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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (c) 2016-2018 ARM Limited.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "arm_compute/core/CL/kernels/CLReduceMaxKernel.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLKernelLibraryEx.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/Validate.h"
#include "arm_compute/core/Window.h"
#include <cmath>
#include <cstdlib>
#include <set>
#include <string>
using namespace arm_compute;
namespace
{
constexpr unsigned int num_elems_processed_per_iteration = 16;
Status validate_arguments(const ITensorInfo *input, int32_t axis, const ITensorInfo *output)
{
// We can handle for simple case only
// Input rank: 2
// Output rank: 1
// Axis: one axis value, restrict to 1
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis != 1, "Axis only allowed 1");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().total_size() == 0,
"Inputs are not broadcast compatible");
// Validate in case of configured output
if (output->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() != input->data_type(),
"Output same type allowed for input and output");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->tensor_shape().num_dimensions() != 1,
"Only support for output dimension 1");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->tensor_shape().num_dimensions() != 2,
"Only support for input dimension 2");
}
return Status{};
}
} // namespace
CLReduceMaxKernel::CLReduceMaxKernel() : _input(nullptr), _output(nullptr), _axis(0) {}
void CLReduceMaxKernel::configure(const ICLTensor *input, int32_t axis, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), axis, output->info()));
_input = input;
_output = output;
_axis = axis;
// Configure kernel window
int cols = _input->info()->tensor_shape()[0];
int rows = _input->info()->tensor_shape()[1];
Window win;
win.set(0, Window::Dimension(0, cols, 1));
win.set(1, Window::Dimension(0, rows, 1));
// Construct kernel name
std::string kernel_name = "reduce_max";
// Set kernel build options
std::set<std::string> build_opts;
build_opts.emplace("-DWIDTH=" + support::cpp11::to_string(cols));
// Create kernel
_kernel =
static_cast<cl::Kernel>(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts));
ICLKernel::configure(win);
}
Status CLReduceMaxKernel::validate(const ITensorInfo *input, int32_t axis,
const ITensorInfo *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, axis, output));
return Status{};
}
void CLReduceMaxKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
Window window_input = window;
Window slice_input = window_input.first_slice_window_1D();
do
{
Window slice_output = slice_input.shift_dimensions(1);
unsigned int idx = 0;
add_1D_tensor_argument(idx, _input, slice_input);
add_1D_tensor_argument(idx, _output, slice_output);
enqueue(queue, *this, slice_input);
} while (window_input.slide_window_slice_1D(slice_input));
}
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