STN论文笔记

# 小郑之家~

paper 地址在 https://arxiv.org/pdf/1506.02025.pdf

### 具体

• Localisation net

• 一个问题是，前面说没有变换的gt，那么变换的参数是如何学到的呢？

• Grid generator

• Sampler

### STN可以应用到哪里，怎么用

STN可以放到CNN中去，并且因为其计算很快，并不会影响训练速度，有时候甚至会加快训练速度，因为STN里面是有采样的，而且作了变换之后，feature更好了一些，算loss的时候可能有用的信息就比较集中。

### 示例代码

# 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)