1 files changed, 111 insertions, 0 deletions
diff --git a/tensorflow/python/kernel_tests/sparse_to_dense_op_py_test.py b/tensorflow/python/kernel_tests/sparse_to_dense_op_py_test.py
new file mode 100644
index 0000000000..2bab89923e
--- /dev/null
+++ b/tensorflow/python/kernel_tests/sparse_to_dense_op_py_test.py
@@ -0,0 +1,111 @@
+"""Tests for tensorflow.kernels.sparse_op."""
+import tensorflow.python.platform
+
+import numpy as np
+import tensorflow as tf
+
+
+def _SparseToDense(sparse_indices, output_size, sparse_values,
+                   default_value):
+  return tf.sparse_to_dense(sparse_indices, output_size,
+                            sparse_values, default_value)
+
+
+class SparseToDenseTest(tf.test.TestCase):
+
+  def testInt(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([1, 3], [5], 1, 0).eval()
+    np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32)
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testFloat(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0).eval()
+    np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32)
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testString(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([1, 3], [5], "a", "b").eval()
+    np_ans = np.array(["b", "a", "b", "a", "b"]).astype(np.string_)
+    self.assertAllEqual(np_ans, tf_ans)
+
+  def testSetValue(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1).eval()
+    np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32)
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testSetSingleValue(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([1, 3], [5], 1, -1).eval()
+    np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32)
+    self.assertAllClose(np_ans, tf_ans)
+
+  def test2d(self):
+    # pylint: disable=bad-whitespace
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1).eval()
+    np_ans = np.array([[-1, -1, -1, -1],
+                       [-1, -1, -1,  1],
+                       [ 1, -1, -1, -1]]).astype(np.int32)
+    self.assertAllClose(np_ans, tf_ans)
+
+  def test3d(self):
+    with self.test_session(use_gpu=False):
+      tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1).eval()
+    np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1
+    np_ans[1, 3, 0] = 1
+    np_ans[2, 0, 1] = 1
+    self.assertAllClose(np_ans, tf_ans)
+
+  def testBadShape(self):
+    with self.test_session():
+      with self.assertRaisesWithPredicateMatch(
+          ValueError, lambda e: ("Input shape should be a vector" == str(e))):
+        _SparseToDense([1, 3], [[5], [3]], 1, -1)
+
+  def testBadValue(self):
+    with self.test_session():
+      dense = _SparseToDense([1, 3], [5], [[5], [3]], -1)
+      with self.assertRaisesOpError(
+          r"sparse_values has incorrect shape \[2,1\], "
+          r"should be \[\] or \[2\]"):
+        dense.eval()
+
+  def testBadNumValues(self):
+    with self.test_session():
+      dense = _SparseToDense([1, 3], [5], [1, 2, 3], -1)
+      with self.assertRaisesOpError(
+          r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"):
+        dense.eval()
+
+  def testBadDefault(self):
+    with self.test_session():
+      dense = _SparseToDense([1, 3], [5], [1, 2], [1, 2])
+      with self.assertRaisesOpError("default_value should be a scalar"):
+        dense.eval()
+
+  def testShapeInferenceKnownShape(self):
+    with self.test_session(use_gpu=False):
+      indices = tf.placeholder(tf.int64)
+
+      shape = [4, 5, 6]
+      output = tf.sparse_to_dense(indices, shape, 1, 0)
+      self.assertEqual(output.get_shape(), [4, 5, 6])
+
+      shape = tf.placeholder(tf.int64, shape=(3,))
+      output = tf.sparse_to_dense(indices, shape, 1, 0)
+      self.assertEqual(output.get_shape().as_list(), [None, None, None])
+
+  def testShapeInferenceUnknownShape(self):
+    with self.test_session(use_gpu=False):
+      indices = tf.placeholder(tf.int64)
+      shape = tf.placeholder(tf.int64)
+      output = tf.sparse_to_dense(indices, shape, 1, 0)
+      self.assertEqual(output.get_shape().ndims, None)
+
+
+if __name__ == "__main__":
+  tf.test.main()