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Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch. AV-Learn has been tested and works on Linux, and requires the following packages:

  • Python 3.7+
  • PyTorch 1.3+
  • CUDA 9.2+
  • GCC 5+
  • MMCV

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment and activate it.

conda create --name avlearn python=3.7 -y
conda activate avlearn

Step 2. Install necessary packages

git clone https://github.com/nightrome/av-learn
cd av-learn/

pip install -r requirements.txt

mim install mmcv-full
mim install mmdet

pip install cumm-cuXXX && pip install spconv-cuXXX

pip install .

Where XXX is the CUDA version that you use.
E.g. pip install cumm-cu113 && pip install spconv-cu113 for CUDA 11.3.

Dataset Preparation

Before Preparation

It is recommended to use $av-learn/data as the root folder for all the datasets.

Download and Data Preparation

NuScenes

Download nuScenes V1.0 full dataset data HERE. Prepare nuscenes data by running

python avlearn/datasets/tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes

In order to use the LaneGCN network for the prediction task, you also need map representations for nuScenes.

First, you have to download the map expansion package from nuScenes. Then, you could either create the map representations using the following script:

python avlearn/datasets/tools/create_nuscenes_graph.py --data_dir ./data/nuscenes --output_dir ./data/nuscenes_representations --maps_dir ./data/nuscenes/maps

or download them from here.

Initialize a pipeline

You can initialize an AV Learn pipeline using a list of modules. The list does not need to contain modules for all of the corresponding tasks of the pipeline, unless end-to-end evaluation is to be performed. In the examples below we initialize different pipelines.

Example 1

In this pipeline we use a CenterPointDetector, a non-learning based CenterPointTracker, and the LaneGCN predictor.

from avlearn.pipeline import Pipeline
from avlearn.modules.detectors import CenterPointDetector
from avlearn.modules.trackers import CenterPointTracker
from avlearn.modules.predictors import LaneGCN

detector = CenterPointDetector()
tracker = CenterPointTracker()
predictor = LaneGCN()

pipeline = Pipeline([detector, tracker, predictor])

Example 2

In this pipeline we use the KalmanTracker for tracking. Thus the velocity head of the CenterPointDetector can be disabled (optional).

from avlearn.pipeline import Pipeline
from avlearn.modules.detectors import CenterPointDetector
from avlearn.modules.trackers import KalmanTracker
from avlearn.modules.predictors import LaneGCN

detector = CenterPointDetector(velocity=False)
tracker = KalmanTracker()
predictor = LaneGCN()

pipeline = Pipeline([detector, tracker, predictor])

Example 3

In this pipeline we only define a predictor. We can still use the pipeline for training and individual evaluation of the predictor, but the end-to-end evaluation is disabled.

from avlearn.pipeline import Pipeline
from avlearn.modules.predictors import LaneGCN

predictor = LaneGCN()

pipeline = Pipeline([predictor])

Training

The AV Learn framework simplifies the process of trainning the corresponding modules of a pipeline. Using the example below you can train all of the pipeline's trainable modules.

pipeline.train(
    dataroot="./data/nuscenes",
    work_dir="./results/training",
    n_epochs=[20, 0, 36],
    batch_size=[8, 0, 4],
    map_dataroot="./data/nuscenes_representations" # Model specific (LaneGCN)
)

Evaluation

Once you obtain the pretrained models, and their checkpoints, evaluating them either individually, or the end-to-end performance of the pipeline, can be performed using the following block of code.

detector = CenterPointDetector(checkpoint="/path/to/checkpoint/")
tracker = CenterPointTracker()
predictor = LaneGCN(checkpoint_pth="/path/to/checkpoint/")

pipeline = Pipeline([detector, tracker, predictor])

pipeline.evaluate(
    dataroot="./data/nuscenes",
    work_dir="./results/evaluation",  
    end_to_end=False, # or set True for end-to-end evaluation
    map_dataroot="./data/nuscenes_representations" # Model specific (LaneGCN)
)