From 88d5b22eb5574d8b564474df2c758d222b3b5547 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Fri, 6 Apr 2018 12:24:55 +0100 Subject: COMPMID-1035 - Add ResneXt50 as a graph example Change-Id: I42f0e7dab38e45b5eecfe6858eaecee8939c8585 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129291 Reviewed-by: Georgios Pinitas Reviewed-by: Anthony Barbier Tested-by: Jenkins --- examples/graph_resnext50.cpp | 208 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 208 insertions(+) create mode 100644 examples/graph_resnext50.cpp (limited to 'examples/graph_resnext50.cpp') diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp new file mode 100644 index 0000000000..f96a02e6d6 --- /dev/null +++ b/examples/graph_resnext50.cpp @@ -0,0 +1,208 @@ +/* + * Copyright (c) 2018 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 "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include + +using namespace arm_compute::utils; +using namespace arm_compute::graph::frontend; +using namespace arm_compute::graph_utils; + +/** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) + */ +class GraphResNeXt50Example : public Example +{ +public: + void do_setup(int argc, char **argv) override + { + std::string data_path; /* Path to the trainable data */ + std::string npy_in; /* Input npy data */ + std::string npy_out; /* Output npy data */ + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::DISABLED; + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_in] [npy_out] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_in] [npy_out] [fast_math_hint]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [npy_in] [npy_out] [fast_math_hint]\n\n"; + std::cout << "No input npy file provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + npy_in = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n"; + std::cout << "No output npy file provided: skipping output accessor\n\n"; + } + else if(argc == 5) + { + data_path = argv[2]; + npy_in = argv[3]; + npy_out = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + npy_in = argv[3]; + npy_out = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; + } + + graph << target_hint + << fast_math_hint + << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), + get_input_accessor(npy_in)) + << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), + get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) + .set_name("bn_data/Scale") + << ConvolutionLayer( + 7U, 7U, 64U, + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), + PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) + .set_name("conv0/Convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); + + add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); + add_residual_block(data_path, 512, 2, 4, 2); + add_residual_block(data_path, 1024, 3, 6, 2); + add_residual_block(data_path, 2048, 4, 3, 2); + + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") + << FlattenLayer().set_name("predictions/Reshape") + << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32)); + + // Finalize graph + GraphConfig config; + config.use_tuner = (target == 2); + graph.finalize(target_hint, config); + } + + void do_run() override + { + // Run graph + graph.run(); + } + +private: + Stream graph{ 0, "ResNeXt50" }; + + void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) + { + for(unsigned int i = 0; i < num_units; ++i) + { + std::stringstream unit_path_ss; + unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_"; + std::string unit_path = unit_path_ss.str(); + + std::stringstream unit_name_ss; + unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/"; + std::string unit_name = unit_name_ss.str(); + + PadStrideInfo pad_grouped_conv(1, 1, 1, 1); + if(i == 0) + { + pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR); + } + + SubStream right(graph); + right << ConvolutionLayer( + 1U, 1U, base_depth / 2, + get_weights_accessor(data_path, unit_path + "conv1_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv1/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") + + << ConvolutionLayer( + 3U, 3U, base_depth / 2, + get_weights_accessor(data_path, unit_path + "conv2_weights.npy"), + std::unique_ptr(nullptr), + pad_grouped_conv, 32) + .set_name(unit_name + "conv2/convolution") + << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"), + get_weights_accessor(data_path, unit_path + "bn2_add.npy")) + .set_name(unit_name + "conv1/Scale") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu") + + << ConvolutionLayer( + 1U, 1U, base_depth, + get_weights_accessor(data_path, unit_path + "conv3_weights.npy"), + get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv3/convolution"); + + SubStream left(graph); + if(i == 0) + { + left << ConvolutionLayer( + 1U, 1U, base_depth, + get_weights_accessor(data_path, unit_path + "sc_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) + .set_name(unit_name + "sc/convolution") + << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"), + get_weights_accessor(data_path, unit_path + "sc_bn_add.npy")) + .set_name(unit_name + "sc/scale"); + } + + graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add"); + graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); + } + } +}; + +/** Main program for ResNeXt50 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [[optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_in, [optional] npy_out ) + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1