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# Lloyd algorithm while estimating average cost?
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
from pytest import param
from pytorch_lightning.utilities.seed import seed_everything
import torch
from risk_biased.scene_dataset.loaders import SceneDataLoaders
from risk_biased.scene_dataset.scene import RandomScene, RandomSceneParams
from matplotlib.patches import Ellipse
from risk_biased.utils.callbacks import get_fast_slow_scenes, DrawCallbackParams
from risk_biased.utils.config_argparse import config_argparse
from risk_biased.utils.load_model import load_from_config
def draw_latent_biased(
model: torch.nn.Module,
device,
scene: RandomScene,
n_samples: int,
params: DrawCallbackParams,
):
ped_trajs = scene.get_pedestrians_trajectories()
n_scenes, n_agents, n_steps, features = ped_trajs.shape
ego_traj = scene.get_ego_ref_trajectory([t * params.dt for t in range(n_steps)])
ego_past, ego_future = torch.split(
torch.from_numpy(ego_traj.astype("float32")),
[params.num_steps, params.num_steps_future],
dim=2,
)
ego_past = ego_past.repeat(n_scenes, n_samples, 1, 1).view(
-1, 1, params.num_steps, features
)
ego_future = ego_future.repeat(n_scenes, n_samples, 1, 1).view(
-1, 1, params.num_steps_future, features
)
input_traj = ped_trajs[:, :, : params.num_steps]
normalized_input, offset = SceneDataLoaders.normalize_trajectory(
torch.from_numpy(input_traj.astype("float32")).contiguous().to(device)
)
normalized_input = (
normalized_input.view(n_scenes, 1, n_agents, params.num_steps, features)
.repeat(1, n_samples, 1, 1, 1)
.view(-1, n_agents, params.num_steps, features)
)
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)
offset = (
offset.view(n_scenes, 1, n_agents, features)
.repeat(1, n_samples, 1, 1)
.view(-1, n_agents, features)
)
n_scenes = ped_trajs.shape[0]
risk_level = (
torch.linspace(0, 1, n_samples)
.view(1, n_samples, 1)
.repeat(n_scenes, 1, n_agents)
)
y_samples, biased_mu, biased_log_std = model(
normalized_input,
mask_input,
map,
mask_map,
offset=offset,
x_ego=ego_past,
y_ego=ego_future,
risk_level=risk_level.view(-1, n_agents),
)
biased_mu = (
biased_mu.reshape(n_scenes, n_samples, n_agents, 2)
.permute(0, 2, 1, 3)
.mean(0)
.cpu()
.detach()
.numpy()
)
biased_std = (
(
2
* biased_log_std.reshape(n_scenes, n_samples, n_agents, 2).permute(
0, 2, 1, 3
)
)
.exp()
.mean(0)
.sqrt()
.cpu()
.detach()
.numpy()
)
risk_level = risk_level.permute(0, 2, 1).mean(0).cpu().detach().numpy()
fig, ax = plt.subplots()
cmap = plt.get_cmap("RdBu_r")
for a in range(n_agents):
for j in range(n_samples):
ellipse = Ellipse(
(biased_mu[a, j, 0], biased_mu[a, j, 1]),
width=biased_std[a, j, 0] * 2,
height=biased_std[a, j, 1] * 2,
facecolor=cmap(risk_level[a, j]),
alpha=0.1,
)
ax.add_patch(ellipse)
norm = mpl.colors.Normalize(vmin=0, vmax=1, clip=True)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
plt.colorbar(sm, label="Risk level")
plt.axis([-3, 3, -3, 3])
plt.show()
if __name__ == "__main__":
# Draws the biased distributions in latent space for different risk levels 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, 128)
draw_params = DrawCallbackParams.from_config(cfg)
if cfg.seed is not None:
seed_everything(cfg.seed)
n_samples = 64
draw_latent_biased(
model.model, model.device, safer_fast_scene, n_samples, draw_params
)
draw_latent_biased(
model.model, model.device, safer_slow_scene, n_samples, draw_params
)
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