(dataset_list, transforms, dataset_catalog, is_train=True)
return [dataset]
Arguments:
        dataset_list (list[str]): Contains the names of the datasets, i.e.,
            coco_2014_trian, coco_2014_val, etc
        transforms (callable): transforms to apply to each (image, target) sample
        dataset_catalog (DatasetCatalog): contains the information on how to
            construct a dataset.
        is_train (bool): whether to setup the dataset for training or testing
Arguments:
        dataset_list (list[str]): Contains the names of the datasets, i.e.,
            coco_2014_trian, coco_2014_val, etc
        transforms (callable): transforms to apply to each (image, target) sample
        dataset_catalog (DatasetCatalog): contains the information on how to
            construct a dataset.
        is_train (bool): whether to setup the dataset for training or testing
Arguments : dataset_list ( list [ str ] ) : Contains the names of the datasets i . e . coco_2014_trian coco_2014_val etc transforms ( callable ) : transforms to apply to each ( image target ) sample dataset_catalog ( DatasetCatalog ) : contains the information on how to construct a dataset . is_train ( bool ) : whether to setup the dataset for training or testing
def build_dataset(dataset_list, transforms, dataset_catalog, is_train=True):
    """
    Arguments:
        dataset_list (list[str]): Contains the names of the datasets, i.e.,
            coco_2014_trian, coco_2014_val, etc
        transforms (callable): transforms to apply to each (image, target) sample
        dataset_catalog (DatasetCatalog): contains the information on how to
            construct a dataset.
        is_train (bool): whether to setup the dataset for training or testing
    """
    if not isinstance(dataset_list, (list, tuple)):
        raise RuntimeError(
            "dataset_list should be a list of strings, got {}".format(dataset_list)
        )
    datasets = []
    for dataset_name in dataset_list:
        data = dataset_catalog.get(dataset_name)
        factory = getattr(D, data["factory"])
        args = data["args"]
        # for COCODataset, we want to remove images without annotations
        # during training
        if data["factory"] in ["COCODataset",
                               "WordDataset"]:
            args["remove_images_without_annotations"] = is_train
        if data["factory"] == "PascalVOCDataset":
            args["use_difficult"] = not is_train
        args["transforms"] = transforms
        # make dataset from factory
        dataset = factory(**args)
        datasets.append(dataset)

    # for testing, return a list of datasets
    if not is_train:
        return datasets

    # for training, concatenate all datasets into a single one
    dataset = datasets[0]
    if len(datasets) > 1:
        dataset = D.ConcatDataset(datasets)

    return [dataset]
def build_dataset ( dataset_list , transforms , dataset_catalog , is_train = True ) : if not isinstance ( dataset_list , ( list , tuple ) ) : raise RuntimeError ( "dataset_list should be a list of strings, got {}" . format ( dataset_list ) ) datasets = [ ] for dataset_name in dataset_list : data = dataset_catalog . get ( dataset_name ) factory = getattr ( D , data [ "factory" ] ) args = data [ "args" ] # for COCODataset, we want to remove images without annotations # during training if data [ "factory" ] in [ "COCODataset" , "WordDataset" ] : args [ "remove_images_without_annotations" ] = is_train if data [ "factory" ] == "PascalVOCDataset" : args [ "use_difficult" ] = not is_train args [ "transforms" ] = transforms # make dataset from factory dataset = factory ( * * args ) datasets . append ( dataset ) # for testing, return a list of datasets if not is_train : return datasets # for training, concatenate all datasets into a single one dataset = datasets [ 0 ] if len ( datasets ) > 1 : dataset = D . ConcatDataset ( datasets ) return [ dataset ]