def test_snapshot_distribution(self):
"""Test that snapshotter correctly calls saves/restores snapshots."""
# Create a test network.
net1 = snt.Sequential([
networks.LayerNormMLP([10, 10]),
networks.MultivariateNormalDiagHead(1)
])
spec = specs.Array([10], dtype=np.float32)
tf2_utils.create_variables(net1, [spec])
# Save the test network.
directory = self.get_tempdir()
objects_to_save = {'net': net1}
snapshotter = tf2_savers.Snapshotter(objects_to_save,
directory=directory)
snapshotter.save()
# Reload the test network.
net2 = tf.saved_model.load(os.path.join(snapshotter.directory, 'net'))
inputs = tf2_utils.add_batch_dim(tf2_utils.zeros_like(spec))
with tf.GradientTape() as tape:
dist1 = net1(inputs)
loss1 = tf.math.reduce_sum(dist1.mean() + dist1.variance())
grads1 = tape.gradient(loss1, net1.trainable_variables)
with tf.GradientTape() as tape:
dist2 = net2(inputs)
loss2 = tf.math.reduce_sum(dist2.mean() + dist2.variance())
grads2 = tape.gradient(loss2, net2.trainable_variables)
assert all(tree.map_structure(np.allclose, list(grads1), list(grads2)))
def test_rnn_snapshot(self):
"""Test that snapshotter correctly calls saves/restores snapshots on RNNs."""
# Create a test network.
net = snt.LSTM(10)
spec = specs.Array([10], dtype=np.float32)
tf2_utils.create_variables(net, [spec])
# Test that if you add some postprocessing without rerunning
# create_variables, it still works.
wrapped_net = snt.DeepRNN([net, lambda x: x])
for net1 in [net, wrapped_net]:
# Save the test network.
directory = self.get_tempdir()
objects_to_save = {'net': net1}
snapshotter = tf2_savers.Snapshotter(objects_to_save,
directory=directory)
snapshotter.save()
# Reload the test network.
net2 = tf.saved_model.load(
os.path.join(snapshotter.directory, 'net'))
inputs = tf2_utils.add_batch_dim(tf2_utils.zeros_like(spec))
with tf.GradientTape() as tape:
outputs1, next_state1 = net1(inputs, net1.initial_state(1))
loss1 = tf.math.reduce_sum(outputs1)
grads1 = tape.gradient(loss1, net1.trainable_variables)
with tf.GradientTape() as tape:
outputs2, next_state2 = net2(inputs, net2.initial_state(1))
loss2 = tf.math.reduce_sum(outputs2)
grads2 = tape.gradient(loss2, net2.trainable_variables)
assert np.allclose(outputs1, outputs2)
assert np.allclose(tree.flatten(next_state1),
tree.flatten(next_state2))
assert all(
tree.map_structure(np.allclose, list(grads1), list(grads2)))