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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 classification network."""
# Import libraries
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.vision.modeling import backbones
from official.vision.modeling import classification_model
class ClassificationNetworkTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(192 * 4, 3, 12, 192, 5524416),
(384 * 4, 6, 12, 384, 21665664),
)
def test_vision_transformer_creation(self, mlp_dim, num_heads, num_layers,
hidden_size, num_params):
"""Test for creation of a Vision Transformer classifier."""
inputs = np.random.rand(2, 224, 224, 3)
tf_keras.backend.set_image_data_format('channels_last')
backbone = backbones.VisionTransformer(
mlp_dim=mlp_dim,
num_heads=num_heads,
num_layers=num_layers,
hidden_size=hidden_size,
input_specs=tf_keras.layers.InputSpec(shape=[None, 224, 224, 3]),
)
self.assertEqual(backbone.count_params(), num_params)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
@parameterized.parameters(
(128, 50, 'relu'),
(128, 50, 'relu'),
(128, 50, 'swish'),
)
def test_resnet_network_creation(self, input_size, resnet_model_id,
activation):
"""Test for creation of a ResNet-50 classifier."""
inputs = np.random.rand(2, input_size, input_size, 3)
tf_keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(model_id=resnet_model_id, activation=activation)
self.assertEqual(backbone.count_params(), 23561152)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
self.assertEqual(model.count_params(), 25610152)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
def test_revnet_network_creation(self):
"""Test for creation of a RevNet-56 classifier."""
revnet_model_id = 56
inputs = np.random.rand(2, 224, 224, 3)
tf_keras.backend.set_image_data_format('channels_last')
backbone = backbones.RevNet(model_id=revnet_model_id)
self.assertEqual(backbone.count_params(), 19473792)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
add_head_batch_norm=True,
)
self.assertEqual(model.count_params(), 22816104)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
@combinations.generate(
combinations.combine(
mobilenet_model_id=[
'MobileNetV1',
'MobileNetV2',
'MobileNetV3Large',
'MobileNetV3Small',
'MobileNetV3EdgeTPU',
'MobileNetMultiAVG',
'MobileNetMultiMAX',
],
filter_size_scale=[1.0, 0.75],
))
def test_mobilenet_network_creation(self, mobilenet_model_id,
filter_size_scale):
"""Test for creation of a MobileNet classifier."""
inputs = np.random.rand(2, 224, 224, 3)
tf_keras.backend.set_image_data_format('channels_last')
backbone = backbones.MobileNet(
model_id=mobilenet_model_id, filter_size_scale=filter_size_scale)
num_classes = 1001
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
@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(64, 128, 128, 3)
tf_keras.backend.set_image_data_format('channels_last')
with strategy.scope():
backbone = backbones.ResNet(model_id=50, use_sync_bn=use_sync_bn)
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=1000,
dropout_rate=0.2,
)
_ = model(inputs)
@combinations.generate(
combinations.combine(
strategy=[
strategy_combinations.one_device_strategy_gpu,
],
data_format=['channels_last', 'channels_first'],
input_dim=[1, 3, 4]))
def test_data_format_gpu(self, strategy, data_format, input_dim):
"""Test for different data formats on GPU devices."""
if data_format == 'channels_last':
inputs = np.random.rand(2, 128, 128, input_dim)
else:
inputs = np.random.rand(2, input_dim, 128, 128)
input_specs = tf_keras.layers.InputSpec(shape=inputs.shape)
tf_keras.backend.set_image_data_format(data_format)
with strategy.scope():
backbone = backbones.ResNet(model_id=50, input_specs=input_specs)
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=1000,
input_specs=input_specs,
)
_ = model(inputs)
def test_serialize_deserialize(self):
"""Validate the classification net can be serialized and deserialized."""
tf_keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(model_id=50)
model = classification_model.ClassificationModel(
backbone=backbone, num_classes=1000)
config = model.get_config()
new_model = classification_model.ClassificationModel.from_config(config)
# Validate that the config can be forced to JSON.
_ = new_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(model.get_config(), new_model.get_config())
if __name__ == '__main__':
tf.test.main()
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