test_datasets_download.py

import contextlib
import itertools
import tempfile
import time
import unittest.mock
import warnings
from datetime import datetime
from distutils import dir_util
from os import path
from urllib.error import HTTPError, URLError
from urllib.parse import urlparse
from urllib.request import urlopen, Request

import pytest
from torchvision import datasets
from torchvision.datasets.utils import (
    download_url,
    check_integrity,
    download_file_from_google_drive,
    _get_redirect_url,
    USER_AGENT,
)


def limit_requests_per_time(min_secs_between_requests=2.0):
    last_requests = {}

    def outer_wrapper(fn):
        def inner_wrapper(request, *args, **kwargs):
            url = request.full_url if isinstance(request, Request) else request

            netloc = urlparse(url).netloc
            last_request = last_requests.get(netloc)
            if last_request is not None:
                elapsed_secs = (datetime.now() - last_request).total_seconds()
                delta = min_secs_between_requests - elapsed_secs
                if delta > 0:
                    time.sleep(delta)

            response = fn(request, *args, **kwargs)
            last_requests[netloc] = datetime.now()

            return response

        return inner_wrapper

    return outer_wrapper


urlopen = limit_requests_per_time()(urlopen)


def resolve_redirects(max_hops=3):
    def outer_wrapper(fn):
        def inner_wrapper(request, *args, **kwargs):
            initial_url = request.full_url if isinstance(request, Request) else request
            url = _get_redirect_url(initial_url, max_hops=max_hops)

            if url == initial_url:
                return fn(request, *args, **kwargs)

            warnings.warn(f"The URL {initial_url} ultimately redirects to {url}.")

            if not isinstance(request, Request):
                return fn(url, *args, **kwargs)

            request_attrs = {
                attr: getattr(request, attr) for attr in ("data", "headers", "origin_req_host", "unverifiable")
            }
            # the 'method' attribute does only exist if the request was created with it
            if hasattr(request, "method"):
                request_attrs["method"] = request.method

            return fn(Request(url, **request_attrs), *args, **kwargs)

        return inner_wrapper

    return outer_wrapper


urlopen = resolve_redirects()(urlopen)


@contextlib.contextmanager
def log_download_attempts(
    urls_and_md5s=None,
    file="utils",
    patch=True,
    mock_auxiliaries=None,
):
    def add_mock(stack, name, file, **kwargs):
        try:
            return stack.enter_context(unittest.mock.patch(f"torchvision.datasets.{file}.{name}", **kwargs))
        except AttributeError as error:
            if file != "utils":
                return add_mock(stack, name, "utils", **kwargs)
            else:
                raise pytest.UsageError from error

    if urls_and_md5s is None:
        urls_and_md5s = set()
    if mock_auxiliaries is None:
        mock_auxiliaries = patch

    with contextlib.ExitStack() as stack:
        url_mock = add_mock(stack, "download_url", file, wraps=None if patch else download_url)
        google_drive_mock = add_mock(
            stack, "download_file_from_google_drive", file, wraps=None if patch else download_file_from_google_drive
        )

        if mock_auxiliaries:
            add_mock(stack, "extract_archive", file)

        try:
            yield urls_and_md5s
        finally:
            for args, kwargs in url_mock.call_args_list:
                url = args[0]
                md5 = args[-1] if len(args) == 4 else kwargs.get("md5")
                urls_and_md5s.add((url, md5))

            for args, kwargs in google_drive_mock.call_args_list:
                id = args[0]
                url = f"https://drive.google.com/file/d/{id}"
                md5 = args[3] if len(args) == 4 else kwargs.get("md5")
                urls_and_md5s.add((url, md5))


def retry(fn, times=1, wait=5.0):
    msgs = []
    for _ in range(times + 1):
        try:
            return fn()
        except AssertionError as error:
            msgs.append(str(error))
            time.sleep(wait)
    else:
        raise AssertionError(
            "\n".join(
                (
                    f"Assertion failed {times + 1} times with {wait:.1f} seconds intermediate wait time.\n",
                    *(f"{idx}: {error}" for idx, error in enumerate(msgs, 1)),
                )
            )
        )


@contextlib.contextmanager
def assert_server_response_ok():
    try:
        yield
    except URLError as error:
        raise AssertionError("The request timed out.") from error
    except HTTPError as error:
        raise AssertionError(f"The server returned {error.code}: {error.reason}.") from error
    except RecursionError as error:
        raise AssertionError(str(error)) from error


def assert_url_is_accessible(url, timeout=5.0):
    request = Request(url, headers={"User-Agent": USER_AGENT}, method="HEAD")
    with assert_server_response_ok():
        urlopen(request, timeout=timeout)


def assert_file_downloads_correctly(url, md5, tmpdir, timeout=5.0):
    file = path.join(tmpdir, path.basename(url))
    with assert_server_response_ok():
        with open(file, "wb") as fh:
            request = Request(url, headers={"User-Agent": USER_AGENT})
            response = urlopen(request, timeout=timeout)
            fh.write(response.read())

    assert check_integrity(file, md5=md5), "The MD5 checksums mismatch"


class DownloadConfig:
    def __init__(self, url, md5=None, id=None):
        self.url = url
        self.md5 = md5
        self.id = id or url

    def __repr__(self):
        return self.id


def make_download_configs(urls_and_md5s, name=None):
    return [
        DownloadConfig(url, md5=md5, id=f"{name}, {url}" if name is not None else None) for url, md5 in urls_and_md5s
    ]


def collect_download_configs(dataset_loader, name=None, **kwargs):
    urls_and_md5s = set()
    try:
        with log_download_attempts(urls_and_md5s=urls_and_md5s, **kwargs):
            dataset = dataset_loader()
    except Exception:
        dataset = None

    if name is None and dataset is not None:
        name = type(dataset).__name__

    return make_download_configs(urls_and_md5s, name)


# This is a workaround since fixtures, such as the built-in tmp_dir, can only be used within a test but not within a
# parametrization. Thus, we use a single root directory for all datasets and remove it when all download tests are run.
ROOT = tempfile.mkdtemp()


@pytest.fixture(scope="module", autouse=True)
def root():
    yield ROOT
    dir_util.remove_tree(ROOT)


def places365():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.Places365(ROOT, split=split, small=small, download=True),
                name=f"Places365, {split}, {'small' if small else 'large'}",
                file="places365",
            )
            for split, small in itertools.product(("train-standard", "train-challenge", "val"), (False, True))
        ]
    )


def caltech101():
    return collect_download_configs(lambda: datasets.Caltech101(ROOT, download=True), name="Caltech101")


def caltech256():
    return collect_download_configs(lambda: datasets.Caltech256(ROOT, download=True), name="Caltech256")


def cifar10():
    return collect_download_configs(lambda: datasets.CIFAR10(ROOT, download=True), name="CIFAR10")


def cifar100():
    return collect_download_configs(lambda: datasets.CIFAR100(ROOT, download=True), name="CIFAR100")


def voc():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.VOCSegmentation(ROOT, year=year, download=True),
                name=f"VOC, {year}",
                file="voc",
            )
            for year in ("2007", "2007-test", "2008", "2009", "2010", "2011", "2012")
        ]
    )


def mnist():
    with unittest.mock.patch.object(datasets.MNIST, "mirrors", datasets.MNIST.mirrors[-1:]):
        return collect_download_configs(lambda: datasets.MNIST(ROOT, download=True), name="MNIST")


def fashion_mnist():
    return collect_download_configs(lambda: datasets.FashionMNIST(ROOT, download=True), name="FashionMNIST")


def kmnist():
    return collect_download_configs(lambda: datasets.KMNIST(ROOT, download=True), name="KMNIST")


def emnist():
    # the 'split' argument can be any valid one, since everything is downloaded anyway
    return collect_download_configs(lambda: datasets.EMNIST(ROOT, split="byclass", download=True), name="EMNIST")


def qmnist():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.QMNIST(ROOT, what=what, download=True),
                name=f"QMNIST, {what}",
                file="mnist",
            )
            for what in ("train", "test", "nist")
        ]
    )


def omniglot():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.Omniglot(ROOT, background=background, download=True),
                name=f"Omniglot, {'background' if background else 'evaluation'}",
            )
            for background in (True, False)
        ]
    )


def phototour():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.PhotoTour(ROOT, name=name, download=True),
                name=f"PhotoTour, {name}",
                file="phototour",
            )
            # The names postfixed with '_harris' point to the domain 'matthewalunbrown.com'. For some reason all
            # requests timeout from within CI. They are disabled until this is resolved.
            for name in ("notredame", "yosemite", "liberty")  # "notredame_harris", "yosemite_harris", "liberty_harris"
        ]
    )


def sbdataset():
    return collect_download_configs(
        lambda: datasets.SBDataset(ROOT, download=True),
        name="SBDataset",
        file="voc",
    )


def sbu():
    return collect_download_configs(
        lambda: datasets.SBU(ROOT, download=True),
        name="SBU",
        file="sbu",
    )


def semeion():
    return collect_download_configs(
        lambda: datasets.SEMEION(ROOT, download=True),
        name="SEMEION",
        file="semeion",
    )


def stl10():
    return collect_download_configs(
        lambda: datasets.STL10(ROOT, download=True),
        name="STL10",
    )


def svhn():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.SVHN(ROOT, split=split, download=True),
                name=f"SVHN, {split}",
                file="svhn",
            )
            for split in ("train", "test", "extra")
        ]
    )


def usps():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.USPS(ROOT, train=train, download=True),
                name=f"USPS, {'train' if train else 'test'}",
                file="usps",
            )
            for train in (True, False)
        ]
    )


def celeba():
    return collect_download_configs(
        lambda: datasets.CelebA(ROOT, download=True),
        name="CelebA",
        file="celeba",
    )


def widerface():
    return collect_download_configs(
        lambda: datasets.WIDERFace(ROOT, download=True),
        name="WIDERFace",
        file="widerface",
    )


def kinetics():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda: datasets.Kinetics(
                    path.join(ROOT, f"Kinetics{num_classes}"),
                    frames_per_clip=1,
                    num_classes=num_classes,
                    split=split,
                    download=True,
                ),
                name=f"Kinetics, {num_classes}, {split}",
                file="kinetics",
            )
            for num_classes, split in itertools.product(("400", "600", "700"), ("train", "val"))
        ]
    )


def kitti():
    return itertools.chain(
        *[
            collect_download_configs(
                lambda train=train: datasets.Kitti(ROOT, train=train, download=True),
                name=f"Kitti, {'train' if train else 'test'}",
                file="kitti",
            )
            for train in (True, False)
        ]
    )


def make_parametrize_kwargs(download_configs):
    argvalues = []
    ids = []
    for config in download_configs:
        argvalues.append((config.url, config.md5))
        ids.append(config.id)

    return dict(argnames=("url", "md5"), argvalues=argvalues, ids=ids)


@pytest.mark.parametrize(
    **make_parametrize_kwargs(
        itertools.chain(
            places365(),
            caltech101(),
            caltech256(),
            cifar10(),
            cifar100(),
            # The VOC download server is unstable. See https://github.com/pytorch/vision/issues/2953 for details.
            # voc(),
            mnist(),
            fashion_mnist(),
            kmnist(),
            emnist(),
            qmnist(),
            omniglot(),
            phototour(),
            sbdataset(),
            sbu(),
            semeion(),
            stl10(),
            svhn(),
            usps(),
            celeba(),
            widerface(),
            kinetics(),
            kitti(),
        )
    )
)
def test_url_is_accessible(url, md5):
    retry(lambda: assert_url_is_accessible(url))


@pytest.mark.parametrize(**make_parametrize_kwargs(itertools.chain()))
def test_file_downloads_correctly(url, md5):
    retry(lambda: assert_file_downloads_correctly(url, md5))