diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/pooling_ops_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/pooling_ops_test.py | 60 |
1 files changed, 2 insertions, 58 deletions
diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py index 150e2ff7f2..a126180414 100644 --- a/tensorflow/python/kernel_tests/pooling_ops_test.py +++ b/tensorflow/python/kernel_tests/pooling_ops_test.py @@ -19,7 +19,6 @@ from __future__ import division from __future__ import print_function import numpy as np -import os from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes @@ -1342,33 +1341,11 @@ class PoolingTest(test.TestCase): return # Test the GPU implementation that uses cudnn for now. - 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" + # It does not propagate the diff in cases of NaNs 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, - 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) - - # 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, @@ -1384,11 +1361,6 @@ class PoolingTest(test.TestCase): 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 = [ @@ -1419,14 +1391,11 @@ class PoolingTest(test.TestCase): return # Test the GPU implementation that uses cudnn for now. - 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" + # It does not propagate the diff in cases of NaNs 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, @@ -1442,31 +1411,6 @@ 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() |