facedetection add 310 infer

model_zoo/research/cv/FaceDetection/README.md

@@ -87,6 +87,7 @@ The entire code structure is as following:
 .
 └─ Face Detection
   ├─ README.md
+  ├─ ascend310_infer                        # application for 310 inference
   ├─ model_utils
     ├─ __init__.py                          # init file
     ├─ config.py                            # Parse arguments
@@ -97,6 +98,7 @@ The entire code structure is as following:
     ├─ run_standalone_train.sh              # launch standalone training(1p) in ascend
     ├─ run_distribute_train.sh              # launch distributed training(8p) in ascend
     ├─ run_eval.sh                          # launch evaluating in ascend
+    ├─ run_infer_310.sh                     # launch inference on Ascend310
     └─ run_export.sh                        # launch exporting air model
   ├─ src
     ├─ FaceDetection
@@ -115,6 +117,9 @@ The entire code structure is as following:
   ├─ default_config.yaml                    # default configurations
   ├─ train.py                               # training scripts
   ├─ eval.py                                # evaluation scripts
+  ├─ postprocess.py                         # postprocess script
+  ├─ preprocess.py                          # preprocess script
+  ├─ bin.py                                 # bin script
   └─ export.py                              # export air model
 ```
 
@@ -266,13 +271,39 @@ Saving ../../results/0-2441_61000/.._.._results_0-2441_61000_face_AP_0.760.png
 
 And the detect result and P-R graph will also be saved in "./results/[MODEL_NAME]/"
 
-### 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:
+
+```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] [MINDRECORD_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 map.log file.
 
 ```bash
-cd ./scripts
-bash run_export.sh [PLATFORM] [BATCH_SIZE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]
+calculate [recall | persicion | ap]...
+Saving ../../results/0-2441_61000/.._.._results_0-2441_61000_face_AP_0.7575.png
 ```
 
 # [Model Description](#contents)
@@ -310,6 +341,20 @@ bash run_export.sh [PLATFORM] [BATCH_SIZE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]
 | Accuracy            | 8pcs: 76.0%                 |
 | Model for inference | 37M (.ckpt file)            |
 
+### Inference Performance
+
+| Parameters          | Ascend                      |
+| ------------------- | --------------------------- |
+| Model Version       | Face Detection              |
+| Resource            | Ascend 310; Euler2.8        |
+| Uploaded Date       | 19/06/2021 (month/day/year) |
+| MindSpore Version   | 1.2.0                       |
+| Dataset             | 3K images                   |
+| batch_size          | 1                           |
+| outputs             | mAP                         |
+| mAP                 | mAP=75.75%                  |
+| Model for inference | 37M(.ckpt file)             |
+
 # [ModelZoo Homepage](#contents)
 
 Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/research/cv/FaceDetection/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/FaceDetection/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/FaceDetection/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/FaceDetection/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/FaceDetection/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/FaceDetection/bin.py

@@ -0,0 +1,76 @@
+# 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.
+# ============================================================================
+"""bin for 310 inference"""
+import os
+import numpy as np
+from PIL import Image, ImageOps
+from model_utils.config import config
+
+
+def tf_pil(img):
+    """ Letterbox an image to fit in the network """
+
+    net_w, net_h = config.input_shape
+    fill_color = 127
+    im_w, im_h = img.size
+
+    if im_w == net_w and im_h == net_h:
+        return img
+
+    # Rescaling
+    if im_w / net_w >= im_h / net_h:
+        scale = net_w / im_w
+    else:
+        scale = net_h / im_h
+    if scale != 1:
+        resample_mode = Image.NEAREST
+        img = img.resize((int(scale * im_w), int(scale * im_h)), resample_mode)
+        im_w, im_h = img.size
+
+    if im_w == net_w and im_h == net_h:
+        return img
+
+    # Padding
+    img_np = np.array(img)
+    channels = img_np.shape[2] if len(img_np.shape) > 2 else 1
+    pad_w = (net_w - im_w) / 2
+    pad_h = (net_h - im_h) / 2
+    pad = (int(pad_w), int(pad_h), int(pad_w + .5), int(pad_h + .5))
+    img = ImageOps.expand(img, border=pad, fill=(fill_color,) * channels)
+    return img
+
+
+def hwc2chw(img_np):
+    return img_np.transpose(2, 0, 1).copy()
+
+
+def to_tensor(image):
+    image = np.asarray(image)
+    image = hwc2chw(image)
+    image = image / 255.
+    return image.astype(np.float32)
+
+
+if __name__ == '__main__':
+    result_path = os.path.join(config.preprocess_path, 'images_bin')
+    if not os.path.isdir(result_path):
+        os.makedirs(result_path, exist_ok=True)
+    data_path = os.path.join(config.preprocess_path, "images")
+    files = os.listdir(data_path)
+    for file in files:
+        img_pil = Image.open(os.path.join(data_path, file)).convert("RGB")
+        img_pil = tf_pil(img_pil)
+        img_pil = to_tensor(img_pil)
+        img_pil.tofile(os.path.join(result_path, file.split('.')[0] + '.bin'))

model_zoo/research/cv/FaceDetection/default_config.yaml

@@ -58,6 +58,15 @@ anchors_mask: [[8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]]
 conf_thresh: 0.1
 nms_thresh: 0.45
 
+#export
+file_name: "FaceDetection"
+file_format: "AIR"
+
+#310 infer
+preprocess_path: ""
+save_output_path: ""
+data_dir: ""
+
 ---
 
 # Help description for each configuration

model_zoo/research/cv/FaceDetection/export.py

@@ -19,13 +19,17 @@ import numpy as np
 from mindspore import context
 from mindspore import Tensor
 from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
-
+from src.network_define import BuildTestNetwork
 from src.FaceDetection.yolov3 import HwYolov3 as backbone_HwYolov3
 from model_utils.config import config
 
 def save_air():
-    '''save air'''
-    print('============= yolov3 start save air ==================')
+    '''save air or mindir'''
+    anchors = config.anchors
+    reduction_0 = 64.0
+    reduction_1 = 32.0
+    reduction_2 = 16.0
+    print('============= yolov3 start save air or mindir ==================')
     devid = int(os.getenv('DEVICE_ID', '0')) if config.run_platform != 'CPU' else 0
     context.set_context(mode=context.GRAPH_MODE, device_target=config.run_platform, save_graphs=False, device_id=devid)
 
@@ -47,12 +51,12 @@ def save_air():
                 param_dict_new[key] = values
         load_param_into_net(network, param_dict_new)
         print('load model {} success'.format(config.pretrained))
-
+        test_net = BuildTestNetwork(network, reduction_0, reduction_1, reduction_2, anchors, anchors_mask, num_classes,
+                                    config)
         input_data = np.random.uniform(low=0, high=1.0, size=(config.batch_size, 3, 448, 768)).astype(np.float32)
 
         tensor_input_data = Tensor(input_data)
-        export(network, tensor_input_data,
-               file_name=config.pretrained.replace('.ckpt', '_' + str(config.batch_size) + 'b.air'), file_format='AIR')
+        export(test_net, tensor_input_data, file_name=config.file_name, file_format=config.file_format)
 
         print("export model success.")
 

model_zoo/research/cv/FaceDetection/postprocess.py

@@ -0,0 +1,115 @@
+# 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 matplotlib.pyplot as plt
+import numpy as np
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+from src.FaceDetection import voc_wrapper
+from src.network_define import get_bounding_boxes, tensor_to_brambox, \
+    parse_gt_from_anno, parse_rets, calc_recall_precision_ap
+
+from model_utils.config import config
+
+
+def cal_map(result_path, data_dir, save_output_path):
+    """cal map"""
+    labels = ['face']
+    det = {}
+    img_size = {}
+    img_anno = {}
+    eval_times = 0
+    classes = {0: 'face'}
+    ret_files_set = {'face': os.path.join(save_output_path, 'comp4_det_test_face_rm5050.txt')}
+    files = os.listdir(os.path.join(data_dir, "labels"))
+    for file in files:
+        image_name = file.split('.')[0]
+        label = np.fromfile(os.path.join(data_dir, "labels", file), dtype=np.float64).reshape((1, 200, 6))
+        image_size = np.fromfile(os.path.join(data_dir, "image_size", file), dtype=np.int32).reshape((1, 1, 2))
+        eval_times += 1
+        dets = []
+        tdets = []
+        file_path = os.path.join(result_path, image_name)
+        coords_0 = np.fromfile(file_path + '_0.bin', dtype=np.float32).reshape((1, 4, 84, 4))
+        coords_0 = Tensor(coords_0, mstype.float32)
+        cls_scores_0 = np.fromfile(file_path + '_1.bin', dtype=np.float32).reshape((1, 4, 84))
+        cls_scores_0 = Tensor(cls_scores_0, mstype.float32)
+        coords_1 = np.fromfile(file_path + '_2.bin', dtype=np.float32).reshape((1, 4, 336, 4))
+        coords_1 = Tensor(coords_1, mstype.float32)
+        cls_scores_1 = np.fromfile(file_path + '_3.bin', dtype=np.float32).reshape((1, 4, 336))
+        cls_scores_1 = Tensor(cls_scores_1, mstype.float32)
+        coords_2 = np.fromfile(file_path + '_4.bin', dtype=np.float32).reshape((1, 4, 1344, 4))
+        coords_2 = Tensor(coords_2, mstype.float32)
+        cls_scores_2 = np.fromfile(file_path + '_5.bin', dtype=np.float32).reshape((1, 4, 1344))
+        cls_scores_2 = Tensor(cls_scores_2, mstype.float32)
+
+        boxes_0, boxes_1, boxes_2 = get_bounding_boxes(coords_0, cls_scores_0, coords_1, cls_scores_1, coords_2,
+                                                       cls_scores_2, config.conf_thresh, config.input_shape,
+                                                       config.num_classes)
+
+        converted_boxes_0, converted_boxes_1, converted_boxes_2 = tensor_to_brambox(boxes_0, boxes_1, boxes_2,
+                                                                                    config.input_shape, labels)
+
+        tdets.append(converted_boxes_0)
+        tdets.append(converted_boxes_1)
+        tdets.append(converted_boxes_2)
+        batch = len(tdets[0])
+        for b in range(batch):
+            single_dets = []
+            for op in range(3):
+                single_dets.extend(tdets[op][b])
+            dets.append(single_dets)
+
+        det.update({image_name: v for k, v in enumerate(dets)})
+        img_size.update({image_name: v for k, v in enumerate(image_size)})
+        img_anno.update({image_name: v for k, v in enumerate(label)})
+
+    netw, neth = config.input_shape
+    reorg_dets = voc_wrapper.reorg_detection(det, netw, neth, img_size)
+    voc_wrapper.gen_results(reorg_dets, save_output_path, img_size, config.nms_thresh)
+
+    # compute mAP
+    ground_truth = parse_gt_from_anno(img_anno, classes)
+
+    ret_list = parse_rets(ret_files_set)
+    iou_thr = 0.5
+    evaluate = calc_recall_precision_ap(ground_truth, ret_list, iou_thr)
+    print(evaluate)
+
+    aps_str = ''
+    for cls in evaluate:
+        per_line, = plt.plot(evaluate[cls]['recall'], evaluate[cls]['precision'], 'b-')
+        per_line.set_label('%s:AP=%.4f' % (cls, evaluate[cls]['ap']))
+        aps_str += '_%s_AP_%.4f' % (cls, evaluate[cls]['ap'])
+        plt.plot([i / 1000.0 for i in range(1, 1001)], [i / 1000.0 for i in range(1, 1001)], 'y--')
+        plt.axis([0, 1.2, 0, 1.2])  # [x_min, x_max, y_min, y_max]
+        plt.xlabel('recall')
+        plt.ylabel('precision')
+        plt.grid()
+
+        plt.legend()
+        plt.title('PR')
+
+    # save mAP
+    ap_save_path = os.path.join(save_output_path, save_output_path.replace('/', '_') + aps_str + '.png')
+    print('Saving {}'.format(ap_save_path))
+    plt.savefig(ap_save_path)
+
+    print('=============yolov3 evaluating finished==================')
+
+
+if __name__ == '__main__':
+    cal_map(config.result_path, config.data_dir, config.save_output_path)

model_zoo/research/cv/FaceDetection/preprocess.py

@@ -0,0 +1,88 @@
+# 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.
+# ============================================================================
+"""pre process for 310 inference"""
+import os
+import numpy as np
+from PIL import Image
+import mindspore.dataset.vision.py_transforms as P
+import mindspore.dataset as de
+from model_utils.config import config
+
+
+class SingleScaleTrans_Infer:
+    '''SingleScaleTrans'''
+
+    def __init__(self, resize, max_anno_count=200):
+        self.resize = (resize[0], resize[1])
+        self.max_anno_count = max_anno_count
+
+    def __call__(self, imgs, ann, image_names, image_size, batch_info):
+
+        decode = P.Decode()
+        ret_imgs = []
+        ret_anno = []
+
+        for i, image in enumerate(imgs):
+            img_pil = decode(image)
+            input_data = img_pil, ann[i]
+            ret_imgs.append(np.array(input_data[0]))
+            ret_anno.append(input_data[1])
+
+        for i, anno in enumerate(ret_anno):
+            anno_count = anno.shape[0]
+            if anno_count < self.max_anno_count:
+                ret_anno[i] = np.concatenate(
+                    (ret_anno[i], np.zeros((self.max_anno_count - anno_count, 6), dtype=float)), axis=0)
+            else:
+                ret_anno[i] = ret_anno[i][:self.max_anno_count]
+
+        return np.array(ret_imgs), np.array(ret_anno), image_names, image_size
+
+
+def preprocess():
+    """preprocess"""
+    preprocess_path = config.preprocess_path
+    images_path = os.path.join(preprocess_path, 'images')
+    if not os.path.isdir(images_path):
+        os.makedirs(images_path, exist_ok=True)
+
+    labels_path = os.path.join(preprocess_path, 'labels')
+    if not os.path.isdir(labels_path):
+        os.makedirs(labels_path, exist_ok=True)
+
+    image_name_path = os.path.join(preprocess_path, 'image_name')
+    if not os.path.isdir(image_name_path):
+        os.makedirs(image_name_path, exist_ok=True)
+    image_size_path = os.path.join(preprocess_path, 'image_size')
+    if not os.path.isdir(image_size_path):
+        os.makedirs(image_size_path, exist_ok=True)
+
+    ds = de.MindDataset(os.path.join(config.mindrecord_path, "data.mindrecord0"),
+                        columns_list=["image", "annotation", "image_name", "image_size"])
+    single_scale_trans = SingleScaleTrans_Infer(resize=config.input_shape)
+    ds = ds.batch(config.batch_size, per_batch_map=single_scale_trans,
+                  input_columns=["image", "annotation", "image_name", "image_size"], num_parallel_workers=8)
+    ds = ds.repeat(1)
+    for data in ds.create_tuple_iterator(output_numpy=True):
+        images, labels, image_name, image_size = data[0:4]
+        images = Image.fromarray(images[0].astype('uint8')).convert('RGB')
+        images.save(os.path.join(images_path, image_name[0].decode() + ".jpg"))
+        labels.tofile(os.path.join(labels_path, image_name[0].decode() + ".bin"))
+        image_name.tofile(os.path.join(image_name_path, image_name[0].decode() + ".bin"))
+        image_size.tofile(os.path.join(image_size_path, image_name[0].decode() + ".bin"))
+
+
+if __name__ == '__main__':
+    preprocess()

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

@@ -0,0 +1,114 @@
+#!/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 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [MINDRECORD_DIR] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+mindrecord_dir=$(get_real_path $2)
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+echo "mindrecord dir: "$mindrecord_dir
+echo "device id: "$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=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py --preprocess_path=./preprocess_Result --mindrecord_path=$mindrecord_dir --batch_size=1 &> preprocess.log
+    python3.7 ../bin.py --preprocess_path=./preprocess_Result
+    data_dir=./preprocess_Result
+    input0_path=./preprocess_Result/images_bin
+}
+
+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=$model --input0_path=$input0_path --device_id=$device_id &> infer.log
+}
+
+function cal_map()
+{
+    if [ -d infer_output ]; then
+        rm -rf ./infer_output
+    fi
+    mkdir infer_output
+    python3.7 ../postprocess.py --result_path=./result_Files --data_dir=$data_dir --save_output_path=./infer_output &> map.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_map
+if [ $? -ne 0 ]; then
+    echo "calculate map failed"
+    exit 1
+fi