def __init__(self, num_data, batch_size):
self.batch_size = batch_size
config = OmegaConf.create({
"use_features": True,
"annotations": {
"train": "not_a_real_annotations_dataset",
"val": "not_a_real_annotations_dataset",
},
"features": {
"train": "not_a_real_features_dataset",
"val": "not_a_real_features_dataset",
},
"dataset_config": {
"simple": 0
},
})
self._num_data = num_data
self._batch_size = batch_size
self.config = config
self.dataset_list = []
dataset_builder = MMFDatasetBuilder(
"simple", functools.partial(NumbersDataset, num_examples=num_data))
dataset_builder.train_dataloader = self._get_dataloader
dataset_builder.val_dataloader = self._get_dataloader
dataset_builder.test_dataloader = self._get_dataloader
self.datamodules = {"simple": dataset_builder}
def __init__(self, config, num_data):
super().__init__(config)
batch_size = config.training.batch_size
config = OmegaConf.create(
{
"use_features": True,
"annotations": {
"train": "not_a_real_annotations_dataset",
"val": "not_a_real_annotations_dataset",
},
"features": {
"train": "not_a_real_features_dataset",
"val": "not_a_real_features_dataset",
},
"dataset_config": {"numbers": 0},
}
)
self._num_data = num_data
self.batch_size = batch_size
self.config = config
self.dataset_list = []
dataset_builder = MMFDatasetBuilder(
"numbers",
functools.partial(NumbersDataset, num_examples=num_data, always_one=True),
)
dataset_builder.train_dataloader = self._get_dataloader_train
dataset_builder.val_dataloader = self._get_dataloader_val
dataset_builder.test_dataloader = self._get_dataloader_test
self.datamodules = {"numbers": dataset_builder}
def _create_dataset(self, dataset_type):
dataset_builder = MMFDatasetBuilder(
"vqa", functools.partial(SimpleMMFDataset, num_examples=100))
return dataset_builder.load(self.config, dataset_type)