每日Attention学习10——Scale-Aware Modulation

模块出处

[ICCV 23] [link] [code] Scale-Aware Modulation Meet Transformer

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模块名称

Scale-Aware Modulation (SAM)

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模块作用

改进的自注意力

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模块结构

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模块代码

import torch
import torch.nn as nn
import torch.nn.functional as Fclass SAM(nn.Module):def __init__(self, dim, ca_num_heads=4, sa_num_heads=8, qkv_bias=False, qk_scale=None,attn_drop=0., proj_drop=0., expand_ratio=2):super().__init__()self.ca_attention = 1self.dim = dimself.ca_num_heads = ca_num_headsself.sa_num_heads = sa_num_headsassert dim % ca_num_heads == 0, f"dim {dim} should be divided by num_heads {ca_num_heads}."assert dim % sa_num_heads == 0, f"dim {dim} should be divided by num_heads {sa_num_heads}."self.act = nn.GELU()self.proj = nn.Linear(dim, dim)self.proj_drop = nn.Dropout(proj_drop)self.split_groups=self.dim//ca_num_headsself.v = nn.Linear(dim, dim, bias=qkv_bias)self.s = nn.Linear(dim, dim, bias=qkv_bias)for i in range(self.ca_num_heads):local_conv = nn.Conv2d(dim//self.ca_num_heads, dim//self.ca_num_heads, kernel_size=(3+i*2), padding=(1+i), stride=1, groups=dim//self.ca_num_heads)setattr(self, f"local_conv_{i + 1}", local_conv)self.proj0 = nn.Conv2d(dim, dim*expand_ratio, kernel_size=1, padding=0, stride=1, groups=self.split_groups)self.bn = nn.BatchNorm2d(dim*expand_ratio)self.proj1 = nn.Conv2d(dim*expand_ratio, dim, kernel_size=1, padding=0, stride=1)def forward(self, x, H, W):# InB, N, C = x.shapev = self.v(x)s = self.s(x).reshape(B, H, W, self.ca_num_heads, C//self.ca_num_heads).permute(3, 0, 4, 1, 2)# Multi-Head Mixed Convolutionfor i in range(self.ca_num_heads):local_conv = getattr(self, f"local_conv_{i + 1}")s_i= s[i]s_i = local_conv(s_i).reshape(B, self.split_groups, -1, H, W)if i == 0:s_out = s_ielse:s_out = torch.cat([s_out,s_i],2)s_out = s_out.reshape(B, C, H, W)# Scale-Aware Aggregation (SAA)s_out = self.proj1(self.act(self.bn(self.proj0(s_out))))self.modulator = s_outs_out = s_out.reshape(B, C, N).permute(0, 2, 1)x = s_out * v# Outx = self.proj(x)x = self.proj_drop(x)return xif __name__ == '__main__':x = torch.randn([3, 1024, 256])  # B, N, Csam = SAM(dim=256)out = sam(x, H=32, W=32)  # H=N*Wprint(out.shape)  # 3, 1024, 256

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原文表述

我们提出了一种新颖的卷积调制，称为尺度感知调制 (SAM)，它包含两个新模块：多头混合卷积 (MHMC) 和尺度感知聚合 (SAA)。MHMC 模块旨在增强感受野并同时捕获多尺度特征。SAA 模块旨在有效地聚合不同头部之间的特征，同时保持轻量级架构。