微信开发中 ACCESS TOKEN 过期失效的解决方案详解
648
2022-10-27
基于PyTorch的深度学习项目高级框架
bootstrap.pytorch is a high-level extension for deep learning projects with PyTorch. It aims at accelerating research projects and prototyping by providing a powerful workflow focused on your dataset and model.
And it is:
ScalableModularShareableExtendableUncomplicatedBuilt for reproducibilityEasy to log and plot anything
Unlike many others, bootstrap.pytorch is not a wrapper over pytorch, but a powerful extension.
Quick tour
To run an experiment (training + evaluation):
python -m bootstrap.run -o myproject/options/sgd.yaml
To display parsed options from the yaml file:
python -m bootstrap.run -o myproject/options/sgd.yaml -h
Running an experiment will create 4 files, here is an example with mnist:
options.yaml contains the options used for the experimentlogs.txt contains all the information given to the loggerlogs.json contains the following data: train_epoch.loss, train_batch.loss, eval_epoch.accuracy_top1, etcview.html contains training and evaluation curves with javascript utilities (plotly)
To save the next experiment in a specific directory:
python -m bootstrap.run -o myproject/options/sgd.yaml --exp.dir logs/custom
To reload an experiment:
python -m bootstrap.run -o logs/custom/options.yaml --exp.resume last
Documentation
The package reference is available on the documentation website.
It also contains some notes:
InstallationConceptsQuickstartDirectoriesExamples
Official project modules
mnist.bootstrap.pytorch is a useful example for starting a quick project with bootstrapvision.bootstrap.pytorch contains utilities to train image classifier, object detector, etc. on usual datasets like imagenet, cifar10, cifar100, coco, visual genome, etcrecipe1m.bootstrap.pytorch is a project for image-text retrieval related to the Recip1M dataset developped in the context of a SIGIR18 paperblock.bootstrap.pytorch is a project focused on fusion modules related to the VQA 2.0, TDIUC and VRD datasets developped in the context of a AAAI19 paper
Poster
Contribute
Contributions to this repository are welcome and encouraged. We also have a public trello board with prospect features, as well as an indication of those currently being developed. Feel free to contact us with suggestions, or send a pull request.
We use flake8 to perform early semantic checking of submitted code. After installing all the requirements in requirements.txt, please run the following to activate the pre-commit hooks for flake8: pre-commit install
To manually trigger the pre-commit checks for a file without creating a commit, you can run the following command: pre-commit run --files
bootstrap.pytorch was conceived and is maintained by Rémi Cadène and Micael Carvalho, with helpful discussions and insights from Thomas Robert and Hedi Ben-Younes. We chose to adopt the [very permissive] BSD-3 license, which allows for commercial and private use, making it compatible with both academy and industry standards.
版权声明:本文内容由网络用户投稿,版权归原作者所有,本站不拥有其著作权,亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容,请联系我们jiasou666@gmail.com 处理,核实后本网站将在24小时内删除侵权内容。
发表评论
暂时没有评论,来抢沙发吧~