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add training scripts and modify readme of mobilenetv2_quant and 

resnet50_quant

modify readme
tags/v1.0.0
hwjiaorui 5 years ago
parent
a50bc2182e
commit
aaa0436882
5 changed files with 598 additions and 146 deletions
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  1. +10
    -7
      model_zoo/official/cv/mobilenetv2_quant/Readme.md
  2. +300
    -0
      model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh
  3. +0
    -96
      model_zoo/official/cv/mobilenetv2_quant/scripts/run_train_quant.sh
  4. +43
    -8
      model_zoo/official/cv/resnet50_quant/Readme.md
  5. +245
    -35
      model_zoo/official/cv/resnet50_quant/scripts/run_train.sh

+ 10
- 7
model_zoo/official/cv/mobilenetv2_quant/Readme.md View File

@@ -70,7 +70,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
├── mobileNetv2_quant
├── Readme.md # descriptions about MobileNetV2-Quant
├── scripts
│ ├──run_train_quant.sh # shell script for train on Ascend
│ ├──run_train.sh # shell script for train on Ascend and GPU
│ ├──run_infer_quant.sh # shell script for evaluation on Ascend
├── src
│ ├──config.py # parameter configuration
@@ -91,19 +91,22 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil

You can start training using python or shell scripts. The usage of shell scripts as follows:

- Ascend: sh run_train_quant.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
- bash run_train.sh [Ascend] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
- bash run_train.sh [GPU] [DEVICE_ID_LIST] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)


### Launch

```
# training example
shell:
Ascend: sh run_train_quant.sh Ascend 8 10.222.223.224 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt
``` bash
# training example
>>> bash run_train.sh Ascend ~/hccl_4p_0123_x.x.x.x.json ~/imagenet/train/ ~/mobilenet.ckpt
>>> bash run_train.sh GPU 1,2 ~/imagenet/train/ ~/mobilenet.ckpt
```

### Result

Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
Training result will be stored in the example path. Checkpoints trained by `Ascend` will be stored at `./train/device$i/checkpoint` by default, and training log will be redirected to `./train/device$i/train.log`. Checkpoints trained by `GPU` will be stored in `./train/checkpointckpt_$i` by default, and training log will be redirected to `./train/train.log`.
`train.log` is as follows:

```
epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]


+ 300
- 0
model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh View File

@@ -0,0 +1,300 @@
#!/bin/bash
# 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.
# ============================================================================

get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}


# check_and_get_Ascend_device(){

# #device_list=(${1//,/ })
# IFS=',' read -ra device_list <<<"$1"
# last_device_id=0
# first_device_id=8
# device_used=(0 0 0 0 0 0 0 0)

# for var in "${device_list[@]}"
# do
# if [ $((var)) -lt 0 ] || [ $((var)) -ge 8 ]
# then
# echo "error: device id=${var} is incorrect, device id must be in range [0,8), please check your device id list!"
# exit 1
# fi

# if [ ${device_used[$((var))]} -eq 0 ]
# then
# device_used[ $((var)) ]=1
# else
# echo "error: device id is duplicate, please check your device id list!"
# exit 1
# fi

# if [ ${last_device_id} \< $((var)) ]
# then
# last_device_id=$((var))
# fi
# if [ ${first_device_id} \> $((var)) ]
# then
# first_device_id=$((var))
# fi
# done

# device_num=`expr ${last_device_id} - ${first_device_id} + 1`
# if [ ${device_num} != ${#device_list[*]} ]
# then
# echo "error: the Ascend chips used must be continuous, please check your device id list!"
# exit 1
# fi

# if [ ${first_device_id} -lt 4 ] && [ ${last_device_id} -ge 4 ]
# then
# if [ ${first_device_id} != 0 ] || [ ${last_device_id} != 7 ]
# then
# echo "error: device id list must be in the same group of [0,4) or [4,8) when using Ascend chips."
# exit 1
# fi
# fi

# echo "${first_device_id},`expr ${last_device_id} + 1`"
# }

# get_hccl_name(){

# server_ip=$(ifconfig -a | grep inet | grep -v 127.0.0.1 | grep -v inet6 | awk '{print $2}' | tr -d "addr:")
# device_num=`expr $2 - $1`
# device_id_list=""

# for(( i=$1 ; i < $2 ; i++ ))
# do
# device_id_list=${device_id_list}$i
# done
# hccl_name="hccl_${device_num}p_${device_id_list}_${server_ip}.json"

# echo ${hccl_name}
# }


get_gpu_device_num(){

#device_list=(${1//,/ })
IFS=',' read -ra device_list <<<"$1"
device_used=(0 0 0 0 0 0 0 0)
device_num=0
for var in "${device_list[@]}"
do
if [ $((var)) -lt 0 ] || [ $((var)) -ge 8 ]
then
echo "error: device id=${var} is incorrect, device id must be in range [0,8), please check your device id list!"
exit 1
fi

if [ ${device_used[$((var))]} -eq 0 ]
then
device_used[ $((var)) ]=1
device_num=$((device_num+1))
fi
done

echo ${device_num}
}


run_ascend(){

if [ $# -gt 4 ] || [ $# -lt 3 ]
then
echo "Usage: bash run_train.sh [Ascend] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)\n "
exit 1
fi

#first_last_device=$(check_and_get_Ascend_device $2)
#devices=(${first_last_device//,/ })
#IFS=',' read -ra devices <<<"${first_last_device}"
# first_device=${first_last_device:0:1}
# last_device=${first_last_device:2:1}
# device_num=`expr $((last_device)) - $((first_device))`
#single ascend or multiple ascend
# if [ ${device_num} -gt 1 ]
# then
# ori_path=$(dirname "$(readlink -f "$0" )")
# #generate hccl config file
# cd ../../../../utils/hccl_tools/ || exit
# device_num_arg="[${first_device},${last_device})"

# python hccl_tools.py --device_num=${device_num_arg}

# hccl_name=$(get_hccl_name ${first_device} ${last_device})

# if [ ! -e ${hccl_name} ]
# then
# echo "error: failed to generate the hccl config file!"
# exit 1
# fi

# mv ${hccl_name} ${ori_path}
# cd ${ori_path} || exit

# PATH1=$(get_real_path ${hccl_name})

# if [ ! -f $PATH1 ]
# then
# echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
# exit 1
# fi

# export RANK_TABLE_FILE=$PATH1
# fi

PATH1=$(get_real_path $2)
PATH2=$(get_real_path $3)

if [ $# == 4 ]
then
PATH3=$(get_real_path $4)
fi

if [ ! -f $PATH1 ]
then
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
exit 1
fi

if [ ! -d $PATH2 ]
then
echo "error: DATASET_PATH=$PATH2 is not a directory"
exit 1
fi

if [ $# == 4 ] && [ ! -f $PATH3 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
exit 1
fi


rank_file_name=${2##*/}
IFS='_' read -ra array <<<"${rank_file_name}"
device_id_list=${array[2]}
first_device=${device_id_list:0:1}
#last_device=${device_list:${#device_list}-1:1}
device_num=${#device_id_list}
ulimit -u unlimited
export DEVICE_NUM=${device_num}
export RANK_SIZE=${device_num}
export RANK_TABLE_FILE=$PATH1

export SERVER_ID=0
rank_start=$((DEVICE_NUM * SERVER_ID))

rm -rf ./train
mkdir ./train
for((i=0; i<${device_num}; i++))
do
export DEVICE_ID=$((first_device+i))
export RANK_ID=$((rank_start + i))
mkdir ./train/device$i
cp ../*.py ./train/device$i
cp *.sh ./train/device$i
cp -r ../src ./train/device$i
cd ./train/device$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
if [ $# == 3 ]
then
python train.py --device_target=$1 --dataset_path=$PATH2 &> train.log &
fi
if [ $# == 4 ]
then
python train.py --device_traget=$1 --dataset_path=$PATH2 --pre_trained=$PATH3 &> train.log &
fi

cd ../.. || exit
done
}

run_gpu(){
if [ $# -gt 3 ] || [ $# -lt 2 ]
then
echo "Usage: bash run_train.sh [GPU] [DEVICE_ID_LIST] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)\n "
exit 1
fi

PATH1=$(get_real_path $3)
if [ $# == 4 ]
then
PATH2=$(get_real_path $4)
fi

if [ ! -d $PATH1 ]
then
echo "error: DATASET_PATH=$PATH1 is not a directory"
exit 1
fi

if [ $# == 4 ] && [ ! -f $PATH2 ]
then
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
exit 1
fi

device_num=$(get_gpu_device_num $2)

ulimit -u unlimited
export DEVICE_NUM=${device_num}
export RANK_SIZE=${device_num}
export CUDA_VISIBLE_DEVICES=$2
rm -rf ./train
mkdir ./train
cp ../*.py ./train
cp *.sh ./train
cp -r ../src ./train
cd ./train || exit
echo "start training"
env > env.log
if [ $# == 3 ]
then
mpirun --allow-run-as-root -n ${RANK_SIZE} \
python train.py --device_target=$1 --dataset_path=$PATH1 &> train.log &
fi
if [ $# == 4 ]
then
mpirun --allow-run-as-root -n ${RANK_SIZE} \
python train.py --device_traget=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 &> train.log &
fi

cd ..
}


if [ $1 = "Ascend" ] ; then
run_ascend "$@"
elif [ $1 = "GPU" ] ; then
run_gpu "$@"
else
echo "Unsupported device target: $1"
fi;

+ 0
- 96
model_zoo/official/cv/mobilenetv2_quant/scripts/run_train_quant.sh View File

@@ -1,96 +0,0 @@
#!/usr/bin/env bash
# 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.
# ============================================================================

run_ascend()
{
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
then
echo "error: DEVICE_NUM=$2 is not in (1-9)"
exit 1
fi

if [ ! -d $5 ] && [ ! -f $5 ]
then
echo "error: DATASET_PATH=$5 is not a directory or file"
exit 1
fi

BASEPATH=$(cd "`dirname $0`" || exit; pwd)
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
if [ -d "../train" ];
then
rm -rf ../train
fi
mkdir ../train
cd ../train || exit
python ${BASEPATH}/../src/launch.py \
--nproc_per_node=$2 \
--visible_devices=$4 \
--server_id=$3 \
--training_script=${BASEPATH}/../train.py \
--dataset_path=$5 \
--pre_trained=$6 \
--device_target=$1 &> train.log & # dataset train folder
}

run_gpu()
{
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
then
echo "error: DEVICE_NUM=$2 is not in (1-8)"
exit 1
fi

if [ ! -d $4 ]
then
echo "error: DATASET_PATH=$4 is not a directory"
exit 1
fi

BASEPATH=$(cd "`dirname $0`" || exit; pwd)
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
if [ -d "../train" ];
then
rm -rf ../train
fi
mkdir ../train
cd ../train || exit

export CUDA_VISIBLE_DEVICES="$3"
mpirun -n $2 --allow-run-as-root \
python ${BASEPATH}/../train.py \
--dataset_path=$4 \
--device_target=$1 \
--pre_trained=$5 &> ../train.log & # dataset train folder
}

if [ $# -gt 6 ] || [ $# -lt 5 ]
then
echo "Usage:\n \
Ascend: sh run_train_quant.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
GPU: sh run_train_quant.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
"
exit 1
fi

if [ $1 = "Ascend" ] ; then
run_ascend "$@"
elif [ $1 = "GPU" ] ; then
run_gpu "$@"
else
echo "Unsupported device target."
fi;


+ 43
- 8
model_zoo/official/cv/resnet50_quant/Readme.md View File

@@ -1,4 +1,43 @@
# Contents
# ResNet-50_quant Example

## Description

This is an example of training ResNet-50_quant with ImageNet2012 dataset in MindSpore.

## Requirements

- Install [MindSpore](https://www.mindspore.cn/install/en).

- Download the dataset ImageNet2012

> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
> ```
> .
> ├── ilsvrc # train dataset
> └── ilsvrc_eval # infer dataset: images should be classified into 1000 directories firstly, just like train images
> ```


## Example structure

```shell
resnet50_quant/
├── eval.py
├── models
│   └── resnet_quant.py
├── Readme.md
├── scripts
│   ├── run_infer.sh
│   └── run_train.sh
├── src
│   ├── config.py
│   ├── crossentropy.py
│   ├── dataset.py
│   ├── launch.py
│   └── lr_generator.py
└── train.py
```

- [resnet50 Description](#resnet50-description)
- [Model Architecture](#model-architecture)
@@ -88,21 +127,17 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil

### Usage


You can start training using python or shell scripts. The usage of shell scripts as follows:

- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH][CKPT_PATH]
- Ascend: sh run_train.sh Ascend [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH]\(optional)
### Launch

```
# training example
shell:
Ascend: sh run_train.sh Ascend 8 10.222.223.224 0,1,2,3,4,5,6,7 ~/resnet/train/ Resnet50-90_5004.ckpt
# training example
Ascend: bash run_train.sh Ascend ~/hccl_4p_0123_x.x.x.x.json ~/imagenet/train/
```

### Result

Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
Training result will be stored in the example path. Checkpoints will be stored at `./train/device$i/` by default, and training log will be redirected to `./train/device$i/train.log` like followings.

```
epoch: 1 step: 5004, loss is 4.8995576


+ 245
- 35
model_zoo/official/cv/resnet50_quant/scripts/run_train.sh View File

@@ -1,4 +1,4 @@
#!/usr/bin/env bash
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,49 +14,259 @@
# limitations under the License.
# ============================================================================

run_ascend()
{
if [ $2 -lt 1 ] && [ $2 -gt 8 ]
then
echo "error: DEVICE_NUM=$2 is not in (1-8)"
exit 1
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}

if [ ! -d $5 ] && [ ! -f $5 ]
then
echo "error: DATASET_PATH=$5 is not a directory or file"
exit 1
# check_and_get_Ascend_device(){

# #device_list=(${1//,/ })
# IFS=',' read -ra device_list <<<"$1"
# last_device_id=0
# first_device_id=8
# device_used=(0 0 0 0 0 0 0 0)

# for var in "${device_list[@]}"
# do
# if [ $((var)) -lt 0 ] || [ $((var)) -ge 8 ]
# then
# echo "error: device id=${var} is incorrect, device id must be in range [0,8), please check your device id list!"
# exit 1
# fi

# if [ ${device_used[$((var))]} -eq 0 ]
# then
# device_used[ $((var)) ]=1
# else
# echo "error: device id is duplicate, please check your device id list!"
# exit 1
# fi

# if [ ${last_device_id} \< $((var)) ]
# then
# last_device_id=$((var))
# fi
# if [ ${first_device_id} \> $((var)) ]
# then
# first_device_id=$((var))
# fi
# done

# device_num=`expr ${last_device_id} - ${first_device_id} + 1`
# if [ ${device_num} != ${#device_list[*]} ]
# then
# echo "error: the Ascend chips used must be continuous, please check your device id list!"
# exit 1
# fi

# if [ ${first_device_id} -lt 4 ] && [ ${last_device_id} -ge 4 ]
# then
# if [ ${first_device_id} != 0 ] || [ ${last_device_id} != 7 ]
# then
# echo "error: device id list must be in the same group of [0,4) or [4,8) when using Ascend chips."
# exit 1
# fi
# fi

# echo "${first_device_id},`expr ${last_device_id} + 1`"
# }

# get_hccl_name(){

# server_ip=$(ifconfig -a | grep inet | grep -v 127.0.0.1 | grep -v inet6 | awk '{print $2}' | tr -d "addr:")
# device_num=`expr $2 - $1`
# device_id_list=""

# for(( i=$1 ; i < $2 ; i++ ))
# do
# device_id_list=${device_id_list}$i
# done
# hccl_name="hccl_${device_num}p_${device_id_list}_${server_ip}.json"

# echo ${hccl_name}
# }


run_ascend(){

if [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: bash run_train.sh [Ascend] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)\n"
exit 1
fi

# first_last_device=$(check_and_get_Ascend_device $2)
# #devices=(${first_last_device//,/ })
# #IFS=',' read -ra devices <<<"${first_last_device}"
# first_device=${first_last_device:0:1}
# last_device=${first_last_device:2:1}
# device_num=`expr $((last_device)) - $((first_device))`

# #single ascend or multiple ascend
# if [ ${device_num} -gt 1 ]
# then
# ori_path=$(dirname "$(readlink -f "$0")")
# #generate hccl config file
# cd ../../../../utils/hccl_tools/ || exit
# device_num_arg="[${first_device},${last_device})"

# python hccl_tools.py --device_num=${device_num_arg}

# hccl_name=$(get_hccl_name ${first_device} ${last_device})

# if [ ! -e ${hccl_name} ]
# then
# echo "error: failed to generate the hccl config file!"
# exit 1
# fi

# mv ${hccl_name} ${ori_path}
# cd ${ori_path} || exit

# PATH1=$(get_real_path ${hccl_name})

# if [ ! -f $PATH1 ]
# then
# echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
# exit 1
# fi

# export RANK_TABLE_FILE=$PATH1
# fi


PATH1=$(get_real_path $2)
PATH2=$(get_real_path $3)

if [ $# == 4 ]
then
PATH3=$(get_real_path $4)
fi

BASEPATH=$(cd "`dirname $0`" || exit; pwd)
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
if [ -d "../train" ];
if [ ! -f $PATH1 ]
then
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
exit 1
fi

if [ ! -d $PATH2 ]
then
echo "error: DATASET_PATH=$PATH2 is not a directory"
exit 1
fi

if [ $# == 4 ] && [ ! -f $PATH3 ]
then
rm -rf ../train
echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
exit 1
fi
mkdir ../train
cd ../train || exit
python ${BASEPATH}/../src/launch.py \
--nproc_per_node=$2 \
--visible_devices=$4 \
--server_id=$3 \
--training_script=${BASEPATH}/../train.py \
--dataset_path=$5 \
--pre_trained=$6 \
--device_target=$1 &> train.log & # dataset train folder

rank_file_name=${2##*/}
IFS='_' read -ra array <<<"${rank_file_name}"
device_id_list=${array[2]}
first_device=${device_id_list:0:1}
device_num=${#device_id_list}


ulimit -u unlimited
export DEVICE_NUM=${device_num}
export RANK_SIZE=${device_num}
export RANK_TABLE_FILE=$PATH1

export SERVER_ID=0
rank_start=$((DEVICE_NUM * SERVER_ID))

rm -rf ./train
mkdir ./train
for((i=0; i<${device_num}; i++))
do
export DEVICE_ID=$((first_device+i))
export RANK_ID=$((rank_start + i))
mkdir ./train/device$i
cp ../*.py ./train/device$i
cp *.sh ./train/device$i
cp -r ../src ./train/device$i
cp -r ../models ./train/device$i
cd ./train/device$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
if [ $# == 3 ]
then
python train.py --device_target=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> train.log &
fi
if [ $# == 4 ]
then
python train.py --device_target=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> train.log &
fi

cd ../.. || exit
done
}

if [ $# -gt 6 ] || [ $# -lt 4 ]
then
echo "Usage:\n \
Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
"
exit 1
fi
# run_gpu(){

# if [ $# -gt 3 ] || [ $# -lt 2 ]
# then
# echo "Usage: sh run_train_distribute_quant.sh [GPU] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)\n "
# exit 1
# fi

# PATH1=$(get_real_path $2)
# if [ $# == 3 ]
# then
# PATH2=$(get_real_path $3)
# fi

# if [ ! -d $PATH1 ]
# then
# echo "error: DATASET_PATH=$PATH1 is not a directory"
# exit 1
# fi

# if [ $# == 3 ] && [ ! -f $PATH2 ]
# then
# echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
# exit 1
# fi

# ulimit -u unlimited
# export RANK_SIZE=2
# #export CUDA_VISIBLE_DEVICES=1,2

# rm -rf ./train_parallel
# mkdir ./train_parallel
# cp ../*.py ./train_parallel
# cp *.sh ./train_parallel
# cp -r ../src ./train_parallel
# cp -r ../models ./train_parallel
# cd ./train_parallel || exit
# echo "start training"
# env > env.log
# if [ $# == 2 ]
# then
# mpirun --allow-run-as-root -n $RANK_SIZE
# python train.py --device_target=$1 --dataset_path=$PATH1 &> log &
# fi
# if [ $# == 3 ]
# then
# mpirun --allow-run-as-root -n $RANK_SIZE
# python train.py --device_traget=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
# fi
# cd ..
# }


if [ $1 = "Ascend" ] ; then
run_ascend "$@"
else
echo "not support platform"
fi;

echo "Unsupported device target: $1"
fi;

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