Spaces:
Sleeping
Sleeping
File size: 4,767 Bytes
5769ee4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import os
import pytest
import torch
from mmcv import Config
from risk_biased.mpc_planner.planner import MPCPlanner, MPCPlannerParams
from risk_biased.predictors.biased_predictor import (
LitTrajectoryPredictorParams,
LitTrajectoryPredictor,
)
from risk_biased.scene_dataset.loaders import SceneDataLoaders
from risk_biased.utils.cost import TTCCostParams
from risk_biased.utils.planner_utils import to_state
@pytest.fixture(scope="module")
def params():
torch.manual_seed(0)
working_dir = os.path.dirname(os.path.realpath(__file__))
config_path = os.path.join(
working_dir, "..", "..", "..", "risk_biased", "config", "learning_config.py"
)
cfg = Config.fromfile(config_path)
cfg.num_control_samples = 10
cfg.num_elite = 3
cfg.iter_max = 3
cfg.smoothing_factor = 0.2
cfg.mean_warm_start = True
cfg.acceleration_std_x_m_s2 = 2.0
cfg.acceleration_std_y_m_s2 = 0.0
cfg.dt = 0.1
cfg.num_steps = 3
cfg.num_steps_future = 5
cfg.tracking_cost_scale_longitudinal = 0.1
cfg.tracking_cost_scale_lateral = 1.0
cfg.tracking_cost_reduce = "mean"
cfg.cost_scale = 10
cfg.cost_reduce = "mean"
cfg.distance_bandwidth = 2
cfg.time_bandwidth = 0.5
cfg.min_velocity_diff = 0.01
cfg.risk_estimator = {"type": "cvar", "eps": 1e-3}
cfg.interaction_type = ""
cfg.mcg_dim_expansion = 2
cfg.mcg_num_layers = 0
cfg.num_attention_heads = 4
cfg.num_blocks = 3
cfg.sequence_encoder_type = "MLP" # one of "MLP", "LSTM", "maskedLSTM"
cfg.sequence_decoder_type = "MLP" # one of "MLP", "LSTM"
cfg.state_dim = 2
cfg.dynamic_state_dim = 2
cfg.map_state_dim = 2
cfg.max_size_lane = 0
cfg.latent_dim = 2
cfg.hidden_dim = 64
cfg.num_hidden_layers = 3
cfg.risk_distribution = {"type": "log-uniform", "min": 0, "max": 1, "scale": 3}
cfg.kl_weight = 1.0
cfg.kl_threshold = 0.1
cfg.learning_rate = 1e-3
cfg.n_mc_samples_risk = 2048
cfg.n_mc_samples_biased = 128
cfg.risk_weight = 1e3
cfg.use_risk_constraint = True
cfg.risk_constraint_update_every_n_epoch = 20
cfg.risk_constraint_weight_update_factor = 1.5
cfg.risk_constraint_weight_maximum = 1e5
cfg.condition_on_ego_future = True
cfg.is_mlp_residual = True
cfg.num_samples_min_fde = 6
return cfg
class TestMPCPlanner:
@pytest.fixture(autouse=True)
def setup(self, params):
self.planner_params = MPCPlannerParams.from_config(params)
predictor_params = LitTrajectoryPredictorParams.from_config(params)
self.predictor = LitTrajectoryPredictor(
predictor_params,
TTCCostParams.from_config(params),
SceneDataLoaders.unnormalize_trajectory,
)
self.normalizer = SceneDataLoaders.normalize_trajectory
self.planner = MPCPlanner(self.planner_params, self.predictor, self.normalizer)
def test_reset(self):
self.planner.reset()
assert torch.allclose(
self.planner.solver.control_input_mean_init,
self.planner.control_input_mean_init,
)
assert torch.allclose(
self.planner.solver.control_input_std_init,
self.planner.control_input_std_init,
)
assert self.planner._ego_state_history == []
assert self.planner._ego_state_target_trajectory == None
assert self.planner._ego_state_planned_trajectory == None
assert self.planner._ado_state_history == []
assert self.planner._latest_ado_position_future_samples == None
def test_replan(self, params):
num_prediction_samples = 100
num_agents = 1
self.planner.reset()
current_ego_state = to_state(torch.Tensor([[1, 1, 0, 0]]), params.dt)
for step in range(params.num_steps + 1):
self.planner._update_ego_state_history(current_ego_state)
current_ado_state = to_state(torch.Tensor([[2.0, 0.0, 0, 0]]), params.dt)
for step in range(params.num_steps + 1):
self.planner._update_ado_state_history(current_ado_state)
target_velocity = torch.Tensor([3.0, 0.0])
self.planner.replan(
current_ado_state,
current_ego_state,
target_velocity,
num_prediction_samples=num_prediction_samples,
)
assert self.planner._ego_state_planned_trajectory.shape == torch.Size(
[num_agents, params.num_steps_future]
)
next_ego_state = self.planner.get_planned_next_ego_state()
assert next_ego_state.shape == torch.Size([1])
assert self.planner.fetch_latest_prediction().shape == torch.Size(
[num_prediction_samples, num_agents, params.num_steps_future]
)
|