forked from mindspore-Ecosystem/mindspore
!7502 delete launch file
Merge pull request !7502 from zhaoting/mobilenet
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Gitee
commit
290b0aa194
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@ -70,7 +70,6 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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│ ├──args.py # parse args
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│ ├──config.py # parameter configuration
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│ ├──dataset.py # creating dataset
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│ ├──launch.py # start python script
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│ ├──lr_generator.py # learning rate config
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│ ├──mobilenetV2.py # MobileNetV2 architecture
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│ ├──models.py # contain define_net and Loss, Monitor
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@ -31,21 +31,32 @@ run_ascend()
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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export RANK_TABLE_FILE=$4
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DEVICE_NUM=$2
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if [ -d "../train" ];
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then
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rm -rf ../train
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fi
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mkdir ../train
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cd ../train || exit
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python ${BASEPATH}/../src/launch.py \
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for((i=0; i<${DEVICE_NUM}; i++))
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do
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export DEVICE_ID=$i
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export RANK_ID=$i
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rm -rf ./rank$i
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mkdir ./rank$i
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cp ../*.py ./rank$i
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cp -r ../src ./rank$i
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cd ./rank$i || exit
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echo "start training for rank $RANK_ID, device $DEVICE_ID"
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env > env.log
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python train.py \
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--platform=$1 \
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--nproc_per_node=$2 \
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--visible_devices=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pretrain_ckpt=$6 \
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--freeze_layer=$7 \
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&> ../train.log & # dataset train folder
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&> log$i.log &
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cd ..
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done
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}
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run_gpu()
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@ -1,64 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""launch train script"""
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import os
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import sys
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import subprocess
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import shutil
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from args import launch_parse_args
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def main():
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print("start", __file__)
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args = launch_parse_args()
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print(args)
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visible_devices = args.visible_devices.split(',')
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assert os.path.isfile(args.training_script)
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assert len(visible_devices) >= args.nproc_per_node
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print('visible_devices:{}'.format(visible_devices))
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# spawn the processes
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processes = []
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cmds = []
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log_files = []
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env = os.environ.copy()
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env['RANK_SIZE'] = str(args.nproc_per_node)
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cur_path = os.getcwd()
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for rank_id in range(0, args.nproc_per_node):
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os.chdir(cur_path)
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device_id = visible_devices[rank_id]
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rank_dir = os.path.join(cur_path, 'rank{}'.format(rank_id))
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env['RANK_ID'] = str(rank_id)
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env['DEVICE_ID'] = str(device_id)
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if os.path.exists(rank_dir):
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shutil.rmtree(rank_dir)
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os.mkdir(rank_dir)
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os.chdir(rank_dir)
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cmd = [sys.executable, '-u']
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cmd.append(args.training_script)
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cmd.extend(args.training_script_args)
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log_file = open(f'{rank_dir}/log{rank_id}.log', 'w')
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process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
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processes.append(process)
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cmds.append(cmd)
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log_files.append(log_file)
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for process, cmd, log_file in zip(processes, cmds, log_files):
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process.wait()
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if process.returncode != 0:
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raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
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log_file.close()
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if __name__ == "__main__":
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main()
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@ -64,7 +64,6 @@ Dataset used: [imagenet](http://www.image-net.org/)
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├── src
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│ ├──config.py # parameter configuration
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│ ├──dataset.py # creating dataset
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│ ├──launch.py # start python script
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│ ├──lr_generator.py # learning rate config
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│ ├──mobilenetV3.py # MobileNetV3 architecture
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├── train.py # training script
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@ -1,162 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""launch train script"""
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import os
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import sys
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import json
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import subprocess
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import shutil
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from argparse import ArgumentParser
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def parse_args():
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"""
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parse args .
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Args:
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Returns:
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args.
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Examples:
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>>> parse_args()
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"""
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parser = ArgumentParser(description="mindspore distributed training launch "
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"helper utilty that will spawn up "
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"multiple distributed processes")
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parser.add_argument("--nproc_per_node", type=int, default=1,
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help="The number of processes to launch on each node, "
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"for D training, this is recommended to be set "
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"to the number of D in your system so that "
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"each process can be bound to a single D.")
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parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
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help="will use the visible devices sequentially")
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parser.add_argument("--server_id", type=str, default="",
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help="server ip")
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parser.add_argument("--training_script", type=str,
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help="The full path to the single D training "
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"program/script to be launched in parallel, "
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"followed by all the arguments for the "
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"training script")
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# rest from the training program
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args, unknown = parser.parse_known_args()
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args.training_script_args = unknown
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return args
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def main():
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print("start", __file__)
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args = parse_args()
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print(args)
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visible_devices = args.visible_devices.split(',')
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assert os.path.isfile(args.training_script)
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assert len(visible_devices) >= args.nproc_per_node
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print('visible_devices:{}'.format(visible_devices))
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if not args.server_id:
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print('pleaser input server ip!!!')
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exit(0)
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print('server_id:{}'.format(args.server_id))
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# construct hccn_table
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hccn_configs = open('/etc/hccn.conf', 'r').readlines()
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device_ips = {}
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for hccn_item in hccn_configs:
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hccn_item = hccn_item.strip()
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if hccn_item.startswith('address_'):
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device_id, device_ip = hccn_item.split('=')
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device_id = device_id.split('_')[1]
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device_ips[device_id] = device_ip
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print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
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hccn_table = {}
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hccn_table['board_id'] = '0x0000'
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hccn_table['chip_info'] = '910'
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hccn_table['deploy_mode'] = 'lab'
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hccn_table['group_count'] = '1'
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hccn_table['group_list'] = []
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instance_list = []
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usable_dev = ''
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for instance_id in range(args.nproc_per_node):
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instance = {}
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instance['devices'] = []
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device_id = visible_devices[instance_id]
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device_ip = device_ips[device_id]
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usable_dev += str(device_id)
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instance['devices'].append({
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'device_id': device_id,
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'device_ip': device_ip,
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})
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instance['rank_id'] = str(instance_id)
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instance['server_id'] = args.server_id
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instance_list.append(instance)
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hccn_table['group_list'].append({
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'device_num': str(args.nproc_per_node),
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'server_num': '1',
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'group_name': '',
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'instance_count': str(args.nproc_per_node),
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'instance_list': instance_list,
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})
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hccn_table['para_plane_nic_location'] = 'device'
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hccn_table['para_plane_nic_name'] = []
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for instance_id in range(args.nproc_per_node):
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eth_id = visible_devices[instance_id]
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hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
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hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
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hccn_table['status'] = 'completed'
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# save hccn_table to file
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table_path = os.getcwd()
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if not os.path.exists(table_path):
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os.mkdir(table_path)
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table_fn = os.path.join(table_path,
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'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
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with open(table_fn, 'w') as table_fp:
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json.dump(hccn_table, table_fp, indent=4)
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sys.stdout.flush()
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# spawn the processes
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processes = []
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cmds = []
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log_files = []
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env = os.environ.copy()
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env['RANK_SIZE'] = str(args.nproc_per_node)
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cur_path = os.getcwd()
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for rank_id in range(0, args.nproc_per_node):
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os.chdir(cur_path)
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device_id = visible_devices[rank_id]
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device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
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env['RANK_ID'] = str(rank_id)
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env['DEVICE_ID'] = str(device_id)
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if args.nproc_per_node > 1:
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env['RANK_TABLE_FILE'] = table_fn
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if os.path.exists(device_dir):
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shutil.rmtree(device_dir)
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os.mkdir(device_dir)
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os.chdir(device_dir)
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cmd = [sys.executable, '-u']
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cmd.append(args.training_script)
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cmd.extend(args.training_script_args)
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log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
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process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
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processes.append(process)
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cmds.append(cmd)
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log_files.append(log_file)
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for process, cmd, log_file in zip(processes, cmds, log_files):
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process.wait()
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if process.returncode != 0:
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raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
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log_file.close()
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if __name__ == "__main__":
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main()
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@ -66,7 +66,7 @@ default_boxes_ltrb = GeneratDefaultBoxes().default_boxes_ltrb
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default_boxes = GeneratDefaultBoxes().default_boxes
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y1, x1, y2, x2 = np.split(default_boxes_ltrb[:, :4], 4, axis=-1)
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vol_anchors = (x2 - x1) * (y2 - y1)
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matching_threshold = config.match_thershold
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matching_threshold = config.match_threshold
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def ssd_bboxes_encode(boxes):
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@ -100,7 +100,7 @@ def metrics(pred_data):
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class_boxes = pred_boxes[score_mask] * [h, w, h, w]
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if score_mask.any():
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nms_index = apply_nms(class_boxes, class_box_scores, config.nms_thershold, config.max_boxes)
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nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, config.max_boxes)
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class_boxes = class_boxes[nms_index]
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class_box_scores = class_box_scores[nms_index]
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@ -21,8 +21,8 @@ config = ed({
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"img_shape": [300, 300],
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"num_ssd_boxes": 1917,
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"neg_pre_positive": 3,
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"match_thershold": 0.5,
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"nms_thershold": 0.6,
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"match_threshold": 0.5,
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"nms_threshold": 0.6,
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"min_score": 0.1,
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"max_boxes": 100,
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@ -38,7 +38,7 @@ config = ed({
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"num_default": [3, 6, 6, 6, 6, 6],
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"extras_in_channels": [256, 576, 1280, 512, 256, 256],
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"extras_out_channels": [576, 1280, 512, 256, 256, 128],
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"extras_srides": [1, 1, 2, 2, 2, 2],
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"extras_strides": [1, 1, 2, 2, 2, 2],
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"extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
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"feature_size": [19, 10, 5, 3, 2, 1],
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"min_scale": 0.2,
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@ -228,7 +228,7 @@ class SSD300(nn.Cell):
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in_channels = config.extras_in_channels
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out_channels = config.extras_out_channels
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ratios = config.extras_ratio
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strides = config.extras_srides
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strides = config.extras_strides
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residual_list = []
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for i in range(2, len(in_channels)):
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residual = InvertedResidual(in_channels[i], out_channels[i], stride=strides[i],
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