def test_tensor_bhw2(self, device):
height, width = 3, 4
grid = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=True).to(device)
expected = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=False).to(device)
grid_norm = kornia.denormalize_pixel_coordinates(grid, height, width)
assert_allclose(grid_norm, expected)
def test_jit(self, device):
@torch.jit.script
def op_script(img: torch.Tensor, height: int, width: int) -> torch.Tensor:
return kornia.geometry.denormalize_pixel_coordinates(img, height, width)
height, width = 3, 4
grid = kornia.utils.create_meshgrid(height, width, normalized_coordinates=True).to(device)
actual = op_script(grid, height, width)
expected = kornia.denormalize_pixel_coordinates(grid, height, width)
assert_close(actual, expected)
def test_list(self, device):
height, width = 3, 4
grid = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=True).to(device)
grid = grid.contiguous().view(-1, 2)
expected = kornia.utils.create_meshgrid(
height, width, normalized_coordinates=False).to(device)
expected = expected.contiguous().view(-1, 2)
grid_norm = kornia.denormalize_pixel_coordinates(grid, height, width)
assert_allclose(grid_norm, expected)
def op_script(input: torch.Tensor, height: int,
width: int) -> torch.Tensor:
return kornia.denormalize_pixel_coordinates(input, height, width)
