diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md index 1f253a1047..92ca328679 100644 --- a/model_zoo/official/cv/deeplabv3/README.md +++ b/model_zoo/official/cv/deeplabv3/README.md @@ -12,6 +12,8 @@ - [Script Parameters](#script-parameters) - [Training Process](#training-process) - [Evaluation Process](#evaluation-process) + - [Export MindIR](#export-mindir) + - [Inference Process](#inference-process) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) @@ -478,6 +480,37 @@ Our result were obtained by running the applicable training script. To achieve t Note: There OS is output stride, and MS is multiscale. +## [Export MindIR](#contents) + +```shell +python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] +``` + +The ckpt_file parameter is required, +`EXPORT_FORMAT` should be in ["AIR", "MINDIR"] + +## [Inference Process](#contents) + +### Usage + +Before performing inference, the air file must bu exported by export script on the 910 environment. +Current batch_Size can only be set to 1. The precision calculation process needs about 70G+ memory space. + +```shell +# Ascend310 inference +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID] +``` + +`DEVICE_ID` is optional, default value is 0. + +### result + +Inference result is saved in current path, you can find result in acc.log file. + +| **Network** | OS=16 | OS=8 | MS | Flip | mIOU | mIOU in paper | +| :----------: | :-----: | :----: | :----: | :-----: | :-----: | :-------------: | +| deeplab_v3 | | √ | | | 78.84 | 78.51 | + # [Model Description](#contents) ## [Performance](#contents) diff --git a/model_zoo/official/cv/deeplabv3/README_CN.md b/model_zoo/official/cv/deeplabv3/README_CN.md index a5e221a7ed..89e4016b09 100644 --- a/model_zoo/official/cv/deeplabv3/README_CN.md +++ b/model_zoo/official/cv/deeplabv3/README_CN.md @@ -23,6 +23,10 @@ - [Ascend处理器环境运行](#ascend处理器环境运行-1) - [结果](#结果-1) - [训练准确率](#训练准确率) + - [导出mindir模型](#导出mindir模型) + - [推理过程](#推理过程) + - [用法](#用法-2) + - [结果](#结果-2) - [模型描述](#模型描述) - [性能](#性能) - [评估性能](#评估性能) @@ -492,6 +496,36 @@ python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA \ 注意：OS指输出步长（output stride）， MS指多尺度（multiscale）。 +## 导出mindir模型 + +```shell +python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] +``` + +参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。 + +## 推理过程 + +### 用法 + +在执行推理前，air文件必须在910上通过export.py文件导出。 +目前仅可处理batch_Size为1。 + +```shell +# Ascend310 推理 +bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID] +``` + +`DEVICE_ID` 可选，默认值为 0。 + +### 结果 + +推理结果保存在当前路径，可在acc.log中看到最终精度结果。 + +| **Network** | OS=16 | OS=8 | MS | Flip | mIOU | mIOU in paper | +| :----------: | :-----: | :----: | :----: | :-----: | :-----: | :-------------: | +| deeplab_v3 | | √ | | | 78.84 | 78.51 | + # 模型描述 ## 性能 diff --git a/model_zoo/official/cv/deeplabv3/ascend310_infer/inc/utils.h b/model_zoo/official/cv/deeplabv3/ascend310_infer/inc/utils.h new file mode 100644 index 0000000000..efebe03a8c --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/ascend310_infer/inc/utils.h @@ -0,0 +1,32 @@ +/** + * 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); +#endif diff --git a/model_zoo/official/cv/deeplabv3/ascend310_infer/src/CMakeLists.txt b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/CMakeLists.txt new file mode 100644 index 0000000000..9550b6a74a --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/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}/../inc) +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/official/cv/deeplabv3/ascend310_infer/src/build.sh b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/build.sh new file mode 100644 index 0000000000..7fac9cff3a --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/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/official/cv/deeplabv3/ascend310_infer/src/main.cc b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/main.cc new file mode 100644 index 0000000000..f627d52d9e --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/main.cc @@ -0,0 +1,208 @@ +/** + * 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/api/context.h" +#include "include/api/model.h" +#include "include/api/types.h" +#include "include/api/serialization.h" +#include "include/minddata/dataset/include/vision.h" +#include "include/minddata/dataset/include/execute.h" +#include "../inc/utils.h" + +using mindspore::GlobalContext; +using mindspore::Serialization; +using mindspore::Model; +using mindspore::ModelContext; +using mindspore::Status; +using mindspore::ModelType; +using mindspore::GraphCell; +using mindspore::kSuccess; +using mindspore::MSTensor; +using mindspore::dataset::Execute; +using mindspore::dataset::TensorTransform; +using mindspore::dataset::vision::Resize; +using mindspore::dataset::vision::Pad; +using mindspore::dataset::vision::HWC2CHW; +using mindspore::dataset::vision::Normalize; +using mindspore::dataset::vision::SwapRedBlue; +using mindspore::dataset::vision::Decode; + + +DEFINE_string(mindir_path, "", "mindir path"); +DEFINE_string(dataset_path, ".", "dataset path"); +DEFINE_int32(device_id, 0, "device id"); + +int PadImage(const MSTensor &input, MSTensor *output) { + std::shared_ptr normalize(new Normalize({103.53, 116.28, 123.675}, + {57.375, 57.120, 58.395})); + Execute composeNormalize({normalize}); + std::vector shape = input.Shape(); + auto imgResize = MSTensor(); + auto imgNormalize = MSTensor(); + int paddingSize; + const int IMAGEWIDTH = 513; + const int IMAGEHEIGHT = 513; + float widthScale, heightScale; + widthScale = static_cast(IMAGEWIDTH) / shape[1]; + heightScale = static_cast(IMAGEHEIGHT) / shape[0]; + Status ret; + if (widthScale < heightScale) { + int heightSize = shape[0]*widthScale; + std::shared_ptr resize(new Resize({heightSize, IMAGEWIDTH})); + Execute composeResizeWidth({resize}); + ret = composeResizeWidth(input, &imgResize); + if (ret != kSuccess) { + std::cout << "ERROR: Resize Width failed." << std::endl; + return 1; + } + ret = composeNormalize(imgResize, &imgNormalize); + if (ret != kSuccess) { + std::cout << "ERROR: Normalize failed." << std::endl; + return 1; + } + paddingSize = IMAGEHEIGHT - heightSize; + std::shared_ptr pad(new Pad({0, 0, 0, paddingSize})); + Execute composePad({pad}); + ret = composePad(imgNormalize, output); + if (ret != kSuccess) { + std::cout << "ERROR: Height Pad failed." << std::endl; + return 1; + } + } else { + int widthSize = shape[1]*heightScale; + std::shared_ptr resize(new Resize({IMAGEHEIGHT, widthSize})); + Execute composeResizeHeight({resize}); + ret = composeResizeHeight(input, &imgResize); + if (ret != kSuccess) { + std::cout << "ERROR: Resize Height failed." << std::endl; + return 1; + } + ret = composeNormalize(imgResize, &imgNormalize); + if (ret != kSuccess) { + std::cout << "ERROR: Normalize failed." << std::endl; + return 1; + } + paddingSize = IMAGEWIDTH - widthSize; + std::shared_ptr pad(new Pad({0, 0, paddingSize, 0})); + Execute composePad({pad}); + ret = composePad(imgNormalize, output); + if (ret != kSuccess) { + std::cout << "ERROR: Width Pad failed." << std::endl; + return 1; + } + } + return 0; +} + +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; + } + + GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310); + GlobalContext::SetGlobalDeviceID(FLAGS_device_id); + auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR); + auto model_context = std::make_shared(); + Model model(GraphCell(graph), model_context); + Status ret = model.Build(); + if (ret != kSuccess) { + std::cout << "ERROR: Build failed." << std::endl; + return 1; + } + std::vector model_inputs = model.GetInputs(); + if (model_inputs.empty()) { + std::cout << "Invalid model, inputs is empty." << std::endl; + return 1; + } + + auto all_files = GetAllFiles(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(); + std::shared_ptr decode(new Decode()); + std::shared_ptr swapredblue(new SwapRedBlue()); + Execute composeDecode({decode, swapredblue}); + std::shared_ptr hwc2chw(new HWC2CHW()); + Execute composeTranspose({hwc2chw}); + + for (size_t i = 0; i < size; ++i) { + 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] << std::endl; + auto imgDecode = MSTensor(); + auto image = ReadFileToTensor(all_files[i]); + ret = composeDecode(image, &imgDecode); + if (ret != kSuccess) { + std::cout << "ERROR: Decode failed." << std::endl; + return 1; + } + auto imgPad = MSTensor(); + PadImage(imgDecode, &imgPad); + auto img = MSTensor(); + composeTranspose(imgPad, &img); + inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(), + img.Data().get(), img.DataSize()); + gettimeofday(&start, nullptr); + ret = model.Predict(inputs, &outputs); + gettimeofday(&end, nullptr); + if (ret != kSuccess) { + std::cout << "Predict " << all_files[i] << " 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], outputs); + } + double average = 0.0; + int inferCount = 0; + char tmpCh[256] = {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; + snprintf(tmpCh, sizeof(tmpCh), \ + "NN inference cost average time: %4.3f ms of infer_count %d \n", average, inferCount); + 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 << tmpCh; + fileStream.close(); + costTime_map.clear(); + return 0; +} diff --git a/model_zoo/official/cv/deeplabv3/ascend310_infer/src/utils.cc b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/utils.cc new file mode 100644 index 0000000000..e383cfc33e --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/ascend310_infer/src/utils.cc @@ -0,0 +1,129 @@ +/** + * 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 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; +} + +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(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/official/cv/deeplabv3/export.py b/model_zoo/official/cv/deeplabv3/export.py index d194546d87..184212e635 100644 --- a/model_zoo/official/cv/deeplabv3/export.py +++ b/model_zoo/official/cv/deeplabv3/export.py @@ -26,7 +26,7 @@ parser.add_argument("--batch_size", type=int, default=1, help="batch size") parser.add_argument("--input_size", type=int, default=513, help="batch size") parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.") parser.add_argument("--file_name", type=str, default="deeplabv3", help="output file name.") -parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format') +parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format') parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend", help="device target") parser.add_argument('--model', type=str.lower, default='deeplab_v3_s8', choices=['deeplab_v3_s16', 'deeplab_v3_s8'], diff --git a/model_zoo/official/cv/deeplabv3/postprocess.py b/model_zoo/official/cv/deeplabv3/postprocess.py new file mode 100644 index 0000000000..67b517e9b2 --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/postprocess.py @@ -0,0 +1,122 @@ +# 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 eval import cal_hist, pre_process + +def parse_args(): + parser = argparse.ArgumentParser(description="deeplabv3 accuracy calculation") + parser.add_argument('--data_root', type=str, default='', help='root path of val data') + parser.add_argument('--data_lst', type=str, default='', help='list of val data') + parser.add_argument('--batch_size', type=int, default=1, help='batch size') + parser.add_argument('--crop_size', type=int, default=513, help='crop size') + parser.add_argument('--scales', type=float, action='append', help='scales of evaluation') + parser.add_argument('--flip', action='store_true', help='perform left-right flip') + parser.add_argument('--ignore_label', type=int, default=255, help='ignore label') + parser.add_argument('--num_classes', type=int, default=21, help='number of classes') + parser.add_argument('--result_path', type=str, default='./result_Files', help='result Files path') + args, _ = parser.parse_known_args() + return args + +def eval_batch(args, result_file, img_lst, crop_size=513, flip=True): + result_lst = [] + batch_size = len(img_lst) + batch_img = np.zeros((args.batch_size, 3, crop_size, crop_size), dtype=np.float32) + resize_hw = [] + for l in range(batch_size): + img_ = img_lst[l] + img_, resize_h, resize_w = pre_process(args, img_, crop_size) + batch_img[l] = img_ + resize_hw.append([resize_h, resize_w]) + + batch_img = np.ascontiguousarray(batch_img) + net_out = np.fromfile(result_file, np.float32).reshape(args.batch_size, args.num_classes, crop_size, crop_size) + + for bs in range(batch_size): + probs_ = net_out[bs][:, :resize_hw[bs][0], :resize_hw[bs][1]].transpose((1, 2, 0)) + ori_h, ori_w = img_lst[bs].shape[0], img_lst[bs].shape[1] + probs_ = cv2.resize(probs_, (ori_w, ori_h)) + result_lst.append(probs_) + + return result_lst + + +def eval_batch_scales(args, eval_net, img_lst, scales, + base_crop_size=513, flip=True): + sizes_ = [int((base_crop_size - 1) * sc) + 1 for sc in scales] + probs_lst = eval_batch(args, eval_net, img_lst, crop_size=sizes_[0], flip=flip) + print(sizes_) + for crop_size_ in sizes_[1:]: + probs_lst_tmp = eval_batch(args, eval_net, img_lst, crop_size=crop_size_, flip=flip) + for pl, _ in enumerate(probs_lst): + probs_lst[pl] += probs_lst_tmp[pl] + + result_msk = [] + for i in probs_lst: + result_msk.append(i.argmax(axis=2)) + return result_msk + + +def acc_cal(): + args = parse_args() + # data list + with open(args.data_lst) as f: + img_lst = f.readlines() + # evaluate + hist = np.zeros((args.num_classes, args.num_classes)) + batch_img_lst = [] + batch_msk_lst = [] + bi = 0 + image_num = 0 + for i, line in enumerate(img_lst): + img_path, msk_path = line.strip().split(' ') + result_file = os.path.join(args.result_path, os.path.basename(img_path).split('.jpg')[0] + '_0.bin') + img_path = os.path.join(args.data_root, img_path) + msk_path = os.path.join(args.data_root, msk_path) + img_ = cv2.imread(img_path) + msk_ = cv2.imread(msk_path, cv2.IMREAD_GRAYSCALE) + batch_img_lst.append(img_) + batch_msk_lst.append(msk_) + bi += 1 + if bi == args.batch_size: + batch_res = eval_batch_scales(args, result_file, batch_img_lst, scales=args.scales, + base_crop_size=args.crop_size, flip=args.flip) + for mi in range(args.batch_size): + hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes) + + bi = 0 + batch_img_lst = [] + batch_msk_lst = [] + print('processed {} images'.format(i+1)) + image_num = i + + if bi > 0: + batch_res = eval_batch_scales(args, result_file, batch_img_lst, scales=args.scales, + base_crop_size=args.crop_size, flip=args.flip) + for mi in range(bi): + hist += cal_hist(batch_msk_lst[mi].flatten(), batch_res[mi].flatten(), args.num_classes) + print('processed {} images'.format(image_num + 1)) + + 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__': + acc_cal() diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_infer_310.sh b/model_zoo/official/cv/deeplabv3/scripts/run_infer_310.sh new file mode 100644 index 0000000000..44b8c1ba24 --- /dev/null +++ b/model_zoo/official/cv/deeplabv3/scripts/run_infer_310.sh @@ -0,0 +1,103 @@ +#!/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 || $# -gt 5 ]]; then + echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_ID] + DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) +data_root=$(get_real_path $3) +data_list_path=$(get_real_path $4) + + +device_id=0 +if [ $# == 5 ]; then + device_id=$5 +fi + +echo "mindir name: "$model +echo "dataset path: "$data_path +echo "data root path: "$data_root +echo "data list path: "$data_list_path +echo "device id: "$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=$ASCEND_HOME/opp +fi + +function compile_app() +{ + cd ../ascend310_infer/src + if [ -f "Makefile" ]; then + make clean + fi + bash build.sh &> build.log +} + +function infer() +{ + cd - + if [ -d result_Files ]; then + rm -rf ./result_Files + fi + if [ -d time_Result ]; then + rm -rf ./time_Result + fi + mkdir result_Files + mkdir time_Result + ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log +} + +function cal_acc() +{ + python3.7 ../postprocess.py --data_root=$data_root --data_lst=$data_list_path --scales=1.0 --result_path=./result_Files &> 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