def test_reset(self):
# Node and Marble have some arbitrary nonzero initial motion vars.
node = Node(torch.tensor([1.]), torch.tensor([2.]), torch.tensor([3.]),
4, NewtonianGravity(), 1, 1, 1, 1)
marble = Marble(torch.tensor([1.1]), torch.tensor([2.2]),
torch.tensor([3.3]), 4.4, NewtonianGravity(), None)
model = NenwinModel([node], [marble])
model.make_timestep(10)
model.make_timestep(10)
model.make_timestep(10)
# Verify that the motion variables have changed
self.assertFalse(check_close(node.pos, node.init_pos))
self.assertFalse(check_close(marble.vel, marble.init_vel))
self.assertFalse(check_close(marble.acc, marble.init_acc))
model.reset()
# Now they should be the original values again.
self.assertTrue(check_close(node.pos, node.init_pos))
self.assertTrue(check_close(node.vel, node.init_vel))
self.assertTrue(check_close(node.acc, node.init_acc))
self.assertTrue(check_close(marble.pos, marble.init_pos))
self.assertTrue(check_close(marble.vel, marble.init_vel))
self.assertTrue(check_close(marble.acc, marble.init_acc))
def test_reset_removed_added_marbles(self):
"""
NenwinModel.reset() should remove all Marbles
added with NenwinModel.add_marbles()
"""
original_marble = Marble(ZERO,
ZERO,
ZERO,
10,
NewtonianGravity(),
datum=None)
added_marble = original_marble.copy()
assert added_marble is not original_marble
model = NenwinModel([], [original_marble])
model.add_marbles([added_marble])
model.reset()
self.assertIn(original_marble, model.marbles)
self.assertIn(original_marble, model._NenwinModel__all_particles)
self.assertNotIn(added_marble, model.marbles)
self.assertNotIn(added_marble, model._NenwinModel__all_particles)
class LossFunctionCallWrongPredTestCase(unittest.TestCase):
"""
Testcases for NenwinLossFunction.__call__() for when another than the target
output-MarbleEaterNodes has eaten a Marble.
"""
def setUp(self):
"""
Sketch:
|
^|
y|
|
0| N_1 <M N_0
|
|
+-------------------------------
-10 0 10 x>
Marble M starts with moving towards N_1, but should arrive at N_0.
"""
self.pos_weight = 0.5
self.vel_weight = 0.5
self.nodes = (gen_node_at(torch.tensor([10.0, 0])),
gen_node_at(torch.tensor([-10.0, 0])))
self.marble = gen_marble_at(ZERO,
vel=torch.tensor([-3.0, 0]),
datum="original")
self.model = NenwinModel(self.nodes, [self.marble])
for _ in range(50): # Should be enough for the Marble to be eaten
self.model.make_timestep(0.1)
assert len(self.model.marbles) == 0, "Testcase badly designed."
self.loss_fun = NenwinLossFunction(self.nodes, self.model,
vel_weight=self.vel_weight,
pos_weight=self.pos_weight)
self.target_index = 0
self.wrong_node_index = 1
self.loss = self.loss_fun(self.target_index)
def test_value_loss_wrong_pred_no_marble_left(self):
"""
The loss should equal the velocity-weighted distance
of the target Node to the nearest Marble,
plus the *negative reciprocal* of the distance of the wrong node
(self.nodes[1]) to the Marble.
Case where no non-eaten Marble is available.
"""
target_node = self.nodes[self.target_index]
wrong_node = self.nodes[self.wrong_node_index]
expected = velocity_weighted_distance(target_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
expected -= 1/velocity_weighted_distance(wrong_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
torch.testing.assert_allclose(self.loss, expected)
def test_value_loss_wrong_pred_some_marble_left(self):
"""
The loss should equal the velocity-weighted distance
of the target Node to the nearest Marble,
plus the *negative reciprocal* of the distance of the wrong node
(self.nodes[1]) to the Marble.
Case where another non-eaten Marble is still available
at time of loss computation.
"""
second_marble = gen_marble_at(ZERO, datum="second")
self.model.add_marbles([second_marble])
self.loss = self.loss_fun(self.target_index)
target_node = self.nodes[self.target_index]
wrong_node = self.nodes[self.wrong_node_index]
expected = velocity_weighted_distance(target_node, second_marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
expected -= 1/velocity_weighted_distance(wrong_node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
torch.testing.assert_allclose(self.loss, expected)
def test_grads_no_error_wrong_pred(self):
"""
Case in which no prediction output is given.
All learnable parameters should have a gradient value
that does not cause errors with an optimizer.
Case where another non-eaten Marble is still available
at time of loss computation.
"""
second_marble = gen_marble_at(ZERO)
self.model.add_marbles([second_marble])
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
try:
self.loss.backward()
optim.step()
except RuntimeError as e:
self.fail(f"Error occurred during backprop/optim step: {e}")
def test_grads_value_wrong_pred(self):
"""
Case in which no prediction output is given.
The learnable parameters of the Marble should have a gradient value
that does affect the values of the weights.
The loss should be lower for the second run.
"""
second_marble = gen_marble_at(ZERO)
self.model.add_marbles([second_marble])
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
self.loss.backward()
self.assertIsNotNone(self.marble.init_pos.grad)
self.assertIsNotNone(self.marble.init_vel.grad)
self.assertIsNotNone(self.marble.mass.grad)
optim.step()
# Now verify the loss improved.
self.model.reset()
self.model.make_timestep(0.1)
self.model.add_marbles([second_marble])
new_loss = self.loss_fun(self.target_index)
self.assertLess(new_loss.item(), self.loss.item())
class LossFunctionCallNoPredTestCase(unittest.TestCase):
"""
Testcases for NenwinLossFunction.__call__() in case none of the
output-MarbleEaterNodes has eaten any Marble.
"""
def setUp(self):
self.pos_weight = 0.5
self.vel_weight = 0.5
self.node = gen_node_at(ZERO)
self.marble = gen_marble_at(torch.tensor([10., 10.]))
self.model = NenwinModel([self.node], [self.marble])
self.model.make_timestep(0.1) # too short to cross the distance
self.loss_fun = NenwinLossFunction([self.node], self.model,
vel_weight=self.vel_weight,
pos_weight=self.pos_weight)
assert self.node.num_marbles_eaten == 0, "Testcase badly desgined."
self.target_index = 0
self.loss = self.loss_fun(self.target_index)
def test_value_loss_no_output(self):
"""
If no Marble has been eaten, the loss
should equal the velocity-weighted distance
of the target Node to the nearest Marble.
"""
expected = velocity_weighted_distance(self.node, self.marble,
pos_weight=self.pos_weight,
vel_weight=self.vel_weight)
torch.testing.assert_allclose(self.loss, expected)
def test_grads_no_error_no_output(self):
"""
Case in which no prediction output is given.
All learnable parameters should have a gradient value
that does not cause errors with an optimizer.
Passes if no errors occurs.
"""
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
try:
self.loss.backward()
optim.step()
except RuntimeError as e:
self.fail(f"Error occurred during backprop/optim step: {e}")
def test_grads_value_no_output(self):
"""
Case in which no prediction output is given.
The learnable parameters of the Marble should have a gradient value
that does affect the values of the weights.
The loss should be lower for the second run.
"""
optim = torch.optim.Adam(self.model.parameters())
optim.zero_grad()
self.loss.backward()
self.assertIsNotNone(self.marble.init_pos.grad)
self.assertIsNotNone(self.marble.init_vel.grad)
self.assertIsNotNone(self.marble.mass.grad)
optim.step()
# Now verify the loss improved.
self.model.reset()
self.model.make_timestep(0.1)
new_loss = self.loss_fun(self.target_index)
self.assertLess(new_loss.item(), self.loss.item())
