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Running
on
Zero
File size: 3,620 Bytes
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from torch import nn, Tensor
from torch.nn import functional as F
from typing import Union
from functools import partial
from .utils import _init_weights
from .refine import ConvRefine, LightConvRefine, LighterConvRefine
class ConvUpsample(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
scale_factor: int = 2,
norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d,
activation: nn.Module = nn.ReLU(inplace=True),
groups: int = 1,
) -> None:
super().__init__()
assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}"
self.scale_factor = scale_factor
self.upsample = partial(
F.interpolate,
scale_factor=scale_factor,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False,
antialias=False,
) if scale_factor > 1 else nn.Identity()
self.refine = ConvRefine(
in_channels=in_channels,
out_channels=out_channels,
norm_layer=norm_layer,
activation=activation,
groups=groups,
)
self.apply(_init_weights)
def forward(self, x: Tensor) -> Tensor:
x = self.upsample(x)
x = self.refine(x)
return x
class LightConvUpsample(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
scale_factor: int = 2,
norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d,
activation: nn.Module = nn.ReLU(inplace=True),
) -> None:
super().__init__()
assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}"
self.scale_factor = scale_factor
self.upsample = partial(
F.interpolate,
scale_factor=scale_factor,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False,
antialias=False,
) if scale_factor > 1 else nn.Identity()
self.refine = LightConvRefine(
in_channels=in_channels,
out_channels=out_channels,
norm_layer=norm_layer,
activation=activation,
)
self.apply(_init_weights)
def forward(self, x: Tensor) -> Tensor:
x = self.upsample(x)
x = self.refine(x)
return x
class LighterConvUpsample(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
scale_factor: int = 2,
norm_layer: Union[nn.BatchNorm2d, nn.GroupNorm, None] = nn.BatchNorm2d,
activation: nn.Module = nn.ReLU(inplace=True),
) -> None:
super().__init__()
assert scale_factor >= 1, f"Scale factor should be greater than or equal to 1, but got {scale_factor}"
self.scale_factor = scale_factor
self.upsample = partial(
F.interpolate,
scale_factor=scale_factor,
mode="bilinear",
align_corners=False,
recompute_scale_factor=False,
antialias=False,
) if scale_factor > 1 else nn.Identity()
self.refine = LighterConvRefine(
in_channels=in_channels,
out_channels=out_channels,
norm_layer=norm_layer,
activation=activation,
)
self.apply(_init_weights)
def forward(self, x: Tensor) -> Tensor:
x = self.upsample(x)
x = self.refine(x)
return x
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