diff options
Diffstat (limited to 'tests/validation/fixtures/GEMMLowpFixture.h')
| -rw-r--r-- | tests/validation/fixtures/GEMMLowpFixture.h | 22 |
1 files changed, 9 insertions, 13 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index 11a491faa7..aa4eedb75d 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -31,7 +31,7 @@ #include "tests/validation/Validation.h" #include "tests/validation/reference/GEMMLowp.h" #include "tests/validation/reference/ArithmeticOperations.h" -#include "tests/validation/reference/QuantizationLayer.h" +#include "tests/validation/reference/DequantizationLayer.h" #include <cstdint> #include <vector> @@ -472,20 +472,14 @@ template <typename TensorType, typename AccessorType, typename FunctionType, boo class GEMMLowpDequantizedMatrixMultiplyValidationFixture : public framework::Fixture { public: - void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, bool accumulate) { - // Accumulation is supported for Int8/UInt8 only in aarch64 - bool accumulate = true; - // Accumulation is not supported for Int8/UInt8 in aarch32 -#ifdef __arm__ - accumulate = false; -#endif //__arm__ - bool dynamic_qinfo = false; + const bool dynamic_qinfo = false; const auto a_qinfo = QuantizationInfo(1.0f / 255, a_offset); const auto b_qinfo = QuantizationInfo(5.0f / 255, b_offset); TensorFillInfo finfo; _target = compute_target(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); - _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate); + _reference = compute_reference(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, finfo, accumulate, dynamic_qinfo); } protected: @@ -495,14 +489,16 @@ protected: return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, int32_t, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, output_qinfo, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, GEMMLowpOutputStageInfo(), false, finfo, accumulate, dynamic_qinfo, DataType::F32); } - SimpleTensor<float> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, bool accumulate) + SimpleTensor<float> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, const QuantizationInfo& a_qinfo, const QuantizationInfo& b_qinfo, const TensorFillInfo& finfo, bool accumulate, const bool dynamic_qinfo) { + QuantizationInfo s32_ref_output_quant_info = QuantizationInfo(a_qinfo.uniform().scale * b_qinfo.uniform().scale, 0, dynamic_qinfo); + SimpleTensor<int32_t> s32_ref_output = compute_gemmlowp_reference<reinterpret_input_as_3d, int8_t, int8_t, false, false, run_twice>(shape_a, shape_b, shape_output, a_qinfo, b_qinfo, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, finfo); + s32_ref_output.quantization_info(s32_ref_output_quant_info); SimpleTensor<float> f32_ref_output(s32_ref_output.shape(), DataType::F32); - QuantizationInfo dst_quant_info = QuantizationInfo(a_qinfo.uniform().scale * b_qinfo.uniform().scale, 0); - f32_ref_output = reference::quantization_layer<int32_t, float>(s32_ref_output, DataType::F32, dst_quant_info); + f32_ref_output = reference::dequantization_layer<float, int32_t>(s32_ref_output); if (accumulate) { |