176 lines
8.3 KiB
C++
176 lines
8.3 KiB
C++
/*
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* Copyright (c) 2018 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_LSTM_LAYER_DATASET
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#define ARM_COMPUTE_TEST_LSTM_LAYER_DATASET
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#include "utils/TypePrinter.h"
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace datasets
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{
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class LSTMLayerDataset
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{
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public:
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using type = std::tuple<TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, TensorShape, ActivationLayerInfo, float, float>;
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struct iterator
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{
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iterator(std::vector<TensorShape>::const_iterator src_it,
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std::vector<TensorShape>::const_iterator input_weights_it,
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std::vector<TensorShape>::const_iterator recurrent_weights_it,
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std::vector<TensorShape>::const_iterator cells_bias_it,
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std::vector<TensorShape>::const_iterator output_cell_it,
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std::vector<TensorShape>::const_iterator dst_it,
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std::vector<TensorShape>::const_iterator scratch_it,
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std::vector<ActivationLayerInfo>::const_iterator infos_it,
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std::vector<float>::const_iterator cell_threshold_it,
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std::vector<float>::const_iterator projection_threshold_it)
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: _src_it{ std::move(src_it) },
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_input_weights_it{ std::move(input_weights_it) },
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_recurrent_weights_it{ std::move(recurrent_weights_it) },
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_cells_bias_it{ std::move(cells_bias_it) },
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_output_cell_it{ std::move(output_cell_it) },
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_dst_it{ std::move(dst_it) },
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_scratch_it{ std::move(scratch_it) },
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_infos_it{ std::move(infos_it) },
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_cell_threshold_it{ std::move(cell_threshold_it) },
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_projection_threshold_it{ std::move(projection_threshold_it) }
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{
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}
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std::string description() const
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{
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std::stringstream description;
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description << "In=" << *_src_it << ":";
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description << "InputWeights=" << *_input_weights_it << ":";
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description << "RecurrentWeights=" << *_recurrent_weights_it << ":";
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description << "Biases=" << *_cells_bias_it << ":";
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description << "Scratch=" << *_scratch_it << ":";
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description << "Out=" << *_dst_it;
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return description.str();
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}
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LSTMLayerDataset::type operator*() const
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{
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return std::make_tuple(*_src_it, *_input_weights_it, *_recurrent_weights_it, *_cells_bias_it, *_output_cell_it, *_dst_it, *_scratch_it, *_infos_it, *_cell_threshold_it, *_projection_threshold_it);
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}
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iterator &operator++()
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{
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++_src_it;
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++_input_weights_it;
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++_recurrent_weights_it;
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++_cells_bias_it;
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++_output_cell_it;
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++_dst_it;
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++_scratch_it;
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++_infos_it;
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++_cell_threshold_it;
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++_projection_threshold_it;
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return *this;
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}
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private:
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std::vector<TensorShape>::const_iterator _src_it;
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std::vector<TensorShape>::const_iterator _input_weights_it;
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std::vector<TensorShape>::const_iterator _recurrent_weights_it;
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std::vector<TensorShape>::const_iterator _cells_bias_it;
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std::vector<TensorShape>::const_iterator _output_cell_it;
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std::vector<TensorShape>::const_iterator _dst_it;
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std::vector<TensorShape>::const_iterator _scratch_it;
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std::vector<ActivationLayerInfo>::const_iterator _infos_it;
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std::vector<float>::const_iterator _cell_threshold_it;
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std::vector<float>::const_iterator _projection_threshold_it;
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};
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iterator begin() const
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{
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return iterator(_src_shapes.begin(), _input_weights_shapes.begin(), _recurrent_weights_shapes.begin(), _cell_bias_shapes.begin(), _output_cell_shapes.begin(), _dst_shapes.begin(),
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_scratch_shapes.begin(), _infos.begin(), _cell_threshold.begin(), _projection_threshold.begin());
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}
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int size() const
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{
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return std::min(_src_shapes.size(), std::min(_input_weights_shapes.size(), std::min(_recurrent_weights_shapes.size(), std::min(_cell_bias_shapes.size(), std::min(_output_cell_shapes.size(),
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std::min(_dst_shapes.size(), std::min(_scratch_shapes.size(), std::min(_cell_threshold.size(), std::min(_projection_threshold.size(), _infos.size())))))))));
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}
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void add_config(TensorShape src, TensorShape input_weights, TensorShape recurrent_weights, TensorShape cell_bias_weights, TensorShape output_cell_state, TensorShape dst, TensorShape scratch,
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ActivationLayerInfo info, float cell_threshold, float projection_threshold)
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{
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_src_shapes.emplace_back(std::move(src));
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_input_weights_shapes.emplace_back(std::move(input_weights));
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_recurrent_weights_shapes.emplace_back(std::move(recurrent_weights));
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_cell_bias_shapes.emplace_back(std::move(cell_bias_weights));
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_output_cell_shapes.emplace_back(std::move(output_cell_state));
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_dst_shapes.emplace_back(std::move(dst));
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_scratch_shapes.emplace_back(std::move(scratch));
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_infos.emplace_back(std::move(info));
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_cell_threshold.emplace_back(std::move(cell_threshold));
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_projection_threshold.emplace_back(std::move(projection_threshold));
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}
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protected:
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LSTMLayerDataset() = default;
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LSTMLayerDataset(LSTMLayerDataset &&) = default;
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private:
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std::vector<TensorShape> _src_shapes{};
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std::vector<TensorShape> _input_weights_shapes{};
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std::vector<TensorShape> _recurrent_weights_shapes{};
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std::vector<TensorShape> _cell_bias_shapes{};
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std::vector<TensorShape> _output_cell_shapes{};
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std::vector<TensorShape> _dst_shapes{};
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std::vector<TensorShape> _scratch_shapes{};
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std::vector<ActivationLayerInfo> _infos{};
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std::vector<float> _cell_threshold{};
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std::vector<float> _projection_threshold{};
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};
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class SmallLSTMLayerDataset final : public LSTMLayerDataset
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{
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public:
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SmallLSTMLayerDataset()
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{
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add_config(TensorShape(8U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U), TensorShape(16U), TensorShape(64U),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), 0.05f, 0.93f);
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add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), TensorShape(16U, 2U), TensorShape(64U, 2U),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), 0.05f, 0.93f);
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add_config(TensorShape(8U, 2U), TensorShape(8U, 16U), TensorShape(16U, 16U), TensorShape(16U), TensorShape(16U, 2U), TensorShape(16U, 2U), TensorShape(48U, 2U),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), 0.05f, 0.93f);
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}
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};
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} // namespace datasets
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_LSTM_LAYER_DATASET */
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