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100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
from typing import Callable
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from torch import Size, Tensor, device as Device, dtype as DType
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from torch.nn.functional import pad
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from imaginairy.vendored.refiners.fluxion.layers.basics import Identity
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from imaginairy.vendored.refiners.fluxion.layers.chain import Chain, Lambda, Parallel, SetContext, UseContext
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from imaginairy.vendored.refiners.fluxion.layers.conv import Conv2d
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from imaginairy.vendored.refiners.fluxion.layers.module import Module
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from imaginairy.vendored.refiners.fluxion.utils import interpolate
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class Downsample(Chain):
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def __init__(
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self,
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channels: int,
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scale_factor: int,
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padding: int = 0,
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register_shape: bool = True,
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device: Device | str | None = None,
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dtype: DType | None = None,
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):
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"""Downsamples the input by the given scale factor.
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If register_shape is True, the input shape is registered in the context. It will throw an error if the context
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sampling is not set or if the context does not contain a list.
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"""
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self.channels = channels
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self.in_channels = channels
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self.out_channels = channels
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self.scale_factor = scale_factor
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self.padding = padding
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super().__init__(
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Conv2d(
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in_channels=channels,
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out_channels=channels,
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kernel_size=3,
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stride=scale_factor,
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padding=padding,
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device=device,
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dtype=dtype,
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),
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)
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if padding == 0:
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zero_pad: Callable[[Tensor], Tensor] = lambda x: pad(x, (0, 1, 0, 1))
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self.insert(0, Lambda(zero_pad))
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if register_shape:
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self.insert(0, SetContext(context="sampling", key="shapes", callback=self.register_shape))
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def register_shape(self, shapes: list[Size], x: Tensor) -> None:
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shapes.append(x.shape[2:])
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class Interpolate(Module):
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def __init__(self) -> None:
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super().__init__()
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def forward(self, x: Tensor, shape: Size) -> Tensor:
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return interpolate(x, shape)
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class Upsample(Chain):
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def __init__(
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self,
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channels: int,
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upsample_factor: int | None = None,
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device: Device | str | None = None,
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dtype: DType | None = None,
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):
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"""Upsamples the input by the given scale factor.
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If upsample_factor is None, the input shape is taken from the context. It will throw an error if the context
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sampling is not set or if the context is empty (then you should use the dynamic version of Downsample).
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"""
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self.channels = channels
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self.upsample_factor = upsample_factor
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super().__init__(
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Parallel(
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Identity(),
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(
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Lambda(self._get_static_shape)
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if upsample_factor is not None
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else UseContext(context="sampling", key="shapes").compose(lambda x: x.pop())
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),
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),
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Interpolate(),
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Conv2d(
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in_channels=channels,
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out_channels=channels,
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kernel_size=3,
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padding=1,
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device=device,
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dtype=dtype,
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),
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)
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def _get_static_shape(self, x: Tensor) -> Size:
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assert self.upsample_factor is not None
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return Size([size * self.upsample_factor for size in x.shape[2:]])
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