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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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.
*/
/*
* Copyright (c) 2018 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 "io_accessor.h"
#include <ostream>
#include <android/log.h>
bool InputAccessor::access_tensor(arm_compute::ITensor &tensor)
{
// Subtract the mean value from each channel
arm_compute::Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
execute_window_loop(window, [&](const arm_compute::Coordinates& id)
{
*reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_input;
_test_input += _inc ? 1.0 : 0.0;
__android_log_print(ANDROID_LOG_DEBUG, "LOG_TAG", "Input %d, %d = %lf\r\n",
id.y(), id.x(), *reinterpret_cast<float *>(tensor.ptr_to_element(id)));
});
return true;
}
bool OutputAccessor::access_tensor(arm_compute::ITensor &tensor)
{
// Subtract the mean value from each channel
arm_compute::Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
execute_window_loop(window, [&](const arm_compute::Coordinates& id)
{
__android_log_print(ANDROID_LOG_DEBUG, "Output", "Input %d, %d = %lf\r\n",
id.y(), id.x(), *reinterpret_cast<float *>(tensor.ptr_to_element(id)));
});
return false; // end the network
}
bool WeightAccessor::access_tensor(arm_compute::ITensor &tensor)
{
// Subtract the mean value from each channel
arm_compute::Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
execute_window_loop(window, [&](const arm_compute::Coordinates& id)
{
*reinterpret_cast<float *>(tensor.ptr_to_element(id)) = _test_weight;
_test_weight += _inc ? 1.0 : 0.0;
});
return true;
}
bool BiasAccessor::access_tensor(arm_compute::ITensor &tensor)
{
// Subtract the mean value from each channel
arm_compute::Window window;
window.use_tensor_dimensions(tensor.info()->tensor_shape());
execute_window_loop(window, [&](const arm_compute::Coordinates& id)
{
*reinterpret_cast<float *>(tensor.ptr_to_element(id)) = 0.0;
});
return true;
}
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