Demos

These demos show how to integrate ACCV-Lab packages into training code and measure their performance.

StreamPETR

This is a collection of demos based on the StreamPETR model. The implementation is based on the open-source release of StreamPETR (https://github.com/exiawsh/StreamPETR).

This documentation explains the optimizations, environment setup, configuration files and evaluation commands.

Note

For the differences from the original StreamPETR implementation, including removed functionality, additional ACCV-Lab implementations, and the GridMask replacement, read demos/streampetr/README.md in the ACCV-Lab repository. That README describes the code in its checkout; use it together with documentation from the same revision.

For license and attribution information about third-party code used in ACCV-Lab and the StreamPETR demo, see Third-Party Licenses.

Important

The StreamPETR demos share one common implementation under demos/streampetr/ in the ACCV-Lab repository. The documentation is organized by package. Configuration files select the baseline or optimized implementation for each comparison. See the corresponding evaluation page for further details.

The tutorials explain the integration into StreamPETR as a representative real-world example. Stand-alone examples for the use of the individual packages are available in the corresponding package documentation.

Demos for Contained Packages

Multiple modifications have been made to the original implementation to demonstrate different contained packages. This includes demos of:

Description

Link
Use of batching-helpers:

  • Batches the loss computation (which is already partially batched in the original implementation).

  • Demo contains a tutorial on how to implement the batching for both matching and loss computation.

Batching Helpers Demo
Use of dali-pipeline-framework:

  • An efficient alternative to the PyTorch DataLoader used in the original implementation is implemented.

  • Demo contains a tutorial on how to use the DALI Pipeline Framework to build the data-loading pipeline.

DALI Pipeline Framework Demo
Use of optim-test-tools:

  • Enables runtime evaluation and profiling of the optimizations. This functionality is used in the other demos to perform performance evaluation.

  • Enables dumping of the training input data for debugging/demonstration purposes. This functionality is used in the DALI Pipeline Framework demo for debugging/demonstration purposes.

Optimization Testing Tools Demo

Running the Demos

To run the demos, you need to perform the steps described in the table below.

Step

Description

  1. Set up the environment

Set up the Docker image and container as described in the StreamPETR Docker setup.

  1. Prepare the data

The StreamPETR demo uses the NuScenes dataset (the mini version in our evaluations), and assumes that the data was converted as needed for StreamPETR (see the Data Preparation documentation of the original StreamPETR repository, linked from its README: https://github.com/exiawsh/StreamPETR).

For the provided mini-dataset configurations, generate the reference loader’s metadata from inside the container with:

cd /streampetr
python tools/create_data_nusc.py \
  --root-path /data/nuscenes \
  --version v1.0-mini \
  --extra-tag nuscenes2d

This creates nuscenes2d_temporal_infos_train.pkl and nuscenes2d_temporal_infos_val.pkl under /data/nuscenes. With this command, the camera paths stored in these files are absolute and are used directly by the reference loader. Keep the same in-container dataset location during conversion and training, and regenerate the files if the location is changed.

The DALI Pipeline Framework data loader generates its projected annotations and v1.0-mini_preproc_streampetr.pkl cache automatically on the first run; the dataset directory must be writable to store it.

  1. Run the package-specific demo

Run the demo for the contained package you are interested in by following the steps outlined in the demo description (typically in the evaluation section). For example, for the batching-helpers demo, see the Batching Helpers evaluation page for commands.

Choosing a Configuration

The configuration files are under demos/streampetr/projects/configs/StreamPETR/. They include configurations for the reference demo, Batching Helpers, DALI Pipeline Framework data loading, and profiling. For the appropriate configuration, comparison baseline, supported GPU counts, and commands, see the evaluation section of the corresponding demo, for example the Batching Helpers evaluation or DALI Pipeline Framework evaluation.

For the demo’s regression tests, see Running Regression Tests in the Docker setup guide.

Running All Evaluations

The evaluation configurations are under projects/configs/StreamPETR/Evals/. Follow the corresponding evaluation pages for the measured comparisons and commands. To run every configuration in that directory sequentially, including the additional variants, use:

bash /streampetr/tools/run_all_evaluations.sh

This requires the prepared dataset and enough GPUs for all configurations (up to eight). Logs are written under eval_res_all_evaluations/ in the current directory.

The script sorts configuration filenames alphabetically before running them. If the first two configurations have already completed, for example, pass 2 to skip them and start with the third:

bash /streampetr/tools/run_all_evaluations.sh 2

The script does not check previous results; it simply skips the specified number of configurations at the beginning of the sorted list.

Measurement and Profiling

Use the Optimization Testing Tools to measure and inspect the training process:

  • Runtime evaluation

  • NVTX range-based profiling (including optional CUDA synchronizations for ranges)

  • Dumping of the training input data for debugging/demonstration purposes

See Optimization Testing Tools Demo for configuration options and usage instructions, and the optim-test-tools package documentation for the API.