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Diffstat (limited to 'tests/validate_examples/graph_depthwiseconvolution.cpp')
| -rw-r--r-- | tests/validate_examples/graph_depthwiseconvolution.cpp | 394 |
1 files changed, 394 insertions, 0 deletions
diff --git a/tests/validate_examples/graph_depthwiseconvolution.cpp b/tests/validate_examples/graph_depthwiseconvolution.cpp new file mode 100644 index 0000000000..3ea33e1deb --- /dev/null +++ b/tests/validate_examples/graph_depthwiseconvolution.cpp @@ -0,0 +1,394 @@ +/* + * 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/DepthwiseConvolutionLayer.h" +#include "tests/validation/reference/Permute.h" + +#include "utils/CommonGraphOptions.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include "ValidateExample.h" +#include "graph_validate_utils.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 +{ +/** Depthwise 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 DepthConvolutionOptions final : public CommonGraphValidateOptions +{ +public: + explicit DepthConvolutionOptions(CommandLineParser &parser) noexcept + : CommonGraphValidateOptions(parser), + 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)), + 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)), + padding_mode(), + conv_mode(), + depth_multiplier(parser.add_option<SimpleOption<int>>("depth_multiplier", 1)), + data_layout(), + 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<ConvolutionPaddingMode> available_padding_modes + { + ConvolutionPaddingMode::Valid, + ConvolutionPaddingMode::Same + }; + + const std::set<arm_compute::graph::DepthwiseConvolutionMethod> supported_convolution_methods + { + arm_compute::graph::DepthwiseConvolutionMethod::Default, + arm_compute::graph::DepthwiseConvolutionMethod::GEMV, + arm_compute::graph::DepthwiseConvolutionMethod::Optimized3x3, + }; + + const std::set<DataLayout> supported_data_layouts + { + DataLayout::NHWC, + DataLayout::NCHW, + }; + + padding_mode = parser.add_option<EnumOption<ConvolutionPaddingMode>>("padding_mode", available_padding_modes, ConvolutionPaddingMode::Valid); + conv_mode = parser.add_option<EnumOption<arm_compute::graph::DepthwiseConvolutionMethod>>("convolution_method", supported_convolution_methods, + arm_compute::graph::DepthwiseConvolutionMethod::Default); + data_layout = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC); + + padding_mode->set_help("Set padding mode"); + 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"); + 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"); + scale->set_help("Quantization scale from QASYMM8"); + 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_scale->set_help("Quantization scale from QASYMM8"); + weights_offset->set_help("Quantization offset from QASYMM8"); + weights_range_low->set_help("Lower bound for input randomization range"); + weights_range_high->set_help("Lower bound for input randomization range"); + depth_multiplier->set_help("Depth multiplier"); + } + + /** Fill out the supplied parameters with user supplied parameters + * + * @param[out] os Output stream. + * @param[in] common_params Example parameters to output + * + * @return None. + */ + void consume_parameters(ExampleParams &common_params) + { + common_params.input.width = width->value(); + common_params.input.height = height->value(); + common_params.input.fm = channels->value(); + common_params.input.batch = batch->value(); + common_params.input.quant_info = QuantizationInfo(scale->value(), offset->value()); + common_params.input.npy = input_npy->value(); + common_params.input.range_low = input_range_low->value(); + common_params.input.range_high = input_range_high->value(); + + common_params.weights.width = weights_width->value(); + common_params.weights.height = weights_height->value(); + common_params.weights.npy = weights_npy->value(); + common_params.weights.range_low = weights_range_low->value(); + common_params.weights.range_high = weights_range_high->value(); + common_params.weights.quant_info = QuantizationInfo(weights_scale->value(), weights_offset->value()); + + common_params.bias.npy = bias_npy->value(); + + common_params.output.quant_info = QuantizationInfo(output_scale->value(), output_offset->value()); + common_params.output.npy = output_npy->value(); + + common_params.convolution.padding_mode = padding_mode->value(); + common_params.convolution.padding_top = padding_top->value(); + common_params.convolution.padding_bottom = padding_bottom->value(); + common_params.convolution.padding_left = padding_left->value(); + common_params.convolution.padding_right = padding_right->value(); + common_params.convolution.padding_stride_x = stride_x->value(); + common_params.convolution.padding_stride_y = stride_y->value(); + common_params.convolution.depth_multiplier = depth_multiplier->value(); + + common_params.data_type = data_type->value(); + common_params.data_layout = data_layout->value(); + common_params.depth_convolution_method = conv_mode->value(); + } + + void print_parameters(::std::ostream &os, const ExampleParams &common_params) override + { + os << "Threads : " << common_params.common_params.threads << std::endl; + os << "Target : " << common_params.common_params.target << std::endl; + os << "Data type : " << common_params.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)) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.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.depth_convolution_method << std::endl; + os << "Depth multiplier: " << common_params.convolution.depth_multiplier; + } + + /** Prevent instances of this class from being copied (As this class contains pointers) */ + DepthConvolutionOptions(const DepthConvolutionOptions &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + DepthConvolutionOptions &operator=(const DepthConvolutionOptions &) = delete; + /** Allow instances of this class to be moved */ + DepthConvolutionOptions(DepthConvolutionOptions &&) noexcept(true) = default; + /** Allow instances of this class to be moved */ + DepthConvolutionOptions &operator=(DepthConvolutionOptions &&) noexcept(true) = default; + /** Default destructor */ + ~DepthConvolutionOptions() override = default; + +private: + 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> *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 */ + EnumOption<ConvolutionPaddingMode> *padding_mode; /**< Padding mode */ + EnumOption<arm_compute::graph::DepthwiseConvolutionMethod> *conv_mode; /**< Convolution method */ + SimpleOption<int> *depth_multiplier; /**< Depth multiplier */ + EnumOption<arm_compute::DataLayout> *data_layout; /**< Graph data layout */ + 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 */ +}; + +/** DepthwiseConvolutionLayer Graph example validation accessor class */ +template <typename D> +class DepthConvolutionVerifyAccessor final : public VerifyAccessor<D> +{ +public: + using BaseClassType = VerifyAccessor<D>; + using BaseClassType::BaseClassType; + using BaseClassType::_params; + using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type; + +public: + SimpleTensor<D> reference(SimpleTensor<D> &src, SimpleTensor<D> &weights, SimpleTensor<TBias> &bias, const TensorShape &output_shape) override + { + // Calculate padding information + const PadStrideInfo padding_info = calculate_convolution_padding(_params); + + //Calculate reference + return reference::depthwise_convolution<D>(src, weights, bias, output_shape, padding_info, + _params.convolution.depth_multiplier, + Size2D(1U, 1U), + _params.output.quant_info); + } + + float relative_tolerance() override + { + const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance + { + { + arm_compute::graph::Target::CL, + { { DataType::F16, 0.01f }, + { DataType::F32, 0.01f }, + { DataType::QASYMM8, 0.0f } + } + }, + { + arm_compute::graph::Target::NEON, + { { DataType::F16, 0.01f }, + { DataType::F32, 0.01f }, + { DataType::QASYMM8, 1.0f } + } + } + }; + + return relative_tolerance.at(_params.common_params.target).at(_params.data_type); + } + + float absolute_tolerance() override + { + const std::map<Target, const std::map<DataType, float>> absolute_tolerance + { + { + Target::CL, + { { DataType::F16, 0.0f }, + { DataType::F32, 0.0000f }, + { DataType::QASYMM8, 0.0f } + } + }, + { + Target::NEON, + { { DataType::F16, 0.2f }, + { DataType::F32, 0.002f }, + { DataType::QASYMM8, 0.0f } + } + } + }; + + return absolute_tolerance.at(_params.common_params.target).at(_params.data_type); + } + + float tolerance_number() override + { + const std::map<Target, const std::map<DataType, float>> absolute_tolerance + { + { + Target::CL, + { { DataType::F16, 0.05f }, + { DataType::F32, 0.00f }, + { DataType::QASYMM8, 0.0f } + } + }, + { + Target::NEON, + { { DataType::F16, 0.05f }, + { DataType::F32, 0.0f }, + { DataType::QASYMM8, 0.0f } + } + } + }; + + return absolute_tolerance.at(_params.common_params.target).at(_params.data_type); + } +}; + +} // namespace + +class GraphDepthwiseConvolutionValidateExample final : public GraphValidateExample<DepthwiseConvolutionLayer, DepthConvolutionOptions, DepthConvolutionVerifyAccessor> +{ + using GraphValidateExample::graph; + +public: + GraphDepthwiseConvolutionValidateExample() + : GraphValidateExample("DepthWiseConvolution Graph example") + { + } + + DepthwiseConvolutionLayer GraphFunctionLayer(ExampleParams ¶ms) override + { + const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info); + const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info); + + const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info); + const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info); + + // Calculate padding information + const PadStrideInfo padding_info = calculate_convolution_padding(params); + + return DepthwiseConvolutionLayer(params.weights.width, params.weights.height, + get_accessor(params.weights, weights_lower, weights_upper, 1), + get_accessor(params.bias, lower, upper, 2), + padding_info, params.convolution.depth_multiplier, params.weights.quant_info, params.output.quant_info); + } +}; + +/** Main program for Graph Depthwise Convolution test + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( Input dimensions [width, height, channels, batch] + * Weights dimensions [width, height, channels] + * Padding [top,bottom,left,right, Stride x, Stride y, mode [Valid / Same / Manual] ) + * Convolution Method[ Default/GEMV/Optimized3x3] + * Verification[tolerance_number,absolute_tolerance,relative_tolerance] ) + * + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example<GraphDepthwiseConvolutionValidateExample>(argc, argv); +} |