def test_compute_metrics_correct(self):
"""Tests output when logit outputs indicate correct classification."""
logits, labels_correct = self._create_logits_labels(True)
logits = training._shard_batch(logits)
labels_correct = training._shard_batch(labels_correct)
p_compute_metrics = jax.pmap(utils.compute_metrics, axis_name='batch')
metrics = p_compute_metrics(logits, labels_correct)
loss = metrics['loss']
accuracy = metrics['accuracy']
with self.subTest(name='loss_type'):
self.assertIsInstance(loss, jnp.ndarray)
with self.subTest(name='loss_len'):
self.assertEqual(loss.size, 1)
with self.subTest(name='loss_values'):
self.assertGreaterEqual(loss.all(), 0)
with self.subTest(name='accuracy_type'):
self.assertIsInstance(accuracy, jnp.ndarray)
with self.subTest(name='accuracy_Len'):
self.assertEqual(accuracy.size, 1)
with self.subTest(name='accuracy_values'):
self.assertAlmostEqual(accuracy.all(), 1.0)
def test_train_one_step(self):
"""Tests training loop over one step."""
iterator = self._dataset.get_train()
batch = next(iterator)
state = jax_utils.replicate(self._state)
optimizer = jax_utils.replicate(self._optimizer.create(self._model))
self._rng, step_key = jax.random.split(self._rng)
batch = training._shard_batch(batch)
sharded_keys = common_utils.shard_prng_key(step_key)
p_train_step = jax.pmap(functools.partial(
training.train_step, learning_rate_fn=self._learning_rate_fn),
axis_name='batch')
_, _, loss, gradient_norm = p_train_step(optimizer, batch,
sharded_keys, state)
loss = jnp.mean(loss)
gradient_norm = jax_utils.unreplicate(gradient_norm)
with self.subTest(name='test_loss_range'):
self.assertBetween(loss, self._min_loss, self._max_loss)
with self.subTest(name='test_gradient_norm'):
self.assertGreaterEqual(gradient_norm, 0)
def test_eval_batch(self):
"""Tests model per-batch evaluation function."""
state = jax_utils.replicate(self._state)
optimizer = jax_utils.replicate(self._optimizer.create(self._model))
iterator = self._dataset.get_test()
batch = next(iterator)
batch = training._shard_batch(batch)
metrics = jax.pmap(training._eval_step,
axis_name='batch')(optimizer.target, state, batch)
loss = jnp.mean(metrics['loss'])
accuracy = jnp.mean(metrics['accuracy'])
with self.subTest(name='test_eval_batch_loss'):
self.assertBetween(loss, self._min_loss, self._max_loss)
with self.subTest(name='test_eval_batch_accuracy'):
self.assertBetween(accuracy, 0., 1.)
