From dcf5be98380dd48b5058f1dd4fa14350e752f1bb Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Mon, 6 Dec 2021 11:08:50 +0300 Subject: [PATCH] Specify DetectionOutput-8 operation (#8704) * Specify DetectionOutput-8 operation The delta is small where we just remove num_classes attribute to support OD models out of the box Signed-off-by: Roman Kazantsev * Remove a link to DetectionOutput-1 from opset-8 Signed-off-by: Roman Kazantsev * Update a list in ie_docs.xml Signed-off-by: Roman Kazantsev --- docs/doxygen/ie_docs.xml | 1 + docs/ops/detection/DetectionOutput_8.md | 198 ++++++++++++++++++++++++ docs/ops/opset8.md | 2 +- 3 files changed, 200 insertions(+), 1 deletion(-) create mode 100644 docs/ops/detection/DetectionOutput_8.md diff --git a/docs/doxygen/ie_docs.xml b/docs/doxygen/ie_docs.xml index 1a86428580f..3a0f6d854eb 100644 --- a/docs/doxygen/ie_docs.xml +++ b/docs/doxygen/ie_docs.xml @@ -139,6 +139,7 @@ limitations under the License. + diff --git a/docs/ops/detection/DetectionOutput_8.md b/docs/ops/detection/DetectionOutput_8.md new file mode 100644 index 00000000000..c4138f2ec8f --- /dev/null +++ b/docs/ops/detection/DetectionOutput_8.md @@ -0,0 +1,198 @@ +## DetectionOutput {#openvino_docs_ops_detection_DetectionOutput_8} + +**Versioned name**: *DetectionOutput-8* + +**Category**: *Object detection* + +**Short description**: *DetectionOutput* performs non-maximum suppression to generate the detection output using information on location and +confidence predictions. + +**Detailed description**: [Reference](https://arxiv.org/pdf/1512.02325.pdf). The layer has 3 mandatory inputs: tensor with box logits, +tensor with confidence predictions and tensor with box coordinates (proposals). It can have 2 additional inputs with additional confidence +predictions and box coordinates described in the [article](https://arxiv.org/pdf/1711.06897.pdf). The output tensor contains information +about filtered detections described with 7 element tuples: `[batch_id, class_id, confidence, x_1, y_1, x_2, y_2]`. The first tuple +with `batch_id` equal to `-1` means end of output. + +At each feature map cell, *DetectionOutput* predicts the offsets relative to the default box shapes in the cell, as well as the per-class +scores that indicate the presence of a class instance in each of those boxes. Specifically, for each box out of k at a given +location, *DetectionOutput* computes class scores and the four offsets relative to the original default box shape. +This results in a total of \f$(c + 4)k\f$ filters that are applied around each location in the feature map, +yielding \f$(c + 4)kmn\f$ outputs for a *m \* n* feature map. + +**Attributes**: + +**NOTE:** *num_classes*, a number of classes attribute, presents in [DetectionOutput_1](DetectionOutput_1.md) has been removed. +It can be computed as `cls_pred_shape[-1] // num_prior_boxes` where `cls_pred_shape` and `num_prior_boxes` are class predictions tensor shape and +a number of prior boxes. + +* *background_label_id* + + * **Description**: background label id. If there is no background class, set it to -1. + * **Range of values**: integer values + * **Type**: int + * **Default value**: 0 + * **Required**: *no* + +* *top_k* + + * **Description**: maximum number of results to be kept per batch after NMS step. -1 means keeping all bounding boxes. + * **Range of values**: integer values + * **Type**: int + * **Default value**: -1 + * **Required**: *no* + +* *variance_encoded_in_target* + + * **Description**: *variance_encoded_in_target* is a flag that denotes if variance is encoded in target. If flag is false then +it is necessary to adjust the predicted offset accordingly. + * **Range of values**: false or true + * **Type**: boolean + * **Default value**: false + * **Required**: *no* + +* *keep_top_k* + + * **Description**: maximum number of bounding boxes per batch to be kept after NMS step. -1 means keeping all bounding boxes after NMS step. + * **Range of values**: integer values + * **Type**: int[] + * **Required**: *yes* + +* *code_type* + + * **Description**: type of coding method for bounding boxes + * **Range of values**: "caffe.PriorBoxParameter.CENTER_SIZE", "caffe.PriorBoxParameter.CORNER" + * **Type**: string + * **Default value**: "caffe.PriorBoxParameter.CORNER" + * **Required**: *no* + +* *share_location* + + * **Description**: *share_location* is a flag that denotes if bounding boxes are shared among different classes. + * **Range of values**: false or true + * **Type**: boolean + * **Default value**: true + * **Required**: *no* + +* *nms_threshold* + + * **Description**: threshold to be used in the NMS stage + * **Range of values**: floating-point values + * **Type**: float + * **Required**: *yes* + +* *confidence_threshold* + + * **Description**: only consider detections whose confidences are larger than a threshold. If not provided, consider all boxes. + * **Range of values**: floating-point values + * **Type**: float + * **Default value**: 0 + * **Required**: *no* + +* *clip_after_nms* + + * **Description**: *clip_after_nms* flag that denotes whether to perform clip bounding boxes after non-maximum suppression or not. + * **Range of values**: false or true + * **Type**: boolean + * **Default value**: false + * **Required**: *no* + +* *clip_before_nms* + + * **Description**: *clip_before_nms* flag that denotes whether to perform clip bounding boxes before non-maximum suppression or not. + * **Range of values**: false or true + * **Type**: boolean + * **Default value**: false + * **Required**: *no* + +* *decrease_label_id* + + * **Description**: *decrease_label_id* flag that denotes how to perform NMS. + * **Range of values**: + * false - perform NMS like in Caffe\*. + * true - perform NMS like in MxNet\*. + * **Type**: boolean + * **Default value**: false + * **Required**: *no* + +* *normalized* + + * **Description**: *normalized* flag that denotes whether input tensor with proposal boxes is normalized. +If tensor is not normalized then *input_height* and *input_width* attributes are used to normalize box coordinates. + * **Range of values**: false or true + * **Type**: boolean + * **Default value**: false + * **Required**: *no* + +* *input_height (input_width)* + + * **Description**: input image height (width). If the *normalized* is 1 then these attributes are not used. + * **Range of values**: positive integer number + * **Type**: int + * **Default value**: 1 + * **Required**: *no* + +* *objectness_score* + + * **Description**: threshold to sort out confidence predictions. Used only when the *DetectionOutput* layer has 5 inputs. + * **Range of values**: non-negative float number + * **Type**: float + * **Default value**: 0 + * **Required**: *no* + +**Inputs** + +* **1**: 2D input tensor with box logits with shape `[N, num_prior_boxes * num_loc_classes * 4]` and type *T*. `num_loc_classes` +is equal to `num_classes` when `share_location` is 0 or it's equal to 1 otherwise. **Required.** +* **2**: 2D input tensor with class predictions with shape `[N, num_prior_boxes * num_classes]` and type *T*. **Required.** +* **3**: 3D input tensor with proposals with shape `[priors_batch_size, 1, num_prior_boxes * prior_box_size]` or +`[priors_batch_size, 2, num_prior_boxes * prior_box_size]`. `priors_batch_size` is either 1 or `N`. +Size of the second dimension depends on `variance_encoded_in_target`. If `variance_encoded_in_target` is equal to 0, +the second dimension equals to 2 and variance values are provided for each boxes coordinates. +If `variance_encoded_in_target` is equal to 1, the second dimension equals to 1 and this tensor contains proposals boxes only. +`prior_box_size` is equal to 4 when `normalized` is set to 1 or it's equal to 5 otherwise. **Required.** +* **4**: 2D input tensor with additional class predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). +Its shape must be equal to `[N, num_prior_boxes * 2]`. **Optional.** +* **5**: 2D input tensor with additional box predictions information described in the [article](https://arxiv.org/pdf/1711.06897.pdf). +Its shape must be equal to first input tensor shape. **Optional.** + +**Outputs** + +* **1**: 4D output tensor with type *T*. Its shape depends on `keep_top_k` or `top_k` being set. It `keep_top_k[0]` is greater than zero, +then the shape is `[1, 1, N * keep_top_k[0], 7]`. If `keep_top_k[0]` is set to -1 and `top_k` is greater than zero, +then the shape is `[1, 1, N * top_k * num_classes, 7]`. Otherwise, the output shape is equal to `[1, 1, N * num_classes * num_prior_boxes, 7]`. + +**Types** + +* *T*: any supported floating-point type. + + +**Example** + +```xml + + + + + 1 + 5376 + + + 1 + 2688 + + + 1 + 2 + 5376 + + + + + 1 + 1 + 200 + 7 + + + +``` diff --git a/docs/ops/opset8.md b/docs/ops/opset8.md index d3dab4299f2..d20b2a46ae7 100644 --- a/docs/ops/opset8.md +++ b/docs/ops/opset8.md @@ -43,7 +43,7 @@ declared in `namespace opset8`. * [DeformableConvolution](convolution/DeformableConvolution_8.md) * [DeformablePSROIPooling](detection/DeformablePSROIPooling_1.md) * [DepthToSpace](movement/DepthToSpace_1.md) -* [DetectionOutput](detection/DetectionOutput_1.md) +* [DetectionOutput](detection/DetectionOutput_8.md) * [DFT](signals/DFT_7.md) * [Divide](arithmetic/Divide_1.md) * [Einsum](matrix/Einsum_7.md)