8d99c2c4f8
- 引入新的 SCSA（空间和通道协同注意力）模块 - 用 SCSA 替换原有的 Pooling层作为 token_mixer - 删除了未使用的 SEBlock.py 文件- 移除了与当前项目无关的 TIAM(CV).py 文件
157 lines
7.3 KiB
Python
157 lines
7.3 KiB
Python
import typing as t
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import torch
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import torch.nn as nn
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from einops.einops import rearrange
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from mmengine.model import BaseModule
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__all__ = ['SCSA']
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"""SCSA：探索空间注意力和通道注意力之间的协同作用
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通道和空间注意力分别在为各种下游视觉任务提取特征依赖性和空间结构关系方面带来了显着的改进。
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虽然它们的结合更有利于发挥各自的优势，但通道和空间注意力之间的协同作用尚未得到充分探索，缺乏充分利用多语义信息的协同潜力来进行特征引导和缓解语义差异。
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我们的研究试图在多个语义层面揭示空间和通道注意力之间的协同关系，提出了一种新颖的空间和通道协同注意力模块（SCSA）。我们的SCSA由两部分组成：可共享的多语义空间注意力（SMSA）和渐进式通道自注意力（PCSA）。
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SMSA 集成多语义信息并利用渐进式压缩策略将判别性空间先验注入 PCSA 的通道自注意力中，有效地指导通道重新校准。此外，PCSA 中基于自注意力机制的稳健特征交互进一步缓解了 SMSA 中不同子特征之间多语义信息的差异。
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我们在七个基准数据集上进行了广泛的实验，包括 ImageNet-1K 上的分类、MSCOCO 2017 上的对象检测、ADE20K 上的分割以及其他四个复杂场景检测数据集。我们的结果表明，我们提出的 SCSA 不仅超越了当前最先进的注意力机制，
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而且在各种任务场景中表现出增强的泛化能力。
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"""
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class SCSA(BaseModule):
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def __init__(
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self,
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dim: int,
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head_num: int,
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window_size: int = 7,
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group_kernel_sizes: t.List[int] = [3, 5, 7, 9],
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qkv_bias: bool = False,
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fuse_bn: bool = False,
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norm_cfg: t.Dict = dict(type='BN'),
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act_cfg: t.Dict = dict(type='ReLU'),
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down_sample_mode: str = 'avg_pool',
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attn_drop_ratio: float = 0.,
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gate_layer: str = 'sigmoid',
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):
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super(SCSA, self).__init__()
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self.dim = dim
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self.head_num = head_num
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self.head_dim = dim // head_num
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self.scaler = self.head_dim ** -0.5
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self.group_kernel_sizes = group_kernel_sizes
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self.window_size = window_size
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self.qkv_bias = qkv_bias
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self.fuse_bn = fuse_bn
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self.down_sample_mode = down_sample_mode
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assert self.dim // 4, 'The dimension of input feature should be divisible by 4.'
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self.group_chans = group_chans = self.dim // 4
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self.local_dwc = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[0],
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padding=group_kernel_sizes[0] // 2, groups=group_chans)
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self.global_dwc_s = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[1],
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padding=group_kernel_sizes[1] // 2, groups=group_chans)
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self.global_dwc_m = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[2],
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padding=group_kernel_sizes[2] // 2, groups=group_chans)
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self.global_dwc_l = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[3],
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padding=group_kernel_sizes[3] // 2, groups=group_chans)
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self.sa_gate = nn.Softmax(dim=2) if gate_layer == 'softmax' else nn.Sigmoid()
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self.norm_h = nn.GroupNorm(4, dim)
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self.norm_w = nn.GroupNorm(4, dim)
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self.conv_d = nn.Identity()
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self.norm = nn.GroupNorm(1, dim)
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self.q = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
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self.k = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
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self.v = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
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self.attn_drop = nn.Dropout(attn_drop_ratio)
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self.ca_gate = nn.Softmax(dim=1) if gate_layer == 'softmax' else nn.Sigmoid()
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if window_size == -1:
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self.down_func = nn.AdaptiveAvgPool2d((1, 1))
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else:
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if down_sample_mode == 'recombination':
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self.down_func = self.space_to_chans
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# dimensionality reduction
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self.conv_d = nn.Conv2d(in_channels=dim * window_size ** 2, out_channels=dim, kernel_size=1, bias=False)
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elif down_sample_mode == 'avg_pool':
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self.down_func = nn.AvgPool2d(kernel_size=(window_size, window_size), stride=window_size)
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elif down_sample_mode == 'max_pool':
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self.down_func = nn.MaxPool2d(kernel_size=(window_size, window_size), stride=window_size)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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The dim of x is (B, C, H, W)
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"""
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# Spatial attention priority calculation
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b, c, h_, w_ = x.size()
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# (B, C, H)
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x_h = x.mean(dim=3)
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l_x_h, g_x_h_s, g_x_h_m, g_x_h_l = torch.split(x_h, self.group_chans, dim=1)
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# (B, C, W)
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x_w = x.mean(dim=2)
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l_x_w, g_x_w_s, g_x_w_m, g_x_w_l = torch.split(x_w, self.group_chans, dim=1)
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x_h_attn = self.sa_gate(self.norm_h(torch.cat((
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self.local_dwc(l_x_h),
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self.global_dwc_s(g_x_h_s),
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self.global_dwc_m(g_x_h_m),
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self.global_dwc_l(g_x_h_l),
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), dim=1)))
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x_h_attn = x_h_attn.view(b, c, h_, 1)
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x_w_attn = self.sa_gate(self.norm_w(torch.cat((
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self.local_dwc(l_x_w),
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self.global_dwc_s(g_x_w_s),
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self.global_dwc_m(g_x_w_m),
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self.global_dwc_l(g_x_w_l)
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), dim=1)))
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x_w_attn = x_w_attn.view(b, c, 1, w_)
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x = x * x_h_attn * x_w_attn
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# Channel attention based on self attention
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# reduce calculations
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y = self.down_func(x)
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y = self.conv_d(y)
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_, _, h_, w_ = y.size()
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# normalization first, then reshape -> (B, H, W, C) -> (B, C, H * W) and generate q, k and v
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y = self.norm(y)
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q = self.q(y)
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k = self.k(y)
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v = self.v(y)
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# (B, C, H, W) -> (B, head_num, head_dim, N)
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q = rearrange(q, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
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head_dim=int(self.head_dim))
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k = rearrange(k, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
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head_dim=int(self.head_dim))
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v = rearrange(v, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
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head_dim=int(self.head_dim))
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# (B, head_num, head_dim, head_dim)
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attn = q @ k.transpose(-2, -1) * self.scaler
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attn = self.attn_drop(attn.softmax(dim=-1))
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# (B, head_num, head_dim, N)
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attn = attn @ v
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# (B, C, H_, W_)
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attn = rearrange(attn, 'b head_num head_dim (h w) -> b (head_num head_dim) h w', h=int(h_), w=int(w_))
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# (B, C, 1, 1)
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attn = attn.mean((2, 3), keepdim=True)
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attn = self.ca_gate(attn)
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return attn * x
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if __name__ == '__main__':
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block = SCSA(
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dim=256,
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head_num=8,
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)
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input_tensor = torch.rand(1, 256, 32, 32)
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# 调用模块进行前向传播
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output_tensor = block(input_tensor)
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# 打印输入和输出张量的大小
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print("Input size:", input_tensor.size())
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print("Output size:", output_tensor.size())
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