GPU-Accelerated Deep Learning Library in Python

Related tags

Deep Learninghebel
Overview

Hebel

GPU-Accelerated Deep Learning Library in Python

Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.

I no longer actively develop Hebel. If you are looking for a deep learning framework in Python, I now recommend Chainer.

Models

Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future.

Hebel implements dropout as well as L1 and L2 weight decay for regularization.

Optimization

Hebel implements stochastic gradient descent (SGD) with regular and Nesterov momentum.

Compatibility

Currently, Hebel will run on Linux and Windows, and probably Mac OS X (not tested).

Dependencies

  • PyCUDA
  • numpy
  • PyYAML
  • skdata (only for MNIST example)

Installation

Hebel is on PyPi, so you can install it with

pip install hebel

Getting started

Study the yaml configuration files in examples/ and run

python train_model.py examples/mnist_neural_net_shallow.yml

The script will create a directory in examples/mnist where the models and logs are saved.

Read the Getting started guide at hebel.readthedocs.org/en/latest/getting_started.html for more information.

Documentation

hebel.readthedocs.org

Contact

Maintained by Hannes Bretschneider ([email protected]). If your are using Hebel, please let me know whether you find it useful and file a Github issue if you find any bugs or have feature requests.

Citing

http://dx.doi.org/10.5281/zenodo.10050

If you make use of Hebel in your research, please cite it. The BibTeX reference is

@article{Bretschneider:10050,
  author        = "Hannes Bretschneider",
  title         = "{Hebel - GPU-Accelerated Deep Learning Library in Python}",
  month         = "May",
  year          = "2014",
  doi           = "10.5281/zenodo.10050",
  url           = "https://zenodo.org/record/10050",
}

What's with the name?

Hebel is the German word for lever, one of the oldest tools that humans use. As Archimedes said it: "Give me a lever long enough and a fulcrum on which to place it, and I shall move the world."

Comments
  • Contributing PyCUDA routines

    Contributing PyCUDA routines

    Heya

    I stumbled across this project looking for some PyCUDA routines that operate on matrices per-row or per-column. It seems you have a bunch of handy routines for this, which is awesome, e.g. row-wise maximum, add_vec_to_mat etc.

    Would you be willing to contribute them back to PyCUDA? a lot of these routines seem like they'd definitely be useful more widely. And perhaps offering the contribution might give the PyCUDA guys some inspiration or a kick in the arse to create a more general partial reductions API (like numpy's axis=0 arguments) and broadcasting behaviour for element-wise operations on GPUArrays? (I would attempt this myself but my CUDA-fu is weak)

    Just a thought anyway. I would suggest it to them myself but the licencing is different (GPL vs MIT)

    Cheers!

    opened by mjwillson 6
  • [WIP][HEP3] Implement convolution for DNA sequence

    [WIP][HEP3] Implement convolution for DNA sequence

    I am merging my code for training conv-nets from DNA sequence into Hebel. This should be done by the end of January 2015. Please follow this issue if you are interested in using Hebel for learning from DNA sequence or would like to test it.

    Hebel Enhancement Proposal 
    opened by hannes-brt 3
  • Compiling issues with MacOSX

    Compiling issues with MacOSX

    I am trying to compile in Mac OSX yosemite and it seems hebel is not running. i installed PyCUDA and other libraries needed but stuck at this error.

    $ python hebel_test.py Traceback (most recent call last): File "hebel_test.py", line 18, in hebel.init(0) File "/Users/prabhubalakrishnan/Desktop/hebel/hebel/init.py", line 131, in init from pycuda import gpuarray, driver, curandom File "/Library/Python/2.7/site-packages/pycuda-2014.1-py2.7-macosx-10.10-intel.egg/pycuda/gpuarray.py", line 3, in import pycuda.elementwise as elementwise File "/Library/Python/2.7/site-packages/pycuda-2014.1-py2.7-macosx-10.10-intel.egg/pycuda/elementwise.py", line 34, in from pytools import memoize_method File "/Library/Python/2.7/site-packages/pytools-2014.3.5-py2.7.egg/pytools/init.py", line 5, in from six.moves import range, zip, intern, input ImportError: cannot import name intern

    How to fix?

    opened by olddocks 3
  • Global name 'hidden_inputs' is not defined

    Global name 'hidden_inputs' is not defined

    When running optimizer.run(100), an error occurred: global name 'hidden_inputs' is not defined in line 323 of ./hebel/hebel/models/neurals_net.py

    Where to define the global variable 'hidden_inputs'? Thanks!

    opened by Robert0812 3
  • AttributeError: python: undefined symbol: cuPointerGetAttribute

    AttributeError: python: undefined symbol: cuPointerGetAttribute

    [email protected]:~/github/hebel$ echo $LD_LIBRARY_PATH /usr/local/cuda:/usr/local/cuda/bin:/usr/local/cuda/lib64:/home/ubgpu/torch/install/lib:/home/ubgpu/torch/install/lib [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ python train_model.py examples/mnist_neural_net_shallow.yml Traceback (most recent call last): File "train_model.py", line 39, in run_from_config(yaml_src) File "/home/ubgpu/github/hebel/hebel/config.py", line 41, in run_from_config config = load(yaml_src) File "/home/ubgpu/github/hebel/hebel/config.py", line 92, in load proxy_graph = yaml.load(string, **kwargs) File "/usr/local/lib/python2.7/dist-packages/yaml/init.py", line 71, in load return loader.get_single_data() File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 39, in get_single_data return self.construct_document(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 48, in construct_document for dummy in generator: File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 398, in construct_yaml_map value = self.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 318, in multi_constructor mapping = loader.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 318, in multi_constructor mapping = loader.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 323, in multi_constructor classname = try_to_import(tag_suffix) File "/home/ubgpu/github/hebel/hebel/config.py", line 251, in try_to_import exec('import %s' % modulename) File "", line 1, in File "/home/ubgpu/github/hebel/hebel/layers/init.py", line 17, in from .dummy_layer import DummyLayer File "/home/ubgpu/github/hebel/hebel/layers/dummy_layer.py", line 17, in from .hidden_layer import HiddenLayer File "/home/ubgpu/github/hebel/hebel/layers/hidden_layer.py", line 25, in from ..pycuda_ops import linalg File "/home/ubgpu/github/hebel/hebel/pycuda_ops/linalg.py", line 32, in from . import cublas File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cublas.py", line 47, in import cuda File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cuda.py", line 36, in from cudadrv import * File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cudadrv.py", line 233, in _libcuda.cuPointerGetAttribute.restype = int File "/usr/lib/python2.7/ctypes/init.py", line 378, in getattr func = self.getitem(name) File "/usr/lib/python2.7/ctypes/init.py", line 383, in getitem func = self._FuncPtr((name_or_ordinal, self)) AttributeError: python: undefined symbol: cuPointerGetAttribute [email protected]:~/github/hebel$

    opened by andyyuan78 2
  • OSError: CUDA runtime library not found

    OSError: CUDA runtime library not found

    [email protected]:~/github/hebel$ sudo pip install pyCUDA Requirement already satisfied (use --upgrade to upgrade): pyCUDA in /usr/local/lib/python2.7/dist-packages Requirement already satisfied (use --upgrade to upgrade): decorator>=3.2.0 in /usr/local/lib/python2.7/dist-packages (from pyCUDA) Requirement already satisfied (use --upgrade to upgrade): pytools>=2011.2 in /usr/local/lib/python2.7/dist-packages (from pyCUDA) Requirement already satisfied (use --upgrade to upgrade): pytest>=2 in /usr/local/lib/python2.7/dist-packages (from pyCUDA) Requirement already satisfied (use --upgrade to upgrade): appdirs>=1.4.0 in /usr/local/lib/python2.7/dist-packages (from pytools>=2011.2->pyCUDA) Requirement already satisfied (use --upgrade to upgrade): six in /usr/local/lib/python2.7/dist-packages (from pytools>=2011.2->pyCUDA) Requirement already satisfied (use --upgrade to upgrade): py>=1.4.25 in /usr/local/lib/python2.7/dist-packages (from pytest>=2->pyCUDA) [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ python Python 2.7.6 (default, Mar 22 2014, 22:59:56) [GCC 4.8.2] on linux2 Type "help", "copyright", "credits" or "license" for more information.

    quit() [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ sudo pip install PyCUDA Requirement already satisfied (use --upgrade to upgrade): PyCUDA in /usr/local/lib/python2.7/dist-packages Requirement already satisfied (use --upgrade to upgrade): decorator>=3.2.0 in /usr/local/lib/python2.7/dist-packages (from PyCUDA) Requirement already satisfied (use --upgrade to upgrade): pytools>=2011.2 in /usr/local/lib/python2.7/dist-packages (from PyCUDA) Requirement already satisfied (use --upgrade to upgrade): pytest>=2 in /usr/local/lib/python2.7/dist-packages (from PyCUDA) Requirement already satisfied (use --upgrade to upgrade): appdirs>=1.4.0 in /usr/local/lib/python2.7/dist-packages (from pytools>=2011.2->PyCUDA) Requirement already satisfied (use --upgrade to upgrade): six in /usr/local/lib/python2.7/dist-packages (from pytools>=2011.2->PyCUDA) Requirement already satisfied (use --upgrade to upgrade): py>=1.4.25 in /usr/local/lib/python2.7/dist-packages (from pytest>=2->PyCUDA) [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ echo $PYTHONPATH /usr/local/lib/python2.7/dist-packages [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ [email protected]:~/github/hebel$ python train_model.py examples/mnist_neural_net_shallow.yml Traceback (most recent call last): File "train_model.py", line 39, in run_from_config(yaml_src) File "/home/ubgpu/github/hebel/hebel/config.py", line 41, in run_from_config config = load(yaml_src) File "/home/ubgpu/github/hebel/hebel/config.py", line 92, in load proxy_graph = yaml.load(string, **kwargs) File "/usr/local/lib/python2.7/dist-packages/yaml/init.py", line 71, in load return loader.get_single_data() File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 39, in get_single_data return self.construct_document(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 48, in construct_document for dummy in generator: File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 398, in construct_yaml_map value = self.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 318, in multi_constructor mapping = loader.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 318, in multi_constructor mapping = loader.construct_mapping(node) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 208, in construct_mapping return BaseConstructor.construct_mapping(self, node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 133, in construct_mapping value = self.construct_object(value_node, deep=deep) File "/usr/local/lib/python2.7/dist-packages/yaml/constructor.py", line 90, in construct_object data = constructor(self, tag_suffix, node) File "/home/ubgpu/github/hebel/hebel/config.py", line 323, in multi_constructor classname = try_to_import(tag_suffix) File "/home/ubgpu/github/hebel/hebel/config.py", line 251, in try_to_import exec('import %s' % modulename) File "", line 1, in File "/home/ubgpu/github/hebel/hebel/layers/init.py", line 17, in from .dummy_layer import DummyLayer File "/home/ubgpu/github/hebel/hebel/layers/dummy_layer.py", line 17, in from .hidden_layer import HiddenLayer File "/home/ubgpu/github/hebel/hebel/layers/hidden_layer.py", line 25, in from ..pycuda_ops import linalg File "/home/ubgpu/github/hebel/hebel/pycuda_ops/linalg.py", line 32, in from . import cublas File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cublas.py", line 47, in import cuda File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cuda.py", line 35, in from cudart import * File "/home/ubgpu/github/hebel/hebel/pycuda_ops/cudart.py", line 60, in raise OSError('CUDA runtime library not found') OSError: CUDA runtime library not found

    opened by andyyuan78 1
  • AttributeError: 'NoneType' object has no attribute 'cudaGetErrorString'

    AttributeError: 'NoneType' object has no attribute 'cudaGetErrorString'

    at commit a7f4cbb91c029c344921db76850bf9dc8eb47af4 with python 2.7.6 I try: 'python train_model.py examples/mnist_neural_net_shallow.yml' And i get the following

    Traceback (most recent call last):
  File "train_model.py", line 39, in <module>
    run_from_config(yaml_src)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 41, in run_from_config
    config = load(yaml_src)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 92, in load
    proxy_graph = yaml.load(string, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/yaml/__init__.py", line 71, in load
    return loader.get_single_data()
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 39, in get_single_data
    return self.construct_document(node)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 48, in construct_document
    for dummy in generator:
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 398, in construct_yaml_map
    value = self.construct_mapping(node)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 208, in construct_mapping
    return BaseConstructor.construct_mapping(self, node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 133, in construct_mapping
    value = self.construct_object(value_node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 90, in construct_object
    data = constructor(self, tag_suffix, node)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 318, in multi_constructor
    mapping = loader.construct_mapping(node)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 208, in construct_mapping
    return BaseConstructor.construct_mapping(self, node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 133, in construct_mapping
    value = self.construct_object(value_node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 90, in construct_object
    data = constructor(self, tag_suffix, node)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 318, in multi_constructor
    mapping = loader.construct_mapping(node)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 208, in construct_mapping
    return BaseConstructor.construct_mapping(self, node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 133, in construct_mapping
    value = self.construct_object(value_node, deep=deep)
  File "/usr/local/lib/python2.7/site-packages/yaml/constructor.py", line 90, in construct_object
    data = constructor(self, tag_suffix, node)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 323, in multi_constructor
    classname = try_to_import(tag_suffix)
  File "/Users/epic/Documents/git/hebel/hebel/config.py", line 251, in try_to_import
    exec('import %s' % modulename)
  File "<string>", line 1, in <module>
  File "/Users/epic/Documents/git/hebel/hebel/layers/__init__.py", line 17, in <module>
    from .dummy_layer import DummyLayer
  File "/Users/epic/Documents/git/hebel/hebel/layers/dummy_layer.py", line 17, in <module>
    from .hidden_layer import HiddenLayer
  File "/Users/epic/Documents/git/hebel/hebel/layers/hidden_layer.py", line 25, in <module>
    from ..pycuda_ops import linalg
  File "/Users/epic/Documents/git/hebel/hebel/pycuda_ops/linalg.py", line 32, in <module>
    from . import cublas
  File "/Users/epic/Documents/git/hebel/hebel/pycuda_ops/cublas.py", line 47, in <module>
    import cuda
  File "/Users/epic/Documents/git/hebel/hebel/pycuda_ops/cuda.py", line 35, in <module>
    from cudart import *
  File "/Users/epic/Documents/git/hebel/hebel/pycuda_ops/cudart.py", line 142, in <module>
    _libcudart.cudaGetErrorString.restype = ctypes.c_char_p
AttributeError: 'NoneType' object has no attribute 'cudaGetErrorString'
    opened by epichub 1
  • Report a tiny bug in running example script

    Report a tiny bug in running example script

    Example script data_providers.py imports skdata by
    from skdata.mnist.view import OfficialVectorClassification

    It should be skdata.mnist.views, otherwise errors occur.

    opened by Robert0812 1
  • Missing packages added.

    Missing packages added.

    It's not enough to specify root package (i.e. hebel) in packages argument of setup.py. In order to successfully install hebel, subpackages must be listed as well.

    opened by mnowotka 0
  • docs: fix simple typo, initalized -> initialized

    docs: fix simple typo, initalized -> initialized

    There is a small typo in hebel/layers/hidden_layer.py, hebel/layers/linear_regression_layer.py, hebel/layers/logistic_layer.py, hebel/layers/softmax_layer.py.

    Should read initialized rather than initalized.

    Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

    opened by timgates42 0
  • Small documentation enhancement request

    Small documentation enhancement request

    Hi there, I really appreciate Hebel. It was a good first step for me to "take the plunge" into using GPU.

    I struggled a bit after going through the example (MNIST) script. In particular, it wasn't clear how to have the model predict new data (i.e., data you don't have targets for).

    The first (small) stumble was what to with the DataProvider. I just put in dummy zero targets. Perhaps targets could be an optional field somehow?

    A more thorny issue was how to actually do the predictions. I couldn't for the life of me figure out how to feed the DataProvider data into the feed_forward without getting the error:

      File "/usr/local/lib/python2.7/dist-packages/hebel/models/neural_net.py", line 422, in feed_forward
    prediction=prediction))
  File "/usr/local/lib/python2.7/dist-packages/hebel/layers/input_dropout.py", line 96, in feed_forward
    return (input_data * (1 - self.dropout_probability),)
TypeError: unsupported operand type(s) for *: 'MiniBatchDataProvider' and 'float'

    This was my original attempt:

    # After loading in the data . . .
Xv = Xv.astype(np.float32)
yv = pd.get_dummies(yv).values.astype(np.float32)
valid_data = MiniBatchDataProvider(Xv, yv, batch_size=5000)

    I finally resorted to useing a gpu array which worked:

    from pycuda import gpuarray
valid_data = gpuarray.to_gpu(Xt)
y_pred = model.feed_forward(valid_data, return_cache=False, prediction=True).get()

    The .get() at the end of the last statement was also something I had to figure out going through code.

    Having an example in the documentation would be helpful.

    opened by walterreade 1
Releases(v0.02.1)
Owner
Hannes Bretschneider
Postdoctoral Fellow in the Blencowe Lab at University of Toronto
Hannes Bretschneider
GitHub Repository
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision Kehong Gong*, Bingbing Li*, Jianfeng Zhang*, Ta

256 Dec 28, 2022
Lab Materials for MIT 6.S191: Introduction to Deep Learning

This repository contains all of the code and software labs for MIT 6.S191: Introduction to Deep Learning! All lecture slides and videos are available

Alexander Amini 5.6k Dec 26, 2022
Pytorch code for semantic segmentation using ERFNet

ERFNet (PyTorch version) This code is a toolbox that uses PyTorch for training and evaluating the ERFNet architecture for semantic segmentation. For t

Edu 394 Jan 01, 2023
Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

gapmm2: gapped alignment using minimap2 This tool is a wrapper for minimap2 to r

Jon Palmer 2 Jan 27, 2022
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

Ritchie Ng 9.2k Jan 02, 2023
Official implementation of the paper ``Unifying Nonlocal Blocks for Neural Networks'' (ICCV'21)

Spectral Nonlocal Block Overview Official implementation of the paper: Unifying Nonlocal Blocks for Neural Networks (ICCV'21) Spectral View of Nonloca

91 Dec 14, 2022
Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

snc4onnx Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX. https://github.com/PINTO0309/simple-onnx-processing-tools 1.

Katsuya Hyodo 8 Oct 13, 2022
Virtual Dance Reality Stage: a feature that offers you to share a stage with another user virtually

Portrait Segmentation using Tensorflow This script removes the background from an input image. You can read more about segmentation here Setup The scr

291 Dec 24, 2022
Qlib is an AI-oriented quantitative investment platform

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

Microsoft 10.1k Dec 30, 2022
Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra

850-Safra-DS-ModuloI Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra Para aprender mais Git https://learngitbranc

Brian Nunes 7 Dec 10, 2022
Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"

BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A

Nguyen Duy Kien 111 Dec 07, 2022
Predictive Modeling on Electronic Health Records(EHR) using Pytorch

Predictive Modeling on Electronic Health Records(EHR) using Pytorch Overview Although there are plenty of repos on vision and NLP models, there are ve

81 Jan 01, 2023
Trainable Bilateral Filter Layer (PyTorch)

Trainable Bilateral Filter Layer (PyTorch) This repository contains our GPU-accelerated trainable bilateral filter layer (three spatial and one range

FabianWagner 26 Dec 25, 2022
Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

HAIS Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021) by Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang*. (*) Corresp

Hust Visual Learning Team 145 Jan 05, 2023
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
SatelliteSfM - A library for solving the satellite structure from motion problem

Satellite Structure from Motion Maintained by Kai Zhang. Overview This is a libr

Kai Zhang 190 Dec 08, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
Code for Deep Single-image Portrait Image Relighting

Deep Single-Image Portrait Relighting [Project Page] Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs. In ICCV, 2019 Overview Test script for

438 Jan 05, 2023
Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiati

8 Aug 28, 2022