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/*
* Copyright (c) 2017-2019 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.
*/
#include "SoftmaxLayer.h"
#include "arm_compute/core/Types.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
{
// Create reference
SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 };
// Compute reference. Lower dims are the collapsing of the first axis
// dimensions (i.e., the flattened dimension of each batch). The upper dims are
// instead the batches we want to normalize
int lower_dims = 1;
for(size_t i = 0; i < axis; i++)
{
lower_dims *= src.shape()[i];
}
int upper_dims = 1;
for(size_t i = axis; i < TensorShape::num_max_dimensions; i++)
{
upper_dims *= src.shape()[i];
}
for(int r = 0; r < upper_dims; ++r)
{
const T *src_row_ptr = src.data() + r * lower_dims;
T *dst_row_ptr = dst.data() + r * lower_dims;
// Find max
const T max = *std::max_element(src_row_ptr, src_row_ptr + lower_dims);
// Regularize
T sum(0.f);
std::transform(src_row_ptr, src_row_ptr + lower_dims, dst_row_ptr, [&sum, max, beta](T val)
{
const T res(std::exp((val - max) * beta));
sum += res;
return res;
});
// Normalize
std::transform(dst_row_ptr, dst_row_ptr + lower_dims, dst_row_ptr, [sum](T val)
{
return val / sum;
});
}
return dst;
}
template <typename T, typename std::enable_if<std::is_same<T, uint8_t>::value, int>::type>
SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, size_t axis)
{
// Note: Output quantization info should always have scale = 1/256 and offset = 0
const QuantizationInfo output_quantization_info = QuantizationInfo(1.f / 256, 0);
SimpleTensor<float> src_tmp = convert_from_asymmetric(src);
SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta, axis);
SimpleTensor<T> dst = convert_to_asymmetric<uint8_t>(dst_tmp, output_quantization_info);
return dst;
}
template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta, size_t axis);
template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta, size_t axis);
template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta, size_t axis);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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