/
dtypes.py
650 lines (586 loc) · 21.3 KB
/
dtypes.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
# 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.
# ==============================================================================
"""Library of dtypes (Tensor element types)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import builtins
from tensorflow.core.framework import types_pb2
# We need to import pywrap_tensorflow prior to the bfloat wrapper to avoid
# protobuf errors where a file is defined twice on MacOS.
# pylint: disable=invalid-import-order,g-bad-import-order
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
from tensorflow.python import _pywrap_bfloat16
from tensorflow.python import _dtypes
from tensorflow.python.util.tf_export import tf_export
_np_bfloat16 = _pywrap_bfloat16.TF_bfloat16_type()
# pylint: disable=slots-on-old-class
@tf_export("dtypes.DType", "DType")
class DType(_dtypes.DType):
"""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.uint32`: 32-bit unsigned integer.
* `tf.uint64`: 64-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.
* `tf.variant`: Values of arbitrary types.
The `tf.as_dtype()` function converts numpy types and string type
names to a `DType` object.
"""
__slots__ = ()
@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` corresponding 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 as_numpy_dtype(self):
"""Returns a Python `type` object based on this `DType`."""
return _TF_TO_NP[self._type_enum]
@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:
if self.base_dtype == bfloat16:
return _np_bfloat16(float.fromhex("-0x1.FEp127"))
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:
if self.base_dtype == bfloat16:
return _np_bfloat16(float.fromhex("0x1.FEp127"))
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] # pylint: disable=redefined-builtin
if clip_negative:
min = 0 # pylint: disable=redefined-builtin
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
```
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
if type(other) != DType: # pylint: disable=unidiomatic-typecheck
try:
other = as_dtype(other)
except TypeError:
return False
return self._type_enum == other._type_enum # pylint: disable=protected-access
def __ne__(self, other):
"""Returns True iff self != other."""
return not self.__eq__(other)
# "If a class that overrides __eq__() needs to retain the implementation
# of __hash__() from a parent class, the interpreter must be told this
# explicitly by setting __hash__ = <ParentClass>.__hash__."
# TODO(slebedev): Remove once __eq__ and __ne__ are implemented in C++.
__hash__ = _dtypes.DType.__hash__
def __reduce__(self):
return as_dtype, (self.name,)
# pylint: enable=slots-on-old-class
# 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)
tf_export("dtypes.resource", "resource").export_constant(__name__, "resource")
variant = DType(types_pb2.DT_VARIANT)
tf_export("dtypes.variant", "variant").export_constant(__name__, "variant")
float16 = DType(types_pb2.DT_HALF)
tf_export("dtypes.float16", "float16").export_constant(__name__, "float16")
half = float16
tf_export("dtypes.half", "half").export_constant(__name__, "half")
float32 = DType(types_pb2.DT_FLOAT)
tf_export("dtypes.float32", "float32").export_constant(__name__, "float32")
float64 = DType(types_pb2.DT_DOUBLE)
tf_export("dtypes.float64", "float64").export_constant(__name__, "float64")
double = float64
tf_export("dtypes.double", "double").export_constant(__name__, "double")
int32 = DType(types_pb2.DT_INT32)
tf_export("dtypes.int32", "int32").export_constant(__name__, "int32")
uint8 = DType(types_pb2.DT_UINT8)
tf_export("dtypes.uint8", "uint8").export_constant(__name__, "uint8")
uint16 = DType(types_pb2.DT_UINT16)
tf_export("dtypes.uint16", "uint16").export_constant(__name__, "uint16")
uint32 = DType(types_pb2.DT_UINT32)
tf_export("dtypes.uint32", "uint32").export_constant(__name__, "uint32")
uint64 = DType(types_pb2.DT_UINT64)
tf_export("dtypes.uint64", "uint64").export_constant(__name__, "uint64")
int16 = DType(types_pb2.DT_INT16)
tf_export("dtypes.int16", "int16").export_constant(__name__, "int16")
int8 = DType(types_pb2.DT_INT8)
tf_export("dtypes.int8", "int8").export_constant(__name__, "int8")
string = DType(types_pb2.DT_STRING)
tf_export("dtypes.string", "string").export_constant(__name__, "string")
complex64 = DType(types_pb2.DT_COMPLEX64)
tf_export("dtypes.complex64",
"complex64").export_constant(__name__, "complex64")
complex128 = DType(types_pb2.DT_COMPLEX128)
tf_export("dtypes.complex128",
"complex128").export_constant(__name__, "complex128")
int64 = DType(types_pb2.DT_INT64)
tf_export("dtypes.int64", "int64").export_constant(__name__, "int64")
bool = DType(types_pb2.DT_BOOL) # pylint: disable=redefined-builtin
tf_export("dtypes.bool", "bool").export_constant(__name__, "bool")
qint8 = DType(types_pb2.DT_QINT8)
tf_export("dtypes.qint8", "qint8").export_constant(__name__, "qint8")
quint8 = DType(types_pb2.DT_QUINT8)
tf_export("dtypes.quint8", "quint8").export_constant(__name__, "quint8")
qint16 = DType(types_pb2.DT_QINT16)
tf_export("dtypes.qint16", "qint16").export_constant(__name__, "qint16")
quint16 = DType(types_pb2.DT_QUINT16)
tf_export("dtypes.quint16", "quint16").export_constant(__name__, "quint16")
qint32 = DType(types_pb2.DT_QINT32)
tf_export("dtypes.qint32", "qint32").export_constant(__name__, "qint32")
resource_ref = DType(types_pb2.DT_RESOURCE_REF)
variant_ref = DType(types_pb2.DT_VARIANT_REF)
bfloat16 = DType(types_pb2.DT_BFLOAT16)
tf_export("dtypes.bfloat16", "bfloat16").export_constant(__name__, "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)
uint32_ref = DType(types_pb2.DT_UINT32_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)
uint64_ref = DType(types_pb2.DT_UINT64_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_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,
}
# 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 = {
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.string_: string,
np.unicode_: string,
np.bool_: bool,
_np_qint8: qint8,
_np_quint8: quint8,
_np_qint16: qint16,
_np_quint16: quint16,
_np_qint32: qint32,
_np_bfloat16: bfloat16,
}
# Map (some) NumPy platform dtypes to TF ones using their fixed-width
# synonyms. Note that platform dtypes are not always simples aliases,
# i.e. reference equality is not guaranteed. See e.g. numpy/numpy#9799.
for pdt in [
np.intc,
np.uintc,
np.int_,
np.uint,
np.longlong,
np.ulonglong,
]:
if pdt not in _NP_TO_TF:
_NP_TO_TF[pdt] = next(
_NP_TO_TF[dt] for dt in _NP_TO_TF if dt == pdt().dtype)
TF_VALUE_DTYPES = set(_NP_TO_TF.values())
_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(
"dtypes.QUANTIZED_DTYPES",
v1=["dtypes.QUANTIZED_DTYPES",
"QUANTIZED_DTYPES"]).export_constant(__name__, "QUANTIZED_DTYPES")
_PYTHON_TO_TF = {
builtins.float: float32,
builtins.bool: bool,
builtins.object: string
}
_ANY_TO_TF = {}
_ANY_TO_TF.update(_INTERN_TABLE)
_ANY_TO_TF.update(_STRING_TO_TF)
_ANY_TO_TF.update(_PYTHON_TO_TF)
_ANY_TO_TF.update(_NP_TO_TF)
# Ensure no collisions.
assert len(_ANY_TO_TF) == sum(
len(d) for d in [_INTERN_TABLE, _STRING_TO_TF, _PYTHON_TO_TF, _NP_TO_TF])
@tf_export("dtypes.as_dtype", "as_dtype")
def as_dtype(type_value):
"""Converts the given `type_value` to a `DType`.
Note: `DType` values are interned. When passed a new `DType` object,
`as_dtype` always returns the interned value.
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 _INTERN_TABLE[type_value.as_datatype_enum]
if isinstance(type_value, np.dtype):
try:
return _NP_TO_TF[type_value.type]
except KeyError:
pass
try:
return _ANY_TO_TF[type_value]
except (KeyError, TypeError):
# TypeError indicates that type_value is not hashable.
pass
if hasattr(type_value, "dtype"):
try:
return _NP_TO_TF[np.dtype(type_value.dtype).type]
except (KeyError, TypeError):
pass
if isinstance(type_value, _dtypes.DType):
return _INTERN_TABLE[type_value.as_datatype_enum]
raise TypeError("Cannot convert value %r to a TensorFlow DType." %
(type_value,))