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Support Range operator in ONNX (#2242)
Range operators was reviewed in #2087
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‎docs/Changelog.md‎

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@@ -10841,6 +10841,67 @@ This version of the operator has been available since version 11 of the default
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<dd>tensor of bool, which should be a scalar.</dd>
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</dl>
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### <a name="Range-11"></a>**Range-11**</a>
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Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
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up to `limit` (exclusive).
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The number of elements in the output of range is computed as below-
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`number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`
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The pseudocode determining the contents of the output is shown below-
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`for(int i=0; i<number_of_elements; ++i)`
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`{`
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` output[i] = start + (i * delta); `
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`}`
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`Example 1`
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Inputs: start = 3, limit = 9, delta = 3
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Output: [3, 6]
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`Example 2`
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Inputs: start = 10, limit = 4, delta = -2
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Output: [10, 8, 6]
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#### Version
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This version of the operator has been available since version 11 of the default ONNX operator set.
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#### Inputs
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<dl>
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<dt><tt>start</tt> : T</dt>
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<dd>Scalar. First entry for the range of output values.</dd>
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<dt><tt>limit</tt> : T</dt>
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<dd>Scalar. Exclusive upper limit for the range of output values.</dd>
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<dt><tt>delta</tt> : T</dt>
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<dd>Scalar. Value to step by.</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>output</tt> : T</dt>
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<dd>A 1-D tensor with same type as the inputs containing generated range of values.</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)</dt>
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<dd>Constrain input types to common numeric type tensors.</dd>
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</dl>
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#### Function
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The Function can be represented as a function.
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### <a name="Resize-11"></a>**Resize-11**</a>
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Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.

‎docs/Operators.md‎

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**Operators with function registered:**
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* <a href="#DynamicQuantizeLinear">DynamicQuantizeLinear</a>
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* <a href="#MeanVarianceNormalization">MeanVarianceNormalization</a>
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* <a href="#Range">Range</a>
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## ai.onnx (default)
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### <a name="Abs"></a><a name="abs">**Abs**</a>
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</dl>
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### <a name="Range"></a><a name="range">**Range**</a>
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Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
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up to `limit` (exclusive).
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The number of elements in the output of range is computed as below-
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`number_of_elements = max( ceil( (limit - start) / delta ) , 0 )`
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The pseudocode determining the contents of the output is shown below-
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`for(int i=0; i<number_of_elements; ++i)`
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`{`
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` output[i] = start + (i * delta); `
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`}`
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`Example 1`
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Inputs: start = 3, limit = 9, delta = 3
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Output: [3, 6]
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`Example 2`
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Inputs: start = 10, limit = 4, delta = -2
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Output: [10, 8, 6]
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#### Version
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This version of the operator has been available since version 11 of the default ONNX operator set.
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#### Inputs
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<dl>
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<dt><tt>start</tt> : T</dt>
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<dd>Scalar. First entry for the range of output values.</dd>
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<dt><tt>limit</tt> : T</dt>
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<dd>Scalar. Exclusive upper limit for the range of output values.</dd>
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<dt><tt>delta</tt> : T</dt>
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<dd>Scalar. Value to step by.</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>output</tt> : T</dt>
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<dd>A 1-D tensor with same type as the inputs containing generated range of values.</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T</tt> : tensor(float), tensor(double), tensor(int16), tensor(int32), tensor(int64)</dt>
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<dd>Constrain input types to common numeric type tensors.</dd>
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</dl>
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#### Function
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The Function can be represented as a function.
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#### Examples
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<details>
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<summary>range_float_type_positive_delta</summary>
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```python
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.float32(1)
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limit = np.float32(5)
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delta = np.float32(2)
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output = np.arange(start, limit, delta, dtype=np.float32) # expected output [1.0, 3.0]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_float_type_positive_delta')
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```
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</details>
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<details>
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<summary>range_int32_type_negative_delta</summary>
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```python
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.int32(10)
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limit = np.int32(6)
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delta = np.int32(-3)
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output = np.arange(start, limit, delta, dtype=np.int32) # expected output [10, 7]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_int32_type_negative_delta')
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```
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</details>
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### <a name="Reciprocal"></a><a name="reciprocal">**Reciprocal**</a>
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Reciprocal takes one input data (Tensor<T>) and produces one output data

‎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 132/139 (94.96%, 5 generators excluded) common operators.
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Node tests have covered 133/140 (95.00%, 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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### Range
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There are 2 test cases, listed as following:
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<details>
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<summary>range_float_type_positive_delta</summary>
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```python
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.float32(1)
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limit = np.float32(5)
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delta = np.float32(2)
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output = np.arange(start, limit, delta, dtype=np.float32) # expected output [1.0, 3.0]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_float_type_positive_delta')
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```
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</details>
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<details>
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<summary>range_int32_type_negative_delta</summary>
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```python
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.int32(10)
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limit = np.int32(6)
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delta = np.int32(-3)
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output = np.arange(start, limit, delta, dtype=np.int32) # expected output [10, 7]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_int32_type_negative_delta')
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```
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</details>
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### Reciprocal
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There are 1 test cases, listed as following:
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<details>

‎onnx/backend/test/case/node/range.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 Range(Base):
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@staticmethod
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def export_range_float_type_positive_delta(): # type: () -> None
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.float32(1)
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limit = np.float32(5)
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delta = np.float32(2)
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output = np.arange(start, limit, delta, dtype=np.float32) # expected output [1.0, 3.0]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_float_type_positive_delta')
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@staticmethod
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def export_range_int32_type_negative_delta(): # type: () -> None
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node = onnx.helper.make_node(
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'Range',
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inputs=['start', 'limit', 'delta'],
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outputs=['output'],
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)
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start = np.int32(10)
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limit = np.int32(6)
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delta = np.int32(-3)
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output = np.arange(start, limit, delta, dtype=np.int32) # expected output [10, 7]
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expect(node, inputs=[start, limit, delta], outputs=[output],
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name='test_range_int32_type_negative_delta')

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

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

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‎onnx/backend/test/data/node/test_range_float_type_positive_delta/test_data_set_0/input_1.pb‎

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‎onnx/backend/test/data/node/test_range_float_type_positive_delta/test_data_set_0/input_2.pb‎

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

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

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