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dunedn-1.0.1
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ProtoDUNE raw data denoising with DL
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dunedn-1.0.1 |
| نام | dunedn |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | M. Rossi |
| ایمیل نویسنده | marco.rossi@cern.ch |
| آدرس صفحه اصلی | https://github.com/marcorossi5/DUNEdn.git |
| آدرس اینترنتی | https://pypi.org/project/dunedn/ |
| مجوز | - |
# DUNEdn
[](https://arxiv.org/abs/2103.01596)
[](https://zenodo.org/badge/latestdoi/248536693)
If you use this software please cite this [paper](https://doi.org/10.1007/s41781-021-00077-9)
```bibtex
@article{dunedn,
author={Rossi, Marco
and Vallecorsa, Sofia},
title={Deep Learning Strategies for ProtoDUNE Raw Data Denoising},
journal={Computing and Software for Big Science},
year={2022},
month={Jan},
day={07},
volume={6},
number={1},
numpages={9},
issn={2510-2044},
doi={10.1007/s41781-021-00077-9},
url={https://doi.org/10.1007/s41781-021-00077-9}
}
```
DUNEdn is a denoising algorithm for ProtoDUNE-SP raw data with Neural Networks.
## Installation
The package can be installed with Python's pip package manager:
```bash
git clone https://github.com/marcorossi5/DUNEdn.git
cd DUNEdn
pip install .
```
This process will copy the DUNEdn program to your environment python path.
### Note
The [saved_models](saved_models) directory contains the checkpoints to reproduce
the results presented in [arXiv:2103.01596](https://arxiv.org/abs/2103.01596).
Since some of the saved models files are quite large (~100 MB), they are uploaded
via [git-lfs](https://git-lfs.github.com/). when cloning the repo, it is possible
to download pointers to those large files rather than the whole binaries. This can
be achieved adding the flag `--config lfs.fetchexclude="*.pth*"` to the `git clone`
command above.
### Requirements
DUNEdn requires the following packages:
- python3
- numpy
- pytorch
- torchvision
- matplotlib
- hyperopt
## Running the code
In order to launch the code
```bash
dunedn <subcommand> [options]
```
Valid subcommands are: `preprocess|train|inference`.
Use `dunedn <subcommand> --help` to print the correspondent help message.
For example, the help message for `train` subcommand is:
```bash
$ dunedn train --help
usage: dunedn train [-h] [--output OUTPUT] [--force] configcard
Train model loading settings from configcard.
positional arguments:
configcard yaml configcard path
optional arguments:
-h, --help show this help message and exit
--output OUTPUT output folder
--force overwrite existing files if present
```
### Configuration cards
Models' parameter settings are stored in configcards. The [configcards](configcards)
folder contains some examples. These can be extended providing the path to user
defined cards directly to the command line interface.
Setting the `DUNEDN_SEARCH_PATH` environment variable it is possible to let DUNEdn
looking for configcards into different directories automatically. More on the
search behavior can be found at the `get_configcard_path` function's docstring
in the [utils/ultis.py](src/dunedn/utils/utils.py) file.
### Preprocess a dataset
At first, a dataset directory should have the following structure:
```text
dataset directory tree structure:
dataset_dir
|-- train
| |--- evts
|-- val
| |--- evts
|-- test
| |--- evts
```
where each `evts` folder contains a collection of ProtoDUNE events stored as raw
digits (numpy array format).
It is possible to generate the correspondent dataset to train an USCG or a GCNN
network with the command:
```bash
dunedn preprocess <configcard.yaml> --dir_name <dataset directory>
```
This will modify the dataset directory tree in the following way:
```txt
dataset directory tree structure:
dataset_dir
|-- train
| |--- evts
| |-- planes (preprocess product)
| |-- crops (preprocess product)
|-- val
| |--- evts
| |--- planes (preprocess product)
|-- test
| |--- evts
| |--- planes (preprocess product)
```
### Training a model
After specifying parameters inside a configuration card, leverage DUNEdn to train
the correspondent model with:
```bash
dunedn train <configcard.yaml>
```
The output directory is set by default to `output`. Optionally, the
`DUNEDN_OUTPUT_PATH` environment variable could be set to override this choice.
### Inference
```bash
dunedn inference -i <input.npy> -o <output.npy> -m <modeltype> [--model_path <checkpoint.pth>]
```
DUNEdn inference takes the `input.npy` array and forwards it to the desired model
`modeltype`. The output array is saved to `output.npy`.
If a checkpoint directory path is given with the optional `--model_path` flag, a
saved model checkpoint could be loaded for inference.
The checkpoint directory should have the following structure:
```text
model_path
|-- collection
| |-- <ckpt directory name>_dn_collection.pth
|-- induction
| |-- <ckpt directory name>_dn_induction.pth
```
On the other hand, if `--model_path` is not specified, an un-trained network is issued.
### Benchmark
The paper results can be reproduced through the
[compute_denoising_performance.py](benchmarks/compute_denoising_performance.py) benchmark.
Please, see the script's docstring for further information.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pyyaml |
| - | torch |
| - | torchvision |
| - | matplotlib |
| - | hyperopt |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl dunedn-1.0.1:
pip install dunedn-1.0.1.whl
نصب پکیج tar.gz dunedn-1.0.1:
pip install dunedn-1.0.1.tar.gz