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/*
* Copyright (c) 2017-2019 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_GC_HELPER_H
#define ARM_COMPUTE_TEST_GC_HELPER_H
#include "tests/Globals.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h"
#include <iostream>
namespace arm_compute
{
namespace test
{
/** Helper to create an empty tensor.
*
* @param[in] shape Desired shape.
* @param[in] data_type Desired data type.
* @param[in] num_channels (Optional) It indicates the number of channels for each tensor element
*
* @return Empty @ref GCTensor with the specified shape and data type.
*/
inline GCTensor create_tensor(const TensorShape &shape, DataType data_type, int num_channels = 1)
{
GCTensor tensor;
tensor.allocator()->init(TensorInfo(shape, num_channels, data_type));
return tensor;
}
/** Helper to create an empty tensor.
*
* @param[in] name File name from which to get the dimensions.
* @param[in] data_type Desired data type.
*
* @return Empty @ref GCTensor with the specified shape and data type.
*/
inline GCTensor create_tensor(const std::string &name, DataType data_type)
{
constexpr unsigned int num_channels = 1;
const RawTensor &raw = library->get(name);
GCTensor tensor;
tensor.allocator()->init(TensorInfo(raw.shape(), num_channels, data_type));
return tensor;
}
/** Helper to print tensor.
*
* @param[in] tensor Tensor to print.
* @param[in] name Tensor name.
* @param[in] info Format information.
*
* @return Empty @ref GCTensor with the specified shape and data type.
*/
inline void print_tensor(ITensor &tensor, const std::string &name, IOFormatInfo info = IOFormatInfo(IOFormatInfo::PrintRegion::Full))
{
std::ostringstream s;
IGCTensor &t = dynamic_cast<IGCTensor &>(tensor);
t.map();
t.print(s, info);
std::cout << name << ":" << std::endl;
std::cout << s.str().c_str();
t.unmap();
}
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_GC_HELPER_H */
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