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"""Tests for tensorflow.python.framework.importer."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.core.framework import types_pb2
from tensorflow.python.framework import test_util
from tensorflow.python.framework import types
from tensorflow.python.platform import googletest
class TypesTest(test_util.TensorFlowTestCase):
def testAllTypesConstructible(self):
for datatype_enum in types_pb2.DataType.values():
if datatype_enum == types_pb2.DT_INVALID:
continue
self.assertEqual(
datatype_enum, types.DType(datatype_enum).as_datatype_enum)
def testAllTypesConvertibleToDType(self):
for datatype_enum in types_pb2.DataType.values():
if datatype_enum == types_pb2.DT_INVALID:
continue
self.assertEqual(
datatype_enum, types.as_dtype(datatype_enum).as_datatype_enum)
def testAllTypesConvertibleToNumpyDtype(self):
for datatype_enum in types_pb2.DataType.values():
if datatype_enum == types_pb2.DT_INVALID:
continue
dtype = types.as_dtype(datatype_enum)
numpy_dtype = dtype.as_numpy_dtype
_ = np.empty((1, 1, 1, 1), dtype=numpy_dtype)
if dtype.base_dtype != types.bfloat16:
# NOTE(mdevin): Intentionally no way to feed a DT_BFLOAT16.
self.assertEqual(
types.as_dtype(datatype_enum).base_dtype, types.as_dtype(numpy_dtype))
def testInvalid(self):
with self.assertRaises(TypeError):
types.DType(types_pb2.DT_INVALID)
with self.assertRaises(TypeError):
types.as_dtype(types_pb2.DT_INVALID)
def testNumpyConversion(self):
self.assertIs(types.float32, types.as_dtype(np.float32))
self.assertIs(types.float64, types.as_dtype(np.float64))
self.assertIs(types.int32, types.as_dtype(np.int32))
self.assertIs(types.int64, types.as_dtype(np.int64))
self.assertIs(types.uint8, types.as_dtype(np.uint8))
self.assertIs(types.int16, types.as_dtype(np.int16))
self.assertIs(types.int8, types.as_dtype(np.int8))
self.assertIs(types.complex64, types.as_dtype(np.complex64))
self.assertIs(types.string, types.as_dtype(np.object))
self.assertIs(types.string, types.as_dtype(np.array(["foo", "bar"]).dtype))
self.assertIs(types.bool, types.as_dtype(np.bool))
with self.assertRaises(TypeError):
types.as_dtype(np.dtype([("f1", np.uint), ("f2", np.int32)]))
def testStringConversion(self):
self.assertIs(types.float32, types.as_dtype("float32"))
self.assertIs(types.float64, types.as_dtype("float64"))
self.assertIs(types.int32, types.as_dtype("int32"))
self.assertIs(types.uint8, types.as_dtype("uint8"))
self.assertIs(types.int16, types.as_dtype("int16"))
self.assertIs(types.int8, types.as_dtype("int8"))
self.assertIs(types.string, types.as_dtype("string"))
self.assertIs(types.complex64, types.as_dtype("complex64"))
self.assertIs(types.int64, types.as_dtype("int64"))
self.assertIs(types.bool, types.as_dtype("bool"))
self.assertIs(types.qint8, types.as_dtype("qint8"))
self.assertIs(types.quint8, types.as_dtype("quint8"))
self.assertIs(types.qint32, types.as_dtype("qint32"))
self.assertIs(types.bfloat16, types.as_dtype("bfloat16"))
self.assertIs(types.float32_ref, types.as_dtype("float32_ref"))
self.assertIs(types.float64_ref, types.as_dtype("float64_ref"))
self.assertIs(types.int32_ref, types.as_dtype("int32_ref"))
self.assertIs(types.uint8_ref, types.as_dtype("uint8_ref"))
self.assertIs(types.int16_ref, types.as_dtype("int16_ref"))
self.assertIs(types.int8_ref, types.as_dtype("int8_ref"))
self.assertIs(types.string_ref, types.as_dtype("string_ref"))
self.assertIs(types.complex64_ref, types.as_dtype("complex64_ref"))
self.assertIs(types.int64_ref, types.as_dtype("int64_ref"))
self.assertIs(types.bool_ref, types.as_dtype("bool_ref"))
self.assertIs(types.qint8_ref, types.as_dtype("qint8_ref"))
self.assertIs(types.quint8_ref, types.as_dtype("quint8_ref"))
self.assertIs(types.qint32_ref, types.as_dtype("qint32_ref"))
self.assertIs(types.bfloat16_ref, types.as_dtype("bfloat16_ref"))
with self.assertRaises(TypeError):
types.as_dtype("not_a_type")
def testDTypesHaveUniqueNames(self):
dtypes = []
names = set()
for datatype_enum in types_pb2.DataType.values():
if datatype_enum == types_pb2.DT_INVALID:
continue
dtype = types.as_dtype(datatype_enum)
dtypes.append(dtype)
names.add(dtype.name)
self.assertEqual(len(dtypes), len(names))
def testIsInteger(self):
self.assertEqual(types.as_dtype("int8").is_integer, True)
self.assertEqual(types.as_dtype("int16").is_integer, True)
self.assertEqual(types.as_dtype("int32").is_integer, True)
self.assertEqual(types.as_dtype("int64").is_integer, True)
self.assertEqual(types.as_dtype("uint8").is_integer, True)
self.assertEqual(types.as_dtype("complex64").is_integer, False)
self.assertEqual(types.as_dtype("float").is_integer, False)
self.assertEqual(types.as_dtype("double").is_integer, False)
self.assertEqual(types.as_dtype("string").is_integer, False)
self.assertEqual(types.as_dtype("bool").is_integer, False)
def testMinMax(self):
# make sure min/max evaluates for all data types that have min/max
for datatype_enum in types_pb2.DataType.values():
if datatype_enum == types_pb2.DT_INVALID:
continue
dtype = types.as_dtype(datatype_enum)
numpy_dtype = dtype.as_numpy_dtype
# ignore types for which there are no minimum/maximum (or we cannot
# compute it, such as for the q* types)
if (dtype.is_quantized or
dtype.base_dtype == types.bool or
dtype.base_dtype == types.string or
dtype.base_dtype == types.complex64):
continue
print "%s: %s - %s" % (dtype, dtype.min, dtype.max)
# check some values that are known
if numpy_dtype == np.bool_:
self.assertEquals(dtype.min, 0)
self.assertEquals(dtype.max, 1)
if numpy_dtype == np.int8:
self.assertEquals(dtype.min, -128)
self.assertEquals(dtype.max, 127)
if numpy_dtype == np.int16:
self.assertEquals(dtype.min, -32768)
self.assertEquals(dtype.max, 32767)
if numpy_dtype == np.int32:
self.assertEquals(dtype.min, -2147483648)
self.assertEquals(dtype.max, 2147483647)
if numpy_dtype == np.int64:
self.assertEquals(dtype.min, -9223372036854775808)
self.assertEquals(dtype.max, 9223372036854775807)
if numpy_dtype == np.uint8:
self.assertEquals(dtype.min, 0)
self.assertEquals(dtype.max, 255)
if numpy_dtype == np.uint16:
self.assertEquals(dtype.min, 0)
self.assertEquals(dtype.max, 4294967295)
if numpy_dtype == np.uint32:
self.assertEquals(dtype.min, 0)
self.assertEquals(dtype.max, 18446744073709551615)
if numpy_dtype in (np.float16, np.float32, np.float64):
self.assertEquals(dtype.min, np.finfo(numpy_dtype).min)
self.assertEquals(dtype.max, np.finfo(numpy_dtype).max)
def testRepr(self):
for enum, name in types._TYPE_TO_STRING.iteritems():
dtype = types.DType(enum)
self.assertEquals(repr(dtype), 'tf.' + name)
dtype2 = eval(repr(dtype))
self.assertEquals(type(dtype2), types.DType)
self.assertEquals(dtype, dtype2)
if __name__ == "__main__":
googletest.main()
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