--- /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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