Tree Nested PyTorch Tensor Lib

Last update: Dec 29, 2022

Overview

DI-treetensor

treetensor is a generalized tree-based tensor structure mainly developed by OpenDILab Contributors.

Almost all the operation can be supported in form of trees in a convenient way to simplify the structure processing when the calculation is tree-based.

Installation

You can simply install it with pip command line from the official PyPI site.

pip install di-treetensor

For more information about installation, you can refer to Installation.

Documentation

The detailed documentation are hosted on https://opendilab.github.io/DI-treetensor.

Only english version is provided now, the chinese documentation is still under development.

Quick Start

You can easily create a tree value object based on FastTreeValue.

import builtins
import os
from functools import partial

import treetensor.torch as torch

print = partial(builtins.print, sep=os.linesep)

if __name__ == '__main__':
    # create a tree tensor
    t = torch.randn({'a': (2, 3), 'b': {'x': (3, 4)}})
    print(t)
    print(torch.randn(4, 5))  # create a normal tensor
    print()

    # structure of tree
    print('Structure of tree')
    print('t.a:', t.a)  # t.a is a native tensor
    print('t.b:', t.b)  # t.b is a tree tensor
    print('t.b.x', t.b.x)  # t.b.x is a native tensor
    print()

    # math calculations
    print('Math calculation')
    print('t ** 2:', t ** 2)
    print('torch.sin(t).cos()', torch.sin(t).cos())
    print()

    # backward calculation
    print('Backward calculation')
    t.requires_grad_(True)
    t.std().arctan().backward()
    print('grad of t:', t.grad)
    print()

    # native operation
    # all the ops can be used as the original usage of `torch`
    print('Native operation')
    print('torch.sin(t.a)', torch.sin(t.a))  # sin of native tensor

The result should be

<Tensor 0x7f0dae602760>
├── a --> tensor([[-1.2672, -1.5817, -0.3141],
│                 [ 1.8107, -0.1023,  0.0940]])
└── b --> <Tensor 0x7f0dae602820>
    └── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                      [ 1.5956,  0.8825, -0.5702, -0.2247],
                      [ 0.9235,  0.4538,  0.8775, -0.2642]])

tensor([[-0.9559,  0.7684,  0.2682, -0.6419,  0.8637],
        [ 0.9526,  0.2927, -0.0591,  1.2804, -0.2455],
        [ 0.4699, -0.9998,  0.6324, -0.6885,  1.1488],
        [ 0.8920,  0.4401, -0.7785,  0.5931,  0.0435]])

Structure of tree
t.a:
tensor([[-1.2672, -1.5817, -0.3141],
        [ 1.8107, -0.1023,  0.0940]])
t.b:
<Tensor 0x7f0dae602820>
└── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                  [ 1.5956,  0.8825, -0.5702, -0.2247],
                  [ 0.9235,  0.4538,  0.8775, -0.2642]])

t.b.x
tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
        [ 1.5956,  0.8825, -0.5702, -0.2247],
        [ 0.9235,  0.4538,  0.8775, -0.2642]])

Math calculation
t ** 2:
<Tensor 0x7f0dae602eb0>
├── a --> tensor([[1.6057, 2.5018, 0.0986],
│                 [3.2786, 0.0105, 0.0088]])
└── b --> <Tensor 0x7f0dae60c040>
    └── x --> tensor([[1.4943, 0.1187, 0.9960, 0.1669],
                      [2.5458, 0.7789, 0.3252, 0.0505],
                      [0.8528, 0.2059, 0.7699, 0.0698]])

torch.sin(t).cos()
<Tensor 0x7f0dae621910>
├── a --> tensor([[0.5782, 0.5404, 0.9527],
│                 [0.5642, 0.9948, 0.9956]])
└── b --> <Tensor 0x7f0dae6216a0>
    └── x --> tensor([[0.5898, 0.9435, 0.6672, 0.9221],
                      [0.5406, 0.7163, 0.8578, 0.9753],
                      [0.6983, 0.9054, 0.7185, 0.9661]])


Backward calculation
grad of t:
<Tensor 0x7f0dae60c400>
├── a --> tensor([[-0.0435, -0.0535, -0.0131],
│                 [ 0.0545, -0.0064, -0.0002]])
└── b --> <Tensor 0x7f0dae60cbe0>
    └── x --> tensor([[ 0.0357, -0.0141, -0.0349, -0.0162],
                      [ 0.0476,  0.0249, -0.0213, -0.0103],
                      [ 0.0262,  0.0113,  0.0248, -0.0116]])


Native operation
torch.sin(t.a)
tensor([[-0.9543, -0.9999, -0.3089],
        [ 0.9714, -0.1021,  0.0939]], grad_fn=<SinBackward>)

For more quick start explanation and further usage, take a look at:

Quick Start

Extension

If you need to translate treevalue object to runnable source code, you may use the potc-treevalue plugin with the installation command below

pip install DI-treetensor[potc]

In potc, you can translate the objects to runnable python source code, which can be loaded to objects afterwards by the python interpreter, like the following graph

For more information, you can refer to

Contribution

We appreciate all contributions to improve DI-treetensor, both logic and system designs. Please refer to CONTRIBUTING.md for more guides.

And users can join our slack communication channel, or contact the core developer HansBug for more detailed discussion.

License

DI-treetensor released under the Apache 2.0 license.

You might also like...

Pretty Tensor - Fluent Neural Networks in TensorFlow

Pretty Tensor provides a high level builder API for TensorFlow. It provides thin wrappers on Tensors so that you can easily build multi-layer neural networks.

1.2k Dec 29, 2022

A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

TorchArrow (Warning: Unstable Prototype) This is a prototype library currently under heavy development. It does not currently have stable releases, an

536 Jan 6, 2023

Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train format

ttopt Description Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train (TT) format and maximu

5 May 23, 2022

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework Background: Outlier detection (OD) is a key data mining task for identify

127 Jan 5, 2023

Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

Tensor Component Analysis for Interpreting the Latent Space of GANs [ paper | project page ] Code to reproduce the results in the paper "Tensor Compon

4 Jun 17, 2022

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

7 Apr 23, 2022

mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms.

mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms. It provides easily interchangeable modeling and planning components, and a set of utility functions that allow writing model-based RL algorithms with only a few lines of code.

724 Jan 4, 2023

OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

205 Dec 29, 2022

Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割模型列表本仓库收录了课程作业中同学们采用jittor框架实现的如下模型： UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022

Comments

PyTorch OP List(P0)
reference: https://pytorch.org/docs/1.8.0/torch.html

common

[x] numel

[x] cpu

[x] cuda

[x] to

Creation Ops

[x] torch.zeros_like

[x] torch.randn_like

[x] torch.randint_like

[x] torch.ones_like

[x] torch.full_like

[x] torch.empty_like

[x] torch.zeros

[x] torch.randn

[x] torch.randint

[x] torch.ones

[x] torch.full

[x] torch.empty

Indexing, Slicing, Joining, Mutating Ops

[x] cat

[x] chunk

[ ] gather

[x] index_select

[x] masked_select

[x] reshape

[ ] scatter

[x] split

[x] squeeze

[x] stack

[ ] tile

[ ] unbind

[x] unsqueeze

[x] where

Math Ops

Pointwise Ops

[x] add

[x] sub

[x] mul

[x] div

[x] pow

[x] neg

[x] abs

[x] sign

[x] floor

[x] ceil

[x] round

[x] sigmoid

[x] clamp

[x] exp

[x] exp2

[x] sqrt

[x] log

[x] log10

[x] log2

Reduction Ops

[ ] argmax

[ ] argmin

[x] all

[x] any

[x] max

[x] min

[x] dist

[ ] logsumexp

[x] mean

[ ] median

[x] norm

[ ] prod

[x] std

[x] sum

[ ] unique

Comparison Ops

[ ] argsort

[x] eq

[x] ge

[x] gt

[x] isfinite

[x] isinf

[x] isnan

[x] le

[x] lt

[x] ne

[ ] sort

[ ] topk

Other Ops

[ ] cdist

[x] clone

[ ] flip

BLAS and LAPACK Ops

[ ] addbmm

[ ] addmm

[ ] bmm

[x] dot

[x] matmul

[x] mm

enhancement
opened by PaParaZz1 3
PyTorch OP Doc List
P0

[x] cpu

[x] cuda

[x] to

[x] torch.zeros_like

[x] torch.randn_like

[x] torch.ones_like

[x] torch.zeros

[x] torch.randn

[x] torch.randint

[x] torch.ones

[x] cat

[x] reshape

[x] split

[x] squeeze

[x] stack

[x] unsqueeze

[x] where

[x] abs

[x] add

[x] clamp

[x] div

[x] exp

[x] log

[x] sqrt

[x] sub

[x] sigmoid

[x] pow

[x] mul

[ ] argmax

[ ] argmin

[x] all

[x] any

[x] max

[x] min

[x] dist

[x] mean

[x] std

[x] sum

[x] eq

[x] ge

[x] gt

[x] le

[x] lt

[x] ne

[x] clone

[x] dot

[x] matmul

[x] mm

P1

[x] numel

[x] torch.randint_like

[x] torch.full_like

[x] torch.empty_like

[x] torch.full

[x] torch.empty

[x] chunk

[ ] gather

[x] index_select

[x] masked_select

[ ] scatter

[ ] tile

[ ] unbind

[x] ceil

[x] exp2

[x] floor

[x] log10

[x] log2

[x] neg

[x] round

[x] sign

[ ] bmm

P2

[ ] logsumexp

[ ] median

[x] norm

[ ] prod

[ ] unique

[ ] argsort

[x] isfinite

[x] isinf

[x] isnan

[ ] sort

[ ] topk

[ ] cdist

[ ] flip

[ ] addbmm

[ ] addmm
opened by PaParaZz1 2

dev(hansbug): add stream support for paralleling the calculations in tree

Here is an example:

import time

import numpy as np
import torch

import treetensor.torch as ttorch

N, M, T = 200, 2, 50
S1, S2, S3 = 512, 1024, 2048


def test_min():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N // M)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N // M)}, device='cuda')

    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_native():
    a = {f'a{i}': torch.randn(S1, S2, device='cuda') for i in range(N)}
    b = {f'a{i}': torch.randn(S2, S3, device='cuda') for i in range(N)}

    result = []
    for i in range(T):
        _start_time = time.time()

        for key in a.keys():
            _ = torch.matmul(a[key], b[key])
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_linear():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N)}, device='cuda')

    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_stream():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N)}, device='cuda')

    ttorch.stream(M)
    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def warmup():
    # warm up
    a = torch.randn(1024, 1024).cuda()
    b = torch.randn(1024, 1024).cuda()
    for _ in range(20):
        c = torch.matmul(a, b)


if __name__ == '__main__':
    warmup()
    test_min()
    test_native()
    test_linear()
    test_stream()

不过讲真，这个stream实际效果挺脆弱的，非常看tensor尺寸，大了小了都不行，GPU性能不够也不行，一弄不好还容易负优化，总之挺难伺候的。这部分如果想实用化的话得再研究研究。

enhancement

opened by HansBug 1

Failure when try to convert between numpy and torch on Windows Python3.10
See here: https://github.com/opendilab/DI-treetensor/runs/7820313811?check_suite_focus=true

The bug is like

@method_treelize(return_type=_get_tensor_class) def tensor(self: numpy.ndarray, *args, **kwargs): > tensor_: torch.Tensor = torch.from_numpy(self) E RuntimeError: Numpy is not available

The only way I found to 'solve' this is to downgrade python to version3.9 to lower. So these tests will be skipped temporarily.
bug
opened by HansBug 0

Releases(v0.4.0)

v0.4.0(Aug 14, 2022)
What's Changed

dev(hansbug): remove support for py3.6 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/12

pytorch upgrade to 1.12 by @zjowowen in https://github.com/opendilab/DI-treetensor/pull/11

dev(hansbug): add test for torch1.12.0 and python3.10 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/13

dev(hansbug): add stream support for paralleling the calculations in tree by @HansBug in https://github.com/opendilab/DI-treetensor/pull/10

New Contributors

@zjowowen made their first contribution in https://github.com/opendilab/DI-treetensor/pull/11

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.3.0...v0.4.0
Source code(tar.gz)
Source code(zip)
v0.3.0(Jul 15, 2022)
What's Changed

dev(hansbug): use newer version of treevalue 1.4.1 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/9

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.2.1...v0.3.0
Source code(tar.gz)
Source code(zip)
v0.2.1(Mar 22, 2022)
What's Changed

fix(hansbug): fix uncompitable problem with walk by @HansBug in https://github.com/opendilab/DI-treetensor/pull/5

dev(hansbug): add tensor method for treetensor.numpy.ndarray by @HansBug in https://github.com/opendilab/DI-treetensor/pull/6

fix(hansbug): add subside support to all the functions. by @HansBug in https://github.com/opendilab/DI-treetensor/pull/7

doc(hansbug): add documentation for np.stack, np.split and other 3 functions. by @HansBug in https://github.com/opendilab/DI-treetensor/pull/8

release(hansbug): use version 0.2.1 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/4

New Contributors

@HansBug made their first contribution in https://github.com/opendilab/DI-treetensor/pull/5

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.2.0...v0.2.1
Source code(tar.gz)
Source code(zip)
v0.2.0(Jan 4, 2022)
Use newer version of treevalue>=1.2.0

Add support of torch 1.10.0

Add support of potc

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.1.0...v0.2.0
Source code(tar.gz)
Source code(zip)
v0.1.0(Dec 26, 2021)

Use treevalue>=1.1.0 for tree structure support.

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.0.1...v0.1.0
Source code(tar.gz)
Source code(zip)
v0.0.1(Sep 30, 2021)

First simple relase of treetensor.

For further information: https://opendilab.github.io/DI-treetensor/v0.0.1/index.html
Source code(tar.gz)
Source code(zip)

Owner

OpenDILab

Open sourced Decision Intelligence (DI)

GitHub Repository https://opendilab.github.io/DI-treetensor/

This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

📈 Automated Time Series Forecasting Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to gene

42 Jan 04, 2023

[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)

Feel free to visit my homepage Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DIMP) [ECCVW2020 paper] Presentation

35 Oct 26, 2022

Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

A310 Computational Neuroscience - Okinawa Institute of Science and Technology, 2022 This repository contains modeling practice materials and homework

1 Jan 24, 2022

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Exercises and project documentation for the 3. Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

1 Jan 13, 2022

Implicit Deep Adaptive Design (iDAD)

Implicit Deep Adaptive Design (iDAD) This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Lik

12 Aug 14, 2022

Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

7.8k Jan 03, 2023

Optimising chemical reactions using machine learning

Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat

75 Dec 14, 2022

A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.

Realistic galaxy simulation via score-based generative models Official code for 'Realistic galaxy simulation via score-based generative models'. We us

32 Dec 20, 2022

Experiments and examples converting Transformers to ONNX

Experiments and examples converting Transformers to ONNX This repository containes experiments and examples on converting different Transformers to ON

4 Dec 24, 2022

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

67 Dec 20, 2022

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

201 Nov 21, 2022

Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

Approximate Outer Product Gradient Descent with Memory Code for the numerical experiment of the paper Speeding-Up Back-Propagation in DNN: Approximate

2 Mar 02, 2022

Deep Compression for Dense Point Cloud Maps.

DEPOCO This repository implements the algorithms described in our paper Deep Compression for Dense Point Cloud Maps. How to get started (using Docker)

67 Dec 06, 2022

Image-to-Image Translation in PyTorch

CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e

19k Jan 07, 2023

JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

43 Sep 28, 2022

Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

27 Oct 15, 2022

A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

This repository contains a collection of resources and papers on Diffusion Models and Score-based Models. If there are any missing valuable resources

5.1k Jan 08, 2023

This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

You can use this simple crypto backtesting script to ensure your trading strategy is successful Minimal setup required and works well with static TP a

154 Sep 12, 2022

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

AdversarialTexture Adversarial Texture Optimization from RGB-D Scans (CVPR 2020). Scanning Data Download Please refer to data directory for details. B

153 Nov 28, 2022

Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.

Knowledge Graph Embeddings and Chemical Effect Prediction, 2020. Scripts and outputs related to the paper Prediction of Adverse Biological Effects of

1 Nov 01, 2021

Tree Nested PyTorch Tensor Lib

Related tags

Overview

DI-treetensor

Installation

Documentation

Quick Start

Extension

Contribution

License

You might also like...

Pretty Tensor - Fluent Neural Networks in TensorFlow

A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

Gradient-free global optimization algorithm for multidimensional functions based on the low rank tensor train format

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms.

OpenDILab RL Kubernetes Custom Resource and Operator Lib

Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

Comments

PyTorch OP List(P0)

common

Creation Ops

Indexing, Slicing, Joining, Mutating Ops

Math Ops

Pointwise Ops

Reduction Ops

Comparison Ops

Other Ops

BLAS and LAPACK Ops

PyTorch OP Doc List

P0

P1

P2

dev(hansbug): add stream support for paralleling the calculations in tree

Failure when try to convert between numpy and torch on Windows Python3.10

Releases(v0.4.0)

v0.4.0(Aug 14, 2022)

What's Changed

New Contributors

v0.3.0(Jul 15, 2022)

What's Changed

v0.2.1(Mar 22, 2022)

What's Changed

New Contributors

v0.2.0(Jan 4, 2022)

v0.1.0(Dec 26, 2021)

v0.0.1(Sep 30, 2021)

Owner

OpenDILab

This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)

Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Implicit Deep Adaptive Design (iDAD)

Py-faster-rcnn - Faster R-CNN (Python implementation)

Optimising chemical reactions using machine learning

A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.

Experiments and examples converting Transformers to ONNX

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

Deep Compression for Dense Point Cloud Maps.

Image-to-Image Translation in PyTorch

JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Active Offline Policy Selection With Python

A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.