diff options
Diffstat (limited to 'tests/validation/reference/SoftmaxLayer.cpp')
| -rw-r--r-- | tests/validation/reference/SoftmaxLayer.cpp | 97 |
1 files changed, 47 insertions, 50 deletions
diff --git a/tests/validation/reference/SoftmaxLayer.cpp b/tests/validation/reference/SoftmaxLayer.cpp index 2fe1faef50..3fbac32a9b 100644 --- a/tests/validation/reference/SoftmaxLayer.cpp +++ b/tests/validation/reference/SoftmaxLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 ARM Limited. + * Copyright (c) 2017-2020 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,7 +23,9 @@ */ #include "SoftmaxLayer.h" +#include "arm_compute/core/Helpers.h" #include "arm_compute/core/Types.h" +#include "utils/TypePrinter.h" namespace arm_compute { @@ -39,45 +41,38 @@ SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, in // Create reference SimpleTensor<T> dst{ src.shape(), src.data_type(), 1 }; - // Negative index is used to specify axis from the end (e.g. -1 for the last axis). - if(axis < 0) - { - axis += src.shape().num_dimensions(); - } - - // 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 < static_cast<size_t>(axis); ++i) - { - lower_dims *= src.shape()[i]; - } + const int32_t n_dims = static_cast<int32_t>(src.shape().num_dimensions()); + ARM_COMPUTE_ERROR_ON(axis < -n_dims || axis >= n_dims); - int upper_dims = 1; - for(size_t i = static_cast<size_t>(axis); i < TensorShape::num_max_dimensions; ++i) - { - upper_dims *= src.shape()[i]; - } + const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, n_dims)); + Window window; + window.use_tensor_dimensions(src.shape()); + const unsigned int axis_dimension = src.shape()[actual_axis]; + window.set(actual_axis, Window::Dimension(0, 1, 1)); -#if defined(_OPENMP) - #pragma omp parallel for -#endif /* _OPENMP */ - for(int r = 0; r < upper_dims; ++r) + execute_window_loop(window, [&](const Coordinates & id) { - 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); + // Find max along axis + Coordinates offset(id); + offset.set(actual_axis, 0); + T max = *reinterpret_cast<const T *>(src(offset)); + for(unsigned int axis_id = 1; axis_id < axis_dimension; ++axis_id) + { + offset.set(actual_axis, axis_id); + const T val = *reinterpret_cast<const T *>(src(offset)); + if(val > max) + { + max = val; + } + } // Regularize T sum(0.f); - std::transform(src_row_ptr, src_row_ptr + lower_dims, dst_row_ptr, [&sum, max, beta, is_log](T val) + for(unsigned int axis_id = 0; axis_id < axis_dimension; ++axis_id) { - T res{ (val - max) *beta }; - + offset.set(actual_axis, axis_id); + const T val = *reinterpret_cast<const T *>(src(offset)); + T res{ (val - max) *beta }; if(is_log) { sum += std::exp(res); @@ -87,23 +82,24 @@ SimpleTensor<T> softmax_layer_generic(const SimpleTensor<T> &src, float beta, in res = std::exp(res); sum += res; } - return res; - }); + *reinterpret_cast<T *>(dst(offset)) = res; + } // Normalize - std::transform(dst_row_ptr, dst_row_ptr + lower_dims, dst_row_ptr, [sum, is_log](T val) + for(unsigned int axis_id = 0; axis_id < axis_dimension; ++axis_id) { + offset.set(actual_axis, axis_id); + const T val = *reinterpret_cast<const T *>(dst(offset)); if(is_log) { - return val - sum; + *reinterpret_cast<T *>(dst(offset)) = val - static_cast<T>(std::log(sum)); } else { - return val / sum; + *reinterpret_cast<T *>(dst(offset)) = val / sum; } - }); - } - + } + }); return dst; } @@ -111,26 +107,27 @@ template SimpleTensor<float> softmax_layer_generic(const SimpleTensor<float> &sr template SimpleTensor<half> softmax_layer_generic(const SimpleTensor<half> &src, float beta, int32_t axis, bool is_log); template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> -SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis) +SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis, bool is_log) { - return softmax_layer_generic<T>(src, beta, axis, false); + return softmax_layer_generic<T>(src, beta, axis, is_log); } template < typename T, typename std::enable_if < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int >::type > -SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis) +SimpleTensor<T> softmax_layer(const SimpleTensor<T> &src, float beta, int32_t axis, bool is_log) { - const QuantizationInfo output_quantization_info = arm_compute::get_softmax_output_quantization_info(src.data_type(), false); + const QuantizationInfo output_quantization_info = arm_compute::get_softmax_output_quantization_info(src.data_type(), is_log); SimpleTensor<float> src_tmp = convert_from_asymmetric(src); - SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta, axis); + SimpleTensor<float> dst_tmp = softmax_layer<float>(src_tmp, beta, axis, is_log); SimpleTensor<T> dst = convert_to_asymmetric<T>(dst_tmp, output_quantization_info); return dst; } -template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta, int32_t axis); -template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta, int32_t axis); -template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta, int32_t axis); -template SimpleTensor<int8_t> softmax_layer(const SimpleTensor<int8_t> &src, float beta, int32_t axis); +template SimpleTensor<float> softmax_layer(const SimpleTensor<float> &src, float beta, int32_t axis, bool is_log); +template SimpleTensor<half> softmax_layer(const SimpleTensor<half> &src, float beta, int32_t axis, bool is_log); +template SimpleTensor<uint8_t> softmax_layer(const SimpleTensor<uint8_t> &src, float beta, int32_t axis, bool is_log); +template SimpleTensor<int8_t> softmax_layer(const SimpleTensor<int8_t> &src, float beta, int32_t axis, bool is_log); + } // namespace reference } // namespace validation } // namespace test |