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Removed confusing information about required/optional output of the operation. The output exists always but it may be not connected to anywhere. (#3423)

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This commit is contained in:
Evgeny Lazarev
2020-11-30 20:02:09 +03:00
committed by GitHub
parent e910902c9d
commit 0a52702e6a
16 changed files with 35 additions and 22 deletions
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docs/ops/activation/Clamp_1.md
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@@ -30,7 +30,7 @@
**Outputs**:
* **1**: Multidimensional output tensor with shape and type matching the input tensor. Required.
* **1**: Multidimensional output tensor with shape and type matching the input tensor.
**Detailed description**:
docs/ops/activation/Elu_1.md
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@@ -33,4 +33,4 @@ elu(x) = \left\{\begin{array}{ll}
**Outputs**:
* **1**: Result of Elu function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
* **1**: Result of Elu function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
docs/ops/activation/Exp_1.md
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@@ -14,4 +14,4 @@
**Outputs**:
* **1**: Result of Exp function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
* **1**: Result of Exp function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
docs/ops/activation/GELU_2.md
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@@ -28,6 +28,10 @@ Similarly, the following Gelu approximation (typical for the TensorFlow*) is rec
* **1**: Multidimensional input tensor. Required.
**Outputs**:
* **1**: Floating point tensor with shape and type matching the input tensor.
**Example**
```xml
@@ -46,4 +50,4 @@ Similarly, the following Gelu approximation (typical for the TensorFlow*) is rec
</output>
</layer>
```
```
docs/ops/activation/Mish_4.md
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@@ -16,7 +16,7 @@
**Outputs**:
* **1**: Floating point tensor with shape and type matching the input tensor. Required.
* **1**: Floating point tensor with shape and type matching the input tensor.
**Types**
@@ -47,4 +47,4 @@
</port>
</output>
</layer>
```
```
docs/ops/activation/Sigmoid_1.md
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@@ -24,7 +24,7 @@
**Outputs**:
* **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
* **1**: Result of Sigmoid function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
**Example**
@@ -44,4 +44,4 @@
</output>
</layer>
```
```
docs/ops/arithmetic/Sinh_1.md
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@@ -16,7 +16,7 @@
**Outputs**
* **1**: The result of element-wise sinh operation. A tensor of type T.
* **1**: The result of element-wise sinh operation. A tensor of type *T*.
**Types**
@@ -47,4 +47,4 @@ a_{i} = sinh(a_{i})
</port>
</output>
</layer>
```
```
docs/ops/arithmetic/Tanh_1.md
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@@ -14,7 +14,7 @@
**Outputs**:
* **1**: Result of Tanh function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor. Required.
* **1**: Result of Tanh function applied to the input tensor *x*. Floating point tensor with shape and type matching the input tensor.
**Detailed description**
@@ -22,4 +22,4 @@ For each element from the input tensor calculates corresponding
element in the output tensor with the following formula:
\f[
tanh ( x ) = \frac{2}{1+e^{-2x}} - 1 = 2sigmoid(2x) - 1
\f]
\f]
docs/ops/convolution/BinaryConvolution_1.md
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@@ -35,4 +35,5 @@ The operation has the same attributes as a regular *Convolution* layer and sever
**Outputs**:
* **1**: output tensor containing float values. Required.
* **1**: output tensor containing float values.
docs/ops/detection/Proposal_4.md
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@@ -153,7 +153,7 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
* **1**: tensor of type *T* and shape `[batch_size * post_nms_topn, 5]`.
* **2**: tensor of type *T* and shape `[batch_size * post_nms_topn]` with probabilities. *Optional*.
* **2**: tensor of type *T* and shape `[batch_size * post_nms_topn]` with probabilities.
**Types**
@@ -191,4 +191,4 @@ the second optional tensor of shape `[batch_size * post_nms_topn]` with probabil
</port>
</output>
</layer>
```
```
docs/ops/detection/ROIPooling_1.md
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@@ -63,7 +63,7 @@ Batch indices must be in the range of `[0, N-1]`.
**Outputs**:
* **1**: 4D output tensor of shape `[NUM_ROIS, C, pooled_h, pooled_w]` with feature maps of type *T*. Required.
* **1**: 4D output tensor of shape `[NUM_ROIS, C, pooled_h, pooled_w]` with feature maps of type *T*.
**Types**
@@ -77,4 +77,4 @@ Batch indices must be in the range of `[0, N-1]`.
<input> ... </input>
<output> ... </output>
</layer>
```
```
docs/ops/detection/ReorgYolo_1.md
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@@ -26,7 +26,7 @@
**Outputs**:
* **1**: 4D output tensor of the same type as input tensor and shape `[N, C*stride*stride, H/stride, W/stride]`. Required.
* **1**: 4D output tensor of the same type as input tensor and shape `[N, C*stride*stride, H/stride, W/stride]`.
**Example**
docs/ops/shape/Squeeze_1.md
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@@ -14,6 +14,10 @@
* **2**: 0D or 1D tensor of type *T_SHAPE* with dimensions indices to squeeze. Values could be negative. *Optional*.
**Outputs**:
* **1**: Tensor with squeezed values of type *T*.
**Types**
* *T*: supported type.
@@ -65,4 +69,4 @@
</port>
</output>
</layer>
```
```
docs/ops/shape/Unsqueeze_1.md
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@@ -14,6 +14,10 @@
* **2**: OD or 1D tensor of type *T_SHAPE* with dimensions indices to be set to 1. Values could be negative. *Required*.
**Outputs**:
* **1**: Tensor with unsqueezed values of type *T*.
**Types**
* *T*: supported type.
@@ -65,4 +69,4 @@
</port>
</output>
</layer>
```
```
docs/ops/sort/NonMaxSuppression_5.md
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@@ -72,7 +72,7 @@ class must not exceed `max_output_boxes_per_class`.
* **2**: `selected_scores` - tensor of type *T_THRESHOLDS* and shape `[number of selected boxes, 3]` containing information about scores for each selected box as triplets `[batch_index, class_index, box_score]`.
* **3**: `valid_outputs` - 1D tensor with 1 element of type *T_IND* representing the total number of selected boxes. Optional.
* **3**: `valid_outputs` - 1D tensor with 1 element of type *T_IND* representing the total number of selected boxes.
Plugins which do not support dynamic output tensors produce `selected_indices` and `selected_scores` tensors of shape `[min(num_boxes, max_output_boxes_per_class) * num_batches * num_classes, 3]` which is an upper bound for the number of possible selected boxes. Output tensor elements following the really selected boxes are filled with value -1.
docs/ops/sort/TopK_3.md
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@@ -51,7 +51,7 @@
* **1**: Output tensor of type *T* with top *k* values from the input tensor along specified dimension *axis*. The shape of the tensor is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`.
* **2**: Output tensor with top *k* indices for each slice along *axis* dimension of type *T_IND*. The shape of the tensor is the same as for the 1st output, that is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`
* **2**: Output tensor with top *k* indices for each slice along *axis* dimension of type *T_IND*. The shape of the tensor is the same as for the 1st output, that is `[input1.shape[0], ..., input1.shape[axis-1], k, input1.shape[axis+1], ...]`.
**Types**