def test_perspective(self):
from torchvision.transforms import RandomPerspective
data = [
self._create_data(26, 34, device=self.device),
self._create_data(26, 26, device=self.device)
]
for tensor, pil_img in data:
scripted_tranform = torch.jit.script(F.perspective)
test_configs = [
[[[0, 0], [33, 0], [33, 25], [0, 25]],
[[3, 2], [32, 3], [30, 24], [2, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]],
[[0, 0], [33, 0], [33, 25], [0, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]],
[[5, 5], [30, 3], [33, 19], [4, 25]]],
]
n = 10
test_configs += [
RandomPerspective.get_params(pil_img.size[0], pil_img.size[1],
i / n) for i in range(n)
]
for r in [
0,
]:
for spoints, epoints in test_configs:
out_pil_img = F.perspective(pil_img,
startpoints=spoints,
endpoints=epoints,
interpolation=r)
out_pil_tensor = torch.from_numpy(
np.array(out_pil_img).transpose((2, 0, 1)))
for fn in [F.perspective, scripted_tranform]:
out_tensor = fn(tensor,
startpoints=spoints,
endpoints=epoints,
interpolation=r).cpu()
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 5% of different pixels
self.assertLess(ratio_diff_pixels,
0.05,
msg="{}: {}\n{} vs \n{}".format(
(r, spoints, epoints),
ratio_diff_pixels,
out_tensor[0, :7, :7],
out_pil_tensor[0, :7, :7]))
def test_perspective(self):
from torchvision.transforms import RandomPerspective
data = [
self._create_data(26, 34, device=self.device),
self._create_data(26, 26, device=self.device)
]
scripted_transform = torch.jit.script(F.perspective)
for tensor, pil_img in data:
test_configs = [
[[[0, 0], [33, 0], [33, 25], [0, 25]],
[[3, 2], [32, 3], [30, 24], [2, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]],
[[0, 0], [33, 0], [33, 25], [0, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]],
[[5, 5], [30, 3], [33, 19], [4, 25]]],
]
n = 10
test_configs += [
RandomPerspective.get_params(pil_img.size[0], pil_img.size[1],
i / n) for i in range(n)
]
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_perspective(tensor, pil_img, scripted_transform,
test_configs)
batch_tensors = self._create_data_batch(26,
36,
num_samples=4,
device=self.device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
for spoints, epoints in test_configs:
self._test_fn_on_batch(batch_tensors,
F.perspective,
startpoints=spoints,
endpoints=epoints,
interpolation=0)
def test_perspective(self):
from torchvision.transforms import RandomPerspective
data = [self._create_data(26, 34, device=self.device), self._create_data(26, 26, device=self.device)]
scripted_transform = torch.jit.script(F.perspective)
for tensor, pil_img in data:
test_configs = [
[[[0, 0], [33, 0], [33, 25], [0, 25]], [[3, 2], [32, 3], [30, 24], [2, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]], [[0, 0], [33, 0], [33, 25], [0, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]], [[5, 5], [30, 3], [33, 19], [4, 25]]],
]
n = 10
test_configs += [
RandomPerspective.get_params(pil_img.size[0], pil_img.size[1], i / n) for i in range(n)
]
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_perspective(tensor, pil_img, scripted_transform, test_configs)
batch_tensors = self._create_data_batch(26, 36, num_samples=4, device=self.device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
for spoints, epoints in test_configs:
self._test_fn_on_batch(
batch_tensors, F.perspective, startpoints=spoints, endpoints=epoints, interpolation=NEAREST
)
# assert changed type warning
spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
with self.assertWarnsRegex(UserWarning, r"Argument interpolation should be of type InterpolationModes"):
res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
self.assertTrue(res1.equal(res2))