def build_dataloader(self, dataset_instance: Optional[Dataset],
dataset_type: str, *args, **kwargs):
if dataset_instance is None:
raise TypeError(
f"dataset instance for {dataset_type} hasn't been set and is None"
)
dataset_instance.dataset_type = dataset_type
dataloader, _ = build_dataloader_and_sampler(dataset_instance,
self.config)
return dataloader
def build_dataloaders(self):
assert len(self._datasets) > 0, "Call build_datasets first"
for dataset_instance in self.datasets:
loader_instance, _ = build_dataloader_and_sampler(
dataset_instance, self.config.training)
sampler_instance = loader_instance.sampler
self.loaders[dataset_instance.name] = loader_instance
self.samplers[dataset_instance.name] = sampler_instance
self.current_loader = self.loaders[self.current_dataset_name]
def build_dataloaders(self):
assert len(self._datasets) > 0, "Call build_datasets first"
for dataset_instance in self.datasets:
loader_instance, sampler_instance = build_dataloader_and_sampler(
dataset_instance, self.config.training)
self.loaders.append(loader_instance)
self.samplers.append(sampler_instance)
self.current_loader = self.loaders[self.current_index]
def get_dataloader(self):
dataloader, _ = build_dataloader_and_sampler(self.current_dataset,
self.training_config)
return dataloader
