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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef NV_INFER_PLUGIN_UTILS_H
#define NV_INFER_PLUGIN_UTILS_H
#include "NvInferRuntimeCommon.h"
//!
//! \file NvInferPluginUtils.h
//!
//! This is the API for the Nvidia provided TensorRT plugin utilities.
//! It lists all the parameters utilized by the TensorRT plugins.
//!
namespace nvinfer1
{
namespace plugin
{
//!
//! \struct PriorBoxParameters
//!
//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all
//! dimensions (H x W).
//!
//! PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer.
//!
struct PriorBoxParameters
{
float *minSize; //!< Minimum box size in pixels. Can not be nullptr.
float *maxSize; //!< Maximum box size in pixels. Can be nullptr.
float *aspectRatios; //!< Aspect ratios of the boxes. Can be nullptr.
int32_t numMinSize; //!< Number of elements in minSize. Must be larger than 0.
int32_t numMaxSize; //!< Number of elements in maxSize. Can be 0 or same as numMinSize.
int32_t numAspectRatios; //!< Number of elements in aspectRatios. Can be 0.
bool flip; //!< If true, will flip each aspect ratio. For example,
//!< if there is an aspect ratio "r", the aspect ratio "1.0/r" will be generated as well.
bool clip; //!< If true, will clip the prior so that it is within [0,1].
float variance[4]; //!< Variance for adjusting the prior boxes.
int32_t imgH; //!< Image height. If 0, then the H dimension of the data tensor will be used.
int32_t imgW; //!< Image width. If 0, then the W dimension of the data tensor will be used.
float stepH; //!< Step in H. If 0, then (float)imgH/h will be used where h is the H dimension of the 1st input tensor.
float stepW; //!< Step in W. If 0, then (float)imgW/w will be used where w is the W dimension of the 1st input tensor.
float offset; //!< Offset to the top left corner of each cell.
};
//!
//! \struct RPROIParams
//!
//! \brief RPROIParams is used to create the RPROIPlugin instance.
//!
struct RPROIParams
{
int32_t poolingH; //!< Height of the output in pixels after ROI pooling on feature map.
int32_t poolingW; //!< Width of the output in pixels after ROI pooling on feature map.
int32_t featureStride; //!< Feature stride; ratio of input image size to feature map size.
//!< Assuming that max pooling layers in the neural network use square filters.
int32_t preNmsTop; //!< Number of proposals to keep before applying NMS.
int32_t nmsMaxOut; //!< Number of remaining proposals after applying NMS.
int32_t anchorsRatioCount; //!< Number of anchor box ratios.
int32_t anchorsScaleCount; //!< Number of anchor box scales.
float iouThreshold; //!< IoU (Intersection over Union) threshold used for the NMS step.
float minBoxSize; //!< Minimum allowed bounding box size before scaling, used for anchor box calculation.
float spatialScale; //!< Spatial scale between the input image and the last feature map.
};
//!
//! \struct GridAnchorParameters
//!
//! \brief The Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W).
//! GridAnchorParameters defines a set of parameters for creating the plugin layer for all feature maps.
//!
struct GridAnchorParameters
{
float minSize; //!< Scale of anchors corresponding to finest resolution.
float maxSize; //!< Scale of anchors corresponding to coarsest resolution.
float* aspectRatios; //!< List of aspect ratios to place on each grid point.
int32_t numAspectRatios; //!< Number of elements in aspectRatios.
int32_t H; //!< Height of feature map to generate anchors for.
int32_t W; //!< Width of feature map to generate anchors for.
float variance[4]; //!< Variance for adjusting the prior boxes.
};
//!
//! \enum CodeTypeSSD
//!
//! \brief The type of encoding used for decoding the bounding boxes and loc_data.
//!
//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated.
//!
enum class CodeTypeSSD : int32_t
{
CORNER TRT_DEPRECATED_ENUM = 0, //!< Use box corners.
CENTER_SIZE TRT_DEPRECATED_ENUM = 1, //!< Use box centers and size.
CORNER_SIZE TRT_DEPRECATED_ENUM = 2, //!< Use box centers and size.
TF_CENTER TRT_DEPRECATED_ENUM = 3 //!< Use box centers and size but flip x and y coordinates.
};
//!
//! \struct DetectionOutputParameters
//!
//! \brief The DetectionOutput plugin layer generates the detection output
//! based on location and confidence predictions by doing non maximum suppression.
//!
//! This plugin first decodes the bounding boxes based on the anchors generated.
//! It then performs non_max_suppression on the decoded bounding boxes.
//! DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer.
//!
//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated.
//!
struct TRT_DEPRECATED DetectionOutputParameters
{
bool shareLocation; //!< If true, bounding box are shared among different classes.
bool varianceEncodedInTarget; //!< If true, variance is encoded in target.
//!< Otherwise we need to adjust the predicted offset accordingly.
int32_t backgroundLabelId; //!< Background label ID. If there is no background class, set it as -1.
int32_t numClasses; //!< Number of classes to be predicted.
int32_t topK; //!< Number of boxes per image with top confidence scores that are fed
//!< into the NMS algorithm.
int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step.
float confidenceThreshold; //!< Only consider detections whose confidences are larger than a threshold.
float nmsThreshold; //!< Threshold to be used in NMS.
CodeTypeSSD codeType; //!< Type of coding method for bbox.
int32_t inputOrder[3]; //!< Specifies the order of inputs {loc_data, conf_data, priorbox_data}.
bool confSigmoid; //!< Set to true to calculate sigmoid of confidence scores.
bool isNormalized; //!< Set to true if bounding box data is normalized by the network.
bool isBatchAgnostic{true}; //!< Defaults to true. Set to false if prior boxes are unique per batch.
};
//!
//! \brief When performing yolo9000, softmaxTree is helping to do softmax on confidence scores,
//! for element to get the precise classification through word-tree structured classification definition.
//!
struct softmaxTree
{
int32_t* leaf;
int32_t n;
int32_t* parent;
int32_t* child;
int32_t* group;
char** name;
int32_t groups;
int32_t* groupSize;
int32_t* groupOffset;
};
//!
//! \brief The Region plugin layer performs region proposal calculation.
//!
//! Generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell).
//! For each box, calculating its probabilities of objects detections from 80 pre-defined classifications
//! (yolo9000 has 9418 pre-defined classifications, and these 9418 items are organized as work-tree structure).
//! RegionParameters defines a set of parameters for creating the Region plugin layer.
//!
struct RegionParameters
{
int32_t num; //!< Number of predicted bounding box for each grid cell.
int32_t coords; //!< Number of coordinates for a bounding box.
int32_t classes; //!< Number of classifications to be predicted.
softmaxTree* smTree; //!< Helping structure to do softmax on confidence scores.
};
//!
//! \brief The NMSParameters are used by the BatchedNMSPlugin for performing
//! the non_max_suppression operation over boxes for object detection networks.
//!
//! \deprecated Deprecated in TensorRT 10.0. BatchedNMSPlugin plugin is deprecated.
//!
struct TRT_DEPRECATED NMSParameters
{
bool shareLocation; //!< If set to true, the boxes inputs are shared across all classes.
//!< If set to false, the boxes input should account for per class box data.
int32_t backgroundLabelId; //!< Label ID for the background class.
//!< If there is no background class, set it as -1
int32_t numClasses; //!< Number of classes in the network.
int32_t topK; //!< Number of bounding boxes to be fed into the NMS step.
int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step.
//!< Should be less than or equal to the topK value.
float scoreThreshold; //!< Scalar threshold for score (low scoring boxes are removed).
float iouThreshold; //!< A scalar threshold for IOU (new boxes that have high IOU overlap
//!< with previously selected boxes are removed).
bool isNormalized; //!< Set to false, if the box coordinates are not normalized,
//!< i.e. not in the range [0,1]. Defaults to false.
};
} // namespace plugin
} // namespace nvinfer1
#endif // NV_INFER_PLUGIN_UTILS_H
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