gen_onnx.py 58.2 KB
Newer Older
Khalique's avatar
Khalique committed
1
2
3
4
5
6
import numpy as np
import onnx
from onnx import helper
from onnx import numpy_helper
from onnx import AttributeProto, TensorProto, GraphProto

Khalique's avatar
Khalique committed
7

Khalique's avatar
Khalique committed
8
9
def onnx_test(op_test):
    def run_test():
Khalique's avatar
Khalique committed
10
11
        op_info = op_test()
        if len(op_info) > 3:
Khalique's avatar
Khalique committed
12
13
14
15
16
            graph_def = helper.make_graph(op_info[0],
                                          op_test.__name__,
                                          op_info[1],
                                          op_info[2],
                                          initializer=op_info[3])
Khalique's avatar
Khalique committed
17
        else:
Khalique's avatar
Khalique committed
18
19
20
21
            graph_def = helper.make_graph(op_info[0], op_test.__name__,
                                          op_info[1], op_info[2])
        model_def = helper.make_model(graph_def,
                                      producer_name=op_test.__name__)
Khalique's avatar
Khalique committed
22
        onnx.save(model_def, '{}.onnx'.format(op_test.__name__))
Khalique's avatar
Khalique committed
23

Khalique's avatar
Khalique committed
24
25
    return run_test

Khalique's avatar
Khalique committed
26

Khalique's avatar
Khalique committed
27
@onnx_test
Khalique's avatar
Khalique committed
28
29
30
31
32
33
34
35
36
37
def acos_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Acos',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
38
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
39

Khalique's avatar
Khalique committed
40

41
42
43
44
45
46
47
48
49
50
51
52
53
54
@onnx_test
def acosh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Acosh',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])


Khalique's avatar
Khalique committed
55
@onnx_test
Khalique's avatar
Khalique committed
56
57
58
def add_bcast_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
Khalique's avatar
Khalique committed
59
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
Khalique's avatar
Khalique committed
60

Khalique's avatar
Khalique committed
61
62
63
64
65
66
67
    node = onnx.helper.make_node('Add',
                                 inputs=['0', '1'],
                                 broadcast=1,
                                 axis=1,
                                 outputs=['2'])

    return ([node], [x, y], [z])
Khalique's avatar
Khalique committed
68
69


Khalique's avatar
Khalique committed
70
@onnx_test
Khalique's avatar
Khalique committed
71
72
73
74
75
76
77
78
79
80
81
def add_fp16_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        outputs=['2'],
    )

Khalique's avatar
Khalique committed
82
    return (
Khalique's avatar
Khalique committed
83
        [node],
Khalique's avatar
Khalique committed
84
        [x, y],
Khalique's avatar
Khalique committed
85
86
        [z],
        # '0' -> 1.5, '1' -> 2.5
Khalique's avatar
Khalique committed
87
88
89
90
        [
            onnx.helper.make_tensor('0', TensorProto.FLOAT16, [1], [15872]),
            onnx.helper.make_tensor('1', TensorProto.FLOAT16, [1], [16640])
        ])
Khalique's avatar
Khalique committed
91
92


Khalique's avatar
Khalique committed
93
@onnx_test
Khalique's avatar
Khalique committed
94
95
96
def add_scalar_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [])
Khalique's avatar
Khalique committed
97
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
Khalique's avatar
Khalique committed
98

Khalique's avatar
Khalique committed
99
100
101
102
    node = onnx.helper.make_node('Add', inputs=['0', '1'], outputs=['2'])

    return ([node], [x, y], [z],
            [helper.make_tensor('1', TensorProto.FLOAT, [], [1])])
Khalique's avatar
Khalique committed
103
104


Khalique's avatar
Khalique committed
105
@onnx_test
Khalique's avatar
Khalique committed
106
107
108
109
def argmax_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])

Khalique's avatar
Khalique committed
110
111
112
113
114
    node = onnx.helper.make_node('ArgMax',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axis=2,
                                 keepdims=0)
Khalique's avatar
Khalique committed
115

Khalique's avatar
Khalique committed
116
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
117

Khalique's avatar
Khalique committed
118

Khalique's avatar
Khalique committed
119
@onnx_test
Khalique's avatar
Khalique committed
120
121
122
123
def argmin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])

Khalique's avatar
Khalique committed
124
125
126
127
128
    node = onnx.helper.make_node('ArgMin',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axis=3,
                                 keepdims=0)
Khalique's avatar
Khalique committed
129

Khalique's avatar
Khalique committed
130
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
131

Khalique's avatar
Khalique committed
132

Khalique's avatar
Khalique committed
133
@onnx_test
Khalique's avatar
Khalique committed
134
135
136
137
138
139
140
141
142
143
def asin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Asin',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
144
145
    return ([node], [x], [y])

Khalique's avatar
Khalique committed
146

147
148
149
150
151
152
153
154
155
156
157
158
159
160
@onnx_test
def asinh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Asinh',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])


Khalique's avatar
Khalique committed
161
@onnx_test
Khalique's avatar
Khalique committed
162
163
164
165
166
167
168
169
170
def atan_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Atan',
        inputs=['x'],
        outputs=['y'],
    )
Khalique's avatar
Khalique committed
171

Khalique's avatar
Khalique committed
172
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
173

Khalique's avatar
Khalique committed
174

175
176
177
178
179
180
181
182
183
184
185
186
187
188
@onnx_test
def atanh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Atanh',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])


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
@onnx_test
def averagepool_notset_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1])

    node = onnx.helper.make_node('AveragePool',
                                 inputs=['x'],
                                 outputs=['y'],
                                 kernel_shape=[6, 6],
                                 strides=[2, 2],
                                 pads=[0, 0, 1, 1],
                                 auto_pad='NOTSET')

    return ([node], [x], [y])


@onnx_test
def averagepool_same_lower_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])

    node = onnx.helper.make_node('AveragePool',
                                 inputs=['x'],
                                 outputs=['y'],
                                 kernel_shape=[2, 2],
                                 auto_pad='SAME_LOWER')

    return ([node], [x], [y])


@onnx_test
def averagepool_same_upper_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])

    node = onnx.helper.make_node('AveragePool',
                                 inputs=['x'],
                                 outputs=['y'],
                                 kernel_shape=[2, 2],
                                 auto_pad='SAME_UPPER')

    return ([node], [x], [y])


Khalique's avatar
Khalique committed
233
@onnx_test
Khalique's avatar
Khalique committed
234
235
236
237
def cast_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

Khalique's avatar
Khalique committed
238
239
    node = onnx.helper.make_node('Cast', inputs=['x'], outputs=['y'], to=1)

Khalique's avatar
Khalique committed
240
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
241

kahmed10's avatar
kahmed10 committed
242

Shucai Xiao's avatar
Shucai Xiao committed
243
244
245
246
247
248
249
250
251
252
253
254
@onnx_test
def ceil_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Ceil',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])
Khalique's avatar
Khalique committed
255

kahmed10's avatar
kahmed10 committed
256

Khalique's avatar
Khalique committed
257
@onnx_test
Khalique's avatar
Khalique committed
258
259
260
261
def clip_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])

Khalique's avatar
Khalique committed
262
263
264
265
266
    node = onnx.helper.make_node('Clip',
                                 inputs=['0'],
                                 outputs=['1'],
                                 max=6.0,
                                 min=0.0)
Khalique's avatar
Khalique committed
267

Khalique's avatar
Khalique committed
268
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
269

Khalique's avatar
Khalique committed
270

Khalique's avatar
Khalique committed
271
@onnx_test
Khalique's avatar
Khalique committed
272
273
274
275
276
277
278
279
280
281
282
283
def concat_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [9, 4, 3])

    node = onnx.helper.make_node(
        'Concat',
        inputs=['0', '1'],
        axis=0,
        outputs=['2'],
    )

Khalique's avatar
Khalique committed
284
285
    return ([node], [x, y], [z])

Khalique's avatar
Khalique committed
286

Khalique's avatar
Khalique committed
287
@onnx_test
Khalique's avatar
Khalique committed
288
289
290
def constant_test():
    x = np.array([0, 1, 2])
    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
Khalique's avatar
Khalique committed
291

Khalique's avatar
Khalique committed
292
293
294
295
296
297
298
299
300
301
302
303
    node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['0'],
        value=onnx.helper.make_tensor(
            name='const_tensor',
            data_type=TensorProto.FLOAT,
            dims=x.shape,
            vals=x.flatten().astype(float),
        ),
    )

Khalique's avatar
Khalique committed
304
    return ([node], [], [y])
Khalique's avatar
Khalique committed
305

Khalique's avatar
Khalique committed
306

Khalique's avatar
Khalique committed
307
@onnx_test
Khalique's avatar
Khalique committed
308
def constant_fill_test():
Khalique's avatar
Khalique committed
309
310
311
312
313
314
    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])

    node = onnx.helper.make_node(
        'ConstantFill',
        inputs=[],
        outputs=['value'],
Khalique's avatar
Khalique committed
315
316
317
318
        dtype=1,
        value=1.0,
        shape=[2, 3],
        input_as_shape=0,
Khalique's avatar
Khalique committed
319
320
    )

Khalique's avatar
Khalique committed
321
    return ([node], [], [value])
Khalique's avatar
Khalique committed
322

Khalique's avatar
Khalique committed
323

Khalique's avatar
Khalique committed
324
@onnx_test
Khalique's avatar
Khalique committed
325
def constant_fill_input_as_shape_test():
Khalique's avatar
Khalique committed
326
    np_shape = np.array([2, 3])
Khalique's avatar
Khalique committed
327
328
329
    shape = helper.make_tensor_value_info('shape', TensorProto.INT32, [2])
    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])

Khalique's avatar
Khalique committed
330
331
332
333
    ts_shape = helper.make_tensor(name='shape_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=np_shape.shape,
                                  vals=np_shape.flatten().astype(int))
Khalique's avatar
Khalique committed
334
335
336
337
338
339
340
341
342
343
344
345

    const_shape_node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=ts_shape,
    )

    node = onnx.helper.make_node(
        'ConstantFill',
        inputs=['shape'],
        outputs=['value'],
Khalique's avatar
Khalique committed
346
347
348
        dtype=1,
        value=1.0,
        input_as_shape=1,
Khalique's avatar
Khalique committed
349
350
    )

Khalique's avatar
Khalique committed
351
    return ([const_shape_node, node], [], [value])
Khalique's avatar
Khalique committed
352

Khalique's avatar
Khalique committed
353

Khalique's avatar
Khalique committed
354
@onnx_test
Khalique's avatar
Khalique committed
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
def constant_scalar_test():
    x = np.array([1])
    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['0'],
        value=onnx.helper.make_tensor(
            name='const_tensor',
            data_type=TensorProto.INT32,
            dims=x.shape,
            vals=x.flatten().astype(int),
        ),
    )

Khalique's avatar
Khalique committed
371
    return ([node], [], [y])
Khalique's avatar
Khalique committed
372

Khalique's avatar
Khalique committed
373

Khalique's avatar
Khalique committed
374
@onnx_test
Khalique's avatar
Khalique committed
375
def const_of_shape_empty_input_test():
Khalique's avatar
Khalique committed
376
377
    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
                                         [10])
Khalique's avatar
Khalique committed
378
379
    shape_val = np.array([2, 3, 4]).astype(np.int64)
    empty_val = np.array([]).astype(np.int64)
Khalique's avatar
Khalique committed
380
381
382
383
    empty_ts = helper.make_tensor(name='empty_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=empty_val.shape,
                                  vals=empty_val.flatten().astype(int))
Khalique's avatar
Khalique committed
384
385
386
387
388
389
390
391
392
393
394
395
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=empty_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])

    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
Khalique's avatar
Khalique committed
396
        value=tensor_val,
Khalique's avatar
Khalique committed
397
398
    )

Khalique's avatar
Khalique committed
399
    return ([shape_const, node], [], [y])
Khalique's avatar
Khalique committed
400

Khalique's avatar
Khalique committed
401

Khalique's avatar
Khalique committed
402
@onnx_test
Khalique's avatar
Khalique committed
403
def const_of_shape_float_test():
Khalique's avatar
Khalique committed
404
405
    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.FLOAT, [1],
                                         [10])
Khalique's avatar
Khalique committed
406
407

    shape_val = np.array([2, 3, 4]).astype(np.int64)
Khalique's avatar
Khalique committed
408
409
410
411
    shape_ts = helper.make_tensor(name='shape_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=shape_val.shape,
                                  vals=shape_val.flatten().astype(int))
Khalique's avatar
Khalique committed
412
413
414
415
416
417
418
419
420

    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])

Khalique's avatar
Khalique committed
421
422
423
424
    node = onnx.helper.make_node('ConstantOfShape',
                                 inputs=['shape'],
                                 outputs=['y'],
                                 value=tensor_val)
Khalique's avatar
Khalique committed
425

Khalique's avatar
Khalique committed
426
    return ([shape_const, node], [], [y])
Khalique's avatar
Khalique committed
427

Khalique's avatar
Khalique committed
428

Khalique's avatar
Khalique committed
429
@onnx_test
Khalique's avatar
Khalique committed
430
def const_of_shape_int64_test():
Khalique's avatar
Khalique committed
431
432
    tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
                                         [10])
Khalique's avatar
Khalique committed
433
    shape_val = np.array([2, 3, 4]).astype(np.int64)
Khalique's avatar
Khalique committed
434
435
436
437
    shape_ts = helper.make_tensor(name='shape_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=shape_val.shape,
                                  vals=shape_val.flatten().astype(int))
Khalique's avatar
Khalique committed
438
    shape_const = helper.make_node(
Khalique's avatar
Khalique committed
439
440
441
442
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
Khalique's avatar
Khalique committed
443
444
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
Khalique's avatar
Khalique committed
445
446
447
448
449

    node = onnx.helper.make_node('ConstantOfShape',
                                 inputs=['shape'],
                                 outputs=['y'],
                                 value=tensor_val)
Khalique's avatar
Khalique committed
450

Khalique's avatar
Khalique committed
451
    return ([shape_const, node], [], [y])
Khalique's avatar
Khalique committed
452

Khalique's avatar
Khalique committed
453

Khalique's avatar
Khalique committed
454
@onnx_test
Khalique's avatar
Khalique committed
455
456
def const_of_shape_no_value_attr_test():
    shape_val = np.array([2, 3, 4]).astype(np.int64)
Khalique's avatar
Khalique committed
457
458
459
460
    shape_ts = helper.make_tensor(name='shape_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=shape_val.shape,
                                  vals=shape_val.flatten().astype(int))
Khalique's avatar
Khalique committed
461
462
463
464
465
466
467
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
Khalique's avatar
Khalique committed
468

Khalique's avatar
Khalique committed
469
470
471
472
473
474
    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
475
    return ([shape_const, node], [], [y])
Khalique's avatar
Khalique committed
476

Khalique's avatar
Khalique committed
477

Khalique's avatar
Khalique committed
478
@onnx_test
Khalique's avatar
Khalique committed
479
480
481
482
483
def conv_autopad_fail_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
    out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 34, 34])

Khalique's avatar
Khalique committed
484
485
486
487
488
489
490
491
492
    node = onnx.helper.make_node('Conv',
                                 inputs=['0', '1'],
                                 outputs=['2'],
                                 dilations=[1, 1],
                                 strides=[1, 1],
                                 auto_pad='SAME',
                                 pads=[0, 0, 1, 1, 0, 0, 1, 1])

    return ([node], [x, y], [out])
Khalique's avatar
Khalique committed
493
494


Khalique's avatar
Khalique committed
495
@onnx_test
Khalique's avatar
Khalique committed
496
497
498
499
500
501
def conv_bias_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 28, 28])

Khalique's avatar
Khalique committed
502
503
504
505
506
507
508
    node = onnx.helper.make_node('Conv',
                                 inputs=['0', '1', '2'],
                                 outputs=['3'],
                                 dilations=[1, 1],
                                 strides=[1, 1])

    return ([node], [x, y, z], [out])
Khalique's avatar
Khalique committed
509
510


Khalique's avatar
Khalique committed
511
@onnx_test
Khalique's avatar
Khalique committed
512
513
514
515
516
517
518
519
def conv_bn_relu_maxpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1])
    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
    k = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1])
    l = helper.make_tensor_value_info('6', TensorProto.FLOAT, [1])
Khalique's avatar
Khalique committed
520
521
    out = helper.make_tensor_value_info('10', TensorProto.FLOAT,
                                        [1, 1, 14, 14])
Khalique's avatar
Khalique committed
522

Khalique's avatar
Khalique committed
523
524
525
526
527
528
    node0 = onnx.helper.make_node('Conv',
                                  inputs=['0', '1', '2'],
                                  outputs=['7'],
                                  dilations=[1, 1],
                                  strides=[1, 1],
                                  pads=[0, 0, 0, 0])
Khalique's avatar
Khalique committed
529

Khalique's avatar
Khalique committed
530
531
532
533
534
    node1 = onnx.helper.make_node('BatchNormalization',
                                  inputs=['7', '3', '4', '5', '6'],
                                  outputs=['8'],
                                  epsilon=9.99999974737875e-06,
                                  momentum=0.899999976158142)
Khalique's avatar
Khalique committed
535

Khalique's avatar
Khalique committed
536
537
538
539
540
541
542
543
544
    node2 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9'])
    node3 = onnx.helper.make_node('MaxPool',
                                  inputs=['9'],
                                  outputs=['10'],
                                  pads=[0, 0, 0, 0],
                                  strides=[2, 2],
                                  kernel_shape=[2, 2])

    return ([node0, node1, node2, node3], [x, y, z, m, n, k, l], [out])
Khalique's avatar
Khalique committed
545
546


Khalique's avatar
Khalique committed
547
@onnx_test
Khalique's avatar
Khalique committed
548
549
550
551
552
553
def conv_relu_maxpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 1, 14, 14])

Khalique's avatar
Khalique committed
554
555
556
557
558
559
    node1 = onnx.helper.make_node('Conv',
                                  inputs=['0', '1', '2'],
                                  outputs=['3'],
                                  dilations=[1, 1],
                                  strides=[1, 1],
                                  pads=[0, 0, 0, 0])
Khalique's avatar
Khalique committed
560

Khalique's avatar
Khalique committed
561
    node2 = onnx.helper.make_node('Relu', inputs=['3'], outputs=['4'])
Khalique's avatar
Khalique committed
562

Khalique's avatar
Khalique committed
563
564
565
566
567
568
569
570
    node3 = onnx.helper.make_node('MaxPool',
                                  inputs=['4'],
                                  outputs=['5'],
                                  pads=[0, 0, 0, 0],
                                  strides=[2, 2],
                                  kernel_shape=[2, 2])

    return ([node1, node2, node3], [x, y, z], [out])
Khalique's avatar
Khalique committed
571
572


Khalique's avatar
Khalique committed
573
@onnx_test
Khalique's avatar
Khalique committed
574
575
576
577
578
579
580
581
def conv_relu_maxpool_x2_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5])
    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 5, 5, 5])
    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 5, 5])

Khalique's avatar
Khalique committed
582
583
584
585
586
587
    node1 = onnx.helper.make_node('Conv',
                                  inputs=['0', '1', '2'],
                                  outputs=['5'],
                                  dilations=[1, 1],
                                  strides=[1, 1],
                                  pads=[0, 0, 0, 0])
Khalique's avatar
Khalique committed
588

Khalique's avatar
Khalique committed
589
    node2 = onnx.helper.make_node('Relu', inputs=['5'], outputs=['6'])
Khalique's avatar
Khalique committed
590

Khalique's avatar
Khalique committed
591
592
593
594
595
596
    node3 = onnx.helper.make_node('MaxPool',
                                  inputs=['6'],
                                  outputs=['7'],
                                  pads=[0, 0, 0, 0],
                                  strides=[2, 2],
                                  kernel_shape=[2, 2])
Khalique's avatar
Khalique committed
597

Khalique's avatar
Khalique committed
598
599
600
601
602
603
    node4 = onnx.helper.make_node('Conv',
                                  inputs=['7', '3', '4'],
                                  outputs=['8'],
                                  dilations=[1, 1],
                                  strides=[1, 1],
                                  pads=[0, 0, 0, 0])
Khalique's avatar
Khalique committed
604

Khalique's avatar
Khalique committed
605
    node5 = onnx.helper.make_node('Relu', inputs=['8'], outputs=['9'])
Khalique's avatar
Khalique committed
606

Khalique's avatar
Khalique committed
607
608
609
610
611
612
613
614
    node6 = onnx.helper.make_node('MaxPool',
                                  inputs=['9'],
                                  outputs=['10'],
                                  pads=[0, 0, 0, 0],
                                  strides=[2, 2],
                                  kernel_shape=[2, 2])

    return ([node1, node2, node3, node4, node5, node6], [x, y, z, m, n], [out])
Khalique's avatar
Khalique committed
615
616


617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
@onnx_test
def convinteger_bias_test():
    x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.INT32, [1])
    out = helper.make_tensor_value_info('3', TensorProto.INT32, [1, 2, 28, 28])

    node = onnx.helper.make_node('ConvInteger',
                                 inputs=['0', '1', '2'],
                                 outputs=['3'],
                                 dilations=[1, 1],
                                 strides=[1, 1])

    return ([node], [x, y, z], [out])


Khalique's avatar
Khalique committed
633
@onnx_test
Khalique's avatar
Khalique committed
634
635
636
637
638
639
640
641
642
643
def cos_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Cos',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
644
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
645

Khalique's avatar
Khalique committed
646

Khalique's avatar
Khalique committed
647
@onnx_test
Khalique's avatar
Khalique committed
648
649
650
651
652
653
654
655
656
657
def cosh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'Cosh',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
658
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
659

Khalique's avatar
Khalique committed
660

kahmed10's avatar
kahmed10 committed
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
@onnx_test
def deconv_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])

    node = onnx.helper.make_node('ConvTranspose',
                                 name='conv1',
                                 inputs=['x', 'w'],
                                 outputs=['y'])

    return ([node], [x, w], [y])


@onnx_test
def deconv_bias_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 1, 3, 3])
    b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])

    node = onnx.helper.make_node('ConvTranspose',
                                 name='conv1',
                                 inputs=['x', 'w', 'b'],
                                 outputs=['y'])

    return ([node], [x, w, b], [y])


@onnx_test
def deconv_input_pads_strides_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 7, 5])

    node = onnx.helper.make_node('ConvTranspose',
                                 inputs=['x', 'w'],
                                 outputs=['y'],
                                 strides=[3, 2],
                                 pads=[1, 1, 1, 1])

    return ([node], [x, w], [y])


@onnx_test
def deconv_input_pads_asymm_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 8, 6])

    node = onnx.helper.make_node('ConvTranspose',
                                 inputs=['x', 'w'],
                                 outputs=['y'],
                                 strides=[3, 2],
                                 pads=[0, 0, 1, 1])

    return ([node], [x, w], [y])


@onnx_test
def deconv_output_shape_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8])

    node = onnx.helper.make_node('ConvTranspose',
                                 inputs=['x', 'w'],
                                 outputs=['y'],
                                 strides=[3, 2],
                                 output_shape=[10, 8])

    return ([node], [x, w], [y])


@onnx_test
def deconv_output_padding_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 10, 8])

    node = onnx.helper.make_node('ConvTranspose',
                                 inputs=['x', 'w'],
                                 outputs=['y'],
                                 strides=[3, 2],
                                 output_padding=[1, 1])

    return ([node], [x, w], [y])


@onnx_test
def deconv_stride_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 3, 3])
    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 2, 3, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 7, 3])

    node = onnx.helper.make_node('ConvTranspose',
                                 inputs=['x', 'w'],
                                 outputs=['y'],
                                 strides=[3, 2])

    return ([node], [x, w], [y])


Khalique's avatar
Khalique committed
764
@onnx_test
Khalique's avatar
Khalique committed
765
def dropout_test():
Khalique's avatar
Khalique committed
766
767
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])
Khalique's avatar
Khalique committed
768

Khalique's avatar
Khalique committed
769
770
771
772
773
774
775
    node = onnx.helper.make_node(
        'Dropout',
        inputs=['0'],
        outputs=['1'],
    )

    return ([node], [x], [y])
Khalique's avatar
Khalique committed
776
777


Khalique's avatar
Khalique committed
778
@onnx_test
Khalique's avatar
Khalique committed
779
780
781
782
def elu_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])

Khalique's avatar
Khalique committed
783
784
785
786
    node = onnx.helper.make_node('Elu',
                                 inputs=['0'],
                                 outputs=['1'],
                                 alpha=0.01)
Khalique's avatar
Khalique committed
787

Khalique's avatar
Khalique committed
788
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
789

Khalique's avatar
Khalique committed
790

Khalique's avatar
Khalique committed
791
@onnx_test
Khalique's avatar
Khalique committed
792
793
794
795
796
797
798
799
800
801
def erf_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])

    node = onnx.helper.make_node(
        'Erf',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
802
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
803

Khalique's avatar
Khalique committed
804

Khalique's avatar
Khalique committed
805
@onnx_test
Khalique's avatar
Khalique committed
806
807
808
809
810
811
812
813
814
815
def exp_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Exp',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
816
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
817

Khalique's avatar
Khalique committed
818

Khalique's avatar
Khalique committed
819
@onnx_test
Khalique's avatar
Khalique committed
820
821
def expand_test():
    shape_val = np.array([2, 3, 4, 5]).astype(np.int64)
Khalique's avatar
Khalique committed
822
823
824
825
    shape_ts = helper.make_tensor(name='shape_tensor',
                                  data_type=TensorProto.INT32,
                                  dims=shape_val.shape,
                                  vals=shape_val.flatten().astype(int))
Khalique's avatar
Khalique committed
826
827
828
829
830
831
832
833
834
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])

Khalique's avatar
Khalique committed
835
836
837
838
839
840
    node = onnx.helper.make_node('Expand',
                                 inputs=['x', 'shape'],
                                 outputs=['y'])

    return ([shape_const, node], [x], [y])

Khalique's avatar
Khalique committed
841

Khalique's avatar
Khalique committed
842
@onnx_test
Khalique's avatar
Khalique committed
843
844
def flatten_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
Khalique's avatar
Khalique committed
845
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6, 20])
Khalique's avatar
Khalique committed
846
847
    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60])

Khalique's avatar
Khalique committed
848
849
850
851
    node = onnx.helper.make_node('Flatten',
                                 inputs=['0'],
                                 axis=2,
                                 outputs=['2'])
Khalique's avatar
Khalique committed
852

Khalique's avatar
Khalique committed
853
854
855
    node2 = onnx.helper.make_node('Flatten', inputs=['0'], outputs=['3'])

    return ([node, node2], [x], [y, y2])
Khalique's avatar
Khalique committed
856

kahmed10's avatar
kahmed10 committed
857

Shucai Xiao's avatar
Shucai Xiao committed
858
859
860
861
862
863
864
865
866
867
868
869
@onnx_test
def floor_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Floor',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])
Khalique's avatar
Khalique committed
870

kahmed10's avatar
kahmed10 committed
871

Khalique's avatar
Khalique committed
872
@onnx_test
Khalique's avatar
Khalique committed
873
874
def gather_test():
    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
Khalique's avatar
Khalique committed
875
876
    i = helper.make_tensor_value_info('indices', TensorProto.INT32,
                                      [2, 3, 4, 5])
Khalique's avatar
Khalique committed
877
878
879
880
881
882
883
884
885
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Gather',
        inputs=['data', 'indices'],
        outputs=['y'],
        axis=1,
    )

Khalique's avatar
Khalique committed
886
887
    return ([node], [x, i], [y])

Khalique's avatar
Khalique committed
888

Khalique's avatar
Khalique committed
889
@onnx_test
Khalique's avatar
Khalique committed
890
891
892
893
894
895
def gemm_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [])
    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11])

Khalique's avatar
Khalique committed
896
897
898
899
900
901
902
903
904
    node = onnx.helper.make_node('Gemm',
                                 inputs=['0', '1', '2'],
                                 outputs=['3'],
                                 alpha=2.0,
                                 beta=2.0,
                                 transA=1,
                                 transB=1)

    return ([node], [x, y, z], [a])
Khalique's avatar
Khalique committed
905
906


Khalique's avatar
Khalique committed
907
@onnx_test
Khalique's avatar
Khalique committed
908
909
910
911
912
913
def gemm_ex_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])

Khalique's avatar
Khalique committed
914
915
916
917
918
919
920
921
    node = onnx.helper.make_node('Gemm',
                                 inputs=['1', '2', '3'],
                                 outputs=['y'],
                                 alpha=0.5,
                                 beta=0.8,
                                 transA=1)

    return ([node], [m1, m2, m3], [y])
Khalique's avatar
Khalique committed
922
923


Khalique's avatar
Khalique committed
924
@onnx_test
Khalique's avatar
Khalique committed
925
926
927
928
929
930
def gemm_ex_brcst_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])

Khalique's avatar
Khalique committed
931
932
933
934
935
936
937
938
    node = onnx.helper.make_node('Gemm',
                                 inputs=['1', '2', '3'],
                                 outputs=['y'],
                                 alpha=0.5,
                                 beta=0.8,
                                 transA=1)

    return ([node], [m1, m2, m3], [y])
Khalique's avatar
Khalique committed
939
940


Khalique's avatar
Khalique committed
941
@onnx_test
Khalique's avatar
Khalique committed
942
def globalavgpool_test():
Khalique's avatar
Khalique committed
943
944
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
Khalique's avatar
Khalique committed
945
946
947
948
949
950
951

    node = onnx.helper.make_node(
        'GlobalAveragePool',
        inputs=['0'],
        outputs=['1'],
    )

Khalique's avatar
Khalique committed
952
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
953

Khalique's avatar
Khalique committed
954

Khalique's avatar
Khalique committed
955
@onnx_test
Khalique's avatar
Khalique committed
956
def globalmaxpool_test():
Khalique's avatar
Khalique committed
957
958
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
Khalique's avatar
Khalique committed
959
960
961
962
963
964
965

    node = onnx.helper.make_node(
        'GlobalMaxPool',
        inputs=['0'],
        outputs=['1'],
    )

Khalique's avatar
Khalique committed
966
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
967

Khalique's avatar
Khalique committed
968

Khalique's avatar
Khalique committed
969
@onnx_test
Khalique's avatar
Khalique committed
970
971
972
973
974
975
976
977
978
979
980
981
def group_conv_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 4, 14, 14])

    node = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1'],
        group=4,
        outputs=['2'],
    )

Khalique's avatar
Khalique committed
982
983
    return ([node], [x, y], [z])

Khalique's avatar
Khalique committed
984

Khalique's avatar
Khalique committed
985
@onnx_test
Khalique's avatar
Khalique committed
986
def imagescaler_test():
Khalique's avatar
Khalique committed
987
988
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 16, 16])
Khalique's avatar
Khalique committed
989

Khalique's avatar
Khalique committed
990
991
992
993
994
    node = onnx.helper.make_node('ImageScaler',
                                 inputs=['0'],
                                 outputs=['1'],
                                 bias=[0.01, 0.02, 0.03],
                                 scale=0.5)
Khalique's avatar
Khalique committed
995

Khalique's avatar
Khalique committed
996
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
997

Khalique's avatar
Khalique committed
998

Khalique's avatar
Khalique committed
999
@onnx_test
Khalique's avatar
Khalique committed
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
def implicit_add_bcast_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        outputs=['2'],
    )

Khalique's avatar
Khalique committed
1011
1012
    return ([node], [x, y], [z])

Khalique's avatar
Khalique committed
1013

Khalique's avatar
Khalique committed
1014
@onnx_test
Khalique's avatar
Khalique committed
1015
1016
1017
def implicit_pow_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
Khalique's avatar
Khalique committed
1018
1019
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
                                            [2, 3, 4, 5])
Khalique's avatar
Khalique committed
1020
1021
1022
1023
1024
1025
1026

    node = onnx.helper.make_node(
        'Pow',
        inputs=['0', '1'],
        outputs=['out'],
    )

Khalique's avatar
Khalique committed
1027
1028
    return ([node], [arg0, arg1], [arg_out])

Khalique's avatar
Khalique committed
1029

Khalique's avatar
Khalique committed
1030
@onnx_test
Khalique's avatar
Khalique committed
1031
1032
1033
def implicit_sub_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
Khalique's avatar
Khalique committed
1034
1035
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
                                            [2, 3, 4, 5])
Khalique's avatar
Khalique committed
1036
1037
1038
1039
1040
1041
1042

    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', '1'],
        outputs=['out'],
    )

Khalique's avatar
Khalique committed
1043
1044
    return ([node], [arg0, arg1], [arg_out])

Khalique's avatar
Khalique committed
1045

1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
@onnx_test
def initializer_not_an_input():
    values = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
    w = helper.make_tensor(name='w',
                           data_type=TensorProto.FLOAT,
                           dims=values.shape,
                           vals=values.flatten().astype(np.float))

    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [5, 2])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 4])

    node = onnx.helper.make_node(
        'Gemm',
        inputs=['x', 'w'],
        outputs=['y'],
    )

    return ([node], [x], [y], [w])


kahmed10's avatar
kahmed10 committed
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
@onnx_test
def instance_norm_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
    scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2])
    bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2])
    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 3, 3])

    node = onnx.helper.make_node('InstanceNormalization',
                                 inputs=['0', '1', '2'],
                                 outputs=['3'])

    return ([node], [x, scale, bias], [y])


@onnx_test
def instance_norm_val_test():
    x = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
                   [[0, 1, 2], [3, 4, 5], [6, 7, 8]]]])
    scale = np.array([1, 2])
    bias = np.array([0, 1])

    x_tensor = helper.make_tensor(name='x_tensor',
                                  data_type=TensorProto.FLOAT,
                                  dims=x.shape,
                                  vals=x.flatten().astype(np.float))
    scale_tensor = helper.make_tensor(name='scale_tensor',
                                      data_type=TensorProto.FLOAT,
                                      dims=scale.shape,
                                      vals=scale.flatten().astype(np.float))
    bias_tensor = helper.make_tensor(name='bias_tensor',
                                     data_type=TensorProto.FLOAT,
                                     dims=bias.shape,
                                     vals=bias.flatten().astype(np.float))

    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 2, 3, 3])

    node = onnx.helper.make_node(
        'InstanceNormalization',
        inputs=['x_tensor', 'scale_tensor', 'bias_tensor'],
        outputs=['y'])

    return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor])


Khalique's avatar
Khalique committed
1110
@onnx_test
Khalique's avatar
Khalique committed
1111
1112
1113
1114
def leaky_relu_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])

Khalique's avatar
Khalique committed
1115
1116
1117
1118
    node = onnx.helper.make_node('LeakyRelu',
                                 inputs=['0'],
                                 outputs=['1'],
                                 alpha=0.01)
Khalique's avatar
Khalique committed
1119

Khalique's avatar
Khalique committed
1120
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1121

Khalique's avatar
Khalique committed
1122

Khalique's avatar
Khalique committed
1123
@onnx_test
Khalique's avatar
Khalique committed
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
def log_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Log',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1134
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1135

Khalique's avatar
Khalique committed
1136

Khalique's avatar
Khalique committed
1137
@onnx_test
Khalique's avatar
Khalique committed
1138
1139
1140
1141
def logsoftmax_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6])

Khalique's avatar
Khalique committed
1142
1143
1144
1145
    node = onnx.helper.make_node('LogSoftmax',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axis=1)
Khalique's avatar
Khalique committed
1146

Khalique's avatar
Khalique committed
1147
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1148

Khalique's avatar
Khalique committed
1149

Khalique's avatar
Khalique committed
1150
@onnx_test
Khalique's avatar
Khalique committed
1151
1152
1153
1154
def lrn_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24])

Khalique's avatar
Khalique committed
1155
1156
1157
1158
1159
1160
1161
    node = onnx.helper.make_node('LRN',
                                 inputs=['0'],
                                 size=5,
                                 alpha=0.0001,
                                 beta=0.75,
                                 bias=1.0,
                                 outputs=['1'])
Khalique's avatar
Khalique committed
1162

Khalique's avatar
Khalique committed
1163
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1164

Khalique's avatar
Khalique committed
1165

Khalique's avatar
Khalique committed
1166
@onnx_test
Khalique's avatar
Khalique committed
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
def matmul_bmbm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 2, 3, 6, 8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1178
1179
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1180

Khalique's avatar
Khalique committed
1181
@onnx_test
Khalique's avatar
Khalique committed
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
def matmul_bmv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 6])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1193
1194
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1195

Khalique's avatar
Khalique committed
1196
@onnx_test
Khalique's avatar
Khalique committed
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
def matmul_mv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [6])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1208
1209
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1210

Khalique's avatar
Khalique committed
1211
@onnx_test
Khalique's avatar
Khalique committed
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
def matmul_vbm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1223
1224
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1225

Khalique's avatar
Khalique committed
1226
@onnx_test
Khalique's avatar
Khalique committed
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
def matmul_vm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1238
1239
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1240

Khalique's avatar
Khalique committed
1241
@onnx_test
Khalique's avatar
Khalique committed
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
def matmul_vv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1253
1254
    return ([node], [m1, m2], [y])

Khalique's avatar
Khalique committed
1255

1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
@onnx_test
def matmulinteger_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16])
    m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [3, 16, 8])
    y = helper.make_tensor_value_info('y', TensorProto.INT32, [3, 6, 8])

    node = onnx.helper.make_node(
        'MatMulInteger',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1, m2], [y])


Khalique's avatar
Khalique committed
1271
@onnx_test
Khalique's avatar
Khalique committed
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
def max_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Max',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

Khalique's avatar
Khalique committed
1284
1285
    return ([node], [a, b, c], [y])

Khalique's avatar
Khalique committed
1286

1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
@onnx_test
def maxpool_notset_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 1, 1])

    node = onnx.helper.make_node('MaxPool',
                                 inputs=['x'],
                                 outputs=['y'],
                                 kernel_shape=[6, 6],
                                 strides=[2, 2],
                                 pads=[0, 0, 1, 1],
                                 auto_pad='NOTSET')

    return ([node], [x], [y])


@onnx_test
def maxpool_same_upper_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 5, 5])

    node = onnx.helper.make_node('MaxPool',
                                 inputs=['x'],
                                 outputs=['y'],
                                 kernel_shape=[2, 2],
                                 auto_pad='SAME_UPPER')

    return ([node], [x], [y])


Khalique's avatar
Khalique committed
1317
@onnx_test
Khalique's avatar
Khalique committed
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
def min_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Min',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

Khalique's avatar
Khalique committed
1330
1331
    return ([node], [a, b, c], [y])

Khalique's avatar
Khalique committed
1332

Khalique's avatar
Khalique committed
1333
@onnx_test
Khalique's avatar
Khalique committed
1334
1335
1336
1337
def no_pad_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2])

Khalique's avatar
Khalique committed
1338
1339
1340
1341
    node = onnx.helper.make_node('Pad',
                                 inputs=['0'],
                                 pads=[0, 0, 0, 0],
                                 outputs=['1'])
Khalique's avatar
Khalique committed
1342

Khalique's avatar
Khalique committed
1343
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1344

Khalique's avatar
Khalique committed
1345

Khalique's avatar
Khalique committed
1346
@onnx_test
Khalique's avatar
Khalique committed
1347
1348
1349
1350
def pad_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4])

Khalique's avatar
Khalique committed
1351
1352
1353
1354
    node = onnx.helper.make_node('Pad',
                                 inputs=['0'],
                                 pads=[1, 1, 1, 1],
                                 outputs=['1'])
Khalique's avatar
Khalique committed
1355

Khalique's avatar
Khalique committed
1356
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1357

Khalique's avatar
Khalique committed
1358

Khalique's avatar
Khalique committed
1359
@onnx_test
Khalique's avatar
Khalique committed
1360
1361
1362
def pow_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
Khalique's avatar
Khalique committed
1363
1364
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
                                            [2, 3, 4, 5])
Khalique's avatar
Khalique committed
1365
1366
1367
1368
1369
1370
1371

    node = onnx.helper.make_node(
        'Pow',
        inputs=['0', '1'],
        outputs=['out'],
    )

Khalique's avatar
Khalique committed
1372
    return ([node], [arg0, arg1], [arg_out])
Khalique's avatar
Khalique committed
1373

kahmed10's avatar
kahmed10 committed
1374

Shucai Xiao's avatar
Shucai Xiao committed
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
@onnx_test
def reducel1_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
    axes = [-2]

    node = onnx.helper.make_node('ReduceL1',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)

    return ([node], [x], [y])


@onnx_test
def reducel2_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])
    axes = [-1]

    node = onnx.helper.make_node('ReduceL2',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)

    return ([node], [x], [y])


@onnx_test
def reduce_log_sum_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 6])
    axes = [-3]

    node = onnx.helper.make_node('ReduceLogSum',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=1)

    return ([node], [x], [y])


@onnx_test
def reduce_log_sum_exp_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 5, 6])
    axes = [-4]

    node = onnx.helper.make_node('ReduceLogSumExp',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=1)

    return ([node], [x], [y])


Shucai Xiao's avatar
Shucai Xiao committed
1435
1436
1437
@onnx_test
def reducemax_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
Shucai Xiao's avatar
Shucai Xiao committed
1438
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
Shucai Xiao's avatar
Shucai Xiao committed
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
    axes = [2]

    node = onnx.helper.make_node('ReduceMax',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)

    return ([node], [x], [y])

Khalique's avatar
Khalique committed
1449

Khalique's avatar
Khalique committed
1450
@onnx_test
Khalique's avatar
Khalique committed
1451
1452
1453
def reducemean_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
Khalique's avatar
Khalique committed
1454
    axes = [2, 3]
Khalique's avatar
Khalique committed
1455

Khalique's avatar
Khalique committed
1456
1457
1458
1459
1460
    node = onnx.helper.make_node('ReduceMean',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)
Khalique's avatar
Khalique committed
1461

Khalique's avatar
Khalique committed
1462
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1463

kahmed10's avatar
kahmed10 committed
1464

Khalique's avatar
Khalique committed
1465
@onnx_test
Khalique's avatar
Khalique committed
1466
1467
1468
def reducemean_keepdims_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
Khalique's avatar
Khalique committed
1469
    axes = [2]
Khalique's avatar
Khalique committed
1470

Khalique's avatar
Khalique committed
1471
1472
1473
1474
1475
    node = onnx.helper.make_node('ReduceMean',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=1)
Khalique's avatar
Khalique committed
1476

Khalique's avatar
Khalique committed
1477
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1478

kahmed10's avatar
kahmed10 committed
1479

Shucai Xiao's avatar
Shucai Xiao committed
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
@onnx_test
def reducemin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 1, 5, 1])
    axes = [1, 3]

    node = onnx.helper.make_node('ReduceMin',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=1)

    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1493

kahmed10's avatar
kahmed10 committed
1494

Khalique's avatar
Khalique committed
1495
@onnx_test
Shucai Xiao's avatar
Shucai Xiao committed
1496
def reduceprod_test():
Khalique's avatar
Khalique committed
1497
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
Shucai Xiao's avatar
Shucai Xiao committed
1498
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
Khalique's avatar
Khalique committed
1499
    axes = [2]
Khalique's avatar
Khalique committed
1500

Shucai Xiao's avatar
Shucai Xiao committed
1501
    node = onnx.helper.make_node('ReduceProd',
Khalique's avatar
Khalique committed
1502
1503
1504
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
Shucai Xiao's avatar
Shucai Xiao committed
1505
                                 keepdims=1)
Khalique's avatar
Khalique committed
1506

Khalique's avatar
Khalique committed
1507
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1508

Khalique's avatar
Khalique committed
1509

Khalique's avatar
Khalique committed
1510
@onnx_test
Shucai Xiao's avatar
Shucai Xiao committed
1511
def reducesum_test():
Khalique's avatar
Khalique committed
1512
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
Shucai Xiao's avatar
Shucai Xiao committed
1513
1514
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
    axes = [2]
Khalique's avatar
Khalique committed
1515

Khalique's avatar
Khalique committed
1516
1517
1518
1519
1520
    node = onnx.helper.make_node('ReduceSum',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)
Khalique's avatar
Khalique committed
1521

Khalique's avatar
Khalique committed
1522
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1523

Khalique's avatar
Khalique committed
1524

Khalique's avatar
Khalique committed
1525
@onnx_test
Khalique's avatar
Khalique committed
1526
1527
1528
def reducesum_keepdims_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
Khalique's avatar
Khalique committed
1529
    axes = [2, 3]
Khalique's avatar
Khalique committed
1530

Khalique's avatar
Khalique committed
1531
1532
1533
1534
1535
    node = onnx.helper.make_node('ReduceSum',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=1)
Khalique's avatar
Khalique committed
1536

Khalique's avatar
Khalique committed
1537
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1538

Khalique's avatar
Khalique committed
1539

Shucai Xiao's avatar
Shucai Xiao committed
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
@onnx_test
def reducesum_multiaxis_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
    axes = [2, 3]

    node = onnx.helper.make_node('ReduceSum',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)

    return ([node], [x], [y])


@onnx_test
def reducesum_square_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
    axes = [-2]

    node = onnx.helper.make_node('ReduceSumSquare',
                                 inputs=['x'],
                                 outputs=['y'],
                                 axes=axes,
                                 keepdims=0)

    return ([node], [x], [y])


Khalique's avatar
Khalique committed
1570
@onnx_test
Khalique's avatar
Khalique committed
1571
1572
1573
def reshape_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3])
    x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2])
Khalique's avatar
Khalique committed
1574
    x_shape_list = [3, 8]
Khalique's avatar
Khalique committed
1575
1576
1577
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 8])
    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3, 8])

Khalique's avatar
Khalique committed
1578
    node = onnx.helper.make_node('Reshape', inputs=['0', '1'], outputs=['2'])
Khalique's avatar
Khalique committed
1579

Khalique's avatar
Khalique committed
1580
1581
1582
1583
1584
1585
1586
    node2 = onnx.helper.make_node('Reshape',
                                  inputs=['0'],
                                  shape=x_shape_list,
                                  outputs=['3'])

    return ([node, node2], [x, x_shape], [y, y2],
            [helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])])
Khalique's avatar
Khalique committed
1587
1588


Khalique's avatar
Khalique committed
1589
@onnx_test
Khalique's avatar
Khalique committed
1590
1591
def reshape_non_standard_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])
Khalique's avatar
Khalique committed
1592
1593
    trans_x = helper.make_tensor_value_info('trans_x', TensorProto.FLOAT,
                                            [2, 4, 3])
Khalique's avatar
Khalique committed
1594
1595
1596
1597
1598
1599
1600
1601
1602
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 3, 2])

    trans = helper.make_node(
        'Transpose',
        inputs=['x'],
        outputs=['trans_x'],
        perm=[0, 2, 1],
    )

Khalique's avatar
Khalique committed
1603
1604
1605
1606
1607
1608
    res = onnx.helper.make_node('Reshape',
                                inputs=['trans_x'],
                                outputs=['y'],
                                shape=[4, 3, 2])

    return ([trans, res], [x], [y])
Khalique's avatar
Khalique committed
1609
1610


Khalique's avatar
Khalique committed
1611
@onnx_test
Khalique's avatar
Khalique committed
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
def shape_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.INT64, [4])

    node = onnx.helper.make_node(
        'Shape',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1622
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1623

Khalique's avatar
Khalique committed
1624

Khalique's avatar
Khalique committed
1625
@onnx_test
Khalique's avatar
Khalique committed
1626
1627
def shape_gather_test():
    values = np.array([1])
kahmed10's avatar
kahmed10 committed
1628
    # value = helper.make_tensor_value_info('value', TensorProto.INT32, [1])
Khalique's avatar
Khalique committed
1629
1630
1631
1632
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [7, 3, 10])
    y = helper.make_tensor_value_info('y', TensorProto.INT64, [3])
    z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [1])

Khalique's avatar
Khalique committed
1633
1634
1635
1636
    value_tensor = helper.make_tensor(name='const_tensor',
                                      data_type=TensorProto.INT32,
                                      dims=values.shape,
                                      vals=values.flatten().astype(int))
Khalique's avatar
Khalique committed
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657

    node_const = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['value'],
        value=value_tensor,
    )

    node_shape = onnx.helper.make_node(
        'Shape',
        inputs=['x'],
        outputs=['y'],
    )

    node_gather = helper.make_node(
        'Gather',
        inputs=['y', 'value'],
        outputs=['z'],
        axis=0,
    )

Khalique's avatar
Khalique committed
1658
1659
    return ([node_const, node_shape, node_gather], [x], [z])

Khalique's avatar
Khalique committed
1660

Khalique's avatar
Khalique committed
1661
@onnx_test
Khalique's avatar
Khalique committed
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
def sign_test():
    x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5])
    y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [10, 5])

    node = onnx.helper.make_node(
        'Sign',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1672
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1673

Khalique's avatar
Khalique committed
1674

Khalique's avatar
Khalique committed
1675
@onnx_test
Khalique's avatar
Khalique committed
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
def sin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Sin',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1686
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1687

Khalique's avatar
Khalique committed
1688

Khalique's avatar
Khalique committed
1689
@onnx_test
Khalique's avatar
Khalique committed
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
def sinh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Sinh',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1700
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1701

Khalique's avatar
Khalique committed
1702

Khalique's avatar
Khalique committed
1703
@onnx_test
Khalique's avatar
Khalique committed
1704
1705
1706
1707
def slice_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])

Khalique's avatar
Khalique committed
1708
1709
1710
1711
1712
1713
    node = onnx.helper.make_node('Slice',
                                 inputs=['0'],
                                 axes=[0, 1],
                                 starts=[1, 0],
                                 ends=[2, 2],
                                 outputs=['1'])
Khalique's avatar
Khalique committed
1714

Khalique's avatar
Khalique committed
1715
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1716

Khalique's avatar
Khalique committed
1717

Khalique's avatar
Khalique committed
1718
@onnx_test
Khalique's avatar
Khalique committed
1719
1720
1721
1722
def softmax_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3])

Khalique's avatar
Khalique committed
1723
    node = onnx.helper.make_node('Softmax', inputs=['0'], outputs=['1'])
Khalique's avatar
Khalique committed
1724

Khalique's avatar
Khalique committed
1725
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1726

Khalique's avatar
Khalique committed
1727

Khalique's avatar
Khalique committed
1728
@onnx_test
Khalique's avatar
Khalique committed
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
def sqrt_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])

    node = onnx.helper.make_node(
        'Sqrt',
        inputs=['x'],
        outputs=['y'],
    )

Khalique's avatar
Khalique committed
1739
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1740

Khalique's avatar
Khalique committed
1741

Khalique's avatar
Khalique committed
1742
@onnx_test
Khalique's avatar
Khalique committed
1743
def squeeze_unsqueeze_test():
Khalique's avatar
Khalique committed
1744
1745
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
                                      [1, 3, 1, 1, 2, 1])
Khalique's avatar
Khalique committed
1746
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 2])
Khalique's avatar
Khalique committed
1747
1748
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT,
                                      [1, 1, 3, 1, 2, 1])
Khalique's avatar
Khalique committed
1749

Khalique's avatar
Khalique committed
1750
1751
1752
1753
    node = onnx.helper.make_node('Squeeze',
                                 inputs=['0'],
                                 axes=[0, 2, 3, 5],
                                 outputs=['1'])
Khalique's avatar
Khalique committed
1754

Khalique's avatar
Khalique committed
1755
1756
1757
1758
1759
1760
    node2 = onnx.helper.make_node('Unsqueeze',
                                  inputs=['1'],
                                  axes=[0, 1, 3, 5],
                                  outputs=['2'])

    return ([node, node2], [x], [z])
Khalique's avatar
Khalique committed
1761
1762


Khalique's avatar
Khalique committed
1763
@onnx_test
Khalique's avatar
Khalique committed
1764
1765
1766
def sub_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
Khalique's avatar
Khalique committed
1767
1768
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
                                            [2, 3, 4, 5])
Khalique's avatar
Khalique committed
1769
1770
1771
1772
1773

    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', '1'],
        outputs=['out'],
Khalique's avatar
Khalique committed
1774
1775
        broadcast=1,
        axis=1,
Khalique's avatar
Khalique committed
1776
1777
    )

Khalique's avatar
Khalique committed
1778
1779
    return ([node], [arg0, arg1], [arg_out])

Khalique's avatar
Khalique committed
1780

Khalique's avatar
Khalique committed
1781
@onnx_test
Khalique's avatar
Khalique committed
1782
1783
def sub_scalar_test():
    values = np.array([1])
Khalique's avatar
Khalique committed
1784
1785
1786
1787
1788
1789
1790
1791
1792
    arg_node = helper.make_tensor_value_info('0', TensorProto.FLOAT,
                                             [2, 3, 4, 5])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
                                            [2, 3, 4, 5])

    values_tensor = helper.make_tensor(name='const',
                                       data_type=TensorProto.FLOAT,
                                       dims=values.shape,
                                       vals=values.flatten().astype(float))
Khalique's avatar
Khalique committed
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806

    arg_const = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['arg_const'],
        value=values_tensor,
    )

    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', 'arg_const'],
        outputs=['out'],
    )

Khalique's avatar
Khalique committed
1807
1808
    return ([arg_const, node], [arg_node], [arg_out])

Khalique's avatar
Khalique committed
1809

Khalique's avatar
Khalique committed
1810
@onnx_test
Khalique's avatar
Khalique committed
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
def sum_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Sum',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

Khalique's avatar
Khalique committed
1823
1824
    return ([node], [a, b, c], [y])

Khalique's avatar
Khalique committed
1825

Khalique's avatar
Khalique committed
1826
@onnx_test
Khalique's avatar
Khalique committed
1827
1828
1829
1830
1831
def tan_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
Khalique's avatar
Khalique committed
1832
1833
1834
1835
        'Tan',
        inputs=['x'],
        outputs=['y'],
    )
Khalique's avatar
Khalique committed
1836

Khalique's avatar
Khalique committed
1837
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1838

Khalique's avatar
Khalique committed
1839

Khalique's avatar
Khalique committed
1840
@onnx_test
Khalique's avatar
Khalique committed
1841
1842
1843
1844
1845
def tanh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
Khalique's avatar
Khalique committed
1846
1847
1848
1849
        'Tanh',
        inputs=['x'],
        outputs=['y'],
    )
Khalique's avatar
Khalique committed
1850

Khalique's avatar
Khalique committed
1851
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1852

Khalique's avatar
Khalique committed
1853

Khalique's avatar
Khalique committed
1854
@onnx_test
Khalique's avatar
Khalique committed
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
def transpose_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])

    node = onnx.helper.make_node(
        'Transpose',
        perm=[0, 3, 1, 2],
        inputs=['0'],
        outputs=['1'],
    )

Khalique's avatar
Khalique committed
1866
    return ([node], [x], [y])
Khalique's avatar
Khalique committed
1867

Khalique's avatar
Khalique committed
1868

Khalique's avatar
Khalique committed
1869
1870
1871
@onnx_test
def transpose_gather_test():
    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6])
Khalique's avatar
Khalique committed
1872
1873
1874
1875
    i = helper.make_tensor_value_info('indices', TensorProto.INT32,
                                      [2, 4, 3, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT,
                                      [3, 2, 3, 4, 5, 4, 5, 6])
Khalique's avatar
Khalique committed
1876
1877
1878
1879
1880
1881
1882
1883

    td = onnx.helper.make_node(
        'Transpose',
        inputs=['data'],
        outputs=['tdata'],
        perm=[0, 2, 1, 3],
    )

Khalique's avatar
Khalique committed
1884
1885
1886
1887
    ti = onnx.helper.make_node('Transpose',
                               inputs=['indices'],
                               outputs=['tindices'],
                               perm=[0, 2, 1, 3])
Khalique's avatar
Khalique committed
1888
1889
1890
1891
1892
1893
1894
1895

    node = onnx.helper.make_node(
        'Gather',
        inputs=['tdata', 'tindices'],
        outputs=['y'],
        axis=1,
    )

Khalique's avatar
Khalique committed
1896
    return ([td, ti, node], [x, i], [y])
Khalique's avatar
Khalique committed
1897

Khalique's avatar
Khalique committed
1898

Khalique's avatar
Khalique committed
1899
@onnx_test
Khalique's avatar
Khalique committed
1900
1901
1902
def unknown_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
1903
1904
1905

    helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])

Khalique's avatar
Khalique committed
1906
1907
    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5])

Khalique's avatar
Khalique committed
1908
    node = onnx.helper.make_node('Unknown', inputs=['0', '1'], outputs=['2'])
Khalique's avatar
Khalique committed
1909

Khalique's avatar
Khalique committed
1910
    node2 = onnx.helper.make_node('Unknown', inputs=['2'], outputs=['3'])
Khalique's avatar
Khalique committed
1911

Khalique's avatar
Khalique committed
1912
    return ([node, node2], [x, y], [a])
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935


@onnx_test
def variable_batch_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
                                      [None, 3, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
                                      [None, 3, 16, 16])

    node = onnx.helper.make_node('Identity', inputs=['0'], outputs=['1'])

    return ([node], [x], [y])


@onnx_test
def variable_batch_leq_zero_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [0, 3, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [-1, 3, 16, 16])

    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [-1, 3, 16, 16])
    node = onnx.helper.make_node('Add', inputs=['0', '1'], outputs=['2'])

    return ([node], [x, y], [z])