+++ /dev/null
-# 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.slim_nets.vgg."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from nets import vgg
-
-slim = tf.contrib.slim
-
-
-class VGGATest(tf.test.TestCase):
-
- def testBuild(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_a(inputs, num_classes)
- self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
-
- def testFullyConvolutional(self):
- batch_size = 1
- height, width = 256, 256
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
- self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, 2, 2, num_classes])
-
- def testEndPoints(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = vgg.vgg_a(inputs, num_classes)
- expected_names = ['vgg_a/conv1/conv1_1',
- 'vgg_a/pool1',
- 'vgg_a/conv2/conv2_1',
- 'vgg_a/pool2',
- 'vgg_a/conv3/conv3_1',
- 'vgg_a/conv3/conv3_2',
- 'vgg_a/pool3',
- 'vgg_a/conv4/conv4_1',
- 'vgg_a/conv4/conv4_2',
- 'vgg_a/pool4',
- 'vgg_a/conv5/conv5_1',
- 'vgg_a/conv5/conv5_2',
- 'vgg_a/pool5',
- 'vgg_a/fc6',
- 'vgg_a/fc7',
- 'vgg_a/fc8'
- ]
- self.assertSetEqual(set(end_points.keys()), set(expected_names))
-
- def testModelVariables(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- vgg.vgg_a(inputs, num_classes)
- expected_names = ['vgg_a/conv1/conv1_1/weights',
- 'vgg_a/conv1/conv1_1/biases',
- 'vgg_a/conv2/conv2_1/weights',
- 'vgg_a/conv2/conv2_1/biases',
- 'vgg_a/conv3/conv3_1/weights',
- 'vgg_a/conv3/conv3_1/biases',
- 'vgg_a/conv3/conv3_2/weights',
- 'vgg_a/conv3/conv3_2/biases',
- 'vgg_a/conv4/conv4_1/weights',
- 'vgg_a/conv4/conv4_1/biases',
- 'vgg_a/conv4/conv4_2/weights',
- 'vgg_a/conv4/conv4_2/biases',
- 'vgg_a/conv5/conv5_1/weights',
- 'vgg_a/conv5/conv5_1/biases',
- 'vgg_a/conv5/conv5_2/weights',
- 'vgg_a/conv5/conv5_2/biases',
- 'vgg_a/fc6/weights',
- 'vgg_a/fc6/biases',
- 'vgg_a/fc7/weights',
- 'vgg_a/fc7/biases',
- 'vgg_a/fc8/weights',
- 'vgg_a/fc8/biases',
- ]
- model_variables = [v.op.name for v in slim.get_model_variables()]
- self.assertSetEqual(set(model_variables), set(expected_names))
-
- def testEvaluation(self):
- batch_size = 2
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- predictions = tf.argmax(logits, 1)
- self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
-
- def testTrainEvalWithReuse(self):
- train_batch_size = 2
- eval_batch_size = 1
- 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_a(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_a(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_a(inputs)
- sess.run(tf.global_variables_initializer())
- output = sess.run(logits)
- self.assertTrue(output.any())
-
-
-class VGG16Test(tf.test.TestCase):
-
- def testBuild(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_16(inputs, num_classes)
- self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
-
- def testFullyConvolutional(self):
- batch_size = 1
- height, width = 256, 256
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
- self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, 2, 2, num_classes])
-
- def testEndPoints(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = vgg.vgg_16(inputs, num_classes)
- expected_names = ['vgg_16/conv1/conv1_1',
- 'vgg_16/conv1/conv1_2',
- 'vgg_16/pool1',
- 'vgg_16/conv2/conv2_1',
- 'vgg_16/conv2/conv2_2',
- 'vgg_16/pool2',
- 'vgg_16/conv3/conv3_1',
- 'vgg_16/conv3/conv3_2',
- 'vgg_16/conv3/conv3_3',
- 'vgg_16/pool3',
- 'vgg_16/conv4/conv4_1',
- 'vgg_16/conv4/conv4_2',
- 'vgg_16/conv4/conv4_3',
- 'vgg_16/pool4',
- 'vgg_16/conv5/conv5_1',
- 'vgg_16/conv5/conv5_2',
- 'vgg_16/conv5/conv5_3',
- 'vgg_16/pool5',
- 'vgg_16/fc6',
- 'vgg_16/fc7',
- 'vgg_16/fc8'
- ]
- self.assertSetEqual(set(end_points.keys()), set(expected_names))
-
- def testModelVariables(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- vgg.vgg_16(inputs, num_classes)
- expected_names = ['vgg_16/conv1/conv1_1/weights',
- 'vgg_16/conv1/conv1_1/biases',
- 'vgg_16/conv1/conv1_2/weights',
- 'vgg_16/conv1/conv1_2/biases',
- 'vgg_16/conv2/conv2_1/weights',
- 'vgg_16/conv2/conv2_1/biases',
- 'vgg_16/conv2/conv2_2/weights',
- 'vgg_16/conv2/conv2_2/biases',
- 'vgg_16/conv3/conv3_1/weights',
- 'vgg_16/conv3/conv3_1/biases',
- 'vgg_16/conv3/conv3_2/weights',
- 'vgg_16/conv3/conv3_2/biases',
- 'vgg_16/conv3/conv3_3/weights',
- 'vgg_16/conv3/conv3_3/biases',
- 'vgg_16/conv4/conv4_1/weights',
- 'vgg_16/conv4/conv4_1/biases',
- 'vgg_16/conv4/conv4_2/weights',
- 'vgg_16/conv4/conv4_2/biases',
- 'vgg_16/conv4/conv4_3/weights',
- 'vgg_16/conv4/conv4_3/biases',
- 'vgg_16/conv5/conv5_1/weights',
- 'vgg_16/conv5/conv5_1/biases',
- 'vgg_16/conv5/conv5_2/weights',
- 'vgg_16/conv5/conv5_2/biases',
- 'vgg_16/conv5/conv5_3/weights',
- 'vgg_16/conv5/conv5_3/biases',
- 'vgg_16/fc6/weights',
- 'vgg_16/fc6/biases',
- 'vgg_16/fc7/weights',
- 'vgg_16/fc7/biases',
- 'vgg_16/fc8/weights',
- 'vgg_16/fc8/biases',
- ]
- model_variables = [v.op.name for v in slim.get_model_variables()]
- self.assertSetEqual(set(model_variables), set(expected_names))
-
- def testEvaluation(self):
- batch_size = 2
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- predictions = tf.argmax(logits, 1)
- self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
-
- def testTrainEvalWithReuse(self):
- train_batch_size = 2
- eval_batch_size = 1
- 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_16(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_16(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_16(inputs)
- sess.run(tf.global_variables_initializer())
- output = sess.run(logits)
- self.assertTrue(output.any())
-
-
-class VGG19Test(tf.test.TestCase):
-
- def testBuild(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_19(inputs, num_classes)
- self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
-
- def testFullyConvolutional(self):
- batch_size = 1
- height, width = 256, 256
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
- self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, 2, 2, num_classes])
-
- def testEndPoints(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- _, end_points = vgg.vgg_19(inputs, num_classes)
- expected_names = [
- 'vgg_19/conv1/conv1_1',
- 'vgg_19/conv1/conv1_2',
- 'vgg_19/pool1',
- 'vgg_19/conv2/conv2_1',
- 'vgg_19/conv2/conv2_2',
- 'vgg_19/pool2',
- 'vgg_19/conv3/conv3_1',
- 'vgg_19/conv3/conv3_2',
- 'vgg_19/conv3/conv3_3',
- 'vgg_19/conv3/conv3_4',
- 'vgg_19/pool3',
- 'vgg_19/conv4/conv4_1',
- 'vgg_19/conv4/conv4_2',
- 'vgg_19/conv4/conv4_3',
- 'vgg_19/conv4/conv4_4',
- 'vgg_19/pool4',
- 'vgg_19/conv5/conv5_1',
- 'vgg_19/conv5/conv5_2',
- 'vgg_19/conv5/conv5_3',
- 'vgg_19/conv5/conv5_4',
- 'vgg_19/pool5',
- 'vgg_19/fc6',
- 'vgg_19/fc7',
- 'vgg_19/fc8'
- ]
- self.assertSetEqual(set(end_points.keys()), set(expected_names))
-
- def testModelVariables(self):
- batch_size = 5
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- inputs = tf.random_uniform((batch_size, height, width, 3))
- vgg.vgg_19(inputs, num_classes)
- expected_names = [
- 'vgg_19/conv1/conv1_1/weights',
- 'vgg_19/conv1/conv1_1/biases',
- 'vgg_19/conv1/conv1_2/weights',
- 'vgg_19/conv1/conv1_2/biases',
- 'vgg_19/conv2/conv2_1/weights',
- 'vgg_19/conv2/conv2_1/biases',
- 'vgg_19/conv2/conv2_2/weights',
- 'vgg_19/conv2/conv2_2/biases',
- 'vgg_19/conv3/conv3_1/weights',
- 'vgg_19/conv3/conv3_1/biases',
- 'vgg_19/conv3/conv3_2/weights',
- 'vgg_19/conv3/conv3_2/biases',
- 'vgg_19/conv3/conv3_3/weights',
- 'vgg_19/conv3/conv3_3/biases',
- 'vgg_19/conv3/conv3_4/weights',
- 'vgg_19/conv3/conv3_4/biases',
- 'vgg_19/conv4/conv4_1/weights',
- 'vgg_19/conv4/conv4_1/biases',
- 'vgg_19/conv4/conv4_2/weights',
- 'vgg_19/conv4/conv4_2/biases',
- 'vgg_19/conv4/conv4_3/weights',
- 'vgg_19/conv4/conv4_3/biases',
- 'vgg_19/conv4/conv4_4/weights',
- 'vgg_19/conv4/conv4_4/biases',
- 'vgg_19/conv5/conv5_1/weights',
- 'vgg_19/conv5/conv5_1/biases',
- 'vgg_19/conv5/conv5_2/weights',
- 'vgg_19/conv5/conv5_2/biases',
- 'vgg_19/conv5/conv5_3/weights',
- 'vgg_19/conv5/conv5_3/biases',
- 'vgg_19/conv5/conv5_4/weights',
- 'vgg_19/conv5/conv5_4/biases',
- 'vgg_19/fc6/weights',
- 'vgg_19/fc6/biases',
- 'vgg_19/fc7/weights',
- 'vgg_19/fc7/biases',
- 'vgg_19/fc8/weights',
- 'vgg_19/fc8/biases',
- ]
- model_variables = [v.op.name for v in slim.get_model_variables()]
- self.assertSetEqual(set(model_variables), set(expected_names))
-
- def testEvaluation(self):
- batch_size = 2
- height, width = 224, 224
- num_classes = 1000
- with self.test_session():
- eval_inputs = tf.random_uniform((batch_size, height, width, 3))
- logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
- self.assertListEqual(logits.get_shape().as_list(),
- [batch_size, num_classes])
- predictions = tf.argmax(logits, 1)
- self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
-
- def testTrainEvalWithReuse(self):
- train_batch_size = 2
- eval_batch_size = 1
- 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.global_variables_initializer())
- output = sess.run(logits)
- self.assertTrue(output.any())
-
-if __name__ == '__main__':
- tf.test.main()
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