l5kit.dataset.ego module¶
- class l5kit.dataset.ego.BaseEgoDataset(cfg: dict, zarr_dataset: l5kit.data.zarr_dataset.ChunkedDataset)¶
Bases:
Generic[torch.utils.data.dataset.T_co]- get_frame(scene_index: int, state_index: int, track_id: Optional[int] = None) dict¶
A utility function to get the rasterisation and trajectory target for a given agent in a given frame
- Parameters
scene_index (int) – the index of the scene in the zarr
state_index (int) – a relative frame index in the scene
track_id (Optional[int]) – the agent to rasterize or None for the AV
- Returns
the rasterised image in (Cx0x1) if the rast is not None, the target trajectory (position and yaw) along with their availability, the 2D matrix to center that agent, the agent track (-1 if ego) and the timestamp
- Return type
dict
- get_frame_indices(frame_idx: int) numpy.ndarray¶
Get indices for the given frame. EgoDataset iterates over frames, so this will be a single element :param frame_idx: index of the scene :type frame_idx: int
- Returns
indices that can be used for indexing with __getitem__
- Return type
np.ndarray
- get_scene_dataset(scene_index: int) l5kit.dataset.ego.BaseEgoDataset¶
Returns another EgoDataset dataset where the underlying data can be modified. This is possible because, even if it supports the same interface, this dataset is np.ndarray based.
- Parameters
scene_index (int) – the scene index of the new dataset
- Returns
A valid EgoDataset dataset with a copy of the data
- Return type
- get_scene_indices(scene_idx: int) numpy.ndarray¶
Get indices for the given scene. EgoDataset iterates over frames, so this is just a matter of finding the scene boundaries. :param scene_idx: index of the scene :type scene_idx: int
- Returns
indices that can be used for indexing with __getitem__
- Return type
np.ndarray
- class l5kit.dataset.ego.EgoAgentDatasetVectorized(cfg: dict, zarr_dataset: l5kit.data.zarr_dataset.ChunkedDataset, vectorizer: l5kit.vectorization.vectorizer.Vectorizer, perturbation: Optional[l5kit.kinematic.perturbation.Perturbation] = None, agents_mask: Optional[numpy.ndarray] = None, eval_mode: bool = False)¶
Bases:
Generic[torch.utils.data.dataset.T_co]Get a PyTorch dataset object that can be used to train DNNs with vectorized input. Ego features are added to the agent features to treat ego as an agent.
- Parameters
cfg (dict) – configuration file
zarr_dataset (ChunkedDataset) – the raw zarr dataset
vectorizer (Vectorizer) – a object that supports vectorization around an AV
perturbation (Optional[Perturbation]) – an object that takes care of applying trajectory perturbations. None if not desired
agents_mask (Optional[np.ndarray]) – custom boolean mask of the agent availability.
eval_mode (bool) – enable eval mode (iterates over agent, similarly to AgentDataset).
- get_frame_indices(frame_idx: int) numpy.ndarray¶
Get indices for the given frame. EgoDataset iterates over frames, so this will be a single element :param frame_idx: index of the scene :type frame_idx: int
- Returns
indices that can be used for indexing with __getitem__
- Return type
np.ndarray
- get_scene_dataset(scene_index: int) l5kit.dataset.ego.EgoAgentDatasetVectorized¶
Returns another EgoAgentDatasetVectorized dataset where the underlying data can be modified. This is possible because, even if it supports the same interface, this dataset is np.ndarray based.
- Parameters
scene_index (int) – the scene index of the new dataset
- Returns
A valid EgoAgentDatasetVectorized dataset with a copy of the data
- Return type
- get_scene_indices(scene_idx: int) numpy.ndarray¶
Get indices for the given scene. EgoDataset iterates over frames, so this is just a matter of finding the scene boundaries. :param scene_idx: index of the scene :type scene_idx: int
- Returns
indices that can be used for indexing with __getitem__
- Return type
np.ndarray
- load_agents_mask() numpy.ndarray¶
Loads a boolean mask of the agent availability stored into the zarr. Performs some sanity check against cfg. Returns: a boolean mask of the same length of the dataset agents
- class l5kit.dataset.ego.EgoDataset(cfg: dict, zarr_dataset: l5kit.data.zarr_dataset.ChunkedDataset, rasterizer: l5kit.rasterization.rasterizer.Rasterizer, perturbation: Optional[l5kit.kinematic.perturbation.Perturbation] = None)¶
Bases:
Generic[torch.utils.data.dataset.T_co]- get_frame(scene_index: int, state_index: int, track_id: Optional[int] = None) dict¶
A utility function to get the rasterisation and trajectory target for a given agent in a given frame
- Parameters
scene_index (int) – the index of the scene in the zarr
state_index (int) – a relative frame index in the scene
track_id (Optional[int]) – the agent to rasterize or None for the AV
- Returns
the rasterised image in (Cx0x1) if the rast is not None, the target trajectory (position and yaw) along with their availability, the 2D matrix to center that agent, the agent track (-1 if ego) and the timestamp
- Return type
dict
- get_scene_dataset(scene_index: int) l5kit.dataset.ego.EgoDataset¶
Returns another EgoDataset dataset where the underlying data can be modified. This is possible because, even if it supports the same interface, this dataset is np.ndarray based.
- Parameters
scene_index (int) – the scene index of the new dataset
- Returns
A valid EgoDataset dataset with a copy of the data
- Return type
- class l5kit.dataset.ego.EgoDatasetVectorized(cfg: dict, zarr_dataset: l5kit.data.zarr_dataset.ChunkedDataset, vectorizer: l5kit.vectorization.vectorizer.Vectorizer, perturbation: Optional[l5kit.kinematic.perturbation.Perturbation] = None)¶
Bases:
Generic[torch.utils.data.dataset.T_co]- get_scene_dataset(scene_index: int) l5kit.dataset.ego.EgoDatasetVectorized¶
Returns another EgoDataset dataset where the underlying data can be modified. This is possible because, even if it supports the same interface, this dataset is np.ndarray based.
- Parameters
scene_index (int) – the scene index of the new dataset
- Returns
A valid EgoDataset dataset with a copy of the data
- Return type