diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/scatter_nd_ops_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/scatter_nd_ops_test.py | 32 |
1 files changed, 16 insertions, 16 deletions
diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index f2f3023469..86e063cb36 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -294,7 +294,7 @@ class StatefulScatterNdTest(test.TestCase): self.assertAllEqual(scatter_update.get_shape().as_list(), shape) expected_result = np.zeros([2, 2], dtype=np.int32) - with self.test_session(): + with self.cached_session(): ref.initializer.run() self.assertAllEqual(expected_result, scatter_update.eval()) @@ -409,7 +409,7 @@ class ScatterNdTest(test.TestCase): expected = np.array([b"", b"one", b"", b"three", b"four", b"", b"", b"seven"]) scatter = self.scatter_nd(indices, updates, shape=(8,)) - with self.test_session() as sess: + with self.cached_session() as sess: result = sess.run(scatter) self.assertAllEqual(expected, result) @@ -420,7 +420,7 @@ class ScatterNdTest(test.TestCase): dtype=dtypes.string) expected = np.array([b"", b"", b"", b"bb", b"a", b"", b"", b"c"]) scatter = self.scatter_nd(indices, updates, shape=(8,)) - with self.test_session() as sess: + with self.cached_session() as sess: result = sess.run(scatter) self.assertAllEqual(expected, result) @@ -432,7 +432,7 @@ class ScatterNdTest(test.TestCase): expected = [np.array([b"", b"", b"", b"bc", b"a", b"", b"", b"d"]), np.array([b"", b"", b"", b"cb", b"a", b"", b"", b"d"])] scatter = self.scatter_nd(indices, updates, shape=(8,)) - with self.test_session() as sess: + with self.cached_session() as sess: result = sess.run(scatter) self.assertTrue(np.array_equal(result, expected[0]) or np.array_equal(result, expected[1])) @@ -451,7 +451,7 @@ class ScatterNdTest(test.TestCase): scatter = self.scatter_nd(indices, updates, shape) self.assertAllEqual(scatter.get_shape().as_list(), shape) expected_result = np.zeros([2, 2], dtype=np.int32) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(expected_result, scatter.eval()) def testUndefinedIndicesShape(self): @@ -486,7 +486,7 @@ class ScatterNdTest(test.TestCase): updates = array_ops.placeholder(dtypes.int32, shape=None) shape = constant_op.constant([0, 3, 2], dtypes.int32) - with self.test_session(): + with self.cached_session(): with self.assertRaisesOpError( "Indices and updates specified for empty output"): self.scatter_nd(indices, updates, shape).eval(feed_dict={ @@ -500,7 +500,7 @@ class ScatterNdTest(test.TestCase): shape = constant_op.constant([0], dtypes.int32) scatter = self.scatter_nd(indices, updates, shape) - with self.test_session(): + with self.cached_session(): self.assertEqual(scatter.eval().size, 0) def testRank3InvalidShape1(self): @@ -531,7 +531,7 @@ class ScatterNdTest(test.TestCase): [outputs], [updates, input_], [grad_vals]) expected_updates_grad = np.array([1, 4], dtype=np.float64) expected_input_grad = np.array([[1, 2], [3, 4]], dtype=np.float64) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(expected_updates_grad, updates_grad.eval()) if self.non_aliasing_add_test: self.assertAllEqual(expected_input_grad, input_grad.eval()) @@ -548,7 +548,7 @@ class ScatterNdTest(test.TestCase): [outputs], [updates, input_], [grad_vals]) expected_updates_grad = np.array([[1, 2], [3, 4]], dtype=np.float64) expected_input_grad = np.array([[3, 4], [1, 2]], dtype=np.float64) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(expected_updates_grad, updates_grad.eval()) if self.non_aliasing_add_test: self.assertAllEqual(expected_input_grad, input_grad.eval()) @@ -570,7 +570,7 @@ class ScatterNdTest(test.TestCase): [[[3, 4], [5, 6]], [[1, 2], [7, 8]]], dtype=np.float64) expected_input_grad = np.array( [[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float64) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(expected_updates_grad, updates_grad.eval()) if self.non_aliasing_add_test: self.assertAllEqual(expected_input_grad, input_grad.eval()) @@ -607,7 +607,7 @@ class ScatterNdTest(test.TestCase): [[[[1, 2], [3, 4]]]], [[[[5, 6], [7, 8]]]] ]]], dtype=np.float64) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(expected_updates_grad, updates_grad.eval()) if self.non_aliasing_add_test: self.assertAllEqual(expected_input_grad, input_grad.eval()) @@ -616,33 +616,33 @@ class ScatterNdTest(test.TestCase): indices = array_ops.zeros([100000, 1], dtypes.int32) values = np.random.randn(100000) shape = [1] - with self.test_session(): + with self.cached_session(): val = self.scatter_nd(indices, values, shape).eval() self.assertAllClose([np.sum(values)], val) def testSmokeScatterNdBatch2DSliceDim2(self): - with self.test_session(): + with self.cached_session(): indices = array_ops.zeros([3, 5, 2], dtype=dtypes.int32) values = array_ops.zeros([3, 5, 7]) shape = [4, 6, 7] self.scatter_nd(indices, values, shape).eval() def testSmokeScatterNdBatch1DSliceDim2(self): - with self.test_session(): + with self.cached_session(): indices = array_ops.zeros([0, 2], dtype=dtypes.int32) values = array_ops.zeros([0, 7]) shape = [4, 6, 7] self.scatter_nd(indices, values, shape).eval() def testSmokeScatterNdBatch1DSliceDim3ShapeRank7(self): - with self.test_session(): + with self.cached_session(): indices = array_ops.zeros([1, 3], dtype=dtypes.int32) values = array_ops.zeros([1, 6, 7, 8, 9]) shape = [3, 4, 5, 6, 7, 8, 9] self.scatter_nd(indices, values, shape).eval() def testSmokeScatterNdBatch2DSliceDim3ShapeRank7(self): - with self.test_session(): + with self.cached_session(): indices = array_ops.zeros([1, 2, 3], dtype=dtypes.int32) values = array_ops.zeros([1, 2, 6, 7, 8, 9]) shape = [3, 4, 5, 6, 7, 8, 9] |