【PyTorch】教程:Spatial transformer network

SPATIAL TRANSFORMER NETWORKS

【PyTorch】教程:Spatial transformer network_第1张图片

在这个教程中,我们将学习利用视觉注意力机制(spatial transformer networks DeepMind paper )增强我们的网络。

Spatial transformer networks (以下简称 STN)是任何空间变换的可微注意力概括。STN 允许一个神经网络学习如何执行空间变换,从而可以增强模型的几何鲁棒性。例如,可以截取ROI,尺度变换,角度旋转或更多的放射变换等等。

STN 一个很重要的特性就是在可以插入到任意的CNN里,只需要少量的修改。

Loading the data

本节中,我们使用 MNIST 数据集,利用一个标准的卷积网络并且使用 STN 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")

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='../../../datasets',
                train=True,
                download=True,
                transform=transforms.Compose([
                    transforms.ToTensor(),
                    transforms.Normalize((0.1307,), (0.3081,))])), 
                batch_size=64, 
                shuffle=True,
                num_workers=4)
                

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='../../../datasets',
                train=False,
                download=True,
                transform=transforms.Compose([
                    transforms.ToTensor(),
                    transforms.Normalize((0.1307,), (0.3081,))])), 
                batch_size=64, 
                shuffle=True,
                num_workers=4)

Depicting spatial transformer networks

STN 主要有3个结构:

  • Localisation net:是一个常规的 CNN,回归转换参数,转换的参数从来没有在数据集里学习过,网络自动学习空间变换信息从而可以增强全局的准确率;
  • Grid generator:生成与输出图像中的每个像素对应的输入图像中的坐标网格。
  • Sample:使用变换的参数并将其应用于输入图像。

【PyTorch】教程:Spatial transformer network_第2张图片

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)         # 特征提取
        xs = xs.view(-1, 10 * 3 * 3)      # feature map resize到对应的维度
        theta = self.fc_loc(xs)           # 局部网络 回归 参数 θ
        theta = theta.view(-1, 2, 3)      # 参数θ resize 到对应的维度
        
        grid = F.affine_grid(theta, x.size()) # 对于 θ 计算输出对应的原图位置
        x = F.grid_sample(x, grid)        # 对原图进行 sample 得到目标输出
        
        return x 
        
    def forward(self, x):
        x = self.stn(x)
        
        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=device)

Training the model

SGD 算法训练网络,网络以可监督的方式训练分类任务,同时自动学习 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)))

Visualizing the STN results

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()

【PyTorch】教程:Spatial transformer network_第3张图片

【参考】

SPATIAL TRANSFORMER NETWORKS TUTORIAL

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