feat(net): 替换 token_mixer 为 SCSA 模块
- 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
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componets/SCSA.py
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156
componets/SCSA.py
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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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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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"""Elsevier2024
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变化检测 (CD) 是地球观测中一种重要的监测方法，尤其适用于土地利用分析、城市管理和灾害损失评估。然而，在星座互联和空天协作时代，感兴趣区域 (ROI) 的变化由于几何透视旋转和时间风格差异而导致许多错误检测。
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为了应对这些挑战，我们引入了 CDNeXt，该框架阐明了一种稳健而有效的方法，用于将基于预训练主干的 Siamese 网络与用于遥感图像的创新时空交互注意模块 (TIAM) 相结合。
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CDNeXt 可分为四个主要组件：编码器、交互器、解码器和检测器。值得注意的是，由 TIAM 提供支持的交互器从编码器提取的二进制时间特征中查询和重建空间透视依赖关系和时间风格相关性，以扩大 ROI 变化的差异。
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最后，检测器集成解码器生成的分层特征，随后生成二进制变化掩码。
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"""
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class SpatiotemporalAttentionFullNotWeightShared(nn.Module):
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def __init__(self, in_channels, inter_channels=None, dimension=2, sub_sample=False):
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super(SpatiotemporalAttentionFullNotWeightShared, self).__init__()
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assert dimension in [2, ]
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self.dimension = dimension
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self.sub_sample = sub_sample
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self.in_channels = in_channels
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self.inter_channels = inter_channels
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if self.inter_channels is None:
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self.inter_channels = in_channels // 2
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if self.inter_channels == 0:
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self.inter_channels = 1
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self.g1 = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0)
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)
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self.g2 = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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self.W1 = nn.Sequential(
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nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
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kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(self.in_channels)
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)
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self.W2 = nn.Sequential(
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nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
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kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(self.in_channels)
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)
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self.theta = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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self.phi = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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def forward(self, x1, x2):
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"""
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:param x: (b, c, h, w)
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:param return_nl_map: if True return z, nl_map, else only return z.
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:return:
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"""
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batch_size = x1.size(0)
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g_x11 = self.g1(x1).reshape(batch_size, self.inter_channels, -1)
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g_x12 = g_x11.permute(0, 2, 1)
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g_x21 = self.g2(x2).reshape(batch_size, self.inter_channels, -1)
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g_x22 = g_x21.permute(0, 2, 1)
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theta_x1 = self.theta(x1).reshape(batch_size, self.inter_channels, -1)
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theta_x2 = theta_x1.permute(0, 2, 1)
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phi_x1 = self.phi(x2).reshape(batch_size, self.inter_channels, -1)
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phi_x2 = phi_x1.permute(0, 2, 1)
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energy_time_1 = torch.matmul(theta_x1, phi_x2)
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energy_time_2 = energy_time_1.permute(0, 2, 1)
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energy_space_1 = torch.matmul(theta_x2, phi_x1)
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energy_space_2 = energy_space_1.permute(0, 2, 1)
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energy_time_1s = F.softmax(energy_time_1, dim=-1)
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energy_time_2s = F.softmax(energy_time_2, dim=-1)
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energy_space_2s = F.softmax(energy_space_1, dim=-2)
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energy_space_1s = F.softmax(energy_space_2, dim=-2)
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# C1*S(C2) energy_time_1s * C1*H1W1 g_x12 * energy_space_1s S(H2W2)*H1W1 -> C1*H1W1
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y1 = torch.matmul(torch.matmul(energy_time_2s, g_x11), energy_space_2s).contiguous() # C2*H2W2
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# C2*S(C1) energy_time_2s * C2*H2W2 g_x21 * energy_space_2s S(H1W1)*H2W2 -> C2*H2W2
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y2 = torch.matmul(torch.matmul(energy_time_1s, g_x21), energy_space_1s).contiguous() # C1*H1W1
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y1 = y1.reshape(batch_size, self.inter_channels, *x2.size()[2:])
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y2 = y2.reshape(batch_size, self.inter_channels, *x1.size()[2:])
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return x1 + self.W1(y1), x2 + self.W2(y2)
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if __name__ == '__main__':
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in_channels = 64
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batch_size = 8
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height = 32
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width = 32
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block = SpatiotemporalAttentionFullNotWeightShared(in_channels=in_channels)
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input1 = torch.rand(batch_size, in_channels, height, width)
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input2 = torch.rand(batch_size, in_channels, height, width)
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output1, output2 = block(input1, input2)
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print(f"Input1 size: {input1.size()}")
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print(f"Input2 size: {input2.size()}")
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print(f"Output1 size: {output1.size()}")
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print(f"Output2 size: {output2.size()}")
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5
net.py
5
net.py
@ -6,6 +6,8 @@ import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from einops import rearrange
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from componets.SCSA import SCSA
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def drop_path(x, drop_prob: float = 0., training: bool = False):
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if drop_prob == 0. or not training:
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@ -164,7 +166,8 @@ class BaseFeatureExtractionSAR(nn.Module):
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super().__init__()
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self.norm1 = LayerNorm(dim, 'WithBias')
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self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代
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self.token_mixer = SCSA(dim=dim,head_num=8)
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# self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代
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self.norm2 = LayerNorm(dim, 'WithBias')
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
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