207 lines
8.8 KiB
C++
207 lines
8.8 KiB
C++
/*
|
|
* 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 */
|