137 lines
5.6 KiB
Python
137 lines
5.6 KiB
Python
# Copyright 2016 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 slim.inception_resnet_v2."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import tensorflow as tf
|
|
|
|
from nets import inception
|
|
|
|
|
|
class InceptionTest(tf.test.TestCase):
|
|
|
|
def testBuildLogits(self):
|
|
batch_size = 5
|
|
height, width = 299, 299
|
|
num_classes = 1000
|
|
with self.test_session():
|
|
inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
logits, _ = inception.inception_resnet_v2(inputs, num_classes)
|
|
self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[batch_size, num_classes])
|
|
|
|
def testBuildEndPoints(self):
|
|
batch_size = 5
|
|
height, width = 299, 299
|
|
num_classes = 1000
|
|
with self.test_session():
|
|
inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
_, end_points = inception.inception_resnet_v2(inputs, num_classes)
|
|
self.assertTrue('Logits' in end_points)
|
|
logits = end_points['Logits']
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[batch_size, num_classes])
|
|
self.assertTrue('AuxLogits' in end_points)
|
|
aux_logits = end_points['AuxLogits']
|
|
self.assertListEqual(aux_logits.get_shape().as_list(),
|
|
[batch_size, num_classes])
|
|
pre_pool = end_points['PrePool']
|
|
self.assertListEqual(pre_pool.get_shape().as_list(),
|
|
[batch_size, 8, 8, 1536])
|
|
|
|
def testVariablesSetDevice(self):
|
|
batch_size = 5
|
|
height, width = 299, 299
|
|
num_classes = 1000
|
|
with self.test_session():
|
|
inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
# Force all Variables to reside on the device.
|
|
with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
|
|
inception.inception_resnet_v2(inputs, num_classes)
|
|
with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
|
|
inception.inception_resnet_v2(inputs, num_classes)
|
|
for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_cpu'):
|
|
self.assertDeviceEqual(v.device, '/cpu:0')
|
|
for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_gpu'):
|
|
self.assertDeviceEqual(v.device, '/gpu:0')
|
|
|
|
def testHalfSizeImages(self):
|
|
batch_size = 5
|
|
height, width = 150, 150
|
|
num_classes = 1000
|
|
with self.test_session():
|
|
inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
logits, end_points = inception.inception_resnet_v2(inputs, num_classes)
|
|
self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[batch_size, num_classes])
|
|
pre_pool = end_points['PrePool']
|
|
self.assertListEqual(pre_pool.get_shape().as_list(),
|
|
[batch_size, 3, 3, 1536])
|
|
|
|
def testUnknownBatchSize(self):
|
|
batch_size = 1
|
|
height, width = 299, 299
|
|
num_classes = 1000
|
|
with self.test_session() as sess:
|
|
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
|
|
logits, _ = inception.inception_resnet_v2(inputs, num_classes)
|
|
self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits'))
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[None, num_classes])
|
|
images = tf.random_uniform((batch_size, height, width, 3))
|
|
sess.run(tf.initialize_all_variables())
|
|
output = sess.run(logits, {inputs: images.eval()})
|
|
self.assertEquals(output.shape, (batch_size, num_classes))
|
|
|
|
def testEvaluation(self):
|
|
batch_size = 2
|
|
height, width = 299, 299
|
|
num_classes = 1000
|
|
with self.test_session() as sess:
|
|
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
logits, _ = inception.inception_resnet_v2(eval_inputs,
|
|
num_classes,
|
|
is_training=False)
|
|
predictions = tf.argmax(logits, 1)
|
|
sess.run(tf.initialize_all_variables())
|
|
output = sess.run(predictions)
|
|
self.assertEquals(output.shape, (batch_size,))
|
|
|
|
def testTrainEvalWithReuse(self):
|
|
train_batch_size = 5
|
|
eval_batch_size = 2
|
|
height, width = 150, 150
|
|
num_classes = 1000
|
|
with self.test_session() as sess:
|
|
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
|
|
inception.inception_resnet_v2(train_inputs, num_classes)
|
|
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
|
|
logits, _ = inception.inception_resnet_v2(eval_inputs,
|
|
num_classes,
|
|
is_training=False,
|
|
reuse=True)
|
|
predictions = tf.argmax(logits, 1)
|
|
sess.run(tf.initialize_all_variables())
|
|
output = sess.run(predictions)
|
|
self.assertEquals(output.shape, (eval_batch_size,))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|