From 56770f2233814db64e8b79bc37a7c356e980073c Mon Sep 17 00:00:00 2001
From: Debottam Bakshi Gupta <debottam.bakshi.gupta@cern.ch>
Date: Thu, 8 Jul 2021 18:11:41 +0000
Subject: [PATCH] improving Flattening algorithm from 2Dto1D to multiDto1D
---
.../AthOnnxruntimeUtils/FlattenInput.h | 24 ++++++++++++-------
.../AthExOnnxRuntime/src/EvaluateModel.cxx | 4 ++--
2 files changed, 17 insertions(+), 11 deletions(-)
diff --git a/Control/AthOnnxruntimeUtils/AthOnnxruntimeUtils/FlattenInput.h b/Control/AthOnnxruntimeUtils/AthOnnxruntimeUtils/FlattenInput.h
index 0a4abfaf7970..3d472dc3a6c4 100644
--- a/Control/AthOnnxruntimeUtils/AthOnnxruntimeUtils/FlattenInput.h
+++ b/Control/AthOnnxruntimeUtils/AthOnnxruntimeUtils/FlattenInput.h
@@ -13,15 +13,21 @@
namespace AthONNX {
template<typename T>
- inline std::vector<T> FlattenInput2D_1D( std::vector<std::vector<T>> features, int size){
- std::vector<float> Flatten1D(size);
- for(std::size_t r=0;r<features.size();++r){
- int n_cols = features[r].size();
- for(int c=0;c<n_cols;++c){
- float a = float(features[r][c]);
- Flatten1D[r*n_cols+c] = a;
- }
- }
+
+ inline std::vector<T> FlattenInput_multiD_1D( std::vector<std::vector<T>> features){
+ // 1. Compute the total size required.
+ int total_size = 0;
+ for (auto& feature : features) total_size += feature.size();
+
+ // 2. Create a vector to hold the data.
+ std::vector<T> Flatten1D;
+ Flatten1D.reserve(total_size);
+
+ // 3. Fill it
+ for (auto& feature : features)
+ for (auto& elem : feature)
+ Flatten1D.push_back(elem);
+
return Flatten1D;
}
diff --git a/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
index 98edd86c7d19..45200d5e44da 100644
--- a/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
+++ b/Control/AthenaExamples/AthExOnnxRuntime/src/EvaluateModel.cxx
@@ -167,7 +167,7 @@ namespace AthONNX {
/***************** Choose an example sample randomly ****************************/
std::vector<std::vector<float>> input_tensor_values = m_input_tensor_values_notFlat[m_testSample];
- std::vector<float> flatten = AthONNX::FlattenInput2D_1D(input_tensor_values, 784);
+ std::vector<float> flatten = AthONNX::FlattenInput_multiD_1D(input_tensor_values);
// 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];
@@ -235,7 +235,7 @@ namespace AthONNX {
std::vector<float> batch_input_tensor_values;
for (int j = l; j < l+m_sizeOfBatch; j++) {
- std::vector<float> flattened_input = AthONNX::FlattenInput2D_1D(m_input_tensor_values_notFlat[j],784);
+ std::vector<float> flattened_input = AthONNX::FlattenInput_multiD_1D(m_input_tensor_values_notFlat[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(), flattened_input.begin(), flattened_input.end());
}
--
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