COMPMID-661: Convolution quantized (#32)

Change-Id: Id69df4ce98d1d89bdf9c9aa5c4d909659909b30f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110456
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

5 files changed, 141 insertions, 46 deletions

diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index a6e07248aa..56e10f01b3 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -45,7 +45,8 @@ namespace
 {
 RelativeTolerance<float>            tolerance_f32(0.05f);                 /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
 RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
-constexpr AbsoluteTolerance<float>  tolerance_q(1.0f);                    /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
+constexpr AbsoluteTolerance<float>  tolerance_fixed(1.0f);                /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
+constexpr AbsoluteTolerance<float>  tolerance_qasymm8(0.0);               /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
 constexpr float                     tolerance_num = 0.07f;                /**< Tolerance number */
 
 /** CNN data types */
@@ -55,6 +56,7 @@ const auto CNNDataTypes = framework::dataset::make("DataType",
     DataType::F32,
     DataType::QS8,
     DataType::QS16,
+    DataType::QASYMM8,
 });
 } // namespace
 
@@ -67,17 +69,22 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     // Set fixed point position data type allowed
     int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
 
+    auto bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+
     // Create tensors
-    CLTensor src     = create_tensor<CLTensor>(input_shape, data_type, 1, fixed_point_position);
-    CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, fixed_point_position);
-    CLTensor bias    = create_tensor<CLTensor>(bias_shape, data_type, 1, fixed_point_position);
-    CLTensor dst     = create_tensor<CLTensor>(output_shape, data_type, 1, fixed_point_position);
+    CLTensor src     = create_tensor<CLTensor>(input_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127));
+    CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127));
+    CLTensor bias    = create_tensor<CLTensor>(bias_shape, bias_data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127));
+    CLTensor dst     = create_tensor<CLTensor>(output_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127));
 
     ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+    const QuantizationInfo src_quantization_info     = src.info()->quantization_info();
+    const QuantizationInfo weights_quantization_info = weights.info()->quantization_info();
+
     // Create and configure function
     CLConvolutionLayer conv;
     conv.configure(&src, &weights, &bias, &dst, info);
@@ -93,6 +100,10 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
     validate(bias.info()->valid_region(), bias_valid_region);
     validate(dst.info()->valid_region(), dst_valid_region);
 
+    // Validate QuantizationInfo
+    ARM_COMPUTE_EXPECT(src.info()->quantization_info() == src_quantization_info, framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(weights.info()->quantization_info() == weights_quantization_info, framework::LogLevel::ERRORS);
+
     // Validate padding
     //TODO(COMPMID-415) Need to validate padding?
 }
@@ -143,7 +154,7 @@ TEST_SUITE_END()
 template <typename T>
 using CLConvolutionLayerFixedPointFixture = ConvolutionValidationFixedPointFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>;
 
-TEST_SUITE(Quantized)
+TEST_SUITE(FixedPoint)
 TEST_SUITE(QS8)
 // We test for fixed point precision [4,6]
 FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
@@ -153,7 +164,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int8_t>, fr
                        framework::dataset::make("FractionalBits", 4, 7)))
 {
     // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_q);
+    validate(CLAccessor(_target), _reference, tolerance_fixed);
 }
 FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
                                                                                                                        framework::dataset::make("ReshapeWeights", { true, false })),
@@ -162,7 +173,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int8_t>, fr
                                                                                                                        framework::dataset::make("FractionalBits", 4, 7)))
 {
     // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_q);
+    validate(CLAccessor(_target), _reference, tolerance_fixed);
 }
 TEST_SUITE_END()
 
@@ -175,7 +186,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int16_t>, f
                        framework::dataset::make("FractionalBits", 1, 14)))
 {
     // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_q);
+    validate(CLAccessor(_target), _reference, tolerance_fixed);
 }
 FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
                                                                                                                         framework::dataset::make("ReshapeWeights", { true, false })),
@@ -184,7 +195,31 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int16_t>, f
                                                                                                                         framework::dataset::make("FractionalBits", 1, 14)))
 {
     // Validate output
-    validate(CLAccessor(_target), _reference, tolerance_q);
+    validate(CLAccessor(_target), _reference, tolerance_fixed);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+template <typename T>
+using CLConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+                       framework::dataset::make("ReshapeWeights", { true })),
+                       framework::dataset::make("DataType", DataType::QASYMM8)),
+                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_qasymm8);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+                                                                                                                       framework::dataset::make("ReshapeWeights", { true })),
+                                                                                                                       framework::dataset::make("DataType", DataType::QASYMM8)),
+                                                                                                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
 TEST_SUITE_END()
 TEST_SUITE_END()
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index 84e1bca6a5..4b747d64f7 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -232,4 +232,4 @@ TEST_SUITE_END()
 TEST_SUITE_END()
 } // namespace validation
 } // namespace test
-} // namespace arm_compute
+} // namespace arm_compute
\ No newline at end of file
diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp
index e53f5fd407..0d8c8774b8 100644
--- a/tests/validation/CL/FullyConnectedLayer.cpp
+++ b/tests/validation/CL/FullyConnectedLayer.cpp
@@ -99,6 +99,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame
     ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
+    const QuantizationInfo src_quantization_info     = src.info()->quantization_info();
+    const QuantizationInfo weights_quantization_info = weights.info()->quantization_info();
+
     // Create and configure function.
     CLFullyConnectedLayer fc;
     fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
@@ -106,6 +109,10 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame
     // Validate valid region
     const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape);
     validate(dst.info()->valid_region(), dst_valid_region);
+
+    // Validate QuantizationInfo
+    ARM_COMPUTE_EXPECT(src.info()->quantization_info() == src_quantization_info, framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(weights.info()->quantization_info() == weights_quantization_info, framework::LogLevel::ERRORS);
 }
 
 template <typename T>
diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index 859780812a..48b4665fe7 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -47,17 +47,24 @@ namespace test
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class ConvolutionValidationFixedPointFixture : public framework::Fixture
+class ConvolutionValidationGenericFixture : public framework::Fixture
 {
 public:
+    using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;
+
+public:
     template <typename...>
-    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits)
+    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights,
+               DataType data_type, int fractional_bits, QuantizationInfo quantization_info)
     {
-        _fractional_bits = fractional_bits;
-        _data_type       = data_type;
-
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits);
+        _data_type         = data_type;
+        _is_quantized      = is_data_type_quantized_asymmetric(data_type);
+        _bias_data_type    = _is_quantized ? DataType::S32 : data_type;
+        _fractional_bits   = fractional_bits;
+        _quantization_info = quantization_info;
+
+        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights);
+        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info);
     }
 
 protected:
@@ -66,6 +73,18 @@ protected:
     {
         switch(tensor.data_type())
         {
+            case DataType::QASYMM8:
+            {
+                std::uniform_int_distribution<uint8_t> distribution(0, 3);
+                library->fill(tensor, distribution, i);
+                break;
+            }
+            case DataType::S32:
+            {
+                std::uniform_int_distribution<int32_t> distribution(-100, 100);
+                library->fill(tensor, distribution, i);
+                break;
+            }
             case DataType::F16:
             case DataType::F32:
             {
@@ -79,7 +98,7 @@ protected:
     }
 
     TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
-                              bool reshape_weights, DataType data_type, int fixed_point_position)
+                              bool reshape_weights)
     {
         WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]);
         TensorShape reshaped_weights_shape(weights_shape);
@@ -90,15 +109,16 @@ protected:
             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
             bool       is_optimised                   = false;
 #if defined(__arm__)
-            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32;
+            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && _data_type == DataType::F32;
 #elif defined(__aarch64__)
-            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
+            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && _data_type == DataType::F32;
 #endif /* defined(__arm__) || defined(__aarch64__) */
 
             reshaped_weights_shape.collapse(3);
 
-            if(bias_shape.total_size() > 0)
+            if(bias_shape.total_size() > 0 && !_is_quantized)
             {
+                // Add bias to the weights reshaped matrix
                 reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1);
             }
 
@@ -110,17 +130,17 @@ protected:
             }
             else
             {
-                const int interleave_width = 16 / data_size_from_type(data_type);
+                const int interleave_width = 16 / data_size_from_type(_data_type);
                 reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width);
                 reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width))));
             }
         }
 
         // Create tensors
-        TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
-        TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
-        TensorType bias    = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
-        TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
+        TensorType src     = create_tensor<TensorType>(input_shape, _data_type, 1, _fractional_bits, _quantization_info);
+        TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _fractional_bits, _quantization_info);
+        TensorType bias    = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info);
+        TensorType dst     = create_tensor<TensorType>(output_shape, _data_type, 1, _fractional_bits, _quantization_info);
 
         // Create and configure function
         FunctionType conv;
@@ -150,20 +170,28 @@ protected:
             const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
             bool       is_optimised                   = false;
 #if defined(__arm__)
-            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32;
+            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && _data_type == DataType::F32;
 #elif defined(__aarch64__)
-            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32;
+            is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && _data_type == DataType::F32;
 #endif /* defined(__arm__) || defined(__aarch64__) */
 
             TensorShape     tmp_weights_shape(weights_shape);
-            SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position);
-            SimpleTensor<T> tmp_bias(bias_shape, data_type, 1, fixed_point_position);
+            SimpleTensor<T> tmp_weights(tmp_weights_shape, _data_type, 1, _fractional_bits, _quantization_info);
 
             // Fill with original shape
             fill(tmp_weights, 1);
-            fill(tmp_bias, 2);
 
-            tmp_weights = linearise_weights(tmp_weights, &tmp_bias);
+            if(_is_quantized)
+            {
+                fill(AccessorType(bias), 2);
+                tmp_weights = linearise_weights(tmp_weights);
+            }
+            else
+            {
+                SimpleTensor<T> tmp_bias(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info);
+                fill(tmp_bias, 2);
+                tmp_weights = linearise_weights(tmp_weights, &tmp_bias);
+            }
 
             if(!is_fully_connected_convolution && !is_optimised)
             {
@@ -192,13 +220,12 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
-                                      DataType data_type, int fixed_point_position)
+    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info)
     {
         // Create reference
-        SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
-        SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
-        SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
+        SimpleTensor<T>     src{ input_shape, _data_type, 1, _fractional_bits, _quantization_info };
+        SimpleTensor<T>     weights{ weights_shape, _data_type, 1, _fractional_bits, _quantization_info };
+        SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info };
 
         // Fill reference
         fill(src, 0);
@@ -208,10 +235,13 @@ protected:
         return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
     }
 
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-    int             _fractional_bits{};
-    DataType        _data_type{};
+    TensorType       _target{};
+    SimpleTensor<T>  _reference{};
+    DataType         _data_type{};
+    DataType         _bias_data_type{};
+    int              _fractional_bits{};
+    QuantizationInfo _quantization_info{};
+    bool             _is_quantized = false;
 
 private:
     template <typename U>
@@ -241,7 +271,6 @@ private:
 
             dst[dst_idx] = weights[weights_idx];
         }
-
         if(biases != nullptr)
         {
             // Fill last row with biases
@@ -260,13 +289,37 @@ private:
 };
 
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
+class ConvolutionValidationFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type)
     {
-        ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0);
+        ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0, QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ConvolutionValidationFixedPointFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits)
+    {
+        ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits,
+                                                                                              QuantizationInfo());
+    }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class ConvolutionValidationQuantizedFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type,
+               QuantizationInfo quantization_info)
+    {
+        ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0, quantization_info);
     }
 };
 } // namespace validation
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index c504e2e10d..06d6be3fa4 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -302,4 +302,4 @@ protected:
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
+#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
\ No newline at end of file