forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
filler_op.cc
695 lines (558 loc) · 21 KB
/
filler_op.cc
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
#include "caffe2/operators/filler_op.h"
namespace caffe2 {
template <>
bool RangeFillOp<float, CPUContext>::Fill(Tensor* output) {
float* data = output->template mutable_data<float>();
for (int i = 0; i < output->numel(); ++i) {
data[i] = i;
}
return true;
}
template <>
template <typename T>
bool DiagonalFillOp<CPUContext>::FillWithType(Tensor* output) {
VerifyOutputShape(output);
T value = OperatorBase::GetSingleArgument<T>("value", 0);
auto* data = output->template mutable_data<T>();
// first fill everything with 0
math::Set<T, CPUContext>(output->numel(), T(0), data, &context_);
// then calculate step size for diagonal
auto step = GetStepSize(output);
for (int64_t i = 0; i < output->numel(); i += step) {
math::Set<T, CPUContext>(1, value, data, &context_);
data += step;
}
return true;
}
REGISTER_CPU_OPERATOR(UniformFill, UniformFillOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(UniformIntFill, UniformFillOp<int, CPUContext>);
REGISTER_CPU_OPERATOR(UniqueUniformFill, UniqueUniformFillOp<CPUContext>);
REGISTER_CPU_OPERATOR(ConstantFill, ConstantFillOp<CPUContext>);
REGISTER_CPU_OPERATOR(DiagonalFill, DiagonalFillOp<CPUContext>);
REGISTER_CPU_OPERATOR(GaussianFill, GaussianFillOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(XavierFill, XavierFillOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(MSRAFill, MSRAFillOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(RangeFill, RangeFillOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(LengthsRangeFill, LengthsRangeFillOp<CPUContext>);
OPERATOR_SCHEMA(ConstantFill)
.NumInputs(0, 2)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
This operator fills the elements of the output tensor with a constant value
specified by the `value` argument.
- The data type is specified by the `dtype` argument
- Currently, the data types supported are *float*, *int32*, *int64*, and *bool*
- If the `dtype` argument is not provided, the data type of `value` is used
- The output tensor shape is either specified by the `shape` argument or will
match the shape of the input tensor if one is provided (if an input tensor is
provided, a shape argument should not be set)
- Optional additional dimensions can be appended at the end as specified by
`extra_shape` argument
- If `input_as_shape` is set to True, the input should be a 1D tensor
containing the desired output shape (the dimensions specified in `extra_shape`
will also be appended)
- If a second input V is passed, fill the output with the first element of V
When specifying `dtype` argument, use the integer keys from the *DataType* enum
in TensorProto:
```
message TensorProto {
...
enum DataType {
UNDEFINED = 0;
FLOAT = 1; // float
INT32 = 2; // int
BYTE = 3; // BYTE, when deserialized, is going to be restored as uint8.
STRING = 4; // string
BOOL = 5; // bool
UINT8 = 6; // uint8_t
INT8 = 7; // int8_t
UINT16 = 8; // uint16_t
INT16 = 9; // int16_t
INT64 = 10; // int64_t
FLOAT16 = 12; // at::Half
DOUBLE = 13; // double
}
```
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"ConstantFill",
[],
["Y"],
shape=(1,5,5)
)
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
Y: [[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]]
```
</details>
<details>
<summary> <b>Example 2</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"ConstantFill",
["X"],
["Y"],
value=4.0,
dtype=1,
extra_shape=(1,2)
)
workspace.FeedBlob("X", (np.random.randint(100, size=(3,3))).astype(np.float32))
print("X:", workspace.FetchBlob("X"))
workspace.RunOperatorOnce(op)
print("Y:", workspace.FetchBlob("Y"))
```
**Result**
```
X: [[86. 30. 84.]
[34. 51. 9.]
[29. 86. 59.]]
Y: [[[[4. 4.]]
[[4. 4.]]
[[4. 4.]]]
[[[4. 4.]]
[[4. 4.]]
[[4. 4.]]]
[[[4. 4.]]
[[4. 4.]]
[[4. 4.]]]]
```
</details>
)DOC")
.Arg(
"value",
"*(type: primitive; default: 0.0f) value to populate output tensor with.")
.Arg(
"dtype",
"*(type: int)* The data type for the elements of the output tensor. "
"Strictly must be one of the types from *DataType* enum in TensorProto.")
.Arg(
"shape",
"*(type: int | Tuple(int))* Shape of the output tensor. Cannot pass an "
"input blob and this arg at the same time.")
.Arg(
"extra_shape",
"*(type: int | Tuple(int))* Additional dimensions appended at the end "
"of the shape indicated by the input blob. Cannot set this"
"argument when there is no input blob.")
.Arg(
"input_as_shape",
"*(type: int | Tuple(int))* 1D tensor containing the desired output "
"shape. First input must be in CPU context.")
.Input(
0,
"X",
"*(type: Tensor)* [OPTIONAL] Input tensor to provide shape information.")
.Output(
0,
"Y",
"*(type: Tensor)* Output tensor of constant values.");
OPERATOR_SCHEMA(DiagonalFill)
.NumInputs(0, 1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
The operator fills the diagonal elements of the output tensor (>= 2D)
with a constant value specified by the 'value' argument, and others 0. If
number of dimensions of the output tensor is greater than 2, all dimensions
must be equal.
The data type is specified by the 'dtype' argument. The 'dtype' argument must
be one of the data types specified in the 'DataType' enum field in the
TensorProto message. If the 'dtype' argument is not provided, the data type of
'value' is used.
The output tensor shape is specified by the 'shape' argument. If the number of
input is 1, the shape will be identical to that of the input at run time with
optional additional dimensions appended at the end as specified by 'extra_shape'
argument. In that case the 'shape' argument should not be set.
If input_as_shape is set to true, then the input should be a 1D tensor
containing the desired output shape (the dimensions specified in extra_shape
will also be appended)
NOTE: Currently, it supports data type of float, int32, int64, and bool.
)DOC")
.Arg("value", "The value for the elements of the output tensor.")
.Arg(
"dtype",
"The data type for the elements of the output tensor."
"Strictly must be one of the types from DataType enum in TensorProto.")
.Arg(
"shape",
"The shape of the output tensor."
"Cannot set the shape argument and pass in an input at the same time.")
.Arg(
"extra_shape",
"The additional dimensions appended at the end of the shape indicated"
"by the input blob."
"Cannot set the extra_shape argument when there is no input blob.")
.Arg("input_as_shape", "1D tensor containing the desired output shape")
.Input(0, "input", "Input tensor (optional) to provide shape information.")
.Output(
0,
"output",
"Output tensor"
"argument and its type is specified by the 'dtype' argument");
OPERATOR_SCHEMA(UniformFill)
.NumInputs({0, 1, 3})
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
Fill the output tensor with float samples from uniform distribution [`min`, `max`].
- The range can be defined either by arguments or input blobs. `min` and `max` are inclusive.
- If the range is given by input blobs, you also need to give the shape as input.
- When the range is given as arguments, this operator enforces min <= max. When the range is given as inputs, the constraint is not enforced.
- When the range is given as inputs and max < min, the first dimension of the output is set to 0. This behavior is allowed so that dynamically sampling indices into a dynamically sized tensor is possible.
- The shape of the output can be given as argument or input.
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op_1 = core.CreateOperator(
"UniformFill",
[],
["output"],
min=5.5,
max=10.5,
shape=(3,3)
)
op_2 = core.CreateOperator(
"UniformFill",
["shape", "min", "max"],
["output"],
input_as_shape=1
)
// Test arg-based op
workspace.RunOperatorOnce(op_1)
print("output (op_1):\n", workspace.FetchBlob("output"))
// Test input-based op
workspace.ResetWorkspace()
workspace.FeedBlob("shape", np.array([5,5]))
workspace.FeedBlob("min", np.array(13.8, dtype=np.float32))
workspace.FeedBlob("max", np.array(19.3, dtype=np.float32))
workspace.RunOperatorOnce(op_2)
print("output (op_2):\n", workspace.FetchBlob("output"))
```
**Result**
```
output (op_1):
[[8.894862 8.225005 6.7890406]
[9.588293 7.1072135 7.7234955]
[8.210596 6.0202913 9.665462 ]]
output (op_2):
[[18.965155 15.603871 15.038921 17.14872 18.134571]
[18.84237 17.845276 19.214737 16.970337 15.494069]
[18.754795 16.724329 15.311974 16.962536 18.60965 ]
[15.186268 15.264773 18.73341 19.077969 14.237255]
[15.917589 15.844325 16.248466 17.006554 17.502048]]
```
</details>
)DOC")
.Arg("min", "(*float*): minimum value, inclusive")
.Arg("max", "(*float*): maximum value, inclusive")
.Arg("shape", "(*Tuple(int)*): shape of the output, do not set when `input_as_shape`=1")
.Arg(
"input_as_shape",
"(*int*): set to 1 to use the first input as shape; `shape` input must be in CPU context")
.Input(
0,
"shape",
"(*Tensor`<int>`*): 1-D tensor of the shape of the output, must be used with `input_as_shape` argument")
.Input(1, "min", "(*Tensor`<float>`*): scalar tensor containing minimum value, inclusive")
.Input(2, "max", "(*Tensor`<float>`*): scalar tensor containing maximum value, inclusive")
.Output(0, "output", "(*Tensor`<float>`*): filled output tensor");
OPERATOR_SCHEMA(UniformIntFill)
.NumInputs({0, 1, 3})
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<TensorProto_DataType_INT32>)
.SetDoc(R"DOC(
Fill the output tensor with int32 samples from uniform distribution [`min`, `max`].
- The range can be defined either by arguments or input blobs. `min` and `max` are inclusive.
- If the range is given by input blobs, you also need to give the shape as input.
- When the range is given as arguments, this operator enforces min <= max. When the range is given as inputs, the constraint is not enforced.
- When the range is given as inputs and max < min, the first dimension of the output is set to 0. This behavior is allowed so that dynamically sampling indices into a dynamically sized tensor is possible.
- The shape of the output can be given as argument or input.
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op_1 = core.CreateOperator(
"UniformIntFill",
[],
["output"],
min=5,
max=10,
shape=(3,3)
)
op_2 = core.CreateOperator(
"UniformIntFill",
["shape", "min", "max"],
["output"],
input_as_shape=1
)
// Test arg-based op
workspace.RunOperatorOnce(op_1)
print("output (op_1):\n", workspace.FetchBlob("output"))
// Test input-based op
workspace.ResetWorkspace()
workspace.FeedBlob("shape", np.array([5,5]))
workspace.FeedBlob("min", np.array(13, dtype=np.int32))
workspace.FeedBlob("max", np.array(19, dtype=np.int32))
workspace.RunOperatorOnce(op_2)
print("output (op_2):\n", workspace.FetchBlob("output"))
```
**Result**
```
output (op_1):
[[ 6 10 7]
[ 5 10 6]
[ 7 5 10]]
output (op_2):
[[19 13 15 13 13]
[14 17 14 15 15]
[17 14 19 13 13]
[17 18 16 13 18]
[14 15 16 18 16]]
```
</details>
)DOC")
.Arg("min", "(*int*): minimum value, inclusive")
.Arg("max", "(*int*): maximum value, inclusive")
.Arg(
"shape",
"(*Tuple(int)*): shape of the output, do not set when `input_as_shape`=1")
.Arg(
"input_as_shape",
"(*int*): set to 1 to use the first input as shape; `shape` input must be in CPU context")
.Input(0, "shape", "(*Tensor`<int>`*): 1-D tensor of the shape of the output, must be used with `input_as_shape` argument")
.Input(1, "min", "(*Tensor`<int>`*): scalar tensor containing minimum value, inclusive")
.Input(2, "max", "(*Tensor`<int>`*): scalar tensor containing maximum value, inclusive")
.Output(0, "output", "(*Tensor`<int>`*): filled output tensor");
OPERATOR_SCHEMA(UniqueUniformFill)
.NumInputs(0, 2)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
Fill the output tensor with uniform samples between min and max (inclusive).
If the second input is given, its elements will be excluded from uniform
sampling. Using the second input will require you to provide shape via the first
input.
)DOC")
.Arg("min", "Minimum value, inclusive")
.Arg("max", "Maximum value, inclusive")
.Arg(
"dtype",
"The data type for the elements of the output tensor."
"Strictly must be one of the types from DataType enum in TensorProto."
"This only supports INT32 and INT64 now. If not set, assume INT32")
.Arg(
"shape",
"The shape of the output tensor."
"Cannot set the shape argument and pass in an input at the same time.")
.Arg(
"extra_shape",
"The additional dimensions appended at the end of the shape indicated"
"by the input blob. "
"Cannot set the extra_shape argument when there is no input blob.")
.Arg(
"input_as_shape",
"1D tensor containing the desired output shape. First input must be in CPU context.")
.Input(0, "input", "Input tensor to provide shape information")
.Input(
1,
"avoid",
"(optional) Avoid elements in this tensor. Elements must be unique.")
.Output(0, "output", "Output tensor of unique uniform samples");
OPERATOR_SCHEMA(GaussianFill)
.NumInputs(0, 1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
This op fills an output tensor with samples drawn from a normal distribution specified by the mean and standard deviation arguments. The output tensor shape is specified by the *shape* argument. However, if *input_as_shape* is set to *true*, then the *input* should be a 1D tensor containing the desired output shape (the dimensions specified in *extra_shape* will also be appended). In this case, the *shape* argument should **not** be set.
*Note: cannot set the shape argument and pass in an input at the same time.*
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"GaussianFill",
[],
["out"],
shape=[3,3],
mean=2.0,
std=1.1
)
workspace.RunOperatorOnce(op)
print("Out:\n", workspace.FetchBlob("out"))
```
**Result**
```
Out:
[[1.2084167 2.3336504 2.827349 ]
[2.7108908 0.9374752 1.7173369 ]
[0.03320992 2.1775863 1.0894578 ]]
```
</details>
)DOC")
.Arg(
"mean",
"*(type: float; default: 0.)* Mean of the distribution to draw from.")
.Arg(
"std",
"*(type: float; default: 1.)* Standard deviation of the distribution to draw from.")
.Arg(
"shape",
"*(type: [int])* Desired shape of the *output* tensor.")
.Arg(
"extra_shape",
"*(type: [int])* The additional dimensions appended at the end of the *shape* indicated by the input blob. Cannot set the *extra_shape* argument when there is no input blob.")
.Arg(
"input_as_shape",
"*(type: bool; default: False)* set to *True* to use the *input* as shape. First, input must be in CPU context.")
.Input(
0,
"input",
"(Optional) 1D tensor specifying the shape of the output. Must be used with *input_as_shape=True*")
.Output(
0,
"output",
"Output tensor of random values drawn from a normal distribution. If the shape argument is set, this is the shape specified, and if the *input* exists and *input_as_shape=True*, it is the shape specified by the *input* tensor.");
OPERATOR_SCHEMA(XavierFill)
.NumInputs(0, 1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>)
.SetDoc(R"DOC(
This op fills an output tensor with values sampled from a uniform distribution with the range determined by the desired shape of the output. Rather, than specifying the range of values manually, the novelty of Xavier Fill is that it automatically scales the range of the distribution it draws from based on the size of the desired output tensor. For more information check out the paper [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf). The output tensor shape is specified by the *shape* argument. However, if *input_as_shape* is set to *true*, then the *input* should be a 1D tensor containing the desired output shape (the dimensions specified in *extra_shape* will also be appended). In this case, the *shape* argument should **not** be set.
*Note: Do not set the shape argument and pass in an input at the same time.*
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"XavierFill",
[],
["out"],
shape=[3,3],
)
workspace.RunOperatorOnce(op)
print("Out:\n", workspace.FetchBlob("out"))
```
**Result**
```
Out:
[[-0.8412168 0.33207083 -0.88418937]
[ 0.43059897 -0.8340702 0.07781601]
[ 0.93261135 -0.24542928 -0.3980782 ]]
```
</details>
)DOC")
.Arg(
"shape",
"*(type: [int])* Desired shape of the *output* tensor.")
.Arg(
"extra_shape",
"*(type: [int])* The additional dimensions appended at the end of the *shape* indicated by the input blob. Cannot set the *extra_shape* argument when there is no input blob.")
.Arg(
"input_as_shape",
"*(type: bool; default: False)* set to *True* to use the *input* as shape. First, input must be in CPU context.")
.Input(
0,
"input",
"(Optional) 1D tensor specifying the shape of the output. Must be used with *input_as_shape=True*")
.Output(
0,
"output",
"Output tensor of random values drawn from an automatically scaled uniform distribution, based on the size of the output tensor. If the shape argument is set, this is the shape specified by the shape argument, and if the *input* exists and *input_as_shape=True*, it is the shape specified by the *input* tensor.");
OPERATOR_SCHEMA(MSRAFill)
.NumInputs(0, 1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>);
OPERATOR_SCHEMA(RangeFill)
.NumInputs(0, 1)
.NumOutputs(1)
.AllowInplace({{0, 0}})
.TensorInferenceFunction(FillerTensorInference<>);
NO_GRADIENT(UniformFill);
NO_GRADIENT(UniformIntFill);
NO_GRADIENT(UniqueUniformFill);
NO_GRADIENT(ConstantFill);
NO_GRADIENT(DiagonalFill);
NO_GRADIENT(GaussianFill);
NO_GRADIENT(XavierFill);
NO_GRADIENT(MSRAFill);
NO_GRADIENT(RangeFill);
OPERATOR_SCHEMA(LengthsRangeFill)
.NumInputs(1)
.NumOutputs(1)
.SetDoc(R"DOC(
The *LengthsRangeFill* op takes a single input *lengths* and outputs a single tensor *range_sequence*. For each element of *lengths*, the op appends the range(0,lengths) vector to the end of *range_sequence*. For example, if input=[2,4,1], the output would be [0,1,0,1,2,3,0].
Github Links:
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.h
- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/filler_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"LengthsRangeFill",
["lengths"],
["range_sequence"],
)
workspace.FeedBlob("lengths", np.array([2,4,1]).astype(np.int32))
print("lengths:\n", workspace.FetchBlob("lengths"))
workspace.RunOperatorOnce(op)
print("range_sequence: \n", workspace.FetchBlob("range_sequence"))
```
**Result**
```
lengths:
[2 4 1]
range_sequence:
[0 1 0 1 2 3 0]
```
</details>
)DOC")
.Input(0, "lengths", "1D tensor of int32 or int64 segment lengths.")
.Output(
0,
"range_sequence",
"1D tensor whose size is the sum of *lengths*");
NO_GRADIENT(LengthsRangeFill);
} // namespace caffe2