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Jun 5, 2021
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port tests in test_functional_tensor to pytest #3977

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port tests in test_functional_tensor to pytest
sahilg06 Jun 5, 2021
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format code in test_functional_tensor
sahilg06 Jun 5, 2021
6810f3d
format code in test_functional_tensor
sahilg06 Jun 5, 2021
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279 changes: 141 additions & 138 deletions test/test_functional_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,82 +36,6 @@ class Tester(unittest.TestCase):
def setUp(self):
self.device = "cpu"

def test_assert_image_tensor(self):
shape = (100,)
tensor = torch.rand(*shape, dtype=torch.float, device=self.device)

list_of_methods = [(F_t._get_image_size, (tensor, )), (F_t.vflip, (tensor, )),
(F_t.hflip, (tensor, )), (F_t.crop, (tensor, 1, 2, 4, 5)),
(F_t.adjust_brightness, (tensor, 0.)), (F_t.adjust_contrast, (tensor, 1.)),
(F_t.adjust_hue, (tensor, -0.5)), (F_t.adjust_saturation, (tensor, 2.)),
(F_t.center_crop, (tensor, [10, 11])), (F_t.five_crop, (tensor, [10, 11])),
(F_t.ten_crop, (tensor, [10, 11])), (F_t.pad, (tensor, [2, ], 2, "constant")),
(F_t.resize, (tensor, [10, 11])), (F_t.perspective, (tensor, [0.2, ])),
(F_t.gaussian_blur, (tensor, (2, 2), (0.7, 0.5))),
(F_t.invert, (tensor, )), (F_t.posterize, (tensor, 0)),
(F_t.solarize, (tensor, 0.3)), (F_t.adjust_sharpness, (tensor, 0.3)),
(F_t.autocontrast, (tensor, )), (F_t.equalize, (tensor, ))]

for func, args in list_of_methods:
with self.assertRaises(Exception) as context:
func(*args)

self.assertTrue('Tensor is not a torch image.' in str(context.exception))

def test_vflip(self):
script_vflip = torch.jit.script(F.vflip)

img_tensor, pil_img = _create_data(16, 18, device=self.device)
vflipped_img = F.vflip(img_tensor)
vflipped_pil_img = F.vflip(pil_img)
_assert_equal_tensor_to_pil(vflipped_img, vflipped_pil_img)

# scriptable function test
vflipped_img_script = script_vflip(img_tensor)
assert_equal(vflipped_img, vflipped_img_script)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=self.device)
_test_fn_on_batch(batch_tensors, F.vflip)

def test_hflip(self):
script_hflip = torch.jit.script(F.hflip)

img_tensor, pil_img = _create_data(16, 18, device=self.device)
hflipped_img = F.hflip(img_tensor)
hflipped_pil_img = F.hflip(pil_img)
_assert_equal_tensor_to_pil(hflipped_img, hflipped_pil_img)

# scriptable function test
hflipped_img_script = script_hflip(img_tensor)
assert_equal(hflipped_img, hflipped_img_script)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=self.device)
_test_fn_on_batch(batch_tensors, F.hflip)

def test_crop(self):
script_crop = torch.jit.script(F.crop)

img_tensor, pil_img = _create_data(16, 18, device=self.device)

test_configs = [
(1, 2, 4, 5), # crop inside top-left corner
(2, 12, 3, 4), # crop inside top-right corner
(8, 3, 5, 6), # crop inside bottom-left corner
(8, 11, 4, 3), # crop inside bottom-right corner
]

for top, left, height, width in test_configs:
pil_img_cropped = F.crop(pil_img, top, left, height, width)

img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)

img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=self.device)
_test_fn_on_batch(batch_tensors, F.crop, top=top, left=left, height=height, width=width)

def test_hsv2rgb(self):
scripted_fn = torch.jit.script(F_t._hsv2rgb)
shape = (3, 100, 150)
Expand Down Expand Up @@ -610,68 +534,6 @@ def test_rotate(self):
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
assert_equal(res1, res2)

def test_gaussian_blur(self):
small_image_tensor = torch.from_numpy(
np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
).permute(2, 0, 1).to(self.device)

large_image_tensor = torch.from_numpy(
np.arange(26 * 28, dtype="uint8").reshape((1, 26, 28))
).to(self.device)

scripted_transform = torch.jit.script(F.gaussian_blur)

# true_cv2_results = {
# # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
# "3_3_0.8": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.5)
# "3_3_0.5": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.8)
# "3_5_0.8": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.5)
# "3_5_0.5": ...
# # np_img2 = np.arange(26 * 28, dtype="uint8").reshape((26, 28))
# # cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7)
# "23_23_1.7": ...
# }
p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'gaussian_blur_opencv_results.pt')
true_cv2_results = torch.load(p)

for tensor in [small_image_tensor, large_image_tensor]:

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)

for ksize in [(3, 3), [3, 5], (23, 23)]:
for sigma in [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)]:

_ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
_sigma = sigma[0] if sigma is not None else None
shape = tensor.shape
gt_key = "{}_{}_{}__{}_{}_{}".format(
shape[-2], shape[-1], shape[-3],
_ksize[0], _ksize[1], _sigma
)
if gt_key not in true_cv2_results:
continue

true_out = torch.tensor(
true_cv2_results[gt_key]
).reshape(shape[-2], shape[-1], shape[-3]).permute(2, 0, 1).to(tensor)

for fn in [F.gaussian_blur, scripted_transform]:
out = fn(tensor, kernel_size=ksize, sigma=sigma)
torch.testing.assert_close(
out, true_out, rtol=0.0, atol=1.0, check_stride=False,
msg="{}, {}".format(ksize, sigma)
)


@unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
class CUDATester(Tester):
Expand Down Expand Up @@ -1141,5 +1003,146 @@ def test_adjust_gamma(device, dtype, config):
)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('func, args', [
(F_t._get_image_size, ()), (F_t.vflip, ()),
(F_t.hflip, ()), (F_t.crop, (1, 2, 4, 5)),
(F_t.adjust_brightness, (0., )), (F_t.adjust_contrast, (1., )),
(F_t.adjust_hue, (-0.5, )), (F_t.adjust_saturation, (2., )),
(F_t.center_crop, ([10, 11], )), (F_t.five_crop, ([10, 11], )),
(F_t.ten_crop, ([10, 11], )), (F_t.pad, ([2, ], 2, "constant")),
(F_t.resize, ([10, 11], )), (F_t.perspective, ([0.2, ])),
(F_t.gaussian_blur, ((2, 2), (0.7, 0.5))),
(F_t.invert, ()), (F_t.posterize, (0, )),
(F_t.solarize, (0.3, )), (F_t.adjust_sharpness, (0.3, )),
(F_t.autocontrast, ()), (F_t.equalize, ())
])
def test_assert_image_tensor(device, func, args):
shape = (100,)
tensor = torch.rand(*shape, dtype=torch.float, device=device)
with pytest.raises(Exception, match=r"Tensor is not a torch image."):
func(tensor, *args)


@pytest.mark.parametrize('device', cpu_and_gpu())
def test_vflip(device):
script_vflip = torch.jit.script(F.vflip)

img_tensor, pil_img = _create_data(16, 18, device=device)
vflipped_img = F.vflip(img_tensor)
vflipped_pil_img = F.vflip(pil_img)
_assert_equal_tensor_to_pil(vflipped_img, vflipped_pil_img)

# scriptable function test
vflipped_img_script = script_vflip(img_tensor)
assert_equal(vflipped_img, vflipped_img_script)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.vflip)


@pytest.mark.parametrize('device', cpu_and_gpu())
def test_hflip(device):
script_hflip = torch.jit.script(F.hflip)

img_tensor, pil_img = _create_data(16, 18, device=device)
hflipped_img = F.hflip(img_tensor)
hflipped_pil_img = F.hflip(pil_img)
_assert_equal_tensor_to_pil(hflipped_img, hflipped_pil_img)

# scriptable function test
hflipped_img_script = script_hflip(img_tensor)
assert_equal(hflipped_img, hflipped_img_script)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.hflip)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('top, left, height, width', [
(1, 2, 4, 5), # crop inside top-left corner
(2, 12, 3, 4), # crop inside top-right corner
(8, 3, 5, 6), # crop inside bottom-left corner
(8, 11, 4, 3), # crop inside bottom-right corner
])
def test_crop(device, top, left, height, width):
script_crop = torch.jit.script(F.crop)

img_tensor, pil_img = _create_data(16, 18, device=device)

pil_img_cropped = F.crop(pil_img, top, left, height, width)

img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)

img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)

batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.crop, top=top, left=left, height=height, width=width)


@pytest.mark.parametrize('device', cpu_and_gpu())
@pytest.mark.parametrize('image_size', ('small', 'large'))
@pytest.mark.parametrize('dt', [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize('ksize', [(3, 3), [3, 5], (23, 23)])
@pytest.mark.parametrize('sigma', [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
@pytest.mark.parametrize('fn', [F.gaussian_blur, torch.jit.script(F.gaussian_blur)])
def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):

# true_cv2_results = {
# # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
# "3_3_0.8": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.5)
# "3_3_0.5": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.8)
# "3_5_0.8": ...
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.5)
# "3_5_0.5": ...
# # np_img2 = np.arange(26 * 28, dtype="uint8").reshape((26, 28))
# # cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7)
# "23_23_1.7": ...
# }
p = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'gaussian_blur_opencv_results.pt')
true_cv2_results = torch.load(p)

if image_size == 'small':
tensor = torch.from_numpy(
np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
).permute(2, 0, 1).to(device)
else:
tensor = torch.from_numpy(
np.arange(26 * 28, dtype="uint8").reshape((1, 26, 28))
).to(device)

if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return

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

_ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
_sigma = sigma[0] if sigma is not None else None
shape = tensor.shape
gt_key = "{}_{}_{}__{}_{}_{}".format(
shape[-2], shape[-1], shape[-3],
_ksize[0], _ksize[1], _sigma
)
if gt_key not in true_cv2_results:
return

true_out = torch.tensor(
true_cv2_results[gt_key]
).reshape(shape[-2], shape[-1], shape[-3]).permute(2, 0, 1).to(tensor)

out = fn(tensor, kernel_size=ksize, sigma=sigma)
torch.testing.assert_close(
out, true_out, rtol=0.0, atol=1.0, check_stride=False,
msg="{}, {}".format(ksize, sigma)
)


if __name__ == '__main__':
unittest.main()