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/*
* Copyright (c) 2016-2020 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.
*/
#include "arm_compute/core/CL/kernels/CLMeanStdDevKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Window.h"
#include <cmath>
#include <set>
#include <string>
using namespace arm_compute;
CLMeanStdDevKernel::CLMeanStdDevKernel()
: _input(nullptr), _mean(nullptr), _stddev(nullptr), _global_sum(nullptr), _global_sum_squared(nullptr), _border_size(0)
{
}
BorderSize CLMeanStdDevKernel::border_size() const
{
return _border_size;
}
Status CLMeanStdDevKernel::validate(const ITensorInfo *input, float *mean, cl::Buffer *global_sum, float *stddev, cl::Buffer *global_sum_squared)
{
ARM_COMPUTE_UNUSED(mean);
ARM_COMPUTE_UNUSED(stddev);
ARM_COMPUTE_UNUSED(global_sum);
ARM_COMPUTE_UNUSED(global_sum_squared);
ARM_COMPUTE_RETURN_ERROR_ON_INT64_BASE_ATOMICS_UNSUPPORTED();
ARM_COMPUTE_RETURN_ERROR_ON_TENSOR_NOT_2D(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
return Status{};
}
void CLMeanStdDevKernel::configure(const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev, cl::Buffer *global_sum_squared)
{
configure(CLKernelLibrary::get().get_compile_context(), input, mean, global_sum, stddev, global_sum_squared);
}
void CLMeanStdDevKernel::configure(const CLCompileContext &compile_context, const ICLImage *input, float *mean, cl::Buffer *global_sum, float *stddev, cl::Buffer *global_sum_squared)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, global_sum);
ARM_COMPUTE_ERROR_ON(stddev && nullptr == global_sum_squared);
ARM_COMPUTE_ERROR_THROW_ON(CLMeanStdDevKernel::validate(input->info(), mean, global_sum, stddev, global_sum_squared));
_input = input;
_mean = mean;
_stddev = stddev;
_global_sum = global_sum;
_global_sum_squared = global_sum_squared;
// Create kernel
std::set<std::string> build_opts;
if(_stddev != nullptr)
{
build_opts.insert("-DSTDDEV");
}
_kernel = create_kernel(compile_context, "mean_stddev_accumulate", build_opts);
// Set fixed arguments
unsigned int idx = num_arguments_per_2D_tensor(); //Skip the input parameters
_kernel.setArg(idx++, static_cast<cl_uint>(input->info()->dimension(1)));
_kernel.setArg(idx++, *_global_sum);
if(_stddev != nullptr)
{
_kernel.setArg(idx++, *_global_sum_squared);
}
// Configure kernel window
constexpr unsigned int num_elems_processed_per_iteration_x = 8;
const unsigned int num_elems_processed_per_iteration_y = input->info()->dimension(1);
_border_size = BorderSize(ceil_to_multiple(input->info()->dimension(0), num_elems_processed_per_iteration_x) - input->info()->dimension(0));
Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
AccessWindowRectangle input_access(input->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);
update_window_and_padding(win, input_access);
ICLKernel::configure_internal(win);
}
void CLMeanStdDevKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
// Clear sums
static const cl_ulong zero = 0;
queue.enqueueWriteBuffer(*_global_sum, CL_FALSE, 0, sizeof(cl_ulong), &zero);
if(_stddev != nullptr)
{
queue.enqueueWriteBuffer(*_global_sum_squared, CL_FALSE, 0, sizeof(cl_ulong), &zero);
}
Window slice = window.first_slice_window_2D();
do
{
unsigned int idx = 0;
add_2D_tensor_argument(idx, _input, slice);
// Set slice step equal to height to force gws[1] to 1,
// as each thread calculates the sum across all rows and columns equal to the number of elements processed by each work-item
slice.set_dimension_step(Window::DimY, _input->info()->dimension(1));
enqueue(queue, *this, slice, lws_hint());
}
while(window.slide_window_slice_2D(slice));
// Calculate mean and stddev
cl_ulong global_sum = 0;
cl_ulong global_sum_squared = 0;
const float num_pixels = _input->info()->dimension(0) * _input->info()->dimension(1);
queue.enqueueReadBuffer(*_global_sum, CL_TRUE, 0, sizeof(cl_ulong), static_cast<void *>(&global_sum));
const float mean = global_sum / num_pixels;
*_mean = mean;
if(_stddev != nullptr)
{
queue.enqueueReadBuffer(*_global_sum_squared, CL_TRUE, 0, sizeof(cl_ulong), static_cast<void *>(&global_sum_squared));
*_stddev = std::sqrt((global_sum_squared / num_pixels) - (mean * mean));
}
}
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