GitLab

* Added general reader for sn3 tensors in "pascalvincent" format

* Added class QMNIST into mnist.py

* QMNIST dataset: make some pt files smaller

* Change request from fmassa.

* read_sn3_pascalvincent_tensor: cse

* read_sn3_pascalvincent_tensor: check file size (when strict!=False)

* Fix lint

* More lint

* Add documentation and expose QMNIST to dataset namespace

* CelebA: track attr names, support split="all", code cleanup

* fix typo

Changes made in 7716aba5 broke calls to this method.

* added fake data

* fixed fake data

* renamed extract and download methods and added functionality

* added raw fake data

* refactored imagenet and added test

* flake8

* added fake devkit and mocked download_url

* reversed uncommenting

* added mock to CI

* fixed tests for imagefolder

* flake8

* modif of the STL10 loader

* missing space

* Fix STL10 repr

* Do not inherit from Cifar10

* Make it safer to inherit from VisionDataset

* WIP

* WIP: minor improvements

* Add tests

* Fix typo

* Use download_and_extract on caltech, cifar and omniglot

* Add a print message during extraction

* Remove EMNIST from test

* add USPS dataset

* minor fixes

* Improvements to the USPS dataset

Add it to the documentation, expose it to torchvision.datasets
and inherit from VisionDataset

* [RFC] Add support for joint transformations in VisionDataset

* Add joints transforms for VOC and SBD

Breaking change in SBD, the xy_transform has been renamed transforms. I think this is fine given that we have not released a version of torchvision that contains it

* fixed check integrity

* stylistic changes

* flake8

* added is_valid_file option

* small fixes

* fixes

* flake8 fixes

* some test

* flake8 fixes

* improvements

* modifications on tests
* fixes

* minor fix

* Fix wrong doc string

* bug fix

* fix inconsistent behavior for ~ expression

* remove duplicated code

* introduced new super class for all vision datasets

* Removed root from repr if dataset has no root

* reverted some overly-ambitious autoformatting

* reverted some overly-ambitious autoformatting

* added split attribute to repr of STL10 dataset

* made Python2 friendly and more robust

* Fixed call of the superclass constructor

* moved transform and target_transform back to the base classes

* added check if transforms are present before printing to avoid setting them within the constructor

* added missing transforms and target_transforms to base classes

* fixed linter error

str.endswith can take a single string, or a tuple of strings. This replaces a loop over each extension and a call to endswith with a single call to endswith passing in all the allowed extensions. This has the advantage that the loop over each extension is done in c rather than python, and the code is a little less verbose.

* Added Semantic Boundaries Dataset

* Updated docs and datasets importing module

* Improved docs

* initial commit

* fixed Python2 issue

* fixed naming incorrectness and Python2 compability

* fixed preparation of train folder

* removed detection dataset

* added docstring and repr

* moved import of scipy to make the import of torchvision independent of it

* improved conversion from class string to index

* removed support for other years than 2012

* removed accidentally added file

* moved emptying of split folder to avoid accidental deletion

* removed deletion of the images

* removed error conversion for Python2

* Aligned class indices with the indices identified by ImageFolder class

Raise an `IndexError` for `FakeData.__getitem__()` if the index would be out of range.

* Add Caltech101 and Caltech256

* Add information about default for target_type

* Fix docs

* Add function to download from Google Drive

* Add CelebA dataset

* Only import pandas when needed

* Addressing comments

* Remove trailing whitespace

* Replace torch.LongTensor with torch.as_tensor

* Fixes for PyTorch version of tqdm

* Flake

* Flake fix

* Update URL for EMNIST

Update the dataset and download URL for EMNIST to the url from the updated arxiv version.

* Update mnist.py

* Add KMNIST

* Add documentation

* Add KMNIST to module's __all__ list

* Add Cityscapes Dataset

* Rename 'label' to 'semantic' to make the meaning more clear

* Add support for gtCoarse target set