pytorch代码实现注意力机制之GC

GC注意力机制

GC注意力机制来源于《GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond》一文当中，作者从Non-local Network的角度出发，发现对于不同位置点的attention map是几乎一致的，说明non-local中每个点计算attention map存在很大的计算浪费，从而提出了简化的NL，也就是SNL。
更进一步地，作者还研究了和SENet的关联，基于SENet和SNL，提出了统一的框架，并结合两者优点提出了GCNet，计算量相对较小，又能很好地融合全局信息。

论文地址：GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

代码实现：

import torch
from torch import nn as nn
import torch.nn.functional as F
from timm.models.layers.create_act import create_act_layer, get_act_layer
from timm.models.layers import make_divisible
from timm.models.layers.mlp import ConvMlp
from timm.models.layers.norm import LayerNorm2d


class GlobalContext(nn.Module):

    def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
                 rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
        super(GlobalContext, self).__init__()
        act_layer = get_act_layer(act_layer)

        self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None

        if rd_channels is None:
            rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
        if fuse_add:
            self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
        else:
            self.mlp_add = None
        if fuse_scale:
            self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
        else:
            self.mlp_scale = None

        self.gate = create_act_layer(gate_layer)
        self.init_last_zero = init_last_zero
        self.reset_parameters()

    def reset_parameters(self):
        if self.conv_attn is not None:
            nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
        if self.mlp_add is not None:
            nn.init.zeros_(self.mlp_add.fc2.weight)

    def forward(self, x):
        B, C, H, W = x.shape

        if self.conv_attn is not None:
            attn = self.conv_attn(x).reshape(B, 1, H * W)  # (B, 1, H * W)
            attn = F.softmax(attn, dim=-1).unsqueeze(3)  # (B, 1, H * W, 1)
            context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
            context = context.view(B, C, 1, 1)
        else:
            context = x.mean(dim=(2, 3), keepdim=True)

        if self.mlp_scale is not None:
            mlp_x = self.mlp_scale(context)
            x = x * self.gate(mlp_x)
        if self.mlp_add is not None:
            mlp_x = self.mlp_add(context)
            x = x + mlp_x

        return x

if __name__ == '__main__':
    input=torch.randn(50,512,7,7)
    gc = GlobalContext(512)
    output=gc(input)
    print(output.shape)
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