apeforest commented on a change in pull request #16654: Multithreaded Inference Support URL: https://github.com/apache/incubator-mxnet/pull/16654#discussion_r365506405
########## File path: example/multi_threaded_inference/multi_threaded_inference.cc ########## @@ -0,0 +1,351 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2017 by Contributors + * \file multi_threaded_inference.cc + * \brief Multi Threaded inference example with CachedOp +*/ + +#include <cstdio> +#include <cstdlib> +#include <iostream> +#include <fstream> +#include <thread> +#include <iomanip> +#include <chrono> +#include <mxnet/ndarray.h> +#include <opencv2/opencv.hpp> +#include <mxnet/c_predict_api.h> +#include "mxnet-cpp/MxNetCpp.h" + +const mx_float DEFAULT_MEAN = 117.0; + + +// Code to load image, PrintOutput results, helper functions for the same obtained from: +// https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/predict-cpp/ + +static std::string trim(const std::string &input) { + auto not_space = [](int ch) { return !std::isspace(ch); }; + auto output = input; + output.erase(output.begin(), + std::find_if(output.begin(), output.end(), not_space)); + output.erase(std::find_if(output.rbegin(), output.rend(), not_space).base(), + output.end()); + return output; +} + +std::vector<std::string> LoadSynset(const std::string& synset_file) { + std::ifstream fi(synset_file.c_str()); + + if (!fi.is_open()) { + std::cerr << "Error opening synset file " << synset_file << std::endl; + assert(false); + } + + std::vector<std::string> output; + + std::string synset, lemma; + while (fi >> synset) { + getline(fi, lemma); + output.push_back(lemma); + } + + fi.close(); + + return output; +} + +void PrintOutputResult(const float* data, size_t size, const std::vector<std::string>& synset) { + if (size != synset.size()) { + std::cerr << "Result data and synset size do not match!" << std::endl; + } + + float best_accuracy = 0.0; + std::size_t best_idx = 0; + + for (std::size_t i = 0; i < size; ++i) { + if (data[i] > best_accuracy) { + best_accuracy = data[i]; + best_idx = i; + } + } + + std::cout << "Best Result: " << trim(synset[best_idx]) << " (id=" << best_idx << ", " << + "accuracy=" << std::setprecision(8) << best_accuracy << ")" << std::endl; +} + + +// Read Image data into a float array +void GetImageFile(const std::string &image_file, mx_float *image_data, + int channels, cv::Size resize_size) { + // Read all kinds of file into a BGR color 3 channels image + cv::Mat im_ori = cv::imread(image_file, cv::IMREAD_COLOR); + + if (im_ori.empty()) { + std::cerr << "Can't open the image. Plase check " << image_file << ". \n"; + assert(false); + } + + cv::Mat im; + resize(im_ori, im, resize_size); + + int size = im.rows * im.cols * channels; + + mx_float* ptr_image_r = image_data; + mx_float* ptr_image_g = image_data + size / 3; + mx_float* ptr_image_b = image_data + size / 3 * 2; + + float mean_b, mean_g, mean_r; + mean_b = mean_g = mean_r = DEFAULT_MEAN; + + for (int i = 0; i < im.rows; ++i) { + auto data = im.ptr<uchar>(i); + for (int j = 0; j < im.cols; j++) { + if (channels > 1) { + *ptr_image_b++ = static_cast<mx_float>(*data++) - mean_b; + *ptr_image_g++ = static_cast<mx_float>(*data++) - mean_g; + } + } + *ptr_image_r++ = static_cast<mx_float>(*data++) - mean_r; + } +} + +void prepare_input_data(const mxnet::cpp::Shape& shape, const mxnet::cpp::Context& ctx, + int num_threads, + std::vector<mxnet::cpp::NDArray>* data_arr, + bool random_uniform = false) { + for (size_t i = 0; i < num_threads; ++i) { + data_arr->emplace_back(shape, ctx, false, 0); + int begin = i * 100; + int end = begin + 100; + if (random_uniform) { + mxnet::cpp::Operator("_random_uniform")(begin, end) + .Invoke((*data_arr)[i]); + } + mxnet::cpp::NDArray::WaitAll(); + } +} + +// Run inference on a model +void run_inference(const std::string& model_name, const std::vector<mxnet::cpp::NDArray>& input_arrs, + std::vector<mxnet::NDArray*> *output_mx_arr, + int num_inf_per_thread = 1, bool random_sleep = false, + int num_threads = 1, bool static_alloc = false, + bool static_shape = false, + bool is_gpu = false) { + LOG(INFO) << "Running inference for " + model_name + + " num_threads: " + std::to_string(num_threads) + + " num_inf_per_thread: " + std::to_string(num_inf_per_thread) + + " random_sleep: " + std::to_string(random_sleep) + + " static_alloc: " + std::to_string(static_alloc) + + " static_shape: " + std::to_string(static_shape); + std::string json_file = model_name + "-symbol.json"; + std::string param_file = model_name + "-0000.params"; + auto out = mxnet::cpp::Symbol::Load(json_file); + std::string static_alloc_str = static_alloc ? "true" : "false"; + std::string static_shape_str = static_shape ? "true" : "false"; + + // Prepare context +# if MXNET_USE_CUDA == 1 + mxnet::Context backend_ctx; + mxnet::cpp::Context ctx = mxnet::cpp::Context::cpu(0); + if (is_gpu) { + backend_ctx = mxnet::Context::GPU(0); + ctx = mxnet::cpp::Context::gpu(0); + } else { + backend_ctx = mxnet::Context::CPU(0); + ctx = mxnet::cpp::Context::cpu(0); + } +# else + mxnet::Context backend_ctx = mxnet::Context::CPU(0); + mxnet::cpp::Context ctx = mxnet::cpp::Context::cpu(0); +#endif + + // Prepare input data and parameters + std::vector<mxnet::cpp::NDArray> data_arr(num_threads); + std::vector<mxnet::cpp::NDArray> softmax_arr; + std::vector<mxnet::cpp::NDArray> params; + mxnet::cpp::Shape data_shape = mxnet::cpp::Shape(1, 3, 224, 224); + mxnet::cpp::Shape softmax_shape = mxnet::cpp::Shape(1); + int num_inputs = out.ListInputs().size(); + + for (size_t i = 0; i < data_arr.size(); ++i) { + data_arr[i] = input_arrs[i].Copy(ctx); + } + prepare_input_data(softmax_shape, ctx, num_threads, &softmax_arr); + std::map<std::string, mxnet::cpp::NDArray> parameters; + mxnet::cpp::NDArray::Load(param_file, 0, ¶meters); + + for (const std::string& name : out.ListInputs()) { + if (name == "arg:data") { + continue; + } + if (parameters.find("arg:" + name) != parameters.end()) { + params.push_back(parameters["arg:" + name].Copy(ctx)); + } else if (parameters.find("aux:" + name) != parameters.end()) { + params.push_back(parameters["aux:" + name].Copy(ctx)); + } + } + + CachedOpHandle hdl = CachedOpHandle(); + + std::vector<std::string> flag_keys{"data_indices", "param_indices", + "static_alloc", "static_shape"}; + std::string param_indices = "["; + for (size_t i = 1; i < num_inputs; ++i) { + param_indices += std::to_string(i); + param_indices += std::string(", "); + } + param_indices += "]"; + std::vector<std::string> flag_vals{"[0]", param_indices, static_alloc_str, + static_shape_str}; + std::vector<const char*> flag_key_cstrs, flag_val_cstrs; + flag_key_cstrs.reserve(flag_keys.size()); + for (size_t i = 0; i < flag_keys.size(); ++i) { + flag_key_cstrs.emplace_back(flag_keys[i].c_str()); + } + for (size_t i = 0; i < flag_vals.size(); ++i) { + flag_val_cstrs.emplace_back(flag_vals[i].c_str()); + } + + int ret1 = MXCreateCachedOpEX(out.GetHandle(), flag_keys.size(), + flag_key_cstrs.data(), flag_val_cstrs.data(), + &hdl, true); + if (ret1 < 0) { + LOG(FATAL) << MXGetLastError(); + } + + // Prepare data structures and lambda to run in different threads + std::vector<NDArrayHandle *> cached_op_handles(num_threads); + + std::vector<std::vector<NDArrayHandle>> arr_handles(num_threads); + for (size_t i = 0; i < num_threads; ++i) { + arr_handles[i].reserve(num_inputs); + arr_handles[i].emplace_back(data_arr[i].GetHandle()); + for (size_t j = 1; j < num_inputs - 1; ++j) { + arr_handles[i].emplace_back(params[j - 1].GetHandle()); + } + arr_handles[i].emplace_back(softmax_arr[i].GetHandle()); + } + + auto func = [&](int num) { + unsigned next = num; + if (random_sleep) { + int sleep_time = rand_r(&next) % 5; + std::this_thread::sleep_for(std::chrono::seconds(sleep_time)); + } + int num_output = 0; + const int *stypes; + int ret = MXInvokeCachedOpEx(hdl, arr_handles[num].size(), arr_handles[num].data(), + &num_output, &(cached_op_handles[num]), &stypes); + if (ret < 0) { + LOG(FATAL) << MXGetLastError(); + } + (*output_mx_arr)[num] = static_cast<mxnet::NDArray *>(*cached_op_handles[num]); + }; + + // Spawn multiple threads, join and wait for threads to complete + std::vector<std::thread> worker_threads(num_threads); + int count = 0; + for (auto &&i : worker_threads) { + i = std::thread(func, count); + count++; + } + + for (auto &&i : worker_threads) { + i.join(); + } + + mxnet::cpp::NDArray::WaitAll(); + + std::string synset_file = "synset.txt"; + auto synset = LoadSynset(synset_file); + std::vector<mxnet::NDArray> tmp(num_threads); + for (size_t i = 0; i < num_threads; i++) { + tmp[i] = (*output_mx_arr)[i]->Copy(mxnet::Context::CPU(0)); + tmp[i].WaitToRead(); + (*output_mx_arr)[i] = &tmp[i]; + } + for (size_t i = 0; i < num_threads; ++i) { + PrintOutputResult(static_cast<float *>((*output_mx_arr)[i]->data().dptr_), + (*output_mx_arr)[i]->shape().Size(), synset); + } + int ret2 = MXFreeCachedOp(hdl); + if (ret2 < 0) { + LOG(FATAL) << MXGetLastError(); + } + + mxnet::cpp::NDArray::WaitAll(); + +} + +int main(int argc, char *argv[]) { + if (argc < 5) { + std::cout << "Please provide a model name, num_threads, is_gpu, test_image" << std::endl + << "Usage: ./multi_threaded_inference [model_name] [num_threads] [is_gpu] [file_names]" + << std::endl + << "Example: ./.multi_threaded_inference imagenet1k-inception-bn 1 0 apple.jpg" + << std::endl + << "NOTE: Thread number ordering will be based on the ordering of file inputs" << std::endl + << "NOTE: Epoch is assumed to be 0" << std::endl; + return EXIT_FAILURE; + } + std::string model_name = std::string(argv[1]); + int num_threads = std::atoi(argv[2]); + bool is_gpu = std::atoi(argv[3]); + CHECK(num_threads == argc - 4) << "Number of files provided, should be same as num_threads"; + std::vector<std::string> test_files; + for (size_t i = 0; i < argc - 4; ++i) { + test_files.emplace_back(argv[4 + i]); Review comment: indentation ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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