Split-Attention Module in PyTorch

Just in case someone want to use the Split-Attention Module. The module is provided here:

import torch
import torch.nn as nn
from torch.nn import functional as F

class rSoftMax(nn.Module):
    def __init__(self, radix, cardinality):
        super().__init__()
        assert radix > 0
        self.radix = radix
        self.cardinality = cardinality

    def forward(self, x):
        batch = x.size(0)
        if self.radix > 1:
            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
            x = F.softmax(x, dim=1)
            x = x.reshape(batch, -1)
        else:
            x = torch.sigmoid(x)
        return x

class Splat(nn.Module):
    def __init__(self, channels, radix, cardinality, reduction_factor=4):
        super(Splat, self).__init__()
        self.radix = radix
        self.cardinality = cardinality
        self.channels = channels
        inter_channels = max(channels*radix//reduction_factor, 32)
        self.fc1 = nn.Conv2d(channels//radix, inter_channels, 1, groups=cardinality)
        self.bn1 = nn.BatchNorm2d(inter_channels)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(inter_channels, channels*radix, 1, groups=cardinality)
        self.rsoftmax = rSoftMax(radix, cardinality)

    def forward(self, x):
        batch, rchannel = x.shape[:2]
        if self.radix > 1:
            splited = torch.split(x, rchannel//self.radix, dim=1)
            gap = sum(splited) 
        else:
            gap = x
        gap = F.adaptive_avg_pool2d(gap, 1)
        gap = self.fc1(gap)

        gap = self.bn1(gap)
        gap = self.relu(gap)

        atten = self.fc2(gap)
        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)

        if self.radix > 1:
            attens = torch.split(atten, rchannel//self.radix, dim=1)
            out = sum([att*split for (att, split) in zip(attens, splited)])
        else:
            out = atten * x
        return out.contiguous()

[zhanghang1989]

@zhanghang1989 thanks for sharing the split attention module implementation, can we integrate this with FPN module instead of Resnet backbone ??

Yes, that should work.

[Hi-Jingzhi]

Hi! some questions about the parameter channels of the layer fn1 in class Splat, as follows. The one is “what’s mean of the parameter channels”, the other one is “the input channels of the layer fn1 is channels//radix, or channels?”.