From d4c80fb89b6a78100a3abf7d461b16f42abe1f3b Mon Sep 17 00:00:00 2001
From: Debo <debottam.bakshi.gupta@cern.ch>
Date: Thu, 3 Dec 2020 18:58:41 +0100
Subject: [PATCH] Renaming old files and adding provision for batch execution
in onnx models
---
.../AthExOnnxRuntime/CMakeLists.txt | 4 +-
.../AthenaExamples/AthExOnnxRuntime/README.md | 63 ++--
.../share/AthExOnnxRuntime_jobOptions.py | 10 +-
.../AthExOnnxRuntime/src/CxxApiAlgorithm.cxx | 198 ------------
.../AthExOnnxRuntime/src/EvaluateModel.cxx | 296 ++++++++++++++++++
.../{CxxApiAlgorithm.h => EvaluateModel.h} | 20 +-
.../components/AthExOnnxRuntime_entries.cxx | 5 +-
7 files changed, 358 insertions(+), 238 deletions(-)
delete mode 100644 Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.cxx
create mode 100644 Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
rename Control/AthenaExamples/AthExOnnxRuntime/src/{CxxApiAlgorithm.h => EvaluateModel.h} (76%)
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/CMakeLists.txt b/Control/AthenaExamples/AthExOnnxRuntime/CMakeLists.txt
index 8361d3a13413..2cf4c6266bf7 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/CMakeLists.txt
+++ b/Control/AthenaExamples/AthExOnnxRuntime/CMakeLists.txt
@@ -3,9 +3,6 @@
# Declare the package's name.
atlas_subdir( AthExOnnxRuntime )
-# External dependencies.
-find_package( onnxruntime )
-
# Component(s) in the package.
atlas_add_library( AthExOnnxRuntimeLib
INTERFACE
@@ -25,6 +22,7 @@ atlas_install_joboptions( share/*.py )
atlas_add_test( AthExOnnxRuntimeJob_serial
SCRIPT athena.py AthExOnnxRuntime/AthExOnnxRuntime_jobOptions.py )
+
atlas_add_test( AthExOnnxRuntimeJob_mt
SCRIPT athena.py --threads=2
AthExOnnxRuntime/AthExOnnxRuntime_jobOptions.py )
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/README.md b/Control/AthenaExamples/AthExOnnxRuntime/README.md
index 34afcb68bea5..79efde7c5724 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/README.md
+++ b/Control/AthenaExamples/AthExOnnxRuntime/README.md
@@ -4,37 +4,48 @@ This package is meant to hold code demonstrating how to use onnxruntime
from Athena.
The package loads a MNIST_testModel.onnx model previously trained to recognize
-handwritten digits in the form of 28x28 pixels from `0 to 9`.
-
-The package further loads test pixel dataset and their labels. CxxApiAlgorithm
+handwritten digits in the form of 28x28 pixels from `0 to 9`. When Run in a DEBUG
+mode the logistics of the input and output layers can be seen
+```
+AthONNX DEBUG Input 0 : name= flatten_input:0
+AthONNX DEBUG Input 0 : num_dims= 3
+AthONNX DEBUG Input 0 : dim 0= -1
+AthONNX DEBUG Input 0 : dim 1= 28
+AthONNX DEBUG Input 0 : dim 2= 28
+AthONNX DEBUG Output 0 : name= dense_1/Softmax:0
+AthONNX DEBUG Output 0 : num_dims= 2
+AthONNX DEBUG Output 0 : dim 0= -1
+AthONNX DEBUG Output 0 : dim 1= 10
+```
+The `dim 0= -1` always represents the batch size and ready to take any intger no. The model in this
+example package has provition to run both No-BatchMode and BatchMode. For No-BatchMode `dim 0= 1` for
+BatchMode `dim 0= batch size`
+
+The package further loads test pixel dataset and their labels. When not running in batch mode EvaluateModel
randomly picks a sample from the dataset and passes through MNIST_testModel.onnx
model and predicts its value among 0 to 9. The highest probable(score) value should
match with actual label of the sample e.g.:
```
-AthONNX INFO Label for the input test data = 9
-AthONNX INFO Score for class 0 = 3.32061e-09
-
-AthONNX INFO Score for class 1 = 1.52828e-08
-
-AthONNX INFO Score for class 2 = 3.08599e-08
-
-AthONNX INFO Score for class 3 = 1.14768e-07
-
-AthONNX INFO Score for class 4 = 2.05344e-05
-
-AthONNX INFO Score for class 5 = 1.54014e-11
-
-AthONNX INFO Score for class 6 = 1.0932e-10
-
-AthONNX INFO Score for class 7 = 0.000205113
-
-AthONNX INFO Score for class 8 = 5.37525e-08
-
-AthONNX INFO Score for class 9 = 0.999774
-
-AthONNX INFO Class: 9 has the highest score: 0.999774
+INFO Label for the input test data = 1
+AthONNX DEBUG Score for class 0 = 1.07293e-07
+AthONNX DEBUG Score for class 1 = 0.999818
+AthONNX DEBUG Score for class 2 = 1.18024e-05
+AthONNX DEBUG Score for class 3 = 2.53529e-05
+AthONNX DEBUG Score for class 4 = 4.19157e-06
+AthONNX DEBUG Score for class 5 = 1.66088e-06
+AthONNX DEBUG Score for class 6 = 7.7723e-06
+AthONNX DEBUG Score for class 7 = 6.33801e-05
+AthONNX DEBUG Score for class 8 = 5.83467e-05
+AthONNX DEBUG Score for class 9 = 9.74693e-06
+AthONNX INFO Class: 1 has the highest score: 0.999818
```
The above result can be obtained by running `athena AthExOnnxRuntime_jobOptions.py`
-in your run directory
+in your run directory. Edit AthExOnnxRuntime_jobOptions.py's `AthONNX.DoBatches = False` to run in No Batch
+mode.
+
+To Run in Batch mode edit `AthONNX.DoBatches = True` and provide number of batches and each batch size to
+`AthONNX.NumberOfBatches` and to `AthONNX.SizeOfBatch` respectively.
+
+Fyi. by default the algorithms will run in No-Batch mode
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/share/AthExOnnxRuntime_jobOptions.py b/Control/AthenaExamples/AthExOnnxRuntime/share/AthExOnnxRuntime_jobOptions.py
index 20b057cc213c..ac1beaef5aeb 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/share/AthExOnnxRuntime_jobOptions.py
+++ b/Control/AthenaExamples/AthExOnnxRuntime/share/AthExOnnxRuntime_jobOptions.py
@@ -5,17 +5,21 @@ from AthenaCommon.AlgSequence import AlgSequence
algSequence = AlgSequence()
# Set up the job.
-from AthExOnnxRuntime.AthExOnnxRuntimeConf import AthONNX__CxxApiAlgorithm
+from AthExOnnxRuntime.AthExOnnxRuntimeConf import AthONNX__EvaluateModel
from AthOnnxruntimeService.AthOnnxruntimeServiceConf import AthONNX__ONNXRuntimeSvc
from AthenaCommon.AppMgr import ServiceMgr
ServiceMgr += AthONNX__ONNXRuntimeSvc( OutputLevel = DEBUG )
-algSequence += AthONNX__CxxApiAlgorithm("AthONNX")
+algSequence += AthONNX__EvaluateModel("AthONNX")
# Get a random no. between 0 to 10k for test sample
from random import randint
AthONNX = algSequence.AthONNX
AthONNX.TestSample = randint(0, 9999)
+AthONNX.DoBatches = False
+AthONNX.NumberOfBatches = 1
+AthONNX.SizeOfBatch = 1
+
# Run for 10 "events".
-theApp.EvtMax = 10
+theApp.EvtMax = 2
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.cxx b/Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.cxx
deleted file mode 100644
index 0efc20418790..000000000000
--- a/Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.cxx
+++ /dev/null
@@ -1,198 +0,0 @@
-// Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration
-
-// Local include(s).
-#include "CxxApiAlgorithm.h"
-
-// Framework include(s).
-#include "PathResolver/PathResolver.h"
-
-namespace AthONNX {
-
- //*******************************************************************
- // for reading MNIST images
- std::vector<std::vector<float>> read_mnist_pixel(const std::string &full_path) //function to load test images
- {
- std::vector<std::vector<float>> input_tensor_values;
- input_tensor_values.resize(10000, std::vector<float>(28*28*1));
- std::ifstream file (full_path.c_str(), std::ios::binary);
- int magic_number=0;
- int number_of_images=0;
- int n_rows=0;
- int n_cols=0;
- file.read((char*)&magic_number,sizeof(magic_number));
- magic_number= ntohl(magic_number);
- file.read((char*)&number_of_images,sizeof(number_of_images));
- number_of_images= ntohl(number_of_images);
- file.read((char*)&n_rows,sizeof(n_rows));
- n_rows= ntohl(n_rows);
- file.read((char*)&n_cols,sizeof(n_cols));
- n_cols= ntohl(n_cols);
- for(int i=0;i<number_of_images;++i)
- {
- for(int r=0;r<n_rows;++r)
- {
- for(int c=0;c<n_cols;++c)
- {
- unsigned char temp=0;
- file.read((char*)&temp,sizeof(temp));
- input_tensor_values[i][r*n_cols+c]= float(temp)/255;
- }
- }
- }
- return input_tensor_values;
- }
-
- //********************************************************************************
- // for reading MNIST labels
- std::vector<int> read_mnist_label(const std::string &full_path) //function to load test labels
- {
- std::vector<int> output_tensor_values(1*10000);
- std::ifstream file (full_path.c_str(), std::ios::binary);
- int magic_number=0;
- int number_of_labels=0;
- file.read((char*)&magic_number,sizeof(magic_number));
- magic_number= ntohl(magic_number);
- file.read((char*)&number_of_labels,sizeof(number_of_labels));
- number_of_labels= ntohl(number_of_labels);
- for(int i=0;i<number_of_labels;++i)
- {
- unsigned char temp=0;
- file.read((char*)&temp,sizeof(temp));
- output_tensor_values[i]= int(temp);
- }
- return output_tensor_values;
- }
-
- StatusCode CxxApiAlgorithm::initialize() {
-
- // Access the service.
- ATH_CHECK( m_svc.retrieve() );
-
- // Find the model file.
- const std::string modelFileName =
- PathResolverFindCalibFile( m_modelFileName );
- const std::string pixelFileName =
- PathResolverFindCalibFile( m_pixelFileName );
- const std::string labelFileName =
- PathResolverFindCalibFile( m_labelFileName );
- ATH_MSG_INFO( "Using model file: " << modelFileName );
- ATH_MSG_INFO( "Using pixel file: " << pixelFileName );
- ATH_MSG_INFO( "Using pixel file: " << labelFileName );
- // Set up the ONNX Runtime session.
- Ort::SessionOptions sessionOptions;
- sessionOptions.SetIntraOpNumThreads( 1 );
- sessionOptions.SetGraphOptimizationLevel( ORT_ENABLE_BASIC );
- Ort::AllocatorWithDefaultOptions allocator;
- m_session = std::make_unique< Ort::Session >( m_svc->env(),
- modelFileName.c_str(),
- sessionOptions );
- ATH_MSG_INFO( "Created the ONNX Runtime session" );
- std::vector<int64_t> input_node_dims;
- size_t num_input_nodes = m_session->GetInputCount();
- std::vector<const char*> input_node_names(num_input_nodes);
- for( std::size_t i = 0; i < num_input_nodes; i++ ) {
- // print input node names
- char* input_name = m_session->GetInputName(i, allocator);
- ATH_MSG_DEBUG("Input "<<i<<" : "<<" name= "<<input_name);
- input_node_names[i] = input_name;
- // print input node types
- Ort::TypeInfo type_info = m_session->GetInputTypeInfo(i);
- auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
- ONNXTensorElementDataType type = tensor_info.GetElementType();
- ATH_MSG_DEBUG("Input "<<i<<" : "<<" type= "<<type);
-
- // print input shapes/dims
- input_node_dims = tensor_info.GetShape();
- ATH_MSG_DEBUG("Input "<<i<<" : num_dims= "<<input_node_dims.size());
- for (std::size_t j = 0; j < input_node_dims.size(); j++){
- if(input_node_dims[j]<0)
- input_node_dims[j] =1;
- ATH_MSG_DEBUG("Input"<<i<<" : dim "<<j<<"= "<<input_node_dims[j]);
- }
- }
-
- //output nodes
- std::vector<int64_t> output_node_dims;
- size_t num_output_nodes = m_session->GetOutputCount();
- std::vector<const char*> output_node_names(num_output_nodes);
-
- for( std::size_t i = 0; i < num_output_nodes; i++ ) {
- // print output node names
- char* output_name = m_session->GetOutputName(i, allocator);
- ATH_MSG_DEBUG("Output "<<i<<" : "<<" name= "<<output_name);
- output_node_names[i] = output_name;
-
- Ort::TypeInfo type_info = m_session->GetOutputTypeInfo(i);
- auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
- ONNXTensorElementDataType type = tensor_info.GetElementType();
- ATH_MSG_DEBUG("Output "<<i<<" : "<<" type= "<<type);
-
- // print output shapes/dims
- output_node_dims = tensor_info.GetShape();
- ATH_MSG_DEBUG("Output "<<i<<" : num_dims= "<<output_node_dims.size());
- for (std::size_t j = 0; j < output_node_dims.size(); j++){
- if(output_node_dims[j]<0)
- output_node_dims[j] =1;
- ATH_MSG_DEBUG("Output"<<i<<" : dim "<<j<<"= "<<output_node_dims[j]);
- }
- }
- //*************************************************************************
- // Score the model using sample data, and inspect values
- //loading input data
-
- std::vector<std::vector<float>> input_tensor_values_ = read_mnist_pixel(pixelFileName);
- std::vector<int> output_tensor_values_ = read_mnist_label(labelFileName);
-
- //preparing container to hold input data
-
- size_t input_tensor_size = 1*28*28;
- std::vector<float> input_tensor_values(input_tensor_size);
- input_tensor_values = input_tensor_values_[m_testSample];
-
- //preparing container to hold output data
- int output_tensor_values = output_tensor_values_[m_testSample];
-
- // create input tensor object from data values
- auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
- Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_tensor_values.data(), input_tensor_size, input_node_dims.data(), input_node_dims.size());
- assert(input_tensor.IsTensor());
-
- // score model & input tensor, get back output tensor
- auto output_tensors = m_session->Run(Ort::RunOptions{nullptr}, input_node_names.data(), &input_tensor, input_node_names.size(), output_node_names.data(), output_node_names.size());
- assert(output_tensors.size() == 1 && output_tensors.front().IsTensor());
-
- // Get pointer to output tensor float values
- float* floatarr = output_tensors.front().GetTensorMutableData<float>();
-
- // show true label for the test input
- ATH_MSG_INFO("Label for the input test data = "<<output_tensor_values);
- float max = -999;
- int max_index;
- for (int i = 0; i < 10; i++){
- ATH_MSG_INFO("Score for class "<<i<<" = "<<floatarr[i]<<std::endl);
- if (max<floatarr[i]){
- max = floatarr[i];
- max_index = i;
- }
- }
- ATH_MSG_INFO("Class: "<<max_index<<" has the highest score: "<<floatarr[max_index]);
- // Return gracefully.
- return StatusCode::SUCCESS;
- }
-
- StatusCode CxxApiAlgorithm::execute( const EventContext& /*ctx*/ ) const {
-
- // Return gracefully.
- return StatusCode::SUCCESS;
- }
-
- StatusCode CxxApiAlgorithm::finalize() {
-
- // Delete the session object.
- m_session.reset();
-
- // Return gracefully.
- return StatusCode::SUCCESS;
- }
-
-} // namespace AthONNX
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
new file mode 100644
index 000000000000..f4a9ce0b0f8e
--- /dev/null
+++ b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
@@ -0,0 +1,296 @@
+// Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration
+
+// Local include(s).
+#include "EvaluateModel.h"
+
+// Framework include(s).
+#include "PathResolver/PathResolver.h"
+
+namespace AthONNX {
+
+ //*******************************************************************
+ // for reading MNIST images
+ std::vector<std::vector<float>> read_mnist_pixel(const std::string &full_path) //function to load test images
+ {
+ std::vector<std::vector<float>> input_tensor_values;
+ input_tensor_values.resize(10000, std::vector<float>(28*28*1));
+ std::ifstream file (full_path.c_str(), std::ios::binary);
+ int magic_number=0;
+ int number_of_images=0;
+ int n_rows=0;
+ int n_cols=0;
+ file.read((char*)&magic_number,sizeof(magic_number));
+ magic_number= ntohl(magic_number);
+ file.read((char*)&number_of_images,sizeof(number_of_images));
+ number_of_images= ntohl(number_of_images);
+ file.read((char*)&n_rows,sizeof(n_rows));
+ n_rows= ntohl(n_rows);
+ file.read((char*)&n_cols,sizeof(n_cols));
+ n_cols= ntohl(n_cols);
+ for(int i=0;i<number_of_images;++i)
+ {
+ for(int r=0;r<n_rows;++r)
+ {
+ for(int c=0;c<n_cols;++c)
+ {
+ unsigned char temp=0;
+ file.read((char*)&temp,sizeof(temp));
+ input_tensor_values[i][r*n_cols+c]= float(temp)/255;
+ }
+ }
+ }
+ return input_tensor_values;
+ }
+
+ //********************************************************************************
+ // for reading MNIST labels
+ std::vector<int> read_mnist_label(const std::string &full_path) //function to load test labels
+ {
+ std::vector<int> output_tensor_values(1*10000);
+ std::ifstream file (full_path.c_str(), std::ios::binary);
+ int magic_number=0;
+ int number_of_labels=0;
+ file.read((char*)&magic_number,sizeof(magic_number));
+ magic_number= ntohl(magic_number);
+ file.read((char*)&number_of_labels,sizeof(number_of_labels));
+ number_of_labels= ntohl(number_of_labels);
+ for(int i=0;i<number_of_labels;++i)
+ {
+ unsigned char temp=0;
+ file.read((char*)&temp,sizeof(temp));
+ output_tensor_values[i]= int(temp);
+ }
+ return output_tensor_values;
+ }
+
+ StatusCode EvaluateModel::initialize() {
+
+ // Access the service.
+ ATH_CHECK( m_svc.retrieve() );
+
+ /*****
+ The combination of no. of batches and batch size shouldn't cross
+ the total smple size which is 10000 for this example
+ *****/
+ if(m_doBatches && (m_numberOfBatches*m_sizeOfBatch)>10000){
+ ATH_MSG_INFO("The total no. of sample crossed the no. of available sample ....");
+ return StatusCode::FAILURE;
+ }
+ // Find the model file.
+ const std::string modelFileName =
+ PathResolverFindCalibFile( m_modelFileName );
+ const std::string pixelFileName =
+ PathResolverFindCalibFile( m_pixelFileName );
+ const std::string labelFileName =
+ PathResolverFindCalibFile( m_labelFileName );
+ ATH_MSG_INFO( "Using model file: " << modelFileName );
+ ATH_MSG_INFO( "Using pixel file: " << pixelFileName );
+ ATH_MSG_INFO( "Using pixel file: " << labelFileName );
+ // Set up the ONNX Runtime session.
+ Ort::SessionOptions sessionOptions;
+ sessionOptions.SetIntraOpNumThreads( 1 );
+ sessionOptions.SetGraphOptimizationLevel( ORT_ENABLE_BASIC );
+ Ort::AllocatorWithDefaultOptions allocator;
+ m_session = std::make_unique< Ort::Session >( m_svc->env(),
+ modelFileName.c_str(),
+ sessionOptions );
+ ATH_MSG_INFO( "Created the ONNX Runtime session" );
+ m_input_tensor_values = read_mnist_pixel(pixelFileName);
+ m_output_tensor_values = read_mnist_label(labelFileName);
+
+ // Return gracefully.
+ return StatusCode::SUCCESS;
+ }
+
+ StatusCode EvaluateModel::execute( const EventContext& /*ctx*/ ) const {
+
+ Ort::AllocatorWithDefaultOptions allocator;
+ auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+ /************************** Input Nodes *****************************/
+ /*********************************************************************/
+ std::vector<int64_t> input_node_dims;
+ size_t num_input_nodes = m_session->GetInputCount();
+ std::vector<const char*> input_node_names(num_input_nodes);
+ for( std::size_t i = 0; i < num_input_nodes; i++ ) {
+ // print input node names
+ char* input_name = m_session->GetInputName(i, allocator);
+ ATH_MSG_DEBUG("Input "<<i<<" : "<<" name= "<<input_name);
+ input_node_names[i] = input_name;
+ Ort::TypeInfo type_info = m_session->GetInputTypeInfo(i);
+ auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+ // print input shapes/dims
+ input_node_dims = tensor_info.GetShape();
+ ATH_MSG_DEBUG("Input "<<i<<" : num_dims= "<<input_node_dims.size());
+ for (std::size_t j = 0; j < input_node_dims.size(); j++){
+ ATH_MSG_DEBUG("Input "<<i<<" : dim "<<j<<"= "<<input_node_dims[j]);
+ }
+ }
+
+ /************************** Output Nodes *****************************/
+ /*********************************************************************/
+ //output nodes
+ std::vector<int64_t> output_node_dims;
+ size_t num_output_nodes = m_session->GetOutputCount();
+ std::vector<const char*> output_node_names(num_output_nodes);
+
+ for( std::size_t i = 0; i < num_output_nodes; i++ ) {
+ // print output node names
+ char* output_name = m_session->GetOutputName(i, allocator);
+ ATH_MSG_DEBUG("Output "<<i<<" : "<<" name= "<<output_name);
+ output_node_names[i] = output_name;
+
+ Ort::TypeInfo type_info = m_session->GetOutputTypeInfo(i);
+ auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+
+ // print output shapes/dims
+ output_node_dims = tensor_info.GetShape();
+ ATH_MSG_DEBUG("Output "<<i<<" : num_dims= "<<output_node_dims.size());
+ for (std::size_t j = 0; j < output_node_dims.size(); j++){
+ ATH_MSG_DEBUG("Output "<<i<<" : dim "<<j<<"= "<<output_node_dims[j]);
+ }
+ }
+
+ /************************* Score if input is not a batch ********************/
+ /****************************************************************************/
+ if(m_doBatches == false){
+
+ /**************************************************************************************
+ * input_node_dims[0] = -1; -1 needs to be replaced by the batch size; for no batch is 1
+ * input_node_dims[1] = 28
+ * input_node_dims[2] = 28
+ ****************************************************************************************/
+
+ input_node_dims[0] = 1;
+ output_node_dims[0] = 1;
+
+ /***************** Choose an example sample randomly ****************************/
+ std::vector<float> input_tensor_values = m_input_tensor_values[m_testSample];
+ // Output label of corresponding m_input_tensor_values[m_testSample]; e.g 0, 1, 2, 3 etc
+ int output_tensor_values = m_output_tensor_values[m_testSample];
+
+ // For a check that the sample dimension is fully flatten (1x28x28 = 784)
+ ATH_MSG_DEBUG("Size of Input tensor: "<<input_tensor_values.size());
+
+ /************** Create input tensor object from input data values to feed into your model *********************/
+ Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info,
+ input_tensor_values.data(),
+ input_tensor_values.size(), /*** 1x28x28 = 784 ***/
+ input_node_dims.data(),
+ input_node_dims.size()); /*** [1, 28, 28] = 3 ***/
+
+ /********* Convert 784 elements long flattened 1D array to 3D (1, 28, 28) onnx compatible tensor ************/
+ ATH_MSG_DEBUG("Input tensor size after converted to Ort tensor: "<<input_tensor.GetTensorTypeAndShapeInfo().GetShape());
+ // Makes sure input tensor has same dimensions as input layer of the model
+ assert(input_tensor.IsTensor()&&
+ input_tensors.GetTensorTypeAndShapeInfo().GetShape() == input_node_dims.size());
+
+ /********* Score model by feeding input tensor and get output tensor in return *****************************/
+ auto output_tensor = m_session->Run(Ort::RunOptions{nullptr},
+ input_node_names.data(),
+ &input_tensor,
+ input_node_names.size(), /** 1, flatten_input:0 **/
+ output_node_names.data(),
+ output_node_names.size()); /** 1, dense_1/Softmax:0 **/
+
+ assert(output_tensor.size() == output_node_names.size() && output_tensor.front().IsTensor());
+
+ // Get pointer to output tensor float values
+ float* floatarr = output_tensor.front().GetTensorMutableData<float>();
+
+ // show true label for the test input
+ ATH_MSG_INFO("Label for the input test data = "<<output_tensor_values);
+ float max = -999;
+ int max_index;
+ for (int i = 0; i < 10; i++){
+ ATH_MSG_DEBUG("Score for class "<<i<<" = "<<floatarr[i]);
+ if (max<floatarr[i]){
+ max = floatarr[i];
+ max_index = i;
+ }
+ }
+ ATH_MSG_INFO("Class: "<<max_index<<" has the highest score: "<<floatarr[max_index]);
+
+ } // m_doBatches == false codition ends
+ /************************* Score if input is a batch ********************/
+ /****************************************************************************/
+ else {
+ /**************************************************************************************
+ Similar scoring structure like non batch execution but 1st demention needs to be replaced by batch size
+ for this example lets take 3 batches with batch size 5
+ * input_node_dims[0] = 5
+ * input_node_dims[1] = 28
+ * input_node_dims[2] = 28
+ ****************************************************************************************/
+
+ input_node_dims[0] = m_sizeOfBatch;
+ output_node_dims[0] = m_sizeOfBatch;
+
+ /************************** process multiple batches ********************************/
+ for (int i = 0; i < m_numberOfBatches; i++) {
+ ATH_MSG_DEBUG("Processing batch #" << i);
+ std::vector<float> batch_input_tensor_values;
+
+ for (int j = 0; j < m_sizeOfBatch; j++) {
+
+ /******************For each batch we need a flattened (5 x 28 x 28 = 3920) 1D array******************************/
+ batch_input_tensor_values.insert(batch_input_tensor_values.end(),m_input_tensor_values[j].begin(),m_input_tensor_values[j].end());
+ }
+
+ Ort::Value batch_input_tensors = Ort::Value::CreateTensor<float>(memory_info,
+ batch_input_tensor_values.data(),
+ batch_input_tensor_values.size(), /*** 5x28x28 = 3920 ***/
+ input_node_dims.data(),
+ input_node_dims.size()); /*** [5, 28, 28] = 3 ***/
+
+ assert(batch_input_tensors.IsTensor()&&
+ batch_input_tensors.GetTensorTypeAndShapeInfo().GetShape() == input_node_dims.size());
+
+ auto batch_output_tensors = m_session->Run(Ort::RunOptions{nullptr},
+ input_node_names.data(),
+ &batch_input_tensors,
+ input_node_names.size(), /** 1, flatten_input:0 **/
+ output_node_names.data(),
+ output_node_names.size()); /** 1, dense_1/Softmax:0 **/
+
+ assert(batch_output_tensors.size() == output_names.size() &&
+ batch_output_tensors.IsTensor() &&
+ batch_output_tensors.GetTensorTypeAndShapeInfo().GetShape()[0] == m_sizeOfBatch);
+
+ // Get pointer to output tensor float values
+
+ ATH_MSG_DEBUG("output vector size: "<<batch_output_tensors[0].GetTensorTypeAndShapeInfo().GetShape());
+ float* floatarr = batch_output_tensors.front().GetTensorMutableData<float>();
+
+ // show true label for the test input
+ for(int i = 0; i<m_sizeOfBatch; i++){
+ ATH_MSG_INFO("Label for the input test data = "<<m_output_tensor_values[i]);
+ int k = i*10;
+ float max = -999;
+ int max_index = 0;
+ for (int j =k ; j < k+10; j++){
+ ATH_MSG_INFO("Score for class "<<j-k<<" = "<<floatarr[j]);
+ if (max<floatarr[j]){
+ max = floatarr[j];
+ max_index = j;
+ }
+ }
+ ATH_MSG_INFO("Class: "<<max_index-k<<" has the highest score: "<<floatarr[max_index]);
+ }
+
+ }
+ } // else/m_doBatches == True codition ends
+ // Return gracefully.
+ return StatusCode::SUCCESS;
+ }
+ StatusCode EvaluateModel::finalize() {
+
+ // Delete the session object.
+ m_session.reset();
+
+ // Return gracefully.
+ return StatusCode::SUCCESS;
+ }
+
+} // namespace AthONNX
+
+
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.h b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.h
similarity index 76%
rename from Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.h
rename to Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.h
index 0ebf218804d2..59e0fc5933e6 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/src/CxxApiAlgorithm.h
+++ b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.h
@@ -1,7 +1,7 @@
// Dear emacs, this is -*- c++ -*-
// Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration
-#ifndef ATHEXONNXRUNTIME_CXXAPIALGORITHM_H
-#define ATHEXONNXRUNTIME_CXXAPIALGORITHM_H
+#ifndef ATHEXONNXRUNTIME_EVALUATEMODEL_H
+#define ATHEXONNXRUNTIME_EVALUATEMODEL_H
// Local include(s).
#include "AthOnnxruntimeService/IONNXRuntimeSvc.h"
@@ -19,6 +19,8 @@
#include <iostream>
#include <fstream>
#include <arpa/inet.h>
+#include <vector>
+#include <iterator>
namespace AthONNX {
@@ -29,7 +31,7 @@ namespace AthONNX {
/// @author Debottam Bakshi Gupta <Debottam.Bakshi.Gupta@cern.ch>
/// @author Attila Krasznahorkay <Attila.Krasznahorkay@cern.ch>
///
- class CxxApiAlgorithm : public AthReentrantAlgorithm {
+ class EvaluateModel: public AthReentrantAlgorithm {
public:
/// Inherit the base class's constructor
@@ -62,6 +64,12 @@ namespace AthONNX {
"dev/MLTest/2020-03-31/t10k-labels-idx1-ubyte",
"Name of the label file to load" };
Gaudi::Property<int> m_testSample {this, "TestSample", 0, "A Random Test Sample"};
+
+ /// Following properties needed to be consdered if the .onnx model is evaluated in batch mode
+ Gaudi::Property<bool> m_doBatches {this, "DoBatches", false, "Processing events by batches"};
+ Gaudi::Property<int> m_numberOfBatches {this, "NumberOfBatches", 1, "No. of batches to be passed"};
+ Gaudi::Property<int> m_sizeOfBatch {this, "SizeOfBatch", 1, "No. of elements/example in a batch"};
+
/// Handle to @c AthONNX::IONNXRuntimeSvc
ServiceHandle< IONNXRuntimeSvc > m_svc{ this, "ONNXRuntimeSvc",
"AthONNX::ONNXRuntimeSvc",
@@ -71,9 +79,11 @@ namespace AthONNX {
/// The "session" of ONNX Runtime that we'll be using
std::unique_ptr< Ort::Session > m_session;
+ std::vector<std::vector<float>> m_input_tensor_values;
+ std::vector<int> m_output_tensor_values;
- }; // class CxxApiAlgorithm
+ }; // class EvaluateModel
} // namespace AthONNX
-#endif // ATHEXONNXRUNTIME_CXXAPIALGORITHM_H
+#endif // ATHEXONNXRUNTIME_EVALUATEMODEL_H
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/src/components/AthExOnnxRuntime_entries.cxx b/Control/AthenaExamples/AthExOnnxRuntime/src/components/AthExOnnxRuntime_entries.cxx
index ca1006fe95b4..cb25a4bfd9d2 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/src/components/AthExOnnxRuntime_entries.cxx
+++ b/Control/AthenaExamples/AthExOnnxRuntime/src/components/AthExOnnxRuntime_entries.cxx
@@ -1,7 +1,6 @@
// Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration
// Local include(s).
-#include "../CxxApiAlgorithm.h"
-
+#include "../EvaluateModel.h"
// Declare the package's components.
-DECLARE_COMPONENT( AthONNX::CxxApiAlgorithm )
+DECLARE_COMPONENT( AthONNX::EvaluateModel )
--
GitLab