Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch.

Overview

Jittor-MLP

Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch.

What's New

Rearrange, Reduce in einops for Jittor is support ! Easier to convert Transformer-based and MLP-based models from PyTorch to Jittor!

  • from .einops_my.layers.jittor import Rearrange, Reduce (shown in ./models_jittor/raft_mlp.py)

Models

  • Jittor and Pytorch implementaion of gMLP

Usage

import jittor as jt
from models_jittor import gMLPForImageClassification as gMLP_jt
from models_jittor import ResMLPForImageClassification as ResMLP_jt
from models_jittor import MLPMixerForImageClassification as MLPMixer_jt
from models_jittor import ViP as ViP_jt
from models_jittor import S2MLPv2 as S2MLPv2_jt
from models_jittor import ConvMixer as ConvMixer_jt
from models_jittor import convmlp_s as ConvMLP_s_jt 
from models_jittor import convmlp_l as ConvMLP_l_jt 
from models_jittor import convmlp_m as ConvMLP_m_jt 
from models_jittor import RaftMLP as RaftMLP_jt

model_jt = MLPMixer_jt(
    image_size=(224,112),
    patch_size=16,
    in_channels=3,
    num_classes=1000,
    d_model=256,
    depth=12,
)

images = jt.randn(8, 3, 224, 224)
with jt.no_grad():
    output = model_jt(images)
print(output.shape) # (8, 1000)

############################################################################

import torch
from models_pytorch import gMLPForImageClassification as gMLP_pt
from models_pytorch import ResMLPForImageClassification as ResMLP_pt
from models_pytorch import MLPMixerForImageClassification as MLPMixer_pt
from models_pytorch import ViP as ViP_pt
from models_pytorch import S2MLPv2 as S2MLPv2_pt 
from models_pytorch import ConvMixer as ConvMixer_pt 
from models_pytorch import convmlp_s as ConvMLP_s_pt 
from models_pytorch import convmlp_l as ConvMLP_l_pt 
from models_pytorch import convmlp_m as ConvMLP_m_pt 
from models_pytorch import RaftMLP as RaftMLP_pt

model_pt = ViP_pt(
    image_size=224,
    patch_size=16,
    in_channels=3,
    num_classes=1000,
    d_model=256,
    depth=30,
    segments = 16,
    weighted = True
)

images = torch.randn(8, 3, 224, 224)

with torch.no_grad():
    output = model_pt(images)
print(output.shape) # (8, 1000)


############################## Non-square images and patch sizes #########################

model_jt = ViP_jt(
    image_size=(224, 112),
    patch_size=(16, 8),
    in_channels=3,
    num_classes=1000,
    d_model=256,
    depth=30,
    segments = 16,
    weighted = True
)
images = jt.randn(8, 3, 224, 112)
with jt.no_grad():
    output = model_jt(images)
print(output.shape) # (8, 1000)

############################## 2 Stages S2MLPv2 #########################
model_pt = S2MLPv2_pt(
    in_channels = 3,
    image_size = (224,224),
    patch_size = [(7,7), (2,2)],
    d_model = [192, 384],
    depth = [4, 14],
    num_classes = 1000, 
    expansion_factor = [3, 3]
)

############################## ConvMLP With Pretrain Params #########################
model_jt = ConvMLP_s_jt(pretrained = True, num_classes = 1000)


############################## RaftMLP #########################
model_jt = RaftMLP_jt(
        layers = [
            {"depth": 12,
            "dim": 768,
            "patch_size": 16,
            "raft_size": 4}
        ],
        gap = True
    )

Citations

@misc{tolstikhin2021mlpmixer,
    title   = {MLP-Mixer: An all-MLP Architecture for Vision},
    author  = {Ilya Tolstikhin and Neil Houlsby and Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Thomas Unterthiner and Jessica Yung and Daniel Keysers and Jakob Uszkoreit and Mario Lucic and Alexey Dosovitskiy},
    year    = {2021},
    eprint  = {2105.01601},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
@misc{hou2021vision,
    title   = {Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition},
    author  = {Qibin Hou and Zihang Jiang and Li Yuan and Ming-Ming Cheng and Shuicheng Yan and Jiashi Feng},
    year    = {2021},
    eprint  = {2106.12368},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
@article{liu2021pay,
  title={Pay Attention to MLPs},
  author={Liu, Hanxiao and Dai, Zihang and So, David R and Le, Quoc V},
  journal={arXiv preprint arXiv:2105.08050},
  year={2021}
}
@article{touvron2021resmlp,
  title={Resmlp: Feedforward networks for image classification with data-efficient training},
  author={Touvron, Hugo and Bojanowski, Piotr and Caron, Mathilde and Cord, Matthieu and El-Nouby, Alaaeldin and Grave, Edouard and Joulin, Armand and Synnaeve, Gabriel and Verbeek, Jakob and J{\'e}gou, Herv{\'e}},
  journal={arXiv preprint arXiv:2105.03404},
  year={2021}
}
@article{yu2021s,
  title={S $\^{} 2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision},
  author={Yu, Tan and Li, Xu and Cai, Yunfeng and Sun, Mingming and Li, Ping},
  journal={arXiv preprint arXiv:2108.01072},
  year={2021}
}
@article{li2021convmlp,
  title={ConvMLP: Hierarchical Convolutional MLPs for Vision},
  author={Li, Jiachen and Hassani, Ali and Walton, Steven and Shi, Humphrey},
  journal={arXiv preprint arXiv:2109.04454},
  year={2021}
}
@article{tatsunami2021raftmlp,
  title={RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision?},
  author={Tatsunami, Yuki and Taki, Masato},
  journal={arXiv preprint arXiv:2108.04384},
  year={2021}
}
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Dec 31, 2022
Python code for loading the Aschaffenburg Pose Dataset.

Aschaffenburg Pose Dataset (APD) This repository contains Python code for loading and filtering the Aschaffenburg Pose Dataset. The dataset itself and

1 Nov 26, 2021
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 02, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning Yansong Tang *, Zhenyu Jiang *, Zhenda Xie *, Yue

Zhenyu Jiang 12 Nov 16, 2022
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training By Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue. This

290 Dec 29, 2022
[ACMMM 2021, Oral] Code release for "Elastic Tactile Simulation Towards Tactile-Visual Perception"

EIP: Elastic Interaction of Particles Code release for "Elastic Tactile Simulation Towards Tactile-Visual Perception", in ACMMM (Oral) 2021. By Yikai

Yikai Wang 37 Dec 20, 2022
Dynamic Realtime Animation Control

Our project is targeted at making an application that dynamically detects the user’s expressions and gestures and projects it onto an animation software which then renders a 2D/3D animation realtime

Harsh Avinash 10 Aug 01, 2022
blind SQLIpy sebuah alat injeksi sql yang menggunakan waktu sql untuk mendapatkan sebuah server database.

blind SQLIpy Alat blind SQLIpy ini merupakan alat injeksi sql yang menggunakan metode time based blind sql injection metode tersebut membutuhkan waktu

Galih Anggoro Prasetya 4 Feb 24, 2022
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation (CVPR2019) This is a pytorch implementatio

Yawei Luo 280 Jan 01, 2023
⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.

Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co

Daniel Steinberg 0 Nov 06, 2022
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

CIFS This repository provides codes for CIFS (ICML 2021). CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Sel

Hanshu YAN 19 Nov 12, 2022
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory"

memory_efficient_attention.pytorch A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory" (Rabe&Staats'21). def effic

Ryuichiro Hataya 7 Dec 26, 2022
Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

hao 214 Dec 28, 2022
Sign Language Translation with Transformers (COLING'2020, ECCV'20 SLRTP Workshop)

transformer-slt This repository gathers data and code supporting the experiments in the paper Better Sign Language Translation with STMC-Transformer.

Kayo Yin 107 Dec 27, 2022
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience This repository is the official implementation of [https://www.bi

Eulerlab 6 Oct 09, 2022
Code for "Learning the Best Pooling Strategy for Visual Semantic Embedding", CVPR 2021

Learning the Best Pooling Strategy for Visual Semantic Embedding Official PyTorch implementation of the paper Learning the Best Pooling Strategy for V

Jiacheng Chen 106 Jan 06, 2023
Dataloader tools for language modelling

Installation: pip install lm_dataloader Design Philosophy A library to unify lm dataloading at large scale Simple interface, any tokenizer can be inte

5 Mar 25, 2022
Robust Lane Detection via Expanded Self Attention (WACV 2022)

Robust Lane Detection via Expanded Self Attention (WACV 2022) Minhyeok Lee, Junhyeop Lee, Dogyoon Lee, Woojin Kim, Sangwon Hwang, Sangyoun Lee Overvie

Min Hyeok Lee 18 Nov 12, 2022