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# Lloyd algorithm while estimating average cost?
import os
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from pytorch_lightning.utilities.seed import seed_everything
# from scipy.cluster.vq import kmeans2
# from scipy.spatial import voronoi_plot_2d, Voronoi
import torch
import torch.nn as nn
from risk_biased.scene_dataset.loaders import SceneDataLoaders
from risk_biased.scene_dataset.scene import RandomScene, RandomSceneParams
# from risk_biased.scene_dataset.scene_plotter import ScenePlotter
from risk_biased.utils.callbacks import get_fast_slow_scenes, DrawCallbackParams
from risk_biased.utils.config_argparse import config_argparse
from risk_biased.utils.cost import TTCCostNumpy
from risk_biased.utils.load_model import load_from_config
def draw_cost_map(
model: nn.Module,
selected_agent: int,
device,
scene: RandomScene,
sqrt_n_samples: int,
params: DrawCallbackParams,
):
n_samples = sqrt_n_samples**2
ped_trajs = scene.get_pedestrians_trajectories()
n_scenes, n_agents, n_steps, features = ped_trajs.shape
input_traj = ped_trajs[:, :, : params.num_steps]
normalized_input, offset = SceneDataLoaders.normalize_trajectory(
torch.from_numpy(input_traj.astype("float32")).contiguous().to(device)
)
n_scenes = ped_trajs.shape[0]
x = np.linspace(-3, 3, sqrt_n_samples)
y = np.linspace(-3, 3, sqrt_n_samples)
xx, yy = np.meshgrid(x, y)
prior_samples = (
torch.from_numpy(np.stack((xx, yy), -1).astype("float32"))
.view(1, 1, n_samples, 2)
.repeat(n_scenes, n_agents, 1, 1)
)
mask_z = torch.ones_like(prior_samples[..., 0, 0])
mask_input = torch.ones_like(normalized_input[..., 0])
map = torch.empty(n_scenes, 0, 0, features, device=device)
mask_map = torch.empty(n_scenes, 0, 0)
generated_trajs = (
SceneDataLoaders.unnormalize_trajectory(
model.decode(
z_samples=prior_samples,
mask_z=mask_z,
x=normalized_input,
mask_x=mask_input,
map=map,
mask_map=mask_map,
offset=offset,
),
offset,
)
.cpu()
.detach()
.numpy()
)
input_traj = np.repeat(
input_traj.reshape((n_scenes, n_agents, 1, params.num_steps, features)),
n_samples,
axis=2,
)
generated_ped_trajs = np.concatenate((input_traj, generated_trajs), axis=3)
ego_traj = scene.get_ego_ref_trajectory(params.scene_params.sample_times)[
None, :, :
]
ttc_cost_func = TTCCostNumpy(params.ttc_cost_params)
sample_times = np.array(params.scene_params.sample_times)
ped_velocities = (
generated_ped_trajs[:, :, :, 1:] - generated_ped_trajs[:, :, :, :-1]
) / ((sample_times[1:] - sample_times[:-1])[None, None, None, :, None])
ped_velocities = np.concatenate((ped_velocities[:, :, :, 0:1], ped_velocities), 3)
ttc_cost_pred, (ttc_pred, dist_pred) = ttc_cost_func(
ego_traj[:, :, :, params.num_steps :],
generated_ped_trajs[:, :, :, params.num_steps :],
scene.get_ego_ref_velocity()[:, :, None],
ped_velocities[:, :, :, params.num_steps :],
)
ttc_cost_pred = (
ttc_cost_pred[:, selected_agent]
.reshape(n_scenes, sqrt_n_samples, sqrt_n_samples)
.mean(0)
)
cmap = plt.get_cmap("RdBu_r")
plt.contourf(
xx,
yy,
ttc_cost_pred.reshape((sqrt_n_samples, sqrt_n_samples)),
50,
cmap=cmap,
extent=(-3, 3, -3, 3),
vmin=0,
vmax=2,
)
norm = matplotlib.colors.Normalize(vmin=0, vmax=2, clip=True)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
plt.colorbar(sm, label="TTC cost")
plt.axis([-3, 3, -3, 3])
plt.show()
if __name__ == "__main__":
# Draws a contour plot of the cost associated with the latent samples in two scenarios: safer_fast and safer_slow
working_dir = os.path.dirname(os.path.realpath(__file__))
config_path = os.path.join(
working_dir, "..", "..", "risk_biased", "config", "learning_config.py"
)
cfg = config_argparse(config_path)
model, loaders, cfg = load_from_config(cfg)
assert (
cfg.latent_dim == 2
and "The latent dimension of the model must be exactly 2 to be plotted (no dimensionality reduction capabilities)"
)
scene_params = RandomSceneParams.from_config(cfg)
safer_fast_scene, safer_slow_scene = get_fast_slow_scenes(scene_params, 100)
draw_params = DrawCallbackParams.from_config(cfg)
if cfg.seed is not None:
seed_everything(cfg.seed)
sqrt_n_samples = 100
n_quantize = 100
draw_cost_map(
model.model,
0,
model.device,
safer_fast_scene,
sqrt_n_samples,
draw_params,
)
draw_cost_map(
model.model,
0,
model.device,
safer_slow_scene,
sqrt_n_samples,
draw_params,
)
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