diff options
author | giuros01 <giuseppe.rossini@arm.com> | 2019-05-14 16:12:53 +0100 |
---|---|---|
committer | Giuseppe Rossini <giuseppe.rossini@arm.com> | 2019-06-11 10:38:21 +0000 |
commit | d5134364fc4ca40ea65635192e7959327d690a01 (patch) | |
tree | d6781cc0319e54e538ea2b02ea59e842acfd6e49 | |
parent | e7510622419a63315e5ad5ed7de61a2ce4bd0b49 (diff) | |
download | ComputeLibrary-d5134364fc4ca40ea65635192e7959327d690a01.tar.gz |
COMPMID-2321: PRELU support in NEActivationLayer
Change-Id: Ib320ee7772492cd1b86eba624438da826d47b984
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1224
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
-rw-r--r-- | arm_compute/core/NEON/wrapper/traits.h | 42 | ||||
-rw-r--r-- | arm_compute/runtime/NEON/NEFunctions.h | 1 | ||||
-rw-r--r-- | arm_compute/runtime/NEON/functions/NEPReluLayer.h | 59 | ||||
-rw-r--r-- | src/core/NEON/kernels/NEElementwiseOperationKernel.cpp | 84 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NEPReluLayer.cpp | 43 | ||||
-rw-r--r-- | tests/validation/NEON/PReluLayer.cpp | 218 |
6 files changed, 398 insertions, 49 deletions
diff --git a/arm_compute/core/NEON/wrapper/traits.h b/arm_compute/core/NEON/wrapper/traits.h index 0dbd90ddf8..cc22597c29 100644 --- a/arm_compute/core/NEON/wrapper/traits.h +++ b/arm_compute/core/NEON/wrapper/traits.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -44,27 +44,27 @@ struct vector_128_tag {}; template <typename T, int S> struct neon_vector; // Specializations #ifndef DOXYGEN_SKIP_THIS -template <> struct neon_vector<uint8_t, 8>{ using type = uint8x8_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<int8_t, 8>{ using type = int8x8_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<uint8_t, 16>{ using type = uint8x16_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<int8_t, 16>{ using type = int8x16_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<uint16_t, 4>{ using type = uint16x4_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<int16_t, 4>{ using type = int16x4_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<uint16_t, 8>{ using type = uint16x8_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<int16_t, 8>{ using type = int16x8_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<uint32_t, 2>{ using type = uint32x2_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<int32_t, 2>{ using type = int32x2_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<uint32_t, 4>{ using type = uint32x4_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<int32_t, 4>{ using type = int32x4_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<uint64_t, 1>{ using type = uint64x1_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<int64_t, 1>{ using type = int64x1_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<uint64_t, 2>{ using type = uint64x2_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<int64_t, 2>{ using type = int64x2_t; using tag_type = vector_128_tag; }; -template <> struct neon_vector<float_t, 2>{ using type = float32x2_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<float_t, 4>{ using type = float32x4_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<uint8_t, 8>{ using scalar_type = uint8_t; using type = uint8x8_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<int8_t, 8>{ using scalar_type = int8_t; using type = int8x8_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<uint8_t, 16>{ using scalar_type = uint8_t; using type = uint8x16_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<int8_t, 16>{ using scalar_type = int8_t; using type = int8x16_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<uint16_t, 4>{ using scalar_type = uint16_t; using type = uint16x4_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<int16_t, 4>{ using scalar_type = int16_t; using type = int16x4_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<uint16_t, 8>{ using scalar_type = uint16_t; using type = uint16x8_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<int16_t, 8>{ using scalar_type = int16_t; using type = int16x8_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<uint32_t, 2>{ using scalar_type = uint32_t; using type = uint32x2_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<int32_t, 2>{ using scalar_type = int32_t; using type = int32x2_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<uint32_t, 4>{ using scalar_type = uint32_t; using type = uint32x4_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<int32_t, 4>{ using scalar_type = int32_t; using type = int32x4_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<uint64_t, 1>{ using scalar_type = uint64_t;using type = uint64x1_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<int64_t, 1>{ using scalar_type = int64_t; using type = int64x1_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<uint64_t, 2>{ using scalar_type = uint64_t; using type = uint64x2_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<int64_t, 2>{ using scalar_type = int64_t; using type = int64x2_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<float_t, 2>{ using scalar_type = float_t; using type = float32x2_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<float_t, 4>{ using scalar_type = float_t; using type = float32x4_t; using tag_type = vector_128_tag; }; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template <> struct neon_vector<float16_t, 4>{ using type = float16x4_t; using tag_type = vector_64_tag; }; -template <> struct neon_vector<float16_t, 8>{ using type = float16x8_t; using tag_type = vector_128_tag; }; +template <> struct neon_vector<float16_t, 4>{ using scalar_type = float16_t; using type = float16x4_t; using tag_type = vector_64_tag; }; +template <> struct neon_vector<float16_t, 8>{ using scalar_type = float16_t; using type = float16x8_t; using tag_type = vector_128_tag; }; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC #endif /* DOXYGEN_SKIP_THIS */ diff --git a/arm_compute/runtime/NEON/NEFunctions.h b/arm_compute/runtime/NEON/NEFunctions.h index 4e0cdd7a0a..94607364b3 100644 --- a/arm_compute/runtime/NEON/NEFunctions.h +++ b/arm_compute/runtime/NEON/NEFunctions.h @@ -106,6 +106,7 @@ #include "arm_compute/runtime/NEON/functions/NENonMaximaSuppression3x3.h" #include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h" #include "arm_compute/runtime/NEON/functions/NEOpticalFlow.h" +#include "arm_compute/runtime/NEON/functions/NEPReluLayer.h" #include "arm_compute/runtime/NEON/functions/NEPadLayer.h" #include "arm_compute/runtime/NEON/functions/NEPermute.h" #include "arm_compute/runtime/NEON/functions/NEPhase.h" diff --git a/arm_compute/runtime/NEON/functions/NEPReluLayer.h b/arm_compute/runtime/NEON/functions/NEPReluLayer.h new file mode 100644 index 0000000000..52db4279cd --- /dev/null +++ b/arm_compute/runtime/NEON/functions/NEPReluLayer.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef __ARM_COMPUTE_NEPRELULAYER_H__ +#define __ARM_COMPUTE_NEPRELULAYER_H__ + +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/INESimpleFunction.h" + +namespace arm_compute +{ +class ITensor; + +/** Basic function to run @ref NEArithmeticOperationKernel for PRELU + * + * @note The function implements an activation layer with the PRELU activation function. + */ +class NEPReluLayer : public INESimpleFunction +{ +public: + /** Set the input and output tensor. + * + * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32. + * @param[in] alpha Source alpha tensor. Data types supported: same of @p input. + * @param[out] output Destination tensor. Data type supported: same as @p input + */ + void configure(const ITensor *input, const ITensor *alpha, ITensor *output); + /** Static function to check if given info will lead to a valid configuration of @ref NEPReluLayer + * + * @param[in] input Source tensor info. Data types supported: QASYMM8/F16/F32. + * @param[in] alpha Source alpha tensor info. Data types supported: same of @p input. + * @param[in] output Destination tensor info. Data type supported: same as @p input + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *alpha, const ITensorInfo *output); +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_NEPRELULAYER_H__ */ diff --git a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp index 0fe05d2044..8bd37d5913 100644 --- a/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp +++ b/src/core/NEON/kernels/NEElementwiseOperationKernel.cpp @@ -125,6 +125,11 @@ inline ScalarType elementwise_arithm_op_scalar(const ScalarType &a, const Scalar res = (a - b) * (a - b); break; } + case ArithmeticOperation::PRELU: + { + res = (a > 0 ? a : a * b); + break; + } case ArithmeticOperation::DIV: { res = a / b; @@ -147,10 +152,14 @@ inline uint8_t elementwise_arithm_op_quantized_scalar(const float &a, const floa return quantize_qasymm8(elementwise_arithm_op_scalar<op>(a, b), qinfo); } -template <ArithmeticOperation op, typename VectorType> -inline VectorType elementwise_arithm_op(const VectorType &a, const VectorType &b) +template <ArithmeticOperation op, typename VectorType> +inline typename VectorType::type elementwise_arithm_op(const typename VectorType::type &a, const typename VectorType::type &b) { - VectorType res = { 0, 0, 0, 0 }; + using vec_type = typename VectorType::type; + using scalar_type = typename VectorType::scalar_type; + using tag_type = typename VectorType::tag_type; + + vec_type res = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); switch(op) { @@ -162,10 +171,20 @@ inline VectorType elementwise_arithm_op(const VectorType &a, const VectorType &b break; case ArithmeticOperation::SQUARED_DIFF: { - const VectorType tmp = wrapper::vsub(a, b); - res = wrapper::vmul(tmp, tmp); + const vec_type tmp = wrapper::vsub(a, b); + res = wrapper::vmul(tmp, tmp); + break; + } + case ArithmeticOperation::PRELU: + { + const vec_type zero = wrapper::vdup_n(static_cast<scalar_type>(0), tag_type{}); + const vec_type tmp = wrapper::vmul(a, b); + const auto gt = wrapper::vcgt(a, zero); + + res = wrapper::vbsl(gt, a, tmp); break; } + default: ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); } @@ -174,26 +193,26 @@ inline VectorType elementwise_arithm_op(const VectorType &a, const VectorType &b } template <> -inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, float32x4_t>(const float32x4_t &a, const float32x4_t &b) +inline float32x4_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) { return wrapper::vdiv(a, b); } template <> -inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, float32x4_t>(const float32x4_t &a, const float32x4_t &b) +inline float32x4_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector<float, 4>>(const float32x4_t &a, const float32x4_t &b) { return wrapper::vpow(a, b); } #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC template <> -inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, float16x8_t>(const float16x8_t &a, const float16x8_t &b) +inline float16x8_t elementwise_arithm_op<ArithmeticOperation::DIV, typename wrapper::traits::neon_vector_t<float16_t, 4>>(const float16x8_t &a, const float16x8_t &b) { return wrapper::vdiv(a, b); } template <> -inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, float16x8_t>(const float16x8_t &a, const float16x8_t &b) +inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, typename wrapper::traits::neon_vector_t<float16_t, 4>>(const float16x8_t &a, const float16x8_t &b) { return wrapper::vpow(a, b); } @@ -202,23 +221,27 @@ inline float16x8_t elementwise_arithm_op<ArithmeticOperation::POWER, float16x8_t template <ArithmeticOperation op> inline float32x4x4_t elementwise_arithm_op(const float32x4x4_t &a, const float32x4x4_t &b) { + using neon_vector_float = wrapper::traits::neon_vector<float, 4>; float32x4x4_t out = { { - elementwise_arithm_op<op>(a.val[0], b.val[0]), - elementwise_arithm_op<op>(a.val[1], b.val[1]), - elementwise_arithm_op<op>(a.val[2], b.val[2]), - elementwise_arithm_op<op>(a.val[3], b.val[3]), + elementwise_arithm_op<op, neon_vector_float>(a.val[0], b.val[0]), + elementwise_arithm_op<op, neon_vector_float>(a.val[1], b.val[1]), + elementwise_arithm_op<op, neon_vector_float>(a.val[2], b.val[2]), + elementwise_arithm_op<op, neon_vector_float>(a.val[3], b.val[3]), } }; return out; } -template <ArithmeticOperation op, typename ScalarType, typename VectorType> -inline VectorType elementwise_arithm_op_broadcast(const VectorType &a, const ScalarType &broadcast_value, const bool reorder) +template <ArithmeticOperation op, typename ScalarType, typename VectorType> +inline typename VectorType::type elementwise_arithm_op_broadcast(const typename VectorType::type &a, const ScalarType &broadcast_value, const bool reorder) { - VectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag()); - return elementwise_arithm_op<op>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); + using tag_type = typename VectorType::tag_type; + using vec_type = typename VectorType::type; + + vec_type broadcast_vector = wrapper::vdup_n(broadcast_value, tag_type{}); + return elementwise_arithm_op<op, VectorType>(reorder ? broadcast_vector : a, reorder ? a : broadcast_vector); } template <ComparisonOperation op, typename InputScalarType> @@ -322,7 +345,7 @@ inline int elementwise_arithm_op_loop(int window_start_x, int window_end_x, int { const auto a = wrapper::vloadq(input1_ptr + x); const auto b = wrapper::vloadq(input2_ptr + x); - wrapper::vstore(output_ptr + x, elementwise_arithm_op<op>(a, b)); + wrapper::vstore(output_ptr + x, elementwise_arithm_op<op, VectorType>(a, b)); } return x; } @@ -353,7 +376,7 @@ inline int elementwise_arithm_op_broadcast_loop(int window_start_x, int window_e for(; x <= (window_end_x - window_step_x); x += window_step_x) { const auto a = wrapper::vloadq((non_broadcast_input_ptr + x)); - wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op>(a, broadcast_value, reorder)); + wrapper::vstore(output_ptr + x, elementwise_arithm_op_broadcast<op, ScalarType, VectorType>(a, broadcast_value, reorder)); } return x; } @@ -692,13 +715,15 @@ void elementwise_comp_op_32(const ITensor *in1, const ITensor *in2, ITensor *out &elementwise_comp_op_32_loop<op, InputScalarType, InputVectorType>); } -template <ArithmeticOperation op, typename ScalarType, typename VectorType> +template <ArithmeticOperation op, typename VectorType> void elementwise_arithm_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - elementwise_op<ScalarType, ScalarType, VectorType>(in1, in2, out, window, - &elementwise_arithm_op_scalar<op, ScalarType>, - &elementwise_arithm_op_broadcast_loop<op, ScalarType, VectorType>, - &elementwise_arithm_op_loop<op, ScalarType, VectorType>); + using scalar_type = typename VectorType::scalar_type; + + elementwise_op<scalar_type, scalar_type, VectorType>(in1, in2, out, window, + &elementwise_arithm_op_scalar<op, scalar_type>, + &elementwise_arithm_op_broadcast_loop<op, scalar_type, VectorType>, + &elementwise_arithm_op_loop<op, scalar_type, VectorType>); } template <ArithmeticOperation op> @@ -745,13 +770,13 @@ configure_arithm_func(const ITensor *input1, const ITensor *input2, ITensor *out { static std::map<std::string, NEElementwiseOperationKernel::ElementwiseFunction *> map_function = { - { "op_F32_F32_F32", &elementwise_arithm_op<op, float, float32x4_t> }, - { "op_S16_S16_S16", &elementwise_arithm_op<op, int16_t, int16x8_t> }, - { "op_S32_S32_S32", &elementwise_arithm_op<op, int32_t, int32x4_t> }, + { "op_F32_F32_F32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float, 4>> }, + { "op_S16_S16_S16", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int16_t, 8>> }, + { "op_S32_S32_S32", &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<int32_t, 4>> }, { "op_QASYMM8_QASYMM8_QASYMM8", &elementwise_arithm_op_quantized<op> } }; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - map_function["op_F16_F16_F16"] = &elementwise_arithm_op<op, float16_t, float16x8_t>; + map_function["op_F16_F16_F16"] = &elementwise_arithm_op<op, typename wrapper::traits::neon_vector<float16_t, 8>>; #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ return configure_func(input1, input2, output, map_function); @@ -849,6 +874,9 @@ void NEArithmeticOperationKernel::configure(ArithmeticOperation op, const ITenso case ArithmeticOperation::SQUARED_DIFF: _function = configure_arithm_func<ArithmeticOperation::SQUARED_DIFF>(input1, input2, output); break; + case ArithmeticOperation::PRELU: + _function = configure_arithm_func<ArithmeticOperation::PRELU>(input1, input2, output); + break; default: ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); } diff --git a/src/runtime/NEON/functions/NEPReluLayer.cpp b/src/runtime/NEON/functions/NEPReluLayer.cpp new file mode 100644 index 0000000000..b386fed575 --- /dev/null +++ b/src/runtime/NEON/functions/NEPReluLayer.cpp @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/NEON/functions/NEPReluLayer.h" + +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/kernels/NEElementwiseOperationKernel.h" +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +void NEPReluLayer::configure(const ITensor *input, const ITensor *alpha, ITensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique<NEArithmeticOperationKernel>(); + k->configure(ArithmeticOperation::PRELU, input, alpha, output); + _kernel = std::move(k); +} + +Status NEPReluLayer::validate(const ITensorInfo *input, const ITensorInfo *alpha, const ITensorInfo *output) +{ + return NEArithmeticOperationKernel::validate(ArithmeticOperation::PRELU, input, alpha, output); +} +} // namespace arm_compute diff --git a/tests/validation/NEON/PReluLayer.cpp b/tests/validation/NEON/PReluLayer.cpp new file mode 100644 index 0000000000..95dbf33393 --- /dev/null +++ b/tests/validation/NEON/PReluLayer.cpp @@ -0,0 +1,218 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEPReluLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" +#include "tests/NEON/Accessor.h" +#include "tests/PaddingCalculator.h" +#include "tests/datasets/ShapeDatasets.h" +#include "tests/framework/Asserts.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/ElementwiseOperationsFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +RelativeTolerance<float> tolerance_fp32(0.000001f); + +constexpr unsigned int num_elems_processed_per_iteration = 16; +/** Input data sets **/ +const auto PReluLayerQASYMM8Dataset = combine(combine(framework::dataset::make("DataType", DataType::QASYMM8), framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataType", + DataType::QASYMM8)); +const auto PReluLayerFP32Dataset = combine(combine(framework::dataset::make("DataType", DataType::F32), framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataType", DataType::F32)); + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +RelativeTolerance<float> tolerance_fp16(0.001f); + +const auto PReluLayerFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("DataType", DataType::F16)); + +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(PReluLayer) + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( + framework::dataset::make("Input1Info", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8), // Window shrink + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8), // Invalid data type combination + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching shapes + }), + framework::dataset::make("Input2Info",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::S16), + TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32), + })), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::U8), + TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::U8), + TensorInfo(TensorShape(48U, 11U, 2U), 1, DataType::F32), + })), + framework::dataset::make("Expected", { true, true, false, false, false})), + input1_info, input2_info, output_info, expected) +{ + ARM_COMPUTE_EXPECT(bool(NEPReluLayer::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + +template <typename T> +using NEPReluLayerFixture = PReluLayerValidationFixture<Tensor, Accessor, NEPReluLayer, T>; + +template <typename T> +using NEPReluLayerQuantizedFixture = PReluLayerValidationQuantizedFixture<Tensor, Accessor, NEPReluLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, datasets::SmallShapes(), + shape) +{ + // Create tensors + Tensor ref_src1 = create_tensor<Tensor>(shape, DataType::QASYMM8); + Tensor ref_src2 = create_tensor<Tensor>(shape, DataType::QASYMM8); + Tensor dst = create_tensor<Tensor>(shape, DataType::QASYMM8); + + // Create and Configure function + NEPReluLayer prelu; + prelu.configure(&ref_src1, &ref_src2, &dst); + + // Validate valid region + const ValidRegion valid_region = shape_to_valid_region(shape); + validate(dst.info()->valid_region(), valid_region); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, NEPReluLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallShapes(), + PReluLayerQASYMM8Dataset), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) })) + + ) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32, 0.01); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, NEPReluLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeShapes(), + PReluLayerQASYMM8Dataset), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(5.f / 255.f, 20) })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 255.f, 5) })) + + ) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32, 0.01); +} +TEST_SUITE_END() +TEST_SUITE_END() + +TEST_SUITE(Float) +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmall, NEPReluLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), PReluLayerFP16Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp16, 0.01); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, NEPReluLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), PReluLayerFP16Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp16, 0.01); +} +TEST_SUITE_END() // FP16 +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + +TEST_SUITE(FP32) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, datasets::SmallShapes(), + shape) +{ + // Create tensors + Tensor ref_src1 = create_tensor<Tensor>(shape, DataType::F32); + Tensor ref_src2 = create_tensor<Tensor>(shape, DataType::F32); + Tensor dst = create_tensor<Tensor>(shape, DataType::F32); + + // Create and Configure function + NEPReluLayer prelu; + prelu.configure(&ref_src1, &ref_src2, &dst); + + // Validate valid region + const ValidRegion valid_region = shape_to_valid_region(shape); + validate(dst.info()->valid_region(), valid_region); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, NEPReluLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), PReluLayerFP32Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, NEPReluLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapes(), PReluLayerFP32Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} + +template <typename T> +using NEPReluLayerBroadcastFixture = PReluLayerBroadcastValidationFixture<Tensor, Accessor, NEPReluLayer, T>; + +FIXTURE_DATA_TEST_CASE(RunSmallBroadcast, NEPReluLayerBroadcastFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallShapesBroadcast(), + PReluLayerFP32Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} + +FIXTURE_DATA_TEST_CASE(RunLargeBroadcast, NEPReluLayerBroadcastFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeShapesBroadcast(), + PReluLayerFP32Dataset)) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_fp32); +} +TEST_SUITE_END() // FP32 +TEST_SUITE_END() // Float + +TEST_SUITE_END() // PReluLayer +TEST_SUITE_END() // NEON +} // namespace validation +} // namespace test +} // namespace arm_compute |