def test_ac1(self):
problem = prob.setup_2room_rect()
title = 'ac-10step_penalty100k'
args = base_args('-')
# -----------------------------
viz = visdom.Visdom()
viz.text(args.print())
print(args)
base = 10
goal = [base, base]
size = math.ceil(sum(goal) ** 0.5)
act = UDLRStep(always_draw=False)
obj = DiscreteSpaceObjective(goal)
env = DiscreteEnv(problem, obj, size, act, random_init=True)
trainer = gr.ACTrainer(env, title=args.title, log_every=args.log_every)
trainer.train(episodes=100000, steps=base, lr=0.0005, loss_args=loss_args)
insts = trainer._instances
num_insts = len(insts)
print(num_insts)
gr.save_model(trainer.model, './data/{}.pkl'.format(title))
for i in range(5):
utils.layout_disc_to_viz(insts[-i][-1])
print('num_solutions', len(trainer._solutions))
def test4(self):
problem = prob.setup_2room_rect()
layout = PointsInBound(problem, None)()
room = layout.random()
print(room)
assert len(room[0]) == 4
assert len(room[4]) == 2
[print(x) for x in [room[1], room[2], room[3], room[4]]]
move_action = MoveWall()
new_layout = move_action(layout)
def test3(self):
problem = prob.setup_2room_rect()
layout = PointsInBound(problem, None)()
cost_fn = objectives.ConstraintsHeur(problem, AreaConstraint=3)
print(layout.is_valid, cost_fn(layout))
print(layout.to_vec4())
transition = MoveWall()
model = gr.Annealer()
state = model(layout, cost_fn, transition, num_iter=10000)
return state
def test_run1(self):
problem = prob.setup_2room_rect()
goal = [10, 10]
size = math.ceil(sum(goal) ** 0.5)
act = UDLRStep(False)
obj = DiscreteSpaceObjective(goal)
env = DiscreteEnv(problem, True, obj, size, act)
policy = nns.LSTMDQN(size, size, act.num_actions,
in_size=2, feats_in=2, feats_size=10)
target = nns.LSTMDQN(size, size, act.num_actions,
in_size=2, feats_in=2, feats_size=10)
def test5(self):
# wierd polygon intersections
l1 = [(1, 1), (1, 10), (10, 10), (10, 5) , (15, 5), (15, 1)]
l2 = [(7, 5), (7, 10), (17, 10), (17, 5), (7, 5)]
p1, p2 = Room(l1, name='r1'), Room(l2, name='r2')
# print(Polygon(l1).intersection(Polygon(l2)))
print(p2.intersection(p1))
inters = p2.intersection(p1)
p2 = p2.difference(inters)
problem = prob.setup_2room_rect()
lyt = BuildingLayout(problem, rooms=[p1, p2])
utils.plotpoly([lyt])
def test_disclyt(self):
problem = prob.setup_2room_rect()
state = StackedRooms(problem, size=(10, 10))
objective = DiscProbDim(None)
print(objective.keys)
# action = DrawBox()
state.add_step([0, 0, 0, 4, 4])
r1, m1 = objective.reward(state)
print(r1)
print(m1)
state.add_step([1, 5, 5, 8, 8])
# print(np.stack(state.rooms()))
r2, m2 = objective.reward(state)
print(r2)
print(m2)
print(state.state)
def test3(self):
problem = prob.setup_2room_rect()
goal = [10, 10]
size = math.ceil(sum(goal) ** 0.5)
lyt = CellComplexLayout(problem, size=(size, size))
obj = DiscreteSpaceObjective(goal)
env_action = UDLRStep(False)
for i in range(3):
lyt, success = env_action.forward(lyt, action=6)
lyt, success = env_action.forward(lyt, action=4)
for i in range(3):
lyt, success = env_action.forward(lyt, action=7)
print(obj.reward(lyt, True))
#for i in range(4):
# lyt, success = env_action.forward(lyt, action=4)
utils.layout_disc_to_viz(lyt)
def test2(self):
problem = prob.setup_2room_rect()
goal = [10, 10]
size = math.ceil(sum(goal) ** 0.5)
lyt = CellComplexLayout(problem, size=(size, size))
obj = DiscreteSpaceObjective(goal)
env_action = UDLRStep(False)
# for i in range(2):
lyt, success = env_action.forward(lyt, action=2)
lyt, success = env_action.forward(lyt, action=6)
print(obj.reward(lyt, True))
for i in range(4):
lyt, success = env_action.forward(lyt, action=4)
# img = lyt.to_image()
# viz.image(img)
utils.layout_disc_to_viz(lyt)
r, m = obj.reward(lyt, True)
print(r, m)
def make_env():
problem = prob.setup_2room_rect()
return MockEnv(problem, PointsInBound(problem, None))