l5kit.planning.vectorized.closed_loop_model module

class l5kit.planning.vectorized.closed_loop_model.VectorizedUnrollModel(history_num_frames_ego: int, history_num_frames_agents: int, num_targets: int, weights_scaling: List[float], criterion: torch.nn.modules.module.Module, global_head_dropout: float, disable_other_agents: bool, disable_map: bool, disable_lane_boundaries: bool, detach_unroll: bool, warmup_num_frames: int, discount_factor: float, limit_predicted_yaw: bool = True)

Bases: l5kit.planning.vectorized.open_loop_model.VectorizedModel

Vectorized closed-loop planning model.

forward(data_batch: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

update_transformation_matrices(pred_xy_step_unnorm: torch.Tensor, pred_yaw_step: torch.Tensor, t0_from_ts: torch.Tensor, ts_from_t0: torch.Tensor, yaw_t0_from_ts: torch.Tensor, yaw_ts_from_t0: torch.Tensor, zero: torch.Tensor, one: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]

Updates the used transformation matrices to reflect AoI’s new position.