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/*
* 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
* 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_UNIT_WEIGHTS_RETENTION
#define ARM_COMPUTE_TEST_UNIT_WEIGHTS_RETENTION
#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/FullyConnectedLayer.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
/** Test case to run a fully connected layer with weights retention, reconfigure
* with different shapes and rerun making sure the weights are retained.
*
* Runs a fully connected layer stimulating is_interleaved_transpose CLGEMM,
* then reconfigures with different batch size and reruns.
*/
template <typename TensorType, typename AccessorType, typename FullyConnectedFunction>
class WeightsRetentionReconfigureTestCaseFixture : public framework::Fixture
{
using T = float;
public:
void setup()
{
_max_batches = 8;
_cur_batches = 6;
_target = compute_target();
_reference = compute_reference();
};
protected:
template <typename U>
void fill(U &&tensor, int i)
{
std::uniform_real_distribution<> distribution(0.5f, 1.f);
library->fill(tensor, distribution, i);
}
TensorType compute_target()
{
// Create tensors
TensorType w1 = create_tensor<TensorType>(TensorShape(180000U, 150U), DataType::F32, 1);
TensorType b1 = create_tensor<TensorType>(TensorShape(150U), DataType::F32, 1);
TensorType src = create_tensor<TensorType>(TensorShape(1U, 150U, 1200U, _max_batches), DataType::F32, 1);
TensorType dst = create_tensor<TensorType>(TensorShape(150U, _max_batches), DataType::F32, 1);
// Create and configure function
FullyConnectedFunction fc_layer_1;
fc_layer_1.configure(&src, &w1, &b1, &dst);
// Allocate persistent tensors
w1.allocator()->allocate();
b1.allocator()->allocate();
// Allocate tensors (1st iteration)
src.allocator()->allocate();
dst.allocator()->allocate();
// Fill tensors (1st iteration)
fill(AccessorType(src), 0);
fill(AccessorType(w1), 1);
fill(AccessorType(b1), 2);
// Compute functions (1st iteration)
fc_layer_1.run();
// Update tensor shapes (2nd iteration)
auto src_padding = src.allocator()->info().padding();
auto dst_padding = dst.allocator()->info().padding();
int diff = _max_batches - _cur_batches;
auto new_src_padding = PaddingSize(src_padding.top, src_padding.right, src_padding.bottom + diff, src_padding.left);
auto new_dst_padding = PaddingSize(dst_padding.top, dst_padding.right, dst_padding.bottom + diff, dst_padding.left);
src.allocator()->info().set_tensor_shape(TensorShape(1U, 150U, 1200U, _cur_batches)).set_is_resizable(true).extend_padding(new_src_padding);
src.allocator()->info().set_is_resizable(false);
dst.allocator()->info().set_tensor_shape(TensorShape(150U, _cur_batches)).set_is_resizable(true).extend_padding(new_dst_padding);
dst.allocator()->info().set_is_resizable(false);
// Configure FC info
FullyConnectedLayerInfo fc_info;
fc_info.retain_internal_weights = true;
// Configure functions (2nd iteration)
fc_layer_1.configure(&src, &w1, &b1, &dst, fc_info);
// Fill tensors (2nd iteration)
fill(AccessorType(src), 5);
// Compute functions (2nd iteration)
fc_layer_1.run();
return dst;
}
SimpleTensor<T> compute_reference()
{
// Create reference
SimpleTensor<T> w1{ TensorShape(180000U, 150U), DataType::F32 };
SimpleTensor<T> b1{ TensorShape(150U), DataType::F32 };
SimpleTensor<T> src{ TensorShape(1U, 150U, 1200U, _cur_batches), DataType::F32 };
// Fill reference
fill(src, 5);
fill(w1, 1);
fill(b1, 2);
return reference::fully_connected_layer(src, w1, b1, TensorShape(150U, _cur_batches));
}
protected:
TensorType _target{};
SimpleTensor<T> _reference{};
unsigned int _max_batches{};
unsigned int _cur_batches{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_UNIT_WEIGHTS_RETENTION */
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