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Update specification for ConvolutionBackpropData. (#4679)

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* Update specification for ConvolutionBackpropData.

* Add backticks to attribute types, changed layout description for input, filter and output.

* Correct xml example.

* Add new examples.

* Add link with convolution backprop description.

* Repleace additional link with argxiv website.

* Insert enumeration for examples.

* Fix example with output_shape input.
This commit is contained in:
Szymon Durawa
2021-05-17 08:21:07 +02:00
committed by GitHub
parent d2fb57dfe0
commit d5a8861475
1 changed files with 93 additions and 18 deletions
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111
docs/ops/convolution/ConvolutionBackpropData_1.md
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@@ -4,15 +4,15 @@
**Category**: Convolution
**Short description**: Computes the gradients of a Convolution operation with respect to the input. Also known as a Deconvolution or a Transposed Convolution.
**Short description**: Computes 1D, 2D or 3D *ConvolutionBackpropData* operation with respect to the input and kernel tensors. Also known as a Transposed Convolution.
**Detailed description**:
ConvolutionBackpropData takes the input tensor, weights tensor and output shape and computes the output tensor of a given shape. The shape of the output can be specified as an input 1D integer tensor explicitly or determined by other attributes implicitly. If output shape is specified as an explicit input, shape of the output exactly matches the specified size and required amount of padding is computed.
ConvolutionBackpropData takes the input tensor, weights tensor and output shape and computes the output tensor of a given shape. The shape of the output can be specified as an input 1D integer tensor explicitly or determined by other attributes implicitly. If output shape is specified as an explicit input, shape of the output exactly matches the specified size and required amount of padding is computed. More thorough explanation can be found in [Transposed Convolutions](https://arxiv.org/abs/1603.07285).
ConvolutionBackpropData accepts the same set of attributes as a regular Convolution operation, but they are interpreted in a "backward way", so they are applied to the output of ConvolutionBackpropData, but not to the input. Refer to a regular Convolution operation for detailed description of each attribute.
ConvolutionBackpropData accepts the same set of attributes as a regular Convolution operation and additionally `output_padding` attribute, but they are interpreted in a "backward way", so they are applied to the output of ConvolutionBackpropData, but not to the input. Refer to a regular [Convolution](Convolution_1.md) operation for detailed description of each Convolution attribute.
Output shape when specified as an input `output_shape`, specifies only spatial dimensions. No batch or channel dimension should be passed along with H, W or other spatial dimensions. If `output_shape` is omitted, then `pads_begin`, `pads_end` or `auto_pad` are used to determine output spatial shape `[Y_1, Y_2, ..., Y_D]` by input spatial shape `[X_1, X_2, ..., X_D]` in the following way:
When output shape is specified as an input tensor `output_shape` then it specifies only spatial dimensions. No batch or channel dimension should be passed along with spatial dimensions. If `output_shape` is omitted, then `pads_begin`, `pads_end` or `auto_pad` are used to determine output spatial shape `[O_z, O_y, O_x]` by input spatial shape `[I_z, I_y, I_x]` in the following way:
```
if auto_pads != None:
@@ -24,7 +24,7 @@ Y_i = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1) - pads_begin[i] - p
where `K_i` filter kernel dimension along spatial axis `i`.
If `output_shape` is specified, `pads_begin` and `pads_end` are ignored, and `auto_pad` defines how to distribute padding amount around the tensor. In this case pads are determined based on the next formulas to correctly align input and output tensors (similar to ONNX definition at https://github.com/onnx/onnx/blob/master/docs/Operators.md#convtranspose):
If `output_shape` is specified, `pads_begin` and `pads_end` are ignored, and `auto_pad` defines how to distribute padding amount around the tensor. In this case pads are determined based on the next formulas to correctly align input and output tensors:
```
total_padding[i] = stride[i] * (X_i - 1) + ((K_i - 1) * dilations[i] + 1) - output_shape[i] + output_padding[i]
@@ -42,7 +42,7 @@ else:
* **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[]
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
@@ -50,7 +50,7 @@ else:
* **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 specified, in which case pads are calculated automatically.
* **Range of values**: non-negative integers
* **Type**: int[]
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
@@ -59,7 +59,7 @@ else:
* **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[]
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
@@ -68,7 +68,7 @@ else:
* **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[]
* **Type**: `int[]`
* **Default value**: None
* **Required**: *yes*
@@ -76,9 +76,10 @@ else:
* **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 (same_lower)* the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning).
* *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.
* *valid* - do not use padding.
* **Type**: string
* **Type**: `string`
* **Default value**: None
* **Required**: *no*
* **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified.
@@ -87,27 +88,38 @@ else:
* **Description**: *output_padding* adds additional amount of paddings per each spatial axis in the `output` tensor. It unlocks more elements in the output allowing them to be computed. Elements are added at the higher coordinate indices for the spatial dimensions. Number of elements in *output_padding* list matches the number of spatial dimensions in `data` and `output` tensors.
* **Range of values**: non-negative integer values
* **Type**: int[]
* **Type**: `int[]`
* **Default value**: all zeros
* **Required**: *no*
**Inputs**:
* **1**: `data` -- input tensor of rank 3 or greater. Layout is `[N, C_INPUT, X1, ..., XD]`. *Required*.
* **1**: Input tensor of type *T1* and rank 3, 4 or 5. Layout is `[N, C_INPUT, Z, Y, X]` (number of batches, number of input channels, spatial axes Z, Y, X). *Required*.
* **2**: `filter` -- convolution kernel tensor. Weights have shape `[C_INPUT, C_OUTPUT, K_D, ..., K_1]`. `C_INPUT` is the number of channels in input `data` tensor shape, and `C_OUTPUT` is the number of channels in the `output` tensor. Spatial size of the kernel `[K_D, ..., K_1]` is derived from the shape of this input and aren't specified by any attribute. *Required*.
* **2**: Convolution kernel tensor of type *T1* and rank 3, 4 or 5. Layout is `[C_INPUT, C_OUTPUT, Z, Y, X]` (number of input channels, number of output channels, spatial axes Z, Y, X). Spatial size of the kernel is derived from the shape of this input and aren't specified by any attribute. *Required*.
* **3**: `output_shape` is 1D integer tensor that specifies spatial shape of the output. *Optional*. If specified, *padding amount* is deduced from relation of input and output spatial shapes according to formulas in the description. If not specified, *output shape* is calculated based on the `pads_begin` and `pads_end` or completely according to `auto_pad`.
* **3**: `output_shape` is 1D tensor of type *T2* that specifies spatial shape of the output. If specified, *padding amount* is deduced from relation of input and output spatial shapes according to formulas in the description. If not specified, *output shape* is calculated based on the `pads_begin` and `pads_end` or completely according to `auto_pad`. *Optional*.
* **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` -- output tensor of the same rank as input `data` tensor and shape `[N, C_OUTPUT, Y1, ..., YD]`.
* **1**: Output tensor of type *T1* and rank 3, 4 or 5. Layout is `[N, C_OUTPUT, Z, Y, X]` (number of batches, number of kernel output channels, spatial axes Z, Y, X).
**Example**
**Types**:
* *T1*: any numeric type.
* *T2*: any integer type.
**Examples**
*Example 1: 2D ConvolutionBackpropData*
```xml
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="2,2" output_padding="0,0" auto_pad="explicit"/>
<input>
<port id="0">
<dim>1</dim>
@@ -132,3 +144,66 @@ else:
</output>
</layer>
```
*Example 2: 2D ConvolutionBackpropData with output_padding*
```xml
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="3,3" output_padding="2,2" auto_pad="explicit"/>
<input>
<port id="0">
<dim>1</dim>
<dim>20</dim>
<dim>2</dim>
<dim>2</dim>
</port>
<port id="1">
<dim>20</dim>
<dim>10</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>10</dim>
<dim>8</dim>
<dim>8</dim>
</port>
</output>
</layer>
```
*Example 3: 2D ConvolutionBackpropData with output_shape input*
```xml
<layer id="5" name="upsampling_node" type="ConvolutionBackpropData">
<data dilations="1,1" pads_begin="1,1" pads_end="1,1" strides="1,1" output_padding="0,0" auto_pad="valid"/>
<input>
<port id="0">
<dim>1</dim>
<dim>20</dim>
<dim>224</dim>
<dim>224</dim>
</port>
<port id="1">
<dim>20</dim>
<dim>10</dim>
<dim>3</dim>
<dim>3</dim>
</port>
<port id="2">
<dim>2</dim> <!-- output_shape value is: [450, 450]-->
</port>
</input>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>10</dim>
<dim>450</dim>
<dim>450</dim>
</port>
</output>
</layer>
```