diff --git a/official/cv/crnn/README.md b/official/cv/crnn/README.md
index 237142ab15a570bfb6efcc26846e14cd9f6c541a..02226ad55614c09f9e7c90105f0365459b19abe5 100644
--- a/official/cv/crnn/README.md
+++ b/official/cv/crnn/README.md
@@ -24,7 +24,6 @@
         - [Export MindIR](#export-mindir)
         - [Infer on Ascend310](#infer-on-ascend310)
         - [result](#result)
-        - [Post Training Quantization](#post-training-quantization)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Training Performance](#training-performance)
@@ -422,41 +421,6 @@ correct num: 2042 , total num: 3000
 result CRNNAccuracy is: 0.806666666666
 ```
 
-### [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on IIIT5K dataset.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --eval_dataset [DATASET NAME] --eval_dataset_path [DATA PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --eval_dataset [DATASET NAME] --eval_dataset_path [DATA PATH] --ckpt_file [CKPT_PATH]
-```
-
-The quantized AIR file will be stored as "./results/crnn_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-correct num: 2398 , total num: 3000
-result CRNNAccuracy is: 0.7933333333333
-```
-
 ## [Model Description](#contents)
 
 ### [Performance](#contents)
diff --git a/official/cv/crnn/ascend310_quant_infer/acc.py b/official/cv/crnn/ascend310_quant_infer/acc.py
deleted file mode 100644
index 6438f9d970856861c9757da2b6c9ebf8b85b3c4c..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# 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 argparse
-import numpy as np
-import Levenshtein
-from mindspore import nn
-
-
-label_dict = "abcdefghijklmnopqrstuvwxyz0123456789"
-
-parser = argparse.ArgumentParser("crnn quant postprocess")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-parser.add_argument("--label_path", type=str, required=True, help="path to label.npy.")
-parser.add_argument("--num_step", type=int, default=24, help="num step for LSTM layer")
-
-args, _ = parser.parse_known_args()
-
-
-class CRNNAccuracy(nn.Metric):
-    """
-    Define accuracy metric for warpctc network.
-    """
-
-    def __init__(self, print_flag=True):
-        super(CRNNAccuracy, self).__init__()
-        self._correct_num = 0
-        self._total_num = 0
-        self.blank = len(label_dict)
-        self.print_flag = print_flag
-
-    def clear(self):
-        self._correct_num = 0
-        self._total_num = 0
-
-    def update(self, *inputs):
-        if len(inputs) != 2:
-            raise ValueError('CRNNAccuracy need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
-        y_pred = self._convert_data(inputs[0])
-        str_pred = self._ctc_greedy_decoder(y_pred)
-        if isinstance(inputs[1], list) and isinstance(inputs[1][0], str):
-            str_label = [x.lower() for x in inputs[1]]
-        else:
-            y = self._convert_data(inputs[1])
-            str_label = self._convert_labels(y)
-
-        for pred, label in zip(str_pred, str_label):
-            if self.print_flag:
-                print(pred, " :: ", label)
-            edit_distance = Levenshtein.distance(pred, label)
-            self._total_num += 1
-            if edit_distance == 0:
-                self._correct_num += 1
-
-    def eval(self):
-        if self._total_num == 0:
-            raise RuntimeError('Accuary can not be calculated, because the number of samples is 0.')
-        print('correct num: ', self._correct_num, ', total num: ', self._total_num)
-        sequence_accurancy = self._correct_num / self._total_num
-        return sequence_accurancy
-
-    def _arr2char(self, inputs):
-        string = ""
-        for i in inputs:
-            if i < self.blank:
-                string += label_dict[i]
-        return string
-
-    def _convert_labels(self, inputs):
-        str_list = []
-        for label in inputs:
-            str_temp = self._arr2char(label)
-            str_list.append(str_temp)
-        return str_list
-
-    def _ctc_greedy_decoder(self, y_pred):
-        """
-        parse predict result to labels
-        """
-        indices = []
-        seq_len, batch_size, _ = y_pred.shape
-        indices = y_pred.argmax(axis=2)
-        lens = [seq_len] * batch_size
-        pred_labels = []
-        for i in range(batch_size):
-            idx = indices[:, i]
-            last_idx = self.blank
-            pred_label = []
-            for j in range(lens[i]):
-                cur_idx = idx[j]
-                if cur_idx not in [last_idx, self.blank]:
-                    pred_label.append(cur_idx)
-                last_idx = cur_idx
-            pred_labels.append(pred_label)
-        str_results = []
-        for i in pred_labels:
-            str_results.append(self._arr2char(i))
-        return str_results
-
-
-def calculate_acc(result_path, label_path):
-    """
-    Calculate accuracy according to the annotation file and result file.
-    """
-    metrics = CRNNAccuracy()
-    label_list = np.load(label_path)
-    prefix = "crnn_data_bs_1_"
-    for i in range(len(os.listdir(result_path))):
-        result_file = os.path.join(result_path, prefix + str(i) + "_output_0.bin")
-        class_num = len(label_dict) + 1
-        pred_y = np.fromfile(result_file, dtype=np.float16).reshape(args.num_step, -1, class_num)
-        label = label_list[i]
-        label_str = str()
-        for index in label[0]:
-            if int(index) < len(label_dict):
-                label_str += label_dict[int(index)]
-        metrics.update(pred_y, [label_str])
-
-    print("result CRNNAccuracy is: ", metrics.eval())
-    metrics.clear()
-
-
-if __name__ == '__main__':
-    calculate_acc(args.result_path, args.label_path)
diff --git a/official/cv/crnn/ascend310_quant_infer/export_bin.py b/official/cv/crnn/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index 85ea3b1ddfcf06fbfa522be478d19cb38d3fd0e3..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import shutil
-import numpy as np
-from mindspore import context
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    result_path = "./data"
-    if os.path.exists(result_path):
-        shutil.rmtree(result_path)
-    os.makedirs(result_path)
-
-    data_path = os.path.join(result_path, "00_input")
-    os.makedirs(data_path)
-
-    dataset = create_dataset(name=config.eval_dataset,
-                             dataset_path=config.eval_dataset_path,
-                             batch_size=1,
-                             is_training=False,
-                             config=config)
-    labels_list = []
-    prefix = "crnn_data_bs_1_"
-    for i, data in enumerate(dataset):
-        file_path = os.path.join(data_path, prefix + str(i) + ".bin")
-        data[0].asnumpy().tofile(file_path)
-        labels_list.append(data[1].asnumpy())
-    np.save(os.path.join(result_path, "label.npy"), labels_list)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.dataset import create_dataset
-    from src.model_utils.config import config
-
-    generate_data()
diff --git a/official/cv/crnn/ascend310_quant_infer/inc/model_process.h b/official/cv/crnn/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/crnn/ascend310_quant_infer/inc/sample_process.h b/official/cv/crnn/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/crnn/ascend310_quant_infer/inc/utils.h b/official/cv/crnn/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/crnn/ascend310_quant_infer/post_quant.py b/official/cv/crnn/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index b7b28717da618e5b7a783cc730e7b1459c2d6082..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore as ms
-from mindspore import Tensor, context, load_checkpoint
-
-
-def quant_crnn(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    for data in dataset.create_dict_iterator(num_epochs=1):
-        _ = calibration_network(data["image"])
-
-    # step5: export the air file
-    save_model("results/crnn_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/crnn_quant.air"))
-
-
-def model_export():
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
-    config.batch_size = 1
-    net = crnn(config)
-    load_checkpoint(config.ckpt_file, net=net)
-    net.set_train(False)
-
-    input_data = Tensor(np.zeros([config.batch_size, 3, config.image_height, config.image_width]), ms.float32)
-
-    ds = create_dataset(name=config.eval_dataset,
-                        dataset_path=config.eval_dataset_path,
-                        batch_size=config.batch_size,
-                        is_training=False,
-                        config=config)
-    dataset = ds.take(1)
-    quant_crnn(net, dataset, input_data)
-
-
-if __name__ == '__main__':
-    sys.path.append("..")
-    from src.crnn import crnn
-    from src.model_utils.config import config
-    from src.model_utils.device_adapter import get_device_id
-    from src.dataset import create_dataset
-
-    model_export()
diff --git a/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh b/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index b034e5b293a114a7b6b05904b4880bc5e2e0cc9b..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/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 [ $# -lt 3 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]"
-    echo "Example: bash run_quant_infer.sh ./crnn_quant.air ./00_data ./label.npy"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-label_path=$(get_real_path $3)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "label path: "$label_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=crnn_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./crnn_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --label_path=$label_path  &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/crnn/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/crnn/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/crnn/ascend310_quant_infer/src/acl.json b/official/cv/crnn/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/crnn/ascend310_quant_infer/src/build.sh b/official/cv/crnn/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/crnn/ascend310_quant_infer/src/main.cpp b/official/cv/crnn/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/crnn/ascend310_quant_infer/src/model_process.cpp b/official/cv/crnn/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/crnn/ascend310_quant_infer/src/sample_process.cpp b/official/cv/crnn/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 5f8f20f297bf6101d3a68ffd65bd0e6ed69d9486..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/crnn/ascend310_quant_infer/src/utils.cpp b/official/cv/crnn/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/crnn/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/deeplabv3/README.md b/official/cv/deeplabv3/README.md
index 185eda20a7a842bb85f193591a817be50b710630..4f55129f70865688a18f8eacd4cf68287714c4ab 100644
--- a/official/cv/deeplabv3/README.md
+++ b/official/cv/deeplabv3/README.md
@@ -30,7 +30,6 @@
     - [Inference Process](#inference-process)
         - [Usage](#usage-2)
         - [result](#result-2)
-    - [Post Training Quantization](#post-training-quantization)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
@@ -868,40 +867,6 @@ Inference result is saved in current path, you can find result in acc.log file.
 | :----------: | :-----: | :----: | :----: | :-----: | :-----: | :-------------: |
 | deeplab_v3 |       | √    |      |       | 78.84 | 78.51    |
 
-## [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-In this project, the model is set as deeplab_v3_s8.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --model [MODEL] --data_root [DATA ROOT] --data_lst [DATA LST]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --model [MODEL] --data_root [DATA ROOT] --data_lst [DATA LST] --ckpt_file [CKPT_PATH]
-```
-
-The quantized AIR file will be stored as "./results/deeplabv3_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH] [SHAPE_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-mean Iou 0.7854572371350974
-```
-
 # [Model Description](#contents)
 
 ## [Performance](#contents)
diff --git a/official/cv/deeplabv3/README_CN.md b/official/cv/deeplabv3/README_CN.md
index 8017d3f9c8278f9a6afcb46792c969e05a80cf0e..63b111ea4c9b20932b734be6e7200d29d84f4fcd 100644
--- a/official/cv/deeplabv3/README_CN.md
+++ b/official/cv/deeplabv3/README_CN.md
@@ -31,7 +31,6 @@
     - [推理过程](#推理过程)
         - [用法](#用法-2)
         - [结果](#结果-2)
-    - [训练后量化推理](#训练后量化推理)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [训练性能](#训练性能)
@@ -835,40 +834,6 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DATA_ROOT] [DATA_LIST] [DEVICE_
 | :----------: | :-----: | :----: | :----: | :-----: | :-----: | :-------------: |
 | deeplab_v3 |       | √    |      |       | 78.84 | 78.51    |
 
-## [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。
-本训练后量化工程的模型类型是deeplab_v3_s8。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --model [MODEL] --data_root [DATA ROOT] --data_lst [DATA LST]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --model [MODEL] --data_root [DATA ROOT] --data_lst [DATA LST] --ckpt_file [CKPT_PATH]
-```
-
-导出的模型会存储在./result/deeplabv3_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH] [SHAPE_PATH]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-mean Iou 0.7854572371350974
-```
-
 # 模型描述
 
 ## 性能
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/acc.py b/official/cv/deeplabv3/ascend310_quant_infer/acc.py
deleted file mode 100644
index 49ba00d34407a2b1247c87e18738ee837f4a9a0c..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# 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.
-# ============================================================================
-"""post process for 310 inference"""
-import os
-import argparse
-import numpy as np
-from PIL import Image
-import cv2
-
-
-parser = argparse.ArgumentParser(description="deeplabv3 accuracy calculation")
-parser.add_argument('--crop_size', type=int, default=513, help='crop size')
-parser.add_argument('--num_classes', type=int, default=21, help='number of classes')
-parser.add_argument('--result_path', type=str, default='./result', help='result Files path')
-parser.add_argument('--label_path', type=str, default='./01_label', help='result Files path')
-parser.add_argument('--shape_path', type=str, default='./shape.npy', help='path of image shape')
-args, _ = parser.parse_known_args()
-
-
-def get_img_size(file_name):
-    img = Image.open(file_name)
-    return img.size
-
-
-def get_resized_size(org_h, org_w, long_size=513):
-    if org_h > org_w:
-        new_h = long_size
-        new_w = int(1.0 * long_size * org_w / org_h)
-    else:
-        new_w = long_size
-        new_h = int(1.0 * long_size * org_h / org_w)
-    return new_h, new_w
-
-
-def cal_hist(a, b, n):
-    k = (a >= 0) & (a < n)
-    return np.bincount(n * a[k].astype(np.int32) + b[k], minlength=n ** 2).reshape(n, n)
-
-
-def acc_cal(result_path, label_path, shape_path):
-    hist = np.zeros((args.num_classes, args.num_classes))
-    mask_shape = np.load(shape_path)
-    prefix = "deeplabv3_data_bs_1_"
-    for i in range(len(mask_shape)):
-        output = os.path.join(result_path, prefix + str(i) + "_output_0.bin")
-        net_out = np.fromfile(output, np.float32).reshape(args.num_classes, args.crop_size, args.crop_size)
-        ori_height, ori_width = mask_shape[i][0], mask_shape[i][1]
-        resize_h, resize_w = get_resized_size(ori_height, ori_width)
-        probs_ = net_out[:, :resize_h, :resize_w].transpose((1, 2, 0))
-        probs_ = cv2.resize(probs_, (ori_width, ori_height))
-
-        result_msk = probs_.argmax(axis=2)
-        label = os.path.join(label_path, prefix + str(i) + ".bin")
-        mask = np.fromfile(label, np.uint8).reshape(mask_shape[i])
-
-        hist += cal_hist(mask.flatten(), result_msk.flatten(), args.num_classes)
-
-    print(hist)
-    iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
-    print('per-class IoU', iu)
-    print('mean IoU', np.nanmean(iu))
-
-
-if __name__ == '__main__':
-    acc_cal(args.result_path, args.label_path, args.shape_path)
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/config.cfg b/official/cv/deeplabv3/ascend310_quant_infer/config.cfg
deleted file mode 100644
index 66fab9aad80306f03f3d869786384ea69437147f..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/config.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-skip_layers:"network.resnet.layer4.0.downsample.0"
-skip_fusion_layers:"network.resnet.layer4.0.downsample.0"
\ No newline at end of file
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py b/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index ad29f34553438d094fd973842bbcd5002c98a8e0..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import shutil
-import cv2
-import numpy as np
-from PIL import Image
-
-
-prefix = "deeplabv3_data_bs_"
-data_path = "./data"
-if os.path.exists(data_path):
-    shutil.rmtree(data_path)
-os.makedirs(data_path)
-
-
-def get_img_size(file_name):
-    img = Image.open(file_name)
-    return img.size
-
-
-def resize_long(img, long_size=513):
-    h, w, _ = img.shape
-    if h > w:
-        new_h = long_size
-        new_w = int(1.0 * long_size * w / h)
-    else:
-        new_w = long_size
-        new_h = int(1.0 * long_size * h / w)
-    imo = cv2.resize(img, (new_w, new_h))
-    return imo
-
-
-def pre_process(args, img_, crop_size=513):
-    # resize
-    img_ = resize_long(img_, crop_size)
-    resize_h, resize_w, _ = img_.shape
-
-    # mean, std
-    image_mean = np.array(args.image_mean)
-    image_std = np.array(args.image_std)
-    img_ = (img_ - image_mean) / image_std
-
-    # pad to crop_size
-    pad_h = crop_size - img_.shape[0]
-    pad_w = crop_size - img_.shape[1]
-    if pad_h > 0 or pad_w > 0:
-        img_ = cv2.copyMakeBorder(img_, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)
-    # hwc to chw
-    img_ = img_.transpose((2, 0, 1))
-    return img_, resize_h, resize_w
-
-
-def eval_batch(args, img_lst, crop_size, index):
-    batch_size = len(img_lst)
-    batch_img = np.zeros((batch_size, 3, crop_size, crop_size), dtype=np.float32)
-    resize_hw = []
-    for l in range(batch_size):
-        img_ = img_lst[l]
-        img_, resize_h, resize_w = pre_process(args, img_, crop_size)
-        batch_img[l] = img_
-        resize_hw.append([resize_h, resize_w])
-
-    batch_img = np.ascontiguousarray(batch_img)
-    data_dir = os.path.join(data_path, "00_data")
-    if not os.path.exists(data_dir):
-        os.makedirs(data_dir)
-    data_file = os.path.join(data_dir, prefix + str(batch_size) + "_" + str(index) + ".bin")
-    batch_img.tofile(data_file)
-
-def eval_batch_scales(args, img_lst, scales, base_crop_size, index):
-    sizes_ = [int((base_crop_size - 1) * sc) + 1 for sc in scales]
-    return eval_batch(args, img_lst, crop_size=sizes_[0], index=index)
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    config.scales = config.scales_list[config.scales_type]
-    args = config
-    # data list
-    with open(args.data_lst) as f:
-        img_lst = f.readlines()
-
-    # evaluate
-    batch_img_lst = []
-    batch_msk_lst = []
-    shape_lst = []
-    for i, line in enumerate(img_lst):
-        ori_img_path, ori_msk_path = line.strip().split(" ")
-        img_path = "VOCdevkit" + ori_img_path.split("VOCdevkit")[1]
-        msk_path = "VOCdevkit" + ori_msk_path.split("VOCdevkit")[1]
-        img_path = os.path.join(args.data_root, img_path)
-        msk_path = os.path.join(args.data_root, msk_path)
-        org_width, org_height = get_img_size(img_path)
-        shape_lst.append([org_height, org_width])
-        img_ = cv2.imread(img_path)
-        msk_ = cv2.imread(msk_path, cv2.IMREAD_GRAYSCALE)
-        batch_img_lst.append(img_)
-        batch_msk_lst.append(msk_)
-        eval_batch_scales(args, batch_img_lst, scales=args.scales, base_crop_size=args.crop_size, index=i)
-        label_dir = os.path.join(data_path, "01_label")
-        if not os.path.exists(label_dir):
-            os.makedirs(label_dir)
-        label_path = os.path.join(label_dir, prefix + str(len(batch_img_lst)) + "_" + str(i) + ".bin")
-        msk_.tofile(label_path)
-        batch_img_lst = []
-        batch_msk_lst = []
-    np.save(os.path.join(data_path, "shape.npy"), shape_lst)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from model_utils.config import config
-
-    generate_data()
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/fusion_switch.cfg b/official/cv/deeplabv3/ascend310_quant_infer/fusion_switch.cfg
deleted file mode 100644
index f5fadd59385362ae3ce752172c1226162776c447..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/fusion_switch.cfg
+++ /dev/null
@@ -1 +0,0 @@
-ConvBatchnormFusionPass:off
\ No newline at end of file
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/inc/model_process.h b/official/cv/deeplabv3/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/inc/sample_process.h b/official/cv/deeplabv3/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/inc/utils.h b/official/cv/deeplabv3/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py b/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 125d65741d533d781e6c44a1055f77d2fa988417..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import os
-import sys
-import cv2
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore.nn as nn
-import mindspore.ops as ops
-from mindspore import Tensor, context
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-def resize_long(img, long_size=513):
-    h, w, _ = img.shape
-    if h > w:
-        new_h = long_size
-        new_w = int(1.0 * long_size * w / h)
-    else:
-        new_w = long_size
-        new_h = int(1.0 * long_size * h / w)
-    imo = cv2.resize(img, (new_w, new_h))
-    return imo
-
-
-def pre_process(args, img_, crop_size=513):
-    # resize
-    img_ = resize_long(img_, crop_size)
-    resize_h, resize_w, _ = img_.shape
-
-    # mean, std
-    image_mean = np.array(args.image_mean)
-    image_std = np.array(args.image_std)
-    img_ = (img_ - image_mean) / image_std
-
-    # pad to crop_size
-    pad_h = crop_size - img_.shape[0]
-    pad_w = crop_size - img_.shape[1]
-    if pad_h > 0 or pad_w > 0:
-        img_ = cv2.copyMakeBorder(img_, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=0)
-    # hwc to chw
-    img_ = img_.transpose((2, 0, 1))
-    return img_, resize_h, resize_w
-
-
-def eval_batch(args, img_lst, crop_size=513):
-    batch_size = len(img_lst)
-    batch_img = np.zeros((batch_size, 3, crop_size, crop_size), dtype=np.float32)
-    resize_hw = []
-    for l in range(batch_size):
-        img_ = img_lst[l]
-        img_, resize_h, resize_w = pre_process(args, img_, crop_size)
-        batch_img[l] = img_
-        resize_hw.append([resize_h, resize_w])
-
-    batch_img = np.ascontiguousarray(batch_img)
-    return batch_img
-
-
-def eval_batch_scales(args, img_lst, scales, base_crop_size):
-    sizes_ = [int((base_crop_size - 1) * sc) + 1 for sc in scales]
-    return eval_batch(args, img_lst, crop_size=sizes_[0])
-
-
-def generate_batch_data():
-    config.scales = config.scales_list[config.scales_type]
-    args = config
-    # data list
-    with open(args.data_lst) as f:
-        img_lst = f.readlines()
-
-    # evaluate
-    batch_img_lst = []
-    ori_img_path, _ = img_lst[0].strip().split(" ")
-    img_path = "VOCdevkit" + ori_img_path.split("VOCdevkit")[1]
-
-    img_path = os.path.join(args.data_root, img_path)
-    img_ = cv2.imread(img_path)
-    batch_img_lst.append(img_)
-    return eval_batch_scales(args, batch_img_lst, scales=args.scales, base_crop_size=args.crop_size)
-
-
-def quant_deeplabv3(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data, config_defination="./config.cfg")
-    # There is value beyond 1e30 in this layer. So this layer will not be quantized.
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    _ = calibration_network(Tensor(dataset))
-
-    # step5: export the air file
-    save_model("results/deeplabv3_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/deeplabv3_quant.air"))
-
-
-class BuildEvalNetwork(nn.Cell):
-    def __init__(self, net, input_format="NCHW"):
-        super(BuildEvalNetwork, self).__init__()
-        self.network = net
-        self.softmax = nn.Softmax(axis=1)
-        self.transpose = ops.Transpose()
-        self.format = input_format
-
-    def construct(self, x):
-        if self.format == "NHWC":
-            x = self.transpose(x, (0, 3, 1, 2))
-        output = self.network(x)
-        output = self.softmax(output)
-        return output
-
-
-def run_export():
-    '''run export.'''
-    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
-    config.freeze_bn = True
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=config.device_id)
-
-    if config.export_model == 'deeplab_v3_s16':
-        network = net_factory.nets_map['deeplab_v3_s16']('eval', config.num_classes, 16, True)
-    else:
-        network = net_factory.nets_map['deeplab_v3_s8']('eval', config.num_classes, 8, True)
-    network = BuildEvalNetwork(network, config.input_format)
-    param_dict = load_checkpoint(config.ckpt_file)
-
-    # load the parameter into net
-    load_param_into_net(network, param_dict)
-    batch_size = 1
-    if config.input_format == "NHWC":
-        input_data = Tensor(
-            np.ones([batch_size, config.input_size, config.input_size, 3]).astype(np.float32))
-    else:
-        input_data = Tensor(
-            np.ones([batch_size, 3, config.input_size, config.input_size]).astype(np.float32))
-    batch_data = generate_batch_data()
-    quant_deeplabv3(network, batch_data, input_data)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.nets import net_factory
-    from model_utils.config import config
-
-    run_export()
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh b/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index 832ef3cd6f5d393715d617fce8ac5b9820a7f595..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/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 [ $# -lt 4 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH] [SHAPE_PATH]"
-    echo "Example: bash run_quant_infer.sh ./deeplabv3_quant.air ./00_data ./01_label ./shape.npy"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-label_path=$(get_real_path $3)
-shape_path=$(get_real_path $4)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "label path: "$label_path
-echo "shape path: "$shape_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=deeplabv3_quant --soc_version=Ascend310 --fusion_switch_file=./fusion_switch.cfg &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./deeplabv3_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --label_path=$label_path --shape_path=$shape_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/deeplabv3/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/acl.json b/official/cv/deeplabv3/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/build.sh b/official/cv/deeplabv3/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/main.cpp b/official/cv/deeplabv3/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/model_process.cpp b/official/cv/deeplabv3/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/sample_process.cpp b/official/cv/deeplabv3/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 5f8f20f297bf6101d3a68ffd65bd0e6ed69d9486..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/deeplabv3/ascend310_quant_infer/src/utils.cpp b/official/cv/deeplabv3/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/deeplabv3/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/maskrcnn/README.md b/official/cv/maskrcnn/README.md
index cc3be810288457cc8e242eed0466b85a637f8206..bd70672b1802b094e3c8fc5aa9f0b6600ef113e2 100644
--- a/official/cv/maskrcnn/README.md
+++ b/official/cv/maskrcnn/README.md
@@ -26,7 +26,6 @@
     - [Inference Process](#inference-process)
         - [Usage](#usage)
         - [result](#result)
-    - [Post Training Quantization](#post-training-quantization)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
@@ -753,69 +752,6 @@ Accumulating evaluation results...
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.594
 ```
 
-## [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on COCO2017 dataset.
-The inference process needs about 600G hard disk space to save the reasoning results.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --coco_root [COCO DATA PATH] --mindrecord_dir [MINDRECORD PATH] --ann_file [ANNOTATION PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --coco_root [COCO DATA PATH] --mindrecord_dir [MINDRECORD PATH] --ckpt_file [CKPT_PATH]
-```
-
-The quantized AIR file will be stored as "./results/maskrcnn_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-Evaluate annotation type *bbox*
-Accumulating evaluation results...
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.378
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.602
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.240
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.500
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.528
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.572
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657
-
-Evaluate annotation type *segm*
-Accumulating evaluation results...
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.321
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.553
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.328
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.276
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.422
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.441
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.279
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.476
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578
-```
-
 # Model Description
 
 ## Performance
diff --git a/official/cv/maskrcnn/README_CN.md b/official/cv/maskrcnn/README_CN.md
index fcca9b9d0acdac977d664df459eeff5b0f753dfd..2e37e9c03d776c751c6c07fb00f443d234ebee96 100644
--- a/official/cv/maskrcnn/README_CN.md
+++ b/official/cv/maskrcnn/README_CN.md
@@ -25,7 +25,6 @@
     - [推理过程](#推理过程)
         - [使用方法](#使用方法)
         - [结果](#结果)
-    - [训练后量化推理](#训练后量化推理)
 - [模型说明](#模型说明)
     - [性能](#性能)
         - [训练性能](#训练性能)
@@ -700,68 +699,6 @@ Accumulating evaluation results...
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.594
 ```
 
-## [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于COCO2017数据集。
-推理过程需要占用大约600G的硬盘空间来保存推理的结果。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --coco_root [COCO DATA PATH] --mindrecord_dir [MINDRECORD PATH] --ann_file [ANNOTATION PATH]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --coco_root [COCO DATA PATH] --mindrecord_dir [MINDRECORD PATH] --ckpt_file [CKPT_PATH]
-```
-
-导出的模型会存储在./result/maskrcnn_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-Evaluate annotation type *bbox*
-Accumulating evaluation results...
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.378
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.602
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.240
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.420
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.481
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.500
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.528
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.572
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657
-
-Evaluate annotation type *segm*
-Accumulating evaluation results...
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.321
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.553
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.328
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.164
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.350
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.276
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.422
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.441
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.279
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.476
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.578
-```
-
 # 模型说明
 
 ## 性能
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/acc.py b/official/cv/maskrcnn/ascend310_quant_infer/acc.py
deleted file mode 100644
index c6eaba9e73d675dabba8cb8d63dbb48bd7b9a006..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# 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.
-# ============================================================================
-"""post process for 310 inference"""
-import os
-import argparse
-import numpy as np
-from pycocotools.coco import COCO
-from util import coco_eval, bbox2result_1image, results2json, get_seg_masks
-
-
-parser = argparse.ArgumentParser("maskrcnn quant postprocess")
-parser.add_argument("--shape_path", type=str, required=True, help="path to image meta directory")
-parser.add_argument("--annotation_path", type=str, required=True, help="path to instance_xxx.json")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-
-args, _ = parser.parse_known_args()
-
-
-def get_eval_result(shape_data, ann_file, result_path):
-    """ Get metrics result according to the annotation file and result file"""
-    max_num = 128
-    result_path = result_path
-    outputs = []
-
-    dataset_coco = COCO(ann_file)
-    for index in range(len(os.listdir(shape_data))):
-        prefix = "coco2017_maskrcnn_bs_1_"
-        shape_file_path = os.path.join(shape_data, prefix + str(index) + ".bin")
-        shape_file = np.fromfile(shape_file_path, dtype=np.float16).reshape(1, 4)
-
-        bbox_result_file = os.path.join(result_path, prefix + str(index) + "_output_0.bin")
-        label_result_file = os.path.join(result_path, prefix + str(index) + "_output_1.bin")
-        mask_result_file = os.path.join(result_path, prefix + str(index) + "_output_2.bin")
-        mask_fb_result_file = os.path.join(result_path, prefix + str(index) + "_output_3.bin")
-
-        all_bbox = np.fromfile(bbox_result_file, dtype=np.float16).reshape(80000, 5)
-        all_label = np.fromfile(label_result_file, dtype=np.int32).reshape(80000, 1)
-        all_mask = np.fromfile(mask_result_file, dtype=np.bool_).reshape(80000, 1)
-        all_mask_fb = np.fromfile(mask_fb_result_file, dtype=np.float16).reshape(80000, 28, 28)
-
-        all_bbox_squee = np.squeeze(all_bbox)
-        all_label_squee = np.squeeze(all_label)
-        all_mask_squee = np.squeeze(all_mask)
-        all_mask_fb_squee = np.squeeze(all_mask_fb)
-
-        all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
-        all_labels_tmp_mask = all_label_squee[all_mask_squee]
-        all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]
-
-        if all_bboxes_tmp_mask.shape[0] > max_num:
-            inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
-            inds = inds[:max_num]
-            all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
-            all_labels_tmp_mask = all_labels_tmp_mask[inds]
-            all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]
-        num_classes = 81
-        bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, num_classes)
-        segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, shape_file[0],
-                                     True, num_classes)
-        outputs.append((bbox_results, segm_results))
-
-    eval_types = ["bbox", "segm"]
-    result_files = results2json(dataset_coco, outputs, "./results.pkl")
-    coco_eval(result_files, eval_types, dataset_coco, single_result=False)
-
-
-if __name__ == '__main__':
-    get_eval_result(args.shape_path, args.annotation_path, args.result_path)
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/export_bin.py b/official/cv/maskrcnn/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index 1e651feadd86811f36bb05e285c7b04c6ff7cad6..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-from mindspore import context
-
-
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
-def generate_data(dataset_path):
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    batch_size = 1
-    ds = create_maskrcnn_dataset(dataset_path, batch_size=batch_size, is_training=False)
-    total = ds.get_dataset_size()
-
-    cur_dir = os.getcwd() + "/data"
-    img_path = os.path.join(cur_dir, "00_img_data")
-    if not os.path.exists(img_path):
-        os.makedirs(img_path)
-    meta_path = os.path.join(cur_dir, "01_meta_data")
-    if not os.path.exists(meta_path):
-        os.makedirs(meta_path)
-    print("\n========================================\n")
-    print("total images num: ", total)
-    print("Processing, please wait a moment.")
-    bin_prefix = "coco2017_maskrcnn_bs_1_"
-    for i, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)):
-        img_data = data["image"]
-        img_metas = data["image_shape"]
-        file_name = bin_prefix + str(i) + ".bin"
-        img_data.tofile(os.path.join(img_path, file_name))
-        img_metas.tofile(os.path.join(meta_path, file_name))
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.model_utils.config import config
-    from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
-
-    prefix = "MaskRcnn_eval.mindrecord"
-    mindrecord_dir = config.mindrecord_dir
-    mindrecord_file = os.path.join(mindrecord_dir, prefix)
-    if not os.path.exists(mindrecord_file):
-        if not os.path.isdir(mindrecord_dir):
-            os.makedirs(mindrecord_dir)
-        if config.dataset == "coco":
-            if os.path.isdir(config.coco_root):
-                print("Create Mindrecord.")
-                data_to_mindrecord_byte_image("coco", False, prefix, file_num=1)
-                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
-            else:
-                print("coco_root not exits.")
-        else:
-            if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
-                print("Create Mindrecord.")
-                data_to_mindrecord_byte_image("other", False, prefix, file_num=1)
-                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
-            else:
-                print("IMAGE_DIR or ANNO_PATH not exits.")
-
-    generate_data(mindrecord_file)
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/inc/model_process.h b/official/cv/maskrcnn/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 3706b955b97ec4e4cacde04e8d7889a78522785d..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(const std::vector<void *> &inputDataBuffer, const std::vector<size_t> &bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/inc/sample_process.h b/official/cv/maskrcnn/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 4e28dff95b2913a7223c4a48908640a0fc4e3d01..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder, char *shape_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/inc/utils.h b/official/cv/maskrcnn/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/post_quant.py b/official/cv/maskrcnn/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 90baada8a784257e84288f656a9e96891bd5c339..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import os
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-from mindspore import Tensor, context
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
-def quant_maskrcnn(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, *input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, *input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    for data in dataset.create_dict_iterator(num_epochs=1):
-        _ = calibration_network(data["image"], data["image_shape"])
-
-    # step5: export the air file
-    save_model("results/maskrcnn_quant", calibration_network, *input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/maskrcnn_quant.air"))
-
-
-def export_maskrcnn():
-    """ export_maskrcnn """
-    config.test_batch_size = 1
-    config.batch_size = config.test_batch_size
-    net = MaskRcnn_Infer(config=config)
-    param_dict = load_checkpoint(config.ckpt_file)
-    param_dict_new = {}
-    for key, value in param_dict.items():
-        param_dict_new["network." + key] = value
-    load_param_into_net(net, param_dict_new)
-    net.set_train(False)
-
-    img = Tensor(np.zeros([config.batch_size, 3, config.img_height, config.img_width], np.float16))
-    img_metas = Tensor(np.zeros([config.batch_size, 4], np.float16))
-    input_data = [img, img_metas]
-
-    mindrecord_file = os.path.join(config.mindrecord_dir, "MaskRcnn_eval.mindrecord")
-    ds = create_maskrcnn_dataset(mindrecord_file, batch_size=config.batch_size, is_training=False)
-    dataset = ds.take(1)
-    quant_maskrcnn(net, dataset, input_data)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.model_utils.config import config
-    from src.maskrcnn.mask_rcnn_r50 import MaskRcnn_Infer
-    from src.dataset import create_maskrcnn_dataset
-
-    export_maskrcnn()
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh b/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index 2926bf28507a67a08d415e95c17059a148015a5e..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/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 [ $# -lt 4 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]"
-    echo "Example: bash run_quant_infer.sh ./maskrcnn_quant.air ./00_img_data ./01_meta_data ./instances_val2017.json"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-shape_path=$(get_real_path $3)
-annotation_path=$(get_real_path $4)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "shape path: "$shape_path
-echo "annotation path: "$annotation_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=maskrcnn_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./maskrcnn_quant.om $data_path $shape_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --shape_path=$shape_path --annotation_path=$annotation_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/maskrcnn/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/acl.json b/official/cv/maskrcnn/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/build.sh b/official/cv/maskrcnn/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/main.cpp b/official/cv/maskrcnn/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 5fb6bad7044e27eb7958277b7cc191ea8924e9df..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 4) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder path_of_shapeFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2], argv[3]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/model_process.cpp b/official/cv/maskrcnn/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 59f2f7a2509d63e77ebcec596e73ae8225ec7e02..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(const std::vector<void *> &inputDataBuffer, const std::vector<size_t> &bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-    for (size_t i = 0; i < inputDataBuffer.size(); ++i) {
-        aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer[i], bufferSize[i]);
-        if (inputData == nullptr) {
-            ERROR_LOG("can't create data buffer, create input failed");
-            return FAILED;
-        }
-
-        aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("add input dataset buffer failed");
-            aclDestroyDataBuffer(inputData);
-            inputData = nullptr;
-            return FAILED;
-        }
-    }
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/sample_process.cpp b/official/cv/maskrcnn/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 10e36d747d7dd42643d750948fe9a69913cff315..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,263 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder, char *shape_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-    std::vector<string> shapeFile;
-    GetAllFiles(shape_folder, &shapeFile);
-
-    if (testFile.size() !=shapeFile.size()) {
-        ERROR_LOG("number of data files is not equal to shape file");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process data file:%s", testFile[index].c_str());
-        INFO_LOG("start to process shape file:%s", shapeFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed, index is %zu", index);
-            return FAILED;
-        }
-
-        uint32_t devBufferShapeSize;
-        void *shapeDevBuffer = Utils::GetDeviceBufferOfFile(shapeFile[index], &devBufferShapeSize);
-        if (shapeDevBuffer == nullptr) {
-            ERROR_LOG("get shape device buffer failed, index is %zu", index);
-            return FAILED;
-        }
-
-        std::vector<void *> inputBuffers({picDevBuffer, shapeDevBuffer});
-        std::vector<size_t> inputSizes({devBufferSize, devBufferShapeSize});
-
-        ret = processModel.CreateInput(inputBuffers, inputSizes);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        gettimeofday(&start, nullptr);
-        double startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        double endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/src/utils.cpp b/official/cv/maskrcnn/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/maskrcnn/ascend310_quant_infer/util.py b/official/cv/maskrcnn/ascend310_quant_infer/util.py
deleted file mode 100644
index 391bc24a47d5413c8800cc93c2e12348abb61853..0000000000000000000000000000000000000000
--- a/official/cv/maskrcnn/ascend310_quant_infer/util.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# 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.
-# ============================================================================
-"""coco eval for maskrcnn"""
-import json
-import cv2
-import numpy as np
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-from pycocotools import mask as maskUtils
-
-
-_init_value = np.array(0.0)
-summary_init = {
-    'Precision/mAP': _init_value,
-    'Precision/mAP@.50IOU': _init_value,
-    'Precision/mAP@.75IOU': _init_value,
-    'Precision/mAP (small)': _init_value,
-    'Precision/mAP (medium)': _init_value,
-    'Precision/mAP (large)': _init_value,
-    'Recall/AR@1': _init_value,
-    'Recall/AR@10': _init_value,
-    'Recall/AR@100': _init_value,
-    'Recall/AR@100 (small)': _init_value,
-    'Recall/AR@100 (medium)': _init_value,
-    'Recall/AR@100 (large)': _init_value,
-}
-
-
-def coco_eval(result_files, result_types, coco, max_dets=(100, 300, 1000), single_result=False):
-    """coco eval for maskrcnn"""
-    anns = json.load(open(result_files['bbox']))
-    if not anns:
-        return summary_init
-    if isinstance(coco, str):
-        coco = COCO(coco)
-    assert isinstance(coco, COCO)
-
-    for res_type in result_types:
-        result_file = result_files[res_type]
-        assert result_file.endswith('.json')
-
-        coco_dets = coco.loadRes(result_file)
-        gt_img_ids = coco.getImgIds()
-        det_img_ids = coco_dets.getImgIds()
-        iou_type = 'bbox' if res_type == 'proposal' else res_type
-        cocoEval = COCOeval(coco, coco_dets, iou_type)
-        if res_type == 'proposal':
-            cocoEval.params.useCats = 0
-            cocoEval.params.maxDets = list(max_dets)
-
-        tgt_ids = gt_img_ids if not single_result else det_img_ids
-
-        if single_result:
-            res_dict = dict()
-            for id_i in tgt_ids:
-                cocoEval = COCOeval(coco, coco_dets, iou_type)
-                if res_type == 'proposal':
-                    cocoEval.params.useCats = 0
-                    cocoEval.params.maxDets = list(max_dets)
-
-                cocoEval.params.imgIds = [id_i]
-                cocoEval.evaluate()
-                cocoEval.accumulate()
-                cocoEval.summarize()
-                res_dict.update({coco.imgs[id_i]['file_name']: cocoEval.stats[1]})
-
-        cocoEval = COCOeval(coco, coco_dets, iou_type)
-        if res_type == 'proposal':
-            cocoEval.params.useCats = 0
-            cocoEval.params.maxDets = list(max_dets)
-
-        cocoEval.params.imgIds = tgt_ids
-        cocoEval.evaluate()
-        cocoEval.accumulate()
-        cocoEval.summarize()
-
-        summary_metrics = {
-            'Precision/mAP': cocoEval.stats[0],
-            'Precision/mAP@.50IOU': cocoEval.stats[1],
-            'Precision/mAP@.75IOU': cocoEval.stats[2],
-            'Precision/mAP (small)': cocoEval.stats[3],
-            'Precision/mAP (medium)': cocoEval.stats[4],
-            'Precision/mAP (large)': cocoEval.stats[5],
-            'Recall/AR@1': cocoEval.stats[6],
-            'Recall/AR@10': cocoEval.stats[7],
-            'Recall/AR@100': cocoEval.stats[8],
-            'Recall/AR@100 (small)': cocoEval.stats[9],
-            'Recall/AR@100 (medium)': cocoEval.stats[10],
-            'Recall/AR@100 (large)': cocoEval.stats[11],
-        }
-
-    return summary_metrics
-
-
-def xyxy2xywh(bbox):
-    """convert format of coordinate point"""
-    _bbox = bbox.tolist()
-    return [
-        _bbox[0],
-        _bbox[1],
-        _bbox[2] - _bbox[0] + 1,
-        _bbox[3] - _bbox[1] + 1,
-        ]
-
-def bbox2result_1image(bboxes, labels, num_classes):
-    """Convert detection results to a list of numpy arrays.
-
-    Args:
-        bboxes (Tensor): shape (n, 5)
-        labels (Tensor): shape (n, )
-        num_classes (int): class number, including background class
-
-    Returns:
-        list(ndarray): bbox results of each class
-    """
-    if bboxes.shape[0] == 0:
-        result = [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes - 1)]
-    else:
-        result = [bboxes[labels == i, :] for i in range(num_classes - 1)]
-
-    return result
-
-def proposal2json(dataset, results):
-    """convert proposal to json mode"""
-    img_ids = dataset.getImgIds()
-    json_results = []
-    dataset_len = dataset.get_dataset_size()*2
-    for idx in range(dataset_len):
-        img_id = img_ids[idx]
-        bboxes = results[idx]
-        for i in range(bboxes.shape[0]):
-            data = dict()
-            data['image_id'] = img_id
-            data['bbox'] = xyxy2xywh(bboxes[i])
-            data['score'] = float(bboxes[i][4])
-            data['category_id'] = 1
-            json_results.append(data)
-    return json_results
-
-def det2json(dataset, results):
-    """convert det to json mode"""
-    cat_ids = dataset.getCatIds()
-    img_ids = dataset.getImgIds()
-    json_results = []
-    dataset_len = len(img_ids)
-    for idx in range(dataset_len):
-        img_id = img_ids[idx]
-        if idx == len(results): break
-        result = results[idx]
-        for label, result_label in enumerate(result):
-            bboxes = result_label
-            for i in range(bboxes.shape[0]):
-                data = dict()
-                data['image_id'] = img_id
-                data['bbox'] = xyxy2xywh(bboxes[i])
-                data['score'] = float(bboxes[i][4])
-                data['category_id'] = cat_ids[label]
-                json_results.append(data)
-    return json_results
-
-def segm2json(dataset, results):
-    """convert segm to json mode"""
-    cat_ids = dataset.getCatIds()
-    img_ids = dataset.getImgIds()
-    bbox_json_results = []
-    segm_json_results = []
-
-    dataset_len = len(img_ids)
-    assert dataset_len == len(results)
-    for idx in range(dataset_len):
-        img_id = img_ids[idx]
-        if idx == len(results): break
-        det, seg = results[idx]
-        for label, det_label in enumerate(det):
-            bboxes = det_label
-            for i in range(bboxes.shape[0]):
-                data = dict()
-                data['image_id'] = img_id
-                data['bbox'] = xyxy2xywh(bboxes[i])
-                data['score'] = float(bboxes[i][4])
-                data['category_id'] = cat_ids[label]
-                bbox_json_results.append(data)
-
-            if len(seg) == 2:
-                segms = seg[0][label]
-                mask_score = seg[1][label]
-            else:
-                segms = seg[label]
-                mask_score = [bbox[4] for bbox in bboxes]
-            for i in range(bboxes.shape[0]):
-                data = dict()
-                data['image_id'] = img_id
-                data['score'] = float(mask_score[i])
-                data['category_id'] = cat_ids[label]
-                segms[i]['counts'] = segms[i]['counts'].decode()
-                data['segmentation'] = segms[i]
-                segm_json_results.append(data)
-    return bbox_json_results, segm_json_results
-
-def results2json(dataset, results, out_file):
-    """convert result convert to json mode"""
-    result_files = dict()
-    if isinstance(results[0], list):
-        json_results = det2json(dataset, results)
-        result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
-        result_files['proposal'] = '{}.{}.json'.format(out_file, 'bbox')
-        with open(result_files['bbox'], 'w') as fp:
-            json.dump(json_results, fp)
-    elif isinstance(results[0], tuple):
-        json_results = segm2json(dataset, results)
-        result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
-        result_files['segm'] = '{}.{}.json'.format(out_file, 'segm')
-        with open(result_files['bbox'], 'w') as fp:
-            json.dump(json_results[0], fp)
-        with open(result_files['segm'], 'w') as fp:
-            json.dump(json_results[1], fp)
-    elif isinstance(results[0], np.ndarray):
-        json_results = proposal2json(dataset, results)
-        result_files['proposal'] = '{}.{}.json'.format(out_file, 'proposal')
-        with open(result_files['proposal'], 'w') as fp:
-            json.dump(json_results, fp)
-    else:
-        raise TypeError('invalid type of results')
-    return result_files
-
-def get_seg_masks(mask_pred, det_bboxes, det_labels, img_meta, rescale, num_classes):
-    """Get segmentation masks from mask_pred and bboxes"""
-    mask_pred = mask_pred.astype(np.float32)
-
-    cls_segms = [[] for _ in range(num_classes - 1)]
-    bboxes = det_bboxes[:, :4]
-    labels = det_labels + 1
-
-    ori_shape = img_meta[:2].astype(np.int32)
-    scale_factor = img_meta[2:].astype(np.int32)
-
-    if rescale:
-        img_h, img_w = ori_shape[:2]
-    else:
-        img_h = np.round(ori_shape[0] * scale_factor[0]).astype(np.int32)
-        img_w = np.round(ori_shape[1] * scale_factor[1]).astype(np.int32)
-
-    for i in range(bboxes.shape[0]):
-        bbox = (bboxes[i, :] / 1.0).astype(np.int32)
-        label = labels[i]
-        w = max(bbox[2] - bbox[0] + 1, 1)
-        h = max(bbox[3] - bbox[1] + 1, 1)
-        w = min(w, img_w - bbox[0])
-        h = min(h, img_h - bbox[1])
-        if w <= 0 or h <= 0:
-            print("there is invalid proposal bbox, index={} bbox={} w={} h={}".format(i, bbox, w, h))
-            w = max(w, 1)
-            h = max(h, 1)
-        mask_pred_ = mask_pred[i, :, :]
-        im_mask = np.zeros((img_h, img_w), dtype=np.uint8)
-        bbox_mask = cv2.resize(mask_pred_, (w, h), interpolation=cv2.INTER_LINEAR)
-        mask_thr_binary = 0.5
-        bbox_mask = (bbox_mask > mask_thr_binary).astype(np.uint8)
-        im_mask[bbox[1]:bbox[1] + h, bbox[0]:bbox[0] + w] = bbox_mask
-
-        rle = maskUtils.encode(
-            np.array(im_mask[:, :, np.newaxis], order='F'))[0]
-        cls_segms[label - 1].append(rle)
-
-    return cls_segms
diff --git a/official/cv/ssd/README.md b/official/cv/ssd/README.md
index 3ab719b6b5beade3056d0d6e7ff1b40bee4c916e..1298b5b49a54b8ee6c796d6de0309b341abdfeb9 100644
--- a/official/cv/ssd/README.md
+++ b/official/cv/ssd/README.md
@@ -24,7 +24,6 @@
         - [Export MindIR](#export-mindir)
         - [Infer on Ascend310](#infer-on-ascend310)
         - [result](#result)
-        - [Post Training Quantization](#post-training-quantization)
     - [Model Description](#model-description)
         - [Performance](#performance)
     - [Description of Random Situation](#description-of-random-situation)
@@ -571,52 +570,6 @@ Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
 mAP: 0.33880018942412393
 ```
 
-### [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on COCO2017 dataset.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --checkpoint_path [CKPT_PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]
-```
-
-The quantized AIR file will be stored as "./results/ssd_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [IMAGE_DATA] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.237
-Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.386
-Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.240
-Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042
-Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200
-Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.255
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.404
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.441
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.136
-Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707
-mAP: 0.23657619676441116
-```
-
 ## [Model Description](#contents)
 
 ### [Performance](#contents)
diff --git a/official/cv/ssd/README_CN.md b/official/cv/ssd/README_CN.md
index 40fed4347d7856fc20070b9d2f7cc338b997b00f..20dfd85491d4841913bf471a67ca7268f8dd4550 100644
--- a/official/cv/ssd/README_CN.md
+++ b/official/cv/ssd/README_CN.md
@@ -21,7 +21,6 @@
         - [导出MindIR](#导出mindir)
         - [在Ascend310执行推理](#在ascend310执行推理)
         - [结果](#结果)
-        - [训练后量化推理](#训练后量化推理)
 - [模型描述](#模型描述)
     - [性能](#性能)
 - [随机情况说明](#随机情况说明)
@@ -459,51 +458,6 @@ Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
 mAP: 0.33880018942412393
 ```
 
-### [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于COCO2017数据集。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --checkpoint_path [CKPT_PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]
-```
-
-导出的模型会存储在./result/ssd_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [IMAGE_DATA] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.237
-Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.386
-Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.240
-Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042
-Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200
-Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.255
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.404
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.441
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.136
-Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455
-Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707
-mAP: 0.23657619676441116
-```
-
 # 模型描述
 
 ## 性能
diff --git a/official/cv/ssd/ascend310_quant_infer/acc.py b/official/cv/ssd/ascend310_quant_infer/acc.py
deleted file mode 100644
index a07505e4eb5a51b8837ec00d0e2f92d01a0db13f..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# 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
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-
-parser = argparse.ArgumentParser("ssd quant postprocess")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-parser.add_argument("--image_shape", type=str, required=True, help="path to image shape directory.")
-parser.add_argument("--image_id", type=str, required=True, help="path to image id directory.")
-parser.add_argument("--ann_file", type=str, required=True, help="path to annotation file.")
-parser.add_argument("--min_score", type=float, default=0.1, help="min box score threshold.")
-parser.add_argument("--nms_threshold", type=float, default=0.6, help="threshold of nms process.")
-parser.add_argument("--max_boxes", type=int, default=100, help="max number of detection boxes.")
-
-args, _ = parser.parse_known_args()
-
-
-def apply_nms(all_boxes, all_scores, thres, max_boxes):
-    """Apply NMS to bboxes."""
-    y1 = all_boxes[:, 0]
-    x1 = all_boxes[:, 1]
-    y2 = all_boxes[:, 2]
-    x2 = all_boxes[:, 3]
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-
-    order = all_scores.argsort()[::-1]
-    keep = []
-
-    while order.size > 0:
-        i = order[0]
-        keep.append(i)
-
-        if len(keep) >= max_boxes:
-            break
-
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-
-        ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
-        inds = np.where(ovr <= thres)[0]
-
-        order = order[inds + 1]
-    return keep
-
-
-def metrics(pred_data, anno_json):
-    """Calculate mAP of predicted bboxes."""
-
-    #Classes need to train or test.
-    val_cls = ['background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
-               'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
-               'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
-               'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
-               'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
-               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
-               'kite', 'baseball bat', 'baseball glove', 'skateboard',
-               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
-               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
-               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
-               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
-               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
-               'refrigerator', 'book', 'clock', 'vase', 'scissors',
-               'teddy bear', 'hair drier', 'toothbrush']
-    num_classes = len(val_cls)
-    val_cls_dict = {}
-    for i, cls in enumerate(val_cls):
-        val_cls_dict[i] = cls
-    coco_gt = COCO(anno_json)
-    classs_dict = {}
-    cat_ids = coco_gt.loadCats(coco_gt.getCatIds())
-    for cat in cat_ids:
-        classs_dict[cat["name"]] = cat["id"]
-
-    predictions = []
-    img_ids = []
-
-    for sample in pred_data:
-        pred_boxes = sample['boxes']
-        box_scores = sample['box_scores']
-        img_id = sample['img_id']
-        h, w = sample['image_shape']
-
-        final_boxes = []
-        final_label = []
-        final_score = []
-        img_ids.append(img_id)
-
-        for c in range(1, num_classes):
-            class_box_scores = box_scores[:, c]
-            score_mask = class_box_scores > args.min_score
-            class_box_scores = class_box_scores[score_mask]
-            class_boxes = pred_boxes[score_mask] * [h, w, h, w]
-
-            if score_mask.any():
-                nms_index = apply_nms(class_boxes, class_box_scores, args.nms_threshold, args.max_boxes)
-                class_boxes = class_boxes[nms_index]
-                class_box_scores = class_box_scores[nms_index]
-
-                final_boxes += class_boxes.tolist()
-                final_score += class_box_scores.tolist()
-                final_label += [classs_dict[val_cls_dict[c]]] * len(class_box_scores)
-
-        for loc, label, score in zip(final_boxes, final_label, final_score):
-            res = {}
-            res['image_id'] = img_id
-            res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]
-            res['score'] = score
-            res['category_id'] = label
-            predictions.append(res)
-    with open('predictions.json', 'w') as f:
-        json.dump(predictions, f)
-
-    coco_dt = coco_gt.loadRes('predictions.json')
-    E = COCOeval(coco_gt, coco_dt, iouType='bbox')
-    E.params.imgIds = img_ids
-    E.evaluate()
-    E.accumulate()
-    E.summarize()
-    return E.stats[0]
-
-
-def calculate_acc(result_path, image_id):
-    """
-    Calculate accuracy of VGG16 inference.
-
-    Args:
-        result_path (str): the directory or inference result.
-        image_id (str): the path of image_id directory.
-    """
-    pred_data = []
-    for file in os.listdir(image_id):
-        id_num = int(np.fromfile(os.path.join(image_id, file), dtype=np.int32)[0])
-        img_size = np.fromfile(os.path.join(args.image_shape, file), dtype=np.float32).reshape(1, -1)[0]
-        img_ids_name = file.split(".")[0]
-        image_shape = np.array([img_size[0], img_size[1]])
-        result_path_0 = os.path.join(result_path, img_ids_name + "_output_0.bin")
-        result_path_1 = os.path.join(result_path, img_ids_name + "_output_1.bin")
-        boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(1917, 4)
-        box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(1917, 81)
-        pred_data.append({"boxes": boxes, "box_scores": box_scores, "img_id": id_num, "image_shape": image_shape})
-    mAP = metrics(pred_data, args.ann_file)
-    print(f" mAP:{mAP}")
-
-
-if __name__ == '__main__':
-    calculate_acc(args.result_path, args.image_id)
diff --git a/official/cv/ssd/ascend310_quant_infer/config.cfg b/official/cv/ssd/ascend310_quant_infer/config.cfg
deleted file mode 100644
index f5da94987ca5dceab7ac820c7c39cdfab17a5a27..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/config.cfg
+++ /dev/null
@@ -1 +0,0 @@
-do_fusion:false
\ No newline at end of file
diff --git a/official/cv/ssd/ascend310_quant_infer/export_bin.py b/official/cv/ssd/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index 8ff4c3637e80724e9f84935be7ceec494989f82c..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    mindrecord_file = create_mindrecord("coco", "ssd_eval.mindrecord", False)
-    batch_size = 1
-    ds = create_ssd_dataset(mindrecord_file, batch_size=batch_size, repeat_num=1, is_training=False,
-                            use_multiprocessing=False)
-    cur_dir = os.getcwd()
-    image_path = os.path.join(cur_dir, "./data/00_image_data")
-    if not os.path.isdir(image_path):
-        os.makedirs(image_path)
-    img_id_path = os.path.join(cur_dir, "./data/01_image_id")
-    if not os.path.isdir(img_id_path):
-        os.makedirs(img_id_path)
-    img_shape_path = os.path.join(cur_dir, "./data/02_image_shape")
-    if not os.path.isdir(img_shape_path):
-        os.makedirs(img_shape_path)
-    total = ds.get_dataset_size()
-    iter_num = 0
-    for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
-        file_name = "coco_bs_" + str(batch_size) + "_" + str(iter_num) + ".bin"
-        img_id = data["img_id"]
-        img_np = data["image"]
-        img_shape = data["image_shape"]
-        img_id.tofile(os.path.join(img_id_path, file_name))
-        img_np.tofile(os.path.join(image_path, file_name))
-        img_shape.tofile(os.path.join(img_shape_path, file_name))
-
-        iter_num += 1
-    print("total images num: ", total)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.dataset import create_ssd_dataset, create_mindrecord
-
-    generate_data()
diff --git a/official/cv/ssd/ascend310_quant_infer/inc/model_process.h b/official/cv/ssd/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/ssd/ascend310_quant_infer/inc/sample_process.h b/official/cv/ssd/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/ssd/ascend310_quant_infer/inc/utils.h b/official/cv/ssd/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/ssd/ascend310_quant_infer/post_quant.py b/official/cv/ssd/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 5f10e88baa6ec01402cf0a326ed339af29f32dd0..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore
-from mindspore import Tensor, context
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-def quant_ssd(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data, config_defination="./config.cfg")
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    for data in dataset.create_dict_iterator(num_epochs=1):
-        img_data = data["image"]
-        _ = calibration_network(img_data)
-
-    # step5: export the air file
-    save_model("results/ssd_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/ssd_quant.air"))
-
-
-def run_export():
-    """
-    Prepare input parameters needed for exporting quantization model.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=config.device_id)
-    if hasattr(config, "num_ssd_boxes") and config.num_ssd_boxes == -1:
-        num = 0
-        h, w = config.img_shape
-        for i in range(len(config.steps)):
-            num += (h // config.step[i]) * (w // config.step[i]) * config.num_default[i]
-        config.num_ssd_boxes = num
-    net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
-    net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
-    param_dict = load_checkpoint(config.checkpoint_path)
-    net.init_parameters_data()
-    load_param_into_net(net, param_dict)
-    net.set_train(False)
-    batch_size = 1
-    input_shp = [batch_size, 3] + config.img_shape
-    inputs = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp), mindspore.float32)
-    mindrecord_file = create_mindrecord("coco", "ssd_eval.mindrecord", False)
-    batch_size = 1
-    datasets = create_ssd_dataset(mindrecord_file, batch_size=batch_size, repeat_num=1, is_training=False,
-                                  use_multiprocessing=False)
-    ds = datasets.take(1)
-    quant_ssd(net, ds, inputs)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2
-    from src.model_utils.config import config
-    from src.dataset import create_ssd_dataset, create_mindrecord
-    from src.box_utils import default_boxes
-    run_export()
diff --git a/official/cv/ssd/ascend310_quant_infer/run_quant_infer.sh b/official/cv/ssd/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index ccda13914100eb5d7d903b8d38605d54b06e9fda..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/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 [ $# -lt 5 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [IMAGE_DATA] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]"
-    echo "Example: bash run_quant_infer.sh ./ssd_quant.air ./00_image_data ./01_image_id ./02_image_shape \
-./instances_val2017.json"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-id_path=$(get_real_path $3)
-shape_path=$(get_real_path $4)
-ann_path=$(get_real_path $5)
-
-echo "air name: "$model
-echo "image data path: "$data_path
-echo "image id path: "$id_path
-echo "image shape path: "$shape_path
-echo "annotation path: "$ann_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=ssd_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./ssd_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --image_id=$id_path --image_shape=$shape_path --ann_file=$ann_path  &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/ssd/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/ssd/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/ssd/ascend310_quant_infer/src/acl.json b/official/cv/ssd/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/ssd/ascend310_quant_infer/src/build.sh b/official/cv/ssd/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/ssd/ascend310_quant_infer/src/main.cpp b/official/cv/ssd/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/ssd/ascend310_quant_infer/src/model_process.cpp b/official/cv/ssd/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/ssd/ascend310_quant_infer/src/sample_process.cpp b/official/cv/ssd/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index cbf58dbae68ff8b4c1b662f228218fe607810512..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,256 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    // loop begin
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    // loop end
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/ssd/ascend310_quant_infer/src/utils.cpp b/official/cv/ssd/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/ssd/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/unet/README.md b/official/cv/unet/README.md
index 3093c54e5b20b6dfb11251848a366e03ca1495ff..1804f306acaeccbddd1030f3b6826cec607ee07b 100644
--- a/official/cv/unet/README.md
+++ b/official/cv/unet/README.md
@@ -21,7 +21,6 @@
         - [How to use](#how-to-use)
             - [Inference](#inference)
                 - [Running on Ascend 310](#running-on-ascend-310)
-                - [Post Training Quantization](#post-training-quantization)
             - [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
             - [Transfer training](#transfer-training)
     - [Description of Random Situation](#description-of-random-situation)
@@ -561,40 +560,6 @@ Inference result is saved in current path, you can find result in acc.log file.
 Cross valid dice coeff is: 0.9054352151297033
 ```
 
-##### [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on ISBI dataset.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_path [DATA DIR] --result_path [RESULT PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --data_path [DATASET PATH] --checkpoint_file_path [CKPT_PATH]
-```
-
-The quantized AIR file will be stored as "./results/unet_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-Cross valid dice coeff is: 0.9139793866877975
-```
-
 #### Continue Training on the Pretrained Model
 
 Set options `resume` to True in `*.yaml`, and set `resume_ckpt` to the path of your checkpoint. e.g.
@@ -623,4 +588,4 @@ In data_loader.py, we set the seed inside “_get_val_train_indices" function. W
 
 ## [ModelZoo Homepage](#contents)
 
-Please check the official [homepage](https://gitee.com/mindspore/models).
\ No newline at end of file
+Please check the official [homepage](https://gitee.com/mindspore/models).
diff --git a/official/cv/unet/README_CN.md b/official/cv/unet/README_CN.md
index 1bba434d8cb5e31e715b63ff47129a9acdc94f36..41c4ed74595853219ef10d522b353bb8d8302712 100644
--- a/official/cv/unet/README_CN.md
+++ b/official/cv/unet/README_CN.md
@@ -22,7 +22,6 @@
         - [用法](#用法-1)
             - [推理](#推理)
                 - [Ascend 310环境运行](#ascend-310环境运行)
-                - [训练后量化推理](#训练后量化推理)
             - [继续训练预训练模型](#继续训练预训练模型)
             - [迁移学习](#迁移学习)
     - [随机情况说明](#随机情况说明)
@@ -556,39 +555,6 @@ bash run_infer_310.sh [NETWORK] [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
 Cross valid dice coeff is: 0.9054352151297033
 ```
 
-##### [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于ISBI数据集。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_path [DATA DIR] --result_path [RESULT PATH]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --data_path [DATASET PATH] --checkpoint_file_path [CKPT_PATH]
-```
-
-导出的模型会存储在./result/unet_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-Cross valid dice coeff is: 0.9139793866877975
-```
-
 #### 继续训练预训练模型
 
 在`*.yaml`里将`resume`设置成True，并将`resume_ckpt`设置成对应的权重文件路径，例如：
diff --git a/official/cv/unet/ascend310_quant_infer/acc.py b/official/cv/unet/ascend310_quant_infer/acc.py
deleted file mode 100644
index 305abd85991fd71cd06562807fc2688d86b9031b..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# 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 argparse
-import numpy as np
-import cv2
-
-import mindspore.nn as nn
-
-
-parser = argparse.ArgumentParser("unet quant postprocess")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-parser.add_argument("--label_path", type=str, required=True, help="path to label.npy.")
-parser.add_argument("--input_path", type=str, required=True, help="path to input data.")
-
-args, _ = parser.parse_known_args()
-
-class dice_coeff(nn.Metric):
-    """Unet Metric, return dice coefficient and IOU."""
-
-    def __init__(self, print_res=True, show_eval=False):
-        super(dice_coeff, self).__init__()
-        self.clear()
-        self.show_eval = show_eval
-        self.print_res = print_res
-        self.img_num = 0
-        # network config
-        self.include_background = True
-        self.eval_resize = False
-        self.num_classes = 2
-
-    def clear(self):
-        self._dice_coeff_sum = 0
-        self._iou_sum = 0
-        self._samples_num = 0
-        self.img_num = 0
-
-    def update(self, *inputs):
-        if len(inputs) != 2:
-            raise ValueError('Need 2 inputs (y_predict, y), but got {}'.format(len(inputs)))
-        y = self._convert_data(inputs[1])
-        self._samples_num += y.shape[0]
-        y = y.transpose(0, 2, 3, 1)
-        b, h, w, c = y.shape
-        if b != 1:
-            raise ValueError('Batch size should be 1 when in evaluation.')
-        y = y.reshape((h, w, c))
-        start_index = 0
-        if not self.include_background:
-            y = y[:, :, 1:]
-            start_index = 1
-
-        y_softmax = np.squeeze(self._convert_data(inputs[0]), axis=0)
-        if self.eval_resize:
-            y_pred = []
-            for i in range(start_index, self.num_classes):
-                y_pred.append(cv2.resize(np.uint8(y_softmax[:, :, i] * 255), (w, h)) / 255)
-            y_pred = np.stack(y_pred, axis=-1)
-        else:
-            y_pred = y_softmax
-            if not self.include_background:
-                y_pred = y_softmax[:, :, start_index:]
-
-        y_pred = y_pred.astype(np.float32)
-        inter = np.dot(y_pred.flatten(), y.flatten())
-        union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
-
-        single_dice_coeff = 2 * float(inter) / float(union + 1e-6)
-        single_iou = single_dice_coeff / (2 - single_dice_coeff)
-        if self.print_res:
-            print("single dice coeff is: {}, IOU is: {}".format(single_dice_coeff, single_iou))
-        self._dice_coeff_sum += single_dice_coeff
-        self._iou_sum += single_iou
-
-    def eval(self):
-        if self._samples_num == 0:
-            raise RuntimeError('Total samples num must not be 0.')
-        return (self._dice_coeff_sum / float(self._samples_num), self._iou_sum / float(self._samples_num))
-
-
-if __name__ == '__main__':
-    metrics = dice_coeff()
-    # eval_activate = "softmax"
-
-    label_list = np.load(args.label_path)
-    for j in range(len(os.listdir(args.input_path))):
-        file_name = os.path.join(args.result_path, "ISBI_test_bs_1_" + str(j) + "_output_0.bin")
-        rst_out = np.fromfile(file_name, np.float32).reshape(1, 388, 388, 2)
-        label = label_list[j]
-        metrics.update(rst_out, label)
-    eval_score = metrics.eval()
-    print("==================== Cross valid dice coeff is:", eval_score[0])
-    print("==================== Cross valid dice IOU is:", eval_score[1])
diff --git a/official/cv/unet/ascend310_quant_infer/export_bin.py b/official/cv/unet/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index c84b1dae0edffe2dc5d559b6e8e63ac136386e5d..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# 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.
-# ============================================================================
-"""unet 310 infer preprocess dataset"""
-import os
-import sys
-import shutil
-import numpy as np
-
-
-def generate_data(data_dir, result_path, cross_valid_ind=1):
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-
-    _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=config.crop,
-                                      img_size=config.image_size)
-    labels_list = []
-    img_path = os.path.join(result_path, "00_data")
-    if os.path.exists(img_path):
-        shutil.rmtree(img_path)
-    os.makedirs(img_path)
-
-    for i, data in enumerate(valid_dataset):
-        file_name = "ISBI_test_bs_1_" + str(i) + ".bin"
-        file_path = os.path.join(img_path, file_name)
-        data[0].asnumpy().tofile(file_path)
-        labels_list.append(data[1].asnumpy())
-    np.save(os.path.join(result_path, "label.npy"), labels_list)
-
-
-if __name__ == '__main__':
-    sys.path.append("..")
-    from src.data_loader import create_dataset
-    from src.model_utils.config import config
-
-    generate_data(data_dir=config.data_path, cross_valid_ind=config.cross_valid_ind, result_path=config.result_path)
diff --git a/official/cv/unet/ascend310_quant_infer/inc/model_process.h b/official/cv/unet/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/unet/ascend310_quant_infer/inc/sample_process.h b/official/cv/unet/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/unet/ascend310_quant_infer/inc/utils.h b/official/cv/unet/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/unet/ascend310_quant_infer/post_quant.py b/official/cv/unet/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 37dc08282c4b52a41cabb46aa60bdb1452b17c70..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-from mindspore import Tensor, context
-from mindspore.train.model import Model
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-def quant_unet(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    model = Model(calibration_network, loss_fn=TempLoss(), metrics={"dice_coff": dice_coeff()})
-    _ = model.eval(dataset, dataset_sink_mode=False)
-
-    # step5: export the air file
-    save_model("results/unet_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/unet_quant.air"))
-
-
-def run_export():
-    """run export."""
-    if config.model_name == 'unet_medical':
-        net = UNetMedical(n_channels=config.num_channels, n_classes=config.num_classes)
-    else:
-        raise ValueError("post training quantization currently does not support model: {}".format(config.model_name))
-    # return a parameter dict for model
-    param_dict = load_checkpoint(config.checkpoint_file_path)
-    # load the parameter into net
-    load_param_into_net(net, param_dict)
-    net = UnetEval(net, eval_activate="softmax")
-    batch_size = 1
-    input_data = Tensor(np.ones([batch_size, config.num_channels, config.height, config.width]).astype(np.float32))
-    _, valid_dataset = create_dataset(config.data_path, 1, batch_size, False, 1, False, do_crop=config.crop,
-                                      img_size=config.image_size)
-    dataset = valid_dataset.take(1)
-    quant_unet(net, dataset, input_data)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.data_loader import create_dataset
-    from src.unet_medical import UNetMedical
-    from src.utils import UnetEval, TempLoss, dice_coeff
-    from src.model_utils.config import config
-    from src.model_utils.device_adapter import get_device_id
-
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=get_device_id())
-
-    run_export()
diff --git a/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh b/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index 6877e451a3adb2ff02923957643f0afcc43d7072..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/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 [ $# -lt 3 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]"
-    echo "Example: bash run_quant_infer.sh ./unet_quant.air ./data ./label_ids.npy"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-label_path=$(get_real_path $3)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "label path: "$label_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=unet_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./unet_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --label_path=$label_path --input_path=$data_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/unet/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/unet/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/unet/ascend310_quant_infer/src/acl.json b/official/cv/unet/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/unet/ascend310_quant_infer/src/build.sh b/official/cv/unet/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/unet/ascend310_quant_infer/src/main.cpp b/official/cv/unet/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/unet/ascend310_quant_infer/src/model_process.cpp b/official/cv/unet/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/unet/ascend310_quant_infer/src/sample_process.cpp b/official/cv/unet/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 5f8f20f297bf6101d3a68ffd65bd0e6ed69d9486..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/unet/ascend310_quant_infer/src/utils.cpp b/official/cv/unet/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/unet/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/vgg16/README.md b/official/cv/vgg16/README.md
index e47a112fb56e63c7605ee125f8466efce876e7a7..a2431e540781a9ec5370b7873cfd3a9cec2c8574 100644
--- a/official/cv/vgg16/README.md
+++ b/official/cv/vgg16/README.md
@@ -28,7 +28,6 @@
         - [Export MindIR](#export-mindir)
         - [Infer on Ascend310](#infer-on-ascend310)
         - [result](#result)
-        - [Post Training Quantization](#post-training-quantization)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Training Performance](#training-performance)
@@ -280,7 +279,6 @@ python eval.py --config_path=[YAML_CONFIG_PATH] --device_target="GPU" --dataset=
         ├── README.md                             // descriptions about vgg
         ├── README_CN.md                          // descriptions about vgg with Chinese
         ├── ascend310_infer                       // Ascend310 infer folder
-        ├── ascend310_quant_infer                 // Ascend310 infer folder (quant)
         ├── infer                                 // MindX infer folder
         ├── model_utils
         │   ├── __init__.py                       // init file
@@ -573,40 +571,6 @@ Inference result is saved in current path, you can find result like this in acc.
 'acc': 0.92
 ```
 
-### [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on CIFAR-10 dataset.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --result_path [RESULT PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH]
-```
-
-The quantized AIR file will be stored as "./results/vgg_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-'acc': 0.91
-```
-
 ## [Model Description](#contents)
 
 ### [Performance](#contents)
diff --git a/official/cv/vgg16/README_CN.md b/official/cv/vgg16/README_CN.md
index 62a4695255b16a02ddafbf7fd52b5278902ae2e3..46b4e6f514a2becff571f8793e73021839668956 100644
--- a/official/cv/vgg16/README_CN.md
+++ b/official/cv/vgg16/README_CN.md
@@ -29,7 +29,6 @@
         - [导出MindIR](#导出mindir)
         - [在Ascend310执行推理](#在ascend310执行推理)
         - [结果](#结果)
-        - [训练后量化推理](#训练后量化推理)
     - [模型描述](#模型描述)
         - [性能](#性能)
             - [训练性能](#训练性能)
@@ -281,7 +280,6 @@ python eval.py --config_path=[YAML_CONFIG_PATH] --device_target="GPU" --dataset=
         ├── README.md                             // VGG 相关说明
         ├── README_CN.md                          // VGG 相关中文说明
         ├── ascend310_infer                       // Ascend310 推理目录
-        ├── ascend310_quant_infer                 // Ascend310 推理目录（量化）
         ├── infer                                 // MindX 推理目录
         ├── model_utils
         │   ├── __init__.py                 // 初始化文件
@@ -539,39 +537,6 @@ bash run_infer_310.sh [MINDIR_PATH] [DATASET_NAME] [DATASET_PATH] [NEED_PREPROCE
 'acc': 0.92
 ```
 
-### [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于CIFAR-10数据集。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --result_path [RESULT PATH]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --config_path [YMAL_CONFIG_PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH]
-```
-
-导出的模型会存储在./result/vgg_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-'acc': 0.91
-```
-
 ## 模型描述
 
 ### 性能
diff --git a/official/cv/vgg16/ascend310_quant_infer/acc.py b/official/cv/vgg16/ascend310_quant_infer/acc.py
deleted file mode 100644
index 1204fe43203f6bf9a48efea1a8253e7dc3da668d..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# 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 argparse
-import numpy as np
-
-from mindspore.nn import Top1CategoricalAccuracy
-
-
-parser = argparse.ArgumentParser("vgg16 quant postprocess")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-parser.add_argument("--label_path", type=str, required=True, help="path to label.npy.")
-
-args, _ = parser.parse_known_args()
-
-def calculate_acc(result_path, label_path):
-    """
-    Calculate accuracy of VGG16 inference.
-
-    Args:
-        result_path (str): the directory or inference result.
-        label_path (str): the path of data label in .npy format.
-    """
-    top1_acc = Top1CategoricalAccuracy()
-    labels = np.load(label_path, allow_pickle=True)
-    batch_size = 1
-    for idx, _ in enumerate(labels):
-        f_name = os.path.join(result_path, "VGG16_data_bs" + str(batch_size) + "_" + str(idx) + "_output_0.bin")
-        pred = np.fromfile(f_name, np.float32)
-        pred = pred.reshape(batch_size, int(pred.shape[0] / batch_size))
-        top1_acc.update(pred, labels[idx])
-    print("acc: ", top1_acc.eval())
-
-
-if __name__ == '__main__':
-    calculate_acc(args.result_path, args.label_path)
diff --git a/official/cv/vgg16/ascend310_quant_infer/export_bin.py b/official/cv/vgg16/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index eeddb5cd8cc8ef197348d037ce0b5773ddad812f..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import shutil
-import numpy as np
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    config.batch_size = 1
-    config.image_size = list(map(int, config.image_size.split(',')))
-    config.dataset = "cifar10"
-
-    dataset = vgg_create_dataset(config.data_dir, config.image_size, config.batch_size, training=False)
-    img_path = os.path.join(config.result_path, "00_data")
-    if os.path.exists(img_path):
-        shutil.rmtree(img_path)
-    os.makedirs(img_path)
-    label_list = []
-    for idx, data in enumerate(dataset.create_dict_iterator(output_numpy=True)):
-        file_name = "VGG16_data_bs" + str(config.batch_size) + "_" + str(idx) + ".bin"
-        file_path = os.path.join(img_path, file_name)
-        data["image"].tofile(file_path)
-        label_list.append(data["label"])
-    np.save(os.path.join(config.result_path, "cifar10_label_ids.npy"), label_list)
-    print("=" * 20, "export bin files finished", "=" * 20)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.dataset import vgg_create_dataset
-    from model_utils.moxing_adapter import config
-
-    generate_data()
diff --git a/official/cv/vgg16/ascend310_quant_infer/inc/model_process.h b/official/cv/vgg16/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/vgg16/ascend310_quant_infer/inc/sample_process.h b/official/cv/vgg16/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/vgg16/ascend310_quant_infer/inc/utils.h b/official/cv/vgg16/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/vgg16/ascend310_quant_infer/post_quant.py b/official/cv/vgg16/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 16f1274155dfbae878d36580ab15d72b0ad0dc3f..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore.nn as nn
-from mindspore import Tensor, context
-from mindspore.nn.optim.momentum import Momentum
-from mindspore.train.model import Model
-from mindspore.train.serialization import load_checkpoint
-from mindspore.common import dtype as mstype
-
-
-def quant_vgg(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-    opt = Momentum(filter(lambda x: x.requires_grad, calibration_network.get_parameters()), 0.01, config.momentum,
-                   weight_decay=config.weight_decay)
-    model = Model(calibration_network, loss_fn=loss, optimizer=opt, metrics={"acc"})
-    _ = model.eval(dataset, dataset_sink_mode=False)
-
-    # step5: export the air file
-    save_model("results/vgg_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/vgg_quant.air"))
-
-
-def run_export():
-    """
-    Prepare input parameters needed for exporting quantization model.
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
-    if config.device_target == "Ascend":
-        config.device_id = get_device_id()
-        context.set_context(device_id=config.device_id)
-
-    config.image_size = list(map(int, config.image_size.split(',')))
-    if config.dataset == "cifar10":
-        net = vgg16(num_classes=config.num_classes, args=config)
-    else:
-        net = vgg16(config.num_classes, config, phase="test")
-
-    load_checkpoint(config.ckpt_file, net=net)
-    net.set_train(False)
-    batch_size = 1
-    input_data = Tensor(np.zeros([batch_size, 3, config.image_size[0], config.image_size[1]]), mstype.float32)
-    dataset = vgg_create_dataset(config.data_dir, config.image_size, batch_size, training=False)
-    ds = dataset.take(1)
-    quant_vgg(net, ds, input_data)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.vgg import vgg16
-    from src.dataset import vgg_create_dataset
-    from model_utils.moxing_adapter import config
-    from model_utils.device_adapter import get_device_id
-    run_export()
diff --git a/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh b/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index eb157c53eb576aa16cd15250f3114ef8dc6c0bb5..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/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 [ $# -lt 3 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [LABEL_PATH]"
-    echo "Example: bash run_quant_infer.sh ./vgg_quant.air ./00_data ./cifar10_label_ids.npy"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-label_path=$(get_real_path $3)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "label path: "$label_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=vgg_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./vgg_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --label_path=$label_path  &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/vgg16/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/acl.json b/official/cv/vgg16/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/build.sh b/official/cv/vgg16/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/main.cpp b/official/cv/vgg16/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/model_process.cpp b/official/cv/vgg16/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/sample_process.cpp b/official/cv/vgg16/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index cbf58dbae68ff8b4c1b662f228218fe607810512..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,256 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    // loop begin
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    // loop end
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/vgg16/ascend310_quant_infer/src/utils.cpp b/official/cv/vgg16/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/vgg16/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/yolov3_darknet53/README.md b/official/cv/yolov3_darknet53/README.md
index e0e8c528012aa05ffb00d587c6e93b6f23b39dc1..56541538e43319cb2b3fdecb8ed8d8dd9f601ace 100644
--- a/official/cv/yolov3_darknet53/README.md
+++ b/official/cv/yolov3_darknet53/README.md
@@ -16,7 +16,6 @@
             - [Evaluation](#evaluation)
         - [Export MindIR](#export-mindir)
         - [Inference Process](#inference-process)
-        - [Post Training Quantization](#post-training-quantization)
     - [Model Description](#model-description)
         - [Performance](#performance)
             - [Evaluation Performance](#evaluation-performance)
@@ -449,59 +448,6 @@ Inference result is saved in current path, you can find result in acc.log file.
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
 ```
 
-### [Post Training Quantization(Only useful for Ascend device)](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on COCO2014 dataset.
-
-1. In Ascend910 platform, generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --annFile [ANNOTATION FILE PATH]
-```
-
-[DATA DIR] is for eval data path, [ANNOTATION FILE PATH] is for annotation path.
-
-2. In Ascend910 platform, export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH] --annFile [ANNOTATION FILE PATH]
-```
-
-[DATASET PATH] is for eval data path, [ANNOTATION FILE PATH] is for annotation path.
-
-The quantized AIR file will be stored as "./results/yolov3_quant.air".
-
-3. Copy bin data and AIR model to Ascend310 platform, and implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-cd ascend310_quant_infer/
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-AIR_PATH is air path generated in step 2, DATA_PATH is evaluation data path, IMAGE_ID is image binary file generated in step 1，IMAGE_SHAPE is shape binary file generated in step 2, ANN_FILE is annotation file.
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-=============coco eval result=========
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.306
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.524
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.314
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.256
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.395
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.419
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.548
-```
-
 ## [Model Description](#contents)
 
 ### [Performance](#contents)
diff --git a/official/cv/yolov3_darknet53/README_CN.md b/official/cv/yolov3_darknet53/README_CN.md
index c806a37209faec68ee7c4eddb97d89040b2831e4..7632049cf2863f361a97c1bc5f68a13faa040bd5 100644
--- a/official/cv/yolov3_darknet53/README_CN.md
+++ b/official/cv/yolov3_darknet53/README_CN.md
@@ -20,7 +20,6 @@
     - [推理过程](#推理过程)
         - [用法](#用法-2)
         - [结果](#结果-2)
-    - [训练后量化推理](#训练后量化推理)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [评估性能](#评估性能)
@@ -443,58 +442,6 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
 ```
 
-## [训练后量化推理（仅在Ascend场景下）](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于COCO2014数据集。
-
-1、在Ascend910环境上，生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --annFile [ANNOTATION FILE PATH]
-```
-
-其中，[DATA DIR]为推理数据的路径，[ANNOTATION FILE PATH]为用到的注解文件路径
-
-2、在Ascend910环境上，生成训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH] --annFile [ANNOTATION FILE PATH]
-```
-
-其中，[DATASET PATH]为推理数据的路径，[ANNOTATION FILE PATH]为注解文件的路径。
-
-导出的模型会存储在./result/yolov3_quant.air。
-
-3、将前两步生成的数据拷贝到310环境上，在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 quant inference
-cd ascend310_quant_infer/
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-其中，AIR_PATH为第二步生成的air文件路径，DATA_PATH为推理数据的路径，IMAGE_ID为第一步生成的image_id相关二进制文件，IMAGE_SHAPE为第一步生成的shape相关二进制文件，ANN_FILE为注解文件路径
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-=============coco eval result=========
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.306
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.524
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.314
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.319
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.423
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.256
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.395
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.419
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.219
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.548
-```
-
 # 模型描述
 
 ## 性能
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/acc.py b/official/cv/yolov3_darknet53/ascend310_quant_infer/acc.py
deleted file mode 100644
index a6d72c158daed3c659a39bd02fbb17cb61235dbc..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# 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 datetime
-import argparse
-import sys
-from collections import defaultdict
-import numpy as np
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-
-parser = argparse.ArgumentParser('YoloV3 quant postprocess')
-parser.add_argument('--result_path', type=str, required=True, help='result files path.')
-parser.add_argument('--batch_size', default=1, type=int, help='batch size for per gpu')
-parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
-parser.add_argument('--eval_ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
-parser.add_argument('--annFile', type=str, default='', help='path to annotation')
-parser.add_argument('--image_shape', type=str, default='./image_shape.npy', help='path to image_shape.npy')
-parser.add_argument('--image_id', type=str, default='./image_id.npy', help='path to image_id.npy')
-parser.add_argument('--log_path', type=str, default='outputs/', help='inference result save location')
-
-args, _ = parser.parse_known_args()
-
-
-class Redirct:
-    def __init__(self):
-        self.content = ""
-
-    def write(self, content):
-        self.content += content
-
-    def flush(self):
-        self.content = ""
-
-
-class DetectionEngine:
-    """Detection engine."""
-    def __init__(self):
-        self.eval_ignore_threshold = args.eval_ignore_threshold
-        self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
-                       'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
-                       'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
-                       'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
-                       'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
-                       'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-                       'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
-                       'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
-                       'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
-                       'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
-        self.num_classes = len(self.labels)
-        self.results = {}
-        self.file_path = ''
-        self.save_prefix = args.outputs_dir
-        self.annFile = args.annFile
-        self._coco = COCO(self.annFile)
-        self._img_ids = list(sorted(self._coco.imgs.keys()))
-        self.det_boxes = []
-        self.nms_thresh = args.nms_thresh
-        self.coco_catIds = self._coco.getCatIds()
-
-    def do_nms_for_results(self):
-        """Get result boxes."""
-        for img_id in self.results:
-            for clsi in self.results[img_id]:
-                dets = self.results[img_id][clsi]
-                dets = np.array(dets)
-                keep_index = self._nms(dets, self.nms_thresh)
-
-                keep_box = [{'image_id': int(img_id),
-                             'category_id': int(clsi),
-                             'bbox': list(dets[i][:4].astype(float)),
-                             'score': dets[i][4].astype(float)}
-                            for i in keep_index]
-                self.det_boxes.extend(keep_box)
-
-    def _nms(self, predicts, threshold):
-        """Calculate NMS."""
-        # convert xywh -> xmin ymin xmax ymax
-        x1 = predicts[:, 0]
-        y1 = predicts[:, 1]
-        x2 = x1 + predicts[:, 2]
-        y2 = y1 + predicts[:, 3]
-        scores = predicts[:, 4]
-
-        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-        order = scores.argsort()[::-1]
-
-        reserved_boxes = []
-        while order.size > 0:
-            i = order[0]
-            reserved_boxes.append(i)
-            max_x1 = np.maximum(x1[i], x1[order[1:]])
-            max_y1 = np.maximum(y1[i], y1[order[1:]])
-            min_x2 = np.minimum(x2[i], x2[order[1:]])
-            min_y2 = np.minimum(y2[i], y2[order[1:]])
-
-            intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1)
-            intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1)
-            intersect_area = intersect_w * intersect_h
-            ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
-
-            indexes = np.where(ovr <= threshold)[0]
-            order = order[indexes + 1]
-        return reserved_boxes
-
-    def write_result(self):
-        """Save result to file."""
-        import json
-        t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
-        try:
-            self.file_path = self.save_prefix + '/predict' + t + '.json'
-            f = open(self.file_path, 'w')
-            json.dump(self.det_boxes, f)
-        except IOError as e:
-            raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
-        else:
-            f.close()
-            return self.file_path
-
-    def get_eval_result(self):
-        """Get eval result."""
-        cocoGt = COCO(self.annFile)
-        cocoDt = cocoGt.loadRes(self.file_path)
-        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
-        cocoEval.evaluate()
-        cocoEval.accumulate()
-        rdct = Redirct()
-        stdout = sys.stdout
-        sys.stdout = rdct
-        cocoEval.summarize()
-        sys.stdout = stdout
-        return rdct.content
-
-    def detect(self, outputs, batch, image_shape, image_id):
-        """Detect boxes."""
-        outputs_num = len(outputs)
-        # output [|32, 52, 52, 3, 85| ]
-        for batch_id in range(batch):
-            for out_id in range(outputs_num):
-                # 32, 52, 52, 3, 85
-                out_item = outputs[out_id]
-                # 52, 52, 3, 85
-                out_item_single = out_item[batch_id, :]
-                # get number of items in one head, [B, gx, gy, anchors, 5+80]
-                dimensions = out_item_single.shape[:-1]
-                out_num = 1
-                for d in dimensions:
-                    out_num *= d
-                ori_w, ori_h = image_shape[batch_id]
-                img_id = int(image_id[batch_id])
-                x = out_item_single[..., 0] * ori_w
-                y = out_item_single[..., 1] * ori_h
-                w = out_item_single[..., 2] * ori_w
-                h = out_item_single[..., 3] * ori_h
-
-                conf = out_item_single[..., 4:5]
-                cls_emb = out_item_single[..., 5:]
-
-                cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
-                x = x.reshape(-1)
-                y = y.reshape(-1)
-                w = w.reshape(-1)
-                h = h.reshape(-1)
-                cls_emb = cls_emb.reshape(-1, self.num_classes)
-                conf = conf.reshape(-1)
-                cls_argmax = cls_argmax.reshape(-1)
-
-                x_top_left = x - w / 2.
-                y_top_left = y - h / 2.
-                # create all False
-                flag = np.random.random(cls_emb.shape) > sys.maxsize
-                for i in range(flag.shape[0]):
-                    c = cls_argmax[i]
-                    flag[i, c] = True
-                confidence = cls_emb[flag] * conf
-                for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence, cls_argmax):
-                    if confi < self.eval_ignore_threshold:
-                        continue
-                    if img_id not in self.results:
-                        self.results[img_id] = defaultdict(list)
-                    x_lefti = max(0, x_lefti)
-                    y_lefti = max(0, y_lefti)
-                    wi = min(wi, ori_w)
-                    hi = min(hi, ori_h)
-                    # transform catId to match coco
-                    coco_clsi = self.coco_catIds[clsi]
-                    self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
-
-
-if __name__ == "__main__":
-    args.outputs_dir = os.path.join(args.log_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-    if not os.path.exists(args.outputs_dir):
-        os.makedirs(args.outputs_dir)
-    detection = DetectionEngine()
-    bs = args.batch_size
-    shape_list = np.load(args.image_shape)
-    id_list = np.load(args.image_id)
-    prefix = "YoloV3-DarkNet_coco_bs_" + str(bs) + "_"
-    iter_num = 0
-    for id_img in id_list:
-        shape_img = shape_list[iter_num]
-        path_small = os.path.join(args.result_path, prefix + str(iter_num) + '_output_0.bin')
-        path_medium = os.path.join(args.result_path, prefix + str(iter_num) + '_output_1.bin')
-        path_big = os.path.join(args.result_path, prefix + str(iter_num) + '_output_2.bin')
-        if os.path.exists(path_small) and os.path.exists(path_medium) and os.path.exists(path_big):
-            output_small = np.fromfile(path_small, np.float32).reshape(bs, 13, 13, 3, 85)
-            output_medium = np.fromfile(path_medium, np.float32).reshape(bs, 26, 26, 3, 85)
-            output_big = np.fromfile(path_big, np.float32).reshape(bs, 52, 52, 3, 85)
-            detection.detect([output_small, output_medium, output_big], bs, shape_img, id_img)
-        else:
-            print("Error: Image ", iter_num, " is not exist.")
-        iter_num += 1
-
-    detection.do_nms_for_results()
-    result_file_path = detection.write_result()
-    eval_result = detection.get_eval_result()
-    print('\n=============coco eval result=========\n' + eval_result)
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/export_bin.py b/official/cv/yolov3_darknet53/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index 6403824e82519d9e9b96f9b67539e17b5a3f80f4..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import numpy as np
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    config.batch_size = 1
-    data_path = os.path.join(config.data_dir, "val2014")
-    ds, data_size = create_yolo_dataset(data_path, config.annFile, is_training=False, batch_size=config.batch_size,
-                                        max_epoch=1, device_num=1, rank=0, shuffle=False, config=config)
-    print('testing shape : {}'.format(config.test_img_shape))
-    print('total {} images to eval'.format(data_size))
-
-    save_folder = "./data"
-    image_folder = os.path.join(save_folder, "image_bin")
-    if not os.path.exists(image_folder):
-        os.makedirs(image_folder)
-
-    list_image_shape = []
-    list_image_id = []
-
-    for i, data in enumerate(ds.create_dict_iterator()):
-        image = data["image"].asnumpy()
-        image_shape = data["image_shape"]
-        image_id = data["img_id"]
-        file_name = "YoloV3-DarkNet_coco_bs_" + str(config.batch_size) + "_" + str(i) + ".bin"
-        file_path = image_folder + "/" + file_name
-        image.tofile(file_path)
-        list_image_shape.append(image_shape.asnumpy())
-        list_image_id.append(image_id.asnumpy())
-    shapes = np.array(list_image_shape)
-    ids = np.array(list_image_id)
-    np.save(save_folder + "/image_shape.npy", shapes)
-    np.save(save_folder + "/image_id.npy", ids)
-
-
-if __name__ == '__main__':
-    sys.path.append("..")
-    from model_utils.config import config
-    from src.yolo_dataset import create_yolo_dataset
-
-    generate_data()
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/model_process.h b/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/sample_process.h b/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/utils.h b/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/post_quant.py b/official/cv/yolov3_darknet53/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 89adccb605ceca0c1b6695d0c9b36c42b45edc04..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import os
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore as ms
-from mindspore import context, Tensor
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-def quant_yolov3(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    for _, data in enumerate(dataset.create_dict_iterator(num_epochs=1)):
-        image = data["image"]
-        _ = calibration_network(image)
-
-    # step5: export the air file
-    save_model("results/yolov3_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/yolov3_quant.air"))
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.yolo import YOLOV3DarkNet53
-    from src.yolo_dataset import create_yolo_dataset
-    from model_utils.config import config
-
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    net = YOLOV3DarkNet53(is_training=False)
-
-    param_dict = load_checkpoint(config.ckpt_file)
-    load_param_into_net(net, param_dict)
-    net.set_train(False)
-
-    config.batch_size = 16
-    data_path = os.path.join(config.data_dir, "val2014")
-    datasets, data_size = create_yolo_dataset(data_path, config.annFile, is_training=False,
-                                              batch_size=config.batch_size, max_epoch=1, device_num=1, rank=0,
-                                              shuffle=False, config=config)
-    ds = datasets.take(1)
-    export_batch_size = 1
-    shape = [export_batch_size, 3] + config.test_img_shape
-    inputs = Tensor(np.zeros(shape), ms.float32)
-    quant_yolov3(net, ds, inputs)
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh b/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index 016ed0133c814c7558c30842e191fad50e163614..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/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 [ $# -lt 5 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]"
-    echo "Example: bash run_quant_infer.sh ./yolov3_quant.air ./image_bin ./image_id.npy ./image_shape.npy \
-./instances_val2014.json"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-id_path=$(get_real_path $3)
-shape_path=$(get_real_path $4)
-ann_path=$(get_real_path $5)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "id path: "$id_path
-echo "shape path: "$shape_path
-echo "ann path: "$ann_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=yolov3_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./yolov3_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --annFile=$ann_path --image_shape=$shape_path \
-    --image_id=$id_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/acl.json b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/build.sh b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/main.cpp b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/model_process.cpp b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/sample_process.cpp b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 5f8f20f297bf6101d3a68ffd65bd0e6ed69d9486..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/utils.cpp b/official/cv/yolov3_darknet53/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_darknet53/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/yolov3_resnet18/README.md b/official/cv/yolov3_resnet18/README.md
index 14e4425575a5c255cc2aaec2327eae7e6ed3935e..91dead8e25de60c0726ec8ede0af3c51b8eabb5f 100644
--- a/official/cv/yolov3_resnet18/README.md
+++ b/official/cv/yolov3_resnet18/README.md
@@ -17,7 +17,6 @@
     - [Inference Process](#inference-process)
         - [Usage](#usage)
         - [result](#result)
-    - [Post Training Quantization](#post-training-quantization)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
@@ -362,44 +361,6 @@ Inference result is saved in current path, you can find result in acc.log file.
   class 1 precision is 85.34%, recall is 79.13%
   ```
 
-## [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Note the precision and recall values are results of two-classification(person and face) used our own annotations with COCO2017 dataset.
-Note quantization-related config file utils.py is located in the directory ascend310_quant_infer.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --anno_path [LABEL PATH]
-```
-
-Note that image_dir is set as the parent directory of COCO dataset.
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --anno_path [LABEL PATH] --ckpt_file [CKPT_PATH]
-```
-
-The quantized AIR file will be stored as "./results/yolov3_resnet_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-class 0 precision is 91.34%, recall is 64.92%
-class 1 precision is 94.61%, recall is 64.07%
-```
-
 # [Model Description](#contents)
 
 ## [Performance](#contents)
diff --git a/official/cv/yolov3_resnet18/README_CN.md b/official/cv/yolov3_resnet18/README_CN.md
index 6dd798f1a82e0bad702613b56f88e34710fa68c9..9a9239e7200e03aa7b05fb7cf7e21fc10f9c1107 100644
--- a/official/cv/yolov3_resnet18/README_CN.md
+++ b/official/cv/yolov3_resnet18/README_CN.md
@@ -19,7 +19,6 @@
     - [推理过程](#推理过程)
         - [用法](#用法)
         - [结果](#结果)
-    - [训练后量化推理](#训练后量化推理)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [评估性能](#评估性能)
@@ -358,44 +357,6 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
   class 1 precision is 85.34%, recall is 79.13%
   ```
 
-## [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。
-注意精度和召回值是使用我们自己的标注和COCO2017的两种分类（人与脸）的结果。
-注意训练后量化端测推理有关的文件utils.py位于ascend310_quant_infer目录下。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --ann_file [ANNOTATION PATH]
-```
-
-注意image_dir设置成COCO数据集的上级目录。
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --ckpt_file [CKPT_PATH]
-```
-
-导出的模型会存储在./result/yolov3_resnet_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-class 0 precision is 91.34%, recall is 64.92%
-class 1 precision is 94.61%, recall is 64.07%
-```
-
 # 模型描述
 
 ## 性能
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py b/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py
deleted file mode 100644
index da1990b0db509ba81a77de28c5940e1ecffc2c29..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# 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.
-# ============================================================================
-"""post process for 310 inference"""
-import os
-import argparse
-import numpy as np
-from utils import metrics
-
-
-parser = argparse.ArgumentParser("yolov3_resnet18 quant postprocess")
-parser.add_argument("--anno_path", type=str, required=True, help="path to annotation.npy")
-parser.add_argument("--result_path", type=str, required=True, help="path to inference results.")
-parser.add_argument("--batch_size", type=int, default=1, help="batch size of data.")
-parser.add_argument("--num_classes", type=int, default=2, help="number of classed to detect.")
-
-args, _ = parser.parse_known_args()
-
-
-def calculate_acc():
-    """ Calculate accuracy of yolov3_resnet18 inference"""
-    ann = np.load(args.anno_path, allow_pickle=True)
-    pred_data = []
-    prefix = "Yolov3-resnet18_coco_bs_" + str(args.batch_size) + "_"
-    for i in range(len(ann)):
-        result0 = os.path.join(args.result_path, prefix + str(i) + "_output_0.bin")
-        result1 = os.path.join(args.result_path, prefix + str(i) + "_output_1.bin")
-        output0 = np.fromfile(result0, np.float32).reshape(args.batch_size, 13860, 4)
-        output1 = np.fromfile(result1, np.float32).reshape(args.batch_size, 13860, 2)
-        for batch_idx in range(args.batch_size):
-            pred_data.append({"boxes": output0[batch_idx],
-                              "box_scores": output1[batch_idx],
-                              "annotation": ann[i]})
-    precisions, recalls = metrics(pred_data)
-    for j in range(args.num_classes):
-        print("class {} precision is {:.2f}%, recall is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100))
-
-
-if __name__ == '__main__':
-    calculate_acc()
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py b/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index b1950a668d22c5c0d323b470943ffd57cd51b2dc..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import numpy as np
-from mindspore import context
-
-
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
-def generate_data(dataset_path):
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    ds = create_yolo_dataset(dataset_path, is_training=False)
-    cur_dir = os.getcwd() + "/data"
-    img_folder = cur_dir + "/00_image"
-    if not os.path.exists(img_folder):
-        os.makedirs(img_folder)
-    shape_folder = cur_dir + "/01_image_shape"
-    if not os.path.exists(shape_folder):
-        os.makedirs(shape_folder)
-    total = ds.get_dataset_size()
-    ann_list = []
-    print("\n========================================\n")
-    print("total images num: ", total)
-    print("Processing, please wait a moment.")
-    prefix = "Yolov3-resnet18_coco_bs_1_"
-    for i, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)):
-        image_np = data['image']
-        image_shape = data['image_shape']
-        annotation = data['annotation']
-        file_name = prefix + str(i) + ".bin"
-        image_path = os.path.join(img_folder, file_name)
-        image_np.tofile(image_path)
-        shape_path = os.path.join(shape_folder, file_name)
-        image_shape.tofile(shape_path)
-
-        ann_list.append(annotation)
-    ann_file = np.array(ann_list)
-    np.save(os.path.join(cur_dir, "annotation_list.npy"), ann_file)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
-    from model_utils.config import config as default_config
-
-    if not os.path.isdir(default_config.eval_mindrecord_dir):
-        os.makedirs(default_config.eval_mindrecord_dir)
-
-    yolo_prefix = "yolo.mindrecord"
-    mindrecord_file = os.path.join(default_config.eval_mindrecord_dir, yolo_prefix + "0")
-    if not os.path.exists(mindrecord_file):
-        if os.path.isdir(default_config.image_dir) and os.path.exists(default_config.anno_path):
-            print("Create Mindrecord")
-            data_to_mindrecord_byte_image(default_config.image_dir,
-                                          default_config.anno_path,
-                                          default_config.eval_mindrecord_dir,
-                                          prefix=yolo_prefix,
-                                          file_num=8)
-            print("Create Mindrecord Done, at {}".format(default_config.eval_mindrecord_dir))
-        else:
-            print("image_dir or anno_path not exits")
-
-    generate_data(mindrecord_file)
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h b/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 3706b955b97ec4e4cacde04e8d7889a78522785d..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(const std::vector<void *> &inputDataBuffer, const std::vector<size_t> &bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h b/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 4e28dff95b2913a7223c4a48908640a0fc4e3d01..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder, char *shape_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h b/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py b/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index ca00080a41a5070ee582e1ebaa9aa121277f0813..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import os
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore as ms
-import mindspore.ops as ops
-from mindspore import context, Tensor
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
-def quant_yolov3_resnet(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, *input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, *input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    concat = ops.Concat()
-    index = 0
-    image_data = []
-    for data in dataset.create_dict_iterator(num_epochs=1):
-        index += 1
-        if index == 1:
-            image_data = data["image"]
-        else:
-            image_data = concat((image_data, data["image"]))
-        if index == dataset.get_dataset_size():
-            _ = calibration_network(image_data, data["image_shape"])
-
-    # step5: export the air file
-    save_model("results/yolov3_resnet_quant", calibration_network, *input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/yolov3_resnet_quant.air"))
-
-
-def export_yolov3_resnet():
-    """ prepare for quantization of yolov3_resnet """
-    cfg = ConfigYOLOV3ResNet18()
-    net = yolov3_resnet18(cfg)
-    eval_net = YoloWithEval(net, cfg)
-    param_dict = load_checkpoint(default_config.ckpt_file)
-    load_param_into_net(eval_net, param_dict)
-    eval_net.set_train(False)
-
-    default_config.export_batch_size = 1
-    shape = [default_config.export_batch_size, 3] + cfg.img_shape
-    input_data = Tensor(np.zeros(shape), ms.float32)
-    input_shape = Tensor(np.zeros([1, 2]), ms.float32)
-    inputs = (input_data, input_shape)
-
-    if not os.path.isdir(default_config.eval_mindrecord_dir):
-        os.makedirs(default_config.eval_mindrecord_dir)
-
-    yolo_prefix = "yolo.mindrecord"
-    mindrecord_file = os.path.join(default_config.eval_mindrecord_dir, yolo_prefix + "0")
-    if not os.path.exists(mindrecord_file):
-        if os.path.isdir(default_config.image_dir) and os.path.exists(default_config.anno_path):
-            print("Create Mindrecord")
-            data_to_mindrecord_byte_image(default_config.image_dir,
-                                          default_config.anno_path,
-                                          default_config.eval_mindrecord_dir,
-                                          prefix=yolo_prefix,
-                                          file_num=8)
-            print("Create Mindrecord Done, at {}".format(default_config.eval_mindrecord_dir))
-        else:
-            print("image_dir or anno_path not exits")
-    datasets = create_yolo_dataset(mindrecord_file, is_training=False)
-    ds = datasets.take(16)
-    quant_yolov3_resnet(eval_net, ds, inputs)
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.yolov3 import yolov3_resnet18, YoloWithEval
-    from src.config import ConfigYOLOV3ResNet18
-    from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
-    from model_utils.config import config as default_config
-
-    export_yolov3_resnet()
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh b/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index c118d783e4149050b77b234d5511f5309710cbd9..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/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 [ $# -lt 4 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]"
-    echo "Example: bash run_quant_infer.sh ./yolov3_resnet_quant.air ./00_image ./01_image_shape ./annotation_list.npy"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-shape_path=$(get_real_path $3)
-annotation_path=$(get_real_path $4)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "shape path: "$shape_path
-echo "annotation path: "$annotation_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=yolov3_resnet_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./yolov3_resnet_quant.om $data_path $shape_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --anno_path=$annotation_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 5fb6bad7044e27eb7958277b7cc191ea8924e9df..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 4) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder path_of_shapeFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2], argv[3]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 59f2f7a2509d63e77ebcec596e73ae8225ec7e02..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(const std::vector<void *> &inputDataBuffer, const std::vector<size_t> &bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-    for (size_t i = 0; i < inputDataBuffer.size(); ++i) {
-        aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer[i], bufferSize[i]);
-        if (inputData == nullptr) {
-            ERROR_LOG("can't create data buffer, create input failed");
-            return FAILED;
-        }
-
-        aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("add input dataset buffer failed");
-            aclDestroyDataBuffer(inputData);
-            inputData = nullptr;
-            return FAILED;
-        }
-    }
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 10e36d747d7dd42643d750948fe9a69913cff315..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,263 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder, char *shape_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-    std::vector<string> shapeFile;
-    GetAllFiles(shape_folder, &shapeFile);
-
-    if (testFile.size() !=shapeFile.size()) {
-        ERROR_LOG("number of data files is not equal to shape file");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process data file:%s", testFile[index].c_str());
-        INFO_LOG("start to process shape file:%s", shapeFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed, index is %zu", index);
-            return FAILED;
-        }
-
-        uint32_t devBufferShapeSize;
-        void *shapeDevBuffer = Utils::GetDeviceBufferOfFile(shapeFile[index], &devBufferShapeSize);
-        if (shapeDevBuffer == nullptr) {
-            ERROR_LOG("get shape device buffer failed, index is %zu", index);
-            return FAILED;
-        }
-
-        std::vector<void *> inputBuffers({picDevBuffer, shapeDevBuffer});
-        std::vector<size_t> inputSizes({devBufferSize, devBufferShapeSize});
-
-        ret = processModel.CreateInput(inputBuffers, inputSizes);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        gettimeofday(&start, nullptr);
-        double startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        double endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp b/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}
diff --git a/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py b/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py
deleted file mode 100644
index f2d78f31b2fcc88373faaa6dac36dad86363b30f..0000000000000000000000000000000000000000
--- a/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# Copyright 2020 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.
-# ============================================================================
-"""metrics utils"""
-
-import numpy as np
-
-
-def calc_iou(bbox_pred, bbox_ground):
-    """Calculate iou of predicted bbox and ground truth."""
-    x1 = bbox_pred[0]
-    y1 = bbox_pred[1]
-    width1 = bbox_pred[2] - bbox_pred[0]
-    height1 = bbox_pred[3] - bbox_pred[1]
-
-    x2 = bbox_ground[0]
-    y2 = bbox_ground[1]
-    width2 = bbox_ground[2] - bbox_ground[0]
-    height2 = bbox_ground[3] - bbox_ground[1]
-
-    endx = max(x1 + width1, x2 + width2)
-    startx = min(x1, x2)
-    width = width1 + width2 - (endx - startx)
-
-    endy = max(y1 + height1, y2 + height2)
-    starty = min(y1, y2)
-    height = height1 + height2 - (endy - starty)
-
-    if width <= 0 or height <= 0:
-        iou = 0
-    else:
-        area = width * height
-        area1 = width1 * height1
-        area2 = width2 * height2
-        iou = area * 1. / (area1 + area2 - area)
-
-    return iou
-
-
-def apply_nms(all_boxes, all_scores, thres, max_boxes):
-    """Apply NMS to bboxes."""
-    x1 = all_boxes[:, 0]
-    y1 = all_boxes[:, 1]
-    x2 = all_boxes[:, 2]
-    y2 = all_boxes[:, 3]
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-
-    order = all_scores.argsort()[::-1]
-    keep = []
-
-    while order.size > 0:
-        i = order[0]
-        keep.append(i)
-
-        if len(keep) >= max_boxes:
-            break
-
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-
-        ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
-        inds = np.where(ovr <= thres)[0]
-
-        order = order[inds + 1]
-    return keep
-
-
-class ConfigYOLOV3ResNet18:
-    """
-    Config parameters for YOLOv3.
-
-    Examples:
-        ConfigYoloV3ResNet18.
-    """
-    def __init__(self):
-        self.img_shape = [352, 640]
-        self.feature_shape = [32, 3, 352, 640]
-        self.num_classes = 2
-        self.nms_max_num = 50
-
-        self.backbone_input_shape = [64, 64, 128, 256]
-        self.backbone_shape = [64, 128, 256, 512]
-        self.backbone_layers = [2, 2, 2, 2]
-        self.backbone_stride = [1, 2, 2, 2]
-        self.ignore_threshold = 0.5
-        self.obj_threshold = 0.3
-        self.nms_threshold = 0.4
-        self.anchor_scales = [(10, 13),
-                              (16, 30),
-                              (33, 23),
-                              (30, 61),
-                              (62, 45),
-                              (59, 119),
-                              (116, 90),
-                              (156, 198),
-                              (163, 326)]
-        self.out_channel = int(len(self.anchor_scales) / 3 * (self.num_classes + 5))
-
-
-def metrics(pred_data):
-    """Calculate precision and recall of predicted bboxes."""
-    config = ConfigYOLOV3ResNet18()
-    num_classes = config.num_classes
-    count_corrects = [1e-6 for _ in range(num_classes)]
-    count_grounds = [1e-6 for _ in range(num_classes)]
-    count_preds = [1e-6 for _ in range(num_classes)]
-
-    for i, sample in enumerate(pred_data):
-        gt_anno = sample["annotation"]
-        box_scores = sample['box_scores']
-        boxes = sample['boxes']
-        mask = box_scores >= config.obj_threshold
-        boxes_ = []
-        scores_ = []
-        classes_ = []
-        max_boxes = config.nms_max_num
-        for c in range(num_classes):
-            class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])]
-            class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])]
-            nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes)
-            class_boxes = class_boxes[nms_index]
-            class_box_scores = class_box_scores[nms_index]
-            classes = np.ones_like(class_box_scores, 'int32') * c
-            boxes_.append(class_boxes)
-            scores_.append(class_box_scores)
-            classes_.append(classes)
-
-        boxes = np.concatenate(boxes_, axis=0)
-        classes = np.concatenate(classes_, axis=0)
-
-        # metric
-        count_correct = [1e-6 for _ in range(num_classes)]
-        count_ground = [1e-6 for _ in range(num_classes)]
-        count_pred = [1e-6 for _ in range(num_classes)]
-
-        for anno in gt_anno:
-            count_ground[anno[4]] += 1
-
-        for box_index, box in enumerate(boxes):
-            bbox_pred = [box[1], box[0], box[3], box[2]]
-            count_pred[classes[box_index]] += 1
-
-            for anno in gt_anno:
-                class_ground = anno[4]
-
-                if classes[box_index] == class_ground:
-                    iou = calc_iou(bbox_pred, anno)
-                    if iou >= 0.5:
-                        count_correct[class_ground] += 1
-                        break
-
-        count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)]
-        count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)]
-        count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)]
-
-    precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)])
-    recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)])
-    return precision, recall
diff --git a/official/cv/yolov4/README.md b/official/cv/yolov4/README.md
index 89bc1a5f9431430815ea3140364b566dc1667095..0399575d558dadf142a6fc1cc97e8f606f576e9c 100644
--- a/official/cv/yolov4/README.md
+++ b/official/cv/yolov4/README.md
@@ -15,7 +15,6 @@
         - [Evaluation](#evaluation)
     - [Convert Process](#convert-process)
         - [Convert](#convert)
-    - [Post Training Quantization](#post-training-quantization)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
@@ -530,52 +529,6 @@ Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636
 Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
 ```
 
-## [Post Training Quantization](#contents)
-
-Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization.
-Current quantization project bases on COCO2017 dataset.
-
-1. Generate data of .bin format required for AIR model inference at Ascend310 platform.
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --annFile [ANNOTATION FILE PATH]
-```
-
-2. Export quantized AIR model.
-
-Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community).
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH] --annFile [ANNOTATION FILE PATH]
-```
-
-The quantized AIR file will be stored as "./results/yolov4_quant.air".
-
-3. Implement inference at Ascend310 platform.
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-Inference result is saved in current path, you can find result like this in acc.log file.
-
-```bash
-=============coco eval result=========
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.433
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.633
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.467
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.273
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.475
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.555
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.329
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.532
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.568
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.395
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.690
-```
-
 # [Model Description](#contents)
 
 ## [Performance](#contents)
diff --git a/official/cv/yolov4/README_CN.md b/official/cv/yolov4/README_CN.md
index 39529d507a6a1de44a304a476ca79f31188a1967..9baa09db39ff0c7d7a19c0fad3a7a51ddb36d620 100644
--- a/official/cv/yolov4/README_CN.md
+++ b/official/cv/yolov4/README_CN.md
@@ -22,7 +22,6 @@
     - [推理过程](#推理过程)
         - [用法](#用法)
         - [结果](#结果)
-    - [训练后量化推理](#训练后量化推理)
 - [模型说明](#模型说明)
     - [性能](#性能)
         - [评估性能](#评估性能)
@@ -537,51 +536,6 @@ Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.636
 Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
 ```
 
-## [训练后量化推理](#contents)
-
-训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。本训练后量化工程基于COCO2017数据集。
-
-1、生成Ascend310平台AIR模型推理需要的.bin格式数据。
-
-```shell
-python export_bin.py --config_path [YMAL CONFIG PATH] --data_dir [DATA DIR] --annFile [ANNOTATION FILE PATH]
-```
-
-2、导出训练后量化的AIR格式模型。
-
-导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community)
-
-```shell
-python post_quant.py --config_path [YMAL CONFIG PATH] --ckpt_file [CKPT_PATH] --data_dir [DATASET PATH] --annFile [ANNOTATION FILE PATH]
-```
-
-导出的模型会存储在./result/yolov4_quant.air。
-
-3、在Ascend310执行推理量化模型。
-
-```shell
-# Ascend310 quant inference
-bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]
-```
-
-推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。
-
-```bash
-=============coco eval result=========
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.433
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.633
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.467
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.273
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.475
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.555
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.329
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.532
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.568
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.395
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.690
-```
-
 # [模型说明](#目录)
 
 ## [性能](#目录)
diff --git a/official/cv/yolov4/ascend310_quant_infer/acc.py b/official/cv/yolov4/ascend310_quant_infer/acc.py
deleted file mode 100644
index 2a3a947a900fcee410b07934de37ebf04a4922e5..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/acc.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# 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 datetime
-import argparse
-import sys
-from collections import defaultdict
-import numpy as np
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-
-parser = argparse.ArgumentParser('YoloV4 quant postprocess')
-parser.add_argument('--result_path', type=str, required=True, help='result files path.')
-parser.add_argument('--batch_size', default=1, type=int, help='batch size for per gpu')
-parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
-parser.add_argument('--eval_ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
-parser.add_argument('--annFile', type=str, default='', help='path to annotation')
-parser.add_argument('--image_shape', type=str, default='./image_shape.npy', help='path to image_shape.npy')
-parser.add_argument('--image_id', type=str, default='./image_id.npy', help='path to image_id.npy')
-parser.add_argument('--log_path', type=str, default='outputs/', help='inference result save location')
-
-args, _ = parser.parse_known_args()
-
-
-class Redirct:
-    def __init__(self):
-        self.content = ""
-
-    def write(self, content):
-        self.content += content
-
-    def flush(self):
-        self.content = ""
-
-
-class DetectionEngine:
-    """Detection engine."""
-    def __init__(self):
-        self.eval_ignore_threshold = args.eval_ignore_threshold
-        self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
-                       'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
-                       'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
-                       'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
-                       'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
-                       'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-                       'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
-                       'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
-                       'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
-                       'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
-        self.num_classes = len(self.labels)
-        self.results = {}
-        self.file_path = ''
-        self.save_prefix = args.outputs_dir
-        self.annFile = args.annFile
-        self._coco = COCO(self.annFile)
-        self._img_ids = list(sorted(self._coco.imgs.keys()))
-        self.det_boxes = []
-        self.nms_thresh = args.nms_thresh
-        self.coco_catIds = self._coco.getCatIds()
-
-    def do_nms_for_results(self):
-        """Get result boxes."""
-        for img_id in self.results:
-            for clsi in self.results[img_id]:
-                dets = self.results[img_id][clsi]
-                dets = np.array(dets)
-                keep_index = self._nms(dets, self.nms_thresh)
-
-                keep_box = [{'image_id': int(img_id),
-                             'category_id': int(clsi),
-                             'bbox': list(dets[i][:4].astype(float)),
-                             'score': dets[i][4].astype(float)}
-                            for i in keep_index]
-                self.det_boxes.extend(keep_box)
-
-    def _nms(self, predicts, threshold):
-        """Calculate NMS."""
-        # convert xywh -> xmin ymin xmax ymax
-        x1 = predicts[:, 0]
-        y1 = predicts[:, 1]
-        x2 = x1 + predicts[:, 2]
-        y2 = y1 + predicts[:, 3]
-        scores = predicts[:, 4]
-
-        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-        order = scores.argsort()[::-1]
-
-        reserved_boxes = []
-        while order.size > 0:
-            i = order[0]
-            reserved_boxes.append(i)
-            max_x1 = np.maximum(x1[i], x1[order[1:]])
-            max_y1 = np.maximum(y1[i], y1[order[1:]])
-            min_x2 = np.minimum(x2[i], x2[order[1:]])
-            min_y2 = np.minimum(y2[i], y2[order[1:]])
-
-            intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1)
-            intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1)
-            intersect_area = intersect_w * intersect_h
-            ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
-
-            indexes = np.where(ovr <= threshold)[0]
-            order = order[indexes + 1]
-        return reserved_boxes
-
-    def write_result(self):
-        """Save result to file."""
-        import json
-        t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
-        try:
-            self.file_path = self.save_prefix + '/predict' + t + '.json'
-            f = open(self.file_path, 'w')
-            json.dump(self.det_boxes, f)
-        except IOError as e:
-            raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
-        else:
-            f.close()
-            return self.file_path
-
-    def get_eval_result(self):
-        """Get eval result."""
-        cocoGt = COCO(self.annFile)
-        cocoDt = cocoGt.loadRes(self.file_path)
-        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
-        cocoEval.evaluate()
-        cocoEval.accumulate()
-        rdct = Redirct()
-        stdout = sys.stdout
-        sys.stdout = rdct
-        cocoEval.summarize()
-        sys.stdout = stdout
-        return rdct.content
-
-    def detect(self, outputs, batch, image_shape, image_id):
-        """Detect boxes."""
-        outputs_num = len(outputs)
-        # output [|32, 52, 52, 3, 85| ]
-        for batch_id in range(batch):
-            for out_id in range(outputs_num):
-                # 32, 52, 52, 3, 85
-                out_item = outputs[out_id]
-                # 52, 52, 3, 85
-                out_item_single = out_item[batch_id, :]
-                # get number of items in one head, [B, gx, gy, anchors, 5+80]
-                dimensions = out_item_single.shape[:-1]
-                out_num = 1
-                for d in dimensions:
-                    out_num *= d
-                ori_w, ori_h = image_shape[batch_id]
-                img_id = int(image_id[batch_id])
-                x = out_item_single[..., 0] * ori_w
-                y = out_item_single[..., 1] * ori_h
-                w = out_item_single[..., 2] * ori_w
-                h = out_item_single[..., 3] * ori_h
-
-                conf = out_item_single[..., 4:5]
-                cls_emb = out_item_single[..., 5:]
-
-                cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
-                x = x.reshape(-1)
-                y = y.reshape(-1)
-                w = w.reshape(-1)
-                h = h.reshape(-1)
-                cls_emb = cls_emb.reshape(-1, self.num_classes)
-                conf = conf.reshape(-1)
-                cls_argmax = cls_argmax.reshape(-1)
-
-                x_top_left = x - w / 2.
-                y_top_left = y - h / 2.
-                # create all False
-                flag = np.random.random(cls_emb.shape) > sys.maxsize
-                for i in range(flag.shape[0]):
-                    c = cls_argmax[i]
-                    flag[i, c] = True
-                confidence = cls_emb[flag] * conf
-                for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence, cls_argmax):
-                    if confi < self.eval_ignore_threshold:
-                        continue
-                    if img_id not in self.results:
-                        self.results[img_id] = defaultdict(list)
-                    x_lefti = max(0, x_lefti)
-                    y_lefti = max(0, y_lefti)
-                    wi = min(wi, ori_w)
-                    hi = min(hi, ori_h)
-                    # transform catId to match coco
-                    coco_clsi = self.coco_catIds[clsi]
-                    self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
-
-
-if __name__ == "__main__":
-    args.outputs_dir = os.path.join(args.log_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-    if not os.path.exists(args.outputs_dir):
-        os.makedirs(args.outputs_dir)
-    detection = DetectionEngine()
-    bs = args.batch_size
-    shape_list = np.load(args.image_shape)
-    id_list = np.load(args.image_id)
-    prefix = "YoloV4_coco_bs_" + str(bs) + "_"
-    iter_num = 0
-    for id_img in id_list:
-        shape_img = shape_list[iter_num]
-        path_small = os.path.join(args.result_path, prefix + str(iter_num) + '_output_0.bin')
-        path_medium = os.path.join(args.result_path, prefix + str(iter_num) + '_output_1.bin')
-        path_big = os.path.join(args.result_path, prefix + str(iter_num) + '_output_2.bin')
-        if os.path.exists(path_small) and os.path.exists(path_medium) and os.path.exists(path_big):
-            output_small = np.fromfile(path_small, np.float32).reshape(bs, 19, 19, 3, 85)
-            output_medium = np.fromfile(path_medium, np.float32).reshape(bs, 38, 38, 3, 85)
-            output_big = np.fromfile(path_big, np.float32).reshape(bs, 76, 76, 3, 85)
-            detection.detect([output_small, output_medium, output_big], bs, shape_img, id_img)
-        else:
-            print("Error: Image ", iter_num, " is not exist.")
-        iter_num += 1
-
-    detection.do_nms_for_results()
-    result_file_path = detection.write_result()
-    eval_result = detection.get_eval_result()
-    print('\n=============coco eval result=========\n' + eval_result)
diff --git a/official/cv/yolov4/ascend310_quant_infer/export_bin.py b/official/cv/yolov4/ascend310_quant_infer/export_bin.py
deleted file mode 100644
index 17761a914f4a5f88b26abd7fb25ca8391381994b..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/export_bin.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# 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.
-# ============================================================================
-"""generate data and label needed for AIR model inference"""
-import os
-import sys
-import numpy as np
-
-
-def generate_data():
-    """
-    Generate data and label needed for AIR model inference at Ascend310 platform.
-    """
-    config.batch_size = 1
-    data_path = os.path.join(config.data_dir, "val2017")
-    ann_file = os.path.join(config.data_dir, "annotations/instances_val2017.json")
-    ds, data_size = create_yolo_dataset(data_path, ann_file, is_training=False, batch_size=config.batch_size,
-                                        max_epoch=1, device_num=1, rank=0, shuffle=False, default_config=config)
-    print('testing shape : {}'.format(config.test_img_shape))
-    print('total {} images to eval'.format(data_size))
-
-    save_folder = "./data"
-    image_folder = os.path.join(save_folder, "00_image")
-    if not os.path.exists(image_folder):
-        os.makedirs(image_folder)
-
-    list_image_shape = []
-    list_image_id = []
-
-    for i, data in enumerate(ds.create_dict_iterator()):
-        image = data["image"].asnumpy()
-        image_shape = data["image_shape"]
-        image_id = data["img_id"]
-        file_name = "YoloV4_coco_bs_" + str(config.batch_size) + "_" + str(i) + ".bin"
-        file_path = image_folder + "/" + file_name
-        image.tofile(file_path)
-        list_image_shape.append(image_shape.asnumpy())
-        list_image_id.append(image_id.asnumpy())
-    shapes = np.array(list_image_shape)
-    ids = np.array(list_image_id)
-    np.save(save_folder + "/image_shape.npy", shapes)
-    np.save(save_folder + "/image_id.npy", ids)
-
-
-if __name__ == '__main__':
-    sys.path.append("..")
-    from model_utils.config import config
-    from src.yolo_dataset import create_yolo_dataset
-
-    generate_data()
diff --git a/official/cv/yolov4/ascend310_quant_infer/inc/model_process.h b/official/cv/yolov4/ascend310_quant_infer/inc/model_process.h
deleted file mode 100644
index 79e19833c7a1b3e428458c23aaba953dc40b1f01..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/inc/model_process.h
+++ /dev/null
@@ -1,111 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* ModelProcess
-*/
-class ModelProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  ModelProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~ModelProcess();
-
-  /**
-  * @brief load model from file with mem
-  * @param [in] modelPath: model path
-  * @return result
-  */
-  Result LoadModelFromFileWithMem(const char *modelPath);
-
-  /**
-  * @brief unload model
-  */
-  void Unload();
-
-  /**
-  * @brief create model desc
-  * @return result
-  */
-  Result CreateDesc();
-
-  /**
-  * @brief destroy desc
-  */
-  void DestroyDesc();
-
-  /**
-  * @brief create model input
-  * @param [in] inputDataBuffer: input buffer
-  * @param [in] bufferSize: input buffer size
-  * @return result
-  */
-  Result CreateInput(void *inputDataBuffer, size_t bufferSize);
-
-  /**
-  * @brief destroy input resource
-  */
-  void DestroyInput();
-
-  /**
-  * @brief create output buffer
-  * @return result
-  */
-  Result CreateOutput();
-
-  /**
-  * @brief destroy output resource
-  */
-  void DestroyOutput();
-
-  /**
-  * @brief model execute
-  * @return result
-  */
-  Result Execute();
-
-  /**
-  * @brief dump model output result to file
-  */
-  void DumpModelOutputResult(char *output_name);
-
-  /**
-  * @brief get model output result
-  */
-  void OutputModelResult();
-
- private:
-  uint32_t modelId_;
-  size_t modelMemSize_;
-  size_t modelWeightSize_;
-  void *modelMemPtr_;
-  void *modelWeightPtr_;
-  bool loadFlag_;  // model load flag
-  aclmdlDesc *modelDesc_;
-  aclmdlDataset *input_;
-  aclmdlDataset *output_;
-};
-
diff --git a/official/cv/yolov4/ascend310_quant_infer/inc/sample_process.h b/official/cv/yolov4/ascend310_quant_infer/inc/sample_process.h
deleted file mode 100644
index 24d6ea01e59925673a548a7873ab310623235549..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/inc/sample_process.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <string>
-#include <vector>
-#include "../inc/utils.h"
-#include "acl/acl.h"
-
-/**
-* SampleProcess
-*/
-class SampleProcess {
- public:
-  /**
-  * @brief Constructor
-  */
-  SampleProcess();
-
-  /**
-  * @brief Destructor
-  */
-  ~SampleProcess();
-
-  /**
-  * @brief init reousce
-  * @return result
-  */
-  Result InitResource();
-
-  /**
-  * @brief sample process
-  * @return result
-  */
-  Result Process(char *om_path, char *input_folder);
-
-  void GetAllFiles(std::string path, std::vector<std::string> *files);
-
- private:
-  void DestroyResource();
-
-  int32_t deviceId_;
-  aclrtContext context_;
-  aclrtStream stream_;
-};
diff --git a/official/cv/yolov4/ascend310_quant_infer/inc/utils.h b/official/cv/yolov4/ascend310_quant_infer/inc/utils.h
deleted file mode 100644
index 3ae2a571b8e8ba51c01b02e23f36dfad5a7b18f1..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/inc/utils.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/**
- * 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.
- */
-
-#pragma once
-#include <iostream>
-#include <string>
-
-#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO]  " fmt "\n", ##args)
-#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN]  " fmt "\n", ##args)
-#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args)
-
-typedef enum Result {
-  SUCCESS = 0,
-  FAILED = 1
-} Result;
-
-/**
-* Utils
-*/
-class Utils {
- public:
-  /**
-  * @brief create device buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return device buffer of file
-  */
-  static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize);
-
-  /**
-  * @brief create buffer of file
-  * @param [in] fileName: file name
-  * @param [out] fileSize: size of file
-  * @return buffer of pic
-  */
-  static void* ReadBinFile(std::string fileName, uint32_t *fileSize);
-};
-
-#pragma once
diff --git a/official/cv/yolov4/ascend310_quant_infer/post_quant.py b/official/cv/yolov4/ascend310_quant_infer/post_quant.py
deleted file mode 100644
index 815034a5e92e8cf1e5ee0f471595f333ede10ef8..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/post_quant.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# 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.
-# ============================================================================
-"""do post training quantization for Ascend310"""
-import os
-import sys
-import numpy as np
-
-from amct_mindspore.quantize_tool import create_quant_config
-from amct_mindspore.quantize_tool import quantize_model
-from amct_mindspore.quantize_tool import save_model
-import mindspore as ms
-from mindspore import context, Tensor
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-
-def quant_yolov4(network, dataset, input_data):
-    """
-    Export post training quantization model of AIR format.
-
-    Args:
-        network: the origin network for inference.YOLOV3DarkNet53
-        dataset: the data for inference.
-        input_data: the data used for constructing network. The shape and format of input data should be the same as
-                    actual data for inference.
-    """
-
-    # step2: create the quant config json file
-    create_quant_config("./config.json", network, input_data)
-
-    # step3: do some network modification and return the modified network
-    calibration_network = quantize_model("./config.json", network, input_data)
-    calibration_network.set_train(False)
-
-    # step4: perform the evaluation of network to do activation calibration
-    for _, data in enumerate(dataset.create_dict_iterator(num_epochs=1)):
-        image = data["image"]
-        _ = calibration_network(image)
-
-    # step5: export the air file
-    save_model("results/yolov4_quant", calibration_network, input_data)
-    print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/yolov4_quant.air"))
-
-
-if __name__ == "__main__":
-    sys.path.append("..")
-    from src.yolo import YOLOV4CspDarkNet53
-    from src.yolo_dataset import create_yolo_dataset
-    from model_utils.config import config
-
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    net = YOLOV4CspDarkNet53()
-
-    param_dict = load_checkpoint(config.ckpt_file)
-    load_param_into_net(net, param_dict)
-    net.set_train(False)
-
-    config.batch_size = 1
-    data_path = os.path.join(config.data_dir, "val2017")
-    ann_file = os.path.join(config.data_dir, "annotations/instances_val2017.json")
-    datasets, data_size = create_yolo_dataset(data_path, ann_file, is_training=False, batch_size=config.batch_size,
-                                              max_epoch=1, device_num=1, rank=0, shuffle=False, default_config=config)
-    ds = datasets.take(1)
-    shape = [config.batch_size, 3] + config.test_img_shape
-    inputs = Tensor(np.zeros(shape), ms.float32)
-    quant_yolov4(net, ds, inputs)
diff --git a/official/cv/yolov4/ascend310_quant_infer/run_quant_infer.sh b/official/cv/yolov4/ascend310_quant_infer/run_quant_infer.sh
deleted file mode 100644
index 6b66b0eabeebbc49b75823466827a8149b7b8d56..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/run_quant_infer.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/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 [ $# -lt 5 ]; then
-    echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]"
-    echo "Example: bash run_quant_infer.sh ./yolov4_quant.air ./image_bin ./image_id.npy ./image_shape.npy \
-./instances_val2017.json"
-exit 1
-fi
-
-get_real_path(){
-    if [ "${1:0:1}" == "/" ]; then
-        echo "$1"
-    else
-        echo "$(realpath -m $PWD/$1)"
-    fi
-}
-model=$(get_real_path $1)
-data_path=$(get_real_path $2)
-id_path=$(get_real_path $3)
-shape_path=$(get_real_path $4)
-ann_path=$(get_real_path $5)
-
-echo "air name: "$model
-echo "dataset path: "$data_path
-echo "id path: "$id_path
-echo "shape path: "$shape_path
-echo "ann path: "$ann_path
-
-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 ASCEND_HOME=/usr/local/Ascend/latest/
-    export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$PATH
-    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$LD_LIBRARY_PATH
-    export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe
-    export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH
-    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
-fi
-
-function air_to_om()
-{
-    atc --input_format=NCHW --framework=1 --model=$model --output=yolov4_quant --soc_version=Ascend310 &> atc.log
-}
-
-function compile_app()
-{
-    bash ./src/build.sh &> build.log
-}
-
-function infer()
-{
-    if [ -d result ]; then
-        rm -rf ./result
-    fi
-    mkdir result
-    ./out/main ./yolov4_quant.om $data_path &> infer.log
-}
-
-function cal_acc()
-{
-    python3.7 ./acc.py --result_path=./result --annFile=$ann_path --image_shape=$shape_path \
-    --image_id=$id_path &> acc.log
-}
-
-echo "start atc================================================"
-air_to_om
-if [ $? -ne 0 ]; then
-    echo "air to om code failed"
-    exit 1
-fi
-
-echo "start compile============================================"
-compile_app
-if [ $? -ne 0 ]; then
-    echo "compile app code failed"
-    exit 1
-fi
-
-echo "start infer=============================================="
-infer
-if [ $? -ne 0 ]; then
-    echo " execute inference failed"
-    exit 1
-fi
-
-echo "start calculate acc======================================"
-cal_acc
-if [ $? -ne 0 ]; then
-    echo "calculate accuracy failed"
-    exit 1
-fi
\ No newline at end of file
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/CMakeLists.txt b/official/cv/yolov4/ascend310_quant_infer/src/CMakeLists.txt
deleted file mode 100644
index 655026d7d91612267a287e83e886ba2ce1304d18..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/CMakeLists.txt
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved.
-
-# CMake lowest version requirement
-cmake_minimum_required(VERSION 3.5.1)
-# project information
-project(InferClassification)
-# Check environment variable
-if(NOT DEFINED ENV{ASCEND_HOME})
-    message(FATAL_ERROR "please define environment variable:ASCEND_HOME")
-endif()
-
-# Compile options
-add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
-
-# Skip build rpath
-set(CMAKE_SKIP_BUILD_RPATH True)
-
-# Set output directory
-set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out)
-
-# Set include directory and library directory
-set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib)
-set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib)
-set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
-
-# Header path
-include_directories(${ACL_LIB_DIR}/include/)
-include_directories(${FWKACL_LIB_DIR}/include/)
-include_directories(${ATLAS_ACL_LIB_DIR}/include/)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
-
-# add host lib path
-link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR})
-find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64)
-
-add_executable(main utils.cpp
-        sample_process.cpp
-        model_process.cpp
-        main.cpp)
-
-target_link_libraries(main ${acl} gflags pthread)
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/acl.json b/official/cv/yolov4/ascend310_quant_infer/src/acl.json
deleted file mode 100644
index 0967ef424bce6791893e9a57bb952f80fd536e93..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/acl.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/build.sh b/official/cv/yolov4/ascend310_quant_infer/src/build.sh
deleted file mode 100644
index b5979b68e60b673f763a3cac98c5e147e7085291..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/build.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-path_cur=$(cd "`dirname $0`" || exit; pwd)
-
-function preparePath() {
-    rm -rf $1
-    mkdir -p $1
-    cd $1 || exit
-}
-
-function buildA300() {
-    if [ ! "${ARCH_PATTERN}" ]; then
-        # set ARCH_PATTERN to acllib when it was not specified by user
-        export ARCH_PATTERN=acllib
-        echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}"
-    else
-        echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib"
-        export ARCH_PATTERN=${ARCH_PATTERN}/acllib
-    fi
-
-    path_build=$path_cur/build
-    preparePath $path_build
-    cmake ..
-    make -j
-    ret=$?
-    cd ..
-    return ${ret}
-}
-
-# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user
-if [ ! "${ASCEND_VERSION}" ]; then
-    export ASCEND_VERSION=ascend-toolkit/latest
-    echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}"
-else
-    echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user"
-fi
-
-buildA300
-
-if [ $? -ne 0 ]; then
-    exit 1
-fi
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/main.cpp b/official/cv/yolov4/ascend310_quant_infer/src/main.cpp
deleted file mode 100644
index 80165505f447d418e0f107b76d04ffae59b89a73..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/main.cpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * 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 <iostream>
-#include "../inc/sample_process.h"
-#include "../inc/utils.h"
-bool g_is_device = false;
-
-int main(int argc, char **argv) {
-    if (argc != 3) {
-        ERROR_LOG("usage:./main path_of_om path_of_inputFolder");
-        return FAILED;
-    }
-    SampleProcess processSample;
-    Result ret = processSample.InitResource();
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample init resource failed");
-        return FAILED;
-    }
-
-    ret = processSample.Process(argv[1], argv[2]);
-    if (ret != SUCCESS) {
-        ERROR_LOG("sample process failed");
-        return FAILED;
-    }
-
-    INFO_LOG("execute sample success");
-    return SUCCESS;
-}
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/model_process.cpp b/official/cv/yolov4/ascend310_quant_infer/src/model_process.cpp
deleted file mode 100644
index 04e6a42ab43cbc41720fe6b9e30bf919323c9f9e..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/model_process.cpp
+++ /dev/null
@@ -1,339 +0,0 @@
-/**
- * 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/model_process.h"
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <algorithm>
-#include "../inc/utils.h"
-extern bool g_is_device;
-
-ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr),
-modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) {
-}
-
-ModelProcess::~ModelProcess() {
-    Unload();
-    DestroyDesc();
-    DestroyInput();
-    DestroyOutput();
-}
-
-Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) {
-    if (loadFlag_) {
-        ERROR_LOG("has already loaded a model");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("query model failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_);
-        return FAILED;
-    }
-
-    ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_);
-        return FAILED;
-    }
-
-    ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_,
-        modelMemSize_, modelWeightPtr_, modelWeightSize_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("load model from file failed, model file is %s", modelPath);
-        return FAILED;
-    }
-
-    loadFlag_ = true;
-    INFO_LOG("load model %s success", modelPath);
-    return SUCCESS;
-}
-
-Result ModelProcess::CreateDesc() {
-    modelDesc_ = aclmdlCreateDesc();
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("create model description failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlGetDesc(modelDesc_, modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("get model description failed");
-        return FAILED;
-    }
-
-    INFO_LOG("create model description success");
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyDesc() {
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-}
-
-Result ModelProcess::CreateInput(void *inputDataBuffer, size_t bufferSize) {
-    input_ = aclmdlCreateDataset();
-    if (input_ == nullptr) {
-        ERROR_LOG("can't create dataset, create input failed");
-        return FAILED;
-    }
-
-    aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer, bufferSize);
-    if (inputData == nullptr) {
-        ERROR_LOG("can't create data buffer, create input failed");
-        return FAILED;
-    }
-
-    aclError ret = aclmdlAddDatasetBuffer(input_, inputData);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("add input dataset buffer failed");
-        aclDestroyDataBuffer(inputData);
-        inputData = nullptr;
-        return FAILED;
-    }
-
-    return SUCCESS;
-}
-
-void ModelProcess::DestroyInput() {
-    if (input_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i);
-        aclDestroyDataBuffer(dataBuffer);
-    }
-    aclmdlDestroyDataset(input_);
-    input_ = nullptr;
-}
-
-Result ModelProcess::CreateOutput() {
-    if (modelDesc_ == nullptr) {
-        ERROR_LOG("no model description, create output failed");
-        return FAILED;
-    }
-
-    output_ = aclmdlCreateDataset();
-    if (output_ == nullptr) {
-        ERROR_LOG("can't create dataset, create output failed");
-        return FAILED;
-    }
-
-    size_t outputSize = aclmdlGetNumOutputs(modelDesc_);
-    for (size_t i = 0; i < outputSize; ++i) {
-        size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i);
-
-        void *outputBuffer = nullptr;
-        aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size);
-            return FAILED;
-        }
-
-        aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't create data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            return FAILED;
-        }
-
-        ret = aclmdlAddDatasetBuffer(output_, outputData);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("can't add data buffer, create output failed");
-            aclrtFree(outputBuffer);
-            aclDestroyDataBuffer(outputData);
-            return FAILED;
-        }
-    }
-
-    INFO_LOG("create model output success");
-    return SUCCESS;
-}
-
-void ModelProcess::DumpModelOutputResult(char *output_name) {
-    size_t outputNum = aclmdlGetDatasetNumBuffers(output_);
-
-    for (size_t i = 0; i < outputNum; ++i) {
-        std::stringstream ss;
-        ss << "result/" << output_name << "_output_" << i << ".bin";
-        std::string outputFileName = ss.str();
-        FILE *outputFile = fopen(outputFileName.c_str(), "wb");
-        if (outputFile != nullptr) {
-            aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-            void* data = aclGetDataBufferAddr(dataBuffer);
-            uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-            void* outHostData = NULL;
-            aclError ret = ACL_ERROR_NONE;
-            if (!g_is_device) {
-                ret = aclrtMallocHost(&outHostData, len);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                    return;
-                }
-
-                ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                    (void)aclrtFreeHost(outHostData);
-                    return;
-                }
-
-                fwrite(outHostData, len, sizeof(char), outputFile);
-
-                ret = aclrtFreeHost(outHostData);
-                if (ret != ACL_ERROR_NONE) {
-                    ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                    return;
-                }
-            } else {
-                fwrite(data, len, sizeof(char), outputFile);
-            }
-            fclose(outputFile);
-            outputFile = nullptr;
-        } else {
-            ERROR_LOG("create output file [%s] failed", outputFileName.c_str());
-            return;
-        }
-    }
-
-    INFO_LOG("dump data success");
-    return;
-}
-
-void ModelProcess::OutputModelResult() {
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        uint32_t len = aclGetDataBufferSizeV2(dataBuffer);
-
-        void *outHostData = NULL;
-        aclError ret = ACL_ERROR_NONE;
-        float *outData = NULL;
-        if (!g_is_device) {
-            ret = aclrtMallocHost(&outHostData, len);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret);
-                return;
-            }
-
-            ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret);
-                return;
-            }
-
-            outData = reinterpret_cast<float*>(outHostData);
-        } else {
-            outData = reinterpret_cast<float*>(data);
-        }
-        std::map<float, unsigned int, std::greater<float> > resultMap;
-        for (unsigned int j = 0; j < len / sizeof(float); ++j) {
-            resultMap[*outData] = j;
-            outData++;
-        }
-
-        int cnt = 0;
-        for (auto it = resultMap.begin(); it != resultMap.end(); ++it) {
-            // print top 5
-            if (++cnt > 5) {
-                break;
-            }
-
-            INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first);
-        }
-        if (!g_is_device) {
-            ret = aclrtFreeHost(outHostData);
-            if (ret != ACL_ERROR_NONE) {
-                ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret);
-                return;
-            }
-        }
-    }
-
-    INFO_LOG("output data success");
-    return;
-}
-
-void ModelProcess::DestroyOutput() {
-    if (output_ == nullptr) {
-        return;
-    }
-
-    for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) {
-        aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i);
-        void* data = aclGetDataBufferAddr(dataBuffer);
-        (void)aclrtFree(data);
-        (void)aclDestroyDataBuffer(dataBuffer);
-    }
-
-    (void)aclmdlDestroyDataset(output_);
-    output_ = nullptr;
-}
-
-Result ModelProcess::Execute() {
-    aclError ret = aclmdlExecute(modelId_, input_, output_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("execute model failed, modelId is %u", modelId_);
-        return FAILED;
-    }
-
-    INFO_LOG("model execute success");
-    return SUCCESS;
-}
-
-void ModelProcess::Unload() {
-    if (!loadFlag_) {
-        WARN_LOG("no model had been loaded, unload failed");
-        return;
-    }
-
-    aclError ret = aclmdlUnload(modelId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("unload model failed, modelId is %u", modelId_);
-    }
-
-    if (modelDesc_ != nullptr) {
-        (void)aclmdlDestroyDesc(modelDesc_);
-        modelDesc_ = nullptr;
-    }
-
-    if (modelMemPtr_ != nullptr) {
-        aclrtFree(modelMemPtr_);
-        modelMemPtr_ = nullptr;
-        modelMemSize_ = 0;
-    }
-
-    if (modelWeightPtr_ != nullptr) {
-        aclrtFree(modelWeightPtr_);
-        modelWeightPtr_ = nullptr;
-        modelWeightSize_ = 0;
-    }
-
-    loadFlag_ = false;
-    INFO_LOG("unload model success, modelId is %u", modelId_);
-}
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/sample_process.cpp b/official/cv/yolov4/ascend310_quant_infer/src/sample_process.cpp
deleted file mode 100644
index 5f8f20f297bf6101d3a68ffd65bd0e6ed69d9486..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/sample_process.cpp
+++ /dev/null
@@ -1,252 +0,0 @@
-/**
- * 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/sample_process.h"
-#include <sys/time.h>
-#include <sys/types.h>
-#include <dirent.h>
-#include <string.h>
-#include <iostream>
-#include <fstream>
-#include "../inc/model_process.h"
-#include "acl/acl.h"
-#include "../inc/utils.h"
-extern bool g_is_device;
-using std::string;
-using std::vector;
-
-SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) {
-}
-
-SampleProcess::~SampleProcess() {
-    DestroyResource();
-}
-
-Result SampleProcess::InitResource() {
-    // ACL init
-
-    const char *aclConfigPath = "./src/acl.json";
-    aclError ret = aclInit(aclConfigPath);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl init failed");
-        return FAILED;
-    }
-    INFO_LOG("acl init success");
-
-    // open device
-    ret = aclrtSetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl open device %d failed", deviceId_);
-        return FAILED;
-    }
-    INFO_LOG("open device %d success", deviceId_);
-
-    // create context (set current)
-    ret = aclrtCreateContext(&context_, deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create context failed");
-        return FAILED;
-    }
-    INFO_LOG("create context success");
-
-    // create stream
-    ret = aclrtCreateStream(&stream_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl create stream failed");
-        return FAILED;
-    }
-    INFO_LOG("create stream success");
-
-    // get run mode
-    aclrtRunMode runMode;
-    ret = aclrtGetRunMode(&runMode);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("acl get run mode failed");
-        return FAILED;
-    }
-    g_is_device = (runMode == ACL_DEVICE);
-    INFO_LOG("get run mode success");
-    return SUCCESS;
-}
-
-void SampleProcess::GetAllFiles(std::string path, std::vector<string> *files) {
-    DIR *pDir = NULL;
-    struct dirent* ptr = nullptr;
-    if (!(pDir = opendir(path.c_str()))) {
-        return;
-    }
-    while ((ptr = readdir(pDir)) != 0) {
-        if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) {
-            files->push_back(path + "/" + ptr->d_name);
-        }
-    }
-    closedir(pDir);
-}
-
-Result SampleProcess::Process(char *om_path, char *input_folder) {
-    // model init
-    const double second_to_millisecond = 1000;
-    const double second_to_microsecond = 1000000;
-
-    double whole_cost_time = 0.0;
-    struct timeval start_global = {0};
-    struct timeval end_global = {0};
-    double startTimeMs_global = 0.0;
-    double endTimeMs_global = 0.0;
-
-    gettimeofday(&start_global, nullptr);
-
-    ModelProcess processModel;
-    const char* omModelPath = om_path;
-
-    Result ret = processModel.LoadModelFromFileWithMem(omModelPath);
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute LoadModelFromFileWithMem failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateDesc();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateDesc failed");
-        return FAILED;
-    }
-
-    ret = processModel.CreateOutput();
-    if (ret != SUCCESS) {
-        ERROR_LOG("execute CreateOutput failed");
-        return FAILED;
-    }
-
-    std::vector<string> testFile;
-    GetAllFiles(input_folder, &testFile);
-
-    if (testFile.size() == 0) {
-        WARN_LOG("no input data under folder");
-    }
-
-    double model_cost_time = 0.0;
-    double edge_to_edge_model_cost_time = 0.0;
-
-    for (size_t index = 0; index < testFile.size(); ++index) {
-        INFO_LOG("start to process file:%s", testFile[index].c_str());
-        // model process
-
-        struct timeval time_init = {0};
-        double timeval_init = 0.0;
-        gettimeofday(&time_init, nullptr);
-        timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond;
-
-        uint32_t devBufferSize;
-        void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize);
-        if (picDevBuffer == nullptr) {
-            ERROR_LOG("get pic device buffer failed,index is %zu", index);
-            return FAILED;
-        }
-        ret = processModel.CreateInput(picDevBuffer, devBufferSize);
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute CreateInput failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        struct timeval start = {0};
-        struct timeval end = {0};
-        double startTimeMs = 0.0;
-        double endTimeMs = 0.0;
-        gettimeofday(&start, nullptr);
-        startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond;
-
-        ret = processModel.Execute();
-
-        gettimeofday(&end, nullptr);
-        endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond;
-
-        double cost_time = endTimeMs - startTimeMs;
-        INFO_LOG("model infer time: %lf ms", cost_time);
-
-        model_cost_time += cost_time;
-
-        double edge_to_edge_cost_time = endTimeMs - timeval_init;
-        edge_to_edge_model_cost_time += edge_to_edge_cost_time;
-
-        if (ret != SUCCESS) {
-            ERROR_LOG("execute inference failed");
-            aclrtFree(picDevBuffer);
-            return FAILED;
-        }
-
-        int pos = testFile[index].find_last_of('/');
-        std::string name = testFile[index].substr(pos+1);
-        std::string outputname = name.substr(0, name.rfind("."));
-
-        // dump output result to file in the current directory
-        processModel.DumpModelOutputResult(const_cast<char *>(outputname.c_str()));
-
-        // release model input buffer
-        aclrtFree(picDevBuffer);
-        processModel.DestroyInput();
-    }
-    double test_file_size = 0.0;
-    test_file_size = testFile.size();
-    INFO_LOG("infer dataset size:%lf", test_file_size);
-
-    gettimeofday(&end_global, nullptr);
-    startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond;
-    endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond;
-    whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size;
-
-    model_cost_time /= test_file_size;
-    INFO_LOG("model cost time per sample: %lf ms", model_cost_time);
-    edge_to_edge_model_cost_time /= test_file_size;
-    INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time);
-    INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time);
-
-    return SUCCESS;
-}
-
-void SampleProcess::DestroyResource() {
-    aclError ret;
-    if (stream_ != nullptr) {
-        ret = aclrtDestroyStream(stream_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy stream failed");
-        }
-        stream_ = nullptr;
-    }
-    INFO_LOG("end to destroy stream");
-
-    if (context_ != nullptr) {
-        ret = aclrtDestroyContext(context_);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("destroy context failed");
-        }
-        context_ = nullptr;
-    }
-    INFO_LOG("end to destroy context");
-
-    ret = aclrtResetDevice(deviceId_);
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("reset device failed");
-    }
-    INFO_LOG("end to reset device is %d", deviceId_);
-
-    ret = aclFinalize();
-    if (ret != ACL_ERROR_NONE) {
-        ERROR_LOG("finalize acl failed");
-    }
-    INFO_LOG("end to finalize acl");
-}
-
diff --git a/official/cv/yolov4/ascend310_quant_infer/src/utils.cpp b/official/cv/yolov4/ascend310_quant_infer/src/utils.cpp
deleted file mode 100644
index d9208c8cfd9979e9248046e7325f260eb2d14d80..0000000000000000000000000000000000000000
--- a/official/cv/yolov4/ascend310_quant_infer/src/utils.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/**
- * 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 <sys/stat.h>
-#include <iostream>
-#include <fstream>
-#include <cstring>
-#include "acl/acl.h"
-
-extern bool g_is_device;
-
-void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) {
-    struct stat sBuf;
-    int fileStatus = stat(fileName.data(), &sBuf);
-    if (fileStatus == -1) {
-        ERROR_LOG("failed to get file");
-        return nullptr;
-    }
-    if (S_ISREG(sBuf.st_mode) == 0) {
-        ERROR_LOG("%s is not a file, please enter a file", fileName.c_str());
-        return nullptr;
-    }
-
-    std::ifstream binFile(fileName, std::ifstream::binary);
-    if (binFile.is_open() == false) {
-        ERROR_LOG("open file %s failed", fileName.c_str());
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.end);
-    uint32_t binFileBufferLen = binFile.tellg();
-    if (binFileBufferLen == 0) {
-        ERROR_LOG("binfile is empty, filename is %s", fileName.c_str());
-        binFile.close();
-        return nullptr;
-    }
-
-    binFile.seekg(0, binFile.beg);
-
-    void* binFileBufferData = nullptr;
-    aclError ret = ACL_ERROR_NONE;
-    if (!g_is_device) {
-        ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc for binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-        if (binFileBufferData == nullptr) {
-            ERROR_LOG("malloc binFileBufferData failed");
-            binFile.close();
-            return nullptr;
-        }
-    } else {
-        ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen);
-            binFile.close();
-            return nullptr;
-        }
-    }
-    binFile.read(static_cast<char *>(binFileBufferData), binFileBufferLen);
-    binFile.close();
-    *fileSize = binFileBufferLen;
-    return binFileBufferData;
-}
-
-void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) {
-    uint32_t inputHostBuffSize = 0;
-    void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize);
-    if (inputHostBuff == nullptr) {
-        return nullptr;
-    }
-    if (!g_is_device) {
-        void *inBufferDev = nullptr;
-        uint32_t inBufferSize = inputHostBuffSize;
-        aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-
-        ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE);
-        if (ret != ACL_ERROR_NONE) {
-            ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u",
-                inBufferSize, inputHostBuffSize);
-            aclrtFree(inBufferDev);
-            aclrtFreeHost(inputHostBuff);
-            return nullptr;
-        }
-        aclrtFreeHost(inputHostBuff);
-        *fileSize = inBufferSize;
-        return inBufferDev;
-    } else {
-        *fileSize = inputHostBuffSize;
-        return inputHostBuff;
-    }
-}