SocialCircle

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⭕️ Official codes for "SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction" (CVPR2024)

SocialCircle 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# SocialCircle

This is the official codes (*TensorFlow2 version*) for "SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction".

We also provide the corresponding PyTorch codes (beta version for now) in the [TorchVersion(beta)](https://github.com/cocoon2wong/SocialCircle/tree/TorchVersion(beta)) branch.
Please note that model weights that trained with different backends (TensorFlow2 or PyTorch) can not be used in the other version of codes.
Weights released in [this page](https://github.com/cocoon2wong/SocialCircle/releases) are trained under *TensorFlow2*.
For weights trained with PyTorch, please refer to [this page](https://github.com/cocoon2wong/Project-Monandaeg/tree/SocialCircle).

**2024.06.06 Update**: The code repository for our subsequent work "SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction" is now available at [SocialCirclePlus](https://github.com/cocoon2wong/SocialCirclePlus).
If you are interested in our work, feel free to try it!
It is used in exactly the same way as the SocialCircle repository, and is compatible with the pytorch version of SocialCircle model weights.

## Authors' Note

This work is the first part in our *Echolocation Trilogy*.
It focuses on how to describe and locate echoes from agents spatially.
The third work in this series, *Reverberation*, is now available on arXiv.

Here are all the repositories involved in our trilogy:

- Part I, **Where do the echoes come from?**: [*SocialCircle*](https://github.com/cocoon2wong/SocialCircle)  (CVPR 2024) and [*SocialCirclePlus*](https://github.com/cocoon2wong/SocialCirclePlus) (Journal, Under Review);
- Part II, **How echoes interact with each other?**: [*Resonance*](https://github.com/cocoon2wong/Re) (ICCV 2025);
- Part III, **How long do the echoes last?**: [*Reverberation*](https://github.com/cocoon2wong/Rev) (Journal, Under Review).

Note that these repositories share the same training engine and the weight files are compatible with each other (in the order in which the repositories were released, the later released repositories are compatible with the weights of the previous releases, the [*Reverberation*](https://github.com/cocoon2wong/Rev) repository is recommended as it is compatible with all previous models).
You can copy only the core model folders, e.g. `SocialCircle`, `Re`, `Rev`, etc., to a repository's root path (i.e., the folder where `qpid` is located), and train and test the models via `main.py`.

## Get Started

You can clone [this repository](https://github.com/cocoon2wong/SocialCircle) by the following command:

```bash
git clone https://github.com/cocoon2wong/SocialCircle.git
```

Then, run the following command to initialize all submodules:

```bash
git submodule update --init --recursive
```

## Requirements

The codes are developed with Python 3.9.
Additional packages used are included in the `requirements.txt` file.

{: .box-warning}
**Warning:** We recommend installing all required Python packages in a virtual environment (like the `conda` environment).
Otherwise, there *COULD* be other problems due to the package version conflicts.

Run the following command to install the required packages in your Python environment:

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

## Dataset Prepare and Process

### ETH-UCY, SDD, NBA, nuScenes

{: .box-warning}
**Warning:** If you want to validate `SocialCircle` models on these datasets, make sure you are getting this repository via `git clone` and that all `gitsubmodules` have been properly initialized via `git submodule update --init --recursive`.

You can run the following commands to prepare dataset files that have been validated in our paper:

1. Run Python the script inner the `dataset_original` folder:

```bash
    cd dataset_original
    ```

- For `ETH-UCY` and `SDD`, run

```bash
      python main_ethucysdd.py
      ```

- For `NBA` or `nuScenes`, you can download their original dataset files, put them into the given path listed within `dataset_original/main_nba.py` or `dataset_original/main_nuscenes.py`, then run

```bash
      python main_nba.py
      python main_nuscenes.py
      ```
      
      (You can also download the processed dataset files manually from [here](https://github.com/cocoon2wong/Project-Monandaeg/tree/main/Dataset), and put them into `dataset_processed` and `dataset_configs` folders.)

2. Back to the repo folder and create soft links:

```bash
    cd ..
    ln -s dataset_original/dataset_processed ./
    ln -s dataset_original/dataset_configs ./
    ```

**Dataset Corrections**:
The `univ13` split (ETH-UCY) takes `univ` and `univ3` as test sets, and other sets {`eth`, `hotel`, `unive`, `zara1`, `zara2`, `zara3`} as training sets.
Differently, the `univ` split only includes `univ` for testing models.
Our reported results in this conference paper are tested under split `univ`.
Following most current approaches, we have test them and report results with the new split `univ13` in the corresponding journal-expanded paper `SocialCircle+` (codes repo [here](https://github.com/cocoon2wong/SocialCirclePlus), paper available on [arXiv](https://arxiv.org/abs/2409.14984)).
Correspondingly, some `SocialCircle` results have been corrected, and please check them in the [`SocialCirclePlus` repo](https://github.com/cocoon2wong/SocialCirclePlus) with the newly trained weights in the [weights repo](https://github.com/cocoon2wong/Project-Monandaeg/tree/SocialCircle) (postfixed with `univ13`).

Click the following button to learn more about how to process these dataset files.

<div style="text-align: center;">
    <a class="btn btn-colorful btn-lg" href="https://cocoon2wong.github.io/Project-Luna/howToUse/">💡 Dataset Guidelines</a>
</div>

### Prepare Your New Datasets

Before training `SocialCircle` models on your own dataset, you should add your dataset information.
See [this document](https://cocoon2wong.github.io/Project-Luna/) for details.

## Pre-Trained Model Weights and Evaluation

We have provided our pre-trained model weights to help you quickly evaluate the `SocialCircle` models' performance.

Click the following buttons to download our model weights.
We recommend that you download the weights and place them in the `weights/SocialCircle` folder.

<div style="text-align: center;">
    <a class="btn btn-colorful btn-lg" href="https://github.com/cocoon2wong/SocialCircle/releases">⬇️ Download Weights (TensorFlow 2)</a>
    <a class="btn btn-colorful btn-lg" href="https://github.com/cocoon2wong/Project-Monandaeg/tree/SocialCircle">⬇️ Download Weights (PyTorch)</a>
</div>

{: .box-warning}
**Warning:** The TensorFlow 2 version of codes only support weights that trained with TensorFlow 2, and the PyTorch version of codes only support weights that trained with PyTorch.
Please download the correct weights file or the program will not run correctly.

You can start evaluating models by

```bash
python main.py --sc SOME_MODEL_WEIGHTS
```

Here, `SOME_MODEL_WEIGHTS` is the path of the weights folder, for example, `weights/SocialCircle/evsc_P8_sdd`.

## Training

You can start training a `SocialCircle` model via the following command:

```bash
python main.py --model MODEL_IDENTIFIER --split DATASET_SPLIT
```

Here, `MODEL_IDENTIFIER` is the identifier of the model.
These identifiers are supported in current codes:

- The basic transformer model for trajectory prediction:
  - `trans` (named the `Transformer` in the paper);
  - `transsc` (SocialCircle variation `Transformer-SC`).
- MSN ([🔗homepage](https://northocean.github.io/MSN/)):
  - `msna` (original model);
  - `msnsc` (SocialCircle variation).
- V^2-Net ([🔗homepage](https://cocoon2wong.github.io/Vertical/)):
  - `va` (original model);
  - `vsc` (SocialCircle variation).
- E-V^2-Net ([🔗homepage](https://cocoon2wong.github.io/E-Vertical/)):
  - `eva` (original model);
  - `evsc` (SocialCircle variation).

`DATASET_SPLIT` is the identifier (i.e., the name of dataset's split files in `dataset_configs`, for example `eth` is the identifier of the split list in `dataset_configs/ETH-UCY/eth.plist`) of the dataset or splits used for training.
It accepts:

- ETH-UCY: {`eth`, `hotel`, `univ`, `zara1`, `zara2`};
- SDD: `sdd`;
- NBA: `nba50k`;
- nuScenes: {`nuScenes_v1.0`, `nuScenes_ov_v1.0`};

For example, you can start training the `E-V^2-Net-SC` model by

```bash
python main.py --model evsc --split zara1
```

You can also specify other needed args, like the learning rate `--lr`, batch size `--batch_size`, etc.
See detailed args in the `Args Used` Section.

In addition, the simplest way to reproduce our results is to copy all training args we used in the provided weights.
For example, you can start a training of `E-V^2-Net-SC` on `zara1` by:

```bash
python main.py --restore_args weights/SocialCircle/evsczara1
```

### Toy Example

You can run the following script to learn how the proposed `SocialCircle` works in an interactive way:

```bash
python scripts/socialcircle_toy_example.py
```

Set positions of the manual neighbor to see model's outputs like:

## Args Used

Please specify your customized args when training or testing your model in the following way:

```bash
python main.py --ARG_KEY1 ARG_VALUE2 --ARG_KEY2 ARG_VALUE2 -SHORT_ARG_KEY3 ARG_VALUE3 ...
```

where `ARG_KEY` is the name of args, and `ARG_VALUE` is the corresponding value.
All args and their usages are listed below.

About the `argtype`:

- Args with argtype=`static` can not be changed once after training.
  When testing the model, the program will not parse these args to overwrite the saved values.
- Args with argtype=`dynamic` can be changed anytime.
  The program will try to first parse inputs from the terminal and then try to load from the saved JSON file.
- Args with argtype=`temporary` will not be saved into JSON files.
  The program will parse these args from the terminal at each time.

### Basic Args

- `--K_train`: type=`int`, argtype=`static`.
  The number of multiple generations when training. This arg only works for multiple-generation models. 
  The default value is `10`.
- `--K`: type=`int`, argtype=`dynamic`.
  The number of multiple generations when testing. This arg only works for multiple-generation models. 
  The default value is `20`.
- `--anntype`: type=`str`, argtype=`static`.
  Model's predicted annotation type. Can be `'coordinate'` or `'boundingbox'`. 
  The default value is `coordinate`.
- `--batch_size` (short for `-bs`): type=`int`, argtype=`dynamic`.
  Batch size when implementation. 
  The default value is `5000`.
- `--compute_loss`: type=`int`, argtype=`temporary`.
  Controls whether compute losses when testing. 
  The default value is `0`.
- `--dataset`: type=`str`, argtype=`static`.
  Name of the video dataset to train or evaluate. For example, `'ETH-UCY'` or `'SDD'`. NOTE: DO NOT set this argument manually. 
  The default value is `Unavailable`.
- `--draw_distribution` (short for `-dd`): type=`int`, argtype=`temporary`.
  Controls whether to draw distributions of predictions instead of points. If `draw_distribution == 0`, it will draw results as normal coordinates; If `draw_distribution == 1`, it will draw all results in the distribution way, and points from different time steps will be drawn with different colors. 
  The default value is `0`.
- `--draw_exclude_type` (short for `-det`): type=`str`, argtype=`temporary`.
  Draw visualized results of agents except for user-assigned types. If the assigned types are `"Biker_Cart"` and the `draw_results` or `draw_videos` is not `"null"`, it will draw results of all types of agents except "Biker" and "Cart". It supports partial match, and it is case-sensitive. 
  The default value is `null`.
- `--draw_extra_outputs`: type=`int`, argtype=`temporary`.
  Choose whether to draw (put text) extra model outputs on the visualized images. 
  The default value is `0`.
- `--draw_index`: type=`str`, argtype=`temporary`.
  Indexes of test agents to visualize. Numbers are split with `_`. For example, `'123_456_789'`. 
  The default value is `all`.
- `--draw_results` (short for `-dr`): type=`str`, argtype=`temporary`.
  Controls whether to draw visualized results on video frames. Accept the name of one video clip. The codes will first try to load the video file according to the path saved in the `plist` file (saved in `dataset_configs` folder), and if it loads successfully it will draw the results on that video, otherwise it will draw results on a blank canvas. Note that `test_mode` will be set to `'one'` and `force_split` will be set to `draw_results` if `draw_results != 'null'`. 
  The default value is `null`.
- `--draw_videos`: type=`str`, argtype=`temporary`.
  Controls whether draw visualized results on video frames and save as images. Accept the name of one video clip. The codes will first try to load the video according to the path saved in the `plist` file, and if successful it will draw the visualization on the video, otherwise it will draw on a blank canvas. Note that `test_mode` will be set to `'one'` and `force_split` will be set to `draw_videos` if `draw_videos != 'null'`. 
  The default value is `null`.
- `--epochs`: type=`int`, argtype=`static`.
  Maximum training epochs. 
  The default value is `500`.
- `--experimental`: type=`bool`, argtype=`temporary`.
  NOTE: It is only used for code test. 
  The default value is `False`.
- `--force_anntype`: type=`str`, argtype=`temporary`.
  Assign the prediction type. It is now only used for silverballers models that are trained with annotation type `coordinate` but want to test on datasets with annotation type `boundingbox`. 
  The default value is `null`.
- `--force_clip`: type=`str`, argtype=`temporary`.
  Force test video clip (ignore the train/test split). It only works when `test_mode` has been set to `one`. 
  The default value is `null`.
- `--force_dataset`: type=`str`, argtype=`temporary`.
  Force test dataset (ignore the train/test split). It only works when `test_mode` has been set to `one`. 
  The default value is `null`.
- `--force_split`: type=`str`, argtype=`temporary`.
  Force test dataset (ignore the train/test split). It only works when `test_mode` has been set to `one`. 
  The default value is `null`.
- `--gpu`: type=`str`, argtype=`temporary`.
  Speed up training or test if you have at least one NVidia GPU. If you have no GPUs or want to run the code on your CPU, please set it to `-1`. NOTE: It only supports training or testing on one GPU. 
  The default value is `0`.
- `--interval`: type=`float`, argtype=`static`.
  Time interval of each sampled trajectory point. 
  The default value is `0.4`.
- `--load` (short for `-l`): type=`str`, argtype=`temporary`.
  Folder to load model (to test). If set to `null`, the training manager will start training new models according to other given args. 
  The default value is `null`.
- `--log_dir`: type=`str`, argtype=`static`.
  Folder to save training logs and model weights. Logs will save at `args.save_base_dir/current_model`. DO NOT change this arg manually. (You can still change the path by passing the `save_base_dir` arg.) 
  The default value is `Unavailable`.
- `--lr` (short for `-lr`): type=`float`, argtype=`static`.
  Learning rate. 
  The default value is `0.001`.
- `--max_agents`: type=`int`, argtype=`static`.
  Max number of agents to predict in one frame. It only works when `model_type == 'frame-based'`. 
  The default value is `50`.
- `--model_name`: type=`str`, argtype=`static`.
  Customized model name. 
  The default value is `model`.
- `--model_type`: type=`str`, argtype=`static`.
  Model type, canbe `'agent-based'` or `'frame-based'`. 
  The default value is `agent-based`.
- `--model`: type=`str`, argtype=`static`.
  The model type used to train or test. 
  The default value is `none`.
- `--obs_frames` (short for `-obs`): type=`int`, argtype=`static`.
  Observation frames for prediction. 
  The default value is `8`.
- `--only_process_trajectory`: type=`int`, argtype=`static`.
  (Pre/post-process Arg) Controls whether to process non-trajectory model inputs or not. 
  The default value is `0`.
- `--pmove`: type=`int`, argtype=`static`.
  (Pre/post-process Arg) Index of the reference point when moving trajectories. 
  The default value is `-1`.
- `--pred_frames` (short for `-pred`): type=`int`, argtype=`static`.
  Prediction frames. 
  The default value is `12`.
- `--restore_args`: type=`str`, argtype=`temporary`.
  Path to restore the reference args before training. It will not restore any args if `args.restore_args == 'null'`. 
  The default value is `null`.
- `--restore`: type=`str`, argtype=`temporary`.
  Path to restore the pre-trained weights before training. It will not restore any weights if `args.restore == 'null'`. 
  The default value is `null`.
- `--save_base_dir`: type=`str`, argtype=`static`.
  Base folder to save all running logs. 
  The default value is `./logs`.
- `--split` (short for `-s`): type=`str`, argtype=`static`.
  The dataset split that used to train and evaluate. 
  The default value is `zara1`.
- `--start_test_percent`: type=`float`, argtype=`static`.
  Set when (at which epoch) to start validation during training. The range of this arg should be `0 <= x <= 1`. Validation may start at epoch `args.epochs * args.start_test_percent`. 
  The default value is `0.0`.
- `--step`: type=`float`, argtype=`dynamic`.
  Frame interval for sampling training data. 
  The default value is `1.0`.
- `--test_mode`: type=`str`, argtype=`temporary`.
  Test settings. It can be `'one'`, `'all'`, or `'mix'`. When setting it to `one`, it will test the model on the `args.force_split` only; When setting it to `all`, it will test on each of the test datasets in `args.split`; When setting it to `mix`, it will test on all test datasets in `args.split` together. 
  The default value is `mix`.
- `--test_step`: type=`int`, argtype=`static`.
  Epoch interval to run validation during training. 
  The default value is `1`.
- `--update_saved_args`: type=`int`, argtype=`temporary`.
  Choose whether to update (overwrite) the saved arg files or not. 
  The default value is `0`.
- `--use_seg_maps`: type=`int`, argtype=`dynamic`.
  Controls whether to use segmentation maps instead of the calculated trajectory maps. 
  The default value is `0`.
- `--verbose` (short for `-v`): type=`int`, argtype=`temporary`.
  Controls if print verbose logs and outputs to the terminal. 
  The default value is `0`.

### First-stage Silverballers Args

- `--Kc`: type=`int`, argtype=`static`.
  The number of style channels in `Agent` model. 
  The default value is `20`.
- `--T` (short for `-T`): type=`str`, argtype=`static`.
  Type of transformations used when encoding or decoding trajectories. It could be: - `none`: no transformations - `fft`: fast Fourier transform - `fft2d`: 2D fast Fourier transform - `haar`: haar wavelet transform - `db2`: DB2 wavelet transform 
  The default value is `fft`.
- `--depth`: type=`int`, argtype=`static`.
  Depth of the random noise vector. 
  The default value is `16`.
- `--deterministic`: type=`int`, argtype=`static`.
  Controls if predict trajectories in the deterministic way. 
  The default value is `0`.
- `--feature_dim`: type=`int`, argtype=`static`.
  Feature dimensions that are used in most layers. 
  The default value is `128`.
- `--key_points`: type=`str`, argtype=`static`.
  A list of key time steps to be predicted in the agent model. For example, `'0_6_11'`. 
  The default value is `0_6_11`.
- `--loss`: type=`str`, argtype=`dynamic`.
  Loss used to train agent models. Canbe `'avgkey'` or `'keyl2'` (default). 
  The default value is `keyl2`.
- `--preprocess`: type=`str`, argtype=`static`.
  Controls whether to run any pre-process before the model inference. It accepts a 3-bit-like string value (like `'111'`): - The first bit: `MOVE` trajectories to (0, 0); - The second bit: re-`SCALE` trajectories; - The third bit: `ROTATE` trajectories. 
  The default value is `100`.

### Second-stage Silverballers Args

- `--Kc`: type=`int`, argtype=`static`.
  The number of style channels in `Agent` model. 
  The default value is `20`.
- `--T` (short for `-T`): type=`str`, argtype=`static`.
  Type of transformations used when encoding or decoding trajectories. It could be: - `none`: no transformations - `fft`: fast Fourier transform - `fft2d`: 2D fast Fourier transform - `haar`: haar wavelet transform - `db2`: DB2 wavelet transform 
  The default value is `fft`.
- `--feature_dim`: type=`int`, argtype=`static`.
  Feature dimensions that are used in most layers. 
  The default value is `128`.
- `--key_points`: type=`str`, argtype=`static`.
  A list of key time steps to be predicted in the agent model. For example, `'0_6_11'`. 
  The default value is `0_6_11`.
- `--points`: type=`int`, argtype=`static`.
  The number of keypoints accepted in the handler model. 
  The default value is `1`.
- `--preprocess`: type=`str`, argtype=`static`.
  Controls whether to run any pre-process before the model inference. It accepts a 3-bit-like string value (like `'111'`): - The first bit: `MOVE` trajectories to (0, 0); - The second bit: re-`SCALE` trajectories; - The third bit: `ROTATE` trajectories. 
  The default value is `100`.

### Silverballers Args

- `--channel` (short for `-c`): type=`int`, argtype=`temporary`.
  Specify the k-th channel of the model output. If `channel == -1`, it outputs all channels' predictions. 
  The default value is `-1`.
- `--down_sampling_rate`: type=`float`, argtype=`temporary`.
  Down sampling rate to sample trajectories from all N = K*Kc trajectories. 
  The default value is `1.0`.
- `--loada` (short for `-la`): type=`str`, argtype=`temporary`.
  Path to load the first-stage agent model. 
  The default value is `null`.
- `--loadb` (short for `-lb`): type=`str`, argtype=`temporary`.
  Path to load the second-stage handler model. 
  The default value is `null`.
- `--pick_trajectories` (short for `-p`): type=`float`, argtype=`temporary`.
  Calculates the sum of the context map values of the predicted trajectories and picks the top n (percentage) best predictions. This parameter is only valid when the model's input contains `MAPS` and `MAP_PARAS`. 
  The default value is `1.0`.

### SocialCircle Args

- `--Ts` (short for `-Ts`): type=`str`, argtype=`static`.
  The transformation on SocialCircle. It could be: - `none`: no transformations - `fft`: fast Fourier transform - `haar`: haar wavelet transform - `db2`: DB2 wavelet transform 
  The default value is `none`.
- `--partitions`: type=`int`, argtype=`static`.
  Partitions in the SocialCircle. Set it to `-1` to adapt to different observation/prediction length settings. 
  The default value is `-1`.
- `--rel_speed`: type=`int`, argtype=`static`.
  Choose whether to use the relative speed or the absolute speed as the speed factor in the SocialCircle. (Default to the `absolute speed`) 
  The default value is `0`.
- `--use_direction`: type=`int`, argtype=`static`.
  Choose whether to use the direction factor in the SocialCircle. 
  The default value is `1`.
- `--use_distance`: type=`int`, argtype=`static`.
  Choose whether to use the distance factor in the SocialCircle. 
  The default value is `1`.
- `--use_move_direction`: type=`int`, argtype=`static`.
  Choose whether to use the move direction factor in the SocialCircle. 
  The default value is `0`.
- `--use_velocity`: type=`int`, argtype=`static`.
  Choose whether to use the velocity factor in the SocialCircle. 
  The default value is `1`.

## Contact us

Conghao Wong ([@cocoon2wong](https://github.com/cocoon2wong)): conghaowong@icloud.com  
Beihao Xia ([@NorthOcean](https://github.com/NorthOcean)): xbh_hust@hust.edu.cn  
Ziqian Zou ([@LivepoolQ](https://github.com/LivepoolQ)): ziqianzoulive@icloud.com

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