Python _define_model Examples

Example #1

File: train_test.py Project: ALISCIFP/models

  def test_define_model(self, mock_eval):
    FLAGS.batch_size = 2
    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images_x_np = np.zeros(shape=images_shape)
    images_y_np = np.zeros(shape=images_shape)
    images_x = tf.constant(images_x_np, dtype=tf.float32)
    images_y = tf.constant(images_y_np, dtype=tf.float32)

    cyclegan_model = train._define_model(images_x, images_y)
    self.assertIsInstance(cyclegan_model, tfgan.CycleGANModel)
    self.assertShapeEqual(images_x_np, cyclegan_model.reconstructed_x)
    self.assertShapeEqual(images_y_np, cyclegan_model.reconstructed_y)

Example #2

  def test_define_model(self, mock_eval):
    FLAGS.batch_size = 2
    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images_x_np = np.zeros(shape=images_shape)
    images_y_np = np.zeros(shape=images_shape)
    images_x = tf.constant(images_x_np, dtype=tf.float32)
    images_y = tf.constant(images_y_np, dtype=tf.float32)

    cyclegan_model = train._define_model(images_x, images_y)
    self.assertIsInstance(cyclegan_model, tfgan.CycleGANModel)
    self.assertShapeEqual(images_x_np, cyclegan_model.reconstructed_x)
    self.assertShapeEqual(images_y_np, cyclegan_model.reconstructed_y)

Example #3

  def test_define_model(self):
    FLAGS.batch_size = 2
    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images_np = np.zeros(shape=images_shape)
    images = tf.constant(images_np, dtype=tf.float32)
    labels = tf.one_hot([0] * FLAGS.batch_size, 2)

    model = train._define_model(images, labels)
    self.assertIsInstance(model, tfgan.StarGANModel)
    self.assertShapeEqual(images_np, model.generated_data)
    self.assertShapeEqual(images_np, model.reconstructed_data)
    self.assertTrue(isinstance(model.discriminator_variables, list))
    self.assertTrue(isinstance(model.generator_variables, list))
    self.assertIsInstance(model.discriminator_scope, tf.VariableScope)
    self.assertTrue(model.generator_scope, tf.VariableScope)
    self.assertTrue(callable(model.discriminator_fn))
    self.assertTrue(callable(model.generator_fn))

Example #4

File: train_test.py Project: Exscotticus/models

  def test_define_model(self):
    FLAGS.batch_size = 2
    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images_np = np.zeros(shape=images_shape)
    images = tf.constant(images_np, dtype=tf.float32)
    labels = tf.one_hot([0] * FLAGS.batch_size, 2)

    model = train._define_model(images, labels)
    self.assertIsInstance(model, tfgan.StarGANModel)
    self.assertShapeEqual(images_np, model.generated_data)
    self.assertShapeEqual(images_np, model.reconstructed_data)
    self.assertTrue(isinstance(model.discriminator_variables, list))
    self.assertTrue(isinstance(model.generator_variables, list))
    self.assertIsInstance(model.discriminator_scope, tf.VariableScope)
    self.assertTrue(model.generator_scope, tf.VariableScope)
    self.assertTrue(callable(model.discriminator_fn))
    self.assertTrue(callable(model.generator_fn))

Example #5

File: train_test.py Project: leizeling/Base_tensorflow-object_detection_2Dcord

    def test_define_train_ops(self, mock_summary_scalar):
        FLAGS.batch_size = 2
        FLAGS.generator_lr = 0.1
        FLAGS.discriminator_lr = 0.01

        images_shape = [FLAGS.batch_size, 4, 4, 3]
        images_x = tf.zeros(images_shape, dtype=tf.float32)
        images_y = tf.zeros(images_shape, dtype=tf.float32)

        cyclegan_model = train._define_model(images_x, images_y)
        cyclegan_loss = tfgan.cyclegan_loss(cyclegan_model,
                                            cycle_consistency_loss_weight=10.0)
        train_ops = train._define_train_ops(cyclegan_model, cyclegan_loss)

        self.assertIsInstance(train_ops, tfgan.GANTrainOps)
        mock_summary_scalar.assert_has_calls([
            mock.call('generator_lr', mock.ANY),
            mock.call('discriminator_lr', mock.ANY)
        ])

Example #6

  def test_define_train_ops(self, mock_summary_scalar):

    FLAGS.batch_size = 2
    FLAGS.generator_lr = 0.1
    FLAGS.discriminator_lr = 0.01

    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images = tf.zeros(images_shape, dtype=tf.float32)
    labels = tf.one_hot([0] * FLAGS.batch_size, 2)

    model = train._define_model(images, labels)
    loss = tfgan.stargan_loss(model)
    train_ops = train._define_train_ops(model, loss)

    self.assertIsInstance(train_ops, tfgan.GANTrainOps)
    mock_summary_scalar.assert_has_calls([
        mock.call('generator_lr', mock.ANY),
        mock.call('discriminator_lr', mock.ANY)
    ])

Example #7

File: train_test.py Project: ALISCIFP/models

  def test_define_train_ops(self, mock_summary_scalar):
    FLAGS.batch_size = 2
    FLAGS.generator_lr = 0.1
    FLAGS.discriminator_lr = 0.01

    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images_x = tf.zeros(images_shape, dtype=tf.float32)
    images_y = tf.zeros(images_shape, dtype=tf.float32)

    cyclegan_model = train._define_model(images_x, images_y)
    cyclegan_loss = tfgan.cyclegan_loss(
        cyclegan_model, cycle_consistency_loss_weight=10.0)
    train_ops = train._define_train_ops(cyclegan_model, cyclegan_loss)

    self.assertIsInstance(train_ops, tfgan.GANTrainOps)
    mock_summary_scalar.assert_has_calls([
        mock.call('generator_lr', mock.ANY),
        mock.call('discriminator_lr', mock.ANY)
    ])

Example #8

File: train_test.py Project: Exscotticus/models

  def test_define_train_ops(self, mock_summary_scalar):

    FLAGS.batch_size = 2
    FLAGS.generator_lr = 0.1
    FLAGS.discriminator_lr = 0.01

    images_shape = [FLAGS.batch_size, 4, 4, 3]
    images = tf.zeros(images_shape, dtype=tf.float32)
    labels = tf.one_hot([0] * FLAGS.batch_size, 2)

    model = train._define_model(images, labels)
    loss = tfgan.stargan_loss(model)
    train_ops = train._define_train_ops(model, loss)

    self.assertIsInstance(train_ops, tfgan.GANTrainOps)
    mock_summary_scalar.assert_has_calls([
        mock.call('generator_lr', mock.ANY),
        mock.call('discriminator_lr', mock.ANY)
    ])