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"""Fetches a DataLoader for EMNIST dataset with PyTorch."""
from typing import Callable
from torch.utils.data import DataLoader
from torchvision.datasets import EMNIST
def fetch_dataloader(
root: str,
split: str,
train: bool,
download: bool,
transform: Callable = None,
target_transform: Callable = None,
batch_size: int = 128,
shuffle: bool = False,
num_workers: int = 0,
cuda: bool = True,
) -> DataLoader:
"""Down/load the EMNIST dataset and return a PyTorch DataLoader.
Args:
root (str): Root directory of dataset where EMNIST/processed/training.pt and EMNIST/processed/test.pt
exist.
split (str): The dataset has 6 different splits: byclass, bymerge, balanced, letters, digits and mnist.
This argument specifies which one to use.
train (bool): If True, creates dataset from training.pt, otherwise from test.pt.
download (bool): If true, downloads the dataset from the internet and puts it in root directory. If
dataset is already downloaded, it is not downloaded again.
transform (Callable): A function/transform that takes in an PIL image and returns a transformed version.
E.g, transforms.RandomCrop.
target_transform (Callable): A function/transform that takes in the target and transforms it.
batch_size (int): How many samples per batch to load (the default is 128).
shuffle (bool): Set to True to have the data reshuffled at every epoch (the default is False).
num_workers (int): How many subprocesses to use for data loading. 0 means that the data will be loaded in
the main process (default: 0).
cuda (bool): If True, the data loader will copy Tensors into CUDA pinned memory before returning them.
Returns:
DataLoader: A PyTorch DataLoader with emnist characters.
"""
dataset = EMNIST(
root=root,
split=split,
train=train,
download=download,
transform=transform,
target_transform=target_transform,
)
data_loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=cuda,
)
return data_loader
|
