693 lines
24 KiB
Python
693 lines
24 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Library of dtypes (Tensor element types)."""
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import numpy as np
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from . import pywrap_tensorflow
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from tensorboard.compat.proto import types_pb2
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_np_bfloat16 = pywrap_tensorflow.TF_bfloat16_type()
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# @tf_export("DType")
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class DType:
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"""Represents the type of the elements in a `Tensor`.
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The following `DType` objects are defined:
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* `tf.float16`: 16-bit half-precision floating-point.
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* `tf.float32`: 32-bit single-precision floating-point.
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* `tf.float64`: 64-bit double-precision floating-point.
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* `tf.bfloat16`: 16-bit truncated floating-point.
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* `tf.complex64`: 64-bit single-precision complex.
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* `tf.complex128`: 128-bit double-precision complex.
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* `tf.int8`: 8-bit signed integer.
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* `tf.uint8`: 8-bit unsigned integer.
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* `tf.uint16`: 16-bit unsigned integer.
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* `tf.uint32`: 32-bit unsigned integer.
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* `tf.uint64`: 64-bit unsigned integer.
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* `tf.int16`: 16-bit signed integer.
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* `tf.int32`: 32-bit signed integer.
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* `tf.int64`: 64-bit signed integer.
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* `tf.bool`: Boolean.
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* `tf.string`: String.
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* `tf.qint8`: Quantized 8-bit signed integer.
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* `tf.quint8`: Quantized 8-bit unsigned integer.
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* `tf.qint16`: Quantized 16-bit signed integer.
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* `tf.quint16`: Quantized 16-bit unsigned integer.
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* `tf.qint32`: Quantized 32-bit signed integer.
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* `tf.resource`: Handle to a mutable resource.
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* `tf.variant`: Values of arbitrary types.
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In addition, variants of these types with the `_ref` suffix are
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defined for reference-typed tensors.
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The `tf.as_dtype()` function converts numpy types and string type
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names to a `DType` object.
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"""
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def __init__(self, type_enum):
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"""Creates a new `DataType`.
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NOTE(mrry): In normal circumstances, you should not need to
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construct a `DataType` object directly. Instead, use the
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`tf.as_dtype()` function.
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Args:
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type_enum: A `types_pb2.DataType` enum value.
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Raises:
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TypeError: If `type_enum` is not a value `types_pb2.DataType`.
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"""
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# TODO(mrry): Make the necessary changes (using __new__) to ensure
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# that calling this returns one of the interned values.
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type_enum = int(type_enum)
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if (
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type_enum not in types_pb2.DataType.values()
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or type_enum == types_pb2.DT_INVALID
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):
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raise TypeError(
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"type_enum is not a valid types_pb2.DataType: %s" % type_enum
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)
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self._type_enum = type_enum
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@property
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def _is_ref_dtype(self):
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"""Returns `True` if this `DType` represents a reference type."""
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return self._type_enum > 100
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@property
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def _as_ref(self):
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"""Returns a reference `DType` based on this `DType`."""
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if self._is_ref_dtype:
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return self
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else:
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return _INTERN_TABLE[self._type_enum + 100]
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@property
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def base_dtype(self):
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"""Returns a non-reference `DType` based on this `DType`."""
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if self._is_ref_dtype:
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return _INTERN_TABLE[self._type_enum - 100]
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else:
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return self
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@property
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def real_dtype(self):
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"""Returns the dtype correspond to this dtype's real part."""
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base = self.base_dtype
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if base == complex64:
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return float32
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elif base == complex128:
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return float64
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else:
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return self
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@property
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def is_numpy_compatible(self):
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return self._type_enum not in _NUMPY_INCOMPATIBLE
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@property
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def as_numpy_dtype(self):
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"""Returns a `numpy.dtype` based on this `DType`."""
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return _TF_TO_NP[self._type_enum]
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@property
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def as_datatype_enum(self):
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"""Returns a `types_pb2.DataType` enum value based on this `DType`."""
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return self._type_enum
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@property
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def is_bool(self):
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"""Returns whether this is a boolean data type."""
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return self.base_dtype == bool
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@property
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def is_integer(self):
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"""Returns whether this is a (non-quantized) integer type."""
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return (
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self.is_numpy_compatible
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and not self.is_quantized
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and np.issubdtype(self.as_numpy_dtype, np.integer)
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)
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@property
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def is_floating(self):
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"""Returns whether this is a (non-quantized, real) floating point
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type."""
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return (
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self.is_numpy_compatible
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and np.issubdtype(self.as_numpy_dtype, np.floating)
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) or self.base_dtype == bfloat16
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@property
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def is_complex(self):
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"""Returns whether this is a complex floating point type."""
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return self.base_dtype in (complex64, complex128)
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@property
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def is_quantized(self):
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"""Returns whether this is a quantized data type."""
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return self.base_dtype in _QUANTIZED_DTYPES_NO_REF
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@property
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def is_unsigned(self):
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"""Returns whether this type is unsigned.
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Non-numeric, unordered, and quantized types are not considered unsigned, and
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this function returns `False`.
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Returns:
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Whether a `DType` is unsigned.
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"""
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try:
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return self.min == 0
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except TypeError:
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return False
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@property
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def min(self):
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"""Returns the minimum representable value in this data type.
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Raises:
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TypeError: if this is a non-numeric, unordered, or quantized type.
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"""
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if self.is_quantized or self.base_dtype in (
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bool,
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string,
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complex64,
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complex128,
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):
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raise TypeError("Cannot find minimum value of %s." % self)
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# there is no simple way to get the min value of a dtype, we have to check
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# float and int types separately
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try:
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return np.finfo(self.as_numpy_dtype).min
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except: # bare except as possible raises by finfo not documented
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try:
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return np.iinfo(self.as_numpy_dtype).min
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except:
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if self.base_dtype == bfloat16:
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return _np_bfloat16(float.fromhex("-0x1.FEp127"))
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raise TypeError("Cannot find minimum value of %s." % self)
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@property
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def max(self):
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"""Returns the maximum representable value in this data type.
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Raises:
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TypeError: if this is a non-numeric, unordered, or quantized type.
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"""
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if self.is_quantized or self.base_dtype in (
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bool,
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string,
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complex64,
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complex128,
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):
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raise TypeError("Cannot find maximum value of %s." % self)
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# there is no simple way to get the max value of a dtype, we have to check
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# float and int types separately
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try:
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return np.finfo(self.as_numpy_dtype).max
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except: # bare except as possible raises by finfo not documented
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try:
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return np.iinfo(self.as_numpy_dtype).max
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except:
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if self.base_dtype == bfloat16:
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return _np_bfloat16(float.fromhex("0x1.FEp127"))
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raise TypeError("Cannot find maximum value of %s." % self)
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@property
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def limits(self, clip_negative=True):
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"""Return intensity limits, i.e. (min, max) tuple, of the dtype.
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Args:
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clip_negative : bool, optional
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If True, clip the negative range (i.e. return 0 for min intensity)
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even if the image dtype allows negative values.
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Returns
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min, max : tuple
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Lower and upper intensity limits.
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"""
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min, max = dtype_range[
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self.as_numpy_dtype
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] # pylint: disable=redefined-builtin
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if clip_negative:
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min = 0 # pylint: disable=redefined-builtin
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return min, max
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def is_compatible_with(self, other):
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"""Returns True if the `other` DType will be converted to this DType.
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The conversion rules are as follows:
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```python
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DType(T) .is_compatible_with(DType(T)) == True
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DType(T) .is_compatible_with(DType(T).as_ref) == True
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DType(T).as_ref.is_compatible_with(DType(T)) == False
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DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
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```
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Args:
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other: A `DType` (or object that may be converted to a `DType`).
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Returns:
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True if a Tensor of the `other` `DType` will be implicitly converted to
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this `DType`.
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"""
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other = as_dtype(other)
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return self._type_enum in (
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other.as_datatype_enum,
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other.base_dtype.as_datatype_enum,
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)
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def __eq__(self, other):
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"""Returns True iff this DType refers to the same type as `other`."""
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if other is None:
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return False
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try:
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dtype = as_dtype(other).as_datatype_enum
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return self._type_enum == dtype # pylint: disable=protected-access
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except TypeError:
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return False
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def __ne__(self, other):
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"""Returns True iff self != other."""
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return not self.__eq__(other)
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@property
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def name(self):
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"""Returns the string name for this `DType`."""
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return _TYPE_TO_STRING[self._type_enum]
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def __int__(self):
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return self._type_enum
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def __str__(self):
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return "<dtype: %r>" % self.name
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def __repr__(self):
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return "tf." + self.name
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def __hash__(self):
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return self._type_enum
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def __reduce__(self):
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return as_dtype, (self.name,)
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@property
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def size(self):
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if (
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self._type_enum == types_pb2.DT_VARIANT
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or self._type_enum == types_pb2.DT_RESOURCE
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):
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return 1
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return np.dtype(self.as_numpy_dtype).itemsize
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# Define data type range of numpy dtype
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dtype_range = {
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np.bool_: (False, True),
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np.uint8: (0, 255),
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np.uint16: (0, 65535),
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np.int8: (-128, 127),
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np.int16: (-32768, 32767),
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np.int64: (-(2**63), 2**63 - 1),
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np.uint64: (0, 2**64 - 1),
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np.int32: (-(2**31), 2**31 - 1),
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np.uint32: (0, 2**32 - 1),
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np.float32: (-1, 1),
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np.float64: (-1, 1),
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}
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# Define standard wrappers for the types_pb2.DataType enum.
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resource = DType(types_pb2.DT_RESOURCE)
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# tf_export("resource").export_constant(__name__, "resource")
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variant = DType(types_pb2.DT_VARIANT)
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# tf_export("variant").export_constant(__name__, "variant")
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float16 = DType(types_pb2.DT_HALF)
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# tf_export("float16").export_constant(__name__, "float16")
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half = float16
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# tf_export("half").export_constant(__name__, "half")
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float32 = DType(types_pb2.DT_FLOAT)
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# tf_export("float32").export_constant(__name__, "float32")
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float64 = DType(types_pb2.DT_DOUBLE)
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# tf_export("float64").export_constant(__name__, "float64")
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double = float64
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# tf_export("double").export_constant(__name__, "double")
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int32 = DType(types_pb2.DT_INT32)
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# tf_export("int32").export_constant(__name__, "int32")
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uint8 = DType(types_pb2.DT_UINT8)
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# tf_export("uint8").export_constant(__name__, "uint8")
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uint16 = DType(types_pb2.DT_UINT16)
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# tf_export("uint16").export_constant(__name__, "uint16")
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uint32 = DType(types_pb2.DT_UINT32)
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# tf_export("uint32").export_constant(__name__, "uint32")
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uint64 = DType(types_pb2.DT_UINT64)
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# tf_export("uint64").export_constant(__name__, "uint64")
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int16 = DType(types_pb2.DT_INT16)
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# tf_export("int16").export_constant(__name__, "int16")
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int8 = DType(types_pb2.DT_INT8)
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# tf_export("int8").export_constant(__name__, "int8")
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string = DType(types_pb2.DT_STRING)
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# tf_export("string").export_constant(__name__, "string")
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complex64 = DType(types_pb2.DT_COMPLEX64)
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# tf_export("complex64").export_constant(__name__, "complex64")
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complex128 = DType(types_pb2.DT_COMPLEX128)
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# tf_export("complex128").export_constant(__name__, "complex128")
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int64 = DType(types_pb2.DT_INT64)
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# tf_export("int64").export_constant(__name__, "int64")
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bool = DType(types_pb2.DT_BOOL) # pylint: disable=redefined-builtin
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# tf_export("bool").export_constant(__name__, "bool")
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qint8 = DType(types_pb2.DT_QINT8)
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# tf_export("qint8").export_constant(__name__, "qint8")
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quint8 = DType(types_pb2.DT_QUINT8)
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# tf_export("quint8").export_constant(__name__, "quint8")
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qint16 = DType(types_pb2.DT_QINT16)
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# tf_export("qint16").export_constant(__name__, "qint16")
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quint16 = DType(types_pb2.DT_QUINT16)
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# tf_export("quint16").export_constant(__name__, "quint16")
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qint32 = DType(types_pb2.DT_QINT32)
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# tf_export("qint32").export_constant(__name__, "qint32")
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resource_ref = DType(types_pb2.DT_RESOURCE_REF)
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variant_ref = DType(types_pb2.DT_VARIANT_REF)
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bfloat16 = DType(types_pb2.DT_BFLOAT16)
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# tf_export("bfloat16").export_constant(__name__, "bfloat16")
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float16_ref = DType(types_pb2.DT_HALF_REF)
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half_ref = float16_ref
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float32_ref = DType(types_pb2.DT_FLOAT_REF)
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float64_ref = DType(types_pb2.DT_DOUBLE_REF)
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double_ref = float64_ref
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int32_ref = DType(types_pb2.DT_INT32_REF)
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uint32_ref = DType(types_pb2.DT_UINT32_REF)
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uint8_ref = DType(types_pb2.DT_UINT8_REF)
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uint16_ref = DType(types_pb2.DT_UINT16_REF)
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int16_ref = DType(types_pb2.DT_INT16_REF)
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int8_ref = DType(types_pb2.DT_INT8_REF)
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string_ref = DType(types_pb2.DT_STRING_REF)
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complex64_ref = DType(types_pb2.DT_COMPLEX64_REF)
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complex128_ref = DType(types_pb2.DT_COMPLEX128_REF)
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int64_ref = DType(types_pb2.DT_INT64_REF)
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uint64_ref = DType(types_pb2.DT_UINT64_REF)
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bool_ref = DType(types_pb2.DT_BOOL_REF)
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qint8_ref = DType(types_pb2.DT_QINT8_REF)
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quint8_ref = DType(types_pb2.DT_QUINT8_REF)
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qint16_ref = DType(types_pb2.DT_QINT16_REF)
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quint16_ref = DType(types_pb2.DT_QUINT16_REF)
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qint32_ref = DType(types_pb2.DT_QINT32_REF)
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bfloat16_ref = DType(types_pb2.DT_BFLOAT16_REF)
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_NUMPY_INCOMPATIBLE = frozenset(
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[
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types_pb2.DT_VARIANT,
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types_pb2.DT_VARIANT_REF,
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types_pb2.DT_RESOURCE,
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types_pb2.DT_RESOURCE_REF,
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]
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)
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# Maintain an intern table so that we don't have to create a large
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# number of small objects.
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_INTERN_TABLE = {
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types_pb2.DT_HALF: float16,
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types_pb2.DT_FLOAT: float32,
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types_pb2.DT_DOUBLE: float64,
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types_pb2.DT_INT32: int32,
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types_pb2.DT_UINT8: uint8,
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types_pb2.DT_UINT16: uint16,
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types_pb2.DT_UINT32: uint32,
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types_pb2.DT_UINT64: uint64,
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types_pb2.DT_INT16: int16,
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types_pb2.DT_INT8: int8,
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types_pb2.DT_STRING: string,
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types_pb2.DT_COMPLEX64: complex64,
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types_pb2.DT_COMPLEX128: complex128,
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types_pb2.DT_INT64: int64,
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types_pb2.DT_BOOL: bool,
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types_pb2.DT_QINT8: qint8,
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types_pb2.DT_QUINT8: quint8,
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types_pb2.DT_QINT16: qint16,
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types_pb2.DT_QUINT16: quint16,
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types_pb2.DT_QINT32: qint32,
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types_pb2.DT_BFLOAT16: bfloat16,
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types_pb2.DT_RESOURCE: resource,
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types_pb2.DT_VARIANT: variant,
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types_pb2.DT_HALF_REF: float16_ref,
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types_pb2.DT_FLOAT_REF: float32_ref,
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types_pb2.DT_DOUBLE_REF: float64_ref,
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types_pb2.DT_INT32_REF: int32_ref,
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types_pb2.DT_UINT32_REF: uint32_ref,
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types_pb2.DT_UINT8_REF: uint8_ref,
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types_pb2.DT_UINT16_REF: uint16_ref,
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types_pb2.DT_INT16_REF: int16_ref,
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types_pb2.DT_INT8_REF: int8_ref,
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types_pb2.DT_STRING_REF: string_ref,
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types_pb2.DT_COMPLEX64_REF: complex64_ref,
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types_pb2.DT_COMPLEX128_REF: complex128_ref,
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|
types_pb2.DT_INT64_REF: int64_ref,
|
|
types_pb2.DT_UINT64_REF: uint64_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,
|
|
types_pb2.DT_VARIANT_REF: variant_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_UINT32: "uint32",
|
|
types_pb2.DT_UINT64: "uint64",
|
|
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_VARIANT: "variant",
|
|
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_UINT32_REF: "uint32_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_UINT64_REF: "uint64_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",
|
|
types_pb2.DT_VARIANT_REF: "variant_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)])
|
|
_np_quint8 = np.dtype([("quint8", np.uint8)])
|
|
_np_qint16 = np.dtype([("qint16", np.int16)])
|
|
_np_quint16 = np.dtype([("quint16", np.uint16)])
|
|
_np_qint32 = np.dtype([("qint32", np.int32)])
|
|
|
|
# _np_bfloat16 is defined by a module import.
|
|
|
|
# Custom struct dtype for directly-fed ResourceHandles of supported type(s).
|
|
np_resource = np.dtype([("resource", np.ubyte)])
|
|
|
|
# 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.uint32, uint32),
|
|
(np.uint64, uint64),
|
|
(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),
|
|
# TODO(#1677): _np_bfloat16 is defined as 0. This causes `as_dtype` to
|
|
# error. Add below back after we fix `TF_bfloat16_type`.
|
|
# (_np_bfloat16, 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_UINT32: np.uint32,
|
|
types_pb2.DT_UINT64: np.uint64,
|
|
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_bfloat16,
|
|
# 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_UINT32_REF: np.uint32,
|
|
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_UINT64_REF: np.uint64,
|
|
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_bfloat16,
|
|
}
|
|
|
|
_QUANTIZED_DTYPES_NO_REF = frozenset([qint8, quint8, qint16, quint16, qint32])
|
|
_QUANTIZED_DTYPES_REF = frozenset(
|
|
[qint8_ref, quint8_ref, qint16_ref, quint16_ref, qint32_ref]
|
|
)
|
|
QUANTIZED_DTYPES = _QUANTIZED_DTYPES_REF.union(_QUANTIZED_DTYPES_NO_REF)
|
|
# tf_export("QUANTIZED_DTYPES").export_constant(__name__, "QUANTIZED_DTYPES")
|
|
|
|
_PYTHON_TO_TF = {float: float32, bool: bool}
|
|
|
|
|
|
# @tf_export("as_dtype")
|
|
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
|
|
|
|
try:
|
|
return _PYTHON_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
|
|
|
|
if isinstance(type_value, (type, np.dtype)):
|
|
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
|
|
)
|