| /* |
| * Copyright (c) 2017-2021 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
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| * 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 |
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| * SOFTWARE. |
| */ |
| #ifndef ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/RawTensor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/ActivationLayer.h" |
| #include "tests/validation/reference/FullyConnectedLayer.h" |
| #include "tests/validation/reference/Utils.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class FullyConnectedLayerValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TDecay = typename std::decay<T>::type; |
| using TBias = typename std::conditional < (std::is_same<TDecay, uint8_t>::value || std::is_same<TDecay, int8_t>::value), int32_t, T >::type; |
| |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, |
| DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo activation_info, bool mixed_layout = false) |
| { |
| ARM_COMPUTE_UNUSED(weights_shape); |
| ARM_COMPUTE_UNUSED(bias_shape); |
| |
| _mixed_layout = mixed_layout; |
| _data_type = data_type; |
| _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| _quantization_info = quantization_info; |
| _activation_info = activation_info; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape); |
| } |
| |
| protected: |
| void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) |
| { |
| const DataLayout data_layout = src.info()->data_layout(); |
| // Test Multi DataLayout graph cases, when the data layout changes after configure |
| src.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); |
| dst.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); |
| |
| // Compute Convolution function |
| layer.run(); |
| |
| // Reinstating original data layout for the test suite to properly check the values |
| src.info()->set_data_layout(data_layout); |
| dst.info()->set_data_layout(data_layout); |
| } |
| |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| if(_data_type == DataType::QASYMM8) |
| { |
| std::uniform_int_distribution<uint8_t> distribution(0, 30); |
| library->fill(tensor, distribution, i); |
| } |
| else if(_data_type == DataType::QASYMM8_SIGNED) |
| { |
| std::uniform_int_distribution<int8_t> distribution(-15, 15); |
| library->fill(tensor, distribution, i); |
| } |
| else if(_data_type == DataType::S32) |
| { |
| std::uniform_int_distribution<int32_t> distribution(-50, 50); |
| library->fill(tensor, distribution, i); |
| } |
| else if(_data_type == DataType::F16) |
| { |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| } |
| else if(_data_type == DataType::F32) |
| { |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| } |
| else |
| { |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights, |
| bool reshape_weights) |
| { |
| TensorShape reshaped_weights_shape(weights_shape); |
| |
| // Test actions depending on the target settings |
| // |
| // | reshape | !reshape |
| // -----------+-----------+--------------------------- |
| // transpose | | *** |
| // -----------+-----------+--------------------------- |
| // !transpose | transpose | transpose |
| // | | |
| // |
| // ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same |
| if(!reshape_weights || !transpose_weights) |
| { |
| const size_t shape_x = reshaped_weights_shape.x(); |
| reshaped_weights_shape.set(0, reshaped_weights_shape.y()); |
| reshaped_weights_shape.set(1, shape_x); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info); |
| TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _quantization_info); |
| TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info); |
| TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _quantization_info); |
| |
| // Create Fully Connected layer info |
| FullyConnectedLayerInfo fc_info; |
| fc_info.transpose_weights = transpose_weights; |
| fc_info.are_weights_reshaped = !reshape_weights; |
| fc_info.activation_info = _activation_info; |
| |
| // Create and configure function. |
| FunctionType fc; |
| fc.configure(&src, &weights, &bias, &dst, fc_info); |
| |
| ARM_COMPUTE_ASSERT(src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(weights.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); |
| |
| add_padding_x({ &src, &weights, &bias, &dst }); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| weights.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!weights.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); |
| |
| // Fill tensors |
| fill(AccessorType(src), 0); |
| fill(AccessorType(bias), 2); |
| |
| if(!reshape_weights || !transpose_weights) |
| { |
| TensorShape tmp_shape(weights_shape); |
| RawTensor tmp(tmp_shape, _data_type, 1); |
| |
| // Fill with original shape |
| fill(tmp, 1); |
| |
| // Transpose elementwise |
| tmp = transpose(tmp); |
| |
| AccessorType weights_accessor(weights); |
| |
| for(int i = 0; i < tmp.num_elements(); ++i) |
| { |
| Coordinates coord = index2coord(tmp.shape(), i); |
| std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)), |
| tmp.element_size(), |
| static_cast<RawTensor::value_type *>(weights_accessor(coord))); |
| } |
| } |
| else |
| { |
| fill(AccessorType(weights), 1); |
| } |
| |
| if(_mixed_layout) |
| { |
| mix_layout(fc, src, dst); |
| } |
| else |
| { |
| // Compute NEFullyConnectedLayer function |
| fc.run(); |
| } |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info }; |
| SimpleTensor<T> weights{ weights_shape, _data_type, 1, _quantization_info }; |
| SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _quantization_info }; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| return reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, output_shape), _activation_info, _quantization_info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataType _bias_data_type{}; |
| bool _mixed_layout{ false }; |
| QuantizationInfo _quantization_info{}; |
| ActivationLayerInfo _activation_info{}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, |
| ActivationLayerInfo activation_info) |
| { |
| FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, |
| reshape_weights, data_type, |
| QuantizationInfo(), activation_info, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, |
| QuantizationInfo quantization_info, ActivationLayerInfo activation_info) |
| { |
| FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, |
| reshape_weights, data_type, |
| quantization_info, activation_info, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class FullyConnectedWithDynamicWeightsFixture : public framework::Fixture |
| { |
| private: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| if(_data_type == DataType::F16) |
| { |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| } |
| else if(_data_type == DataType::F32) |
| { |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| } |
| else |
| { |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| void fill_transposed_weights(TensorType &weights, TensorShape weights_shape, int seed) |
| { |
| RawTensor tmp(weights_shape, _data_type, 1); |
| |
| // Fill with original shape |
| fill(tmp, seed); |
| |
| // Transpose elementwise |
| tmp = transpose(tmp); |
| |
| AccessorType weights_accessor(weights); |
| |
| for(int i = 0; i < tmp.num_elements(); ++i) |
| { |
| Coordinates coord = index2coord(tmp.shape(), i); |
| std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)), |
| tmp.element_size(), |
| static_cast<RawTensor::value_type *>(weights_accessor(coord))); |
| } |
| } |
| |
| void validate_with_tolerance(TensorType &target, SimpleTensor<T> &ref) |
| { |
| if(_data_type == DataType::F32) |
| { |
| constexpr RelativeTolerance<float> rel_tolerance_f32(0.05f); |
| constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f); |
| validate(AccessorType(target), ref, rel_tolerance_f32, 0, abs_tolerance_f32); |
| } |
| else |
| { |
| validate(AccessorType(target), ref); |
| } |
| } |
| |
| public: |
| template <typename...> |
| void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape dst_shape, |
| DataType data_type, ActivationLayerInfo activation_info) |
| { |
| _data_type = data_type; |
| |
| // Setup tensor meta-data |
| TensorInfo src_info(src_shape, 1, data_type); |
| _src.allocator()->init(src_info); |
| |
| TensorShape tr_weights_shape{ weights_shape[1], weights_shape[0] }; |
| TensorInfo wei_info(tr_weights_shape, 1, data_type); |
| _weights.allocator()->init(wei_info); |
| |
| TensorInfo bias_info(bias_shape, 1, data_type); |
| _bias.allocator()->init(bias_info); |
| |
| TensorInfo dst_info(dst_shape, 1, data_type); |
| _dst.allocator()->init(dst_info); |
| |
| // Configure FC layer and mark the weights as non constant |
| FullyConnectedLayerInfo fc_info; |
| fc_info.activation_info = activation_info; |
| fc_info.are_weights_reshaped = true; |
| fc_info.transpose_weights = false; |
| fc_info.constant_weights = false; |
| FunctionType fc; |
| fc.configure(&_src, &_weights, &_bias, &_dst, fc_info); |
| |
| // Allocate all the tensors |
| _src.allocator()->allocate(); |
| _weights.allocator()->allocate(); |
| _bias.allocator()->allocate(); |
| _dst.allocator()->allocate(); |
| |
| // Run multiple iterations with different inputs |
| constexpr int num_iterations = 5; |
| int randomizer_offset = 0; |
| for(int i = 0; i < num_iterations; ++i) |
| { |
| // Run target |
| { |
| fill(AccessorType(_src), randomizer_offset); |
| fill_transposed_weights(_weights, weights_shape, randomizer_offset + 1); |
| fill(AccessorType(_bias), randomizer_offset + 2); |
| |
| fc.run(); |
| } |
| |
| // Run reference and compare |
| { |
| SimpleTensor<T> src{ src_shape, data_type }; |
| SimpleTensor<T> weights{ weights_shape, data_type }; |
| SimpleTensor<T> bias{ bias_shape, data_type }; |
| |
| // Fill reference |
| fill(src, randomizer_offset); |
| fill(weights, randomizer_offset + 1); |
| fill(bias, randomizer_offset + 2); |
| |
| auto dst = reference::activation_layer(reference::fully_connected_layer<T>(src, weights, bias, dst_shape), activation_info); |
| |
| // Validate |
| validate_with_tolerance(_dst, dst); |
| } |
| |
| randomizer_offset += 100; |
| } |
| } |
| |
| private: |
| TensorType _src{}, _weights{}, _bias{}, _dst{}; |
| DataType _data_type{ DataType::UNKNOWN }; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */ |