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"""Tests for tensorflow.ops.tf.cast."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker as gc
class CastOpTest(tf.test.TestCase):
def _toDataType(self, dtype):
"""Returns TensorFlow data type for numpy type."""
if dtype == np.float32:
return tf.float32
elif dtype == np.float64:
return tf.float64
elif dtype == np.int32:
return tf.int32
elif dtype == np.int64:
return tf.int64
elif dtype == np.bool:
return tf.bool
else:
return None
def _cast(self, x, dtype, use_gpu=False):
with self.test_session(use_gpu=use_gpu):
val = tf.constant(x, self._toDataType(np.array([x]).dtype))
return tf.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]
else:
type_list = [np.float32, np.float64, np.int32, np.int64]
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.test_session(use_gpu=False):
b = tf.cast(tf.cast(a, tf.bfloat16), tf.float32)
self.assertAllClose(a, b.eval(), rtol=1/128.)
with self.test_session(use_gpu=True):
b = tf.cast(tf.cast(a, tf.bfloat16), tf.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)
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.test_session():
with self.assertRaisesOpError(err):
tf.cast(x, dtype).eval()
def testNotImplemented(self):
self._OpError(np.arange(0, 10), tf.string,
"Cast.*int64.*string.*")
def testGradients(self):
t = [tf.float32, tf.float64]
for src_t in t:
for dst_t in t:
with self.test_session():
x = tf.constant(1.0, src_t)
z = tf.identity(x)
y = tf.cast(z, dst_t)
err = gc.ComputeGradientError(x, [1], y, [1])
self.assertLess(err, 1e-3)
class SparseTensorCastTest(tf.test.TestCase):
def testCast(self):
indices = tf.constant([[0L], [1L], [2L]])
values = tf.constant(np.array([1, 2, 3], np.int64))
shape = tf.constant([3L])
st = tf.SparseTensor(indices, values, shape)
st_cast = tf.cast(st, tf.float32)
with self.test_session():
self.assertAllEqual(st_cast.indices.eval(), [[0L], [1L], [2L]])
self.assertAllEqual(st_cast.values.eval(),
np.array([1, 2, 3], np.float32))
self.assertAllEqual(st_cast.shape.eval(), [3L])
if __name__ == "__main__":
tf.test.main()
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