def build_iteration_strategy(
config: DictConfig,
dataloaders: Dict[str, torch.utils.data.DataLoader],
*args,
**kwargs,
) -> IterationStrategy:
if not config.get("enabled", True):
return ConstantIterationStrategy.from_params(dataloaders, *args, **kwargs)
else:
assert (
"type" in config
), "multitasking config must define 'type' attribute if enabled"
# This assumes all dataloaders will have same dataset type
iteration_strategy_class = registry.get_iteration_strategy_class(config.type)
config = config.get("params", {})
dataset_type = dataloaders[list(dataloaders.keys())[0]].dataset.dataset_type
if dataset_type != "train":
logger.info(
f"{iteration_strategy_class.__name__} updated to size "
+ f"proportional for {dataset_type}"
)
return SizeProportionalIterationStrategy.from_params(
dataloaders, *args, **kwargs
)
else:
return iteration_strategy_class(config, dataloaders, *args, **kwargs)
def build_iteration_strategy(
config: DictConfig,
dataloaders: Dict[str, torch.utils.data.DataLoader],
*args,
**kwargs,
) -> IterationStrategy:
if not config.get("enabled", True):
return ConstantIterationStrategy.from_params(dataloaders, *args, **kwargs)
else:
assert (
"type" in config
), "multitasking config must define 'type' attribute if enabled"
# This assumes all dataloaders will have same dataset type
iteration_strategy_class = registry.get_iteration_strategy_class(config.type)
config = config.get("params", {})
# val and test splits won't be affected as test reporter iterates
# over the datasets one by one without using any iteration strategy
return iteration_strategy_class(config, dataloaders, *args, **kwargs)