deeplabv3 310 inference

modified:   postprocess.py

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)

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

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 <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+std::vector<std::string> 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<mindspore::MSTensor> &outputs);
+#endif

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)

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

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 <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+
+#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<TensorTransform> normalize(new Normalize({103.53, 116.28, 123.675},
+                                                           {57.375, 57.120, 58.395}));
+  Execute composeNormalize({normalize});
+  std::vector<int64_t> 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<float>(IMAGEWIDTH) / shape[1];
+  heightScale = static_cast<float>(IMAGEHEIGHT) / shape[0];
+  Status ret;
+  if (widthScale < heightScale) {
+    int heightSize = shape[0]*widthScale;
+    std::shared_ptr<TensorTransform> 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<TensorTransform> 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<TensorTransform> 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<TensorTransform> 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<mindspore::Context>();
+  Model model(GraphCell(graph), model_context);
+  Status ret = model.Build();
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+  std::vector<MSTensor> 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<double, double> costTime_map;
+  size_t size = all_files.size();
+  std::shared_ptr<TensorTransform> decode(new Decode());
+  std::shared_ptr<TensorTransform> swapredblue(new SwapRedBlue());
+  Execute composeDecode({decode, swapredblue});
+  std::shared_ptr<TensorTransform> 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<MSTensor> inputs;
+    std::vector<MSTensor> 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<double, double>(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;
+}

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 <fstream>
+#include <algorithm>
+#include <iostream>
+#include "../inc/utils.h"
+
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+std::vector<std::string> GetAllFiles(std::string_view dirName) {
+  struct dirent *filename;
+  DIR *dir = OpenDir(dirName);
+  if (dir == nullptr) {
+    return {};
+  }
+  std::vector<std::string> 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<MSTensor> &outputs) {
+  std::string homePath = "./result_Files";
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    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;
+}

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'],

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()

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