BReG-NeXt

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Implementation of the BReG-NeXt architecture

BReG-NeXt 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# BReG-NeXt

Implementation of the paper **BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient**

BReG-NeXt paper can be found on
[IEEE Xplore](https://ieeexplore.ieee.org/document/9064942) and
[arXiv](https://arxiv.org/abs/2004.08495)

![overview](https://raw.githubusercontent.com/behzadhsni/BReG-NeXt/master/overview_modular3.png)

# Requirements

Tensorflow 1.14.0 is suggested to run the code. For installing the rest of the required packages, run the following command:

```
pip install -r requirements.txt
```

# Content

- tfrecords: Sample tfrecords for training and validation from FER2013 database
- Snapshots: Example model trained on AffectNet database on BReG-NeXt-50
- Logs: Log report of the BReG-NeXt-50 trained model on AffectNet database

# Pre-Trained Parameters

As mentioned above trained parameters on the AffectNet database on BReG-NeXt-50 is provided in the [Snapshots folder](https://github.com/behzadhsni/BReG-NeXt/tree/master/codes/Snapshots/categorical_attempt_1). You can find various trained parameter values in this file. More specifically, the adaptive coefficient (alpha and beta) are stored in variables that have the following regex:

```
ResidualBlock[_]*[1-9]*/shortcut_mod[_]*[1-9]*/[a,c][_]*[1-9]*
```

# How To Run

Simply run the `BReG-NeXt.py` file:

```
codes/>> python BReG-NeXt.py
```

# Try on binder or google colab (with GPU or TPU)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/psnegi/BReG-NeXt/master?filepath=codes%2FBReG_NeXt.ipynb)

# Write Your Own Complex Mapping

To write your own customized complex mapping (given the restrictions and properties mentioned in the [BReG-NeXt paper](https://ieeexplore.ieee.org/document/9064942)), you need to modify [Lines 136 to 140](https://github.com/behzadhsni/BReG-NeXt/blob/master/codes/BReG-NeXt.py#L136) of the `BReG-NeXt.py` file. Simply write your own function for the mapping and assign it to the `identity` variable.

```
 with tf.name_scope('shortcut_mod'): # Write your customized function
  multiplier1 = tf.Variable(1, dtype=tf.float32, trainable=True, name='alpha') # first optional coefficient
  multiplier2 = tf.Variable(1, dtype=tf.float32, trainable=True, name='beta') # second optional coefficient
  with tf.name_scope('shortcut_mod_function'):
    identity = your_function(multiplier1, multiplier2) # must assign your function to the 'identity' variable
```

# Running on PyTorch

This repository also supports running on PyTorch with PyTorch lightning. The models can be found in the `codes/torch/` directory.

The requirements (which can be installed with pip) are listed in `codes/torch/requirements.txt`.

To train a model with PyTorch, you should use:

```bash
>> python codes/torch/trainer.py --train_data_path <path to train tfrecord> --val_data_path <path to val tfrecord>
```

To enable GPU training, pass a non-zero value to the trainer:

```bash
>> python codes/torch/trainer.py --train_data_path <path to train tfrecord> --val_data_path <path to val tfrecord> --num_gpus N
```

There are several other training options (View the docs here: https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.html?highlight=Trainer). You can view them using:

```bash
>> python codes/torch/trainer.py --help
```

# Citation

All submitted papers (or any publically available text) that uses the entire or parts of this code, must cite the following paper:

**B. Hasani, P. S. Negi and M. Mahoor, "BReG-NeXt: Facial affect computing using adaptive residual networks with bounded gradient," in IEEE Transactions on Affective Computing, 2020.**

BibTex:

```
@ARTICLE{9064942,  author={B. {Hasani} and P. S. {Negi} and M. {Mahoor}},  journal={IEEE Transactions on Affective Computing},  title={BReG-NeXt: Facial affect computing using adaptive residual networks with bounded gradient},   year={2020},  volume={},  number={},  pages={1-1},}
```

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