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from typing import Union
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]],
**kwargs
):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
class VGGConv2d(BasicConv2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]] = 3,
padding: int = 1,
**kwargs
):
super().__init__(in_channels, out_channels, kernel_size,
padding=padding, **kwargs)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return F.leaky_relu(x, 0.2, inplace=True)
class BasicConvTranspose2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]],
**kwargs
):
super().__init__()
self.trans_conv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.trans_conv(x)
x = self.bn(x)
return F.relu(x, inplace=True)
class DCGANConvTranspose2d(BasicConvTranspose2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]] = 4,
stride: int = 2,
padding: int = 1,
is_last_layer: bool = False,
**kwargs
):
super().__init__(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, **kwargs)
self.is_last_layer = is_last_layer
def forward(self, x):
if self.is_last_layer:
return self.trans_conv(x)
else:
return super().forward(x)
class BasicLinear(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
):
super().__init__()
self.fc = nn.Linear(in_features, out_features, bias=False)
self.bn = nn.BatchNorm1d(out_features)
def forward(self, x):
x = self.fc(x)
x = self.bn(x)
return x
class FocalConv2d(BasicConv2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]],
halving: int,
**kwargs
):
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
self.halving = halving
def forward(self, x):
h = x.size(2)
split_size = h // 2 ** self.halving
z = x.split(split_size, dim=2)
z = torch.cat([self.conv(_) for _ in z], dim=2)
return F.leaky_relu(z, inplace=True)
class BasicConv1d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int]],
**kwargs
):
super(BasicConv1d, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
bias=False, **kwargs)
def forward(self, x):
return self.conv(x)
class HorizontalPyramidPooling(BasicConv2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]] = 1,
use_avg_pool: bool = True,
use_max_pool: bool = True,
**kwargs
):
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
self.use_avg_pool = use_avg_pool
self.use_max_pool = use_max_pool
assert use_avg_pool or use_max_pool, 'Pooling layer(s) required.'
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
def forward(self, x):
if self.use_avg_pool and self.use_max_pool:
x = self.avg_pool(x) + self.max_pool(x)
elif self.use_avg_pool and not self.use_max_pool:
x = self.avg_pool(x)
elif not self.use_avg_pool and self.use_max_pool:
x = self.max_pool(x)
x = super().forward(x)
return x
|
