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https://github.com/kaylode/theseus
General template for most Pytorch projects
https://github.com/kaylode/theseus
classification computer-vision natural-language-processing object-detection pytorch segmentation structured-data template
Last synced: about 2 months ago
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General template for most Pytorch projects
- Host: GitHub
- URL: https://github.com/kaylode/theseus
- Owner: kaylode
- License: mit
- Created: 2020-07-24T17:46:29.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-09-12T18:25:15.000Z (3 months ago)
- Last Synced: 2024-09-13T07:53:58.868Z (3 months ago)
- Topics: classification, computer-vision, natural-language-processing, object-detection, pytorch, segmentation, structured-data, template
- Language: Python
- Homepage:
- Size: 108 MB
- Stars: 34
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
#
:sailboat: Ship of Theseus
> The ship wherein Theseus and the youth of Athens returned from Crete had thirty oars, and was preserved by the Athenians down even to the time of Demetrius Phalereus, for they took away the old planks as they decayed, putting in new and stronger timber in their places, insomuch that this ship became a standing example among the philosophers, for the logical question of things that grow; one side holding that the ship remained the same, and the other contending that it was not the same.
> — Plutarch, Theseus
-------------------------------------------------------
# :pencil: Instructions
### Installation
- Create virtual environment: `conda create -n myenv python=3.10`
- Install [Pytorch](https://pytorch.org/). Currently support `torch==1.13.1`
- Inside your project, install this package by `git+https://github.com/kaylode/theseus.git@master#egg=theseus[cv,cv.classification,cv.detection,cv.semantic]`
***extra packages can be identified from the pyproject.toml***.
### To adapt for personal project
1. Create your own dataset, dataloader, model, loss function, metric function, ... and register it to the registry so that it can be generated from config at runtime.
2. Customize inherited trainer and pipeline to your need, such as what to do before/after training/validating step,...
3. Write custom callbacks (recommended!), follow [Lightning](https://lightning.ai/docs/pytorch/latest/)
4. Modify configuration file
*See ```theseus/cv/classification``` for example*
### To execute scripts with arguments
- Run the script with `--config-dir` flag with a specified config folder that contains the yaml file. And `--config-name` is that file's name.
Example:
```
python train.py \
--config-dir configs \
--config-name pipeline.yaml
```
- To override arguments inside the .yaml file, follow the instructions from [Hydra](https://hydra.cc/docs/intro/). For example, to train 50 epochs and resume training from checkpoints:
```
python train.py \
--config-dir configs \
--config-name pipeline.yaml \
trainer.args.max_epochs=5000 \
global.resume=checkpoint.pth
```
**Notice: There are no spaces between keys and values in -o flag**
# :school_satchel: Resources
- Example colab notebooks for classification tasks: [](https://colab.research.google.com/drive/1mZmT1B5zI1j_0w1MbP-kq8_Tbcx_tIFq?usp=sharing)
- Example colab notebooks for semantic segmentation tasks: [](https://colab.research.google.com/drive/1VlR1JolMmEX2OtNLvzfnHhHAfI4lJ-Qo?usp=sharing)
# :blue_book: References
- This repo is inspired by https://github.com/vltanh/torchan **Remember to give it a star** .
- The big refactor is mostly adapted from https://github.com/HCMUS-ROBOTICS/ssdf-nncore 's nncore, **which also deserves stars**.