Skip to content

Finish porting test_functional_tensor.py to pytest #3990

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 7, 2021
Merged

Finish porting test_functional_tensor.py to pytest #3990

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
23b6675
Finish porting test_functional_tensor.py
NicolasHug Jun 7, 2021
0d00e70
forgot to remove self
NicolasHug Jun 7, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
122 changes: 13 additions & 109 deletions test/test_functional_tensor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
import itertools
import os
import unittest
import colorsys
import math

Expand Down Expand Up @@ -31,113 +30,18 @@
NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC


class Tester(unittest.TestCase):

def setUp(self):
self.device = "cpu"

def _test_rotate_all_options(self, tensor, pil_img, scripted_rotate, centers):
img_size = pil_img.size
dt = tensor.dtype
for r in [NEAREST, ]:
for a in range(-180, 180, 17):
for e in [True, False]:
for c in centers:
for f in [None, [0, 0, 0], (1, 2, 3), [255, 255, 255], [1, ], (2.0, )]:
f_pil = int(f[0]) if f is not None and len(f) == 1 else f
out_pil_img = F.rotate(pil_img, angle=a, interpolation=r, expand=e, center=c, fill=f_pil)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.rotate, scripted_rotate]:
out_tensor = fn(tensor, angle=a, interpolation=r, expand=e, center=c, fill=f).cpu()

if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)

self.assertEqual(
out_tensor.shape,
out_pil_tensor.shape,
msg="{}: {} vs {}".format(
(img_size, r, dt, a, e, c), out_tensor.shape, out_pil_tensor.shape
))

num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 3% of different pixels
self.assertLess(
ratio_diff_pixels,
0.03,
msg="{}: {}\n{} vs \n{}".format(
(img_size, r, dt, a, e, c, f),
ratio_diff_pixels,
out_tensor[0, :7, :7],
out_pil_tensor[0, :7, :7]
)
)

def test_rotate(self):
# Tests on square image
scripted_rotate = torch.jit.script(F.rotate)

data = [_create_data(26, 26, device=self.device), _create_data(32, 26, device=self.device)]
for tensor, pil_img in data:

img_size = pil_img.size
centers = [
None,
(int(img_size[0] * 0.3), int(img_size[0] * 0.4)),
[int(img_size[0] * 0.5), int(img_size[0] * 0.6)]
]

for dt in [None, torch.float32, torch.float64, torch.float16]:

if dt == torch.float16 and torch.device(self.device).type == "cpu":
# skip float16 on CPU case
continue

if dt is not None:
tensor = tensor.to(dtype=dt)

self._test_rotate_all_options(tensor, pil_img, scripted_rotate, centers)

batch_tensors = _create_data_batch(26, 36, num_samples=4, device=self.device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)

center = (20, 22)
_test_fn_on_batch(
batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center
)
tensor, pil_img = data[0]
# assert deprecation warning and non-BC
with self.assertWarnsRegex(UserWarning, r"Argument resample is deprecated and will be removed"):
res1 = F.rotate(tensor, 45, resample=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
assert_equal(res1, res2)

# assert changed type warning
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationMode"):
res1 = F.rotate(tensor, 45, interpolation=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
assert_equal(res1, res2)


@unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
class CUDATester(Tester):

def setUp(self):
self.device = "cuda"

def test_scale_channel(self):
"""Make sure that _scale_channel gives the same results on CPU and GPU as
histc or bincount are used depending on the device.
"""
# TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
# only use bincount and remove that test.
size = (1_000,)
img_chan = torch.randint(0, 256, size=size).to('cpu')
scaled_cpu = F_t._scale_channel(img_chan)
scaled_cuda = F_t._scale_channel(img_chan.to('cuda'))
assert_equal(scaled_cpu, scaled_cuda.to('cpu'))
@needs_cuda
def test_scale_channel():
"""Make sure that _scale_channel gives the same results on CPU and GPU as
histc or bincount are used depending on the device.
"""
# TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
# only use bincount and remove that test.
size = (1_000,)
img_chan = torch.randint(0, 256, size=size).to('cpu')
scaled_cpu = F_t._scale_channel(img_chan)
scaled_cuda = F_t._scale_channel(img_chan.to('cuda'))
assert_equal(scaled_cpu, scaled_cuda.to('cpu'))


class TestRotate:
Expand Down Expand Up @@ -1271,4 +1175,4 @@ def test_ten_crop(device):


if __name__ == '__main__':
unittest.main()
pytest.main([__file__])