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56 lines
2.0 KiB
56 lines
2.0 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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blur_filter_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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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, blur_filter_size=3, stride=2) -> None:
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super(BlurPool2d, self).__init__()
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assert blur_filter_size > 1
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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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pad_size = [get_padding(blur_filter_size, stride, dilation=1)] * 4
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self.padding = nn.ReflectionPad2d(pad_size)
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blur_matrix = (np.poly1d((0.5, 0.5)) ** (blur_filter_size - 1)).coeffs
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blur_filter = torch.Tensor(blur_matrix[:, None] * blur_matrix[None, :])
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self.blur_filter = blur_filter[None, None, :, :]
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def _apply(self, fn):
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# override nn.Module _apply to prevent need for blur_filter to be registered as a buffer,
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# this keeps it out of state dict, but allows .cuda(), .type(), etc to work as expected
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super(BlurPool2d, self)._apply(fn)
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self.blur_filter = fn(self.blur_filter)
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def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: # type: ignore
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C = input_tensor.shape[1]
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return F.conv2d(
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self.padding(input_tensor),
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self.blur_filter.type(input_tensor.dtype).expand(C, -1, -1, -1), stride=self.stride, groups=C)
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