2024-02-08 nightly release (81e2831)

Author: pytorchbot

.github/workflows/tests-schedule.yml

@@ -36,7 +36,7 @@ jobs:
         run: pip install --no-build-isolation --editable .
 
       - name: Install all optional dataset requirements
-        run: pip install scipy pycocotools lmdb requests
+        run: pip install scipy pycocotools lmdb gdown
 
       - name: Install tests requirements
         run: pip install pytest

mypy.ini

@@ -142,3 +142,7 @@ ignore_missing_imports = True
 [mypy-h5py.*]
 
 ignore_missing_imports = True
+
+[mypy-gdown.*]
+
+ignore_missing_imports = True

setup.py

@@ -59,7 +59,6 @@ def write_version_file():
 
 requirements = [
     "numpy",
-    "requests",
     pytorch_dep,
 ]
 

torchvision/datasets/caltech.py

@@ -30,6 +30,10 @@ class Caltech101(VisionDataset):
         download (bool, optional): If true, downloads the dataset from the internet and
             puts it in root directory. If dataset is already downloaded, it is not
             downloaded again.
+
+            .. warning::
+
+                To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
     """
 
     def __init__(

torchvision/datasets/celeba.py

@@ -38,6 +38,10 @@ class CelebA(VisionDataset):
         download (bool, optional): If true, downloads the dataset from the internet and
             puts it in root directory. If dataset is already downloaded, it is not
             downloaded again.
+
+            .. warning::
+
+                To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
     """
 
     base_folder = "celeba"

torchvision/datasets/pcam.py

@@ -25,6 +25,10 @@ class PCAM(VisionDataset):
          target_transform (callable, optional): A function/transform that takes in the target and transforms it.
          download (bool, optional): If True, downloads the dataset from the internet and puts it into ``root/pcam``. If
              dataset is already downloaded, it is not downloaded again.
+
+             .. warning::
+
+                To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
     """
 
     _FILES = {

torchvision/datasets/utils.py

@@ -1,8 +1,6 @@
 import bz2
-import contextlib
 import gzip
 import hashlib
-import itertools
 import lzma
 import os
 import os.path
@@ -13,13 +11,11 @@
 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, Union
 from urllib.parse import urlparse
 
 import numpy as np
-import requests
 import torch
 from torch.utils.model_zoo import tqdm
 
@@ -191,22 +187,6 @@ def list_files(root: Union[str, pathlib.Path], suffix: str, prefix: bool = False
     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: Union[str, pathlib.Path],
@@ -221,7 +201,12 @@ def download_file_from_google_drive(
         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
+    try:
+        import gdown
+    except ModuleNotFoundError:
+        raise RuntimeError(
+            "To download files from GDrive, 'gdown' is required. You can install it with 'pip install gdown'."
+        )
 
     root = os.path.expanduser(root)
     if not filename:
@@ -234,51 +219,10 @@ def download_file_from_google_drive(
         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)
+    gdown.download(id=file_id, output=fpath, quiet=False, user_agent=USER_AGENT)
 
-        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."
-        )
+    if not check_integrity(fpath, md5):
+        raise RuntimeError("File not found or corrupted.")
 
 
 def _extract_tar(

torchvision/datasets/widerface.py

@@ -34,6 +34,10 @@ class WIDERFace(VisionDataset):
             puts it in root directory. If dataset is already downloaded, it is not
             downloaded again.
 
+            .. warning::
+
+                To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
+
     """
 
     BASE_FOLDER = "widerface"

torchvision/transforms/functional.py

@@ -8,6 +8,7 @@
 import numpy as np
 import torch
 from PIL import Image
+from PIL.Image import Image as PILImage
 from torch import Tensor
 
 try:
@@ -123,7 +124,7 @@ def _is_numpy_image(img: Any) -> bool:
     return img.ndim in {2, 3}
 
 
-def to_tensor(pic) -> Tensor:
+def to_tensor(pic: Union[PILImage, np.ndarray]) -> Tensor:
     """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
     This function does not support torchscript.