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/*
* Copyright (c) 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_FUSEBATCHNORMALIZATION_FIXTURE
#define ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/FuseBatchNormalization.h"
#include <tuple>
#include <utility>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, int dims_weights, typename T>
class FuseBatchNormalizationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta)
{
std::tie(_target_w, _target_b) = compute_target(shape_w, data_type, data_layout, in_place, with_bias, with_gamma, with_beta);
std::tie(_reference_w, _reference_b) = compute_reference(shape_w, data_type, with_bias, with_gamma, with_beta);
}
protected:
template <typename U>
void fill(U &&tensor, int i, float min, float max)
{
library->fill_tensor_uniform(tensor, i, min, max);
}
std::pair<TensorType, TensorType> compute_target(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta)
{
const TensorShape shape_v(shape_w[dims_weights - 1]);
if(data_layout == DataLayout::NHWC)
{
permute(shape_w, PermutationVector(2U, 0U, 1U));
}
const bool in_place_w = in_place;
const bool in_place_b = with_bias ? in_place : false;
// Create tensors
TensorType w = create_tensor<TensorType>(shape_w, data_type, 1, QuantizationInfo(), data_layout);
TensorType b = create_tensor<TensorType>(shape_v, data_type);
TensorType mean = create_tensor<TensorType>(shape_v, data_type);
TensorType var = create_tensor<TensorType>(shape_v, data_type);
TensorType w_fused = create_tensor<TensorType>(shape_w, data_type, 1, QuantizationInfo(), data_layout);
TensorType b_fused = create_tensor<TensorType>(shape_v, data_type);
TensorType beta = create_tensor<TensorType>(shape_v, data_type);
TensorType gamma = create_tensor<TensorType>(shape_v, data_type);
auto b_to_use = with_bias ? &b : nullptr;
auto gamma_to_use = with_gamma ? &gamma : nullptr;
auto beta_to_use = with_beta ? &beta : nullptr;
auto w_fused_to_use = in_place_w ? nullptr : &w_fused;
auto b_fused_to_use = in_place_b ? nullptr : &b_fused;
const FuseBatchNormalizationType fuse_bn_type = dims_weights == 3 ?
FuseBatchNormalizationType::DEPTHWISECONVOLUTION :
FuseBatchNormalizationType::CONVOLUTION;
// Create and configure function
FunctionType fuse_batch_normalization;
fuse_batch_normalization.configure(&w, &mean, &var, w_fused_to_use, b_fused_to_use, b_to_use, beta_to_use, gamma_to_use, _epsilon, fuse_bn_type);
ARM_COMPUTE_EXPECT(w.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(var.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(w_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(b_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(beta.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
w.allocator()->allocate();
b.allocator()->allocate();
mean.allocator()->allocate();
var.allocator()->allocate();
w_fused.allocator()->allocate();
b_fused.allocator()->allocate();
beta.allocator()->allocate();
gamma.allocator()->allocate();
ARM_COMPUTE_EXPECT(!w.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!var.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!w_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!b_fused.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!beta.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!gamma.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(w), 0U, -1.0f, 1.0f);
fill(AccessorType(b), 1U, -1.0f, 1.0f);
fill(AccessorType(mean), 2U, -1.0f, 1.0f);
fill(AccessorType(var), 3U, 0.0f, 1.0f);
fill(AccessorType(beta), 4U, -1.0f, 1.0f);
fill(AccessorType(gamma), 5U, -1.0f, 1.0f);
// Compute function
fuse_batch_normalization.run();
return std::make_pair(std::move(in_place_w ? w : w_fused), std::move(in_place_b ? b : b_fused));
}
std::pair<SimpleTensor<T>, SimpleTensor<T>> compute_reference(TensorShape shape_w, DataType data_type, bool with_bias, bool with_gamma, bool with_beta)
{
const TensorShape shape_v(shape_w[dims_weights - 1]);
SimpleTensor<T> w{ shape_w, data_type };
SimpleTensor<T> b{ shape_v, data_type };
SimpleTensor<T> mean{ shape_v, data_type };
SimpleTensor<T> var{ shape_v, data_type };
SimpleTensor<T> w_fused{ shape_w, data_type };
SimpleTensor<T> b_fused{ shape_v, data_type };
SimpleTensor<T> beta{ shape_v, data_type };
SimpleTensor<T> gamma{ shape_v, data_type };
// Fill reference tensor
fill(w, 0U, -1.0f, 1.0f);
fill(b, 1U, -1.0f, 1.0f);
fill(mean, 2U, -1.0f, 1.0f);
fill(var, 3U, 0.0f, 1.0f);
fill(beta, 4U, -1.0f, 1.0f);
fill(gamma, 5U, -1.0f, 1.0f);
if(!with_bias)
{
// Fill with zeros
fill(b, 0U, 0.0f, 0.0f);
}
if(!with_gamma)
{
// Fill with ones
fill(gamma, 0U, 1.0f, 1.0f);
}
if(!with_beta)
{
// Fill with zeros
fill(beta, 0U, 0.0f, 0.0f);
}
switch(dims_weights)
{
case 3:
// Weights for depth wise convolution layer
reference::fuse_batch_normalization_dwc_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon);
break;
case 4:
// Weights for convolution layer
reference::fuse_batch_normalization_conv_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon);
break;
default:
ARM_COMPUTE_ERROR("Not supported number of dimensions for the input weights tensor");
}
return std::make_pair(std::move(w_fused), std::move(b_fused));
}
const float _epsilon{ 0.0001f };
TensorType _target_w{};
TensorType _target_b{};
SimpleTensor<T> _reference_w{};
SimpleTensor<T> _reference_b{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_FIXTURE */
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