| /* |
| * Copyright (c) 2017-2023 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
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| * The above copyright notice and this permission notice shall be included in all |
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| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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| |
| #ifndef ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H |
| #define ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/fixtures/ConvolutionLayerFixture.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/Permute.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type; |
| |
| void setup_quantization(const TensorShape &input_shape, const TensorShape &weights_shape, QuantizationInfo &input_q_info, |
| QuantizationInfo &weights_q_info, DataType data_type) |
| { |
| const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); |
| const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); |
| |
| std::mt19937 generator(library->seed() + _hash); |
| std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); |
| std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); |
| |
| const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] |
| const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] |
| |
| const int32_t offset_lhs = distribution_t(generator); |
| const int32_t offset_rhs = distribution_t(generator); |
| |
| input_q_info = QuantizationInfo(scale_lhs, offset_lhs); |
| weights_q_info = QuantizationInfo(scale_rhs, offset_rhs); |
| |
| QuantizationHint q_hint = suggest_conv_dst_q_info_and_bias(input_q_info, weights_q_info, |
| weights_shape.y() /* heights */, weights_shape.x() /* width */, input_shape.z() /* channels */, |
| data_type, 0.5f /* bias_fraction */); |
| |
| _dst_q_info = q_hint.q_info; |
| _min_bias = q_hint.bias_min; |
| _max_bias = q_hint.bias_max; |
| |
| // Do not change here as these limits are the natural limits of the associated data types and |
| // are embeded in the computation of the dst quantization info. |
| _min_u8 = 0; |
| _max_u8 = 255; |
| _min_s8 = -128; |
| _max_s8 = 127; |
| } |
| |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, |
| DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout, bool mixed_layout = false) |
| { |
| // This hash is used by random generators. There may be hash collisions but |
| // this is intentional as it's a very easy way to make the the current |
| // random generation process almost different for many test configurations, |
| // which were using the same set of values before. |
| _hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] + |
| stride_x + stride_y + pad_x + pad_y + kernel_size + num_kernels + mixed_layout |
| + (data_layout == DataLayout::NHWC); |
| |
| _data_type = data_type; |
| _mixed_layout = mixed_layout; |
| |
| TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); |
| const TensorShape bias_shape(num_kernels); |
| const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); |
| const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| TensorInfo input_info = TensorInfo(input_shape, 1, data_type); |
| TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type); |
| |
| const TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info); |
| |
| QuantizationInfo input_q_info = quantization_info; |
| QuantizationInfo weights_q_info = quantization_info; |
| _dst_q_info = quantization_info; |
| |
| if(is_data_type_quantized(data_type) && (!act_info.enabled() || act_info.activation() == ActivationFunction::IDENTITY)) |
| { |
| setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type); |
| } |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info); |
| } |
| |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, |
| DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout) |
| { |
| ARM_COMPUTE_ERROR_ON(data_layout == DataLayout::UNKNOWN); |
| ARM_COMPUTE_UNUSED(dilation); |
| |
| // This hash is used by random generators. There may be hash collisions but |
| // this is intentional as it's a very easy way to make the the current |
| // random generation process almost different for many test configurations, |
| // which were using the same set of values before. |
| _hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] + |
| weights_shape[0] + weights_shape[1] + weights_shape[2] + weights_shape[3] + dilation.x() + |
| dilation.y() + info.pad_bottom() + info.pad_left() + info.pad_right() + info.pad_top(); |
| |
| _data_type = data_type; |
| |
| const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; |
| |
| QuantizationInfo input_q_info = quantization_info; |
| QuantizationInfo weights_q_info = quantization_info; |
| _dst_q_info = quantization_info; |
| |
| if(is_data_type_quantized(data_type) && (!act_info.enabled() || act_info.activation() == ActivationFunction::IDENTITY)) |
| { |
| setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type); |
| } |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info); |
| } |
| |
| protected: |
| void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) |
| { |
| 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) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QASYMM8_SIGNED: |
| { |
| // Use small input range to avoid all the test results being saturated at the end. |
| std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::F16: |
| { |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f }; |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info, |
| DataType data_type, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info, const DataLayout &data_layout) |
| { |
| if(data_layout == DataLayout::NHWC) |
| { |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, input_q_info, data_layout); |
| TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_q_info, data_layout); |
| TensorType bias = create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo()); |
| TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, _dst_q_info, data_layout); |
| |
| add_padding_x({ &src, &bias, &dst }, data_layout); |
| add_padding_x({ &weights }, data_layout, input_shape[0] % 4 == 0); // Don't add left padding if cl image will be used |
| |
| // Create and configure function |
| FunctionType conv; |
| conv.configure(&src, &weights, &bias, &dst, info, act_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()); |
| |
| // 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 + _hash); |
| fill(AccessorType(weights), 1 + _hash); |
| fill(AccessorType(bias), 2 + _hash); |
| |
| if(_mixed_layout) |
| { |
| mix_layout(conv, src, dst); |
| } |
| else |
| { |
| // Compute Convolution function |
| conv.run(); |
| } |
| |
| 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, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, data_type, 1, input_q_info }; |
| SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_q_info }; |
| SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, QuantizationInfo() }; |
| |
| // Fill reference |
| fill(src, 0 + _hash); |
| fill(weights, 1 + _hash); |
| fill(bias, 2 + _hash); |
| |
| SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info, |
| Size2D(1U, 1U) /* dilation */, 1 /* num_groups */, _dst_q_info); |
| SimpleTensor<T> dst2 = (act_info.enabled()) ? reference::activation_layer<T>(dst, act_info) : dst; |
| return dst2; |
| } |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| QuantizationInfo _dst_q_info{}; |
| DataType _data_type{}; |
| bool _mixed_layout{ false }; |
| int32_t _hash{0}; |
| |
| // Random initialization limits |
| // Default values are previously handcrafted limits |
| // that sould be used when we don't use dynamic quantization |
| int32_t _min_bias{-5}; |
| int32_t _max_bias{5}; |
| int32_t _min_u8{0}; |
| int32_t _max_u8{50}; |
| int32_t _min_s8{-25}; |
| int32_t _max_s8{25}; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class DirectConvolutionValidationFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, ActivationLayerInfo act_info, |
| DataLayout data_layout) |
| { |
| DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, QuantizationInfo(), |
| act_info, data_layout, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class DirectConvolutionValidationQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info, |
| ActivationLayerInfo act_info, DataLayout data_layout) |
| { |
| DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, quantization_info, |
| act_info, data_layout, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationWithTensorShapesQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, |
| DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout) |
| { |
| DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, quantization_info, |
| act_info, data_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class DirectConvolutionValidationWithTensorShapesFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> |
| { |
| public: |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, |
| DataType data_type, ActivationLayerInfo act_info) |
| { |
| DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, QuantizationInfo(), |
| act_info, DataLayout::NCHW); |
| } |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| |
| #endif // ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H |