def modify_config(self, config: FaceGroupingConfig, i):
if i < self.num_runs / 2:
config.dataset = self.bf
else:
config.dataset = self.bbt
return super().modify_config(config, i)
def modify_config(self, config: FaceGroupingConfig, i):
if i < 6:
config.dataset = BigBangTheory(episode_index_test=i)
else:
config.dataset = Buffy(episode_index_test=i - 6)
return super().modify_config(config, i)
def modify_config(self, config: FaceGroupingConfig, i):
from FGG.dataset.split_strategy import SplitEveryXFrames
x = 10
config.graph_builder_params["split_strategy"] = SplitEveryXFrames(x=x)
config.pool_before_clustering = True
config.dataset = Buffy(episode_index_val=None,
episode_index_train=None,
episode_index_test=i)
return (str(x), *super().modify_config(config, i))
def modify_config(self, config: FaceGroupingConfig, i):
config.dataset = self.accio
config.model_params["sparse_adjacency"] = True
return (str(self.num_clusters), *super().modify_config(config, i))
def modify_config(self, config: FaceGroupingConfig, i):
config.dataset = self.bf
return super().create_model_name(config, i)
def modify_config(self, config: FaceGroupingConfig, i):
config.dataset = self.dataset
return super().modify_config(config, i)
def modify_config(self, config: FaceGroupingConfig, i):
config.dataset = Buffy(episode_index_test=i)
return super().modify_config(config, i)
def modify_config(self, config: FaceGroupingConfig, i):
config.dataset = Accio()
config.model_params["sparse_adjacency"] = True
return super().modify_config(config, i)