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