179 Test set: Average loss: 0.1226, Accuracy: 9648/10000 (96%) Train Epoch: 3 [0/60000 (0%)] Loss: 0.187958 Train Epoch: 3 [32000/60000 (53%)] Loss: 0.465108 Test set: Average loss: 0.1741, Accuracy: 9489/10000 (95%) Train Epoch: 4 [0/60000 (0%)] Loss: 0.414825 Train Epoch: 4 [32000/60000 (53%)] Loss: 0.190623 Test set: Average loss: 0.0719, Accuracy: 9775/10000 (98%) Train Epoch: 5 [0/60000 (0%)] Loss: 0.326834 Train Epoch: 5 [32000/60000 (53%)] Loss: 0.207580 Test set: Average loss: 0.0712, Accuracy: 9773/10000 (98%) Train Epoch: 6 [0/60000 (0%)] Loss: 0.213898 Train Epoch: 6 [32000/60000 (53%)] Loss: 0.246923 Test set: Average loss: 0.0848, Accuracy: 9747/10000 (97%) Train Epoch: 7 [0/60000 (0%)] Loss: 0.081923 Train Epoch: 7 [32000/60000 (53%)] Loss: 0.152231 Test set: Average loss: 0.0660, Accuracy: 9808/10000 (98%) Train Epoch: 8 [0/60000 (0%)] Loss: 0.131550 Train Epoch: 8 [32000/60000 (53%)] Loss: 0.176575 Test set: Average loss: 0.0542, Accuracy: 9838/10000 (98%) Train Epoch: 9 [0/60000 (0%)] Loss: 0.237464 Train Epoch: 9 [32000/60000 (53%)] Loss: 0.034639 Test set: Average loss: 0.0516, Accuracy: 9846/10000 (98%) Train Epoch: 10 [0/60000 (0%)] Loss: 0.056443 Train Epoch: 10 [32000/60000 (53%)] Loss: 0.136011 Test set: Average loss: 0.0478, Accuracy: 9842/10000 (98%) Train Epoch: 11 [0/60000 (0%)] Loss: 0.197817 Train Epoch: 11 [32000/60000 (53%)] Loss: 0.065170 Test set: Average loss: 0.0475, Accuracy: 9864/10000 (99%) Train Epoch: 12 [0/60000 (0%)] Loss: 0.123880 Train Epoch: 12 [32000/60000 (53%)] Loss: 0.062649 Test set: Average loss: 0.0628, Accuracy: 9802/10000 (98%) Train Epoch: 13 [0/60000 (0%)] Loss: 0.134825 Train Epoch: 13 [32000/60000 (53%)] Loss: 0.110926 Test set: Average loss: 0.0602, Accuracy: 9810/10000 (98%) Train Epoch: 14 [0/60000 (0%)] Loss: 0.069058 Train Epoch: 14 [32000/60000 (53%)] Loss: 0.094049 Test set: Average loss: 0.0472, Accuracy: 9856/10000 (99%) Train Epoch: 15 [0/60000 (0%)] Loss: 0.035787 Train Epoch: 15 [32000/60000 (53%)] Loss: 0.113708 Test set: Average loss: 0.0480, Accuracy: 9860/10000 (99%) Train Epoch: 16 [0/60000 (0%)] Loss: 0.112437 Train Epoch: 16 [32000/60000 (53%)] Loss: 0.118043 Test set: Average loss: 0.0386, Accuracy: 9885/10000 (99%) Train Epoch: 17 [0/60000 (0%)] Loss: 0.090034 Train Epoch: 17 [32000/60000 (53%)] Loss: 0.300923 Test set: Average loss: 0.0539, Accuracy: 9834/10000 (98%) Train Epoch: 18 [0/60000 (0%)] Loss: 0.428552 Train Epoch: 18 [32000/60000 (53%)] Loss: 0.071741 Test set: Average loss: 0.0536, Accuracy: 9829/10000 (98%) Train Epoch: 19 [0/60000 (0%)] Loss: 0.134506 Train Epoch: 19 [32000/60000 (53%)] Loss: 0.049006 Test set: Average loss: 0.0386, Accuracy: 9876/10000 (99%) Train Epoch: 20 [0/60000 (0%)] Loss: 0.091285 Train Epoch: 20 [32000/60000 (53%)] Loss: 0.068183 Test set: Average loss: 0.0394, Accuracy: 9875/10000 (99%) **Total running time of the script:** ( 1 minutes 42.444 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 `_