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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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194 changes: 98 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 @@ -1831,6 +1735,104 @@ 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)


def test_resize():

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):
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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)
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

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

for osize in test_output_sizes_2:
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oheight, owidth = osize

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

assert (owidth, oheight) == result.size

with pytest.warns(UserWarning, match=r"Anti-alias option is always applied for PIL Image input"):
t = transforms.Resize(osize, antialias=False)
t(img)
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def test_adjust_brightness():
x_shape = [2, 2, 3]
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
Expand Down