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393 lines
20 KiB
393 lines
20 KiB
/*
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* Copyright (c) 2019-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/graph.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/reference/DepthwiseConvolutionLayer.h"
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#include "tests/validation/reference/Permute.h"
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#include "utils/CommonGraphOptions.h"
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#include "utils/GraphUtils.h"
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#include "utils/Utils.h"
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#include "ValidateExample.h"
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#include "graph_validate_utils.h"
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#include <utility>
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using namespace arm_compute::utils;
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using namespace arm_compute::graph::frontend;
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using namespace arm_compute::graph_utils;
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using namespace arm_compute::graph;
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using namespace arm_compute;
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using namespace arm_compute::test;
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using namespace arm_compute::test::validation;
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namespace
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{
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/** Depthwise Convolution command line options used to configure the graph examples
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*
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* (Similar to common options)
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* The options in this object get populated when "parse()" is called on the parser used to construct it.
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* The expected workflow is:
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*
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* CommandLineParser parser;
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* CommonOptions options( parser );
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* parser.parse(argc, argv);
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*/
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class DepthConvolutionOptions final : public CommonGraphValidateOptions
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{
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public:
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explicit DepthConvolutionOptions(CommandLineParser &parser) noexcept
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: CommonGraphValidateOptions(parser),
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width(parser.add_option<SimpleOption<int>>("width", 9)),
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height(parser.add_option<SimpleOption<int>>("height", 9)),
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channels(parser.add_option<SimpleOption<int>>("channels", 1)),
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batch(parser.add_option<SimpleOption<int>>("batch", 1)),
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weights_width(parser.add_option<SimpleOption<int>>("weights_width", 3)),
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weights_height(parser.add_option<SimpleOption<int>>("weights_height", 3)),
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padding_top(parser.add_option<SimpleOption<int>>("padding_top", 0)),
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padding_left(parser.add_option<SimpleOption<int>>("padding_left", 0)),
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padding_bottom(parser.add_option<SimpleOption<int>>("padding_bottom", 0)),
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padding_right(parser.add_option<SimpleOption<int>>("padding_right", 0)),
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stride_x(parser.add_option<SimpleOption<int>>("stride_x", 1)),
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stride_y(parser.add_option<SimpleOption<int>>("stride_y", 1)),
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padding_mode(),
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conv_mode(),
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depth_multiplier(parser.add_option<SimpleOption<int>>("depth_multiplier", 1)),
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data_layout(),
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scale(parser.add_option<SimpleOption<float>>("scale", 1.0f)),
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offset(parser.add_option<SimpleOption<int>>("offset", 0)),
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weights_scale(parser.add_option<SimpleOption<float>>("weights_scale", 1.0f)),
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weights_offset(parser.add_option<SimpleOption<int>>("weights_offset", 0)),
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output_scale(parser.add_option<SimpleOption<float>>("output_scale", 1.0f)),
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output_offset(parser.add_option<SimpleOption<int>>("output_offset", 0)),
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input_range_low(parser.add_option<SimpleOption<uint64_t>>("input_range_low")),
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input_range_high(parser.add_option<SimpleOption<uint64_t>>("input_range_high")),
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weights_range_low(parser.add_option<SimpleOption<uint64_t>>("weights_range_low")),
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weights_range_high(parser.add_option<SimpleOption<uint64_t>>("weights_range_high")),
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input_npy(parser.add_option<SimpleOption<std::string>>("input_image")),
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output_npy(parser.add_option<SimpleOption<std::string>>("reference_image")),
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weights_npy(parser.add_option<SimpleOption<std::string>>("weights_npy")),
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bias_npy(parser.add_option<SimpleOption<std::string>>("bias_image"))
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{
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const std::set<ConvolutionPaddingMode> available_padding_modes
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{
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ConvolutionPaddingMode::Valid,
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ConvolutionPaddingMode::Same
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};
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const std::set<arm_compute::graph::DepthwiseConvolutionMethod> supported_convolution_methods
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{
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arm_compute::graph::DepthwiseConvolutionMethod::Default,
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arm_compute::graph::DepthwiseConvolutionMethod::GEMV,
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arm_compute::graph::DepthwiseConvolutionMethod::Optimized3x3,
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};
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const std::set<DataLayout> supported_data_layouts
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{
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DataLayout::NHWC,
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DataLayout::NCHW,
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};
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padding_mode = parser.add_option<EnumOption<ConvolutionPaddingMode>>("padding_mode", available_padding_modes, ConvolutionPaddingMode::Valid);
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conv_mode = parser.add_option<EnumOption<arm_compute::graph::DepthwiseConvolutionMethod>>("convolution_method", supported_convolution_methods,
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arm_compute::graph::DepthwiseConvolutionMethod::Default);
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data_layout = parser.add_option<EnumOption<DataLayout>>("layout", supported_data_layouts, DataLayout::NHWC);
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padding_mode->set_help("Set padding mode");
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width->set_help("Set Input dimension width");
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height->set_help("Set Input dimension height");
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channels->set_help("Set Input dimension channels");
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batch->set_help("Set Input dimension batch");
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weights_width->set_help("Set weights_dimensions width");
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weights_height->set_help("Set weights_dimensions height");
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padding_top->set_help("Set padding top");
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padding_bottom->set_help("Set padding bottom");
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padding_left->set_help("Set padding left");
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padding_right->set_help("Set padding right");
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stride_x->set_help("Set padding stride x");
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stride_y->set_help("Set padding stride y");
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conv_mode->set_help("Set convolution method");
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data_layout->set_help("Data layout to use");
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scale->set_help("Quantization scale from QASYMM8");
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offset->set_help("Quantization offset from QASYMM8");
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output_scale->set_help("Quantization scale from QASYMM8");
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output_offset->set_help("Quantization offset from QASYMM8");
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input_npy->set_help("Use input .npy instead");
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output_npy->set_help("Use .npy as a reference");
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input_range_low->set_help("Lower bound for input randomization range");
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input_range_high->set_help("Lower bound for input randomization range");
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weights_scale->set_help("Quantization scale from QASYMM8");
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weights_offset->set_help("Quantization offset from QASYMM8");
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weights_range_low->set_help("Lower bound for input randomization range");
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weights_range_high->set_help("Lower bound for input randomization range");
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depth_multiplier->set_help("Depth multiplier");
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}
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/** Fill out the supplied parameters with user supplied parameters
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*
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* @param[out] os Output stream.
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* @param[in] common_params Example parameters to output
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*
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* @return None.
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*/
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void consume_parameters(ExampleParams &common_params)
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{
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common_params.input.width = width->value();
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common_params.input.height = height->value();
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common_params.input.fm = channels->value();
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common_params.input.batch = batch->value();
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common_params.input.quant_info = QuantizationInfo(scale->value(), offset->value());
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common_params.input.npy = input_npy->value();
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common_params.input.range_low = input_range_low->value();
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common_params.input.range_high = input_range_high->value();
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common_params.weights.width = weights_width->value();
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common_params.weights.height = weights_height->value();
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common_params.weights.npy = weights_npy->value();
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common_params.weights.range_low = weights_range_low->value();
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common_params.weights.range_high = weights_range_high->value();
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common_params.weights.quant_info = QuantizationInfo(weights_scale->value(), weights_offset->value());
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common_params.bias.npy = bias_npy->value();
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common_params.output.quant_info = QuantizationInfo(output_scale->value(), output_offset->value());
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common_params.output.npy = output_npy->value();
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common_params.convolution.padding_mode = padding_mode->value();
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common_params.convolution.padding_top = padding_top->value();
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common_params.convolution.padding_bottom = padding_bottom->value();
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common_params.convolution.padding_left = padding_left->value();
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common_params.convolution.padding_right = padding_right->value();
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common_params.convolution.padding_stride_x = stride_x->value();
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common_params.convolution.padding_stride_y = stride_y->value();
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common_params.convolution.depth_multiplier = depth_multiplier->value();
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common_params.data_type = data_type->value();
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common_params.data_layout = data_layout->value();
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common_params.depth_convolution_method = conv_mode->value();
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}
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void print_parameters(::std::ostream &os, const ExampleParams &common_params) override
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{
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os << "Threads : " << common_params.common_params.threads << std::endl;
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os << "Target : " << common_params.common_params.target << std::endl;
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os << "Data type : " << common_params.data_type << std::endl;
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os << "Input dimensions(X,Y, Channels, Batch) : (" << common_params.input.width << "," << common_params.input.height << "," << common_params.input.fm << "," << common_params.input.batch << ")"
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<< std::endl;
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os << "Weight dimensions(X,Y, Channels(same as input)) : (" << common_params.weights.width << "," << common_params.weights.height << "," << common_params.input.fm << ","
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<< ")" << std::endl;
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os << "Padding(top, bottom, left, right) (stride x, stride y) : (" << common_params.convolution.padding_top << "," << common_params.convolution.padding_bottom << "," <<
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common_params.convolution.padding_left << "," << common_params.convolution.padding_right << ") (" << common_params.convolution.padding_stride_x << "," << common_params.convolution.padding_stride_y <<
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")" << std::endl;
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os << "Padding Mode: " << common_params.convolution.padding_mode << std::endl;
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os << "Convolution Method: " << common_params.depth_convolution_method << std::endl;
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os << "Depth multiplier: " << common_params.convolution.depth_multiplier;
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}
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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DepthConvolutionOptions(const DepthConvolutionOptions &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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DepthConvolutionOptions &operator=(const DepthConvolutionOptions &) = delete;
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/** Allow instances of this class to be moved */
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DepthConvolutionOptions(DepthConvolutionOptions &&) noexcept(true) = default;
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/** Allow instances of this class to be moved */
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DepthConvolutionOptions &operator=(DepthConvolutionOptions &&) noexcept(true) = default;
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/** Default destructor */
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~DepthConvolutionOptions() override = default;
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private:
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SimpleOption<int> *width; /**< Input width */
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SimpleOption<int> *height; /**< Input height */
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SimpleOption<int> *channels; /**< Input channels */
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SimpleOption<int> *batch; /**< Input batch */
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SimpleOption<int> *weights_width; /**< weights width */
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SimpleOption<int> *weights_height; /**< weights height */
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SimpleOption<int> *padding_top; /**< Padding top */
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SimpleOption<int> *padding_left; /**< Padding left */
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SimpleOption<int> *padding_bottom; /**< Padding bottom */
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SimpleOption<int> *padding_right; /**< Padding right */
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SimpleOption<int> *stride_x; /**< Padding stride x */
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SimpleOption<int> *stride_y; /**< Padding stride y */
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EnumOption<ConvolutionPaddingMode> *padding_mode; /**< Padding mode */
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EnumOption<arm_compute::graph::DepthwiseConvolutionMethod> *conv_mode; /**< Convolution method */
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SimpleOption<int> *depth_multiplier; /**< Depth multiplier */
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EnumOption<arm_compute::DataLayout> *data_layout; /**< Graph data layout */
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SimpleOption<float> *scale; /**< Input Quantization scale from QASYMM8 */
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SimpleOption<int> *offset; /**< Input Quantization offset from QASYMM8 */
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SimpleOption<float> *weights_scale; /**< Weights Quantization scale from QASYMM8 */
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SimpleOption<int> *weights_offset; /**< Weights Quantization offset from QASYMM8 */
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SimpleOption<float> *output_scale; /**< Output Quantization scale from QASYMM8 */
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SimpleOption<int> *output_offset; /**< Output Quantization offset from QASYMM8 */
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SimpleOption<uint64_t> *input_range_low; /**< Lower bound for input randomization range */
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SimpleOption<uint64_t> *input_range_high; /**< Upper bound for input randomization range */
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SimpleOption<uint64_t> *weights_range_low; /**< Lower bound for weights randomization range */
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SimpleOption<uint64_t> *weights_range_high; /**< Upper bound for weights randomization range */
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SimpleOption<std::string> *input_npy; /**< Use input .npy image */
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SimpleOption<std::string> *output_npy; /**< Use output .npy image to verify*/
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SimpleOption<std::string> *weights_npy; /**< Use weights .npy image */
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SimpleOption<std::string> *bias_npy; /**< Use bias .npy image */
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};
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/** DepthwiseConvolutionLayer Graph example validation accessor class */
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template <typename D>
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class DepthConvolutionVerifyAccessor final : public VerifyAccessor<D>
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{
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public:
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using BaseClassType = VerifyAccessor<D>;
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using BaseClassType::BaseClassType;
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using BaseClassType::_params;
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using TBias = typename std::conditional<std::is_same<typename std::decay<D>::type, uint8_t>::value, int32_t, D>::type;
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public:
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SimpleTensor<D> reference(SimpleTensor<D> &src, SimpleTensor<D> &weights, SimpleTensor<TBias> &bias, const TensorShape &output_shape) override
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{
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// Calculate padding information
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const PadStrideInfo padding_info = calculate_convolution_padding(_params);
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//Calculate reference
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return reference::depthwise_convolution<D>(src, weights, bias, output_shape, padding_info,
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_params.convolution.depth_multiplier,
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Size2D(1U, 1U),
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_params.output.quant_info);
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}
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float relative_tolerance() override
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{
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const std::map<arm_compute::graph::Target, const std::map<DataType, float>> relative_tolerance
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{
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{
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arm_compute::graph::Target::CL,
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{ { DataType::F16, 0.01f },
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{ DataType::F32, 0.01f },
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{ DataType::QASYMM8, 0.0f }
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}
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},
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{
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arm_compute::graph::Target::NEON,
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{ { DataType::F16, 0.01f },
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{ DataType::F32, 0.01f },
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{ DataType::QASYMM8, 1.0f }
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}
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}
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};
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return relative_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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float absolute_tolerance() override
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{
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const std::map<Target, const std::map<DataType, float>> absolute_tolerance
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{
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{
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Target::CL,
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{ { DataType::F16, 0.0f },
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{ DataType::F32, 0.0000f },
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{ DataType::QASYMM8, 0.0f }
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}
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},
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{
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Target::NEON,
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{ { DataType::F16, 0.2f },
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{ DataType::F32, 0.002f },
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{ DataType::QASYMM8, 0.0f }
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}
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}
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};
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return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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float tolerance_number() override
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{
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const std::map<Target, const std::map<DataType, float>> absolute_tolerance
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{
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{
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Target::CL,
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{ { DataType::F16, 0.05f },
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{ DataType::F32, 0.00f },
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{ DataType::QASYMM8, 0.0f }
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}
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},
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{
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Target::NEON,
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{ { DataType::F16, 0.05f },
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{ DataType::F32, 0.0f },
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{ DataType::QASYMM8, 0.0f }
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}
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}
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};
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return absolute_tolerance.at(_params.common_params.target).at(_params.data_type);
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}
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};
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} // namespace
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class GraphDepthwiseConvolutionValidateExample final : public GraphValidateExample<DepthwiseConvolutionLayer, DepthConvolutionOptions, DepthConvolutionVerifyAccessor>
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{
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using GraphValidateExample::graph;
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public:
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GraphDepthwiseConvolutionValidateExample()
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: GraphValidateExample("DepthWiseConvolution Graph example")
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{
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}
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DepthwiseConvolutionLayer GraphFunctionLayer(ExampleParams ¶ms) override
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{
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const PixelValue lower = PixelValue(params.input.range_low, params.data_type, params.input.quant_info);
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const PixelValue upper = PixelValue(params.input.range_high, params.data_type, params.input.quant_info);
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const PixelValue weights_lower = PixelValue(params.weights.range_low, params.data_type, params.weights.quant_info);
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const PixelValue weights_upper = PixelValue(params.weights.range_high, params.data_type, params.weights.quant_info);
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// Calculate padding information
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const PadStrideInfo padding_info = calculate_convolution_padding(params);
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return DepthwiseConvolutionLayer(params.weights.width, params.weights.height,
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get_accessor(params.weights, weights_lower, weights_upper, 1),
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get_accessor(params.bias, lower, upper, 2),
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padding_info, params.convolution.depth_multiplier, params.weights.quant_info, params.output.quant_info);
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}
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};
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/** Main program for Graph Depthwise Convolution test
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*
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* @param[in] argc Number of arguments
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* @param[in] argv Arguments ( Input dimensions [width, height, channels, batch]
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* Weights dimensions [width, height, channels]
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* Padding [top,bottom,left,right, Stride x, Stride y, mode [Valid / Same / Manual] )
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* Convolution Method[ Default/GEMV/Optimized3x3]
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* Verification[tolerance_number,absolute_tolerance,relative_tolerance] )
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*
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*/
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int main(int argc, char **argv)
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{
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return arm_compute::utils::run_example<GraphDepthwiseConvolutionValidateExample>(argc, argv);
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}
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