!1137 ntsnet 310 pr

Merge pull request !1137 from hit_zyh/master

research/cv/ntsnet/README.md

@@ -36,7 +36,7 @@ Dataset used: [Caltech-UCSD Birds-200-2011](<http://www.vision.caltech.edu/visip
 
 Please download the datasets [CUB_200_2011.tgz] and unzip it, then put all training images into a directory named "train", put all testing images into a directory named "test".
 
-The directory structure is as follows:
+The directory structure is as follows, you need to split the dataset by yourself followed by "train_test_split.txt" in the original dataset:
 
 ```path
 ├─resnet50.ckpt
@@ -64,10 +64,13 @@ The directory structure is as follows:
 └─ntsnet
   ├─README.md                             # README
   ├─scripts                               # shell script
-    ├─run_standalone_train.sh             # training in standalone mode(1pcs)
-    ├─run_distribute_train.sh             # training in parallel mode(8 pcs)
-    └─run_eval.sh                         # evaluation
+    ├─run_standalone_train_ascend.sh             # training in standalone mode(1pcs)
+    ├─run_distribute_train_ascend.sh             # training in parallel mode(8 pcs)
+    └─run_eval_ascend.sh                         # evaluation
   ├─src
+    ├─config_gpu.py                       # network configuration
+    ├─dataset_gpu.py                      # dataset utils
+    ├─lr_generator_gpu.py                 # leanring rate generator
     ├─config.py                           # network configuration
     ├─dataset.py                          # dataset utils
     ├─lr_generator.py                     # leanring rate generator
@@ -76,7 +79,9 @@ The directory structure is as follows:
   ├─mindspore_hub_conf.py                 # mindspore hub interface
   ├─export.py                             # script to export MINDIR model
   ├─eval.py                               # evaluation scripts
-  └─train.py                              # training scripts
+  ├─train.py                              # training scripts
+  ├─eval_gpu.py                               # evaluation scripts
+  └─train_gpu.py                              # training scripts
 ```
 
 ## [Script Parameters](#contents)
@@ -85,10 +90,10 @@ The directory structure is as follows:
 
 ```shell
 # distributed training
-Usage: bash run_train.sh [RANK_TABLE_FILE] [DATA_URL] [TRAIN_URL]
+Usage: bash run_train_ascend.sh [RANK_TABLE_FILE] [DATA_URL] [TRAIN_URL]
 
 # standalone training
-Usage: bash run_standalone_train.sh [DATA_URL] [TRAIN_URL]
+Usage: bash run_standalone_train_ascend.sh [DATA_URL] [TRAIN_URL]
 ```
 
 ### [Parameters Configuration](#contents)
@@ -133,7 +138,7 @@ Usage: bash run_standalone_train.sh [DATA_URL] [TRAIN_URL]
 
 ### [Training](#content)
 
-- Run `run_standalone_train_ascend.sh` for non-distributed training of NTS-Net model in Ascend.
+- Run `run_standalone_train_ascend.sh` for non-distributed training of NTS-Net model.
 
 ```bash
 # standalone training in ascend
@@ -208,11 +213,35 @@ accuracy: 0.876
 
 ## Model Export
 
+### [Export MindIR](#contents)
+
+when export mindir file in Ascend 910, the cropAndResize operator differs from 310 and 910. Specifically, 310 requires an input shape (N,C,H,W) while 910 requires an input shape (N,H,W,C). You need to invalid the CropAndResize Validator check in 910 mindspore environment to export successfully.
+
 ```shell
-python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format [EXPORT_FORMAT]
+python export.py --ckpt_file [CKPT_PATH] --train_url [TRAIN_URL]
 ```
 
-`EXPORT_FORMAT` should be "MINDIR"
+- `ckpt_file` Checkpoint file name.
+- `train_url` should be Directory contains checkpoint file.
+
+## Inference Process
+
+### 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] [DATASET_PATH] [DEVICE_ID]
+```
+
+- `MINDIR_PATH` The absolute path of ntsnet.mindir.
+- `DATASET_PATH` The CUB_200_2011 dataset test directory.
+- `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.
 
 # Model Description
 

research/cv/ntsnet/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} -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)
+find_package(gflags REQUIRED)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
\ No newline at end of file

research/cv/ntsnet/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
+  mkdir out
+fi
+cd out || exit
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

research/cv/ntsnet/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 <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+
+#include "../inc/utils.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "include/dataset/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::Serialization;
+using mindspore::Model;
+using mindspore::Context;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::Graph;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::DataType;
+using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+
+DEFINE_string(mindir_path, "", "model path");
+DEFINE_string(dataset_path, ".", "dataset 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 model" << 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->SetPrecisionMode("allow_fp32_to_fp16");
+    context->MutableDeviceInfo().push_back(ascend310_info);
+
+    Graph graph;
+    Status ret = Serialization::Load(FLAGS_mindir_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;
+    }
+
+    auto decode = Decode();
+    auto resize = Resize({448, 448});
+    auto normalize = Normalize({0, 0, 0}, {255, 255, 255});
+    auto hwc2chw = HWC2CHW();
+
+    mindspore::dataset::Execute transform({decode, resize, normalize, hwc2chw});
+
+    std::map<double, double> costTime_map;
+
+    size_t size = all_files.size();
+    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 image =  ReadFileToTensor(all_files[i]);
+        transform(image, &image);
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            image.Data().get(), image.DataSize());
+        gettimeofday(&start, NULL);
+        model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+        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;
+    char tmpCh[256] = {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;
+
+    snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d\n", average, infer_cnt);
+    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 << tmpCh;
+    file_stream.close();
+    costTime_map.clear();
+  return 0;
+}

research/cv/ntsnet/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 <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;
+    std::vector<std::string> res_temp;
+    while ((filename = readdir(dir)) != nullptr) {
+        std::string dName = std::string(filename->d_name);
+        if (dName != "." && dName != ".." && filename->d_type == DT_DIR) {
+            res_temp = GetAllFiles(std::string(dirName) + "/" + filename->d_name);
+            for (int i = 0; i < static_cast<int>(res_temp.size()); i++) {
+                res.emplace_back(res_temp[i]);
+            }
+            continue;
+        }
+        if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+            continue;
+        }
+        res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+    }
+    std::sort(res.begin(), res.end());
+    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/ntsnet/eval.py

@@ -16,21 +16,17 @@
 import argparse
 import ast
 import os
-
-import mindspore.common.dtype as mstype
 from mindspore import context, set_seed, Tensor, load_checkpoint, load_param_into_net, ops
+import mindspore.common.dtype as mstype
 
 from src.config import config
 from src.dataset import create_dataset_test
 from src.network import NTS_NET
-
 parser = argparse.ArgumentParser(description='ntsnet eval running')
 parser.add_argument("--run_modelart", type=ast.literal_eval, default=False, help="Run on modelArt, default is false.")
-parser.add_argument('--data_url', default="./data", help='Directory contains CUB_200_2011 dataset.')
-parser.add_argument('--train_url', default="./ckpt_0", help='Directory contains checkpoint file and eval.log')
+parser.add_argument('--data_url', default=None, help='Directory contains CUB_200_2011 dataset.')
+parser.add_argument('--train_url', default=None, help='Directory contains checkpoint file and eval.log')
 parser.add_argument('--ckpt_filename', default=None, help='checkpoint file name')
-parser.add_argument("--device_target", type=str, default="Ascend", help="Device Target, default Ascend",
-                    choices=["Ascend", "GPU"])
 parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
 args = parser.parse_args()
 run_modelart = args.run_modelart
@@ -43,20 +39,9 @@ batch_size = config.batch_size
 
 resnet50Path = ""
 
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=False)
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
 context.set_context(device_id=device_id)
 
-if args.ckpt_filename is None:
-    files = os.listdir(args.train_url)
-    time = 0.
-    for file in files:
-        if file.endswith('.ckpt'):
-            time_temp = os.path.getatime(os.path.join(args.train_url, file))
-            if time_temp > time:
-                time = time_temp
-                args.ckpt_filename = file
-    print(f"Get ckpt file {args.ckpt_filename}")
-
 if run_modelart:
     import moxing as mox
 
@@ -101,8 +86,6 @@ if __name__ == '__main__':
         total_num = total_num + float(image_data.shape[0])
     print2file("ckpt file name: ", args.ckpt_filename)
     print2file("accuracy: ", round(success_num / total_num, 3))
-    print("accuracy: ", round(success_num / total_num, 3))
-    print("ckpt file name: ", args.ckpt_filename)
     if run_modelart:
         mox.file.copy_parallel(src_url=os.path.join(local_output_url, "eval.log"),
                                dst_url=os.path.join(args.train_url, "eval.log"))

research/cv/ntsnet/eval_gpu.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.
+# ============================================================================
+"""ntsnet eval."""
+import argparse
+import ast
+import os
+
+import mindspore.common.dtype as mstype
+from mindspore import context, set_seed, Tensor, load_checkpoint, load_param_into_net, ops
+
+from src.configgpu import config
+from src.dataset_gpu import create_dataset_test
+from src.network import NTS_NET
+
+parser = argparse.ArgumentParser(description='ntsnet eval running')
+parser.add_argument("--run_modelart", type=ast.literal_eval, default=False, help="Run on modelArt, default is false.")
+parser.add_argument('--data_url', default="./data", help='Directory contains CUB_200_2011 dataset.')
+parser.add_argument('--train_url', default="./ckpt_0", help='Directory contains checkpoint file and eval.log')
+parser.add_argument('--ckpt_filename', default=None, help='checkpoint file name')
+parser.add_argument("--device_target", type=str, default="Ascend", help="Device Target, default Ascend",
+                    choices=["Ascend", "GPU"])
+parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+args = parser.parse_args()
+run_modelart = args.run_modelart
+if not run_modelart:
+    device_id = args.device_id
+else:
+    device_id = int(os.getenv('DEVICE_ID'))
+
+batch_size = config.batch_size
+
+resnet50Path = ""
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=False)
+context.set_context(device_id=device_id)
+
+if args.ckpt_filename is None:
+    files = os.listdir(args.train_url)
+    time = 0.
+    for file in files:
+        if file.endswith('.ckpt'):
+            time_temp = os.path.getatime(os.path.join(args.train_url, file))
+            if time_temp > time:
+                time = time_temp
+                args.ckpt_filename = file
+    print(f"Get ckpt file {args.ckpt_filename}")
+
+if run_modelart:
+    import moxing as mox
+
+    local_input_url = '/cache/data' + str(device_id)
+    local_output_url = '/cache/ckpt' + str(device_id)
+    mox.file.copy_parallel(src_url=args.data_url, dst_url=local_input_url)
+    mox.file.copy_parallel(src_url=os.path.join(args.train_url, args.ckpt_filename),
+                           dst_url=os.path.join(local_output_url, args.ckpt_filename))
+    mox.file.copy_parallel(src_url=os.path.join(args.train_url, "eval.log"),
+                           dst_url=os.path.join(local_output_url, "eval.log"))
+else:
+    local_input_url = args.data_url
+    local_output_url = args.train_url
+
+
+def print2file(obj1, obj2):
+    with open(os.path.join(local_output_url, 'eval.log'), 'a') as f:
+        f.write(str(obj1))
+        f.write(' ')
+        f.write(str(obj2))
+        f.write(' \r\n')
+
+
+if __name__ == '__main__':
+    set_seed(1)
+    test_data_set = create_dataset_test(test_path=os.path.join(local_input_url, "CUB_200_2011/test"),
+                                        batch_size=batch_size)
+    test_data_loader = test_data_set.create_dict_iterator(output_numpy=True)
+
+    ntsnet = NTS_NET(topK=6, resnet50Path=resnet50Path)
+    param_dict = load_checkpoint(os.path.join(local_output_url, args.ckpt_filename))
+    load_param_into_net(ntsnet, param_dict)
+    ntsnet.set_train(False)
+    success_num = 0.0
+    total_num = 0.0
+    for _, data in enumerate(test_data_loader):
+        image_data = Tensor(data['image'], mstype.float32)
+        label = Tensor(data["label"], mstype.int32)
+        _, scrutinizer_out, _, _ = ntsnet(image_data)
+        result_label, _ = ops.ArgMaxWithValue(1)(scrutinizer_out)
+        success_num = success_num + sum((result_label == label).asnumpy())
+        total_num = total_num + float(image_data.shape[0])
+    print2file("ckpt file name: ", args.ckpt_filename)
+    print2file("accuracy: ", round(success_num / total_num, 3))
+    print("accuracy: ", round(success_num / total_num, 3))
+    print("ckpt file name: ", args.ckpt_filename)
+    if run_modelart:
+        mox.file.copy_parallel(src_url=os.path.join(local_output_url, "eval.log"),
+                               dst_url=os.path.join(args.train_url, "eval.log"))

research/cv/ntsnet/export.py

@@ -18,19 +18,18 @@ import ast
 import os
 
 import numpy as np
-import mindspore.common.dtype as mstype
-from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-
 
+from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
+import mindspore.common.dtype as mstype
 from src.network import NTS_NET
 
 parser = argparse.ArgumentParser(description='ntsnet export')
 parser.add_argument("--run_modelart", type=ast.literal_eval, default=False, help="Run on modelArt, default is false.")
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
-parser.add_argument("--batch_size", type=int, default=8, help="batch size")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
 parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file name.")
-parser.add_argument('--data_url', default=None, help='Directory contains CUB_200_2011 dataset.')
 parser.add_argument('--train_url', default=None, help='Directory contains checkpoint file')
+parser.add_argument('--data_url', default=None, help='Directory contains CUB_200_2011 dataset.')
 parser.add_argument("--file_name", type=str, default="ntsnet", help="output file name.")
 parser.add_argument("--file_format", type=str, default="MINDIR", help="file format")
 parser.add_argument('--device_target', type=str, default="Ascend",
@@ -38,8 +37,8 @@ parser.add_argument('--device_target', type=str, default="Ascend",
 args = parser.parse_args()
 
 context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-context.set_context(device_id=args.device_id)
-
+if args.device_target == "Ascend":
+    context.set_context(device_id=args.device_id)
 if args.run_modelart:
     import moxing as mox
 
@@ -49,7 +48,7 @@ if args.run_modelart:
                            dst_url=os.path.join(local_output_url, args.ckpt_file))
 
 if __name__ == '__main__':
-    net = NTS_NET(topK=6)
+    net = NTS_NET(topK=6, flag910=False)
     if args.run_modelart:
         param_dict = load_checkpoint(os.path.join(local_output_url, args.ckpt_file))
     else:

research/cv/ntsnet/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 for 310 inference"""
+import os
+import json
+import argparse
+import numpy as np
+
+
+
+parser = argparse.ArgumentParser(description="postprocess")
+parser.add_argument("--result_dir", type=str, required=True, help="result files path.")
+parser.add_argument("--label_dir", type=str, required=True, help="image file path.")
+args = parser.parse_args()
+
+
+
+if __name__ == '__main__':
+    batch_size = 1
+    rst_path = args.result_dir
+    file_list = os.listdir(rst_path)
+    with open(args.label_dir, "r") as label:
+        labels = json.load(label)
+    success_num = 0.0
+    total_num = 0.0
+    acc = 0.0
+    for f in file_list:
+        if f.find("_1.bin") != -1:
+            label = f.split("_1.bin")[0] + ".jpg"
+            scrutinizer_out = np.fromfile(os.path.join(rst_path, f), np.float32)
+            scrutinizer_out = scrutinizer_out.reshape(batch_size, 200)
+            pred = np.argmax(scrutinizer_out, axis=1)[0]
+            print("pred: ", pred)
+            print("labels[label]: ", labels[label])
+            total_num = total_num + 1
+            if pred == labels[label]:
+                success_num = success_num + 1
+    acc = success_num / total_num
+    print("success_num: ", success_num)
+    print("total_num: ", total_num)
+    print("acc: ", acc)

research/cv/ntsnet/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 os
+import argparse
+import json
+def create_label(result_path, dir_path):
+    """create label"""
+    print("Create label. ")
+    dirs = os.listdir(dir_path)
+    file_list = []
+    for file in dirs:
+        file_list.append(file)
+    file_list = sorted(file_list)
+
+    total = 0
+    img_label = {}
+    for i, file_dir in enumerate(file_list):
+        print("dir_path: ", dir_path)
+        print("file_dir: ", file_dir)
+        files = os.listdir(os.path.join(dir_path, file_dir))
+        for f in files:
+            img_label[f] = i
+        total += len(files)
+
+    json_file = os.path.join(result_path, "label.json")
+    with open(json_file, "w+") as label:
+        json.dump(img_label, label)
+
+    print("[INFO] Completed! Total {} data.".format(total))
+
+parser = argparse.ArgumentParser('preprocess')
+parser.add_argument('--data_path', type=str, default='', help='eval data dir')
+parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    create_label(args.result_path, args.data_path)

research/cv/ntsnet/scripts/run_distribute_train_ascend.sh

@@ -16,7 +16,7 @@
 
 if [ $# != 3 ]
 then
-    echo "Usage: bash run_distribute_train_ascend.sh [RANK_TABLE_FILE] [DATA_URL] [TRAIN_URL]"
+    echo "Usage: bash run_train_ascend.sh [RANK_TABLE_FILE] [DATA_URL] [TRAIN_URL]"
 exit 1
 fi
 
@@ -54,7 +54,7 @@ exit 1
 fi
 
 ulimit -u unlimited
-export HCCL_CONNECT_TIMEOUT=1200
+export HCCL_CONNECT_TIMEOUT=600
 export DEVICE_NUM=8
 export RANK_SIZE=8
 export RANK_TABLE_FILE=$PATH1
@@ -81,7 +81,7 @@ do
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
-    taskset -c $cmdopt python train.py --device_id=$i --device_target="Ascend" --run_distribute=True --device_num=$DEVICE_NUM \
+    taskset -c $cmdopt python train.py --device_id=$i --run_distribute=True --device_num=$DEVICE_NUM \
                     --data_url=$PATH2 --train_url=$PATH3 &> log &
     cd ..
 done

research/cv/ntsnet/scripts/run_distribute_train_gpu.sh

@@ -47,7 +47,7 @@ export CUDA_VISIBLE_DEVICES="$2"
 
 if [ $1 -gt 1 ]; then
   mpirun -n $1 --allow-run-as-root --output-filename log_output --merge-stderr-to-stdout \
-  python3 ${BASEPATH}/../train.py --device_target="GPU" --run_distribute True --data_url=$DATA_DIR --train_url=$TRAIN_URL >train_gpu.log 2>&1 &
+  python3 ${BASEPATH}/../train_gpu.py --device_target="GPU" --run_distribute True --data_url=$DATA_DIR --train_url=$TRAIN_URL >train_gpu.log 2>&1 &
 else
-  python3 ${BASEPATH}/../train.py --device_target="GPU" --data_url=$DATA_DIR --train_url=$TRAIN_URL >train_gpu.log 2>&1 &
+  python3 ${BASEPATH}/../train_gpu.py --device_target="GPU" --data_url=$DATA_DIR --train_url=$TRAIN_URL >train_gpu.log 2>&1 &
 fi

research/cv/ntsnet/scripts/run_eval_ascend.sh

@@ -14,18 +14,12 @@
 # limitations under the License.
 # ============================================================================
 
-if [ $# -lt 3 ] ||  [ $# -gt 4 ]
+if [ $# != 3 ]
 then 
-    echo "Usage: sh run_eval_ascend.sh [DATA_URL] [TRAIN_URL] [CKPT_FILENAME] [DEVICE_ID(optional)]"
+    echo "Usage: sh run_eval_ascend.sh [DATA_URL] [TRAIN_URL] [CKPT_FILENAME]"
 exit 1
 fi
 
-export DEVICE_ID=0
-
-if [ $# = 4 ] ; then
-  export DEVICE_ID=$4
-fi;
-
 get_real_path(){
   if [ "${1:0:1}" == "/" ]; then
     echo "$1"
@@ -36,7 +30,7 @@ get_real_path(){
 
 PATH1=$(get_real_path $1)
 PATH2=$(get_real_path $2)
-PATH3=$3
+PATH3=$(get_real_path $3)
 
 if [ ! -d $PATH1 ]
 then 
@@ -50,9 +44,15 @@ then
 exit 1
 fi
 
+if [ ! -f $PATH3 ]
+then
+    echo "error: CKPT_FILENAME=$PATH3 is not a file"
+exit 1
+fi
 
 ulimit -u unlimited
 export DEVICE_NUM=1
+export DEVICE_ID=0
 export RANK_SIZE=$DEVICE_NUM
 export RANK_ID=0
 
@@ -68,5 +68,5 @@ cd ./eval || exit
 env > env.log
 echo "start evaluation for device $DEVICE_ID"
 python eval.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 \
-                --ckpt_filename=$PATH3 --device_target="Ascend" &> log &
+                --ckpt_filename=$PATH3 &> log &
 cd ..

research/cv/ntsnet/scripts/run_eval_gpu.sh

@@ -67,6 +67,6 @@ cp -r ../src ./eval
 cd ./eval || exit
 env > env.log
 echo "start evaluation for device $DEVICE_ID"
-python eval.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 \
+python eval_gpu.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 \
                 --ckpt_filename=$PATH3 --device_target="GPU" &> log &
 cd ..

research/cv/ntsnet/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 2 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+dataset_path=$(get_real_path $2)
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+echo "mindir name: "$model
+echo "dataset path: "$dataset_path
+echo "device id: "$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=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+   if [ -d preprocess_Result ]; then
+       rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py --data_path=$dataset_path --result_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=$model --dataset_path=$dataset_path --device_id=$device_id   &> infer.log
+}
+function cal_acc()
+{
+
+    python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/label.json   &> acc.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
+cal_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi

research/cv/ntsnet/scripts/run_standalone_train_ascend.sh

@@ -16,16 +16,10 @@
 
 if [ $# != 2 ]
 then
-    echo "Usage: bash run_standalone_train_ascend.sh [DATA_URL] [TRAIN_URL] [DEVICE_ID(optional)]"
+    echo "Usage: bash run_standalone_train_ascend.sh [DATA_URL] [TRAIN_URL]"
 exit 1
 fi
 
-export DEVICE_ID=0
-
-if [ $# = 3 ] ; then
-  export DEVICE_ID=$3
-fi;
-
 get_real_path(){
   if [ "${1:0:1}" == "/" ]; then
     echo "$1"
@@ -52,6 +46,7 @@ fi
 
 ulimit -u unlimited
 export DEVICE_NUM=1
+export DEVICE_ID=0
 export RANK_ID=0
 export RANK_SIZE=1
 
@@ -66,5 +61,5 @@ cp -r ../src ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
-python train.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 --device_target="Ascend"&> log &
+python train.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 &> log &
 cd ..

research/cv/ntsnet/scripts/run_standalone_train_gpu.sh

@@ -61,5 +61,5 @@ cp -r ../src ./train$3
 cd ./train$3 || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
-python train.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 --device_target="GPU"&> log &
+python train_gpu.py --device_id=$DEVICE_ID --data_url=$PATH1 --train_url=$PATH2 --device_target="GPU"&> log &
 cd ..

research/cv/ntsnet/src/config.py

@@ -17,48 +17,21 @@ network config setting, will be used in train.py and eval.py
 """
 from easydict import EasyDict as ed
 
-config_ascend = ed({
+config = ed({
     "save_checkpoint": True,
-    "save_checkpoint_epochs": 2,
-    "keep_checkpoint_max": 10,
+    "save_checkpoint_epochs": 112,
+    "keep_checkpoint_max": 10000,
     "learning_rate": 0.001,
     "m_for_scrutinizer": 4,
     "topK": 6,
     "input_size": (448, 448),
-    "crop_pct_size": (600, 600),
     "weight_decay": 1e-4,
     "momentum": 0.9,
-    "num_epochs": 200,
+    "num_epochs": 112,
     "num_classes": 200,
     "num_train_images": 5994,
     "num_test_images": 5794,
     "batch_size": 8,
     "prefix": "ntsnet",
-    "lossLogName": "loss.log",
-    "lr_scheduler": "cosine",
-    "lr_step": [200, 200],
-    "optimizer": "momentum"
-})
-
-config_gpu = ed({
-    "save_checkpoint": True,
-    "save_checkpoint_epochs": 2,
-    "keep_checkpoint_max": 10,
-    "learning_rate": 0.001,
-    "m_for_scrutinizer": 4,
-    "topK": 6,
-    "input_size": (448, 448),
-    "crop_pct_size": (600, 600),
-    "weight_decay": 1e-4,
-    "momentum": 0.9,
-    "num_epochs": 200,
-    "num_classes": 200,
-    "num_train_images": 5994,
-    "num_test_images": 5794,
-    "batch_size": 16,
-    "prefix": "ntsnet",
-    "lossLogName": "loss.log",
-    "lr_scheduler": "cosine",
-    "lr_step": [60, 100],
-    "optimizer": "momentum"
+    "lossLogName": "loss.log"
 })

research/cv/ntsnet/src/config_gpu.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.
+# ============================================================================
+"""
+network config setting, will be used in train_gpu.py and eval_gpu.py
+"""
+from easydict import EasyDict as ed
+
+config_ascend = ed({
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 2,
+    "keep_checkpoint_max": 10,
+    "learning_rate": 0.001,
+    "m_for_scrutinizer": 4,
+    "topK": 6,
+    "input_size": (448, 448),
+    "crop_pct_size": (600, 600),
+    "weight_decay": 1e-4,
+    "momentum": 0.9,
+    "num_epochs": 200,
+    "num_classes": 200,
+    "num_train_images": 5994,
+    "num_test_images": 5794,
+    "batch_size": 8,
+    "prefix": "ntsnet",
+    "lossLogName": "loss.log",
+    "lr_scheduler": "cosine",
+    "lr_step": [200, 200],
+    "optimizer": "momentum"
+})
+
+config_gpu = ed({
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 2,
+    "keep_checkpoint_max": 10,
+    "learning_rate": 0.001,
+    "m_for_scrutinizer": 4,
+    "topK": 6,
+    "input_size": (448, 448),
+    "crop_pct_size": (600, 600),
+    "weight_decay": 1e-4,
+    "momentum": 0.9,
+    "num_epochs": 200,
+    "num_classes": 200,
+    "num_train_images": 5994,
+    "num_test_images": 5794,
+    "batch_size": 16,
+    "prefix": "ntsnet",
+    "lossLogName": "loss.log",
+    "lr_scheduler": "cosine",
+    "lr_step": [60, 100],
+    "optimizer": "momentum"
+})

research/cv/ntsnet/src/dataset.py

@@ -14,69 +14,41 @@
 # ============================================================================
 
 """ntsnet dataset"""
-import os
-
-import mindspore.dataset as de
+import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as vision
 from mindspore.dataset.vision import Inter
 
-from src.config import config
-
-mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
-std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
 
 def create_dataset_train(train_path, batch_size):
     """create train dataset"""
-    device_num, rank_id = _get_rank_info()
-
-    if device_num == 1:
-        train_data_set = de.ImageFolderDataset(train_path, num_parallel_workers=8, shuffle=True)
-    else:
-        train_data_set = de.ImageFolderDataset(train_path, num_parallel_workers=8,
-                                               shuffle=True, num_shards=device_num, shard_id=rank_id)
-
+    train_data_set = ds.ImageFolderDataset(train_path, shuffle=True)
     # define map operations
     transform_img = [
         vision.Decode(),
-        vision.Resize(config.crop_pct_size, Inter.BILINEAR),
-        vision.RandomCrop(config.input_size),
+        vision.Resize([448, 448], Inter.LINEAR),
         vision.RandomHorizontalFlip(),
-        vision.Normalize(mean=mean, std=std),
         vision.HWC2CHW()
     ]
-    train_data_set = train_data_set.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
-    train_data_set = train_data_set.batch(batch_size, drop_remainder=True)
+    train_data_set = train_data_set.map(input_columns="image", num_parallel_workers=8, operations=transform_img,
+                                        output_columns="image")
+    train_data_set = train_data_set.map(input_columns="image", num_parallel_workers=8,
+                                        operations=lambda x: (x / 255).astype("float32"))
+    train_data_set = train_data_set.batch(batch_size)
     return train_data_set
 
 
 def create_dataset_test(test_path, batch_size):
     """create test dataset"""
-    test_data_set = de.ImageFolderDataset(test_path, shuffle=False)
+    test_data_set = ds.ImageFolderDataset(test_path, shuffle=False)
     # define map operations
     transform_img = [
         vision.Decode(),
-        vision.Resize(config.crop_pct_size, Inter.BILINEAR),
-        vision.CenterCrop(config.input_size),
-        vision.Normalize(mean=mean, std=std),
+        vision.Resize([448, 448], Inter.LINEAR),
         vision.HWC2CHW()
     ]
-    test_data_set = test_data_set.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
-    test_data_set = test_data_set.batch(batch_size, drop_remainder=True)
+    test_data_set = test_data_set.map(input_columns="image", num_parallel_workers=8, operations=transform_img,
+                                      output_columns="image")
+    test_data_set = test_data_set.map(input_columns="image", num_parallel_workers=8,
+                                      operations=lambda x: (x / 255).astype("float32"))
+    test_data_set = test_data_set.batch(batch_size)
     return test_data_set
-
-
-def _get_rank_info():
-    """
-    get rank size and rank id
-    """
-    rank_size = int(os.environ.get("RANK_SIZE", 1))
-
-    if rank_size > 1:
-        from mindspore.communication.management import get_rank, get_group_size
-        rank_size = get_group_size()
-        rank_id = get_rank()
-    else:
-        rank_size = rank_id = None
-
-    return rank_size, rank_id