def test_deg2rad(self):
# generate input data
x_deg = 180. * torch.rand(2, 3, 4)
# convert radians/degrees
x_rad = tgm.deg2rad(x_deg)
x_rad_to_deg = tgm.rad2deg(x_rad)
# compute error
error = utils.compute_mse(x_deg, x_rad_to_deg)
self.assertAlmostEqual(error.item(), 0.0, places=4)
# functional
self.assertTrue(torch.allclose(x_rad, tgm.DegToRad()(x_deg)))
def batch_polygonToRotRectangle(bbox):
"""
:param bbox: The polygon stored in format [x1, y1, x2, y2, x3, y3, x4, y4]
:return: Rotated Rectangle in format [cx, cy, w, h, theta]
"""
bbox = torch.stack([bbox[:, 0::2], bbox[:, 1::2]], dim=1)
angle = torch.atan2(-(bbox[:, 0, 1] - bbox[:, 0, 0]),
bbox[:, 1, 1] - bbox[:, 1, 0])
center = torch.zeros(bbox.size(0),
2,
1,
dtype=bbox.dtype,
device=bbox.device)
for i in range(4):
center[:, 0, 0] += bbox[:, 0, i]
center[:, 1, 0] += bbox[:, 1, i]
center = center / 4.0
R = torch.stack([
torch.cos(angle), -torch.sin(angle),
torch.sin(angle),
torch.cos(angle)
],
dim=1)
R = R.reshape(-1, 2, 2)
normalized = torch.matmul(R.transpose(2, 1), bbox - center)
if bbox.size(0) == 0:
return torch.empty((0, 5), dtype=bbox.dtype, device=bbox.device)
xmin = torch.min(normalized[:, 0, :], dim=1)[0]
xmax = torch.max(normalized[:, 0, :], dim=1)[0]
ymin = torch.min(normalized[:, 1, :], dim=1)[0]
ymax = torch.max(normalized[:, 1, :], dim=1)[0]
w = xmax - xmin
h = ymax - ymin
center = center.squeeze(-1)
center_x = center[:, 0]
center_y = center[:, 1]
new_box = torch.stack([center_x, center_y, w, h, -tgm.rad2deg(angle)],
dim=1)
return new_box
def test_deg2rad(batch_shape, device_type):
# generate input data
x_deg = 180. * torch.rand(batch_shape)
x_deg = x_deg.to(torch.device(device_type))
# convert radians/degrees
x_rad = tgm.deg2rad(x_deg)
x_rad_to_deg = tgm.rad2deg(x_rad)
# compute error
error = utils.compute_mse(x_deg, x_rad_to_deg)
assert pytest.approx(error.item(), 0.0)
# functional
assert torch.allclose(x_rad, tgm.DegToRad()(x_deg))
assert gradcheck(tgm.deg2rad, (utils.tensor_to_gradcheck_var(x_deg), ),
raise_exception=True)
def test_rad2deg(batch_shape, device_type):
# generate input data
x_rad = tgm.pi * torch.rand(batch_shape)
x_rad = x_rad.to(torch.device(device_type))
# convert radians/degrees
x_deg = tgm.rad2deg(x_rad)
x_deg_to_rad = tgm.deg2rad(x_deg)
# compute error
error = utils.compute_mse(x_rad, x_deg_to_rad)
assert pytest.approx(error.item(), 0.0)
# functional
assert torch.allclose(x_deg, tgm.RadToDeg()(x_rad))
# evaluate function gradient
assert gradcheck(tgm.rad2deg, (utils.tensor_to_gradcheck_var(x_rad), ),
raise_exception=True)
