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-rw-r--r--source/application/main/Classifier.cc105
-rw-r--r--source/application/main/PlatformMath.cc20
-rw-r--r--source/application/main/include/Classifier.hpp23
-rw-r--r--source/application/main/include/PlatformMath.hpp7
4 files changed, 99 insertions, 56 deletions
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
index c5519fb..a6ff532 100644
--- a/source/application/main/Classifier.cc
+++ b/source/application/main/Classifier.cc
@@ -24,61 +24,40 @@
#include <set>
#include <cstdint>
#include <inttypes.h>
+#include "PlatformMath.hpp"
namespace arm {
namespace app {
- template<typename T>
- void SetVectorResults(std::set<std::pair<T, uint32_t>>& topNSet,
+ void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet,
std::vector<ClassificationResult>& vecResults,
- TfLiteTensor* tensor,
- const std::vector <std::string>& labels) {
-
- /* For getting the floating point values, we need quantization parameters. */
- QuantParams quantParams = GetTensorQuantParams(tensor);
+ const std::vector <std::string>& labels)
+ {
/* Reset the iterator to the largest element - use reverse iterator. */
- auto topNIter = topNSet.rbegin();
- for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
- T score = topNIter->first;
- vecResults[i].m_normalisedVal = quantParams.scale * (score - quantParams.offset);
- vecResults[i].m_label = labels[topNIter->second];
- vecResults[i].m_labelIdx = topNIter->second;
- }
- }
-
- template<>
- void SetVectorResults<float>(std::set<std::pair<float, uint32_t>>& topNSet,
- std::vector<ClassificationResult>& vecResults,
- TfLiteTensor* tensor,
- const std::vector <std::string>& labels) {
- UNUSED(tensor);
- /* Reset the iterator to the largest element - use reverse iterator. */
auto topNIter = topNSet.rbegin();
for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
vecResults[i].m_normalisedVal = topNIter->first;
vecResults[i].m_label = labels[topNIter->second];
vecResults[i].m_labelIdx = topNIter->second;
}
-
}
- template<typename T>
- bool Classifier::GetTopNResults(TfLiteTensor* tensor,
+ bool Classifier::GetTopNResults(const std::vector<float>& tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount,
const std::vector <std::string>& labels)
{
- std::set<std::pair<T, uint32_t>> sortedSet;
+
+ std::set<std::pair<float , uint32_t>> sortedSet;
/* NOTE: inputVec's size verification against labels should be
* checked by the calling/public function. */
- T* tensorData = tflite::GetTensorData<T>(tensor);
/* Set initial elements. */
for (uint32_t i = 0; i < topNCount; ++i) {
- sortedSet.insert({tensorData[i], i});
+ sortedSet.insert({tensor[i], i});
}
/* Initialise iterator. */
@@ -86,33 +65,26 @@ namespace app {
/* Scan through the rest of elements with compare operations. */
for (uint32_t i = topNCount; i < labels.size(); ++i) {
- if (setFwdIter->first < tensorData[i]) {
+ if (setFwdIter->first < tensor[i]) {
sortedSet.erase(*setFwdIter);
- sortedSet.insert({tensorData[i], i});
+ sortedSet.insert({tensor[i], i});
setFwdIter = sortedSet.begin();
}
}
/* Final results' container. */
vecResults = std::vector<ClassificationResult>(topNCount);
-
- SetVectorResults<T>(sortedSet, vecResults, tensor, labels);
+ SetVectorResults(sortedSet, vecResults, labels);
return true;
}
- template bool Classifier::GetTopNResults<uint8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount, const std::vector <std::string>& labels);
-
- template bool Classifier::GetTopNResults<int8_t>(TfLiteTensor* tensor,
- std::vector<ClassificationResult>& vecResults,
- uint32_t topNCount, const std::vector <std::string>& labels);
-
bool Classifier::GetClassificationResults(
TfLiteTensor* outputTensor,
std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, uint32_t topNCount)
+ const std::vector <std::string>& labels,
+ uint32_t topNCount,
+ bool useSoftmax)
{
if (outputTensor == nullptr) {
printf_err("Output vector is null pointer.\n");
@@ -120,7 +92,7 @@ namespace app {
}
uint32_t totalOutputSize = 1;
- for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++){
+ for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++) {
totalOutputSize *= outputTensor->dims->data[inputDim];
}
@@ -139,22 +111,52 @@ namespace app {
bool resultState;
vecResults.clear();
- /* Get the top N results. */
+ /* De-Quantize Output Tensor */
+ QuantParams quantParams = GetTensorQuantParams(outputTensor);
+
+ /* Floating point tensor data to be populated
+ * NOTE: The assumption here is that the output tensor size isn't too
+ * big and therefore, there's neglibible impact on heap usage. */
+ std::vector<float> tensorData(totalOutputSize);
+
+ /* Populate the floating point buffer */
switch (outputTensor->type) {
- case kTfLiteUInt8:
- resultState = GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+ case kTfLiteUInt8: {
+ uint8_t *tensor_buffer = tflite::GetTensorData<uint8_t>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = quantParams.scale *
+ (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+ }
break;
- case kTfLiteInt8:
- resultState = GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+ }
+ case kTfLiteInt8: {
+ int8_t *tensor_buffer = tflite::GetTensorData<int8_t>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = quantParams.scale *
+ (static_cast<float>(tensor_buffer[i]) - quantParams.offset);
+ }
break;
- case kTfLiteFloat32:
- resultState = GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+ }
+ case kTfLiteFloat32: {
+ float *tensor_buffer = tflite::GetTensorData<float>(outputTensor);
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ tensorData[i] = tensor_buffer[i];
+ }
break;
+ }
default:
- printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
+ printf_err("Tensor type %s not supported by classifier\n",
+ TfLiteTypeGetName(outputTensor->type));
return false;
}
+ if (useSoftmax) {
+ math::MathUtils::SoftmaxF32(tensorData);
+ }
+
+ /* Get the top N results. */
+ resultState = GetTopNResults(tensorData, vecResults, topNCount, labels);
+
if (!resultState) {
printf_err("Failed to get top N results set\n");
return false;
@@ -162,6 +164,5 @@ namespace app {
return true;
}
-
} /* namespace app */
} /* namespace arm */ \ No newline at end of file
diff --git a/source/application/main/PlatformMath.cc b/source/application/main/PlatformMath.cc
index 0b8882a..26b4b72 100644
--- a/source/application/main/PlatformMath.cc
+++ b/source/application/main/PlatformMath.cc
@@ -15,6 +15,8 @@
* limitations under the License.
*/
#include "PlatformMath.hpp"
+#include <algorithm>
+#include <numeric>
#if 0 == ARM_DSP_AVAILABLE
#include <cmath>
@@ -290,6 +292,24 @@ namespace math {
return true;
}
+ void MathUtils::SoftmaxF32(std::vector<float>& vec)
+ {
+ /* Fix for numerical stability and apply exp. */
+ auto start = vec.begin();
+ auto end = vec.end();
+
+ float maxValue = *std::max_element(start, end);
+ for (auto it = start; it != end; ++it) {
+ *it = std::exp((*it) - maxValue);
+ }
+
+ float sumExp = std::accumulate(start, end, 0.0f);
+
+ for (auto it = start; it != end; ++it) {
+ *it = (*it)/sumExp;
+ }
+ }
+
} /* namespace math */
} /* namespace app */
} /* namespace arm */
diff --git a/source/application/main/include/Classifier.hpp b/source/application/main/include/Classifier.hpp
index 3ee3148..d899e8e 100644
--- a/source/application/main/include/Classifier.hpp
+++ b/source/application/main/include/Classifier.hpp
@@ -42,18 +42,33 @@ namespace app {
* populated by this function.
* @param[in] labels Labels vector to match classified classes.
* @param[in] topNCount Number of top classifications to pick. Default is 1.
+ * @param[in] useSoftmax Whether Softmax normalisation should be applied to output. Default is false.
* @return true if successful, false otherwise.
**/
+
virtual bool GetClassificationResults(
TfLiteTensor* outputTensor,
std::vector<ClassificationResult>& vecResults,
- const std::vector <std::string>& labels, uint32_t topNCount);
+ const std::vector <std::string>& labels, uint32_t topNCount,
+ bool use_softmax = false);
+
+ /**
+ * @brief Populate the elements of the Classification Result object.
+ * @param[in] topNSet Ordered set of top 5 output class scores and labels.
+ * @param[out] vecResults A vector of classification results.
+ * populated by this function.
+ * @param[in] labels Labels vector to match classified classes.
+ **/
+
+ void SetVectorResults(
+ std::set<std::pair<float, uint32_t>>& topNSet,
+ std::vector<ClassificationResult>& vecResults,
+ const std::vector <std::string>& labels);
private:
/**
* @brief Utility function that gets the top N classification results from the
* output vector.
- * @tparam T value type
* @param[in] tensor Inference output tensor from an NN model.
* @param[out] vecResults A vector of classification results
* populated by this function.
@@ -61,8 +76,8 @@ namespace app {
* @param[in] labels Labels vector to match classified classes.
* @return true if successful, false otherwise.
**/
- template<typename T>
- bool GetTopNResults(TfLiteTensor* tensor,
+
+ bool GetTopNResults(const std::vector<float>& tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount,
const std::vector <std::string>& labels);
diff --git a/source/application/main/include/PlatformMath.hpp b/source/application/main/include/PlatformMath.hpp
index 6804025..fdb51b2 100644
--- a/source/application/main/include/PlatformMath.hpp
+++ b/source/application/main/include/PlatformMath.hpp
@@ -161,7 +161,14 @@ namespace math {
float* ptrDst,
const uint32_t dstLen);
+ /**
+ * @brief Scales output scores for an arbitrary number of classes so
+ * that they sum to 1, allowing output to be expressed as a probability.
+ * @param[in] vector Vector of floats modified in-place
+ */
+ static void SoftmaxF32(std::vector<float>& vec);
};
+
} /* namespace math */
} /* namespace app */
} /* namespace arm */
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-13 19:29:27 +0000