!2801 add mobilenet v2 quant and resnet50 quant to model_zoo
Merge pull request !2801 from chenzhongming/quant_scriptpull/2801/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
663726e3c1
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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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"""Evaluate MobilenetV2 on ImageNet"""
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import os
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import argparse
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from mindspore import context
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.quant import quant
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from src.mobilenetV2 import mobilenetV2
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from src.dataset import create_dataset
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from src.config import config_ascend
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--device_target', type=str, default=None, help='Run device target')
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parser.add_argument('--quantization_aware', type=bool, default=False, help='Use quantization aware training')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_device_target = None
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if args_opt.device_target == "Ascend":
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config_device_target = config_ascend
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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device_id=device_id, save_graphs=False)
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else:
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raise ValueError("Unsupported device target: {}.".format(args_opt.device_target))
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# define fusion network
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network = mobilenetV2(num_classes=config_device_target.num_classes)
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if args_opt.quantization_aware:
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# convert fusion network to quantization aware network
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network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
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# define network loss
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
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# define dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_device_target,
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device_target=args_opt.device_target,
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batch_size=config_device_target.batch_size)
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step_size = dataset.get_dataset_size()
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# load checkpoint
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(network, param_dict)
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network.set_train(False)
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# define model
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model = Model(network, loss_fn=loss, metrics={'acc'})
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print("============== Starting Validation ==============")
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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print("============== End Validation ==============")
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#!/usr/bin/env bash
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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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if [ $# != 3 ]
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then
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echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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# check dataset path
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if [ ! -d $2 ] && [ ! -f $2 ]
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then
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echo "error: DATASET_PATH=$2 is not a directory or file"
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exit 1
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fi
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# check checkpoint file
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if [ ! -f $3 ]
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then
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echo "error: CHECKPOINT_PATH=$3 is not a file"
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exit 1
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fi
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# set environment
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export DEVICE_ID=0
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export RANK_ID=0
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export RANK_SIZE=1
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if [ -d "../eval" ];
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then
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rm -rf ../eval
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fi
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mkdir ../eval
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cd ../eval || exit
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# launch
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python ${BASEPATH}/../eval.py \
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--device_target=$1 \
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--dataset_path=$2 \
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--checkpoint_path=$3 \
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&> infer.log & # dataset val folder path
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#!/usr/bin/env bash
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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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if [ $# != 3 ]
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then
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echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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# check dataset path
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if [ ! -d $2 ] && [ ! -f $2 ]
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then
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echo "error: DATASET_PATH=$2 is not a directory or file"
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exit 1
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fi
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# check checkpoint file
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if [ ! -f $3 ]
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then
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echo "error: CHECKPOINT_PATH=$3 is not a file"
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exit 1
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fi
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# set environment
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export DEVICE_ID=0
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export RANK_ID=0
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export RANK_SIZE=1
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if [ -d "../eval" ];
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then
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rm -rf ../eval
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fi
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mkdir ../eval
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cd ../eval || exit
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# launch
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python ${BASEPATH}/../eval.py \
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--device_target=$1 \
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--dataset_path=$2 \
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--checkpoint_path=$3 \
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--quantization_aware=True \
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&> infer.log & # dataset val folder path
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#!/usr/bin/env bash
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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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run_ascend()
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{
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if [ $2 -lt 1 ] && [ $2 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$2 is not in (1-9)"
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exit 1
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fi
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if [ ! -d $5 ] && [ ! -f $5 ]
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then
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echo "error: DATASET_PATH=$5 is not a directory or file"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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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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--nproc_per_node=$2 \
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--visible_devices=$4 \
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--server_id=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pre_trained=$6 \
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--device_target=$1 &> train.log & # dataset train folder
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}
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if [ $# -gt 6 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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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 \
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"
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exit 1
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fi
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if [ $1 = "Ascend" ] ; then
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run_ascend "$@"
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else
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echo "Unsupported device target."
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fi;
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#!/usr/bin/env bash
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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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run_ascend()
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{
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if [ $2 -lt 1 ] && [ $2 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$2 is not in (1-9)"
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exit 1
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fi
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if [ ! -d $5 ] && [ ! -f $5 ]
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then
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echo "error: DATASET_PATH=$5 is not a directory or file"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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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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--nproc_per_node=$2 \
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--visible_devices=$4 \
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--server_id=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pre_trained=$6 \
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--quantization_aware=True \
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--device_target=$1 &> train.log & # dataset train folder
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}
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if [ $# -gt 6 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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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 \
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"
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exit 1
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fi
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if [ $1 = "Ascend" ] ; then
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run_ascend "$@"
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else
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echo "Unsupported device target."
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fi;
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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.
|
||||
# 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.
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# ============================================================================
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"""
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network config setting, will be used in train.py and eval.py
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"""
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from easydict import EasyDict as ed
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config_ascend = ed({
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"num_classes": 1000,
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"image_height": 224,
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"image_width": 224,
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"batch_size": 256,
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"data_load_mode": "mindrecord",
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"epoch_size": 200,
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"start_epoch": 0,
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"warmup_epochs": 4,
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"lr": 0.4,
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"momentum": 0.9,
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"weight_decay": 4e-5,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 200,
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"save_checkpoint_path": "./checkpoint",
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"quantization_aware": False,
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})
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config_ascend_quant = ed({
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"num_classes": 1000,
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"image_height": 224,
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"image_width": 224,
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"batch_size": 192,
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"data_load_mode": "mindrecord",
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"epoch_size": 60,
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"start_epoch": 200,
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"warmup_epochs": 1,
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"lr": 0.3,
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"momentum": 0.9,
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"weight_decay": 4e-5,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 200,
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"save_checkpoint_path": "./checkpoint",
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"quantization_aware": True,
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})
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@ -0,0 +1,156 @@
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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");
|
||||
# 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.
|
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# ============================================================================
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"""
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create train or eval dataset.
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"""
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import os
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from functools import partial
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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import mindspore.dataset.transforms.c_transforms as C2
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import mindspore.dataset.transforms.vision.py_transforms as P
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from src.config import config_ascend
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def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32):
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"""
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create a train or eval dataset
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Args:
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dataset_path(string): the path of dataset.
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do_train(bool): whether dataset is used for train or eval.
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repeat_num(int): the repeat times of dataset. Default: 1.
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batch_size(int): the batch size of dataset. Default: 32.
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Returns:
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dataset
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"""
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if device_target == "Ascend":
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rank_size = int(os.getenv("RANK_SIZE"))
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rank_id = int(os.getenv("RANK_ID"))
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columns_list = ['image', 'label']
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if config_ascend.data_load_mode == "mindrecord":
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load_func = partial(de.MindDataset, dataset_path, columns_list)
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else:
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load_func = partial(de.ImageFolderDatasetV2, dataset_path)
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if do_train:
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if rank_size == 1:
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ds = load_func(num_parallel_workers=8, shuffle=True)
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else:
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ds = load_func(num_parallel_workers=8, shuffle=True,
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num_shards=rank_size, shard_id=rank_id)
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else:
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ds = load_func(num_parallel_workers=8, shuffle=False)
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else:
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raise ValueError("Unsupport device_target.")
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resize_height = config.image_height
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if do_train:
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buffer_size = 20480
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# apply shuffle operations
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ds = ds.shuffle(buffer_size=buffer_size)
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# define map operations
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decode_op = C.Decode()
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resize_crop_decode_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
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horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
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resize_op = C.Resize(256)
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center_crop = C.CenterCrop(resize_height)
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normalize_op = C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
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std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
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change_swap_op = C.HWC2CHW()
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if do_train:
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trans = [resize_crop_decode_op, horizontal_flip_op, normalize_op, change_swap_op]
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else:
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trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
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type_cast_op = C2.TypeCast(mstype.int32)
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ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=16)
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ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
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# apply batch operations
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||||
ds = ds.batch(batch_size, drop_remainder=True)
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||||
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||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
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||||
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return ds
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||||
|
||||
|
||||
def create_dataset_py(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1.
|
||||
batch_size(int): the batch size of dataset. Default: 32.
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if device_target == "Ascend":
|
||||
rank_size = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
if do_train:
|
||||
if rank_size == 1:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=rank_size, shard_id=rank_id)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False)
|
||||
else:
|
||||
raise ValueError("Unsupported device target.")
|
||||
|
||||
resize_height = config.image_height
|
||||
|
||||
if do_train:
|
||||
buffer_size = 20480
|
||||
# apply shuffle operations
|
||||
ds = ds.shuffle(buffer_size=buffer_size)
|
||||
|
||||
# define map operations
|
||||
decode_op = P.Decode()
|
||||
resize_crop_op = P.RandomResizedCrop(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
|
||||
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
|
||||
|
||||
resize_op = P.Resize(256)
|
||||
center_crop = P.CenterCrop(resize_height)
|
||||
to_tensor = P.ToTensor()
|
||||
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
|
||||
if do_train:
|
||||
trans = [decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op]
|
||||
else:
|
||||
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
|
||||
|
||||
compose = P.ComposeOp(trans)
|
||||
|
||||
ds = ds.map(input_columns="image", operations=compose(), num_parallel_workers=8, python_multiprocessing=True)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
@ -0,0 +1,166 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""launch train script"""
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
import shutil
|
||||
import platform
|
||||
from argparse import ArgumentParser
|
||||
|
||||
|
||||
def parse_args():
|
||||
"""
|
||||
parse args .
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
args.
|
||||
|
||||
Examples:
|
||||
>>> parse_args()
|
||||
"""
|
||||
parser = ArgumentParser(description="mindspore distributed training launch "
|
||||
"helper utilty that will spawn up "
|
||||
"multiple distributed processes")
|
||||
parser.add_argument("--nproc_per_node", type=int, default=1,
|
||||
help="The number of processes to launch on each node, "
|
||||
"for D training, this is recommended to be set "
|
||||
"to the number of D in your system so that "
|
||||
"each process can be bound to a single D.")
|
||||
parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
|
||||
help="will use the visible devices sequentially")
|
||||
parser.add_argument("--server_id", type=str, default="",
|
||||
help="server ip")
|
||||
parser.add_argument("--training_script", type=str,
|
||||
help="The full path to the single D training "
|
||||
"program/script to be launched in parallel, "
|
||||
"followed by all the arguments for the "
|
||||
"training script")
|
||||
# rest from the training program
|
||||
args, unknown = parser.parse_known_args()
|
||||
args.training_script_args = unknown
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
print("start", __file__)
|
||||
args = parse_args()
|
||||
print(args)
|
||||
visible_devices = args.visible_devices.split(',')
|
||||
assert os.path.isfile(args.training_script)
|
||||
assert len(visible_devices) >= args.nproc_per_node
|
||||
print('visible_devices:{}'.format(visible_devices))
|
||||
if not args.server_id:
|
||||
print('pleaser input server ip!!!')
|
||||
exit(0)
|
||||
print('server_id:{}'.format(args.server_id))
|
||||
|
||||
# construct hccn_table
|
||||
hccn_configs = open('/etc/hccn.conf', 'r').readlines()
|
||||
device_ips = {}
|
||||
for hccn_item in hccn_configs:
|
||||
hccn_item = hccn_item.strip()
|
||||
if hccn_item.startswith('address_'):
|
||||
device_id, device_ip = hccn_item.split('=')
|
||||
device_id = device_id.split('_')[1]
|
||||
device_ips[device_id] = device_ip
|
||||
print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
|
||||
hccn_table = {}
|
||||
arch = platform.processor()
|
||||
hccn_table['board_id'] = {'aarch64': '0x002f', 'x86_64': '0x0000'}[arch]
|
||||
hccn_table['chip_info'] = '910'
|
||||
hccn_table['deploy_mode'] = 'lab'
|
||||
hccn_table['group_count'] = '1'
|
||||
hccn_table['group_list'] = []
|
||||
instance_list = []
|
||||
usable_dev = ''
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
instance = {}
|
||||
instance['devices'] = []
|
||||
device_id = visible_devices[instance_id]
|
||||
device_ip = device_ips[device_id]
|
||||
usable_dev += str(device_id)
|
||||
instance['devices'].append({
|
||||
'device_id': device_id,
|
||||
'device_ip': device_ip,
|
||||
})
|
||||
instance['rank_id'] = str(instance_id)
|
||||
instance['server_id'] = args.server_id
|
||||
instance_list.append(instance)
|
||||
hccn_table['group_list'].append({
|
||||
'device_num': str(args.nproc_per_node),
|
||||
'server_num': '1',
|
||||
'group_name': '',
|
||||
'instance_count': str(args.nproc_per_node),
|
||||
'instance_list': instance_list,
|
||||
})
|
||||
hccn_table['para_plane_nic_location'] = 'device'
|
||||
hccn_table['para_plane_nic_name'] = []
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
eth_id = visible_devices[instance_id]
|
||||
hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
|
||||
hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
|
||||
hccn_table['status'] = 'completed'
|
||||
|
||||
# save hccn_table to file
|
||||
table_path = os.getcwd()
|
||||
if not os.path.exists(table_path):
|
||||
os.mkdir(table_path)
|
||||
table_fn = os.path.join(table_path,
|
||||
'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
|
||||
with open(table_fn, 'w') as table_fp:
|
||||
json.dump(hccn_table, table_fp, indent=4)
|
||||
sys.stdout.flush()
|
||||
|
||||
# spawn the processes
|
||||
processes = []
|
||||
cmds = []
|
||||
log_files = []
|
||||
env = os.environ.copy()
|
||||
env['RANK_SIZE'] = str(args.nproc_per_node)
|
||||
cur_path = os.getcwd()
|
||||
for rank_id in range(0, args.nproc_per_node):
|
||||
os.chdir(cur_path)
|
||||
device_id = visible_devices[rank_id]
|
||||
device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
|
||||
env['RANK_ID'] = str(rank_id)
|
||||
env['DEVICE_ID'] = str(device_id)
|
||||
if args.nproc_per_node > 1:
|
||||
env['MINDSPORE_HCCL_CONFIG_PATH'] = table_fn
|
||||
env['RANK_TABLE_FILE'] = table_fn
|
||||
if os.path.exists(device_dir):
|
||||
shutil.rmtree(device_dir)
|
||||
os.mkdir(device_dir)
|
||||
os.chdir(device_dir)
|
||||
cmd = [sys.executable, '-u']
|
||||
cmd.append(args.training_script)
|
||||
cmd.extend(args.training_script_args)
|
||||
log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
|
||||
process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
|
||||
processes.append(process)
|
||||
cmds.append(cmd)
|
||||
log_files.append(log_file)
|
||||
for process, cmd, log_file in zip(processes, cmds, log_files):
|
||||
process.wait()
|
||||
if process.returncode != 0:
|
||||
raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
|
||||
log_file.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,54 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""learning rate generator"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""
|
||||
generate learning rate array
|
||||
|
||||
Args:
|
||||
global_step(int): total steps of the training
|
||||
lr_init(float): init learning rate
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
total_epochs(int): total epoch of training
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
lr = lr_end + \
|
||||
(lr_max - lr_end) * \
|
||||
(1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
|
||||
if lr < 0.0:
|
||||
lr = 0.0
|
||||
lr_each_step.append(lr)
|
||||
|
||||
current_step = global_step
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[current_step:]
|
||||
|
||||
return learning_rate
|
||||
@ -0,0 +1,231 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""MobileNetV2 Quant model define"""
|
||||
|
||||
import numpy as np
|
||||
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore import Tensor
|
||||
|
||||
__all__ = ['mobilenetV2']
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
Global avg pooling definition.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=False)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class ConvBNReLU(nn.Cell):
|
||||
"""
|
||||
Convolution/Depthwise fused with Batchnorm and ReLU block definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
out_planes (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
|
||||
"""
|
||||
|
||||
def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
super(ConvBNReLU, self).__init__()
|
||||
padding = (kernel_size - 1) // 2
|
||||
self.conv = nn.Conv2dBnAct(in_planes, out_planes, kernel_size,
|
||||
stride=stride,
|
||||
pad_mode='pad',
|
||||
padding=padding,
|
||||
group=groups,
|
||||
has_bn=True,
|
||||
activation='relu')
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class InvertedResidual(nn.Cell):
|
||||
"""
|
||||
Mobilenetv2 residual block definition.
|
||||
|
||||
Args:
|
||||
inp (int): Input channel.
|
||||
oup (int): Output channel.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
expand_ratio (int): expand ration of input channel
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ResidualBlock(3, 256, 1, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, inp, oup, stride, expand_ratio):
|
||||
super(InvertedResidual, self).__init__()
|
||||
assert stride in [1, 2]
|
||||
|
||||
hidden_dim = int(round(inp * expand_ratio))
|
||||
self.use_res_connect = stride == 1 and inp == oup
|
||||
|
||||
layers = []
|
||||
if expand_ratio != 1:
|
||||
layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
|
||||
layers.extend([
|
||||
# dw
|
||||
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
|
||||
# pw-linear
|
||||
nn.Conv2dBnAct(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1, has_bn=True)
|
||||
])
|
||||
self.conv = nn.SequentialCell(layers)
|
||||
self.add = P.TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
out = self.conv(x)
|
||||
if self.use_res_connect:
|
||||
out = self.add(out, x)
|
||||
return out
|
||||
|
||||
|
||||
class mobilenetV2(nn.Cell):
|
||||
"""
|
||||
mobilenetV2 fusion architecture.
|
||||
|
||||
Args:
|
||||
class_num (Cell): number of classes.
|
||||
width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
|
||||
has_dropout (bool): Is dropout used. Default is false
|
||||
inverted_residual_setting (list): Inverted residual settings. Default is None
|
||||
round_nearest (list): Channel round to . Default is 8
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> mobilenetV2(num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, num_classes=1000, width_mult=1.,
|
||||
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
|
||||
super(mobilenetV2, self).__init__()
|
||||
block = InvertedResidual
|
||||
input_channel = 32
|
||||
last_channel = 1280
|
||||
# setting of inverted residual blocks
|
||||
self.cfgs = inverted_residual_setting
|
||||
if inverted_residual_setting is None:
|
||||
self.cfgs = [
|
||||
# t, c, n, s
|
||||
[1, 16, 1, 1],
|
||||
[6, 24, 2, 2],
|
||||
[6, 32, 3, 2],
|
||||
[6, 64, 4, 2],
|
||||
[6, 96, 3, 1],
|
||||
[6, 160, 3, 2],
|
||||
[6, 320, 1, 1],
|
||||
]
|
||||
|
||||
# building first layer
|
||||
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
|
||||
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
|
||||
|
||||
features = [ConvBNReLU(3, input_channel, stride=2)]
|
||||
# building inverted residual blocks
|
||||
for t, c, n, s in self.cfgs:
|
||||
output_channel = _make_divisible(c * width_mult, round_nearest)
|
||||
for i in range(n):
|
||||
stride = s if i == 0 else 1
|
||||
features.append(block(input_channel, output_channel, stride, expand_ratio=t))
|
||||
input_channel = output_channel
|
||||
# building last several layers
|
||||
features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(features)
|
||||
# mobilenet head
|
||||
head = ([GlobalAvgPooling(),
|
||||
nn.DenseBnAct(self.out_channels, num_classes, has_bias=True, has_bn=False)
|
||||
] if not has_dropout else
|
||||
[GlobalAvgPooling(),
|
||||
nn.Dropout(0.2),
|
||||
nn.DenseBnAct(self.out_channels, num_classes, has_bias=True, has_bn=False)
|
||||
])
|
||||
self.head = nn.SequentialCell(head)
|
||||
|
||||
# init weights
|
||||
self._initialize_weights()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
def _initialize_weights(self):
|
||||
"""
|
||||
Initialize weights.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> _initialize_weights()
|
||||
"""
|
||||
for _, m in self.cells_and_names():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
w = Tensor(np.random.normal(0, np.sqrt(2. / n), m.weight.data.shape).astype("float32"))
|
||||
m.weight.set_parameter_data(w)
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.gamma.set_parameter_data(Tensor(np.ones(m.gamma.data.shape, dtype="float32")))
|
||||
m.beta.set_parameter_data(Tensor(np.zeros(m.beta.data.shape, dtype="float32")))
|
||||
elif isinstance(m, nn.Dense):
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))
|
||||
@ -0,0 +1,113 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""MobileNetV2 utils"""
|
||||
|
||||
import time
|
||||
import numpy as np
|
||||
|
||||
from mindspore.train.callback import Callback
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common import dtype as mstype
|
||||
|
||||
|
||||
class Monitor(Callback):
|
||||
"""
|
||||
Monitor loss and time.
|
||||
|
||||
Args:
|
||||
lr_init (numpy array): train lr
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
|
||||
"""
|
||||
|
||||
def __init__(self, lr_init=None):
|
||||
super(Monitor, self).__init__()
|
||||
self.lr_init = lr_init
|
||||
self.lr_init_len = len(lr_init)
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
self.losses = []
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
per_step_mseconds = epoch_mseconds / cb_params.batch_num
|
||||
print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
|
||||
per_step_mseconds,
|
||||
np.mean(self.losses)))
|
||||
|
||||
def step_begin(self, run_context):
|
||||
self.step_time = time.time()
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
step_mseconds = (time.time() - self.step_time) * 1000
|
||||
step_loss = cb_params.net_outputs
|
||||
|
||||
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
|
||||
step_loss = step_loss[0]
|
||||
if isinstance(step_loss, Tensor):
|
||||
step_loss = np.mean(step_loss.asnumpy())
|
||||
|
||||
self.losses.append(step_loss)
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
|
||||
|
||||
print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
|
||||
cb_params.cur_epoch_num -
|
||||
1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
|
||||
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
|
||||
|
||||
|
||||
class CrossEntropyWithLabelSmooth(_Loss):
|
||||
"""
|
||||
CrossEntropyWith LabelSmooth.
|
||||
|
||||
Args:
|
||||
smooth_factor (float): smooth factor, default=0.
|
||||
num_classes (int): num classes
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropyWithLabelSmooth, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor /
|
||||
(num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
|
||||
self.on_value, self.off_value)
|
||||
out_loss = self.ce(logit, one_hot_label)
|
||||
out_loss = self.mean(out_loss, 0)
|
||||
return out_loss
|
||||
@ -0,0 +1,131 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Train mobilenetV2 on ImageNet"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
import random
|
||||
import numpy as np
|
||||
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.train.model import Model, ParallelMode
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.communication.management import init
|
||||
from mindspore.train.quant import quant
|
||||
import mindspore.dataset.engine as de
|
||||
|
||||
from src.dataset import create_dataset
|
||||
from src.lr_generator import get_lr
|
||||
from src.utils import Monitor, CrossEntropyWithLabelSmooth
|
||||
from src.config import config_ascend, config_ascend_quant
|
||||
from src.mobilenetV2 import mobilenetV2
|
||||
|
||||
random.seed(1)
|
||||
np.random.seed(1)
|
||||
de.config.set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pertained checkpoint path')
|
||||
parser.add_argument('--device_target', type=str, default=None, help='Run device target')
|
||||
parser.add_argument('--quantization_aware', type=bool, default=False, help='Use quantization aware training')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if args_opt.device_target == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
rank_id = int(os.getenv('RANK_ID'))
|
||||
rank_size = int(os.getenv('RANK_SIZE'))
|
||||
run_distribute = rank_size > 1
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupported device target.")
|
||||
|
||||
if __name__ == '__main__':
|
||||
# train on ascend
|
||||
config = config_ascend_quant if args_opt.quantization_aware else config_ascend
|
||||
print("training args: {}".format(args_opt))
|
||||
print("training configure: {}".format(config))
|
||||
print("parallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
|
||||
epoch_size = config.epoch_size
|
||||
|
||||
# distribute init
|
||||
if run_distribute:
|
||||
context.set_auto_parallel_context(device_num=rank_size,
|
||||
parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
parameter_broadcast=True,
|
||||
mirror_mean=True)
|
||||
init()
|
||||
|
||||
# define network
|
||||
network = mobilenetV2(num_classes=config.num_classes)
|
||||
# define loss
|
||||
if config.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes)
|
||||
else:
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config,
|
||||
device_target=args_opt.device_target,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
# load pre trained ckpt
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(network, param_dict)
|
||||
|
||||
# convert fusion network to quantization aware network
|
||||
if config.quantization_aware:
|
||||
network = quant.convert_quant_network(network,
|
||||
bn_fold=True,
|
||||
per_channel=[True, False],
|
||||
symmetric=[True, False])
|
||||
|
||||
# get learning rate
|
||||
lr = Tensor(get_lr(global_step=config.start_epoch * step_size,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config.lr,
|
||||
warmup_epochs=config.warmup_epochs,
|
||||
total_epochs=epoch_size + config.start_epoch,
|
||||
steps_per_epoch=step_size))
|
||||
|
||||
# define optimization
|
||||
opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum,
|
||||
config.weight_decay)
|
||||
# define model
|
||||
model = Model(network, loss_fn=loss, optimizer=opt)
|
||||
|
||||
print("============== Starting Training ==============")
|
||||
callback = None
|
||||
if rank_id == 0:
|
||||
callback = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix="mobilenetV2",
|
||||
directory=config.save_checkpoint_path,
|
||||
config=config_ck)
|
||||
callback += [ckpt_cb]
|
||||
model.train(epoch_size, dataset, callbacks=callback)
|
||||
print("============== End Training ==============")
|
||||
@ -0,0 +1,122 @@
|
||||
# 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
|
||||
├── Readme.md
|
||||
├── scripts
|
||||
│ ├──run_train.sh
|
||||
│ ├──run_eval.sh
|
||||
├── src
|
||||
│ ├──config.py
|
||||
│ ├──crossentropy.py
|
||||
│ ├──dataset.py
|
||||
│ ├──luanch.py
|
||||
│ ├──lr_generator.py
|
||||
│ ├──utils.py
|
||||
├── models
|
||||
│ ├──resnet_quant.py
|
||||
├── train.py
|
||||
├── eval.py
|
||||
```
|
||||
|
||||
|
||||
## Parameter configuration
|
||||
|
||||
Parameters for both training and inference can be set in config.py.
|
||||
|
||||
```
|
||||
"class_num": 1001, # dataset class number
|
||||
"batch_size": 32, # batch size of input tensor
|
||||
"loss_scale": 1024, # loss scale
|
||||
"momentum": 0.9, # momentum optimizer
|
||||
"weight_decay": 1e-4, # weight decay
|
||||
"epoch_size": 120, # only valid for taining, which is always 1 for inference
|
||||
"pretrained_epoch_size": 90, # epoch size that model has been trained before load pretrained checkpoint
|
||||
"buffer_size": 1000, # number of queue size in data preprocessing
|
||||
"image_height": 224, # image height
|
||||
"image_width": 224, # image width
|
||||
"save_checkpoint": True, # whether save checkpoint or not
|
||||
"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
|
||||
"keep_checkpoint_max": 50, # only keep the last keep_checkpoint_max checkpoint
|
||||
"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
|
||||
"warmup_epochs": 0, # number of warmup epoch
|
||||
"lr_decay_mode": "cosine", # decay mode for generating learning rate
|
||||
"label_smooth": True, # label smooth
|
||||
"label_smooth_factor": 0.1, # label smooth factor
|
||||
"lr_init": 0, # initial learning rate
|
||||
"lr_max": 0.005, # maximum learning rate
|
||||
```
|
||||
|
||||
## Running the example
|
||||
|
||||
### Train
|
||||
|
||||
### Usage
|
||||
|
||||
- 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]
|
||||
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
# training example
|
||||
Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/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.
|
||||
|
||||
```
|
||||
epoch: 1 step: 5004, loss is 4.8995576
|
||||
epoch: 2 step: 5004, loss is 3.9235563
|
||||
epoch: 3 step: 5004, loss is 3.833077
|
||||
epoch: 4 step: 5004, loss is 3.2795618
|
||||
epoch: 5 step: 5004, loss is 3.1978393
|
||||
```
|
||||
|
||||
## Eval process
|
||||
|
||||
### Usage
|
||||
|
||||
- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
# infer example
|
||||
Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/checkpoint/resnet50-110_5004.ckpt
|
||||
```
|
||||
|
||||
|
||||
> checkpoint can be produced in training process.
|
||||
|
||||
#### Result
|
||||
|
||||
Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
|
||||
|
||||
```
|
||||
result: {'acc': 0.75.252054737516005} ckpt=train_parallel0/resnet-110_5004.ckpt
|
||||
```
|
||||
|
||||
@ -0,0 +1,78 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Evaluate Resnet50 on ImageNet"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
|
||||
from src.config import quant_set, config_quant, config_noquant
|
||||
from src.dataset import create_dataset
|
||||
from src.crossentropy import CrossEntropy
|
||||
from src.utils import _load_param_into_net
|
||||
from models.resnet_quant import resnet50_quant
|
||||
|
||||
from mindspore import context
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.train.serialization import load_checkpoint
|
||||
from mindspore.train.quant import quant
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
|
||||
config = config_quant if quant_set.quantization_aware else config_noquant
|
||||
|
||||
if args_opt.device_target == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(device_id=device_id)
|
||||
|
||||
if __name__ == '__main__':
|
||||
# define fusion network
|
||||
net = resnet50_quant(class_num=config.class_num)
|
||||
if quant_set.quantization_aware:
|
||||
# convert fusion network to quantization aware network
|
||||
net = quant.convert_quant_network(net,
|
||||
bn_fold=True,
|
||||
per_channel=[True, False],
|
||||
symmetric=[True, False])
|
||||
# define network loss
|
||||
if not config.use_label_smooth:
|
||||
config.label_smooth_factor = 0.0
|
||||
loss = CrossEntropy(smooth_factor=config.label_smooth_factor,
|
||||
num_classes=config.class_num)
|
||||
|
||||
# define dataset
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=False,
|
||||
batch_size=config.batch_size,
|
||||
target=args_opt.device_target)
|
||||
step_size = dataset.get_dataset_size()
|
||||
|
||||
# load checkpoint
|
||||
if args_opt.checkpoint_path:
|
||||
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
||||
_load_param_into_net(net, param_dict)
|
||||
net.set_train(False)
|
||||
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, metrics={'acc'})
|
||||
|
||||
print("============== Starting Validation ==============")
|
||||
res = model.eval(dataset)
|
||||
print("result:", res, "ckpt=", args_opt.checkpoint_path)
|
||||
print("============== End Validation ==============")
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,54 @@
|
||||
#!/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.
|
||||
# ============================================================================
|
||||
if [ $# != 3 ]
|
||||
then
|
||||
echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check dataset path
|
||||
if [ ! -d $2 ] && [ ! -f $2 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$2 is not a directory or file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# check checkpoint file
|
||||
if [ ! -f $3 ]
|
||||
then
|
||||
echo "error: CHECKPOINT_PATH=$3 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# set environment
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
|
||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
export RANK_SIZE=1
|
||||
if [ -d "../eval" ];
|
||||
then
|
||||
rm -rf ../eval
|
||||
fi
|
||||
mkdir ../eval
|
||||
cd ../eval || exit
|
||||
|
||||
# luanch
|
||||
python ${BASEPATH}/../eval.py \
|
||||
--device_target=$1 \
|
||||
--dataset_path=$2 \
|
||||
--checkpoint_path=$3 \
|
||||
&> infer.log & # dataset val folder path
|
||||
@ -0,0 +1,62 @@
|
||||
#!/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 (1-8)"
|
||||
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
|
||||
}
|
||||
|
||||
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
|
||||
|
||||
if [ $1 = "Ascend" ] ; then
|
||||
run_ascend "$@"
|
||||
else
|
||||
echo "not support platform"
|
||||
fi;
|
||||
|
||||
@ -0,0 +1,68 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
network config setting, will be used in train.py and eval.py
|
||||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
quant_set = ed({
|
||||
"quantization_aware": True,
|
||||
})
|
||||
config_noquant = ed({
|
||||
"class_num": 1001,
|
||||
"batch_size": 32,
|
||||
"loss_scale": 1024,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 1e-4,
|
||||
"epoch_size": 90,
|
||||
"pretrained_epoch_size": 1,
|
||||
"buffer_size": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"data_load_mode": "mindrecord",
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 50,
|
||||
"save_checkpoint_path": "./",
|
||||
"warmup_epochs": 0,
|
||||
"lr_decay_mode": "cosine",
|
||||
"use_label_smooth": True,
|
||||
"label_smooth_factor": 0.1,
|
||||
"lr_init": 0,
|
||||
"lr_max": 0.1,
|
||||
})
|
||||
config_quant = ed({
|
||||
"class_num": 1001,
|
||||
"batch_size": 32,
|
||||
"loss_scale": 1024,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 1e-4,
|
||||
"epoch_size": 120,
|
||||
"pretrained_epoch_size": 90,
|
||||
"buffer_size": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"data_load_mode": "mindrecord",
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 50,
|
||||
"save_checkpoint_path": "./",
|
||||
"warmup_epochs": 0,
|
||||
"lr_decay_mode": "cosine",
|
||||
"use_label_smooth": True,
|
||||
"label_smooth_factor": 0.1,
|
||||
"lr_init": 0,
|
||||
"lr_max": 0.005,
|
||||
})
|
||||
@ -0,0 +1,39 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""define loss function for network"""
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
|
||||
|
||||
class CrossEntropy(_Loss):
|
||||
"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
|
||||
|
||||
def __init__(self, smooth_factor=0, num_classes=1001):
|
||||
super(CrossEntropy, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
|
||||
loss = self.ce(logit, one_hot_label)
|
||||
loss = self.mean(loss, 0)
|
||||
return loss
|
||||
@ -0,0 +1,157 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
create train or eval dataset.
|
||||
"""
|
||||
import os
|
||||
from functools import partial
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.dataset.engine as de
|
||||
import mindspore.dataset.transforms.vision.c_transforms as C
|
||||
import mindspore.dataset.transforms.c_transforms as C2
|
||||
import mindspore.dataset.transforms.vision.py_transforms as P
|
||||
from mindspore.communication.management import init, get_rank, get_group_size
|
||||
from src.config import quant_set, config_quant, config_noquant
|
||||
|
||||
config = config_quant if quant_set.quantization_aware else config_noquant
|
||||
|
||||
|
||||
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1
|
||||
batch_size(int): the batch size of dataset. Default: 32
|
||||
target(str): the device target. Default: Ascend
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if target == "Ascend":
|
||||
device_num = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
else:
|
||||
init("nccl")
|
||||
rank_id = get_rank()
|
||||
device_num = get_group_size()
|
||||
|
||||
columns_list = ['image', 'label']
|
||||
if config.data_load_mode == "mindrecord":
|
||||
load_func = partial(de.MindDataset, dataset_path, columns_list)
|
||||
else:
|
||||
load_func = partial(de.ImageFolderDatasetV2, dataset_path)
|
||||
if device_num == 1:
|
||||
ds = load_func(num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = load_func(num_parallel_workers=8, shuffle=True,
|
||||
num_shards=device_num, shard_id=rank_id)
|
||||
|
||||
image_size = config.image_height
|
||||
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
|
||||
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
|
||||
|
||||
# define map operations
|
||||
if do_train:
|
||||
trans = [
|
||||
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
|
||||
C.RandomHorizontalFlip(prob=0.5),
|
||||
C.Normalize(mean=mean, std=std),
|
||||
C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
trans = [
|
||||
C.Decode(),
|
||||
C.Resize(256),
|
||||
C.CenterCrop(image_size),
|
||||
C.Normalize(mean=mean, std=std),
|
||||
C.HWC2CHW()
|
||||
]
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
|
||||
ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
|
||||
ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
|
||||
|
||||
def create_dataset_py(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1
|
||||
batch_size(int): the batch size of dataset. Default: 32
|
||||
target(str): the device target. Default: Ascend
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if target == "Ascend":
|
||||
device_num = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
else:
|
||||
init("nccl")
|
||||
rank_id = get_rank()
|
||||
device_num = get_group_size()
|
||||
|
||||
if do_train:
|
||||
if device_num == 1:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=device_num, shard_id=rank_id)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False)
|
||||
|
||||
image_size = 224
|
||||
|
||||
# define map operations
|
||||
decode_op = P.Decode()
|
||||
resize_crop_op = P.RandomResizedCrop(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333))
|
||||
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
|
||||
|
||||
resize_op = P.Resize(256)
|
||||
center_crop = P.CenterCrop(image_size)
|
||||
to_tensor = P.ToTensor()
|
||||
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
|
||||
# define map operations
|
||||
if do_train:
|
||||
trans = [decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op]
|
||||
else:
|
||||
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
|
||||
|
||||
compose = P.ComposeOp(trans)
|
||||
ds = ds.map(input_columns="image", operations=compose(), num_parallel_workers=8, python_multiprocessing=True)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
@ -0,0 +1,165 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""launch train script"""
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
import shutil
|
||||
import platform
|
||||
from argparse import ArgumentParser
|
||||
|
||||
def parse_args():
|
||||
"""
|
||||
parse args .
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
args.
|
||||
|
||||
Examples:
|
||||
>>> parse_args()
|
||||
"""
|
||||
parser = ArgumentParser(description="mindspore distributed training launch "
|
||||
"helper utilty that will spawn up "
|
||||
"multiple distributed processes")
|
||||
parser.add_argument("--nproc_per_node", type=int, default=1,
|
||||
help="The number of processes to launch on each node, "
|
||||
"for D training, this is recommended to be set "
|
||||
"to the number of D in your system so that "
|
||||
"each process can be bound to a single D.")
|
||||
parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
|
||||
help="will use the visible devices sequentially")
|
||||
parser.add_argument("--server_id", type=str, default="",
|
||||
help="server ip")
|
||||
parser.add_argument("--training_script", type=str,
|
||||
help="The full path to the single D training "
|
||||
"program/script to be launched in parallel, "
|
||||
"followed by all the arguments for the "
|
||||
"training script")
|
||||
# rest from the training program
|
||||
args, unknown = parser.parse_known_args()
|
||||
args.training_script_args = unknown
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
print("start", __file__)
|
||||
args = parse_args()
|
||||
print(args)
|
||||
visible_devices = args.visible_devices.split(',')
|
||||
assert os.path.isfile(args.training_script)
|
||||
assert len(visible_devices) >= args.nproc_per_node
|
||||
print('visible_devices:{}'.format(visible_devices))
|
||||
if not args.server_id:
|
||||
print('pleaser input server ip!!!')
|
||||
exit(0)
|
||||
print('server_id:{}'.format(args.server_id))
|
||||
|
||||
# construct hccn_table
|
||||
hccn_configs = open('/etc/hccn.conf', 'r').readlines()
|
||||
device_ips = {}
|
||||
for hccn_item in hccn_configs:
|
||||
hccn_item = hccn_item.strip()
|
||||
if hccn_item.startswith('address_'):
|
||||
device_id, device_ip = hccn_item.split('=')
|
||||
device_id = device_id.split('_')[1]
|
||||
device_ips[device_id] = device_ip
|
||||
print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
|
||||
hccn_table = {}
|
||||
arch = platform.processor()
|
||||
hccn_table['board_id'] = {'aarch64': '0x002f', 'x86_64': '0x0000'}[arch]
|
||||
hccn_table['chip_info'] = '910'
|
||||
hccn_table['deploy_mode'] = 'lab'
|
||||
hccn_table['group_count'] = '1'
|
||||
hccn_table['group_list'] = []
|
||||
instance_list = []
|
||||
usable_dev = ''
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
instance = {}
|
||||
instance['devices'] = []
|
||||
device_id = visible_devices[instance_id]
|
||||
device_ip = device_ips[device_id]
|
||||
usable_dev += str(device_id)
|
||||
instance['devices'].append({
|
||||
'device_id': device_id,
|
||||
'device_ip': device_ip,
|
||||
})
|
||||
instance['rank_id'] = str(instance_id)
|
||||
instance['server_id'] = args.server_id
|
||||
instance_list.append(instance)
|
||||
hccn_table['group_list'].append({
|
||||
'device_num': str(args.nproc_per_node),
|
||||
'server_num': '1',
|
||||
'group_name': '',
|
||||
'instance_count': str(args.nproc_per_node),
|
||||
'instance_list': instance_list,
|
||||
})
|
||||
hccn_table['para_plane_nic_location'] = 'device'
|
||||
hccn_table['para_plane_nic_name'] = []
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
eth_id = visible_devices[instance_id]
|
||||
hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
|
||||
hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
|
||||
hccn_table['status'] = 'completed'
|
||||
|
||||
# save hccn_table to file
|
||||
table_path = os.getcwd()
|
||||
if not os.path.exists(table_path):
|
||||
os.mkdir(table_path)
|
||||
table_fn = os.path.join(table_path,
|
||||
'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
|
||||
with open(table_fn, 'w') as table_fp:
|
||||
json.dump(hccn_table, table_fp, indent=4)
|
||||
sys.stdout.flush()
|
||||
|
||||
# spawn the processes
|
||||
processes = []
|
||||
cmds = []
|
||||
log_files = []
|
||||
env = os.environ.copy()
|
||||
env['RANK_SIZE'] = str(args.nproc_per_node)
|
||||
cur_path = os.getcwd()
|
||||
for rank_id in range(0, args.nproc_per_node):
|
||||
os.chdir(cur_path)
|
||||
device_id = visible_devices[rank_id]
|
||||
device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
|
||||
env['RANK_ID'] = str(rank_id)
|
||||
env['DEVICE_ID'] = str(device_id)
|
||||
if args.nproc_per_node > 1:
|
||||
env['MINDSPORE_HCCL_CONFIG_PATH'] = table_fn
|
||||
env['RANK_TABLE_FILE'] = table_fn
|
||||
if os.path.exists(device_dir):
|
||||
shutil.rmtree(device_dir)
|
||||
os.mkdir(device_dir)
|
||||
os.chdir(device_dir)
|
||||
cmd = [sys.executable, '-u']
|
||||
cmd.append(args.training_script)
|
||||
cmd.extend(args.training_script_args)
|
||||
log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
|
||||
process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
|
||||
processes.append(process)
|
||||
cmds.append(cmd)
|
||||
log_files.append(log_file)
|
||||
for process, cmd, log_file in zip(processes, cmds, log_files):
|
||||
process.wait()
|
||||
if process.returncode != 0:
|
||||
raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
|
||||
log_file.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,87 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""learning rate generator"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
|
||||
"""
|
||||
generate learning rate array
|
||||
|
||||
Args:
|
||||
lr_init(float): init learning rate
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
total_epochs(int): total epoch of training
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or default
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
if lr_decay_mode == 'steps':
|
||||
decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
|
||||
for i in range(total_steps):
|
||||
if i < decay_epoch_index[0]:
|
||||
lr = lr_max
|
||||
elif i < decay_epoch_index[1]:
|
||||
lr = lr_max * 0.1
|
||||
elif i < decay_epoch_index[2]:
|
||||
lr = lr_max * 0.01
|
||||
else:
|
||||
lr = lr_max * 0.001
|
||||
lr_each_step.append(lr)
|
||||
elif lr_decay_mode == 'poly':
|
||||
if warmup_steps != 0:
|
||||
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
else:
|
||||
inc_each_step = 0
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = float(lr_init) + inc_each_step * float(i)
|
||||
else:
|
||||
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
||||
lr = float(lr_max) * base * base
|
||||
if lr < 0.0:
|
||||
lr = 0.0
|
||||
lr_each_step.append(lr)
|
||||
elif lr_decay_mode == 'cosine':
|
||||
decay_steps = total_steps - warmup_steps
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
lr = float(lr_init) + lr_inc * (i + 1)
|
||||
else:
|
||||
linear_decay = (total_steps - i) / decay_steps
|
||||
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
|
||||
decayed = linear_decay * cosine_decay + 0.00001
|
||||
lr = lr_max * decayed
|
||||
lr_each_step.append(lr)
|
||||
else:
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
|
||||
lr_each_step.append(lr)
|
||||
|
||||
learning_rate = np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
return learning_rate
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue