You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
54 lines
1.9 KiB
54 lines
1.9 KiB
# 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."""
|
|
|
|
import tensorflow as tf
|
|
|
|
from object_detection.utils import model_util
|
|
|
|
|
|
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()
|