class Config(_NewTask.Config):
tasks: Dict[str, NewTask.Config] = {}
task_weights: Dict[str, float] = {}
target_task_name: Optional[str] = None # for selecting best epoch
data: DisjointMultitaskData.Config = DisjointMultitaskData.Config()
metric_reporter: DisjointMultitaskMetricReporter.Config = (
DisjointMultitaskMetricReporter.Config())
def from_config(cls, task_config, metadata=None, model_state=None):
datas = OrderedDict()
models = OrderedDict()
metric_reporters = OrderedDict()
for name, task in task_config.tasks.items():
tensorizers, data = NewTask._init_tensorizers(task)
datas[name] = data
models[name] = NewTask._init_model(task, tensorizers)
metric_reporters[name] = create_component(
ComponentType.METRIC_REPORTER,
task.metric_reporter,
tensorizers=tensorizers,
)
task_weights = {
task_name: task_config.task_weights.get(task_name, 1.0)
for task_name in task_config.tasks.keys()
}
data = DisjointMultitaskData(task_config.data, datas)
model = NewDisjointMultitaskModel(models, loss_weights=task_weights)
if model_state:
model.load_state_dict(model_state)
metric_reporter = DisjointMultitaskMetricReporter(
metric_reporters,
loss_weights=task_weights,
target_task_name=task_config.target_task_name,
use_subtask_select_metric=(
task_config.metric_reporter.use_subtask_select_metric
),
)
trainer = create_trainer(task_config.trainer, model)
return cls(data, model, metric_reporter, trainer)
class Config(TaskBase.Config):
tasks: Dict[str, Task.Config]
task_weights: Dict[str, float] = {}
data_handler: DisjointMultitaskDataHandler.Config = DisjointMultitaskDataHandler.Config(
)
metric_reporter: DisjointMultitaskMetricReporter.Config = DisjointMultitaskMetricReporter.Config(
)
class Config(TaskBase.Config):
tasks: Dict[str, Task_Deprecated.Config]
task_weights: Dict[str, float] = {}
target_task_name: Optional[str] = None # for selecting best epoch
data_handler: DisjointMultitaskDataHandler.Config = DisjointMultitaskDataHandler.Config(
)
metric_reporter: DisjointMultitaskMetricReporter.Config = DisjointMultitaskMetricReporter.Config(
)
def from_config(cls, task_config, metadata=None, model_state=None):
print("Task parameters:\n")
pprint(config_to_json(type(task_config), task_config))
data_handlers = OrderedDict()
exporters = OrderedDict()
for name, task in task_config.tasks.items():
featurizer = create_featurizer(task.featurizer, task.features)
data_handlers[name] = create_data_handler(task.data_handler,
task.features,
task.labels,
featurizer=featurizer)
data_handler = DisjointMultitaskDataHandler(task_config.data_handler,
data_handlers)
print("\nLoading data...")
if metadata:
data_handler.load_metadata(metadata)
else:
data_handler.init_metadata()
metadata = data_handler.metadata
exporters = {
name: (create_exporter(
task.exporter,
task.features,
task.labels,
data_handler.data_handlers[name].metadata,
task.model,
) if task.exporter else None)
for name, task in task_config.tasks.items()
}
metric_reporter = DisjointMultitaskMetricReporter(
OrderedDict(
(name,
create_metric_reporter(task.metric_reporter, metadata[name]))
for name, task in task_config.tasks.items()),
target_task_name=task_config.metric_reporter.target_task_name,
)
model = DisjointMultitaskModel(
OrderedDict(
(name, create_model(task.model, task.features, metadata[name]))
for name, task in task_config.tasks.items()))
if model_state:
model.load_state_dict(model_state)
if cuda_utils.CUDA_ENABLED:
model = model.cuda()
optimizers = create_optimizer(model, task_config.optimizer)
return cls(
exporters=exporters,
trainer=create_trainer(task_config.trainer),
data_handler=data_handler,
model=model,
metric_reporter=metric_reporter,
optimizers=optimizers,
lr_scheduler=Scheduler(optimizers, task_config.scheduler,
metric_reporter.lower_is_better),
)
def from_config(
cls,
task_config: Config,
unused_metadata=None,
model_state=None,
tensorizers=None,
rank=0,
world_size=1,
):
data_dict = OrderedDict()
models = OrderedDict()
metric_reporters = OrderedDict()
tensorizers_dict = tensorizers or {}
# We can't really re-use the tensorizers, and the tensorizers saved for disjoint
# multitask are an empty dict right now anyway. Really we should serialize
# all of the subtasks individually.
for name, task in task_config.tasks.items():
tensorizers, data = cls._init_tensorizers(
task, tensorizers_dict.get(name), rank, world_size
)
data_dict[name] = data
models[name] = NewTask._init_model(task.model, tensorizers)
metric_reporters[name] = create_component(
ComponentType.METRIC_REPORTER,
task.metric_reporter,
tensorizers=tensorizers,
)
task_weights = {
task_name: task_config.task_weights.get(task_name, 1.0)
for task_name in task_config.tasks.keys()
}
data = DisjointMultitaskData.from_config(
task_config.data, data_dict=data_dict, rank=rank, world_size=world_size
)
# for serialization
data.tensorizers = {
name: data.data_dict[name].tensorizers for name in data.data_dict
}
model = NewDisjointMultitaskModel(models, loss_weights=task_weights)
if model_state:
model.load_state_dict(model_state)
metric_reporter = DisjointMultitaskMetricReporter(
metric_reporters,
loss_weights=task_weights,
target_task_name=task_config.target_task_name,
use_subtask_select_metric=(
task_config.metric_reporter.use_subtask_select_metric
),
)
trainer = create_trainer(task_config.trainer, model)
return cls(data, model, metric_reporter, trainer)
def from_config(
cls,
task_config: Config,
metadata=None,
model_state=None,
tensorizers=None,
rank=0,
world_size=1,
):
print("Task parameters:\n")
pprint(config_to_json(type(task_config), task_config))
data_handlers = OrderedDict()
exporters = OrderedDict()
for name, task in task_config.tasks.items():
featurizer = create_featurizer(task.featurizer, task.features)
data_handlers[name] = create_data_handler(
task.data_handler, task.features, task.labels, featurizer=featurizer
)
data_handler = DisjointMultitaskDataHandler(
task_config.data_handler,
data_handlers,
target_task_name=task_config.target_task_name,
)
print("\nLoading data...")
if metadata:
data_handler.load_metadata(metadata)
else:
data_handler.init_metadata()
metadata = data_handler.metadata
exporters = {
name: (
create_exporter(
task.exporter,
task.features,
task.labels,
data_handler.data_handlers[name].metadata,
task.model,
)
if task.exporter
else None
)
for name, task in task_config.tasks.items()
}
task_weights = {
task_name: task_config.task_weights.get(task_name, 1)
for task_name in task_config.tasks.keys()
}
metric_reporter = DisjointMultitaskMetricReporter(
OrderedDict(
(name, create_metric_reporter(task.metric_reporter, metadata[name]))
for name, task in task_config.tasks.items()
),
loss_weights=task_weights,
target_task_name=task_config.target_task_name,
use_subtask_select_metric=(
task_config.metric_reporter.use_subtask_select_metric
),
)
model = DisjointMultitaskModel(
OrderedDict(
(name, create_model(task.model, task.features, metadata[name]))
for name, task in task_config.tasks.items()
),
loss_weights=task_weights,
)
if model_state:
model.load_state_dict(model_state)
if cuda.CUDA_ENABLED:
model = model.cuda()
return cls(
target_task_name=task_config.target_task_name,
exporters=exporters,
trainer=create_trainer(task_config.trainer, model),
data_handler=data_handler,
model=model,
metric_reporter=metric_reporter,
)
