-
Notifications
You must be signed in to change notification settings - Fork 1.3k
/
convnet.py
994 lines (882 loc) · 43 KB
/
convnet.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
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
# Copyright 2017 The Sonnet 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.
# ============================================================================
"""A minimal interface convolutional networks module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import six
from six.moves import xrange # pylint: disable=redefined-builtin
from sonnet.python.modules import base
from sonnet.python.modules import batch_norm
from sonnet.python.modules import batch_norm_v2
from sonnet.python.modules import conv
from sonnet.python.modules import util
import tensorflow.compat.v1 as tf
DATA_FORMAT_NCHW = "NCHW"
DATA_FORMAT_NHWC = "NHWC"
SUPPORTED_2D_DATA_FORMATS = {DATA_FORMAT_NCHW, DATA_FORMAT_NHWC}
def _replicate_elements(input_iterable, num_times):
"""Replicates entry in `input_iterable` if `input_iterable` is of length 1."""
if len(input_iterable) == 1:
return (input_iterable[0],) * num_times
return tuple(input_iterable)
class ConvNet2D(base.AbstractModule, base.Transposable):
"""A 2D Convolutional Network module."""
POSSIBLE_INITIALIZER_KEYS = {"w", "b"}
def __init__(self,
output_channels,
kernel_shapes,
strides,
paddings,
rates=(1,),
activation=tf.nn.relu,
activate_final=False,
normalization_ctor=None,
normalization_kwargs=None,
normalize_final=None,
initializers=None,
partitioners=None,
regularizers=None,
use_batch_norm=None, # Deprecated.
use_bias=True,
batch_norm_config=None, # Deprecated.
data_format=DATA_FORMAT_NHWC,
custom_getter=None,
name="conv_net_2d"):
"""Constructs a `ConvNet2D` module.
By default, neither batch normalization nor activation are applied to the
output of the final layer.
Args:
output_channels: Iterable of output channels, as defined in
`conv.Conv2D`. Output channels can be defined either as number or via a
callable. In the latter case, since the function invocation is deferred
to graph construction time, the user must only ensure that entries can
be called when build is called. Each entry in the iterable defines
properties in the corresponding convolutional layer.
kernel_shapes: Iterable of kernel sizes as defined in `conv.Conv2D`; if
the list contains one element only, the same kernel shape is used in
each layer of the network.
strides: Iterable of kernel strides as defined in `conv.Conv2D`; if the
list contains one element only, the same stride is used in each layer of
the network.
paddings: Iterable of padding options as defined in `conv.Conv2D`. Each
can be `snt.SAME`, `snt.VALID`, `snt.FULL`, `snt.CAUSAL`,
`snt.REVERSE_CAUSAL` or a pair of these to use for height and width.
If the Iterable contains one element only, the same padding is used in
each layer of the network.
rates: Iterable of dilation rates as defined in `conv.Conv2D`; if the
list contains one element only, the same rate is used in each layer of
the network.
activation: An activation op.
activate_final: Boolean determining if the activation and batch
normalization, if turned on, are applied to the final layer.
normalization_ctor: Constructor to return a callable which will perform
normalization at each layer. Defaults to None / no normalization.
Examples of what could go here: `snt.BatchNormV2`, `snt.LayerNorm`. If
a string is provided, importlib is used to convert the string to a
callable, so either `snt.LayerNorm` or `"snt.LayerNorm"` can be
provided.
normalization_kwargs: kwargs to be provided to `normalization_ctor` when
it is called.
normalize_final: Whether to apply normalization after the final conv
layer. Default is to take the value of activate_final.
initializers: Optional dict containing ops to initialize the filters of
the whole network (with key 'w') or biases (with key 'b').
partitioners: Optional dict containing partitioners to partition
weights (with key 'w') or biases (with key 'b'). As a default, no
partitioners are used.
regularizers: Optional dict containing regularizers for the filters of the
whole network (with key 'w') or biases (with key 'b'). As a default, no
regularizers are used. A regularizer should be a function that takes a
single `Tensor` as an input and returns a scalar `Tensor` output, e.g.
the L1 and L2 regularizers in `tf.contrib.layers`.
use_batch_norm: Boolean determining if batch normalization is applied
after convolution. Deprecated, use `normalization_ctor` instead.
use_bias: Boolean or iterable of booleans determining whether to include
bias parameters in the convolutional layers. Default `True`.
batch_norm_config: Optional mapping of additional configuration for the
`snt.BatchNorm` modules. Deprecated, use `normalization_kwargs` instead.
data_format: A string, one of "NCHW" or "NHWC". Specifies whether the
channel dimension of the input and output is the last dimension
(default, "NHWC"), or the second dimension ("NCHW").
custom_getter: Callable or dictionary of callables to use as
custom getters inside the module. If a dictionary, the keys
correspond to regexes to match variable names. See the
`tf.get_variable` documentation for information about the
custom_getter API. Note that this `custom_getter` will not be passed
to the `transpose` method. If you want to use a custom getter with
the transposed of this convolutional network, you should provide one
to the `transpose` method instead.
name: Name of the module.
Raises:
TypeError: If `output_channels` is not iterable; or if `kernel_shapes` is
not iterable; or `strides` is not iterable; or `paddings` is not
iterable; or if `activation` is not callable.
ValueError: If `output_channels` is empty; or if `kernel_shapes` has not
length 1 or `len(output_channels)`; or if `strides` has not
length 1 or `len(output_channels)`; or if `paddings` has not
length 1 or `len(output_channels)`; or if `rates` has not
length 1 or `len(output_channels)`; or if the given data_format is not a
supported format ("NHWC" or "NCHW"); or if `normalization_ctor` is
provided but cannot be mapped to a callable.
KeyError: If `initializers`, `partitioners` or `regularizers` contain any
keys other than 'w' or 'b'.
TypeError: If any of the given initializers, partitioners or regularizers
are not callable.
"""
if not isinstance(output_channels, collections.Iterable):
raise TypeError("output_channels must be iterable")
output_channels = tuple(output_channels)
if not isinstance(kernel_shapes, collections.Iterable):
raise TypeError("kernel_shapes must be iterable")
kernel_shapes = tuple(kernel_shapes)
if not isinstance(strides, collections.Iterable):
raise TypeError("strides must be iterable")
strides = tuple(strides)
if not isinstance(paddings, collections.Iterable):
raise TypeError("paddings must be iterable")
paddings = tuple(paddings)
if not isinstance(rates, collections.Iterable):
raise TypeError("rates must be iterable")
rates = tuple(rates)
if isinstance(use_batch_norm, collections.Iterable):
raise TypeError("use_batch_norm must be a boolean. Per-layer use of "
"batch normalization is not supported. Previously, a "
"test erroneously suggested use_batch_norm can be an "
"iterable of booleans.")
super(ConvNet2D, self).__init__(name=name, custom_getter=custom_getter)
if not output_channels:
raise ValueError("output_channels must not be empty")
self._output_channels = tuple(output_channels)
self._num_layers = len(self._output_channels)
self._input_shape = None
if data_format not in SUPPORTED_2D_DATA_FORMATS:
raise ValueError("Invalid data_format {}. Allowed formats "
"{}".format(data_format, SUPPORTED_2D_DATA_FORMATS))
self._data_format = data_format
self._initializers = util.check_initializers(
initializers, self.POSSIBLE_INITIALIZER_KEYS)
self._partitioners = util.check_partitioners(
partitioners, self.POSSIBLE_INITIALIZER_KEYS)
self._regularizers = util.check_regularizers(
regularizers, self.POSSIBLE_INITIALIZER_KEYS)
if not callable(activation):
raise TypeError("Input 'activation' must be callable")
self._activation = activation
self._activate_final = activate_final
self._kernel_shapes = _replicate_elements(kernel_shapes, self._num_layers)
if len(self._kernel_shapes) != self._num_layers:
raise ValueError(
"kernel_shapes must be of length 1 or len(output_channels)")
self._strides = _replicate_elements(strides, self._num_layers)
if len(self._strides) != self._num_layers:
raise ValueError(
"""strides must be of length 1 or len(output_channels)""")
self._paddings = _replicate_elements(paddings, self._num_layers)
if len(self._paddings) != self._num_layers:
raise ValueError(
"""paddings must be of length 1 or len(output_channels)""")
self._rates = _replicate_elements(rates, self._num_layers)
if len(self._rates) != self._num_layers:
raise ValueError(
"""rates must be of length 1 or len(output_channels)""")
self._parse_normalization_kwargs(
use_batch_norm, batch_norm_config,
normalization_ctor, normalization_kwargs)
if normalize_final is None:
util.deprecation_warning(
"normalize_final is not specified, so using the value of "
"activate_final = {}. Change your code to set this kwarg explicitly. "
"In the future, normalize_final will default to True.".format(
activate_final))
self._normalize_final = activate_final
else:
# User has provided an override, so don't link to activate_final.
self._normalize_final = normalize_final
if isinstance(use_bias, bool):
use_bias = (use_bias,)
else:
if not isinstance(use_bias, collections.Iterable):
raise TypeError("use_bias must be either a bool or an iterable")
use_bias = tuple(use_bias)
self._use_bias = _replicate_elements(use_bias, self._num_layers)
self._instantiate_layers()
def _check_and_assign_normalization_members(self, normalization_ctor,
normalization_kwargs):
"""Checks that the normalization constructor is callable."""
if isinstance(normalization_ctor, six.string_types):
normalization_ctor = util.parse_string_to_constructor(normalization_ctor)
if normalization_ctor is not None and not callable(normalization_ctor):
raise ValueError(
"normalization_ctor must be a callable or a string that specifies "
"a callable, got {}.".format(normalization_ctor))
self._normalization_ctor = normalization_ctor
self._normalization_kwargs = normalization_kwargs
def _parse_normalization_kwargs(self, use_batch_norm, batch_norm_config,
normalization_ctor, normalization_kwargs):
"""Sets up normalization, checking old and new flags."""
if use_batch_norm is not None:
# Delete this whole block when deprecation is done.
util.deprecation_warning(
"`use_batch_norm` kwarg is deprecated. Change your code to instead "
"specify `normalization_ctor` and `normalization_kwargs`.")
if not use_batch_norm:
# Explicitly set to False - normalization_{ctor,kwargs} has precedence.
self._check_and_assign_normalization_members(normalization_ctor,
normalization_kwargs or {})
else: # Explicitly set to true - new kwargs must not be used.
if normalization_ctor is not None or normalization_kwargs is not None:
raise ValueError(
"if use_batch_norm is specified, normalization_ctor and "
"normalization_kwargs must not be.")
self._check_and_assign_normalization_members(batch_norm.BatchNorm,
batch_norm_config or {})
else:
# Old kwargs not set, this block will remain after removing old kwarg.
self._check_and_assign_normalization_members(normalization_ctor,
normalization_kwargs or {})
def _instantiate_layers(self):
"""Instantiates all the convolutional modules used in the network."""
# Here we are entering the module's variable scope to name our submodules
# correctly (not to create variables). As such it's safe to not check
# whether we're in the same graph. This is important if we're constructing
# the module in one graph and connecting it in another (e.g. with `defun`
# the module is created in some default graph, and connected to a capturing
# graph in order to turn it into a graph function).
with self._enter_variable_scope(check_same_graph=False):
self._layers = tuple(conv.Conv2D(name="conv_2d_{}".format(i), # pylint: disable=g-complex-comprehension
output_channels=self._output_channels[i],
kernel_shape=self._kernel_shapes[i],
stride=self._strides[i],
rate=self._rates[i],
padding=self._paddings[i],
use_bias=self._use_bias[i],
initializers=self._initializers,
partitioners=self._partitioners,
regularizers=self._regularizers,
data_format=self._data_format)
for i in xrange(self._num_layers))
def _build(self, inputs, **normalization_build_kwargs):
"""Assembles the `ConvNet2D` and connects it to the graph.
Args:
inputs: A 4D Tensor of shape `[batch_size, input_height, input_width,
input_channels]`.
**normalization_build_kwargs: kwargs passed to the normalization module
at _build time.
Returns:
A 4D Tensor of shape `[batch_size, output_height, output_width,
output_channels[-1]]`.
Raises:
ValueError: If `is_training` is not explicitly specified when using
batch normalization.
"""
if (self._normalization_ctor in {batch_norm.BatchNorm,
batch_norm_v2.BatchNormV2} and
"is_training" not in normalization_build_kwargs):
raise ValueError("Boolean is_training flag must be explicitly specified "
"when using batch normalization.")
self._input_shape = tuple(inputs.get_shape().as_list())
net = inputs
final_index = len(self._layers) - 1
for i, layer in enumerate(self._layers):
net = layer(net)
if i != final_index or self._normalize_final:
if self._normalization_ctor is not None:
# The name 'batch_norm' is used even if something else like
# LayerNorm is being used. This is to avoid breaking old checkpoints.
normalizer = self._normalization_ctor(
name="batch_norm_{}".format(i),
**self._normalization_kwargs)
net = normalizer(
net, **util.remove_unsupported_kwargs(
normalizer, normalization_build_kwargs))
else:
if normalization_build_kwargs:
tf.logging.warning(
"No normalization configured, but extra kwargs "
"provided: {}".format(normalization_build_kwargs))
if i != final_index or self._activate_final:
net = self._activation(net)
return net
@property
def layers(self):
"""Returns a tuple containing the convolutional layers of the network."""
return self._layers
@property
def initializers(self):
return self._initializers
@property
def partitioners(self):
return self._partitioners
@property
def regularizers(self):
return self._regularizers
@property
def strides(self):
return self._strides
@property
def paddings(self):
return self._paddings
@property
def rates(self):
return self._rates
@property
def kernel_shapes(self):
return self._kernel_shapes
@property
def output_channels(self):
return tuple([l() if callable(l) else l for l in self._output_channels])
@property
def use_bias(self):
return self._use_bias
@property
def use_batch_norm(self):
util.deprecation_warning(
"The `.use_batch_norm` property is deprecated. Check "
"`.normalization_ctor` instead.")
return self._normalization_ctor == batch_norm.BatchNorm
@property
def batch_norm_config(self):
util.deprecation_warning(
"The `.batch_norm_config` property is deprecated. Check "
"`.normalization_kwargs` instead.")
return self._normalization_kwargs
@property
def normalization_ctor(self):
return self._normalization_ctor
@property
def normalization_kwargs(self):
return self._normalization_kwargs
@property
def normalize_final(self):
return self._normalize_final
@property
def activation(self):
return self._activation
@property
def activate_final(self):
return self._activate_final
# Implements Transposable interface.
@property
def input_shape(self):
"""Returns shape of input `Tensor` passed at last call to `build`."""
self._ensure_is_connected()
return self._input_shape
def _transpose(self,
transpose_constructor,
name=None,
output_channels=None,
kernel_shapes=None,
strides=None,
paddings=None,
activation=None,
activate_final=None,
normalization_ctor=None,
normalization_kwargs=None,
normalize_final=None,
initializers=None,
partitioners=None,
regularizers=None,
use_bias=None,
data_format=None):
"""Returns transposed version of this network.
Args:
transpose_constructor: A method that creates an instance of the transposed
network type. The method must accept the same kwargs as this methods
with the exception of the `transpose_constructor` argument.
name: Optional string specifying the name of the transposed module. The
default name is constructed by appending "_transpose"
to `self.module_name`.
output_channels: Optional iterable of numbers of output channels.
kernel_shapes: Optional iterable of kernel sizes. The default value is
constructed by reversing `self.kernel_shapes`.
strides: Optional iterable of kernel strides. The default value is
constructed by reversing `self.strides`.
paddings: Optional iterable of padding options, either `snt.SAME` or
`snt.VALID`; The default value is constructed by reversing
`self.paddings`.
activation: Optional activation op. Default value is `self.activation`.
activate_final: Optional boolean determining if the activation and batch
normalization, if turned on, are applied to the final layer.
normalization_ctor: Constructor to return a callable which will perform
normalization at each layer. Defaults to None / no normalization.
Examples of what could go here: `snt.BatchNormV2`, `snt.LayerNorm`. If
a string is provided, importlib is used to convert the string to a
callable, so either `snt.LayerNorm` or `"snt.LayerNorm"` can be
provided.
normalization_kwargs: kwargs to be provided to `normalization_ctor` when
it is called.
normalize_final: Whether to apply normalization after the final conv
layer. Default is to take the value of activate_final.
initializers: Optional dict containing ops to initialize the filters of
the whole network (with key 'w') or biases (with key 'b'). The default
value is `self.initializers`.
partitioners: Optional dict containing partitioners to partition
weights (with key 'w') or biases (with key 'b'). The default value is
`self.partitioners`.
regularizers: Optional dict containing regularizers for the filters of the
whole network (with key 'w') or biases (with key 'b'). The default is
`self.regularizers`.
use_bias: Optional boolean or iterable of booleans determining whether to
include bias parameters in the convolutional layers. Default
is constructed by reversing `self.use_bias`.
data_format: Optional string, one of "NCHW" or "NHWC". Specifies whether
the channel dimension of the input and output is the last dimension.
Default is `self._data_format`.
Returns:
Matching transposed module.
Raises:
ValueError: If output_channels is specified and its length does not match
the number of layers.
"""
if data_format is None:
data_format = self._data_format
if output_channels is None:
output_channels = []
channel_dim = -1 if data_format == DATA_FORMAT_NHWC else 1
for layer in reversed(self._layers):
output_channels.append(lambda l=layer: l.input_shape[channel_dim])
elif len(output_channels) != len(self._layers):
# Note that we only have to do this check for the output channels. Any
# other inconsistencies will be picked up by ConvNet2D.__init__.
raise ValueError("Iterable output_channels length must match the "
"number of layers ({}), but is {} instead.".format(
len(self._layers), len(output_channels)))
if kernel_shapes is None:
kernel_shapes = reversed(self.kernel_shapes)
if strides is None:
strides = reversed(self.strides)
if paddings is None:
paddings = reversed(self.paddings)
if activation is None:
activation = self.activation
if activate_final is None:
activate_final = self.activate_final
if normalization_ctor is None:
normalization_ctor = self.normalization_ctor
if normalization_kwargs is None:
normalization_kwargs = self.normalization_kwargs
if normalize_final is None:
normalize_final = self.normalize_final
if initializers is None:
initializers = self.initializers
if partitioners is None:
partitioners = self.partitioners
if regularizers is None:
regularizers = self.regularizers
if use_bias is None:
use_bias = reversed(self.use_bias)
if name is None:
name = self.module_name + "_transpose"
return transpose_constructor(
output_channels=output_channels,
kernel_shapes=kernel_shapes,
strides=strides,
paddings=paddings,
activation=activation,
activate_final=activate_final,
normalization_ctor=normalization_ctor,
normalization_kwargs=normalization_kwargs,
normalize_final=normalize_final,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
use_bias=use_bias,
data_format=data_format,
name=name)
# Implements Transposable interface.
def transpose(self,
name=None,
output_channels=None,
kernel_shapes=None,
strides=None,
paddings=None,
activation=None,
activate_final=None,
normalization_ctor=None,
normalization_kwargs=None,
normalize_final=None,
initializers=None,
partitioners=None,
regularizers=None,
use_batch_norm=None,
use_bias=None,
batch_norm_config=None,
data_format=None,
custom_getter=None):
"""Returns transposed version of this network.
Args:
name: Optional string specifying the name of the transposed module. The
default name is constructed by appending "_transpose"
to `self.module_name`.
output_channels: Optional iterable of numbers of output channels.
kernel_shapes: Optional iterable of kernel sizes. The default value is
constructed by reversing `self.kernel_shapes`.
strides: Optional iterable of kernel strides. The default value is
constructed by reversing `self.strides`.
paddings: Optional iterable of padding options, either `snt.SAME` or
`snt.VALID`; The default value is constructed by reversing
`self.paddings`.
activation: Optional activation op. Default value is `self.activation`.
activate_final: Optional boolean determining if the activation and batch
normalization, if turned on, are applied to the final layer.
normalization_ctor: Constructor to return a callable which will perform
normalization at each layer. Defaults to None / no normalization.
Examples of what could go here: `snt.BatchNormV2`, `snt.LayerNorm`. If
a string is provided, importlib is used to convert the string to a
callable, so either `snt.LayerNorm` or `"snt.LayerNorm"` can be
provided.
normalization_kwargs: kwargs to be provided to `normalization_ctor` when
it is called.
normalize_final: Whether to apply normalization after the final conv
layer. Default is to take the value of activate_final.
initializers: Optional dict containing ops to initialize the filters of
the whole network (with key 'w') or biases (with key 'b'). The default
value is `self.initializers`.
partitioners: Optional dict containing partitioners to partition
weights (with key 'w') or biases (with key 'b'). The default value is
`self.partitioners`.
regularizers: Optional dict containing regularizers for the filters of the
whole network (with key 'w') or biases (with key 'b'). The default is
`self.regularizers`.
use_batch_norm: Optional boolean determining if batch normalization is
applied after convolution. The default value is `self.use_batch_norm`.
use_bias: Optional boolean or iterable of booleans determining whether to
include bias parameters in the convolutional layers. Default
is constructed by reversing `self.use_bias`.
batch_norm_config: Optional mapping of additional configuration for the
`snt.BatchNorm` modules. Default is `self.batch_norm_config`.
data_format: Optional string, one of "NCHW" or "NHWC". Specifies whether
the channel dimension of the input and output is the last dimension.
Default is `self._data_format`.
custom_getter: Callable or dictionary of callables to use as
custom getters inside the module. If a dictionary, the keys
correspond to regexes to match variable names. See the
`tf.get_variable` documentation for information about the
custom_getter API.
Returns:
Matching `ConvNet2DTranspose` module.
Raises:
ValueError: If output_channels is specified and its length does not match
the number of layers.
ValueError: If the given data_format is not a supported format ("NHWC" or
"NCHW").
NotImplementedError: If the convolutions are dilated.
"""
for rate in self._rates:
if rate != 1:
raise NotImplementedError("Transpose dilated convolutions "
"are not supported")
output_shapes = []
if data_format is None:
data_format = self._data_format
if data_format == DATA_FORMAT_NHWC:
start_dim, end_dim = 1, -1
elif data_format == DATA_FORMAT_NCHW:
start_dim, end_dim = 2, 4
else:
raise ValueError("Invalid data_format {:s}. Allowed formats "
"{}".format(data_format, SUPPORTED_2D_DATA_FORMATS))
if custom_getter is None and self._custom_getter is not None:
tf.logging.warning(
"This convnet was constructed with a custom getter, but the "
"`transpose` method was not given any. The transposed ConvNet will "
"not be using any custom_getter.")
for layer in reversed(self._layers):
output_shapes.append(lambda l=layer: l.input_shape[start_dim:end_dim])
transpose_constructor = functools.partial(ConvNet2DTranspose,
output_shapes=output_shapes,
custom_getter=custom_getter)
return self._transpose(
transpose_constructor=transpose_constructor,
name=name,
output_channels=output_channels,
kernel_shapes=kernel_shapes,
strides=strides,
paddings=paddings,
activation=activation,
activate_final=activate_final,
normalization_ctor=normalization_ctor,
normalization_kwargs=normalization_kwargs,
normalize_final=normalize_final,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
use_bias=use_bias,
data_format=data_format)
class ConvNet2DTranspose(ConvNet2D):
"""A 2D Transpose-Convolutional Network module."""
def __init__(self,
output_channels,
output_shapes,
kernel_shapes,
strides,
paddings,
activation=tf.nn.relu,
activate_final=False,
normalization_ctor=None,
normalization_kwargs=None,
normalize_final=None,
initializers=None,
partitioners=None,
regularizers=None,
use_batch_norm=False,
use_bias=True,
batch_norm_config=None,
data_format=DATA_FORMAT_NHWC,
custom_getter=None,
name="conv_net_2d_transpose"):
"""Constructs a `ConvNetTranspose2D` module.
`output_{shapes,channels}` can be defined either as iterable of
{iterables,integers} or via a callable. In the latter case, since the
function invocation is deferred to graph construction time, the user
must only ensure that entries can be called returning meaningful values when
build is called. Each entry in the iterable defines properties in the
corresponding convolutional layer.
By default, neither batch normalization nor activation are applied to the
output of the final layer.
Args:
output_channels: Iterable of numbers of output channels.
output_shapes: Iterable of output shapes as defined in
`conv.conv2DTranpose`; if the iterable contains one element only, the
same shape is used in each layer of the network.
kernel_shapes: Iterable of kernel sizes as defined in `conv.Conv2D`; if
the list contains one element only, the same kernel shape is used in
each layer of the network.
strides: Iterable of kernel strides as defined in `conv.Conv2D`; if the
list contains one element only, the same stride is used in each layer of
the network.
paddings: Iterable of padding options, either `snt.SAME` or
`snt.VALID`; if the Iterable contains one element only, the same padding
is used in each layer of the network.
activation: An activation op.
activate_final: Boolean determining if the activation and batch
normalization, if turned on, are applied to the final layer.
normalization_ctor: Constructor to return a callable which will perform
normalization at each layer. Defaults to None / no normalization.
Examples of what could go here: `snt.BatchNormV2`, `snt.LayerNorm`. If
a string is provided, importlib is used to convert the string to a
callable, so either `snt.LayerNorm` or `"snt.LayerNorm"` can be
provided.
normalization_kwargs: kwargs to be provided to `normalization_ctor` when
it is called.
normalize_final: Whether to apply normalization after the final conv
layer. Default is to take the value of activate_final.
initializers: Optional dict containing ops to initialize the filters of
the whole network (with key 'w') or biases (with key 'b').
partitioners: Optional dict containing partitioners to partition
weights (with key 'w') or biases (with key 'b'). As a default, no
partitioners are used.
regularizers: Optional dict containing regularizers for the filters of the
whole network (with key 'w') or biases (with key 'b'). As a default, no
regularizers are used. A regularizer should be a function that takes a
single `Tensor` as an input and returns a scalar `Tensor` output, e.g.
the L1 and L2 regularizers in `tf.contrib.layers`.
use_batch_norm: Boolean determining if batch normalization is applied
after convolution.
use_bias: Boolean or iterable of booleans determining whether to include
bias parameters in the convolutional layers. Default `True`.
batch_norm_config: Optional mapping of additional configuration for the
`snt.BatchNorm` modules.
data_format: A string, one of "NCHW" or "NHWC". Specifies whether the
channel dimension of the input and output is the last dimension
(default, "NHWC"), or the second dimension ("NCHW").
custom_getter: Callable or dictionary of callables to use as
custom getters inside the module. If a dictionary, the keys
correspond to regexes to match variable names. See the
`tf.get_variable` documentation for information about the
custom_getter API.
name: Name of the module.
Raises:
TypeError: If `output_channels` is not iterable; or if `output_shapes`
is not iterable; or if `kernel_shapes` is not iterable; or if `strides`
is not iterable; or if `paddings` is not iterable; or if `activation` is
not callable.
ValueError: If `output_channels` is empty; or if `kernel_shapes` has not
length 1 or `len(output_channels)`; or if `strides` has not
length 1 or `len(output_channels)`; or if `paddings` has not
length 1 or `len(output_channels)`.
ValueError: If the given data_format is not a supported format ("NHWC" or
"NCHW").
ValueError: If `normalization_ctor` is provided but cannot be converted
to a callable.
KeyError: If `initializers`, `partitioners` or `regularizers` contain any
keys other than 'w' or 'b'.
TypeError: If any of the given initializers, partitioners or regularizers
are not callable.
"""
if not isinstance(output_channels, collections.Iterable):
raise TypeError("output_channels must be iterable")
output_channels = tuple(output_channels)
num_layers = len(output_channels)
if not isinstance(output_shapes, collections.Iterable):
raise TypeError("output_shapes must be iterable")
output_shapes = tuple(output_shapes)
self._output_shapes = _replicate_elements(output_shapes, num_layers)
if len(self._output_shapes) != num_layers:
raise ValueError(
"output_shapes must be of length 1 or len(output_channels)")
super(ConvNet2DTranspose, self).__init__(
output_channels,
kernel_shapes,
strides,
paddings,
activation=activation,
activate_final=activate_final,
normalization_ctor=normalization_ctor,
normalization_kwargs=normalization_kwargs,
normalize_final=normalize_final,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
use_batch_norm=use_batch_norm,
use_bias=use_bias,
batch_norm_config=batch_norm_config,
data_format=data_format,
custom_getter=custom_getter,
name=name)
def _instantiate_layers(self):
"""Instantiates all the convolutional modules used in the network."""
# See `ConvNet2D._instantiate_layers` for more information about why we are
# using `check_same_graph=False`.
with self._enter_variable_scope(check_same_graph=False):
self._layers = tuple(
conv.Conv2DTranspose(name="conv_2d_transpose_{}".format(i), # pylint: disable=g-complex-comprehension
output_channels=self._output_channels[i],
output_shape=self._output_shapes[i],
kernel_shape=self._kernel_shapes[i],
stride=self._strides[i],
padding=self._paddings[i],
initializers=self._initializers,
partitioners=self._partitioners,
regularizers=self._regularizers,
data_format=self._data_format,
use_bias=self._use_bias[i])
for i in xrange(self._num_layers))
@property
def output_shapes(self):
return tuple([l() if callable(l) else l for l in self._output_shapes])
# Implements Transposable interface.
def transpose(self,
name=None,
output_channels=None,
kernel_shapes=None,
strides=None,
paddings=None,
activation=None,
activate_final=None,
normalization_ctor=None,
normalization_kwargs=None,
normalize_final=None,
initializers=None,
partitioners=None,
regularizers=None,
use_batch_norm=None,
use_bias=None,
batch_norm_config=None,
data_format=None,
custom_getter=None):
"""Returns transposed version of this network.
Args:
name: Optional string specifying the name of the transposed module. The
default name is constructed by appending "_transpose"
to `self.module_name`.
output_channels: Optional iterable of numbers of output channels.
kernel_shapes: Optional iterable of kernel sizes. The default value is
constructed by reversing `self.kernel_shapes`.
strides: Optional iterable of kernel strides. The default value is
constructed by reversing `self.strides`.
paddings: Optional iterable of padding options, either `snt.SAME` or
`snt.VALID`; The default value is constructed by reversing
`self.paddings`.
activation: Optional activation op. Default value is `self.activation`.
activate_final: Optional boolean determining if the activation and batch
normalization, if turned on, are applied to the final layer.
normalization_ctor: Constructor to return a callable which will perform
normalization at each layer. Defaults to None / no normalization.
Examples of what could go here: `snt.BatchNormV2`, `snt.LayerNorm`. If
a string is provided, importlib is used to convert the string to a
callable, so either `snt.LayerNorm` or `"snt.LayerNorm"` can be
provided.
normalization_kwargs: kwargs to be provided to `normalization_ctor` when
it is called.
normalize_final: Whether to apply normalization after the final conv
layer. Default is to take the value of activate_final.
initializers: Optional dict containing ops to initialize the filters of
the whole network (with key 'w') or biases (with key 'b'). The default
value is `self.initializers`.
partitioners: Optional dict containing partitioners to partition
weights (with key 'w') or biases (with key 'b'). The default value is
`self.partitioners`.
regularizers: Optional dict containing regularizers for the filters of the
whole network (with key 'w') or biases (with key 'b'). The default is
`self.regularizers`.
use_batch_norm: Optional boolean determining if batch normalization is
applied after convolution. The default value is `self.use_batch_norm`.
use_bias: Optional boolean or iterable of booleans determining whether to
include bias parameters in the convolutional layers. Default
is constructed by reversing `self.use_bias`.
batch_norm_config: Optional mapping of additional configuration for the
`snt.BatchNorm` modules. Default is `self.batch_norm_config`.
data_format: Optional string, one of "NCHW" or "NHWC". Specifies whether
the channel dimension of the input and output is the last dimension.
Default is `self._data_format`.
custom_getter: Callable or dictionary of callables to use as
custom getters inside the module. If a dictionary, the keys
correspond to regexes to match variable names. See the
`tf.get_variable` documentation for information about the
custom_getter API.
Returns:
Matching `ConvNet2D` module.
Raises:
ValueError: If output_channels is specified and its length does not match
the number of layers.
"""
if use_batch_norm is not None:
if normalization_ctor is not None or normalization_kwargs is not None:
raise ValueError(
"If use_batch_norm is specified, normalization_ctor and "
"normalization_kwargs must not be.")
if use_batch_norm:
normalization_ctor = batch_norm.BatchNorm
else:
normalization_ctor = None
normalization_kwargs = batch_norm_config
if custom_getter is None and self._custom_getter is not None:
tf.logging.warning(
"This convnet was constructed with a custom getter, but the "
"`transpose` method was not given any. The transposed ConvNet will "
"not be using any custom_getter.")
transpose_constructor = functools.partial(ConvNet2D,
custom_getter=custom_getter)
return self._transpose(
transpose_constructor=transpose_constructor,
name=name,
output_channels=output_channels,
kernel_shapes=kernel_shapes,
strides=strides,
paddings=paddings,
activation=activation,
activate_final=activate_final,
normalization_ctor=normalization_ctor,
normalization_kwargs=normalization_kwargs,
normalize_final=normalize_final,
initializers=initializers,
partitioners=partitioners,
regularizers=regularizers,
use_bias=use_bias,
data_format=data_format)