def __init__(self,
task_graph=None,
input_module=None,
seed=None,
**kwargs):
defaults = recursive_merge_dicts(em_default_config,
mt_em_default_config,
misses='insert')
self.config = recursive_merge_dicts(defaults, kwargs)
# If no task_graph is specified, default to a single binary task
if task_graph is None:
task_graph = TaskHierarchy(edges=[], cardinalities=[2])
self.task_graph = task_graph
self.K_t = self.task_graph.K_t # Cardinalities by task
self.T = self.task_graph.T # Total number of tasks
MTClassifier.__init__(self, cardinalities=self.K_t, seed=seed)
if input_module is None:
input_module = IdentityModule(self.config['layer_output_dims'][0])
self._build(input_module)
# Show network
if self.config['verbose']:
print("\nNetwork architecture:")
self._print()
print()
def __init__(
self,
layer_out_dims,
input_modules=None,
middle_modules=None,
head_modules=None,
K=[],
task_graph=None,
**kwargs,
):
kwargs["layer_out_dims"] = layer_out_dims
config = recursive_merge_dicts(em_default_config,
mt_em_default_config,
misses="insert")
config = recursive_merge_dicts(config, kwargs)
MTClassifier.__init__(self, K, config)
if task_graph is None:
if K is None:
raise ValueError("You must supply either a list of "
"cardinalities (K) or a TaskGraph.")
task_graph = TaskGraph(K)
self.task_graph = task_graph
self.K = self.task_graph.K # Cardinalities by task
self.t = self.task_graph.t # Total number of tasks
assert len(self.K) == self.t
self._build(input_modules, middle_modules, head_modules)
# Show network
if self.config["verbose"]:
print("\nNetwork architecture:")
self._print()
print()
def __init__(self, K=None, task_graph=None, **kwargs):
"""
Args:
K: A t-length list of task cardinalities (overrided by task_graph
if task_graph is not None)
task_graph: TaskGraph: A TaskGraph which defines a feasible set of
task label vectors; overrides K if provided
"""
config = recursive_merge_dicts(lm_default_config, kwargs)
MTClassifier.__init__(self, K, config)
if task_graph is None:
task_graph = TaskGraph(K)
self.task_graph = task_graph
# Note: While K is a list of the cardinalities of the tasks, k is the
# cardinality of the feasible set. These are always the same for a
# single-task model, but rarely the same for a multi-task model.
self.k = self.task_graph.k