text: """ CBAM (sort-of) Attention
  
  Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
  
  WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
  some tasks, especially fine-grained it seems. I may end up removing this impl.
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import torch
  from torch import nn as nn
  import torch.nn.functional as F
  
  from .conv_bn_act import ConvNormAct
  from .create_act import create_act_layer, get_act_layer
  from .helpers import make_divisible
  
  
  class ChannelAttn(nn.Module):
      """ Original CBAM channel attention module, currently avg + max pool variant only.
      """
      def __init__(
              self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
              act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
          super(ChannelAttn, self).__init__()
          if not rd_channels:
              rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
          self.fc1 = nn.Conv2d(channels, rd_channels, 1, bias=mlp_bias)
          self.act = act_layer(inplace=True)
          self.fc2 = nn.Conv2d(rd_channels, channels, 1, bias=mlp_bias)
          self.gate = create_act_layer(gate_layer)
  
      def forward(self, x):
          x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
          x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
          return x * self.gate(x_avg + x_max)
  
  
  class LightChannelAttn(ChannelAttn):
      """An experimental 'lightweight' that sums avg + max pool first
      """
      def __init__(
              self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
              act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
          super(LightChannelAttn, self).__init__(
              channels, rd_ratio, rd_channels, rd_divisor, act_layer, gate_layer, mlp_bias)
  
      def forward(self, x):
          x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
          x_attn = self.fc2(self.act(self.fc1(x_pool)))
          return x * F.sigmoid(x_attn)
  
  
  class SpatialAttn(nn.Module):
      """ Original CBAM spatial attention module
      """
      def __init__(self, kernel_size=7, gate_layer='sigmoid'):
          super(SpatialAttn, self).__init__()
          self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
          self.gate = create_act_layer(gate_layer)
  
      def forward(self, x):
          x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
          x_attn = self.conv(x_attn)
          return x * self.gate(x_attn)
  
  
  class LightSpatialAttn(nn.Module):
      """An experimental 'lightweight' variant that sums avg_pool and max_pool results.
      """
      def __init__(self, kernel_size=7, gate_layer='sigmoid'):
          super(LightSpatialAttn, self).__init__()
          self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
          self.gate = create_act_layer(gate_layer)
  
      def forward(self, x):
          x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
          x_attn = self.conv(x_attn)
          return x * self.gate(x_attn)
  
  
  class CbamModule(nn.Module):
      def __init__(
              self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
              spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
          super(CbamModule, self).__init__()
          self.channel = ChannelAttn(
              channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
              rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
          self.spatial = SpatialAttn(spatial_kernel_size, gate_layer=gate_layer)
  
      def forward(self, x):
          x = self.channel(x)
          x = self.spatial(x)
          return x
  
  
  class LightCbamModule(nn.Module):
      def __init__(
              self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
              spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
          super(LightCbamModule, self).__init__()
          self.channel = LightChannelAttn(
              channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
              rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
          self.spatial = LightSpatialAttn(spatial_kernel_size)
  
      def forward(self, x):
          x = self.channel(x)
          x = self.spatial(x)
          return x
  
  