读gaitset代码

输入：128个30,64,64预处理后
ipts[128, 30, 64, 44]->sils[128,1, 1186, 64, 44]
GaitSet(
  (set_block1): SetBlockWrapper(
    sils先transpose且reshape成torch.Size([3840, 1, 64, 44])
    (forward_block): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)
      )
      (1): LeakyReLU(negative_slope=0.01, inplace=True)
      (2): BasicConv2d(
        (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (3): LeakyReLU(negative_slope=0.01, inplace=True)
      (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    得到torch.Size([3840, 32, 32, 22])后
    再reshape且transpose成torch.Size([128, 32, 30, 32, 22])outs
  )
  (set_pooling): PackSequenceWrapper()
  对outs最大池化得到gl
  
  (gl_block2): Sequential(
    这里其实是set_block2的深拷贝gl_block2
  	输入gl[128, 32, 32, 22]
    (0): BasicConv2d(
      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): LeakyReLU(negative_slope=0.01, inplace=True)
    (2): BasicConv2d(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (3): LeakyReLU(negative_slope=0.01, inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )得到gl
  
  (set_block2): SetBlockWrapper(
    也是先把outs[128, 32, 30, 32, 22]转置
    (forward_block): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (1): LeakyReLU(negative_slope=0.01, inplace=True)
      (2): BasicConv2d(
        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (3): LeakyReLU(negative_slope=0.01, inplace=True)
      (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )得到torch.Size([128, 64, 30, 16, 11])-》outs
  )
  
  (set_pooling): PackSequenceWrapper()
  outs再最大池化后与gl求和得到torch.Size([128, 64, 16, 11])-》gl
  
  (gl_block3): Sequential(
    输入gl
    (0): BasicConv2d(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (1): LeakyReLU(negative_slope=0.01, inplace=True)
    (2): BasicConv2d(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    )
    (3): LeakyReLU(negative_slope=0.01, inplace=True)
  )得到gl[128, 128, 16, 11]
  
  (set_block3): SetBlockWrapper(
    这里输入的是outs[128, 64, 30, 16, 11]
    (forward_block): Sequential(
      (0): BasicConv2d(
        (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (1): LeakyReLU(negative_slope=0.01, inplace=True)
      (2): BasicConv2d(
        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (3): LeakyReLU(negative_slope=0.01, inplace=True)
    )
  )得到outs[128, 128, 30, 16, 11]

  (set_pooling): PackSequenceWrapper()
  outs再最大池化后得到[128, 128, 16, 11]
  与gl求和得到torch.Size([128, 128, 16, 11])-》gl
  
  (HPP): HorizontalPoolingPyramid()
  输入outs[128, 128, 16, 11]
  根据bin[16, 8, 4, 2, 1]把outs给view成对应形状的z，例如取8时有[128, 128, 8, 22]
  随后将z的最大值和均值求和得到[128, 128, 16]、[128, 128, 8]等等
  最后拼接得到feature1[128, 128, 31]
  
  同样输入gl[128, 128, 16, 11]
  得到feature2[128, 128, 31]
  两个feature拼接得到feature[128, 128, 62]

  (Head): SeparateFCs()
  输入feature
  得到embs[128, 256, 62]
  
  (loss_aggregator): LossAggregator(
    (losses): ModuleDict(
      (triplet): TripletLoss()
    )
  )
)
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最后有

{
	'training_feat': {
		'triplet': {
			'embeddings': embs[128, 256, 62], 
			'labels': 128维}},
	'visual_summary': {
		'image/sils': sils给view成[3840, 1, 64, 44]}, 
	'inference_feat': {	
		'embeddings': embs[128, 256, 62]}}
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