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Port test resize in test_transforms to pytest #3952

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merged 3 commits into from
Jun 5, 2021
Merged

Port test resize in test_transforms to pytest #3952

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b3f3739
Port test resize in test_transforms to pytest
AnirudhDagar Jun 3, 2021
85dc2a5
Merge branch 'master' into refactor-test-resize
AnirudhDagar Jun 5, 2021
85cdd80
parameterize resize tests
AnirudhDagar Jun 5, 2021
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205 changes: 109 additions & 96 deletions test/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,31 +202,6 @@ def test_ten_crop(self):
self.assertEqual(len(results), 10)
self.assertEqual(results, expected_output)

def test_randomresized_params(self):
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
img = torch.ones(3, height, width)
to_pil_image = transforms.ToPILImage()
img = to_pil_image(img)
size = 100
epsilon = 0.05
min_scale = 0.25
for _ in range(10):
scale_min = max(round(random.random(), 2), min_scale)
scale_range = (scale_min, scale_min + round(random.random(), 2))
aspect_min = max(round(random.random(), 2), epsilon)
aspect_ratio_range = (aspect_min, aspect_min + round(random.random(), 2))
randresizecrop = transforms.RandomResizedCrop(size, scale_range, aspect_ratio_range)
i, j, h, w = randresizecrop.get_params(img, scale_range, aspect_ratio_range)
aspect_ratio_obtained = w / h
self.assertTrue((min(aspect_ratio_range) - epsilon <= aspect_ratio_obtained and
aspect_ratio_obtained <= max(aspect_ratio_range) + epsilon) or
aspect_ratio_obtained == 1.0)
self.assertIsInstance(i, int)
self.assertIsInstance(j, int)
self.assertIsInstance(h, int)
self.assertIsInstance(w, int)

def test_randomperspective(self):
for _ in range(10):
height = random.randint(24, 32) * 2
Expand Down Expand Up @@ -287,77 +262,6 @@ def test_randomperspective_fill(self):
with self.assertRaises(ValueError):
F.perspective(img_conv, startpoints, endpoints, fill=tuple([fill] * wrong_num_bands))

def test_resize(self):

input_sizes = [
# height, width
# square image
(28, 28),
(27, 27),
# rectangular image: h < w
(28, 34),
(29, 35),
# rectangular image: h > w
(34, 28),
(35, 29),
]
test_output_sizes_1 = [
# single integer
22, 27, 28, 36,
# single integer in tuple/list
[22, ], (27, ),
]
test_output_sizes_2 = [
# two integers
[22, 22], [22, 28], [22, 36],
[27, 22], [36, 22], [28, 28],
[28, 37], [37, 27], [37, 37]
]

for height, width in input_sizes:
img = Image.new("RGB", size=(width, height), color=127)

for osize in test_output_sizes_1:
for max_size in (None, 37, 1000):

t = transforms.Resize(osize, max_size=max_size)
result = t(img)

msg = "{}, {} - {} - {}".format(height, width, osize, max_size)
osize = osize[0] if isinstance(osize, (list, tuple)) else osize
# If size is an int, smaller edge of the image will be matched to this number.
# i.e, if height > width, then image will be rescaled to (size * height / width, size).
if height < width:
exp_w, exp_h = (int(osize * width / height), osize) # (w, h)
if max_size is not None and max_size < exp_w:
exp_w, exp_h = max_size, int(max_size * exp_h / exp_w)
self.assertEqual(result.size, (exp_w, exp_h), msg=msg)
elif width < height:
exp_w, exp_h = (osize, int(osize * height / width)) # (w, h)
if max_size is not None and max_size < exp_h:
exp_w, exp_h = int(max_size * exp_w / exp_h), max_size
self.assertEqual(result.size, (exp_w, exp_h), msg=msg)
else:
exp_w, exp_h = (osize, osize) # (w, h)
if max_size is not None and max_size < osize:
exp_w, exp_h = max_size, max_size
self.assertEqual(result.size, (exp_w, exp_h), msg=msg)

for height, width in input_sizes:
img = Image.new("RGB", size=(width, height), color=127)

for osize in test_output_sizes_2:
oheight, owidth = osize

t = transforms.Resize(osize)
result = t(img)

self.assertEqual((owidth, oheight), result.size)

with self.assertWarnsRegex(UserWarning, r"Anti-alias option is always applied for PIL Image input"):
t = transforms.Resize(osize, antialias=False)
t(img)

def test_random_crop(self):
height = random.randint(10, 32) * 2
width = random.randint(10, 32) * 2
Expand Down Expand Up @@ -1315,6 +1219,115 @@ def test_random_erasing(self):
t.__repr__()


def test_randomresized_params():
height = random.randint(24, 32) * 2
width = random.randint(24, 32) * 2
img = torch.ones(3, height, width)
to_pil_image = transforms.ToPILImage()
img = to_pil_image(img)
size = 100
epsilon = 0.05
min_scale = 0.25
for _ in range(10):
scale_min = max(round(random.random(), 2), min_scale)
scale_range = (scale_min, scale_min + round(random.random(), 2))
aspect_min = max(round(random.random(), 2), epsilon)
aspect_ratio_range = (aspect_min, aspect_min + round(random.random(), 2))
randresizecrop = transforms.RandomResizedCrop(size, scale_range, aspect_ratio_range)
i, j, h, w = randresizecrop.get_params(img, scale_range, aspect_ratio_range)
aspect_ratio_obtained = w / h
assert((min(aspect_ratio_range) - epsilon <= aspect_ratio_obtained and
aspect_ratio_obtained <= max(aspect_ratio_range) + epsilon) or
aspect_ratio_obtained == 1.0)
assert isinstance(i, int)
assert isinstance(j, int)
assert isinstance(h, int)
assert isinstance(w, int)


@pytest.mark.parametrize('height, width', [
# height, width
# square image
(28, 28),
(27, 27),
# rectangular image: h < w
(28, 34),
(29, 35),
# rectangular image: h > w
(34, 28),
(35, 29),
])
@pytest.mark.parametrize('osize', [
# single integer
22, 27, 28, 36,
# single integer in tuple/list
[22, ], (27, ),
])
@pytest.mark.parametrize('max_size', (None, 37, 1000))
def test_resize(height, width, osize, max_size):
img = Image.new("RGB", size=(width, height), color=127)

t = transforms.Resize(osize, max_size=max_size)
result = t(img)

msg = "{}, {} - {} - {}".format(height, width, osize, max_size)
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Just for info: we don't need these kind of error message anymore as they just print the compared values. Now that we're using pytest, we can just rely on pytest introspection. It's fine to leave as-is though ;)

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Awesome! I'll keep this in mind with my future PRs.

osize = osize[0] if isinstance(osize, (list, tuple)) else osize
# If size is an int, smaller edge of the image will be matched to this number.
# i.e, if height > width, then image will be rescaled to (size * height / width, size).
if height < width:
exp_w, exp_h = (int(osize * width / height), osize) # (w, h)
if max_size is not None and max_size < exp_w:
exp_w, exp_h = max_size, int(max_size * exp_h / exp_w)
assert result.size == (exp_w, exp_h), msg
elif width < height:
exp_w, exp_h = (osize, int(osize * height / width)) # (w, h)
if max_size is not None and max_size < exp_h:
exp_w, exp_h = int(max_size * exp_w / exp_h), max_size
assert result.size == (exp_w, exp_h), msg
else:
exp_w, exp_h = (osize, osize) # (w, h)
if max_size is not None and max_size < osize:
exp_w, exp_h = max_size, max_size
assert result.size == (exp_w, exp_h), msg


@pytest.mark.parametrize('height, width', [
# height, width
# square image
(28, 28),
(27, 27),
# rectangular image: h < w
(28, 34),
(29, 35),
# rectangular image: h > w
(34, 28),
(35, 29),
])
@pytest.mark.parametrize('osize', [
# two integers sequence output
[22, 22], [22, 28], [22, 36],
[27, 22], [36, 22], [28, 28],
[28, 37], [37, 27], [37, 37]
])
def test_resize_sequence_output(height, width, osize):
img = Image.new("RGB", size=(width, height), color=127)
oheight, owidth = osize

t = transforms.Resize(osize)
result = t(img)

assert (owidth, oheight) == result.size


def test_resize_antialias_error():
osize = [37, 37]
img = Image.new("RGB", size=(35, 29), color=127)

with pytest.warns(UserWarning, match=r"Anti-alias option is always applied for PIL Image input"):
t = transforms.Resize(osize, antialias=False)
t(img)


class TestPad:

def test_pad(self):
Expand Down