COMPMID-639 - Add PPMAccessor and TopNPredictionsAccessor in graph_utils

Change-Id: I40c3e2dfcde10c65ed9c86f9283a53f9e679d4fa
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92437
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

3 files changed, 238 insertions, 11 deletions

diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index bdd831075d..bcfc0f7994 100644
--- a/utils/GraphUtils.cpp
+++ b/utils/GraphUtils.cpp
@@ -34,8 +34,10 @@
 #include "arm_compute/core/PixelValue.h"
 #include "libnpy/npy.hpp"
 
+#include <algorithm>
+#include <iomanip>
+#include <ostream>
 #include <random>
-#include <sstream>
 
 using namespace arm_compute::graph_utils;
 
@@ -48,16 +50,8 @@ bool PPMWriter::access_tensor(ITensor &tensor)
 {
     std::stringstream ss;
     ss << _name << _iterator << ".ppm";
-    if(dynamic_cast<Tensor *>(&tensor) != nullptr)
-    {
-        arm_compute::utils::save_to_ppm(dynamic_cast<Tensor &>(tensor), ss.str());
-    }
-#ifdef ARM_COMPUTE_CL
-    else if(dynamic_cast<CLTensor *>(&tensor) != nullptr)
-    {
-        arm_compute::utils::save_to_ppm(dynamic_cast<CLTensor &>(tensor), ss.str());
-    }
-#endif /* ARM_COMPUTE_CL */
+
+    arm_compute::utils::save_to_ppm(tensor, ss.str());
 
     _iterator++;
     if(_maximum == 0)
@@ -87,6 +81,101 @@ bool DummyAccessor::access_tensor(ITensor &tensor)
     return ret;
 }
 
+PPMAccessor::PPMAccessor(const std::string &ppm_path, bool bgr, float mean_r, float mean_g, float mean_b)
+    : _ppm_path(ppm_path), _bgr(bgr), _mean_r(mean_r), _mean_g(mean_g), _mean_b(mean_b)
+{
+}
+
+bool PPMAccessor::access_tensor(ITensor &tensor)
+{
+    utils::PPMLoader ppm;
+    const float      mean[3] =
+    {
+        _bgr ? _mean_b : _mean_r,
+        _mean_g,
+        _bgr ? _mean_r : _mean_b
+    };
+
+    // Open PPM file
+    ppm.open(_ppm_path);
+
+    // Fill the tensor with the PPM content (BGR)
+    ppm.fill_planar_tensor(tensor, _bgr);
+
+    // Subtract the mean value from each channel
+    Window window;
+    window.use_tensor_dimensions(tensor.info()->tensor_shape());
+
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        const float value                                     = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - mean[id.z()];
+        *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value;
+    });
+
+    return true;
+}
+
+TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream)
+    : _labels(), _output_stream(output_stream), _top_n(top_n)
+{
+    _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());
+    }
+}
+
+bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0));
+
+    // Get the predicted class
+    std::vector<float>  classes_prob;
+    std::vector<size_t> index;
+
+    const auto   output_net  = reinterpret_cast<float *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
+    const size_t num_classes = tensor.info()->dimension(0);
+
+    classes_prob.resize(num_classes);
+    index.resize(num_classes);
+
+    std::copy(output_net, output_net + num_classes, classes_prob.begin());
+
+    // Sort results
+    std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));
+    std::sort(std::begin(index), std::end(index),
+              [&](size_t a, size_t b)
+    {
+        return classes_prob[a] > classes_prob[b];
+    });
+
+    _output_stream << "---------- Top " << _top_n << " predictions ----------" << std::endl
+                   << std::endl;
+    for(size_t i = 0; i < _top_n; ++i)
+    {
+        _output_stream << std::fixed << std::setprecision(4)
+                       << classes_prob[index.at(i)]
+                       << " - [id = " << index.at(i) << "]"
+                       << ", " << _labels[index.at(i)] << std::endl;
+    }
+
+    return false;
+}
+
 RandomAccessor::RandomAccessor(PixelValue lower, PixelValue upper, std::random_device::result_type seed)
     : _lower(lower), _upper(upper), _seed(seed)
 {
diff --git a/utils/GraphUtils.h b/utils/GraphUtils.h
index 5c370e5eba..39b3f115bd 100644
--- a/utils/GraphUtils.h
+++ b/utils/GraphUtils.h
@@ -29,6 +29,8 @@
 #include "arm_compute/graph/Types.h"
 
 #include <random>
+#include <string>
+#include <vector>
 
 namespace arm_compute
 {
@@ -76,6 +78,60 @@ private:
     unsigned int _maximum;
 };
 
+/** PPM accessor class */
+class PPMAccessor final : public graph::ITensorAccessor
+{
+public:
+    /** Constructor
+     *
+     * @param[in] ppm_path Path to PPM file
+     * @param[in] bgr      (Optional) Fill the first plane with blue channel (default = false)
+     * @param[in] mean_r   (Optional) Red mean value to be subtracted from red channel
+     * @param[in] mean_g   (Optional) Green mean value to be subtracted from green channel
+     * @param[in] mean_b   (Optional) Blue mean value to be subtracted from blue channel
+     */
+    PPMAccessor(const std::string &ppm_path, bool bgr = true, float mean_r = 0.0f, float mean_g = 0.0f, float mean_b = 0.0f);
+    /** Allow instances of this class to be move constructed */
+    PPMAccessor(PPMAccessor &&) = default;
+
+    // Inherited methods overriden:
+    bool access_tensor(ITensor &tensor) override;
+
+private:
+    const std::string &_ppm_path;
+    const bool         _bgr;
+    const float        _mean_r;
+    const float        _mean_g;
+    const float        _mean_b;
+};
+
+/** Result accessor class */
+class TopNPredictionsAccessor final : public graph::ITensorAccessor
+{
+public:
+    /** Constructor
+     *
+     * @param[in]  labels_path   Path to labels text file.
+     * @param[in]  top_n         (Optional) Number of output classes to print
+     * @param[out] output_stream (Optional) Output stream
+     */
+    TopNPredictionsAccessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout);
+    /** Allow instances of this class to be move constructed */
+    TopNPredictionsAccessor(TopNPredictionsAccessor &&) = default;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    TopNPredictionsAccessor(const TopNPredictionsAccessor &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    TopNPredictionsAccessor &operator=(const TopNPredictionsAccessor &) = delete;
+
+    // Inherited methods overriden:
+    bool access_tensor(ITensor &tensor) override;
+
+private:
+    std::vector<std::string> _labels;
+    std::ostream            &_output_stream;
+    size_t                   _top_n;
+};
+
 /** Random accessor class */
 class RandomAccessor final : public graph::ITensorAccessor
 {
diff --git a/utils/Utils.h b/utils/Utils.h
index b0e1abeb5f..c88de0e16b 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -263,6 +263,88 @@ public:
         }
     }
 
+    /** Fill a tensor with 3 planes (one for each channel) with the content of the currently open PPM file.
+     *
+     * @note If the image is a CLImage, the function maps and unmaps the image
+     *
+     * @param[in,out] tensor Tensor with 3 planes to fill (Must be allocated, and of matching dimensions with the opened PPM). Data types supported: U8/F32
+     * @param[in]     bgr    (Optional) Fill the first plane with blue channel (default = false)
+     */
+    template <typename T>
+    void fill_planar_tensor(T &tensor, bool bgr = false)
+    {
+        ARM_COMPUTE_ERROR_ON(!is_open());
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::U8, DataType::F32);
+        ARM_COMPUTE_ERROR_ON(tensor.info()->dimension(0) != _width || tensor.info()->dimension(1) != _height || tensor.info()->dimension(2) != 3);
+
+        try
+        {
+            // Map buffer if creating a CLTensor
+            map(tensor, true);
+
+            // Check if the file is large enough to fill the image
+            const size_t current_position = _fs.tellg();
+            _fs.seekg(0, std::ios_base::end);
+            const size_t end_position = _fs.tellg();
+            _fs.seekg(current_position, std::ios_base::beg);
+
+            ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < tensor.info()->tensor_shape().total_size(),
+                                     "Not enough data in file");
+            ARM_COMPUTE_UNUSED(end_position);
+
+            // Iterate through every pixel of the image
+            arm_compute::Window window;
+            window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, _width, 1));
+            window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, _height, 1));
+            window.set(arm_compute::Window::DimZ, arm_compute::Window::Dimension(0, 1, 1));
+
+            arm_compute::Iterator out(&tensor, window);
+
+            unsigned char red   = 0;
+            unsigned char green = 0;
+            unsigned char blue  = 0;
+
+            size_t stride_z = tensor.info()->strides_in_bytes()[2];
+
+            arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates & id)
+            {
+                red   = _fs.get();
+                green = _fs.get();
+                blue  = _fs.get();
+
+                switch(tensor.info()->data_type())
+                {
+                    case arm_compute::DataType::U8:
+                    {
+                        *(out.ptr() + 0 * stride_z) = bgr ? blue : red;
+                        *(out.ptr() + 1 * stride_z) = green;
+                        *(out.ptr() + 2 * stride_z) = bgr ? red : blue;
+                        break;
+                    }
+                    case arm_compute::DataType::F32:
+                    {
+                        *reinterpret_cast<float *>(out.ptr() + 0 * stride_z) = static_cast<float>(bgr ? blue : red);
+                        *reinterpret_cast<float *>(out.ptr() + 1 * stride_z) = static_cast<float>(green);
+                        *reinterpret_cast<float *>(out.ptr() + 2 * stride_z) = static_cast<float>(bgr ? red : blue);
+                        break;
+                    }
+                    default:
+                    {
+                        ARM_COMPUTE_ERROR("Unsupported data type");
+                    }
+                }
+            },
+            out);
+
+            // Unmap buffer if creating a CLTensor
+            unmap(tensor);
+        }
+        catch(const std::ifstream::failure &e)
+        {
+            ARM_COMPUTE_ERROR("Loading PPM file: %s", e.what());
+        }
+    }
+
 private:
     std::ifstream _fs;
     unsigned int  _width, _height;