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