unet.py 文件源码

def predict(self, model_path, x_test):
        """
        Uses the model to create a prediction for the given data

        :param model_path: path to the model checkpoint to restore
        :param x_test: Data to predict on. Shape [n, nx, ny, channels]
        :returns prediction: The unet prediction Shape [n, px, py, labels] (px=nx-self.offset/2) 
        """

        init = tf.global_variables_initializer()
        with tf.Session() as sess:
            # Initialize variables
            sess.run(init)

            # Restore model weights from previously saved model
            self.restore(sess, model_path)

            y_dummy = np.empty((x_test.shape[0], x_test.shape[1], x_test.shape[2], self.n_class))
            prediction = sess.run(self.predicter, feed_dict={self.x: x_test, self.y: y_dummy, self.keep_prob: 1.})

        return prediction