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c7f3ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import numpy as np
import torch
import torch.nn as nn
def get_filter_2d(kernel, kernel_size, channels, no_grad=True):
# Reshape to 2d depthwise convolutional weight
kernel = kernel.view(1, 1, kernel_size, kernel_size)
kernel = kernel.repeat(channels, 1, 1, 1)
filter = nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=kernel_size, groups=channels,
bias=False, padding=kernel_size // 2)
filter.weight.data = kernel
if no_grad:
filter.weight.requires_grad = False
return filter
def get_filter_1d(kernel, kernel_size, channels, no_grad=True):
kernel = kernel.view(1, 1, kernel_size)
kernel = kernel.repeat(channels, 1, 1)
filter = nn.Conv1d(in_channels=channels, out_channels=channels, kernel_size=kernel_size, groups=channels,
bias=False, padding=kernel_size // 2)
filter.weight.data = kernel
if no_grad:
filter.weight.requires_grad = False
return filter
def get_gaussian_kernel_2d(kernel_size, sigma):
# Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
x_coord = torch.arange(kernel_size)
x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
y_grid = x_grid.t()
xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
mean = (kernel_size - 1) / 2.
variance = sigma ** 2.
# Calculate the 2-dimensional gaussian kernel which is
# the product of two gaussian distributions for two different
# variables (in this case called x and y)
gaussian_kernel = (1. / (2. * np.pi * variance)) * torch.exp(
-torch.sum((xy_grid - mean) ** 2., dim=-1) / (2 * variance))
# Make sure sum of values in gaussian kernel equals 1.
gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
return gaussian_kernel
def get_gaussian_kernel_1d(kernel_size, sigma):
x_grid = torch.arange(kernel_size)
mean = (kernel_size - 1) / 2.
variance = sigma ** 2.
gaussian_kernel = (1. / ((2. * np.pi) ** 0.5 * sigma)) * torch.exp(-(x_grid - mean) ** 2. / (2 * variance))
gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
return gaussian_kernel
def get_hann_kernel_1d(kernel_size, periodic=False):
# periodic=False gives symmetric kernel, otherwise equivalent to hann(kernel_size + 1)
return torch.hann_window(kernel_size, periodic)
def get_triangle_kernel_1d(kernel_size):
kernel = torch.zeros(kernel_size)
for idx in range(kernel_size):
kernel[idx] = 1 - abs((idx - (kernel_size - 1) / 2) / ((kernel_size - 1) / 2))
return kernel
def add_gaussian_noise(tensor, mean=0, std=1):
noise = torch.randn(tensor.size()) * std + mean
noisy_tensor = tensor + noise
return noisy_tensor
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