crnn_seq2seq_ocr 310 infer

new file:   export.py
	new file:   postprocess.py
	new file:   preprocess.py

model_zoo/official/cv/crnn_seq2seq_ocr/README.md

@@ -18,6 +18,10 @@
             - [Distributed Training](#distributed-training)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
+    - [Inference Process](#inference-process)
+        - [Export MindIR](#export-mindir)
+        - [Infer on Ascend310](#infer-on-ascend310)
+        - [result](#result)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Training Performance](#training-performance)
@@ -214,6 +218,39 @@ character precision = 0.967522
 Annotation precision precision = 0.746213
 ```
 
+## Inference Process
+
+### [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,
+`file_format` should be in ["AIR", "MINDIR"]
+
+### 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] [NEED_PREPROCESS] [DEVICE_ID]
+```
+
+- `NEED_PREPROCESS` means weather the dataset is processed in binary format, it's value is 'y' or 'n'.
+- `DEVICE_ID` is optional, default value is 0.
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```bash
+character precision = 0.967522
+
+Annotation precision precision = 0.746213
+```
+
 # Model Description
 
 ## Performance

model_zoo/official/cv/crnn_seq2seq_ocr/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} -O2 -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/crnn_seq2seq_ocr/ascend310_infer/build.sh

@@ -0,0 +1,29 @@
+#!/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
+    rm -rf out
+fi
+
+mkdir out
+cd out || exit
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake .. \
+    -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/crnn_seq2seq_ocr/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/crnn_seq2seq_ocr/ascend310_infer/src/main.cc

@@ -0,0 +1,141 @@
+/**
+ * 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 "include/dataset/execute.h"
+#include "include/dataset/vision.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input0_path, ".", "input0 path");
+DEFINE_string(input1_path, ".", "input1 path");
+DEFINE_string(input2_path, ".", "input2 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);
+  auto input1_files = GetAllFiles(FLAGS_input1_path);
+  auto input2_files = GetAllFiles(FLAGS_input2_path);
+
+  if (input0_files.empty() || input1_files.empty() || input2_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]);
+    auto input1 = ReadFileToTensor(input1_files[i]);
+    auto input2 = ReadFileToTensor(input2_files[i]);
+
+    inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                        input0.Data().get(), input0.DataSize());
+    inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
+                        input1.Data().get(), input1.DataSize());
+    inputs.emplace_back(model_inputs[2].Name(), model_inputs[2].DataType(), model_inputs[2].Shape(),
+                        input2.Data().get(), input2.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/official/cv/crnn_seq2seq_ocr/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;
+}
+
+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/crnn_seq2seq_ocr/default_config.yaml

@@ -1,4 +1,4 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
 enable_modelarts: False
 # Url for modelarts
 data_url: ""
@@ -59,7 +59,13 @@ adam_beta1: 0.5
 adam_beta2: 0.999
 loss_scale: 1024
 
+#export-related
+file_name: "crnn-seq2seq-ocr"
+file_format: "MINDIR"
 
+#310 infer-related
+pre_result_path: './preprocess_Result'
+post_result_path: './result_Files'
 
 ---
 

model_zoo/official/cv/crnn_seq2seq_ocr/export.py

@@ -0,0 +1,58 @@
+# 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.
+
+"""
+
+import os
+import numpy as np
+from mindspore import context, Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.attention_ocr import AttentionOCRInfer
+
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+
+
+def get_model():
+    '''generate model'''
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
+    # Network
+    network = AttentionOCRInfer(config.eval_batch_size,
+                                int(config.img_width / 4),
+                                config.encoder_hidden_size,
+                                config.decoder_hidden_size,
+                                config.decoder_output_size,
+                                config.max_length,
+                                config.dropout_p)
+    checkpoint_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), config.checkpoint_path)
+    ckpt = load_checkpoint(checkpoint_path)
+    load_param_into_net(network, ckpt)
+    network.set_train(False)
+    print("Checkpoint loading Done!")
+
+    sos_id = config.characters_dictionary.go_id
+    images = Tensor(np.zeros((config.eval_batch_size, 3, config.img_height, config.img_width),
+                             dtype=np.float32))
+    decoder_hidden = Tensor(np.zeros((1, config.eval_batch_size, config.decoder_hidden_size),
+                                     dtype=np.float16))
+    decoder_input = Tensor((np.ones((config.eval_batch_size, 1)) * sos_id).astype(np.int32))
+    inputs = (images, decoder_input, decoder_hidden)
+    export(network, *inputs, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    get_model()

model_zoo/official/cv/crnn_seq2seq_ocr/postprocess.py

@@ -0,0 +1,91 @@
+# 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.
+# ============================================================================
+"""
+postprocess.
+
+"""
+
+import os
+import codecs
+import numpy as np
+
+from src.utils import initialize_vocabulary
+from src.model_utils.config import config
+from eval import text_standardization, LCS_length
+
+
+def get_acc():
+    '''generate accuracy'''
+    vocab_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), config.vocab_path)
+    _, rev_vocab = initialize_vocabulary(vocab_path)
+    eos_id = config.characters_dictionary.eos_id
+
+    num_correct_char = 0
+    num_total_char = 0
+    num_correct_word = 0
+    num_total_word = 0
+
+    correct_file = 'result_correct.txt'
+    incorrect_file = 'result_incorrect.txt'
+
+    with codecs.open(correct_file, 'w', encoding='utf-8') as fp_output_correct, \
+            codecs.open(incorrect_file, 'w', encoding='utf-8') as fp_output_incorrect:
+
+        file_num = len(os.listdir(config.post_result_path)) // config.max_length
+        for i in range(file_num):
+            batch_decoded_label = []
+            for j in range(config.max_length):
+                f = "ocr_bs" + str(config.eval_batch_size) + "_" + str(i) + "_" + str(j) + ".bin"
+                t = np.fromfile(os.path.join(config.post_result_path, f), np.int32)
+                t = t.reshape(config.eval_batch_size,)
+                batch_decoded_label.append(t)
+            ann_f = os.path.join(config.pre_result_path, "annotation")
+            annotation = np.load(os.path.join(ann_f, "ocr_bs" + str(config.eval_batch_size) + "_" + str(i) + ".npy"))
+
+            for b in range(config.eval_batch_size):
+                text = annotation[b].decode("utf8")
+                text = text_standardization(text)
+                decoded_label = list(np.array(batch_decoded_label)[:, b])
+                decoded_words = []
+                for idx in decoded_label:
+                    if idx == eos_id:
+                        break
+                    else:
+                        decoded_words.append(rev_vocab[idx])
+                predict = text_standardization("".join(decoded_words))
+
+                if predict == text:
+                    num_correct_word += 1
+                    fp_output_correct.write('\t\t' + text + '\n')
+                    fp_output_correct.write('\t\t' + predict + '\n\n')
+                    print('correctly predicted : pred: {}, gt: {}'.format(predict, text))
+
+                else:
+                    fp_output_incorrect.write('\t\t' + text + '\n')
+                    fp_output_incorrect.write('\t\t' + predict + '\n\n')
+                    print('incorrectly predicted : pred: {}, gt: {}'.format(predict, text))
+
+                num_total_word += 1
+                num_correct_char += 2 * LCS_length(text, predict)
+                num_total_char += len(text) + len(predict)
+
+        print('\nnum of correct characters = %d' % (num_correct_char))
+        print('\nnum of total characters = %d' % (num_total_char))
+        print('\nnum of correct words = %d' % (num_correct_word))
+        print('\nnum of total words = %d' % (num_total_word))
+        print('\ncharacter precision = %f' % (float(num_correct_char) / num_total_char))
+        print('\nAnnotation precision precision = %f' % (float(num_correct_word) / num_total_word))
+if __name__ == '__main__':
+    get_acc()

model_zoo/official/cv/crnn_seq2seq_ocr/preprocess.py

@@ -0,0 +1,66 @@
+# 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.
+# ============================================================================
+"""
+preprocess.
+
+"""
+
+import os
+import numpy as np
+
+from src.dataset import create_ocr_val_dataset
+from src.model_utils.config import config
+
+def get_bin():
+    '''generate bin files.'''
+    prefix = "fsns.mindrecord"
+    if config.enable_modelarts:
+        mindrecord_file = os.path.join(config.data_path, prefix + "0")
+    else:
+        mindrecord_file = os.path.join(config.test_data_dir, prefix + "0")
+    print("mindrecord_file", mindrecord_file)
+    dataset = create_ocr_val_dataset(mindrecord_file, config.eval_batch_size)
+    data_loader = dataset.create_dict_iterator(num_epochs=1, output_numpy=True)
+    print("Dataset creation Done!")
+
+    sos_id = config.characters_dictionary.go_id
+
+    images_path = os.path.join(config.pre_result_path, "00_images")
+    decoder_input_path = os.path.join(config.pre_result_path, "01_decoder_input")
+    decoder_hidden_path = os.path.join(config.pre_result_path, "02_decoder_hidden")
+    annotation_path = os.path.join(config.pre_result_path, "annotation")
+    os.makedirs(images_path)
+    os.makedirs(decoder_input_path)
+    os.makedirs(decoder_hidden_path)
+    os.makedirs(annotation_path)
+
+    for i, data in enumerate(data_loader):
+        annotation = data["annotation"]
+        images = data["image"].astype(np.float32)
+        decoder_hidden = np.zeros((1, config.eval_batch_size, config.decoder_hidden_size),
+                                  dtype=np.float16)
+        decoder_input = (np.ones((config.eval_batch_size, 1)) * sos_id).astype(np.int32)
+
+        file_name = "ocr_bs" + str(config.eval_batch_size) + "_" + str(i) + ".bin"
+        images.tofile(os.path.join(images_path, file_name))
+        decoder_input.tofile(os.path.join(decoder_input_path, file_name))
+        decoder_hidden.tofile(os.path.join(decoder_hidden_path, file_name))
+
+        file_name = "ocr_bs" + str(config.eval_batch_size) + "_" + str(i) + ".npy"
+        np.save(os.path.join(annotation_path, file_name), annotation)
+    print("=" * 10, "export bin files finished.", "=" * 10)
+
+if __name__ == '__main__':
+    get_bin()

model_zoo/official/cv/crnn_seq2seq_ocr/scripts/run_infer_310.sh

@@ -0,0 +1,121 @@
+#!/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 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    DEVICE_TARGET must choose from ['GPU', 'CPU', 'Ascend']
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+
+if [ "$2" == "y" ] || [ "$2" == "n" ];then
+    need_preprocess=$2
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+echo "need preprocess: "$need_preprocess
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_images --input1_path=./preprocess_Result/01_decoder_input --input2_path=./preprocess_Result/02_decoder_hidden --device_id=$device_id &> infer.log
+
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py &> acc.log
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+    if [ $? -ne 0 ]; then
+        echo "preprocess dataset failed"
+        exit 1
+    fi
+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_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi