456 lines
18 KiB
Python
456 lines
18 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for slim.nets.vgg."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from nets import vgg
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slim = tf.contrib.slim
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class VGGATest(tf.test.TestCase):
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def testBuild(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_a(inputs, num_classes)
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self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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def testFullyConvolutional(self):
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batch_size = 1
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height, width = 256, 256
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
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self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, 2, 2, num_classes])
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def testEndPoints(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = vgg.vgg_a(inputs, num_classes)
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expected_names = ['vgg_a/conv1/conv1_1',
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'vgg_a/pool1',
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'vgg_a/conv2/conv2_1',
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'vgg_a/pool2',
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'vgg_a/conv3/conv3_1',
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'vgg_a/conv3/conv3_2',
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'vgg_a/pool3',
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'vgg_a/conv4/conv4_1',
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'vgg_a/conv4/conv4_2',
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'vgg_a/pool4',
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'vgg_a/conv5/conv5_1',
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'vgg_a/conv5/conv5_2',
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'vgg_a/pool5',
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'vgg_a/fc6',
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'vgg_a/fc7',
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'vgg_a/fc8'
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]
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self.assertSetEqual(set(end_points.keys()), set(expected_names))
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def testModelVariables(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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vgg.vgg_a(inputs, num_classes)
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expected_names = ['vgg_a/conv1/conv1_1/weights',
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'vgg_a/conv1/conv1_1/biases',
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'vgg_a/conv2/conv2_1/weights',
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'vgg_a/conv2/conv2_1/biases',
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'vgg_a/conv3/conv3_1/weights',
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'vgg_a/conv3/conv3_1/biases',
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'vgg_a/conv3/conv3_2/weights',
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'vgg_a/conv3/conv3_2/biases',
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'vgg_a/conv4/conv4_1/weights',
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'vgg_a/conv4/conv4_1/biases',
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'vgg_a/conv4/conv4_2/weights',
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'vgg_a/conv4/conv4_2/biases',
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'vgg_a/conv5/conv5_1/weights',
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'vgg_a/conv5/conv5_1/biases',
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'vgg_a/conv5/conv5_2/weights',
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'vgg_a/conv5/conv5_2/biases',
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'vgg_a/fc6/weights',
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'vgg_a/fc6/biases',
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'vgg_a/fc7/weights',
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'vgg_a/fc7/biases',
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'vgg_a/fc8/weights',
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'vgg_a/fc8/biases',
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]
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model_variables = [v.op.name for v in slim.get_model_variables()]
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self.assertSetEqual(set(model_variables), set(expected_names))
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def testEvaluation(self):
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batch_size = 2
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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predictions = tf.argmax(logits, 1)
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self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
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def testTrainEvalWithReuse(self):
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train_batch_size = 2
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eval_batch_size = 1
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train_height, train_width = 224, 224
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eval_height, eval_width = 256, 256
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num_classes = 1000
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with self.test_session():
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train_inputs = tf.random_uniform(
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(train_batch_size, train_height, train_width, 3))
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logits, _ = vgg.vgg_a(train_inputs)
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self.assertListEqual(logits.get_shape().as_list(),
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[train_batch_size, num_classes])
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tf.get_variable_scope().reuse_variables()
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eval_inputs = tf.random_uniform(
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(eval_batch_size, eval_height, eval_width, 3))
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logits, _ = vgg.vgg_a(eval_inputs, is_training=False,
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spatial_squeeze=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[eval_batch_size, 2, 2, num_classes])
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logits = tf.reduce_mean(logits, [1, 2])
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predictions = tf.argmax(logits, 1)
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self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
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def testForward(self):
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batch_size = 1
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height, width = 224, 224
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with self.test_session() as sess:
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_a(inputs)
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sess.run(tf.initialize_all_variables())
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output = sess.run(logits)
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self.assertTrue(output.any())
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class VGG16Test(tf.test.TestCase):
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def testBuild(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_16(inputs, num_classes)
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self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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def testFullyConvolutional(self):
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batch_size = 1
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height, width = 256, 256
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
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self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, 2, 2, num_classes])
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def testEndPoints(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = vgg.vgg_16(inputs, num_classes)
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expected_names = ['vgg_16/conv1/conv1_1',
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'vgg_16/conv1/conv1_2',
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'vgg_16/pool1',
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'vgg_16/conv2/conv2_1',
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'vgg_16/conv2/conv2_2',
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'vgg_16/pool2',
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'vgg_16/conv3/conv3_1',
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'vgg_16/conv3/conv3_2',
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'vgg_16/conv3/conv3_3',
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'vgg_16/pool3',
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'vgg_16/conv4/conv4_1',
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'vgg_16/conv4/conv4_2',
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'vgg_16/conv4/conv4_3',
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'vgg_16/pool4',
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'vgg_16/conv5/conv5_1',
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'vgg_16/conv5/conv5_2',
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'vgg_16/conv5/conv5_3',
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'vgg_16/pool5',
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'vgg_16/fc6',
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'vgg_16/fc7',
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'vgg_16/fc8'
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]
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self.assertSetEqual(set(end_points.keys()), set(expected_names))
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def testModelVariables(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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vgg.vgg_16(inputs, num_classes)
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expected_names = ['vgg_16/conv1/conv1_1/weights',
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'vgg_16/conv1/conv1_1/biases',
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'vgg_16/conv1/conv1_2/weights',
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'vgg_16/conv1/conv1_2/biases',
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'vgg_16/conv2/conv2_1/weights',
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'vgg_16/conv2/conv2_1/biases',
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'vgg_16/conv2/conv2_2/weights',
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'vgg_16/conv2/conv2_2/biases',
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'vgg_16/conv3/conv3_1/weights',
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'vgg_16/conv3/conv3_1/biases',
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'vgg_16/conv3/conv3_2/weights',
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'vgg_16/conv3/conv3_2/biases',
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'vgg_16/conv3/conv3_3/weights',
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'vgg_16/conv3/conv3_3/biases',
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'vgg_16/conv4/conv4_1/weights',
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'vgg_16/conv4/conv4_1/biases',
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'vgg_16/conv4/conv4_2/weights',
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'vgg_16/conv4/conv4_2/biases',
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'vgg_16/conv4/conv4_3/weights',
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'vgg_16/conv4/conv4_3/biases',
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'vgg_16/conv5/conv5_1/weights',
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'vgg_16/conv5/conv5_1/biases',
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'vgg_16/conv5/conv5_2/weights',
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'vgg_16/conv5/conv5_2/biases',
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'vgg_16/conv5/conv5_3/weights',
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'vgg_16/conv5/conv5_3/biases',
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'vgg_16/fc6/weights',
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'vgg_16/fc6/biases',
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'vgg_16/fc7/weights',
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'vgg_16/fc7/biases',
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'vgg_16/fc8/weights',
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'vgg_16/fc8/biases',
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]
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model_variables = [v.op.name for v in slim.get_model_variables()]
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self.assertSetEqual(set(model_variables), set(expected_names))
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def testEvaluation(self):
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batch_size = 2
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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predictions = tf.argmax(logits, 1)
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self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
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def testTrainEvalWithReuse(self):
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train_batch_size = 2
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eval_batch_size = 1
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train_height, train_width = 224, 224
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eval_height, eval_width = 256, 256
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num_classes = 1000
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with self.test_session():
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train_inputs = tf.random_uniform(
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(train_batch_size, train_height, train_width, 3))
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logits, _ = vgg.vgg_16(train_inputs)
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self.assertListEqual(logits.get_shape().as_list(),
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[train_batch_size, num_classes])
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tf.get_variable_scope().reuse_variables()
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eval_inputs = tf.random_uniform(
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(eval_batch_size, eval_height, eval_width, 3))
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logits, _ = vgg.vgg_16(eval_inputs, is_training=False,
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spatial_squeeze=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[eval_batch_size, 2, 2, num_classes])
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logits = tf.reduce_mean(logits, [1, 2])
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predictions = tf.argmax(logits, 1)
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self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
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def testForward(self):
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batch_size = 1
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height, width = 224, 224
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with self.test_session() as sess:
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_16(inputs)
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sess.run(tf.initialize_all_variables())
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output = sess.run(logits)
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self.assertTrue(output.any())
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class VGG19Test(tf.test.TestCase):
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def testBuild(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_19(inputs, num_classes)
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self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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def testFullyConvolutional(self):
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batch_size = 1
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height, width = 256, 256
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
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self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, 2, 2, num_classes])
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def testEndPoints(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = vgg.vgg_19(inputs, num_classes)
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expected_names = [
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'vgg_19/conv1/conv1_1',
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'vgg_19/conv1/conv1_2',
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'vgg_19/pool1',
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'vgg_19/conv2/conv2_1',
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'vgg_19/conv2/conv2_2',
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'vgg_19/pool2',
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'vgg_19/conv3/conv3_1',
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'vgg_19/conv3/conv3_2',
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'vgg_19/conv3/conv3_3',
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'vgg_19/conv3/conv3_4',
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'vgg_19/pool3',
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'vgg_19/conv4/conv4_1',
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'vgg_19/conv4/conv4_2',
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'vgg_19/conv4/conv4_3',
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'vgg_19/conv4/conv4_4',
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'vgg_19/pool4',
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'vgg_19/conv5/conv5_1',
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'vgg_19/conv5/conv5_2',
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'vgg_19/conv5/conv5_3',
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'vgg_19/conv5/conv5_4',
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'vgg_19/pool5',
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'vgg_19/fc6',
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'vgg_19/fc7',
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'vgg_19/fc8'
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]
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self.assertSetEqual(set(end_points.keys()), set(expected_names))
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def testModelVariables(self):
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batch_size = 5
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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vgg.vgg_19(inputs, num_classes)
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expected_names = [
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'vgg_19/conv1/conv1_1/weights',
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'vgg_19/conv1/conv1_1/biases',
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'vgg_19/conv1/conv1_2/weights',
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'vgg_19/conv1/conv1_2/biases',
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'vgg_19/conv2/conv2_1/weights',
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'vgg_19/conv2/conv2_1/biases',
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'vgg_19/conv2/conv2_2/weights',
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'vgg_19/conv2/conv2_2/biases',
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'vgg_19/conv3/conv3_1/weights',
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'vgg_19/conv3/conv3_1/biases',
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'vgg_19/conv3/conv3_2/weights',
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'vgg_19/conv3/conv3_2/biases',
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'vgg_19/conv3/conv3_3/weights',
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'vgg_19/conv3/conv3_3/biases',
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'vgg_19/conv3/conv3_4/weights',
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'vgg_19/conv3/conv3_4/biases',
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'vgg_19/conv4/conv4_1/weights',
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'vgg_19/conv4/conv4_1/biases',
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'vgg_19/conv4/conv4_2/weights',
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'vgg_19/conv4/conv4_2/biases',
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'vgg_19/conv4/conv4_3/weights',
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'vgg_19/conv4/conv4_3/biases',
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'vgg_19/conv4/conv4_4/weights',
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'vgg_19/conv4/conv4_4/biases',
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'vgg_19/conv5/conv5_1/weights',
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'vgg_19/conv5/conv5_1/biases',
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'vgg_19/conv5/conv5_2/weights',
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'vgg_19/conv5/conv5_2/biases',
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'vgg_19/conv5/conv5_3/weights',
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'vgg_19/conv5/conv5_3/biases',
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'vgg_19/conv5/conv5_4/weights',
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'vgg_19/conv5/conv5_4/biases',
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'vgg_19/fc6/weights',
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'vgg_19/fc6/biases',
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'vgg_19/fc7/weights',
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'vgg_19/fc7/biases',
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'vgg_19/fc8/weights',
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'vgg_19/fc8/biases',
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]
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model_variables = [v.op.name for v in slim.get_model_variables()]
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self.assertSetEqual(set(model_variables), set(expected_names))
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def testEvaluation(self):
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batch_size = 2
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height, width = 224, 224
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num_classes = 1000
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with self.test_session():
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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predictions = tf.argmax(logits, 1)
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self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
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|
|
|
def testTrainEvalWithReuse(self):
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train_batch_size = 2
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|
eval_batch_size = 1
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|
train_height, train_width = 224, 224
|
|
eval_height, eval_width = 256, 256
|
|
num_classes = 1000
|
|
with self.test_session():
|
|
train_inputs = tf.random_uniform(
|
|
(train_batch_size, train_height, train_width, 3))
|
|
logits, _ = vgg.vgg_19(train_inputs)
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[train_batch_size, num_classes])
|
|
tf.get_variable_scope().reuse_variables()
|
|
eval_inputs = tf.random_uniform(
|
|
(eval_batch_size, eval_height, eval_width, 3))
|
|
logits, _ = vgg.vgg_19(eval_inputs, is_training=False,
|
|
spatial_squeeze=False)
|
|
self.assertListEqual(logits.get_shape().as_list(),
|
|
[eval_batch_size, 2, 2, num_classes])
|
|
logits = tf.reduce_mean(logits, [1, 2])
|
|
predictions = tf.argmax(logits, 1)
|
|
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
|
|
|
|
def testForward(self):
|
|
batch_size = 1
|
|
height, width = 224, 224
|
|
with self.test_session() as sess:
|
|
inputs = tf.random_uniform((batch_size, height, width, 3))
|
|
logits, _ = vgg.vgg_19(inputs)
|
|
sess.run(tf.initialize_all_variables())
|
|
output = sess.run(logits)
|
|
self.assertTrue(output.any())
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|