def test_ill_opts(self):
pred = torch.rand(1, 3, 5, 5, 5)
with self.assertRaisesRegex(ValueError, ""):
BendingEnergyLoss(reduction="unknown")(pred)
with self.assertRaisesRegex(ValueError, ""):
BendingEnergyLoss(reduction=None)(pred)
def test_shape(self, input_param, input_data, expected_val):
result = BendingEnergyLoss(**input_param).forward(**input_data)
np.testing.assert_allclose(result.detach().cpu().numpy(),
expected_val,
rtol=1e-5)
def test_ill_shape(self):
loss = BendingEnergyLoss()
# not in 3-d, 4-d, 5-d
with self.assertRaisesRegex(ValueError, ""):
loss.forward(torch.ones((1, 3)))
with self.assertRaisesRegex(ValueError, ""):
loss.forward(torch.ones((1, 3, 5, 5, 5, 5)))
# spatial_dim < 5
with self.assertRaisesRegex(ValueError, ""):
loss.forward(torch.ones((1, 3, 4, 5, 5)))
with self.assertRaisesRegex(ValueError, ""):
loss.forward(torch.ones((1, 3, 5, 4, 5)))
with self.assertRaisesRegex(ValueError, ""):
loss.forward(torch.ones((1, 3, 5, 5, 4)))
def test_ill_shape(self):
loss = BendingEnergyLoss()
# not in 3-d, 4-d, 5-d
with self.assertRaisesRegex(ValueError, "Expecting 3-d, 4-d or 5-d"):
loss.forward(torch.ones((1, 3), device=device))
with self.assertRaisesRegex(ValueError, "Expecting 3-d, 4-d or 5-d"):
loss.forward(torch.ones((1, 4, 5, 5, 5, 5), device=device))
with self.assertRaisesRegex(ValueError, "All spatial dimensions"):
loss.forward(torch.ones((1, 3, 4, 5, 5), device=device))
with self.assertRaisesRegex(ValueError, "All spatial dimensions"):
loss.forward(torch.ones((1, 3, 5, 4, 5)))
with self.assertRaisesRegex(ValueError, "All spatial dimensions"):
loss.forward(torch.ones((1, 3, 5, 5, 4)))
# number of vector components unequal to number of spatial dims
with self.assertRaisesRegex(ValueError, "Number of vector components"):
loss.forward(torch.ones((1, 2, 5, 5, 5)))
with self.assertRaisesRegex(ValueError, "Number of vector components"):
loss.forward(torch.ones((1, 2, 5, 5, 5)))