用pytorch写一段四元数卷积
时间: 2023-07-01 13:24:00 浏览: 42
好的,下面是一个简单的四元数卷积的pytorch实现:
```python
import torch
class QuaternionConv(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
super(QuaternionConv, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.in_channels = in_channels
self.out_channels = out_channels
self.weight = torch.nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size, kernel_size, 4, 4))
self.bias = torch.nn.Parameter(torch.randn(out_channels))
def forward(self, x):
batch_size, in_channels, input_dim_z, input_dim_y, input_dim_x = x.shape
output_dim_z = int((input_dim_z + 2 * self.padding - self.kernel_size) / self.stride) + 1
output_dim_y = int((input_dim_y + 2 * self.padding - self.kernel_size) / self.stride) + 1
output_dim_x = int((input_dim_x + 2 * self.padding - self.kernel_size) / self.stride) + 1
output = torch.zeros(batch_size, self.out_channels, output_dim_z, output_dim_y, output_dim_x, 4)
for z in range(output_dim_z):
for y in range(output_dim_y):
for x in range(output_dim_x):
input_patch = x[:, :, z*self.stride:z*self.stride+self.kernel_size, y*self.stride:y*self.stride+self.kernel_size, x*self.stride:x*self.stride+self.kernel_size]
for k in range(self.kernel_size):
for l in range(self.kernel_size):
for m in range(self.kernel_size):
input_quaternion = input_patch[:, :, k, l, m]
input_quaternion = input_quaternion.view(batch_size, self.in_channels, 1, 1, 1, 4)
weight_quaternion = self.weight[:, :, k, l, m, :, :]
weight_quaternion = weight_quaternion.view(1, self.out_channels, 1, 1, 1, 4, 4)
output_quaternion = torch.matmul(input_quaternion, weight_quaternion)
output_quaternion = output_quaternion.view(batch_size, self.out_channels, 4)
output[:, :, z, y, x] += output_quaternion
output += self.bias.view(1, self.out_channels, 1, 1, 1).expand_as(output)
return output
```
这是一个3D四元数卷积,使用了pytorch的nn.Module进行实现。它具有4个维度的输入和输出,其中最后一个维度表示四元数。权重张量也具有4个维度,其中最后两个维度表示两个四元数的乘积。在实现中,我们对每个输入的3D块进行迭代,并使用matmul运算符计算每个位置上的输出四元数。这个实现可以根据需要进行调整,以适应不同的卷积核大小和步幅。
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