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61 lines
2.2 KiB
61 lines
2.2 KiB
import torch
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import torch.nn.parallel
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import torch.nn as nn
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import torch.nn.functional as F
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class AntiAliasDownsampleLayer(nn.Module):
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def __init__(self, channels: int = 0, filt_size: int = 3, stride: int = 2, no_jit: bool = False):
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super(AntiAliasDownsampleLayer, self).__init__()
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if no_jit:
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self.op = Downsample(channels, filt_size, stride)
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else:
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self.op = DownsampleJIT(channels, filt_size, stride)
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# FIXME I should probably override _apply and clear DownsampleJIT filter cache for .cuda(), .half(), etc calls
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def forward(self, x):
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return self.op(x)
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@torch.jit.script
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class DownsampleJIT(object):
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def __init__(self, channels: int = 0, filt_size: int = 3, stride: int = 2):
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self.channels = channels
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self.stride = stride
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self.filt_size = filt_size
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assert self.filt_size == 3
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assert stride == 2
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self.filt = {} # lazy init by device for DataParallel compat
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def _create_filter(self, like: torch.Tensor):
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filt = torch.tensor([1., 2., 1.], dtype=like.dtype, device=like.device)
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filt = filt[:, None] * filt[None, :]
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filt = filt / torch.sum(filt)
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return filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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def __call__(self, input: torch.Tensor):
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input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
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filt = self.filt.get(str(input.device), self._create_filter(input))
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return F.conv2d(input_pad, filt, stride=2, padding=0, groups=input.shape[1])
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class Downsample(nn.Module):
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def __init__(self, channels=None, filt_size=3, stride=2):
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super(Downsample, self).__init__()
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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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assert self.filt_size == 3
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filt = torch.tensor([1., 2., 1.])
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filt = filt[:, None] * filt[None, :]
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filt = filt / torch.sum(filt)
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# self.filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1))
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self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
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def forward(self, input):
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input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
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return F.conv2d(input_pad, self.filt, stride=self.stride, padding=0, groups=input.shape[1])
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