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import os
import pytest
import torch
import numpy as np
from mmcv import Config
from risk_biased.utils.cost import BaseCostTorch, TTCCostTorch, DistanceCostTorch
from risk_biased.utils.cost import BaseCostNumpy, TTCCostNumpy, DistanceCostNumpy
from risk_biased.utils.cost import (
CostParams,
TTCCostParams,
DistanceCostParams,
)
@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.cost_scale = 1
cfg.cost_reduce = "mean"
cfg.ego_length = 4
cfg.ego_width = 1.75
cfg.distance_bandwidth = 2
cfg.time_bandwidth = 2
cfg.min_velocity_diff = 0.01
return cfg
def get_fake_input(batch_size, num_steps, is_torch, use_mask, num_agents=0):
if num_agents <= 0:
shape = [batch_size, num_steps, 2]
else:
shape = [batch_size, num_agents, num_steps, 2]
if is_torch:
x1 = torch.rand(shape)
x2 = torch.rand(shape)
v1 = torch.rand(shape)
v2 = torch.rand(shape)
if use_mask:
mask = torch.rand(shape[:-1]) > 0.1
else:
mask = None
else:
x1 = np.random.uniform(size=shape)
x2 = np.random.uniform(size=shape)
v1 = np.random.uniform(size=shape)
v2 = np.random.uniform(size=shape)
if use_mask:
mask = np.random.uniform(size=shape[:-1]) > 0.1
else:
mask = None
return x1, x2, v1, v2, mask
@pytest.mark.parametrize(
"reduce, batch_size, num_steps, is_torch, use_mask, num_agents",
[
("mean", 8, 5, True, True, 0),
("min", 4, 2, False, True, 2),
("max", 4, 2, True, False, 3),
("now", 16, 1, False, False, 1),
("final", 1, 4, True, True, 0),
],
)
def test_base_cost(
params,
reduce: str,
batch_size: int,
num_steps: int,
is_torch: bool,
use_mask: bool,
num_agents: int,
):
params.cost_reduce = reduce
cost_params = CostParams.from_config(params)
if is_torch:
base_cost = BaseCostTorch(cost_params)
else:
base_cost = BaseCostNumpy(cost_params)
x1, x2, v1, v2, mask = get_fake_input(
batch_size, num_steps, is_torch, use_mask, num_agents
)
cost, _ = base_cost(x1, x2, v1, v2, mask)
if num_agents > 0:
assert cost.shape == (
batch_size,
num_agents,
)
else:
assert cost.shape == (batch_size,)
assert (cost == 0).all()
assert base_cost.scale == params.cost_scale
assert base_cost.distance_bandwidth == 1
assert base_cost.time_bandwidth == 1
@pytest.mark.parametrize(
"param_class, cost_class, reduce, batch_size, num_steps, is_torch, use_mask, num_agents",
[
(DistanceCostParams, DistanceCostTorch, "max", 4, 2, True, True, 3),
(DistanceCostParams, DistanceCostNumpy, "now", 16, 1, False, True, 0),
(DistanceCostParams, DistanceCostTorch, "final", 1, 4, True, False, 2),
(TTCCostParams, TTCCostTorch, "max", 4, 2, True, False, 0),
(TTCCostParams, TTCCostNumpy, "now", 16, 1, False, True, 3),
(TTCCostParams, TTCCostNumpy, "final", 1, 4, False, True, 1),
],
)
def test_generic_cost(
params,
param_class,
cost_class,
reduce: str,
batch_size: int,
num_steps: int,
is_torch: bool,
use_mask: bool,
num_agents: int,
):
params.cost_reduce = reduce
cost_params = param_class.from_config(params)
x1, x2, v1, v2, mask = get_fake_input(
batch_size, num_steps, is_torch, use_mask, num_agents
)
compute_cost = cost_class(cost_params)
cost, _ = compute_cost(x1, x2, v1, v2, mask)
# Shaped is reduced
if num_agents > 0:
assert cost.shape == (batch_size, num_agents)
else:
assert cost.shape == (batch_size,)
assert (cost != 0).any()
assert compute_cost.scale == params.cost_scale
# Rescale the cost for comparison
compute_cost.scale = params.cost_scale + 10
assert compute_cost.scale != params.cost_scale
rescaled_cost, _ = compute_cost(x1, x2, v1, v2, mask)
# all rescaled cost are larger but 0 cost is equal to rescaled cost
assert (rescaled_cost >= cost).all()
# at least some rescaled cost are strictly larger than normal scale cost
assert (rescaled_cost > cost).any()
# Compute mean and min costs to compare
params.cost_reduce = "mean"
cost_params_mean = param_class.from_config(params)
cost_function_mean = cost_class(cost_params_mean)
cost_mean, _ = cost_function_mean(x1, x2, v1, v2)
params.cost_reduce = "min"
cost_params_min = param_class.from_config(params)
cost_function_min = cost_class(cost_params_min)
cost_min, _ = cost_function_min(x1, x2, v1, v2)
# max reduce is larger than mean
if reduce == "max":
assert (cost >= cost_mean).all()
# min reduce is lower than any othir
assert (cost_mean >= cost_min).all()
assert (cost >= cost_min).all()
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