You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
122 lines
5.2 KiB
122 lines
5.2 KiB
""" Selective Kernel Convolution/Attention
|
|
|
|
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
|
|
|
|
Hacked together by Ross Wightman
|
|
"""
|
|
|
|
import torch
|
|
from torch import nn as nn
|
|
|
|
from .conv_bn_act import ConvBnAct
|
|
|
|
|
|
def _kernel_valid(k):
|
|
if isinstance(k, (list, tuple)):
|
|
for ki in k:
|
|
return _kernel_valid(ki)
|
|
assert k >= 3 and k % 2
|
|
|
|
|
|
class SelectiveKernelAttn(nn.Module):
|
|
def __init__(self, channels, num_paths=2, attn_channels=32,
|
|
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
|
|
""" Selective Kernel Attention Module
|
|
|
|
Selective Kernel attention mechanism factored out into its own module.
|
|
|
|
"""
|
|
super(SelectiveKernelAttn, self).__init__()
|
|
self.num_paths = num_paths
|
|
self.pool = nn.AdaptiveAvgPool2d(1)
|
|
self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=False)
|
|
self.bn = norm_layer(attn_channels)
|
|
self.act = act_layer(inplace=True)
|
|
self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
|
|
|
|
def forward(self, x):
|
|
assert x.shape[1] == self.num_paths
|
|
x = torch.sum(x, dim=1)
|
|
x = self.pool(x)
|
|
x = self.fc_reduce(x)
|
|
x = self.bn(x)
|
|
x = self.act(x)
|
|
x = self.fc_select(x)
|
|
B, C, H, W = x.shape
|
|
x = x.view(B, self.num_paths, C // self.num_paths, H, W)
|
|
x = torch.softmax(x, dim=1)
|
|
return x
|
|
|
|
|
|
class SelectiveKernelConv(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels, kernel_size=None, stride=1, dilation=1, groups=1,
|
|
attn_reduction=16, min_attn_channels=32, keep_3x3=True, split_input=False,
|
|
drop_block=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None):
|
|
""" Selective Kernel Convolution Module
|
|
|
|
As described in Selective Kernel Networks (https://arxiv.org/abs/1903.06586) with some modifications.
|
|
|
|
Largest change is the input split, which divides the input channels across each convolution path, this can
|
|
be viewed as a grouping of sorts, but the output channel counts expand to the module level value. This keeps
|
|
the parameter count from ballooning when the convolutions themselves don't have groups, but still provides
|
|
a noteworthy increase in performance over similar param count models without this attention layer. -Ross W
|
|
|
|
Args:
|
|
in_channels (int): module input (feature) channel count
|
|
out_channels (int): module output (feature) channel count
|
|
kernel_size (int, list): kernel size for each convolution branch
|
|
stride (int): stride for convolutions
|
|
dilation (int): dilation for module as a whole, impacts dilation of each branch
|
|
groups (int): number of groups for each branch
|
|
attn_reduction (int, float): reduction factor for attention features
|
|
min_attn_channels (int): minimum attention feature channels
|
|
keep_3x3 (bool): keep all branch convolution kernels as 3x3, changing larger kernels for dilations
|
|
split_input (bool): split input channels evenly across each convolution branch, keeps param count lower,
|
|
can be viewed as grouping by path, output expands to module out_channels count
|
|
drop_block (nn.Module): drop block module
|
|
act_layer (nn.Module): activation layer to use
|
|
norm_layer (nn.Module): batchnorm/norm layer to use
|
|
"""
|
|
super(SelectiveKernelConv, self).__init__()
|
|
kernel_size = kernel_size or [3, 5] # default to one 3x3 and one 5x5 branch. 5x5 -> 3x3 + dilation
|
|
_kernel_valid(kernel_size)
|
|
if not isinstance(kernel_size, list):
|
|
kernel_size = [kernel_size] * 2
|
|
if keep_3x3:
|
|
dilation = [dilation * (k - 1) // 2 for k in kernel_size]
|
|
kernel_size = [3] * len(kernel_size)
|
|
else:
|
|
dilation = [dilation] * len(kernel_size)
|
|
self.num_paths = len(kernel_size)
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.split_input = split_input
|
|
if self.split_input:
|
|
assert in_channels % self.num_paths == 0
|
|
in_channels = in_channels // self.num_paths
|
|
groups = min(out_channels, groups)
|
|
|
|
conv_kwargs = dict(
|
|
stride=stride, groups=groups, drop_block=drop_block, act_layer=act_layer, norm_layer=norm_layer,
|
|
aa_layer=aa_layer)
|
|
self.paths = nn.ModuleList([
|
|
ConvBnAct(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
|
|
for k, d in zip(kernel_size, dilation)])
|
|
|
|
attn_channels = max(int(out_channels / attn_reduction), min_attn_channels)
|
|
self.attn = SelectiveKernelAttn(out_channels, self.num_paths, attn_channels)
|
|
self.drop_block = drop_block
|
|
|
|
def forward(self, x):
|
|
if self.split_input:
|
|
x_split = torch.split(x, self.in_channels // self.num_paths, 1)
|
|
x_paths = [op(x_split[i]) for i, op in enumerate(self.paths)]
|
|
else:
|
|
x_paths = [op(x) for op in self.paths]
|
|
x = torch.stack(x_paths, dim=1)
|
|
x_attn = self.attn(x)
|
|
x = x * x_attn
|
|
x = torch.sum(x, dim=1)
|
|
return x
|