读gaitset代码

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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, 16]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()))
)

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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