点击上方“小白学视觉”，选择加"星标"或“置顶”

重磅干货，第一时间送达

在本教程中，您将学习如何使用称为空间变换器网络的视觉注意机制来扩充您的网络。你可以在DeepMind paper 有关空间变换器网络的内容。

空间变换器网络是对任何空间变换的差异化关注的概括。空间变换器网络（简称STN）允许神经网络学习如何在输入图像上执行空间变换，
以增强模型的几何不变性。例如，它可以裁剪感兴趣的区域，缩放并校正图像的方向。而这可能是一种有用的机制，因为CNN对于旋转和
缩放以及更一般的仿射变换并不是不变的。

关于STN的最棒的事情之一是能够简单地将其插入任何现有的CNN，而且只需很少的修改。

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 npplt.ion()   # 交互模式

1.加载数据

在这篇文章中，我们尝试了经典的 MNIST 数据集。使用标准卷积网络增强空间变换器网络。

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 训练数据集
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)
# 测试数据集
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)

• 输出结果

Downloading http://yann.lecun.com/exdb/mnist/train-http://pytorch.panchuang.net/FourSection/images-idx3-ubyte.gz to ./MNIST/raw/train-http://pytorch.panchuang.net/FourSection/images-idx3-ubyte.gz
Extracting ./MNIST/raw/train-http://pytorch.panchuang.net/FourSection/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-http://pytorch.panchuang.net/FourSection/images-idx3-ubyte.gz to ./MNIST/raw/t10k-http://pytorch.panchuang.net/FourSection/images-idx3-ubyte.gz
Extracting ./MNIST/raw/t10k-http://pytorch.panchuang.net/FourSection/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!

2.什么是空间变换器网络？

空间变换器网络归结为三个主要组成部分：

• 本地网络（Localisation Network）是常规CNN，其对变换参数进行回归。不会从该数据集中明确地学习转换，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( Grid Genator)在输入图像中生成与输出图像中的每个像素相对应的坐标网格。

• 采样器（Sampler）使用变换的参数并将其应用于输入图像。

注意：

我们使用最新版本的Pytorch，它应该包含affine_grid和grid_sample模块。

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)# 空间变换器定位 - 网络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))# 3 * 2 affine矩阵的回归量self.fc_loc = nn.Sequential(nn.Linear(10 * 3 * 3, 32),nn.ReLU(True),nn.Linear(32, 3 * 2))# 使用身份转换初始化权重/偏差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))# 空间变换器网络转发功能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 xdef forward(self, x):# transform the inputx = self.stn(x)# 执行一般的前进传递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)

3.训练模型

训练模型
现在我们使用 SGD（随机梯度下降）算法来训练模型。网络正在以有监督的方式学习分类任务。同时，该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()))
#
# 一种简单的测试程序，用于测量STN在MNIST上的性能。.
#def test():with torch.no_grad():model.eval()test_loss = 0correct = 0for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)# 累加批量损失test_loss += F.nll_loss(output, target, size_average=False).item()# 获取最大对数概率的索引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)))

4.可视化 STN 结果

现在，我们将检查我们学习的视觉注意机制的结果。

我们定义了一个小辅助函数，以便在训练时可视化变换。

def convert_http://pytorch.panchuang.net/FourSection/image_np(inp):"""Convert a Tensor to numpy http://pytorch.panchuang.net/FourSection/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 + meaninp = np.clip(inp, 0, 1)return inp# 我们想要在训练之后可视化空间变换器层的输出
# 我们使用STN可视化一批输入图像和相应的变换批次。
def visualize_stn():with torch.no_grad():# Get a batch of training datadata = next(iter(test_loader))[0].to(device)input_tensor = data.cpu()transformed_input_tensor = model.stn(data).cpu()in_grid = convert_http://pytorch.panchuang.net/FourSection/image_np(torchvision.utils.make_grid(input_tensor))out_grid = convert_http://pytorch.panchuang.net/FourSection/image_np(torchvision.utils.make_grid(transformed_input_tensor))# Plot the results side-by-sidef, axarr = plt.subplots(1, 2)axarr[0].imshow(in_grid)axarr[0].set_title('Dataset http://pytorch.panchuang.net/FourSection/images')axarr[1].imshow(out_grid)axarr[1].set_title('Transformed http://pytorch.panchuang.net/FourSection/images')for epoch in range(1, 20 + 1):train(epoch)test()# 在某些输入批处理上可视化STN转换
visualize_stn()plt.ioff()
plt.show()

• 输出结果

Train Epoch: 1 [0/60000 (0%)]   Loss: 2.336866
Train Epoch: 1 [32000/60000 (53%)]      Loss: 0.841600Test set: Average loss: 0.2624, Accuracy: 9212/10000 (92%)Train Epoch: 2 [0/60000 (0%)]   Loss: 0.527656
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.428908Test set: Average loss: 0.1176, Accuracy: 9632/10000 (96%)Train Epoch: 3 [0/60000 (0%)]   Loss: 0.305364
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.263615Test set: Average loss: 0.1099, Accuracy: 9677/10000 (97%)Train Epoch: 4 [0/60000 (0%)]   Loss: 0.169776
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.408683Test set: Average loss: 0.0861, Accuracy: 9734/10000 (97%)Train Epoch: 5 [0/60000 (0%)]   Loss: 0.286635
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.122162Test set: Average loss: 0.0817, Accuracy: 9743/10000 (97%)Train Epoch: 6 [0/60000 (0%)]   Loss: 0.331074
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.126413Test set: Average loss: 0.0589, Accuracy: 9822/10000 (98%)Train Epoch: 7 [0/60000 (0%)]   Loss: 0.109780
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.172199Test set: Average loss: 0.0629, Accuracy: 9814/10000 (98%)Train Epoch: 8 [0/60000 (0%)]   Loss: 0.078934
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.156452Test set: Average loss: 0.0563, Accuracy: 9839/10000 (98%)Train Epoch: 9 [0/60000 (0%)]   Loss: 0.063500
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.186023Test set: Average loss: 0.0713, Accuracy: 9799/10000 (98%)Train Epoch: 10 [0/60000 (0%)]  Loss: 0.199808
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.083502Test set: Average loss: 0.0528, Accuracy: 9850/10000 (98%)Train Epoch: 11 [0/60000 (0%)]  Loss: 0.092909
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.204410Test set: Average loss: 0.0471, Accuracy: 9857/10000 (99%)Train Epoch: 12 [0/60000 (0%)]  Loss: 0.078322
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.041391Test set: Average loss: 0.0634, Accuracy: 9796/10000 (98%)Train Epoch: 13 [0/60000 (0%)]  Loss: 0.061228
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.137952Test set: Average loss: 0.0654, Accuracy: 9802/10000 (98%)Train Epoch: 14 [0/60000 (0%)]  Loss: 0.068635
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.084583Test set: Average loss: 0.0515, Accuracy: 9853/10000 (99%)Train Epoch: 15 [0/60000 (0%)]  Loss: 0.263158
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.127036Test set: Average loss: 0.0493, Accuracy: 9851/10000 (99%)Train Epoch: 16 [0/60000 (0%)]  Loss: 0.083642
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.028274Test set: Average loss: 0.0461, Accuracy: 9867/10000 (99%)Train Epoch: 17 [0/60000 (0%)]  Loss: 0.076734
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.034796Test set: Average loss: 0.0409, Accuracy: 9864/10000 (99%)Train Epoch: 18 [0/60000 (0%)]  Loss: 0.122501
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.152187Test set: Average loss: 0.0474, Accuracy: 9860/10000 (99%)Train Epoch: 19 [0/60000 (0%)]  Loss: 0.050512
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.270055Test set: Average loss: 0.0416, Accuracy: 9878/10000 (99%)Train Epoch: 20 [0/60000 (0%)]  Loss: 0.073357
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.017542Test set: Average loss: 0.0713, Accuracy: 9816/10000 (98%)

脚本的总运行时间：1分48.736秒

下载1：OpenCV-Contrib扩展模块中文版教程

在「小白学视觉」公众号后台回复：扩展模块中文教程，即可下载全网第一份OpenCV扩展模块教程中文版，涵盖扩展模块安装、SFM算法、立体视觉、目标跟踪、生物视觉、超分辨率处理等二十多章内容。

下载2：Python视觉实战项目52讲

在「小白学视觉」公众号后台回复：Python视觉实战项目，即可下载包括图像分割、口罩检测、车道线检测、车辆计数、添加眼线、车牌识别、字符识别、情绪检测、文本内容提取、面部识别等31个视觉实战项目，助力快速学校计算机视觉。

下载3：OpenCV实战项目20讲

在「小白学视觉」公众号后台回复：OpenCV实战项目20讲，即可下载含有20个基于OpenCV实现20个实战项目，实现OpenCV学习进阶。

交流群

欢迎加入公众号读者群一起和同行交流，目前有SLAM、三维视觉、传感器、自动驾驶、计算摄影、检测、分割、识别、医学影像、GAN、算法竞赛等微信群（以后会逐渐细分），请扫描下面微信号加群，备注：”昵称+学校/公司+研究方向“，例如：”张三 + 上海交大 + 视觉SLAM“。请按照格式备注，否则不予通过。添加成功后会根据研究方向邀请进入相关微信群。请勿在群内发送广告，否则会请出群，谢谢理解~