.. 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:: default # 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:: default 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) .. 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-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-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 to ./MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-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 to ./MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-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 to ./MNIST/raw 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:: default 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:: default 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. .. code-block:: default 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: 2.368588 Train Epoch: 1 [32000/60000 (53%)] Loss: 0.916856 Test set: Average loss: 0.4112, Accuracy: 8697/10000 (87%) Train Epoch: 2 [0/60000 (0%)] Loss: 0.908913 Train Epoch: 2 [32000/60000 (53%)] Loss: 0.345142 Test set: Average loss: 0.1291, Accuracy: 9593/10000 (96%) Train Epoch: 3 [0/60000 (0%)] Loss: 0.250927 Train Epoch: 3 [32000/60000 (53%)] Loss: 0.421475 Test set: Average loss: 0.1086, Accuracy: 9668/10000 (97%) Train Epoch: 4 [0/60000 (0%)] Loss: 0.350522 Train Epoch: 4 [32000/60000 (53%)] Loss: 0.257925 Test set: Average loss: 0.2147, Accuracy: 9354/10000 (94%) Train Epoch: 5 [0/60000 (0%)] Loss: 0.305506 Train Epoch: 5 [32000/60000 (53%)] Loss: 0.082600 Test set: Average loss: 0.0722, Accuracy: 9768/10000 (98%) Train Epoch: 6 [0/60000 (0%)] Loss: 0.212339 Train Epoch: 6 [32000/60000 (53%)] Loss: 0.183269 Test set: Average loss: 0.0848, Accuracy: 9741/10000 (97%) Train Epoch: 7 [0/60000 (0%)] Loss: 0.096175 Train Epoch: 7 [32000/60000 (53%)] Loss: 0.140066 Test set: Average loss: 0.0562, Accuracy: 9827/10000 (98%) Train Epoch: 8 [0/60000 (0%)] Loss: 0.126675 Train Epoch: 8 [32000/60000 (53%)] Loss: 0.115889 Test set: Average loss: 0.0520, Accuracy: 9836/10000 (98%) Train Epoch: 9 [0/60000 (0%)] Loss: 0.131406 Train Epoch: 9 [32000/60000 (53%)] Loss: 0.263442 Test set: Average loss: 0.0458, Accuracy: 9866/10000 (99%) Train Epoch: 10 [0/60000 (0%)] Loss: 0.093719 Train Epoch: 10 [32000/60000 (53%)] Loss: 0.089051 Test set: Average loss: 0.0555, Accuracy: 9830/10000 (98%) Train Epoch: 11 [0/60000 (0%)] Loss: 0.225552 Train Epoch: 11 [32000/60000 (53%)] Loss: 0.092615 Test set: Average loss: 0.0527, Accuracy: 9840/10000 (98%) Train Epoch: 12 [0/60000 (0%)] Loss: 0.420041 Train Epoch: 12 [32000/60000 (53%)] Loss: 0.050414 Test set: Average loss: 0.0470, Accuracy: 9863/10000 (99%) Train Epoch: 13 [0/60000 (0%)] Loss: 0.097507 Train Epoch: 13 [32000/60000 (53%)] Loss: 0.069199 Test set: Average loss: 0.0468, Accuracy: 9855/10000 (99%) Train Epoch: 14 [0/60000 (0%)] Loss: 0.207414 Train Epoch: 14 [32000/60000 (53%)] Loss: 0.027101 Test set: Average loss: 0.0422, Accuracy: 9871/10000 (99%) Train Epoch: 15 [0/60000 (0%)] Loss: 0.091398 Train Epoch: 15 [32000/60000 (53%)] Loss: 0.211614 Test set: Average loss: 0.0369, Accuracy: 9887/10000 (99%) Train Epoch: 16 [0/60000 (0%)] Loss: 0.035318 Train Epoch: 16 [32000/60000 (53%)] Loss: 0.092014 Test set: Average loss: 0.0369, Accuracy: 9890/10000 (99%) Train Epoch: 17 [0/60000 (0%)] Loss: 0.055495 Train Epoch: 17 [32000/60000 (53%)] Loss: 0.119085 Test set: Average loss: 0.0412, Accuracy: 9870/10000 (99%) Train Epoch: 18 [0/60000 (0%)] Loss: 0.028833 Train Epoch: 18 [32000/60000 (53%)] Loss: 0.044220 Test set: Average loss: 0.0388, Accuracy: 9889/10000 (99%) Train Epoch: 19 [0/60000 (0%)] Loss: 0.074087 Train Epoch: 19 [32000/60000 (53%)] Loss: 0.046246 Test set: Average loss: 0.0690, Accuracy: 9816/10000 (98%) Train Epoch: 20 [0/60000 (0%)] Loss: 0.154425 Train Epoch: 20 [32000/60000 (53%)] Loss: 0.059043 Test set: Average loss: 0.0380, Accuracy: 9894/10000 (99%) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 45.248 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 `_