频率分量的确定

[yueshuheng]

想请问一下，这些频率分量是怎么确定的呀？

[Frank Star]

作者在这里给出了一种两步准则来选择MCA模块中的频率分量。其主要**是为：

第一步先分别计算出通道注意力中每个频率分量的结果；
第二步再根据所得结果筛选出Top-k个性能最佳的频率分量。

可以参考这篇文章：
https://zhuanlan.zhihu.com/p/339215696

[Zhongrocky]

Hi, this work is amazing! However, I found that the module of the MultispectralDCTlayer is empty while debugging. May I ask what is the reason for this result.

[cfzd]

@Zhongrocky
It is not empty. It just don't fave any learnable parameters. The implementation of MultispectralDCTlayer is here:

FcaNet/model/layer.py

Lines 65 to 117 in aa5fb63

 class MultiSpectralDCTLayer(nn.Module): 
     """ 
     Generate dct filters 
     """ 
     def __init__(self, height, width, mapper_x, mapper_y, channel): 
         super(MultiSpectralDCTLayer, self).__init__() 
          
         assert len(mapper_x) == len(mapper_y) 
         assert channel % len(mapper_x) == 0 
  
         self.num_freq = len(mapper_x) 
  
         # fixed DCT init 
         self.register_buffer('weight', self.get_dct_filter(height, width, mapper_x, mapper_y, channel)) 
          
         # fixed random init 
         # self.register_buffer('weight', torch.rand(channel, height, width)) 
  
         # learnable DCT init 
         # self.register_parameter('weight', self.get_dct_filter(height, width, mapper_x, mapper_y, channel)) 
          
         # learnable random init 
         # self.register_parameter('weight', torch.rand(channel, height, width)) 
  
         # num_freq, h, w 
  
     def forward(self, x): 
         assert len(x.shape) == 4, 'x must been 4 dimensions, but got ' + str(len(x.shape)) 
         # n, c, h, w = x.shape 
  
         x = x * self.weight 
  
         result = torch.sum(x, dim=[2,3]) 
         return result 
  
     def build_filter(self, pos, freq, POS): 
         result = math.cos(math.pi * freq * (pos + 0.5) / POS) / math.sqrt(POS)  
         if freq == 0: 
             return result 
         else: 
             return result * math.sqrt(2) 
      
     def get_dct_filter(self, tile_size_x, tile_size_y, mapper_x, mapper_y, channel): 
         dct_filter = torch.zeros(channel, tile_size_x, tile_size_y) 
  
         c_part = channel // len(mapper_x) 
  
         for i, (u_x, v_y) in enumerate(zip(mapper_x, mapper_y)): 
             for t_x in range(tile_size_x): 
                 for t_y in range(tile_size_y): 
                     dct_filter[i * c_part: (i+1)*c_part, t_x, t_y] = self.build_filter(t_x, u_x, tile_size_x) * self.build_filter(t_y, v_y, tile_size_y) 
                          
         return dct_filter 

[Zhongrocky]

@cfzd Thank you a lot. Got it.