VectorNet源代码（GNN+Attention+MLP）《VectorNet: Encoding HD Maps and》

[](https://www.python.org/downloads/release/python-370/) [](https://www.python.org/downloads/release/python-380/) [](./LICENSE)  > ### [Argoverse 2 API](https://github.com/argoai/av2-api) has been released! Check out our [NeurIPS 2021 Datasets and Benchmarks publication](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/4734ba6f3de83d861c3176a6273cac6d-Abstract-round2.html) to learn more about the datasets. --- # Argoverse API > Official GitHub repository for [Argoverse dataset](https://www.argoverse.org) --- ## Table of Contents > If you have any questions, feel free to open a [GitHub issue](https://github.com/argoai/argoverse-api/issues) describing the problem. - [Installation](#installation) - [Usage](#usage) - [Demo](#demo) - [Baselines](#baselines) - [Contributing](#contributing) - [Disclaimer](#disclaimer) - [License](#license) - [Open-Source Libraries Using Argoverse](#other-repos) --- ## Installation - Linux - MacOS ### 1) Clone - Clone this repo to your local machine using: ```git clone https://github.com/argoai/argoverse-api.git``` ### 2) Download HD map data - Download `hd_maps.tar.gz` from [our website](https://www.argoverse.org/av1.html#download-link) and extract into the root directory of the repo. Your directory structure should look something like this: ``` argodataset └── argoverse └── data_loading └── evaluation └── map_representation └── utils └── visualization └── map_files └── license ... ``` ### 3) Download Argoverse-Tracking and Argoverse-Forecasting We provide both the full dataset and the sample version of the dataset for testing purposes. Head to [our website](https://www.argoverse.org/data.html#download-link) to see the download option. * **Argoverse-Tracking** provides track annotations, egovehicle poses, and *undistorted*, raw data from camera (@30hz) and lidar sensors (@10hz) as well as two stereo cameras (@5hz). We've released a total 113 scenes/logs, separated into 65 logs for training, 24 logs for validating, and 24 logs for testing. We've separated training data into smaller files to make it easier to download, but you should extract them all into one folder. We also provide sample data (1 log) in `tracking_sample.tar.gz`. * **Argoverse-Forecasting** contains 327790 sequences of interesting scenarios. Each sequence follows the trajectory of the main agent for 5 seconds, while keeping track of all other actors (e.g car, pedestrian). We've separated them into 208272 training sequences, 40127 validation sequences, and 79391 test sequences. We also provide sample data (5 sequences) in `forecasting_sample.tar.gz`. Note that you need to download HD map data (and extract them into project root folder) for the API to function properly. You can selectively download either **Argoverse-Tracking** or **Argoverse-Forecasting** or both, depending on what type of data you need. The data can be extracted to any location in your local machine. ### 4) Install argoverse package * `argoverse` can be installed as a python package using pip install -e /path_to_root_directory_of_the_repo/ Make sure that you can run `python -c "import argoverse"` in python, and you are good to go! ### (optional) Install mayavi * Some visualizations may require `mayavi`. See instructions on how to install Mayavi [here](https://docs.enthought.com/mayavi/mayavi/installation.html). ### (optional) Install ffmpeg * Some visualizations may require `ffmpeg`. See instructions on how to install ffmpeg [here](https://ffmpeg.org/download.html). ### (optional) Stereo tutorial dependencies * You will need to install three dependencies to run the [stereo tutorial](https://github.com/argoai/argoverse-api/blob/master/demo_usage/competition_stereo_tutorial.ipynb): * **Open3D**: See instructions on how to install [here](https://github.com/intel-isl/Open3D). * **OpenCV contrib**: See instructions on how to install [here](https://pypi.org/project/opencv-contrib-python). * **Plotly**: See instructions on how to install [here](https://github.com/plotly/plotly.py). ### (optional) Remake the object-oriented label folders * The `track_labels_amodal` folders contains object-oriented labels (in contrast to per-frame labels in `per_sweep_annotations_amodal` folders. Run following script to remake `track_labels_amodal` folders and fix existing issues: python3 argoverse/utils/make_track_label_folders.py argoverse-tracking/train/ python3 argoverse/utils/make_track_label_folders.py argoverse-tracking/val/ --- ## Usage The Argoverse API provides useful functionality to interact with the 3 main components of our dataset: the HD Map, the Argoverse Tracking Dataset and the Argoverse Forecasting Dataset. ```python from argoverse.map_representation.map_api import ArgoverseMap from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader from argoverse.data_loading.argoverse_forecasting_loader import ArgoverseForecastingLoader avm = ArgoverseMap() argoverse_tracker_loader = ArgoverseTrackingLoader('argoverse-tracking/') #simply change to your local path of the data argoverse_forecasting_loader = ArgoverseForecastingLoader('argoverse-forecasting/') #simply change to your local path of the data ``` API documentation is available [here](https://argoai.github.io/argoverse-api/). We recommend you get started by working through the demo tutorials below. --- ## Demo To make it easier to use our API, we provide demo tutorials in the form of Jupyter Notebooks. To run them, you'll need to first install Jupyter Notebook `pip install jupyter`. Then navigate to the repo directory and open a server with `jupyter notebook`. When you run the command, it will open your browser automatically. If you lose the page, you can click on the link in your terminal to re-open the Jupyter notebook. Once it's running, just navigate to the `demo_usage` folder and open any tutorial! Note that to use the tracking and forecasting tutorials, you'll need to download the tracking and forecasting sample data from [our website](https://www.argoverse.org/data.html#download-link) and extract the folders into the root of the repo. ### **Argoverse Map Tutorial** [](./demo_usage/argoverse_map_tutorial.ipynb) ### **Argoverse-Tracking Tutorial** [](./demo_usage/argoverse_tracking_tutorial.ipynb) ### **Argoverse-Forecasting Tutorial** [](./demo_usage/argoverse_forecasting_tutorial.ipynb) ### Rendering birds-eye-view Run the following script to render cuboids from a birds-eye-view on the map. ``` $ python visualize_30hz_benchmark_data_on_map.py --dataset_dir <path/to/logs> --log_id <id of the specific log> --experiment_prefix <prefix of the output directory> ``` For example, the path to the logs might be `argoverse-tracking/train4` and the log id might be `2bc6a872-9979-3493-82eb-fb55407473c9`. This script will write to `<experiment prefix>_per_log_viz/<log id>` in the current working directory with images that look like the following:  It will also generate a video visualization at `<experiment prefix>_per_log_viz/<log id>_lidar_roi_nonground.mp4` ### Rendering cuboids on images Run the following script to render cuboids on images. ``` $ python cuboids_to_bboxes.py --dataset-dir <path/to/logs> --log-ids <id of specific log> --experiment-prefix <prefix for output directory> ``` This script can process multiple logs if desired. They can be passed as a comma separated list to `--log-ids`. Images will b