COMPMID-1632 Add CLL2NormalizationLayer for NHWC and FP32
Change-Id: Iae22554d5fe893fd22a000eab5bfd8275ea06eb3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154102
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
diff --git a/tests/validation/CL/L2NormalizeLayer.cpp b/tests/validation/CL/L2NormalizeLayer.cpp
index 3d121b0..517ba84 100644
--- a/tests/validation/CL/L2NormalizeLayer.cpp
+++ b/tests/validation/CL/L2NormalizeLayer.cpp
@@ -44,6 +44,10 @@
{
/** Tolerance for float operations */
constexpr AbsoluteTolerance<float> tolerance_f32(0.00001f);
+constexpr AbsoluteTolerance<float> tolerance_f16(0.01f);
+
+auto data = concat(combine(framework::dataset::make("DataLayout", { DataLayout::NCHW }), framework::dataset::make("Axis", { 0 })), combine(framework::dataset::make("DataLayout", { DataLayout::NHWC }),
+ framework::dataset::make("Axis", { 1 })));
} // namespace
@@ -58,7 +62,7 @@
TensorInfo(TensorShape(128U, 64U), 2, DataType::F32), // Number of Input channels != 1
TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != F32
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis >= num_max_dimensions
- TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 0
+ TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 3
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32)
}),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(128U, 64U), 1, DataType::F16),
@@ -69,7 +73,7 @@
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32)
})),
- framework::dataset::make("Axis", { 0U, 0U, 0U, 0U, static_cast<unsigned int>(TensorShape::num_max_dimensions), 1U, 0U })),
+ framework::dataset::make("Axis", { 0U, 0U, 0U, 0U, static_cast<unsigned int>(TensorShape::num_max_dimensions), 4U, 0U })),
framework::dataset::make("Expected", { false, false, false, false, false, false, true })),
input_info, output_info, axis, expected)
{
@@ -87,22 +91,36 @@
TEST_SUITE(Float)
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLL2NormalizeLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 })))
+ combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), data), framework::dataset::make("Epsilon", { 1e-12 })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLL2NormalizeLayerFixture<float>, framework::DatasetMode::NIGHTLY,
- combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 })))
+ combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), data), framework::dataset::make("Epsilon", { 1e-12 })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-TEST_SUITE_END()
-TEST_SUITE_END()
+TEST_SUITE_END() // FP32
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLL2NormalizeLayerFixture<half>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)), data), framework::dataset::make("Epsilon", { 1e-6 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLL2NormalizeLayerFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F16)), data), framework::dataset::make("Epsilon", { 1e-6 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // Float
-TEST_SUITE_END()
-TEST_SUITE_END()
+TEST_SUITE_END() // L2NormalizeLayer
+TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
diff --git a/tests/validation/CL/ReductionOperation.cpp b/tests/validation/CL/ReductionOperation.cpp
index 516a134..2adb4e9 100644
--- a/tests/validation/CL/ReductionOperation.cpp
+++ b/tests/validation/CL/ReductionOperation.cpp
@@ -58,16 +58,16 @@
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Mismatching data type input/output
TensorInfo(TensorShape(128U, 64U), 2, DataType::F32), // Number of Input channels != 1
- TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != F16/F32
+ TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != QASYMM8/F16/F32
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis >= num_max_dimensions
- TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 0 and SUM_SQUARE
+ TensorInfo(TensorShape(128U, 64U), 1, DataType::QASYMM8), // Axis == 0 and SUM_SQUARE and QASYMM8
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32)
}),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(1U, 64U), 1, DataType::F16),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
TensorInfo(TensorShape(1U, 64U), 1, DataType::S16),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
- TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
+ TensorInfo(TensorShape(1U, 64U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32)
})),
framework::dataset::make("Axis", { 0U, 0U, 0U, static_cast<unsigned int>(TensorShape::num_max_dimensions), 1U, 0U })),
diff --git a/tests/validation/NEON/L2NormalizeLayer.cpp b/tests/validation/NEON/L2NormalizeLayer.cpp
index f868ade..0a1ddba 100644
--- a/tests/validation/NEON/L2NormalizeLayer.cpp
+++ b/tests/validation/NEON/L2NormalizeLayer.cpp
@@ -85,14 +85,18 @@
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, NEL2NormalizeLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
- combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 })))
+ combine(combine(combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("Axis", { 0 })),
+ framework::dataset::make("Epsilon", { 1e-12 })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, NEL2NormalizeLayerFixture<float>, framework::DatasetMode::NIGHTLY,
- combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0 })), framework::dataset::make("Epsilon", { 1e-12 })))
+ combine(combine(combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+ framework::dataset::make("Axis", { 0 })),
+ framework::dataset::make("Epsilon", { 1e-12 })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
diff --git a/tests/validation/fixtures/L2NormalizeLayerFixture.h b/tests/validation/fixtures/L2NormalizeLayerFixture.h
index 6f11dcb..097d1c4 100644
--- a/tests/validation/fixtures/L2NormalizeLayerFixture.h
+++ b/tests/validation/fixtures/L2NormalizeLayerFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -45,10 +45,10 @@
{
public:
template <typename...>
- void setup(TensorShape shape, DataType data_type, unsigned int axis, float epsilon)
+ void setup(TensorShape shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon)
{
- _target = compute_target(shape, data_type, axis, epsilon);
- _reference = compute_reference(shape, data_type, axis, epsilon);
+ _target = compute_target(shape, data_type, data_layout, axis, epsilon);
+ _reference = compute_reference(shape, data_type, data_layout, axis, epsilon);
}
protected:
@@ -58,11 +58,16 @@
library->fill_tensor_uniform(tensor, 0);
}
- TensorType compute_target(const TensorShape &shape, DataType data_type, unsigned int axis, float epsilon)
+ TensorType compute_target(TensorShape shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon)
{
+ if(data_layout == DataLayout::NHWC)
+ {
+ permute(shape, PermutationVector(2U, 0U, 1U));
+ }
+
// Create tensors
- TensorType src = create_tensor<TensorType>(shape, data_type);
- TensorType dst = create_tensor<TensorType>(shape, data_type);
+ TensorType src = create_tensor<TensorType>(shape, data_type, 1, QuantizationInfo(), data_layout);
+ TensorType dst = create_tensor<TensorType>(shape, data_type, 1, QuantizationInfo(), data_layout);
// Create and configure function
FunctionType l2_norm_func;
@@ -87,8 +92,25 @@
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &shape, DataType data_type, unsigned int axis, float epsilon)
+ SimpleTensor<T> compute_reference(const TensorShape &shape, DataType data_type, DataLayout data_layout, unsigned int axis, float epsilon)
{
+ if(data_layout == DataLayout::NHWC)
+ {
+ switch(axis)
+ {
+ case 0:
+ axis = 2;
+ break;
+ case 1:
+ axis = 0;
+ break;
+ case 2:
+ axis = 1;
+ break;
+ default:
+ break;
+ }
+ }
// Create reference
SimpleTensor<T> src{ shape, data_type };
diff --git a/tests/validation/reference/L2NormalizeLayer.cpp b/tests/validation/reference/L2NormalizeLayer.cpp
index 99f4e8a..2667751 100644
--- a/tests/validation/reference/L2NormalizeLayer.cpp
+++ b/tests/validation/reference/L2NormalizeLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -66,7 +66,7 @@
{
const T *src_row_ptr = src.data() + du * elems;
T *dst_row_ptr = dst.data() + du * elems;
- const T normalization_value = std::sqrt(std::max(sum[du], epsilon));
+ const T normalization_value = sqrt(std::max(sum[du], static_cast<T>(epsilon)));
std::transform(src_row_ptr, src_row_ptr + elems, dst_row_ptr, [normalization_value](T val)
{
return val / normalization_value;
@@ -82,6 +82,7 @@
}
template SimpleTensor<float> l2_normalize(const SimpleTensor<float> &src, unsigned int axis, float epsilon);
+template SimpleTensor<half> l2_normalize(const SimpleTensor<half> &src, unsigned int axis, float epsilon);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/ReductionOperation.cpp b/tests/validation/reference/ReductionOperation.cpp
index 499263f..2f103a6 100644
--- a/tests/validation/reference/ReductionOperation.cpp
+++ b/tests/validation/reference/ReductionOperation.cpp
@@ -39,36 +39,39 @@
namespace
{
template <typename T>
-struct square
+T reduce_operation(T *ptr, int reduce_elements, ReductionOperation op, int stride)
{
- T operator()(const T &lhs, const T &rhs) const
- {
- return (lhs + rhs * rhs);
- }
-};
+ using type = typename std::remove_cv<T>::type;
+ auto res = type(0);
-template <typename T>
-struct sum
-{
- T operator()(const T &lhs, const T &rhs) const
+ if(std::is_integral<type>::value)
{
- return (lhs + rhs);
+ uint32_t int_res = 0;
+ for(int i = 0; i < reduce_elements; ++i)
+ {
+ auto elem = static_cast<uint32_t>(*(ptr + stride * i));
+ int_res += (op == ReductionOperation::SUM_SQUARE) ? elem * elem : elem;
+ }
+ if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0)
+ {
+ int_res /= reduce_elements;
+ }
+ res = saturate_cast<type>(int_res);
}
-};
+ else
+ {
+ for(int i = 0; i < reduce_elements; ++i)
+ {
+ auto elem = *(ptr + stride * i);
+ res += (op == ReductionOperation::SUM_SQUARE) ? elem * elem : elem;
+ }
+ if(op == ReductionOperation::MEAN_SUM && reduce_elements > 0)
+ {
+ res /= reduce_elements;
+ }
+ }
-template <typename T>
-T reduce_operation(T *ptr, int reduce_elements, ReductionOperation op)
-{
- switch(op)
- {
- case ReductionOperation::SUM_SQUARE:
- return std::accumulate(ptr, ptr + reduce_elements, static_cast<T>(0), square<T>());
- case ReductionOperation::SUM:
- case ReductionOperation::MEAN_SUM:
- return std::accumulate(ptr, ptr + reduce_elements, static_cast<T>(0), sum<T>());
- default:
- ARM_COMPUTE_ERROR("Unsupported reduction operation");
- }
+ return res;
}
} // namespace
@@ -77,44 +80,22 @@
{
// Create reference
SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.quantization_info() };
- const unsigned int src_width = src.shape().x();
- const unsigned int src_height = src.shape().y();
- const unsigned int src_depth = src.shape().z();
- const unsigned int src_batch = src.shape()[3];
- const bool mean = op == ReductionOperation::MEAN_SUM;
+ const unsigned int src_width = src.shape().x();
+ const unsigned int src_height = src.shape().y();
+ const unsigned int src_depth = src.shape().z();
+ const unsigned int src_batch = src.shape()[3];
+ const int reduce_elems = src.shape()[axis];
switch(axis)
{
case 0:
{
- const int reduce_elems = src.shape()[axis];
- const unsigned int upper_dims = src.shape().total_size_upper(1);
+ const unsigned int upper_dims = src.shape().total_size_upper(1);
for(unsigned int du = 0; du < upper_dims; ++du)
{
- if(std::is_integral<T>::value)
- {
- uint32_t res = 0;
- for(unsigned int x = 0; x < src_width; ++x)
- {
- res += static_cast<uint32_t>(src[du * src_width + x]);
- }
- if(mean && src_width > 0)
- {
- res /= src_width;
- }
- dst[du] = saturate_cast<uint8_t>(res);
- }
- else
- {
- const T *src_row_ptr = src.data() + du * reduce_elems;
-
- auto res = reduce_operation(src_row_ptr, reduce_elems, op);
- if(mean && src_width > 0)
- {
- res /= src_width;
- }
- dst[du] = res;
- }
+ const T *src_row_ptr = src.data() + du * reduce_elems;
+ auto res = reduce_operation(src_row_ptr, reduce_elems, op, 1);
+ dst[du] = res;
}
}
break;
@@ -125,32 +106,11 @@
{
for(unsigned int x = 0; x < src_width; ++x)
{
- if(std::is_integral<T>::value)
- {
- uint32_t res = 0;
- for(unsigned int y = 0; y < src_height; ++y)
- {
- res += static_cast<uint32_t>(src[du * src_height * src_width + y * src_width + x]);
- }
- if(mean && src_height > 0)
- {
- res /= src_height;
- }
- dst[du * src_width + x] = saturate_cast<uint8_t>(res);
- }
- else
- {
- auto res = T(0);
- for(unsigned int y = 0; y < src_height; ++y)
- {
- res += src[du * src_height * src_width + y * src_width + x];
- }
- if(mean && src_height > 0)
- {
- res /= src_height;
- }
- dst[du * src_width + x] = res;
- }
+ const int in_offset = du * src_height * src_width + x;
+ const int out_offset = du * src_width + x;
+ const T *src_row_ptr = src.data() + in_offset;
+ auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_width);
+ dst[out_offset] = res;
}
}
}
@@ -164,32 +124,11 @@
{
for(unsigned int y = 0; y < src_height; ++y)
{
- if(std::is_integral<T>::value)
- {
- uint32_t res = T(0);
- for(unsigned int z = 0; z < src_depth; ++z)
- {
- res += static_cast<uint32_t>(src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x]);
- }
- if(mean && src_depth > 0)
- {
- res /= src_depth;
- }
- dst[du * src_width * src_height + y * src_width + x] = saturate_cast<uint8_t>(res);
- }
- else
- {
- auto res = T(0);
- for(unsigned int z = 0; z < src_depth; ++z)
- {
- res += src[du * src_depth * src_height * src_width + z * src_height * src_width + y * src_width + x];
- }
- if(mean && src_depth > 0)
- {
- res /= src_depth;
- }
- dst[du * src_width * src_height + y * src_width + x] = res;
- }
+ const int in_offset = du * src_depth * src_height * src_width + y * src_width + x;
+ const int out_offset = du * src_width * src_height + y * src_width + x;
+ const T *src_row_ptr = src.data() + in_offset;
+ auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_height * src_width);
+ dst[out_offset] = res;
}
}
}
@@ -206,34 +145,11 @@
{
for(unsigned int x = 0; x < src_width; ++x)
{
- if(std::is_integral<T>::value)
- {
- uint32_t res = 0;
- for(unsigned int w = 0; w < src_batch; ++w)
- {
- res += static_cast<uint32_t>(src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x]);
- }
- if(mean && src_batch > 0)
- {
- res /= src_batch;
- }
-
- dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = saturate_cast<uint8_t>(res);
- }
- else
- {
- auto res = T(0);
- for(unsigned int w = 0; w < src_batch; ++w)
- {
- res += src[du * src_batch * src_depth * src_height * src_width + w * src_width * src_height * src_depth + z * src_width * src_height + y * src_width + x];
- }
- if(mean && src_batch > 0)
- {
- res /= src_batch;
- }
-
- dst[du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x] = res;
- }
+ const int in_offset = du * src_batch * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x;
+ const int out_offset = du * src_depth * src_height * src_width + z * src_width * src_height + y * src_width + x;
+ const T *src_row_ptr = src.data() + in_offset;
+ auto res = reduce_operation(src_row_ptr, reduce_elems, op, src_width * src_height * src_depth);
+ dst[out_offset] = res;
}
}
}