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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_DEPTHWISECONVOLUTIONFIXTURE
#define ARM_COMPUTE_TEST_DEPTHWISECONVOLUTIONFIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Fixture.h"
namespace arm_compute
{
namespace test
{
namespace benchmark
{
using namespace arm_compute::misc::shape_calculator;
/** Fixture that can be used for NEON and CL */
template <typename TensorType, typename Function, typename Accessor>
class DepthwiseConvolutionLayerFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape src_shape, Size2D kernel_size, PadStrideInfo info, Size2D Dilation, DataType data_type, int batches)
{
// Get shapes
TensorShape weights_shape(kernel_size.width, kernel_size.height);
const TensorInfo in_info(src_shape, 1, data_type);
const TensorInfo we_info(weights_shape, 1, data_type);
TensorShape dst_shape = compute_depthwise_convolution_shape(in_info, we_info, info, 1);
weights_shape.set(2, dst_shape.z());
// Set batched in source and destination shapes
src_shape.set(3 /* batch */, batches);
dst_shape.set(3 /* batch */, batches);
// Create tensors
src = create_tensor<TensorType>(src_shape, data_type, 1, QuantizationInfo(0.5f, 10));
weights = create_tensor<TensorType>(weights_shape, data_type, 1, QuantizationInfo(0.5f, 10));
biases = create_tensor<TensorType>(TensorShape(weights_shape[2]), is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type, 1);
dst = create_tensor<TensorType>(dst_shape, data_type, 1, QuantizationInfo(0.5f, 10));
// Create and configure function
depth_conv.configure(&src, &weights, &biases, &dst, info);
// Allocate tensors
src.allocator()->allocate();
weights.allocator()->allocate();
biases.allocator()->allocate();
dst.allocator()->allocate();
}
void run()
{
depth_conv.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 depth_conv{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_DEPTHWISECONVOLUTIONFIXTURE */
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