extern "C" {
//!
//! \brief Create a plugin layer that fuses the RPN and ROI pooling using user-defined parameters.
//! Registered plugin type "RPROI_TRT". Registered plugin version "1".
//! \param featureStride Feature stride.
//! \param preNmsTop Number of proposals to keep before applying NMS.
//! \param nmsMaxOut Number of remaining proposals after applying NMS.
//! \param iouThreshold IoU threshold.
//! \param minBoxSize Minimum allowed bounding box size before scaling.
//! \param spatialScale Spatial scale between the input image and the last feature map.
//! \param pooling Spatial dimensions of pooled ROIs.
//! \param anchorRatios Aspect ratios for generating anchor windows.
//! \param anchorScales Scales for generating anchor windows.
//!
//! \return Returns a FasterRCNN fused RPN+ROI pooling plugin. Returns nullptr on invalid inputs.
//!
TENSORRTAPI nvinfer1::IPluginV2* createRPNROIPlugin(int featureStride, int preNmsTop,
                                                                int nmsMaxOut, float iouThreshold, float minBoxSize,
                                                                float spatialScale, nvinfer1::DimsHW pooling,
                                                                nvinfer1::Weights anchorRatios, nvinfer1::Weights anchorScales);

//!
//! \brief The Normalize plugin layer normalizes the input to have L2 norm of 1 with scale learnable.
//! Registered plugin type "Normalize_TRT". Registered plugin version "1".
//! \param scales Scale weights that are applied to the output tensor.
//! \param acrossSpatial Whether to compute the norm over adjacent channels (acrossSpatial is true) or nearby spatial locations (within channel in which case acrossSpatial is false).
//! \param channelShared Whether the scale weight(s) is shared across channels.
//! \param eps Epsilon for not diviiding by zero.
//!
TENSORRTAPI nvinfer1::IPluginV2* createNormalizePlugin(const nvinfer1::Weights* scales, bool acrossSpatial, bool channelShared, float eps);

//!
//! \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.
//! Registered plugin type "PriorBox_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createPriorBoxPlugin(nvinfer1::plugin::PriorBoxParameters param);

//!
//! \brief The Grid Anchor Generator plugin layer generates the prior boxes of
//! designated sizes and aspect ratios across all dimensions (H x W) for all feature maps.
//! GridAnchorParameters defines a set of parameters for creating the GridAnchorGenerator plugin layer.
//! Registered plugin type "GridAnchor_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createAnchorGeneratorPlugin(nvinfer1::plugin::GridAnchorParameters* param, int numLayers);

//!
//! \brief The DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression.
//! DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer.
//! Registered plugin type "NMS_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createNMSPlugin(nvinfer1::plugin::DetectionOutputParameters param);

//!
//! \brief The LReLu plugin layer performs leaky ReLU for 4D tensors. Give an input value x, the PReLU layer computes the output as x if x > 0 and negative_slope //! x if x <= 0.
//! Registered plugin type "LReLU_TRT". Registered plugin version "1".
//! \param negSlope Negative_slope value.
//!
TENSORRTAPI nvinfer1::IPluginV2* createLReLUPlugin(float negSlope);

//!
//! \brief The Reorg plugin reshapes input of shape CxHxW into a (C*stride*stride)x(H/stride)x(W/stride) shape, used in YOLOv2.
//! It does that by taking 1 x stride x stride slices from tensor and flattening them into (stridexstride) x 1 x 1 shape.
//! Registered plugin type "Reorg_TRT". Registered plugin version "1".
//! \param stride Strides in H and W, it should divide both H and W. Also stride * stride should be less than or equal to C.
//!
TENSORRTAPI nvinfer1::IPluginV2* createReorgPlugin(int stride);

//!
//! \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 probablities of objects detections from 80 pre-defined classifications (yolo9000 has 9416 pre-defined classifications,
//! and these 9416 items are organized as work-tree structure).
//! RegionParameters defines a set of parameters for creating the Region plugin layer.
//! Registered plugin type "Region_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createRegionPlugin(nvinfer1::plugin::RegionParameters params);

//!
//! \brief The Clip Plugin performs a clip operation on the input tensor. It
//! clips the tensor values to a specified min and max. Any value less than clipMin are set to clipMin.
//! Any values greater than clipMax are set to clipMax. For example, this plugin can be used
//! to perform a Relu6 operation by specifying clipMin=0.0 and clipMax=6.0
//! Registered plugin type "Clip_TRT". Registered plugin version "1".
//! \param layerName The name of the TensorRT layer.
//! \param clipMin The minimum value to clip to.
//! \param clipMax The maximum value to clip to.
//!
TENSORRTAPI nvinfer1::IPluginV2* createClipPlugin(const char* layerName, float clipMin, float clipMax);

//!
//! \brief The BatchedNMS Plugin performs non_max_suppression on the input boxes, per batch, across all classes.
//! It greedily selects a subset of bounding boxes in descending order of
//! score. Prunes away boxes that have a high intersection-over-union (IOU)
//! overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2],
//! where (y1, x1) and (y2, x2) are the coordinates of any
//! diagonal pair of box corners and the coordinates can be provided as normalized
//! (i.e., lying in the interval [0, 1]) or absolute.
//! The plugin expects two inputs.
//! Input0 is expected to be 4-D float boxes tensor of shape [batch_size, num_boxes,
//! q, 4], where q can be either 1 (if shareLocation is true) or num_classes.
//! Input1 is expected to be a 3-D float scores tensor of shape [batch_size, num_boxes, num_classes]
//! representing a single score corresponding to each box.
//! The plugin returns four outputs.
//! num_detections : A [batch_size] int32 tensor indicating the number of valid
//! detections per batch item. Can be less than keepTopK. Only the top num_detections[i] entries in
//! nmsed_boxes[i], nmsed_scores[i] and nmsed_classes[i] are valid.
//! nmsed_boxes : A [batch_size, max_detections, 4] float32 tensor containing
//! the co-ordinates of non-max suppressed boxes.
//! nmsed_scores : A [batch_size, max_detections] float32 tensor containing the
//! scores for the boxes.
//! nmsed_classes :  A [batch_size, max_detections] float32 tensor containing the
//! classes for the boxes.
//!
//! Registered plugin type "BatchedNMS_TRT". Registered plugin version "1".
//!
TENSORRTAPI nvinfer1::IPluginV2* createBatchedNMSPlugin(nvinfer1::plugin::NMSParameters param);

//!
//! \brief Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional namespace.
//! The plugin library author should ensure that this function name is unique to the library.
//! This function should be called once before accessing the Plugin Registry.
//! \param logger Logger object to print plugin registration information
//! \param libNamespace Namespace used to register all the plugins in this library
//!
TENSORRTAPI bool initLibNvInferPlugins(void* logger, const char* libNamespace);