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DeepStream 7.1 + Fixes + New model output format

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This commit is contained in:
Marcos Luciano
2024-11-07 11:25:17 -03:00
parent bca9e59d07
commit b451b036b2
75 changed files with 2383 additions and 1113 deletions
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77
nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
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@@ -1,5 +1,5 @@
/*
* Copyright (c) 2018-2023, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2018-2024, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
@@ -38,19 +38,19 @@ namespace {
}
}
cudaError_t cudaYoloLayer_nc(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
const uint64_t& inputSize, const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth,
const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
cudaError_t cudaYoloLayer_nc(const void* input, void* output, const uint& batchSize, const uint64_t& inputSize,
const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth, const uint& netHeight,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
cudaError_t cudaYoloLayer(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
const uint64_t& inputSize, const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth,
const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
cudaError_t cudaYoloLayer(const void* input, void* output, const uint& batchSize, const uint64_t& inputSize,
const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth, const uint& netHeight,
const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses, const uint& numBBoxes,
const float& scaleXY, const void* anchors, const void* mask, cudaStream_t stream);
cudaError_t cudaRegionLayer(const void* input, void* softmax, void* boxes, void* scores, void* classes,
const uint& batchSize, const uint64_t& inputSize, const uint64_t& outputSize, const uint64_t& lastInputSize,
const uint& netWidth, const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
cudaError_t cudaRegionLayer(const void* input, void* softmax, void* output, const uint& batchSize,
const uint64_t& inputSize, const uint64_t& outputSize, const uint64_t& lastInputSize, const uint& netWidth,
const uint& netHeight, const uint& gridSizeX, const uint& gridSizeY, const uint& numOutputClasses,
const uint& numBBoxes, const void* anchors, cudaStream_t stream);
YoloLayer::YoloLayer(const void* data, size_t length) {
@@ -98,6 +98,8 @@ YoloLayer::YoloLayer(const uint& netWidth, const uint& netHeight, const uint& nu
{
assert(m_NetWidth > 0);
assert(m_NetHeight > 0);
assert(m_NumClasses > 0);
assert(m_OutputSize > 0);
};
nvinfer1::IPluginV2DynamicExt*
@@ -155,13 +157,15 @@ YoloLayer::serialize(void* buffer) const noexcept
uint anchorsSize = curYoloTensor.anchors.size();
write(d, anchorsSize);
for (uint j = 0; j < anchorsSize; ++j)
for (uint j = 0; j < anchorsSize; ++j) {
write(d, curYoloTensor.anchors[j]);
}
uint maskSize = curYoloTensor.mask.size();
write(d, maskSize);
for (uint j = 0; j < maskSize; ++j)
for (uint j = 0; j < maskSize; ++j) {
write(d, curYoloTensor.mask[j]);
}
}
}
@@ -169,17 +173,14 @@ nvinfer1::DimsExprs
YoloLayer::getOutputDimensions(INT index, const nvinfer1::DimsExprs* inputs, INT nbInputDims,
nvinfer1::IExprBuilder& exprBuilder)noexcept
{
assert(index < 3);
if (index == 0) {
return nvinfer1::DimsExprs{3, {inputs->d[0], exprBuilder.constant(static_cast<int>(m_OutputSize)),
exprBuilder.constant(4)}};
}
assert(index < 1);
return nvinfer1::DimsExprs{3, {inputs->d[0], exprBuilder.constant(static_cast<int>(m_OutputSize)),
exprBuilder.constant(1)}};
exprBuilder.constant(6)}};
}
bool
YoloLayer::supportsFormatCombination(INT pos, const nvinfer1::PluginTensorDesc* inOut, INT nbInputs, INT nbOutputs) noexcept
YoloLayer::supportsFormatCombination(INT pos, const nvinfer1::PluginTensorDesc* inOut, INT nbInputs, INT nbOutputs)
noexcept
{
return inOut[pos].format == nvinfer1::TensorFormat::kLINEAR && inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
@@ -187,7 +188,7 @@ YoloLayer::supportsFormatCombination(INT pos, const nvinfer1::PluginTensorDesc*
nvinfer1::DataType
YoloLayer::getOutputDataType(INT index, const nvinfer1::DataType* inputTypes, INT nbInputs) const noexcept
{
assert(index < 3);
assert(index < 1);
return nvinfer1::DataType::kFLOAT;
}
@@ -206,10 +207,6 @@ YoloLayer::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::
{
INT batchSize = inputDesc[0].dims.d[0];
void* boxes = outputs[0];
void* scores = outputs[1];
void* classes = outputs[2];
uint64_t lastInputSize = 0;
uint yoloTensorsSize = m_YoloTensors.size();
@@ -223,45 +220,47 @@ YoloLayer::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::
const std::vector<float> anchors = curYoloTensor.anchors;
const std::vector<int> mask = curYoloTensor.mask;
void* v_anchors;
void* v_mask;
void* d_anchors;
void* d_mask;
if (anchors.size() > 0) {
CUDA_CHECK(cudaMalloc(&v_anchors, sizeof(float) * anchors.size()));
CUDA_CHECK(cudaMemcpyAsync(v_anchors, anchors.data(), sizeof(float) * anchors.size(), cudaMemcpyHostToDevice, stream));
CUDA_CHECK(cudaMalloc(&d_anchors, sizeof(float) * anchors.size()));
CUDA_CHECK(cudaMemcpyAsync(d_anchors, anchors.data(), sizeof(float) * anchors.size(), cudaMemcpyHostToDevice,
stream));
}
if (mask.size() > 0) {
CUDA_CHECK(cudaMalloc(&v_mask, sizeof(int) * mask.size()));
CUDA_CHECK(cudaMemcpyAsync(v_mask, mask.data(), sizeof(int) * mask.size(), cudaMemcpyHostToDevice, stream));
CUDA_CHECK(cudaMalloc(&d_mask, sizeof(int) * mask.size()));
CUDA_CHECK(cudaMemcpyAsync(d_mask, mask.data(), sizeof(int) * mask.size(), cudaMemcpyHostToDevice, stream));
}
const uint64_t inputSize = (numBBoxes * (4 + 1 + m_NumClasses)) * gridSizeY * gridSizeX;
if (mask.size() > 0) {
if (m_NewCoords) {
CUDA_CHECK(cudaYoloLayer_nc(inputs[i], boxes, scores, classes, batchSize, inputSize, m_OutputSize, lastInputSize,
m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY, v_anchors, v_mask, stream));
CUDA_CHECK(cudaYoloLayer_nc(inputs[i], outputs[0], batchSize, inputSize, m_OutputSize, lastInputSize,
m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY, d_anchors, d_mask,
stream));
}
else {
CUDA_CHECK(cudaYoloLayer(inputs[i], boxes, scores, classes, batchSize, inputSize, m_OutputSize, lastInputSize,
m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY, v_anchors, v_mask, stream));
CUDA_CHECK(cudaYoloLayer(inputs[i], outputs[0], batchSize, inputSize, m_OutputSize, lastInputSize, m_NetWidth,
m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, scaleXY, d_anchors, d_mask, stream));
}
}
else {
void* softmax;
CUDA_CHECK(cudaMalloc(&softmax, sizeof(float) * inputSize * batchSize));
CUDA_CHECK(cudaMemsetAsync((float*) softmax, 0, sizeof(float) * inputSize * batchSize, stream));
CUDA_CHECK(cudaMemsetAsync((float*)softmax, 0, sizeof(float) * inputSize * batchSize, stream));
CUDA_CHECK(cudaRegionLayer(inputs[i], softmax, boxes, scores, classes, batchSize, inputSize, m_OutputSize,
lastInputSize, m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, v_anchors, stream));
CUDA_CHECK(cudaRegionLayer(inputs[i], softmax, outputs[0], batchSize, inputSize, m_OutputSize, lastInputSize,
m_NetWidth, m_NetHeight, gridSizeX, gridSizeY, m_NumClasses, numBBoxes, d_anchors, stream));
CUDA_CHECK(cudaFree(softmax));
}
if (anchors.size() > 0) {
CUDA_CHECK(cudaFree(v_anchors));
CUDA_CHECK(cudaFree(d_anchors));
}
if (mask.size() > 0) {
CUDA_CHECK(cudaFree(v_mask));
CUDA_CHECK(cudaFree(d_mask));
}
lastInputSize += numBBoxes * gridSizeY * gridSizeX;