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from typing import Union, Tuple
import torch
import torch.nn as nn
class BasicConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
**kwargs
):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
bias=False, **kwargs)
def forward(self, x):
return self.conv(x)
class VGGConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]] = 3,
padding: int = 1,
**kwargs
):
super(VGGConv2d, self).__init__()
self.conv = BasicConv2d(in_channels, out_channels, kernel_size,
padding=padding, **kwargs)
def forward(self, x):
return self.conv(x)
class BasicConvTranspose2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
**kwargs
):
super(BasicConvTranspose2d, self).__init__()
self.trans_conv = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, bias=False, **kwargs)
def forward(self, x):
return self.trans_conv(x)
class DCGANConvTranspose2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]] = 4,
stride: int = 2,
padding: int = 1,
**kwargs
):
super(DCGANConvTranspose2d).__init__()
self.trans_conv = BasicConvTranspose2d(in_channels, out_channels,
kernel_size, stride=stride,
padding=padding, **kwargs)
def forward(self, x):
return self.trans_conv(x)
class FocalConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
halving: int,
**kwargs
):
super(FocalConv2d, self).__init__()
self.halving = halving
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
bias=False, **kwargs)
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 z
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)
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