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from typing import Union, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
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 F.leaky_relu(z, inplace=True)
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