From e1a339e04bf4ad120a516d0fe0b02500c826f56d Mon Sep 17 00:00:00 2001 From: zjut Date: Fri, 8 Nov 2024 12:04:52 +0800 Subject: [PATCH] =?UTF-8?q?feat(net):=20=E6=9B=BF=E6=8D=A2=20SMFA=20?= =?UTF-8?q?=E4=B8=BA=20SCSA=20=E5=B9=B6=E8=B0=83=E6=95=B4=E7=9B=B8?= =?UTF-8?q?=E5=85=B3=E9=85=8D=E7=BD=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 将 SMFA 模块替换为 SCSA 模块 - 更新项目配置,使用本地 Python 3.8 环境 -调整 SCSA 模块参数,如维度、头数等 - 优化注意力机制,提高模型性能 --- .idea/PFCFuse.iml | 2 +- .idea/misc.xml | 2 +- componets/SCSA(CV2维图像).pdf | Bin 0 -> 2240254 bytes componets/SCSA.py | 156 ++++++++++++++++++++++ componets/{UFFC(CV2维任务).py => UFFC.py} | 0 net.py | 12 +- 6 files changed, 166 insertions(+), 6 deletions(-) create mode 100644 componets/SCSA(CV2维图像).pdf create mode 100644 componets/SCSA.py rename componets/{UFFC(CV2维任务).py => UFFC.py} (100%) diff --git a/.idea/PFCFuse.iml b/.idea/PFCFuse.iml index a8d74d0..8b8c395 100644 --- a/.idea/PFCFuse.iml +++ 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z(#`jeMIGQ^fdn`Z!2%py3>V;F4~T6<*$Q8emNvKqHuNu^5ulL{=fr4-g51L;77;e& zGEdv-O4t|1T+ikA4}w9&`Ny4@e*ZvC9rU(nCj;+;MnA5tDcVDi1LV9gMpNz#tMak- zIX10v-y1SB9PiB>YT3)inBE3>5lzGRVw@V_#hS8imo~WPEsP~Xj^_pN&Ix-N&f`uF zP<_$%i2G!N%sGL5uJHmzMCSB;;r8Ib|3OUy?}Ld~SVv61pzoYu4j0;B@9{WA#0k9P z1mVEr1@0yG4|AW;2DSzPUi7hk4ne^J?$Hzk+)t46{MzT{W+&5uQyn;k@z-AV3fJ!02fhlZAL-!i`!bA=2B65Z{ zgakjn*hmJH-)DhD-x$>}~>kGJ= zMSUVim2REm1CPy}$LSO^GG8C2p-x`y;X)esU(QaP9$jsit3bv3kQQhirlw&}CN1!j zoPve%;M^tX4rQiVj}aO&!mrnBs|N#u7OKK20u!V-;D8n4*DvQdfqy{*>U2=xfF!@p z5n;o8=Jxi$GmsYcOQvL4=V;c$IwvjgFIde*JahgQVdF%l!i(UT1%8*baK?Qf z+OW<^bG%BA&6fje-!5vYFdi}z3HhSl8|$z*9>^&HUTneqI_F3|jxQ!au`F&6zAu>n z`L)V%eBfD8TZBD@o0=W33=e@0)YCA|%OXx&?6f$64Ezs;QLO7jRzU4o(;?I)@M{5o9Q)N}frEqM zFi$a=2=fJU5O9i1Qph*+HUzXWi*hp6xp|Kb8D?%C&p77=K9Sur=%mr;2fHs=hA@Zl zehYjZYRoX^vwqYGaqD~K(zPsU^a+4ZahN59O8U1&`DIou7BW)MOed&G8Saj zcwt>}9Zo&}IKzg#xUw*R99LHK3ON_ma6RvJO6TVq_Z%ZGxHgD4jnb{Ek1@j5pl*Y- z7Ws{|IOZ+(m^2&s9MRKyO|$l=7+h3+VWLg^Sr+`;76N9SZw5Q=>?3qU+H-KA1Hrth z#;F}uI=Mnei(baYz>9TW;}5)WR@9OfXfYUdv`%H23KbkmL-P~TP}IbMJRefe9U?@|=%h3~O>NO36__0UTxMIjL>ZsQ$qBy;Yc!_*5 zcPa<@7mDrL1<&vTb-PNlI82os1#jWb4PepXZEKvWtYV3~af6K4_#+>?ZH+6Xwa^0= zSLh#fE!4*bSNBDy=ibJFHX!)*xLI14=aJ}3kk92h=hq-1Cu+qE)fiZy{+5= z9c=K=96^Tu%>rG61!l$hzQCH*{ez7ia;zTm!6{a0u(22__lZRUc6$o_LqAm0HOMJ!SB+EU8@jUOL+Mtxp?uzE4DyHBL#&S-v+AFb4X@K;0ix^j z`4Hm+o^fk)Ynllj&M>GC#kZ@EV{xxnAs@~ZJ|BllE)8SJaCVH-V$bW*$v5&eqKDOu z*JqP2=rUNU;fxCTGMtrTzQ*aI>od_Uyqt$EVxViJ1%Gku6N8@vno@|YYXkd4w-ykX zg8jJCEcS^eWuxonXo{8(yh^svDbx;d#vgS>^&>%i|l2y~IR)xPECBxHll zqJ~#q%t61#hzFe!TC*T;^>Bsv^c2o6&I8Wb9iAZup8sRLamEtX7Uv|c&+xV!gw=46 zw&nB|)W^YY=pRBvpf9*{# torch.Tensor: + """ + The dim of x is (B, C, H, W) + """ + # Spatial attention priority calculation + b, c, h_, w_ = x.size() + # (B, C, H) + x_h = x.mean(dim=3) + l_x_h, g_x_h_s, g_x_h_m, g_x_h_l = torch.split(x_h, self.group_chans, dim=1) + # (B, C, W) + x_w = x.mean(dim=2) + l_x_w, g_x_w_s, g_x_w_m, g_x_w_l = torch.split(x_w, self.group_chans, dim=1) + + x_h_attn = self.sa_gate(self.norm_h(torch.cat(( + self.local_dwc(l_x_h), + self.global_dwc_s(g_x_h_s), + self.global_dwc_m(g_x_h_m), + self.global_dwc_l(g_x_h_l), + ), dim=1))) + x_h_attn = x_h_attn.view(b, c, h_, 1) + + x_w_attn = self.sa_gate(self.norm_w(torch.cat(( + self.local_dwc(l_x_w), + self.global_dwc_s(g_x_w_s), + self.global_dwc_m(g_x_w_m), + self.global_dwc_l(g_x_w_l) + ), dim=1))) + x_w_attn = x_w_attn.view(b, c, 1, w_) + + x = x * x_h_attn * x_w_attn + + # Channel attention based on self attention + # reduce calculations + y = self.down_func(x) + y = self.conv_d(y) + _, _, h_, w_ = y.size() + + # normalization first, then reshape -> (B, H, W, C) -> (B, C, H * W) and generate q, k and v + y = self.norm(y) + q = self.q(y) + k = self.k(y) + v = self.v(y) + # (B, C, H, W) -> (B, head_num, head_dim, N) + q = rearrange(q, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num), + head_dim=int(self.head_dim)) + k = rearrange(k, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num), + head_dim=int(self.head_dim)) + v = rearrange(v, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num), + head_dim=int(self.head_dim)) + + # (B, head_num, head_dim, head_dim) + attn = q @ k.transpose(-2, -1) * self.scaler + attn = self.attn_drop(attn.softmax(dim=-1)) + # (B, head_num, head_dim, N) + attn = attn @ v + # (B, C, H_, W_) + attn = rearrange(attn, 'b head_num head_dim (h w) -> b (head_num head_dim) h w', h=int(h_), w=int(w_)) + # (B, C, 1, 1) + attn = attn.mean((2, 3), keepdim=True) + attn = self.ca_gate(attn) + return attn * x + +if __name__ == '__main__': + + block = SCSA( + dim=256, + head_num=8, + ) + + input_tensor = torch.rand(1, 256, 32, 32) + + # 调用模块进行前向传播 + output_tensor = block(input_tensor) + + # 打印输入和输出张量的大小 + print("Input size:", input_tensor.size()) + print("Output size:", output_tensor.size()) diff --git a/componets/UFFC(CV2维任务).py b/componets/UFFC.py similarity index 100% rename from componets/UFFC(CV2维任务).py rename to componets/UFFC.py diff --git a/net.py b/net.py index c47aa5b..3d457ae 100644 --- a/net.py +++ b/net.py @@ -6,7 +6,8 @@ import torch.utils.checkpoint as checkpoint from timm.models.layers import DropPath, to_2tuple, trunc_normal_ from einops import rearrange -from componets.SMFA import SMFA +from componets.SCSA import SCSA + from componets.TIAM import SpatiotemporalAttentionFullNotWeightShared from componets.WTConvCV2 import WTConv2d @@ -159,7 +160,8 @@ class BaseFeatureExtraction(nn.Module): self.WTConv2d = WTConv2d(dim, dim) self.norm1 = LayerNorm(dim, 'WithBias') - self.token_mixer = SMFA(dim=dim) + # self.token_mixer = SMFA(dim=dim) + self.token_mixer = SCSA(dim=dim,head_num=8) # self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa,MLPs是mlp,这个用pool来替代 self.norm2 = LayerNorm(dim, 'WithBias') @@ -195,9 +197,11 @@ class BaseFeatureExtraction(nn.Module): ) # 该表达式将 self.layer_scale_1 这个一维张量(或变量)在维度末尾添加两个新的维度,使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作,如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。 ) + pol = self.poolmlp(self.norm2(x)) + x = wtConvX + self.drop_path( self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) - * self.poolmlp(self.norm2(x))) + * pol) else: x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse x = x + self.drop_path(self.poolmlp(self.norm2(x))) @@ -569,7 +573,7 @@ class Restormer_Decoder(nn.Module): nn.Conv2d(int(dim) // 2, out_channels, kernel_size=3, stride=1, padding=1, bias=bias), ) self.sigmoid = nn.Sigmoid() - self.spatiotemporalAttentionFullNotWeightShared = SpatiotemporalAttentionFullNotWeightShared(dim=dim) + self.spatiotemporalAttentionFullNotWeightShared = SpatiotemporalAttentionFullNotWeightShared(in_channels=dim) def forward(self, inp_img, base_feature, detail_feature):