-
Notifications
You must be signed in to change notification settings - Fork 1.5k
/
tensor_feature.py
341 lines (294 loc) · 12.1 KB
/
tensor_feature.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
# coding=utf-8
# Copyright 2023 The TensorFlow Datasets Authors.
#
# 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.
"""Feature connector."""
from __future__ import annotations
import enum
import functools
from typing import Optional, Protocol, Tuple, TypeVar, Union
import zlib
from etils import enp
import numpy as np
from tensorflow_datasets.core import utils
from tensorflow_datasets.core.features import feature as feature_lib
from tensorflow_datasets.core.proto import feature_pb2
from tensorflow_datasets.core.utils import dtype_utils
from tensorflow_datasets.core.utils import np_utils
from tensorflow_datasets.core.utils import py_utils
from tensorflow_datasets.core.utils import type_utils
from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow as tf
Json = utils.Json
Shape = utils.Shape
T = TypeVar('T')
class Encoding(enum.Enum):
"""Encoding type of `tfds.features.Tensor`.
For higher dimension tensors, it is recommended to define the encoding as
zlib or bytes to save space on disk.
Attributes:
NONE: No compression (default). bools/integers will be upcasted to int64 as
this is the only integer format supported by the
[`tf.train.Example`](https://www.tensorflow.org/tutorials/load_data/tfrecord#tftrainexample)
protobufs in which examples are saved.
BYTES: Stored as raw bytes (avoid the upcasting from above).
ZLIB: The raw bytes are compressed using zlib.
"""
NONE = 'none'
BYTES = 'bytes'
ZLIB = 'zlib'
# Could eventually add GZIP too (as supported by `tf.io.decode_compressed`
# but feel redundant with ZLIB.
class Tensor(feature_lib.FeatureConnector):
"""`FeatureConnector` for generic data of arbitrary shape and type."""
# For backward compatibility with the `features.json` saved by
# `FeatureConnector.save_config`
ALIASES = ['tensorflow_datasets.core.features.feature.Tensor']
def __init__(
self,
*,
shape: utils.Shape,
dtype: type_utils.TfdsDType,
# TODO(tfds): Could add an Encoding.AUTO to automatically compress
# tensors using some heuristic. However, careful about backward
# compatibility.
# Would require some `DatasetInfo.api_version = 1` which would be
# increased when triggering backward-incompatible changes.
encoding: Union[str, Encoding] = Encoding.NONE,
doc: feature_lib.DocArg = None,
serialized_dtype: Optional[type_utils.TfdsDType] = None,
serialized_shape: Optional[utils.Shape] = None,
):
"""Construct a Tensor feature.
Args:
shape: Tensor shape
dtype: Tensor dtype
encoding: Internal encoding. See `tfds.features.Encoding` for available
values.
doc: Documentation of this feature (e.g. description).
serialized_dtype: Tensor dtype. Used to validate that serialized examples
have this dtype. If `None` then defaults to `dtype`
serialized_shape: Tensor shape. Used to validate that serialized examples
have this shape. If `None` then defaults to `shape`
"""
super().__init__(doc=doc)
self._shape = tuple(shape)
self._dtype = dtype_utils.cast_to_numpy(dtype)
self._serialized_dtype = dtype_utils.cast_to_numpy(
serialized_dtype or self._dtype
)
self._serialized_shape = tuple(
self._shape if serialized_shape is None else serialized_shape
)
if isinstance(encoding, str):
encoding = encoding.lower()
self._encoding = Encoding(encoding)
self._encoded_to_bytes = self._encoding != Encoding.NONE
self._dynamic_shape = self._shape.count(None) > 1
if dtype_utils.is_string(self._dtype) and self._encoded_to_bytes:
raise NotImplementedError(
'tfds.features.Tensor() does not support `encoding=` when '
'dtype is string. Please open a PR if you need this feature.'
)
@py_utils.memoize()
def get_tensor_info(self) -> feature_lib.TensorInfo:
"""See base class for details."""
return feature_lib.TensorInfo(shape=self._shape, dtype=self._dtype)
@py_utils.memoize()
def get_serialized_info(self):
"""See base class for details."""
if self._encoded_to_bytes: # Values encoded (stored as bytes)
serialized_spec = feature_lib.TensorInfo(shape=(), dtype=np.object_)
else:
serialized_spec = feature_lib.TensorInfo(
shape=self._serialized_shape,
dtype=self._serialized_dtype,
)
# Dynamic shape, need an additional field to restore the shape after
# de-serialization.
if self._dynamic_shape:
return {
'shape': feature_lib.TensorInfo(
shape=(len(self._shape),),
dtype=np.int32,
),
'value': serialized_spec,
}
return serialized_spec
def encode_example(self, example_data):
"""See base class for details."""
# TODO(epot): Is there a better workaround ?
# It seems some user have non-conventional use of tfds.features.Tensor where
# they defined shape=(None, None) even if it wasn't supported.
# For backward compatibility, the check is moved inside encode example.
if self._dynamic_shape and not self._encoded_to_bytes:
raise ValueError(
'Multiple unknown dimensions Tensor require to set '
"`Tensor(..., encoding='zlib')` (or 'bytes'). "
f'For {self}'
)
np_dtype = self._serialized_dtype
if np_dtype == np.bool_ and isinstance(example_data, str):
raise TypeError(
f'Error encoding: {example_data!r}. {example_data!r} is a string, so '
'converting it to `bool` will always output `True`. Please, fix '
'`_generate_examples` with a better parsing.'
)
if enp.lazy.has_tf and isinstance(example_data, tf.Tensor):
raise TypeError(
f'Error encoding: {example_data!r}. `_generate_examples` should '
'yield `np.array` compatible values, not `tf.Tensor`'
)
if not isinstance(example_data, np.ndarray):
example_data = np.array(example_data, dtype=np_dtype)
# Ensure the shape and dtype match
if example_data.dtype != np_dtype:
raise ValueError(
'Dtype {} do not match {}'.format(example_data.dtype, np_dtype)
)
shape = example_data.shape
utils.assert_shape_match(shape, self._serialized_shape)
# Eventually encode the data
if self._encoded_to_bytes:
example_data = example_data.tobytes()
if self._encoding == Encoding.ZLIB:
example_data = zlib.compress(example_data)
# For dynamically shaped tensors, also save the shape (the proto
# flatten all values so we need a way to recover the shape).
if self._dynamic_shape:
return {
'value': example_data,
'shape': shape,
}
else:
return example_data
def _get_value_and_shape(self, example_data):
if self._dynamic_shape:
value = example_data['value']
# Extract the shape (while using static values when available)
if enp.lazy.has_tf and isinstance(example_data['shape'], tf.Tensor):
shape = utils.merge_shape(example_data['shape'], self._shape)
else:
shape = utils.merge_shape(
example_data['shape'][: len(value)], self._shape
)
else:
value = example_data
shape = np_utils.to_np_shape(self._shape)
return value, shape
def decode_example(self, tfexample_data):
"""See base class for details."""
value, shape = self._get_value_and_shape(tfexample_data)
if self._encoded_to_bytes:
if self._encoding == Encoding.ZLIB:
value = tf.io.decode_compressed(value, compression_type='ZLIB')
value = tf.io.decode_raw(value, self.tf_dtype)
value = tf.reshape(value, shape)
return value
def decode_example_np(
self, example_data: type_utils.NpArrayOrScalar
) -> type_utils.NpArrayOrScalar:
example_data, shape = self._get_value_and_shape(example_data)
if not self._encoded_to_bytes:
if isinstance(example_data, np.ndarray) and shape:
return example_data.reshape(shape)
return example_data
if self._encoding == Encoding.ZLIB:
example_data = _execute_function_on_array_or_scalar(
zlib.decompress, example_data
)
return _execute_function_on_array_or_scalar(
_bytes_to_np_array, example_data, dtype=self._dtype, shape=shape
)
def decode_batch_example(self, example_data):
"""See base class for details."""
if self._dynamic_shape or self._encoded_to_bytes:
# For Sequence(Tensor()), use `tf.map_fn` to decode/reshape individual
# tensors.
return super().decode_batch_example(example_data)
else:
# For regular tensors, `decode_example` is a no-op so can be applied
# directly (avoid `tf.map_fn`)
return self.decode_example(example_data)
def decode_ragged_example(self, example_data):
"""See base class for details."""
if self._dynamic_shape or self._encoded_to_bytes:
# For dynamic/bytes, we need to decode individual values, so call
# `tf.ragged.map_flat_values`
return super().decode_ragged_example(example_data)
else:
# For regular tensors, `decode_example` is a no-op so can be applied
# directly (avoid `tf.ragged.map_flat_values overhead`)
return self.decode_example(example_data)
@classmethod
def from_json_content(
cls, value: Union[Json, feature_pb2.TensorFeature]
) -> 'Tensor':
if isinstance(value, dict):
return cls(
shape=tuple(value['shape']),
dtype=feature_lib.dtype_from_str(value['dtype']),
# Use .get for backward-compatibility
encoding=value.get('encoding', Encoding.NONE),
)
return cls(
shape=feature_lib.from_shape_proto(value.shape),
dtype=feature_lib.dtype_from_str(value.dtype),
encoding=value.encoding or Encoding.NONE,
)
def to_json_content(self) -> feature_pb2.TensorFeature:
return feature_pb2.TensorFeature(
shape=feature_lib.to_shape_proto(self._shape),
dtype=feature_lib.dtype_to_str(self._dtype),
encoding=self._encoding.value,
)
def _bytes_to_np_array(example_data: bytes, dtype: np.dtype, shape: Tuple[int]):
return np.frombuffer(example_data, dtype=dtype).reshape(shape)
class InputFunc(Protocol[T]):
"""This protocol is used to type _execute_function_on_array_or_scalar."""
def __call__(
self, example_data: type_utils.NpArrayOrScalar, *args, **kwargs
) -> T:
...
def _execute_function_on_array_or_scalar(
function: InputFunc[T],
data: type_utils.NpArrayOrScalar,
*args,
**kwargs,
) -> T:
"""Runs `function` on `data`, or each element of `data` if it's an array."""
if isinstance(data, bytes):
return function(data, *args, **kwargs)
if isinstance(data, np.ndarray):
partial_function = functools.partial(function, *args, **kwargs)
return np.array(list(map(partial_function, data)))
raise ValueError(
'example should have type `bytes` or `np.ndarray(dtype=bytes)`, but'
f' has wrong type {type(data)}'
)
def get_inner_feature_repr(feature):
"""Utils which returns the object which should get printed in __repr__.
This is used in container features (Sequence, FeatureDict) to print scalar
Tensor in a less verbose way `Sequence(int32)` rather than
`Sequence(Tensor(shape=(), dtype=int32))`.
Args:
feature: The feature to display
Returns:
Either the feature or it's inner value.
"""
# We only print `int32` rather than `Tensor(shape=(), dtype=int32)`
# * For the base `Tensor` class (and not subclass).
# * When shape is scalar (explicit check to avoid trigger when `shape=None`).
if type(feature) == Tensor and feature.shape == (): # pylint: disable=unidiomatic-typecheck,g-explicit-bool-comparison
return feature_lib.dtype_to_str(feature.np_dtype)
else:
return repr(feature)