diff options
author | Pablo Tello <pablo.tello@arm.com> | 2017-06-23 10:40:05 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-09-17 14:14:20 +0100 |
commit | 383deec6b38f8b00f901d475000d46f8d3e5fb97 (patch) | |
tree | dc2e72587ea624d1b0eb06d8559af0e7783d90d0 /tests | |
parent | fabb038a54ca217497c17e31ba7ae098690f2f69 (diff) | |
download | ComputeLibrary-383deec6b38f8b00f901d475000d46f8d3e5fb97.tar.gz |
COMPMID-345: Added support for arm8.2+FP16 in the the validation framework.
Change-Id: Ifef2133d4a0da5456bec147330405b6d58cf6a71
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78676
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'tests')
-rw-r--r-- | tests/SConscript | 4 | ||||
-rw-r--r-- | tests/TensorLibrary.h | 13 | ||||
-rw-r--r-- | tests/Utils.h | 12 | ||||
-rw-r--r-- | tests/validation/NEON/ConvolutionLayer.cpp | 2 | ||||
-rw-r--r-- | tests/validation/TensorFactory.h | 10 | ||||
-rw-r--r-- | tests/validation/TensorOperations.h | 27 | ||||
-rw-r--r-- | tests/validation/Validation.cpp | 8 |
7 files changed, 52 insertions, 24 deletions
diff --git a/tests/SConscript b/tests/SConscript index ef39595feb..6be4ddb35e 100644 --- a/tests/SConscript +++ b/tests/SConscript @@ -56,10 +56,6 @@ else: common_env.Append(LIBS = ["arm_compute"]) arm_compute_lib = arm_compute_so -if env['arch'] == 'arm64-v8.2-a' and ( common_env['validation_tests'] or common_env['benchmark_tests']): - print("validation_tests=1 and benchmark_tests=1 are not currently supported for arch=arm64-v8.2-a") - Exit(1) - #FIXME Delete before release if common_env['internal_only']: common_env.Append(CPPDEFINES=['INTERNAL_ONLY']) diff --git a/tests/TensorLibrary.h b/tests/TensorLibrary.h index bdf91c6eda..b05302a9b0 100644 --- a/tests/TensorLibrary.h +++ b/tests/TensorLibrary.h @@ -43,6 +43,10 @@ #include <string> #include <type_traits> +#if ARM_COMPUTE_ENABLE_FP16 +#include <arm_fp16.h> // needed for float16_t +#endif + namespace arm_compute { namespace test @@ -494,10 +498,10 @@ void TensorLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_t fill(tensor, distribution_s64, seed_offset); break; } -#ifdef ENABLE_FP16 +#if ARM_COMPUTE_ENABLE_FP16 case DataType::F16: { - std::uniform_real_distribution<float16_t> distribution_f16(std::numeric_limits<float16_t>::lowest(), std::numeric_limits<float16_t>::max()); + std::uniform_real_distribution<float> distribution_f16(std::numeric_limits<float16_t>::lowest(), std::numeric_limits<float16_t>::max()); fill(tensor, distribution_f16, seed_offset); break; } @@ -589,11 +593,10 @@ void TensorLibrary::fill_tensor_uniform(T &&tensor, std::random_device::result_t fill(tensor, distribution_s64, seed_offset); break; } -#if ENABLE_FP16 +#if ARM_COMPUTE_ENABLE_FP16 case DataType::F16: { - ARM_COMPUTE_ERROR_ON(!(std::is_same<float16_t, D>::value)); - std::uniform_real_distribution<float16_t> distribution_f16(low, high); + std::uniform_real_distribution<float_t> distribution_f16(low, high); fill(tensor, distribution_f16, seed_offset); break; } diff --git a/tests/Utils.h b/tests/Utils.h index f3622cafaa..b2d4bf4f90 100644 --- a/tests/Utils.h +++ b/tests/Utils.h @@ -38,6 +38,10 @@ #include <string> #include <type_traits> +#if ARM_COMPUTE_ENABLE_FP16 +#include <arm_fp16.h> // needed for float16_t +#endif + namespace arm_compute { namespace test @@ -362,6 +366,10 @@ template <> struct promote<int16_t> { using type = int32_t; }; template <> struct promote<uint32_t> { using type = uint64_t; }; template <> struct promote<int32_t> { using type = int64_t; }; template <> struct promote<float> { using type = float; }; +#ifdef ARM_COMPUTE_ENABLE_FP16 +template <> struct promote<float16_t> { using type = float16_t; }; +#endif + template <typename T> using promote_t = typename promote<T>::type; @@ -513,11 +521,11 @@ void store_value_with_data_type(void *ptr, T value, DataType data_type) case DataType::S64: *reinterpret_cast<int64_t *>(ptr) = value; break; -#ifdef ENABLE_FP16 +#if ARM_COMPUTE_ENABLE_FP16 case DataType::F16: *reinterpret_cast<float16_t *>(ptr) = value; break; -#endif /* ENABLE_FP16 */ +#endif /* ARM_COMPUTE_ENABLE_FP16 */ case DataType::F32: *reinterpret_cast<float *>(ptr) = value; break; diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp index a1dbe38bbf..ee2b24db96 100644 --- a/tests/validation/NEON/ConvolutionLayer.cpp +++ b/tests/validation/NEON/ConvolutionLayer.cpp @@ -197,4 +197,4 @@ BOOST_AUTO_TEST_SUITE_END() BOOST_AUTO_TEST_SUITE_END() BOOST_AUTO_TEST_SUITE_END() BOOST_AUTO_TEST_SUITE_END() -#endif
\ No newline at end of file +#endif diff --git a/tests/validation/TensorFactory.h b/tests/validation/TensorFactory.h index 48f9d6702f..610425bbfb 100644 --- a/tests/validation/TensorFactory.h +++ b/tests/validation/TensorFactory.h @@ -30,6 +30,10 @@ #include "boost_wrapper.h" +#if ARM_COMPUTE_ENABLE_FP16 +#include <arm_fp16.h> // needed for float16_t +#endif + namespace arm_compute { namespace test @@ -39,7 +43,7 @@ namespace validation using TensorVariant = boost::variant < Tensor<uint8_t>, Tensor<int8_t>, Tensor<uint16_t>, Tensor<int16_t>, Tensor<uint32_t>, Tensor<int32_t>, -#ifdef ENABLE_FP16 +#ifdef ARM_COMPUTE_ENABLE_FP16 Tensor<float16_t>, #endif Tensor<float >>; @@ -90,10 +94,10 @@ public: using value_type_s32 = typename match_const<R, int32_t>::type; v = Tensor<int32_t>(shape, dt, fixed_point_position, reinterpret_cast<value_type_s32 *>(data)); break; -#ifdef ENABLE_FP16 +#ifdef ARM_COMPUTE_ENABLE_FP16 case DataType::F16: using value_type_f16 = typename match_const<R, float16_t>::type; - v = Tensor<float16_t>(raw.shape(), dt, reinterpret_cast<value_type_f16 *>(raw.data())); + v = Tensor<float16_t>(shape, dt, fixed_point_position, reinterpret_cast<value_type_f16 *>(data)); break; #endif case DataType::F32: diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h index 7337924b47..56cc657daa 100644 --- a/tests/validation/TensorOperations.h +++ b/tests/validation/TensorOperations.h @@ -49,13 +49,24 @@ namespace tensor_operations { namespace { +template <class T> +struct is_floating_point + : std::integral_constant < bool, + std::is_same<float, typename std::remove_cv<T>::type>::value || +#if ARM_COMPUTE_ENABLE_FP16 + std::is_same<float16_t, typename std::remove_cv<T>::type>::value || +#endif + std::is_same<double, typename std::remove_cv<T>::type>::value || std::is_same<long double, typename std::remove_cv<T>::type>::value > +{ +}; + bool is_valid_pixel(int i, int min, int max) { return (i >= min && i < max); } // 3D convolution for floating point type -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int8_t fixed_point_position) { const int half_width_weights = width_weights / 2; @@ -525,7 +536,7 @@ void depth_convert<int16_t, int32_t>(const Tensor<int16_t> &in, Tensor<int32_t> } // Matrix multiplication for floating point type -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void gemm(const Tensor<T> &in1, const Tensor<T> &in2, const Tensor<T> &in3, Tensor<T> &out, float alpha, float beta) { const int M = out.shape().y(); @@ -609,7 +620,7 @@ void pixel_wise_multiplication(const Tensor<T1> &in1, const Tensor<T2> &in2, Ten for(int i = 0; i < in1.num_elements(); ++i) { double val = static_cast<intermediate_type>(in1[i]) * static_cast<intermediate_type>(in2[i]) * static_cast<double>(scale); - if(std::is_floating_point<T3>::value) + if(is_floating_point<T3>::value) { out[i] = val; } @@ -705,7 +716,7 @@ void threshold(const Tensor<T> &in, Tensor<T> &out, uint8_t threshold, uint8_t f } // Activation Layer for floating point type -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void activation_layer(const Tensor<T> &in, Tensor<T> &out, ActivationLayerInfo act_info) { const T a = static_cast<T>(act_info.a()); @@ -838,7 +849,7 @@ void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor } // Batch Normalization Layer for floating point type -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position) { const int cols = static_cast<int>(in.shape()[0]); @@ -940,7 +951,7 @@ void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const } // Normalization Layer for floating point type -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void normalization_layer(const Tensor<T> &in, Tensor<T> &out, NormalizationLayerInfo norm_info) { const uint32_t norm_size = norm_info.norm_size(); @@ -1235,7 +1246,7 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in hstart = std::max(hstart, 0); wend = std::min(wend, w_in); hend = std::min(hend, h_in); - if(std::is_floating_point<T>::value) + if(is_floating_point<T>::value) { for(int y = hstart; y < hend; ++y) { @@ -1267,7 +1278,7 @@ void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_in } // Softmax Layer -template <typename T, typename std::enable_if<std::is_floating_point<T>::value, int>::type * = nullptr> +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr> void softmax_layer(const Tensor<T> &in, Tensor<T> &out) { const int cols = static_cast<int>(in.shape()[0]); diff --git a/tests/validation/Validation.cpp b/tests/validation/Validation.cpp index 17dc6952ca..8aada0cb0e 100644 --- a/tests/validation/Validation.cpp +++ b/tests/validation/Validation.cpp @@ -40,6 +40,10 @@ #include <cstdint> #include <iomanip> +#if ARM_COMPUTE_ENABLE_FP16 +#include <arm_fp16.h> // needed for float16_t +#endif + namespace arm_compute { namespace test @@ -82,7 +86,7 @@ double get_double_data(const void *ptr, DataType data_type) return *reinterpret_cast<const uint64_t *>(ptr); case DataType::S64: return *reinterpret_cast<const int64_t *>(ptr); -#if ENABLE_FP16 +#if ARM_COMPUTE_ENABLE_FP16 case DataType::F16: return *reinterpret_cast<const float16_t *>(ptr); #endif @@ -384,6 +388,8 @@ void validate(const IAccessor &tensor, BorderSize border_size, const BorderMode void validate(std::vector<unsigned int> classified_labels, std::vector<unsigned int> expected_labels) { + ARM_COMPUTE_UNUSED(classified_labels); + ARM_COMPUTE_UNUSED(expected_labels); BOOST_TEST(expected_labels.size() != 0); BOOST_TEST(classified_labels.size() == expected_labels.size()); |