diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/sparse_ops_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/sparse_ops_test.py | 49 |
1 files changed, 37 insertions, 12 deletions
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py index e67a2c25e9..14eb2cba68 100644 --- a/tensorflow/python/kernel_tests/sparse_ops_test.py +++ b/tensorflow/python/kernel_tests/sparse_ops_test.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import numpy as np +import unittest from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -544,21 +545,24 @@ class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase): self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool)) -class SparseReduceSumTest(test_util.TensorFlowTestCase): +class SparseReduceTest(test_util.TensorFlowTestCase): - # [[1, ?, 1] - # [?, 1, ?]] + # [[1, ?, 2] + # [?, 3, ?]] # where ? is implictly-zero. ind = np.array([[0, 0], [0, 2], [1, 1]]).astype(np.int64) vals = np.array([1, 1, 1]).astype(np.int32) dense_shape = np.array([2, 3]).astype(np.int64) - def _compare(self, sp_t, reduction_axes, ndims, keep_dims): + def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum): densified = sparse_ops.sparse_tensor_to_dense(sp_t).eval() np_ans = densified if reduction_axes is None: - np_ans = np.sum(np_ans, keepdims=keep_dims) + if do_sum: + np_ans = np.sum(np_ans, keepdims=keep_dims) + else: + np_ans = np.max(np_ans, keepdims=keep_dims) else: if not isinstance(reduction_axes, list): # Single scalar. reduction_axes = [reduction_axes] @@ -568,15 +572,28 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase): # Loop below depends on sorted. reduction_axes.sort() for ra in reduction_axes.ravel()[::-1]: - np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims) + if do_sum: + np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims) + else: + np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims) with self.test_session(): - tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes, - keep_dims) + if do_sum: + tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes, + keep_dims) + else: + tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes, + keep_dims) out_dense = tf_dense_ans.eval() - tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, reduction_axes, - keep_dims) + if do_sum: + tf_sparse_ans = sparse_ops.sparse_reduce_sum_sparse(sp_t, + reduction_axes, + keep_dims) + else: + tf_sparse_ans = sparse_ops.sparse_reduce_max_sparse(sp_t, + reduction_axes, + keep_dims) # Convert to dense for comparison purposes. out_sparse = sparse_ops.sparse_tensor_to_dense(tf_sparse_ans).eval() @@ -584,9 +601,12 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase): self.assertAllClose(np_ans, out_sparse) def _compare_all(self, sp_t, reduction_axes, ndims): - self._compare(sp_t, reduction_axes, ndims, False) - self._compare(sp_t, reduction_axes, ndims, True) + self._compare(sp_t, reduction_axes, ndims, False, False) + self._compare(sp_t, reduction_axes, ndims, False, True) + self._compare(sp_t, reduction_axes, ndims, True, False) + self._compare(sp_t, reduction_axes, ndims, True, True) + @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug") def testSimpleAndRandomInputs(self): if np.__version__ == "1.13.0": self.skipTest("numpy 1.13.0 bug") @@ -621,7 +641,12 @@ class SparseReduceSumTest(test_util.TensorFlowTestCase): sparse_ops.sparse_reduce_sum(sp_t, -3).eval() with self.assertRaisesOpError("Invalid reduction dimension 2"): sparse_ops.sparse_reduce_sum(sp_t, 2).eval() + with self.assertRaisesOpError("Invalid reduction dimension -3"): + sparse_ops.sparse_reduce_max(sp_t, -3).eval() + with self.assertRaisesOpError("Invalid reduction dimension 2"): + sparse_ops.sparse_reduce_max(sp_t, 2).eval() + @unittest.skipIf(np.__version__ == "1.13.0", "numpy 1.13 bug") def testGradient(self): if np.__version__ == "1.13.0": self.skipTest("numpy 1.13.0 bug") |