def test_fluid_tf_equality(self):
tf.reset_default_graph()
_sess = tf.InteractiveSession()
for domain in [
Domain([8, 6], boundaries=OPEN),
Domain([8, 6], boundaries=STICKY),
Domain([8, 6], boundaries=SLIPPERY),
Domain([8, 6], boundaries=PERIODIC),
Domain([8, 6], boundaries=[PERIODIC, [OPEN, STICKY]])
]:
print('Comparing on domain %s' % (domain.boundaries, ))
np_fluid = Fluid(domain,
density=Noise(),
velocity=Noise(),
batch_size=10)
tf_fluid = constant(np_fluid)
physics = IncompressibleFlow(conserve_density=False)
for _ in range(3):
np_fluid = physics.step(np_fluid, 1.0)
tf_fluid = physics.step(tf_fluid, 1.0)
for np_tensor, tf_tensor in zip(struct.flatten(np_fluid),
struct.flatten(tf_fluid)):
tf_eval = tf_tensor.eval()
numpy.testing.assert_almost_equal(np_tensor,
tf_eval,
decimal=5)
def test_gradient_batch_independence(self):
session = Session(None) # Used to run the TensorFlow graph
world = World()
fluid = world.add(Fluid(Domain([40, 32], boundaries=CLOSED),
buoyancy_factor=0.1,
batch_size=2),
physics=IncompressibleFlow())
world.add(
Inflow(Sphere(center=numpy.array([[5, 4], [5, 8]]), radius=3),
rate=0.2))
fluid.velocity = variable(fluid.velocity) # create TensorFlow variable
# fluid.velocity *= 0
initial_state = fluid.state # Remember the state at t=0 for later visualization
session.initialize_variables()
for frame in range(3):
world.step(dt=1.5)
target = session.run(fluid.density).data[0, ...]
loss = tf.nn.l2_loss(fluid.density.data[1, ...] - target)
self_loss = tf.nn.l2_loss(fluid.density.data[0, ...] - target)
# loss = self_loss
optim = tf.train.GradientDescentOptimizer(
learning_rate=0.2).minimize(loss)
session.initialize_variables()
for optim_step in range(3):
_, loss_value, sl_value = session.run([optim, loss, self_loss])
staggered_velocity = session.run(
initial_state.velocity).staggered_tensor()
numpy.testing.assert_equal(staggered_velocity[0, ...], 0)
assert numpy.all(~numpy.isnan(staggered_velocity))
def test_tf_worldgraph(self):
tf.reset_default_graph()
world = World()
fluid = world.add(Fluid(Domain([16, 16])),
physics=IncompressibleFlow())
tf_bake_graph(world,
Session(Scene.create('data', copy_calling_script=False)))
world.step()
self.assertIsInstance(fluid.state, Fluid)
self.assertIsInstance(fluid.state.density.data, numpy.ndarray)
def test_fluid_tf(self):
tf.reset_default_graph()
world = World()
fluid = Fluid(Domain([16, 16]))
world.add(fluid, physics=IncompressibleFlow())
world.add(Inflow(Sphere((8, 8), radius=4)))
world.add(Obstacle(box[4:16, 0:8]))
fluid_in = fluid.copied_with(density=placeholder, velocity=placeholder)
fluid_out = world.step(fluid_in)
self.assertIsInstance(fluid_out, Fluid)
session = Session(Scene.create('data', copy_calling_script=False))
fluid = session.run(fluid_out, {fluid_in: fluid})
fluid = session.run(fluid_out, {fluid_in: fluid})
self.assertIsInstance(fluid, Fluid)
def test_precision_16(self):
try:
math.set_precision(16)
fluid = Fluid(Domain([16, 16]), density=math.maximum(0, Noise()))
fluid = variable(fluid)
self.assertEqual(fluid.density.data.dtype.as_numpy_dtype,
numpy.float16)
self.assertEqual(
fluid.velocity.unstack()[0].data.dtype.as_numpy_dtype,
numpy.float16)
fluid = IncompressibleFlow().step(fluid, dt=1.0)
self.assertEqual(fluid.density.data.dtype.as_numpy_dtype,
numpy.float16)
self.assertEqual(
fluid.velocity.unstack()[0].data.dtype.as_numpy_dtype,
numpy.float16)
finally:
math.set_precision(32) # Reset environment