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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 Mobiledet."""
import itertools
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.modeling.backbones import mobiledet
class MobileDetTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
'MobileDetCPU',
'MobileDetDSP',
'MobileDetEdgeTPU',
'MobileDetGPU',
)
def test_serialize_deserialize(self, model_id):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id=model_id,
filter_size_scale=1.0,
use_sync_bn=False,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
norm_momentum=0.99,
norm_epsilon=0.001,
min_depth=8,
divisible_by=8,
regularize_depthwise=False,
)
network = mobiledet.MobileDet(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = mobiledet.MobileDet.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())
@parameterized.parameters(
itertools.product(
[1, 3],
[
'MobileDetCPU',
'MobileDetDSP',
'MobileDetEdgeTPU',
'MobileDetGPU',
],
))
def test_input_specs(self, input_dim, model_id):
"""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 = mobiledet.MobileDet(model_id=model_id, input_specs=input_specs)
inputs = tf_keras.Input(shape=(128, 128, input_dim), batch_size=1)
_ = network(inputs)
@parameterized.parameters(
itertools.product(
[
'MobileDetCPU',
'MobileDetDSP',
'MobileDetEdgeTPU',
'MobileDetGPU',
],
[32, 224],
))
def test_mobiledet_creation(self, model_id, input_size):
"""Test creation of MobileDet family models."""
tf_keras.backend.set_image_data_format('channels_last')
mobiledet_layers = {
# The number of filters of layers having outputs been collected
# for filter_size_scale = 1.0
'MobileDetCPU': [8, 16, 32, 72, 144],
'MobileDetDSP': [24, 32, 64, 144, 240],
'MobileDetEdgeTPU': [16, 16, 40, 96, 384],
'MobileDetGPU': [16, 32, 64, 128, 384],
}
network = mobiledet.MobileDet(model_id=model_id,
filter_size_scale=1.0)
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = network(inputs)
for idx, num_filter in enumerate(mobiledet_layers[model_id]):
self.assertAllEqual(
[1, input_size / 2 ** (idx+1), input_size / 2 ** (idx+1), num_filter],
endpoints[str(idx+1)].shape.as_list())
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