Port test/test_transforms_tensor.py to pytest

Currently, most tests in test/test_transforms_tensor.py rely on unittest.TestCase. Now that we support pytest, we want to remove the use of the unittest module.

Instructions

There are many tests in this file, so I bundled them in multiple related groups below. If you’re interested in working on this issue, please comment below with “I’m working on <group X>, <group Y>, etc…” so that others don’t pick the same tests as you do. Feel free to pick as many groups as you wish, but please don’t submit more than 2 groups per PR in order to keep the reviews manageable. Before picking a group, make sure it wasn’t picked by another contributor first. Thanks!!

How to port a test to pytest

Porting a test from unittest to pytest is usually fairly straightforward. For a typical example, see https://github.com/pytorch/vision/pull/3907/files:

• take the test method out of the Tester(unittest.TestCase) class and just declare it as a function
• Replace @unittest.skipIf with pytest.mark.skipif(cond, reason=...)
• remove any use of self.assertXYZ.
  • Typically assertEqual(a, b) can be replaced by assert a == b when a and b are pure python objects (scalars, tuples, lists), and otherwise we can rely on assert_equal which is already used in the file.
  • self.assertRaises should be replaced with the pytest.raises(Exp, match=...): context manager, as done in https://github.com/pytorch/vision/pull/3907/files. Same for warnings with pytest.warns
  • self.assertTrue should be replaced with a plain assert
• When a function uses for loops to tests multiple parameter values, one should usepytest.mark.parametrize instead, as done e.g. in https://github.com/pytorch/vision/pull/3907/files.
• It may make sense to keep related tests within a single class. Not all groups need a dedicated class though, it’s on a case-by-case basis.
• Important: a lot of these tests rely on self.device because they need to be run on both CPU and GPU. For these, use the cpu_and_gpu() from common_utils instead, e.g.:

https://github.com/pytorch/vision/blob/f7b4cb0438702f67cf71cdd7dd8057fc377fb816/test/test_functional_tensor.py#L845-L846

and you can just replace self.device by device in the test

• The tests that only need a CPU should use the cpu_only decorator from common_utils, and the tests that need a cuda device should use the needs_cuda decorator (unless they already use cpu_and_gpu()).

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• group A https://github.com/pytorch/vision/pull/3996 These ones could be bundled into a single test_random() function and be parametrized over func, method, fn_kwargs and match_kwargs.

  • test_random_horizontal_flip
  • test_random_vertical_flip
  • test_random_invert
  • test_random_posterize
  • test_random_solarize
  • test_random_adjust_sharpness
  • test_random_autocontrast
  • test_random_equalize

• group B https://github.com/pytorch/vision/pull/4008

  • test_color_jitter – make 5 new test functions and parametrize 4 of them (over brightness, contrast, saturation, hue)
  • test_pad – parametrize over m and mul
  • test_crop – can be split and parametrized over different things, or not.
  • test_center_crop – same

• group C https://github.com/pytorch/vision/pull/4010 These 2 below can probably be merged into _test_op_list_output (which should be renamed to e.g. test_x_crop) by parametrizing over func, method, out_length and fn_kwargs

  • test_five_crop
  • test_ten_crop
  • test_resize – parametrize over all for loop variables
  • test_resized_crop – same

• group D https://github.com/pytorch/vision/pull/4000

  • test_random_affine – split this into different parametrized functions (one for shear, one for scale, etc.)
  • test_random_rotate – parametrize over all loop variables
  • test_random_perspective – parametrize over all loop variables
  • test_to_grayscale – parametrize over meth_kwargs

• group E https://github.com/pytorch/vision/pull/4023

  • test_normalize
  • test_linear_transformation
  • test_compose
  • test_random_apply
  • test_gaussian_blur – parametrize over meth_kwargs

• group F https://github.com/pytorch/vision/pull/3996

  • test_random_erasing – maybe split this one into different functions, and parametrize over test_configs
  • test_convert_image_dtype – parametrize over all loop variables and convert the continue and the assertRaises into a pytest.xfail
  • test_autoaugment – parametrize over policy and fill

cc @pmeier @vfdev-5

[vivekkumar7089]

I would like to work on D and E.

[AnirudhDagar]

I’ll start working on group B and C.