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1. Replace inplace Leaky ReLU in auto-encoder classifier with non-inplace one
2. Replace rank number with get_ordinal method in xmp
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# Conflicts:
# models/model.py
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1. Features used in HPM is decoded canonical embedding without transpose convolution
2. Decode pose embedding to image for Part Net
3. Backbone seems to be redundant, we can use feature map given by auto-decoder
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1. Triplet loss function and weight init function haven't been implement yet
2. Tuplize features returned by auto-encoder for later unpack
3. Correct comment error in auto-encoder
4. Swap batch_size dim and time dim in HPM and PartNet in case of redundant transpose
5. Find backbone problems in HPM and disable it temporarily
6. Make feature structure by HPM consistent to that by PartNet
7. Fix average pooling dimension issue and incorrect view change in HP
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1. Add default output channels of decoder
2. Replace deprecated torch.nn.functional.sigmoid with torch.sigmoid
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1. Wrap fully connected layers
2. Introduce hyperparameter tuning in constructor
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1. Add batch normalization and activation to layers
2. VGGConv2d and FocalConv2d inherits to BasicConv2d; DCGANConvTranspose2d inherits to BasicConvTranspose2d
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1. Make activation functions be inplace ops
2. Change Leaky ReLU to ReLU in decoder
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1. Wrap Conv2d 3x3-padding-1 to VGGConv2d
2. Wrap ConvTranspose2d 4x4-stride-4-padding-1 to DCGANConvTranspose2d
3. Turn off bias in conv since the employment of batch normalization
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