def test_get_scene_indices_ego(scene_idx: int, zarr_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
rasterizer = build_rasterizer(cfg, dmg)
dataset = EgoDataset(cfg, zarr_dataset, rasterizer)
scene_indices = dataset.get_scene_indices(scene_idx)
frame_slice = get_frames_slice_from_scenes(zarr_dataset.scenes[scene_idx])
assert scene_indices[0] == frame_slice.start
assert scene_indices[-1] == frame_slice.stop - 1
class L5Env(gym.Env):
"""Custom Environment of L5 Kit that can be registered in OpenAI Gym.
:param env_config_path: path to the L5Kit environment configuration file
:param dmg: local data manager object
:param simulation_cfg: configuration of the L5Kit closed loop simulator
:param train: flag to determine whether to use train or validation dataset
:param reward: calculates the reward for the gym environment
:param cle: flag to enable close loop environment updates
:param rescale_action: flag to rescale the model action back to the un-normalized action space
:param use_kinematic: flag to use the kinematic model
:param kin_model: the kinematic model
:param return_info: flag to return info when a episode ends
:param randomize_start: flag to randomize the start frame of episode
"""
def __init__(self,
env_config_path: Optional[str] = None,
dmg: Optional[LocalDataManager] = None,
sim_cfg: Optional[SimulationConfig] = None,
train: bool = True,
reward: Optional[Reward] = None,
cle: bool = True,
rescale_action: bool = True,
use_kinematic: bool = False,
kin_model: Optional[KinematicModel] = None,
reset_scene_id: Optional[int] = None,
return_info: bool = False,
randomize_start: bool = True) -> None:
"""Constructor method
"""
super(L5Env, self).__init__()
# Required to register environment
if env_config_path is None:
return
# env config
dm = dmg if dmg is not None else LocalDataManager(None)
cfg = load_config_data(env_config_path)
self.step_time = cfg["model_params"]["step_time"]
# rasterisation
rasterizer = build_rasterizer(cfg, dm)
raster_size = cfg["raster_params"]["raster_size"][0]
n_channels = rasterizer.num_channels()
# load dataset of environment
self.train = train
self.overfit = cfg["gym_params"]["overfit"]
self.randomize_start_frame = randomize_start
if self.train or self.overfit:
loader_key = cfg["train_data_loader"]["key"]
else:
loader_key = cfg["val_data_loader"]["key"]
dataset_zarr = ChunkedDataset(dm.require(loader_key)).open()
self.dataset = EgoDataset(cfg, dataset_zarr, rasterizer)
# Define action and observation space
# Continuous Action Space: gym.spaces.Box (X, Y, Yaw * number of future states)
self.action_space = spaces.Box(low=-1, high=1, shape=(3, ))
# Observation Space: gym.spaces.Dict (image: [n_channels, raster_size, raster_size])
obs_shape = (n_channels, raster_size, raster_size)
self.observation_space = spaces.Dict({
'image':
spaces.Box(low=0, high=1, shape=obs_shape, dtype=np.float32)
})
# Simulator Config within Gym
self.sim_cfg = sim_cfg if sim_cfg is not None else SimulationConfigGym(
)
self.simulator = ClosedLoopSimulator(self.sim_cfg,
self.dataset,
device=torch.device("cpu"),
mode=ClosedLoopSimulatorModes.GYM)
self.reward = reward if reward is not None else L2DisplacementYawReward(
)
self.max_scene_id = cfg["gym_params"]["max_scene_id"]
if not self.train:
self.max_scene_id = cfg["gym_params"]["max_val_scene_id"]
self.randomize_start_frame = False
if self.overfit:
self.overfit_scene_id = cfg["gym_params"]["overfit_id"]
self.randomize_start_frame = False
self.cle = cle
self.rescale_action = rescale_action
self.use_kinematic = use_kinematic
if self.use_kinematic:
self.kin_model = kin_model if kin_model is not None else UnicycleModel(
)
self.kin_rescale = self._get_kin_rescale_params()
else:
self.non_kin_rescale = self._get_non_kin_rescale_params()
# If not None, reset_scene_id is the scene_id that will be rolled out when reset is called
self.reset_scene_id = reset_scene_id
if self.overfit:
self.reset_scene_id = self.overfit_scene_id
# flag to decide whether to return any info at end of episode
# helps to limit the IPC
self.return_info = return_info
self.seed()
def seed(self, seed: int = None) -> List[int]:
"""Generate the random seed.
:param seed: the seed integer
:return: the output random seed
"""
self.np_random, seed = seeding.np_random(seed)
# TODO : add a torch seed for future
return [seed]
def set_reset_id(self, reset_id: int = None) -> None:
"""Set the reset_id to unroll from specific scene_id.
Useful during deterministic evaluation.
:param reset_id: the scene_id to unroll
"""
self.reset_scene_id = reset_id
def reset(self) -> Dict[str, np.ndarray]:
""" Resets the environment and outputs first frame of a new scene sample.
:return: the observation of first frame of sampled scene index
"""
# Define in / outs for new episode scene
self.agents_ins_outs: DefaultDict[
int, List[List[UnrollInputOutput]]] = defaultdict(list)
self.ego_ins_outs: DefaultDict[
int, List[UnrollInputOutput]] = defaultdict(list)
# Select Scene ID
self.scene_index = self.np_random.randint(0, self.max_scene_id)
if self.reset_scene_id is not None:
self.scene_index = min(self.reset_scene_id, self.max_scene_id - 1)
self.reset_scene_id += 1
# Select Frame ID (within bounds of the scene)
if self.randomize_start_frame:
scene_length = len(self.dataset.get_scene_indices(
self.scene_index))
self.eps_length = self.sim_cfg.num_simulation_steps or scene_length
end_frame = scene_length - self.eps_length
self.sim_cfg.start_frame_index = self.np_random.randint(
0, end_frame + 1)
# Prepare episode scene
self.sim_dataset = SimulationDataset.from_dataset_indices(
self.dataset, [self.scene_index], self.sim_cfg)
# Reset CLE evaluator
self.reward.reset()
# Output first observation
self.frame_index = 1 # Frame_index 1 has access to the true ego speed
ego_input = self.sim_dataset.rasterise_frame_batch(self.frame_index)
self.ego_input_dict = {
k: np.expand_dims(v, axis=0)
for k, v in ego_input[0].items()
}
# Reset Kinematic model
if self.use_kinematic:
init_kin_state = np.array(
[0.0, 0.0, 0.0, self.step_time * ego_input[0]['curr_speed']])
self.kin_model.reset(init_kin_state)
# Only output the image attribute
obs = {'image': ego_input[0]["image"]}
return obs
def step(self, action: np.ndarray) -> GymStepOutput:
"""Inputs the action, updates the environment state and outputs the next frame.
:param action: the action to perform on current state/frame
:return: the namedTuple comprising the (next observation, reward, done, info)
based on the current action
"""
frame_index = self.frame_index
next_frame_index = frame_index + 1
episode_over = next_frame_index == (len(self.sim_dataset) - 1)
# EGO
if not self.sim_cfg.use_ego_gt:
action = self._rescale_action(action)
ego_output = self._convert_action_to_ego_output(action)
self.ego_output_dict = ego_output
if self.cle:
# In closed loop training, the raster is updated according to predicted ego positions.
self.simulator.update_ego(self.sim_dataset, next_frame_index,
self.ego_input_dict,
self.ego_output_dict)
ego_frame_in_out = self.simulator.get_ego_in_out(
self.ego_input_dict, self.ego_output_dict,
self.simulator.keys_to_exclude)
self.ego_ins_outs[self.scene_index].append(
ego_frame_in_out[self.scene_index])
# generate simulated_outputs
simulated_outputs = SimulationOutputCLE(self.scene_index,
self.sim_dataset,
self.ego_ins_outs,
self.agents_ins_outs)
# reward calculation
reward = self.reward.get_reward(self.frame_index, [simulated_outputs])
# done is True when episode ends
done = episode_over
# Optionally we can pass additional info
# We are using "info" to output rewards and simulated outputs (during evaluation)
info: Dict[str, Any]
info = {
'reward_tot': reward["total"],
'reward_dist': reward["distance"],
'reward_yaw': reward["yaw"]
}
if done and self.return_info:
info = {
"sim_outs": self.get_episode_outputs(),
"reward_tot": reward["total"],
"reward_dist": reward["distance"],
"reward_yaw": reward["yaw"]
}
# Get next obs
self.frame_index += 1
obs = self._get_obs(self.frame_index, episode_over)
# return obs, reward, done, info
return GymStepOutput(obs, reward["total"], done, info)
def get_episode_outputs(self) -> List[EpisodeOutputGym]:
"""Generate and return the outputs at the end of the episode.
:return: List of episode outputs
"""
episode_outputs = [
EpisodeOutputGym(self.scene_index, self.sim_dataset,
self.ego_ins_outs, self.agents_ins_outs)
]
return episode_outputs
def render(self) -> None:
"""Render a frame during the simulation
"""
raise NotImplementedError
def _get_obs(self, frame_index: int,
episode_over: bool) -> Dict[str, np.ndarray]:
"""Get the observation corresponding to a given frame index in the scene.
:param frame_index: the index of the frame which provides the observation
:param episode_over: flag to determine if the episode is over
:return: the observation corresponding to the frame index
"""
if episode_over:
frame_index = 0 # Dummy final obs (when episode_over)
ego_input = self.sim_dataset.rasterise_frame_batch(frame_index)
self.ego_input_dict = {
k: np.expand_dims(v, axis=0)
for k, v in ego_input[0].items()
}
obs = {"image": ego_input[0]["image"]}
return obs
def _rescale_action(self, action: np.ndarray) -> np.ndarray:
"""Rescale the input action back to the un-normalized action space. PPO and related algorithms work well
with normalized action spaces. The environment receives a normalized action and we un-normalize it back to
the original action space for environment updates.
:param action: the normalized action
:return: the unnormalized action
"""
if self.rescale_action:
if self.use_kinematic:
action[0] = self.kin_rescale.steer_scale * action[0]
action[1] = self.kin_rescale.acc_scale * action[1]
else:
action[
0] = self.non_kin_rescale.x_mu + self.non_kin_rescale.x_scale * action[
0]
action[
1] = self.non_kin_rescale.y_mu + self.non_kin_rescale.y_scale * action[
1]
action[
2] = self.non_kin_rescale.yaw_mu + self.non_kin_rescale.yaw_scale * action[
2]
return action
def _get_kin_rescale_params(self) -> KinematicActionRescaleParams:
"""Determine the action un-normalization parameters for the kinematic model
from the current dataset in the L5Kit environment.
:return: Tuple of the action un-normalization parameters for kinematic model
"""
global MAX_ACC, MAX_STEER
return KinematicActionRescaleParams(MAX_STEER * self.step_time,
MAX_ACC * self.step_time)
def _get_non_kin_rescale_params(self,
max_num_scenes: int = 10
) -> NonKinematicActionRescaleParams:
"""Determine the action un-normalization parameters for the non-kinematic model
from the current dataset in the L5Kit environment.
:param max_num_scenes: maximum number of scenes to consider to determine parameters
:return: Tuple of the action un-normalization parameters for non-kinematic model
"""
scene_ids = list(range(self.max_scene_id)) if not self.overfit else [
self.overfit_scene_id
]
if len(scene_ids) > max_num_scenes: # If too many scenes, CPU crashes
scene_ids = scene_ids[:max_num_scenes]
sim_dataset = SimulationDataset.from_dataset_indices(
self.dataset, scene_ids, self.sim_cfg)
return calculate_non_kinematic_rescale_params(sim_dataset)
def _convert_action_to_ego_output(
self, action: np.ndarray) -> Dict[str, np.ndarray]:
"""Convert the input action into ego output format.
:param action: the input action provided by policy
:return: action in ego output format, a numpy dict with keys 'positions' and 'yaws'
"""
if self.use_kinematic:
data_dict = self.kin_model.update(action[:2])
else:
# [batch_size=1, num_steps, (X, Y, yaw)]
data = action.reshape(1, 1, 3)
pred_positions = data[:, :, :2]
# [batch_size, num_steps, 1->(yaw)]
pred_yaws = data[:, :, 2:3]
data_dict = {"positions": pred_positions, "yaws": pred_yaws}
return data_dict
zarr_dataset.open()
print(zarr_dataset)
# %% [markdown]
# Now, however it's time for us to look at the scenes and analyze them in depth. Theoretically, we could create a nifty little data-loader to do some heavy lifting for us.
# %% [code] {"_kg_hide-input":true}
import numpy as np
from IPython.display import display, clear_output
import PIL
cfg["raster_params"]["map_type"] = "py_semantic"
rast = build_rasterizer(cfg, dm)
dataset = EgoDataset(cfg, zarr_dataset, rast)
scene_idx = 2
indexes = dataset.get_scene_indices(scene_idx)
images = []
for idx in indexes:
data = dataset[idx]
im = data["image"].transpose(1, 2, 0)
im = dataset.rasterizer.to_rgb(im)
target_positions_pixels = transform_points(data["target_positions"] + data["centroid"][:2], data["world_to_image"])
center_in_pixels = np.asarray(cfg["raster_params"]["ego_center"]) * cfg["raster_params"]["raster_size"]
draw_trajectory(im, target_positions_pixels, data["target_yaws"], TARGET_POINTS_COLOR)
clear_output(wait=True)
#display(PIL.Image.fromarray(im[::-1]))
# %% [markdown]
# So, there's a lot of information in this one image. I'll try my best to point everything out, but do notify me if I make any errors. OK, let's get started with dissecting the image:
