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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from .conv import PaddedConv3D
from .ops import video_to_image, cast_tuple
class Upsample(nn.Module):
def __init__(self, in_channels, out_channels, with_conv=True):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=1, padding=1
)
@video_to_image
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, out_channels, with_conv=True):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=2, padding=0
)
@video_to_image
def forward(self, x):
if self.with_conv:
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
# pylint: disable-next=not-callable
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
class SpatialUpsample2x(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size: Union[int, Tuple[int]] = (3, 3),
stride: Union[int, Tuple[int]] = (1, 1),
unup=False,
is_causal=True,
):
super().__init__()
self.chan_in = chan_in
self.chan_out = chan_out
self.kernel_size = kernel_size
self.unup = unup
self.conv = PaddedConv3D(
self.chan_in,
self.chan_out,
(1,) + self.kernel_size,
stride=(1,) + stride,
padding=1,
is_causal=is_causal,
)
def forward(self, x):
if not self.unup:
t = x.shape[2]
x = rearrange(x, "b c t h w -> b (c t) h w")
x = F.interpolate(x, scale_factor=(2, 2), mode="nearest")
x = rearrange(x, "b (c t) h w -> b c t h w", t=t)
x = self.conv(x)
return x
class SpatialDownsample2x(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size: Union[int, Tuple[int]] = (3, 3),
stride: Union[int, Tuple[int]] = (2, 2),
is_causal=True,
**kwargs,
):
super().__init__()
kernel_size = cast_tuple(kernel_size, 2)
stride = cast_tuple(stride, 2)
self.chan_in = chan_in
self.chan_out = chan_out
self.kernel_size = kernel_size
self.conv = PaddedConv3D(
self.chan_in,
self.chan_out,
(1,) + self.kernel_size,
stride=(1,) + stride,
padding=0,
is_causal=is_causal,
)
def forward(self, x):
pad = (0, 1, 0, 1, 0, 0)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
return x
class Spatial2xTime2x3DUpsample(nn.Module):
def __init__(self, in_channels, out_channels, is_causal=True, is_first=False):
super().__init__()
self.conv = PaddedConv3D(
in_channels, out_channels, kernel_size=3, padding=1, is_causal=is_causal
)
self.is_causal = is_causal
if not is_causal and is_first:
self.temporal_up_conv = nn.ConvTranspose3d(
in_channels,
in_channels,
kernel_size=(2, 1, 1),
stride=1,
padding=0,
)
def forward(self, x):
if self.is_causal:
if x.size(2) > 1:
x, x_ = x[:, :, :1], x[:, :, 1:]
x_ = F.interpolate(x_, scale_factor=(2, 2, 2), mode="trilinear")
x = F.interpolate(x, scale_factor=(1, 2, 2), mode="trilinear")
x = torch.concat([x, x_], dim=2)
else:
x = F.interpolate(x, scale_factor=(1, 2, 2), mode="trilinear")
else:
if x.size(2) > 1:
x = F.interpolate(x, scale_factor=(2, 2, 2), mode="trilinear")
else:
# if temporal length is 1,
# we upsample temporally using up conv instead of interpolation because interpolation leads to duplicate frames
x = self.temporal_up_conv(x)
x = F.interpolate(x, scale_factor=(1, 2, 2), mode="trilinear")
return self.conv(x)
class Spatial2xTime2x3DDownsample(nn.Module):
def __init__(self, in_channels, out_channels, is_causal=True):
super().__init__()
self.conv = PaddedConv3D(
in_channels,
out_channels,
kernel_size=3,
padding=0,
stride=2,
is_causal=is_causal,
)
def forward(self, x):
pad = (0, 1, 0, 1, 0, 0)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
return x
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