Open felixt56 opened 2 years ago
I have the same issue. Did you solve it? But I find this issue may help as an alternative: https://github.com/tensorflow/tensorflow/issues/41492
Unfortunately, not yet.
[https://repository-images.githubusercontent.com/190687137/70787000-14ec-11ea-84f7-6cc9843d119e]https://github.com/rasbt/python-machine-learning-book-3rd-edition/tree/master/ch15/downloading-celeba
python-machine-learning-book-3rd-edition/ch15/downloading-celeba at master · rasbt/python-machine-learning-book-3rd-edition · GitHubhttps://github.com/rasbt/python-machine-learning-book-3rd-edition/tree/master/ch15/downloading-celeba github.com The "Python Machine Learning (3rd edition)" book code repository - python-machine-learning-book-3rd-edition/ch15/downloading-celeba at master · rasbt/python-machine-learning-book-3rd-edition
may be it will help U
Regards
Felix Teplitsky RAFAEL Ltd.
From: HouyiDu @.***> Sent: Monday, March 28, 2022 0:19 To: tensorflow/datasets Cc: TEPLITSKY FELIX; Author Subject: [Marketing Mail] Re: [tensorflow/datasets] Can't download "seleb_a" (Issue #3855)
I have the same issue. Did you solve it?
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Short description Can't download "seleb_a"
Environment information
Operating System: <Ubuntu 20.04 focal (x86-64)>
Python version: <3.8.10>
tensorflow-datasets
/tfds-nightly
version: <4.5.2+nightly>tensorflow
/tf-nightly
version: <2.8.0>Does the issue still exists with the last
tfds-nightly
package (pip install --upgrade tfds-nightly
) ? YES Reproduction instructionsIf you share a colab, make sure to update the permissions to share it.
Link to logs If applicable, <link to gist with logs, stack trace>
Expected behavior I need celeb_a dataset
Additional context Output: build_celeba.py
2022-03-23 18:57:25.652770: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2022-03-23 18:57:25.652786: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 2022-03-23 18:57:26.961914: W tensorflow/core/platform/cloud/google_auth_provider.cc:184] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "NOT_FOUND: Could not locate the credentials file.". Retrieving token from GCE failed with "FAILED_PRECONDITION: Error executing an HTTP request: libcurl code 6 meaning 'Couldn't resolve host name', error details: Could not resolve host: metadata". tfds.core.DatasetInfo( name='celeb_a', full_name='celeb_a/2.0.1', description=""" CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including
5 landmark locations, 40 binary attributes annotations per image.
The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, and landmark (or facial part) localization.
Note: CelebA dataset may contain potential bias. The fairness indicators example goes into detail about several considerations to keep in mind while using the CelebA dataset. """, homepage='http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html', data_path='~/tensorflow_datasets/celeb_a/2.0.1', download_size=1.38 GiB, dataset_size=1.62 GiB, features=FeaturesDict({ 'attributes': FeaturesDict({ '5_o_Clock_Shadow': tf.bool, 'Arched_Eyebrows': tf.bool, 'Attractive': tf.bool, 'Bags_Under_Eyes': tf.bool, 'Bald': tf.bool, 'Bangs': tf.bool, 'Big_Lips': tf.bool, 'Big_Nose': tf.bool, 'Black_Hair': tf.bool, 'Blond_Hair': tf.bool, 'Blurry': tf.bool, 'Brown_Hair': tf.bool, 'Bushy_Eyebrows': tf.bool, 'Chubby': tf.bool, 'Double_Chin': tf.bool, 'Eyeglasses': tf.bool, 'Goatee': tf.bool, 'Gray_Hair': tf.bool, 'Heavy_Makeup': tf.bool, 'High_Cheekbones': tf.bool, 'Male': tf.bool, 'Mouth_Slightly_Open': tf.bool, 'Mustache': tf.bool, 'Narrow_Eyes': tf.bool, 'No_Beard': tf.bool, 'Oval_Face': tf.bool, 'Pale_Skin': tf.bool, 'Pointy_Nose': tf.bool, 'Receding_Hairline': tf.bool, 'Rosy_Cheeks': tf.bool, 'Sideburns': tf.bool, 'Smiling': tf.bool, 'Straight_Hair': tf.bool, 'Wavy_Hair': tf.bool, 'Wearing_Earrings': tf.bool, 'Wearing_Hat': tf.bool, 'Wearing_Lipstick': tf.bool, 'Wearing_Necklace': tf.bool, 'Wearing_Necktie': tf.bool, 'Young': tf.bool, }), 'image': Image(shape=(218, 178, 3), dtype=tf.uint8), 'landmarks': FeaturesDict({ 'lefteye_x': tf.int64, 'lefteye_y': tf.int64, 'leftmouth_x': tf.int64, 'leftmouth_y': tf.int64, 'nose_x': tf.int64, 'nose_y': tf.int64, 'righteye_x': tf.int64, 'righteye_y': tf.int64, 'rightmouth_x': tf.int64, 'rightmouth_y': tf.int64, }), }), supervised_keys=None, disable_shuffling=False, splits={ 'test':,
'train': ,
'validation': ,
},
citation="""@inproceedings{conf/iccv/LiuLWT15,
added-at = {2018-10-09T00:00:00.000+0200},
author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
biburl = {https://www.bibsonomy.org/bibtex/250e4959be61db325d2f02c1d8cd7bfbb/dblp},
booktitle = {ICCV},
crossref = {conf/iccv/2015},
ee = {http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.425},
interhash = {3f735aaa11957e73914bbe2ca9d5e702},
intrahash = {50e4959be61db325d2f02c1d8cd7bfbb},
isbn = {978-1-4673-8391-2},
keywords = {dblp},
pages = {3730-3738},
publisher = {IEEE Computer Society},
timestamp = {2018-10-11T11:43:28.000+0200},
title = {Deep Learning Face Attributes in the Wild.},
url = {http://dblp.uni-trier.de/db/conf/iccv/iccv2015.html#LiuLWT15},
year = 2015
}""",
)
Downloading and preparing dataset 1.38 GiB (download: 1.38 GiB, generated: 1.62 GiB, total: 3.00 GiB) to ~/tensorflow_datasets/celeb_a/2.0.1...
Dl Size...: 0 MiB [00:00, ? MiB/s] | 0/4 [00:00<?, ? url/s]
Dl Completed...: 0%| | 0/4 [00:00<?, ? url/s]
Traceback (most recent call last):
File "/media/a1/SW/python-machine-learning-book-3rd-edition/ch15/build_celeba.py", line 5, in
celeba_bldr.download_and_prepare()
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/dataset_builder.py", line 461, in download_and_prepare
self._download_and_prepare(
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/dataset_builder.py", line 1146, in _download_and_prepare
split_generators = self._split_generators( # pylint: disable=unexpected-keyword-arg
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/image/celeba.py", line 125, in _split_generators
downloaded_dirs = dl_manager.download({
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/download_manager.py", line 548, in download
return _map_promise(self._download, url_or_urls)
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/download_manager.py", line 766, in _map_promise
res = tf.nest.map_structure(lambda p: p.get(), all_promises) # Wait promises
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 914, in map_structure
structure[0], [func(x) for x in entries],
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 914, in
structure[0], [func( x) for x in entries],
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/download_manager.py", line 766, in
res = tf.nest.map_structure(lambda p: p.get(), all_promises) # Wait promises
File "/home/felixt/.local/lib/python3.8/site-packages/promise/promise.py", line 512, in get
return self._target_settled_value(_raise=True)
File "/home/felixt/.local/lib/python3.8/site-packages/promise/promise.py", line 516, in _target_settled_value
return self._target()._settled_value(_raise)
File "/home/felixt/.local/lib/python3.8/site-packages/promise/promise.py", line 226, in _settled_value
reraise(type(raise_val), raise_val, self._traceback)
File "/usr/lib/python3/dist-packages/six.py", line 703, in reraise
raise value
File "/home/felixt/.local/lib/python3.8/site-packages/promise/promise.py", line 844, in handle_future_result
resolve(future.result())
File "/usr/lib/python3.8/concurrent/futures/_base.py", line 437, in result
return self.get_result()
File "/usr/lib/python3.8/concurrent/futures/_base.py", line 389, in get_result
raise self._exception
File "/usr/lib/python3.8/concurrent/futures/thread.py", line 57, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/downloader.py", line 217, in _sync_download
with _open_url(url, verify=verify) as (response, iter_content):
File "/usr/lib/python3.8/contextlib.py", line 113, in enter
return next(self.gen)
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/downloader.py", line 279, in _open_with_requests
_assert_status(response)
File "/home/felixt/.local/lib/python3.8/site-packages/tensorflow_datasets/core/download/downloader.py", line 306, in _assert_status
raise DownloadError('Failed to get url {}. HTTP code: {}.'.format(
tensorflow_datasets.core.download.downloader.DownloadError: Failed to get url https://drive.google.com/uc?export=download&id=0B7EVK8r0v71pZjFTYXZWM3FlRnM&confirm=t. HTTP code: 404.