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Disable MLIR bridge for NMS image ops test 

MLIR bridge doesn't support tf.NonMaxSuppressionV4 legalization that is
conditionally generated by non_max_suppression_padded function.

PiperOrigin-RevId: 320197235
Change-Id: If7242133254680b366771ced50de074ed6180563
This commit is contained in:
Smit Hinsu 2020-07-08 08:57:06 -07:00 committed by Sachin Joglekar
parent 1725ab6962
commit bf61dd6420
1 changed files with 16 additions and 0 deletions
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16
tensorflow/compiler/tests/image_ops_test.py
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@ -30,6 +30,7 @@ from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.compiler.tests import xla_test from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_image_ops from tensorflow.python.ops import gen_image_ops
from tensorflow.python.ops import image_ops from tensorflow.python.ops import image_ops
@ -774,6 +775,7 @@ class ResizeBilinearNonAlignCornersTest(xla_test.XLATestCase):
class NonMaxSuppressionTest(xla_test.XLATestCase): class NonMaxSuppressionTest(xla_test.XLATestCase):
@test_util.disable_mlir_bridge("%1")
def testNMS128From1024(self): def testNMS128From1024(self):
num_boxes = 1024 num_boxes = 1024
boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4") boxes_np = np.random.normal(50, 10, (num_boxes, 4)).astype("f4")
@ -808,6 +810,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
self.assertEqual(indices_tf.size, max_output_size) self.assertEqual(indices_tf.size, max_output_size)
@test_util.disable_mlir_bridge("%1")
def testNMS3From6Boxes(self): def testNMS3From6Boxes(self):
# Three boxes are selected based on IOU. # 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], boxes_data = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
@ -849,6 +852,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
self.assertEqual(num_valid, 3) self.assertEqual(num_valid, 3)
self.assertAllClose(indices_tf[:num_valid], [3, 0, 5]) self.assertAllClose(indices_tf[:num_valid], [3, 0, 5])
@test_util.disable_mlir_bridge("%1")
def testNMS3Then2WithScoreThresh(self): def testNMS3Then2WithScoreThresh(self):
# Three boxes are selected based on IOU. # Three boxes are selected based on IOU.
# One is filtered out by score threshold. # One is filtered out by score threshold.
@ -891,6 +895,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
self.assertEqual(num_valid, 2) self.assertEqual(num_valid, 2)
self.assertAllClose(indices_tf[:num_valid], [3, 0]) self.assertAllClose(indices_tf[:num_valid], [3, 0])
@test_util.disable_mlir_bridge("%1")
def testNMS3Then1WithScoreMaxThresh(self): def testNMS3Then1WithScoreMaxThresh(self):
# Three boxes are selected based on IOU. # Three boxes are selected based on IOU.
# One is filtered out by score threshold. # One is filtered out by score threshold.
@ -934,6 +939,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
self.assertEqual(num_valid, 1) self.assertEqual(num_valid, 1)
self.assertAllClose(indices_tf[:num_valid], [3]) self.assertAllClose(indices_tf[:num_valid], [3])
@test_util.disable_mlir_bridge("%1")
def testSelectFromContinuousOverLap(self): def testSelectFromContinuousOverLap(self):
# Tests that a suppressed box does not itself suppress other boxes. # Tests that a suppressed box does not itself suppress other boxes.
@ -978,6 +984,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase):
class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase): class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSFrom6(self): def testBatchedNMSFrom6(self):
boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],
@ -1015,6 +1022,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
indices_output) indices_output)
self.assertAllEqual([5, 4], num_valid_output) self.assertAllEqual([5, 4], num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSFrom6Max3(self): def testBatchedNMSFrom6Max3(self):
boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],
@ -1048,6 +1056,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([[0, 1, 2], [0, 1, 3]], indices_output) self.assertAllEqual([[0, 1, 2], [0, 1, 3]], indices_output)
self.assertAllEqual([3, 3], num_valid_output) self.assertAllEqual([3, 3], num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSSingleFrom6Max3(self): def testBatchedNMSSingleFrom6Max3(self):
boxes_data = [[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]] [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]]
@ -1078,6 +1087,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([0, 1, 2], indices_output) self.assertAllEqual([0, 1, 2], indices_output)
self.assertAllEqual(3, num_valid_output) self.assertAllEqual(3, num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSSingleFrom6NoPad(self): def testBatchedNMSSingleFrom6NoPad(self):
boxes_data = [[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]] [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]]
@ -1107,6 +1117,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([0, 1, 2, 4, 5], indices_output) self.assertAllEqual([0, 1, 2, 4, 5], indices_output)
self.assertAllEqual(5, num_valid_output) self.assertAllEqual(5, num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSBatchDimsFrom6Max3(self): def testBatchedNMSBatchDimsFrom6Max3(self):
boxes_data = [[[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],
@ -1140,6 +1151,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([[[0, 1, 2], [0, 1, 3]]], indices_output) self.assertAllEqual([[[0, 1, 2], [0, 1, 3]]], indices_output)
self.assertAllEqual([[3, 3]], num_valid_output) self.assertAllEqual([[3, 3]], num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSScoreThresholdFrom6Max3(self): def testBatchedNMSScoreThresholdFrom6Max3(self):
boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],
@ -1175,6 +1187,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([3, 2], num_valid_output) self.assertAllEqual([3, 2], num_valid_output)
self.assertAllEqual([[0, 1, 2], [0, 1, invalid_index]], indices_output) self.assertAllEqual([[0, 1, 2], [0, 1, invalid_index]], indices_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSUnsortedInputFrom6(self): def testBatchedNMSUnsortedInputFrom6(self):
boxes_data = [[[0, 2, 1, 2], [3, 3, 4, 4], [0, 0, 1, 1], boxes_data = [[[0, 2, 1, 2], [3, 3, 4, 4], [0, 0, 1, 1],
[0, 0.4, 1, 1.4], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8]], [0, 0.4, 1, 1.4], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8]],
@ -1211,6 +1224,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
indices_output) indices_output)
self.assertAllEqual([5, 4], num_valid_output) self.assertAllEqual([5, 4], num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSNoncanonicalizedInputFrom6(self): def testBatchedNMSNoncanonicalizedInputFrom6(self):
boxes_data = [[[1, 0, 0, 1], [4, 3, 3, 4], [1, 0.4, 0, 1.4], boxes_data = [[[1, 0, 0, 1], [4, 3, 3, 4], [1, 0.4, 0, 1.4],
[1, 0.6, 0, 1.6], [1, 0.8, 0, 1.8], [1, 2, 0, 2]], [1, 0.6, 0, 1.6], [1, 0.8, 0, 1.8], [1, 2, 0, 2]],
@ -1248,6 +1262,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
indices_output) indices_output)
self.assertAllEqual([5, 4], num_valid_output) self.assertAllEqual([5, 4], num_valid_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSScoreThresholdCanInputsFrom6Max3(self): def testBatchedNMSScoreThresholdCanInputsFrom6Max3(self):
boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],
@ -1283,6 +1298,7 @@ class BatchedNonMaxSuppressionCorrectnessTest(xla_test.XLATestCase):
self.assertAllEqual([3, 2], num_valid_output) self.assertAllEqual([3, 2], num_valid_output)
self.assertAllEqual([[0, 1, 2], [0, 1, invalid_index]], indices_output) self.assertAllEqual([[0, 1, 2], [0, 1, invalid_index]], indices_output)
@test_util.disable_mlir_bridge("%1")
def testBatchedNMSFrom6DynamicInput(self): def testBatchedNMSFrom6DynamicInput(self):
boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4], boxes_data = [[[0, 0, 1, 1], [3, 3, 4, 4], [0, 0.4, 1, 1.4],
[0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]], [0, 0.6, 1, 1.6], [0, 0.8, 1, 1.8], [0, 2, 1, 2]],