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_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