arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp
index b9be9d4..2ff40b7 100644
--- a/utils/GraphUtils.cpp
+++ b/utils/GraphUtils.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -30,13 +30,7 @@
#include "arm_compute/runtime/CL/CLTensor.h"
#endif /* ARM_COMPUTE_CL */
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/PixelValue.h"
-
-#include <algorithm>
#include <iomanip>
-#include <ostream>
-#include <random>
using namespace arm_compute::graph_utils;
@@ -80,8 +74,10 @@
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)
+PPMAccessor::PPMAccessor(std::string ppm_path, bool bgr,
+ float mean_r, float mean_g, float mean_b,
+ float std_r, float std_g, float std_b)
+ : _ppm_path(std::move(ppm_path)), _bgr(bgr), _mean_r(mean_r), _mean_g(mean_g), _mean_b(mean_b), _std_r(std_r), _std_g(std_g), _std_b(std_b)
{
}
@@ -94,6 +90,12 @@
_mean_g,
_bgr ? _mean_r : _mean_b
};
+ const float std[3] =
+ {
+ _bgr ? _std_b : _std_r,
+ _std_g,
+ _bgr ? _std_r : _std_b
+ };
// Open PPM file
ppm.open(_ppm_path);
@@ -111,7 +113,7 @@
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;
+ *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value / std[id.z()];
});
return true;
@@ -140,16 +142,14 @@
}
}
-bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
+template <typename T>
+void TopNPredictionsAccessor::access_predictions_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<T> classes_prob;
std::vector<size_t> index;
- const auto output_net = reinterpret_cast<float *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
+ const auto output_net = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
const size_t num_classes = tensor.info()->dimension(0);
classes_prob.resize(num_classes);
@@ -170,10 +170,28 @@
for(size_t i = 0; i < _top_n; ++i)
{
_output_stream << std::fixed << std::setprecision(4)
- << classes_prob[index.at(i)]
+ << +classes_prob[index.at(i)]
<< " - [id = " << index.at(i) << "]"
<< ", " << _labels[index.at(i)] << std::endl;
}
+}
+
+bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8);
+ ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0));
+
+ switch(tensor.info()->data_type())
+ {
+ case DataType::QASYMM8:
+ access_predictions_tensor<uint8_t>(tensor);
+ break;
+ case DataType::F32:
+ access_predictions_tensor<float>(tensor);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+ }
return false;
}
@@ -314,6 +332,7 @@
// Validate tensor shape
ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch");
+
if(fortran_order)
{
for(size_t i = 0; i < shape.size(); ++i)