Pyorch 3D Integrated Cell

Building a 3D Integrated Cell: https://www.biorxiv.org/content/early/2017/12/21/238378

For the original 2D manuscript and software:

Generative Modeling with Conditional Autoencoders: Building an Integrated Cell
Manuscript: https://arxiv.org/abs/1705.00092
GitHub: https://github.com/AllenCellModeling/torch_integrated_cell

Installation

Installing on linux is recommended.

prerequisites

Running on docker is recommended, though not required.

• install pytorch on docker / nvidia-docker as in e.g. this guide: https://github.com/AllenCellModeling/docker_pytorch_extended
  Note: The model will not converge with pytorch versions later that 0.20 due to changes cuDNN. Make sure your version has cuDNN 7.0.2 or earlier.
• download the training images: http://downloads.allencell.org/publication-data/building-a-3d-integrated-cell/
  Note: This tarball will decompress to about 150gb

Running the Code

After you clone this repository, you will need to edit the mount points for the images in start_pytorch_docker.sh to point to where you saved them.

Example of changed mount points in dockerfile:

nvidia-docker run -it \
	-v /allen/aics/modeling/jacksonb/projects:/root/projects \
  	-v /allen/aics/modeling/jacksonb/results:/root/results \
	-v /allen/aics/modeling/jacksonb/data/ipp_17_10_25:/root/data/ipp/ipp_17_10_25 \
	rorydm/pytorch_extras:jupyter \
	bash

Replace any 'jacksonb' or 'gregj' paths with your paths and replace the 'rorydm' or 'gregj' docker image with your docker image tag.

Once those locations are properly set, you can start the docker image with

bash start_pytorch_docker.sh

Once you're in the docker container, you can train the model with

bash start_training.sh

This will take a while, probably about 2 weeks.

Project website

Example outputs of this model can be viewed at http://www.allencell.org

Important files

train_model.py
	Main function

model_utils.py
	Misc functions including...
		Initialization of models, optimizers, loss criteria
		Assignment of models to different GPUs
		Saving and loading

	In theory, model parallelization and data parallelization get set on lines 102-105

models/
	Definitions for variations on the integrated cell model. Each model consists of four parts:
		Encoder 
		Decoder
		Encoder Discriminator
		Decoder Discriminator

		Each model has a data-parallelization module which accepts a list of GPU IDs

train_modules/
	Definitions for training schemas

	aaegan_train2.py is we use now. It is low-memory version of aaegan_train.py

	A general training step is
		Take steps for the discriminators
		Take steps for the encoder and decoder
		Take advarsarial steps for the encoder and decoder WRT the discriminators

data_providers/
	Definitions for DataProvider objects i.e. loading data into pytorch tensors

	DataProvider3Dh5.py is what we use now. 

Citation

If you find this code useful in your research, please consider citing the following paper:

@article {Johnson238378,
author = {Johnson, Gregory R. and Donovan-Maiye, Rory M. and Maleckar, Mary M.},
title = {Building a 3D Integrated Cell},
year = {2017},
doi = {10.1101/238378},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2017/12/21/238378},
eprint = {https://www.biorxiv.org/content/early/2017/12/21/238378.full.pdf},
journal = {bioRxiv}
}

Contact

Gregory Johnson E-mail: [email protected]

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.