From 8913d8d7bc83fdcb6c5dc9baca6bb369418de48b Mon Sep 17 00:00:00 2001 From: Alex Gilday Date: Thu, 15 Feb 2018 11:07:18 +0000 Subject: COMPMID-915: Create ResNet50 example ResidualLayer node (COMPMID-916) also created as required for the ResNet architecture. Change-Id: I4fb4d2e08a8d3ce206f96f7946f5afc3e244676a Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/121185 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- examples/graph_resnet50.cpp | 233 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 233 insertions(+) create mode 100644 examples/graph_resnet50.cpp (limited to 'examples/graph_resnet50.cpp') diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp new file mode 100644 index 0000000000..88f58bf09e --- /dev/null +++ b/examples/graph_resnet50.cpp @@ -0,0 +1,233 @@ +/* + * Copyright (c) 2017-2018 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/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include + +using namespace arm_compute::utils; +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; + +/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class GraphResNet50Example : public Example +{ +public: + void do_setup(int argc, char **argv) override + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + // Create a preprocessor object + const std::array mean_rgb{ { 122.68f, 116.67f, 104.01f } }; + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb, + false /* Do not convert to BGR */); + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + TargetHint target_hint = set_target_hint(int_target_hint); + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\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] [image] [labels]\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] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + } + + // Initialize the graph + graph.graph_init(int_target_hint == 2); + + graph << target_hint + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */)) + << ConvolutionLayer( + 7U, 7U, 64U, + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 3, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); + + add_residual_block(data_path, "block1", 64, 3, 2); + add_residual_block(data_path, "block2", 128, 4, 2); + add_residual_block(data_path, "block3", 256, 6, 2); + add_residual_block(data_path, "block4", 512, 3, 1); + + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << ConvolutionLayer( + 1U, 1U, 1000U, + get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + << FlattenLayer() + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); + } + void do_run() override + { + // Run graph + graph.run(); + } + +private: + Graph graph{}; + + void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride) + { + for(unsigned int i = 0; i < num_units; ++i) + { + std::stringstream unit; + unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_"; + std::string unit_name = unit.str(); + + unsigned int middle_stride = 1; + + if(i == (num_units - 1)) + { + middle_stride = stride; + } + + SubGraph right; + right << ConvolutionLayer( + 1U, 1U, base_depth, + get_weights_accessor(data_path, unit_name + "conv1_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer( + 3U, 3U, base_depth, + get_weights_accessor(data_path, unit_name + "conv2_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(middle_stride, middle_stride, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer( + 1U, 1U, base_depth * 4, + get_weights_accessor(data_path, unit_name + "conv3_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"), + 0.0000100099996416f); + + if(i == 0) + { + SubGraph left; + left << ConvolutionLayer( + 1U, 1U, base_depth * 4, + get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"), + 0.0000100099996416f); + + graph << ResidualLayer(std::move(left), std::move(right)); + } + else if(middle_stride > 1) + { + SubGraph left; + left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)) + // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + + graph << ResidualLayer(std::move(left), std::move(right)); + } + else + { + graph << ResidualLayer(std::move(right)); + } + + graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + } + } +}; + +/** Main program for ResNet50 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1