Update AlexNet example with accessors
diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index bdd8310..bcfc0f7 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 @@
{
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 @@
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)
{