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Stimulus Verifier

This folder contains the codes for CVPR2023 paper "Stimulus Verification is a Universal and Effective Sampler in Multi-modal Human Trajectory Prediction".

Environment

The codes in this folder are developed with PyTorch and CUDA support. As the codes only use relatively basic functionalities, no specific version settings are required.

Dataset

The Stimulus Verifier currently shares the same dataset folder as our prior work PCCSNet, while only using the ETH/UCY dataset.

Specifically, in folder ../dataset/ethucy, you will find:

  • folder processed: containing pre-processed trajectories stored in .npy files, not used for stimulus verifier.
  • folder raw: raw .csv files of the ETH/UCY dataset, each containing 4 rows of data (frame, ID, x, y). In addition to the training process, these files are also used for generating social data as a pre-processing step. See paragraph below.
  • folder semantic_maps : manually annotated semantic maps stored in .npy format, each containing a matrix M that has the same shape (H, W) as the scene image, where M[i, j] = 0 / 1 indicates whether there is an obstacle (therefore impossible to walk on) at that particular location of the scene image.
  • file ethucy.py processing codes for PCCSNet, not used for stimulus verifier.

Preparation for Social Data

After cloning the project, first run python preprocess_social_data.py. This will preprocess social data for the Social Verifier and save them in the social_data/preprocessed folder.

Evaluation

We provide pre-trained models of both Context Verifier and Social Verifier as well as detailed evaluation configurations on the ETH/UCY dataset in StimulusVerifier_AdditionalFiles.zip.

After downloading and unzipping it under folder StimulusVerifier, you will find:

  • base_model_outputs: The outputs of base prediction models. In the folder PCCSNet, you will find the prediction files of our prior work PCCSNet on the ETH/UCY dataset, each containing 200 predictions in absolute coordinates. Besides, the ground-truth files are also provided. Note that these ground-truth files shares the same content as the .npy files under ../dataset/ethucy/processed (concatenated if necessary). Besides, the same predictions can also be acquired by running the 'test' command of PCCSNet using our public models.
  • saved_models: Trained verifier models, separately placed in folders named context and social.
  • scores_cache: Cached likelihoods of PCCSNet's predictions using pre-trained verifiers.
  • verification_configs.py: Detailed configurations of stimulus verification on PCCSNet's predictions. Note that such configurations can be different for other base prediction models' predictions.

Then, the evaluation can be carried out by running

python verify_predictions.py -bm PCCSNet -d <DATASET_NAME>

where <DATASET_NAME> is the name of the dataset, e.g. eth or zara1. See datasets.py for details.

The evaluation will give the following results (ADE/FDE):

Dataset AP Before Verification AP After Verification FP Before Verification FP After Verification
ETH 0.26 / 0.51 0.25 / 0.49 0.29 / 0.43 0.28 / 0.41
HOTEL 0.11 / 0.19 0.10 / 0.17 0.12 / 0.16 0.11 / 0.15
UNIV 0.29 / 0.60 0.26 / 0.52 0.32 / 0.53 0.29 / 0.44
ZARA1 0.21 / 0.44 0.20 / 0.41 0.24 / 0.38 0.22 / 0.35
ZARA2 0.15 / 0.33 0.14 / 0.31 0.17 / 0.29 0.16 / 0.26
Avg 0.20 / 0.41 0.19 / 0.38 0.23 / 0.36 0.21 / 0.32

Here AP stands for ADE-prioritized, where the trajectory with minimum ADE among all 20 predictions is used for evaluation; whereas FP stands for FDE-prioritized, which means that the trajectory with minimum FDE among all 20 predictions is used.

Training

To train a stimulus verifier from scratch, use the following command

python train_stimulus_verifier.py -st <STIMULUS_TYPE> -d <DATASET_NAME>

where <STIMULUS_TYPE> can be context or social in our current implementation. For other possible hyper-parameters, please refer to train_stimulus_verifier.py.

Customizations

If you wish to train Stimulus Verifier using your own data, here are some potentially helpful reminders.

  • For both types of stimulus verifier, our recommendation is that you place your data under ../dataset/<NEW_DATA>, and organize the files similar to the file structure of ../dataset/ethucy. Meanwhile, you should also specify the mappings from dataset names to actual raw data files (see dataset_info.py L1-8 for details).
  • Social Verifier: For social data, if you followed our recommendation above and kept the raw data files under ../dataset/<NEW_DATA>/raw identical to ETH/UCY .csv files, you can prepare the social data using python preprocess_social_data.py with some changes in arguments. Otherwise preprocess_social_data.py needs to be modified so that it supports your own data format.
  • Context Verifier: To train a context verifier, you need to first prepare a semantic map for your data. In our implementation, we use a single-channel 'image' to indicate that (see semantic_maps in Dataset). Yet multi-channel semantic maps are also allowed after modifying the CNN structure of context verifier in model/models.py accordingly. Besides, a set of transformations that translates annotated coordinates to pixel coordinates in the scene image (if applicable) is needed (see dataset_info.py L10-19).

If you wish to verify predictions with trained verifiers, here are also some reminders.

  • Social Verification: Before verifying social stimulus, please make sure that the social information for each of the trajectories to be verified can be generated or has been prepared in advance. In our implementation, the social information of base model outputs is already prepared during the execution of python preprocess_social_data.py.
  • Context Verification: When verifying context stimulus of candidate trajectories, please make sure that the trajectories are in absolute coordinates instead of relative ones so that the coordinate transformations can work properly.