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| import math | |
| import numbers | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| class GaussianSmoothing(nn.Module): | |
| """ | |
| Apply gaussian smoothing on a | |
| 1d, 2d or 3d tensor. Filtering is performed seperately for each channel | |
| in the input using a depthwise convolution. | |
| Arguments: | |
| channels (int, sequence): Number of channels of the input tensors. Output will | |
| have this number of channels as well. | |
| kernel_size (int, sequence): Size of the gaussian kernel. | |
| sigma (float, sequence): Standard deviation of the gaussian kernel. | |
| dim (int, optional): The number of dimensions of the data. | |
| Default value is 2 (spatial). | |
| """ | |
| def __init__(self, channels, kernel_size, sigma, dim=2): | |
| super(GaussianSmoothing, self).__init__() | |
| if isinstance(kernel_size, numbers.Number): | |
| kernel_size = [kernel_size] * dim | |
| if isinstance(sigma, numbers.Number): | |
| sigma = [sigma] * dim | |
| # The gaussian kernel is the product of the | |
| # gaussian function of each dimension. | |
| kernel = 1 | |
| meshgrids = torch.meshgrid( | |
| [ | |
| torch.arange(size, dtype=torch.float32) | |
| for size in kernel_size | |
| ] | |
| ) | |
| for size, std, mgrid in zip(kernel_size, sigma, meshgrids): | |
| mean = (size - 1) / 2 | |
| kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \ | |
| torch.exp(-((mgrid - mean) / (2 * std)) ** 2) | |
| # Make sure sum of values in gaussian kernel equals 1. | |
| kernel = kernel / torch.sum(kernel) | |
| # Reshape to depthwise convolutional weight | |
| kernel = kernel.view(1, 1, *kernel.size()) | |
| kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) | |
| self.register_buffer('weight', kernel) | |
| self.groups = channels | |
| if dim == 1: | |
| self.conv = F.conv1d | |
| elif dim == 2: | |
| self.conv = F.conv2d | |
| elif dim == 3: | |
| self.conv = F.conv3d | |
| else: | |
| raise RuntimeError( | |
| 'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim) | |
| ) | |
| def forward(self, input): | |
| """ | |
| Apply gaussian filter to input. | |
| Arguments: | |
| input (torch.Tensor): Input to apply gaussian filter on. | |
| Returns: | |
| filtered (torch.Tensor): Filtered output. | |
| """ | |
| return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups) | |
| class AverageSmoothing(nn.Module): | |
| """ | |
| Apply average smoothing on a | |
| 1d, 2d or 3d tensor. Filtering is performed seperately for each channel | |
| in the input using a depthwise convolution. | |
| Arguments: | |
| channels (int, sequence): Number of channels of the input tensors. Output will | |
| have this number of channels as well. | |
| kernel_size (int, sequence): Size of the average kernel. | |
| sigma (float, sequence): Standard deviation of the rage kernel. | |
| dim (int, optional): The number of dimensions of the data. | |
| Default value is 2 (spatial). | |
| """ | |
| def __init__(self, channels, kernel_size, dim=2): | |
| super(AverageSmoothing, self).__init__() | |
| # Make sure sum of values in gaussian kernel equals 1. | |
| kernel = torch.ones(size=(kernel_size, kernel_size)) / (kernel_size * kernel_size) | |
| # Reshape to depthwise convolutional weight | |
| kernel = kernel.view(1, 1, *kernel.size()) | |
| kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) | |
| self.register_buffer('weight', kernel) | |
| self.groups = channels | |
| if dim == 1: | |
| self.conv = F.conv1d | |
| elif dim == 2: | |
| self.conv = F.conv2d | |
| elif dim == 3: | |
| self.conv = F.conv3d | |
| else: | |
| raise RuntimeError( | |
| 'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim) | |
| ) | |
| def forward(self, input): | |
| """ | |
| Apply average filter to input. | |
| Arguments: | |
| input (torch.Tensor): Input to apply average filter on. | |
| Returns: | |
| filtered (torch.Tensor): Filtered output. | |
| """ | |
| return self.conv(input, weight=self.weight, groups=self.groups) | |