generateDetectionPlugin.cpp

/*
 * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 *
 * 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.
 */
#include "generateDetectionPlugin.h"
#include "plugin.h"
#include <cuda_runtime_api.h>
#include <algorithm>

using namespace nvinfer1;
using namespace plugin;
using nvinfer1::plugin::GenerateDetection;
using nvinfer1::plugin::GenerateDetectionPluginCreator;

#include <fstream>

namespace
{
const char* GENERATEDETECTION_PLUGIN_VERSION{"1"};
const char* GENERATEDETECTION_PLUGIN_NAME{"GenerateDetection_TRT"};
} // namespace

PluginFieldCollection GenerateDetectionPluginCreator::mFC{};
std::vector<PluginField> GenerateDetectionPluginCreator::mPluginAttributes;

GenerateDetectionPluginCreator::GenerateDetectionPluginCreator()
{

    mPluginAttributes.emplace_back(PluginField("num_classes", nullptr, PluginFieldType::kINT32, 1));
    mPluginAttributes.emplace_back(PluginField("keep_topk", nullptr, PluginFieldType::kINT32, 1));
    mPluginAttributes.emplace_back(PluginField("score_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
    mPluginAttributes.emplace_back(PluginField("iou_threshold", nullptr, PluginFieldType::kFLOAT32, 1));
    mPluginAttributes.emplace_back(PluginField("image_size", nullptr, PluginFieldType::kINT32, 3));

    mFC.nbFields = mPluginAttributes.size();
    mFC.fields = mPluginAttributes.data();
}

const char* GenerateDetectionPluginCreator::getPluginName() const
{
    return GENERATEDETECTION_PLUGIN_NAME;
};

const char* GenerateDetectionPluginCreator::getPluginVersion() const
{
    return GENERATEDETECTION_PLUGIN_VERSION;
};

const PluginFieldCollection* GenerateDetectionPluginCreator::getFieldNames()
{
    return &mFC;
};

IPluginV2Ext* GenerateDetectionPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
    auto image_size = TLTMaskRCNNConfig::IMAGE_SHAPE;
    const PluginField* fields = fc->fields;
    for (int i = 0; i < fc->nbFields; ++i)
    {
        const char* attrName = fields[i].name;
        if (!strcmp(attrName, "num_classes"))
        {
            assert(fields[i].type == PluginFieldType::kINT32);
            mNbClasses = *(static_cast<const int*>(fields[i].data));
        }
        if (!strcmp(attrName, "keep_topk"))
        {
            assert(fields[i].type == PluginFieldType::kINT32);
            mKeepTopK = *(static_cast<const int*>(fields[i].data));
        }
        if (!strcmp(attrName, "score_threshold"))
        {
            assert(fields[i].type == PluginFieldType::kFLOAT32);
            mScoreThreshold = *(static_cast<const float*>(fields[i].data));
        }
        if (!strcmp(attrName, "iou_threshold"))
        {
            assert(fields[i].type == PluginFieldType::kFLOAT32);
            mIOUThreshold = *(static_cast<const float*>(fields[i].data));
        }
        if (!strcmp(attrName, "image_size"))
        {
            assert(fields[i].type == PluginFieldType::kINT32);
            const auto dims = static_cast<const int32_t*>(fields[i].data);
            std::copy_n(dims, 3, image_size.d);
        }
    }
    return new GenerateDetection(mNbClasses, mKeepTopK, mScoreThreshold, mIOUThreshold, image_size);
};

IPluginV2Ext* GenerateDetectionPluginCreator::deserializePlugin(const char* name, const void* data, size_t length)
{
    return new GenerateDetection(data, length);
};

GenerateDetection::GenerateDetection(int num_classes, int keep_topk, float score_threshold, float iou_threshold, const nvinfer1::Dims& image_size)
    : mNbClasses(num_classes)
    , mKeepTopK(keep_topk)
    , mScoreThreshold(score_threshold)
    , mIOUThreshold(iou_threshold)
    , mImageSize(image_size)
{
    mBackgroundLabel = 0;
    assert(mNbClasses > 0);
    assert(mKeepTopK > 0);
    assert(score_threshold >= 0.0f);
    assert(iou_threshold > 0.0f);

    mParam.backgroundLabelId = 0;
    mParam.numClasses = mNbClasses;
    mParam.keepTopK = mKeepTopK;
    mParam.scoreThreshold = mScoreThreshold;
    mParam.iouThreshold = mIOUThreshold;

    mType = DataType::kFLOAT;
};

int GenerateDetection::getNbOutputs() const
{
    return 1;
};

int GenerateDetection::initialize()
{
    // Init the regWeight [10, 10, 5, 5]
    mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
    CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr),
    static_cast<const void*>(TLTMaskRCNNConfig::DETECTION_REG_WEIGHTS), sizeof(float) * 4, cudaMemcpyHostToDevice));

    //@Init the mValidCnt and mDecodedBboxes for max batch size
    std::vector<int> tempValidCnt(mMaxBatchSize, mAnchorsCnt);

    mValidCnt = std::make_shared<CudaBind<int>>(mMaxBatchSize);

    CUASSERT(cudaMemcpy(
        mValidCnt->mPtr, static_cast<void*>(tempValidCnt.data()), sizeof(int) * mMaxBatchSize, cudaMemcpyHostToDevice));

    return 0;
};

void GenerateDetection::terminate(){};

void GenerateDetection::destroy()
{
    delete this;
};

bool GenerateDetection::supportsFormat(DataType type, PluginFormat format) const
{
    return (type == DataType::kFLOAT && format == PluginFormat::kNCHW);
};

const char* GenerateDetection::getPluginType() const
{
    return "GenerateDetection_TRT";
};

const char* GenerateDetection::getPluginVersion() const
{
    return "1";
};

IPluginV2Ext* GenerateDetection::clone() const
{
    return new GenerateDetection(*this);
};

void GenerateDetection::setPluginNamespace(const char* libNamespace)
{
    mNameSpace = libNamespace;
};

const char* GenerateDetection::getPluginNamespace() const
{
    return mNameSpace.c_str();
}

size_t GenerateDetection::getSerializationSize() const
{
    return sizeof(int) * 2 + sizeof(float) * 2 + sizeof(int) * 2 + sizeof(nvinfer1::Dims);
};

void GenerateDetection::serialize(void* buffer) const
{
    char *d = reinterpret_cast<char*>(buffer), *a = d;
    write(d, mNbClasses);
    write(d, mKeepTopK);
    write(d, mScoreThreshold);
    write(d, mIOUThreshold);
    write(d, mMaxBatchSize);
    write(d, mAnchorsCnt);
    write(d, mImageSize);
    ASSERT(d == a + getSerializationSize());
};

GenerateDetection::GenerateDetection(const void* data, size_t length)
{
    const char *d = reinterpret_cast<const char*>(data), *a = d;
    int num_classes = read<int>(d);
    int keep_topk = read<int>(d);
    float score_threshold = read<float>(d);
    float iou_threshold = read<float>(d);
    mMaxBatchSize = read<int>(d);
    mAnchorsCnt = read<int>(d);
    mImageSize = read<nvinfer1::Dims3>(d);
    ASSERT(d == a + length);

    mNbClasses = num_classes;
    mKeepTopK = keep_topk;
    mScoreThreshold = score_threshold;
    mIOUThreshold = iou_threshold;

    mParam.backgroundLabelId = 0;
    mParam.numClasses = mNbClasses;
    mParam.keepTopK = mKeepTopK;
    mParam.scoreThreshold = mScoreThreshold;
    mParam.iouThreshold = mIOUThreshold;

    mType = DataType::kFLOAT;
};

void GenerateDetection::check_valid_inputs(const nvinfer1::Dims* inputs, int nbInputDims)
{
    // classifier_delta_bbox[N, anchors, num_classes*4, 1, 1]
    // classifier_class[N, anchors, num_classes, 1, 1]
    // rpn_rois[N, anchors, 4]
    assert(nbInputDims == 3);

    // score
    assert(inputs[1].nbDims == 4 && inputs[1].d[1] == mNbClasses);
    // delta_bbox
    assert(inputs[0].nbDims == 4 && inputs[0].d[1] == mNbClasses * 4);
    // roi
    assert(inputs[2].nbDims == 2 && inputs[2].d[1] == 4);
};

size_t GenerateDetection::getWorkspaceSize(int batch_size) const
{
    RefineDetectionWorkSpace refine(batch_size, mAnchorsCnt, mParam, mType);
    return refine.totalSize;
};

Dims GenerateDetection::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{

    check_valid_inputs(inputs, nbInputDims);
    assert(index == 0);

    // [N, anchors, (y1, x1, y2, x2, class_id, score)]
    nvinfer1::Dims detections;

    detections.nbDims = 2;
    // number of anchors
    detections.d[0] = mKeepTopK;
    detections.d[1] = 6;

    return detections;
}

int GenerateDetection::enqueue(
    int batch_size, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream)
{

    void* detections = outputs[0];

    // refine detection
    RefineDetectionWorkSpace refDetcWorkspace(batch_size, mAnchorsCnt, mParam, mType);
    cudaError_t status
        = DetectionPostProcess(stream, batch_size, mAnchorsCnt, static_cast<float*>(mRegWeightDevice->mPtr),
            static_cast<float>(mImageSize.d[1]), // Image Height
            static_cast<float>(mImageSize.d[2]), // Image Width
            DataType::kFLOAT,                                        // mType,
            mParam, refDetcWorkspace, workspace,
            inputs[1],       // inputs[InScore]
            inputs[0],       // inputs[InDelta],
            mValidCnt->mPtr, // inputs[InCountValid],
            inputs[2],       // inputs[ROI]
            detections);

    assert(status == cudaSuccess);
    return status;
};

DataType GenerateDetection::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
{
    // Only DataType::kFLOAT is acceptable by the plugin layer
    return DataType::kFLOAT;
}

// Return true if output tensor is broadcast across a batch.
bool GenerateDetection::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
{
    return false;
}

// Return true if plugin can use input that is broadcast across batch without replication.
bool GenerateDetection::canBroadcastInputAcrossBatch(int inputIndex) const
{
    return false;
}

// Configure the layer with input and output data types.
void GenerateDetection::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs,
    const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast,
    const bool* outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize)
{
    check_valid_inputs(inputDims, nbInputs);
    assert(inputDims[0].d[0] == inputDims[1].d[0] && inputDims[1].d[0] == inputDims[2].d[0]);

    mAnchorsCnt = inputDims[2].d[0];
    mType = inputTypes[0];
    mMaxBatchSize = maxBatchSize;
}

// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
void GenerateDetection::attachToContext(
    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
{
}

// Detach the plugin object from its execution context.
void GenerateDetection::detachFromContext() {}