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ST-TR

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Spatial Temporal Transformer Network for Skeleton-Based Activity Recognition

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# Spatial Temporal Transformer Network

### Introduction
This repository contains the implementation of the model presented in the following paper:

> **Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition**, Chiara Plizzari, Marco Cannici, Matteo Matteucci, [ArXiv](https://arxiv.org/abs/2008.07404)

> **Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition**, Chiara Plizzari, Marco Cannici, Matteo Matteucci, Pattern Recognition. ICPR International Workshops and Challenges, 2021, [Proceedings](https://link.springer.com/chapter/10.1007/978-3-030-68796-0_50)

> **Skeleton-based action recognition via spatial and temporal transformer networks**, Chiara Plizzari, Marco Cannici, Matteo Matteucci, Computer Vision and Image Understanding, Volumes 208-209, 2021, 103219, ISSN 1077-3142, [CVIU](https://doi.org/10.1016/j.cviu.2021.103219)

![Alt Text](https://raw.githubusercontent.com/Chiaraplizz/ST-TR/master/additional_files/IMG.png)

### Visualizations of Spatial Transformer logits

The heatmaps are 25 x 25 matrices, where each row and each column represents a body joint. An element in position (i, j) represents the correlation between joint i and joint j, resulting from self-attention.

![Alt Text](https://raw.githubusercontent.com/Chiaraplizz/ST-TR/master/additional_files/ezgif.com-video-to-gif-2.gif)![Alt Text](https://raw.githubusercontent.com/Chiaraplizz/ST-TR/master/additional_files/ezgif.com-video-to-gif.gif)

### Prerequisites
- Python3
- [PyTorch](http://pytorch.org)
- All the libraries in <code>requirements.txt</code>

### Run mode
<pre><code> python3 main.py </pre></code>

**Training**:
Set in <code>/config/st_gcn/nturgbd/train.yaml</code>:
- <code>Training</code>: True

**Testing**:
Set in <code>/config/st_gcn/nturgbd/train.yaml</code>:
- <code>Training</code>: False

### Data generation
We performed our experiments on three datasets: **NTU-RGB+D 60**, **NTU-RGB+D 120** and **Kinetics**. 
#### NTU-RGB+D
The data can downloaded from [their website](http://rose1.ntu.edu.sg/datasets/actionrecognition.asp). You need to download **3D Skeletons** only (5.8G (NTU-60) + 4.5G (NTU-120)). Once downloaded, use the following to generate joint data for NTU-60:
<pre><code> python3 ntu_gendata.py </pre></code>
If you want to generate data and preprocess them, use directly:
<pre><code> python3 preprocess.py </pre></code>
In order to generate bones, you need to run: 
<pre><code> python3 ntu_gen_bones.py </pre></code>
The joint information and bone information can be merged through:
<pre><code> python3 ntu_merge_joint_bones.py </pre></code>

For NTU-120, the samples are divided between training and testing in a different way. Thus, you need to run: 
<pre><code> python3 ntu120_gendata.py </pre></code>
If you want to generate data and process them directly, use: 
<pre><code> python3 preprocess_120.py </pre></code>

#### Kinetics

[Kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/) is a dataset for video action recognition, consisting of raw video data only. The corresponding skeletons are extracted using Openpose, and are available for download at [GoogleDrive](https://drive.google.com/open?id=1SPQ6FmFsjGg3f59uCWfdUWI-5HJM_YhZ) (7.5G). 
From raw skeletons, generate the dataset by running:
<pre><code> python3 kinetics_gendata.py </pre></code>

### Spatial Transformer Stream
Spatial Transformer implementation corresponds to <code>ST-TR/code/st_gcn/net/spatial_transformer.py</code>.
Set in <code>/config/st_gcn/nturgbd/train.yaml</code>:
- <code>attention: True</code>
- <code>tcn_attention: False</code>
- <code>only_attention: True</code>
- <code>all_layers: False</code>

to run the spatial transformer stream (S-TR-stream).

### Temporal Transformer Stream
Temporal Transformer implementation corresponds to <code>ST-TR/code/st_gcn/net/temporal_transformer.py</code>.
Set in <code>/config/st_gcn/nturgbd/train.yaml </code>:
- <code>attention: False</code>
- <code>tcn_attention: True</code>
- <code>only_attention: True</code>
- <code>all_layers: False</code>

to run the temporal transformer stream (T-TR-stream).

### To merge S-TR and T-TR (ST-TR)
The score resulting from the S-TR stream and T-TR stream are combined to produce the final ST-TR score by: 
<pre><code>  python3 ensemble.py </pre></code>

### Adaptive Configuration (AGCN)

In order to run T-TR-agcn and ST-TR-agcn configurations, please set <code>agcn: True</code>.

### Different ST-TR configurations
Set in <code>/config/st_gcn/nturgbd/train.yaml</code>:
- <code>only_attention: False</code>, to use ST-TR as an augmentation procedure to ST-GCN (refer to Sec. V(E) "Effect of Augmenting Convolution with Self-Attention")
- <code>all_layers: True</code>, to apply ST-TR on all layers, otherwise it will be applied from the 4th layer on (refer to Sec. V(D) "Effect of Applying Self-Attention to Feature Extraction")
- Set both <code>attention: True</code> and <code>tcn_attention: True</code> to combine both SSA and TSA on a unique stream (refer to Sec. V(F) "Effect of combining SSA and TSA on one stream")
- <code>more_channels: True</code>, to assign to each head more channels than dk/Nh.
- <code>n</code>: used if <code>more_channels</code> is set to True, in order to assign to each head dk*num/Nh channels

To set the block dimensions of the windowed version of Temporal Transformer:
- <code>dim_block1, dim_block2, dim_block3</code>, respectively to set block dimension where the output channels are equal to 64, 128 and 256.

### Second order information
Set in <code>/config/st_gcn/nturgbd/train.yaml</code>:
- <code>channels: 6 </code>, because on channels dimension we have both the coordinates of joint (3), and coordinates of bones(3)
- <code>double_channel: True </code>, since in this configuration we also doubled the channels in each layer.

### Pre-trained Models
Please notice I have attached pre-trained models of the configurations presented in the paper in the <code>checkpoint_ST-TR</code> folder. Please note that the \*_bones_\*.pth configurations correspond to the models trained with joint+bones information, while the others are trained with joints only.

### Citation 
Please cite one of the following papers if you use this code for your researches:

<pre><code>@article{plizzari2021skeleton,
  title={Skeleton-based action recognition via spatial and temporal transformer networks},
  author={Plizzari, Chiara and Cannici, Marco and Matteucci, Matteo},
  journal={Computer Vision and Image Understanding},
  volume={208},
  pages={103219},
  year={2021},
  publisher={Elsevier}
}
</pre></code>

<pre><code>@inproceedings{plizzari2021spatial,
  title={Spatial temporal transformer network for skeleton-based action recognition},
  author={Plizzari, Chiara and Cannici, Marco and Matteucci, Matteo},
  booktitle={Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part III},
  pages={694--701},
  year={2021},
  organization={Springer}
}
</pre></code>

## Contact :pushpin:
If you have any question, do not hesitate to contact me at <code> chiara.plizzari@mail.polimi.it</code>. I will be glad to clarify your doubts!

<sub> Note: we include LICENSE, LICENSE_1 and LICENSE_2 in this repository since part of the code has been derived respectively
from https://github.com/yysijie/st-gcn, https://github.com/leaderj1001/Attention-Augmented-Conv2d
and https://github.com/kenziyuliu/Unofficial-DGNN-PyTorch/blob/master/README.md </sub>

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