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}
}
5 Sep 15, 2022
43 Dec 28, 2022
18 Nov 17, 2022
23 Nov 30, 2022
20 Dec 12, 2022
138 Dec 24, 2022
22 Dec 14, 2022
211 Dec 28, 2022
2 Nov 20, 2021
2 Dec 15, 2021
0 Aug 13, 2022
87 Dec 19, 2022
217 Dec 05, 2022
30 Dec 10, 2022
7 Mar 12, 2022
143 Dec 14, 2022
99 Jan 06, 2023
775 Jan 08, 2023
2 Feb 03, 2022
1.4k Dec 30, 2022
32.9k Jan 08, 2023
9 Feb 15, 2022
1 Jan 27, 2022
32 Jul 29, 2022
4 Oct 15, 2022
14 Dec 12, 2022
8 Oct 25, 2022
173 Jan 04, 2023