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remove **Default value**: None when **Required**: *yes* (#6638)

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Patryk Elszkowski
2021-07-14 14:00:29 +02:00
committed by GitHub
parent cbad1b2539
commit b9d02939b3
54 changed files with 112 additions and 247 deletions
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12
docs/ops/convolution/GroupConvolutionBackpropData_1.md
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@@ -8,14 +8,13 @@
**Detailed description**: Splits input and filters into multiple groups, computes *ConvolutionBackpropData* on them and concatenates the results. It is equivalent to GroupConvolution and Convolution relationship.
**Attributes**: The operation has the same attributes as a *ConvolutionBackpropData*. Number of groups is derived from the kernel shape.
**Attributes**: The operation has the same attributes as a *ConvolutionBackpropData*. Number of groups is derived from the kernel shape.
* *strides*
* **Description**: *strides* has the same definition as *strides* for a regular Convolution but applied in the backward way, for the output tensor.
* **Range of values**: positive integers
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* *pads_begin*
@@ -23,7 +22,6 @@
* **Description**: *pads_begin* has the same definition as *pads_begin* for a regular Convolution but applied in the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
* **Range of values**: non-negative integers
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
@@ -32,7 +30,6 @@
* **Description**: *pads_end* has the same definition as *pads_end* for a regular Convolution but applied in the backward way, for the output tensor. May be omitted, in which case pads are calculated automatically.
* **Range of values**: non-negative integers
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
@@ -41,12 +38,11 @@
* **Description**: *dilations* has the same definition as *dilations* for a regular Convolution but applied in the backward way, for the output tensor.
* **Range of values**: positive integers
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* *auto_pad*
* **Description**: *auto_pad* has the same definition as *auto_pad* for a regular Convolution but applied in the backward way, for the output tensor.
* **Description**: *auto_pad* has the same definition as *auto_pad* for a regular Convolution but applied in the backward way, for the output tensor.
* *explicit* - use explicit padding values from *pads_begin* and *pads_end*.
* *same_upper* - the input is padded to match the output size. In case of odd padding value an extra padding is added at the end.
* *same_lower* - the input is padded to match the output size. In case of odd padding value an extra padding is added at the beginning.
@@ -71,12 +67,12 @@
* **2**: Kernel tensor of type `T1` and rank 4, 5 or 6. Layout is `[GROUPS, C_IN, C_OUT, X, Y, Z]` (number of groups, number of input channels, number of output channels, spatial axes X, Y, Z). Required.
* **3**: Output shape tensor of type `T2` and rank 1. It specifies spatial shape of the output. Optional.
* **Note** Number of groups is derived from the shape of the kernel and not specified by any attribute.
* **Note** Number of groups is derived from the shape of the kernel and not specified by any attribute.
* **Note**: Type of the convolution (1D, 2D or 3D) is derived from the rank of the input tensors and not specified by any attribute:
* 1D convolution (input tensors rank 3) means that there is only one spatial axis X
* 2D convolution (input tensors rank 4) means that there are two spatial axes Y, X
* 3D convolution (input tensors rank 5) means that there are three spatial axes Z, Y, X
**Outputs**:
* **1**: Output tensor of type `T1` and rank 3, 4 or 5 (the same as input *1*). Layout is `[N, GROUPS * C_OUT, Z, Y, X]` (number of batches, number of kernel output channels, spatial axes Z, Y, X).