-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy path_op_var.py
More file actions
390 lines (337 loc) · 12.1 KB
/
_op_var.py
File metadata and controls
390 lines (337 loc) · 12.1 KB
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
from typing import List, Optional, Union
import numpy as np
from ..reference import from_array_extended
from ..annotations import AI_ONNX_ML, domain
class OpsVar:
"""
Operators taking only one input.
"""
def ArgMax(
self, axis: int = 0, keepdims: int = 1, select_last_index: int = 0
) -> "Var":
return self.make_node(
"ArgMax",
self,
axis=axis,
keepdims=keepdims,
select_last_index=select_last_index,
)
def ArgMin(
self, axis: int = 0, keepdims: int = 1, select_last_index: int = 0
) -> "Var":
return self.make_node(
"ArgMin",
self,
axis=axis,
keepdims=keepdims,
select_last_index=select_last_index,
)
def AveragePool(
self,
auto_pad: str = "NOTSET",
ceil_mode: int = 0,
count_include_pad: int = 0,
dilations: Optional[List[int]] = None,
kernel_shape: Optional[List[int]] = None,
pads: Optional[List[int]] = None,
strides: Optional[List[int]] = None,
) -> "Var":
dilations = dilations or []
kernel_shape = kernel_shape or []
pads = pads or []
strides = strides or []
return self.make_node(
"AveragePool",
self,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
count_include_pad=count_include_pad,
dilations=dilations,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
def Bernoulli(self, dtype: int = 0, seed: float = 0.0) -> "Var":
return self.make_node("Bernoulli", self, dtype=dtype, seed=seed)
def BlackmanWindow(self, output_datatype: int = 1, periodic: int = 1) -> "Var":
return self.make_node(
"BlackmanWindow", self, output_datatype=output_datatype, periodic=periodic
)
def Cast(self, saturate: int = 1, to: int = 0) -> "Var":
return self.make_node("Cast", self, saturate=saturate, to=to)
def Celu(self, alpha: float = 1.0) -> "Var":
return self.make_node("Celu", self, alpha=alpha)
def ConstantOfShape(self, value: Optional[np.array] = None) -> "Var":
if value is None:
return self.make_node("ConstantOfShape", self)
return self.make_node("ConstantOfShape", self, value=from_array_extended(value))
def DepthToSpace(self, blocksize: int = 0, mode: str = "DCR") -> "Var":
return self.make_node("DepthToSpace", self, blocksize=blocksize, mode=mode)
def DynamicQuantizeLinear(
self,
) -> "Vars":
return self.make_node(
"DynamicQuantizeLinear",
self,
)
def Elu(self, alpha: float = 1.0) -> "Var":
return self.make_node("Elu", self, alpha=alpha)
def EyeLike(self, dtype: int = 0, k: int = 0) -> "Var":
return self.make_node("EyeLike", self, dtype=dtype, k=k)
def Flatten(self, axis: int = 1) -> "Var":
return self.make_node("Flatten", self, axis=axis)
def GlobalLpPool(self, p: int = 2) -> "Var":
return self.make_node("GlobalLpPool", self, p=p)
def HammingWindow(self, output_datatype: int = 1, periodic: int = 1) -> "Var":
return self.make_node(
"HammingWindow", self, output_datatype=output_datatype, periodic=periodic
)
def HannWindow(self, output_datatype: int = 1, periodic: int = 1) -> "Var":
return self.make_node(
"HannWindow", self, output_datatype=output_datatype, periodic=periodic
)
def HardSigmoid(
self, alpha: float = 0.20000000298023224, beta: float = 0.5
) -> "Var":
return self.make_node("HardSigmoid", self, alpha=alpha, beta=beta)
def Hardmax(self, axis: int = -1) -> "Var":
return self.make_node("Hardmax", self, axis=axis)
def If(
self,
then_branch: Optional[Union["Var", "Vars", "OnnxGraph"]] = None,
else_branch: Optional[Union["Var", "Vars", "OnnxGraph"]] = None,
) -> Union["Var", "Vars"]:
attr = {}
n_outputs = None
for name, att in zip(
["then_branch", "else_branch"], [then_branch, else_branch]
):
if att is None:
raise ValueError(f"Parameter {name!r} cannot be None.")
if hasattr(att, "to_onnx"):
# Let's overwrite the opsets.
att.parent.opset = self.parent.opset
att.parent.opsets = self.parent.opsets
graph = att.to_onnx()
attr[name] = graph
if n_outputs is None:
n_outputs = len(graph.output)
elif n_outputs != len(graph.output):
raise ValueError(
"then and else branches have different number of outputs."
)
else:
raise ValueError(f"Unexpeted type {type(att)} for parameter {name!r}.")
return self.make_node("If", self, **attr)
def IsInf(self, detect_negative: int = 1, detect_positive: int = 1) -> "Var":
return self.make_node(
"IsInf",
self,
detect_negative=detect_negative,
detect_positive=detect_positive,
)
def LRN(
self,
alpha: float = 9.999999747378752e-05,
beta: float = 0.75,
bias: float = 1.0,
size: int = 0,
) -> "Var":
return self.make_node("LRN", self, alpha=alpha, beta=beta, bias=bias, size=size)
def LeakyRelu(self, alpha: float = 0.009999999776482582) -> "Var":
return self.make_node("LeakyRelu", self, alpha=alpha)
def LogSoftmax(self, axis: int = -1) -> "Var":
return self.make_node("LogSoftmax", self, axis=axis)
def LpNormalization(self, axis: int = -1, p: int = 2) -> "Var":
return self.make_node("LpNormalization", self, axis=axis, p=p)
def LpPool(
self,
auto_pad: str = "NOTSET",
ceil_mode: int = 0,
dilations: Optional[List[int]] = None,
kernel_shape: Optional[List[int]] = None,
p: int = 2,
pads: Optional[List[int]] = None,
strides: Optional[List[int]] = None,
) -> "Var":
dilations = dilations or []
kernel_shape = kernel_shape or []
pads = pads or []
strides = strides or []
return self.make_node(
"LpPool",
self,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=dilations,
kernel_shape=kernel_shape,
p=p,
pads=pads,
strides=strides,
)
def MeanVarianceNormalization(self, axes: Optional[List[int]] = None) -> "Var":
axes = axes or [0, 2, 3]
return self.make_node("MeanVarianceNormalization", self, axes=axes)
def Multinomial(
self, dtype: int = 6, sample_size: int = 1, seed: float = 0.0
) -> "Var":
return self.make_node(
"Multinomial", self, dtype=dtype, sample_size=sample_size, seed=seed
)
def RandomNormalLike(
self, dtype: int = 0, mean: float = 0.0, scale: float = 1.0, seed: float = 0.0
) -> "Var":
return self.make_node(
"RandomNormalLike", self, dtype=dtype, mean=mean, scale=scale, seed=seed
)
def RandomUniformLike(
self, dtype: int = 0, high: float = 1.0, low: float = 0.0, seed: float = 0.0
) -> "Var":
return self.make_node(
"RandomUniformLike", self, dtype=dtype, high=high, low=low, seed=seed
)
def ReduceL1(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceL1",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceL2(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceL2",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceLogSum(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceLogSum",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceLogSumExp(
self, keepdims: int = 1, noop_with_empty_axes: int = 0
) -> "Var":
return self.make_node(
"ReduceLogSumExp",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceMax(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceMax",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceMean(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceMean",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceMin(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceMin",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceProd(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceProd",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceSum(self, keepdims: int = 1, noop_with_empty_axes: int = 0) -> "Var":
return self.make_node(
"ReduceSum",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def ReduceSumSquare(
self, keepdims: int = 1, noop_with_empty_axes: int = 0
) -> "Var":
return self.make_node(
"ReduceSumSquare",
self,
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
)
def Selu(
self, alpha: float = 1.6732631921768188, gamma: float = 1.0507010221481323
) -> "Var":
return self.make_node("Selu", self, alpha=alpha, gamma=gamma)
def Shrink(self, bias: float = 0.0, lambd: float = 0.5) -> "Var":
return self.make_node("Shrink", self, bias=bias, lambd=lambd)
def Slice(
self, starts: "Var", ends: "Var", axes: "Var", steps: Optional["Var"] = None
) -> "Var":
if steps is None:
return self.make_node("Slice", self, starts, ends, axes)
return self.make_node("Slice", self, starts, ends, axes, steps)
def Softmax(self, axis: int = -1) -> "Var":
return self.make_node("Softmax", self, axis=axis)
def SpaceToDepth(self, blocksize: int = 0) -> "Var":
return self.make_node("SpaceToDepth", self, blocksize=blocksize)
def ThresholdedRelu(self, alpha: float = 1.0) -> "Var":
return self.make_node("ThresholdedRelu", self, alpha=alpha)
def Transpose(self, perm: Optional[List[int]] = None) -> "Var":
perm = perm or []
return self.make_node("Transpose", self, perm=perm)
@domain(AI_ONNX_ML)
def Normalizer(self, norm: str = "MAX"):
return self.make_node("Normalizer", self, norm=norm, domain=AI_ONNX_ML)
def _complete():
ops_to_add = [
"Abs",
"Acos",
"Acosh",
"Asin",
"Asinh",
"Atan",
"Atanh",
"BitwiseNot",
"Ceil",
"Cos",
"Cosh",
"Det",
"Erf",
"Exp",
"Floor",
"GlobalAveragePool",
"GlobalMaxPool",
"HardSwish",
"Identity",
"IsNaN",
"Log",
"Mish",
"Neg",
"NonZero",
"Not",
"Reciprocal",
"Relu",
"Round",
"Shape",
"Sigmoid",
"Sign",
"Sin",
"Sinh",
"Size",
"Softplus",
"Softsign",
"Sqrt",
"Tan",
"Tanh",
]
for name in ops_to_add:
if hasattr(OpsVar, name):
continue
setattr(OpsVar, name, lambda self, op_type=name: self.make_node(op_type, self))
_complete()