191 lines
6.9 KiB
Markdown
191 lines
6.9 KiB
Markdown
# ExperimentalDetectronDetectionOutput {#openvino_docs_ops_detection_ExperimentalDetectronDetectionOutput_6}
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@sphinxdirective
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.. meta::
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:description: Learn about ExperimentalDetectronDetectionOutput-6 - an object
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detection operation, which can be performed on four required
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input tensors in OpenVINO.
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**Versioned name**: *ExperimentalDetectronDetectionOutput-6*
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**Category**: *Object detection*
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**Short description**: The *ExperimentalDetectronDetectionOutput* operation performs non-maximum suppression to generate
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the detection output using information on location and score predictions.
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**Detailed description**: The operation performs the following steps:
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1. Applies deltas to boxes sizes [x 1, y 1, x 2, y 2] and takes coordinates of
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refined boxes according to the formulas:
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``x1_new = ctr_x + (dx - 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w``
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``y0_new = ctr_y + (dy - 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h``
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``x1_new = ctr_x + (dx + 0.5 * exp(min(d_log_w, max_delta_log_wh))) * box_w - 1.0``
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``y1_new = ctr_y + (dy + 0.5 * exp(min(d_log_h, max_delta_log_wh))) * box_h - 1.0``
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* ``box_w`` and ``box_h`` are width and height of box, respectively:
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``box_w = x1 - x0 + 1.0``
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``box_h = y1 - y0 + 1.0``
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* ``ctr_x`` and ``ctr_y`` are center location of a box:
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``ctr_x = x0 + 0.5f * box_w``
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``ctr_y = y0 + 0.5f * box_h``
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* ``dx``, ``dy``, ``d_log_w`` and ``d_log_h`` are deltas calculated according to the formulas below, and ``deltas_tensor`` is a
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second input:
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``dx = deltas_tensor[roi_idx, 4 * class_idx + 0] / deltas_weights[0]``
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``dy = deltas_tensor[roi_idx, 4 * class_idx + 1] / deltas_weights[1]``
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``d_log_w = deltas_tensor[roi_idx, 4 * class_idx + 2] / deltas_weights[2]``
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``d_log_h = deltas_tensor[roi_idx, 4 * class_idx + 3] / deltas_weights[3]``
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2. If *class_agnostic_box_regression* is ``true`` removes predictions for background classes.
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3. Clips boxes to the image.
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4. Applies *score_threshold* on detection scores.
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5. Applies non-maximum suppression class-wise with *nms_threshold* and returns *post_nms_count* or less detections per class.
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6. Returns *max_detections_per_image* detections if total number of detections is more than *max_detections_per_image*; otherwise, returns total number of detections and the output tensor is filled with undefined values for rest output tensor elements.
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**Attributes**:
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* *score_threshold*
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* **Description**: The *score_threshold* attribute specifies a threshold to consider only detections whose score are
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larger than the threshold.
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* **Range of values**: non-negative floating-point number
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* **Type**: ``float``
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* **Default value**: None
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* **Required**: *yes*
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* *nms_threshold*
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* **Description**: The *nms_threshold* attribute specifies a 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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* **Default value**: None
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* **Required**: *yes*
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* *num_classes*
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* **Description**: The *num_classes* attribute specifies the number of detected classes.
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* **Range of values**: non-negative integer number
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* **Type**: ``int``
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* **Default value**: None
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* **Required**: *yes*
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* *post_nms_count*
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* **Description**: The *post_nms_count* attribute specifies the maximal number of detections per class.
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* **Range of values**: non-negative integer number
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* **Type**: ``int``
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* **Default value**: None
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* **Required**: *yes*
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* *max_detections_per_image*
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* **Description**: The *max_detections_per_image* attribute specifies maximal number of detections per image.
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* **Range of values**: non-negative integer number
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* **Type**: ``int``
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* **Default value**: None
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* **Required**: *yes*
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* *class_agnostic_box_regression*
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* **Description**: *class_agnostic_box_regression* attribute is a flag that specifies whether to delete background classes or not.
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* **Range of values**:
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* ``true`` means background classes should be deleted
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* ``false`` means background classes should not be deleted
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* **Type**: ``boolean``
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* **Default value**: false
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* **Required**: *no*
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* *max_delta_log_wh*
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* **Description**: The *max_delta_log_wh* attribute specifies maximal delta of logarithms for width and height.
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* **Range of values**: floating-point number
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* **Type**: ``float``
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* **Default value**: None
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* **Required**: *yes*
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* *deltas_weights*
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* **Description**: The *deltas_weights* attribute specifies weights for bounding boxes sizes deltas.
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* **Range of values**: a list of non-negative floating-point numbers
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* **Type**: ``float[]``
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* **Default value**: None
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* **Required**: *yes*
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**Inputs**
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* **1**: A 2D tensor of type *T* with input ROIs, with shape ``[number_of_ROIs, 4]`` providing the ROIs as 4-tuples: [x 1, y 1, x 2, y 2]. The batch dimension of first, second, and third inputs should be the same. **Required.**
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* **2**: A 2D tensor of type *T* with shape ``[number_of_ROIs, num_classes * 4]`` providing deltas for input boxes. **Required.**
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* **3**: A 2D tensor of type *T* with shape ``[number_of_ROIs, num_classes]`` providing detections scores. **Required.**
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* **4**: A 2D tensor of type *T* with shape ``[1, 3]`` contains three elements ``[image_height, image_width, scale_height_and_width]`` providing input image size info. **Required.**
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**Outputs**
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* **1**: A 2D tensor of type *T* with shape ``[max_detections_per_image, 4]`` providing boxes indices.
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* **2**: A 1D tensor of type *T_IND* with shape ``[max_detections_per_image]`` providing classes indices.
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* **3**: A 1D tensor of type *T* with shape ``[max_detections_per_image]`` providing scores indices.
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**Types**
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* *T*: any supported floating-point type.
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* *T_IND*: ``int64`` or ``int32``.
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**Example**
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.. code-block:: xml
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:force:
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<layer ... type="ExperimentalDetectronDetectionOutput" version="opset6">
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<data class_agnostic_box_regression="false" deltas_weights="10.0,10.0,5.0,5.0" max_delta_log_wh="4.135166645050049" max_detections_per_image="100" nms_threshold="0.5" num_classes="81" post_nms_count="2000" score_threshold="0.05000000074505806"/>
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<input>
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<port id="0">
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<dim>1000</dim>
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<dim>4</dim>
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</port>
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<port id="1">
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<dim>1000</dim>
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<dim>324</dim>
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</port>
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<port id="2">
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<dim>1000</dim>
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<dim>81</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>3</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>100</dim>
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<dim>4</dim>
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</port>
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<port id="5" precision="I32">
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<dim>100</dim>
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</port>
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<port id="6" precision="FP32">
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<dim>100</dim>
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</port>
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<port id="7" precision="I32">
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<dim>100</dim>
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</port>
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</output>
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</layer>
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@endsphinxdirective
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