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import math
import os
import matplotlib.pyplot as plt
import numpy as np
from pytorch_lightning.utilities.seed import seed_everything
import torch
from risk_biased.scene_dataset.scene import RandomScene, RandomSceneParams
from risk_biased.utils.cost import (
DistanceCostNumpy,
DistanceCostParams,
TTCCostNumpy,
TTCCostParams,
)
from risk_biased.utils.load_model import load_from_config
from risk_biased.utils.risk import get_risk_level_sampler
from risk_biased.utils.config_argparse import config_argparse
if __name__ == "__main__":
working_dir = os.path.dirname(os.path.realpath(__file__))
config_path = os.path.join(
working_dir, "..", "..", "risk_biased", "config", "learning_config.py"
)
config = config_argparse(config_path)
model, loaders, config = load_from_config(config)
if config.seed is not None:
seed_everything(config.seed)
risk_sampler = get_risk_level_sampler(config.risk_distribution)
is_torch = False
n_scenes = 1000
sample_every = 10
# Get a batch of random pedestrians
scene_params = RandomSceneParams.from_config(config)
scene_params.batch_size = n_scenes
scene = RandomScene(
scene_params,
is_torch=is_torch,
)
dist_cost_func = DistanceCostNumpy(DistanceCostParams.from_config(config))
ttc_cost_func = TTCCostNumpy(TTCCostParams.from_config(config))
len_traj = int(config.time_scene / scene.dt)
ped_trajs = scene.get_pedestrians_trajectories()
ped_trajs_past = ped_trajs[:, :, : config.num_steps]
batch_size = ped_trajs.shape[0]
ego_traj = scene.get_ego_ref_trajectory(config.sample_times).repeat(
batch_size, axis=0
)
normalized_trajs, offset = loaders.normalize_trajectory(
torch.from_numpy(ped_trajs.astype("float32")).contiguous()
)
x = normalized_trajs[:, :, : config.num_steps]
ego_history = (
torch.from_numpy(ego_traj[:, :, : config.num_steps].astype("float32"))
.expand_as(x)
.contiguous()
)
ego_future = (
torch.from_numpy(ego_traj[:, :, -config.num_steps_future :].astype("float32"))
.expand(x.shape[0], x.shape[1], -1, -1)
.contiguous()
)
mask_x = torch.ones_like(x[..., 0])
map = torch.empty(ego_history.shape[0], 0, 0, 2, device=mask_x.device)
mask_map = torch.empty(ego_history.shape[0], 0, 0, device=mask_x.device)
pred_riskier = (
model.predict_step(
(x, mask_x, map, mask_map, offset, ego_history, ego_future),
0,
risk_level=risk_sampler.get_highest_risk(
batch_size=n_scenes, device="cpu"
).unsqueeze(1),
)
.cpu()
.detach()
.numpy()
)
pred = (
model.predict_step(
(x, mask_x, map, mask_map, offset, ego_history, ego_future),
0,
risk_level=None,
)
.cpu()
.detach()
.numpy()
)
ped_trajs_pred = np.concatenate((ped_trajs_past, pred), axis=-2)
ped_trajs_pred_riskier = np.concatenate((ped_trajs_past, pred_riskier), axis=-2)
travel_distances = np.sqrt(
np.square(ped_trajs[..., -1, :] - ped_trajs[..., 0, :]).sum(-1)
)
dist_cost, dist = dist_cost_func(
ego_traj[:, :, config.num_steps :], ped_trajs[:, :, config.num_steps :]
)
ttc_cost, (ttc, dist) = ttc_cost_func(
ego_traj[:, :, config.num_steps :],
ped_trajs[:, :, config.num_steps :],
scene.get_ego_ref_velocity(),
scene.get_pedestrians_velocities(),
)
travel_distances_pred = np.sqrt(
np.square(ped_trajs_pred[..., -1, :] - ped_trajs_pred[..., 0, :]).sum(-1)
)
dist_cost_pred, dist_pred = dist_cost_func(
ego_traj[:, :, config.num_steps :], ped_trajs_pred[:, :, config.num_steps :]
)
sample_times = np.array(config.sample_times)
ped_velocities_pred = (ped_trajs_pred[:, :, 1:] - ped_trajs_pred[:, :, :-1]) / (
(sample_times[1:] - sample_times[:-1])[None, None, :, None]
)
ped_velocities_pred = np.concatenate(
(ped_velocities_pred[:, :, 0:1], ped_velocities_pred), -2
)
ttc_cost_pred, (ttc_pred, dist_pred) = ttc_cost_func(
ego_traj[:, :, config.num_steps :],
ped_trajs_pred[:, :, config.num_steps :],
scene.get_ego_ref_velocity(),
ped_velocities_pred[:, :, config.num_steps :],
)
travel_distances_pred_riskier = np.sqrt(
np.square(
ped_trajs_pred_riskier[..., -1, :] - ped_trajs_pred_riskier[..., 0, :]
).sum(-1)
)
dist_cost_pred_riskier, dist_pred_riskier = dist_cost_func(
ego_traj[:, :, config.num_steps :],
ped_trajs_pred_riskier[:, :, config.num_steps :],
)
sample_times = np.array(config.sample_times)
ped_velocities_pred_riskier = (
ped_trajs_pred_riskier[:, :, 1:] - ped_trajs_pred_riskier[:, :, :-1]
) / ((sample_times[1:] - sample_times[:-1])[None, None, :, None])
ped_velocities_pred_riskier = np.concatenate(
(ped_velocities_pred_riskier[:, :, 0:1], ped_velocities_pred_riskier), 2
)
ttc_cost_pred_riskier, (ttc_pred, dist_pred_riskier) = ttc_cost_func(
ego_traj[:, :, config.num_steps :],
ped_trajs_pred_riskier[:, :, config.num_steps :],
scene.get_ego_ref_velocity(),
ped_velocities_pred_riskier[:, :, config.num_steps :],
)
def plot_histograms(travel_distances, dist_cost, ttc_cost, label=""):
# Open the plots for the sampled future times
fig, ax = plt.subplots(1, 3)
fig.suptitle(label)
# Plot histograms of traveled distances, depending on the parameters.
# It should be multi-modal. There is a minimum distance and a maximum distance and travel distance variations within these bounds.
ax[0].set_title("Travel distance")
ax[1].set_title("Distance cost")
ax[2].set_title("TTC cost")
ax[0].hist(travel_distances, bins=30)
ax[1].hist(dist_cost.flatten(), bins=30)
ax[1].set_ylim([0, 3 * math.sqrt(n_scenes)])
ax[2].hist(ttc_cost.flatten(), bins=30)
ax[2].set_ylim([0, 3 * math.sqrt(n_scenes)])
agent_selected = 0
plot_histograms(
travel_distances[:, agent_selected],
dist_cost[:, agent_selected],
ttc_cost[:, agent_selected],
"Data",
)
plot_histograms(
travel_distances_pred[:, agent_selected],
dist_cost_pred[:, agent_selected],
ttc_cost_pred[:, agent_selected],
"Prediction normal risk",
)
plot_histograms(
travel_distances_pred_riskier[:, agent_selected],
dist_cost_pred_riskier[:, agent_selected],
ttc_cost_pred_riskier[:, agent_selected],
"Prediction high risk",
)
print(f"Average ttc risk")
print(
f"Ground truth: {ttc_cost.mean()}, Prediction: {ttc_cost_pred.mean()}, Biased prediction: {ttc_cost_pred_riskier.mean()}"
)
plt.show()
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