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_fluid_tf(self):
world = World()
fluid = Fluid(Domain([16, 16]))
world.add(fluid)
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)