TensorFlow使用dtype库
2018-10-10 17:04 更新
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＃ ============================================================================
“”“ dtypes库(张量元素类型)“”“
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.framework import types_pb2
class DType(object):
"""Represents the type of the elements in a `Tensor`.
The following `DType` objects are defined:
* `tf.float16`: 16-bit half-precision floating-point.
* `tf.float32`: 32-bit single-precision floating-point.
* `tf.float64`: 64-bit double-precision floating-point.
* `tf.bfloat16`: 16-bit truncated floating-point.
* `tf.complex64`: 64-bit single-precision complex.
* `tf.complex128`: 128-bit double-precision complex.
* `tf.int8`: 8-bit signed integer.
* `tf.uint8`: 8-bit unsigned integer.
* `tf.uint16`: 16-bit unsigned integer.
* `tf.int16`: 16-bit signed integer.
* `tf.int32`: 32-bit signed integer.
* `tf.int64`: 64-bit signed integer.
* `tf.bool`: Boolean.
* `tf.string`: String.
* `tf.qint8`: Quantized 8-bit signed integer.
* `tf.quint8`: Quantized 8-bit unsigned integer.
* `tf.qint16`: Quantized 16-bit signed integer.
* `tf.quint16`: Quantized 16-bit unsigned integer.
* `tf.qint32`: Quantized 32-bit signed integer.
* `tf.resource`: Handle to a mutable resource.
In addition, variants of these types with the `_ref` suffix are
defined for reference-typed tensors.
The `tf.as_dtype()` function converts numpy types and string type
names to a `DType` object.
"""
def __init__(self, type_enum):
"""Creates a new `DataType`.
NOTE(mrry): In normal circumstances, you should not need to
construct a `DataType` object directly. Instead, use the
`tf.as_dtype()` function.
Args:
type_enum: A `types_pb2.DataType` enum value.
Raises:
TypeError: If `type_enum` is not a value `types_pb2.DataType`.
"""
# TODO(mrry): Make the necessary changes (using __new__) to ensure
# that calling this returns one of the interned values.
type_enum = int(type_enum)
if (type_enum not in types_pb2.DataType.values()
or type_enum == types_pb2.DT_INVALID):
raise TypeError(
"type_enum is not a valid types_pb2.DataType: %s" % type_enum)
self._type_enum = type_enum
@property
def _is_ref_dtype(self):
"""Returns `True` if this `DType` represents a reference type."""
return self._type_enum > 100
@property
def _as_ref(self):
"""Returns a reference `DType` based on this `DType`."""
if self._is_ref_dtype:
return self
else:
return _INTERN_TABLE[self._type_enum + 100]
@property
def base_dtype(self):
"""Returns a non-reference `DType` based on this `DType`."""
if self._is_ref_dtype:
return _INTERN_TABLE[self._type_enum - 100]
else:
return self
@property
def real_dtype(self):
"""Returns the dtype correspond to this dtype's real part."""
base = self.base_dtype
if base == complex64:
return float32
elif base == complex128:
return float64
else:
return self
@property
def is_numpy_compatible(self):
return (self._type_enum != types_pb2.DT_RESOURCE and
self._type_enum != types_pb2.DT_RESOURCE_REF)
@property
def as_numpy_dtype(self):
"""Returns a `numpy.dtype` based on this `DType`."""
return _TF_TO_NP[self._type_enum]
@property
def as_datatype_enum(self):
"""Returns a `types_pb2.DataType` enum value based on this `DType`."""
return self._type_enum
@property
def is_bool(self):
"""Returns whether this is a boolean data type"""
return self.base_dtype == bool
@property
def is_integer(self):
"""Returns whether this is a (non-quantized) integer type."""
return (self.is_numpy_compatible and not self.is_quantized and
issubclass(self.as_numpy_dtype, np.integer))
@property
def is_floating(self):
"""Returns whether this is a (non-quantized, real) floating point type."""
return self.is_numpy_compatible and issubclass(self.as_numpy_dtype,
np.floating)
@property
def is_complex(self):
"""Returns whether this is a complex floating point type."""
return self.base_dtype in (complex64, complex128)
@property
def is_quantized(self):
"""Returns whether this is a quantized data type."""
return self.base_dtype in [qint8, quint8, qint16, quint16, qint32, bfloat16]
@property
def is_unsigned(self):
"""Returns whether this type is unsigned.
Non-numeric, unordered, and quantized types are not considered unsigned, and
this function returns `False`.
Returns:
Whether a `DType` is unsigned.
"""
try:
return self.min == 0
except TypeError:
return False
@property
def min(self):
"""Returns the minimum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
if (self.is_quantized or self.base_dtype in
(bool, string, complex64, complex128)):
raise TypeError("Cannot find minimum value of %s." % self)
# there is no simple way to get the min value of a dtype, we have to check
# float and int types separately
try:
return np.finfo(self.as_numpy_dtype()).min
except: # bare except as possible raises by finfo not documented
try:
return np.iinfo(self.as_numpy_dtype()).min
except:
raise TypeError("Cannot find minimum value of %s." % self)
@property
def max(self):
"""Returns the maximum representable value in this data type.
Raises:
TypeError: if this is a non-numeric, unordered, or quantized type.
"""
if (self.is_quantized or self.base_dtype in
(bool, string, complex64, complex128)):
raise TypeError("Cannot find maximum value of %s." % self)
# there is no simple way to get the max value of a dtype, we have to check
# float and int types separately
try:
return np.finfo(self.as_numpy_dtype()).max
except: # bare except as possible raises by finfo not documented
try:
return np.iinfo(self.as_numpy_dtype()).max
except:
raise TypeError("Cannot find maximum value of %s." % self)
@property
def limits(self, clip_negative=True):
"""Return intensity limits, i.e. (min, max) tuple, of the dtype.
Args:
clip_negative : bool, optional
If True, clip the negative range (i.e. return 0 for min intensity)
even if the image dtype allows negative values.
Returns
min, max : tuple
Lower and upper intensity limits.
"""
min, max = dtype_range[self.as_numpy_dtype]
if clip_negative:
min = 0
return min, max
def is_compatible_with(self, other):
"""Returns True if the `other` DType will be converted to this DType.
The conversion rules are as follows:
```python
DType(T) .is_compatible_with(DType(T)) == True
DType(T) .is_compatible_with(DType(T).as_ref) == True
DType(T).as_ref.is_compatible_with(DType(T)) == False
DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
```
Args:
other: A `DType` (or object that may be converted to a `DType`).
Returns:
True if a Tensor of the `other` `DType` will be implicitly converted to
this `DType`.
"""
other = as_dtype(other)
return self._type_enum in (
other.as_datatype_enum, other.base_dtype.as_datatype_enum)
def __eq__(self, other):
"""Returns True iff this DType refers to the same type as `other`."""
if other is None:
return False
try:
dtype = as_dtype(other).as_datatype_enum
return self._type_enum == dtype # pylint: disable=protected-access
except TypeError:
return False
def __ne__(self, other):
"""Returns True iff self != other."""
return not self.__eq__(other)
@property
def name(self):
"""Returns the string name for this `DType`."""
return _TYPE_TO_STRING[self._type_enum]
def __int__(self):
return self._type_enum
def __str__(self):
return "<dtype: %r>" % self.name
def __repr__(self):
return "tf." + self.name
def __hash__(self):
return self._type_enum
@property
def size(self):
if self._type_enum == types_pb2.DT_RESOURCE:
return 1
return np.dtype(self.as_numpy_dtype).itemsize
# Define data type range of numpy dtype
dtype_range = {np.bool_: (False, True),
np.bool8: (False, True),
np.uint8: (0, 255),
np.uint16: (0, 65535),
np.int8: (-128, 127),
np.int16: (-32768, 32767),
np.int64: (-2**63, 2**63 - 1),
np.uint64: (0, 2**64 - 1),
np.int32: (-2**31, 2**31 - 1),
np.uint32: (0, 2**32 - 1),
np.float32: (-1, 1),
np.float64: (-1, 1)}
# Define standard wrappers for the types_pb2.DataType enum.
resource = DType(types_pb2.DT_RESOURCE)
float16 = DType(types_pb2.DT_HALF)
half = float16
float32 = DType(types_pb2.DT_FLOAT)
float64 = DType(types_pb2.DT_DOUBLE)
double = float64
int32 = DType(types_pb2.DT_INT32)
uint8 = DType(types_pb2.DT_UINT8)
uint16 = DType(types_pb2.DT_UINT16)
int16 = DType(types_pb2.DT_INT16)
int8 = DType(types_pb2.DT_INT8)
string = DType(types_pb2.DT_STRING)
complex64 = DType(types_pb2.DT_COMPLEX64)
complex128 = DType(types_pb2.DT_COMPLEX128)
int64 = DType(types_pb2.DT_INT64)
bool = DType(types_pb2.DT_BOOL)
qint8 = DType(types_pb2.DT_QINT8)
quint8 = DType(types_pb2.DT_QUINT8)
qint16 = DType(types_pb2.DT_QINT16)
quint16 = DType(types_pb2.DT_QUINT16)
qint32 = DType(types_pb2.DT_QINT32)
resource_ref = DType(types_pb2.DT_RESOURCE_REF)
bfloat16 = DType(types_pb2.DT_BFLOAT16)
float16_ref = DType(types_pb2.DT_HALF_REF)
half_ref = float16_ref
float32_ref = DType(types_pb2.DT_FLOAT_REF)
float64_ref = DType(types_pb2.DT_DOUBLE_REF)
double_ref = float64_ref
int32_ref = DType(types_pb2.DT_INT32_REF)
uint8_ref = DType(types_pb2.DT_UINT8_REF)
uint16_ref = DType(types_pb2.DT_UINT16_REF)
int16_ref = DType(types_pb2.DT_INT16_REF)
int8_ref = DType(types_pb2.DT_INT8_REF)
string_ref = DType(types_pb2.DT_STRING_REF)
complex64_ref = DType(types_pb2.DT_COMPLEX64_REF)
complex128_ref = DType(types_pb2.DT_COMPLEX128_REF)
int64_ref = DType(types_pb2.DT_INT64_REF)
bool_ref = DType(types_pb2.DT_BOOL_REF)
qint8_ref = DType(types_pb2.DT_QINT8_REF)
quint8_ref = DType(types_pb2.DT_QUINT8_REF)
qint16_ref = DType(types_pb2.DT_QINT16_REF)
quint16_ref = DType(types_pb2.DT_QUINT16_REF)
qint32_ref = DType(types_pb2.DT_QINT32_REF)
bfloat16_ref = DType(types_pb2.DT_BFLOAT16_REF)
# Maintain an intern table so that we don't have to create a large
# number of small objects.
_INTERN_TABLE = {
types_pb2.DT_HALF: float16,
types_pb2.DT_FLOAT: float32,
types_pb2.DT_DOUBLE: float64,
types_pb2.DT_INT32: int32,
types_pb2.DT_UINT8: uint8,
types_pb2.DT_UINT16: uint16,
types_pb2.DT_INT16: int16,
types_pb2.DT_INT8: int8,
types_pb2.DT_STRING: string,
types_pb2.DT_COMPLEX64: complex64,
types_pb2.DT_COMPLEX128: complex128,
types_pb2.DT_INT64: int64,
types_pb2.DT_BOOL: bool,
types_pb2.DT_QINT8: qint8,
types_pb2.DT_QUINT8: quint8,
types_pb2.DT_QINT16: qint16,
types_pb2.DT_QUINT16: quint16,
types_pb2.DT_QINT32: qint32,
types_pb2.DT_BFLOAT16: bfloat16,
types_pb2.DT_RESOURCE: resource,
types_pb2.DT_HALF_REF: float16_ref,
types_pb2.DT_FLOAT_REF: float32_ref,
types_pb2.DT_DOUBLE_REF: float64_ref,
types_pb2.DT_INT32_REF: int32_ref,
types_pb2.DT_UINT8_REF: uint8_ref,
types_pb2.DT_UINT16_REF: uint16_ref,
types_pb2.DT_INT16_REF: int16_ref,
types_pb2.DT_INT8_REF: int8_ref,
types_pb2.DT_STRING_REF: string_ref,
types_pb2.DT_COMPLEX64_REF: complex64_ref,
types_pb2.DT_COMPLEX128_REF: complex128_ref,
types_pb2.DT_INT64_REF: int64_ref,
types_pb2.DT_BOOL_REF: bool_ref,
types_pb2.DT_QINT8_REF: qint8_ref,
types_pb2.DT_QUINT8_REF: quint8_ref,
types_pb2.DT_QINT16_REF: qint16_ref,
types_pb2.DT_QUINT16_REF: quint16_ref,
types_pb2.DT_QINT32_REF: qint32_ref,
types_pb2.DT_BFLOAT16_REF: bfloat16_ref,
types_pb2.DT_RESOURCE_REF: resource_ref,
}
# Standard mappings between types_pb2.DataType values and string names.
_TYPE_TO_STRING = {
types_pb2.DT_HALF: "float16",
types_pb2.DT_FLOAT: "float32",
types_pb2.DT_DOUBLE: "float64",
types_pb2.DT_INT32: "int32",
types_pb2.DT_UINT8: "uint8",
types_pb2.DT_UINT16: "uint16",
types_pb2.DT_INT16: "int16",
types_pb2.DT_INT8: "int8",
types_pb2.DT_STRING: "string",
types_pb2.DT_COMPLEX64: "complex64",
types_pb2.DT_COMPLEX128: "complex128",
types_pb2.DT_INT64: "int64",
types_pb2.DT_BOOL: "bool",
types_pb2.DT_QINT8: "qint8",
types_pb2.DT_QUINT8: "quint8",
types_pb2.DT_QINT16: "qint16",
types_pb2.DT_QUINT16: "quint16",
types_pb2.DT_QINT32: "qint32",
types_pb2.DT_BFLOAT16: "bfloat16",
types_pb2.DT_RESOURCE: "resource",
types_pb2.DT_HALF_REF: "float16_ref",
types_pb2.DT_FLOAT_REF: "float32_ref",
types_pb2.DT_DOUBLE_REF: "float64_ref",
types_pb2.DT_INT32_REF: "int32_ref",
types_pb2.DT_UINT8_REF: "uint8_ref",
types_pb2.DT_UINT16_REF: "uint16_ref",
types_pb2.DT_INT16_REF: "int16_ref",
types_pb2.DT_INT8_REF: "int8_ref",
types_pb2.DT_STRING_REF: "string_ref",
types_pb2.DT_COMPLEX64_REF: "complex64_ref",
types_pb2.DT_COMPLEX128_REF: "complex128_ref",
types_pb2.DT_INT64_REF: "int64_ref",
types_pb2.DT_BOOL_REF: "bool_ref",
types_pb2.DT_QINT8_REF: "qint8_ref",
types_pb2.DT_QUINT8_REF: "quint8_ref",
types_pb2.DT_QINT16_REF: "qint16_ref",
types_pb2.DT_QUINT16_REF: "quint16_ref",
types_pb2.DT_QINT32_REF: "qint32_ref",
types_pb2.DT_BFLOAT16_REF: "bfloat16_ref",
types_pb2.DT_RESOURCE_REF: "resource_ref",
}
_STRING_TO_TF = {value: _INTERN_TABLE[key]
for key, value in _TYPE_TO_STRING.items()}
# Add non-canonical aliases.
_STRING_TO_TF["half"] = float16
_STRING_TO_TF["half_ref"] = float16_ref
_STRING_TO_TF["float"] = float32
_STRING_TO_TF["float_ref"] = float32_ref
_STRING_TO_TF["double"] = float64
_STRING_TO_TF["double_ref"] = float64_ref
# Numpy representation for quantized dtypes.
#
# These are magic strings that are used in the swig wrapper to identify
# quantized types.
# TODO(mrry,keveman): Investigate Numpy type registration to replace this
# hard-coding of names.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
# Custom struct dtype for directly-fed ResourceHandles of supported type(s).
np_resource = np.dtype([("resource", np.ubyte, 1)])
# Standard mappings between types_pb2.DataType values and numpy.dtypes.
_NP_TO_TF = frozenset([
(np.float16, float16),
(np.float32, float32),
(np.float64, float64),
(np.int32, int32),
(np.int64, int64),
(np.uint8, uint8),
(np.uint16, uint16),
(np.int16, int16),
(np.int8, int8),
(np.complex64, complex64),
(np.complex128, complex128),
(np.object, string),
(np.bool, bool),
(_np_qint8, qint8),
(_np_quint8, quint8),
(_np_qint16, qint16),
(_np_quint16, quint16),
(_np_qint32, qint32),
# NOTE(touts): Intentionally no way to feed a DT_BFLOAT16.
])
_TF_TO_NP = {
types_pb2.DT_HALF: np.float16,
types_pb2.DT_FLOAT: np.float32,
types_pb2.DT_DOUBLE: np.float64,
types_pb2.DT_INT32: np.int32,
types_pb2.DT_UINT8: np.uint8,
types_pb2.DT_UINT16: np.uint16,
types_pb2.DT_INT16: np.int16,
types_pb2.DT_INT8: np.int8,
# NOTE(touts): For strings we use np.object as it supports variable length
# strings.
types_pb2.DT_STRING: np.object,
types_pb2.DT_COMPLEX64: np.complex64,
types_pb2.DT_COMPLEX128: np.complex128,
types_pb2.DT_INT64: np.int64,
types_pb2.DT_BOOL: np.bool,
types_pb2.DT_QINT8: _np_qint8,
types_pb2.DT_QUINT8: _np_quint8,
types_pb2.DT_QINT16: _np_qint16,
types_pb2.DT_QUINT16: _np_quint16,
types_pb2.DT_QINT32: _np_qint32,
types_pb2.DT_BFLOAT16: np.uint16,
# Ref types
types_pb2.DT_HALF_REF: np.float16,
types_pb2.DT_FLOAT_REF: np.float32,
types_pb2.DT_DOUBLE_REF: np.float64,
types_pb2.DT_INT32_REF: np.int32,
types_pb2.DT_UINT8_REF: np.uint8,
types_pb2.DT_UINT16_REF: np.uint16,
types_pb2.DT_INT16_REF: np.int16,
types_pb2.DT_INT8_REF: np.int8,
types_pb2.DT_STRING_REF: np.object,
types_pb2.DT_COMPLEX64_REF: np.complex64,
types_pb2.DT_COMPLEX128_REF: np.complex128,
types_pb2.DT_INT64_REF: np.int64,
types_pb2.DT_BOOL_REF: np.bool,
types_pb2.DT_QINT8_REF: _np_qint8,
types_pb2.DT_QUINT8_REF: _np_quint8,
types_pb2.DT_QINT16_REF: _np_qint16,
types_pb2.DT_QUINT16_REF: _np_quint16,
types_pb2.DT_QINT32_REF: _np_qint32,
types_pb2.DT_BFLOAT16_REF: np.uint16,
}
QUANTIZED_DTYPES = frozenset(
[qint8, quint8, qint16, quint16, qint32, qint8_ref, quint8_ref, qint16_ref,
quint16_ref, qint32_ref])
def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Args:
type_value: A value that can be converted to a `tf.DType`
object. This may currently be a `tf.DType` object, a
[`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),
a string type name, or a `numpy.dtype`.
Returns:
A `DType` corresponding to `type_value`.
Raises:
TypeError: If `type_value` cannot be converted to a `DType`.
"""
if isinstance(type_value, DType):
return type_value
try:
return _INTERN_TABLE[type_value]
except KeyError:
pass
try:
return _STRING_TO_TF[type_value]
except KeyError:
pass
if isinstance(type_value, np.dtype):
# The numpy dtype for strings is variable length. We can not compare
# dtype with a single constant (np.string does not exist) to decide
# dtype is a "string" type. We need to compare the dtype.type to be
# sure it's a string type.
if type_value.type == np.string_ or type_value.type == np.unicode_:
return string
for key, val in _NP_TO_TF:
try:
if key == type_value:
return val
except TypeError as e:
raise TypeError("Cannot convert {} to a dtype. {}".format(type_value, e))
raise TypeError(
"Cannot convert value %r to a TensorFlow DType." % type_value)以上内容是否对您有帮助：
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