COMPMID-1808: Add Detection Output Layer to the GraphAPI

COMPMID-1710: Integrate Detection ouput in MobilenetSSD graph example

Change-Id: I384d1eb492ef14ece58f2023ad7bbc16f834450b
Reviewed-on: https://review.mlplatform.org/356
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

2 files changed, 134 insertions, 0 deletions

diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index 2f1df7aef2..ab2c753eac 100644
--- a/utils/GraphUtils.cpp
+++ b/utils/GraphUtils.cpp
@@ -420,6 +420,77 @@ void ValidationOutputAccessor::report_top_n(size_t top_n, size_t total_samples,
     _output_stream << "Accuracy : " << accuracy << std::endl;
 }
 
+DetectionOutputAccessor::DetectionOutputAccessor(const std::string &labels_path, std::vector<TensorShape> &imgs_tensor_shapes, std::ostream &output_stream)
+    : _labels(), _tensor_shapes(std::move(imgs_tensor_shapes)), _output_stream(output_stream)
+{
+    _labels.clear();
+
+    std::ifstream ifs;
+
+    try
+    {
+        ifs.exceptions(std::ifstream::badbit);
+        ifs.open(labels_path, std::ios::in | std::ios::binary);
+
+        for(std::string line; !std::getline(ifs, line).fail();)
+        {
+            _labels.emplace_back(line);
+        }
+    }
+    catch(const std::ifstream::failure &e)
+    {
+        ARM_COMPUTE_ERROR("Accessing %s: %s", labels_path.c_str(), e.what());
+    }
+}
+
+template <typename T>
+void DetectionOutputAccessor::access_predictions_tensor(ITensor &tensor)
+{
+    const size_t num_detection = tensor.info()->valid_region().shape.y();
+    const auto   output_prt    = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
+
+    if(num_detection > 0)
+    {
+        _output_stream << "---------------------- Detections ----------------------" << std::endl
+                       << std::endl;
+
+        _output_stream << std::left << std::setprecision(4) << std::setw(8) << "Image | " << std::setw(8) << "Label | " << std::setw(12) << "Confidence | "
+                       << "[ xmin, ymin, xmax, ymax ]" << std::endl;
+
+        for(size_t i = 0; i < num_detection; ++i)
+        {
+            auto im = static_cast<const int>(output_prt[i * 7]);
+            _output_stream << std::setw(8) << im << std::setw(8)
+                           << _labels[output_prt[i * 7 + 1]] << std::setw(12) << output_prt[i * 7 + 2]
+                           << " [" << (output_prt[i * 7 + 3] * _tensor_shapes[im].x())
+                           << ", " << (output_prt[i * 7 + 4] * _tensor_shapes[im].y())
+                           << ", " << (output_prt[i * 7 + 5] * _tensor_shapes[im].x())
+                           << ", " << (output_prt[i * 7 + 6] * _tensor_shapes[im].y())
+                           << "]" << std::endl;
+        }
+    }
+    else
+    {
+        _output_stream << "No detection found." << std::endl;
+    }
+}
+
+bool DetectionOutputAccessor::access_tensor(ITensor &tensor)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
+
+    switch(tensor.info()->data_type())
+    {
+        case DataType::F32:
+            access_predictions_tensor<float>(tensor);
+            break;
+        default:
+            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+
+    return false;
+}
+
 TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream)
     : _labels(), _output_stream(output_stream), _top_n(top_n)
 {
diff --git a/utils/GraphUtils.h b/utils/GraphUtils.h
index d7f24afdd8..131378e5bd 100644
--- a/utils/GraphUtils.h
+++ b/utils/GraphUtils.h
@@ -283,6 +283,36 @@ private:
     size_t           _positive_samples_top5;
 };
 
+/** Detection output accessor class */
+class DetectionOutputAccessor final : public graph::ITensorAccessor
+{
+public:
+    /** Constructor
+     *
+     * @param[in]  labels_path        Path to labels text file.
+     * @param[in]  imgs_tensor_shapes Network input images tensor shapes.
+     * @param[out] output_stream      (Optional) Output stream
+     */
+    DetectionOutputAccessor(const std::string &labels_path, std::vector<TensorShape> &imgs_tensor_shapes, std::ostream &output_stream = std::cout);
+    /** Allow instances of this class to be move constructed */
+    DetectionOutputAccessor(DetectionOutputAccessor &&) = default;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    DetectionOutputAccessor(const DetectionOutputAccessor &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    DetectionOutputAccessor &operator=(const DetectionOutputAccessor &) = delete;
+
+    // Inherited methods overriden:
+    bool access_tensor(ITensor &tensor) override;
+
+private:
+    template <typename T>
+    void access_predictions_tensor(ITensor &tensor);
+
+    std::vector<std::string> _labels;
+    std::vector<TensorShape> _tensor_shapes;
+    std::ostream            &_output_stream;
+};
+
 /** Result accessor class */
 class TopNPredictionsAccessor final : public graph::ITensorAccessor
 {
@@ -472,6 +502,39 @@ inline std::unique_ptr<graph::ITensorAccessor> get_output_accessor(const arm_com
         return arm_compute::support::cpp14::make_unique<TopNPredictionsAccessor>(graph_parameters.labels, top_n, output_stream);
     }
 }
+/** Generates appropriate output accessor according to the specified graph parameters
+ *
+ * @note If the output accessor is requested to validate the graph then ValidationOutputAccessor is generated
+ *       else if output_accessor_file is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
+ *
+ * @param[in]  graph_parameters Graph parameters
+ * @param[in]  tensor_shapes    Network input images tensor shapes.
+ * @param[in]  is_validation    (Optional) Validation flag (default = false)
+ * @param[out] output_stream    (Optional) Output stream (default = std::cout)
+ *
+ * @return An appropriate tensor accessor
+ */
+inline std::unique_ptr<graph::ITensorAccessor> get_detection_output_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters,
+                                                                             std::vector<TensorShape>                     tensor_shapes,
+                                                                             bool                                         is_validation = false,
+                                                                             std::ostream                                &output_stream = std::cout)
+{
+    if(!graph_parameters.validation_file.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<ValidationOutputAccessor>(graph_parameters.validation_file,
+                                                                                  output_stream,
+                                                                                  graph_parameters.validation_range_start,
+                                                                                  graph_parameters.validation_range_end);
+    }
+    else if(graph_parameters.labels.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>(0);
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<DetectionOutputAccessor>(graph_parameters.labels, tensor_shapes, output_stream);
+    }
+}
 /** Generates appropriate npy output accessor according to the specified npy_path
  *
  * @note If npy_path is empty will generate a DummyAccessor else will generate a NpyAccessor