def test_indexer(self):
indexer = GraspIndexer(gripper_classes=[0.04, 0.06, 0.08])
def define_action(d=0.0, index=0):
a = Action()
a.pose.d = d
a.index = index
return a
self.assertEqual(indexer.from_action(define_action(d=0.04)), 0)
self.assertEqual(indexer.from_action(define_action(d=0.059)), 1)
self.assertEqual(indexer.from_action(define_action(d=0.06)), 1)
self.assertEqual(indexer.from_action(define_action(d=0.07)), 2)
self.assertEqual(indexer.from_action(define_action(d=0.08)), 2)
self.assertEqual(indexer.from_action(define_action(d=0.1)), 2)
a = define_action(index=2)
indexer.to_action(a)
self.assertEqual(indexer.from_action(a), 2)
class Agent:
def __init__(self, **params):
self.model = Config.grasp_model
self.watch_for_model_modification = True
self.model_last_modified = Loader.get_model_path(
self.model).stat().st_mtime
self.monte_carlo = 40 if 'mc' in self.model[1] else None
self.with_types = 'types' in self.model[1]
self.output_layer = 'prob' if not self.with_types else ['prob', 'type']
self.inference = InferencePlanarPose(
model=Loader.get_model(self.model, output_layer=self.output_layer),
box=Config.box,
lower_random_pose=Config.lower_random_pose,
upper_random_pose=Config.upper_random_pose,
monte_carlo=self.monte_carlo,
with_types=self.with_types,
)
self.inference.keep_indixes = None
self.indexer = GraspIndexer(gripper_classes=Config.gripper_classes)
self.converter = Converter(grasp_z_offset=Config.grasp_z_offset,
box=Config.box)
# # self.indexer = GraspFinalDIndexer(gripper_classes=Config.gripper_classes, final_d_classes=[0.0, 0.035])
# self.indexer = LateralIndexer(
# angles=[(0, 0), (0.3, 0)],
# gripper_classes=[0.05, 0.07, 0.084],
# )
# self.converter = Converter(grasp_z_offset=Config.grasp_z_offset, box=Config.box)
self.reinfer_next_time = True # Always true in contrast to AgentPredict
def check_for_model_reload(self):
current_model_st_mtime = Loader.get_model_path(
self.model).stat().st_mtime
if self.watch_for_model_modification and current_model_st_mtime > self.model_last_modified + 0.5: # [s]
logger.warning(f'Reload model {self.model}.')
try:
self.inference.model = Loader.get_model(
self.model, output_layer=self.output_layer)
self.model_last_modified = Loader.get_model_path(
self.model).stat().st_mtime
except OSError:
logger.info('Could not load model, probabily file locked.')
def infer(self, images: List[OrthographicImage], method: SelectionMethod,
**params) -> List[Action]:
if self.monte_carlo: # Adapt monte carlo progress parameter s
epoch_in_collection = Loader.get_episode_count(Config.collection)
s_not_bounded = (epoch_in_collection - 3500) * 1 / (4500 - 3500)
self.inference.current_s = max(min(s_not_bounded, 1.0), 0.0)
self.check_for_model_reload()
if len(images) == 3:
images[2].mat = images[2].mat[:, :, ::-1] # BGR to RGB
action = self.inference.infer(images, method)
self.indexer.to_action(action)
print(action, method)
estimated_reward_lower_than_threshold = action.estimated_reward < Config.bin_empty_at_max_probability
bin_empty = estimated_reward_lower_than_threshold and Epoch.selection_method_should_be_high(
method)
if bin_empty:
return [Action('bin_empty', safe=1)]
self.converter.calculate_pose(action, images)
return [action]
def reward_for_action(self, images: List[OrthographicImage],
action: Action) -> float:
estimated_rewards = self.inference.infer_at_pose(images, action.pose)
if isinstance(estimated_rewards, tuple):
estimated_rewards, _ = estimated_rewards
index = self.indexer.from_action(action)
return estimated_rewards[0][0][index]
