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Add IsInf to detect infinity values (#1884)
* Add IsInf * Add test files * Fix attribute names * Format code * Update doc * Add coverage change
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‎docs/Changelog.md‎

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<dd>Constrain output mask types to boolean tensors.</dd>
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</dl>
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### <a name="IsInf-10"></a>**IsInf-10**</a>
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Map infinity to true and other values to false.
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#### Version
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This version of the operator has been available since version 10 of the default ONNX operator set.
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#### Attributes
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<dl>
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<dt><tt>detect_negative</tt> : int (default is 1)</dt>
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<dd>(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.</dd>
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<dt><tt>detect_positive</tt> : int (default is 1)</dt>
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<dd>(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.</dd>
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</dl>
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#### Inputs
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<dl>
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<dt><tt>X</tt> : T1</dt>
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<dd>input</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>Y</tt> : T2</dt>
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<dd>output</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
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<dd>Constrain input types to float tensors.</dd>
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<dt><tt>T2</tt> : tensor(bool)</dt>
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<dd>Constrain output types to boolean tensors.</dd>
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</dl>
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### <a name="MatMulInteger-10"></a>**MatMulInteger-10**</a>
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Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.

‎docs/Operators.md‎

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* <a href="#Identity">Identity</a>
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* <a href="#If">If</a>
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* <a href="#InstanceNormalization">InstanceNormalization</a>
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* <a href="#IsInf">IsInf</a>
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* <a href="#IsNaN">IsNaN</a>
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* <a href="#LRN">LRN</a>
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* <a href="#LSTM">LSTM</a>
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</details>
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### <a name="IsInf"></a><a name="isinf">**IsInf**</a>
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Map infinity to true and other values to false.
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#### Version
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This version of the operator has been available since version 10 of the default ONNX operator set.
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#### Attributes
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<dl>
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<dt><tt>detect_negative</tt> : int (default is 1)</dt>
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<dd>(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.</dd>
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<dt><tt>detect_positive</tt> : int (default is 1)</dt>
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<dd>(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.</dd>
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</dl>
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#### Inputs
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<dl>
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<dt><tt>X</tt> : T1</dt>
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<dd>input</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>Y</tt> : T2</dt>
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<dd>output</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
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<dd>Constrain input types to float tensors.</dd>
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<dt><tt>T2</tt> : tensor(bool)</dt>
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<dd>Constrain output types to boolean tensors.</dd>
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</dl>
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#### Examples
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<details>
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<summary>infinity</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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)
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x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf')
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```
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</details>
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<details>
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<summary>negative_infinity_only</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_positive=0
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)
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x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isneginf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')
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```
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</details>
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<details>
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<summary>positive_infinity_only</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_negative=0
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)
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x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isposinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
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```
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</details>
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### <a name="IsNaN"></a><a name="isnan">**IsNaN**</a>
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Returns which elements of the input are NaN.

‎docs/TestCoverage.md‎

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* [Overall Test Coverage](#overall-test-coverage)
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# Node Test Coverage
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## Summary
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Node tests have covered 114/127 (89.76%, 5 generators excluded) common operators.
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Node tests have covered 115/128 (89.84%, 5 generators excluded) common operators.
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Node tests have covered 0/0 (N/A) experimental operators.
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</details>
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### IsInf
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There are 3 test cases, listed as following:
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<details>
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<summary>infinity</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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)
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x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf')
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```
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</details>
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<details>
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<summary>negative_infinity_only</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_positive=0
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)
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x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isneginf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')
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```
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</details>
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<details>
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<summary>positive_infinity_only</summary>
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```python
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_negative=0
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)
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x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isposinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
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```
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</details>
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### IsNaN
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There are 1 test cases, listed as following:
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<details>

‎onnx/backend/test/case/node/isinf.py‎

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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np # type: ignore
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import onnx
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from ..base import Base
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from . import expect
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class IsInf(Base):
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@staticmethod
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def export_infinity(): # type: () -> None
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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)
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x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf')
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@staticmethod
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def export_positive_infinity_only(): # type: () -> None
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_negative=0
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)
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x = np.array([-1.7, np.nan, np.inf, 3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isposinf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_positive')
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@staticmethod
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def export_negative_infinity_only(): # type: () -> None
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node = onnx.helper.make_node('IsInf',
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inputs=['x'],
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outputs=['y'],
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detect_positive=0
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)
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x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf],
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dtype=np.float32)
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y = np.isneginf(x)
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expect(node, inputs=[x], outputs=[y], name='test_isinf_negative')

‎onnx/backend/test/data/node/test_isinf/model.onnx‎

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 backend-test:=
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xy"IsInf
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test_isinfZ
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x
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b
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y
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‎onnx/backend/test/data/node/test_isinf/test_data_set_0/input_0.pb‎

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‎onnx/backend/test/data/node/test_isinf/test_data_set_0/output_0.pb‎

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‎onnx/backend/test/data/node/test_isinf_negative/model.onnx‎

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‎onnx/backend/test/data/node/test_isinf_negative/test_data_set_0/input_0.pb‎

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‎onnx/backend/test/data/node/test_isinf_negative/test_data_set_0/output_0.pb‎

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