!18320 FaceRecognitionOnTracking add 310 infer

Merge pull request !18320 from chenweitao_295/FaceRecognitionOnTracking

model_zoo/research/cv/FaceRecognitionForTracking/README.md

@@ -73,6 +73,7 @@ The entire code structure is as following:
 .
 └─ Face Recognition For Tracking
   ├─ README.md
+  ├─ ascend310_infer                        # application for 310 inference
   ├─ scripts
     ├─ run_standalone_train.sh              # launch standalone training(1p) in ascend
     ├─ run_distribute_train.sh              # launch distributed training(8p) in ascend
@@ -84,6 +85,7 @@ The entire code structure is as following:
     ├─ run_export_gpu.sh                    # launch exporting mindir model in gpu
     ├─ run_train_cpu.sh                     # launch standalone training in cpu
     ├─ run_eval_cpu.sh                      # launch evaluating in cpu
+    ├─ run_infer_310.sh                     # launch inference on Ascend310
     └─ run_export_cpu.sh                    # launch exporting mindir model in cpu
   ├─ src
     ├─ config.py                            # parameter configuration
@@ -95,6 +97,8 @@ The entire code structure is as following:
     └─ me_init.py                           # network initialization
   ├─ train.py                               # training scripts
   ├─ eval.py                                # evaluation scripts
+  ├─ postprocess.py                         # postprocess script
+  ├─ preprocess.py                          # preprocess script
   └─ export.py                              # export air/mindir model
 ```
 
@@ -251,9 +255,11 @@ You will get the result as following in "./scripts/device0/eval.log" or txt file
 1e-05: 0.035770748447963394@0.5053771466191392
 ```
 
-### Convert model
+### Inference process
 
-If you want to infer the network on Ascend 310, you should convert the model to AIR:
+#### Convert model
+
+If you want to infer the network on Ascend 310, you should convert the model to MINDIR or AIR:
 
 ```bash
 Ascend:
@@ -278,6 +284,42 @@ cd ./scripts
 sh run_export_cpu.sh [PRETRAINED_BACKBONE] [BATCH_SIZE] [FILE_NAME](optional)
 ```
 
+Export MINDIR:
+
+```shell
+# Ascend310 inference
+python export.py --pretrained [PRETRAIN] --batch_size [BATCH_SIZE] --file_format [EXPORT_FORMAT]
+```
+
+The pretrained parameter is required.
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+Current batch_size can only be set to 1.
+
+#### Infer on Ascend310
+
+Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+```
+
+- `DEVICE_ID` is optional, default value is 0.
+
+#### result
+
+Inference result is saved in current path, you can find result like this in recall.log file.
+
+```bash
+0.5: 0.9096926774720119@0.012683006512816064
+0.3: 0.8121103841852932@0.06735802651382983
+0.1: 0.5893883112042262@0.147308789767686
+0.01: 0.25512525545944137@0.2586851498649049754
+0.001: 0.10664387347206335@0.341498649049754
+0.0001: 0.054125268312746624@0.41116268460973515
+1e-05: 0.03846994254572563@0.47234829963417724
+```
+
 # [Model Description](#contents)
 
 ## [Performance](#contents)
@@ -313,6 +355,20 @@ sh run_export_cpu.sh [PRETRAINED_BACKBONE] [BATCH_SIZE] [FILE_NAME](optional)
 | Recall       | 0.62(FAR=0.1)               | 0.62(FAR=0.1)               | 0.62(FAR=0.1)               |
 | Model for inference | 17M (.ckpt file)            | 17M (.ckpt file)            | 17M (.ckpt file)            |
 
+### Inference Performance
+
+| Parameters          | Ascend                      |
+| ------------------- | --------------------------- |
+| Model Version       | FaceRecognitionForTracking  |
+| Resource            | Ascend 310; Euler2.8        |
+| Uploaded Date       | 11/06/2021 (month/day/year) |
+| MindSpore Version   | 1.2.0                       |
+| Dataset             | 2K images                   |
+| batch_size          | 1                           |
+| outputs             | recall                      |
+| Recall              | 0.589(FAR=0.1)               |
+| Model for inference | 17M(.ckpt file)             |
+
 # [ModelZoo Homepage](#contents)
 
 Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/research/cv/FaceRecognitionForTracking/ascend310_infer/CMakeLists.txt

@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

model_zoo/research/cv/FaceRecognitionForTracking/ascend310_infer/build.sh

@@ -0,0 +1,23 @@
+#!/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 [ ! -d out ]; then
+  mkdir out
+fi
+cd out || exit
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/research/cv/FaceRecognitionForTracking/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/research/cv/FaceRecognitionForTracking/ascend310_infer/src/main.cc

@@ -0,0 +1,127 @@
+/**
+ * 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 <sstream>
+
+#include "include/api/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input0_path, ".", "input0 path");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  if (RealPath(FLAGS_mindir_path).empty()) {
+    std::cout << "Invalid mindir" << std::endl;
+    return 1;
+  }
+
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(FLAGS_device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
+
+  Model model;
+  Status ret = model.Build(GraphCell(graph), context);
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+
+  std::vector<MSTensor> model_inputs = model.GetInputs();
+  if (model_inputs.empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+
+  auto input0_files = GetAllFiles(FLAGS_input0_path);
+
+  if (input0_files.empty()) {
+    std::cout << "ERROR: input data empty." << std::endl;
+    return 1;
+  }
+
+  std::map<double, double> costTime_map;
+  size_t size = input0_files.size();
+
+  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:" << input0_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input0_files[i]);
+    inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                        input0.Data().get(), input0.DataSize());
+
+    gettimeofday(&start, nullptr);
+    ret = model.Predict(inputs, &outputs);
+    gettimeofday(&end, nullptr);
+    if (ret != kSuccess) {
+      std::cout << "Predict " << input0_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(input0_files[i], outputs);
+  }
+  double average = 0.0;
+  int inferCount = 0;
+
+  for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+    double diff = 0.0;
+    diff = iter->second - iter->first;
+    average += diff;
+    inferCount++;
+  }
+  average = average / inferCount;
+  std::stringstream timeCost;
+  timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
+  std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
+  std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
+  std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
+  fileStream << timeCost.str();
+  fileStream.close();
+  costTime_map.clear();
+  return 0;
+}

model_zoo/research/cv/FaceRecognitionForTracking/ascend310_infer/src/utils.cc

@@ -0,0 +1,130 @@
+/**
+ * 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 "inc/utils.h"
+
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+
+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;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+  if (file.empty()) {
+    std::cout << "Pointer file is nullptr" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  std::ifstream ifs(file);
+  if (!ifs.good()) {
+    std::cout << "File: " << file << " is not exist" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  if (!ifs.is_open()) {
+    std::cout << "File: " << file << "open failed" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  ifs.seekg(0, std::ios::end);
+  size_t size = ifs.tellg();
+  mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<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/research/cv/FaceRecognitionForTracking/export.py

@@ -63,12 +63,12 @@ if __name__ == "__main__":
     parser.add_argument('--device_target', type=str, choices=['Ascend', 'GPU', 'CPU'], default='Ascend',
                         help='device_target')
     parser.add_argument('--file_name', type=str, default='FaceRecognitionForTracking', 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')
 
     arg = parser.parse_args()
 
     if arg.device_target == 'Ascend':
-        devid = int(os.getenv('DEVICE_ID'))
+        devid = int(os.getenv('DEVICE_ID', '0'))
         context.set_context(device_id=devid)
 
     context.set_context(mode=context.GRAPH_MODE, device_target=arg.device_target)

model_zoo/research/cv/FaceRecognitionForTracking/postprocess.py

@@ -0,0 +1,128 @@
+# 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 re
+import warnings
+import argparse
+import numpy as np
+from tqdm import tqdm
+
+warnings.filterwarnings('ignore')
+
+parser = argparse.ArgumentParser(description='FaceRecognitionForTracking calcul Recall')
+parser.add_argument("--result_path", type=str, required=True, default='', help="result file path")
+parser.add_argument("--data_dir", type=str, required=True, default='', help="data dir")
+args = parser.parse_args()
+
+
+def inclass_likehood(ims_info, types='cos'):
+    '''Inclass likehood.'''
+    obj_feas = {}
+    likehoods = []
+    for name, _, fea in ims_info:
+        if re.split('_\\d\\d\\d\\d', name)[0] not in obj_feas:
+            obj_feas[re.split('_\\d\\d\\d\\d', name)[0]] = []
+        obj_feas[re.split('_\\d\\d\\d\\d', name)[0]].append(fea)  # pylint: "_\d\d\d\d" -> "_\\d\\d\\d\\d"
+    for _, feas in tqdm(obj_feas.items()):
+        feas = np.array(feas)
+        if types == 'cos':
+            likehood_mat = np.dot(feas, np.transpose(feas)).tolist()
+            for row in likehood_mat:
+                likehoods += row
+        else:
+            for fea in feas.tolist():
+                likehoods += np.sum(-(fea - feas) ** 2, axis=1).tolist()
+
+    likehoods = np.array(likehoods)
+    return likehoods
+
+
+def btclass_likehood(ims_info, types='cos'):
+    '''Btclass likehood.'''
+    likehoods = []
+    count = 0
+    for name1, _, fea1 in tqdm(ims_info):
+        count += 1
+        # pylint: "_\d\d\d\d" -> "_\\d\\d\\d\\d"
+        frame_id1, _ = re.split('_\\d\\d\\d\\d', name1)[0], name1.split('_')[-1]
+        fea1 = np.array(fea1)
+        for name2, _, fea2 in ims_info:
+            # pylint: "_\d\d\d\d" -> "_\\d\\d\\d\\d"
+            frame_id2, _ = re.split('_\\d\\d\\d\\d', name2)[0], name2.split('_')[-1]
+            if frame_id1 == frame_id2:
+                continue
+            fea2 = np.array(fea2)
+            if types == 'cos':
+                likehoods.append(np.sum(fea1 * fea2))
+            else:
+                likehoods.append(np.sum(-(fea1 - fea2) ** 2))
+
+    likehoods = np.array(likehoods)
+    return likehoods
+
+
+def tar_at_far(inlikehoods, btlikehoods):
+    test_point = [0.5, 0.3, 0.1, 0.01, 0.001, 0.0001, 0.00001]
+    tar_far = []
+    for point in test_point:
+        thre = btlikehoods[int(btlikehoods.size * point)]
+        n_ta = np.sum(inlikehoods > thre)
+        tar_far.append((point, float(n_ta) / inlikehoods.size, thre))
+
+    return tar_far
+
+
+def main():
+    with open("result.txt", 'a+') as result_fw:
+        root_path = args.data_dir
+        root_file_list = os.listdir(root_path)
+        ims_info = []
+        for sub_path in root_file_list:
+            for im_path in os.listdir(os.path.join(root_path, sub_path)):
+                ims_info.append((im_path.split('.')[0], os.path.join(root_path, sub_path, im_path)))
+
+        paths = [path for name, path in ims_info]
+        names = [name for name, path in ims_info]
+        print("exact feature...")
+        result_shape = (1, 128)
+        result_path = args.result_path
+        l_t = []
+        for file in [name + "_0.bin" for name in names]:
+            full_file_path = os.path.join(result_path, file)
+            if os.path.isfile(full_file_path):
+                result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape).astype(np.float16)
+                l_t.append(result)
+        feas = np.concatenate(l_t, axis=0)
+        ims_info = list(zip(names, paths, feas.tolist()))
+
+        print("exact inclass likehood...")
+        inlikehoods = inclass_likehood(ims_info)
+        inlikehoods[::-1].sort()
+
+        print("exact btclass likehood...")
+        btlikehoods = btclass_likehood(ims_info)
+        btlikehoods[::-1].sort()
+        tar_far = tar_at_far(inlikehoods, btlikehoods)
+
+        for far, tar, thre in tar_far:
+            print('---{}: {}@{}'.format(far, tar, thre))
+
+        for far, tar, thre in tar_far:
+            result_fw.write('{}: {}@{} \n'.format(far, tar, thre))
+
+
+if __name__ == '__main__':
+    main()

model_zoo/research/cv/FaceRecognitionForTracking/preprocess.py

@@ -0,0 +1,73 @@
+# Copyright 2020-2021 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.
+# ============================================================================
+"""pre process for 310 inference"""
+import os
+import argparse
+import numpy as np
+from PIL import Image
+
+import mindspore.dataset.vision.py_transforms as V
+import mindspore.dataset.transforms.py_transforms as T
+
+
+def load_images(paths, batch_size=1):
+    '''Load images.'''
+    ll = []
+    resize = V.Resize((96, 64))
+    transform = T.Compose([
+        V.ToTensor(),
+        V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+    for i, _ in enumerate(paths):
+        im = Image.open(paths[i])
+        im = resize(im)
+        img = np.array(im)
+        ts = transform(img)
+        ll.append(ts[0])
+        if len(ll) == batch_size:
+            yield np.stack(ll, axis=0)
+            ll.clear()
+    if ll:
+        yield np.stack(ll, axis=0)
+
+
+def preprocess_data(args):
+    """ preprocess data"""
+    root_path = args.data_dir
+    root_file_list = os.listdir(root_path)
+    ims_info = []
+    for sub_path in root_file_list:
+        for im_path in os.listdir(os.path.join(root_path, sub_path)):
+            ims_info.append((im_path.split('.')[0], os.path.join(root_path, sub_path, im_path)))
+
+    paths = [path for name, path in ims_info]
+    names = [name for name, path in ims_info]
+    i = 0
+
+    for img in load_images(paths):
+        img = img.astype(np.float32)
+        file_name = names[i] + ".bin"
+        file_path = os.path.join(args.output_path, file_name)
+        img.tofile(file_path)
+        i += 1
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='preprocess data bin')
+    parser.add_argument('--data_dir', type=str, default='', help='data dir, e.g. /home/test')
+    parser.add_argument('--output_path', type=str, default='', help='output image path, e.g. /home/output')
+
+    arg = parser.parse_args()
+
+    preprocess_data(arg)

model_zoo/research/cv/FaceRecognitionForTracking/scripts/run_infer_310.sh

@@ -0,0 +1,110 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [[ $# -lt 2 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+echo "dataset path: "$data_path
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python ../preprocess.py --output_path=./preprocess_Result --data_dir=$data_path &> preprocess.log
+    preprocess_data_path=./preprocess_Result
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --mindir_path=$model --input0_path=$preprocess_data_path --device_id=$device_id &> infer.log
+}
+
+function cal_recall()
+{
+    python3.7 ../postprocess.py --result_path=./result_Files --data_dir=$data_path &> recall.log &
+}
+
+preprocess_data
+if [ $? -ne 0 ]; then
+    echo "preprocess data failed"
+    exit 1
+fi
+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_recall
+if [ $? -ne 0 ]; then
+    echo "calculate recall failed"
+    exit 1
+fi