From f5fb37cc2130a1a3246f61099c70973f24231065 Mon Sep 17 00:00:00 2001 From: Jozef Daniecki Date: Mon, 8 Feb 2021 10:25:30 +0100 Subject: [PATCH] DeformableConvolution specification refactoring. (#4101) * DeformableConvolution specification refactoring. * Add ticks to types and layouts. * Removed 1D and 3D Convolution type. At the current moment CPU plugin and MXnet implemenatations supports only 2D case. * Fix examples section. * Fix offests input layout description. --- .../convolution/DeformableConvolution_1.md | 141 ++++++++++++++---- 1 file changed, 108 insertions(+), 33 deletions(-) diff --git a/docs/ops/convolution/DeformableConvolution_1.md b/docs/ops/convolution/DeformableConvolution_1.md index 247c52ea121..446d6fd07bb 100644 --- a/docs/ops/convolution/DeformableConvolution_1.md +++ b/docs/ops/convolution/DeformableConvolution_1.md @@ -4,85 +4,160 @@ **Category**: Convolution -**Detailed description**: [Reference](https://arxiv.org/abs/1703.06211) +**Short description**: Computes 2D deformable convolution of input and kernel tensors. -**Attributes** +**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) and [Deformable Convolutional Networks](https://arxiv.org/abs/1703.06211). + +**Attributes**: * *strides* - * **Description**: *strides* is a distance (in pixels) to slide the filter on the feature map over the (z, y, x) axes for 3D convolutions and (y, x) axes for 2D convolutions. For example, *strides* equal *4,2,1* means sliding the filter 4 pixel at a time over depth dimension, 2 over height dimension and 1 over width dimension. - * **Range of values**: integer values starting from 0 - * **Type**: int[] + * **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. + * **Range of values**: integer values starting from `0` + * **Type**: `int[]` * **Default value**: None * **Required**: *yes* * *pads_begin* - * **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. - * **Range of values**: integer values starting from 0 - * **Type**: int[] + * **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. + * **Range of values**: integer values starting from `0` + * **Type**: `int[]` * **Default value**: None * **Required**: *yes* * **Note**: the attribute is ignored when *auto_pad* attribute is specified. * *pads_end* - * **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. - * **Range of values**: integer values starting from 0 - * **Type**: int[] + * **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. + * **Range of values**: integer values starting from `0` + * **Type**: `int[]` * **Default value**: None * **Required**: *yes* * **Note**: the attribute is ignored when *auto_pad* attribute is specified. * *dilations* - * **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. - * **Range of values**: integer value starting from 0 - * **Type**: int[] + * **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. + * **Range of values**: integer value starting from `0` + * **Type**: `int[]` * **Default value**: None * **Required**: *yes* * *auto_pad* * **Description**: *auto_pad* how the padding is calculated. Possible values: - * *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). + * *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. * *valid* - do not use padding. - * **Type**: string - * **Default value**: None + * **Type**: `string` + * **Default value**: explicit * **Required**: *no* * **Note**: *pads_begin* and *pads_end* attributes are ignored when *auto_pad* is specified. + * *group* * **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. - * **Range of values**: integer value starting from 1 - * **Type**: int - * **Default value**: 1 + * **Range of values**: integer value starting from `1` + * **Type**: `int` + * **Default value**: `1` * **Required**: *no* * *deformable_group* - * **Description**: *deformable_group* is the number of groups which deformable values and *output* should be split into along the channel axis. Apply the deformable convolution using the i-th part of the offset part on the i-th out. - * **Range of values**: integer value starting from 1 - * **Type**: int - * **Default value**: 1 + * **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. + * **Range of values**: integer value starting from `1` + * **Type**: `int` + * **Default value**: `1` * **Required**: *no* **Inputs**: -* **1**: Input tensor of rank 3 or greater. Required. +* **1**: Input tensor of type *T* and rank 4. Layout is `NCYX` (number of batches, number of channels, spatial axes Y and X). Required. -* **2**: Deformable values tensor of rank 3 or higher. Required. +* **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. -* **3**: Convolution kernel tensor. Weights layout is OIYX (OIZYX for 3D convolution), which means that *X* is changing the fastest, then *Y*, then *Input* then *Output*. The size of kernel is derived from the shape of this input and not specified by any attribute. Required. +* **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. + +**Outputs**: + +* **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). + +**Types**: + +* *T*: Any floating point type. + **Example** +2D DeformableConvolution (deformable_group=1) ```xml - - - ... - ... + + + + + 1 + 4 + 224 + 224 + + + 1 + 50 + 220 + 220 + + + 64 + 4 + 5 + 5 + + + + + 1 + 64 + 220 + 220 + + -``` \ No newline at end of file +``` + +2D DeformableConvolution (deformable_group=4) +```xml + + + + + 1 + 4 + 224 + 224 + + + 1 + 200 + 220 + 220 + + + 64 + 4 + 5 + 5 + + + + + 1 + 64 + 220 + 220 + + + +```