official/projects/yolo/modeling/backbones/darknet_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.
# 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 yolo."""
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.projects.yolo.modeling.backbones import darknet
class DarknetTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(224, 'darknet53', 2, 1, True),
(224, 'darknettiny', 1, 2, False),
(224, 'cspdarknettiny', 1, 1, False),
(224, 'cspdarknet53', 2, 1, True),
)
def test_network_creation(self, input_size, model_id, endpoint_filter_scale,
scale_final, dilate):
"""Test creation of ResNet family models."""
tf_keras.backend.set_image_data_format('channels_last')
network = darknet.Darknet(
model_id=model_id, min_level=3, max_level=5, dilate=dilate)
self.assertEqual(network.model_id, model_id)
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = network(inputs)
if dilate:
self.assertAllEqual([
1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale
], endpoints['3'].shape.as_list())
self.assertAllEqual([
1, input_size / 2**3, input_size / 2**3, 256 * endpoint_filter_scale
], endpoints['4'].shape.as_list())
self.assertAllEqual([
1, input_size / 2**3, input_size / 2**3,
512 * endpoint_filter_scale * scale_final
], endpoints['5'].shape.as_list())
else:
self.assertAllEqual([
1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale
], endpoints['3'].shape.as_list())
self.assertAllEqual([
1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale
], endpoints['4'].shape.as_list())
self.assertAllEqual([
1, input_size / 2**5, input_size / 2**5,
512 * endpoint_filter_scale * scale_final
], endpoints['5'].shape.as_list())
@combinations.generate(
combinations.combine(
strategy=[
strategy_combinations.cloud_tpu_strategy,
strategy_combinations.one_device_strategy_gpu,
],
use_sync_bn=[False, True],
))
def test_sync_bn_multiple_devices(self, strategy, use_sync_bn):
"""Test for sync bn on TPU and GPU devices."""
inputs = np.random.rand(1, 224, 224, 3)
tf_keras.backend.set_image_data_format('channels_last')
with strategy.scope():
network = darknet.Darknet(
model_id='darknet53',
min_level=3,
max_level=5,
use_sync_bn=use_sync_bn,
)
_ = network(inputs)
@parameterized.parameters(1, 3, 4)
def test_input_specs(self, input_dim):
"""Test different input feature dimensions."""
tf_keras.backend.set_image_data_format('channels_last')
input_specs = tf_keras.layers.InputSpec(shape=[None, None, None, input_dim])
network = darknet.Darknet(
model_id='darknet53', min_level=3, max_level=5, input_specs=input_specs)
inputs = tf_keras.Input(shape=(224, 224, input_dim), batch_size=1)
_ = network(inputs)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id='darknet53',
min_level=3,
max_level=5,
use_sync_bn=False,
activation='relu',
norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
)
network = darknet.Darknet(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = darknet.Darknet.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
if __name__ == '__main__':
tf.test.main()