# 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.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 testGlobalPool(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, global_pool=True) self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 1, 1, 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 testNoClasses(self): batch_size = 5 height, width = 224, 224 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, 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', ] self.assertSetEqual(set(end_points.keys()), set(expected_names)) self.assertTrue(net.op.name.startswith('vgg_a/fc7')) 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 testGlobalPool(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, global_pool=True) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 1, 1, 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 testNoClasses(self): batch_size = 5 height, width = 224, 224 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, 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', ] self.assertSetEqual(set(end_points.keys()), set(expected_names)) self.assertTrue(net.op.name.startswith('vgg_16/fc7')) 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 testGlobalPool(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, global_pool=True) self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 1, 1, 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 testNoClasses(self): batch_size = 5 height, width = 224, 224 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, 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', ] self.assertSetEqual(set(end_points.keys()), set(expected_names)) self.assertTrue(net.op.name.startswith('vgg_19/fc7')) 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()