Home | History | Annotate | Download | only in kernel_tests 
      1 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 #
      3 # Licensed under the Apache License, Version 2.0 (the "License");
      4 # you may not use this file except in compliance with the License.
      5 # You may obtain a copy of the License at
      6 #
      7 #     http://www.apache.org/licenses/LICENSE-2.0
      8 #
      9 # Unless required by applicable law or agreed to in writing, software
     10 # distributed under the License is distributed on an "AS IS" BASIS,
     11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 # See the License for the specific language governing permissions and
     13 # limitations under the License.
     14 # ==============================================================================
     15 """Tests for tensorflow.ops.compare_and_bitpack_op."""
     16 
     17 from __future__ import absolute_import
     18 from __future__ import division
     19 from __future__ import print_function
     20 
     21 import numpy as np
     22 
     23 from tensorflow.python.ops import math_ops
     24 from tensorflow.python.platform import test
     25 
     26 
     27 class CompareAndBitpackTest(test.TestCase):
     28 
     29   def _testCompareAndBitpack(self,
     30                              x, threshold,
     31                              truth,
     32                              expected_err_re=None):
     33     with self.test_session(use_gpu=True):
     34       ans = math_ops.compare_and_bitpack(x, threshold)
     35       if expected_err_re is None:
     36         tf_ans = ans.eval()
     37         self.assertShapeEqual(truth, ans)
     38         self.assertAllEqual(tf_ans, truth)
     39       else:
     40         with self.assertRaisesOpError(expected_err_re):
     41           ans.eval()
     42 
     43   def _testBasic(self, dtype):
     44     rows = 371
     45     cols = 294
     46     x = np.random.randn(rows, cols * 8)
     47     if dtype == np.bool:
     48       x = x > 0
     49     else:
     50       x = x.astype(dtype)
     51     threshold = dtype(0)
     52     # np.packbits flattens the tensor, so we reshape it back to the
     53     # expected dimensions.
     54     truth = np.packbits(x > threshold).reshape(rows, cols)
     55     self._testCompareAndBitpack(x, threshold, truth)
     56 
     57   def testBasicFloat32(self):
     58     self._testBasic(np.float32)
     59 
     60   def testBasicFloat64(self):
     61     self._testBasic(np.float64)
     62 
     63   def testBasicFloat16(self):
     64     self._testBasic(np.float16)
     65 
     66   def testBasicBool(self):
     67     self._testBasic(np.bool)
     68 
     69   def testBasicInt8(self):
     70     self._testBasic(np.int8)
     71 
     72   def testBasicInt16(self):
     73     self._testBasic(np.int16)
     74 
     75   def testBasicInt32(self):
     76     self._testBasic(np.int32)
     77 
     78   def testBasicInt64(self):
     79     self._testBasic(np.int64)
     80 
     81 
     82 if __name__ == "__main__":
     83   test.main()
     84