| /* |
| * 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/graph.h" |
| |
| #include "support/ToolchainSupport.h" |
| |
| #include "tests/NEON/Accessor.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/Permute.h" |
| |
| #include "utils/CommonGraphOptions.h" |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #include "ValidateExample.h" |
| |
| #include <utility> |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| using namespace arm_compute::graph; |
| using namespace arm_compute; |
| using namespace arm_compute::test; |
| using namespace arm_compute::test::validation; |
| namespace |
| { |
| /*Available Padding modes */ |
| enum class PaddingMode |
| { |
| Valid, |
| Same, |
| Manual |
| }; |
| |
| /** Stream Input operator for the PaddingMode type |
| * |
| * @param[in] stream Input stream. |
| * @param[out] Mode Convolution parameters to output |
| * |
| * @return input stream. |
| */ |
| inline ::std::istream &operator>>(::std::istream &stream, PaddingMode &Mode) |
| { |
| static const std::map<std::string, PaddingMode> modes = |
| { |
| { "valid", PaddingMode::Valid }, |
| { "same", PaddingMode::Same }, |
| { "manual", PaddingMode::Manual } |
| }; |
| std::string value; |
| stream >> value; |
| try |
| { |
| Mode = modes.at(arm_compute::utility::tolower(value)); |
| } |
| catch(const std::out_of_range &) |
| { |
| throw std::invalid_argument(value); |
| } |
| |
| return stream; |
| } |
| |
| /** Formatted output of the PaddingMode type |
| * |
| * @param[out] os Output stream. |
| * @param[in] Mode PaddingMode to output |
| * |
| * @return Modified output stream. |
| */ |
| inline ::std::ostream &operator<<(::std::ostream &os, PaddingMode Mode) |
| { |
| switch(Mode) |
| { |
| case PaddingMode::Valid: |
| os << "Valid"; |
| break; |
| case PaddingMode::Same: |
| os << "Same"; |
| break; |
| case PaddingMode::Manual: |
| os << "Manual"; |
| break; |
| default: |
| throw std::invalid_argument("Unsupported padding mode format"); |
| } |
| |
| return os; |
| } |
| /** Structure holding all the input tensor graph parameters */ |
| struct TensorParams |
| { |
| int width{ 0 }; |
| int height{ 0 }; |
| int fm{ 0 }; |
| int batch{ 0 }; |
| QuantizationInfo quant_info{ 1.0f, 0 }; |
| std::string npy{}; |
| uint64_t range_low{ 0 }; |
| uint64_t range_high{ 16 }; |
| }; |
| /** Structure holding all the verification graph parameters */ |
| struct VerificationParams |
| { |
| float absolute_tolerance{ -1.f }; |
| float relative_tolerance{ -1.f }; |
| float tolerance_number{ -1.f }; |
| }; |
| |
| /** Structure holding all the common graph parameters */ |
| struct FrameworkParams |
| { |
| bool help{ false }; |
| int threads{ 0 }; |
| arm_compute::graph::Target target{ arm_compute::graph::Target::NEON }; |
| }; |
| |
| /** Structure holding all the Convolution layer graph parameters */ |
| struct ConvolutionParams |
| { |
| arm_compute::DataType data_type{ DataType::F32 }; |
| arm_compute::DataLayout data_layout{ DataLayout::NCHW }; |
| arm_compute::graph::ConvolutionMethod convolution_method{ arm_compute::graph::ConvolutionMethod::Default }; |
| |
| /** Padding graph parameters */ |
| int padding_top{ 0 }; |
| int padding_bottom{ 0 }; |
| int padding_left{ 0 }; |
| int padding_right{ 0 }; |
| int padding_stride_x{ 0 }; |
| int padding_stride_y{ 0 }; |
| PaddingMode padding_mode{ PaddingMode::Valid }; |
| struct |
| { |
| struct |
| { |
| int X{ 0 }; |
| int Y{ 0 }; |
| } stride{}; |
| PaddingMode mode{ PaddingMode::Valid }; |
| } padding{}; |
| }; |
| |
| /** Structure holding all the graph Example parameters */ |
| struct ExampleParams |
| { |
| FrameworkParams common_params{}; |
| TensorParams input{}; |
| TensorParams weights{}; |
| TensorParams bias{}; |
| TensorParams output{}; |
| VerificationParams verification{}; |
| ConvolutionParams convolution{}; |
| }; |
| |
| /** Formatted output of the ConvolutionParams type |
| * |
| * @param[out] os Output stream. |
| * @param[in] common_params Convolution parameters to output |
| * |
| * @return Modified output stream. |
| */ |
| ::std::ostream &operator<<(::std::ostream &os, const ExampleParams &common_params) |
| { |
| os << "Threads : " << common_params.common_params.threads << std::endl; |
| os << "Target : " << common_params.common_params.target << std::endl; |
| os << "Data type : " << common_params.convolution.data_type << std::endl; |
| os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")" |
| << std::endl; |
| os << "Weight dimensions(X,Y, Channels(same as input), OFM) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << "," << |
| common_params.weights.fm << ")" << std::endl; |
| os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," << |
| common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y << |
| ")" << std::endl; |
| os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl; |
| os << "Convolution Method: " << common_params.convolution.convolution_method << std::endl; |
| return os; |
| } |
| |
| /** Convolution command line options used to configure the graph examples |
| * |
| * (Similar to common options) |
| * The options in this object get populated when "parse()" is called on the parser used to construct it. |
| * The expected workflow is: |
| * |
| * CommandLineParser parser; |
| * CommonOptions options( parser ); |
| * parser.parse(argc, argv); |
| */ |
| class ConvolutionOptions final |
| { |
| public: |
| explicit ConvolutionOptions(CommandLineParser &parser) noexcept |
| : width(parser.add_option<SimpleOption<int>>("width", 9)), |
| height(parser.add_option<SimpleOption<int>>("height", 9)), |
| channels(parser.add_option<SimpleOption<int>>("channels", 1)), |
| batch(parser.add_option<SimpleOption<int>>("batch", 1)), |
| weights_width(parser.add_option<SimpleOption<int>>("weights_width", 3)), |
| weights_height(parser.add_option<SimpleOption<int>>("weights_height", 3)), |
| OFM(parser.add_option<SimpleOption<int>>("OFM", 1)), |
| padding_top(parser.add_option<SimpleOption<int>>("padding_top", 0)), |
| padding_left(parser.add_option<SimpleOption<int>>("padding_left", 0)), |
| padding_bottom(parser.add_option<SimpleOption<int>>("padding_bottom", 0)), |
| padding_right(parser.add_option<SimpleOption<int>>("padding_right", 0)), |
| stride_x(parser.add_option<SimpleOption<int>>("stride_x", 1)), |
| stride_y(parser.add_option<SimpleOption<int>>("stride_y", 1)), |
| help(parser.add_option<ToggleOption>("help")), |
| threads(parser.add_option<SimpleOption<int>>("threads")), |
| target(), |
| data_type(), |
| padding_mode(), |
| conv_mode(), |
| data_layout(), |
| absolute_tolerance(parser.add_option<SimpleOption<float>>("abs_tolerance", -1.0f)), |
| relative_tolerance(parser.add_option<SimpleOption<float>>("rel_tolerance", -1.0f)), |
| tolerance_number(parser.add_option<SimpleOption<float>>("tolerance_num", -1.0f)), |
| scale(parser.add_option<SimpleOption<float>>("scale", 1.0f)), |
| offset(parser.add_option<SimpleOption<int>>("offset", 0)), |
| weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)), |
| weights_offset(parser.add_option<SimpleOption<int>>("weights_offset", 0)), |
| output_scale(parser.add_option<SimpleOption<float>>("output_scale", 1.0f)), |
| output_offset(parser.add_option<SimpleOption<int>>("output_offset", 0)), |
| input_range_low(parser.add_option<SimpleOption<uint64_t>>("input_range_low")), |
| input_range_high(parser.add_option<SimpleOption<uint64_t>>("input_range_high")), |
| weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")), |
| weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high")), |
| input_npy(parser.add_option<SimpleOption<std::string>>("input_image")), |
| output_npy(parser.add_option<SimpleOption<std::string>>("reference_image")), |
| weights_npy(parser.add_option<SimpleOption<std::string>>("weights_npy")), |
| bias_npy(parser.add_option<SimpleOption<std::string>>("bias_image")) |
| { |
| const std::set<PaddingMode> available_padding_modes |
| { |
| PaddingMode::Valid, |
| PaddingMode::Same |
| }; |
| |
| const std::set<arm_compute::graph::Target> supported_targets |
| { |
| Target::NEON, |
| Target::CL, |
| Target::GC, |
| }; |
| |
| const std::set<arm_compute::DataType> supported_data_types |
| { |
| DataType::F16, |
| DataType::F32, |
| DataType::QASYMM8, |
| }; |
| |
| const std::set<arm_compute::graph::ConvolutionMethod> supported_convolution_methods |
| { |
| arm_compute::graph::ConvolutionMethod::Default, |
| arm_compute::graph::ConvolutionMethod::GEMM, |
| arm_compute::graph::ConvolutionMethod::Winograd, |
| arm_compute::graph::ConvolutionMethod::Direct |
| }; |
| |
| const std::set<DataLayout> supported_data_layouts |
| { |
| DataLayout::NHWC, |
| DataLayout::NCHW, |
| }; |
| |
| padding_mode = parser.add_option<EnumOption<PaddingMode>>("padding_mode", available_padding_modes, PaddingMode::Valid); |
| target = parser.add_option<EnumOption<Target>>("target", supported_targets, Target::NEON); |
| data_type = parser.add_option<EnumOption<DataType>>("type", supported_data_types, DataType::F32); |
| conv_mode = parser.add_option<EnumOption<arm_compute::graph::ConvolutionMethod>>("convolution_method", supported_convolution_methods, arm_compute::graph::ConvolutionMethod::Default); |
| data_layout = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC); |
| |
| target->set_help("Target to execute on"); |
| data_type->set_help("Data type to use"); |
| padding_mode->set_help("Set padding mode"); |
| help->set_help("Show this help message"); |
| width->set_help("Set Input dimension width"); |
| height->set_help("Set Input dimension height"); |
| channels->set_help("Set Input dimension channels"); |
| batch->set_help("Set Input dimension batch"); |
| weights_width->set_help("Set weights_dimensions width"); |
| weights_height->set_help("Set weights_dimensions height"); |
| OFM->set_help("Set OFM"); |
| padding_top->set_help("Set padding top"); |
| padding_bottom->set_help("Set padding bottom"); |
| padding_left->set_help("Set padding left"); |
| padding_right->set_help("Set padding right"); |
| stride_x->set_help("Set padding stride x"); |
| stride_y->set_help("Set padding stride y"); |
| conv_mode->set_help("Set convolution method"); |
| data_layout->set_help("Data layout to use"); |
| absolute_tolerance->set_help("Absolute tolerance used for verification"); |
| relative_tolerance->set_help("Absolute tolerance used for verification"); |
| tolerance_number->set_help("Absolute tolerance used for verification"); |
| scale->set_help("Quantization scale from QASYMM8"); |
| offset->set_help("Quantization offset from QASYMM8"); |
| weights_scale->set_help("Quantization scale from QASYMM8"); |
| weights_offset->set_help("Quantization offset from QASYMM8"); |
| output_scale->set_help("Quantization scale from QASYMM8"); |
| output_offset->set_help("Quantization offset from QASYMM8"); |
| input_npy->set_help("Use input .npy instead"); |
| output_npy->set_help("Use .npy as a reference"); |
| input_range_low->set_help("Lower bound for input randomization range"); |
| input_range_high->set_help("Lower bound for input randomization range"); |
| weights_range_low->set_help("Lower bound for input randomization range"); |
| weights_range_high->set_help("Lower bound for input randomization range"); |
| } |
| |
| /** Prevent instances of this class from being copied (As this class contains pointers) */ |
| ConvolutionOptions(const ConvolutionOptions &) = delete; |
| /** Prevent instances of this class from being copied (As this class contains pointers) */ |
| ConvolutionOptions &operator=(const ConvolutionOptions &) = delete; |
| /** Allow instances of this class to be moved */ |
| ConvolutionOptions(ConvolutionOptions &&) noexcept(true) = default; |
| /** Allow instances of this class to be moved */ |
| ConvolutionOptions &operator=(ConvolutionOptions &&) noexcept(true) = default; |
| /** Default destructor */ |
| ~ConvolutionOptions() = default; |
| |
| SimpleOption<int> *width; /**< Input width */ |
| SimpleOption<int> *height; /**< Input height */ |
| SimpleOption<int> *channels; /**< Input channels */ |
| SimpleOption<int> *batch; /**< Input batch */ |
| SimpleOption<int> *weights_width; /**< weights width */ |
| SimpleOption<int> *weights_height; /**< weights height */ |
| SimpleOption<int> *OFM; /**< Output Feature Map */ |
| SimpleOption<int> *padding_top; /**< Padding top */ |
| SimpleOption<int> *padding_left; /**< Padding left */ |
| SimpleOption<int> *padding_bottom; /**< Padding bottom */ |
| SimpleOption<int> *padding_right; /**< Padding right */ |
| SimpleOption<int> *stride_x; /**< Padding stride x */ |
| SimpleOption<int> *stride_y; /**< Padding stride y */ |
| ToggleOption *help; /**< show help message */ |
| SimpleOption<int> *threads; /**< Number of threads option */ |
| EnumOption<arm_compute::graph::Target> *target; /**< Graph execution target */ |
| EnumOption<arm_compute::DataType> *data_type; /**< Graph data type */ |
| EnumOption<PaddingMode> *padding_mode; /**< Padding mode */ |
| EnumOption<arm_compute::graph::ConvolutionMethod> *conv_mode; /**< Convolution method */ |
| EnumOption<arm_compute::DataLayout> *data_layout; /**< Graph data layout */ |
| SimpleOption<float> *absolute_tolerance; /**< Absolute tolerance used in verification */ |
| SimpleOption<float> *relative_tolerance; /**< Relative tolerance used in verification */ |
| SimpleOption<float> *tolerance_number; /**< Tolerance number used in verification */ |
| SimpleOption<float> *scale; /**< Input Quantization scale from QASYMM8 */ |
| SimpleOption<int> *offset; /**< Input Quantization offset from QASYMM8 */ |
| SimpleOption<float> *weights_scale; /**< Weights Quantization scale from QASYMM8 */ |
| SimpleOption<int> *weights_offset; /**< Weights Quantization offset from QASYMM8 */ |
| SimpleOption<float> *output_scale; /**< Output Quantization scale from QASYMM8 */ |
| SimpleOption<int> *output_offset; /**< Output Quantization offset from QASYMM8 */ |
| SimpleOption<uint64_t> *input_range_low; /**< Lower bound for input randomization range */ |
| SimpleOption<uint64_t> *input_range_high; /**< Upper bound for input randomization range */ |
| SimpleOption<uint64_t> *weights_range_low; /**< Lower bound for weights randomization range */ |
| SimpleOption<uint64_t> *weights_range_high; /**< Upper bound for weights randomization range */ |
| |
| SimpleOption<std::string> *input_npy; /**< Use input .npy image */ |
| SimpleOption<std::string> *output_npy; /**< Use output .npy image to verify*/ |
| SimpleOption<std::string> *weights_npy; /**< Use weights .npy image */ |
| SimpleOption<std::string> *bias_npy; /**< Use bias .npy image */ |
| }; |
| |
| /** Consumes the convolution graph options and creates a structure containing any information |
| * |
| * @param[in] options Options to consume |
| * |
| * @return Convolutionparams structure containing the common graph parameters |
| */ |
| ExampleParams consume_covolution_graph_parameters(ConvolutionOptions &options) |
| { |
| ExampleParams common_params; |
| |
| common_params.common_params.help = options.help->is_set() ? options.help->value() : false; |
| common_params.common_params.threads = options.threads->value(); |
| common_params.common_params.target = options.target->value(); |
| |
| common_params.input.width = options.width->value(); |
| common_params.input.height = options.height->value(); |
| common_params.input.fm = options.channels->value(); |
| common_params.input.batch = options.batch->value(); |
| common_params.input.quant_info.scale = options.scale->value(); |
| common_params.input.quant_info.offset = options.offset->value(); |
| common_params.input.npy = options.input_npy->value(); |
| common_params.input.range_low = options.input_range_low->value(); |
| common_params.input.range_high = options.input_range_high->value(); |
| |
| common_params.weights.width = options.weights_width->value(); |
| common_params.weights.height = options.weights_height->value(); |
| common_params.weights.fm = options.OFM->value(); |
| common_params.weights.npy = options.weights_npy->value(); |
| common_params.weights.quant_info.scale = options.weights_scale->value(); |
| common_params.weights.quant_info.offset = options.weights_offset->value(); |
| common_params.weights.range_low = options.weights_range_low->value(); |
| common_params.weights.range_high = options.weights_range_high->value(); |
| |
| common_params.bias.npy = options.bias_npy->value(); |
| |
| common_params.output.quant_info.scale = options.output_scale->value(); |
| common_params.output.quant_info.offset = options.output_offset->value(); |
| common_params.output.npy = options.output_npy->value(); |
| |
| common_params.convolution.padding_mode = options.padding_mode->value(); |
| common_params.convolution.padding_top = options.padding_top->value(); |
| common_params.convolution.padding_bottom = options.padding_bottom->value(); |
| common_params.convolution.padding_left = options.padding_left->value(); |
| common_params.convolution.padding_right = options.padding_right->value(); |
| common_params.convolution.padding_stride_x = options.stride_x->value(); |
| common_params.convolution.padding_stride_y = options.stride_y->value(); |
| common_params.convolution.convolution_method = options.conv_mode->value(); |
| common_params.convolution.data_type = options.data_type->value(); |
| common_params.convolution.data_layout = options.data_layout->value(); |
| |
| common_params.verification.absolute_tolerance = options.absolute_tolerance->value(); |
| common_params.verification.relative_tolerance = options.relative_tolerance->value(); |
| common_params.verification.tolerance_number = options.tolerance_number->value(); |
| |
| return common_params; |
| } |
| |
| /** Calculate stride information. |
| * |
| * Depending on the selected padding mode create the desired PadStrideInfo |
| * |
| * @param[in] params Convolution parameters supplied by the user. |
| * |
| * @return PadStrideInfo with the correct padding mode. |
| */ |
| inline PadStrideInfo calculate_convolution_padding(ExampleParams params) |
| { |
| switch(params.convolution.padding_mode) |
| { |
| case PaddingMode::Manual: |
| { |
| return PadStrideInfo(params.convolution.padding_stride_x, params.convolution.padding_stride_y, params.convolution.padding_left, params.convolution.padding_right, params.convolution.padding_top, |
| params.convolution.padding_bottom, DimensionRoundingType::FLOOR); |
| } |
| case PaddingMode::Valid: |
| { |
| return PadStrideInfo(); |
| } |
| case PaddingMode::Same: |
| { |
| return arm_compute::calculate_same_pad(TensorShape(params.input.width, params.input.height), TensorShape(params.weights.width, params.weights.height), |
| PadStrideInfo(params.convolution.padding_stride_x, |
| params.convolution.padding_stride_y)); |
| } |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| |
| /** ConvolutionLayer Graph example validation accessor class */ |
| template <typename D> |
| class ConvolutionVerifyAccessor final : public graph::ITensorAccessor |
| { |
| public: |
| using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type; |
| |
| /** Constructor |
| * |
| * @param[in] params Convolution parameters |
| */ |
| explicit ConvolutionVerifyAccessor(ExampleParams ¶ms) |
| : _params(std::move(params)) |
| { |
| } |
| |
| // Inherited methods overriden: |
| bool access_tensor(ITensor &tensor) override |
| { |
| if(_params.output.npy.empty()) |
| { |
| const RelativeTolerance<float> rel_tolerance(relative_tolenace(_params.verification.relative_tolerance)); /**< Relative tolerance */ |
| const AbsoluteTolerance<float> abs_tolerance(absolute_tolerance(_params.verification.absolute_tolerance)); /**< Absolute tolerance */ |
| const float tolerance_num(tolerance_number(_params.verification.tolerance_number)); /**< Tolerance number */ |
| |
| //Create Input tensors |
| SimpleTensor<D> src{ TensorShape(_params.input.width, _params.input.height, _params.input.fm, _params.input.batch), _params.convolution.data_type, 1, _params.input.quant_info }; |
| SimpleTensor<D> weights{ TensorShape(_params.weights.width, _params.weights.height, _params.weights.fm), _params.convolution.data_type, 1, _params.weights.quant_info }; |
| SimpleTensor<TBias> bias{ TensorShape(_params.input.height), _params.convolution.data_type, 1, _params.input.quant_info }; |
| |
| //Fill the tenors with random values |
| fill_tensor<D>(src, 0, static_cast<D>(_params.input.range_low), static_cast<D>(_params.input.range_high)); |
| fill_tensor<D>(weights, 1, static_cast<D>(_params.weights.range_low), static_cast<D>(_params.weights.range_high)); |
| fill_tensor<TBias>(bias, 2, static_cast<TBias>(_params.input.range_low), static_cast<TBias>(_params.input.range_high)); |
| |
| // Calculate padding information |
| const PadStrideInfo padding_info = calculate_convolution_padding(_params); |
| |
| //Calculate reference |
| SimpleTensor<D> output = reference::convolution_layer<D>(src, weights, bias, permute_shape(tensor.info()->tensor_shape(), _params.convolution.data_layout, DataLayout::NCHW), padding_info, Size2D(1, |
| 1), |
| 1, |
| _params.output.quant_info); |
| |
| arm_compute::test::validation::validate(Accessor(tensor), output, rel_tolerance, tolerance_num, abs_tolerance); |
| } |
| else |
| { |
| //The user provided a reference file use an npy accessor to validate |
| NumPyAccessor(_params.output.npy, tensor.info()->tensor_shape(), tensor.info()->data_type()).access_tensor(tensor); |
| } |
| return false; |
| } |
| |
| private: |
| /** Fill tensor with Random values. |
| * |
| * Validate the given tensor against the reference result. |
| * |
| * @param[out] tensor The tensor we want to file |
| * @param[in] seed seed for the randomization function |
| * @param[in] low lower bound for random values |
| * @param[in] high upper bound for random values |
| * |
| * @return None. |
| */ |
| template <typename T> |
| void fill_tensor(arm_compute::test::SimpleTensor<T> &tensor, std::random_device::result_type seed, T low, T high) |
| { |
| std::mt19937 gen(seed); |
| switch(tensor.data_type()) |
| { |
| case arm_compute::DataType::QASYMM8: |
| { |
| uint8_t qasymm8_low = tensor.quantization_info().quantize(low, RoundingPolicy::TO_NEAREST_UP); |
| uint8_t qasymm8_high = tensor.quantization_info().quantize(high, RoundingPolicy::TO_NEAREST_UP); |
| |
| std::uniform_int_distribution<uint8_t> distribution(qasymm8_low, qasymm8_high); |
| |
| for(int i = 0; i < tensor.num_elements(); ++i) |
| { |
| tensor[i] = tensor.quantization_info().quantize(distribution(gen), RoundingPolicy::TO_NEAREST_UP); |
| } |
| |
| break; |
| } |
| case arm_compute::DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution(static_cast<int32_t>(low), static_cast<uint32_t>(high)); |
| |
| for(int i = 0; i < tensor.num_elements(); ++i) |
| { |
| tensor[i] = distribution(gen); |
| } |
| |
| break; |
| } |
| |
| case arm_compute::DataType::F16: |
| { |
| std::uniform_real_distribution<float> distribution(static_cast<half>(low), static_cast<half>(high)); |
| |
| for(int i = 0; i < tensor.num_elements(); ++i) |
| { |
| tensor[i] = static_cast<half>(distribution(gen)); |
| } |
| break; |
| } |
| case arm_compute::DataType::F32: |
| { |
| std::uniform_real_distribution<float> distribution(static_cast<float>(low), static_cast<float>(high)); |
| |
| for(int i = 0; i < tensor.num_elements(); ++i) |
| { |
| tensor[i] = distribution(gen); |
| } |
| |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| /** Select relative tolerance. |
| * |
| * Select relative tolerance if not supplied by user. |
| * |
| * @param[in] user_value supplied relative tolerance. -1 designates no user input |
| * |
| * @return Appropriate relative tolerance. |
| */ |
| float relative_tolenace(float user_value) |
| { |
| const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance |
| { |
| { |
| arm_compute::graph::Target::CL, |
| { { DataType::F16, 0.2f }, |
| { DataType::F32, 0.5f }, |
| { DataType::QASYMM8, 1.0f } |
| } |
| }, |
| { |
| arm_compute::graph::Target::NEON, |
| { { DataType::F16, 0.2f }, |
| { DataType::F32, 0.01f }, |
| { DataType::QASYMM8, 0.0f } |
| } |
| } |
| }; |
| if(user_value == -1) |
| { |
| if(_params.convolution.convolution_method == arm_compute::graph::ConvolutionMethod::Winograd |
| && _params.convolution.data_type == DataType::F32 |
| && _params.common_params.target == arm_compute::graph::Target::NEON) |
| { |
| return 0.05f; |
| } |
| else |
| { |
| return relative_tolerance.at(_params.common_params.target).at(_params.convolution.data_type); |
| } |
| } |
| |
| return user_value; |
| } |
| |
| /** Select absolute tolerance. |
| * |
| * Select absolute tolerance if not supplied by user. |
| * |
| * @param[in] user_value supplied absolute tolerance. -1 designates no user input |
| * |
| * @return Appropriate absolute tolerance. |
| */ |
| float absolute_tolerance(float user_value) |
| { |
| const std::map<Target, const std::map<DataType, float>> absolute_tolerance |
| { |
| { |
| Target::CL, |
| { { DataType::F16, 0.0f }, |
| { DataType::F32, 0.0001f }, |
| { DataType::QASYMM8, 0.0f } |
| } |
| }, |
| { |
| Target::NEON, |
| { { DataType::F16, 0.2f }, |
| { DataType::F32, 0.002f }, |
| { DataType::QASYMM8, 0.0f } |
| } |
| } |
| }; |
| |
| if(user_value == -1) |
| { |
| return absolute_tolerance.at(_params.common_params.target).at(_params.convolution.data_type); |
| } |
| return user_value; |
| } |
| /** Select tolerance number. |
| * |
| * Select tolerance number if not supplied by user. |
| * |
| * @param[in] user_value supplied tolerance number. -1 designates no user input |
| * |
| * @return Appropriate tolerance number. |
| */ |
| float tolerance_number(float user_value) |
| { |
| const std::map<Target, const std::map<DataType, float>> absolute_tolerance |
| { |
| { |
| Target::CL, |
| { { DataType::F16, 0.07f }, |
| { DataType::F32, 0.07f }, |
| { DataType::QASYMM8, 0.0f } |
| } |
| }, |
| { |
| Target::NEON, |
| { { DataType::F16, 0.07f }, |
| { DataType::F32, 0.0f }, |
| { DataType::QASYMM8, 0.0f } |
| } |
| } |
| }; |
| |
| if(user_value == -1) |
| { |
| return absolute_tolerance.at(_params.common_params.target).at(_params.convolution.data_type); |
| } |
| return user_value; |
| } |
| |
| ExampleParams _params; |
| }; |
| |
| /** Generates appropriate convolution verify accessor |
| * |
| * @param[in] params User supplied parameters for convolution. |
| * |
| * @return A convolution verify accessor for the requested datatype. |
| */ |
| inline std::unique_ptr<graph::ITensorAccessor> get_convolution_verify_accessor(ExampleParams params) |
| { |
| switch(params.convolution.data_type) |
| { |
| case DataType::QASYMM8: |
| { |
| return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<uint8_t>>( |
| params); |
| } |
| case DataType::F16: |
| { |
| return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<half>>( |
| params); |
| } |
| case DataType::F32: |
| { |
| return arm_compute::support::cpp14::make_unique<ConvolutionVerifyAccessor<float>>( |
| params); |
| } |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| /** Generates appropriate accessor according to the specified graph parameters |
| * |
| * @param[in] graph_parameters Graph parameters |
| * @param[in] lower Lower random values bound |
| * @param[in] upper Upper random values bound |
| * @param[in] seed Random generator seed |
| * |
| * @return An appropriate tensor accessor |
| */ |
| inline std::unique_ptr<graph::ITensorAccessor> get_accessor(const TensorParams &tensor, PixelValue lower, PixelValue upper, const std::random_device::result_type seed = 0) |
| { |
| if(!tensor.npy.empty()) |
| { |
| return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(tensor.npy); |
| } |
| else |
| { |
| return arm_compute::support::cpp14::make_unique<RandomAccessor>(lower, upper, seed); |
| } |
| } |
| } // namespace |
| |
| class GraphConvolutionValidateExample final : public ValidateExample |
| { |
| public: |
| GraphConvolutionValidateExample() |
| : graph(0, "Convolution Graph example") |
| { |
| } |
| bool do_setup(int argc, char **argv) override |
| { |
| CommandLineParser parser; |
| |
| ConvolutionOptions Options(parser); |
| |
| parser.parse(argc, argv); |
| |
| ExampleParams params = consume_covolution_graph_parameters(Options); |
| |
| if(params.common_params.help) |
| { |
| parser.print_help(argv[0]); |
| return false; |
| } |
| |
| std::cout << params << std::endl; |
| |
| // Calculate padding information |
| const PadStrideInfo padding_info = calculate_convolution_padding(params); |
| |
| // Create input descriptor |
| const TensorShape input_shape = permute_shape(TensorShape(params.input.width, params.input.height, params.input.fm, params.input.batch), DataLayout::NCHW, params.convolution.data_layout); |
| TensorDescriptor input_descriptor = TensorDescriptor(input_shape, params.convolution.data_type, params.input.quant_info, params.convolution.data_layout); |
| |
| const PixelValue lower = PixelValue(params.input.range_low, params.convolution.data_type, params.input.quant_info); |
| const PixelValue upper = PixelValue(params.input.range_high, params.convolution.data_type, params.input.quant_info); |
| |
| const PixelValue weights_lower = PixelValue(params.weights.range_low, params.convolution.data_type, params.weights.quant_info); |
| const PixelValue weights_upper = PixelValue(params.weights.range_high, params.convolution.data_type, params.weights.quant_info); |
| |
| graph << params.common_params.target |
| << params.convolution.convolution_method |
| << InputLayer(input_descriptor, get_accessor(params.input, lower, upper, 0)) |
| << ConvolutionLayer(params.weights.width, params.weights.height, params.weights.fm, |
| get_accessor(params.weights, weights_lower, weights_upper, 1), |
| get_accessor(params.bias, lower, upper, 2), |
| padding_info, 1, params.weights.quant_info, params.output.quant_info) |
| << OutputLayer(get_convolution_verify_accessor(params)); |
| |
| GraphConfig config; |
| config.num_threads = params.common_params.threads; |
| |
| graph.finalize(params.common_params.target, config); |
| |
| return true; |
| } |
| |
| void do_run() override |
| { |
| graph.run(); |
| } |
| |
| void do_teardown() override |
| { |
| } |
| |
| private: |
| Stream graph; |
| }; |
| |
| /** Main program for Graph Convolution test |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( Input dimensions [width, height, channels, batch] |
| * Weights dimensions [width, height, OFM] |
| * Padding [top,bottom,left,right, Stride x, Stride y, mode [Valid / Same / Manual] ) |
| * Convolution Method[ Auto/GEMM/Winograd/Direct] |
| * Verification[tolerance_number,absolute_tolerance,relative_tolerance] ) |
| * |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphConvolutionValidateExample>(argc, argv); |
| } |