PhyDNet
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discrepancy between the article and the code?
When I read paper Le_Guen_Disentangling_Physical_Dynamics_CVPR_2020_supplemental.pdf for MNIST dataset there are 6 Encoder Blocks and 6 Decoder Blocks. When I read code from this repo for the MNIST dataset (the only we have here) it seems to me that there are only 3 Encoder Blocks and 3 Decoder Blocks.
So is there discrepancy between the code and the paper here, or have I misunderstood?
models/models.py
class EncoderRNN(torch.nn.Module):
def __init__(self,phycell,convcell, device):
super(EncoderRNN, self).__init__()
self.encoder_E = encoder_E() # general encoder 64x64x1 -> 32x32x32
self.encoder_Ep = encoder_specific() # specific image encoder 32x32x32 -> 16x16x64
self.encoder_Er = encoder_specific()
self.decoder_Dp = decoder_specific() # specific image decoder 16x16x64 -> 32x32x32
self.decoder_Dr = decoder_specific()
self.decoder_D = decoder_D() # general decoder 32x32x32 -> 64x64x1