!18031 ascend 310 inference for fcn8s
Merge pull request !18031 from 于振华/fcn8s_master
This commit is contained in:
Gitee
commit
26aebfba20
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@ -12,6 +12,10 @@
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- [训练](#训练)
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- [评估步骤](#评估步骤)
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- [评估](#评估)
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- [导出过程](#导出过程)
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- [导出](#导出)
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- [推理过程](#推理过程)
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- [推理](#推理)
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- [模型介绍](#模型介绍)
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- [性能](#性能)
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- [评估性能](#评估性能)
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@ -71,10 +75,12 @@ Dataset used:
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├── README.md // descriptions about all the models
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├── FCN8s
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├── README.md // descriptions about FCN
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├── ascend310_infer // 实现310推理源代码
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├── scripts
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├── run_train.sh
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├── run_standalone_train.sh
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├── run_eval.sh
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├── run_infer_310.sh // Ascend推理shell脚本
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├── build_data.sh
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├── src
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│ ├──data
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@ -93,6 +99,8 @@ Dataset used:
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│ ├──moxing_adapter.py // Decorator
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├── default_config.yaml // Parameters config
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├── train.py // training script
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├── postprogress.py // 310推理后处理脚本
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├── export.py // 将checkpoint文件导出到air/mindir
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├── eval.py // evaluation script
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```
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@ -271,6 +279,33 @@ Dataset used:
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mean IoU 0.6467
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```
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## 导出过程
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### 导出
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在导出之前需要修改default_config.yaml配置文件中的ckpt_file配置项，file_name和file_format配置项根据情况修改.
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```shell
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python export.py
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```
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## 推理过程
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### 推理
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在还行推理之前我们需要先导出模型。Air模型只能在昇腾910环境上导出，mindir可以在任意环境上导出。batch_size只支持1。
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_LIST_FILE] [IMAGE_PATH] [MASK_PATH] [DEVICE_ID]
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```
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推理的结果保存在当前目录下，在acc.log日志文件中可以找到类似以下的结果。
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```python
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mean IoU 0.0.64519877
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```
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# [模型介绍](#contents)
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## [性能](#contents)
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@ -0,0 +1,14 @@
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
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set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
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option(MINDSPORE_PATH "mindspore install path" "")
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include_directories(${MINDSPORE_PATH})
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include_directories(${MINDSPORE_PATH}/include)
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include_directories(${PROJECT_SRC_ROOT})
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find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
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file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
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add_executable(main src/main.cc src/utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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@ -0,0 +1,29 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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if [ -d out ]; then
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rm -rf out
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fi
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mkdir out
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cd out || exit
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if [ -f "Makefile" ]; then
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make clean
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fi
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cmake .. \
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-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
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make
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@ -0,0 +1,33 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_INFERENCE_UTILS_H_
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#define MINDSPORE_INFERENCE_UTILS_H_
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#include <sys/stat.h>
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#include <dirent.h>
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#include <vector>
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#include <string>
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#include <memory>
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#include "include/api/types.h"
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std::vector<std::string> GetAllFiles(std::string_view dirName);
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std::vector<std::string> GetImagesById(const std::string &idFIle, const std::string &dirName);
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DIR *OpenDir(std::string_view dirName);
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std::string RealPath(std::string_view path);
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mindspore::MSTensor ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
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#endif
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@ -0,0 +1,223 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <sys/time.h>
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#include <gflags/gflags.h>
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#include <dirent.h>
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#include <iostream>
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#include <string>
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#include <algorithm>
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#include <iosfwd>
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#include <vector>
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#include <fstream>
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#include <sstream>
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#include "include/api/context.h"
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#include "include/api/model.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "include/dataset/vision.h"
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#include "include/dataset/execute.h"
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#include "../inc/utils.h"
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::Status;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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using mindspore::MSTensor;
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using mindspore::DataType;
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using mindspore::dataset::Execute;
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using mindspore::dataset::TensorTransform;
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using mindspore::dataset::vision::Resize;
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using mindspore::dataset::vision::Pad;
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using mindspore::dataset::vision::HWC2CHW;
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using mindspore::dataset::vision::Normalize;
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using mindspore::dataset::vision::SwapRedBlue;
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using mindspore::dataset::vision::Decode;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(image_list, "", "image list");
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DEFINE_string(dataset_path, ".", "dataset path");
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DEFINE_int32(device_id, 0, "device id");
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const int IMAGEWIDTH = 512;
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const int IMAGEHEIGHT = 512;
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int PadImage(const MSTensor &input, MSTensor *output) {
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std::shared_ptr<TensorTransform> normalize(new Normalize({103.53, 116.28, 123.675},
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{57.375, 57.120, 58.395}));
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Execute composeNormalize({normalize});
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std::vector<int64_t> shape = input.Shape();
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auto imgResize = MSTensor();
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auto imgNormalize = MSTensor();
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float widthScale, heightScale;
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widthScale = static_cast<float>(IMAGEWIDTH) / shape[1];
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heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
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Status ret;
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if (widthScale < heightScale) {
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int heightSize = shape[0]*widthScale;
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std::shared_ptr<TensorTransform> resize(new Resize({heightSize, IMAGEWIDTH}));
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Execute composeResizeWidth({resize});
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ret = composeResizeWidth(input, &imgResize);
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if (ret != kSuccess) {
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std::cout << "ERROR: Resize Width failed." << std::endl;
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return 1;
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}
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ret = composeNormalize(imgResize, &imgNormalize);
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if (ret != kSuccess) {
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std::cout << "ERROR: Normalize failed." << std::endl;
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return 1;
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}
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int paddingSize = IMAGEHEIGHT - heightSize;
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std::shared_ptr<TensorTransform> pad(new Pad({0, 0, 0, paddingSize}));
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Execute composePad({pad});
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ret = composePad(imgNormalize, output);
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if (ret != kSuccess) {
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std::cout << "ERROR: Height Pad failed." << std::endl;
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return 1;
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}
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} else {
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int widthSize = shape[1]*heightScale;
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std::shared_ptr<TensorTransform> resize(new Resize({IMAGEHEIGHT, widthSize}));
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Execute composeResizeHeight({resize});
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ret = composeResizeHeight(input, &imgResize);
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if (ret != kSuccess) {
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std::cout << "ERROR: Resize Height failed." << std::endl;
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return 1;
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}
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ret = composeNormalize(imgResize, &imgNormalize);
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if (ret != kSuccess) {
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std::cout << "ERROR: Normalize failed." << std::endl;
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return 1;
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}
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int paddingSize = IMAGEWIDTH - widthSize;
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std::shared_ptr<TensorTransform> pad(new Pad({0, 0, paddingSize, 0}));
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Execute composePad({pad});
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ret = composePad(imgNormalize, output);
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if (ret != kSuccess) {
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std::cout << "ERROR: Width Pad failed." << std::endl;
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return 1;
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}
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}
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return 0;
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}
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int main(int argc, char **argv) {
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (RealPath(FLAGS_mindir_path).empty()) {
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std::cout << "Invalid mindir" << std::endl;
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return 1;
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}
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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ascend310->SetPrecisionMode("allow_fp32_to_fp16");
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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}
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std::vector<MSTensor> model_inputs = model.GetInputs();
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if (model_inputs.empty()) {
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std::cout << "Invalid model, inputs is empty." << std::endl;
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return 1;
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}
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auto all_files = GetImagesById(FLAGS_image_list, FLAGS_dataset_path);
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if (all_files.empty()) {
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std::cout << "ERROR: no input data." << std::endl;
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return 1;
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}
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std::map<double, double> costTime_map;
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size_t size = all_files.size();
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std::shared_ptr<TensorTransform> decode(new Decode());
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Execute composeDecode({decode});
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std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
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Execute composeTranspose({hwc2chw});
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::string file = all_files[i] + ".jpg";
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std::cout << "Start predict input files:" << file << std::endl;
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auto imgDecode = MSTensor();
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auto image = ReadFileToTensor(file);
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ret = composeDecode(image, &imgDecode);
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if (ret != kSuccess) {
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std::cout << "ERROR: Decode failed." << std::endl;
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return 1;
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}
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auto imgPad = MSTensor();
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PadImage(imgDecode, &imgPad);
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auto img = MSTensor();
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composeTranspose(imgPad, &img);
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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img.Data().get(), img.DataSize());
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gettimeofday(&start, nullptr);
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ret = model.Predict(inputs, &outputs);
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gettimeofday(&end, nullptr);
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if (ret != kSuccess) {
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std::cout << "Predict " << file << " failed." << std::endl;
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return 1;
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}
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startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
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WriteResult(file, outputs);
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}
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double average = 0.0;
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int inferCount = 0;
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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average += diff;
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inferCount++;
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}
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average = average / inferCount;
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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}
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@ -0,0 +1,145 @@
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/**
|
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* 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 <fstream>
|
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#include <algorithm>
|
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#include <iostream>
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#include "../inc/utils.h"
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|
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using mindspore::MSTensor;
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using mindspore::DataType;
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std::vector<std::string> GetAllFiles(std::string_view dirName) {
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struct dirent *filename;
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DIR *dir = OpenDir(dirName);
|
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if (dir == nullptr) {
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return {};
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}
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std::vector<std::string> res;
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while ((filename = readdir(dir)) != nullptr) {
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std::string dName = std::string(filename->d_name);
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if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
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continue;
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}
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res.emplace_back(std::string(dirName) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
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for (auto &f : res) {
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std::cout << "image file: " << f << std::endl;
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}
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return res;
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}
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std::vector<std::string> GetImagesById(const std::string &idFile, const std::string &dirName) {
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std::ifstream readFile(idFile);
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std::string id;
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std::vector<std::string> result;
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|
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if (!readFile.is_open()) {
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std::cout << "can not open image id txt file" << std::endl;
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return result;
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}
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while (getline(readFile, id)) {
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result.emplace_back(dirName + "/" + id);
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}
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return result;
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}
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int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
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std::string homePath = "./result_Files";
|
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for (size_t i = 0; i < outputs.size(); ++i) {
|
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size_t outputSize;
|
||||
std::shared_ptr<const void> 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;
|
||||
}
|
||||
|
||||
MSTensor ReadFileToTensor(const std::string &file) {
|
||||
if (file.empty()) {
|
||||
std::cout << "Pointer file is nullptr" << std::endl;
|
||||
return MSTensor();
|
||||
}
|
||||
|
||||
std::ifstream ifs(file);
|
||||
if (!ifs.good()) {
|
||||
std::cout << "File: " << file << " is not exist" << std::endl;
|
||||
return MSTensor();
|
||||
}
|
||||
|
||||
if (!ifs.is_open()) {
|
||||
std::cout << "File: " << file << "open failed" << std::endl;
|
||||
return MSTensor();
|
||||
}
|
||||
|
||||
ifs.seekg(0, std::ios::end);
|
||||
size_t size = ifs.tellg();
|
||||
MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
|
||||
|
||||
ifs.seekg(0, std::ios::beg);
|
||||
ifs.read(reinterpret_cast<char *>(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;
|
||||
}
|
||||
|
|
@ -52,6 +52,10 @@ flip: False
|
|||
freeze_bn: False
|
||||
ckpt_file: "/data/mjq/ckpt/FCN8s_1-133_300.ckpt"
|
||||
|
||||
# ======================================================================================
|
||||
# Export options
|
||||
file_name: "fcn8s"
|
||||
file_format: MINDIR
|
||||
|
||||
---
|
||||
# Help description for each configuration
|
||||
|
|
@ -82,4 +86,6 @@ eval_batch_size: "eval batch size"
|
|||
data_lst: "list of val data"
|
||||
scales: "scales of evaluation"
|
||||
flip: "freeze bn"
|
||||
ckpt_file: "model to evaluate"
|
||||
ckpt_file: "model to evaluate"
|
||||
file_name: "export file name"
|
||||
file_format: "export model type"
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
# 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 FCN8s."""
|
||||
|
||||
import numpy as np
|
||||
|
||||
import mindspore as ms
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
|
||||
from src.nets.FCN8s import FCN8s
|
||||
from src.model_utils.config import config
|
||||
from src.model_utils.device_adapter import get_device_id
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
|
||||
|
||||
if __name__ == '__main__':
|
||||
net = FCN8s(n_class=config.num_classes)
|
||||
|
||||
# load model
|
||||
param_dict = load_checkpoint(config.ckpt_file)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
input_arr = Tensor(np.zeros([1, 3, config.crop_size, config.crop_size]), ms.float32)
|
||||
export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""post process for 310 inference"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
import cv2
|
||||
from PIL import Image
|
||||
|
||||
parser = argparse.ArgumentParser(description="FasterRcnn inference")
|
||||
parser.add_argument("--image_list", type=str, required=True, help="result file path.")
|
||||
parser.add_argument("--result_path", type=str, required=True, help="result file path.")
|
||||
parser.add_argument("--data_path", type=str, required=True, help="mask file path.")
|
||||
parser.add_argument("--mask_path", type=str, required=True, help="mask file path.")
|
||||
args = parser.parse_args()
|
||||
|
||||
NUM_CLASSES = 21
|
||||
|
||||
def get_img_size(file_name):
|
||||
img = Image.open(file_name)
|
||||
return img.size
|
||||
|
||||
def get_resized_size(org_h, org_w, long_size=512):
|
||||
if org_h > org_w:
|
||||
new_h = long_size
|
||||
new_w = int(1.0 * long_size * org_w / org_h)
|
||||
else:
|
||||
new_w = long_size
|
||||
new_h = int(1.0 * long_size * org_h / org_w)
|
||||
|
||||
return new_h, new_w
|
||||
|
||||
def cal_hist(a, b, n):
|
||||
k = (a >= 0) & (a < n)
|
||||
return np.bincount(n * a[k].astype(np.int32) + b[k], minlength=n ** 2).reshape(n, n)
|
||||
|
||||
def cal_acc(image_list, data_path, result_path, mask_path):
|
||||
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
|
||||
with open(image_list) as f:
|
||||
img_list = f.readlines()
|
||||
|
||||
for img in img_list:
|
||||
img_file = os.path.join(data_path, img.strip() + ".jpg")
|
||||
org_width, org_height = get_img_size(img_file)
|
||||
|
||||
resize_h, resize_w = get_resized_size(org_height, org_width)
|
||||
|
||||
result_file = os.path.join(result_path, img.strip() + "_0.bin")
|
||||
result = np.fromfile(result_file, dtype=np.float32).reshape(21, 512, 512)
|
||||
probs_ = result[:, :resize_h, :resize_w].transpose((1, 2, 0))
|
||||
probs_ = cv2.resize(probs_.astype(np.float32), (org_width, org_height))
|
||||
result_msk = probs_.argmax(axis=2)
|
||||
|
||||
mask_file = os.path.join(mask_path, img.strip() + ".png")
|
||||
mask = np.array(Image.open(mask_file), dtype=np.uint8)
|
||||
|
||||
hist += cal_hist(mask.flatten(), result_msk.flatten(), NUM_CLASSES)
|
||||
|
||||
#print(hist)
|
||||
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
|
||||
print('per-class IoU', iu)
|
||||
print('mean IoU', np.nanmean(iu))
|
||||
|
||||
if __name__ == '__main__':
|
||||
cal_acc(args.image_list, args.data_path, args.result_path, args.mask_path)
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
#!/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 4 || $# -gt 5 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_LIST_FILE] [IMAGE_PATH] [MASK_PATH] [DEVICE_ID]
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
|
||||
model=$(get_real_path $1)
|
||||
data_list_file=$(get_real_path $2)
|
||||
image_path=$(get_real_path $3)
|
||||
mask_path=$(get_real_path $4)
|
||||
|
||||
device_id=0
|
||||
if [ $# == 5 ]; then
|
||||
device_id=$5
|
||||
elif [ $# == 4 ]; then
|
||||
if [ ! -z $device_id ]; then
|
||||
device_id=$device_id
|
||||
fi
|
||||
fi
|
||||
|
||||
echo $model
|
||||
echo $image_path
|
||||
echo $mask_path
|
||||
echo $device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
if [ -f "Makefile" ]; then
|
||||
make clean
|
||||
fi
|
||||
sh build.sh &> build.log
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
cd - || exit
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
../ascend310_infer/out/main --image_list=$data_list_file --mindir_path=$model --dataset_path=$image_path --device_id=$device_id &> infer.log
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python ../postprocess.py --image_list=$data_list_file --data_path=$image_path --mask_path=$mask_path --result_path=result_Files &> acc.log
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
compile_app
|
||||
infer
|
||||
cal_acc
|
||||
Loading…
Reference in New Issue