空间变压器网络教程

作者: Ghassen HAMROUNI

https://pytorch.org/tutorials/_images/FSeq.png

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力机制来扩充网络。 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区别关注的概括。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,它可以裁剪感兴趣的区域,缩放并校正图像的方向。这可能是一个有用的机制,因为CNN不会对旋转和缩放以及更一般的仿射变换保持不变。

关于STN的最好的事情之一就是能够将其简单地插入到任何现有的CNN中,而无需进行任何修改。

# 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

加载数据

在本文中,我们将尝试使用经典的MNIST数据集。使用标准卷积网络和空间变换器网络。

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)

Out:

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 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 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 to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw
Processing...
Done!

描绘空间变换器网络

空间变压器网络可归结为三个主要组成部分:

  • 定位网络是常规的CNN,可以对转换参数进行回归。永远不会从该数据集中显式学习变换,而是网络会自动学习增强全局精度的空间变换。
  • 网格生成器在输入图像中生成与来自输出图像的每个像素相对应的坐标网格。
  • 采样器使用转换的参数,并将其应用于输入图像。

https://pytorch.org/tutorials/_images/stn-arch.png

Note 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)

训练模式

现在,让我们使用SGD算法训练模型。网络正在以监督方式学习分类任务。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()))
#
# 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)))

可视化STN结果

现在,我们将检查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转换。

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()

https://pytorch.org/tutorials/_images/sphx_glr_spatial_transformer_tutorial_001.png

Out:

Train Epoch: 1 [0/60000 (0%)]   Loss: 2.290877
Train Epoch: 1 [32000/60000 (53%)]      Loss: 0.910913

Test set: Average loss: 0.2449, Accuracy: 9312/10000 (93%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.489534
Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.296471

Test set: Average loss: 0.1443, Accuracy: 9563/10000 (96%)

Train Epoch: 3 [0/60000 (0%)]   Loss: 0.410248
Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.355454

Test set: Average loss: 0.1019, Accuracy: 9687/10000 (97%)

Train Epoch: 4 [0/60000 (0%)]   Loss: 0.217658
Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.185522

Test set: Average loss: 0.0818, Accuracy: 9751/10000 (98%)

Train Epoch: 5 [0/60000 (0%)]   Loss: 0.471464
Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.591574

Test set: Average loss: 0.0770, Accuracy: 9760/10000 (98%)

Train Epoch: 6 [0/60000 (0%)]   Loss: 0.119462
Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.093015

Test set: Average loss: 0.0817, Accuracy: 9744/10000 (97%)

Train Epoch: 7 [0/60000 (0%)]   Loss: 0.074523
Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.414406

Test set: Average loss: 0.0944, Accuracy: 9714/10000 (97%)

Train Epoch: 8 [0/60000 (0%)]   Loss: 0.100317
Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.114539

Test set: Average loss: 0.1519, Accuracy: 9510/10000 (95%)

Train Epoch: 9 [0/60000 (0%)]   Loss: 0.205053
Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.135724

Test set: Average loss: 0.0892, Accuracy: 9749/10000 (97%)

Train Epoch: 10 [0/60000 (0%)]  Loss: 0.213368
Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.208627

Test set: Average loss: 0.0634, Accuracy: 9813/10000 (98%)

Train Epoch: 11 [0/60000 (0%)]  Loss: 0.078725
Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.099131

Test set: Average loss: 0.0580, Accuracy: 9834/10000 (98%)

Train Epoch: 12 [0/60000 (0%)]  Loss: 0.133572
Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.213358

Test set: Average loss: 0.0506, Accuracy: 9854/10000 (99%)

Train Epoch: 13 [0/60000 (0%)]  Loss: 0.289802
Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.165571

Test set: Average loss: 0.0542, Accuracy: 9842/10000 (98%)

Train Epoch: 14 [0/60000 (0%)]  Loss: 0.219281
Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.284233

Test set: Average loss: 0.0505, Accuracy: 9856/10000 (99%)

Train Epoch: 15 [0/60000 (0%)]  Loss: 0.218599
Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.055698

Test set: Average loss: 0.0507, Accuracy: 9848/10000 (98%)

Train Epoch: 16 [0/60000 (0%)]  Loss: 0.048718
Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.093410

Test set: Average loss: 0.0502, Accuracy: 9855/10000 (99%)

Train Epoch: 17 [0/60000 (0%)]  Loss: 0.071185
Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.053381

Test set: Average loss: 0.0587, Accuracy: 9829/10000 (98%)

Train Epoch: 18 [0/60000 (0%)]  Loss: 0.127790
Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.169319

Test set: Average loss: 0.0484, Accuracy: 9863/10000 (99%)

Train Epoch: 19 [0/60000 (0%)]  Loss: 0.224094
Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.175750

Test set: Average loss: 0.0628, Accuracy: 9817/10000 (98%)

Train Epoch: 20 [0/60000 (0%)]  Loss: 0.251131
Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.024119

Test set: Average loss: 0.0445, Accuracy: 9869/10000 (99%)

脚本的总运行时间: (1分钟44.448秒)

Download Python source code: spatial_transformer_tutorial.py

Download Jupyter notebook: spatial_transformer_tutorial.ipynb


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