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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 tensorflow.ops.tf.cast."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import sys
import platform
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class CastOpTest(test.TestCase):
def _toDataType(self, dtype):
"""Returns TensorFlow data type for numpy type."""
if dtype == np.float32:
return dtypes.float32
elif dtype == np.float64:
return dtypes.float64
elif dtype == np.int32:
return dtypes.int32
elif dtype == np.int64:
return dtypes.int64
elif dtype == np.bool:
return dtypes.bool
elif dtype == np.complex64:
return dtypes.complex64
elif dtype == np.complex128:
return dtypes.complex128
else:
return None
def _cast(self, x, dtype, use_gpu=False):
with self.cached_session(use_gpu=use_gpu):
val = constant_op.constant(x, self._toDataType(np.array([x]).dtype))
return math_ops.cast(val, self._toDataType(dtype), name="cast").eval()
def _test(self, x, dtype, use_gpu=False):
"""Tests cast(x) to dtype behaves the same as numpy.astype."""
np_ans = x.astype(dtype)
tf_ans = self._cast(x, dtype, use_gpu)
self.assertAllEqual(np_ans, tf_ans)
def _testTypes(self, x, use_gpu=False):
"""Tests cast(x) to different tf."""
if use_gpu:
type_list = [
np.float32, np.float64, np.int64, np.complex64, np.complex128
]
else:
type_list = [
np.float32, np.float64, np.int32, np.int64, np.complex64,
np.complex128
]
for from_type in type_list:
for to_type in type_list:
self._test(x.astype(from_type), to_type, use_gpu)
self._test(x.astype(np.bool), np.float32, use_gpu)
self._test(x.astype(np.uint8), np.float32, use_gpu)
if not use_gpu:
self._test(x.astype(np.bool), np.int32, use_gpu)
self._test(x.astype(np.int32), np.int32, use_gpu)
def _testAll(self, x):
self._testTypes(x, use_gpu=False)
if x.dtype == np.float32 or x.dtype == np.float64:
self._testTypes(x, use_gpu=True)
def testBasic(self):
self._testAll(np.arange(-10, 10).reshape(2, 10))
self._testAll(np.linspace(-10, 10, 17))
def testSmallValues(self):
f4 = np.finfo(np.float32)
f8 = np.finfo(np.float64)
self._testAll(
np.array([
0, -1, 1, -f4.resolution, f4.resolution, f8.resolution,
-f8.resolution
]))
def testBfloat16(self):
a = np.random.uniform(-100, 100, 100).astype(np.float32)
with self.cached_session(use_gpu=False):
b = math_ops.cast(math_ops.cast(a, dtypes.bfloat16), dtypes.float32)
self.assertAllClose(a, b.eval(), rtol=1 / 128.)
with self.cached_session(use_gpu=True):
b = math_ops.cast(math_ops.cast(a, dtypes.bfloat16), dtypes.float32)
self.assertAllClose(a, b.eval(), rtol=1 / 128.)
def testRandom(self):
self._testAll(np.random.normal(0, 10, 210).reshape([2, 3, 5, 7]))
self._testAll(np.random.normal(0, 1e6, 210).reshape([2, 3, 5, 7]))
# Special values like int32max, int64min, inf, -inf, nan casted to
# integer values in somewhat unexpected ways. And they behave
# differently on CPU and GPU.
def _compare(self, x, dst_dtype, expected, use_gpu=False):
np.testing.assert_equal(
self._cast(
x, dst_dtype, use_gpu=use_gpu), dst_dtype(expected))
def testIntToFloatBoundary(self):
i4 = np.iinfo(np.int32)
i8 = np.iinfo(np.int64)
self._compare(i4.min, np.float32, i4.min, False)
self._compare(i4.max, np.float32, i4.max, False)
self._compare(i8.min, np.float32, i8.min, False)
self._compare(i8.max, np.float32, i8.max, False)
self._compare(i4.min, np.float64, i4.min, False)
self._compare(i4.max, np.float64, i4.max, False)
self._compare(i8.min, np.float64, i8.min, False)
self._compare(i8.max, np.float64, i8.max, False)
# NOTE: GPU does not support int32/int64 for casting.
def testInfNan(self):
i4 = np.iinfo(np.int32)
i8 = np.iinfo(np.int64)
self._compare(np.inf, np.float32, np.inf, False)
self._compare(np.inf, np.float64, np.inf, False)
if sys.byteorder == "big":
self._compare(np.inf, np.int32, i4.max, False)
self._compare(np.inf, np.int64, i8.max, False)
else:
# np.float64("np.inf").astype(np.int32) is negative on x86 but positive on ppc64le
# Numpy link to relevant discussion - https://github.com/numpy/numpy/issues/9040
# Tensorflow link to relevant discussion - https://github.com/tensorflow/tensorflow/issues/9360
if platform.machine() == "ppc64le":
self._compare(-np.inf, np.int32, i4.min, False)
self._compare(-np.inf, np.int64, i8.min, False)
else:
self._compare(np.inf, np.int32, i4.min, False)
self._compare(np.inf, np.int64, i8.min, False)
self._compare(-np.inf, np.float32, -np.inf, False)
self._compare(-np.inf, np.float64, -np.inf, False)
self._compare(-np.inf, np.int32, i4.min, False)
self._compare(-np.inf, np.int64, i8.min, False)
self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, False)), True)
self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, False)), True)
self._compare(np.nan, np.int32, i4.min, False)
self._compare(np.nan, np.int64, i8.min, False)
self._compare(np.inf, np.float32, np.inf, True)
self._compare(np.inf, np.float64, np.inf, True)
self._compare(-np.inf, np.float32, -np.inf, True)
self._compare(-np.inf, np.float64, -np.inf, True)
self.assertAllEqual(np.isnan(self._cast(np.nan, np.float32, True)), True)
self.assertAllEqual(np.isnan(self._cast(np.nan, np.float64, True)), True)
def _OpError(self, x, dtype, err):
with self.cached_session():
with self.assertRaisesOpError(err):
math_ops.cast(x, dtype).eval()
def testNotImplemented(self):
self._OpError(np.arange(0, 10), dtypes.string, "Cast.*int64.*string.*")
def testCastToTypeOfVariable(self):
with self.cached_session() as sess:
x = variables.Variable(5, dtype=dtypes.float32)
y = variables.Variable(True, dtype=dtypes.bool)
cast = math_ops.cast(y, x.dtype)
variables.global_variables_initializer().run()
self.assertEqual(1.0, sess.run(cast))
def testGradients(self):
t = [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
for src_t in t:
for dst_t in t:
with self.cached_session():
x = constant_op.constant(1.0, src_t)
z = array_ops.identity(x)
y = math_ops.cast(z, dst_t)
err = gradient_checker.compute_gradient_error(x, [], y, [])
self.assertLess(err, 1e-3)
class SparseTensorCastTest(test.TestCase):
def testCast(self):
indices = constant_op.constant([[0], [1], [2]], dtypes.int64)
values = constant_op.constant(np.array([1, 2, 3], np.int64))
shape = constant_op.constant([3], dtypes.int64)
st = sparse_tensor.SparseTensor(indices, values, shape)
st_cast = math_ops.cast(st, dtypes.float32)
with self.cached_session():
self.assertAllEqual(st_cast.indices.eval(), [[0], [1], [2]])
self.assertAllEqual(st_cast.values.eval(),
np.array([1, 2, 3], np.float32))
self.assertAllEqual(st_cast.dense_shape.eval(), [3])
class SaturateCastTest(test.TestCase):
def testSaturate(self):
in_types = dtypes.float32,
out_types = dtypes.int8, dtypes.uint8, dtypes.int16, dtypes.float32
with self.cached_session() as sess:
for in_type in in_types:
for out_type in out_types:
lo, hi = in_type.min, in_type.max
x = constant_op.constant(
[lo, lo + 1, lo // 2, hi // 2, hi - 1, hi], dtype=in_type)
y = math_ops.saturate_cast(x, dtype=out_type)
self.assertEqual(y.dtype, out_type)
x, y = sess.run([x, y])
correct = np.maximum(out_type.min, np.minimum(out_type.max, x))
self.assertAllEqual(correct, y)
if __name__ == "__main__":
test.main()