def get_dataloader(self, context, dataset_name, dataloader):
name = "dataset." + dataset_name + "."
sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
batch_size = envs.get_global_env(name + "batch_size")
reader_class = envs.get_global_env(name + "data_converter")
reader_class_name = envs.get_global_env(name + "reader_class_name",
"Reader")
if sparse_slots == "" and dense_slots == "":
reader = dataloader_instance.dataloader_by_name(
reader_class,
dataset_name,
context["config_yaml"],
context,
reader_class_name=reader_class_name)
reader_class = envs.lazy_instance_by_fliename(
reader_class, reader_class_name)
reader_ins = reader_class(context["config_yaml"])
else:
reader = dataloader_instance.slotdataloader_by_name(
"", dataset_name, context["config_yaml"], context)
reader_ins = SlotReader(context["config_yaml"])
if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
dataloader.set_sample_list_generator(reader)
elif hasattr(reader_ins, 'batch_tensor_creator'):
dataloader.set_batch_generator(reader)
else:
dataloader.set_sample_generator(reader, batch_size)
return dataloader
def _get_dataloader(self, dataset_name, dataloader):
name = "dataset." + dataset_name + "."
thread_num = envs.get_global_env(name + "thread_num")
batch_size = envs.get_global_env(name + "batch_size")
reader_class = envs.get_global_env(name + "data_converter")
abs_dir = os.path.dirname(os.path.abspath(__file__))
sparse_slots = envs.get_global_env(name + "sparse_slots", "").strip()
dense_slots = envs.get_global_env(name + "dense_slots", "").strip()
if sparse_slots == "" and dense_slots == "":
reader = dataloader_instance.dataloader_by_name(
reader_class, dataset_name, self._config_yaml)
reader_class = envs.lazy_instance_by_fliename(
reader_class, "TrainReader")
reader_ins = reader_class(self._config_yaml)
else:
reader = dataloader_instance.slotdataloader_by_name(
"", dataset_name, self._config_yaml)
reader_ins = SlotReader(self._config_yaml)
if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
dataloader.set_sample_list_generator(reader)
else:
dataloader.set_sample_generator(reader, batch_size)
return dataloader
