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https://github.com/neuronets/nobrainer-zoo
A Python CLI to execute a collection of deep learning models for brain imaging.
https://github.com/neuronets/nobrainer-zoo
Last synced: 3 days ago
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A Python CLI to execute a collection of deep learning models for brain imaging.
- Host: GitHub
- URL: https://github.com/neuronets/nobrainer-zoo
- Owner: neuronets
- License: other
- Created: 2021-05-19T14:29:10.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-23T15:04:50.000Z (about 1 year ago)
- Last Synced: 2024-09-18T14:50:18.258Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 298 KB
- Stars: 9
- Watchers: 8
- Forks: 7
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Nobrainer-zoo
Nobrainer-zoo is a toolbox with a collection of deep learning neuroimaging models that eases the use of pretrained
models for various applications. Nobrainer-zoo provides the required environment with all the dependencies for
training/inference of models. You need `singularity/apptainer` (>= 3.7.x/1.0.x) or `docker` to run the Nobrainer-zoo.
## Installation
We highly recommend to create a separate environment for the nobrainer-zoo. You can use conda or python to create the environment.
```
conda create -n nobrainer-zoo python=3
conda activate nobrainer-zoo
```
or
```
python3 -m venv /path/to/new/virtual/environment/nobrainer-zoo
source /path/to/new/virtual/environment/nobrainer-zoo/bin/activate
```
Then install the nobrainer-zoo:
```
[Releases] pip install nobrainer-zoo
[Dev version] pip install https://github.com/neuronets/nobrainer-zoo/archive/refs/heads/main.zip
```
After installation, Nobrainer-zoo should be initialized. It also needs a cache folder to download some helper files based on your needs. By default, it creates a cache folder in your home directory (`~/.nobrainer`). If you do not want the cache folder in your `home` directory, you can setup a different cache location by setting the environmental variable `NOBRAINER_CACHE`. run below command to set it.
```
export NOBRAINER_CACHE=
```
since this environmental variable will be lost when you close your terminal session you need to run it next time or a better solution is, to add it to your `~/.bashrc` file.
simply open the file with your text editor and add the above line at the end. Restart your terminal or re-run your `bashrc` file by `.~/bashrc` to make this change effective.
To initialize the `nobrainer-zoo` run:
```
nobrainer-zoo init
```
*Note: You need to initialize the nobrainer-zoo only once.
Run help to see the functions and each function's options.
```
nobrainer-zoo --help
nobrainer-zoo ls --help
nobrainer-zoo predict --help
nobrainer-zoo fit --help
nobrainer-zoo register --help
nobrainer-zoo generate --help
```
## Available models
To see the list of available models in the nobrainer-zoo run `nobrainer-zoo ls`
Models are added based on their organization, model name , and version. One model might have different versions. Some models (such as `kwyk` or `SyntSR`) have various types which means there was various training method or dataset that lead to different trained models. You can select the model type with `model_type` option for the train and inference.
List of models which will be added in near future can be find [here](https://github.com/Hoda1394/zoo/blob/add/inference_scripts/models_to_add.md). You can suggest a model [here](https://github.com/neuronets/zoo/issues/new/choose).
*Note: models are distributed under their original license.*
## Inference Example
Inference with default options,
```
nobrainer-zoo predict -m neuronets/brainy/0.1.0
nobrainer-zoo register -m DDIG/SynthMorph/1.0.0 --model_type brains
```
pass the model specific options with `--options` argument to the model.
```
nobrainer-zoo predict -m neuronets/brainy/0.1.0 --options verbose block_shape=[128,128,128]
nobrainer-zoo predict -m UCL/SynthSeg/0.1 --options post=
```
**Note**: Nobrainer-zoo will use the gpu by default. So, if you want to force it to use the cpu while the gpu is available you need to pass `--cpu` flag. If you are using docker without any gpu passing the `--cpu` flag is a must. Otherwise, you will get an error.
**Note**: If you are using docker make sure to use the absolute path for input and putput files.
## Train Example
For training with sample dataset you do not need to pass any dataset pattern.
```
nobrainer-zoo fit -m neuronets/brainy
```
To train the network with your own data pass the dataset pattern in the form of tfrecords.
```
nobrainer-zoo fit -m neuronets/brainy "" ""
```
Other parameters can be changed by providing a spec file or changing them with cli command.
```
nobrainer-zoo fit -m neuronets/brainy --spec_file
```
```
nobrainer-zoo fit -m neuronets/brainy --train epoch=2
```