models/research/object_detection/utils/model_util_tf2_test.py

# Copyright 2019 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.
# ==============================================================================

"""Test utility functions for manipulating Keras models."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import unittest
import tensorflow.compat.v1 as tf

from object_detection.utils import model_util
from object_detection.utils import tf_version


@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
class ExtractSubmodelUtilTest(tf.test.TestCase):

  def test_simple_model(self):
    inputs = tf.keras.Input(shape=(256,))  # Returns a placeholder tensor

    # A layer instance is callable on a tensor, and returns a tensor.
    x = tf.keras.layers.Dense(128, activation='relu', name='a')(inputs)
    x = tf.keras.layers.Dense(64, activation='relu', name='b')(x)
    x = tf.keras.layers.Dense(32, activation='relu', name='c')(x)
    x = tf.keras.layers.Dense(16, activation='relu', name='d')(x)
    x = tf.keras.layers.Dense(8, activation='relu', name='e')(x)
    predictions = tf.keras.layers.Dense(10, activation='softmax')(x)

    model = tf.keras.Model(inputs=inputs, outputs=predictions)

    new_in = model.get_layer(
        name='b').input
    new_out = model.get_layer(
        name='d').output

    new_model = model_util.extract_submodel(
        model=model,
        inputs=new_in,
        outputs=new_out)

    batch_size = 3
    ones = tf.ones((batch_size, 128))
    final_out = new_model(ones)
    self.assertAllEqual(final_out.shape, (batch_size, 16))

if __name__ == '__main__':
  tf.test.main()