chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "NvInferPluginUtils.h"
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#include "common/kernels/kernel.h"
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#include "reducedMathPlugin.h"
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#include <iostream>
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using namespace nvinfer1;
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using namespace nvinfer1::plugin;
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using nvinfer1::plugin::ReducedDivisor;
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template <unsigned nthdsPerCTA>
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__launch_bounds__(nthdsPerCTA) __global__ void priorBoxKernel(PriorBoxParameters param, const int H, const int W,
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const int numPriors, const int numAspectRatios, const float* minSize, const float* maxSize,
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const float* aspectRatios, float* outputData)
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{
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// output dims: (H, W, param.numMinSize, (1+haveMaxSize+numAR-1), 4)
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const int dim = H * W * numPriors;
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const bool haveMaxSize = param.numMaxSize > 0;
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const int dimAR = (haveMaxSize ? 1 : 0) + numAspectRatios;
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for (int i = blockIdx.x * nthdsPerCTA + threadIdx.x;
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i < dim; i += gridDim.x * nthdsPerCTA)
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{
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const int w = (i / numPriors) % W;
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const int h = (i / numPriors) / W;
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// Usually param.offset == 0.5
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// Calucate the center of prior box at the input image scale
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const float centerX = (w + param.offset) * param.stepW;
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const float centerY = (h + param.offset) * param.stepH;
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// Minimum size index
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const int minSizeId = (i / dimAR) % param.numMinSize;
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// Aspect ratio index
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const int arId = i % dimAR;
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// Generate square pior box of aspect ratio of 1.0, edge length of minSize[minSizeId]
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if (arId == 0)
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{
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const float boxW = minSize[minSizeId];
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const float boxH = boxW;
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float x, y, z, w;
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// Calculate [x_topleft, y_topleft, x_bottomright, y_bottomright]
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// Coordinates were scaled to [0, 1] against the width or height of the original input image
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x = (centerX - boxW / 2.0f) / param.imgW;
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y = (centerY - boxH / 2.0f) / param.imgH;
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z = (centerX + boxW / 2.0f) / param.imgW;
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w = (centerY + boxH / 2.0f) / param.imgH;
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// If we decided to clip the prior box make sure all the bounding box are inside the original input image
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if (param.clip)
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{
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x = min(max(x, 0.0f), 1.0f);
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y = min(max(y, 0.0f), 1.0f);
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z = min(max(z, 0.0f), 1.0f);
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w = min(max(w, 0.0f), 1.0f);
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}
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// Copy the bounding box coordinates to output
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outputData[i * 4] = x;
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outputData[i * 4 + 1] = y;
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outputData[i * 4 + 2] = z;
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outputData[i * 4 + 3] = w;
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}
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// If have maxSize
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// Generate square pior box for aspect ratio of 1.0, edge length of sqrt(minSize[minSizeId] * maxSize[minSizeId])
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// Described in SSD paper page 6
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else if (haveMaxSize && arId == 1)
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{
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const float boxW = sqrt(minSize[minSizeId] * maxSize[minSizeId]);
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const float boxH = boxW;
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float x, y, z, w;
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x = (centerX - boxW / 2.0f) / param.imgW;
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y = (centerY - boxH / 2.0f) / param.imgH;
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z = (centerX + boxW / 2.0f) / param.imgW;
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w = (centerY + boxH / 2.0f) / param.imgH;
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if (param.clip)
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{
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x = min(max(x, 0.0f), 1.0f);
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y = min(max(y, 0.0f), 1.0f);
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z = min(max(z, 0.0f), 1.0f);
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w = min(max(w, 0.0f), 1.0f);
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}
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outputData[i * 4] = x;
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outputData[i * 4 + 1] = y;
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outputData[i * 4 + 2] = z;
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outputData[i * 4 + 3] = w;
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}
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// Generate other bouding boxes with aspect ratios of not one.
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else
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{
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const int arOffset = haveMaxSize ? arId - 1 : arId; // skip aspectRatios[0] which is 1
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const float boxW = minSize[minSizeId] * sqrt(aspectRatios[arOffset]);
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const float boxH = minSize[minSizeId] / sqrt(aspectRatios[arOffset]);
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float x, y, z, w;
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x = (centerX - boxW / 2.0f) / param.imgW;
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y = (centerY - boxH / 2.0f) / param.imgH;
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z = (centerX + boxW / 2.0f) / param.imgW;
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w = (centerY + boxH / 2.0f) / param.imgH;
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if (param.clip)
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{
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x = min(max(x, 0.0f), 1.0f);
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y = min(max(y, 0.0f), 1.0f);
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z = min(max(z, 0.0f), 1.0f);
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w = min(max(w, 0.0f), 1.0f);
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}
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outputData[i * 4] = x;
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outputData[i * 4 + 1] = y;
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outputData[i * 4 + 2] = z;
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outputData[i * 4 + 3] = w;
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}
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}
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// Simply copy variance to from the parameter to output
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float* output = outputData + dim * 4;
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for (int i = blockIdx.x * nthdsPerCTA + threadIdx.x;
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i < dim; i += gridDim.x * nthdsPerCTA)
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{
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float x, y, z, w;
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x = param.variance[0];
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y = param.variance[1];
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z = param.variance[2];
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w = param.variance[3];
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output[i * 4] = x;
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output[i * 4 + 1] = y;
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output[i * 4 + 2] = z;
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output[i * 4 + 3] = w;
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}
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}
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pluginStatus_t priorBoxGpu(
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cudaStream_t stream,
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const PriorBoxParameters param,
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const int H,
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const int W,
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const int numPriors,
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const int numAspectRatios,
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const void* minSize,
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const void* maxSize,
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const void* aspectRatios,
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void* outputData)
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{
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const int dim = H * W * numPriors;
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if (dim > 5120)
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{
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const int BS = 128;
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const int GS = (dim + BS - 1) / BS;
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priorBoxKernel<BS><<<GS, BS, 0, stream>>>(param, H, W, numPriors, numAspectRatios,
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(const float*) minSize, (const float*) maxSize,
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(const float*) aspectRatios, (float*) outputData);
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CSC(cudaGetLastError(), STATUS_FAILURE);
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return STATUS_SUCCESS;
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}
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else
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{
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const int BS = 32;
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const int GS = (dim + BS - 1) / BS;
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priorBoxKernel<BS><<<GS, BS, 0, stream>>>(param, H, W, numPriors, numAspectRatios,
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(const float*) minSize, (const float*) maxSize,
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(const float*) aspectRatios, (float*) outputData);
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CSC(cudaGetLastError(), STATUS_FAILURE);
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return STATUS_SUCCESS;
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}
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}
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pluginStatus_t priorBoxInference(cudaStream_t stream, const PriorBoxParameters param, const int H, const int W,
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const int numPriors, const int numAspectRatios, const void* minSize, const void* maxSize, const void* aspectRatios,
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void* outputData)
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{
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PLUGIN_ASSERT(param.numMaxSize >= 0);
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if (param.numMaxSize)
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return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios, minSize, maxSize, aspectRatios, outputData);
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else
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return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios,
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minSize, nullptr, aspectRatios, outputData);
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}
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namespace nvinfer1
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{
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namespace plugin
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{
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pluginStatus_t priorBoxInference(cudaStream_t stream, const PriorBoxParameters param, const int H, const int W,
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const int numPriors, const int numAspectRatios, const void* minSize, const void* maxSize, const void* aspectRatios,
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void* outputData)
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{
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PLUGIN_ASSERT(param.numMaxSize >= 0);
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if (param.numMaxSize)
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return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios, minSize, maxSize, aspectRatios, outputData);
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else
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return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios,
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minSize, nullptr, aspectRatios, outputData);
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}
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} // namespace nvinfer1
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} // namespace plugin
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