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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for multitask.evaluator."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from official.core import base_task
from official.core import config_definitions as cfg
from official.modeling.multitask import evaluator
def all_strategy_combinations():
return combinations.combine(
distribution=[
strategy_combinations.default_strategy,
strategy_combinations.cloud_tpu_strategy,
strategy_combinations.one_device_strategy_gpu,
],
mode="eager",
)
class MockModel(tf_keras.Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.dense = tf_keras.layers.Dense(1)
def call(self, inputs):
print(inputs, type(inputs))
if "y" in inputs:
self.add_loss(tf.zeros((1,), dtype=tf.float32))
else:
self.add_loss(tf.ones((1,), dtype=tf.float32))
return self.dense(inputs["x"])
class MockTask(base_task.Task):
"""Mock task object for testing."""
def build_metrics(self, training: bool = True):
del training
return [tf_keras.metrics.Accuracy(name="acc")]
def build_inputs(self, params):
def generate_data(_):
x = tf.zeros(shape=(2,), dtype=tf.float32)
label = tf.zeros([1], dtype=tf.int32)
if self.name == "bar":
return dict(x=x, y=x), label
else:
return dict(x=x), label
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.prefetch(buffer_size=1).batch(2, drop_remainder=True)
def validation_step(self, inputs, model: tf_keras.Model, metrics=None):
logs = super().validation_step(inputs, model, metrics)
logs["counter"] = tf.ones((1,), dtype=tf.float32)
return logs
def aggregate_logs(self, state, step_outputs):
if state is None:
state = {}
for key, value in step_outputs.items():
if key not in state:
state[key] = []
state[key].append(
np.concatenate([np.expand_dims(v.numpy(), axis=0) for v in value]))
return state
def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
for k, v in aggregated_logs.items():
aggregated_logs[k] = np.sum(np.stack(v, axis=0))
return aggregated_logs
class EvaluatorTest(tf.test.TestCase, parameterized.TestCase):
@combinations.generate(all_strategy_combinations())
def test_multitask_evaluator(self, distribution):
with distribution.scope():
tasks = [
MockTask(params=cfg.TaskConfig(), name="bar"),
MockTask(params=cfg.TaskConfig(), name="foo")
]
model = MockModel()
test_evaluator = evaluator.MultiTaskEvaluator(
eval_tasks=tasks, 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)
@combinations.generate(all_strategy_combinations())
def test_multitask_evaluator_numpy_metrics(self, distribution):
with distribution.scope():
tasks = [
MockTask(params=cfg.TaskConfig(), name="bar"),
MockTask(params=cfg.TaskConfig(), name="foo")
]
model = MockModel()
test_evaluator = evaluator.MultiTaskEvaluator(
eval_tasks=tasks, 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)
if __name__ == "__main__":
tf.test.main()
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