STN：空间变换网络(Spatial Transformer Network)

pytorch 源码

``````# -*- coding: utf-8 -*-
"""
Spatial Transformer Networks Tutorial
=====================================
**Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_

.. 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 in the `DeepMind paper <https://arxiv.org/abs/1506.02025>`__

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.
"""
# 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

######################################################################
# ----------------
#
# In this post we experiment with the classic MNIST dataset. Using a
# standard convolutional network augmented with a spatial transformer
# network.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Training dataset
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])), batch_size=64, shuffle=True, num_workers=4)
# Test dataset
datasets.MNIST(root='.', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])), batch_size=64, shuffle=True, num_workers=4)

######################################################################
# 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::
#    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)

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(
100. * batch_idx / len(train_loader), loss.item()))
#
# A simple test procedure to measure STN the performances on MNIST.
#

def test():
model.eval()
test_loss = 0
correct = 0
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()

print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'

######################################################################
# 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():
# Get a batch of training data

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()
``````

Reference
[1] 【论文笔记】Spatial Transformer Networks
[2] Spatial Transformer Networks Tutorial

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