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-rwxr-xr-xscripts/check_clang-tidy.py3
-rw-r--r--tests/SConscript4
-rw-r--r--tests/TensorLibrary.h13
-rw-r--r--tests/Utils.h12
-rw-r--r--tests/validation/NEON/ConvolutionLayer.cpp2
-rw-r--r--tests/validation/TensorFactory.h10
-rw-r--r--tests/validation/TensorOperations.h27
-rw-r--r--tests/validation/Validation.cpp8
8 files changed, 55 insertions, 24 deletions
diff --git a/scripts/check_clang-tidy.py b/scripts/check_clang-tidy.py
index ead2513a93..fc73dca94e 100755
--- a/scripts/check_clang-tidy.py
+++ b/scripts/check_clang-tidy.py
@@ -18,6 +18,7 @@ if __name__ == "__main__":
if "error:" in line:
if (("Utils.cpp" in line and "'arm_compute_version.embed' file not found" in line) or
("cl2.hpp" in line and "cast from pointer to smaller type 'cl_context_properties' (aka 'int') loses information" in line) or
+ ("arm_fp16.h" in line) or
("memory" in line and "cast from pointer to smaller type 'uintptr_t' (aka 'unsigned int') loses information" in line) or
"3rdparty" in line):
continue
@@ -34,6 +35,8 @@ if __name__ == "__main__":
("NEMinMaxLocationKernel.cpp" in line and "move constructors should be marked noexcept" in line) or
("NEMinMaxLocationKernel.cpp" in line and "move assignment operators should be marked noexcept" in line) or
("PMUCounter.cpp" in line and "consider replacing 'long long' with 'int64'" in line) or
+ ("Validation.cpp" in line and "parameter 'classified_labels' is unused" in line) or
+ ("Validation.cpp" in line and "parameter 'expected_labels' is unused" in line) or
"3rdparty" in line):
continue
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());
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-19 18:15:11 +0000