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空间变换器网络教程¶
创建于：2017 年 11 月 08 日 | 最后更新：2024 年 1 月 19 日 | 最后验证：2024 年 11 月 05 日
作者: Ghassen HAMROUNI
在本教程中，您将学习如何使用称为空间变换器网络的可视化注意力机制来增强您的网络。您可以在 DeepMind 论文中阅读有关空间变换器网络的更多信息
空间变换器网络是对任何空间变换的可微注意力的一种泛化。空间变换器网络（简称 STN）允许神经网络学习如何对输入图像执行空间变换，以增强模型的几何不变性。例如，它可以裁剪感兴趣区域、缩放和校正图像的方向。这可能是一种有用的机制，因为 CNN 对旋转和缩放以及更一般的仿射变换是不变的。
关于 STN 最好的事情之一是能够简单地将其插入到任何现有的 CNN 中，而只需进行非常小的修改。
# License: BSD
# Author: Ghassen Hamrouni
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
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加载数据¶
在这篇文章中，我们使用经典的 MNIST 数据集进行实验。使用标准的卷积网络，并使用空间变换器网络进行增强。
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)
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描述空间变换器网络¶
空间变换器网络归结为三个主要组成部分
定位网络是一个常规 CNN，它回归变换参数。变换永远不会从此数据集中显式学习，而是网络自动学习增强全局准确性的空间变换。
网格生成器在输入图像中生成与输出图像中每个像素对应的坐标网格。
采样器使用变换参数并将其应用于输入图像。
注意
我们需要包含 affine_grid 和 grid_sample 模块的最新版本的 PyTorch。
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)
训练模型¶
现在，让我们使用 SGD 算法来训练模型。该网络以监督方式学习分类任务。同时，模型以端到端的方式自动学习 STN。
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)))
可视化 STN 结果¶
现在，我们将检查我们学习到的视觉注意力机制的结果。
我们定义了一个小的辅助函数，以便在训练时可视化变换。
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()
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:5082: UserWarning:
Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:5015: UserWarning:
Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.
Train Epoch: 1 [0/60000 (0%)] Loss: 2.315648
Train Epoch: 1 [32000/60000 (53%)] Loss: 1.070991
/usr/local/lib/python3.10/dist-packages/torch/nn/_reduction.py:51: UserWarning:
size_average and reduce args will be deprecated, please use reduction='sum' instead.
Test set: Average loss: 0.2540, Accuracy: 9309/10000 (93%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.547254
Train Epoch: 2 [32000/60000 (53%)] Loss: 0.306006
Test set: Average loss: 0.1548, Accuracy: 9545/10000 (95%)
Train Epoch: 3 [0/60000 (0%)] Loss: 0.346975
Train Epoch: 3 [32000/60000 (53%)] Loss: 0.237371
Test set: Average loss: 0.1362, Accuracy: 9579/10000 (96%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.410911
Train Epoch: 4 [32000/60000 (53%)] Loss: 0.143433
Test set: Average loss: 0.1182, Accuracy: 9656/10000 (97%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.232937
Train Epoch: 5 [32000/60000 (53%)] Loss: 0.201194
Test set: Average loss: 0.2206, Accuracy: 9347/10000 (93%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.441828
Train Epoch: 6 [32000/60000 (53%)] Loss: 0.130507
Test set: Average loss: 0.0715, Accuracy: 9791/10000 (98%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.089327
Train Epoch: 7 [32000/60000 (53%)] Loss: 0.189949
Test set: Average loss: 0.0669, Accuracy: 9801/10000 (98%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.213020
Train Epoch: 8 [32000/60000 (53%)] Loss: 0.126238
Test set: Average loss: 0.0647, Accuracy: 9810/10000 (98%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.078002
Train Epoch: 9 [32000/60000 (53%)] Loss: 0.104715
Test set: Average loss: 0.0741, Accuracy: 9776/10000 (98%)
Train Epoch: 10 [0/60000 (0%)] Loss: 0.138969
Train Epoch: 10 [32000/60000 (53%)] Loss: 0.205099
Test set: Average loss: 0.0535, Accuracy: 9828/10000 (98%)
Train Epoch: 11 [0/60000 (0%)] Loss: 0.170541
Train Epoch: 11 [32000/60000 (53%)] Loss: 0.079690
Test set: Average loss: 0.0662, Accuracy: 9808/10000 (98%)
Train Epoch: 12 [0/60000 (0%)] Loss: 0.121953
Train Epoch: 12 [32000/60000 (53%)] Loss: 0.219585
Test set: Average loss: 0.0606, Accuracy: 9801/10000 (98%)
Train Epoch: 13 [0/60000 (0%)] Loss: 0.085162
Train Epoch: 13 [32000/60000 (53%)] Loss: 0.081327
Test set: Average loss: 0.0591, Accuracy: 9819/10000 (98%)
Train Epoch: 14 [0/60000 (0%)] Loss: 0.060252
Train Epoch: 14 [32000/60000 (53%)] Loss: 0.164528
Test set: Average loss: 0.0508, Accuracy: 9858/10000 (99%)
Train Epoch: 15 [0/60000 (0%)] Loss: 0.037811
Train Epoch: 15 [32000/60000 (53%)] Loss: 0.106973
Test set: Average loss: 0.0456, Accuracy: 9872/10000 (99%)
Train Epoch: 16 [0/60000 (0%)] Loss: 0.055275
Train Epoch: 16 [32000/60000 (53%)] Loss: 0.125036
Test set: Average loss: 0.0465, Accuracy: 9864/10000 (99%)
Train Epoch: 17 [0/60000 (0%)] Loss: 0.265591
Train Epoch: 17 [32000/60000 (53%)] Loss: 0.253517
Test set: Average loss: 0.0596, Accuracy: 9828/10000 (98%)
Train Epoch: 18 [0/60000 (0%)] Loss: 0.049595
Train Epoch: 18 [32000/60000 (53%)] Loss: 0.096609
Test set: Average loss: 0.0516, Accuracy: 9853/10000 (99%)
Train Epoch: 19 [0/60000 (0%)] Loss: 0.081379
Train Epoch: 19 [32000/60000 (53%)] Loss: 0.112759
Test set: Average loss: 0.0464, Accuracy: 9856/10000 (99%)
Train Epoch: 20 [0/60000 (0%)] Loss: 0.084578
Train Epoch: 20 [32000/60000 (53%)] Loss: 0.020460
Test set: Average loss: 0.0534, Accuracy: 9842/10000 (98%)
脚本的总运行时间：（2 分钟 26.045 秒）
