arm_compute v19.11
diff --git a/src/runtime/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.cpp b/src/runtime/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.cpp
index 2e10152..d4be939 100644
--- a/src/runtime/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.cpp
+++ b/src/runtime/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,14 +24,226 @@
#include "arm_compute/runtime/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.h"
#include "arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h"
-#include "support/ToolchainSupport.h"
+#include "arm_compute/runtime/Scheduler.h"
-using namespace arm_compute;
+namespace arm_compute
+{
+namespace
+{
+void dequantize_tensor(const ITensor *input, ITensor *output)
+{
+ const UniformQuantizationInfo qinfo = input->info()->quantization_info().uniform();
+ const DataType data_type = input->info()->data_type();
+
+ Window window;
+ window.use_tensor_dimensions(input->info()->tensor_shape());
+ Iterator input_it(input, window);
+ Iterator output_it(output, window);
+
+ switch(data_type)
+ {
+ case DataType::QASYMM8:
+ execute_window_loop(window, [&](const Coordinates &)
+ {
+ *reinterpret_cast<float *>(output_it.ptr()) = dequantize(*reinterpret_cast<const uint8_t *>(input_it.ptr()), qinfo.scale, qinfo.offset);
+ },
+ input_it, output_it);
+ break;
+ case DataType::QASYMM16:
+ execute_window_loop(window, [&](const Coordinates &)
+ {
+ *reinterpret_cast<float *>(output_it.ptr()) = dequantize(*reinterpret_cast<const uint16_t *>(input_it.ptr()), qinfo.scale, qinfo.offset);
+ },
+ input_it, output_it);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
+}
+
+void quantize_tensor(const ITensor *input, ITensor *output)
+{
+ const UniformQuantizationInfo qinfo = output->info()->quantization_info().uniform();
+ const DataType data_type = output->info()->data_type();
+
+ Window window;
+ window.use_tensor_dimensions(input->info()->tensor_shape());
+ Iterator input_it(input, window);
+ Iterator output_it(output, window);
+
+ switch(data_type)
+ {
+ case DataType::QASYMM8:
+ execute_window_loop(window, [&](const Coordinates &)
+ {
+ *reinterpret_cast<uint8_t *>(output_it.ptr()) = quantize_qasymm8(*reinterpret_cast<const float *>(input_it.ptr()), qinfo);
+ },
+ input_it, output_it);
+ break;
+ case DataType::QASYMM16:
+ execute_window_loop(window, [&](const Coordinates &)
+ {
+ *reinterpret_cast<uint16_t *>(output_it.ptr()) = quantize_qasymm16(*reinterpret_cast<const float *>(input_it.ptr()), qinfo);
+ },
+ input_it, output_it);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type");
+ }
+}
+} // namespace
+
+CPPBoxWithNonMaximaSuppressionLimit::CPPBoxWithNonMaximaSuppressionLimit(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)),
+ _box_with_nms_limit_kernel(),
+ _scores_in(),
+ _boxes_in(),
+ _batch_splits_in(),
+ _scores_out(),
+ _boxes_out(),
+ _classes(),
+ _batch_splits_out(),
+ _keeps(),
+ _scores_in_f32(),
+ _boxes_in_f32(),
+ _batch_splits_in_f32(),
+ _scores_out_f32(),
+ _boxes_out_f32(),
+ _classes_f32(),
+ _batch_splits_out_f32(),
+ _keeps_f32(),
+ _is_qasymm8(false)
+{
+}
void CPPBoxWithNonMaximaSuppressionLimit::configure(const ITensor *scores_in, const ITensor *boxes_in, const ITensor *batch_splits_in, ITensor *scores_out, ITensor *boxes_out, ITensor *classes,
ITensor *batch_splits_out, ITensor *keeps, ITensor *keeps_size, const BoxNMSLimitInfo info)
{
- auto k = arm_compute::support::cpp14::make_unique<CPPBoxWithNonMaximaSuppressionLimitKernel>();
- k->configure(scores_in, boxes_in, batch_splits_in, scores_out, boxes_out, classes, batch_splits_out, keeps, keeps_size, info);
- _kernel = std::move(k);
-}
\ No newline at end of file
+ ARM_COMPUTE_ERROR_ON_NULLPTR(scores_in, boxes_in, scores_out, boxes_out, classes);
+
+ _is_qasymm8 = scores_in->info()->data_type() == DataType::QASYMM8;
+
+ _scores_in = scores_in;
+ _boxes_in = boxes_in;
+ _batch_splits_in = batch_splits_in;
+ _scores_out = scores_out;
+ _boxes_out = boxes_out;
+ _classes = classes;
+ _batch_splits_out = batch_splits_out;
+ _keeps = keeps;
+
+ if(_is_qasymm8)
+ {
+ // Manage intermediate buffers
+ _memory_group.manage(&_scores_in_f32);
+ _memory_group.manage(&_boxes_in_f32);
+ _memory_group.manage(&_scores_out_f32);
+ _memory_group.manage(&_boxes_out_f32);
+ _memory_group.manage(&_classes_f32);
+ _scores_in_f32.allocator()->init(scores_in->info()->clone()->set_data_type(DataType::F32));
+ _boxes_in_f32.allocator()->init(boxes_in->info()->clone()->set_data_type(DataType::F32));
+ if(batch_splits_in != nullptr)
+ {
+ _memory_group.manage(&_batch_splits_in_f32);
+ _batch_splits_in_f32.allocator()->init(batch_splits_in->info()->clone()->set_data_type(DataType::F32));
+ }
+ _scores_out_f32.allocator()->init(scores_out->info()->clone()->set_data_type(DataType::F32));
+ _boxes_out_f32.allocator()->init(boxes_out->info()->clone()->set_data_type(DataType::F32));
+ _classes_f32.allocator()->init(classes->info()->clone()->set_data_type(DataType::F32));
+ if(batch_splits_out != nullptr)
+ {
+ _memory_group.manage(&_batch_splits_out_f32);
+ _batch_splits_out_f32.allocator()->init(batch_splits_out->info()->clone()->set_data_type(DataType::F32));
+ }
+ if(keeps != nullptr)
+ {
+ _memory_group.manage(&_keeps_f32);
+ _keeps_f32.allocator()->init(keeps->info()->clone()->set_data_type(DataType::F32));
+ }
+
+ _box_with_nms_limit_kernel.configure(&_scores_in_f32, &_boxes_in_f32, (batch_splits_in != nullptr) ? &_batch_splits_in_f32 : nullptr,
+ &_scores_out_f32, &_boxes_out_f32, &_classes_f32,
+ (batch_splits_out != nullptr) ? &_batch_splits_out_f32 : nullptr, (keeps != nullptr) ? &_keeps_f32 : nullptr,
+ keeps_size, info);
+ }
+ else
+ {
+ _box_with_nms_limit_kernel.configure(scores_in, boxes_in, batch_splits_in, scores_out, boxes_out, classes, batch_splits_out, keeps, keeps_size, info);
+ }
+
+ if(_is_qasymm8)
+ {
+ _scores_in_f32.allocator()->allocate();
+ _boxes_in_f32.allocator()->allocate();
+ if(_batch_splits_in != nullptr)
+ {
+ _batch_splits_in_f32.allocator()->allocate();
+ }
+ _scores_out_f32.allocator()->allocate();
+ _boxes_out_f32.allocator()->allocate();
+ _classes_f32.allocator()->allocate();
+ if(batch_splits_out != nullptr)
+ {
+ _batch_splits_out_f32.allocator()->allocate();
+ }
+ if(keeps != nullptr)
+ {
+ _keeps_f32.allocator()->allocate();
+ }
+ }
+}
+
+Status validate(const ITensorInfo *scores_in, const ITensorInfo *boxes_in, const ITensorInfo *batch_splits_in, const ITensorInfo *scores_out, const ITensorInfo *boxes_out, const ITensorInfo *classes,
+ const ITensorInfo *batch_splits_out, const ITensorInfo *keeps, const ITensorInfo *keeps_size, const BoxNMSLimitInfo info)
+{
+ ARM_COMPUTE_UNUSED(batch_splits_in, batch_splits_out, keeps, keeps_size, info);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores_in, boxes_in, scores_out, boxes_out, classes);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(scores_in, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+
+ const bool is_qasymm8 = scores_in->data_type() == DataType::QASYMM8;
+ if(is_qasymm8)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(boxes_in, 1, DataType::QASYMM16);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(boxes_in, boxes_out);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(boxes_in, boxes_out);
+ const UniformQuantizationInfo boxes_qinfo = boxes_in->quantization_info().uniform();
+ ARM_COMPUTE_RETURN_ERROR_ON(boxes_qinfo.scale != 0.125f);
+ ARM_COMPUTE_RETURN_ERROR_ON(boxes_qinfo.offset != 0);
+ }
+
+ return Status{};
+}
+
+void CPPBoxWithNonMaximaSuppressionLimit::run()
+{
+ // Acquire all the temporaries
+ MemoryGroupResourceScope scope_mg(_memory_group);
+
+ if(_is_qasymm8)
+ {
+ dequantize_tensor(_scores_in, &_scores_in_f32);
+ dequantize_tensor(_boxes_in, &_boxes_in_f32);
+ if(_batch_splits_in != nullptr)
+ {
+ dequantize_tensor(_batch_splits_in, &_batch_splits_in_f32);
+ }
+ }
+
+ Scheduler::get().schedule(&_box_with_nms_limit_kernel, Window::DimY);
+
+ if(_is_qasymm8)
+ {
+ quantize_tensor(&_scores_out_f32, _scores_out);
+ quantize_tensor(&_boxes_out_f32, _boxes_out);
+ quantize_tensor(&_classes_f32, _classes);
+ if(_batch_splits_out != nullptr)
+ {
+ quantize_tensor(&_batch_splits_out_f32, _batch_splits_out);
+ }
+ if(_keeps != nullptr)
+ {
+ quantize_tensor(&_keeps_f32, _keeps);
+ }
+ }
+}
+} // namespace arm_compute
diff --git a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
index 13a34b4..e0acf06 100644
--- a/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
+++ b/src/runtime/CPP/functions/CPPDetectionOutputLayer.cpp
@@ -464,7 +464,7 @@
// Ignore background class.
continue;
}
- ARM_COMPUTE_ERROR_ON_MSG(_all_location_predictions[i].find(label) == _all_location_predictions[i].end(), "Could not find location predictions for label %d.", label);
+ ARM_COMPUTE_ERROR_ON_MSG_VAR(_all_location_predictions[i].find(label) == _all_location_predictions[i].end(), "Could not find location predictions for label %d.", label);
const std::vector<BBox> &label_loc_preds = _all_location_predictions[i].find(label)->second;
@@ -497,7 +497,7 @@
const int label = _info.share_location() ? -1 : c;
if(conf_scores.find(c) == conf_scores.end() || decode_bboxes.find(label) == decode_bboxes.end())
{
- ARM_COMPUTE_ERROR("Could not find predictions for label %d.", label);
+ ARM_COMPUTE_ERROR_VAR("Could not find predictions for label %d.", label);
}
const std::vector<float> &scores = conf_scores.find(c)->second;
const std::vector<BBox> &bboxes = decode_bboxes.find(label)->second;
@@ -518,7 +518,7 @@
if(conf_scores.find(label) == conf_scores.end())
{
- ARM_COMPUTE_ERROR("Could not find predictions for label %d.", label);
+ ARM_COMPUTE_ERROR_VAR("Could not find predictions for label %d.", label);
}
const std::vector<float> &scores = conf_scores.find(label)->second;
@@ -570,7 +570,7 @@
{
// Either if there are no confidence predictions
// or there are no location predictions for current label.
- ARM_COMPUTE_ERROR("Could not find predictions for the label %d.", label);
+ ARM_COMPUTE_ERROR_VAR("Could not find predictions for the label %d.", label);
}
const std::vector<BBox> &bboxes = decode_bboxes.find(loc_label)->second;
const std::vector<int> &indices = it.second;
diff --git a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
index 2997b59..bc88f71 100644
--- a/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
+++ b/src/runtime/CPP/functions/CPPDetectionPostProcessLayer.cpp
@@ -42,20 +42,20 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_box_encoding, input_class_score, input_anchors);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_box_encoding, 1, DataType::F32, DataType::QASYMM8);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_box_encoding, input_class_score, input_anchors);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_box_encoding, input_anchors);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_box_encoding->num_dimensions() > 3, "The location input tensor shape should be [4, N, kBatchSize].");
if(input_box_encoding->num_dimensions() > 2)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_box_encoding->dimension(2) != kBatchSize, "The third dimension of the input box_encoding tensor should be equal to %d.", kBatchSize);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_box_encoding->dimension(2) != kBatchSize, "The third dimension of the input box_encoding tensor should be equal to %d.", kBatchSize);
}
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_box_encoding->dimension(0) != kNumCoordBox, "The first dimension of the input box_encoding tensor should be equal to %d.", kNumCoordBox);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_box_encoding->dimension(0) != kNumCoordBox, "The first dimension of the input box_encoding tensor should be equal to %d.", kNumCoordBox);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_class_score->dimension(0) != (info.num_classes() + 1),
"The first dimension of the input class_prediction should be equal to the number of classes plus one.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_anchors->num_dimensions() > 3, "The anchors input tensor shape should be [4, N, kBatchSize].");
if(input_anchors->num_dimensions() > 2)
{
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input_anchors->dimension(0) != kNumCoordBox, "The first dimension of the input anchors tensor should be equal to %d.", kNumCoordBox);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(input_anchors->dimension(0) != kNumCoordBox, "The first dimension of the input anchors tensor should be equal to %d.", kNumCoordBox);
}
ARM_COMPUTE_RETURN_ERROR_ON_MSG((input_box_encoding->dimension(1) != input_class_score->dimension(1))
|| (input_box_encoding->dimension(1) != input_anchors->dimension(1)),
@@ -156,7 +156,7 @@
std::vector<unsigned int> &sorted_indices, const unsigned int num_output, const unsigned int max_detections, ITensor *output_boxes, ITensor *output_classes, ITensor *output_scores,
ITensor *num_detection)
{
- // ymin,xmin,ymax,xmax -> xmin,ymin,xmax,ymax
+ // xmin,ymin,xmax,ymax -> ymin,xmin,ymax,xmax
unsigned int i = 0;
for(; i < num_output; ++i)
{
@@ -183,8 +183,8 @@
CPPDetectionPostProcessLayer::CPPDetectionPostProcessLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _nms(), _input_box_encoding(nullptr), _input_scores(nullptr), _input_anchors(nullptr), _output_boxes(nullptr), _output_classes(nullptr),
- _output_scores(nullptr), _num_detection(nullptr), _info(), _num_boxes(), _num_classes_with_background(), _num_max_detected_boxes(), _decoded_boxes(), _decoded_scores(), _selected_indices(),
- _class_scores(), _input_scores_to_use(nullptr), _result_idx_boxes_after_nms(), _result_classes_after_nms(), _result_scores_after_nms(), _sorted_indices(), _box_scores()
+ _output_scores(nullptr), _num_detection(nullptr), _info(), _num_boxes(), _num_classes_with_background(), _num_max_detected_boxes(), _dequantize_scores(false), _decoded_boxes(), _decoded_scores(),
+ _selected_indices(), _class_scores(), _input_scores_to_use(nullptr)
{
}
@@ -214,15 +214,15 @@
_info = info;
_num_boxes = input_box_encoding->info()->dimension(1);
_num_classes_with_background = _input_scores->info()->dimension(0);
+ _dequantize_scores = (info.dequantize_scores() && is_data_type_quantized(input_box_encoding->info()->data_type()));
auto_init_if_empty(*_decoded_boxes.info(), TensorInfo(TensorShape(_kNumCoordBox, _input_box_encoding->info()->dimension(1), _kBatchSize), 1, DataType::F32));
auto_init_if_empty(*_decoded_scores.info(), TensorInfo(TensorShape(_input_scores->info()->dimension(0), _input_scores->info()->dimension(1), _kBatchSize), 1, DataType::F32));
- auto_init_if_empty(*_selected_indices.info(), TensorInfo(TensorShape(info.max_detections()), 1, DataType::S32));
-
+ auto_init_if_empty(*_selected_indices.info(), TensorInfo(TensorShape(info.use_regular_nms() ? info.detection_per_class() : info.max_detections()), 1, DataType::S32));
const unsigned int num_classes_per_box = std::min(info.max_classes_per_detection(), info.num_classes());
auto_init_if_empty(*_class_scores.info(), TensorInfo(info.use_regular_nms() ? TensorShape(_num_boxes) : TensorShape(_num_boxes * num_classes_per_box), 1, DataType::F32));
- _input_scores_to_use = is_data_type_quantized(input_box_encoding->info()->data_type()) ? &_decoded_scores : _input_scores;
+ _input_scores_to_use = _dequantize_scores ? &_decoded_scores : _input_scores;
// Manage intermediate buffers
_memory_group.manage(&_decoded_boxes);
@@ -236,21 +236,6 @@
_decoded_scores.allocator()->allocate();
_selected_indices.allocator()->allocate();
_class_scores.allocator()->allocate();
-
- if(info.use_regular_nms())
- {
- _result_idx_boxes_after_nms.reserve(_info.detection_per_class() * _info.num_classes());
- _result_classes_after_nms.reserve(_info.detection_per_class() * _info.num_classes());
- _result_scores_after_nms.reserve(_info.detection_per_class() * _info.num_classes());
- }
- else
- {
- _result_scores_after_nms.reserve(num_classes_per_box * _num_boxes);
- _result_classes_after_nms.reserve(num_classes_per_box * _num_boxes);
- _result_scores_after_nms.reserve(num_classes_per_box * _num_boxes);
- _box_scores.reserve(_num_boxes);
- }
- _sorted_indices.resize(info.use_regular_nms() ? info.max_detections() : info.num_classes());
}
Status CPPDetectionPostProcessLayer::validate(const ITensorInfo *input_box_encoding, const ITensorInfo *input_class_score, const ITensorInfo *input_anchors,
@@ -277,7 +262,7 @@
DecodeCenterSizeBoxes(_input_box_encoding, _input_anchors, _info, &_decoded_boxes);
// Decode scores if necessary
- if(is_data_type_quantized(_input_box_encoding->info()->data_type()))
+ if(_dequantize_scores)
{
for(unsigned int idx_c = 0; idx_c < _num_classes_with_background; ++idx_c)
{
@@ -288,9 +273,15 @@
}
}
}
+
// Regular NMS
if(_info.use_regular_nms())
{
+ std::vector<int> result_idx_boxes_after_nms;
+ std::vector<int> result_classes_after_nms;
+ std::vector<float> result_scores_after_nms;
+ std::vector<unsigned int> sorted_indices;
+
for(unsigned int c = 0; c < num_classes; ++c)
{
// For each boxes get scores of the boxes for the class c
@@ -299,6 +290,8 @@
*(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(i)))) =
*(reinterpret_cast<float *>(_input_scores_to_use->ptr_to_element(Coordinates(c + 1, i)))); // i * _num_classes_with_background + c + 1
}
+
+ // Run Non-maxima Suppression
_nms.run();
for(unsigned int i = 0; i < _info.detection_per_class(); ++i)
@@ -307,67 +300,73 @@
if(selected_index == -1)
{
// Nms will return -1 for all the last M-elements not valid
- continue;
+ break;
}
- _result_idx_boxes_after_nms.emplace_back(selected_index);
- _result_scores_after_nms.emplace_back((reinterpret_cast<float *>(_class_scores.buffer()))[selected_index]);
- _result_classes_after_nms.emplace_back(c);
+ result_idx_boxes_after_nms.emplace_back(selected_index);
+ result_scores_after_nms.emplace_back((reinterpret_cast<float *>(_class_scores.buffer()))[selected_index]);
+ result_classes_after_nms.emplace_back(c);
}
}
// We select the max detection numbers of the highest score of all classes
- const auto num_selected = _result_idx_boxes_after_nms.size();
+ const auto num_selected = result_scores_after_nms.size();
const auto num_output = std::min<unsigned int>(max_detections, num_selected);
// Sort selected indices based on result scores
- std::iota(_sorted_indices.begin(), _sorted_indices.end(), 0);
- std::partial_sort(_sorted_indices.data(),
- _sorted_indices.data() + num_output,
- _sorted_indices.data() + num_selected,
+ sorted_indices.resize(num_selected);
+ std::iota(sorted_indices.begin(), sorted_indices.end(), 0);
+ std::partial_sort(sorted_indices.data(),
+ sorted_indices.data() + num_output,
+ sorted_indices.data() + num_selected,
[&](unsigned int first, unsigned int second)
{
- return _result_scores_after_nms[first] > _result_scores_after_nms[second];
+ return result_scores_after_nms[first] > result_scores_after_nms[second];
});
- SaveOutputs(&_decoded_boxes, _result_idx_boxes_after_nms, _result_scores_after_nms, _result_classes_after_nms,
- _sorted_indices, num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);
+ SaveOutputs(&_decoded_boxes, result_idx_boxes_after_nms, result_scores_after_nms, result_classes_after_nms, sorted_indices,
+ num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);
}
// Fast NMS
else
{
const unsigned int num_classes_per_box = std::min<unsigned int>(_info.max_classes_per_detection(), _info.num_classes());
- for(unsigned int b = 0, index = 0; b < _num_boxes; ++b)
+ std::vector<float> max_scores;
+ std::vector<int> box_indices;
+ std::vector<int> max_score_classes;
+
+ for(unsigned int b = 0; b < _num_boxes; ++b)
{
- _box_scores.clear();
- _sorted_indices.clear();
+ std::vector<float> box_scores;
for(unsigned int c = 0; c < num_classes; ++c)
{
- _box_scores.emplace_back(*(reinterpret_cast<float *>(_input_scores_to_use->ptr_to_element(Coordinates(c + 1, b)))));
- _sorted_indices.push_back(c);
+ box_scores.emplace_back(*(reinterpret_cast<float *>(_input_scores_to_use->ptr_to_element(Coordinates(c + 1, b)))));
}
- std::partial_sort(_sorted_indices.data(),
- _sorted_indices.data() + num_classes_per_box,
- _sorted_indices.data() + num_classes,
+
+ std::vector<unsigned int> max_score_indices;
+ max_score_indices.resize(_info.num_classes());
+ std::iota(max_score_indices.data(), max_score_indices.data() + _info.num_classes(), 0);
+ std::partial_sort(max_score_indices.data(),
+ max_score_indices.data() + num_classes_per_box,
+ max_score_indices.data() + num_classes,
[&](unsigned int first, unsigned int second)
{
- return _box_scores[first] > _box_scores[second];
+ return box_scores[first] > box_scores[second];
});
- for(unsigned int i = 0; i < num_classes_per_box; ++i, ++index)
+ for(unsigned int i = 0; i < num_classes_per_box; ++i)
{
- const float score_to_add = _box_scores[_sorted_indices[i]];
- *(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(index)))) = score_to_add;
- _result_scores_after_nms.emplace_back(score_to_add);
- _result_idx_boxes_after_nms.emplace_back(b);
- _result_classes_after_nms.emplace_back(_sorted_indices[i]);
+ const float score_to_add = box_scores[max_score_indices[i]];
+ *(reinterpret_cast<float *>(_class_scores.ptr_to_element(Coordinates(b * num_classes_per_box + i)))) = score_to_add;
+ max_scores.emplace_back(score_to_add);
+ box_indices.emplace_back(b);
+ max_score_classes.emplace_back(max_score_indices[i]);
}
}
- // Run NMS
+ // Run Non-maxima Suppression
_nms.run();
-
- _sorted_indices.clear();
+ std::vector<unsigned int> selected_indices;
for(unsigned int i = 0; i < max_detections; ++i)
{
// NMS returns M valid indices, the not valid tail is filled with -1
@@ -376,13 +375,13 @@
// Nms will return -1 for all the last M-elements not valid
break;
}
- _sorted_indices.emplace_back(*(reinterpret_cast<int *>(_selected_indices.ptr_to_element(Coordinates(i)))));
+ selected_indices.emplace_back(*(reinterpret_cast<int *>(_selected_indices.ptr_to_element(Coordinates(i)))));
}
// We select the max detection numbers of the highest score of all classes
- const auto num_output = std::min<unsigned int>(_info.max_detections(), _sorted_indices.size());
+ const auto num_output = std::min<unsigned int>(_info.max_detections(), selected_indices.size());
- SaveOutputs(&_decoded_boxes, _result_idx_boxes_after_nms, _result_scores_after_nms, _result_classes_after_nms,
- _sorted_indices, num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);
+ SaveOutputs(&_decoded_boxes, box_indices, max_scores, max_score_classes, selected_indices,
+ num_output, max_detections, _output_boxes, _output_classes, _output_scores, _num_detection);
}
}
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute