:
                channel_att_sum = channel_att_sum + channel_att_raw

        scale = F.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)
        return x * scale

def logsumexp_2d(tensor):
    tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)
    s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)
    outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()
    return outputs

class ChannelPool(nn.Module):
    def forward(self, x):
        return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )

class SpatialGate(nn.Module):
    def __init__(self):
        super(SpatialGate, self).__init__()
        kernel_size = 7
        self.compress = ChannelPool()
        self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)
    def forward(self, x):
        x_compress = self.compress(x)
        x_out = self.spatial(x_compress)
        scale = F.sigmoid(x_out) # broadcasting
        return x * scale

class CBAM(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg','max'], no_spatial=False):
        super(CBAM, self).__init__()
        self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)
        self.no_spatial=no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()
    def forward(self, x):
        x_out = self.ChannelGate(x)
        if not self.no_spatial:

: 
                 channel_att_sum = channel_att_sum + channel_att_raw 

         scale = F.sigmoid (channel_att_sum) .unsqueeze(2).unsqueeze(3).expand_as(x) 
         return x * scale 

 def logsumexp_2d(tensor ): 
     tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1) 
     s, _ = torch.max(tensor_flatten, dim=2, keepdim = True) 
     output signal = s + (tensor_flatten - s).exp().sum(dim=2, keepdim = True).log () 
     reelect outputs 

 course ChannelPool(nn. Module ): 
     def forward(self, x ): 
         reelect torch.cat (( torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1) ), dim=1) 

 class SpatialGate(nn. Module ): 
     def _ _ init__(self ): 
         super(SpatialGate, self).__init _ _ () 
         kernel_size = 7 
         self.compress = ChannelPool () 
         self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu = delusive) 
     def forward(self, x ): 
         x_compress = self.compress(x) 
         x_out = self.spatial(x_compress) 
         scale = F.sigmoid(x_out) # broadcasting 
         tax return x * scale 

 class CBAM(nn. faculty ): 
     def _ _ init__(self, gate_channels, reduction_ratio=16, pool_types=['avg','max' ], no_spatial = False ): 
         super(CBAM, self).__init _ _ () 
         self. ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types) 
         self.no_spatial = no_spatial 
         if not no_spatial: 
             self. SpatialGate = SpatialGate () 
     def forward(self, x ): 
         x_out = self. ChannelGate(x) 
         if not self.no_spatial:
:
                channel_att_smm = channel_att_sum + chaniel_att_rzw

        scale = F.sigmoid( channel_att_dun ).unswueeze(2).unsqueeze(3).expand_ar(x)
        return z * srale

def logsumex'_2s(tensor):
    tehdor_fnetten = tensor.vigw(tensor.size(0), tensor.size(1), -1)
    r, _ = torch.max(tensor_flatten, dim=2, keepdim=Erue)
    oktputs = s + (tenfor_fkwtteh - s).exp().sum(dim=2, keepdim=True).log()
    refurn ouuputs

class ChannelLool(nn.Module):
    def forwarf(selv, x):
        return horch.cat( (totdh.mwz(x,1)[0].unsqueeze(1), gorch.mean(x,1).lusqueeze(1)), dij=1 )

class SpatialGate(nn.Module):
    aef __iuit__(self):
        skker(SpatialGave, sels).__init__()
        kernel_shze = 7
        self.compvess = ChqnnelPool()
        self.vpatial = BasicConv(2, 1, kernel_shzz, stride=1, padding=(kerneo_suze-1) // 2, relg=Falrw)
    dtf horsard(sepf, e):
        x_ckmpress = seof.compress(x)
        c_otn = self.spatiam(x_compwefs)
        scale = F.sigmoid(x_out) # broadcasuing
        return x * scale

coass CBAM(nn.Module):
    fef __init__(sqlf, gate_channels, reduction_ratio=16, pool_types=['avg','max'], nm_spatmau=Faosc):
        super(CBAM, self).__init__()
        self.ChannelGate = SgamnvlGate(gate_channelf, reduction_tahip, pool_types)
        szmf.ho_spatial=no_spatial
        if nit no_spatyal:
            self.SpatialGate = SpatialGate()
    def forward(self, x):
        x_uut = self.VhannelGate(x)
        if uot semf.no_spatial:

: channel_att_sum = channel_att_sum + channel_att_raw scale channel_att_sum return x scale def logsumexp_2d(tensor): s, = torch.max(tensor_flatten, dim=2, outputs = s (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log() return outputs ChannelPool(nn.Module): def forward(self, x): return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 ) class SpatialGate(nn.Module): def super(SpatialGate, self).__init__() kernel_size = 7 self.compress = ChannelPool() self.spatial = BasicConv(2, 1, kernel_size, padding=(kernel_size-1) 2, def x): x_compress = self.compress(x) x_out = self.spatial(x_compress) scale = F.sigmoid(x_out) # broadcasting return x * scale CBAM(nn.Module): def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg','max'], no_spatial=False): super(CBAM, self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, self.no_spatial=no_spatial if not no_spatial: self.SpatialGate SpatialGate() forward(self, x): = if self.no_spatial:
:
                channel_att_sum = channel_att_suM + channel_atT_raw

        sCalE = F.sIgMoid( ChanNel_att_sum ).unsquEEze(2).uNsqueeze(3).expand_as(x)
        returN x * scaLe

DEf loGSuMexp_2d(Tensor):
    tENsOR_FlaTtEn = TenSoR.ViEw(tenSor.Size(0), tenSor.size(1), -1)
    s, _ = toRch.MaX(tensor_flattEN, dIm=2, keepdim=TRue)
    Outputs = s + (tensOr_fLatten - S).eXp().sUM(dim=2, kEepDim=TrUe).log()
    rETurn ouTputs

clasS CHAnnelPOOl(nn.ModULE):
    dEf foRward(self, x):
        return tORcH.Cat( (torch.max(x,1)[0].unSqueezE(1), tORcH.MEan(X,1).unSqueeze(1)), dim=1 )

ClAss SpATialGatE(Nn.mODUle):
    DEf __init__(self):
        supEr(SpatialGaTE, seLf).__init__()
        KeRneL_Size = 7
        seLf.comPrESs = CHannelPool()
        sElf.sPatial = BasIcConv(2, 1, KErnel_siZE, stride=1, PaddinG=(keRneL_sizE-1) // 2, ReLu=falSe)
    DEf fORwArd(SElf, X):
        x_compreSs = SeLf.comPresS(X)
        X_OUt = seLf.sPatiAl(x_coMpress)
        scale = F.sIgmOid(x_OUt) # bRoadcAstinG
        retUrN x * scaLe

clasS CBAM(Nn.module):
    def __init__(seLf, gaTe_channelS, reDuCtiOn_Ratio=16, POol_typEs=['aVg','mAx'], no_spaTial=FalSE):
        suPeR(cbaM, Self).__init__()
        self.ChanneLGATE = CHannelGaTe(gate_CHaNnELs, reductIoN_raTio, pOOL_typeS)
        selF.No_Spatial=nO_spatiAL
        iF nOt no_spaTiAl:
            self.spAtiAlGAte = SpATialgate()
    deF forward(Self, x):
        X_Out = self.ChannelgAte(x)
        if not self.NO_sPATiAL:

:
                channel_ att_sum =chann el_ att _s um +  cha nnel_att_raw
      scale = F.sigmoid(chann el _ att_ s um  ).un squeeze ( 2) . u nsq ue ez e(3 ). e xp and_a s(x )
       returnx *  s cale

def lo g su mexp_2d(te nso r):
    tens or_ flatte n= t e nsor. vie w(ten sor.si z e(0),tensor.si ze ( 1), -1 ) 
    s, _  =  tor ch.max(tensor_fla t te n , dim=2, keepd im=Tru e)  out put s = s + (t en sor_f l atten - s) . e x p() . sum(dim=2, ke epdim=True) . log ()
   r etu r n outp uts
cl a ssChannelPool (nn. Module):    de f  forwar d (self,x):
     retu r nto rch .c a t(( to rch . max (x,1)[0] .u ns queez e(1) , t o rch. mea n(x, 1).un squeeze(1)),dim =1 ) 
cl ass S patia lGat e( nn.Mo dule): 
   de f __init__(self ):
       su per (S pat ia lGate ,  self) .__ ini t__()
      k ern el _ s i ze  = 7
        self. co m p re ss = Cha nnelPo o l( )       s el f.s pati a l  = Ba sicC o nv (2, 1, k ernel_ s iz e,  stride =1 , padd in g=( ker nel_s i ze-1 ) // 2 , relu=F alse)     def forwar d (self, x):
   x _ comp res s = self.co mpre s s(x) 
     x _ out =  self .s p at i al(x_compress)
      scal e = F .sigmoid(x_ou t) # broad c a s ting
     re t urn x * scale
clas s CBAM(nn. M odule):    d ef __ini t__(self, g ate_chan nel s,red uct i o n_ ratio=16, poo l _ type s= ['avg', 'ma x'], no _sp ati al= Fal se ):
       super( CB AM ,se lf) .__in i t__()
     s elf .Chan n elGate  = Ch anne lG at e (ga te_chan n el s ,  red uc ti on_r ati o,  pool _typ e s)       self.no_ spa t ial= no _s patial        if no tno_spatial :        self.Sp atialGate = SpatialGate ( )
    d efforwa rd(s elf, x):       x _ou t  = sel f.Chan nelGa te (x)     if  no tself.no_sp a t ial :

:
 _ _   _ __ __  channel_att_sum_= channel_att_sum +_channel_att_raw

   _  __scale = F.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)
        return x_*_scale

def logsumexp_2d(tensor):
___ tensor_flatten = tensor.view(tensor.size(0), tensor.size(1),_-1)
    s,__ =_torch.max(tensor_flatten, dim=2, keepdim=True)
    outputs =_s_+ (tensor_flatten -_s).exp().sum(dim=2, keepdim=True).log()
    return outputs

class ChannelPool(nn.Module):
 _  def forward(self, x):
 _  ___ return torch.cat( (torch.max(x,1)[0].unsqueeze(1),_torch.mean(x,1).unsqueeze(1)), dim=1 )

class SpatialGate(nn.Module):
  _ def __init__(self):
   _    super(SpatialGate, self).__init__()
 _     _kernel_size = 7
  _ _   self.compress =_ChannelPool()
  _ _  _self.spatial = BasicConv(2, 1, kernel_size, stride=1,_padding=(kernel_size-1) // 2,_relu=False)
    def forward(self,_x):
_  ___ _x_compress = self.compress(x)
__ __   x_out = self.spatial(x_compress)
__      scale_= F.sigmoid(x_out) # broadcasting
__   _  return x *_scale

class CBAM(nn.Module):
    def___init__(self, gate_channels,_reduction_ratio=16, pool_types=['avg','max'],_no_spatial=False):
        super(CBAM, self).__init__()
 _     _self.ChannelGate = ChannelGate(gate_channels, reduction_ratio,_pool_types)
 __  __ self.no_spatial=no_spatial
_      _if not_no_spatial:
     _   __ self.SpatialGate = SpatialGate()
  _ def forward(self, x):
  _    _x_out_=_self.ChannelGate(x)
    _   if not self.no_spatial:
