Perceiver IO

class vformer.models.classification.perceiver_io.PerceiverIO(dim=32, depth=6, latent_dim=512, num_latents=512, num_cross_heads=1, num_latent_heads=8, cross_head_dim=64, latent_head_dim=64, queries_dim=32, logits_dim=None, decoder_ff=False)[source]

Bases: Module

Implementation of ‘Perceiver IO: A General Architecture for Structured Inputs & Outputs’ https://arxiv.org/abs/2107.14795

Code Implementation based on: https://github.com/lucidrains/perceiver-pytorch

Parameters

  • dim (int) – Size of sequence to be encoded

  • depth (int) – Depth of latent attention blocks

  • latent_dim (int) – Dimension of latent array

  • num_latents (int) – Number of latent arrays

  • num_cross_heads (int) – Number of heads for cross attention

  • num_latent_heads (int) – Number of heads for latent attention

  • cross_head_dim (int) – Dimension of cross attention head

  • latent_head_dim (int) – Dimension of latent attention head

  • queries_dim (int) – Dimension of queries array

  • logits_dim (int, optional) – Dimension of output logits

  • decoder_ff (bool) – Whether to include a feed forward layer for the decoder attention block

forward(x, queries)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool