reg = torch.unsqueeze(anchor,1) + regression
时间: 2024-05-19 21:11:04 浏览: 18
torch.unsqueeze is a PyTorch function that returns a new tensor with a dimension of size one inserted at the specified position. In this case, it is being used to add a new dimension to the anchor tensor at position one.
The purpose of this operation is to prepare the anchor tensor for regression, which is a machine learning technique used to predict a continuous value (in this case, a set of coordinates) from input data (in this case, an image). The new dimension added by torch.unsqueeze allows the anchor tensor to be used as input for regression models that require a tensor with a specific shape.
相关问题
x=torch.unsqueeze(torch.linspace(-1,1,100),dim=1) x=torch.unsqueezex=torch.unsqueeze(torch.linspace(-1,1,100),dim=1)(torch.linspace(-1,1,100),dim=1)
你好!对于你的代码片段,我注意到你想用`torch.unsqueeze`函数将一维的张量`torch.linspace(-1,1,100)`转换为二维的张量。这个函数的作用是在指定的维度上增加一个维度。在你的代码中,你指定了`dim=1`,所以张量的列数将会增加。
然而,你的代码有一点小错误。你在第一行中正确地使用了`torch.unsqueeze`函数,但是在第二行中错误地将函数名写成了`torch.unsqueezex`。请注意将代码修改为以下形式:
```python
x = torch.unsqueeze(torch.linspace(-1,1,100), dim=1)
```
这样就可以正确地将一维张量转换为二维张量了。希望对你有帮助!如果还有其他问题,请随时提问。
修改下列模块代码,使其能够对三维模型的直线特征进行提取:class FaceKernelCorrelation(nn.Module): def __init__(self, num_kernel=64, sigma=0.2): super(FaceKernelCorrelation, self).__init__() self.num_kernel = num_kernel self.sigma = sigma self.weight_alpha = Parameter(torch.rand(1, num_kernel, 4) * np.pi) self.weight_beta = Parameter(torch.rand(1, num_kernel, 4) * 2 * np.pi) self.bn = nn.BatchNorm1d(num_kernel) self.relu = nn.ReLU() def forward(self, normals, neighbor_index): b, _, n = normals.size() center = normals.unsqueeze(2).expand(-1, -1, self.num_kernel, -1).unsqueeze(4) neighbor = torch.gather(normals.unsqueeze(3).expand(-1, -1, -1, 3), 2, neighbor_index.unsqueeze(1).expand(-1, 3, -1, -1)) neighbor = neighbor.unsqueeze(2).expand(-1, -1, self.num_kernel, -1, -1) fea = torch.cat([center, neighbor], 4) fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 4) weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta), torch.sin(self.weight_alpha) * torch.sin(self.weight_beta), torch.cos(self.weight_alpha)], 0) weight = weight.unsqueeze(0).expand(b, -1, -1, -1) weight = weight.unsqueeze(3).expand(-1, -1, -1, n, -1) weight = weight.unsqueeze(4).expand(-1, -1, -1, -1, 4, -1) dist = torch.sum((fea - weight)**2, 1) fea = torch.sum(torch.sum(np.e**(dist / (-2 * self.sigma**2)), 4), 3) / 16 return self.relu(self.bn(fea))
修改后的代码如下:
```python
class FaceKernelCorrelation(nn.Module):
def __init__(self, num_kernel=64, sigma=0.2):
super(FaceKernelCorrelation, self).__init__()
self.num_kernel = num_kernel
self.sigma = sigma
self.weight_alpha = Parameter(torch.rand(1, num_kernel, 6) * np.pi)
self.weight_beta = Parameter(torch.rand(1, num_kernel, 6) * 2 * np.pi)
self.bn = nn.BatchNorm1d(num_kernel)
self.relu = nn.ReLU()
def forward(self, vertices, neighbor_index):
b, _, n = vertices.size()
center = vertices.unsqueeze(2).expand(-1, -1, self.num_kernel, -1).unsqueeze(4)
neighbor = torch.gather(vertices.unsqueeze(3).expand(-1, -1, -1, 3),
2, neighbor_index.unsqueeze(1).expand(-1, 3, -1, -1))
neighbor = neighbor.unsqueeze(2).expand(-1, -1, self.num_kernel, -1, -1)
# 计算直线特征
line = neighbor - center
length = torch.sqrt(torch.sum(line**2, dim=-1, keepdim=True))
direction = line / (length + 1e-8)
fea = torch.cat([center, direction, length], dim=4)
fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 6)
weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta),
torch.sin(self.weight_alpha) * torch.sin(self.weight_beta),
torch.cos(self.weight_alpha)], 0)
weight = weight.unsqueeze(0).expand(b, -1, -1, -1)
weight = weight.unsqueeze(3).expand(-1, -1, -1, n, -1)
weight = weight.unsqueeze(4).expand(-1, -1, -1, -1, 6, -1)
dist = torch.sum((fea - weight)**2, 1)
fea = torch.sum(torch.sum(np.e**(dist / (-2 * self.sigma**2)), 4), 3) / 16
return self.relu(self.bn(fea))
```
对比原有的代码,主要修改的地方如下:
1. 修改了 weight_alpha 和 weight_beta 的形状,将其从 4 改为 6,以便存储直线特征;
2. 在 forward 函数中,首先计算出所有点的邻居点,然后根据邻居点和中心点计算出直线特征(即方向向量和长度),并将其拼接在一起;
3. 将拼接后的直线特征与权重相减后,进行距离计算和高斯加权求和。
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