l5kit.sampling package¶
-
l5kit.sampling.
generate_agent_sample
(state_index: int, frames: numpy.ndarray, agents: numpy.ndarray, tl_faces: numpy.ndarray, selected_track_id: Optional[int], render_context: l5kit.rasterization.render_context.RenderContext, history_num_frames: int, future_num_frames: int, step_time: float, filter_agents_threshold: float, rasterizer: Optional[l5kit.rasterization.rasterizer.Rasterizer] = None, perturbation: Optional[l5kit.kinematic.perturbation.Perturbation] = None) → dict¶ - Generates the inputs and targets to train a deep prediction model. A deep prediction model takes as input
the state of the world (here: an image we will call the “raster”), and outputs where that agent will be some seconds into the future.
This function has a lot of arguments and is intended for internal use, you should try to use higher level classes and partials that use this function.
- Args:
state_index (int): The anchor frame index, i.e. the “current” timestep in the scene frames (np.ndarray): The scene frames array, can be numpy array or a zarr array agents (np.ndarray): The full agents array, can be numpy array or a zarr array tl_faces (np.ndarray): The full traffic light faces array, can be numpy array or a zarr array selected_track_id (Optional[int]): Either None for AV, or the ID of an agent that you want to predict the future of. This agent is centered in the raster and the returned targets are derived from their future states. raster_size (Tuple[int, int]): Desired output raster dimensions pixel_size (np.ndarray): Size of one pixel in the real world ego_center (np.ndarray): Where in the raster to draw the ego, [0.5,0.5] would be the center history_num_frames (int): Amount of history frames to draw into the rasters future_num_frames (int): Amount of history frames to draw into the rasters step_time (float): seconds between consecutive steps filter_agents_threshold (float): Value between 0 and 1 to use as cutoff value for agent filtering based on their probability of being a relevant agent rasterizer (Optional[Rasterizer]): Rasterizer of some sort that draws a map image perturbation (Optional[Perturbation]): Object that perturbs the input and targets, used
to train models that can recover from slight divergence from training set data
- Raises:
ValueError: A ValueError is returned if the specified
selected_track_id
is not present in the scene or was filtered by applying thefilter_agent_threshold
probability filtering.- Returns:
dict: a dict object with the raster array, the future offset coordinates (meters), the future yaw angular offset, the future_availability as a binary mask
-
l5kit.sampling.
get_future_slice
(frame_index: int, future_num_states: int, future_step_size: int) → slice¶ Given a frame index and future settings returns a slice that returns the given data in the right order. Note that this history returned starts with the most “recent” frame first (e.g.
current_frame``+``future_step_size
).Example:
frame_index=20
,future_num_states=2
,future_step_size=2
would return a slice for frame index 22, 24.- Parameters
state_index (int) – The “anchor” frame index you want to sample from
future_num_states (int) – Number of future frames.
future_step_size (int) – How many frames to step for each future step.
- Raises
IndexError – Returned when
future_step_size
is an invalid value (e.g. 0).- Returns
slice – Slice that when applied to an array returns the future frames in the right order.
-
l5kit.sampling.
get_history_slice
(frame_index: int, history_num_states: int, history_step_size: int, include_current_state: bool = False) → slice¶ Given a frame index and history settings returns a slice that returns the given data in the right order. Note that this history returned starts with the most “recent” frame first (i.e. reverse in time as it’s history).
Example:
frame_index=20
,history_num_frames=2
,history_step_size=2
,include_current_state=True
would return a slice for frame index 20, 18, 16.- Parameters
state_index (int) – The “anchor” frame index you want to sample from
history_num_states (int) – Number of history frames (not including the current frame).
history_step_size (int) – How many frames to step for each history step.
- Keyword Arguments
include_current_state (bool) – Whether the slice should include
frame_index
(default: {False})- Raises
IndexError – Returned when
history_step_size
is an invalid value (e.g. 0).- Returns
slice – Slice that when applied to an array returns the history frames in the right order.