from collections.abc import Callable
from typing import Literal
import torch
from einops import rearrange
from torch import nn
from torch.nn.functional import relu
from .std import _WideResNet
__all__ = [
"mimo_wideresnet28x10",
]
class _MIMOWideResNet(_WideResNet):
def __init__(
self,
depth: int,
widen_factor: int,
in_channels: int,
num_classes: int,
num_estimators: int,
conv_bias: bool,
dropout_rate: float,
groups: int = 1,
style: Literal["imagenet", "cifar"] = "imagenet",
activation_fn: Callable = relu,
normalization_layer: type[nn.Module] = nn.BatchNorm2d,
) -> None:
super().__init__(
depth,
widen_factor=widen_factor,
in_channels=in_channels * num_estimators,
num_classes=num_classes * num_estimators,
conv_bias=conv_bias,
dropout_rate=dropout_rate,
groups=groups,
style=style,
activation_fn=activation_fn,
normalization_layer=normalization_layer,
)
self.num_estimators = num_estimators
def forward(self, x: torch.Tensor) -> torch.Tensor:
if not self.training:
x = x.repeat(self.num_estimators, 1, 1, 1)
out = rearrange(x, "(m b) c h w -> b (m c) h w", m=self.num_estimators)
return rearrange(super().forward(out), "b (m d) -> (m b) d", m=self.num_estimators)
[docs]
def mimo_wideresnet28x10(
in_channels: int,
num_classes: int,
num_estimators: int,
conv_bias: bool = True,
dropout_rate: float = 0.3,
groups: int = 1,
style: Literal["imagenet", "cifar"] = "imagenet",
activation_fn: Callable = relu,
normalization_layer: type[nn.Module] = nn.BatchNorm2d,
) -> _MIMOWideResNet:
return _MIMOWideResNet(
depth=28,
widen_factor=10,
in_channels=in_channels,
num_classes=num_classes,
num_estimators=num_estimators,
conv_bias=conv_bias,
dropout_rate=dropout_rate,
groups=groups,
style=style,
activation_fn=activation_fn,
normalization_layer=normalization_layer,
)
