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/*
* 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.
*/
//FIXME / INTERNAL_ONLY: This file should not be released!
#ifndef ARM_COMPUTE_TEST_DRAGONBENCH_FIXTURE
#define ARM_COMPUTE_TEST_DRAGONBENCH_FIXTURE
#include "arm_compute/core/Error.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Fixture.h"
namespace arm_compute
{
namespace test
{
namespace benchmark
{
/** Fixture that can be used for NEON and CL */
template <typename TensorType, typename Function, typename Accessor, typename Conv2DConfig>
class DragonBenchConv2DFixture : public framework::Fixture
{
public:
template <typename...>
void setup(Conv2DConfig config, DataType data_type, DataLayout data_layout, bool has_bias)
{
// Set tensor shapes in NCHW layout
TensorShape src_shape(config.dim_in_w, config.dim_in_h, config.ch_in, config.ibatch);
TensorShape weights_shape(config.kern_w, config.kern_h, config.ch_in, config.ch_out);
TensorShape biases_shape(config.ch_out);
TensorShape dst_shape(config.dim_out_w, config.dim_out_h, config.ch_out, config.ibatch);
// Set convolution layer info
PadStrideInfo info(config.stride_w, config.stride_h, 0, 0);
if(config.padding)
{
info = calculate_same_pad(src_shape, weights_shape, info);
}
// Permute shapes in case of NHWC
if(data_layout == DataLayout::NHWC)
{
permute(src_shape, PermutationVector(2U, 0U, 1U));
permute(weights_shape, PermutationVector(2U, 0U, 1U));
permute(dst_shape, PermutationVector(2U, 0U, 1U));
}
// Determine bias data type
DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
const QuantizationInfo q_info(2.f, 10);
// Create tensors
src = create_tensor<TensorType>(src_shape, data_type, 1, q_info, data_layout);
weights = create_tensor<TensorType>(weights_shape, data_type, 1, q_info, data_layout);
biases = create_tensor<TensorType>(biases_shape, bias_data_type, 1, q_info, data_layout);
dst = create_tensor<TensorType>(dst_shape, data_type, 1, q_info, data_layout);
// Create and configure function
conv_layer.configure(&src, &weights, has_bias ? &biases : nullptr, &dst, info);
// Allocate tensors
src.allocator()->allocate();
weights.allocator()->allocate();
biases.allocator()->allocate();
dst.allocator()->allocate();
}
void run()
{
conv_layer.run();
}
void sync()
{
sync_if_necessary<TensorType>();
sync_tensor_if_necessary<TensorType>(dst);
}
void teardown()
{
src.allocator()->free();
weights.allocator()->free();
biases.allocator()->free();
dst.allocator()->free();
}
private:
TensorType src{};
TensorType weights{};
TensorType biases{};
TensorType dst{};
Function conv_layer{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_DRAGONBENCH_FIXTURE */
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