def test_multitask_interleaving_trainer(self, distribution):
with distribution.scope():
tasks = [
test_utils.MockFooTask(params=test_utils.FooConfig(),
name="foo"),
test_utils.MockBarTask(params=test_utils.BarConfig(),
name="bar")
]
test_multitask = multitask.MultiTask(tasks=tasks)
test_optimizer = tf.keras.optimizers.SGD(0.1)
model = test_utils.MockMultiTaskModel()
sampler = task_sampler.UniformTaskSampler(
task_weights=test_multitask.task_weights)
test_trainer = interleaving_trainer.MultiTaskInterleavingTrainer(
multi_task=test_multitask,
multi_task_model=model,
optimizer=test_optimizer,
task_sampler=sampler)
results = test_trainer.train(
tf.convert_to_tensor(5, dtype=tf.int32))
self.assertContainsSubset(["training_loss", "bar_acc"],
results["bar"].keys())
self.assertContainsSubset(["training_loss", "foo_acc"],
results["foo"].keys())
self.assertNotIn("total_loss", results)
def test_multitask_evaluator(self, distribution):
with distribution.scope():
tasks = [
MockTask(params=cfg.TaskConfig(), name="bar"),
MockTask(params=cfg.TaskConfig(), name="foo")
]
test_multitask = multitask.MultiTask(tasks=tasks)
model = MockModel()
test_evaluator = evaluator.MultiTaskEvaluator(
task=test_multitask, model=model)
results = test_evaluator.evaluate(tf.convert_to_tensor(1, dtype=tf.int32))
self.assertContainsSubset(["validation_loss", "acc"], results["bar"].keys())
self.assertContainsSubset(["validation_loss", "acc"], results["foo"].keys())
self.assertEqual(results["bar"]["validation_loss"], 0.0)
self.assertEqual(results["foo"]["validation_loss"], 1.0)
def test_multitask_evaluator_numpy_metrics(self, distribution):
with distribution.scope():
tasks = [
MockTask(params=cfg.TaskConfig(), name="bar"),
MockTask(params=cfg.TaskConfig(), name="foo")
]
test_multitask = multitask.MultiTask(tasks=tasks)
model = MockModel()
test_evaluator = evaluator.MultiTaskEvaluator(
task=test_multitask, model=model)
results = test_evaluator.evaluate(tf.convert_to_tensor(5, dtype=tf.int32))
self.assertEqual(results["bar"]["counter"],
5. * distribution.num_replicas_in_sync)
self.assertEqual(results["foo"]["counter"],
5. * distribution.num_replicas_in_sync)
def test_multitask_joint_trainer(self, distribution):
with distribution.scope():
tasks = [
test_utils.MockFooTask(params=test_utils.FooConfig(), name="foo"),
test_utils.MockBarTask(params=test_utils.BarConfig(), name="bar")
]
task_weights = {"foo": 1.0, "bar": 1.0}
test_multitask = multitask.MultiTask(
tasks=tasks, task_weights=task_weights)
test_optimizer = tf.keras.optimizers.SGD(0.1)
model = test_utils.MockMultiTaskModel()
test_trainer = base_trainer.MultiTaskBaseTrainer(
multi_task=test_multitask,
multi_task_model=model,
optimizer=test_optimizer)
results = test_trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
self.assertContainsSubset(["training_loss", "bar_acc"],
results["bar"].keys())
self.assertContainsSubset(["training_loss", "foo_acc"],
results["foo"].keys())
