torch-kitti 0.1.0

utilities and pytorch datasets for the KITTI Vision Benchmark Suite

Pytorch KITTI

This project aims to provide a simple yet effective way to scaffold and load the KITTI Vision Banchmark Dataset providing Datasets, a simple way to download them, metrics and transformations.

Installation

To install torch-kitti

$ pip install torch-kitti

Scaffolding datasets

To manually download the datasets torch-kitti command line utility comes in handy:

$ torch_kitti download --help
usage: Torch Kitti download [-h]
                            {sync_rectified,depth_completion,depth_prediction}
                            path

positional arguments:
  {sync_rectified,depth_completion,depth_prediction}
                        name of the dataset to download
  path                  where scaffold the dataset

optional arguments:
  -h, --help            show this help message and exit

Actually available datasets are:

• KITTI Depth Completion Dataset
• KITTI Depth Prediction Dataset
• KITTI Raw Sync+Rect Dataset

Loading Datasets

All datasets return dictionaries, utilities to manipulate them can be found in torch_kitti.transforms module. Often each dataset provides options to include optional fields, for instance KittiDepthCompletionDataset usually provides simply the img, its sparse depth groundtruth gt and the sparse lidar hints lidar but using load_stereo=True stereo images will be included for each example.

from torchvision.transforms import Compose, RandomCrop, ToTensor

from torch_kitti.depth_completion import KittiDepthCompletionDataset
from torch_kitti.transforms import ApplyToFeatures

transform = ApplyToFeatures(
    Compose(
        [
            ToTensor(),
            RandomCrop([256, 512]),
        ]
    ),
    features=["img", "gt", "lidar"],
)

ds = KittiDepthCompletionDataset(
    "kitti_raw_sync_rect_root",
    "kitti_depth_completion_root",
    load_stereo=False,
    transform=transform,
    download=True,  # download if not found
)

Develop

Download from kitti and cd in the folder then prepare a virtual environment (1), install dev and doc dependencies (2) and pre-commit (3).

$ git clone https://github.com/andreaconti/torch_kitti.git
$ cd torch_kitti
$ python3 -m virtualenv .venv && source .venv/bin/activate  # (1)
$ pip install .[dev, doc] # (2)
$ pre-commit install  # (3)
$ python3 setup.py develop
$ pytest

Tests use some environment variables to locate each dataset on the file system and perform specific tests on it. If they are not found tests are skipped.

• KITTI_SYNC_RECT_ROOT: root of the kitti sync rect dataset
• KITTI_DEPTH_COMPLETION_ROOT: root of the kitti depth completion dataset