utils.py

import bz2
import contextlib
import gzip
import hashlib
import itertools
import lzma
import os
import os.path
import pathlib
import re
import sys
import tarfile
import urllib
import urllib.error
import urllib.request
import warnings
import zipfile
from typing import Any, Callable, Dict, IO, Iterable, Iterator, List, Optional, Tuple, TypeVar
from urllib.parse import urlparse

import numpy as np
import requests
import torch
from torch.utils.model_zoo import tqdm

from .._internally_replaced_utils import _download_file_from_remote_location, _is_remote_location_available

USER_AGENT = "pytorch/vision"


def _save_response_content(
    content: Iterator[bytes],
    destination: str,
    length: Optional[int] = None,
) -> None:
    with open(destination, "wb") as fh, tqdm(total=length) as pbar:
        for chunk in content:
            # filter out keep-alive new chunks
            if not chunk:
                continue

            fh.write(chunk)
            pbar.update(len(chunk))


def _urlretrieve(url: str, filename: str, chunk_size: int = 1024 * 32) -> None:
    with urllib.request.urlopen(urllib.request.Request(url, headers={"User-Agent": USER_AGENT})) as response:
        _save_response_content(iter(lambda: response.read(chunk_size), b""), filename, length=response.length)


def gen_bar_updater() -> Callable[[int, int, int], None]:
    warnings.warn("The function `gen_bar_update` is deprecated since 0.13 and will be removed in 0.15.")
    pbar = tqdm(total=None)

    def bar_update(count, block_size, total_size):
        if pbar.total is None and total_size:
            pbar.total = total_size
        progress_bytes = count * block_size
        pbar.update(progress_bytes - pbar.n)

    return bar_update


def calculate_md5(fpath: str, chunk_size: int = 1024 * 1024) -> str:
    # Setting the `usedforsecurity` flag does not change anything about the functionality, but indicates that we are
    # not using the MD5 checksum for cryptography. This enables its usage in restricted environments like FIPS. Without
    # it torchvision.datasets is unusable in these environments since we perform a MD5 check everywhere.
    md5 = hashlib.md5(**dict(usedforsecurity=False) if sys.version_info >= (3, 9) else dict())
    with open(fpath, "rb") as f:
        for chunk in iter(lambda: f.read(chunk_size), b""):
            md5.update(chunk)
    return md5.hexdigest()


def check_md5(fpath: str, md5: str, **kwargs: Any) -> bool:
    return md5 == calculate_md5(fpath, **kwargs)


def check_integrity(fpath: str, md5: Optional[str] = None) -> bool:
    if not os.path.isfile(fpath):
        return False
    if md5 is None:
        return True
    return check_md5(fpath, md5)


def _get_redirect_url(url: str, max_hops: int = 3) -> str:
    initial_url = url
    headers = {"Method": "HEAD", "User-Agent": USER_AGENT}

    for _ in range(max_hops + 1):
        with urllib.request.urlopen(urllib.request.Request(url, headers=headers)) as response:
            if response.url == url or response.url is None:
                return url

            url = response.url
    else:
        raise RecursionError(
            f"Request to {initial_url} exceeded {max_hops} redirects. The last redirect points to {url}."
        )


def _get_google_drive_file_id(url: str) -> Optional[str]:
    parts = urlparse(url)

    if re.match(r"(drive|docs)[.]google[.]com", parts.netloc) is None:
        return None

    match = re.match(r"/file/d/(?P<id>[^/]*)", parts.path)
    if match is None:
        return None

    return match.group("id")


def download_url(
    url: str, root: str, filename: Optional[str] = None, md5: Optional[str] = None, max_redirect_hops: int = 3
) -> None:
    """Download a file from a url and place it in root.

    Args:
        url (str): URL to download file from
        root (str): Directory to place downloaded file in
        filename (str, optional): Name to save the file under. If None, use the basename of the URL
        md5 (str, optional): MD5 checksum of the download. If None, do not check
        max_redirect_hops (int, optional): Maximum number of redirect hops allowed
    """
    root = os.path.expanduser(root)
    if not filename:
        filename = os.path.basename(url)
    fpath = os.path.join(root, filename)

    os.makedirs(root, exist_ok=True)

    # check if file is already present locally
    if check_integrity(fpath, md5):
        print("Using downloaded and verified file: " + fpath)
        return

    if _is_remote_location_available():
        _download_file_from_remote_location(fpath, url)
    else:
        # expand redirect chain if needed
        url = _get_redirect_url(url, max_hops=max_redirect_hops)

        # check if file is located on Google Drive
        file_id = _get_google_drive_file_id(url)
        if file_id is not None:
            return download_file_from_google_drive(file_id, root, filename, md5)

        # download the file
        try:
            print("Downloading " + url + " to " + fpath)
            _urlretrieve(url, fpath)
        except (urllib.error.URLError, OSError) as e:  # type: ignore[attr-defined]
            if url[:5] == "https":
                url = url.replace("https:", "http:")
                print("Failed download. Trying https -> http instead. Downloading " + url + " to " + fpath)
                _urlretrieve(url, fpath)
            else:
                raise e

    # check integrity of downloaded file
    if not check_integrity(fpath, md5):
        raise RuntimeError("File not found or corrupted.")


def list_dir(root: str, prefix: bool = False) -> List[str]:
    """List all directories at a given root

    Args:
        root (str): Path to directory whose folders need to be listed
        prefix (bool, optional): If true, prepends the path to each result, otherwise
            only returns the name of the directories found
    """
    root = os.path.expanduser(root)
    directories = [p for p in os.listdir(root) if os.path.isdir(os.path.join(root, p))]
    if prefix is True:
        directories = [os.path.join(root, d) for d in directories]
    return directories


def list_files(root: str, suffix: str, prefix: bool = False) -> List[str]:
    """List all files ending with a suffix at a given root

    Args:
        root (str): Path to directory whose folders need to be listed
        suffix (str or tuple): Suffix of the files to match, e.g. '.png' or ('.jpg', '.png').
            It uses the Python "str.endswith" method and is passed directly
        prefix (bool, optional): If true, prepends the path to each result, otherwise
            only returns the name of the files found
    """
    root = os.path.expanduser(root)
    files = [p for p in os.listdir(root) if os.path.isfile(os.path.join(root, p)) and p.endswith(suffix)]
    if prefix is True:
        files = [os.path.join(root, d) for d in files]
    return files


def _extract_gdrive_api_response(response, chunk_size: int = 32 * 1024) -> Tuple[bytes, Iterator[bytes]]:
    content = response.iter_content(chunk_size)
    first_chunk = None
    # filter out keep-alive new chunks
    while not first_chunk:
        first_chunk = next(content)
    content = itertools.chain([first_chunk], content)

    try:
        match = re.search("<title>Google Drive - (?P<api_response>.+?)</title>", first_chunk.decode())
        api_response = match["api_response"] if match is not None else None
    except UnicodeDecodeError:
        api_response = None
    return api_response, content


def download_file_from_google_drive(file_id: str, root: str, filename: Optional[str] = None, md5: Optional[str] = None):
    """Download a Google Drive file from  and place it in root.

    Args:
        file_id (str): id of file to be downloaded
        root (str): Directory to place downloaded file in
        filename (str, optional): Name to save the file under. If None, use the id of the file.
        md5 (str, optional): MD5 checksum of the download. If None, do not check
    """
    # Based on https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url

    root = os.path.expanduser(root)
    if not filename:
        filename = file_id
    fpath = os.path.join(root, filename)

    os.makedirs(root, exist_ok=True)

    if check_integrity(fpath, md5):
        print(f"Using downloaded {'and verified ' if md5 else ''}file: {fpath}")
        return

    url = "https://drive.google.com/uc"
    params = dict(id=file_id, export="download")
    with requests.Session() as session:
        response = session.get(url, params=params, stream=True)

        for key, value in response.cookies.items():
            if key.startswith("download_warning"):
                token = value
                break
        else:
            api_response, content = _extract_gdrive_api_response(response)
            token = "t" if api_response == "Virus scan warning" else None

        if token is not None:
            response = session.get(url, params=dict(params, confirm=token), stream=True)
            api_response, content = _extract_gdrive_api_response(response)

        if api_response == "Quota exceeded":
            raise RuntimeError(
                f"The daily quota of the file {filename} is exceeded and it "
                f"can't be downloaded. This is a limitation of Google Drive "
                f"and can only be overcome by trying again later."
            )

        _save_response_content(content, fpath)

    # In case we deal with an unhandled GDrive API response, the file should be smaller than 10kB and contain only text
    if os.stat(fpath).st_size < 10 * 1024:
        with contextlib.suppress(UnicodeDecodeError), open(fpath) as fh:
            text = fh.read()
            # Regular expression to detect HTML. Copied from https://stackoverflow.com/a/70585604
            if re.search(r"</?\s*[a-z-][^>]*\s*>|(&(?:[\w\d]+|#\d+|#x[a-f\d]+);)", text):
                warnings.warn(
                    f"We detected some HTML elements in the downloaded file. "
                    f"This most likely means that the download triggered an unhandled API response by GDrive. "
                    f"Please report this to torchvision at https://github.com/pytorch/vision/issues including "
                    f"the response:\n\n{text}"
                )

    if md5 and not check_md5(fpath, md5):
        raise RuntimeError(
            f"The MD5 checksum of the download file {fpath} does not match the one on record."
            f"Please delete the file and try again. "
            f"If the issue persists, please report this to torchvision at https://github.com/pytorch/vision/issues."
        )


def _extract_tar(from_path: str, to_path: str, compression: Optional[str]) -> None:
    with tarfile.open(from_path, f"r:{compression[1:]}" if compression else "r") as tar:
        tar.extractall(to_path)


_ZIP_COMPRESSION_MAP: Dict[str, int] = {
    ".bz2": zipfile.ZIP_BZIP2,
    ".xz": zipfile.ZIP_LZMA,
}


def _extract_zip(from_path: str, to_path: str, compression: Optional[str]) -> None:
    with zipfile.ZipFile(
        from_path, "r", compression=_ZIP_COMPRESSION_MAP[compression] if compression else zipfile.ZIP_STORED
    ) as zip:
        zip.extractall(to_path)


_ARCHIVE_EXTRACTORS: Dict[str, Callable[[str, str, Optional[str]], None]] = {
    ".tar": _extract_tar,
    ".zip": _extract_zip,
}
_COMPRESSED_FILE_OPENERS: Dict[str, Callable[..., IO]] = {
    ".bz2": bz2.open,
    ".gz": gzip.open,
    ".xz": lzma.open,
}
_FILE_TYPE_ALIASES: Dict[str, Tuple[Optional[str], Optional[str]]] = {
    ".tbz": (".tar", ".bz2"),
    ".tbz2": (".tar", ".bz2"),
    ".tgz": (".tar", ".gz"),
}


def _detect_file_type(file: str) -> Tuple[str, Optional[str], Optional[str]]:
    """Detect the archive type and/or compression of a file.

    Args:
        file (str): the filename

    Returns:
        (tuple): tuple of suffix, archive type, and compression

    Raises:
        RuntimeError: if file has no suffix or suffix is not supported
    """
    suffixes = pathlib.Path(file).suffixes
    if not suffixes:
        raise RuntimeError(
            f"File '{file}' has no suffixes that could be used to detect the archive type and compression."
        )
    suffix = suffixes[-1]

    # check if the suffix is a known alias
    if suffix in _FILE_TYPE_ALIASES:
        return (suffix, *_FILE_TYPE_ALIASES[suffix])

    # check if the suffix is an archive type
    if suffix in _ARCHIVE_EXTRACTORS:
        return suffix, suffix, None

    # check if the suffix is a compression
    if suffix in _COMPRESSED_FILE_OPENERS:
        # check for suffix hierarchy
        if len(suffixes) > 1:
            suffix2 = suffixes[-2]

            # check if the suffix2 is an archive type
            if suffix2 in _ARCHIVE_EXTRACTORS:
                return suffix2 + suffix, suffix2, suffix

        return suffix, None, suffix

    valid_suffixes = sorted(set(_FILE_TYPE_ALIASES) | set(_ARCHIVE_EXTRACTORS) | set(_COMPRESSED_FILE_OPENERS))
    raise RuntimeError(f"Unknown compression or archive type: '{suffix}'.\nKnown suffixes are: '{valid_suffixes}'.")


def _decompress(from_path: str, to_path: Optional[str] = None, remove_finished: bool = False) -> str:
    r"""Decompress a file.

    The compression is automatically detected from the file name.

    Args:
        from_path (str): Path to the file to be decompressed.
        to_path (str): Path to the decompressed file. If omitted, ``from_path`` without compression extension is used.
        remove_finished (bool): If ``True``, remove the file after the extraction.

    Returns:
        (str): Path to the decompressed file.
    """
    suffix, archive_type, compression = _detect_file_type(from_path)
    if not compression:
        raise RuntimeError(f"Couldn't detect a compression from suffix {suffix}.")

    if to_path is None:
        to_path = from_path.replace(suffix, archive_type if archive_type is not None else "")

    # We don't need to check for a missing key here, since this was already done in _detect_file_type()
    compressed_file_opener = _COMPRESSED_FILE_OPENERS[compression]

    with compressed_file_opener(from_path, "rb") as rfh, open(to_path, "wb") as wfh:
        wfh.write(rfh.read())

    if remove_finished:
        os.remove(from_path)

    return to_path


def extract_archive(from_path: str, to_path: Optional[str] = None, remove_finished: bool = False) -> str:
    """Extract an archive.

    The archive type and a possible compression is automatically detected from the file name. If the file is compressed
    but not an archive the call is dispatched to :func:`decompress`.

    Args:
        from_path (str): Path to the file to be extracted.
        to_path (str): Path to the directory the file will be extracted to. If omitted, the directory of the file is
            used.
        remove_finished (bool): If ``True``, remove the file after the extraction.

    Returns:
        (str): Path to the directory the file was extracted to.
    """
    if to_path is None:
        to_path = os.path.dirname(from_path)

    suffix, archive_type, compression = _detect_file_type(from_path)
    if not archive_type:
        return _decompress(
            from_path,
            os.path.join(to_path, os.path.basename(from_path).replace(suffix, "")),
            remove_finished=remove_finished,
        )

    # We don't need to check for a missing key here, since this was already done in _detect_file_type()
    extractor = _ARCHIVE_EXTRACTORS[archive_type]

    extractor(from_path, to_path, compression)
    if remove_finished:
        os.remove(from_path)

    return to_path


def download_and_extract_archive(
    url: str,
    download_root: str,
    extract_root: Optional[str] = None,
    filename: Optional[str] = None,
    md5: Optional[str] = None,
    remove_finished: bool = False,
) -> None:
    download_root = os.path.expanduser(download_root)
    if extract_root is None:
        extract_root = download_root
    if not filename:
        filename = os.path.basename(url)

    download_url(url, download_root, filename, md5)

    archive = os.path.join(download_root, filename)
    print(f"Extracting {archive} to {extract_root}")
    extract_archive(archive, extract_root, remove_finished)


def iterable_to_str(iterable: Iterable) -> str:
    return "'" + "', '".join([str(item) for item in iterable]) + "'"


T = TypeVar("T", str, bytes)


def verify_str_arg(
    value: T,
    arg: Optional[str] = None,
    valid_values: Optional[Iterable[T]] = None,
    custom_msg: Optional[str] = None,
) -> T:
    if not isinstance(value, torch._six.string_classes):
        if arg is None:
            msg = "Expected type str, but got type {type}."
        else:
            msg = "Expected type str for argument {arg}, but got type {type}."
        msg = msg.format(type=type(value), arg=arg)
        raise ValueError(msg)

    if valid_values is None:
        return value

    if value not in valid_values:
        if custom_msg is not None:
            msg = custom_msg
        else:
            msg = "Unknown value '{value}' for argument {arg}. Valid values are {{{valid_values}}}."
            msg = msg.format(value=value, arg=arg, valid_values=iterable_to_str(valid_values))
        raise ValueError(msg)

    return value


def _read_pfm(file_name: str, slice_channels: int = 2) -> np.ndarray:
    """Read file in .pfm format. Might contain either 1 or 3 channels of data.

    Args:
        file_name (str): Path to the file.
        slice_channels (int): Number of channels to slice out of the file.
            Useful for reading different data formats stored in .pfm files: Optical Flows, Stereo Disparity Maps, etc.
    """

    with open(file_name, "rb") as f:
        header = f.readline().rstrip()
        if header not in [b"PF", b"Pf"]:
            raise ValueError("Invalid PFM file")

        dim_match = re.match(rb"^(\d+)\s(\d+)\s$", f.readline())
        if not dim_match:
            raise Exception("Malformed PFM header.")
        w, h = (int(dim) for dim in dim_match.groups())

        scale = float(f.readline().rstrip())
        if scale < 0:  # little-endian
            endian = "<"
            scale = -scale
        else:
            endian = ">"  # big-endian

        data = np.fromfile(f, dtype=endian + "f")

    pfm_channels = 3 if header == b"PF" else 1

    data = data.reshape(h, w, pfm_channels).transpose(2, 0, 1)
    data = np.flip(data, axis=1)  # flip on h dimension
    data = data[:slice_channels, :, :]
    return data.astype(np.float32)