From 2a2154e6d313c37815cae0137f466886000d6eb6 Mon Sep 17 00:00:00 2001 From: benjijamorris <54606172+benjijamorris@users.noreply.github.com> Date: Fri, 20 Oct 2023 09:50:36 -0700 Subject: [PATCH] Admin/logo (#301) * fix test name * add logos --------- Co-authored-by: Benjamin Morris Co-authored-by: Benjamin Morris --- README.md | 35 ++++++++++++++++++++----------- docs/CytoDL-logo-1C-onDark.png | Bin 0 -> 4069 bytes docs/CytoDL-logo-1C-onLight.png | Bin 0 -> 4112 bytes docs/CytoDL-overview_dark_1.png | Bin 0 -> 163493 bytes docs/CytoDL-overview_light_1.png | Bin 0 -> 163599 bytes tests/conftest.py | 2 +- 6 files changed, 24 insertions(+), 13 deletions(-) create mode 100644 docs/CytoDL-logo-1C-onDark.png create mode 100644 docs/CytoDL-logo-1C-onLight.png create mode 100644 docs/CytoDL-overview_dark_1.png create mode 100644 docs/CytoDL-overview_light_1.png diff --git a/README.md b/README.md index 441459f7f..517e77614 100644 --- a/README.md +++ b/README.md @@ -1,30 +1,41 @@
-# cyto-dl + + + + + PyTorch Lightning Config: Hydra Template
- -

- + + + + +

## Description -In an effort to spend more work on methods development, simplify maintenance, -and create a unified framework for all of AICS's deep learning tools, we have -created `cyto-ml`. As the underlying framework is based on [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template), we recommend that you familiarize yourself with their (short) docs for detailed instructions on running training, overrides, etc. +As part of the [Allen Institute for Cell Science's](allencell.org) mission to understand the principles by which human induced pluripotent stem cells establish and maintain robust dynamic localization of cellular structure, `CytoDL` aims to unify deep learning approaches for understanding 2D and 3D biological data as images, point clouds, and tabular data. + +The bulk of `CytoDL`'s underlying structure bases the [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template) organization - we highly recommend that you familiarize yourself with their (short) docs for detailed instructions on running training, overrides, etc. + +Our currently available code is roughly split into two domains: image-to-image transformations and representation learning. The image-to-image code (denoted im2im) contains configuration files detailing how to train and predict using models for resolution enhancement (e.g. predicting 100x images from 20x images), semantic and instance segmentation, and label-free prediction. Representation learning code includes a wide variety of Variational Auto Encoder (VAE) architectures. + +As we rely on recent versions of pytorch, users wishing to train and run models on GPU hardware will need up-to-date NVIDIA drivers. Users with older GPUs should not expect code to work out of the box. Similarly, we do not currently support training/predicting on Mac GPUs. In most cases, cpu-based training should work when GPU training fails. + +For Im2Im models, we provide a handful of example 3D images for training the basic image-to-image tranformation-type models and default model configuration files for users to become comfortable with the framework and prepare them for training and applying these models on their own data. ## How to run -Install dependencies +Install dependencies. Dependencies are platform specific, please replace `PLATFORM` with your platform - either `linux`, `windows`, or `mac` ```bash # clone project @@ -35,16 +46,16 @@ cd cyto-dl conda create -n myenv python=3.9 conda activate myenv -pip install -r requirements/linux/requirements.txt +pip install -r requirements/PLATFORM/requirements.txt # [OPTIONAL] install extra dependencies - equivariance related -pip install -r requirements/linux/equiv-requirements.txt +pip install -r requirements/PLATFORM/equiv-requirements.txt pip install -e . #[OPTIONAL] if you want to use default experiments on example data -python scripts/download_test_data.py +python scripts download_test_data.py ``` Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/) diff --git a/docs/CytoDL-logo-1C-onDark.png b/docs/CytoDL-logo-1C-onDark.png new file mode 100644 index 0000000000000000000000000000000000000000..1102212becc241ad930affb664f6640093105129 GIT binary patch literal 4069 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@@ -13,7 +13,7 @@ OmegaConf.register_new_resolver("eval", eval) # Experiment configs to test -experiment_types = ["instance_segmentation", "gan", "segmentation", "labelfree"] +experiment_types = ["instance_seg", "gan", "segmentation", "labelfree"] @pytest.fixture(scope="package", params=experiment_types)