TST: Add rudimentary test for tensorflow

odl/contrib/tensorflow/test/tensorflow_test.py

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+# Copyright 2014-2017 The ODL contributors
+#
+# This file is part of ODL.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+
+"""Tests for tensorflow."""
+
+from __future__ import division
+from itertools import permutations
+import pytest
+import numpy as np
+import tensorflow as tf
+
+import odl
+import odl.contrib.tensorflow
+from odl.util import all_almost_equal
+
+
+def test_as_tensorflow_layer():
+    # Define ODL operator
+    matrix = np.random.rand(3, 2)
+    odl_op = odl.MatrixOperator(matrix)
+
+    # Define evaluation points
+    x = np.random.rand(2)
+    z = np.random.rand(3)
+
+    # Add empty axes for batch and channel
+    x_tf = tf.constant(x)[None, ..., None]
+    z_tf = tf.constant(z)[None, ..., None]
+
+    # Create tensorflow layer from odl operator
+    odl_op_layer = odl.contrib.tensorflow.as_tensorflow_layer(
+        odl_op, 'MatrixOperator')
+    y_tf = odl_op_layer(x_tf)
+
+    # Evaluate using tensorflow
+    result = y_tf.eval().ravel()
+    expected = odl_op(x)
+
+    assert all_almost_equal(result, expected)
+
+    # Evaluate the adjoint of the derivative, called gradient in tensorflow
+    result = tf.gradients(y_tf, [x_tf], z_tf)[0].eval().ravel()
+    expected = odl_op.derivative(x).adjoint(z)
+
+    assert all_almost_equal(result, expected)
+
+
+
+if __name__ == '__main__':
+    with tf.Session():
+        pytest.main([str(__file__.replace('\\', '/')), '-v'])