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pytorch-image-models/timm/models/layers/blurpool.py

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'''independent attempt to implement
MaxBlurPool2d in a more general fashion(separate maxpooling from BlurPool)
which was again inspired by
Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
blur_filter_size (int): filter size for blurring. currently supports either 3 or 5 (most common)
defaults to 3.
stride (int): downsampling filter stride
Shape:
Returns:
torch.Tensor: the transformed tensor.
Examples:
"""
def __init__(self, channels=None, blur_filter_size=3, stride=2) -> None:
super(BlurPool2d, self).__init__()
assert blur_filter_size in [3, 5]
self.channels = channels
self.blur_filter_size = blur_filter_size
self.stride = stride
if blur_filter_size == 3:
pad_size = [1] * 4
blur_matrix = torch.Tensor([[1., 2., 1]]) / 4 # binomial kernel b2
else:
pad_size = [2] * 4
blur_matrix = torch.Tensor([[1., 4., 6., 4., 1.]]) / 16 # binomial filter kernel b4
self.padding = nn.ReflectionPad2d(pad_size)
blur_filter = blur_matrix * blur_matrix.T
self.register_buffer('blur_filter', blur_filter[None, None, :, :].repeat((self.channels, 1, 1, 1)))
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: # type: ignore
if not torch.is_tensor(input_tensor):
raise TypeError("Input input type is not a torch.Tensor. Got {}"
.format(type(input_tensor)))
if not len(input_tensor.shape) == 4:
raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
.format(input_tensor.shape))
# apply blur_filter on input
return F.conv2d(self.padding(input_tensor), self.blur_filter, stride=self.stride, groups=input_tensor.shape[1])
######################
# functional interface
######################
'''def blur_pool2d() -> torch.Tensor:
r"""Creates a module that computes pools and blurs and downsample a given
feature map.
See :class:`~kornia.contrib.MaxBlurPool2d` for details.
"""
return BlurPool2d(kernel_size, ceil_mode)(input)'''