benchmarks/benchmark_dataset_iter.py

"""
License:
This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0.
If a copy of the MPL was not distributed with this file, You can obtain one at https://mozilla.org/MPL/2.0/.
"""

import torch

from hub import Dataset
from hub.utils import Timer

DATASET_NAMES = ["activeloop/mnist", "activeloop/cifar10_train"]

BATCH_SIZES = [1, 16, 128]

PREFETCH_SIZES = [1, 4, 16, 128]


def time_iter_pytorch(
    dataset_name="activeloop/mnist", batch_size=1, prefetch_factor=0, process=None
):

    dset = Dataset(dataset_name, cache=False, storage_cache=False, mode="r")

    loader = torch.utils.data.DataLoader(
        dset.to_pytorch(),
        batch_size=batch_size,
        prefetch_factor=prefetch_factor,
        num_workers=1,
    )

    with Timer(
        f"{dataset_name} PyTorch prefetch {prefetch_factor:03} in batches of {batch_size:03}"
    ):
        for idx, (image, label) in enumerate(loader):
            if process is not None:
                process(idx, image, label)


def time_iter_tensorflow(
    dataset_name="activeloop/mnist", batch_size=1, prefetch_factor=0, process=None
):

    dset = Dataset(dataset_name, cache=False, storage_cache=False, mode="r")

    loader = dset.to_tensorflow().batch(batch_size).prefetch(prefetch_factor)

    with Timer(
        f"{dataset_name} TF prefetch {prefetch_factor:03} in batches of {batch_size:03}"
    ):
        for idx, batch in enumerate(loader):
            image = batch["image"]
            label = batch["label"]
            if process is not None:
                process(idx, image, label)


if __name__ == "__main__":
    for name in DATASET_NAMES:
        for size in BATCH_SIZES:
            for prefetch in PREFETCH_SIZES:
                time_iter_pytorch(name, size, prefetch, None)
                time_iter_tensorflow(name, size, prefetch, None)