for deeptext 310 inferenct

model_zoo/official/cv/deeptext/README.md

@@ -64,9 +64,11 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation.
 .
 └─deeptext
   ├─README.md
+  ├─ascend310_infer                     #application for 310 inference
   ├─scripts
     ├─run_standalone_train_ascend.sh    # launch standalone training with ascend platform(1p)
     ├─run_distribute_train_ascend.sh    # launch distributed training with ascend platform(8p)
+    ├─run_infer_310.sh                  # shell script for 310 inference
     └─run_eval_ascend.sh                # launch evaluating with ascend platform
   ├─src
     ├─DeepText
@@ -81,12 +83,14 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation.
       ├─rpn.py                          # region-proposal network
       └─vgg16.py                        # backbone
     ├─config.py                       # training configuration
+    ├─aipp.cfg                        # aipp config file
     ├─dataset.py                      # data proprocessing
     ├─lr_schedule.py                  # learning rate scheduler
     ├─network_define.py               # network definition
     └─utils.py                        # some functions which is commonly used
   ├─eval.py                           # eval net
   ├─export.py                         # export checkpoint, surpport .onnx, .air, .mindir convert
+  ├─postprogress.py                   # post process for 310 inference
   └─train.py                          # train net
 ```
 
@@ -168,6 +172,35 @@ Evaluation result will be stored in the example path, you can find result like t
 class 1 precision is 88.01%, recall is 82.77%
 ```
 
+## Model Export
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
+```
+
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+
+## Inference Process
+
+### Usage
+
+Before performing inference, the air file must bu exported by export script on the Ascend910 environment.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_PATH] [DEVICE_ID]
+```
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```python
+========================================
+
+class 1 precision is 84.24%, recall is 87.40%, F1 is 85.79%
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)
@@ -177,7 +210,7 @@ class 1 precision is 88.01%, recall is 82.77%
 | Parameters                 | Ascend                                                       |
 | -------------------------- | ------------------------------------------------------------ |
 | Model Version              | Deeptext                                                     |
-| Resource                   | Ascend 910, cpu:2.60GHz 192cores, memory:755G                |
+| Resource                   | Ascend 910, cpu:2.60GHz 192cores, memory:755G, OS:Euler2.8   |
 | uploaded Date              | 12/26/2020                                                   |
 | MindSpore Version          | 1.1.0                                                        |
 | Dataset                    | 66040 images                                                 |

model_zoo/official/cv/deeptext/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/official/cv/deeptext/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
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/deeptext/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/deeptext/ascend310_infer/src/main.cc

@@ -0,0 +1,154 @@
+/**
+ * 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 "../inc/utils.h"
+#include "minddata/dataset/include/execute.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/vision.h"
+#include "minddata/dataset/include/vision_ascend.h"
+#include "include/api/types.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::dataset::Execute;
+using mindspore::MSTensor;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::Graph;
+using mindspore::dataset::vision::DvppDecodeResizeJpeg;
+
+DEFINE_string(model_path, "", "model path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(input_width, 960, "input width");
+DEFINE_int32(input_height, 576, "inputheight");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
+DEFINE_string(op_select_impl_mode, "", "op select impl mode");
+DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
+DEFINE_string(dump_config_file, "./acl.json", "dump config file");
+DEFINE_string(device_target, "Ascend310", "device target");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_model_path).empty()) {
+        std::cout << "Invalid mindir" << std::endl;
+        return 1;
+    }
+    if (RealPath(FLAGS_aipp_path).empty()) {
+        std::cout << "Invalid aipp path" << std::endl;
+        return 1;
+    }
+
+    auto context = std::make_shared<Context>();
+    auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
+    ascend310_info->SetDeviceID(FLAGS_device_id);
+    ascend310_info->SetInsertOpConfigPath(FLAGS_aipp_path);
+    context->MutableDeviceInfo().push_back(ascend310_info);
+
+    Graph graph;
+    Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
+    if (ret != kSuccess) {
+        std::cout << "Load model failed." << std::endl;
+        return 1;
+    }
+
+    Model model;
+    ret = model.Build(GraphCell(graph), context);
+    if (ret != kSuccess) {
+        std::cout << "ERROR: Build failed." << std::endl;
+        return 1;
+    }
+
+    std::vector<MSTensor> modelInputs = model.GetInputs();
+
+    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();
+
+    Execute transform(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg({576, 960})));
+
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start;
+        struct timeval end;
+        double startTime_ms;
+        double endTime_ms;
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+
+        mindspore::MSTensor imgDvpp;
+        transform(ReadFileToTensor(all_files[i]), &imgDvpp);
+
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            imgDvpp.Data().get(), imgDvpp.DataSize());
+
+        gettimeofday(&start, NULL);
+        ret = model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+        if (ret != kSuccess) {
+            std::cout << "Predict " << all_files[i] << " failed." << std::endl;
+            return 1;
+        }
+        startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+        endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+        costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+        WriteResult(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int infer_cnt = 0;
+
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        infer_cnt++;
+    }
+
+    average = average/infer_cnt;
+    std::stringstream timeCost;
+    timeCost << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+    std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+    std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+    file_stream << timeCost.str();
+    file_stream.close();
+    costTime_map.clear();
+    return 0;
+}

model_zoo/official/cv/deeptext/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/official/cv/deeptext/export.py

@@ -0,0 +1,53 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""export checkpoint file into air, mindir models"""
+import argparse
+import numpy as np
+
+import mindspore as ms
+from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
+
+from src.Deeptext.deeptext_vgg16 import Deeptext_VGG16_Infer
+from src.config import config
+
+parser = argparse.ArgumentParser(description='deeptext export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument("--file_name", type=str, default="deeptext", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+parser.add_argument('--ckpt_file', type=str, default='', help='deeptext ckpt file.')
+args = parser.parse_args()
+
+config.test_batch_size = args.batch_size
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+context.set_context(device_id=args.device_id)
+
+if __name__ == '__main__':
+    net = Deeptext_VGG16_Infer(config=config)
+    net.set_train(False)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+
+    param_dict_new = {}
+    for key, value in param_dict.items():
+        param_dict_new["network." + key] = value
+
+    load_param_into_net(net, param_dict_new)
+
+    img_data = Tensor(np.zeros([config.test_batch_size, 3, config.img_height, config.img_width]), ms.float16)
+
+    export(net, img_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/deeptext/postprocess.py

@@ -0,0 +1,123 @@
+# 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.
+# ============================================================================
+
+"""Evaluation for Deeptext"""
+import argparse
+import os
+
+import numpy as np
+from src.config import config
+from src.utils import metrics
+from PIL import Image
+import mmcv
+
+parser = argparse.ArgumentParser(description="Deeptext evaluation")
+parser.add_argument("--result_path", type=str, required=True, help="result file path")
+parser.add_argument("--label_path", type=str, required=True, help="label path")
+parser.add_argument("--img_path", type=str, required=True, help="img path")
+args_opt = parser.parse_args()
+
+config.test_batch_size = 1
+
+def get_pred(file, result_path):
+    file_name = file.split('.')[0][3:]
+    all_bbox_file = os.path.join(result_path, file_name + "_0.bin")
+    all_label_file = os.path.join(result_path, file_name + "_1.bin")
+    all_mask_file = os.path.join(result_path, file_name + "_2.bin")
+    all_bbox = np.fromfile(all_bbox_file, dtype=np.float16).reshape(config.test_batch_size, 1000, 5)
+    all_label = np.fromfile(all_label_file, dtype=np.int32).reshape(config.test_batch_size, 1000, 1)
+    all_mask = np.fromfile(all_mask_file, dtype=np.bool).reshape(config.test_batch_size, 1000, 1)
+
+    return all_bbox, all_label, all_mask
+
+def get_gt_bboxes_labels(label_file, img_file):
+    img_data = np.array(Image.open(img_file))
+    img_data, w_scale, h_scale = mmcv.imresize(
+        img_data, (config.img_width, config.img_height), return_scale=True)
+    scale_factor = np.array(
+        [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+    img_shape = (config.img_height, config.img_width, 1.0)
+    img_shape = np.asarray(img_shape, dtype=np.float32)
+
+    file = open(label_file)
+    lines = file.readlines()
+    boxes = []
+    gt_label = []
+    for line in lines:
+        label_info = line.split(",")
+        boxes.append([float(label_info[0]), float(label_info[1]), float(label_info[2]), float(label_info[3])])
+        gt_label.append(int(1))
+
+    gt_bboxes = np.array(boxes)
+    gt_bboxes = gt_bboxes * scale_factor
+
+    gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
+    gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
+
+    return gt_bboxes, gt_label
+
+def deeptext_eval_test(result_path='', label_path='', img_path=''):
+    eval_iter = 0
+
+    print("\n========================================\n")
+    print("Processing, please wait a moment.")
+    max_num = 32
+
+    pred_data = []
+    files = os.listdir(label_path)
+    for file in files:
+        eval_iter = eval_iter + 1
+        img_file = os.path.join(img_path, file.split('gt_')[1].replace("txt", "jpg"))
+
+        label_file = os.path.join(label_path, file)
+        gt_bboxes, gt_labels = get_gt_bboxes_labels(label_file, img_file)
+
+        gt_bboxes = np.array(gt_bboxes).astype(np.float32)
+
+        all_bbox, all_label, all_mask = get_pred(file, result_path)
+        all_label = all_label + 1
+
+
+        for j in range(config.test_batch_size):
+            all_bbox_squee = np.squeeze(all_bbox[j, :, :])
+            all_label_squee = np.squeeze(all_label[j, :, :])
+            all_mask_squee = np.squeeze(all_mask[j, :, :])
+
+            all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
+            all_labels_tmp_mask = all_label_squee[all_mask_squee]
+
+            if all_bboxes_tmp_mask.shape[0] > max_num:
+                inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
+                inds = inds[:max_num]
+                all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
+                all_labels_tmp_mask = all_labels_tmp_mask[inds]
+
+            pred_data.append({"boxes": all_bboxes_tmp_mask,
+                              "labels": all_labels_tmp_mask,
+                              "gt_bboxes": gt_bboxes,
+                              "gt_labels": gt_labels})
+
+    precisions, recalls = metrics(pred_data)
+    print("\n========================================\n")
+    for i in range(config.num_classes - 1):
+        j = i + 1
+        f1 = (2 *  precisions[j] * recalls[j]) / (precisions[j] + recalls[j] + 1e-6)
+        print("class {} precision is {:.2f}%, recall is {:.2f}%,"
+              "F1 is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100, f1 * 100))
+        if config.use_ambigous_sample:
+            break
+
+if __name__ == '__main__':
+    deeptext_eval_test(args_opt.result_path, args_opt.label_path, args_opt.img_path)

model_zoo/official/cv/deeptext/scripts/run_infer_310.sh

@@ -0,0 +1,107 @@
+#!/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 3 || $# -gt 4 ]]; then 
+    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+label_path=$(get_real_path $3)
+if [ $# == 4 ]; then
+    device_id=$4
+elif [ $# == 3 ]; then
+    if [ -z $device_id ]; then
+        device_id=0
+    else
+        device_id=$device_id
+    fi
+fi
+
+echo $model
+echo $data_path
+echo $label_path
+echo $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
+    if [ -f "Makefile" ]; then
+        make clean
+    fi
+    sh build.sh &> build.log
+
+    if [ $? -ne 0 ]; then
+        echo "compile app code failed"
+        exit 1
+    fi
+    cd -
+}
+
+function infer()
+{
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+     if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --model_path=$model --dataset_path=$data_path --device_id=$device_id --aipp_path ../src/aipp.cfg &> infer.log
+
+    if [ $? -ne 0 ]; then
+        echo "execute inference failed"
+        exit 1
+    fi
+}
+
+function cal_acc()
+{
+    python ../postprocess.py --label_path=$label_path --result_path=result_Files --img_path=$data_path &> acc.log &
+    if [ $? -ne 0 ]; then
+        echo "calculate accuracy failed"
+        exit 1
+    fi
+}
+
+compile_app
+infer
+cal_acc

model_zoo/official/cv/deeptext/src/Deeptext/deeptext_vgg16.py

@@ -430,3 +430,13 @@ class Deeptext_VGG16(nn.Cell):
             multi_level_anchors += (Tensor(anchors.astype(np.float32)),)
 
         return multi_level_anchors
+
+class Deeptext_VGG16_Infer(nn.Cell):
+    def __init__(self, config):
+        super(Deeptext_VGG16_Infer, self).__init__()
+        self.network = Deeptext_VGG16(config)
+        self.network.set_train(False)
+
+    def construct(self, img_data):
+        output = self.network(img_data, None, None, None, None)
+        return output

model_zoo/official/cv/deeptext/src/aipp.cfg

@@ -0,0 +1,26 @@
+aipp_op {
+    aipp_mode : static
+    input_format : YUV420SP_U8
+    related_input_rank : 0
+    csc_switch : true
+    rbuv_swap_switch : false
+    matrix_r0c0 : 256
+    matrix_r0c1 : 0
+    matrix_r0c2 : 359
+    matrix_r1c0 : 256
+    matrix_r1c1 : -88
+    matrix_r1c2 : -183
+    matrix_r2c0 : 256
+    matrix_r2c1 : 454
+    matrix_r2c2 : 0
+    input_bias_0 : 0
+    input_bias_1 : 128
+    input_bias_2 : 128
+    
+    mean_chn_0 : 124
+    mean_chn_1 : 117
+    mean_chn_2 : 104
+    var_reci_chn_0 : 0.0171247538316637
+    var_reci_chn_1 : 0.0175070028011204
+    var_reci_chn_2 : 0.0174291938997821
+}