gstreamer/subprojects/gst-plugins-bad/ext/onnx/gstonnxclient.cpp
Daniel Morin 8afc305fe2 onnx: fix, update tensor producer and consumers
- Propagate GstTensor.type becoming to GstTensor.data_type to tensor producer
  (onnx) and consumer (TensorDecoder)

Part-of: <https://gitlab.freedesktop.org/gstreamer/gstreamer/-/merge_requests/6061>
2024-02-05 22:27:59 -05:00

536 lines
17 KiB
C++

/*
* GStreamer gstreamer-onnxclient
* Copyright (C) 2021-2023 Collabora Ltd
*
* gstonnxclient.cpp
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Library General Public
* License as published by the Free Software Foundation; either
* version 2 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library General Public
* License along with this library; if not, write to the
* Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
* Boston, MA 02110-1301, USA.
*/
#include "gstonnxclient.h"
#include <cpu_provider_factory.h>
#include <sstream>
#define GST_CAT_DEFAULT onnx_inference_debug
namespace GstOnnxNamespace
{
template < typename T >
std::ostream & operator<< (std::ostream & os, const std::vector < T > &v)
{
os << "[";
for (size_t i = 0; i < v.size (); ++i)
{
os << v[i];
if (i != v.size () - 1)
{
os << ", ";
}
}
os << "]";
return os;
}
GstOnnxClient::GstOnnxClient (GstElement *debug_parent):debug_parent(debug_parent),
session (nullptr),
width (0),
height (0),
channels (0),
dest (nullptr),
m_provider (GST_ONNX_EXECUTION_PROVIDER_CPU),
inputImageFormat (GST_ML_INPUT_IMAGE_FORMAT_HWC),
inputDatatype (GST_TENSOR_TYPE_UINT8),
inputDatatypeSize (sizeof (uint8_t)),
fixedInputImageSize (false),
inputTensorOffset (0.0),
inputTensorScale (1.0)
{
}
GstOnnxClient::~GstOnnxClient () {
delete session;
delete[]dest;
}
int32_t GstOnnxClient::getWidth (void)
{
return width;
}
int32_t GstOnnxClient::getHeight (void)
{
return height;
}
int32_t GstOnnxClient::getChannels (void)
{
return channels;
}
bool GstOnnxClient::isFixedInputImageSize (void)
{
return fixedInputImageSize;
}
void GstOnnxClient::setInputImageFormat (GstMlInputImageFormat format)
{
inputImageFormat = format;
}
GstMlInputImageFormat GstOnnxClient::getInputImageFormat (void)
{
return inputImageFormat;
}
void GstOnnxClient::setInputImageDatatype(GstTensorDataType datatype)
{
inputDatatype = datatype;
switch (inputDatatype) {
case GST_TENSOR_TYPE_UINT8:
inputDatatypeSize = sizeof (uint8_t);
break;
case GST_TENSOR_TYPE_UINT16:
inputDatatypeSize = sizeof (uint16_t);
break;
case GST_TENSOR_TYPE_UINT32:
inputDatatypeSize = sizeof (uint32_t);
break;
case GST_TENSOR_TYPE_INT32:
inputDatatypeSize = sizeof (int32_t);
break;
case GST_TENSOR_TYPE_FLOAT16:
inputDatatypeSize = 2;
break;
case GST_TENSOR_TYPE_FLOAT32:
inputDatatypeSize = sizeof (float);
break;
default:
g_error ("Data type %d not handled", inputDatatype);
break;
};
}
void GstOnnxClient::setInputImageOffset (float offset)
{
inputTensorOffset = offset;
}
float GstOnnxClient::getInputImageOffset ()
{
return inputTensorOffset;
}
void GstOnnxClient::setInputImageScale (float scale)
{
inputTensorScale = scale;
}
float GstOnnxClient::getInputImageScale ()
{
return inputTensorScale;
}
GstTensorDataType GstOnnxClient::getInputImageDatatype(void)
{
return inputDatatype;
}
std::vector < const char *>GstOnnxClient::genOutputNamesRaw (void)
{
if (!outputNames.empty () && outputNamesRaw.size () != outputNames.size ()) {
outputNamesRaw.resize (outputNames.size ());
for (size_t i = 0; i < outputNamesRaw.size (); i++)
outputNamesRaw[i] = outputNames[i].get ();
}
return outputNamesRaw;
}
bool GstOnnxClient::hasSession (void)
{
return session != nullptr;
}
bool GstOnnxClient::createSession (std::string modelFile,
GstOnnxOptimizationLevel optim, GstOnnxExecutionProvider provider)
{
if (session)
return true;
GraphOptimizationLevel onnx_optim;
switch (optim) {
case GST_ONNX_OPTIMIZATION_LEVEL_DISABLE_ALL:
onnx_optim = GraphOptimizationLevel::ORT_DISABLE_ALL;
break;
case GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_BASIC:
onnx_optim = GraphOptimizationLevel::ORT_ENABLE_BASIC;
break;
case GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_EXTENDED:
onnx_optim = GraphOptimizationLevel::ORT_ENABLE_EXTENDED;
break;
case GST_ONNX_OPTIMIZATION_LEVEL_ENABLE_ALL:
onnx_optim = GraphOptimizationLevel::ORT_ENABLE_ALL;
break;
default:
onnx_optim = GraphOptimizationLevel::ORT_ENABLE_EXTENDED;
break;
};
try {
Ort::SessionOptions sessionOptions;
const auto & api = Ort::GetApi ();
// for debugging
//sessionOptions.SetIntraOpNumThreads (1);
sessionOptions.SetGraphOptimizationLevel (onnx_optim);
m_provider = provider;
switch (m_provider) {
case GST_ONNX_EXECUTION_PROVIDER_CUDA:
try {
OrtCUDAProviderOptionsV2 *cuda_options = nullptr;
Ort::ThrowOnError (api.CreateCUDAProviderOptions (&cuda_options));
std::unique_ptr < OrtCUDAProviderOptionsV2,
decltype (api.ReleaseCUDAProviderOptions) >
rel_cuda_options (cuda_options, api.ReleaseCUDAProviderOptions);
Ort::ThrowOnError (api.SessionOptionsAppendExecutionProvider_CUDA_V2
(static_cast < OrtSessionOptions * >(sessionOptions),
rel_cuda_options.get ()));
}
catch (Ort::Exception & ortex) {
GST_WARNING
("Failed to create CUDA provider - dropping back to CPU");
Ort::ThrowOnError (OrtSessionOptionsAppendExecutionProvider_CPU
(sessionOptions, 1));
}
break;
default:
Ort::ThrowOnError (OrtSessionOptionsAppendExecutionProvider_CPU
(sessionOptions, 1));
break;
};
env =
Ort::Env (OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
"GstOnnxNamespace");
session = new Ort::Session (env, modelFile.c_str (), sessionOptions);
auto inputTypeInfo = session->GetInputTypeInfo (0);
std::vector < int64_t > inputDims =
inputTypeInfo.GetTensorTypeAndShapeInfo ().GetShape ();
if (inputImageFormat == GST_ML_INPUT_IMAGE_FORMAT_HWC) {
height = inputDims[1];
width = inputDims[2];
channels = inputDims[3];
} else {
channels = inputDims[1];
height = inputDims[2];
width = inputDims[3];
}
fixedInputImageSize = width > 0 && height > 0;
GST_DEBUG_OBJECT (debug_parent, "Number of Output Nodes: %d",
(gint) session->GetOutputCount ());
ONNXTensorElementDataType elementType =
inputTypeInfo.GetTensorTypeAndShapeInfo ().GetElementType ();
switch (elementType) {
case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8:
setInputImageDatatype(GST_TENSOR_TYPE_UINT8);
break;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
setInputImageDatatype(GST_TENSOR_TYPE_FLOAT32);
break;
default:
GST_ERROR_OBJECT (debug_parent,
"Only input tensors of type int8 and floatare supported");
return false;
}
Ort::AllocatorWithDefaultOptions allocator;
auto input_name = session->GetInputNameAllocated (0, allocator);
GST_DEBUG_OBJECT (debug_parent, "Input name: %s", input_name.get ());
for (size_t i = 0; i < session->GetOutputCount (); ++i) {
auto output_name = session->GetOutputNameAllocated (i, allocator);
GST_DEBUG_OBJECT (debug_parent, "Output name %lu:%s", i, output_name.get ());
outputNames.push_back (std::move (output_name));
}
genOutputNamesRaw ();
// look up tensor ids
auto metaData = session->GetModelMetadata ();
OrtAllocator *ortAllocator;
auto status =
Ort::GetApi ().GetAllocatorWithDefaultOptions (&ortAllocator);
if (status) {
// Handle the error case
const char *errorString = Ort::GetApi ().GetErrorMessage (status);
GST_WARNING_OBJECT (debug_parent, "Failed to get allocator: %s", errorString);
// Clean up the error status
Ort::GetApi ().ReleaseStatus (status);
return false;
}
for (auto & name:outputNamesRaw) {
Ort::AllocatedStringPtr res =
metaData.LookupCustomMetadataMapAllocated (name, ortAllocator);
if (res)
{
GQuark quark = g_quark_from_string (res.get ());
outputIds.push_back (quark);
} else {
GST_ERROR_OBJECT (debug_parent, "Failed to look up id for key %s", name);
return false;
}
}
}
catch (Ort::Exception & ortex) {
GST_ERROR_OBJECT (debug_parent, "%s", ortex.what ());
return false;
}
return true;
}
void GstOnnxClient::parseDimensions (GstVideoInfo vinfo)
{
int32_t newWidth = fixedInputImageSize ? width : vinfo.width;
int32_t newHeight = fixedInputImageSize ? height : vinfo.height;
if (!fixedInputImageSize) {
GST_WARNING_OBJECT (debug_parent, "Allocating before knowing model input size");
}
if (!dest || width * height < newWidth * newHeight) {
delete[]dest;
dest = new uint8_t[newWidth * newHeight * channels * inputDatatypeSize];
}
width = newWidth;
height = newHeight;
}
// copy tensor data to a GstTensorMeta
GstTensorMeta *GstOnnxClient::copy_tensors_to_meta (std::vector < Ort::Value >
&outputs, GstBuffer * buffer)
{
size_t num_tensors = outputNamesRaw.size ();
GstTensorMeta *tmeta = (GstTensorMeta *) gst_buffer_add_meta (buffer,
gst_tensor_meta_get_info (),
NULL);
tmeta->num_tensors = num_tensors;
tmeta->tensor = (GstTensor *) g_malloc (num_tensors * sizeof (GstTensor));
bool hasIds = outputIds.size () == num_tensors;
for (size_t i = 0; i < num_tensors; i++) {
Ort::Value outputTensor = std::move (outputs[i]);
ONNXTensorElementDataType tensorType =
outputTensor.GetTensorTypeAndShapeInfo ().GetElementType ();
GstTensor *tensor = &tmeta->tensor[i];
if (hasIds)
tensor->id = outputIds[i];
else
tensor->id = 0;
auto tensorShape = outputTensor.GetTensorTypeAndShapeInfo ().GetShape ();
tensor->num_dims = tensorShape.size ();
tensor->dims = g_new (int64_t, tensor->num_dims);
for (size_t j = 0; j < tensorShape.size (); ++j)
tensor->dims[j] = tensorShape[j];
size_t numElements =
outputTensor.GetTensorTypeAndShapeInfo ().GetElementCount ();
if (tensorType == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
size_t buffer_size = 0;
buffer_size = numElements * sizeof (float);
tensor->data = gst_buffer_new_allocate (NULL, buffer_size, NULL);
gst_buffer_fill (tensor->data, 0, outputTensor.GetTensorData < float >(),
buffer_size);
tensor->data_type = GST_TENSOR_TYPE_FLOAT32;
} else if (tensorType == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32) {
size_t buffer_size = 0;
buffer_size = numElements * sizeof (int);
tensor->data = gst_buffer_new_allocate (NULL, buffer_size, NULL);
gst_buffer_fill (tensor->data, 0, outputTensor.GetTensorData < float >(),
buffer_size);
tensor->data_type = GST_TENSOR_TYPE_INT32;
} else {
GST_ERROR_OBJECT (debug_parent, "Output tensor is not FLOAT32 or INT32, not supported");
gst_buffer_remove_meta (buffer, (GstMeta*) tmeta);
return NULL;
}
}
return tmeta;
}
std::vector < Ort::Value > GstOnnxClient::run (uint8_t * img_data,
GstVideoInfo vinfo) {
std::vector < Ort::Value > modelOutput;
doRun (img_data, vinfo, modelOutput);
return modelOutput;
}
bool GstOnnxClient::doRun (uint8_t * img_data, GstVideoInfo vinfo,
std::vector < Ort::Value > &modelOutput)
{
if (!img_data)
return false;
Ort::AllocatorWithDefaultOptions allocator;
auto inputName = session->GetInputNameAllocated (0, allocator);
auto inputTypeInfo = session->GetInputTypeInfo (0);
std::vector < int64_t > inputDims =
inputTypeInfo.GetTensorTypeAndShapeInfo ().GetShape ();
inputDims[0] = 1;
if (inputImageFormat == GST_ML_INPUT_IMAGE_FORMAT_HWC) {
inputDims[1] = height;
inputDims[2] = width;
} else {
inputDims[2] = height;
inputDims[3] = width;
}
std::ostringstream buffer;
buffer << inputDims;
GST_DEBUG_OBJECT (debug_parent, "Input dimensions: %s", buffer.str ().c_str ());
// copy video frame
uint8_t *srcPtr[3] = { img_data, img_data + 1, img_data + 2 };
uint32_t srcSamplesPerPixel = 3;
switch (vinfo.finfo->format) {
case GST_VIDEO_FORMAT_RGBA:
srcSamplesPerPixel = 4;
break;
case GST_VIDEO_FORMAT_BGRA:
srcSamplesPerPixel = 4;
srcPtr[0] = img_data + 2;
srcPtr[1] = img_data + 1;
srcPtr[2] = img_data + 0;
break;
case GST_VIDEO_FORMAT_ARGB:
srcSamplesPerPixel = 4;
srcPtr[0] = img_data + 1;
srcPtr[1] = img_data + 2;
srcPtr[2] = img_data + 3;
break;
case GST_VIDEO_FORMAT_ABGR:
srcSamplesPerPixel = 4;
srcPtr[0] = img_data + 3;
srcPtr[1] = img_data + 2;
srcPtr[2] = img_data + 1;
break;
case GST_VIDEO_FORMAT_BGR:
srcPtr[0] = img_data + 2;
srcPtr[1] = img_data + 1;
srcPtr[2] = img_data + 0;
break;
default:
break;
}
uint32_t stride = vinfo.stride[0];
const size_t inputTensorSize = width * height * channels * inputDatatypeSize;
auto memoryInfo =
Ort::MemoryInfo::CreateCpu (OrtAllocatorType::OrtArenaAllocator,
OrtMemType::OrtMemTypeDefault);
std::vector < Ort::Value > inputTensors;
switch (inputDatatype) {
case GST_TENSOR_TYPE_UINT8:
uint8_t *src_data;
if (inputTensorOffset == 00 && inputTensorScale == 1.0) {
src_data = img_data;
} else {
convert_image_remove_alpha (
dest, inputImageFormat, srcPtr, srcSamplesPerPixel, stride,
(uint8_t)inputTensorOffset, (uint8_t)inputTensorScale);
src_data = dest;
}
inputTensors.push_back (Ort::Value::CreateTensor < uint8_t > (
memoryInfo, src_data, inputTensorSize, inputDims.data (),
inputDims.size ()));
break;
case GST_TENSOR_TYPE_FLOAT32: {
convert_image_remove_alpha ((float*)dest, inputImageFormat , srcPtr,
srcSamplesPerPixel, stride, (float)inputTensorOffset, (float)
inputTensorScale);
inputTensors.push_back (Ort::Value::CreateTensor < float > (
memoryInfo, (float*)dest, inputTensorSize, inputDims.data (),
inputDims.size ()));
}
break;
default:
break;
}
std::vector < const char *>inputNames { inputName.get () };
modelOutput = session->Run (Ort::RunOptions {nullptr},
inputNames.data (),
inputTensors.data (), 1, outputNamesRaw.data (),
outputNamesRaw.size ());
return true;
}
template < typename T>
void GstOnnxClient::convert_image_remove_alpha (T *dst,
GstMlInputImageFormat hwc, uint8_t **srcPtr, uint32_t srcSamplesPerPixel,
uint32_t stride, T offset, T div) {
size_t destIndex = 0;
T tmp;
if (inputImageFormat == GST_ML_INPUT_IMAGE_FORMAT_HWC) {
for (int32_t j = 0; j < height; ++j) {
for (int32_t i = 0; i < width; ++i) {
for (int32_t k = 0; k < channels; ++k) {
tmp = *srcPtr[k];
tmp += offset;
dst[destIndex++] = (T)(tmp / div);
srcPtr[k] += srcSamplesPerPixel;
}
}
// correct for stride
for (uint32_t k = 0; k < 3; ++k)
srcPtr[k] += stride - srcSamplesPerPixel * width;
}
} else {
size_t frameSize = width * height;
T *destPtr[3] = { dst, dst + frameSize, dst + 2 * frameSize };
for (int32_t j = 0; j < height; ++j) {
for (int32_t i = 0; i < width; ++i) {
for (int32_t k = 0; k < channels; ++k) {
tmp = *srcPtr[k];
tmp += offset;
destPtr[k][destIndex] = (T)(tmp / div);
srcPtr[k] += srcSamplesPerPixel;
}
destIndex++;
}
// correct for stride
for (uint32_t k = 0; k < 3; ++k)
srcPtr[k] += stride - srcSamplesPerPixel * width;
}
}
}
}