Specify GenerateProposals-9 (#11004)
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GatherND-8 <openvino_docs_ops_movement_GatherND_8>
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GELU-2 <openvino_docs_ops_activation_GELU_2>
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GELU-7 <openvino_docs_ops_activation_GELU_7>
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GenerateProposals-9 <openvino_docs_ops_detection_GenerateProposals_9>
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GreaterEqual-1 <openvino_docs_ops_comparison_GreaterEqual_1>
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Greater-1 <openvino_docs_ops_comparison_Greater_1>
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GroupConvolutionBackpropData-1 <openvino_docs_ops_convolution_GroupConvolutionBackpropData_1>
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147
docs/ops/detection/GenerateProposals_9.md
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147
docs/ops/detection/GenerateProposals_9.md
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# GenerateProposals {#openvino_docs_ops_detection_GenerateProposals_9}
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**Versioned name**: *GenerateProposals-9*
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**Category**: *Object detection*
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**Short description**: The *GenerateProposals* operation proposes ROIs and their scores
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based on input data for each image in the batch.
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**Detailed description**: The operation performs the following steps for each image:
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1. Transposes and reshapes predicted bounding boxes deltas and scores to get them into the same dimension order as the
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anchors.
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2. Transforms anchors and deltas into proposal bboxes and clips proposal bboxes to an image. The attribute *normalized*
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indicates whether the proposal bboxes are normalized or not.
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3. Sorts all `(proposal, score)` pairs by score from highest to lowest; order of pairs with equal scores is undefined.
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4. Takes top *pre_nms_count* proposals, if total number of proposals is less than *pre_nms_count* takes all proposals.
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5. Removes predicted boxes with either height or width < *min_size*.
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6. Applies non-maximum suppression with *adaptive_nms_threshold*. The initial value of *adaptive_nms_threshold* is
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*nms_threshold*. If `nms_eta < 1` and `adaptive_threshold > 0.5`, update `adaptive_threshold *= nms_eta`.
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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*.
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All proposals of the whole batch are concated image by image, and distinguishable through outputs.
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**Attributes**:
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* *min_size*
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* **Description**: The *min_size* attribute specifies minimum box width and height.
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* **Range of values**: non-negative floating-point number
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* **Type**: float
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* **Required**: *yes*
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* *nms_threshold*
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* **Description**: The *nms_threshold* attribute specifies threshold to be used in the NMS stage.
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* **Range of values**: non-negative floating-point number
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* **Type**: float
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* **Required**: *yes*
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* *pre_nms_count*
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* **Description**: The *pre_nms_count* attribute specifies number of top-n proposals before NMS.
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* **Range of values**: non-negative integer number
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* **Type**: int
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* **Required**: *yes*
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* *post_nms_count*
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* **Description**: The *post_nms_count* attribute specifies number of top-n proposals after NMS.
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* **Range of values**: non-negative integer number
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* **Type**: int
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* **Required**: *yes*
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* *normalized*
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* **Description**: *normalized* is a flag that indicates whether proposal bboxes are normalized or not.
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* **Range of values**: true or false
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* *true* - the bbox coordinates are normalized.
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* *false* - the bbox coordinates are not normalized.
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* **Type**: boolean
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* **Default value**: True
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* **Required**: *no*
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* *nms_eta*
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* **Description**: eta parameter for adaptive NMS.
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* **Range of values**: a floating-point number in close range `[0, 1.0]`.
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* **Type**: float
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* **Default value**: `1.0`
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* **Required**: *no*
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* *roi_num_type*
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* **Description**: the type of element of output 3 `rpnroisnum`.
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* **Range of values**: i32, i64
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* **Type**: string
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* **Default value**: `i64`
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* **Required**: *no*
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**Inputs**
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* **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.**
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* **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.**
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* **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.**
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* **4**: `scores` - tensor of type *T* with shape `[num_batches, number_of_anchors, height, width]` providing proposals scores. **Required.**
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The `height` and `width` from inputs `anchors`, `boxesdeltas` and `scores` are the height and width of feature maps.
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**Outputs**
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* **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.
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* **2**: `rpnscores` - tensor of type *T* with shape `[num_rois]` providing proposed ROIs scores.
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* **3**: `rpnroisnum` - tensor of type *roi_num_type* with shape `[num_batches]` providing the number of proposed ROIs in each image.
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**Types**
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* *T*: any supported floating-point type.
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**Example**
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```xml
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<layer ... type="GenerateProposals" version="opset9">
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<data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000" roi_num_type="i32"/>
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<input>
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<port id="0">
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<dim>8</dim>
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<dim>3</dim>
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</port>
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<port id="1">
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<dim>50</dim>
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<dim>84</dim>
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<dim>3</dim>
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<dim>4</dim>
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</port>
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<port id="2">
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<dim>8</dim>
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<dim>12</dim>
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<dim>50</dim>
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<dim>84</dim>
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</port>
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<port id="3">
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<dim>8</dim>
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<dim>3</dim>
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<dim>50</dim>
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<dim>84</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>4</dim>
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</port>
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<port id="5" precision="FP32">
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<dim>-1</dim>
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</port>
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<port id="6" precision="I32">
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<dim>8</dim>
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</port>
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</output>
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</layer>
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```
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@ -69,6 +69,7 @@ declared in `namespace opset9`.
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* [GatherND](movement/GatherND_8.md)
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* [GatherTree](movement/GatherTree_1.md)
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* [Gelu](activation/GELU_7.md)
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* [GenerateProposals](detection/GenerateProposals_9.md)
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* [Greater](comparison/Greater_1.md)
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* [GreaterEqual](comparison/GreaterEqual_1.md)
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* [GRN](normalization/GRN_1.md)
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