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# Copyright 2015 The TensorFlow Authors. 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
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class BitcastTest(test.TestCase):
def _testBitcast(self, x, datatype, shape):
with self.test_session(use_gpu=True):
tf_ans = array_ops.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)
self.assertEqual(tf_ans.dtype, datatype)
def testSmaller(self):
x = np.random.rand(3, 2)
datatype = dtypes.int8
shape = [3, 2, 8]
self._testBitcast(x, datatype, shape)
def testLarger(self):
x = np.arange(16, dtype=np.int8).reshape([4, 4])
datatype = dtypes.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, dtypes.int64, shape)
def testErrors(self):
x = np.zeros([1, 1], np.int8)
datatype = dtypes.int32
with self.assertRaisesRegexp(ValueError, "Cannot bitcast due to shape"):
array_ops.bitcast(x, datatype, None)
def testEmpty(self):
x = np.ones([], np.int32)
datatype = dtypes.int8
shape = [4]
self._testBitcast(x, datatype, shape)
def testUnknown(self):
x = array_ops.placeholder(dtypes.float32)
datatype = dtypes.int8
array_ops.bitcast(x, datatype, None)
def testQuantizedType(self):
shape = [3, 4]
x = np.zeros(shape, np.uint16)
datatype = dtypes.quint16
self._testBitcast(x, datatype, shape)
def testUnsignedType(self):
shape = [3, 4]
x = np.zeros(shape, np.int64)
datatype = dtypes.uint64
self._testBitcast(x, datatype, shape)
if __name__ == "__main__":
test.main()
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