def __init__(self, args, device, writer=None, writer_counter=None, win_event_counter=None):
super(SpGcnEnv, self).__init__()
self.reset()
self.args = args
self.device = device
self.writer = writer
self.writer_counter = writer_counter
self.discrete_action_space = False
if self.args.reward_function == 'fully_supervised':
self.reward_function = FullySupervisedReward(env=self)
elif self.args.reward_function == 'sub_graph_dice':
self.reward_function = SubGraphDiceReward(env=self)
elif self.args.reward_function == 'defining_rules':
self.reward_function = HoughCircles(env=self, range_num=[8, 10],
range_rad=[max(self.args.data_shape) // 18,
max(self.args.data_shape) // 15], min_hough_confidence=0.7)
elif self.args.reward_function == 'defining_rules_lg':
self.reward_function = HoughCircles_lg(env=self, range_num=[8, 10],
range_rad=[max(self.args.data_shape) // 18,
max(self.args.data_shape) // 15], min_hough_confidence=0.7)
# elif self.args.reward_function == 'focal':
# self.reward_function = FocalReward(env=self)
# elif self.args.reward_function == 'global_sparse':
# self.reward_function = GlobalSparseReward(env=self)
else:
self.reward_function = UnSupervisedReward(env=self)
def __init__(self,
args,
device,
writer=None,
writer_counter=None,
win_event_counter=None):
super(SpGcnEnv, self).__init__()
self.stop_quality = 0
self.reset()
self.args = args
self.device = device
self.writer = writer
self.writer_counter = writer_counter
self.win_event_counter = win_event_counter
self.discrete_action_space = False
if self.args.reward_function == 'fully_supervised':
self.reward_function = FullySupervisedReward(env=self)
elif self.args.reward_function == 'object_level':
self.reward_function = ObjectLevelReward(env=self)
elif self.args.reward_function == 'graph_dice':
self.reward_function = GraphDiceReward(env=self)
elif self.args.reward_function == 'focal':
self.reward_function = FocalReward(env=self)
elif self.args.reward_function == 'global_sparse':
self.reward_function = GlobalSparseReward(env=self)
else:
self.reward_function = UnSupervisedReward(env=self)
def __init__(self, embedding_net, cfg, device, writer=None, writer_counter=None):
super(EmbeddingSpaceEnvNodeBased, self).__init__()
self.embedding_net = embedding_net
self.reset()
self.cfg = cfg
self.device = device
self.writer = writer
self.writer_counter = writer_counter
self.last_final_reward = torch.tensor([0.0])
self.max_p = torch.nn.MaxPool2d(3, padding=1, stride=1)
self.step_encoder = TemporalSineEncoding(max_step=cfg.trn.max_episode_length,
size=cfg.fe.n_embedding_features)
if self.cfg.trn.reward_function == 'sub_graph_dice':
self.reward_function = SubGraphDiceReward()
else:
self.reward_function = UnSupervisedReward(env=self)
self.cluster_policy = nagglo.cosineDistNodeAndEdgeWeightedClusterPolicy
def __init__(self, cfg, device, writer=None, writer_counter=None):
super(SpGcnEnv, self).__init__()
self.reset()
self.cfg = cfg
self.device = device
self.writer = writer
self.writer_counter = writer_counter
self.discrete_action_space = False
self.max_p = torch.nn.MaxPool2d(3, padding=1, stride=1)
if self.cfg.sac.reward_function == 'fully_supervised':
self.reward_function = FullySupervisedReward(env=self)
elif self.cfg.sac.reward_function == 'sub_graph_dice':
self.reward_function = SubGraphDiceReward(env=self)
elif self.cfg.sac.reward_function == 'defining_rules_edge_based':
self.reward_function = HoughCircles(
env=self,
range_num=[8, 10],
range_rad=[
max(self.cfg.sac.data_shape) // 18,
max(self.cfg.sac.data_shape) // 15
],
min_hough_confidence=0.7)
elif self.cfg.sac.reward_function == 'defining_rules_sp_based':
self.reward_function = HoughCirclesOnSp(
env=self,
range_num=[8, 10],
range_rad=[
max(self.cfg.sac.data_shape) // 18,
max(self.cfg.sac.data_shape) // 15
],
min_hough_confidence=0.7)
elif self.cfg.sac.reward_function == 'defining_rules_lg':
assert False
else:
self.reward_function = UnSupervisedReward(env=self)