GitLab

* Add cmake config file generation for torchvision (Torch::Vision)

* Update README

* Update cmake config to export TorchVision::Vision target, adding dependencies

* Update readme

* Fix path issues and rename target to TorchVision::TorchVision

* Update README to reflect strict dependency on torch==1.4.0

* 1.4 or newer.

* Testing CI

* Disable tests for Pillow 7

* Allow forcing GPU build with FORCE_CUDA=1

This is convenient to e.g. build with GPU support inside a docker image

* Document FORCE_CUDA

Test installed version of package

To test against the installed version of the package (which is
preferably since this can catch installation file inclusion bugs) we
have to deal with quirks of coverage.py. By default the ecosystem is
set up to do development coverage tests (e.g. in src, or by using `pip
install -e .` or `python setup.py develop`).

We want to test against the version installed, to do this we have to
find the install path which we'll then pass to the `--cov` arg added by
`pytest-cov`. To do this, we have to cd out of the current folder, and
import the installed version and get it's install path (if we don't cd
out, then we end up getting the existing directory in the cwd since by
default cwd is on `sys.path`)

Once we have the install path, we pass that to the `--cov` of pytest,
however we also want to rewrite the paths for codecov to pick them up on
the website, if they don't have to code coverage with local paths, they
don't register is properly.

In order to that, we have a .coveragerc file that has the contents:

```
[paths]
source =
    torchvision
    /**/site-packages/torchvision
```

This tells codecov to treat all paths with those prefixes as the same,
so anything like
`/home/travis/miniconda/envs/test-environment/lib/python2.7/site-packages/torchvision/models/__init__.py`
would be treated the same as
`torchvision/models/__init__.py` after running the coverage combination
command. The first path seems to be special, in that all subsequent
paths are rewritten to that one.

Once we collect coverage, we then run `coverage run` to rewrite the
installation paths in the coverage file, `.coverage`, to those expected
by codecov.

Phew. And that's it.

N.B: Whilst adding test/__init__.py does solve the issue, it also results in
PWD being added to the path, and then we'll test the development version
of the package, and not the installed version (see
https://docs.pytest.org/en/latest/goodpractices.html#tests-as-part-of-application-code)

Closes #309

* added Whiten transform and relative section in README

* added Whiten transform and its section in README

* fixed lint

* fixed lint again

* added linear transformation and whitening test unitary covariance

* fixed lint and test

* commit before rebasing

* before rebase

* added assertion to check matrix size

* fix readme

* added space around operator

* replace assert with raise ValueError, fix spaces and readme

update SVHN docs to note label change for digit 0

* add Inception and Densenet;
* document that pretrained VGG is available;
* fix a typo.

Some links were not displaying properly

* added FashionMNIST dataset

* documentation

* fixed formatting

* fixed formatting

* add pad_value argument

* add argument to save_image

* fix space

* requested change of order

* update docs

* Fixed border missing on bottom and right side using make_grid

* Add support for Transforms.Scale([h, w]) with specific height and width

if self.size is a int then scale the image with the shorter side,
otherwise if self.size is a list then scale the image to self.size
directly

* Add assert of size and doc for README

Add assert of size and doc for README

* Fix linter problem

Fix linter problem

* Add test for Scale

Add test for Scale

* Add both tuple and list support for Scale.size

Add both tuple and list support for Scale.size

* Add order of Scale.size in document and test case for list type of Scale.size

Add order of Scale.size in document and test case for list type of
Scale.size

is already downloaded* for CIFAR and STL10

Fixed a wrong folder name.

Install pytorch with conda. Most of .travis.yml copied from the example
provided by the conda folks at:

https://conda.io/docs/travis.html