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/*
* Copyright (c) 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_CHANNEL_SHUFFLE_LAYER_DATASET
#define ARM_COMPUTE_TEST_CHANNEL_SHUFFLE_LAYER_DATASET
#include "utils/TypePrinter.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
namespace arm_compute
{
namespace test
{
namespace datasets
{
class ChannelShuffleLayerDataset
{
public:
using type = std::tuple<TensorShape, int>;
struct iterator
{
iterator(std::vector<TensorShape>::const_iterator tensor_it,
std::vector<int>::const_iterator num_groups_it)
: _tensor_it{ std::move(tensor_it) },
_num_groups_it{ std::move(num_groups_it) }
{
}
std::string description() const
{
std::stringstream description;
description << "In=" << *_tensor_it << ":";
description << "NumGroups=" << *_num_groups_it;
return description.str();
}
ChannelShuffleLayerDataset::type operator*() const
{
return std::make_tuple(*_tensor_it, *_num_groups_it);
}
iterator &operator++()
{
++_tensor_it;
++_num_groups_it;
return *this;
}
private:
std::vector<TensorShape>::const_iterator _tensor_it;
std::vector<int>::const_iterator _num_groups_it;
};
iterator begin() const
{
return iterator(_tensor_shapes.begin(), _num_groups.begin());
}
int size() const
{
return std::min(_tensor_shapes.size(), _num_groups.size());
}
void add_config(TensorShape tensor, int num_groups)
{
_tensor_shapes.emplace_back(std::move(tensor));
_num_groups.emplace_back(std::move(num_groups));
}
protected:
ChannelShuffleLayerDataset() = default;
ChannelShuffleLayerDataset(ChannelShuffleLayerDataset &&) = default;
private:
std::vector<TensorShape> _tensor_shapes{};
std::vector<int> _num_groups{};
};
class SmallRandomChannelShuffleLayerDataset final : public ChannelShuffleLayerDataset
{
public:
SmallRandomChannelShuffleLayerDataset()
{
add_config(TensorShape(15U, 16U, 4U, 12U), 2);
add_config(TensorShape(21U, 11U, 12U, 7U), 4);
add_config(TensorShape(21U, 11U, 12U, 7U), 6);
add_config(TensorShape(7U, 3U, 6U, 11U), 3);
}
};
class LargeRandomChannelShuffleLayerDataset final : public ChannelShuffleLayerDataset
{
public:
LargeRandomChannelShuffleLayerDataset()
{
add_config(TensorShape(210U, 43U, 20U, 3U), 5);
add_config(TensorShape(283U, 213U, 15U, 3U), 3);
add_config(TensorShape(500U, 115U, 16U, 2U), 4);
}
};
} // namespace datasets
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_CHANNEL_SHUFFLE_LAYER_DATASET */
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