!1658 u2net 310

Merge pull request !1658 from qqq13579/master

research/cv/u2net/README.md

@@ -9,7 +9,9 @@
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Run On Modelarts](#run-on-modelarts)
+    - [Model Export](#model-export)
     - [Training Process](#training-process)
+    - [Ascend310 Inference Process](#ascend310-inference-process)
 - [Model Description](#model-description)
     - [Performance](#performance)
     - [Training Performance](#training-performance)
@@ -54,7 +56,8 @@ To train U<sup>2</sup>-Net, We use the dataset [DUTS-TR](http://saliencydetectio
 ```shell
 U-2-Net
  ├─  README.md # descriptions about U-2-Net
- ├─  scripts  
+ ├─  scripts
+     ├─ run_infer_310.sh        # 310 inference
      └─ run_distribute_train.sh # launch Ascend training (8 Ascend)
  ├─ assets # save pics for README.MD
  ├─ ckpts  # save ckpt  
@@ -65,7 +68,11 @@ U-2-Net
  ├─  train_modelarts.py  # train script for online train
  ├─  test.py  # generate detection images
  ├─  eval.py  # eval script
- └─  train.py # train script
+ ├─  train.py # train script
+ ├─  ascend310_infer # 310 main
+ ├─  export.py
+ ├─  preprocess.py
+ └─  postprocess.py
 ```
 
 ## [Script Parameters](#contents)
@@ -166,6 +173,30 @@ bash run_distribute_train.sh [/path/to/content] [/path/to/label] [/path/to/RANK_
 
 1. hccl.json which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it
    by using the [hccl_tools](https://gitee.com/mindspore/models/tree/master/utils/hccl_tools).
+## [Model Export](#contents)
+
+```bash
+python export.py --ckpt_dir [/path/to/ckpt_file]
+```
+
+## [Ascend310 Inference Process](#contents)
+
+### Export MINDIR file
+
+```bash
+python  export.py --ckpt_file [/path/to/ckpt_file]
+```
+
+### Ascend310 Inference
+
+- Run `run_infer_310.sh` for Ascend310 inference.
+
+```bash
+# infer
+bash run_infer_310.sh [MINDIR_PATH] [CONTENT_PATH] [LABEL_PATH] [DEVICE_ID]
+```
+
+Semantically segmented pictures will be stored in the postprocess_Result path and the evaluation result will be stored in evaluation.log.
 
 # [Model Description](#contents)
 
@@ -191,7 +222,7 @@ bash run_distribute_train.sh [/path/to/content] [/path/to/label] [/path/to/RANK_
 
 | Parameters        | single Ascend                                    |
 | ----------------- | ------------------------------------------------ |
-| Model Version     | v1                                               |
+| Model Version     | U-2-Net                                          |
 | Resource          | Red Hat 8.3.1; Ascend 910; CPU 2.60GHz; 192cores |
 | MindSpore Version | 1.3.0                                            |
 | Dataset           | content images                                   |
@@ -203,7 +234,7 @@ bash run_distribute_train.sh [/path/to/content] [/path/to/label] [/path/to/RANK_
 
 | Parameters        | single Ascend                                    |
 | ----------------- | ------------------------------------------------ |
-| Model Version     | v1                                               |
+| Model Version     | U-2-Net                                          |
 | Resource          | Red Hat 8.3.1; Ascend 910; CPU 2.60GHz; 192cores |
 | MindSpore Version | 1.3.0                                            |
 | Dataset           | DUTS-TE                                          |

research/cv/u2net/ascend310_infer/CMakeLists.txt

+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)
+find_package(gflags REQUIRED)
\ No newline at end of file

research/cv/u2net/ascend310_infer/build.sh

+#!/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="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

research/cv/u2net/ascend310_infer/inc/utils.h

+/**
+ * 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

research/cv/u2net/ascend310_infer/src/main.cc

+/**
+ * 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 <dirent.h>
+#include <gflags/gflags.h>
+#include <sys/time.h>
+#include <algorithm>
+#include <fstream>
+#include <iosfwd>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "inc/utils.h"
+#include "include/api/context.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/types.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/vision.h"
+
+using mindspore::Context;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::Model;
+using mindspore::ModelType;
+using mindspore::MSTensor;
+using mindspore::Serialization;
+using mindspore::Status;
+using mindspore::dataset::Execute;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input_path, ".", "input 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 input_files = GetAllFiles(FLAGS_input_path);
+
+  if (input_files.empty()) {
+    std::cout << "ERROR: input data empty." << std::endl;
+    return 1;
+  }
+  std::map<double, double> costTime_map;
+  size_t size = input_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:" << input_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input_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 " << input_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(input_files[i], outputs);
+  }
+  double average = 0.0;
+  int inferCount = 0;
+
+  for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+    double 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;
+}

research/cv/u2net/ascend310_infer/src/utils.cc

+/**
+ * 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 <algorithm>
+#include <fstream>
+#include <iostream>
+
+using mindspore::DataType;
+using mindspore::MSTensor;
+
+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;
+}

research/cv/u2net/eval.py

@@ -75,7 +75,7 @@ if __name__ == '__main__':
             pred = np.array(Image.open(pred_path), dtype='float32')
 
             pic_name = content_list[i].replace(".jpg", "").replace(".png", "").replace(".JPEG", "")
-            print("%d / %d ,  %s \n" % (i, len(content_list), pic_name))
+            print("%d / %d ,  %s \n" % (i+1, len(content_list), pic_name))
             label_path = os.path.join(label_directory, pic_name) + ".png"
             label = np.array(Image.open(label_path), dtype='float32')
             if len(label.shape) > 2:

research/cv/u2net/export.py

+# 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 U-2-Net model"""
+
+import argparse
+
+import numpy as np
+from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
+from src.blocks import U2NET
+
+parser = argparse.ArgumentParser(description='checkpoint export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="u2net",
+                    help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="MINDIR", help="file format")
+args = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+if __name__ == '__main__':
+    context.set_context(device_id="Ascend")
+    net = U2NET()
+    param_dict = load_checkpoint(args.ckpt_file)
+    load_param_into_net(net, param_dict)
+    input_data = Tensor(np.zeros([1, 3, 320, 320], np.float32))
+    export(net, input_data, file_name=args.file_name, file_format=args.file_format)

research/cv/u2net/postprocess.py

+# 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 argparse
+import os
+
+import cv2
+import imageio
+import numpy as np
+from PIL import Image
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--bin_path", type=str, help='bin_path, path to binary files generated by 310 model, default: None')
+parser.add_argument("--content_path", type=str, help='content_path, default: None')
+parser.add_argument("--output_dir", type=str, default='output_dir',
+                    help='output_path, path to store output, default: None')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    bin_path = args.bin_path
+    original_dir = args.content_path
+    content_list = os.listdir(args.bin_path)
+
+
+    def normPRED(d):
+        """rescale the value of tensor to between 0 and 1"""
+        ma = d.max()
+        mi = d.min()
+        dn = (d - mi) / (ma - mi)
+        return dn
+
+
+    for i in range(0, len(content_list)):
+        pic_path = os.path.join(args.bin_path, content_list[i])
+        b = np.fromfile(pic_path, dtype=np.float32, count=320 * 320)
+        b = np.reshape(b, (320, 320))
+        file_path = os.path.join(original_dir, content_list[i]).replace("_0.bin", ".jpg")
+        original = np.array(Image.open(file_path), dtype='float32')
+        shape = original.shape
+        b = normPRED(b)
+        image = b
+        content_name = content_list[i].replace("_0.bin", "")
+        image = cv2.resize(image, dsize=(0, 0), fx=shape[1] / image.shape[1], fy=shape[0] / image.shape[0])
+        image_path = os.path.join(args.output_dir, content_name) + ".png"
+        imageio.imsave(image_path, image)
+        print("%d / %d , %s \n" % (i, len(content_list), content_name))

research/cv/u2net/preprocess.py

+# 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 argparse
+import os
+
+import cv2
+import numpy as np
+from PIL import Image
+
+parser = argparse.ArgumentParser('preprocess')
+parser.add_argument("--content_path", type=str, help='content_path, default: None')
+parser.add_argument('--output_path', type=str, default="./preprocess_Result/", help='eval data dir')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+
+    if not os.path.exists(args.output_path):
+        os.makedirs(args.output_path)
+
+
+    def normalize(img, im_type):
+        """normalize tensor"""
+        if im_type == "label":
+            return img
+        if len(img.shape) == 3:
+            img[:, :, 0] = (img[:, :, 0] - 0.485) / 0.229
+            img[:, :, 1] = (img[:, :, 1] - 0.456) / 0.224
+            img[:, :, 2] = (img[:, :, 2] - 0.406) / 0.225
+        else:
+            img = (img - 0.485) / 0.229
+        return img
+
+
+    def crop_and_resize(img_path, im_type, size=320):
+        """crop and resize tensors"""
+        img = np.array(Image.open(img_path), dtype='float32')
+        img = img / 255
+        img = normalize(img, im_type)
+        h, w = img.shape[:2]
+        img = cv2.resize(img, dsize=(0, 0), fx=size / w, fy=size / h)
+        if len(img.shape) == 2:
+            img = np.expand_dims(img, 2).repeat(1, axis=2)
+        im = img
+        im = np.swapaxes(im, 1, 2)
+        im = np.swapaxes(im, 0, 1)
+        im = np.reshape(im, (1, im.shape[0], im.shape[1], im.shape[2]))
+        return im
+
+
+    content_list = os.listdir(args.content_path)
+
+    for j in range(0, len(content_list)):
+        pic_path = os.path.join(args.content_path, content_list[j])
+        content_pic = crop_and_resize(pic_path, im_type="content", size=320)
+        file_name = content_list[j].replace(".jpg", "") + ".bin"
+        image_path = os.path.join(args.output_path, file_name)
+        content_pic.tofile(image_path)
+
+    print("Export bin files finished!")

research/cv/u2net/scripts/run_infer_310.sh

+#!/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 || $# -gt 4 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [CONTENT_PATH] [LABEL_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+get_real_path(){
+
+  if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+content_path=$(get_real_path $2)
+label_path=$(get_real_path $3)
+device_id=0
+if [ $# == 4 ]; then
+    device_id=$4
+fi
+echo "mindir name: "$model
+echo "content path: "$content_path
+echo "device id: "$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 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 --content_path $content_path --output_path='./preprocess_Result/'
+}
+function compile_app()
+{
+    cd ../ascend310_infer/ || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input_path=./preprocess_Result --device_id=$device_id &> infer.log
+}
+
+function post_process()
+{
+   if [ -d postprocess_Result ]; then
+       rm -rf ./postprocess_Result
+    fi
+    mkdir postprocess_Result
+    python3.7 ../postprocess.py --bin_path='./result_Files' --content_path $content_path --output_dir='./postprocess_Result/' &> postprocess.log
+}
+
+function evaluation()
+{
+    python3.7 ../eval.py --pred_dir='./postprocess_Result/' --label_dir $label_path &> evaluation.log
+}
+
+preprocess_data
+if [ $? -ne 0 ]; then
+    echo "preprocess dataset 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
+
+post_process
+if [ $? -ne 0 ]; then
+    echo " execute post_process failed"
+    exit 1
+fi
\ No newline at end of file

research/cv/u2net/test.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
         return img
 
 
-    def crop_and_resize(img_path, im_type, size=320):
+    def resize_im(img_path, size=320):
         """crop and resize tensors"""
         img = np.array(Image.open(img_path), dtype='float32')
         img = img / 255
@@ -105,7 +105,7 @@ if __name__ == '__main__':
     start_time = time.time()
     for j in range(0, len(content_list)):
         pic_path = os.path.join(local_dataset_dir, content_list[j])
-        content_pic = crop_and_resize(pic_path, im_type="content", size=320)
+        content_pic = resize_im(pic_path, size=320)
         image = net(Tensor(content_pic))
         content_name = content_list[j].replace(".jpg", "")
         content_name = content_name.replace(".png", "")