Implementation of the Hybrid Perception Block and Dual-Pruned Self-Attention block from the ITTR paper for Image to Image Translation using Transformers

Related tags

Text Data & NLP deep-learning artificial-intelligence attention-mechanism image-to-image-translation
Overview

ITTR - Pytorch

Implementation of the Hybrid Perception Block (HPB) and Dual-Pruned Self-Attention (DPSA) block from the ITTR paper for Image to Image Translation using Transformers.

Install

$ pip install ITTR-pytorch

Usage

They had 9 blocks of Hybrid Perception Block (HPB) in the paper

import torch
from ITTR_pytorch import HPB

block = HPB(
    dim = 512,              # dimension
    dim_head = 32,          # dimension per attention head
    heads = 8,              # number of attention heads
    attn_height_top_k = 16, # number of top indices to select along height, for the attention pruning
    attn_width_top_k = 16,  # number of top indices to select along width, for the attention pruning
    attn_dropout = 0.,      # attn dropout
    ff_mult = 4,            # expansion factor of feedforward
    ff_dropout = 0.         # feedforward dropout
)

fmap = torch.randn(1, 512, 32, 32)

out = block(fmap) # (1, 512, 32, 32)

You can also use the dual-pruned self-attention as so

import torch
from ITTR_pytorch import DPSA

attn = DPSA(
    dim = 512,         # dimension
    dim_head = 32,     # dimension per attention head
    heads = 8,         # number of attention heads
    height_top_k = 48, # number of top indices to select along height, for the attention pruning
    width_top_k = 48,  # number of top indices to select along width, for the attention pruning
    dropout = 0.       # attn dropout
)

fmap = torch.randn(1, 512, 32, 32)

out = attn(fmap) # (1, 512, 32, 32)

Citations

@inproceedings{Zheng2022ITTRUI,
  title   = {ITTR: Unpaired Image-to-Image Translation with Transformers},
  author  = {Wanfeng Zheng and Qiang Li and Guoxin Zhang and Pengfei Wan and Zhongyuan Wang},
  year    = {2022}
}
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