# Copyright 2015 Google Inc. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Tests for tf.bitcast."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import numpy as np

import tensorflow as tf

class BitcastTest(tf.test.TestCase):

def _testBitcast(self, x, datatype, shape):

with self.test_session():

tf_ans = tf.bitcast(x, datatype)

out = tf_ans.eval()

buff_after = memoryview(out).tobytes()

buff_before = memoryview(x).tobytes()

self.assertEqual(buff_before, buff_after)

self.assertEqual(tf_ans.get_shape(), shape)

def testSmaller(self):

x = np.random.rand(3, 2)

datatype = tf.int8

shape = [3, 2, 8]

self._testBitcast(x, datatype, shape)

def testLarger(self):

x = np.arange(16, dtype=np.int8).reshape([4, 4])

datatype = tf.int32

shape = [4]

self._testBitcast(x, datatype, shape)

def testSameDtype(self):

x = np.random.rand(3, 4)

shape = [3, 4]

self._testBitcast(x, x.dtype, shape)

def testSameSize(self):

x = np.random.rand(3, 4)

shape = [3, 4]

self._testBitcast(x, tf.double, shape)

def testErrors(self):

x = np.zeros([1, 1], np.int8)

datatype = tf.int32

with self.assertRaisesRegexp(ValueError, "Cannot bitcast due to shape"):

tf.bitcast(x, datatype, None)

def testEmpty(self):

x = np.ones([], np.int32)

datatype = tf.int8

shape = [4]

self._testBitcast(x, datatype, shape)

if __name__ == "__main__":

tf.test.main()