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[DOCS] shift to rst - opsets F,G (#17253)

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Karol Blaszczak
2023-04-28 08:47:50 +02:00
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commit 94cf2f8321
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docs/ops/detection/GenerateProposals_9.md
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@@ -1,5 +1,7 @@
# GenerateProposals {#openvino_docs_ops_detection_GenerateProposals_9}
@sphinxdirective
**Versioned name**: *GenerateProposals-9*
**Category**: *Object detection*
@@ -9,18 +11,20 @@ based on input data for each image in the batch.
**Detailed description**: The operation performs the following steps for each image:
1. Transposes and reshapes predicted bounding boxes deltas and scores to get them into the same dimension order as the
anchors.
2. Transforms anchors and deltas into proposal bboxes and clips proposal bboxes to an image. The attribute *normalized*
indicates whether the proposal bboxes are normalized or not.
3. Sorts all `(proposal, score)` pairs by score from highest to lowest; order of pairs with equal scores is undefined.
4. Takes top *pre_nms_count* proposals, if total number of proposals is less than *pre_nms_count* takes all proposals.
5. Removes predicted boxes with either height or width < *min_size*.
6. Applies non-maximum suppression with *adaptive_nms_threshold*. The initial value of *adaptive_nms_threshold* is
*nms_threshold*. If `nms_eta < 1` and `adaptive_threshold > 0.5`, update `adaptive_threshold *= nms_eta`.
7. Takes and returns top proposals after nms operation. The number of returned proposals in each image is dynamic and is specified by output port 3 `rpnroisnum`. And the max number of proposals in each image is specified by attribute *post_nms_count*.
1. Transposes and reshapes predicted bounding boxes deltas and scores to get them into the same dimension order as the
anchors.
2. Transforms anchors and deltas into proposal bboxes and clips proposal bboxes to an image. The attribute *normalized*
indicates whether the proposal bboxes are normalized or not.
3. Sorts all ``(proposal, score)`` pairs by score from highest to lowest; order of pairs with equal scores is undefined.
4. Takes top *pre_nms_count* proposals, if total number of proposals is less than *pre_nms_count* takes all proposals.
5. Removes predicted boxes with either height or width < *min_size*.
6. Applies non-maximum suppression with *adaptive_nms_threshold*. The initial value of *adaptive_nms_threshold* is
*nms_threshold*. If ``nms_eta < 1`` and ``adaptive_threshold > 0.5``, update ``adaptive_threshold *= nms_eta``.
7. Takes and returns top proposals after nms operation. The number of returned proposals in each image is dynamic
and is specified by output port 3 ``rpnroisnum``. And the max number of proposals in each image is specified
by attribute *post_nms_count*.
All proposals of the whole batch are concated image by image, and distinguishable through outputs.
All proposals of the whole batch are concatenated image by image, and distinguishable through outputs.
**Attributes**:
@@ -65,38 +69,40 @@ All proposals of the whole batch are concated image by image, and distinguishabl
* *nms_eta*
* **Description**: eta parameter for adaptive NMS.
* **Range of values**: a floating-point number in closed range `[0, 1.0]`.
* **Range of values**: a floating-point number in closed range ``[0, 1.0]``.
* **Type**: float
* **Default value**: `1.0`
* **Default value**: ``1.0``
* **Required**: *no*
* *roi_num_type*
* **Description**: the type of element of output 3 `rpnroisnum`.
* **Description**: the type of element of output 3 ``rpnroisnum``.
* **Range of values**: i32, i64
* **Type**: string
* **Default value**: `i64`
* **Default value**: ``i64``
* **Required**: *no*
**Inputs**
* **1**: `im_info` - tensor of type *T* and shape `[num_batches, 3]` or `[num_batches, 4]` providing input image info. The image info is layout as `[image_height, image_width, scale_height_and_width]` or as `[image_height, image_width, scale_height, scale_width]`. **Required.**
* **1**: ``im_info`` - tensor of type *T* and shape ``[num_batches, 3]`` or ``[num_batches, 4]`` providing
input image info. The image info is layout as ``[image_height, image_width, scale_height_and_width]`` or as
``[image_height, image_width, scale_height, scale_width]``. **Required.**
* **2**: ``anchors`` - tensor of type *T* with shape ``[height, width, number_of_anchors, 4]`` providing anchors.
Each anchor is layouted as ``[xmin, ymin, xmax, ymax]``. **Required.**
* **3**: ``boxesdeltas`` - tensor of type *T* with shape ``[num_batches, number_of_anchors * 4, height, width]``
providing deltas for anchors. The delta consists of 4 element tuples with layout ``[dx, dy, log(dw), log(dh)]``. **Required.**
* **4**: ``scores`` - tensor of type *T* with shape ``[num_batches, number_of_anchors, height, width]`` providing proposals scores. **Required.**
* **2**: `anchors` - tensor of type *T* with shape `[height, width, number_of_anchors, 4]` providing anchors. Each anchor is layouted as `[xmin, ymin, xmax, ymax]`. **Required.**
* **3**: `boxesdeltas` - tensor of type *T* with shape `[num_batches, number_of_anchors * 4, height, width]` providing deltas for anchors. The delta consists of 4 element tuples with layout `[dx, dy, log(dw), log(dh)]`. **Required.**
* **4**: `scores` - tensor of type *T* with shape `[num_batches, number_of_anchors, height, width]` providing proposals scores. **Required.**
The `height` and `width` from inputs `anchors`, `boxesdeltas` and `scores` are the height and width of feature maps.
The ``height`` and ``width`` from inputs ``anchors``, ``boxesdeltas`` and ``scores`` are the height and width of feature maps.
**Outputs**
* **1**: `rpnrois` - tensor of type *T* with shape `[num_rois, 4]` providing proposed ROIs. The proposals are layouted as `[xmin, ymin, xmax, ymax]`. The `num_rois` means the total proposals number of all the images in one batch. `num_rois` is a dynamic dimension.
* **2**: `rpnscores` - tensor of type *T* with shape `[num_rois]` providing proposed ROIs scores.
* **3**: `rpnroisnum` - tensor of type *roi_num_type* with shape `[num_batches]` providing the number of proposed ROIs in each image.
* **1**: ``rpnrois`` - tensor of type *T* with shape ``[num_rois, 4]`` providing proposed ROIs.
The proposals are layouted as ``[xmin, ymin, xmax, ymax]``. The ``num_rois`` means the total proposals
number of all the images in one batch. ``num_rois`` is a dynamic dimension.
* **2**: ``rpnscores`` - tensor of type *T* with shape ``[num_rois]`` providing proposed ROIs scores.
* **3**: ``rpnroisnum`` - tensor of type *roi_num_type* with shape ``[num_batches]`` providing the number
of proposed ROIs in each image.
**Types**
@@ -104,44 +110,48 @@ The `height` and `width` from inputs `anchors`, `boxesdeltas` and `scores` are t
**Example**
```xml
<layer ... type="GenerateProposals" version="opset9">
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000" roi_num_type="i32"/>
<input>
<port id="0">
<dim>8</dim>
<dim>3</dim>
</port>
<port id="1">
<dim>50</dim>
<dim>84</dim>
<dim>3</dim>
<dim>4</dim>
</port>
<port id="2">
<dim>8</dim>
<dim>12</dim>
<dim>50</dim>
<dim>84</dim>
</port>
<port id="3">
<dim>8</dim>
<dim>3</dim>
<dim>50</dim>
<dim>84</dim>
</port>
</input>
<output>
<port id="4" precision="FP32">
<dim>-1</dim>
<dim>4</dim>
</port>
<port id="5" precision="FP32">
<dim>-1</dim>
</port>
<port id="6" precision="I32">
<dim>8</dim>
</port>
</output>
</layer>
```
.. code-block:: cpp
<layer ... type="GenerateProposals" version="opset9">
<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000" roi_num_type="i32"/>
<input>
<port id="0">
<dim>8</dim>
<dim>3</dim>
</port>
<port id="1">
<dim>50</dim>
<dim>84</dim>
<dim>3</dim>
<dim>4</dim>
</port>
<port id="2">
<dim>8</dim>
<dim>12</dim>
<dim>50</dim>
<dim>84</dim>
</port>
<port id="3">
<dim>8</dim>
<dim>3</dim>
<dim>50</dim>
<dim>84</dim>
</port>
</input>
<output>
<port id="4" precision="FP32">
<dim>-1</dim>
<dim>4</dim>
</port>
<port id="5" precision="FP32">
<dim>-1</dim>
</port>
<port id="6" precision="I32">
<dim>8</dim>
</port>
</output>
</layer>
@endsphinxdirective