给出用pytorch实现canny边缘检测算子，并对图像进行边缘检测的python代码

好的，以下是用PyTorch实现Canny边缘检测算法的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Canny(nn.Module):
    def __init__(self, sigma=1, kernel_size=5, weak_pixel=75, strong_pixel=255, low_threshold=0.05, high_threshold=0.15):
        super(Canny, self).__init__()
        self.sigma = sigma
        self.kernel_size = kernel_size
        self.weak_pixel = weak_pixel
        self.strong_pixel = strong_pixel
        self.low_threshold = low_threshold
        self.high_threshold = high_threshold
        self.padding = int(kernel_size/2)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        self.conv1 = nn.Conv2d(1, 1, kernel_size, stride=1, padding=self.padding, bias=False)
        self.conv2 = nn.Conv2d(1, 1, kernel_size, stride=1, padding=self.padding, bias=False)
        
        # Gaussian
        self.conv1.weight.data = self.gaussian_kernel(self.kernel_size, self.sigma)
        self.conv1.weight.requires_grad = False
        
        # Sobel operators
        self.conv2.weight.data = torch.tensor([[[[-1, 0, 1],
                                                 [-2, 0, 2],
                                                 [-1, 0, 1]]]], dtype=torch.float32)
        self.conv2.weight.requires_grad = False
        
    def gaussian_kernel(self, size, sigma=1):
        kernel = torch.zeros([size, size])
        center = size//2
        
        for i in range(size):
            for j in range(size):
                x = i - center
                y = j - center
                
                kernel[i,j] = torch.exp(-(x**2 + y**2)/(2*sigma**2))
        kernel = kernel / torch.sum(kernel)
        kernel = kernel.view(1, 1, size, size)
        return kernel.to(self.device)
    
    def non_maximum_suppression(self, img, D):
        M, N = img.shape
        Z = torch.zeros(M,N, dtype=torch.float32).to(self.device)
        angle = D * 180. / np.pi
        angle[angle < 0] += 180
        
        for i in range(1,M-1):
            for j in range(1,N-1):
                q = 255
                r = 255
                
                #angle 0
                if (0 <= angle[i,j] < 22.5) or (157.5 <= angle[i,j] <= 180):
                    q = img[i, j+1]
                    r = img[i, j-1]
                #angle 45
                elif (22.5 <= angle[i,j] < 67.5):
                    q = img[i+1, j-1]
                    r = img[i-1, j+1]
                #angle 90
                elif (67.5 <= angle[i,j] < 112.5):
                    q = img[i+1, j]
                    r = img[i-1, j]
                #angle 135
                elif (112.5 <= angle[i,j] < 157.5):
                    q = img[i-1, j-1]
                    r = img[i+1, j+1]
                    
                if (img[i,j] >= q) and (img[i,j] >= r):
                    Z[i,j] = img[i,j]
                else:
                    Z[i,j] = 0
                    
        return Z
    
    def hysteresis(self, img, low_threshold=0.05, high_threshold=0.15):
        high_threshold = img.max() * high_threshold;
        low_threshold = high_threshold * low_threshold;
        
        M, N = img.shape
        res = torch.zeros(M,N, dtype=torch.float32).to(self.device)
        
        weak = torch.tensor(self.weak_pixel, dtype=torch.float32).to(self.device)
        strong = torch.tensor(self.strong_pixel, dtype=torch.float32).to(self.device)
        
        strong_i, strong_j = torch.where(img >= high_threshold)
        weak_i, weak_j = torch.where((img <= high_threshold) & (img >= low_threshold))
        
        res[strong_i, strong_j] = strong
        res[weak_i, weak_j] = weak
        
        # 8-connected component
        edge_i, edge_j = torch.where((img <= high_threshold) & (img >= low_threshold))
        for i, j in zip(edge_i, edge_j):
            if ((res[max(0, i-1):min(M, i+2), max(0, j-1):min(N, j+2)] == strong).any()):
                res[i,j] = strong
            else:
                res[i,j] = 0
                
        return res
    
    def forward(self, x):
        x = F.pad(x, (self.padding, self.padding, self.padding, self.padding), mode='reflect')
        x = self.conv1(x)
        x = self.conv2(x)
        Gx = x[:,:, :-1, :-1]
        Gy = x[:,:, :-1, 1:]
        gradient = torch.sqrt(torch.pow(Gx, 2) + torch.pow(Gy, 2))
        gradient = gradient / gradient.max()
        theta = torch.atan2(Gy, Gx)
        theta[theta<0] = np.pi + theta[theta<0]
        theta[theta>(np.pi*3/4)] -= np.pi
        
        non_maximum = self.non_maximum_suppression(gradient, theta)
        res = self.hysteresis(non_maximum, self.low_threshold, self.high_threshold)
        
        return res

这里使用了PyTorch实现的卷积操作和一些图像处理的函数，包括高斯核生成函数、非极大值抑制函数和滞后阈值函数等。需要注意的是，这里使用了PyTorch的GPU加速，如果没有GPU也可以将代码中的`to(self.device)`去掉。

以下是对一张图像进行Canny边缘检测的示例代码：

import cv2
import numpy as np

# load image
img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE).astype(np.float32)

# normalize image
img = img / 255.0

# apply canny edge detection
canny = Canny().to(device)
edges = canny(torch.from_numpy(img).unsqueeze(0).unsqueeze(0).to(device)).squeeze().cpu().numpy()

# show result
cv2.imshow('original', img)
cv2.imshow('canny', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

这里使用了OpenCV库读取图像，并将图像归一化为[0,1]范围内的浮点数。然后将图像转换为PyTorch张量，并将通道数和批次数扩展为1。最后调用Canny模型进行边缘检测，并将结果转换为NumPy数组并可视化。