https://github.com/czbiohub-sf/pytorch_integrated_cell

Integrated Cell project implemented in pytorch

https://github.com/czbiohub-sf/pytorch_integrated_cell

Repository

Integrated Cell project implemented in pytorch

https://github.com/czbiohub-sf/pytorch_integrated_cell/blob/master/

Pyorch 3D Integrated Cell
===============================

![Model Architecture](doc/images/model_arch.png?raw=true "Model Architecture")

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: gregj@alleninstitute.org

## 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 .

Owner

GitHub Events