| /* |
| * Copyright (c) 2017-2022 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_TEST_CONVOLUTION_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/graph/Utils.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "src/core/NEON/kernels/arm_gemm/utils.hpp" |
| #include "src/graph/mutators/MutatorUtils.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/reference/ActivationLayer.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/PadLayer.h" |
| #include "tests/validation/reference/Permute.h" |
| #include "tests/validation/reference/Utils.h" |
| |
| #include <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace detail |
| { |
| template <typename ConvolutionFunction, typename TensorType> |
| void configure_conv_function(ConvolutionFunction &func, |
| TensorType *src, const TensorType *weights, const TensorType *bias, TensorType *dst, |
| const PadStrideInfo &info, const WeightsInfo &weights_info, |
| const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) |
| { |
| func.configure(src, weights, bias, dst, info, weights_info, dilation, act_info, num_groups); |
| } |
| } // namespace detail |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW> |
| class ConvolutionValidationGenericFixture : public framework::Fixture |
| { |
| public: |
| using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value |
| || std::is_same<typename std::decay<T>::type, int8_t>::value, |
| int32_t, T >::type; |
| |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, |
| DataType data_type, DataType weights_data_type, DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info, ActivationLayerInfo act_info, |
| bool mixed_layout = false, PaddingList pre_pad_layer = PaddingList({})) |
| { |
| _mixed_layout = mixed_layout; |
| _data_type = data_type; |
| _weights_data_type = weights_data_type; |
| _is_quantized = is_data_type_quantized_asymmetric(data_type); |
| _is_bfloat16 = data_type == DataType::BFLOAT16; |
| _bias_data_type = _is_quantized ? DataType::S32 : (_is_bfloat16 ? DataType::F32 : data_type); |
| _output_data_type = _is_bfloat16 ? DataType::F32 : data_type; |
| _quantization_info = quantization_info; |
| _weight_quantization_info = weight_quantization_info; |
| _data_layout = data_layout; |
| |
| _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info, pre_pad_layer); |
| _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info, pre_pad_layer); |
| } |
| |
| protected: |
| void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) |
| { |
| // 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); |
| } |
| |
| void regularize_values(void *values, size_t size) |
| { |
| float *fvalues = static_cast<float *>(values); |
| for(size_t i = 0; i < size; ++i) |
| { |
| fvalues[i] = float(bfloat16(fvalues[i])); |
| } |
| } |
| |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::QASYMM8: |
| { |
| std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f); |
| std::uniform_int_distribution<uint32_t> distribution(bounds.first, bounds.second); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QASYMM8_SIGNED: |
| { |
| std::pair<int, int> bounds = get_quantized_qasymm8_signed_bounds(tensor.quantization_info(), -1.0f, 1.0f); |
| std::uniform_int_distribution<int32_t> distribution(bounds.first, bounds.second); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::QSYMM8_PER_CHANNEL: |
| { |
| int min_bound = 128; |
| int max_bound = -127; |
| for(size_t i = 0; i < _weight_quantization_info.scale().size(); i++) |
| { |
| std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f, i); |
| if(bounds.first < min_bound) |
| { |
| min_bound = bounds.first; |
| } |
| if(bounds.second > max_bound) |
| { |
| max_bound = bounds.second; |
| } |
| } |
| std::uniform_int_distribution<int32_t> distribution(min_bound, max_bound); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(-100, 100); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| case DataType::BFLOAT16: |
| { |
| arm_compute::utils::uniform_real_distribution_16bit<bfloat16> distribution{ -1.0f, 1.0f }; |
| 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; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| // given input is IN nchw format |
| TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info, |
| bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({})) |
| { |
| ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0); |
| |
| const unsigned int num_groups = input_shape[2] / weights_shape[2]; |
| |
| 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)); |
| |
| if(pre_pad_layer.size() > 0) |
| { |
| // make sure paddings exist for each c,h,w dimensions |
| for(unsigned int i = 0; i < 3 - pre_pad_layer.size(); ++i) |
| { |
| pre_pad_layer.push_back({ 0, 0 }); |
| } |
| |
| // rotate padding info from nchw to nhwc |
| std::rotate(pre_pad_layer.begin(), pre_pad_layer.begin() + 2, pre_pad_layer.begin() + 3); |
| } |
| } |
| |
| const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| |
| WeightsInfo weights_info(!reshape_weights, weights_shape[idx_width], weights_shape[idx_height], weights_shape[3]); |
| TensorShape reshaped_weights_shape(weights_shape); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info, _data_layout); |
| TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _weights_data_type, 1, _weight_quantization_info, _data_layout); |
| TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _quantization_info, _data_layout); |
| TensorType dst = create_tensor<TensorType>(output_shape, _output_data_type, 1, _quantization_info, _data_layout); |
| |
| // Create and configure function |
| FunctionType conv; |
| |
| const unsigned int height_index = arm_compute::graph::get_dimension_idx(_data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int width_index = arm_compute::graph::get_dimension_idx(_data_layout, DataLayoutDimension::WIDTH); |
| |
| const PaddingInfo pad_w = width_index < pre_pad_layer.size() ? pre_pad_layer[width_index] : PaddingInfo(0, 0); |
| const PaddingInfo pad_h = height_index < pre_pad_layer.size() ? pre_pad_layer[height_index] : PaddingInfo(0, 0); |
| |
| if(pre_pad_layer.size() > 0 && arm_compute::graph::is_padding_in_height_or_width(_data_layout, pre_pad_layer)) |
| { |
| // this is the logic implemented in NodeFusionMutator -> fuse_pad_with_convolution |
| const PadStrideInfo new_conv_info( |
| info.stride().first, |
| info.stride().second, |
| info.pad_left() + pad_w.first, |
| info.pad_right() + pad_w.second, |
| info.pad_top() + pad_h.first, |
| info.pad_bottom() + pad_h.second, |
| info.round()); |
| detail::configure_conv_function(conv, &src, &weights, &bias, &dst, new_conv_info, weights_info, dilation, act_info, num_groups); |
| } |
| else |
| { |
| detail::configure_conv_function(conv, &src, &weights, &bias, &dst, info, weights_info, dilation, act_info, num_groups); |
| } |
| |
| 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 }, _data_layout); |
| |
| // 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(weights), 1); |
| fill(AccessorType(bias), 2); |
| |
| 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, |
| const Size2D &dilation, const ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({})) |
| { |
| ARM_COMPUTE_ERROR_ON((input_shape[2] % weights_shape[2]) != 0); |
| |
| const unsigned int num_groups = input_shape[2] / weights_shape[2]; |
| |
| // Setup reference data types |
| const DataType src_dt = _is_bfloat16 ? DataType::F32 : _data_type; |
| const DataType weights_dt = _is_bfloat16 ? DataType::F32 : _weights_data_type; |
| const DataType bias_dt = _is_bfloat16 ? DataType::F32 : _bias_data_type; |
| |
| // Create reference |
| SimpleTensor<T> src{ input_shape, src_dt, 1, _quantization_info }; |
| SimpleTensor<TW> weights{ weights_shape, weights_dt, 1, _weight_quantization_info }; |
| SimpleTensor<TBias> bias{ bias_shape, bias_dt, 1, _quantization_info }; |
| |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| // Fill with bfloat16 to perform the conversion and reduce the mismatches in the output |
| if(_is_bfloat16) |
| { |
| regularize_values(static_cast<void *>(src.data()), src.num_elements()); |
| regularize_values(static_cast<void *>(weights.data()), weights.num_elements()); |
| } |
| |
| if(pre_pad_layer.size() > 0) |
| { |
| src = reference::pad_layer<T>(src, pre_pad_layer, PixelValue(0), PaddingMode::CONSTANT); |
| } |
| |
| return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups), |
| act_info) : |
| reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation, num_groups); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataType _weights_data_type{}; |
| DataType _bias_data_type{}; |
| DataType _output_data_type{}; |
| DataLayout _data_layout{}; |
| QuantizationInfo _quantization_info{}; |
| QuantizationInfo _weight_quantization_info{}; |
| bool _is_quantized = false; |
| bool _is_bfloat16 = false; |
| bool _mixed_layout = false; |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class ConvolutionValidationFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, ActivationLayerInfo act_info) |
| { |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, |
| data_type, data_type, data_layout, |
| QuantizationInfo(), QuantizationInfo(), act_info, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class ConvolutionValidationWithPaddingFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, ActivationLayerInfo act_info, PaddingList pre_pad_layer = PaddingList({})) |
| { |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, |
| data_type, data_type, data_layout, |
| QuantizationInfo(), QuantizationInfo(), act_info, mixed_layout, pre_pad_layer); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> |
| class ConvolutionValidationQuantizedFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, QuantizationInfo quantization_info, ActivationLayerInfo act_info) |
| { |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, |
| data_type, data_type, data_layout, quantization_info, quantization_info, act_info, mixed_layout); |
| } |
| }; |
| |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW> |
| class ConvolutionValidationQuantizedPerChannelFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW> |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type, |
| DataLayout data_layout, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataType weights_data_type) |
| { |
| std::vector<float> weights_scales{}; |
| std::mt19937 gen(library->seed()); |
| std::uniform_real_distribution<float> dis(0.01f, 1.f); |
| for(size_t i = 0; i < output_shape[2]; ++i) |
| { |
| weights_scales.push_back(dis(gen)); |
| } |
| ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, |
| reshape_weights, data_type, weights_data_type, data_layout, |
| quantization_info, QuantizationInfo(weights_scales), act_info); |
| } |
| }; |
| |
| #ifdef ARM_COMPUTE_ENABLE_FIXED_FORMAT_KERNELS |
| inline TensorInfo prepare_weights(const TensorInfo tensor_info, const arm_compute::WeightFormat weight_format) |
| { |
| const DataLayout data_layout = tensor_info.data_layout(); |
| ARM_COMPUTE_EXPECT(data_layout == DataLayout::NHWC, framework::LogLevel::ERRORS); |
| const DataType data_type = tensor_info.data_type(); |
| const TensorShape tensor_shape = tensor_info.tensor_shape(); |
| const int N = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES)]; // N=O |
| const int H = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)]; |
| const int W = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)]; |
| const int C = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)]; // C=I |
| |
| const int interleave_by = arm_compute::interleave_by(weight_format); |
| const int block_by = arm_compute::block_by(weight_format); |
| const int Ip = arm_gemm::roundup<unsigned int>(C, block_by); // C'=I' |
| const int Op = arm_gemm::roundup<unsigned int>(N, interleave_by); // O'=N' |
| |
| const TensorShape TS(Ip, W, H, Op); |
| return TensorInfo(TS, 1 /*num_channels*/, data_type, data_layout); |
| } |
| |
| template <typename ScalarType, typename AccessorType> |
| inline void rearrange_data(const AccessorType src, AccessorType dst, const arm_compute::WeightFormat weight_format) |
| { |
| ARM_COMPUTE_EXPECT(arm_compute::is_fixed_format(weight_format), framework::LogLevel::ERRORS); |
| // Data Layout: OHWIo<interleave_by>i<block_by> |
| const int interleave_by = arm_compute::interleave_by(weight_format); |
| const int block_by = arm_compute::block_by(weight_format); |
| const TensorShape src_tensor_shape = src.shape(); |
| const DataLayout data_layout = src.data_layout(); |
| ARM_COMPUTE_EXPECT(data_layout == DataLayout::NHWC, framework::LogLevel::ERRORS); |
| const unsigned int O = src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES)]; // N=O |
| const unsigned int H = src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)]; |
| const unsigned int W = src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)]; |
| const unsigned int I = src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)]; // C=I |
| const unsigned int Ip = arm_gemm::roundup<unsigned int>(I, block_by); // C'=I' |
| const unsigned int Op = arm_gemm::roundup<unsigned int>(O, interleave_by); // N'=O' |
| |
| ARM_COMPUTE_EXPECT_EQUAL(Op * H * W * Ip, (unsigned)dst.num_elements(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(src.num_elements() <= dst.num_elements(), framework::LogLevel::ERRORS); |
| |
| const ScalarType *src_ptr = reinterpret_cast<const ScalarType *>(src.data()); |
| ScalarType *dst_ptr = reinterpret_cast<ScalarType *>(dst.data()); |
| for(unsigned i = 0; i < I; ++i) |
| for(unsigned w = 0; w < W; ++w) |
| for(unsigned h = 0; h < H; ++h) |
| for(unsigned o = 0; o < O; ++o) |
| { |
| ScalarType src_element; |
| switch(data_layout) |
| { |
| case DataLayout::NHWC: |
| { |
| src_element = src_ptr[o * H * W * I + h * W * I + w * I + i]; |
| } |
| break; |
| default: |
| { |
| ARM_COMPUTE_ERROR("Unsupported memory layout."); |
| } |
| } |
| const int x5 = std::floor(((float)o) / interleave_by); |
| const int x4 = h; |
| const int x3 = w; |
| const int x2 = std::floor((float)i / block_by); |
| const int x1 = o % interleave_by; |
| const int x0 = i % block_by; |
| unsigned dst_idx = x5 * H * W * Ip * interleave_by |
| + x4 * W * Ip * interleave_by |
| + x3 * Ip * interleave_by |
| + x2 * interleave_by * block_by |
| + x1 * block_by |
| + x0; |
| dst_ptr[dst_idx] = src_element; |
| } |
| } |
| |
| template <typename ConvolutionFunction, typename TensorClass, typename AccessorType, typename ScalarType> |
| class VariableWeightsFixtureBaseClass : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataLayout data_layout, |
| const DataType data_type) |
| { |
| conv = std::make_unique<ConvolutionFunction>(); |
| // prepare data |
| _data_layout = data_layout; |
| // Fixed format kernels for variable weights can work only with NHWC format. |
| ARM_COMPUTE_EXPECT_EQUAL(_data_layout, DataLayout::NHWC, framework::LogLevel::ERRORS); |
| _data_type = data_type; |
| // run the code |
| compute_target(input_shape, weights_shape, bias_shape, output_shape, info, dilation); |
| compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation); |
| } |
| void teardown() |
| { |
| _target.allocator()->free(); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| 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; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| private: |
| virtual void configure_and_execute_kernel(TensorInfo src_tensor_info, TensorInfo weight_tensor_info, TensorInfo bias_tensor_info, TensorInfo dst_tensor_info, const WeightsInfo weights_info, |
| const PadStrideInfo &conv_info, |
| const Size2D &dilation) = 0; |
| |
| void compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &conv_info, |
| const Size2D &dilation) |
| { |
| // The dataset is always in NCHW format - we need to make C the |
| // innermost dimension because the fixed-format kernel work only |
| // with NHWC layout. |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| const auto src_tensor_info = TensorInfo(input_shape, 1, _data_type, _data_layout); |
| const auto weight_tensor_info = TensorInfo(weights_shape, 1, _data_type, _data_layout); |
| const auto bias_tensor_info = TensorInfo(bias_shape, 1, _data_type, _data_layout); |
| auto dst_tensor_info = TensorInfo(output_shape, 1, _data_type, _data_layout); |
| |
| const int kernel_height = weights_shape[get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT)]; |
| const int kernel_width = weights_shape[get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH)]; |
| const int num_kernels = weights_shape[get_data_layout_dimension_index(_data_layout, DataLayoutDimension::BATCHES)]; |
| |
| const WeightsInfo query_weights_info(/*reshape_weights*/ false, kernel_width, kernel_height, num_kernels, false, arm_compute::WeightFormat::ANY); |
| const bool kernel_found = bool(ConvolutionFunction::has_opt_impl(_computed_weight_format, &src_tensor_info, &weight_tensor_info, |
| &bias_tensor_info, &dst_tensor_info, conv_info, query_weights_info)); |
| // Make surethat the setup founds a fixed-format kernel as requested by the test case. |
| ARM_COMPUTE_EXPECT(kernel_found, framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(arm_compute::is_fixed_format(_computed_weight_format), framework::LogLevel::ERRORS); |
| |
| const WeightsInfo weights_info(/*reshape_weights*/ false, kernel_width, kernel_height, num_kernels, false, _computed_weight_format); |
| configure_and_execute_kernel(src_tensor_info, weight_tensor_info, bias_tensor_info, dst_tensor_info, weights_info, conv_info, |
| dilation); |
| } |
| void compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, |
| const Size2D &dilation) |
| { |
| ARM_COMPUTE_UNUSED(input_shape, weights_shape, bias_shape, output_shape, info, |
| dilation); |
| |
| // Create reference |
| SimpleTensor<ScalarType> src{ input_shape, _data_type }; |
| SimpleTensor<ScalarType> weights{ weights_shape, _data_type }; |
| SimpleTensor<ScalarType> bias{ bias_shape, _data_type }; |
| fill(src, 0); |
| fill(bias, 1); |
| fill(weights, 3); |
| _reference = reference::convolution_layer<ScalarType>(src, weights, bias, output_shape, info, dilation, 1 /*num_groups*/); |
| } |
| DataLayout _data_layout{}; |
| DataType _data_type{}; |
| |
| protected: |
| std::unique_ptr<ConvolutionFunction> conv{}; |
| arm_compute::WeightFormat _computed_weight_format{ arm_compute::WeightFormat::UNSPECIFIED }; |
| TensorClass _target{}; |
| SimpleTensor<ScalarType> _reference{}; |
| }; |
| |
| template <typename ConvolutionFunction, typename TensorClass, typename AccessorType, typename ScalarType> |
| class VariableWeightsFixture : public VariableWeightsFixtureBaseClass<ConvolutionFunction, TensorClass, AccessorType, ScalarType> |
| { |
| void configure_and_execute_kernel(TensorInfo src_tensor_info, TensorInfo weight_tensor_info, TensorInfo bias_tensor_info, TensorInfo dst_tensor_info, const WeightsInfo weights_info, |
| const PadStrideInfo &conv_info, |
| const Size2D &dilation) |
| { |
| this->conv->configure(&src_tensor_info, &weight_tensor_info, &bias_tensor_info, &dst_tensor_info, conv_info, weights_info, dilation); |
| |
| // Allocate input tensors |
| auto src = create_tensor<TensorClass>(src_tensor_info); |
| auto weights_original = create_tensor<TensorClass>(weight_tensor_info); |
| const TensorInfo new_tensor_info = prepare_weights(weight_tensor_info, this->_computed_weight_format); |
| auto weights_transformed = create_tensor<TensorClass>(new_tensor_info); |
| auto bias = create_tensor<TensorClass>(bias_tensor_info); |
| src.allocator()->allocate(); |
| weights_original.allocator()->allocate(); |
| weights_transformed.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| // Allocate destination tensor |
| this->_target = create_tensor<TensorClass>(dst_tensor_info); |
| this->_target.allocator()->allocate(); |
| |
| // Prepare source and biases that are left unchanged. |
| this->fill(AccessorType(src), 0); |
| this->fill(AccessorType(bias), 1); |
| |
| // First run |
| this->fill(AccessorType(weights_original), 2); |
| rearrange_data<ScalarType, AccessorType>(AccessorType(weights_original), AccessorType(weights_transformed), this->_computed_weight_format); |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weights_transformed }, { TensorType::ACL_SRC_2, &bias }, { TensorType::ACL_DST, &(this->_target) } }; |
| this->conv->run(run_pack); |
| // Second run, with new weights |
| this->fill(AccessorType(weights_original), 3); |
| rearrange_data<ScalarType, AccessorType>(AccessorType(weights_original), AccessorType(weights_transformed), this->_computed_weight_format); |
| this->conv->run(run_pack); |
| src.allocator()->free(); |
| weights_original.allocator()->free(); |
| weights_transformed.allocator()->free(); |
| bias.allocator()->free(); |
| } |
| }; |
| |
| template <typename ConvolutionFunction, typename TensorClass, typename AccessorType, typename ScalarType> |
| class VariableWeightsFixtureNEInterface : public VariableWeightsFixtureBaseClass<ConvolutionFunction, TensorClass, AccessorType, ScalarType> |
| { |
| void configure_and_execute_kernel(TensorInfo src_tensor_info, TensorInfo weight_tensor_info, TensorInfo bias_tensor_info, TensorInfo dst_tensor_info, const WeightsInfo weights_info, |
| const PadStrideInfo &conv_info, |
| const Size2D &dilation) |
| { |
| // Allocate input tensors |
| auto src = create_tensor<TensorClass>(src_tensor_info); |
| auto weights_original = create_tensor<TensorClass>(weight_tensor_info); |
| const TensorInfo new_tensor_info = prepare_weights(weight_tensor_info, this->_computed_weight_format); |
| auto weights_transformed = create_tensor<TensorClass>(new_tensor_info); |
| auto bias = create_tensor<TensorClass>(bias_tensor_info); |
| src.allocator()->allocate(); |
| weights_original.allocator()->allocate(); |
| weights_transformed.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| // Allocate destination tensor |
| this->_target = create_tensor<TensorClass>(dst_tensor_info); |
| this->_target.allocator()->allocate(); |
| this->conv->configure(&src, &weights_transformed, &bias, &(this->_target), conv_info, weights_info, dilation); |
| // Prepare source and biases that are left unchanged. |
| this->fill(AccessorType(src), 0); |
| this->fill(AccessorType(bias), 1); |
| |
| // First run |
| this->fill(AccessorType(weights_original), 2); |
| rearrange_data<ScalarType, AccessorType>(AccessorType(weights_original), AccessorType(weights_transformed), this->_computed_weight_format); |
| this->conv->run(); |
| // Second run, with new weights |
| this->fill(AccessorType(weights_original), 3); |
| rearrange_data<ScalarType, AccessorType>(AccessorType(weights_original), AccessorType(weights_transformed), this->_computed_weight_format); |
| this->conv->run(); |
| src.allocator()->free(); |
| weights_original.allocator()->free(); |
| weights_transformed.allocator()->free(); |
| bias.allocator()->free(); |
| } |
| }; |
| |
| template <typename ConvolutionClass> |
| class HasOptImplFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(DataType data_type, arm_compute::WeightFormat query_weight_format) |
| { |
| auto conv = std::make_unique<ConvolutionClass>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 5U, 2U), 1, data_type, DataLayout::NHWC); |
| const auto weight_info = TensorInfo(TensorShape(1U, 3U, 2U, 3U), 1, data_type, DataLayout::NHWC); |
| const auto bias_info = TensorInfo(TensorShape(3U), 1, data_type, DataLayout::NHWC); |
| auto dst_info = TensorInfo(TensorShape(1U, 7U, 3U), 1, data_type, DataLayout::NHWC); |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0, 2, 2, DimensionRoundingType::FLOOR); |
| const WeightsInfo weights_info(false, 3U, 3U, 1U, false, query_weight_format); |
| _kernel_found = bool(ConvolutionClass::has_opt_impl(_computed_weight_format, &src_info, &weight_info, |
| &bias_info, &dst_info, conv_info, weights_info)); |
| } |
| |
| protected: |
| bool _kernel_found{ false }; |
| arm_compute::WeightFormat _computed_weight_format{ arm_compute::WeightFormat::UNSPECIFIED }; |
| }; |
| #endif // ARM_COMPUTE_ENABLE_FIXED_FORMAT_KERNELS |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_CONVOLUTION_LAYER_FIXTURE */ |