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* Removed a redundant image * Fixed ops specifications and other issues * converted html links to anchor links * converted html links to anchor links * Fixed a link * Fixed a link * Changed anchor links according to dev review # Conflicts: # docs/OV_Runtime_UG/Operations_specifications.md
164 lines
7.3 KiB
Markdown
164 lines
7.3 KiB
Markdown
## DeformableConvolution {#openvino_docs_ops_convolution_DeformableConvolution_8}
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**Versioned name**: *DeformableConvolution-8*
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**Category**: *Convolution*
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**Short description**: Computes 2D deformable convolution of input and kernel tensors.
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**Detailed description**: *Deformable Convolution* is similar to regular *Convolution* but its receptive field is deformed because of additional spatial offsets used during input sampling. More thorough explanation can be found in [Deformable Convolutions Demystified](https://towardsdatascience.com/deformable-convolutions-demystified-2a77498699e8), [Deformable Convolutional Networks](https://arxiv.org/abs/1703.06211).
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Modification of DeformableConvolution using modulating scalars is also supported. Please refer to [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/pdf/1811.11168.pdf).
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Output is calculated using the following formula:
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\f[
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y(p) = \displaystyle{\sum_{k = 1}^{K}}w_{k}x(p + p_{k} + {\Delta}p_{k}) \cdot {\Delta}m_{k}
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\f]
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Where
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* K is a number of sampling locations, e.g. for kernel 3x3 and dilation = 1, K = 9
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* \f$x(p)\f$ and \f$y(p)\f$ denote the features at location p from the input feature maps x and output feature maps y
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* \f$w_{k}\f$ is the weight for k-th location.
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* \f$p_{k}\f$ is pre-specified offset for the k-th location, e.g. K = 9 and
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\f$p_{k} \in \{(-1, -1),(-1, 0), . . . ,(1, 1)\}\f$
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* \f${\Delta}p_{k}\f$ is the learnable offset for the k-th location.
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* \f${\Delta}m_{k}\f$ is the modulation scalar from 0 to 1 for the k-th location.
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**Attributes**:
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* *strides*
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* **Description**: *strides* is a distance (in pixels) to slide the filter on the feature map over the `(y,x)` axes. For example, *strides* equal `2,1` means sliding the filter 2 pixel at a time over height dimension and 1 over width dimension.
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* **Range of values**: integer values starting from `0`
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* **Type**: `int[]`
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* **Required**: *yes*
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* *pads_begin*
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* **Description**: *pads_begin* is a number of pixels to add to the beginning along each axis. For example, *pads_begin* equal `1,2` means adding 1 pixel to the top of the input and 2 to the left of the input.
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* **Range of values**: integer values starting from `0`
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* **Type**: `int[]`
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* **Required**: *yes*
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* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
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* *pads_end*
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* **Description**: *pads_end* is a number of pixels to add to the ending along each axis. For example, *pads_end* equal `1,2` means adding 1 pixel to the bottom of the input and 2 to the right of the input.
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* **Range of values**: integer values starting from `0`
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* **Type**: `int[]`
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* **Required**: *yes*
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* **Note**: the attribute is ignored when *auto_pad* attribute is specified.
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* *dilations*
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* **Description**: *dilations* denotes the distance in width and height between elements (weights) in the filter. For example, *dilation* equal `1,1` means that all the elements in the filter are neighbors, so it is the same as for the usual convolution. *dilation* equal `2,2` means that all the elements in the filter are matched not to adjacent elements in the input matrix, but to those that are adjacent with distance 1.
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* **Range of values**: integer value starting from `0`
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* **Type**: `int[]`
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* **Required**: *yes*
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* *auto_pad*
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* **Description**: *auto_pad* how the padding is calculated. Possible values:
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* *explicit* - use explicit padding values from *pads_begin* and *pads_end*.
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* *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.
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* *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.
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* *valid* - do not use padding.
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* **Type**: `string`
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* **Default value**: explicit
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* **Required**: *no*
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* **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified.
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* *group*
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* **Description**: *group* is the number of groups which *output* and *input* should be split into. For example, *group* equal to 1 means that all filters are applied to the whole input (usual convolution), *group* equal to 2 means that both *input* and *output* channels are separated into two groups and the *i-th output* group is connected to the *i-th input* group channel. *group* equal to a number of output feature maps implies depth-wise separable convolution.
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* **Range of values**: integer value starting from `1`
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* **Type**: `int`
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* **Default value**: `1`
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* **Required**: *no*
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* *deformable_group*
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* **Description**: *deformable_group* is the number of groups in which *offsets* input and *output* should be split into along the channel axis. Apply the deformable convolution using the i-th part of the offsets part on the i-th out.
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* **Range of values**: integer value starting from `1`
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* **Type**: `int`
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* **Default value**: `1`
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* **Required**: *no*
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* *bilinear_interpolation_pad*
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* **Description**: if *bilinear_interpolation_pad* is `true` and the sampling location is within one pixel outside of the feature map boundary, then bilinear interpolation is performed on the zero padded feature map. If *bilinear_interpolation_pad* is `false` and the sampling location is within one pixel outside of the feature map boundary, then the sampling location shifts to the inner boundary of the feature map.
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* **Range of values**: `False` or `True`
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* **Type**: `boolean`
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* **Default value**: `False`
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* **Required**: *no*
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**Inputs**:
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* **1**: Input tensor of type *T* and rank 4. Layout is `NCYX` (number of batches, number of channels, spatial axes Y and X). **Required.**
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* **2**: Offsets tensor of type *T* and rank 4. Layout is `NCYX` (number of batches, *deformable_group* \* kernel_Y \* kernel_X \* 2, spatial axes Y and X). **Required.**
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* **3**: Kernel tensor of type *T* and rank 4. Layout is `OIYX` (number of output channels, number of input channels, spatial axes Y and X). **Required.**
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* **4**: Mask tensor of type *T* and rank 4. Layout is `NCYX` (number of batches, *deformable_group* \* kernel_Y \* kernel_X, spatial axes Y and X). If the input is not provided, the values are assumed to be equal to 1. **Optional.**
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**Outputs**:
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* **1**: Output tensor of type *T* and rank 4. Layout is `NOYX` (number of batches, number of kernel output channels, spatial axes Y and X).
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**Types**:
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* *T*: Any numeric type.
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**Example**
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2D DeformableConvolution (deformable_group=1)
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```xml
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<layer type="DeformableConvolution" ...>
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<data dilations="1,1" pads_begin="0,0" pads_end="0,0" strides="1,1" auto_pad="explicit" group="1" deformable_group="1"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>4</dim>
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<dim>224</dim>
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<dim>224</dim>
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</port>
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<port id="1">
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<dim>1</dim>
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<dim>50</dim>
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<dim>220</dim>
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<dim>220</dim>
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</port>
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<port id="2">
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<dim>64</dim>
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<dim>4</dim>
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<dim>5</dim>
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<dim>5</dim>
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</port>
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<port id="3">
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<dim>1</dim>
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<dim>25</dim>
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<dim>220</dim>
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<dim>220</dim>
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</port>
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</input>
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<output>
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<port id="4" precision="FP32">
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<dim>1</dim>
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<dim>64</dim>
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<dim>220</dim>
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<dim>220</dim>
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</port>
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</output>
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</layer>
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```
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