def dump_new_values(self, new_values=[]):
if (not new_values and VERBOSE_FAILURES) or \
(new_values and self.info.verbose):
print('iter={}, outs={}) {}:{}->{}'.format(
self.get_iteration(), len(new_values), self.external.name,
str_from_object(self.get_input_values()),
str_from_object(new_values)))
def next_results(self, accelerate=1, verbose=False):
# TODO: prune repeated values
all_new_values = []
all_new_facts = []
all_results = []
start_calls = self.num_calls
for attempt in range(accelerate):
if all_results or self.enumerated:
break
start_time = time.time()
new_values, new_facts = self._next_outputs()
self._check_output_values(new_values)
self._check_wild_facts(new_facts)
new_results = [self.get_result(map(Object.from_value, output_values), list_index=list_index)
for list_index, output_values in enumerate(new_values)]
all_new_values.extend(new_values)
all_new_facts.extend(new_facts)
all_results.extend(new_results)
self.update_statistics(start_time, new_results)
if verbose and (VERBOSE_FAILURES or all_new_values):
print('{}-{}) {}:{}->{}'.format(start_calls, self.num_calls, self.external.name,
str_from_object(self.get_input_values()),
str_from_object(all_new_values)))
if verbose and all_new_facts:
# TODO: format all_new_facts
print('{}-{}) {}:{}->{}'.format(start_calls, self.num_calls, self.external.name,
str_from_object(self.get_input_values()), all_new_facts))
return all_results, list(map(obj_from_value_expression, all_new_facts))
def next_results(self, verbose=False):
assert not self.enumerated
start_time = time.time()
start_calls = self.num_calls
new_values, new_facts = self._next_outputs()
self._check_output_values(new_values)
self._check_wild_facts(new_facts)
new_objects = [tuple(map(Object.from_value, output_values)) for output_values in new_values]
new_objects = list(filter(lambda o: o not in self.previous_outputs, new_objects))
self.previous_outputs.update(new_objects) # Only counting new outputs as successes
new_results = [self.get_result(output_objects, list_index=list_index, optimistic=False)
for list_index, output_objects in enumerate(new_objects)]
self.update_statistics(start_time, new_results)
if verbose:
if VERBOSE_FAILURES or new_values:
print('{}) {}:{}->{}'.format(start_calls, self.external.name,
str_from_object(self.get_input_values()),
str_from_object(new_values)))
if VERBOSE_WILD and new_facts:
# TODO: format all_new_facts
print('{}) {}:{}->{}'.format(start_calls, self.external.name,
str_from_object(self.get_input_values()), new_facts))
facts = list(map(obj_from_value_expression, new_facts))
if not self.external.outputs and self.successes:
# Set of possible outputs is exhausted
self.enumerated = True
return new_results, facts
def next_results(self, verbose=False):
assert not self.enumerated
start_time = time.time()
start_history = len(self.history)
new_values, new_facts = self._next_outputs()
self._check_output_values(new_values)
self._check_wild_facts(new_facts)
if verbose:
if (not new_values and VERBOSE_FAILURES) or \
(new_values and self.info.verbose):
print('iter={}, outs={}) {}:{}->{}'.format(
self.num_calls, len(new_values), self.external.name,
str_from_object(self.get_input_values()), str_from_object(new_values)))
if VERBOSE_WILD and new_facts:
# TODO: format all_new_facts
print('iter={}, facts={}) {}:{}->{}'.format(
self.num_calls, self.external.name, str_from_object(self.get_input_values()),
new_facts, len(new_facts)))
objects = [objects_from_values(output_values) for output_values in new_values]
new_objects = list(filter(lambda o: o not in self.previous_outputs, objects))
self.previous_outputs.update(new_objects) # Only counting new outputs as successes
new_results = [self.get_result(output_objects, list_index=list_index, optimistic=False)
for list_index, output_objects in enumerate(new_objects)]
if start_history < len(self.history):
self.update_statistics(start_time, new_results)
new_facts = list(map(obj_from_value_expression, new_facts))
self.successful |= any(r.is_successful() for r in new_results)
self.num_calls += 1 # Must be after get_result
#if self.external.is_test() and self.successful:
# # Set of possible test stream outputs is exhausted (excluding wild)
# self.enumerated = True
return new_results, new_facts
def dump_instantiated(instantiated):
print('Instantiated frequencies:\n'
'Atoms: {}\n'
'Actions: {}\n'
'Axioms: {}'.format(
str_from_object(
Counter(atom.predicate for atom in instantiated.atoms)),
str_from_object(
Counter(action.action.name
for action in instantiated.actions)),
str_from_object(
Counter(axiom.axiom.name for axiom in instantiated.axioms))))
def dump_assignment(solution):
bindings, cost, evaluations = solution
print()
print('Solved: {}'.format(bindings is not None))
print('Cost: {}'.format(cost))
print('Total facts: {}'.format(len(evaluations)))
print('Fact counts: {}'.format(str_from_object(Counter(map(get_prefix, evaluations)))))
if bindings is None:
return
print('Assignments:')
for param in sorted(bindings):
print('{} = {}'.format(param, str_from_object(bindings[param])))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--problem', default='mirror', help='The name of the problem to solve')
parser.add_argument('-a', '--algorithm', default='incremental', help='Specifies the algorithm')
parser.add_argument('-c', '--cfree', action='store_true', help='Disables collisions')
parser.add_argument('-d', '--deterministic', action='store_true', help='Uses a deterministic sampler')
parser.add_argument('-g', '--gurobi', action='store_true', help='Uses gurobi')
parser.add_argument('-n', '--number', default=1, type=int, help='The number of blocks')
parser.add_argument('-o', '--optimal', action='store_true', help='Runs in an anytime mode')
parser.add_argument('-s', '--skeleton', action='store_true', help='Enforces skeleton plan constraints')
parser.add_argument('-t', '--max_time', default=30, type=int, help='The max time')
parser.add_argument('-u', '--unit', action='store_true', help='Uses unit costs')
parser.add_argument('-v', '--visualize', action='store_true', help='Visualizes graphs')
args = parser.parse_args()
print('Arguments:', args)
np.set_printoptions(precision=2)
if args.deterministic:
random.seed(seed=0)
np.random.seed(seed=0)
print('Random seed:', get_random_seed())
problem_from_name = {fn.__name__: fn for fn in PROBLEMS}
if args.problem not in problem_from_name:
raise ValueError(args.problem)
print('Problem:', args.problem)
problem_fn = problem_from_name[args.problem]
tamp_problem = problem_fn(args.number)
print(tamp_problem)
pddlstream_problem = pddlstream_from_tamp(tamp_problem, collisions=not args.cfree,
use_stream=not args.gurobi, use_optimizer=args.gurobi)
print('Constants:', str_from_object(pddlstream_problem.constant_map))
print('Initial:', sorted_str_from_list(pddlstream_problem.init))
print('Goal:', str_from_object(pddlstream_problem.goal))
success_cost = 0 if args.optimal else INF
planner = 'max-astar'
#planner = 'ff-wastar1'
with Profiler(field='cumtime', num=20):
if args.algorithm == 'incremental':
solution = solve_incremental(pddlstream_problem,
complexity_step=1, planner=planner,
unit_costs=args.unit, success_cost=success_cost,
max_time=args.max_time, verbose=False)
else:
raise ValueError(args.algorithm)
print_solution(solution)
plan, cost, evaluations = solution
if plan is not None:
display_plan(tamp_problem, plan)
def main(max_time=20):
"""
Creates and solves the 2D motion planning problem.
"""
parser = create_parser()
args = parser.parse_args()
print('Arguments:', args)
obstacles = [create_box((.5, .5), (.2, .2))]
regions = {
'env': create_box((.5, .5), (1, 1)),
'green': create_box((.8, .8), (.4, .4)),
}
goal = 'green'
if goal not in regions:
goal = ARRAY([1, 1])
max_distance = 0.25 # 0.2 | 0.25 | 0.5 | 1.0
problem, samples, roadmap = create_problem(goal,
obstacles,
regions,
max_distance=max_distance)
print('Initial:', str_from_object(problem.init))
print('Goal:', str_from_object(problem.goal))
constraints = PlanConstraints(max_cost=1.25) # max_cost=INF)
with Profiler(field='tottime', num=10):
solution = solve_incremental(problem,
constraints=constraints,
unit_costs=args.unit,
success_cost=0,
max_time=max_time,
verbose=False)
print_solution(solution)
plan, cost, evaluations = solution
#viewer = draw_environment(obstacles, regions)
#for sample in samples:
# viewer.draw_point(sample)
#user_input('Continue?')
# TODO: use the same viewer here
draw_roadmap(roadmap, obstacles, regions) # TODO: do this in realtime
user_input('Continue?')
if plan is None:
return
segments = [args for name, args in plan]
draw_solution(segments, obstacles, regions)
user_input('Finish?')
def main(max_time=20):
"""
Creates and solves the 2D motion planning problem.
"""
obstacles = [create_box((.5, .5), (.2, .2))]
regions = {
'env': create_box((.5, .5), (1, 1)),
'green': create_box((.8, .8), (.4, .4)),
}
goal = 'green'
if goal not in regions:
goal = array([1, 1])
max_distance = 0.25 # 0.2 | 0.25 | 0.5 | 1.0
problem, samples, roadmap = create_problem(goal,
obstacles,
regions,
max_distance=max_distance)
print('Initial:', str_from_object(problem.init))
print('Goal:', str_from_object(problem.goal))
pr = cProfile.Profile()
pr.enable()
solution = solve_incremental(problem,
unit_costs=False,
max_cost=0,
max_time=max_time,
verbose=False)
pr.disable()
pstats.Stats(pr).sort_stats('tottime').print_stats(10)
print_solution(solution)
plan, cost, evaluations = solution
#viewer = draw_environment(obstacles, regions)
#for sample in samples:
# viewer.draw_point(sample)
#user_input('Continue?')
# TODO: use the same viewer here
draw_roadmap(roadmap, obstacles, regions) # TODO: do this in realtime
user_input('Continue?')
if plan is None:
return
segments = [args for name, args in plan]
draw_solution(segments, obstacles, regions)
user_input('Finish?')
def next_results(self, accelerate=1, verbose=False):
start_time = time.time()
assert not self.enumerated
self.enumerated = True
input_values = self.get_input_values()
#try:
value = self.external.fn(*input_values)
#except TypeError as err:
# print('Function [{}] expects {} inputs'.format(self.external.name, len(input_values)))
# raise err
self.value = self.external._codomain(value)
# TODO: cast the inputs and test whether still equal?
#if not (type(self.value) is self.external._codomain):
#if not isinstance(self.value, self.external._codomain):
#if self.value != value:
# raise ValueError('Function [{}] produced a nonintegral value [{}]. '
# 'FastDownward only supports integral costs. '
# 'To "use" real costs, scale each cost by a large factor, '
# 'capturing the most significant bits.'.format(self.external.name, self.value))
if self.value < 0:
raise ValueError(
'Function [{}] produced a negative value [{}]'.format(
self.external.name, self.value))
if (self.value is not False) and verbose:
print('0) {}{}={}'.format(get_prefix(self.external.head),
str_from_object(self.get_input_values()),
self.value))
results = [self.external._Result(self, self.value)]
#if isinstance(self, PredicateInstance) and (self.value != self.external.opt_fn(*input_values)):
# self.update_statistics(start_time, [])
self.update_statistics(start_time, results)
new_facts = []
return results, new_facts
def process_stream_queue(instantiator,
store,
complexity_limit=INF,
verbose=False):
instances = []
results = []
num_successes = 0
while not store.is_terminated() and instantiator and (
instantiator.min_complexity() <= complexity_limit):
instance = instantiator.pop_stream()
if instance.enumerated:
continue
instances.append(instance)
new_results = process_instance(instantiator,
store,
instance,
verbose=verbose)
results.extend(new_results)
num_successes += bool(new_results) # TODO: max_results?
if verbose:
print('Eager Calls: {} | Successes: {} | Results: {} | Counts: {}'.
format(
len(instances), num_successes, len(results),
str_from_object(
Counter(instance.external.name
for instance in instances))))
return len(instances)
def __repr__(self):
return '{}(holding={}, placed={})'.format(
self.__class__.__name__, self.holding,
str_from_object({
name: self.pose_dists[name].surface_dist
for name in self.placed
}))
def dump_new_facts(self, new_facts=[]):
if VERBOSE_WILD and new_facts:
# TODO: format all_new_facts
print('iter={}, facts={}) {}:{}->{}'.format(
self.get_iteration(), self.external.name,
str_from_object(self.get_input_values()), new_facts,
len(new_facts)))
def instantiate_condition(action, is_static, args_from_predicate):
parameters = {p.name for p in action.parameters}
#if not parameters:
# yield {}
# return
static_conditions = list(
filter(is_static, get_literals(get_precondition(action))))
static_parameters = set(
filter(is_parameter, flatten(atom.args for atom in static_conditions)))
if not (parameters <= static_parameters):
raise NotImplementedError(
'Could not instantiate action {} due to parameters: {}'.format(
action.name, str_from_object(parameters - static_parameters)))
atoms_from_cond = {
condition: args_from_predicate[condition.predicate,
get_constants(condition)]
for condition in static_conditions
}
conditions, atoms = zip(*atoms_from_cond.items())
relations = [
Relation(conditions[index].args, atoms[index])
for index in compute_order(conditions, atoms)
]
solution = solve_satisfaction(relations)
for element in solution.body:
yield solution.get_mapping(element)
def create_visualizations(evaluations, stream_plan, iteration):
# TODO: place it in the temp_dir?
# TODO: decompose any joint streams
for result in stream_plan:
create_synthesizer_visualizations(result, iteration)
filename = ITERATION_TEMPLATE.format(iteration)
# visualize_stream_plan(stream_plan, path)
constraints = set() # TODO: approximates needed facts using produced ones
for stream in stream_plan:
constraints.update(
filter(lambda f: evaluation_from_fact(f) not in evaluations,
stream.get_certified()))
print('Constraints:', str_from_object(constraints))
visualize_constraints(constraints,
os.path.join(CONSTRAINT_NETWORK_DIR, filename))
from pddlstream.retired.synthesizer import decompose_stream_plan
decomposed_plan = decompose_stream_plan(stream_plan)
if len(decomposed_plan) != len(stream_plan):
visualize_stream_plan(decompose_stream_plan(stream_plan),
os.path.join(STREAM_PLAN_DIR, filename))
#visualize_stream_plan_bipartite(stream_plan, os.path.join(STREAM_PLAN_DIR, 'fused_' + filename))
visualize_stream_plan(stream_plan,
os.path.join(STREAM_PLAN_DIR, 'fused_' + filename))
def next_results(self, verbose=False):
start_time = time.time()
assert not self.enumerated
self.enumerated = True
input_values = self.get_input_values()
value = self.external.fn(*input_values)
self.value = self.external.codomain(value)
# TODO: cast the inputs and test whether still equal?
#if not (type(self.value) is self.external._codomain):
#if not isinstance(self.value, self.external.codomain):
if self.value < 0:
raise ValueError(
'Function [{}] produced a negative value [{}]'.format(
self.external.name, self.value))
if (self.value is not False) and verbose:
start_call = 0
print('{}) {}{}={}'.format(
start_call, get_prefix(self.external.head),
str_from_object(self.get_input_values()), self.value))
results = [
self._Result(self, self.value, opt_index=None, optimistic=False)
]
#if isinstance(self, PredicateInstance) and (self.value != self.external.opt_fn(*input_values)):
# self.update_statistics(start_time, [])
self.update_statistics(start_time, results)
new_facts = []
return results, new_facts
def read_raw_masses(stackings):
if not stackings:
return {}
weights = {
SCALE_FROM_BUS[bus]: weight
for bus, weight in read_scales().items()
}
print('Raw weights (oz):', str_from_object(weights))
for scale in stackings:
if scale not in weights:
# TODO: return which scale failed
print('Scale {} is not turned on!'.format(scale))
return None
#assert all(0 < weight for weight in weights.values()) # TODO: check that objects are on
masses = {
stackings[scale]: KG_PER_OZ * weight
for scale, weight in weights.items()
}
print('Raw masses (kg):', str_from_object(masses))
return masses
def estimate_masses(masses, bowl_masses, material=None):
print('Bowl masses (kg):', bowl_masses)
#material_masses = {bowl: max(0.0, mass - bowl_masses[bowl])
# for bowl, mass in masses.items()}
material_masses = {
bowl: mass - bowl_masses[bowl]
for bowl, mass in masses.items()
}
print('Material masses (kg):', str_from_object(material_masses))
#if material is not None:
# estimate_particles(masses, material)
return material_masses
def plan_functions(functions, externals):
external_from_function = {}
for external in filter(lambda e: isinstance(e, Function), externals):
assert external.function not in external_from_function
external_from_function[external.function] = external
function_plan = set()
for term in functions:
if get_prefix(term) not in external_from_function:
raise ValueError('{} is not implemented'.format(get_prefix(term)))
external = external_from_function[get_prefix(term)]
instance = external.get_instance(get_args(term))
[result] = instance.next_optimistic()
function_plan.add(result)
print('Function plan:', str_from_object(function_plan))
return function_plan
def next_results(self, verbose=False):
assert not self.enumerated
start_time = time.time()
start_calls = self.num_calls
start_history = len(self.history)
value = self._compute_output()
if (value is not False) and verbose:
print('{}) {}{}={}'.format(
start_calls, get_prefix(self.external.head),
str_from_object(self.get_input_values()), value))
new_results = [self._Result(self, value, optimistic=False)]
new_facts = []
if start_history < len(self.history):
self.update_statistics(start_time, new_results)
self.successful |= any(r.is_successful() for r in new_results)
return new_results, new_facts
def next_results(self, verbose=False):
assert not self.enumerated
start_time = time.time()
start_history = len(self.history)
value = self._compute_output()
new_results = [self._Result(self, value, optimistic=False)]
new_facts = []
if (value is not False) and verbose:
# TODO: str(new_results[-1])
print('iter={}, outs={}) {}{}={:.3f}'.format(
self.get_iteration(), len(new_results), get_prefix(self.external.head),
str_from_object(self.get_input_values()), value))
if start_history <= len(self.history) - 1:
self.update_statistics(start_time, new_results)
self.successful |= any(r.is_successful() for r in new_results)
return new_results, new_facts
def score_masses(args, task, ros_world, init_total_mass, bowl_masses,
final_raw_masses):
# TODO: assert that mass is not zero if object known to be above
# TODO: unify this with the simulator scoring
cup_name, bowl_name = REQUIREMENT_FNS[args.problem](ros_world)
name_from_type = {
'cup': cup_name,
}
if POUR:
name_from_type.update({
'bowl': bowl_name,
})
final_masses = estimate_masses(final_raw_masses, bowl_masses,
args.material)
if POUR:
final_total_mass = sum(final_masses.values())
print('Total mass:', final_total_mass)
print(
'Spilled (%):',
clip(init_total_mass - final_total_mass, min_value=0, max_value=1))
score = {'total_mass': init_total_mass}
score.update({
'mass_in_{}'.format(ty): final_masses[name]
for ty, name in name_from_type.items()
})
if POUR:
final_percentages = {
name: mass / init_total_mass
for name, mass in final_masses.items()
}
print('Percentages (%):', str_from_object(final_percentages))
#score.update({'fraction_in_{}'.format(ty): final_percentages[name]
# for ty, name in name_from_type.items()})
if task.holding:
[spoon_name] = task.holding
spoon_capacity = SPOON_CAPACITIES[get_type(spoon_name), args.material]
fraction_filled = final_masses[cup_name] / spoon_capacity
print('Spoon percentage filled (%):', fraction_filled)
score.update({
'mass_in_spoon': final_masses[cup_name],
#'fraction_in_spoon': final_masses[cup_name] / init_total_mass,
#'spoon_capacity': spoon_capacity,
#'fraction_filled': fraction_filled,
})
return score
def decompose_discrete(results, **kwargs):
data_from_discrete = {}
for result in results:
key = frozenset((feature, result[FEATURE][feature]) for feature in DISCRETE_FEATURES
if feature in result[FEATURE])
data_from_discrete.setdefault(key, []).append(result)
sorted_triplets = []
for key, key_data in data_from_discrete.items():
category_values = get_category_values(key_data, **kwargs)
scores = category_values[SCORE]['score'] if SCORE in category_values else []
sorted_triplets.append((dict(key), len(scores), compute_percentiles(scores, **kwargs)))
for i, (discrete, num, percentiles) in enumerate(
sorted(sorted_triplets, key=lambda t: (t[-1][-1], t[1]), reverse=True)):
print('{}) {} | Num: {} | %: {}'.format(
i, str_from_object(discrete), num, percentiles.round(3).tolist()))
#analyze_data(data_name, data_from_discrete[key])
return data_from_discrete
def run_trials(trials, data_path=None, num_cores=False, **kwargs):
# https://stackoverflow.com/questions/15314189/python-multiprocessing-pool-hangs-at-join
# https://stackoverflow.com/questions/39884898/large-amount-of-multiprocessing-process-causing-deadlock
# TODO: multiprocessing still seems to hang on one thread before starting
assert (get_python_version() == 3)
results = []
if not trials:
return results
start_time = time.time()
serial = (num_cores is False) # None is the max number
failures = 0
scored = 0
try:
for result in map_general(run_trial,
trials,
serial,
num_cores=num_cores,
**kwargs):
num_trials = len(results) + failures
print(
'{}\nTrials: {} | Successes: {} | Failures: {} | Scored: {} | Time: {:.3f}'
.format(SEPARATOR, num_trials, len(results), failures, scored,
elapsed_time(start_time)))
print('Result:', str_from_object(result))
if result is None:
failures += 1
print('Error! Trial resulted in an exception')
continue
scored += int(result.get('score', None) is not None)
results.append(result)
write_results(data_path, results)
# except BaseException as e:
# traceback.print_exc() # e
finally:
print(SEPARATOR)
safe_rm_dir(TEMP_DIRECTORY)
write_results(data_path, results)
print('Hours: {:.3f}'.format(elapsed_time(start_time) / HOURS_TO_SECS))
# TODO: make a generator version of this
return results
def main():
parser = create_parser()
parser.add_argument('-enable', action='store_true', help='Enables rendering during planning')
parser.add_argument('-teleport', action='store_true', help='Teleports between configurations')
parser.add_argument('-simulate', action='store_true', help='Simulates the system')
parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan')
args = parser.parse_args()
print('Arguments:', args)
connect(use_gui=args.viewer)
robot, names, movable = load_world()
print('Objects:', names)
saver = WorldSaver()
problem = pddlstream_from_problem(robot, movable=movable, teleport=args.teleport)
_, _, _, stream_map, init, goal = problem
print('Init:', init)
print('Goal:', goal)
print('Streams:', str_from_object(set(stream_map)))
with Profiler():
with LockRenderer(lock=not args.enable):
solution = solve(problem, algorithm=args.algorithm, unit_costs=args.unit, success_cost=INF)
saver.restore()
print_solution(solution)
plan, cost, evaluations = solution
if (plan is None) or not has_gui():
disconnect()
return
command = postprocess_plan(plan)
if args.simulate:
wait_for_user('Simulate?')
command.control()
else:
wait_for_user('Execute?')
#command.step()
command.refine(num_steps=10).execute(time_step=0.001)
wait_for_user('Finish?')
disconnect()
def solve_serialized(initial_problem,
stream_info={},
unit_costs=False,
unit_efforts=False,
verbose=True,
retain_facts=True,
**kwargs):
# TODO: be careful of CanMove deadends
domain_pddl, constant_map, stream_pddl, stream_map, init, goal = initial_problem
_, _, domain, streams = parse_problem(initial_problem,
stream_info,
constraints=None,
unit_costs=unit_costs,
unit_efforts=unit_efforts)
static_init, _ = partition_facts(
domain, init) # might not be able to reprove static_int
#global_all, global_preimage = [], []
global_plan = []
global_cost = 0
state = list(init)
goals = serialize_goal(goal)
# TODO: instead just track how the true init updates
for i in range(len(goals)):
# TODO: option in algorithms to pass in existing facts
for stream in streams:
stream.reset()
goal = And(*goals[:i + 1])
print('Goal:', str_from_object(goal))
# No strict need to reuse streams because generator functions
#local_problem = PDDLProblem(domain_pddl, constant_map, stream_pddl, stream_map, state, goal)
local_problem = PDDLProblem(domain_pddl, constant_map, streams, None,
state, goal)
with Verbose(verbose):
solution = solve_focused(local_problem,
stream_info=stream_info,
unit_costs=unit_costs,
unit_efforts=unit_efforts,
verbose=True,
**kwargs)
print_solution(solution)
local_plan, local_cost, local_certificate = solution
if local_plan is None:
# TODO: replan upon failure
global_certificate = Certificate(all_facts={}, preimage_facts=None)
return None, INF, global_certificate
if retain_facts:
state = local_certificate.all_facts
else:
_, fluent_facts = partition_facts(domain, state)
state = static_init + fluent_facts + local_certificate.preimage_facts # TODO: include functions
#print('State:', state)
# TODO: indicate when each fact is used
# TODO: record failed facts
global_plan.extend(
local_plan) # TODO: compute preimage of the executed plan
global_cost += local_cost
static_state, _ = partition_facts(domain, state)
#global_all.extend(partition_facts(domain, local_certificate.all_facts)[0])
#global_preimage.extend(static_state)
print('Static:', static_state)
state = apply_actions(domain, state, local_plan, unit_costs=unit_costs)
print(SEPARATOR)
#user_input('Continue?')
# TODO: could also just test the goal here
# TODO: constrain future plan skeletons
global_certificate = Certificate(all_facts={}, preimage_facts=None)
return global_plan, global_cost, global_certificate
def solve_abstract(problem,
constraints=PlanConstraints(),
stream_info={},
replan_actions=set(),
unit_costs=False,
success_cost=INF,
max_time=INF,
max_iterations=INF,
max_memory=INF,
initial_complexity=0,
complexity_step=1,
max_complexity=INF,
max_skeletons=INF,
search_sample_ratio=0,
bind=True,
max_failures=0,
unit_efforts=False,
max_effort=INF,
effort_weight=None,
reorder=True,
visualize=False,
verbose=True,
**search_kwargs):
"""
Solves a PDDLStream problem by first planning with optimistic stream outputs and then querying streams
:param problem: a PDDLStream problem
:param constraints: PlanConstraints on the set of legal solutions
:param stream_info: a dictionary from stream name to StreamInfo altering how individual streams are handled
:param replan_actions: the actions declared to induce replanning for the purpose of deferred stream evaluation
:param unit_costs: use unit action costs rather than numeric costs
:param success_cost: the exclusive (strict) upper bound on plan cost to successfully terminate
:param max_time: the maximum runtime
:param max_iterations: the maximum number of search iterations
:param max_memory: the maximum amount of memory
:param initial_complexity: the initial stream complexity limit
:param complexity_step: the increase in the stream complexity limit per iteration
:param max_complexity: the maximum stream complexity limit
:param max_skeletons: the maximum number of plan skeletons (max_skeletons=None indicates not adaptive)
:param search_sample_ratio: the desired ratio of sample time / search time when max_skeletons!=None
:param bind: if True, propagates parameter bindings when max_skeletons=None
:param max_failures: the maximum number of stream failures before switching phases when max_skeletons=None
:param unit_efforts: use unit stream efforts rather than estimated numeric efforts
:param max_effort: the maximum amount of stream effort
:param effort_weight: a multiplier for stream effort compared to action costs
:param reorder: if True, reorder stream plans to minimize the expected sampling overhead
:param visualize: if True, draw the constraint network and stream plan as a graphviz file
:param verbose: if True, print the result of each stream application
:param search_kwargs: keyword args for the search subroutine
:return: a tuple (plan, cost, evaluations) where plan is a sequence of actions
(or None), cost is the cost of the plan (INF if no plan), and evaluations is init expanded
using stream applications
"""
# TODO: select whether to search or sample based on expected success rates
# TODO: no optimizers during search with relaxed_stream_plan
# TODO: locally optimize only after a solution is identified
# TODO: replan with a better search algorithm after feasible
# TODO: change the search algorithm and unit costs based on the best cost
use_skeletons = (max_skeletons is not None)
#assert implies(use_skeletons, search_sample_ratio > 0)
eager_disabled = (effort_weight is None
) # No point if no stream effort biasing
num_iterations = eager_calls = 0
complexity_limit = initial_complexity
evaluations, goal_exp, domain, externals = parse_problem(
problem,
stream_info=stream_info,
constraints=constraints,
unit_costs=unit_costs,
unit_efforts=unit_efforts)
identify_non_producers(externals)
enforce_simultaneous(domain, externals)
compile_fluent_streams(domain, externals)
# TODO: make effort_weight be a function of the current cost
# if (effort_weight is None) and not has_costs(domain):
# effort_weight = 1
load_stream_statistics(externals)
if visualize and not has_pygraphviz():
visualize = False
print(
'Warning, visualize=True requires pygraphviz. Setting visualize=False'
)
if visualize:
reset_visualizations()
streams, functions, negative, optimizers = partition_externals(
externals, verbose=verbose)
eager_externals = list(filter(lambda e: e.info.eager, externals))
positive_externals = streams + functions + optimizers
has_optimizers = bool(optimizers) # TODO: deprecate
assert implies(has_optimizers, use_skeletons)
################
store = SolutionStore(evaluations,
max_time,
success_cost,
verbose,
max_memory=max_memory)
skeleton_queue = SkeletonQueue(store, domain, disable=not has_optimizers)
disabled = set() # Max skeletons after a solution
while (not store.is_terminated()) and (
num_iterations < max_iterations) and (complexity_limit <=
max_complexity):
num_iterations += 1
eager_instantiator = Instantiator(
eager_externals, evaluations) # Only update after an increase?
if eager_disabled:
push_disabled(eager_instantiator, disabled)
if eager_externals:
eager_calls += process_stream_queue(
eager_instantiator,
store,
complexity_limit=complexity_limit,
verbose=verbose)
################
print(
'\nIteration: {} | Complexity: {} | Skeletons: {} | Skeleton Queue: {} | Disabled: {} | Evaluations: {} | '
'Eager Calls: {} | Cost: {:.3f} | Search Time: {:.3f} | Sample Time: {:.3f} | Total Time: {:.3f}'
.format(num_iterations, complexity_limit,
len(skeleton_queue.skeletons), len(skeleton_queue),
len(disabled), len(evaluations), eager_calls,
store.best_cost, store.search_time, store.sample_time,
store.elapsed_time()))
optimistic_solve_fn = get_optimistic_solve_fn(
goal_exp,
domain,
negative,
replan_actions=replan_actions,
reachieve=use_skeletons,
max_cost=min(store.best_cost, constraints.max_cost),
max_effort=max_effort,
effort_weight=effort_weight,
**search_kwargs)
# TODO: just set unit effort for each stream beforehand
if (max_skeletons is None) or (len(skeleton_queue.skeletons) <
max_skeletons):
disabled_axioms = create_disabled_axioms(
skeleton_queue) if has_optimizers else []
if disabled_axioms:
domain.axioms.extend(disabled_axioms)
stream_plan, opt_plan, cost = iterative_plan_streams(
evaluations,
positive_externals,
optimistic_solve_fn,
complexity_limit,
max_effort=max_effort)
for axiom in disabled_axioms:
domain.axioms.remove(axiom)
else:
stream_plan, opt_plan, cost = OptSolution(
INFEASIBLE, INFEASIBLE, INF) # TODO: apply elsewhere
################
#stream_plan = replan_with_optimizers(evaluations, stream_plan, domain, externals) or stream_plan
stream_plan = combine_optimizers(evaluations, stream_plan)
#stream_plan = get_synthetic_stream_plan(stream_plan, # evaluations
# [s for s in synthesizers if not s.post_only])
#stream_plan = recover_optimistic_outputs(stream_plan)
if reorder:
# TODO: this blows up memory wise for long stream plans
stream_plan = reorder_stream_plan(store, stream_plan)
num_optimistic = sum(r.optimistic
for r in stream_plan) if stream_plan else 0
action_plan = opt_plan.action_plan if is_plan(opt_plan) else opt_plan
print('Stream plan ({}, {}, {:.3f}): {}\nAction plan ({}, {:.3f}): {}'.
format(get_length(stream_plan), num_optimistic,
compute_plan_effort(stream_plan), stream_plan,
get_length(action_plan), cost,
str_from_plan(action_plan)))
if is_plan(stream_plan) and visualize:
log_plans(stream_plan, action_plan, num_iterations)
create_visualizations(evaluations, stream_plan, num_iterations)
################
if (stream_plan is INFEASIBLE) and (not eager_instantiator) and (
not skeleton_queue) and (not disabled):
break
if not is_plan(stream_plan):
print('No plan: increasing complexity from {} to {}'.format(
complexity_limit, complexity_limit + complexity_step))
complexity_limit += complexity_step
if not eager_disabled:
reenable_disabled(evaluations, domain, disabled)
#print(stream_plan_complexity(evaluations, stream_plan))
if not use_skeletons:
process_stream_plan(store,
domain,
disabled,
stream_plan,
opt_plan,
cost,
bind=bind,
max_failures=max_failures)
continue
################
#optimizer_plan = replan_with_optimizers(evaluations, stream_plan, domain, optimizers)
optimizer_plan = None
if optimizer_plan is not None:
# TODO: post process a bound plan
print('Optimizer plan ({}, {:.3f}): {}'.format(
get_length(optimizer_plan),
compute_plan_effort(optimizer_plan), optimizer_plan))
skeleton_queue.new_skeleton(optimizer_plan, opt_plan, cost)
allocated_sample_time = (search_sample_ratio * store.search_time) - store.sample_time \
if len(skeleton_queue.skeletons) <= max_skeletons else INF
if skeleton_queue.process(stream_plan, opt_plan, cost,
complexity_limit,
allocated_sample_time) is INFEASIBLE:
break
################
summary = store.export_summary()
summary.update({
'iterations': num_iterations,
'complexity': complexity_limit,
'skeletons': len(skeleton_queue.skeletons),
})
print('Summary: {}'.format(str_from_object(
summary, ndigits=3))) # TODO: return the summary
write_stream_statistics(externals, verbose)
return store.extract_solution()
def __repr__(self):
return '{}:{}->{}'.format(self.external.name,
str_from_object(self.instance.input_objects),
str_from_object(self.output_objects))
def solve_incremental(problem,
constraints=PlanConstraints(),
unit_costs=False,
success_cost=INF,
max_iterations=INF,
max_time=INF,
max_memory=INF,
initial_complexity=0,
complexity_step=1,
max_complexity=INF,
verbose=False,
**search_args):
"""
Solves a PDDLStream problem by alternating between applying all possible streams and searching
:param problem: a PDDLStream problem
:param constraints: PlanConstraints on the set of legal solutions
:param max_time: the maximum amount of time to apply streams
:param max_iterations: the maximum amount of search iterations
:param initial_complexity: the stream complexity on the first iteration
:param complexity_step: the increase in the complexity limit after each iteration
:param max_complexity: the maximum stream complexity
:param unit_costs: use unit action costs rather than numeric costs
:param success_cost: an exclusive (strict) upper bound on plan cost to terminate
:param verbose: if True, this prints the result of each stream application
:param search_args: keyword args for the search subroutine
:return: a tuple (plan, cost, evaluations) where plan is a sequence of actions
(or None), cost is the cost of the plan, and evaluations is init but expanded
using stream applications
"""
# max_complexity = 0 => current
# complexity_step = INF => exhaustive
# success_cost = terminate_cost = decision_cost
evaluations, goal_expression, domain, externals = parse_problem(
problem, constraints=constraints, unit_costs=unit_costs)
store = SolutionStore(
evaluations, max_time, success_cost, verbose,
max_memory=max_memory) # TODO: include other info here?
if UPDATE_STATISTICS:
load_stream_statistics(externals)
static_externals = compile_fluents_as_attachments(domain, externals)
num_iterations = num_calls = 0
complexity_limit = initial_complexity
instantiator = Instantiator(static_externals, evaluations)
num_calls += process_stream_queue(instantiator,
store,
complexity_limit,
verbose=verbose)
while not store.is_terminated() and (
num_iterations <= max_iterations) and (complexity_limit <=
max_complexity):
num_iterations += 1
print(
'Iteration: {} | Complexity: {} | Calls: {} | Evaluations: {} | Solved: {} | Cost: {} | Time: {:.3f}'
.format(num_iterations, complexity_limit, num_calls,
len(evaluations), store.has_solution(), store.best_cost,
store.elapsed_time()))
plan, cost = solve_finite(evaluations,
goal_expression,
domain,
max_cost=min(store.best_cost,
constraints.max_cost),
**search_args)
if is_plan(plan):
store.add_plan(plan, cost)
if not instantiator:
break
if complexity_step is None:
# TODO: option to select the next k-smallest complexities
complexity_limit = instantiator.min_complexity()
else:
complexity_limit += complexity_step
num_calls += process_stream_queue(instantiator,
store,
complexity_limit,
verbose=verbose)
#retrace_stream_plan(store, domain, goal_expression)
#print('Final queue size: {}'.format(len(instantiator)))
summary = store.export_summary()
summary.update({
# TODO: integrate into store
'iterations': num_iterations,
'complexity': complexity_limit,
# TODO: optimal, infeasible, etc...
})
print(str_from_object(summary)) # TODO: return the summary
if UPDATE_STATISTICS:
write_stream_statistics(externals, verbose)
return store.extract_solution()
def str_from_plan(plan):
if not is_plan(plan):
return str(plan)
return str_from_object(list(map(str_from_action, plan)))
