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/*
* Copyright (c) 2017 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_FULLY_CONNECTED_LAYER_FIXTURE
#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "framework/Asserts.h"
#include "framework/Fixture.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/RawTensor.h"
#include "tests/validation_new/CPP/FullyConnectedLayer.h"
#include "tests/validation_new/Helpers.h"
#include <random>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
RawTensor transpose(const RawTensor &src, int interleave = 1)
{
// Create reference
TensorShape dst_shape(src.shape());
dst_shape.set(0, src.shape().y() * interleave);
dst_shape.set(1, std::ceil(src.shape().x() / static_cast<float>(interleave)));
RawTensor dst{ dst_shape, src.data_type() };
// Compute reference
uint8_t *out_ptr = dst.data();
for(int i = 0; i < dst.num_elements(); i += interleave)
{
Coordinates coord = index2coord(dst.shape(), i);
size_t coord_x = coord.x();
coord.set(0, coord.y() * interleave);
coord.set(1, coord_x / interleave);
const int num_elements = std::min<int>(interleave, src.shape().x() - coord.x());
std::copy_n(static_cast<const uint8_t *>(src(coord)), num_elements * src.element_size(), out_ptr);
out_ptr += interleave * dst.element_size();
}
return dst;
}
} // namespace
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits)
{
ARM_COMPUTE_UNUSED(weights_shape);
ARM_COMPUTE_UNUSED(bias_shape);
_fractional_bits = fractional_bits;
_data_type = data_type;
_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
if(is_data_type_float(_data_type))
{
std::uniform_real_distribution<> distribution(0.5f, 1.f);
library->fill(tensor, distribution, i);
}
else
{
library->fill_tensor_uniform(tensor, i);
}
}
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
bool reshape_weights, DataType data_type, int fixed_point_position)
{
TensorShape reshaped_weights_shape(weights_shape);
// Test actions depending on the target settings
//
// | reshape | !reshape
// -----------+-----------+---------------------------
// transpose | | ***
// -----------+-----------+---------------------------
// !transpose | transpose | transpose &
// | | transpose1xW (if required)
//
// ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
if(!reshape_weights || !transpose_weights)
{
const size_t shape_x = reshaped_weights_shape.x();
reshaped_weights_shape.set(0, reshaped_weights_shape.y());
reshaped_weights_shape.set(1, shape_x);
// Weights have to be passed reshaped
// Transpose 1xW for batched version
if(!reshape_weights && output_shape.y() > 1 && run_interleave)
{
const int transpose_width = 16 / data_size_from_type(data_type);
const float shape_x = reshaped_weights_shape.x();
reshaped_weights_shape.set(0, reshaped_weights_shape.y() * transpose_width);
reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
}
}
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
// Create and configure function.
FunctionType fc;
fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
weights.allocator()->allocate();
bias.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src), 0);
fill(AccessorType(bias), 2);
if(!reshape_weights || !transpose_weights)
{
TensorShape tmp_shape(weights_shape);
RawTensor tmp(tmp_shape, data_type, 1, fixed_point_position);
// Fill with original shape
fill(tmp, 1);
// Transpose elementwise
tmp = transpose(tmp);
// Reshape weights for batched runs
if(!reshape_weights && output_shape.y() > 1 && run_interleave)
{
// Transpose with interleave
const int interleave_size = 16 / tmp.element_size();
tmp = transpose(tmp, interleave_size);
}
AccessorType weights_accessor(weights);
for(int i = 0; i < tmp.num_elements(); ++i)
{
Coordinates coord = index2coord(tmp.shape(), i);
std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
tmp.element_size(),
static_cast<RawTensor::value_type *>(weights_accessor(coord)));
}
}
else
{
fill(AccessorType(weights), 1);
}
// Compute NEFullyConnectedLayer function
fc.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
bool reshape_weights, DataType data_type, int fixed_point_position = 0)
{
// Create reference
SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
// Fill reference
fill(src, 0);
fill(weights, 1);
fill(bias, 2);
return reference::fully_connected_layer<T>(src, weights, bias, output_shape);
}
TensorType _target{};
SimpleTensor<T> _reference{};
int _fractional_bits{};
DataType _data_type{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
{
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
{
FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
reshape_weights, data_type,
0);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */