diff --git a/model_zoo/research/cv/single_path_nas/README_CN.md b/model_zoo/research/cv/single_path_nas/README_CN.md new file mode 100644 index 00000000000..dac32d4c37b --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/README_CN.md @@ -0,0 +1,244 @@ +# 目录 + + + +- [目录](#目录) +- [single-path-nas描述](#single-path-nas描述) +- [数据集](#数据集) +- [特性](#特性) + - [混合精度](#混合精度) +- [环境要求](#环境要求) +- [快速入门](#快速入门) +- [脚本说明](#脚本说明) + - [脚本及样例代码](#脚本及样例代码) + - [脚本参数](#脚本参数) + - [训练过程](#训练过程) + - [训练](#训练) + - [分布式训练](#分布式训练) + - [评估过程](#评估过程) + - [评估](#评估) + - [导出过程](#导出过程) + - [导出](#导出) + - [推理过程](#推理过程) + - [推理](#推理) +- [模型描述](#模型描述) + - [性能](#性能) + - [评估性能](#评估性能) + - [ImageNet-1k上的single-path-nas](#imagenet-1k上的single-path-nas) + - [推理性能](#推理性能) + - [ImageNet-1k上的single-path-nas](#imagenet-1k上的single-path-nas-1) +- [ModelZoo主页](#modelzoo主页) + + + +# single-path-nas描述 + +single-path-nas的作者用一个7x7的大卷积，来代表3x3、5x5和7x7的三种卷积，把外边一圈mask清零掉就变成了3x3或5x5，这个大的卷积成为superkernel，于是整个网络只有一种卷积，看起来是一个直筒结构。搜索空间是基于block的直筒结构，跟ProxylessNAS和FBNet一样，都采用了Inverted Bottleneck 作为cell, 层数跟MobileNetV2都是22层。每层只有两个参数 expansion rate, kernel size是需要搜索的，其他都已固定，比如22层中每层的filter number固定死了，跟FBNet一样，跟MobileNetV2比略有变化。论文中的kernel size和FBNet、 ProxylessNAS一样只有3x3和5x5两种，没有用上7x7。论文中的expansion ratio也只有3和6两种选择。kernel size 和 expansion ratio都只有2中选择，论文选择用Lightnn这篇论文中的手法，把离散选择用连续的光滑函数来表示，阈值用group Lasso term。本论文用了跟ProxylessNAS一样的手法来表达skip connection, 用一个zero layer表示。 +（摘自https://zhuanlan.zhihu.com/p/63605721） + +# 数据集 + +使用的数据集：[ImageNet2012](http://www.image-net.org/) + +- 数据集大小：共1000个类、224*224彩色图像 + - 训练集：共1,281,167张图像 + - 测试集：共50,000张图像 +- 数据格式：JPEG + - 注：数据在dataset.py中处理。 +- 下载数据集，目录结构如下： + + ```text +└─dataset + ├─train # 训练数据集 + └─val # 评估数据集 +``` + +# 特性 + +## 混合精度 + +采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html) 的训练方法，使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。 + +# 环境要求 + +- 硬件（Ascend） + - 使用Ascend来搭建硬件环境。 +- 框架 + - [MindSpore](https://www.mindspore.cn/install/en) +- 如需查看详情，请参见如下资源： + - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/r1.3/index.html) + - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/r1.3/index.html) + +# 快速入门 + +通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估： + +- Ascend处理器环境运行 + + ```bash + # 运行训练示例 + python train.py --device_id=0 > train.log 2>&1 & + + # 运行分布式训练示例 + bash ./scripts/run_train.sh [RANK_TABLE_FILE] imagenet + + # 运行评估示例 + python eval.py --checkpoint_path ./ckpt_0 > ./eval.log 2>&1 & + + # 运行推理示例 + bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] + ``` + + 对于分布式训练，需要提前创建JSON格式的hccl配置文件。 + + 请遵循以下链接中的说明： + + + +# 脚本说明 + +## 脚本及样例代码 + +```bash +├── model_zoo + ├── README_CN.md // Single-Path-NAS相关说明 + ├── scripts + │ ├──run_train.sh // 分布式到Ascend的shell脚本 + │ ├──run_eval.sh // 测试脚本 + │ ├──run_infer_310.sh // 310推理脚本 + ├── src + │ ├──lr_scheduler // 学习率相关文件夹，包含学习率变化策略的py文件 + │ ├──dataset.py // 创建数据集 + │ ├──CrossEntropySmooth.py // 损失函数相关 + │ ├──spnasnet.py // Single-Path-NAS网络架构 + │ ├──config.py // 参数配置 + │ ├──utils.py // spnasnet.py的自定义网络模块 + ├── train.py // 训练和测试文件 +``` + +## 脚本参数 + +在config.py中可以同时配置训练参数和评估参数。 + +- 配置single-path-nas和ImageNet-1k数据集。 + + ```python + 'name':'imagenet' # 数据集 + 'pre_trained':'False' # 是否基于预训练模型训练 + 'num_classes':1000 # 数据集类数 + 'lr_init':0.26 # 初始学习率，单卡训练时设置为0.26，八卡并行训练时设置为1.5 + 'batch_size':128 # 训练批次大小 + 'epoch_size':180 # 总计训练epoch数 + 'momentum':0.9 # 动量 + 'weight_decay':1e-5 # 权重衰减值 + 'image_height':224 # 输入到模型的图像高度 + 'image_width':224 # 输入到模型的图像宽度 + 'data_path':'/data/ILSVRC2012_train/' # 训练数据集的绝对全路径 + 'val_data_path':'/data/ILSVRC2012_val/' # 评估数据集的绝对全路径 + 'device_target':'Ascend' # 运行设备 + 'device_id':0 # 用于训练或评估数据集的设备ID使用run_train.sh进行分布式训练时可以忽略。 + 'keep_checkpoint_max':40 # 最多保存80个ckpt模型文件 + 'checkpoint_path':None # checkpoint文件保存的绝对全路径 + ``` + +更多配置细节请参考脚本`config.py`。 + +## 训练过程 + +### 训练 + +- Ascend处理器环境运行 + + ```bash + python train.py --device_id=0 > train.log 2>&1 & + ``` + + 上述python命令将在后台运行，可以通过生成的train.log文件查看结果。 + +### 分布式训练 + +- Ascend处理器环境运行 + + ```bash + bash ./scripts/run_train.sh [RANK_TABLE_FILE] imagenet + ``` + + 上述shell脚本将在后台运行分布训练。 + +## 评估过程 + +### 评估 + +- 在Ascend环境运行时评估ImageNet-1k数据集 + + “./ckpt_0”是保存了训练好的.ckpt模型文件的目录。 + + ```bash + python eval.py --checkpoint_path ./ckpt_0 > ./eval.log 2>&1 & + OR + bash ./scripts/run_eval.sh + ``` + +## 导出过程 + +### 导出 + + ```shell + python export.py --ckpt_file [CKPT_FILE] + ``` + +## 推理过程 + +### 推理 + +在进行推理之前我们需要先导出模型。mindir可以在任意环境上导出，air模型只能在昇腾910环境上导出。以下展示了使用mindir模型执行推理的示例。 + +- 在昇腾310上使用ImageNet-1k数据集进行推理 + + 推理的结果保存在scripts目录下，在acc.log日志文件中可以找到类似以下的结果。 + + ```shell + # Ascend310 inference + bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] + Total data: 50000, top1 accuracy: 0.74214, top5 accuracy: 0.91652. + ``` + +# 模型描述 + +## 性能 + +### 评估性能 + +#### ImageNet-1k上的single-path-nas + +| 参数 | Ascend | +| -------------------------- | ----------------------------------------------------------- | +| 模型版本 | single-path-nas | +| 资源 | Ascend 910 | +| 上传日期 | 2021-06-27 | +| MindSpore版本 | 1.2.0 | +| 数据集 | ImageNet-1k Train，共1,281,167张图像 | +| 训练参数 | epoch=180, batch_size=128, lr_init=0.26（单卡为0.26,八卡为1.5） | +| 优化器 | Momentum | +| 损失函数 | Softmax交叉熵 | +| 输出 | 概率 | +| 分类准确率 | 八卡：top1:74.21%,top5:91.712% | +| 速度 | 单卡：毫秒/步；八卡：87.173毫秒/步 | + +### 推理性能 + +#### ImageNet-1k上的single-path-nas + +| 参数 | Ascend | +| -------------------------- | ----------------------------------------------------------- | +| 模型版本 | single-path-nas | +| 资源 | Ascend 310 | +| 上传日期 | 2021-06-27 | +| MindSpore版本 | 1.2.0 | +| 数据集 | ImageNet-1k Val，共50,000张图像 | +| 分类准确率 | top1:74.214%,top5:91.652% | +| 速度 | Average time 7.67324 ms of infer_count 50000| + +# ModelZoo主页 + + 请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。 \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/ascend310_infer/inc/utils.h b/model_zoo/research/cv/single_path_nas/ascend310_infer/inc/utils.h new file mode 100644 index 00000000000..f8ae1e5b473 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/ascend310_infer/inc/utils.h @@ -0,0 +1,35 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed 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. + */ + +#ifndef MINDSPORE_INFERENCE_UTILS_H_ +#define MINDSPORE_INFERENCE_UTILS_H_ + +#include +#include +#include +#include +#include +#include "include/api/types.h" + +std::vector GetAllFiles(std::string_view dirName); +DIR *OpenDir(std::string_view dirName); +std::string RealPath(std::string_view path); +mindspore::MSTensor ReadFileToTensor(const std::string &file); +int WriteResult(const std::string& imageFile, const std::vector &outputs); +std::vector GetAllFiles(std::string dir_name); +std::vector> GetAllInputData(std::string dir_name); + +#endif diff --git a/model_zoo/research/cv/single_path_nas/ascend310_infer/src/CMakeLists.txt b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/CMakeLists.txt new file mode 100644 index 00000000000..0397995b0e0 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/CMakeLists.txt @@ -0,0 +1,14 @@ +cmake_minimum_required(VERSION 3.14.1) +project(MindSporeCxxTestcase[CXX]) +add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined") +set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/) +option(MINDSPORE_PATH "mindspore install path" "") +include_directories(${MINDSPORE_PATH}) +include_directories(${MINDSPORE_PATH}/include) +include_directories(${PROJECT_SRC_ROOT}/../) +find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib) +file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*) + +add_executable(main main.cc utils.cc) +target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags) diff --git a/model_zoo/research/cv/single_path_nas/ascend310_infer/src/build.sh b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/build.sh new file mode 100644 index 00000000000..7fac9cff3a9 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/build.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ + +cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`" +make \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/ascend310_infer/src/main.cc b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/main.cc new file mode 100644 index 00000000000..03ab5841af6 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/main.cc @@ -0,0 +1,146 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed 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. + */ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "include/api/model.h" +#include "include/api/context.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "minddata/dataset/include/vision_ascend.h" +#include "minddata/dataset/include/execute.h" +#include "minddata/dataset/include/transforms.h" +#include "minddata/dataset/include/vision.h" +#include "inc/utils.h" + +using mindspore::dataset::vision::Decode; +using mindspore::dataset::vision::Resize; +using mindspore::dataset::vision::CenterCrop; +using mindspore::dataset::vision::Normalize; +using mindspore::dataset::vision::HWC2CHW; +using mindspore::dataset::TensorTransform; +using mindspore::Context; +using mindspore::Serialization; +using mindspore::Model; +using mindspore::Status; +using mindspore::ModelType; +using mindspore::GraphCell; +using mindspore::kSuccess; +using mindspore::MSTensor; +using mindspore::dataset::Execute; + + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + +int main(int argc, char **argv) { + gflags::ParseCommandLineFlags(&argc, &argv, true); + if (RealPath(FLAGS_mindir_path).empty()) { + std::cout << "Invalid mindir" << std::endl; + return 1; + } + + auto context = std::make_shared(); + auto ascend310 = std::make_shared(); + ascend310->SetDeviceID(FLAGS_device_id); + context->MutableDeviceInfo().push_back(ascend310); + mindspore::Graph graph; + Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph); + Model model; + Status ret = model.Build(GraphCell(graph), context); + if (ret != kSuccess) { + std::cout << "ERROR: Build failed." << std::endl; + return 1; + } + + auto all_files = GetAllInputData(FLAGS_dataset_path); + if (all_files.empty()) { + std::cout << "ERROR: no input data." << std::endl; + return 1; + } + + std::map costTime_map; + size_t size = all_files.size(); + // Define transform + std::vector crop_paras = {224}; + std::vector resize_paras = {256}; + std::vector mean = {0.485 * 255, 0.456 * 255, 0.406 * 255}; + std::vector std = {0.229 * 255, 0.224 * 255, 0.225 * 255}; + + auto decode = Decode(); + auto resize = Resize(resize_paras); + auto centercrop = CenterCrop(crop_paras); + auto normalize = Normalize(mean, std); + auto hwc2chw = HWC2CHW(); + + mindspore::dataset::Execute SingleOp({decode, resize, centercrop, normalize, hwc2chw}); + + for (size_t i = 0; i < size; ++i) { + for (size_t j = 0; j < all_files[i].size(); ++j) { + struct timeval start = {0}; + struct timeval end = {0}; + double startTimeMs; + double endTimeMs; + std::vector inputs; + std::vector outputs; + std::cout << "Start predict input files:" << all_files[i][j] <(); + SingleOp(ReadFileToTensor(all_files[i][j]), imgDvpp.get()); + + inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(), + imgDvpp->Data().get(), imgDvpp->DataSize()); + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << all_files[i][j] << " failed." << std::endl; + return 1; + } + startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000; + endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000; + costTime_map.insert(std::pair(startTimeMs, endTimeMs)); + WriteResult(all_files[i][j], outputs); + } + } + double average = 0.0; + int inferCount = 0; + + for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) { + double diff = 0.0; + diff = iter->second - iter->first; + average += diff; + inferCount++; + } + average = average / inferCount; + std::stringstream timeCost; + timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl; + std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl; + std::string fileName = "./time_Result" + std::string("/test_perform_static.txt"); + std::ofstream fileStream(fileName.c_str(), std::ios::trunc); + fileStream << timeCost.str(); + fileStream.close(); + costTime_map.clear(); + return 0; +} diff --git a/model_zoo/research/cv/single_path_nas/ascend310_infer/src/utils.cc b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/utils.cc new file mode 100644 index 00000000000..d71f388b83d --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/ascend310_infer/src/utils.cc @@ -0,0 +1,185 @@ +/** + * Copyright 2021 Huawei Technologies Co., Ltd + * + * Licensed 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. + */ + +#include +#include +#include +#include "inc/utils.h" + +using mindspore::MSTensor; +using mindspore::DataType; + + +std::vector> GetAllInputData(std::string dir_name) { + std::vector> ret; + + DIR *dir = OpenDir(dir_name); + if (dir == nullptr) { + return {}; + } + struct dirent *filename; + /* read all the files in the dir ~ */ + std::vector sub_dirs; + while ((filename = readdir(dir)) != nullptr) { + std::string d_name = std::string(filename->d_name); + // get rid of "." and ".." + if (d_name == "." || d_name == ".." || d_name.empty()) { + continue; + } + std::string dir_path = RealPath(std::string(dir_name) + "/" + filename->d_name); + struct stat s; + lstat(dir_path.c_str(), &s); + if (!S_ISDIR(s.st_mode)) { + continue; + } + + sub_dirs.emplace_back(dir_path); + } + std::sort(sub_dirs.begin(), sub_dirs.end()); + + (void)std::transform(sub_dirs.begin(), sub_dirs.end(), std::back_inserter(ret), + [](const std::string &d) { return GetAllFiles(d); }); + + return ret; +} + + +std::vector GetAllFiles(std::string dir_name) { + struct dirent *filename; + DIR *dir = OpenDir(dir_name); + if (dir == nullptr) { + return {}; + } + + std::vector res; + while ((filename = readdir(dir)) != nullptr) { + std::string d_name = std::string(filename->d_name); + if (d_name == "." || d_name == ".." || d_name.size() <= 3) { + continue; + } + res.emplace_back(std::string(dir_name) + "/" + filename->d_name); + } + std::sort(res.begin(), res.end()); + + return res; +} + + +std::vector GetAllFiles(std::string_view dirName) { + struct dirent *filename; + DIR *dir = OpenDir(dirName); + if (dir == nullptr) { + return {}; + } + std::vector res; + while ((filename = readdir(dir)) != nullptr) { + std::string dName = std::string(filename->d_name); + if (dName == "." || dName == ".." || filename->d_type != DT_REG) { + continue; + } + res.emplace_back(std::string(dirName) + "/" + filename->d_name); + } + std::sort(res.begin(), res.end()); + for (auto &f : res) { + std::cout << "image file: " << f << std::endl; + } + return res; +} + + +int WriteResult(const std::string& imageFile, const std::vector &outputs) { + std::string homePath = "./result_Files"; + for (size_t i = 0; i < outputs.size(); ++i) { + size_t outputSize; + std::shared_ptr netOutput; + netOutput = outputs[i].Data(); + outputSize = outputs[i].DataSize(); + int pos = imageFile.rfind('/'); + std::string fileName(imageFile, pos + 1); + fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin"); + std::string outFileName = homePath + "/" + fileName; + FILE *outputFile = fopen(outFileName.c_str(), "wb"); + fwrite(netOutput.get(), outputSize, sizeof(char), outputFile); + fclose(outputFile); + outputFile = nullptr; + } + return 0; +} + +mindspore::MSTensor ReadFileToTensor(const std::string &file) { + if (file.empty()) { + std::cout << "Pointer file is nullptr" << std::endl; + return mindspore::MSTensor(); + } + + std::ifstream ifs(file); + if (!ifs.good()) { + std::cout << "File: " << file << " is not exist" << std::endl; + return mindspore::MSTensor(); + } + + if (!ifs.is_open()) { + std::cout << "File: " << file << "open failed" << std::endl; + return mindspore::MSTensor(); + } + + ifs.seekg(0, std::ios::end); + size_t size = ifs.tellg(); + mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast(size)}, nullptr, size); + + ifs.seekg(0, std::ios::beg); + ifs.read(reinterpret_cast(buffer.MutableData()), size); + ifs.close(); + + return buffer; +} + + +DIR *OpenDir(std::string_view dirName) { + if (dirName.empty()) { + std::cout << " dirName is null ! " << std::endl; + return nullptr; + } + std::string realPath = RealPath(dirName); + struct stat s; + lstat(realPath.c_str(), &s); + if (!S_ISDIR(s.st_mode)) { + std::cout << "dirName is not a valid directory !" << std::endl; + return nullptr; + } + DIR *dir; + dir = opendir(realPath.c_str()); + if (dir == nullptr) { + std::cout << "Can not open dir " << dirName << std::endl; + return nullptr; + } + std::cout << "Successfully opened the dir " << dirName << std::endl; + return dir; +} + +std::string RealPath(std::string_view path) { + char realPathMem[PATH_MAX] = {0}; + char *realPathRet = nullptr; + realPathRet = realpath(path.data(), realPathMem); + if (realPathRet == nullptr) { + std::cout << "File: " << path << " is not exist."; + return ""; + } + + std::string realPath(realPathMem); + std::cout << path << " realpath is: " << realPath << std::endl; + return realPath; +} diff --git a/model_zoo/research/cv/single_path_nas/create_imagenet2012_label.py b/model_zoo/research/cv/single_path_nas/create_imagenet2012_label.py new file mode 100644 index 00000000000..5caf9b40c25 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/create_imagenet2012_label.py @@ -0,0 +1,51 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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 +# +# less 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. +# ============================================================================ +"""create_imagenet2012_label""" +import os +import json +import argparse + +parser = argparse.ArgumentParser(description="Single-Path-NAS imagenet2012 label") +parser.add_argument("--img_path", type=str, required=True, help="imagenet2012 file path.") +args = parser.parse_args() + + +def create_label(file_path): + """ + create label + """ + print("[WARNING] Create imagenet label. Currently only use for Imagenet2012!") + dirs = os.listdir(file_path) + file_list = [] + for file in dirs: + file_list.append(file) + file_list = sorted(file_list) + + total = 0 + img_label = {} + for i, file_dir in enumerate(file_list): + files = os.listdir(os.path.join(file_path, file_dir)) + for f in files: + img_label[f] = i + total += len(files) + + with open("imagenet_label.json", "w+") as label: + json.dump(img_label, label) + + print("[INFO] Completed! Total {} data.".format(total)) + + +if __name__ == '__main__': + create_label(args.img_path) diff --git a/model_zoo/research/cv/single_path_nas/eval.py b/model_zoo/research/cv/single_path_nas/eval.py new file mode 100644 index 00000000000..a671c7a34af --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/eval.py @@ -0,0 +1,105 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +Process the test set with the .ckpt model in turn. +""" +import argparse +import os + +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import context +from mindspore.common import dtype as mstype +from mindspore.common import set_seed +from mindspore.nn.loss.loss import _Loss +from mindspore.ops import functional as F +from mindspore.ops import operations as P +from mindspore.train.model import Model +from mindspore.train.serialization import load_checkpoint, load_param_into_net + +import src.spnasnet as spnasnet +from src.config import imagenet_cfg +from src.dataset import create_dataset_imagenet + +set_seed(1) + +parser = argparse.ArgumentParser(description='single-path-nas') +parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet',], + help='dataset name.') +parser.add_argument('--checkpoint_path', type=str, default='./ckpt_0', help='Checkpoint file path or dir path') +parser.add_argument('--device_id', type=int, default=None, help='device id of Ascend. (Default: None)') +args_opt = parser.parse_args() + + +class CrossEntropySmooth(_Loss): + """CrossEntropy""" + + def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): + super(CrossEntropySmooth, self).__init__() + self.onehot = P.OneHot() + self.sparse = sparse + self.on_value = Tensor(1.0 - smooth_factor, mstype.float32) + self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32) + self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction) + + def construct(self, logit, label): + if self.sparse: + label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value) + loss_ = self.ce(logit, label) + return loss_ + + +if __name__ == '__main__': + + if args_opt.dataset_name == "imagenet": + cfg = imagenet_cfg + dataset = create_dataset_imagenet(cfg.val_data_path, 1, False) + if not cfg.use_label_smooth: + cfg.label_smooth_factor = 0.0 + loss = CrossEntropySmooth(sparse=True, reduction="mean", + smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes) + net = spnasnet.spnasnet(num_classes=cfg.num_classes) + model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'}) + + else: + raise ValueError("dataset is not support.") + + device_target = cfg.device_target + context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) + if device_target == "Ascend": + if args_opt.device_id is not None: + context.set_context(device_id=args_opt.device_id) + else: + context.set_context(device_id=cfg.device_id) + + if os.path.isfile(args_opt.checkpoint_path) and args_opt.checkpoint_path.endswith('.ckpt'): + param_dict = load_checkpoint(args_opt.checkpoint_path) + load_param_into_net(net, param_dict) + net.set_train(False) + acc = model.eval(dataset) + print(f"model {args_opt.checkpoint_path}'s accuracy is {acc}") + elif os.path.isdir(args_opt.checkpoint_path): + file_list = os.listdir(args_opt.checkpoint_path) + for filename in file_list: + de_path = os.path.join(args_opt.checkpoint_path, filename) + if de_path.endswith('.ckpt'): + param_dict = load_checkpoint(de_path) + load_param_into_net(net, param_dict) + net.set_train(False) + + acc = model.eval(dataset) + print(f"model {de_path}'s accuracy is {acc}") + else: + raise ValueError("args_opt.checkpoint_path must be a checkpoint file or dir contains checkpoint(s)") diff --git a/model_zoo/research/cv/single_path_nas/export.py b/model_zoo/research/cv/single_path_nas/export.py new file mode 100644 index 00000000000..1c2b73de6e2 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/export.py @@ -0,0 +1,54 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +##############export checkpoint file into air, onnx or mindir model################# +python export.py +""" +import argparse + +import numpy as np +from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context + +import src.spnasnet as spnasnet +from src.config import imagenet_cfg + +parser = argparse.ArgumentParser(description='single-path-nas export') +parser.add_argument("--device_id", type=int, default=0, help="Device id") +parser.add_argument("--batch_size", type=int, default=1, help="batch size") +parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") +parser.add_argument("--file_name", type=str, default="single-path-nas", help="output file name.") +parser.add_argument('--width', type=int, default=224, help='input width') +parser.add_argument('--height', type=int, default=224, help='input height') +parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="MINDIR", help="file format") +parser.add_argument("--device_target", type=str, default="Ascend", + choices=["Ascend",], help="device target(default: Ascend)") +args = parser.parse_args() + +context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) +if args.device_target == "Ascend": + context.set_context(device_id=args.device_id) +else: + raise ValueError("Unsupported platform.") + +if __name__ == '__main__': + net = spnasnet.spnasnet(num_classes=imagenet_cfg.num_classes) + + assert args.ckpt_file is not None, "checkpoint_path is None." + + param_dict = load_checkpoint(args.ckpt_file) + load_param_into_net(net, param_dict) + + input_arr = Tensor(np.zeros([args.batch_size, 3, args.height, args.width], np.float32)) + export(net, input_arr, file_name=args.file_name, file_format=args.file_format) diff --git a/model_zoo/research/cv/single_path_nas/postprocess.py b/model_zoo/research/cv/single_path_nas/postprocess.py new file mode 100644 index 00000000000..1a7fa85b297 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/postprocess.py @@ -0,0 +1,54 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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 +# +# less 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. +# ============================================================================ +"""post process for 310 inference""" +import os +import json +import argparse +import numpy as np +from src.config import imagenet_cfg + +batch_size = 1 +parser = argparse.ArgumentParser(description="Single-Path-NAS inference") +parser.add_argument("--result_path", type=str, required=True, help="result files path.") +parser.add_argument("--label_path", type=str, required=True, help="image file path.") +args = parser.parse_args() + + +def get_result(result_path, label_path): + """ + get result + """ + files = os.listdir(result_path) + with open(label_path, "r") as label: + labels = json.load(label) + + top1 = 0 + top5 = 0 + total_data = len(files) + for file in files: + img_ids_name = file.split('_0.')[0] + data_path = os.path.join(result_path, img_ids_name + "_0.bin") + result = np.fromfile(data_path, dtype=np.float32).reshape(batch_size, imagenet_cfg.num_classes) + for batch in range(batch_size): + predict = np.argsort(-result[batch], axis=-1) + if labels[img_ids_name+".JPEG"] == predict[0]: + top1 += 1 + if labels[img_ids_name+".JPEG"] in predict[:5]: + top5 += 1 + print(f"Total data: {total_data}, top1 accuracy: {top1/total_data}, top5 accuracy: {top5/total_data}.") + + +if __name__ == '__main__': + get_result(args.result_path, args.label_path) diff --git a/model_zoo/research/cv/single_path_nas/scripts/run_distribute_train.sh b/model_zoo/research/cv/single_path_nas/scripts/run_distribute_train.sh new file mode 100644 index 00000000000..f4b030f7916 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/scripts/run_distribute_train.sh @@ -0,0 +1,55 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ + +if [ $# != 1 ] +then + echo "Usage: sh run_train.sh [RANK_TABLE_FILE]" +exit 1 +fi + +if [ ! -f $1 ] +then + echo "error: RANK_TABLE_FILE=$1 is not a file" +exit 1 +fi + + +dataset_type='imagenet' + + +ulimit -u unlimited +export DEVICE_NUM=8 +export RANK_SIZE=8 +RANK_TABLE_FILE=$(realpath $1) +export RANK_TABLE_FILE +echo "RANK_TABLE_FILE=${RANK_TABLE_FILE}" + +export SERVER_ID=0 +rank_start=$((DEVICE_NUM * SERVER_ID)) +for((i=0; i<${DEVICE_NUM}; i++)) +do + export DEVICE_ID=$i + export RANK_ID=$((rank_start + i)) + rm -rf ./train_parallel$i + mkdir ./train_parallel$i + cp -r ./src ./train_parallel$i + cp ./train.py ./train_parallel$i + echo "start training for rank $RANK_ID, device $DEVICE_ID, $dataset_type" + cd ./train_parallel$i ||exit + env > env.log + python train.py --device_id=$i --dataset_name=$dataset_type> log 2>&1 & + cd .. +done \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/scripts/run_eval.sh b/model_zoo/research/cv/single_path_nas/scripts/run_eval.sh new file mode 100644 index 00000000000..5d30b6166f3 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/scripts/run_eval.sh @@ -0,0 +1,38 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ + +if [ $# != 1 ] +then + echo "Usage: sh run_eval.sh checkpoint_path_dir/checkpoint_path_file" +exit 1 +fi + + +if [ ! -d $1 ] && [ ! -f $1 ] +then + echo "error: checkpoint_path=$1 is neither a directory nor a file" + exit 1 +fi + + +ulimit -u unlimited +export DEVICE_NUM=1 +export DEVICE_ID=0 +export RANK_SIZE=$DEVICE_NUM +export RANK_ID=0 + +echo "start evaluation for device $DEVICE_ID" +python eval.py --checkpoint_path=$1 > ./eval.log 2>&1 & \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/scripts/run_infer_310.sh b/model_zoo/research/cv/single_path_nas/scripts/run_infer_310.sh new file mode 100644 index 00000000000..c5586cd7fd4 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/scripts/run_infer_310.sh @@ -0,0 +1,99 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ + +if [[ $# -lt 2 || $# -gt 3 ]]; then + echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) + +device_id=0 +if [ $# == 3 ]; then + device_id=$3 +fi + +echo "mindir name: "$model +echo "dataset path: "$data_path +echo "device id: "$device_id + +export ASCEND_HOME=/usr/local/Ascend/ +if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then + export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp +else + export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH + export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/opp +fi + +function compile_app() +{ + cd ../ascend310_infer/src/ || exit + if [ -f "Makefile" ]; then + make clean + fi + sh build.sh &> build.log +} + +function infer() +{ + cd - || exit + if [ -d result_Files ]; then + rm -rf ./result_Files + fi + if [ -d time_Result ]; then + rm -rf ./time_Result + fi + mkdir result_Files + mkdir time_Result + ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log +} + +function cal_acc() +{ + python3.7 ../create_imagenet2012_label.py --img_path=$data_path + python3.7 ../postprocess.py --result_path=./result_Files --label_path=./imagenet_label.json &> acc.log & +} + +compile_app +if [ $? -ne 0 ]; then + echo "compile app code failed" + exit 1 +fi +infer +if [ $? -ne 0 ]; then + echo " execute inference failed" + exit 1 +fi +cal_acc +if [ $? -ne 0 ]; then + echo "calculate accuracy failed" + exit 1 +fi \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/scripts/run_standalone_train.sh b/model_zoo/research/cv/single_path_nas/scripts/run_standalone_train.sh new file mode 100644 index 00000000000..af523897117 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/scripts/run_standalone_train.sh @@ -0,0 +1,40 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ + +if [ $# != 0 ] +then + echo "Usage: sh run_train.sh" +exit 1 +fi + +if [ ! -f $1 ] +then + echo "error: RANK_TABLE_FILE=$1 is not a file" +exit 1 +fi + + +dataset_type='imagenet' + + +ulimit -u unlimited +export DEVICE_ID=0 +export DEVICE_NUM=1 +export RANK_ID=0 +export RANK_SIZE=1 + +echo "start training for device $DEVICE_ID" +python train.py --device_id=$DEVICE_ID --dataset_name=$dataset_type> log 2>&1 & \ No newline at end of file diff --git a/model_zoo/research/cv/single_path_nas/src/CrossEntropySmooth.py b/model_zoo/research/cv/single_path_nas/src/CrossEntropySmooth.py new file mode 100644 index 00000000000..6d63b666946 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/CrossEntropySmooth.py @@ -0,0 +1,38 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""define loss function for network""" +import mindspore.nn as nn +from mindspore import Tensor +from mindspore.common import dtype as mstype +from mindspore.nn.loss.loss import _Loss +from mindspore.ops import functional as F +from mindspore.ops import operations as P + + +class CrossEntropySmooth(_Loss): + """CrossEntropy""" + def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): + super(CrossEntropySmooth, self).__init__() + self.onehot = P.OneHot() + self.sparse = sparse + self.on_value = Tensor(1.0 - smooth_factor, mstype.float32) + self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32) + self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction) + + def construct(self, logit, label): + if self.sparse: + label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value) + loss = self.ce(logit, label) + return loss diff --git a/model_zoo/research/cv/single_path_nas/src/__init__.py b/model_zoo/research/cv/single_path_nas/src/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/model_zoo/research/cv/single_path_nas/src/config.py b/model_zoo/research/cv/single_path_nas/src/config.py new file mode 100644 index 00000000000..fba997b6f09 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/config.py @@ -0,0 +1,53 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +network config setting, will be used in main.py +""" +from easydict import EasyDict as edict + +imagenet_cfg = edict({ + 'name': 'imagenet', + 'pre_trained': False, + 'num_classes': 1000, + 'lr_init': 1.5, # 1p:0.26 8p:1.5 + 'batch_size': 128, + 'epoch_size': 180, + 'momentum': 0.9, + 'weight_decay': 1e-5, + 'image_height': 224, + 'image_width': 224, + 'data_path': '/data/ILSVRC2012_train/', + 'val_data_path': '/data/ILSVRC2012_val/', + 'device_target': 'Ascend', + 'device_id': 0, + 'keep_checkpoint_max': 40, + 'checkpoint_path': None, + 'onnx_filename': 'single-path-nas', + 'air_filename': 'single-path-nas', + + # optimizer and lr related + 'lr_scheduler': 'cosine_annealing', + 'lr_epochs': [30, 60, 90], + 'lr_gamma': 0.3, + 'eta_min': 0.0, + 'T_max': 150, + 'warmup_epochs': 0, + + # loss related + 'is_dynamic_loss_scale': 1, + 'loss_scale': 1024, + 'label_smooth_factor': 0.1, + 'use_label_smooth': True, +}) diff --git a/model_zoo/research/cv/single_path_nas/src/dataset.py b/model_zoo/research/cv/single_path_nas/src/dataset.py new file mode 100644 index 00000000000..97b64529478 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/dataset.py @@ -0,0 +1,104 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +Data operations, will be used in train.py and eval.py +""" +import os + +import mindspore.common.dtype as mstype +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as C +import mindspore.dataset.vision.c_transforms as vision + +from src.config import imagenet_cfg + + +def create_dataset_imagenet(dataset_path, repeat_num=1, training=True, + num_parallel_workers=None, shuffle=True): + """ + create a train or eval imagenet2012 dataset for resnet50 + + Args: + dataset_path(string): the path of dataset. + do_train(bool): whether dataset is used for train or eval. + repeat_num(int): the repeat times of dataset. Default: 1 + batch_size(int): the batch size of dataset. Default: 32 + target(str): the device target. Default: Ascend + + Returns: + dataset + """ + + device_num, rank_id = _get_rank_info() + + if device_num == 1: + data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle) + else: + data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle, + num_shards=device_num, shard_id=rank_id) + + assert imagenet_cfg.image_height == imagenet_cfg.image_width, "image_height not equal image_width" + image_size = imagenet_cfg.image_height + mean = [0.485 * 255, 0.456 * 255, 0.406 * 255] + std = [0.229 * 255, 0.224 * 255, 0.225 * 255] + + # define map operations + if training: + transform_img = [ + vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)), + vision.RandomHorizontalFlip(prob=0.5), + vision.RandomColorAdjust(0.5, 0.4, 0.3, 0.2), + vision.Normalize(mean=mean, std=std), + vision.HWC2CHW() + ] + else: + transform_img = [ + vision.Decode(), + vision.Resize(256), + vision.CenterCrop(image_size), + vision.Normalize(mean=mean, std=std), + vision.HWC2CHW() + ] + + transform_label = [C.TypeCast(mstype.int32)] + if training: + data_set = data_set.map(input_columns="image", num_parallel_workers=16, operations=transform_img) + data_set = data_set.map(input_columns="label", num_parallel_workers=4, operations=transform_label) + else: + data_set = data_set.map(input_columns="image", num_parallel_workers=16, operations=transform_img) + data_set = data_set.map(input_columns="label", num_parallel_workers=4, operations=transform_label) + # apply batch operations + data_set = data_set.batch(imagenet_cfg.batch_size, drop_remainder=False) + + # apply dataset repeat operation + data_set = data_set.repeat(repeat_num) + + return data_set + + +def _get_rank_info(): + """ + get rank size and rank id + """ + rank_size = int(os.environ.get("RANK_SIZE", 1)) + + if rank_size > 1: + from mindspore.communication.management import get_rank, get_group_size + rank_size = get_group_size() + rank_id = get_rank() + else: + rank_size = rank_id = None + + return rank_size, rank_id diff --git a/model_zoo/research/cv/single_path_nas/src/lr_scheduler/__init__.py b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/__init__.py new file mode 100644 index 00000000000..1e5f7fbe57a --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/__init__.py @@ -0,0 +1,14 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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 +# +# httpwww.apache.orglicensesLICENSE-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. +# ============================================================================ diff --git a/model_zoo/research/cv/single_path_nas/src/lr_scheduler/linear_warmup.py b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/linear_warmup.py new file mode 100644 index 00000000000..d43e20648b5 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/linear_warmup.py @@ -0,0 +1,20 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""lr""" + +def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr): + lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps) + lr = float(init_lr) + lr_inc * current_step + return lr diff --git a/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_cosine_annealing_lr.py b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_cosine_annealing_lr.py new file mode 100644 index 00000000000..679c761f0e5 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_cosine_annealing_lr.py @@ -0,0 +1,39 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""lr""" + +import math +import numpy as np + +from .linear_warmup import linear_warmup_lr + + +def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0): + """ warmup cosine annealing lr""" + base_lr = lr + warmup_init_lr = 0 + total_steps = int(max_epoch * steps_per_epoch) + warmup_steps = int(warmup_epochs * steps_per_epoch) + + lr_each_step = [] + for i in range(total_steps): + last_epoch = i // steps_per_epoch + if i < warmup_steps: + lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr) + else: + lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2 + lr_each_step.append(lr) + + return np.array(lr_each_step).astype(np.float32) diff --git a/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_step_lr.py b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_step_lr.py new file mode 100644 index 00000000000..59e845e11eb --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/lr_scheduler/warmup_step_lr.py @@ -0,0 +1,59 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""lr""" + +from collections import Counter +import numpy as np + +from .linear_warmup import linear_warmup_lr + + +def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1): + """warmup step lr""" + base_lr = lr + warmup_init_lr = 0 + total_steps = int(max_epoch * steps_per_epoch) + warmup_steps = int(warmup_epochs * steps_per_epoch) + milestones = lr_epochs + milestones_steps = [] + for milestone in milestones: + milestones_step = milestone * steps_per_epoch + milestones_steps.append(milestones_step) + + lr_each_step = [] + lr = base_lr + milestones_steps_counter = Counter(milestones_steps) + for i in range(total_steps): + if i < warmup_steps: + lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr) + else: + lr = lr * gamma ** milestones_steps_counter[i] + lr_each_step.append(lr) + + return np.array(lr_each_step).astype(np.float32) + + +def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1): + """lr""" + return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma) + + +def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1): + """lr""" + lr_epochs = [] + for i in range(1, max_epoch): + if i % epoch_size == 0: + lr_epochs.append(i) + return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma) diff --git a/model_zoo/research/cv/single_path_nas/src/spnasnet.py b/model_zoo/research/cv/single_path_nas/src/spnasnet.py new file mode 100644 index 00000000000..2219a8ebcca --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/spnasnet.py @@ -0,0 +1,294 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" + Single-Path NASNet for ImageNet-1K, implemented in Mindspore. + Original paper: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours,' + https://arxiv.org/abs/1904.02877. +""" + +import mindspore.nn as nn +import mindspore.ops as ops + +from src.utils import conv1x1_block, conv3x3_block, dwconv3x3_block, dwconv5x5_block + + +class SPNASUnit(nn.Cell): + """ + Single-Path NASNet unit. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int + Strides of the second convolution layer. + use_kernel3 : bool + Whether to use 3x3 (instead of 5x5) kernel. + exp_factor : int + Expansion factor for each unit. + use_skip : bool, default True + Whether to use skip connection. + activation : str, default 'relu' + Activation function or name of activation function. + """ + def __init__(self, + in_channels, + out_channels, + stride, + use_kernel3, + exp_factor, + use_skip=True, + activation="relu"): + super(SPNASUnit, self).__init__() + + self.residual = (in_channels == out_channels) and (stride == 1) and use_skip + self.use_exp_conv = exp_factor > 1 + mid_channels = exp_factor * in_channels + + if self.use_exp_conv: + self.exp_conv = conv1x1_block( + in_channels=in_channels, + out_channels=mid_channels, + activation=activation) + if use_kernel3: + self.conv1 = dwconv3x3_block( + in_channels=mid_channels, + out_channels=mid_channels, + stride=stride, + activation=activation) + else: + self.conv1 = dwconv5x5_block( + in_channels=mid_channels, + out_channels=mid_channels, + stride=stride, + activation=activation) + self.conv2 = conv1x1_block( + in_channels=mid_channels, + out_channels=out_channels, + activation=None) + if self.residual: + self.add = ops.Add() + + def construct(self, x): + """ + Args: + x: Tensor of shape :math:`(N, in_channels, W_{in}, H_{in}) + + Returns: + y: Tensor of shape :math:`(N, out_channels, W_{in}, H_{in}) + """ + + identity = x + if self.use_exp_conv: + y = self.exp_conv(x) + y = self.conv1(y) + y = self.conv2(y) + else: + y = self.conv1(x) + y = self.conv2(y) + if self.residual: + y = self.add(y, identity) + return y + + +class SPNASInitBlock(nn.Cell): + """ + Single-Path NASNet specific initial block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + mid_channels : int + Number of middle channels. + """ + def __init__(self, + in_channels, + out_channels, + mid_channels): + super(SPNASInitBlock, self).__init__() + self.conv1 = conv3x3_block( + in_channels=in_channels, + out_channels=mid_channels, + stride=2) + self.conv2 = SPNASUnit( + in_channels=mid_channels, + out_channels=out_channels, + stride=1, + use_kernel3=True, + exp_factor=1, + use_skip=False) + + def construct(self, x): + x = self.conv1(x) + x = self.conv2(x) + return x + + +class SPNASFinalBlock(nn.Cell): + """ + Single-Path NASNet specific final block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + mid_channels : int + Number of middle channels. + """ + def __init__(self, + in_channels, + out_channels, + mid_channels): + super(SPNASFinalBlock, self).__init__() + self.conv1 = SPNASUnit( + in_channels=in_channels, + out_channels=mid_channels, + stride=1, + use_kernel3=True, + exp_factor=6, + use_skip=False) + self.conv2 = conv1x1_block( + in_channels=mid_channels, + out_channels=out_channels) + + def construct(self, x): + x = self.conv1(x) + x = self.conv2(x) + return x + + +class SPNASNet(nn.Cell): + """ + Single-Path NASNet model from 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours,' + https://arxiv.org/abs/1904.02877. + + Parameters: + ---------- + channels : list of list of int + Number of output channels for each unit. + init_block_channels : list of 2 int + Number of output channels for the initial unit. + final_block_channels : list of 2 int + Number of output channels for the final block of the feature extractor. + kernels3 : list of list of int/bool + Using 3x3 (instead of 5x5) kernel for each unit. + exp_factors : list of list of int + Expansion factor for each unit. + in_channels : int, default 3 + Number of input channels. + in_size : tuple of two ints, default (224, 224) + Spatial size of the expected input image. + num_classes : int, default 1000 + Number of classification classes. + """ + def __init__(self, + channels, + init_block_channels, + final_block_channels, + kernels3, + exp_factors, + in_channels=3, + in_size=(224, 224), + num_classes=1000): + super(SPNASNet, self).__init__() + self.in_size = in_size + self.num_classes = num_classes + + self.features = nn.SequentialCell() + self.features.append(SPNASInitBlock( + in_channels=in_channels, + out_channels=init_block_channels[1], + mid_channels=init_block_channels[0])) + in_channels = init_block_channels[1] + for i, channels_per_stage in enumerate(channels): + stage = nn.SequentialCell() + for j, out_channels in enumerate(channels_per_stage): + stride = 2 if ((j == 0) and (i != 3)) or ((j == len(channels_per_stage) // 2) and (i == 3)) else 1 + use_kernel3 = kernels3[i][j] == 1 + exp_factor = exp_factors[i][j] + stage.append(SPNASUnit( + in_channels=in_channels, + out_channels=out_channels, + stride=stride, + use_kernel3=use_kernel3, + exp_factor=exp_factor)) + in_channels = out_channels + self.features.append(stage) + self.features.append(SPNASFinalBlock( + in_channels=in_channels, + out_channels=final_block_channels[1], + mid_channels=final_block_channels[0])) + in_channels = final_block_channels[1] + self.features.append(nn.AvgPool2d( + kernel_size=7, + stride=1)) + + self.output = nn.Dense( + in_channels=in_channels, + out_channels=num_classes, + weight_init='HeUniform') + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.features(x) + x = self.flatten(x) + x = self.output(x) + return x + + +def get_spnasnet(**kwargs): + """ + Create Single-Path NASNet model with specific parameters. + + Parameters: + ---------- + model_name : str or None, default None + Model name for loading pretrained model. + pretrained : bool, default False + Whether to load the pretrained weights for model. + root : str, default '~/.mindspore/models' + Location for keeping the model parameters. + """ + init_block_channels = [32, 16] + final_block_channels = [320, 1280] + channels = [[24, 24, 24], [40, 40, 40, 40], [80, 80, 80, 80], [96, 96, 96, 96, 192, 192, 192, 192]] + kernels3 = [[1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]] + exp_factors = [[3, 3, 3], [6, 3, 3, 3], [6, 3, 3, 3], [6, 3, 3, 3, 6, 6, 6, 6]] + + net = SPNASNet( + channels=channels, + init_block_channels=init_block_channels, + final_block_channels=final_block_channels, + kernels3=kernels3, + exp_factors=exp_factors, + **kwargs) + + return net + + +def spnasnet(**kwargs): + """ + Single-Path NASNet model from 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours,' + https://arxiv.org/abs/1904.02877. + """ + + return get_spnasnet(**kwargs) diff --git a/model_zoo/research/cv/single_path_nas/src/utils.py b/model_zoo/research/cv/single_path_nas/src/utils.py new file mode 100644 index 00000000000..fe99b85da16 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/src/utils.py @@ -0,0 +1,390 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +utils.py +""" + +from inspect import isfunction + +import mindspore.nn as nn + +conv_weight_init = 'HeNormal' + + +class ConvBlock(nn.Cell): + """ + Standard convolution block with Batch normalization and activation. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple/list of 2 int + Convolution window size. + stride : int or tuple/list of 2 int + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + group : int, default 1 + Number of group. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation=1, + group=1, + has_bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU()): + super(ConvBlock, self).__init__() + self.activate = (activation is not None) + self.use_bn = use_bn + self.use_pad = padding + + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + pad_mode='pad', + padding=padding, + dilation=dilation, + group=group, + has_bias=has_bias, + weight_init=conv_weight_init) + if self.use_bn: + self.bn = nn.BatchNorm2d( + num_features=out_channels, + momentum=0.9, + eps=bn_eps) + if self.activate: + self.active = get_activation_layer(activation) + + def construct(self, x): + x = self.conv(x) + if self.use_bn: + x = self.bn(x) + if self.activate: + x = self.active(x) + return x + + +def conv1x1_block(in_channels, + out_channels, + stride=1, + padding=0, + group=1, + has_bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU()): + """ + 1x1 version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 0 + Padding value for convolution layer. + group : int, default 1 + Number of group. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=stride, + padding=padding, + group=group, + has_bias=has_bias, + use_bn=use_bn, + bn_eps=bn_eps, + activation=activation) + + +def conv3x3_block(in_channels, + out_channels, + stride=1, + padding=1, + dilation=1, + group=1, + has_bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU()): + """ + 3x3 version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + group : int, default 1 + Number of group. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + dilation=dilation, + group=group, + has_bias=has_bias, + use_bn=use_bn, + bn_eps=bn_eps, + activation=activation) + + + +def dwconv3x3_block(in_channels, + out_channels, + stride=1, + padding=1, + dilation=1, + has_bias=False, + bn_eps=1e-5, + activation=nn.ReLU()): + """ + 3x3 depthwise version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + return dwconv_block( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=padding, + dilation=dilation, + has_bias=has_bias, + bn_eps=bn_eps, + activation=activation) + + + +def dwconv5x5_block(in_channels, + out_channels, + stride=1, + padding=2, + dilation=1, + has_bias=False, + bn_eps=1e-5, + activation=nn.ReLU()): + """ + 5x5 depthwise version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 2 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + return dwconv_block( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=5, + stride=stride, + padding=padding, + dilation=dilation, + has_bias=has_bias, + bn_eps=bn_eps, + activation=activation) + + +def dwconv_block(in_channels, + out_channels, + kernel_size, + stride=1, + padding=1, + dilation=1, + has_bias=False, + use_bn=True, + bn_eps=1e-5, + activation=nn.ReLU()): + """ + Depthwise version of the standard convolution block. + + Parameters: + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple/list of 2 int + Convolution window size. + stride : int or tuple/list of 2 int, default 1 + Strides of the convolution. + padding : int, or tuple/list of 2 int, or tuple/list of 4 int, default 1 + Padding value for convolution layer. + dilation : int or tuple/list of 2 int, default 1 + Dilation value for convolution layer. + has_bias : bool, default False + Whether the layer uses a has_bias vector. + use_bn : bool, default True + Whether to use BatchNorm layer. + bn_eps : float, default 1e-5 + Small float added to variance in Batch norm. + activation : function or str or None, default nn.ReLU() + Activation function or name of activation function. + """ + return ConvBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + group=out_channels, + has_bias=has_bias, + use_bn=use_bn, + bn_eps=bn_eps, + activation=activation) + + +class Swish(nn.Cell): + """ + Swish activation function from 'Searching for Activation Functions,' https://arxiv.org/abs/1710.05941. + """ + def __init__(self): + super(Swish, self).__init__() + self.sigmoid = nn.Sigmoid() + def construct(self, x): + return x * self.sigmoid(x) + + +class Identity(nn.Cell): + """ + Identity block. + """ + + def constructor(self, x): + return x + + +def get_activation_layer(activation): + """ + Create activation layer from string/function. + + Parameters: + ---------- + activation : function, or str, or nn.Cell + Activation function or name of activation function. + + Returns: + ------- + nn.Cell + Activation layer. + """ + + if isfunction(activation): + active = activation() + elif isinstance(activation, str): + if activation == "relu": + active = nn.ReLU() + elif activation == "relu6": + active = nn.ReLU6() + elif activation == "swish": + active = Swish() + elif activation == "hswish": + active = nn.HSwish() + elif activation == "sigmoid": + active = nn.Sigmoid() + elif activation == "hsigmoid": + active = nn.HSigmoid() + elif activation == "identity": + active = Identity() + else: + raise NotImplementedError() + elif isinstance(activation, nn.Cell): + active = activation + else: + return ValueError() + return active diff --git a/model_zoo/research/cv/single_path_nas/train.py b/model_zoo/research/cv/single_path_nas/train.py new file mode 100644 index 00000000000..1bc22b118e8 --- /dev/null +++ b/model_zoo/research/cv/single_path_nas/train.py @@ -0,0 +1,166 @@ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed 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 +# +# less 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. +# ============================================================================ +""" +#################train spnasnet example######################## +python train.py +""" +import argparse +import os + +from mindspore import Tensor +from mindspore import context +from mindspore.common import set_seed +from mindspore.communication.management import init, get_rank +from mindspore.context import ParallelMode +from mindspore.nn.optim.momentum import Momentum +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor +from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager +from mindspore.train.model import Model + +from src import spnasnet +from src.CrossEntropySmooth import CrossEntropySmooth +from src.config import imagenet_cfg +from src.dataset import create_dataset_imagenet + +set_seed(1) + + +def lr_steps_imagenet(_cfg, steps_per_epoch): + """lr step for imagenet""" + from src.lr_scheduler.warmup_step_lr import warmup_step_lr + from src.lr_scheduler.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr + if _cfg.lr_scheduler == 'exponential': + _lr = warmup_step_lr(_cfg.lr_init, + _cfg.lr_epochs, + steps_per_epoch, + _cfg.warmup_epochs, + _cfg.epoch_size, + gamma=_cfg.lr_gamma, + ) + elif _cfg.lr_scheduler == 'cosine_annealing': + _lr = warmup_cosine_annealing_lr(_cfg.lr_init, + steps_per_epoch, + _cfg.warmup_epochs, + _cfg.epoch_size, + _cfg.T_max, + _cfg.eta_min) + else: + raise NotImplementedError(_cfg.lr_scheduler) + + return _lr + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Single-Path-NAS Training') + parser.add_argument('--dataset_name', type=str, default='imagenet', choices=['imagenet',], + help='dataset name.') + parser.add_argument('--filter_prefix', type=str, default='huawei', help='filter_prefix name.') + parser.add_argument('--device_id', type=int, default=None, help='device id of Ascend. (Default: None)') + args_opt = parser.parse_args() + + if args_opt.dataset_name == "imagenet": + cfg = imagenet_cfg + else: + raise ValueError("Unsupported dataset.") + + # set context + device_target = cfg.device_target + context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target, enable_graph_kernel=True) + + device_num = int(os.environ.get("DEVICE_NUM", 1)) + + rank = 0 + if device_target == "Ascend": + if args_opt.device_id is not None: + context.set_context(device_id=args_opt.device_id) + else: + context.set_context(device_id=cfg.device_id) + + if device_num > 1: + context.reset_auto_parallel_context() + context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, + gradients_mean=True) + init() + rank = get_rank() + else: + raise ValueError("Unsupported platform.") + + if args_opt.dataset_name == "imagenet": + dataset = create_dataset_imagenet(cfg.data_path, 1) + else: + raise ValueError("Unsupported dataset.") + + batch_num = dataset.get_dataset_size() + + net = spnasnet.get_spnasnet(num_classes=cfg.num_classes) + net.update_parameters_name(args_opt.filter_prefix) + + loss_scale_manager = None + if args_opt.dataset_name == 'imagenet': + lr = lr_steps_imagenet(cfg, batch_num) + + + def get_param_groups(network): + """ get param groups """ + decay_params = [] + no_decay_params = [] + for x in network.trainable_params(): + parameter_name = x.name + if parameter_name.endswith('.bias'): + # all bias not using weight decay + no_decay_params.append(x) + elif parameter_name.endswith('.gamma'): + # bn weight bias not using weight decay, be carefully for now x not include BN + no_decay_params.append(x) + elif parameter_name.endswith('.beta'): + # bn weight bias not using weight decay, be carefully for now x not include BN + no_decay_params.append(x) + else: + decay_params.append(x) + + return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}] + + + if cfg.is_dynamic_loss_scale: + cfg.loss_scale = 1 + + opt = Momentum(params=net.get_parameters(), + learning_rate=Tensor(lr), + momentum=cfg.momentum, + weight_decay=cfg.weight_decay, + loss_scale=cfg.loss_scale) + + if not cfg.use_label_smooth: + cfg.label_smooth_factor = 0.0 + loss = CrossEntropySmooth(sparse=True, reduction="mean", + smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes) + + if cfg.is_dynamic_loss_scale == 1: + loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000) + else: + loss_scale_manager = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False) + + model = Model(net, loss_fn=loss, optimizer=opt, metrics={'top_1_accuracy', 'top_5_accuracy', 'loss'}, + amp_level="O3", keep_batchnorm_fp32=True, loss_scale_manager=loss_scale_manager) + + config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 1, keep_checkpoint_max=cfg.keep_checkpoint_max) + time_cb = TimeMonitor(data_size=batch_num) + ckpt_save_dir = "./ckpt_" + str(rank) + "/" + ckpoint_cb = ModelCheckpoint(prefix="train_spnasnet_" + args_opt.dataset_name, directory=ckpt_save_dir, + config=config_ck) + loss_cb = LossMonitor() + + model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb]) + print("train success")