diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/pooling_ops_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/pooling_ops_test.py | 59 |
1 files changed, 57 insertions, 2 deletions
diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index a126180414..6be8997cab 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import numpy as np from tensorflow.python.framework import constant_op @@ -1341,11 +1342,14 @@ class PoolingTest(test.TestCase): return # Test the GPU implementation that uses cudnn for now. - # It does not propagate the diff in cases of NaNs + saved_nanprop = os.environ.get("TF_ENABLE_MAXPOOL_NANPROP") + # Do not propagate the diff in cases of NaNs + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = "0" expected_input_backprop_cudnn = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] + for v2 in [True, False]: self._testMaxPoolGradDirect( input_data, @@ -1361,6 +1365,30 @@ class PoolingTest(test.TestCase): use_gpu=True, v2=v2) + # Propagate the diff in cases of NaNs + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = "1" + expected_input_backprop_cudnn = expected_input_backprop_tf_cpu + + for v2 in [True, False]: + self._testMaxPoolGradDirect( + input_data, + output_backprop, + expected_input_backprop_cudnn, + input_sizes=[1, 4, 4, 1], + output_sizes=[1, 3, 3, 1], + window_rows=2, + window_cols=2, + row_stride=1, + col_stride=1, + padding="VALID", + use_gpu=True, + v2=v2) + + if saved_nanprop: + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = saved_nanprop + else: + del os.environ["TF_ENABLE_MAXPOOL_NANPROP"] + def _testMaxPoolGradDirectWithNans2_2(self): input_data = [float("nan")] * 16 output_backprop = [ @@ -1391,11 +1419,14 @@ class PoolingTest(test.TestCase): return # Test the GPU implementation that uses cudnn for now. - # It does not propagate the diff in cases of NaNs + saved_nanprop = os.environ.get("TF_ENABLE_MAXPOOL_NANPROP") + # Do not propagate the diff in cases of NaNs + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = "0" expected_input_backprop_cudnn = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] + for v2 in [True, False]: self._testMaxPoolGradDirect( input_data, @@ -1411,6 +1442,30 @@ class PoolingTest(test.TestCase): use_gpu=True, v2=v2) + # Propagate the diff in cases of NaNs + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = "1" + expected_input_backprop_cudnn = expected_input_backprop_tf_cpu + + for v2 in [True, False]: + self._testMaxPoolGradDirect( + input_data, + output_backprop, + expected_input_backprop_cudnn, + input_sizes=[1, 4, 4, 1], + output_sizes=[1, 3, 3, 1], + window_rows=2, + window_cols=2, + row_stride=1, + col_stride=1, + padding="VALID", + use_gpu=True, + v2=v2) + + if saved_nanprop: + os.environ["TF_ENABLE_MAXPOOL_NANPROP"] = saved_nanprop + else: + del os.environ["TF_ENABLE_MAXPOOL_NANPROP"] + def testMaxPoolGradDirect(self): self._testMaxPoolGradDirect1_1() self._testMaxPoolGradDirect1_2() |