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""" PyTorch Involution Layer
Official impl: https://github.com/d-li14/involution/blob/main/cls/mmcls/models/utils/involution_naive.py
Paper: `Involution: Inverting the Inherence of Convolution for Visual Recognition` - https://arxiv.org/abs/2103.06255
"""
import torch.nn as nn
from .conv_bn_act import ConvBnAct
from .create_conv2d import create_conv2d
class Involution(nn.Module):
def __init__(
self,
channels,
kernel_size=3,
stride=1,
group_size=16,
rd_ratio=4,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(Involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.channels = channels
self.group_size = group_size
self.groups = self.channels // self.group_size
self.conv1 = ConvBnAct(
in_channels=channels,
out_channels=channels // rd_ratio,
kernel_size=1,
norm_layer=norm_layer,
act_layer=act_layer)
self.conv2 = self.conv = create_conv2d(
in_channels=channels // rd_ratio,
out_channels=kernel_size**2 * self.groups,
kernel_size=1,
stride=1)
self.avgpool = nn.AvgPool2d(stride, stride) if stride == 2 else nn.Identity()
self.unfold = nn.Unfold(kernel_size, 1, (kernel_size-1)//2, stride)
def forward(self, x):
weight = self.conv2(self.conv1(self.avgpool(x)))
B, C, H, W = weight.shape
KK = int(self.kernel_size ** 2)
weight = weight.view(B, self.groups, KK, H, W).unsqueeze(2)
out = self.unfold(x).view(B, self.groups, self.group_size, KK, H, W)
out = (weight * out).sum(dim=3).view(B, self.channels, H, W)
return out
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