official/projects/qat/vision/tasks/retinanet_test.py
# Copyright 2024 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.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Tests for RetinaNet task."""
# pylint: disable=unused-import
import os
from absl.testing import parameterized
import orbit
import tensorflow as tf, tf_keras
from official import vision
from official.core import exp_factory
from official.modeling import optimization
from official.projects.qat.vision.tasks import retinanet
from official.vision.configs import retinanet as exp_cfg
from official.vision.dataloaders import tfexample_utils
class RetinaNetTaskTest(parameterized.TestCase, tf.test.TestCase):
def _create_test_tfrecord(self, tfrecord_file, example, num_samples):
examples = [example] * num_samples
tfexample_utils.dump_to_tfrecord(
record_file=tfrecord_file, tf_examples=examples)
@parameterized.parameters(
('retinanet_mobile_coco_qat', True),
('retinanet_mobile_coco_qat', False),
)
def test_retinanet_task(self, test_config, is_training):
"""RetinaNet task test for training and val using toy configs."""
input_image_size = [384, 384]
test_tfrecord_file = os.path.join(self.get_temp_dir(), 'det_test.tfrecord')
example = tfexample_utils.create_detection_test_example(
image_height=input_image_size[0],
image_width=input_image_size[1],
image_channel=3,
num_instances=10)
self._create_test_tfrecord(
tfrecord_file=test_tfrecord_file, example=example, num_samples=10)
config = exp_factory.get_exp_config(test_config)
# modify config to suit local testing
config.task.model.input_size = [128, 128, 3]
config.trainer.steps_per_loop = 1
config.task.train_data.global_batch_size = 1
config.task.validation_data.global_batch_size = 1
config.task.train_data.shuffle_buffer_size = 2
config.task.validation_data.shuffle_buffer_size = 2
config.task.validation_data.input_path = test_tfrecord_file
config.task.train_data.input_path = test_tfrecord_file
config.task.annotation_file = None
config.train_steps = 1
task = retinanet.RetinaNetTask(config.task)
model = task.build_model()
self.assertLen(model.weights, 2393)
metrics = task.build_metrics(training=is_training)
strategy = tf.distribute.get_strategy()
data_config = config.task.train_data if is_training else config.task.validation_data
dataset = orbit.utils.make_distributed_dataset(strategy, task.build_inputs,
data_config)
iterator = iter(dataset)
opt_factory = optimization.OptimizerFactory(config.trainer.optimizer_config)
optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
if is_training:
task.train_step(next(iterator), model, optimizer, metrics=metrics)
else:
task.validation_step(next(iterator), model, metrics=metrics)
if __name__ == '__main__':
tf.test.main()