tests/benchmark/fixtures/BatchNormalizationLayerFixture.h

/*
 * Copyright (c) 2017-2018 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
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 * SOFTWARE.
 */
#ifndef ARM_COMPUTE_TEST_BATCHNORMALIZATIONLAYERFIXTURE
#define ARM_COMPUTE_TEST_BATCHNORMALIZATIONLAYERFIXTURE

#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>
class BatchNormalizationLayerFixture : public framework::Fixture
{
public:
    template <typename...>
    void setup(TensorShape tensor_shape, TensorShape param_shape, float epsilon, bool use_gamma, bool use_beta, ActivationLayerInfo act_info, DataType data_type, DataLayout data_layout, int batches)
    {
        // Set batched in source and destination shapes

        tensor_shape.set(tensor_shape.num_dimensions(), batches);
        if(data_layout == DataLayout::NHWC)
        {
            permute(tensor_shape, PermutationVector(2U, 0U, 1U));
        }

        // Create tensors
        src      = create_tensor<TensorType>(tensor_shape, data_type, 1, QuantizationInfo(), data_layout);
        dst      = create_tensor<TensorType>(tensor_shape, data_type, 1, QuantizationInfo(), data_layout);
        mean     = create_tensor<TensorType>(param_shape, data_type, 1);
        variance = create_tensor<TensorType>(param_shape, data_type, 1);
        beta     = create_tensor<TensorType>(param_shape, data_type, 1);
        gamma    = create_tensor<TensorType>(param_shape, data_type, 1);

        // Create and configure function
        TensorType *beta_ptr  = use_beta ? &beta : nullptr;
        TensorType *gamma_ptr = use_gamma ? &gamma : nullptr;
        batch_norm_layer.configure(&src, &dst, &mean, &variance, beta_ptr, gamma_ptr, epsilon, act_info);

        // Allocate tensors
        src.allocator()->allocate();
        dst.allocator()->allocate();
        mean.allocator()->allocate();
        variance.allocator()->allocate();
        beta.allocator()->allocate();
        gamma.allocator()->allocate();
    }

    void run()
    {
        batch_norm_layer.run();
    }

    void sync()
    {
        sync_if_necessary<TensorType>();
        sync_tensor_if_necessary<TensorType>(dst);
    }

    void teardown()
    {
        src.allocator()->free();
        dst.allocator()->free();
        mean.allocator()->free();
        variance.allocator()->free();
        beta.allocator()->free();
        gamma.allocator()->free();
    }

private:
    TensorType src{};
    TensorType dst{};
    TensorType mean{};
    TensorType variance{};
    TensorType beta{};
    TensorType gamma{};
    Function   batch_norm_layer{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_BATCHNORMALIZATIONLAYERFIXTURE */