| Name | kaiko-eva JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | None |
| Summary | Evaluation Framework for oncology foundation models. |
| upload_time | 2024-12-20 11:19:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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| keywords |
machine-learning
evaluation-framework
oncology
foundation-models
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
<div align="center">
<br />
<img src="https://github.com/kaiko-ai/eva/blob/main/docs/images/eva-logo.png?raw=true" width="340">
<br />
<br />
_Oncology FM Evaluation Framework by kaiko.ai_
[](https://pypi.python.org/pypi/kaiko-eva)
[](https://kaiko-ai.github.io/eva/latest)
[](https://github.com/kaiko-ai/eva#license)<br>
[](https://openreview.net/forum?id=FNBQOPj18N¬eId=FNBQOPj18N)
<p align="center">
<a href="https://github.com/kaiko-ai/eva#installation">Installation</a> •
<a href="https://github.com/kaiko-ai/eva#how-to-use">How To Use</a> •
<a href="https://github.com/kaiko-ai/eva#quick-start">Quick Start</a> •
<a href="https://kaiko-ai.github.io/eva/">Documentation</a> •
<a href="https://kaiko-ai.github.io/eva/dev/datasets/">Datasets</a> •
<a href="https://github.com/kaiko-ai/eva#benchmarks">Benchmarks</a> <br>
<a href="https://github.com/kaiko-ai/eva#contributing">Contribute</a> •
<a href="https://github.com/kaiko-ai/eva#acknowledgements">Acknowledgements</a>
</p>
</div>
<br />
_`eva`_ is an evaluation framework for oncology foundation models (FMs) by [kaiko.ai](https://kaiko.ai/).
Check out the [documentation](https://kaiko-ai.github.io/eva/) for more information.
### Highlights:
- Easy and reliable benchmark of Oncology FMs
- Supports path-level classification, slide-level classification and semantic segmentation downstream tasks
- Automatic embedding inference and evaluation of a downstream task
- Native support of popular medical [datasets](https://kaiko-ai.github.io/eva/dev/datasets/) and models
- Produce statistics over multiple evaluation fits and multiple metrics
## Installation
Simple installation from PyPI:
```sh
# to install the core version only
pip install kaiko-eva
# to install the expanded `vision` version
pip install 'kaiko-eva[vision]'
# to install everything
pip install 'kaiko-eva[all]'
```
To install the latest version of the `main` branch:
```sh
pip install "kaiko-eva[all] @ git+https://github.com/kaiko-ai/eva.git"
```
You can verify that the installation was successful by executing:
```sh
eva --version
```
## How To Use
_`eva`_ can be used directly from the terminal as a CLI tool as follows:
```sh
eva {fit,predict,predict_fit} --config url/or/path/to/the/config.yaml
```
_`eva`_ uses [jsonargparse](https://jsonargparse.readthedocs.io/en/v4.31.0/) to
make it easily configurable by automatically generating command line interfaces (CLIs),
which allows to call *any* Python object from the command line. Moreover, the configuration structure is always in sync with the code. Thus, _`eva`_ can be used either directly from Python or as a CLI tool (recommended).
For more information, please refer to the [documentation](https://kaiko-ai.github.io/eva/dev/user-guide/tutorials/offline_vs_online/).
<details>
<summary>Learn about Configs</summary>
The following interfaces are identical:
<table>
<tr>
<th>Python interface</th>
<th>Configuration file</th>
</tr>
<tr>
<td>
<sub>
```Python
# main.py
# execute with: `python main.py`
from torch import nn
from eva import core
from eva.vision import datasets, transforms
# initialize trainer
trainer = core.Trainer(max_steps=100)
# initialize model
model = core.HeadModule(
backbone=nn.Flatten(),
head=nn.Linear(150528, 4),
criterion=nn.CrossEntropyLoss(),
)
# initialize data
data = core.DataModule(
datasets=core.DatasetsSchema(
train=datasets.BACH(
root="data/bach",
split="train",
download=True,
transforms=transforms.ResizeAndCrop(),
),
),
dataloaders=core.DataloadersSchema(
train=core.DataLoader(batch_size=32),
),
)
# perform fit
pipeline = core.Interface()
pipeline.fit(trainer, model=model, data=data)
```
</sub>
<td>
<sub>
```yaml
# main.yaml
# execute with: `eva fit --config main.yaml`
---
trainer:
class_path: eva.Trainer
init_args:
max_steps: 100
model:
class_path: eva.HeadModule
init_args:
backbone: torch.nn.Flatten
head:
class_path: torch.nn.Linear
init_args:
in_features: 150528
out_features: 4
criterion: torch.nn.CrossEntropyLoss
data:
class_path: eva.DataModule
init_args:
datasets:
train:
class_path: eva.vision.datasets.BACH
init_args:
root: ./data/bach
split: train
download: true
transforms: eva.vision.transforms.ResizeAndCrop
dataloaders:
train:
batch_size: 32
```
</sub>
</td>
</tr>
</table>
The `.yaml` file defines the functionality of _`eva`_
by parsing and translating its content to Python objects directly.
Native supported configs can be found at the
[configs](https://github.com/kaiko-ai/eva/tree/main/configs) directory
of the repo, which can be both locally stored or remote.
</details>
## Quick Start
We define two types of evaluations: **online** and **offline**.
While online fit uses the backbone (FM) to perform forward passes
during the fitting process, offline fit first generates embeddings
with the backbone and then fits the model using these embeddings as
input, resulting in a faster evaluation.
Here are some examples to get you started:
- Perform a downstream offline **classification** evaluation of `DINO ViT-S/16`
on the `BACH` dataset with linear probing by first inferring the embeddings
and then performing 5 sequential fits:
```sh
export DOWNLOAD_DATA=true
eva predict_fit --config https://raw.githubusercontent.com/kaiko-ai/eva/main/configs/vision/dino_vit/offline/bach.yaml
```
- Perform a downstream online **segmentation** evaluation of `DINO ViT-S/16` on the
`MoNuSAC` dataset with the `ConvDecoderMS` decoder:
```sh
export DOWNLOAD_DATA=true
eva fit --config https://raw.githubusercontent.com/kaiko-ai/eva/main/configs/vision/dino_vit/online/monusac.yaml
```
For more examples, take a look at the [configs](https://github.com/kaiko-ai/eva/tree/main/configs)
and [tutorials](https://kaiko-ai.github.io/eva/dev/user-guide/advanced/replicate_evaluations/).
> [!NOTE]
> All the datasets that support automatic download in the repo have by default the option to automatically download set to false.
> For automatic download you have to manually set the environmental variable `DOWNLOAD_DATA=true` or in the configuration file `download=true`.
## Leaderboards
The following table shows the FMs we have evaluated with _`eva`_. For more detailed information about the evaluation process, please refer to our [documentation](https://kaiko-ai.github.io/eva/main/leaderboards/).
## Contributing
_`eva`_ is an open source project and welcomes contributions of all kinds. Please checkout the [developer](./docs/DEVELOPER_GUIDE.md)
and [contributing guide](./docs/CONTRIBUTING.md) for help on how to do so.
All contributors must follow the [code of conduct](./docs/CODE_OF_CONDUCT.md).
## Acknowledgements
Our codebase is built using multiple opensource contributions
<div align="center">
[](https://github.com/pre-commit/pre-commit)
[](https://pytorch.org/get-started/locally/)
[](https://pytorchlightning.ai/)<br>
[](https://black.readthedocs.io/en/stable/)
[](https://pycqa.github.io/isort/)
[](https://github.com/astral-sh/ruff)
[](https://microsoft.github.io/pyright/)<br>
[](https://pdm-project.org)
[](https://github.com/wntrblm/nox)
[](https://squidfunk.github.io/mkdocs-material/)
</div>
## Citation
If you find this repository useful, please consider giving a star ⭐ and adding the following citation:
```bibtex
@inproceedings{kaiko.ai2024eva,
title={eva: Evaluation framework for pathology foundation models},
author={kaiko.ai and Ioannis Gatopoulos and Nicolas K{\"a}nzig and Roman Moser and Sebastian Ot{\'a}lora},
booktitle={Medical Imaging with Deep Learning},
year={2024},
url={https://openreview.net/forum?id=FNBQOPj18N}
}
```
<br />
<div align="center">
<img src="https://github.com/kaiko-ai/eva/blob/main/docs/images/kaiko-logo.png?raw=true" width="200">
</div>
Raw data
{
"_id": null,
"home_page": null,
"name": "kaiko-eva",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Ioannis Gatopoulos <ioannis@kaiko.ai>, Nicolas K\u00e4nzig <nicolas@kaiko.ai>, Roman Moser <roman@kaiko.ai>",
"keywords": "machine-learning, evaluation-framework, oncology, foundation-models",
"author": null,
"author_email": "Ioannis Gatopoulos <ioannis@kaiko.ai>, Nicolas K\u00e4nzig <nicolas@kaiko.ai>, Roman Moser <roman@kaiko.ai>",
"download_url": "https://files.pythonhosted.org/packages/66/94/507067dbfbd4e95327cfadd76b0dfc5f8e3d86b1a0d3fda7168d79ed5de1/kaiko_eva-0.1.8.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\n<br />\n\n<img src=\"https://github.com/kaiko-ai/eva/blob/main/docs/images/eva-logo.png?raw=true\" width=\"340\">\n\n<br />\n<br />\n\n_Oncology FM Evaluation Framework by kaiko.ai_\n\n[](https://pypi.python.org/pypi/kaiko-eva)\n[](https://kaiko-ai.github.io/eva/latest)\n[](https://github.com/kaiko-ai/eva#license)<br>\n[](https://openreview.net/forum?id=FNBQOPj18N¬eId=FNBQOPj18N)\n\n<p align=\"center\">\n <a href=\"https://github.com/kaiko-ai/eva#installation\">Installation</a> \u2022\n <a href=\"https://github.com/kaiko-ai/eva#how-to-use\">How To Use</a> \u2022\n <a href=\"https://github.com/kaiko-ai/eva#quick-start\">Quick Start</a> \u2022\n <a href=\"https://kaiko-ai.github.io/eva/\">Documentation</a> \u2022\n <a href=\"https://kaiko-ai.github.io/eva/dev/datasets/\">Datasets</a> \u2022\n <a href=\"https://github.com/kaiko-ai/eva#benchmarks\">Benchmarks</a> <br>\n <a href=\"https://github.com/kaiko-ai/eva#contributing\">Contribute</a> \u2022\n <a href=\"https://github.com/kaiko-ai/eva#acknowledgements\">Acknowledgements</a>\n</p>\n\n</div>\n\n<br />\n\n_`eva`_ is an evaluation framework for oncology foundation models (FMs) by [kaiko.ai](https://kaiko.ai/).\nCheck out the [documentation](https://kaiko-ai.github.io/eva/) for more information.\n\n### Highlights:\n- Easy and reliable benchmark of Oncology FMs\n- Supports path-level classification, slide-level classification and semantic segmentation downstream tasks\n- Automatic embedding inference and evaluation of a downstream task\n- Native support of popular medical [datasets](https://kaiko-ai.github.io/eva/dev/datasets/) and models\n- Produce statistics over multiple evaluation fits and multiple metrics\n\n## Installation\n\nSimple installation from PyPI:\n```sh\n# to install the core version only\npip install kaiko-eva\n\n# to install the expanded `vision` version\npip install 'kaiko-eva[vision]'\n\n# to install everything\npip install 'kaiko-eva[all]'\n```\n\nTo install the latest version of the `main` branch:\n```sh\npip install \"kaiko-eva[all] @ git+https://github.com/kaiko-ai/eva.git\"\n```\n\nYou can verify that the installation was successful by executing:\n```sh\neva --version\n```\n\n## How To Use\n\n_`eva`_ can be used directly from the terminal as a CLI tool as follows:\n```sh\neva {fit,predict,predict_fit} --config url/or/path/to/the/config.yaml \n```\n\n_`eva`_ uses [jsonargparse](https://jsonargparse.readthedocs.io/en/v4.31.0/) to\nmake it easily configurable by automatically generating command line interfaces (CLIs),\nwhich allows to call *any* Python object from the command line. Moreover, the configuration structure is always in sync with the code. Thus, _`eva`_ can be used either directly from Python or as a CLI tool (recommended).\n\nFor more information, please refer to the [documentation](https://kaiko-ai.github.io/eva/dev/user-guide/tutorials/offline_vs_online/).\n\n<details>\n <summary>Learn about Configs</summary>\n\nThe following interfaces are identical:\n<table>\n<tr>\n<th>Python interface</th>\n<th>Configuration file</th>\n</tr>\n<tr>\n<td>\n<sub>\n\n```Python\n# main.py\n# execute with: `python main.py`\n\nfrom torch import nn\n\nfrom eva import core\nfrom eva.vision import datasets, transforms\n\n# initialize trainer\ntrainer = core.Trainer(max_steps=100)\n\n# initialize model\nmodel = core.HeadModule(\n backbone=nn.Flatten(),\n head=nn.Linear(150528, 4),\n criterion=nn.CrossEntropyLoss(),\n)\n\n# initialize data\ndata = core.DataModule(\n datasets=core.DatasetsSchema(\n train=datasets.BACH(\n root=\"data/bach\",\n split=\"train\",\n download=True,\n transforms=transforms.ResizeAndCrop(),\n ),\n ),\n dataloaders=core.DataloadersSchema(\n train=core.DataLoader(batch_size=32),\n ),\n)\n\n# perform fit\npipeline = core.Interface()\npipeline.fit(trainer, model=model, data=data)\n```\n</sub>\n<td>\n<sub>\n\n```yaml\n# main.yaml\n# execute with: `eva fit --config main.yaml`\n\n---\ntrainer:\n class_path: eva.Trainer\n init_args:\n max_steps: 100\nmodel:\n class_path: eva.HeadModule\n init_args:\n backbone: torch.nn.Flatten\n head:\n class_path: torch.nn.Linear\n init_args:\n in_features: 150528\n out_features: 4\n criterion: torch.nn.CrossEntropyLoss\ndata:\n class_path: eva.DataModule\n init_args:\n datasets:\n train:\n class_path: eva.vision.datasets.BACH\n init_args:\n root: ./data/bach\n split: train\n download: true\n transforms: eva.vision.transforms.ResizeAndCrop\n dataloaders:\n train:\n batch_size: 32\n```\n</sub>\n</td>\n</tr>\n</table>\n\nThe `.yaml` file defines the functionality of _`eva`_\nby parsing and translating its content to Python objects directly.\nNative supported configs can be found at the\n[configs](https://github.com/kaiko-ai/eva/tree/main/configs) directory\nof the repo, which can be both locally stored or remote.\n\n</details>\n\n## Quick Start\n\nWe define two types of evaluations: **online** and **offline**.\nWhile online fit uses the backbone (FM) to perform forward passes\nduring the fitting process, offline fit first generates embeddings\nwith the backbone and then fits the model using these embeddings as\ninput, resulting in a faster evaluation.\n\nHere are some examples to get you started:\n\n- Perform a downstream offline **classification** evaluation of `DINO ViT-S/16`\non the `BACH` dataset with linear probing by first inferring the embeddings\nand then performing 5 sequential fits:\n ```sh\n export DOWNLOAD_DATA=true\n eva predict_fit --config https://raw.githubusercontent.com/kaiko-ai/eva/main/configs/vision/dino_vit/offline/bach.yaml\n ```\n\n- Perform a downstream online **segmentation** evaluation of `DINO ViT-S/16` on the\n`MoNuSAC` dataset with the `ConvDecoderMS` decoder:\n ```sh\n export DOWNLOAD_DATA=true\n eva fit --config https://raw.githubusercontent.com/kaiko-ai/eva/main/configs/vision/dino_vit/online/monusac.yaml\n ```\n\nFor more examples, take a look at the [configs](https://github.com/kaiko-ai/eva/tree/main/configs)\nand [tutorials](https://kaiko-ai.github.io/eva/dev/user-guide/advanced/replicate_evaluations/).\n\n> [!NOTE]\n> All the datasets that support automatic download in the repo have by default the option to automatically download set to false.\n> For automatic download you have to manually set the environmental variable `DOWNLOAD_DATA=true` or in the configuration file `download=true`.\n\n## Leaderboards\n\nThe following table shows the FMs we have evaluated with _`eva`_. For more detailed information about the evaluation process, please refer to our [documentation](https://kaiko-ai.github.io/eva/main/leaderboards/).\n\n\n\n\n## Contributing\n\n_`eva`_ is an open source project and welcomes contributions of all kinds. Please checkout the [developer](./docs/DEVELOPER_GUIDE.md)\nand [contributing guide](./docs/CONTRIBUTING.md) for help on how to do so.\n\nAll contributors must follow the [code of conduct](./docs/CODE_OF_CONDUCT.md).\n\n\n## Acknowledgements\n\nOur codebase is built using multiple opensource contributions\n\n<div align=\"center\">\n\n[](https://github.com/pre-commit/pre-commit)\n[](https://pytorch.org/get-started/locally/)\n[](https://pytorchlightning.ai/)<br>\n[](https://black.readthedocs.io/en/stable/)\n[](https://pycqa.github.io/isort/)\n[](https://github.com/astral-sh/ruff)\n[](https://microsoft.github.io/pyright/)<br>\n[](https://pdm-project.org)\n[](https://github.com/wntrblm/nox)\n[](https://squidfunk.github.io/mkdocs-material/)\n\n</div>\n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star \u2b50 and adding the following citation:\n\n```bibtex\n@inproceedings{kaiko.ai2024eva,\n title={eva: Evaluation framework for pathology foundation models},\n author={kaiko.ai and Ioannis Gatopoulos and Nicolas K{\\\"a}nzig and Roman Moser and Sebastian Ot{\\'a}lora},\n booktitle={Medical Imaging with Deep Learning},\n year={2024},\n url={https://openreview.net/forum?id=FNBQOPj18N}\n}\n```\n\n<br />\n\n<div align=\"center\">\n <img src=\"https://github.com/kaiko-ai/eva/blob/main/docs/images/kaiko-logo.png?raw=true\" width=\"200\">\n</div>\n",
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"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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