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 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())
 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 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 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 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 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))