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authorGravatar Adrian Kuegel <akuegel@google.com>2018-09-19 05:20:57 -0700
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2018-09-19 05:26:15 -0700
commit27ea406e6d43ffc0d63f61782c413fe4d8483193 (patch)
treeb6120e9e47251d092e16c50811a8b8809accf6cc /tensorflow/compiler/tests/image_ops_test.py
parent7b936cb6c4ca47c8d3a63b42364998c86d87f2cf (diff)
Enable tests for CPU and GPU backends that involve XlaSort.
PiperOrigin-RevId: 213611371
Diffstat (limited to 'tensorflow/compiler/tests/image_ops_test.py')
-rw-r--r--tensorflow/compiler/tests/image_ops_test.py12
1 files changed, 0 insertions, 12 deletions
diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py
index 6fe5a66e0e..bbe746e28f 100644
--- a/tensorflow/compiler/tests/image_ops_test.py
+++ b/tensorflow/compiler/tests/image_ops_test.py
@@ -605,10 +605,6 @@ class ResizeBilinearTest(xla_test.XLATestCase):
class NonMaxSuppressionTest(xla_test.XLATestCase):
def testNMS128From1024(self):
- # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
- if self.device in ["XLA_CPU", "XLA_GPU"]:
- return
-
with compat.forward_compatibility_horizon(2018, 8, 8):
num_boxes = 1024
boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4")
@@ -644,10 +640,6 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
self.assertEqual(indices_tf.size, max_output_size)
def testNMS3From6Boxes(self):
- # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
- if self.device in ["XLA_CPU", "XLA_GPU"]:
- return
-
with compat.forward_compatibility_horizon(2018, 8, 8):
# Three boxes are selected based on IOU.
boxes_data = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
@@ -693,10 +685,6 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
# Three boxes are selected based on IOU.
# One is filtered out by score threshold.
- # TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
- if self.device in ["XLA_CPU", "XLA_GPU"]:
- return
-
with compat.forward_compatibility_horizon(2018, 8, 8):
boxes_data = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
[0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]]