mirror of
https://github.com/deepmodeling/Uni-Lab-OS
synced 2026-05-23 19:49:56 +00:00
9ef24b7768
Replace scipy differential_evolution with custom DE loop for per-device crossover, circular θ wrapping, and configurable mutation strategy (currenttobest1bin default, best1bin as turbo mode). Key improvements: - Graduate ALL hard constraints during DE (proportional penalty instead of flat inf), giving DE smooth gradient for reachability, min_spacing, etc. Binary inf preserved for final pass/fail reporting. - 2-axis sweep-and-prune AABB broad phase for collision pair pruning - Multi-seed injection from multiple seeder presets + Gaussian variants - snap_theta_safe: collision-check after angle snapping, revert on violation - Weight normalization (100 distance / 60 angle / 5× hard multiplier) - Constraint priority field (critical/high/normal/low → weight multiplier) with LLM intent interpreter setting priority per constraint type - Final success field now checks user hard constraints in binary mode - arm_slider added to mock checker reach table (1.07m) Tests: 202 passed, 24 new tests added (optimizer 7, constraints 6, broad_phase 11) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
424 lines
16 KiB
Python
424 lines
16 KiB
Python
"""约束体系测试。"""
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import math
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import pytest
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from ..constraints import (
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evaluate_constraints,
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evaluate_default_hard_constraints,
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)
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from ..mock_checkers import MockCollisionChecker, MockReachabilityChecker
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from ..models import Constraint, Device, Lab, Placement
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def _make_devices():
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return [
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Device(id="a", name="Device A", bbox=(0.5, 0.5)),
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Device(id="b", name="Device B", bbox=(0.5, 0.5)),
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]
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def _make_lab():
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return Lab(width=5.0, depth=4.0)
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class TestDefaultHardConstraints:
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def test_no_collision_passes(self):
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"""无碰撞的布局应返回 0。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 3.0, 3.0, 0.0),
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]
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
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assert cost == 0.0
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def test_collision_returns_graduated_penalty(self):
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"""碰撞布局应返回正的graduated penalty（非inf）。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 1.2, 1.0, 0.0),
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]
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
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assert cost > 0
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assert not math.isinf(cost)
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def test_collision_returns_inf_binary_mode(self):
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"""Binary mode: 碰撞布局应返回 inf。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 1.2, 1.0, 0.0),
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]
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(
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devices, placements, _make_lab(), checker, graduated=False,
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)
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assert math.isinf(cost)
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def test_out_of_bounds_returns_graduated_penalty(self):
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"""越界布局应返回正的graduated penalty（非inf）。"""
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devices = _make_devices()
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placements = [
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Placement("a", 0.1, 0.1, 0.0), # 左下角越界
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Placement("b", 3.0, 3.0, 0.0),
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]
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
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assert cost > 0
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assert not math.isinf(cost)
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def test_out_of_bounds_returns_inf_binary_mode(self):
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"""Binary mode: 越界布局应返回 inf。"""
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devices = _make_devices()
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placements = [
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Placement("a", 0.1, 0.1, 0.0),
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Placement("b", 3.0, 3.0, 0.0),
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]
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(
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devices, placements, _make_lab(), checker, graduated=False,
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)
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assert math.isinf(cost)
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def test_worse_collision_higher_cost(self):
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"""Deeper penetration should produce higher cost."""
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devices = _make_devices()
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checker = MockCollisionChecker()
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lab = _make_lab()
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# Small overlap
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cost_small = evaluate_default_hard_constraints(
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devices, [Placement("a", 1.0, 1.0, 0.0), Placement("b", 1.4, 1.0, 0.0)],
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lab, checker,
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)
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# Large overlap
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cost_large = evaluate_default_hard_constraints(
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devices, [Placement("a", 1.0, 1.0, 0.0), Placement("b", 1.1, 1.0, 0.0)],
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lab, checker,
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)
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assert cost_large > cost_small > 0
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class TestUserConstraints:
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def test_distance_less_than_satisfied(self):
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"""距离约束满足时 cost=0。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 1.5, 1.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="distance_less_than",
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params={"device_a": "a", "device_b": "b", "distance": 1.0})
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]
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checker = MockCollisionChecker()
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reachability = MockReachabilityChecker()
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reachability
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)
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assert cost == 0.0
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def test_distance_less_than_violated_hard(self):
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"""硬距离约束违反：graduated模式返回有限惩罚，binary模式返回inf。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 4.0, 3.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="distance_less_than",
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params={"device_a": "a", "device_b": "b", "distance": 1.0})
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]
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checker = MockCollisionChecker()
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# graduated=True (default): 有限惩罚
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker
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)
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assert cost > 0
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assert not math.isinf(cost)
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# graduated=False: binary inf
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cost_binary = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker,
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graduated=False,
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)
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assert math.isinf(cost_binary)
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def test_minimize_distance_cost(self):
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"""minimize_distance 约束应返回正比于距离的 cost。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 1.0, 0.0),
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Placement("b", 3.0, 1.0, 0.0),
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]
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constraints = [
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Constraint(type="soft", rule_name="minimize_distance",
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params={"device_a": "a", "device_b": "b"}, weight=2.0)
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]
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checker = MockCollisionChecker()
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker
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)
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# edge-to-edge distance = 2.0 - 0.25 - 0.25 = 1.5, weight = 2.0 → cost = 3.0
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assert abs(cost - 3.0) < 0.01
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def test_reachability_constraint(self):
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"""可达性约束：目标在臂展内应通过（不返回 inf）。
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Opening-faces-arm penalty may add a small soft cost when the
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target's opening doesn't face the arm, but it must not cause
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hard failure (inf).
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"""
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devices = [
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Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
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Device(id="target", name="Target", bbox=(0.5, 0.5)),
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]
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placements = [
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Placement("arm", 1.0, 1.0, 0.0),
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Placement("target", 1.5, 1.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="reachability",
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params={"arm_id": "arm", "target_device_id": "target"})
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]
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checker = MockCollisionChecker()
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reachability = MockReachabilityChecker(arm_reach={"arm": 1.0})
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reachability
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)
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assert not math.isinf(cost) # reachable → no hard failure
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def test_reachability_constraint_violated(self):
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"""可达性约束：目标超出臂展 — graduated返回有限惩罚，binary返回inf。"""
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devices = [
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Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
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Device(id="target", name="Target", bbox=(0.5, 0.5)),
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]
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placements = [
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Placement("arm", 1.0, 1.0, 0.0),
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Placement("target", 4.0, 3.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="reachability",
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params={"arm_id": "arm", "target_device_id": "target"})
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]
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checker = MockCollisionChecker()
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reachability = MockReachabilityChecker(arm_reach={"arm": 1.0})
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# graduated=True (default): 有限惩罚
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reachability
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)
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assert cost > 0
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assert not math.isinf(cost)
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# graduated=False: binary inf
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cost_binary = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reachability,
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graduated=False,
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)
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assert math.isinf(cost_binary)
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def test_distance_less_than_uses_edge_to_edge():
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"""distance_less_than should measure edge-to-edge, not center-to-center.
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Two devices: centers 3m apart, each 2m wide → edge gap = 1m.
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Constraint: distance_less_than 1.5m (edge-to-edge).
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Old center-to-center: 3m > 1.5m → violation.
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New edge-to-edge: 1m < 1.5m → satisfied.
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"""
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devices = [
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Device(id="a", name="A", bbox=(2.0, 1.0)),
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Device(id="b", name="B", bbox=(2.0, 1.0)),
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]
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placements = [
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Placement(device_id="a", x=1.0, y=1.0, theta=0.0),
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Placement(device_id="b", x=4.0, y=1.0, theta=0.0),
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]
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lab = Lab(width=10, depth=10)
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constraint = Constraint(
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type="soft", rule_name="distance_less_than",
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params={"device_a": "a", "device_b": "b", "distance": 1.5},
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weight=1.0,
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)
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checker = MockCollisionChecker()
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cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
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assert cost == pytest.approx(0.0)
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def test_prefer_aligned_zero_at_cardinal():
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"""prefer_aligned cost = 0 when all devices at 0/90/180/270°."""
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devices = [Device(id="a", name="A", bbox=(1.0, 1.0))]
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lab = Lab(width=10, depth=10)
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checker = MockCollisionChecker()
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for angle in [0, math.pi / 2, math.pi, 3 * math.pi / 2]:
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placements = [Placement(device_id="a", x=5, y=5, theta=angle)]
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constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=1.0)
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cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
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assert cost == pytest.approx(0.0, abs=1e-9)
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def test_prefer_aligned_max_at_45():
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"""prefer_aligned cost is maximum when device at 45°."""
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devices = [Device(id="a", name="A", bbox=(1.0, 1.0))]
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placements = [Placement(device_id="a", x=5, y=5, theta=math.pi / 4)]
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lab = Lab(width=10, depth=10)
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constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=1.0)
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checker = MockCollisionChecker()
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cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
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# (1 - cos(4 * pi/4)) / 2 = (1 - cos(pi)) / 2 = (1 - (-1)) / 2 = 1.0
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assert cost == pytest.approx(1.0)
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def test_prefer_aligned_sums_over_devices():
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"""Cost sums across all devices."""
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devices = [
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Device(id="a", name="A", bbox=(1.0, 1.0)),
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Device(id="b", name="B", bbox=(1.0, 1.0)),
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]
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placements = [
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Placement(device_id="a", x=2, y=2, theta=math.pi / 4), # cost = 1.0
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Placement(device_id="b", x=7, y=7, theta=math.pi / 4), # cost = 1.0
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]
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lab = Lab(width=10, depth=10)
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constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=2.0)
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checker = MockCollisionChecker()
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cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
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# 2 devices × 1.0 × weight 2.0 = 4.0
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assert cost == pytest.approx(4.0)
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class TestGraduatedHardConstraints:
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"""graduated 模式下硬约束返回比例惩罚而非 inf。"""
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def test_hard_reachability_graduated_finite(self):
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"""graduated=True: 硬可达性返回有限惩罚。"""
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devices = [
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Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
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Device(id="t", name="Target", bbox=(0.5, 0.5)),
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]
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placements = [
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Placement("arm", 1.0, 1.0, 0.0),
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Placement("t", 4.0, 3.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="reachability",
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params={"arm_id": "arm", "target_device_id": "t"}, weight=1.0)
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]
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checker = MockCollisionChecker()
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reach = MockReachabilityChecker(arm_reach={"arm": 1.0})
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reach,
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graduated=True,
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)
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assert cost > 0
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assert not math.isinf(cost)
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def test_hard_reachability_binary_inf(self):
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"""graduated=False: 硬可达性返回 inf。"""
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devices = [
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Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
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Device(id="t", name="Target", bbox=(0.5, 0.5)),
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]
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placements = [
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Placement("arm", 1.0, 1.0, 0.0),
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Placement("t", 4.0, 3.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="reachability",
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params={"arm_id": "arm", "target_device_id": "t"}, weight=1.0)
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]
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checker = MockCollisionChecker()
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reach = MockReachabilityChecker(arm_reach={"arm": 1.0})
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker, reach,
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graduated=False,
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)
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assert math.isinf(cost)
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def test_hard_min_spacing_graduated_sums_all_pairs(self):
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"""graduated模式：min_spacing 对所有违规对求和（不只第一对）。"""
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devices = [
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Device(id="a", name="A", bbox=(0.5, 0.5)),
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Device(id="b", name="B", bbox=(0.5, 0.5)),
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Device(id="c", name="C", bbox=(0.5, 0.5)),
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]
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# 三个设备间距都小于 min_gap=1.0
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placements = [
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Placement("a", 1.0, 2.0, 0.0),
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Placement("b", 1.3, 2.0, 0.0), # OBB 边缘距 a 约 0.3
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Placement("c", 1.6, 2.0, 0.0), # OBB 边缘距 b 约 0.3, 距 a 约 0.6
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]
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constraints = [
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Constraint(type="hard", rule_name="min_spacing",
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params={"min_gap": 1.0}, weight=1.0)
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]
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checker = MockCollisionChecker()
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker,
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graduated=True,
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)
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# 应大于 0 且有限（累加多对违规）
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assert cost > 0
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assert not math.isinf(cost)
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def test_hard_min_spacing_binary_inf(self):
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"""graduated=False: min_spacing 违规返回 inf。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 2.0, 0.0),
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Placement("b", 1.3, 2.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="min_spacing",
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params={"min_gap": 1.0}, weight=1.0)
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]
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checker = MockCollisionChecker()
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker,
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graduated=False,
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)
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assert math.isinf(cost)
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def test_hard_distance_less_than_graduated(self):
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"""graduated模式：distance_less_than 硬约束返回比例惩罚。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 2.0, 0.0),
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Placement("b", 4.0, 2.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="distance_less_than",
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params={"device_a": "a", "device_b": "b", "distance": 0.5},
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weight=2.0)
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]
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checker = MockCollisionChecker()
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker,
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graduated=True,
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)
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# HARD_MULTIPLIER(5) × weight(2) × overshoot > 0
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assert cost > 0
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assert not math.isinf(cost)
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def test_graduated_default_is_true(self):
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"""不传 graduated 参数时默认使用 graduated 模式。"""
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devices = _make_devices()
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placements = [
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Placement("a", 1.0, 2.0, 0.0),
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Placement("b", 4.0, 2.0, 0.0),
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]
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constraints = [
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Constraint(type="hard", rule_name="distance_less_than",
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params={"device_a": "a", "device_b": "b", "distance": 0.5},
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weight=1.0)
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]
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checker = MockCollisionChecker()
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# 不指定 graduated — 默认应为 True → 有限惩罚
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cost = evaluate_constraints(
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devices, placements, _make_lab(), constraints, checker,
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)
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assert not math.isinf(cost)
|