gabor3d卷积 pytorch

Gabor卷积是一种常见的在图像处理和计算机视觉中使用的卷积操作，它可以用于提取图像的纹理特征。

在Pytorch中实现Gabor卷积操作，可以使用torch.nn.functional中的函数，具体实现如下：

import torch
import torch.nn.functional as F

def gabor_kernel(size, sigma, theta, lambd, gamma=1.0, psi=0.0):
    """Generate 2D Gabor kernel.
    Args:
        size (int): Size of the kernel.
        sigma (float): Standard deviation of the gaussian envelope.
        theta (float): Orientation of the normal to the parallel stripes of a Gabor function.
        lambd (float): Wavelength of the sinusoidal factor.
        gamma (float, optional): Spatial aspect ratio. Default: 1.0.
        psi (float, optional): Phase offset. Default: 0.0.
    Returns:
        torch.Tensor: 2D Gabor kernel.
    """
    sigma_x = sigma
    sigma_y = sigma / gamma

    # Bounding box
    nstds = 3
    xmax = max(abs(nstds * sigma_x * torch.cos(theta)), abs(nstds * sigma_y * torch.sin(theta)))
    xmax = torch.ceil(torch.max(torch.tensor(1), xmax))
    ymax = max(abs(nstds * sigma_x * torch.sin(theta)), abs(nstds * sigma_y * torch.cos(theta)))
    ymax = torch.ceil(torch.max(torch.tensor(1), ymax))
    xmin = -xmax
    ymin = -ymax
    (x, y) = torch.meshgrid(torch.arange(xmin, xmax + 1), torch.arange(ymin, ymax + 1))

    # Rotation
    x_theta = x * torch.cos(theta) + y * torch.sin(theta)
    y_theta = -x * torch.sin(theta) + y * torch.cos(theta)

    gb = torch.exp(-0.5 * (x_theta ** 2 / sigma_x ** 2 + y_theta ** 2 / sigma_y ** 2)) * \
         torch.cos(2 * torch.pi / lambd * x_theta + psi)
    return gb

def gabor_convolution(input, kernel):
    """Apply Gabor convolution on input.
    Args:
        input (torch.Tensor): Input tensor of shape (batch_size, channels, height, width).
        kernel (torch.Tensor): Gabor kernel of shape (channels, kernel_size, kernel_size).
    Returns:
        torch.Tensor: Output tensor of shape (batch_size, channels, height, width).
    """
    batch_size, channels, height, width = input.size()
    kernel_size = kernel.size(-1)
    padding = kernel_size // 2

    # Pad input tensor
    input = F.pad(input, (padding, padding, padding, padding), mode='reflect')

    # Unfold input tensor
    input = input.unfold(2, kernel_size, 1).unfold(3, kernel_size, 1)
    input = input.permute(0, 2, 3, 1, 4, 5).contiguous()
    input = input.view(batch_size, -1, channels * kernel_size ** 2, height, width)

    # Flatten kernel tensor
    kernel = kernel.view(channels, -1)

    # Apply convolution
    output = F.conv3d(input, kernel[:, :, None, None, None], padding=(0, padding, padding))

    # Reshape output tensor
    output = output.view(batch_size, height, width, channels, kernel_size ** 2)
    output = output.permute(0, 3, 1, 2, 4).contiguous()
    output = output.view(batch_size, channels, height, width, kernel_size ** 2)
    output = torch.sum(output, dim=-1)

    return output

其中，gabor_kernel函数用于生成Gabor卷积核，gabor_convolution函数用于在输入图像上应用Gabor卷积。具体使用方法如下：

# Generate Gabor kernel
size = 11
sigma = 2.0
theta = 0.0
lambd = 5.0
kernel = gabor_kernel(size, sigma, theta, lambd)

# Apply Gabor convolution on input
input = torch.randn(1, 3, 224, 224)
output = gabor_convolution(input, kernel)

在上述例子中，使用了一个大小为11x11，标准差为2.0，方向为0度，周期为5.0的Gabor卷积核，并在一个随机生成的3通道图像上应用该卷积核。输出结果为一个3通道的特征图。