ascend 310 inference

model_zoo/official/cv/simclr/README.md

@@ -175,8 +175,11 @@ Major parameters in linear_eval.py as follows:
   ```
 
   The model checkpoint will be saved in the outputs directory.
+
 ### [Evaluation Process](#contents)
+
 #### Evaluation
+
 Before running the command below, please check the checkpoint path used for evaluation.
 
 - running on Ascend
@@ -192,6 +195,34 @@ Before running the command below, please check the checkpoint path used for eval
   'Accuracy': 0.84505
   ```
 
+## [Export MindIR](#contents)
+
+```shell
+python export.py --ckpt_simclr_encoder [SIMCLR_CKPT_PATH] --ckpt_linear_classifier [CLASSIFIER_CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+```
+
+The parameters ckpt_simclr_encoder and ckpt_linear_classifier are required,
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+
+## [Inference Process](#contents)
+
+### Usage
+
+Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [SIMCLR_CLASSIFIER_MINDIR_PATH] [DATA_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+```
+
+`DATA_PATH` is the path to the cifar10 evaluation dataset
+`NEED_PREPROCESS` means weather need preprocess or not, 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 in acc.log file.
+
 ## [Model Description](#contents)
 
 ### [Performance](#contents)

model_zoo/official/cv/simclr/ascend310_infer/CMakeLists.txt

@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

model_zoo/official/cv/simclr/ascend310_infer/build.sh

@@ -0,0 +1,30 @@
+#!/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/simclr/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/simclr/ascend310_infer/src/main.cc

@@ -0,0 +1,134 @@
+/**
+ * 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::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+
+
+DEFINE_string(simclr_classifier_mindir_path, "", "simclr_classifier mindir 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_simclr_classifier_mindir_path).empty()) {
+    std::cout << "Invalid simclr_classifier mindir path" << 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 simclr_classifier_graph;
+  Serialization::Load(FLAGS_simclr_classifier_mindir_path, ModelType::kMindIR, &simclr_classifier_graph);
+
+  Model simclr_classifier_model;
+  Status ret = simclr_classifier_model.Build(GraphCell(simclr_classifier_graph), context);
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build simclr_classifier model failed." << std::endl;
+    return 1;
+  }
+
+  std::vector<MSTensor> simclr_classifier_model_inputs = simclr_classifier_model.GetInputs();
+  if (simclr_classifier_model_inputs.empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+
+  auto input0_files = GetAllFiles(FLAGS_dataset_path);
+  if (input0_files.empty()) {
+    std::cout << "ERROR: no input data." << std::endl;
+    return 1;
+  }
+
+  std::map<double, double> costTime_map;
+  size_t size = input0_files.size();
+  std::cout << "size：" << size << std::endl;
+
+  for (size_t i = 0; i < size; ++i) {
+    struct timeval start = {0};
+    struct timeval end = {0};
+    double startTimeMs;
+    double endTimeMs;
+    std::vector<MSTensor> model_inputs;
+    std::vector<MSTensor> model_outputs;
+    std::cout << "Start predict input files:" << input0_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input0_files[i]);
+    model_inputs.emplace_back(simclr_classifier_model_inputs[0].Name(),
+                              simclr_classifier_model_inputs[0].DataType(),
+                              simclr_classifier_model_inputs[0].Shape(),
+                              input0.Data().get(), input0.DataSize());
+    gettimeofday(&start, nullptr);
+    ret = simclr_classifier_model.Predict(model_inputs, &model_outputs);
+      if (ret != kSuccess) {
+        std::cout << "Predict" << input0_files[i] << "failed." << std::endl;
+        return 1;
+      }
+
+    gettimeofday(&end, nullptr);
+    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], model_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/simclr/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/simclr/export.py

@@ -18,45 +18,51 @@ python export.py
 """
 import argparse
 import numpy as np
-
-import mindspore as ms
-from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
-
-from src.simclr_model import SimCLR
+import mindspore.common.dtype as mstype
+from mindspore import context, Tensor, nn, load_checkpoint, load_param_into_net, export
+from src.simclr_model import SimCLR, SimCLR_Classifier
 from src.resnet import resnet50 as resnet
 
 parser = argparse.ArgumentParser(description='SimCLR')
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
-parser.add_argument("--batch_size", type=int, default=128, help="batch size")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
 parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['cifar10'],
                     help='Dataset, Currently only cifar10 is supported.')
 parser.add_argument('--device_target', type=str, default="Ascend",
                     choices=['Ascend'],
                     help='Device target, Currently only Ascend is supported.')
-parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="simclr", help="output file name.")
-parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--ckpt_simclr_encoder", type=str, required=True, help="Simclr encoder checkpoint file path.")
+parser.add_argument("--ckpt_linear_classifier", type=str, required=True, help="Linear classifier checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="simclr_classifier", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
 args_opt = parser.parse_args()
 
 context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
 if args_opt.device_target == "Ascend":
     context.set_context(device_id=args_opt.device_id)
 
+
 if __name__ == '__main__':
-    if args_opt.dataset_name == 'cifar10':
-        width_multiplier = 1
-        cifar_stem = True
-        projection_dimension = 128
-        image_height = 32
-        image_width = 32
-    else:
+    if args_opt.dataset_name != 'cifar10':
         raise ValueError("dataset is not support.")
+    width_multiplier = 1
+    cifar_stem = True
+    projection_dimension = 128
+    class_num = 10
+    image_height = 32
+    image_width = 32
 
-    base_net = resnet(1, width_multiplier=width_multiplier, cifar_stem=cifar_stem)
-    net = SimCLR(base_net, projection_dimension, base_net.end_point.in_channels)
+    encoder = resnet(1, width_multiplier=width_multiplier, cifar_stem=cifar_stem)
+    classifier = nn.Dense(encoder.end_point.in_channels, class_num)
 
-    param_dict = load_checkpoint(args_opt.ckpt_file)
-    load_param_into_net(net, param_dict)
+    simclr = SimCLR(encoder, projection_dimension, encoder.end_point.in_channels)
+    param_simclr = load_checkpoint(args_opt.ckpt_simclr_encoder)
+    load_param_into_net(simclr, param_simclr)
 
-    input_arr = Tensor(np.zeros([args_opt.batch_size, 3, image_height, image_width]), ms.float32)
-    export(net, input_arr, file_name=args_opt.file_name, file_format=args_opt.file_format)
+    param_classifier = load_checkpoint(args_opt.ckpt_linear_classifier)
+    load_param_into_net(classifier, param_classifier)
+
+    # export SimCLR_Classifier network
+    simclr_classifier = SimCLR_Classifier(simclr.encoder, classifier)
+    input_data = Tensor(np.zeros([args_opt.batch_size, 3, image_height, image_width]), mstype.float32)
+    export(simclr_classifier, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)

model_zoo/official/cv/simclr/postprocess.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+"""
+#################SimCLR postprocess########################
+"""
+import argparse
+import os
+import numpy as np
+
+parser = argparse.ArgumentParser(description='SimCLR Postprocess')
+parser.add_argument('--label_dir', type=str, default='', help='label data directory.')
+parser.add_argument('--result_dir', type=str, default="./result_Files",
+                    help='infer result dir.')
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument('--class_num', type=int, default=10, help='dataset classification number, default is 10.')
+args, _ = parser.parse_known_args()
+
+
+if __name__ == '__main__':
+
+    rst_path = args.result_dir
+    labels = np.load(args.label_dir)
+    top1 = 0
+    total_data = len(os.listdir(rst_path))
+
+    for i in range(total_data):
+        file_name = os.path.join(rst_path, "cifar10_data_bs" + str(args.batch_size) + '_' + str(i) + '_0.bin')
+        output = np.fromfile(file_name, dtype=np.float32).reshape(args.batch_size, args.class_num)
+        for j in range(args.batch_size):
+            predict = np.argmax(output[j], axis=0)
+            y = labels[i][j]
+            if predict == y:
+                top1 += 1
+
+    print("result of Accuracy is: ", top1 / (total_data * args.batch_size))

model_zoo/official/cv/simclr/preprocess.py

@@ -0,0 +1,51 @@
+# 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 cifar-10#################
+"""
+import ast
+import argparse
+import os
+import numpy as np
+from src.dataset import create_dataset
+
+parser = argparse.ArgumentParser(description='preprocess cifar10')
+parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Running distributed evaluation.')
+parser.add_argument('--dataset_name', type=str, default='cifar10', help='Dataset, Currently only cifar10 is supported.')
+parser.add_argument('--eval_dataset_path', type=str, default='./cifar/eval',\
+                    help='Dataset path for evaluating SimCLR.')
+parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument('--use_norm', type=ast.literal_eval, default=False, help='Dataset normalize.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+
+    dataset = create_dataset(args, dataset_mode="eval_classifier")
+    img_path = os.path.join(args.result_path, "00_data")
+    if os.path.exists(img_path):
+        os.rmtree(img_path)
+    os.makedirs(img_path)
+    label_list = []
+
+    for idx, data in enumerate(dataset, start=0):
+        _, images, labels = data
+        file_name = "cifar10_data_bs" + str(args.batch_size) + "_" + str(idx) + ".bin"
+        file_path = img_path + "/" + file_name
+        images.asnumpy().tofile(file_path)
+        label_list.append(labels.asnumpy())
+
+    np.save(args.result_path + "label_ids.npy", label_list)
+    print("="*20, "export bin files finished", "="*20)

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

@@ -0,0 +1,122 @@
+#!/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 || $# -gt 4 ]]; then
+    echo "Usage: bash run_infer_310.sh [SIMCLR_CLASSIFIER_MINDIR_PATH] [DATA_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+
+model=$(get_real_path $1)
+
+data_path=$(get_real_path $2)
+
+if [ "$3" == "y" ] || [ "$3" == "n" ];then
+    need_preprocess=$3
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 4 ]; then
+    device_id=$4
+fi
+
+echo "simclr_classifier mindir: "$model
+echo "dataset path: "$data_path
+echo "need preprocess: "$need_preprocess
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py --eval_dataset_path=$data_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 --simclr_classifier_mindir_path=$model --dataset_path=./preprocess_Result/00_data --device_id=$device_id  &> infer.log
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py --result_dir=./result_Files --label_dir=./preprocess_Result/label_ids.npy &> 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

model_zoo/official/cv/simclr/src/simclr_model.py

@@ -51,3 +51,18 @@ class SimCLR(nn.Cell):
     def inference(self, x):
         h = self.encoder(x)
         return h
+
+class SimCLR_Classifier(nn.Cell):
+    """
+    SimCLR with Classifier.
+    """
+    def __init__(self, encoder, classifier):
+        super(SimCLR_Classifier, self).__init__()
+        self.encoder = encoder
+        self.classifier = classifier
+        self.softmax = nn.Softmax()
+
+    def construct(self, x):
+        y = self.encoder(x)
+        z = self.classifier(y)
+        return self.softmax(z)