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!18386 ascend 310 inference for cnn 

Merge pull request !18386 from 于振华/cnn_master
This commit is contained in:
i-robot 2021-06-21 11:43:28 +08:00 committed by Gitee
parent a98ee2e689 66477506cb
commit ce75b31bc1
15 changed files with 668 additions and 4 deletions
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38
model_zoo/official/cv/cnn_direction_model/README.md
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@ -12,6 +12,10 @@
- [Training](#training)
- [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Export Process](#Export-process)
- [Export](#Export)
- [Inference Process](#Inference-process)
- [Inference](#Inference)
- [Model Description](#model-description)
- [Performance](#performance)
- [Evaluation Performance](#evaluation-performance)
@ -87,10 +91,12 @@ sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]
├── cv
├── cnn_direction_model
├── README.md // descriptions about cnn_direction_model
├── ascend310_infer // application for 310 inference
├── requirements.txt // packages needed
├── scripts
│ ├──run_distribute_train_ascend.sh // distributed training in ascend
│ ├──run_standalone_eval_ascend.sh // evaluate in ascend
│ ├──run_eval.sh // shell script for evaluation on Ascend
│ ├──run_standalone_train_ascend.sh // train standalone in ascend
├── src
│ ├──dataset.py // creating dataset
@ -104,6 +110,8 @@ sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH]
├── train.py // training script
├── eval.py // evaluation script
├── default_config.yaml // config file
├── postprogress.py // post process for 310 inference
├── export.py // export checkpoint files into air/mindir
```
## [Script Parameters](#contents)
@ -222,6 +230,36 @@ sh scripts/run_distribute_train_ascend.sh /home/rank_table.json /home/fsns/train
# (7) Start model inference。
```
## [Export Process](#contents)
### [Export](#content)
```shell
python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]
```
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
## [Inference Process](#contents)
### Usage
Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment.
```shell
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
```
### result
Inference result is saved in current path, you can find result like this in acc.log file.
```python
top1_correct=10096, total=10202, acc=98.96%
top1_correct=8888, total=10202, acc=87.12%
```
# [Model Description](#contents)
## [Performance](#contents)

14
model_zoo/official/cv/cnn_direction_model/ascend310_infer/CMakeLists.txt Normal file
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@ -0,0 +1,14 @@
cmake_minimum_required(VERSION 3.14.1)
project(Ascend310Infer)
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
option(MINDSPORE_PATH "mindspore install path" "")
include_directories(${MINDSPORE_PATH})
include_directories(${MINDSPORE_PATH}/include)
include_directories(${PROJECT_SRC_ROOT})
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
add_executable(main src/main.cc src/utils.cc)
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

23
model_zoo/official/cv/cnn_direction_model/ascend310_infer/build.sh Normal file
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@ -0,0 +1,23 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ ! -d out ]; then
mkdir out
fi
cd out || exit
cmake .. \
-DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

32
model_zoo/official/cv/cnn_direction_model/ascend310_infer/inc/utils.h Normal file
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@ -0,0 +1,32 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_INFERENCE_UTILS_H_
#define MINDSPORE_INFERENCE_UTILS_H_
#include <sys/stat.h>
#include <dirent.h>
#include <vector>
#include <string>
#include <memory>
#include "include/api/types.h"
std::vector<std::string> GetAllFiles(std::string_view dirName);
DIR *OpenDir(std::string_view dirName);
std::string RealPath(std::string_view path);
mindspore::MSTensor ReadFileToTensor(const std::string &file);
int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
#endif

132
model_zoo/official/cv/cnn_direction_model/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,132 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <sys/time.h>
#include <gflags/gflags.h>
#include <dirent.h>
#include <iostream>
#include <string>
#include <algorithm>
#include <iosfwd>
#include <vector>
#include <fstream>
#include <sstream>
#include "../inc/utils.h"
#include "include/dataset/execute.h"
#include "include/dataset/transforms.h"
#include "include/dataset/vision.h"
#include "include/dataset/vision_ascend.h"
#include "include/api/types.h"
#include "include/api/model.h"
#include "include/api/serialization.h"
#include "include/api/context.h"
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Context;
using mindspore::Status;
using mindspore::ModelType;
using mindspore::Graph;
using mindspore::GraphCell;
using mindspore::kSuccess;
using mindspore::MSTensor;
DEFINE_string(model_path, "", "model path");
DEFINE_string(dataset_path, ".", "dataset path");
DEFINE_int32(device_id, 0, "device id");
int main(int argc, char **argv) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (RealPath(FLAGS_model_path).empty()) {
std::cout << "Invalid model" << std::endl;
return 1;
}
auto context = std::make_shared<Context>();
auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
ascend310_info->SetDeviceID(FLAGS_device_id);
context->MutableDeviceInfo().push_back(ascend310_info);
Graph graph;
Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
if (ret != kSuccess) {
std::cout << "Load model failed." << std::endl;
return 1;
}
Model model;
ret = model.Build(GraphCell(graph), context);
if (ret != kSuccess) {
std::cout << "ERROR: Build failed." << std::endl;
return 1;
}
std::vector<MSTensor> modelInputs = model.GetInputs();
auto all_files = GetAllFiles(FLAGS_dataset_path);
if (all_files.empty()) {
std::cout << "ERROR: no input data." << std::endl;
return 1;
}
std::map<double, double> costTime_map;
size_t size = all_files.size();
for (size_t i = 0; i < size; ++i) {
struct timeval start;
struct timeval end;
double startTime_ms;
double endTime_ms;
std::vector<MSTensor> inputs;
std::vector<MSTensor> outputs;
std::cout << "Start predict input files:" << all_files[i] << std::endl;
mindspore::MSTensor image = ReadFileToTensor(all_files[i]);
inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
image.Data().get(), image.DataSize());
gettimeofday(&start, NULL);
model.Predict(inputs, &outputs);
gettimeofday(&end, NULL);
startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
WriteResult(all_files[i], outputs);
}
double average = 0.0;
int infer_cnt = 0;
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
double diff = 0.0;
diff = iter->second - iter->first;
average += diff;
infer_cnt++;
}
average = average / infer_cnt;
std::stringstream timeCost;
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
file_stream << timeCost.str();
file_stream.close();
costTime_map.clear();
return 0;
}

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model_zoo/official/cv/cnn_direction_model/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,147 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "inc/utils.h"
#include <fstream>
#include <algorithm>
#include <iostream>
using mindspore::MSTensor;
using mindspore::DataType;
std::vector<std::string> GetAllFiles(std::string_view dirName) {
struct dirent *filename;
DIR *dir = OpenDir(dirName);
if (dir == nullptr) {
return {};
}
std::vector<std::string> dirs;
std::vector<std::string> files;
while ((filename = readdir(dir)) != nullptr) {
std::string dName = std::string(filename->d_name);
if (dName == "." || dName == "..") {
continue;
} else if (filename->d_type == DT_DIR) {
dirs.emplace_back(std::string(dirName) + "/" + filename->d_name);
} else if (filename->d_type == DT_REG) {
files.emplace_back(std::string(dirName) + "/" + filename->d_name);
} else {
continue;
}
}
for (auto d : dirs) {
dir = OpenDir(d);
while ((filename = readdir(dir)) != nullptr) {
std::string dName = std::string(filename->d_name);
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
continue;
}
files.emplace_back(std::string(d) + "/" + filename->d_name);
}
}
std::sort(files.begin(), files.end());
for (auto &f : files) {
std::cout << "image file: " << f << std::endl;
}
return files;
}
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
std::string homePath = "./result_Files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
std::shared_ptr<const void> netOutput;
netOutput = outputs[i].Data();
outputSize = outputs[i].DataSize();
int pos = imageFile.rfind('/');
std::string fileName(imageFile, pos + 1);
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
std::string outFileName = homePath + "/" + fileName;
FILE * outputFile = fopen(outFileName.c_str(), "wb");
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
fclose(outputFile);
outputFile = nullptr;
}
return 0;
}
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return mindspore::MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return mindspore::MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return mindspore::MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
ifs.close();
return buffer;
}
DIR *OpenDir(std::string_view dirName) {
if (dirName.empty()) {
std::cout << " dirName is null ! " << std::endl;
return nullptr;
}
std::string realPath = RealPath(dirName);
struct stat s;
lstat(realPath.c_str(), &s);
if (!S_ISDIR(s.st_mode)) {
std::cout << "dirName is not a valid directory !" << std::endl;
return nullptr;
}
DIR *dir;
dir = opendir(realPath.c_str());
if (dir == nullptr) {
std::cout << "Can not open dir " << dirName << std::endl;
return nullptr;
}
std::cout << "Successfully opened the dir " << dirName << std::endl;
return dir;
}
std::string RealPath(std::string_view path) {
char realPathMem[PATH_MAX] = {0};
char *realPathRet = nullptr;
realPathRet = realpath(path.data(), realPathMem);
if (realPathRet == nullptr) {
std::cout << "File: " << path << " is not exist.";
return "";
}
std::string realPath(realPathMem);
std::cout << path << " realpath is: " << realPath << std::endl;
return realPath;
}

6
model_zoo/official/cv/cnn_direction_model/default_config.yaml
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@ -61,6 +61,12 @@ eval_dataset_path: ""
checkpoint_path: ""
# export options
ckpt_file: ""
file_name: "cnn"
file_format: "MINDIR"
#310 inferenct options
result_path: "./preprocess_Result/"
---
# Help description for each configuration

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model_zoo/official/cv/cnn_direction_model/export.py Normal file
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@ -0,0 +1,36 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""export script"""
import numpy as np
import mindspore as ms
from mindspore import Tensor, context, load_checkpoint, export
from src.cnn_direction_model import CNNDirectionModel
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=config.device_target)
device_id = get_device_id()
context.set_context(device_id=device_id)
if __name__ == '__main__':
net = CNNDirectionModel([3, 64, 48, 48, 64], [64, 48, 48, 64, 64], [256, 64], [64, 512])
param_dict = load_checkpoint(config.ckpt_file, net=net)
net.set_train(False)
input_data = Tensor(np.zeros([1, 3, config.im_size_h, config.im_size_w]), ms.float32)
export(net, input_data, file_name=config.file_name, file_format=config.file_format)

49
model_zoo/official/cv/cnn_direction_model/postprocess.py Normal file
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@ -0,0 +1,49 @@
# 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
parser = argparse.ArgumentParser(description='post process for cnn')
parser.add_argument("--result_path", type=str, required=True, help="result file path")
parser.add_argument("--label_path", type=str, required=True, help="label file")
args = parser.parse_args()
def cal_acc(result_path, label_path):
img_total = 0
top1_correct = 0
result_shape = (1, 2)
files = os.listdir(result_path)
for file in files:
full_file_path = os.path.join(result_path, file)
if os.path.isfile(full_file_path):
result = np.fromfile(full_file_path, dtype=np.float32).reshape(result_shape)
label_file = os.path.join(label_path, file.split(".bin")[0][:-2] + ".bin")
gt_classes = np.fromfile(label_file, dtype=np.int32)
top1_output = np.argmax(result, (-1))
t1_correct = np.equal(top1_output, gt_classes).sum()
top1_correct += t1_correct
img_total += 1
acc1 = 100.0 * top1_correct / img_total
print('top1_correct={}, total={}, acc={:.2f}%'.format(top1_correct, img_total, acc1))
if __name__ == "__main__":
cal_acc(args.result_path, args.label_path)

68
model_zoo/official/cv/cnn_direction_model/preprocess.py Normal file
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@ -0,0 +1,68 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""preprocess for cnn"""
import os
import random
import numpy as np
from src.model_utils.config import config
from src.dataset import create_dataset_eval
from mindspore import dataset as de
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
def preprocess():
dataset_name = config.dataset_name
dataset_lr, dataset_rl = create_dataset_eval(config.data_root_test + "/" + dataset_name +
".mindrecord0", config=config, dataset_name=dataset_name)
lr_img_path = os.path.join(config.result_path, "lr_dataset/" + "img_data")
lr_label_path = os.path.join(config.result_path, "lr_dataset/" + "label")
os.makedirs(lr_img_path)
os.makedirs(lr_label_path)
for idx, data in enumerate(dataset_lr.create_dict_iterator(output_numpy=True, num_epochs=1)):
img_data = data["image"]
img_label = data["label"]
file_name = "cnn_fsns_1_" + str(idx) + ".bin"
img_file_path = os.path.join(lr_img_path, file_name)
img_data.tofile(img_file_path)
label_file_path = os.path.join(lr_label_path, file_name)
img_label.tofile(label_file_path)
rl_img_path = os.path.join(config.result_path, "rl_dataset/" + "img_data")
rl_label_path = os.path.join(config.result_path, "rl_dataset/" + "label")
os.makedirs(rl_img_path)
os.makedirs(rl_label_path)
for idx, data in enumerate(dataset_rl.create_dict_iterator(output_numpy=True, num_epochs=1)):
img_data = data["image"]
img_label = data["label"]
file_name = "cnn_fsns_1_" + str(idx) + ".bin"
img_file_path = os.path.join(rl_img_path, file_name)
img_data.tofile(img_file_path)
label_file_path = os.path.join(rl_label_path, file_name)
img_label.tofile(label_file_path)
if __name__ == '__main__':
preprocess()

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model_zoo/official/cv/cnn_direction_model/scripts/run_infer_310.sh Normal file
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@ -0,0 +1,119 @@
#!/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 1 || $# -gt 2 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DEVICE_ID]
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
device_id=0
if [ $# == 2 ]; then
device_id=$2
fi
echo "mindir name: "$model
echo "device id: "$device_id
export ASCEND_HOME=/usr/local/Ascend/
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
else
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_Result ]; then
rm -rf ./preprocess_Result
fi
mkdir preprocess_Result
# python3.7 ../preprocess.py --dataset_path=$dataset_path --result_path=./preprocess_Result/
python3.7 ../preprocess.py
}
function compile_app()
{
cd ../ascend310_infer/ || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_Files ]; then
rm -rf ./result_Files
fi
if [ -d time_Result ]; then
rm -rf ./time_Result
fi
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --model_path=$model --dataset_path=./preprocess_Result/lr_dataset/img_data --device_id=$device_id &> lr_infer.log
mv result_Files result_Files_lr
mv time_Result time_Result_lr
mkdir result_Files
mkdir time_Result
../ascend310_infer/out/main --model_path=$model --dataset_path=./preprocess_Result/rl_dataset/img_data --device_id=$device_id &> rl_infer.log
mv result_Files result_Files_rl
mv time_Result time_Result_rl
}
function cal_acc()
{
python3.7 ../postprocess.py --result_path=./result_Files_lr --label_path=./preprocess_Result/lr_dataset/label &> acc.log
python3.7 ../postprocess.py --result_path=./result_Files_rl --label_path=./preprocess_Result/rl_dataset/label &>> acc.log
}
preprocess_data
if [ $? -ne 0 ]; then
echo "preprocess dataset failed"
exit 1
fi
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
infer
if [ $? -ne 0 ]; then
echo " execute inference failed"
exit 1
fi
cal_acc
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi

2
model_zoo/official/cv/googlenet/export.py
View File

@ -33,7 +33,7 @@ if config.device_target == "Ascend":
if __name__ == '__main__':
net = GoogleNet(num_classes=config.num_classes)
assert config.checkpoint_path is not None, "config.checkpoint_path is None."
assert config.ckpt_file is not None, "config.ckpt_file is None."
param_dict = load_checkpoint(config.ckpt_file)
load_param_into_net(net, param_dict)

2
model_zoo/official/cv/googlenet/postprocess.py
View File

@ -17,7 +17,7 @@ import os
import argparse
import numpy as np
parser = argparse.ArgumentParser(description='fasterrcnn_export')
parser = argparse.ArgumentParser(description='postprocess for googlenet')
parser.add_argument("--dataset", type=str, default="imagenet", help="result file path")
parser.add_argument("--result_path", type=str, required=True, help="result file path")
parser.add_argument("--label_file", type=str, required=True, help="label file")

2
model_zoo/official/cv/unet/postprocess.py
View File

@ -78,7 +78,7 @@ if __name__ == '__main__':
metrics = dice_coeff()
if config.dataset == "Cell_nuclei":
img_size = tuple(config.img_size)
img_size = tuple(config.image_size)
for i, bin_name in enumerate(os.listdir('./preprocess_Result/')):
f = bin_name.replace(".png", "")
bin_name_softmax = f + "_0.bin"

2
model_zoo/official/cv/unet/preprocess.py
View File

@ -24,7 +24,7 @@ from src.model_utils.config import config
def preprocess_dataset(data_dir, result_path, cross_valid_ind=1):
_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=config.crop,
img_size=config.img_size)
img_size=config.image_size)
labels_list = []
for i, data in enumerate(valid_dataset):