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author | Sang-Hoon Park <sang-hoon.park@arm.com> | 2021-02-03 10:32:59 +0000 |
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committer | Sang-Hoon Park <sang-hoon.park@arm.com> | 2021-03-08 17:00:45 +0000 |
commit | 668ccdcfb81bfab3a2d44cd1ddd956e83a2dfb09 (patch) | |
tree | d139e1a770bcfc182f1aef38a1b908d634f9bb1c /tests/validation/fixtures/ElementwiseOperationsFixture.h | |
parent | 201e0fee596dafcf9c869a550fae29779aad2394 (diff) | |
download | ComputeLibrary-668ccdcfb81bfab3a2d44cd1ddd956e83a2dfb09.tar.gz |
Add dynamic tensor support to CpuElementwise
The kernels and operators for binary and unary operations
are now capable of being configured with dynamic shapes and
computing windows at run-time.
Additionally, changing arguments' names is done
for consistency.
Partially Implements: COMPMID-4127
Change-Id: I48e5038692db667dec7cb2b2906fe5683214fe19
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4973
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/fixtures/ElementwiseOperationsFixture.h')
-rw-r--r-- | tests/validation/fixtures/ElementwiseOperationsFixture.h | 51 |
1 files changed, 48 insertions, 3 deletions
diff --git a/tests/validation/fixtures/ElementwiseOperationsFixture.h b/tests/validation/fixtures/ElementwiseOperationsFixture.h index dcb408c801..bf51c7e69b 100644 --- a/tests/validation/fixtures/ElementwiseOperationsFixture.h +++ b/tests/validation/fixtures/ElementwiseOperationsFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2020 Arm Limited. + * Copyright (c) 2018-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -48,9 +48,11 @@ public: template <typename...> void setup(ArithmeticOperation op, const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type, - QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out) + QuantizationInfo qinfo0, QuantizationInfo qinfo1, QuantizationInfo qinfo_out, bool use_dyanmic_shape = false) { - _op = op; + _op = op; + _use_dynamic_shape = use_dyanmic_shape; + _target = compute_target(shape0, shape1, data_type0, data_type1, output_data_type, qinfo0, qinfo1, qinfo_out); _reference = compute_reference(shape0, shape1, data_type0, data_type1, output_data_type, qinfo0, qinfo1, qinfo_out); } @@ -87,10 +89,26 @@ protected: TensorType ref_src2 = create_tensor<TensorType>(shape1, data_type1, 1, qinfo1); TensorType dst = create_tensor<TensorType>(TensorShape::broadcast_shape(shape0, shape1), output_data_type, 1, qinfo_out); + // if _use_dynamic_shape is true, this fixture will test scenario for dynamic shapes. + // - At configure time, all input tensors are marked as dynamic using set_tensor_dynamic() + // - After configure, tensors are marked as static for run using set_tensor_static() + // - The tensors with static shape are given to run() + if(_use_dynamic_shape) + { + set_tensor_dynamic(ref_src1); + set_tensor_dynamic(ref_src2); + } + // Create and configure function FunctionType elem_op; elem_op.configure(&ref_src1, &ref_src2, &dst); + if(_use_dynamic_shape) + { + set_tensor_static(ref_src1); + set_tensor_static(ref_src2); + } + ARM_COMPUTE_EXPECT(ref_src1.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(ref_src2.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); @@ -133,6 +151,7 @@ protected: TensorType _target{}; SimpleTensor<T> _reference{}; ArithmeticOperation _op{ ArithmeticOperation::ADD }; + bool _use_dynamic_shape{ false }; }; // Arithmetic operation fused with activation function @@ -226,6 +245,32 @@ public: }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ArithmeticDivisionBroadcastDynamicShapeValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type0, DataType data_type1, DataType output_data_type) + { + ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::DIV, shape0, shape1, + data_type0, data_type1, output_data_type, + QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), true); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ArithmeticDivisionDynamicShapeValidationFixture : public ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(const TensorShape &shape, DataType data_type0, DataType data_type1, DataType output_data_type) + { + ArithmeticOperationsGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(ArithmeticOperation::DIV, shape, shape, + data_type0, data_type1, output_data_type, + QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), true); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> class ArithmeticDivisionBroadcastValidationFloatFixture : public ArithmeticOperationsFuseActivationFixture<TensorType, AccessorType, FunctionType, T> { public: |