Gaussian filter in PyTorch

Question

I am looking for a way to apply a Gaussian filter to an image (tensor) only using PyTorch functions. Using numpy, the equivalent code is

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

# Define 2D Gaussian kernel
def gkern(kernlen=256, std=128):
    """Returns a 2D Gaussian kernel array."""
    gkern1d = signal.gaussian(kernlen, std=std).reshape(kernlen, 1)
    gkern2d = np.outer(gkern1d, gkern1d)
    return gkern2d

# Generate random matrix and multiply the kernel by it
A = np.random.rand(256*256).reshape([256,256])

# Test plot
plt.figure()
plt.imshow(A*gkern(256, std=32))
plt.show()

The closest suggestion I found is based on this post:

import torch.nn as nn

conv = nn.Conv2d(in_channels = 1, out_channels = 1, kernel_size=264, bias=False)
with torch.no_grad():
    conv.weight = gaussian_weights

But it gives me the error NameError: name 'gaussian_weights' is not defined. How can I make it work?

user_4820605 · Answer

Yupp I also had the same idea. So now the question becomes: is there a way to define a Gaussian kernel (or a 2D Gaussian) without using Numpy and/or explicitly specifying the weights?

Yes, it is pretty easy. Just have a look to the function documentation of signal.gaussian. There is a link to the source code. So what the method is doing is the following:

def gaussian(M, std, sym=True):
    if M < 1:
        return np.array([])
    if M == 1:
        return np.ones(1, 'd')
    odd = M % 2
    if not sym and not odd:
        M = M + 1
    n = np.arange(0, M) - (M - 1.0) / 2.0
    sig2 = 2 * std * std
    w = np.exp(-n ** 2 / sig2)
    if not sym and not odd:
        w = w[:-1]
    return w

And you are lucky because is the straightforward to convert in Pytorch, (almost) just replacing np by torch and you are done!

Also, note that np.outer equivalent in torch is ger.

user_15760624 · Answer

There is a Pytorch class to apply Gaussian Blur to your image:

torchvision.transforms.GaussianBlur(kernel_size, sigma=(0.1, 2.0))

Check the documentation for more info

user_10184783 · Answer

Used all the codes from above and updated with Pytorch revision of torch.outer

import torch
def gaussian_fn(M, std):
    n = torch.arange(0, M) - (M - 1.0) / 2.0
    sig2 = 2 * std * std
    w = torch.exp(-n ** 2 / sig2)
    return w

def gkern(kernlen=256, std=128):
    """Returns a 2D Gaussian kernel array."""
    gkern1d = gaussian_fn(kernlen, std=std) 
    gkern2d = torch.outer(gkern1d, gkern1d)
    return gkern2d

# Generate random matrix and multiply the kernel by it
A = np.random.rand(256*256).reshape([256,256])
A = torch.from_numpy(A)
guassian_filter = gkern(256, std=32)

ax=[]
f = plt.figure(figsize=(12,5))
ax.append(f.add_subplot(131))
ax.append(f.add_subplot(132))
ax.append(f.add_subplot(133))
ax[0].imshow(A, cmap='gray')
ax[1].imshow(guassian_filter, cmap='gray')
ax[2].imshow(A*guassian, cmap='gray')
plt.show()

Gaussian filter in PyTorch

3 Answers