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43 lines
1.6 KiB
43 lines
1.6 KiB
"""
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BlurPool layer inspired by
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- Kornia's Max_BlurPool2d
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- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
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Hacked together by Chris Ha and Ross Wightman
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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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import numpy as np
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from .padding import get_padding
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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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filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
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stride (int): downsampling filter stride
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Returns:
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torch.Tensor: the transformed tensor.
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"""
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def __init__(self, channels, filt_size=3, stride=2) -> None:
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super(BlurPool2d, self).__init__()
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assert filt_size > 1
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self.channels = channels
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self.filt_size = filt_size
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self.stride = stride
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self.padding = [get_padding(filt_size, stride, dilation=1)] * 4
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coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32))
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blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1)
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self.register_buffer('filt', blur_filter, persistent=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = F.pad(x, self.padding, 'reflect')
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return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
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