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Refactor boolean attribute type (#3478)

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* Refactor boolean attribute type

* fix python code
This commit is contained in:
Bartek Szmelczynski 2020-12-07 13:42:28 +01:00 committed by GitHub
parent 33ca1760f0
commit 6bad345df9
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27 changed files with 149 additions and 149 deletions
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6
docs/ops/condition/Bucketize_3.md
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@ -24,10 +24,10 @@ For example, if the first input tensor is `[[3, 50], [10, -1]]` and the second i
* **Description**: indicates whether bucket includes the right or the left edge of interval.
* **Range of values**:
* True - bucket includes the right interval edge
* False - bucket includes the left interval edge
* true - bucket includes the right interval edge
* false - bucket includes the left interval edge
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
**Inputs**:

4
docs/ops/detection/DetectionOutput_1.md
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@ -39,9 +39,9 @@ At each feature map cell, *DetectionOutput* predicts the offsets relative to the
* *variance_encoded_in_target*
* **Description**: *variance_encoded_in_target* is a flag that denotes if variance is encoded in target. If flag is false then it is necessary to adjust the predicted offset accordingly.
* **Range of values**: False or True
* **Range of values**: false or true
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *keep_top_k*

6
docs/ops/detection/PriorBoxClustered_1.md
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@ -20,10 +20,10 @@
* **Description**: *clip* is a flag that denotes if each value in the output tensor should be clipped within [0,1].
* **Range of values**:
* False - clipping is not performed
* True - each value in the output tensor is within [0,1]
* false - clipping is not performed
* true - each value in the output tensor is within [0,1]
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *step (step_w, step_h)*

18
docs/ops/detection/PriorBox_1.md
View File

@ -28,20 +28,20 @@
* **Description**: *flip* is a flag that denotes that each *aspect_ratio* is duplicated and flipped. For example, *flip* equals 1 and *aspect_ratio* equals to "4.0,2.0" mean that aspect_ratio is equal to "4.0,2.0,0.25,0.5".
* **Range of values**:
* False - each *aspect_ratio* is flipped
* True - each *aspect_ratio* is not flipped
* false - each *aspect_ratio* is flipped
* true - each *aspect_ratio* is not flipped
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *clip*
* **Description**: *clip* is a flag that denotes if each value in the output tensor should be clipped to [0,1] interval.
* **Range of values**:
* False - clipping is not performed
* True - each value in the output tensor is clipped to [0,1] interval.
* false - clipping is not performed
* true - each value in the output tensor is clipped to [0,1] interval.
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *step*
@ -72,10 +72,10 @@
* **Description**: *scale_all_sizes* is a flag that denotes type of inference. For example, *scale_all_sizes* equals 0 means that the PriorBox layer is inferred in MXNet-like manner. In particular, *max_size* attribute is ignored.
* **Range of values**:
* False - *max_size* is ignored
* True - *max_size* is used
* false - *max_size* is ignored
* true - *max_size* is used
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *fixed_ratio*

12
docs/ops/detection/Proposal_1.md
View File

@ -87,25 +87,25 @@
* *clip_before_nms*
* **Description**: *clip_before_nms* flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *clip_after_nms*
* **Description**: *clip_after_nms* is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *normalize*
* **Description**: *normalize* is a flag that specifies whether to perform normalization of output boxes to *[0,1]* interval or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *box_size_scale*

12
docs/ops/detection/Proposal_4.md
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@ -94,25 +94,25 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
* *clip_before_nms*
* **Description**: *clip_before_nms* flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *clip_after_nms*
* **Description**: *clip_after_nms* is a flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *normalize*
* **Description**: *normalize* is a flag that specifies whether to perform normalization of output boxes to *[0,1]* interval or not.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *box_size_scale*

10
docs/ops/image/Interpolate_1.md
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@ -27,19 +27,19 @@
* *align_corners*
* **Description**: *align_corners* is a flag that specifies whether to align corners or not. 1 means the alignment is applied, 0 means the alignment isn't applied.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *antialias*
* **Description**: *antialias* is a flag that specifies whether to perform anti-aliasing.
* **Range of values**:
* False - do not perform anti-aliasing
* True - perform anti-aliasing
* false - do not perform anti-aliasing
* true - perform anti-aliasing
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *pads_begin*

6
docs/ops/image/Interpolate_4.md
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@ -56,10 +56,10 @@
* **Description**: *antialias* is a flag that specifies whether to perform anti-aliasing.
* **Range of values**:
* False - do not perform anti-aliasing
* True - perform anti-aliasing
* false - do not perform anti-aliasing
* true - perform anti-aliasing
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *pads_begin*

12
docs/ops/matrix/MatMul_1.md
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@ -40,18 +40,18 @@ Two attributes, `transpose_a` and `transpose_b` specify embedded transposition f
* *transpose_a*
* **Description**: transposes dimensions ROW_INDEX_DIM and COL_INDEX_DIM of the 1st input; **False** means no transpose, **True** means transpose. It is ignored if first input is 1D tensor.
* **Range of values**: False or True
* **Description**: transposes dimensions ROW_INDEX_DIM and COL_INDEX_DIM of the 1st input; **false** means no transpose, **true** means transpose. It is ignored if first input is 1D tensor.
* **Range of values**: false or true
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *transpose_b*
* **Description**: transposes dimensions ROW_INDEX_DIM and COL_INDEX_DIM of the 2nd input; **False** means no transpose, **True** means transpose. It is ignored if second input is 1D tensor.
* **Range of values**: False or True
* **Description**: transposes dimensions ROW_INDEX_DIM and COL_INDEX_DIM of the 2nd input; **false** means no transpose, **true** means transpose. It is ignored if second input is 1D tensor.
* **Range of values**: false or true
* **Type**: boolean
* **Default value**: False
* **Default value**: false
* **Required**: *no*

4
docs/ops/movement/Reverse_1.md
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@ -10,9 +10,9 @@
If `index` mode is used, the second tensor should contain indices of axes that should be reversed. The length of the second tensor should be in a range from 0 to rank of the 1st input tensor.
In case if `mask` mode is used, then the second input tensor length should be equal to the rank of the 1st input. And each value has boolean value `True` or `False`. `True` means the corresponding axes should be reverted, `False` means it should be untouched.
In case if `mask` mode is used, then the second input tensor length should be equal to the rank of the 1st input. And each value has boolean value `true` or `false`. `true` means the corresponding axes should be reverted, `false` means it should be untouched.
If no axis specified, that means either the second input is empty if `index` mode is used or second input has only `False` elements if `mask` mode is used, then *Reverse* just passes the source tensor through output not doing any data movements.
If no axis specified, that means either the second input is empty if `index` mode is used or second input has only `false` elements if `mask` mode is used, then *Reverse* just passes the source tensor through output not doing any data movements.
**Attributes**

2
docs/ops/pooling/AvgPool_1.md
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@ -47,7 +47,7 @@
* *exclude_pad*
* **Description**: *exclude_pad* is a type of pooling strategy for values in the padding area. For example, if *exclude_pad* is "true", zero-values in the padding are not used.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: boolean
* **Default value**: None
* **Required**: *yes*

16
docs/ops/reduction/ReduceL1_4.md
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@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -48,7 +48,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL1" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -73,7 +73,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL1" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -96,7 +96,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL1" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -120,7 +120,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL1" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceL2_4.md
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@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -48,7 +48,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL2" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -73,7 +73,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -96,7 +96,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -120,7 +120,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceL2" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceLogicalAnd_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalAnd" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalAnd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalAnd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalAnd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceLogicalOr_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalOr" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalOr" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalOr" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceLogicalOr" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceMax_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
** Types **
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMax" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMax" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMax" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMax" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceMean_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMean" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMean" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMean" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMean" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceMin_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMin" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMin" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMin" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceMin" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceProd_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceProd" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceProd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceProd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceProd" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

16
docs/ops/reduction/ReduceSum_1.md
View File

@ -10,10 +10,10 @@
* *keep_dims*
* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: True or False
* **Description**: If set to `true` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**
@ -24,7 +24,7 @@
**Outputs**
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == true`, or `i`-th dimension is removed from the output otherwise.
**Types**
@ -47,7 +47,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceSum" ...>
<data keep_dims="True" />
<data keep_dims="true" />
<input>
<port id="0">
<dim>6</dim>
@ -72,7 +72,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceSum" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -95,7 +95,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceSum" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>
@ -119,7 +119,7 @@ Corner cases:
```xml
<layer id="1" type="ReduceSum" ...>
<data keep_dims="False" />
<data keep_dims="false" />
<input>
<port id="0">
<dim>6</dim>

4
docs/ops/sequence/CTCGreedyDecoder_1.md
View File

@ -22,9 +22,9 @@ Sequences in the batch can have different length. The lengths of sequences are c
* *merge_repeated*
* **Description**: *merge_repeated* is a flag for merging repeated labels during the CTC calculation.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
**Inputs**

20
docs/ops/sequence/CTCLoss_4.md
View File

@ -27,9 +27,9 @@ p_{i,t,j} = \frac{\exp(logits[i,t,j])}{\sum^{K}_{k=0}{\exp(logits[i,t,k])}}
2. For a given `i`-th target from `labels[i,:]` find all aligned paths.
A path `S = (c1,c2,...,cT)` is aligned with a target `G=(g1,g2,...,gT)` if both chains are equal after decoding.
The decoding extracts substring of length `label_length[i]` from a target `G`, merges repeated characters in `G` in case *preprocess_collapse_repeated* equal to True and
finds unique elements in the order of character occurrence in case *unique* equal to True.
The decoding merges repeated characters in `S` in case *ctc_merge_repeated* equal to True and removes blank characters represented by `blank_index`.
The decoding extracts substring of length `label_length[i]` from a target `G`, merges repeated characters in `G` in case *preprocess_collapse_repeated* equal to true and
finds unique elements in the order of character occurrence in case *unique* equal to true.
The decoding merges repeated characters in `S` in case *ctc_merge_repeated* equal to true and removes blank characters represented by `blank_index`.
By default, `blank_index` is equal to `C-1`, where `C` is a number of classes including the blank.
For example, in case default *ctc_merge_repeated*, *preprocess_collapse_repeated*, *unique* and `blank_index` a target sequence `G=(0,3,2,2,2,2,2,4,3)` of a length `label_length[i]=4` is processed
to `(0,3,2,2)` and a path `S=(0,0,4,3,2,2,4,2,4)` of a length `logit_length[i]=9` is also processed to `(0,3,2,2)`, where `C=5`.
@ -57,25 +57,25 @@ Having log-probabilities for aligned paths, log of summed up probabilities for t
* *preprocess_collapse_repeated*
* **Description**: *preprocess_collapse_repeated* is a flag for a preprocessing step before loss calculation, wherein repeated labels in `labels[i,:]` passed to the loss are merged into single labels.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
* *ctc_merge_repeated*
* **Description**: *ctc_merge_repeated* is a flag for merging repeated characters in a potential alignment during the CTC loss calculation.
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *unique*
* **Description**: *unique* is a flag to find unique elements for a target `labels[i,:]` before matching with potential alignments. Unique elements in the processed `labels[i,:]` are sorted in the order of their occurrence in original `labels[i,:]`. For example, the processed sequence for `labels[i,:]=(0,1,1,0,1,3,3,2,2,3)` of length `label_length[i]=10` will be `(0,1,3,2)` in case *unique* equal to True.
* **Range of values**: True or False
* **Description**: *unique* is a flag to find unique elements for a target `labels[i,:]` before matching with potential alignments. Unique elements in the processed `labels[i,:]` are sorted in the order of their occurrence in original `labels[i,:]`. For example, the processed sequence for `labels[i,:]=(0,1,1,0,1,3,3,2,2,3)` of length `label_length[i]=10` will be `(0,1,3,2)` in case *unique* equal to true.
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**

4
docs/ops/sequence/GRUCell_3.md
View File

@ -43,9 +43,9 @@
* *linear_before_reset*
* **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification of *GRUCell* described in the formula in the [ONNX documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU).
* **Range of values**: True or False
* **Range of values**: true or false
* **Type**: `boolean`
* **Default value**: False
* **Default value**: false
* **Required**: *no*
**Inputs**

8
docs/ops/sort/NonMaxSuppression_1.md
View File

@ -32,11 +32,11 @@ class must not exceed `max_output_boxes_per_class`.
* *sort_result_descending*
* **Description**: *sort_result_descending* is a flag that specifies whenever it is necessary to sort selected boxes across batches or not.
* **Range of values**: True of False
* *True* - sort selected boxes across batches.
* *False* - do not sort selected boxes across batches (boxes are sorted per class).
* **Range of values**: true of false
* *true* - sort selected boxes across batches.
* *false* - do not sort selected boxes across batches (boxes are sorted per class).
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
**Inputs**:

8
docs/ops/sort/NonMaxSuppression_3.md
View File

@ -32,11 +32,11 @@ class must not exceed `max_output_boxes_per_class`.
* *sort_result_descending*
* **Description**: *sort_result_descending* is a flag that specifies whenever it is necessary to sort selected boxes across batches or not.
* **Range of values**: True of False
* *True* - sort selected boxes across batches.
* *False* - do not sort selected boxes across batches (boxes are sorted per class).
* **Range of values**: true of false
* *true* - sort selected boxes across batches.
* *false* - do not sort selected boxes across batches (boxes are sorted per class).
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *output_type*

8
docs/ops/sort/NonMaxSuppression_4.md
View File

@ -32,11 +32,11 @@ class must not exceed `max_output_boxes_per_class`.
* *sort_result_descending*
* **Description**: *sort_result_descending* is a flag that specifies whenever it is necessary to sort selected boxes across batches or not.
* **Range of values**: True of False
* *True* - sort selected boxes across batches.
* *False* - do not sort selected boxes across batches (boxes are sorted per class).
* **Range of values**: true of false
* *true* - sort selected boxes across batches.
* *false* - do not sort selected boxes across batches (boxes are sorted per class).
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *output_type*

8
docs/ops/sort/NonMaxSuppression_5.md
View File

@ -37,11 +37,11 @@ class must not exceed `max_output_boxes_per_class`.
* *sort_result_descending*
* **Description**: *sort_result_descending* is a flag that specifies whenever it is necessary to sort selected boxes across batches or not.
* **Range of values**: True of False
* *True* - sort selected boxes across batches.
* *False* - do not sort selected boxes across batches (boxes are sorted per class).
* **Range of values**: true of false
* *true* - sort selected boxes across batches.
* *false* - do not sort selected boxes across batches (boxes are sorted per class).
* **Type**: boolean
* **Default value**: True
* **Default value**: true
* **Required**: *no*
* *output_type*