Awesome
Transformer Networks for Trajectory Forecasting
This is the code for the paper <a href="https://arxiv.org/abs/2003.08111">Transformer Networks for Trajectory Forecasting</a>
Requirements
- Pytorch 1.0+
- Numpy
- Scipy
- Pandas
- Tensorboard
- <a href="https://github.com/overshiki/kmeans_pytorch">kmeans_pytorch</a> (included in the project is a modified version)
Usage
Data setup
The dataset folder must have the following structure:
- dataset
- dataset_name
- train_folder
- test_folder
- validation_folder (optional)
- clusters.mat (For quantizedTF)
Individual Transformer
To train just run the train_individual.py with different parameters
example: to train on the data for eth
CUDA_VISIBLE_DEVICES=0 python train_individualTF.py --dataset_name eth --name eth --max_epoch 240 --batch_size 100 --name eth_train --factor 1
QuantizedTF
Step1: Create the clusters
NOTE: We used a pytorch based method that use GPUs to lower the computational time, but it requires both a GPU and a high amount of RAM (25 GB).
Since clusters do not change over time they can be created with any code, you just need to create a file with the centroids inside the dataset/dataset_name folder
For ease of use the cluster informations are already upladed for eth+ucy
To create the cluster_mat file run kmeans.py
CUDA_VISIBLE_DEVICES=0 python kmeans.py --dataset_name eth
After that put the clusters.mat inside the appropriate dataset folder.
Step 2: Train the quantized
Run ClassifyTF.py
CUDA_VISIBLE_DEVICES=0 python train_quantizedTF.py --dataset_name zara1 --name zara1 --batch_size 1024
Step 3: Evaluate Best-of-N
Run test_class.py with the parameters for the dataset_name, the name of the trained model, the epoch to test and the number of samples
CUDA_VISIBLE_DEVICES=0 python test_quantizedTF.py --dataset_name eth --name eth --batch_size 1024 --epoch 00030 --num_samples 20
Visualization
The training loss, validation loss, mad and fad for the test can be seen for each epoch by running tensorboard
tensorboard --logdir logs
Citation
If you use the code please cite our paper.
@misc{giuliari2020transformer,
title={Transformer Networks for Trajectory Forecasting},
author={Francesco Giuliari and Irtiza Hasan and Marco Cristani and Fabio Galasso},
year={2020},
eprint={2003.08111},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Thanks
TODO
- Add BERT
- Add QuantizedBert
- Upload Pretrained-Models
Changelog
- 14/05
- Added Quantized Bert
- 27/04
- Added Bert
- Renamed the training files to make more sense
- fixed some issues with the individualTF
- 10/04
- Uploaded the code for the Individual and QuantizedTF