diff options
Diffstat (limited to 'tests/validation/TensorOperations.h')
| -rw-r--r-- | tests/validation/TensorOperations.h | 76 |
1 files changed, 25 insertions, 51 deletions
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index 319047816c..359dfe8d03 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -24,18 +24,15 @@ #ifndef __ARM_COMPUTE_TEST_TENSOR_OPERATIONS_H__ #define __ARM_COMPUTE_TEST_TENSOR_OPERATIONS_H__ -#include "FixedPoint.h" -#include "Tensor.h" -#include "Types.h" -#include "Utils.h" -#include "support/ToolchainSupport.h" - -#include "FixedPoint.h" -#include "Types.h" #include "arm_compute/core/FixedPoint.h" #include "arm_compute/core/Types.h" +#include "support/ToolchainSupport.h" +#include "tests/Types.h" +#include "tests/Utils.h" #include "tests/validation/FixedPoint.h" +#include "tests/validation/Tensor.h" #include "tests/validation/ValidationUserConfiguration.h" +#include "tests/validation/half.h" #include <algorithm> #include <array> @@ -44,26 +41,6 @@ #include <string> #include <vector> -#if ARM_COMPUTE_ENABLE_FP16 -//Beware! most std templates acting on types don't work with the data type float16_t -namespace std -{ -template <> -class numeric_limits<float16_t> -{ -public: - static float16_t lowest() - { - return -std::numeric_limits<float>::max(); // -inf - }; - static float16_t max() - { - return std::numeric_limits<float>::max(); // +inf - }; -}; -} -#endif /* ARM_COMPUTE_ENABLE_FP16 */ - namespace arm_compute { namespace test @@ -77,11 +54,8 @@ namespace template <class T> struct is_floating_point : std::integral_constant < bool, - std::is_same<float, typename std::remove_cv<T>::type>::value || -#ifdef ARM_COMPUTE_ENABLE_FP16 - std::is_same<float16_t, typename std::remove_cv<T>::type>::value || -#endif /* ARM_COMPUTE_ENABLE_FP16 */ - std::is_same<double, typename std::remove_cv<T>::type>::value || std::is_same<long double, typename std::remove_cv<T>::type>::value > + std::is_same<float, typename std::remove_cv<T>::type>::value || std::is_same<half_float::half, typename std::remove_cv<T>::type>::value + || std::is_same<double, typename std::remove_cv<T>::type>::value || std::is_same<long double, typename std::remove_cv<T>::type>::value > { }; @@ -184,7 +158,7 @@ void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out { for(int x = 0; x < cols_weights; ++x) { - T acc = 0.0f; + T acc(0); for(int y = 0; y < rows_weights; ++y) { acc += in[y] * weights[x + y * cols_weights]; @@ -456,8 +430,8 @@ void absolute_difference(const Tensor<T1> &in1, const Tensor<T2> &in2, Tensor<T3 for(int i = 0; i < in1.num_elements(); ++i) { - intermediate_type val = std::abs(static_cast<intermediate_type>(in1[i]) - static_cast<intermediate_type>(in2[i])); - out[i] = saturate_cast<T3>(val); + intermediate_type val(std::abs(static_cast<intermediate_type>(in1[i]) - static_cast<intermediate_type>(in2[i]))); + out[i] = saturate_cast<T3>(val); } } @@ -708,7 +682,7 @@ void gemm(const Tensor<T> &in1, const Tensor<T> &in2, const Tensor<T> &in3, Tens { for(int c = 0; c < N; ++c) { - T acc = 0.0f; + T acc(0); for(int k = 0; k < K; ++k) { @@ -967,10 +941,10 @@ void activation_layer(const Tensor<T> &in, Tensor<T> &out, ActivationLayerInfo a out[i] = static_cast<T>(1) / (static_cast<T>(1) + std::exp(-x)); break; case ActivationLayerInfo::ActivationFunction::RELU: - out[i] = std::max<T>(0, x); + out[i] = std::max(static_cast<T>(0), x); break; case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: - out[i] = std::min<T>(a, std::max<T>(0, x)); + out[i] = std::min<T>(a, std::max(static_cast<T>(0), x)); break; case ActivationLayerInfo::ActivationFunction::LEAKY_RELU: out[i] = (x > 0) ? x : a * x; @@ -1519,16 +1493,16 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in { for(int w = 0; w < pooled_w; ++w) { - T avg_val = 0; - int wstart = w * pool_stride_x - pad_x; - int hstart = h * pool_stride_y - pad_y; - int wend = std::min(wstart + pool_size, w_in + pad_x); - int hend = std::min(hstart + pool_size, h_in + pad_y); - int pool = (hend - hstart) * (wend - wstart); - wstart = std::max(wstart, 0); - hstart = std::max(hstart, 0); - wend = std::min(wend, w_in); - hend = std::min(hend, h_in); + T avg_val(0); + int wstart = w * pool_stride_x - pad_x; + int hstart = h * pool_stride_y - pad_y; + int wend = std::min(wstart + pool_size, w_in + pad_x); + int hend = std::min(hstart + pool_size, h_in + pad_y); + int pool = (hend - hstart) * (wend - wstart); + wstart = std::max(wstart, 0); + hstart = std::max(hstart, 0); + wend = std::min(wend, w_in); + hend = std::min(hend, h_in); if(is_floating_point<T>::value) { for(int y = hstart; y < hend; ++y) @@ -1652,7 +1626,7 @@ void softmax_layer(const Tensor<T> &in, Tensor<T> &out) } // Regularize - T sum = 0; + T sum(0); for(int c = 0; c < cols; ++c) { const T res = exp(in[r * cols + c] - max); @@ -1661,7 +1635,7 @@ void softmax_layer(const Tensor<T> &in, Tensor<T> &out) } // Normalize - const T norm_val = 1 / sum; + const T norm_val = static_cast<T>(1) / sum; for(int c = 0; c < cols; ++c) { out[r * cols + c] *= norm_val; |