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/*
* Copyright (c) 2017 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_TEST_VALIDATION_HELPERS_H__
#define __ARM_COMPUTE_TEST_VALIDATION_HELPERS_H__
#include "Types.h"
#include "ValidationUserConfiguration.h"
#include "arm_compute/core/Types.h"
#include <random>
#include <type_traits>
#include <utility>
#include <vector>
namespace arm_compute
{
namespace test
{
namespace validation
{
/** Helper function to get the testing range for each activation layer.
*
* @param[in] activation Activation function to test.
* @param[in] fixed_point_position (Optional) Number of bits for the fractional part. Defaults to 1.
*
* @return A pair containing the lower upper testing bounds for a given function.
*/
template <typename T>
std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, int fixed_point_position = 1)
{
bool is_float = std::is_floating_point<T>::value;
std::pair<T, T> bounds;
// Set initial values
if(is_float)
{
bounds = std::make_pair(-255.f, 255.f);
}
else
{
bounds = std::make_pair(std::numeric_limits<T>::lowest(), std::numeric_limits<T>::max());
}
// Reduce testing ranges
switch(activation)
{
case ActivationLayerInfo::ActivationFunction::LOGISTIC:
case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
// Reduce range as exponent overflows
if(is_float)
{
bounds.first = -40.f;
bounds.second = 40.f;
}
else
{
bounds.first = -(1 << (fixed_point_position));
bounds.second = 1 << (fixed_point_position);
}
break;
case ActivationLayerInfo::ActivationFunction::TANH:
// Reduce range as exponent overflows
if(!is_float)
{
bounds.first = -(1 << (fixed_point_position));
bounds.second = 1 << (fixed_point_position);
}
break;
case ActivationLayerInfo::ActivationFunction::SQRT:
// Reduce range as sqrt should take a non-negative number
bounds.first = (is_float) ? 0 : 1;
break;
default:
break;
}
return bounds;
}
/** Helper function to get the testing range for batch normalization layer.
*
* @param[in] fixed_point_position (Optional) Number of bits for the fractional part. Defaults to 1.
*
* @return A pair containing the lower upper testing bounds.
*/
template <typename T>
std::pair<T, T> get_batchnormalization_layer_test_bounds(int fixed_point_position = 1)
{
bool is_float = std::is_floating_point<T>::value;
std::pair<T, T> bounds;
// Set initial values
if(is_float)
{
bounds = std::make_pair(-1.f, 1.f);
}
else
{
bounds = std::make_pair(1, 1 << (fixed_point_position));
}
return bounds;
}
/** Fill mask with the corresponding given pattern.
*
* @param[in,out] mask Mask to be filled according to pattern
* @param[in] cols Columns (width) of mask
* @param[in] rows Rows (height) of mask
* @param[in] pattern Pattern to fill the mask according to
*/
inline void fill_mask_from_pattern(uint8_t *mask, int cols, int rows, MatrixPattern pattern)
{
unsigned int v = 0;
std::mt19937 gen(user_config.seed.get());
std::bernoulli_distribution dist(0.5);
for(int r = 0; r < rows; ++r)
{
for(int c = 0; c < cols; ++c, ++v)
{
uint8_t val = 0;
switch(pattern)
{
case MatrixPattern::BOX:
val = 255;
break;
case MatrixPattern::CROSS:
val = ((r == (rows / 2)) || (c == (cols / 2))) ? 255 : 0;
break;
case MatrixPattern::DISK:
val = (((r - rows / 2.0f + 0.5f) * (r - rows / 2.0f + 0.5f)) / ((rows / 2.0f) * (rows / 2.0f)) + ((c - cols / 2.0f + 0.5f) * (c - cols / 2.0f + 0.5f)) / ((cols / 2.0f) *
(cols / 2.0f))) <= 1.0f ? 255 : 0;
break;
case MatrixPattern::OTHER:
val = (dist(gen) ? 0 : 255);
break;
default:
return;
}
mask[v] = val;
}
}
if(pattern == MatrixPattern::OTHER)
{
std::uniform_int_distribution<uint8_t> distribution_u8(0, ((cols * rows) - 1));
mask[distribution_u8(gen)] = 255;
}
}
/** Calculate output tensor shape give a vector of input tensor to concatenate
*
* @param[in] input_shapes Shapes of the tensors to concatenate across depth.
*
* @return The shape of output concatenated tensor.
*/
inline TensorShape calculate_depth_concatenate_shape(std::vector<TensorShape> input_shapes)
{
TensorShape out_shape = input_shapes.at(0);
unsigned int max_x = 0;
unsigned int max_y = 0;
unsigned int depth = 0;
for(auto const &shape : input_shapes)
{
max_x = std::max<unsigned int>(shape.x(), max_x);
max_y = std::max<unsigned int>(shape.y(), max_y);
depth += shape.z();
}
out_shape.set(0, max_x);
out_shape.set(1, max_y);
out_shape.set(2, depth);
return out_shape;
}
/** Create a vector of random ROIs.
*
* @param[in] shape The shape of the input tensor.
* @param[in] pool_info The ROI pooling information.
* @param[in] num_rois The number of ROIs to be created.
* @param[in] seed The random seed to be used.
*
* @return A vector that contains the requested number of random ROIs
*/
std::vector<ROI> generate_random_rois(const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, unsigned int num_rois, std::random_device::result_type seed);
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif //__ARM_COMPUTE_TEST_VALIDATION_HELPERS_H__
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