Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
$ pip install perceiver-pytorchimport torch
from perceiver_pytorch import Perceiver
model = Perceiver(
num_fourier_features = 6, # number of fourier features, with original value (2 * K + 1)
depth = 48, # depth of net, in paper, they went deep, making up for lack of attention
num_latents = 6, # number of latents, or induced set points, or centroids. different papers giving it different names
cross_dim = 512, # cross attention dimension
latent_dim = 512, # latent dimension
cross_heads = 1, # number of heads for cross attention. paper said 1
latent_heads = 8, # number of heads for latent self attention, 8
cross_dim_head = 64,
latent_dim_head = 64,
num_classes = 1000, # output number of classes
attn_dropout = 0.,
ff_dropout = 0.,
weight_tie_layers = False # whether to weight tie layers (optional, as indicated in the diagram)
)
img = torch.randn(1, 224 * 224) # 1 imagenet image, pixelized
model(img) # (1, 1000)@misc{jaegle2021perceiver,
title = {Perceiver: General Perception with Iterative Attention},
author = {Andrew Jaegle and Felix Gimeno and Andrew Brock and Andrew Zisserman and Oriol Vinyals and Joao Carreira},
year = {2021},
eprint = {2103.03206},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}