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# Copyright 2021 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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"""
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resnext export mindir.
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"""
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import argparse
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import numpy as np
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from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
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from src.config import config
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from src.image_classification import get_network
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parser = argparse.ArgumentParser(description='checkpoint export')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--batch_size", type=int, default=1, help="batch size")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument('--width', type=int, default=224, help='input width')
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parser.add_argument('--height', type=int, default=224, help='input height')
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parser.add_argument("--file_name", type=str, default="resnext50", help="output file name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if __name__ == '__main__':
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net = get_network(num_classes=config.num_classes, platform=args.device_target)
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param_dict = load_checkpoint(args.ckpt_file)
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load_param_into_net(net, param_dict)
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input_shp = [args.batch_size, 3, args.height, args.width]
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input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
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export(net, input_array, file_name=args.file_name, file_format=args.file_format)
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#!/bin/bash
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# Copyright 2021 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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DATA_DIR=$2
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export RANK_TABLE_FILE=$1
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export RANK_SIZE=8
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export HCCL_CONNECT_TIMEOUT=600
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echo "hccl connect time out has changed to 600 second"
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PATH_CHECKPOINT=""
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if [ $# == 3 ]
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then
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PATH_CHECKPOINT=$3
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fi
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cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
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echo "the number of logical core" $cores
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avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
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core_gap=`expr $avg_core_per_rank \- 1`
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echo "avg_core_per_rank" $avg_core_per_rank
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echo "core_gap" $core_gap
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for((i=0;i<RANK_SIZE;i++))
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do
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start=`expr $i \* $avg_core_per_rank`
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export DEVICE_ID=$i
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export RANK_ID=$i
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export DEPLOY_MODE=0
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export GE_USE_STATIC_MEMORY=1
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end=`expr $start \+ $core_gap`
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cmdopt=$start"-"$end
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rm -rf LOG$i
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mkdir ./LOG$i
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cp *.py ./LOG$i
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cd ./LOG$i || exit
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echo "start training for rank $i, device $DEVICE_ID"
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env > env.log
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taskset -c $cmdopt python ../train.py \
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--is_distribute \
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--device_id=$DEVICE_ID \
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--pretrained=$PATH_CHECKPOINT \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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cd ../
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done
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#!/bin/bash
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# Copyright 2021 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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DATA_DIR=$1
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export RANK_SIZE=8
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PATH_CHECKPOINT=""
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if [ $# == 2 ]
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then
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PATH_CHECKPOINT=$2
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fi
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mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
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python train.py \
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--is_distribute \
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--platform="GPU" \
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--pretrained=$PATH_CHECKPOINT \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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#!/bin/bash
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# Copyright 2021 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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export DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=$3
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PLATFORM=Ascend
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if [ $# == 4 ]
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then
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PLATFORM=$4
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fi
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python eval.py \
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--pretrained=$PATH_CHECKPOINT \
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--platform=$PLATFORM \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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#!/bin/bash
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# Copyright 2021 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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export DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=""
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if [ $# == 3 ]
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then
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PATH_CHECKPOINT=$3
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fi
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python train.py \
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--device_id=$DEVICE_ID \
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--pretrained=$PATH_CHECKPOINT \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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#!/bin/bash
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# Copyright 2021 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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export DEVICE_ID=$1
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DATA_DIR=$2
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PATH_CHECKPOINT=""
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if [ $# == 3 ]
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then
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PATH_CHECKPOINT=$3
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fi
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python train.py \
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--pretrained=$PATH_CHECKPOINT \
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--platform="GPU" \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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# Copyright 2021 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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"""resnext"""
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from .resnext import *
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# Copyright 2021 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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"""config"""
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from easydict import EasyDict as ed
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config = ed({
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"image_size": '224,224',
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"num_classes": 1000,
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"lr": 0.4,
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"lr_scheduler": 'cosine_annealing',
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"lr_epochs": '30,60,90,120',
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"lr_gamma": 0.1,
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"eta_min": 0,
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"T_max": 150,
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"max_epoch": 150,
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"warmup_epochs": 1,
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"weight_decay": 0.0001,
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"momentum": 0.9,
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"is_dynamic_loss_scale": 0,
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"loss_scale": 1024,
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"label_smooth": 1,
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"label_smooth_factor": 0.1,
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"ckpt_interval": 5,
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"ckpt_save_max": 5,
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"ckpt_path": 'outputs/',
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"is_save_on_master": 1,
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# this two parameter is used for mindspore distributed configuration
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"rank": 0,
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"group_size": 1
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})
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# Copyright 2021 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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"""
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define loss function for network.
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"""
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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import mindspore.nn as nn
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class CrossEntropy(_Loss):
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"""
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the redefined loss function with SoftmaxCrossEntropyWithLogits.
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"""
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def __init__(self, smooth_factor=0., num_classes=1000):
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super(CrossEntropy, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes -1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logit, label):
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one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss = self.ce(logit, one_hot_label)
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loss = self.mean(loss, 0)
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return loss
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# Copyright 2021 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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"""
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dataset processing.
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"""
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import os
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from mindspore.common import dtype as mstype
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import mindspore.dataset as de
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.vision.c_transforms as V_C
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from PIL import Image, ImageFile
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from src.utils.sampler import DistributedSampler
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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class TxtDataset():
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"""
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create txt dataset.
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Args:
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Returns:
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de_dataset.
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"""
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def __init__(self, root, txt_name):
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super(TxtDataset, self).__init__()
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self.imgs = []
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self.labels = []
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fin = open(txt_name, "r")
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for line in fin:
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img_name, label = line.strip().split(' ')
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self.imgs.append(os.path.join(root, img_name))
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self.labels.append(int(label))
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fin.close()
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def __getitem__(self, index):
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img = Image.open(self.imgs[index]).convert('RGB')
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return img, self.labels[index]
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def __len__(self):
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return len(self.imgs)
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def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
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mode='train',
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input_mode='folder',
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root='',
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num_parallel_workers=None,
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shuffle=None,
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sampler=None,
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class_indexing=None,
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drop_remainder=True,
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transform=None,
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target_transform=None):
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"""
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A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
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If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
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are written into a textfile.
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Args:
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data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
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Or path of the textfile that contains every image's path of the dataset.
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image_size (Union(int, sequence)): Size of the input images.
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per_batch_size (int): the batch size of evey step during training.
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max_epoch (int): the number of epochs.
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rank (int): The shard ID within num_shards (default=None).
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group_size (int): Number of shards that the dataset should be divided
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into (default=None).
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mode (str): "train" or others. Default: " train".
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input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
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root (str): the images path for "input_mode="txt"". Default: " ".
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num_parallel_workers (int): Number of workers to read the data. Default: None.
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shuffle (bool): Whether or not to perform shuffle on the dataset
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(default=None, performs shuffle).
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sampler (Sampler): Object used to choose samples from the dataset. Default: None.
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class_indexing (dict): A str-to-int mapping from folder name to index
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(default=None, the folder names will be sorted
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alphabetically and each class will be given a
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unique index starting from 0).
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Examples:
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>>> from src.dataset import classification_dataset
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>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
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>>> data_dir = "/path/to/imagefolder_directory"
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>>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
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>>> per_batch_size=64, max_epoch=100,
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>>> rank=0, group_size=4)
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>>> # Path of the textfile that contains every image's path of the dataset.
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>>> data_dir = "/path/to/dataset/images/train.txt"
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>>> images_dir = "/path/to/dataset/images"
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>>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
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>>> per_batch_size=64, max_epoch=100,
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>>> rank=0, group_size=4,
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>>> input_mode="txt", root=images_dir)
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"""
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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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if transform is None:
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if mode == 'train':
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transform_img = [
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V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
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V_C.RandomHorizontalFlip(prob=0.5),
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V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
|
||||
V_C.Normalize(mean=mean, std=std),
|
||||
V_C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = [
|
||||
V_C.Decode(),
|
||||
V_C.Resize((256, 256)),
|
||||
V_C.CenterCrop(image_size),
|
||||
V_C.Normalize(mean=mean, std=std),
|
||||
V_C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = transform
|
||||
|
||||
if target_transform is None:
|
||||
transform_label = [C.TypeCast(mstype.int32)]
|
||||
else:
|
||||
transform_label = target_transform
|
||||
|
||||
if input_mode == 'folder':
|
||||
de_dataset = de.ImageFolderDataset(data_dir, num_parallel_workers=num_parallel_workers,
|
||||
shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
|
||||
num_shards=group_size, shard_id=rank)
|
||||
else:
|
||||
dataset = TxtDataset(root, data_dir)
|
||||
sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
|
||||
de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
|
||||
|
||||
de_dataset = de_dataset.map(operations=transform_img, input_columns="image",
|
||||
num_parallel_workers=num_parallel_workers)
|
||||
de_dataset = de_dataset.map(operations=transform_label, input_columns="label",
|
||||
num_parallel_workers=num_parallel_workers)
|
||||
|
||||
columns_to_project = ["image", "label"]
|
||||
de_dataset = de_dataset.project(columns=columns_to_project)
|
||||
|
||||
de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
|
||||
de_dataset = de_dataset.repeat(max_epoch)
|
||||
|
||||
return de_dataset
|
||||
@ -0,0 +1,42 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
common architecture.
|
||||
"""
|
||||
import mindspore.nn as nn
|
||||
from src.utils.cunstom_op import GlobalAvgPooling
|
||||
|
||||
__all__ = ['CommonHead']
|
||||
|
||||
class CommonHead(nn.Cell):
|
||||
"""
|
||||
common architecture definition.
|
||||
|
||||
Args:
|
||||
num_classes (int): Number of classes.
|
||||
out_channels (int): Output channels.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, num_classes, out_channels):
|
||||
super(CommonHead, self).__init__()
|
||||
self.avgpool = GlobalAvgPooling()
|
||||
self.fc = nn.Dense(out_channels, num_classes, has_bias=True).add_flags_recursive(fp16=True)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.avgpool(x)
|
||||
x = self.fc(x)
|
||||
return x
|
||||
@ -0,0 +1,98 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
Image classifiation.
|
||||
"""
|
||||
import math
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common import initializer as init
|
||||
import src.backbone as backbones
|
||||
import src.head as heads
|
||||
from src.utils.var_init import default_recurisive_init, KaimingNormal
|
||||
|
||||
|
||||
class ImageClassificationNetwork(nn.Cell):
|
||||
"""
|
||||
architecture of image classification network.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, backbone, head, include_top=True, activation="None"):
|
||||
super(ImageClassificationNetwork, self).__init__()
|
||||
self.backbone = backbone
|
||||
self.include_top = include_top
|
||||
self.need_activation = False
|
||||
if self.include_top:
|
||||
self.head = head
|
||||
if activation != "None":
|
||||
self.need_activation = True
|
||||
if activation == "Sigmoid":
|
||||
self.activation = P.Sigmoid()
|
||||
elif activation == "Softmax":
|
||||
self.activation = P.Softmax()
|
||||
else:
|
||||
raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].")
|
||||
|
||||
def construct(self, x):
|
||||
x = self.backbone(x)
|
||||
if self.include_top:
|
||||
x = self.head(x)
|
||||
if self.need_activation:
|
||||
x = self.activation(x)
|
||||
return x
|
||||
|
||||
|
||||
class ResNeXt(ImageClassificationNetwork):
|
||||
"""
|
||||
ResNeXt architecture.
|
||||
Args:
|
||||
backbone_name (string): backbone.
|
||||
num_classes (int): number of classes, Default is 1000.
|
||||
Returns:
|
||||
Resnet.
|
||||
"""
|
||||
def __init__(self, backbone_name, num_classes=1000, platform="Ascend", include_top=True, activation="None"):
|
||||
self.backbone_name = backbone_name
|
||||
backbone = backbones.__dict__[self.backbone_name](platform=platform)
|
||||
out_channels = backbone.get_out_channels()
|
||||
head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels)
|
||||
super(ResNeXt, self).__init__(backbone, head, include_top, activation)
|
||||
|
||||
default_recurisive_init(self)
|
||||
|
||||
for cell in self.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.set_data(init.initializer(
|
||||
KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
|
||||
cell.weight.shape, cell.weight.dtype))
|
||||
elif isinstance(cell, nn.BatchNorm2d):
|
||||
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
|
||||
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
|
||||
|
||||
# Zero-initialize the last BN in each residual branch,
|
||||
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
||||
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
||||
for cell in self.cells_and_names():
|
||||
if isinstance(cell, backbones.resnet.Bottleneck):
|
||||
cell.bn3.gamma.set_data(init.initializer('zeros', cell.bn3.gamma.shape))
|
||||
elif isinstance(cell, backbones.resnet.BasicBlock):
|
||||
cell.bn2.gamma.set_data(init.initializer('zeros', cell.bn2.gamma.shape))
|
||||
|
||||
|
||||
|
||||
def get_network(**kwargs):
|
||||
return ResNeXt('resnext101', **kwargs)
|
||||
@ -0,0 +1,142 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
learning rate generator.
|
||||
"""
|
||||
import math
|
||||
from collections import Counter
|
||||
import numpy as np
|
||||
|
||||
|
||||
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
|
||||
"""
|
||||
Applies liner Increasing to generate learning rate array in warmup stage.
|
||||
|
||||
Args:
|
||||
current_step(int): current step in warmup stage.
|
||||
warmup_steps(int): all steps in warmup stage.
|
||||
base_lr(float): init learning rate.
|
||||
init_lr(float): end learning rate
|
||||
|
||||
Returns:
|
||||
float, learning rate.
|
||||
"""
|
||||
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
|
||||
lr = float(init_lr) + lr_inc * current_step
|
||||
return lr
|
||||
|
||||
|
||||
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
|
||||
"""
|
||||
Applies cosine decay to generate learning rate array with warmup.
|
||||
|
||||
Args:
|
||||
lr(float): init learning rate
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
max_epoch(int): total epoch of training
|
||||
T_max(int): max epoch in decay.
|
||||
eta_min(float): end learning rate
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
last_epoch = i // steps_per_epoch
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
|
||||
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
|
||||
"""
|
||||
Applies step decay to generate learning rate array with warmup.
|
||||
|
||||
Args:
|
||||
lr(float): init learning rate
|
||||
lr_epochs(list): learning rate decay epoches list
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
max_epoch(int): total epoch of training
|
||||
gamma(float): attenuation constants.
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
milestones = lr_epochs
|
||||
milestones_steps = []
|
||||
for milestone in milestones:
|
||||
milestones_step = milestone * steps_per_epoch
|
||||
milestones_steps.append(milestones_step)
|
||||
|
||||
lr_each_step = []
|
||||
lr = base_lr
|
||||
milestones_steps_counter = Counter(milestones_steps)
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = lr * gamma**milestones_steps_counter[i]
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
|
||||
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
|
||||
|
||||
|
||||
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
lr_epochs = []
|
||||
for i in range(1, max_epoch):
|
||||
if i % epoch_size == 0:
|
||||
lr_epochs.append(i)
|
||||
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
|
||||
|
||||
|
||||
def get_lr(args):
|
||||
"""generate learning rate array."""
|
||||
if args.lr_scheduler == 'exponential':
|
||||
lr = warmup_step_lr(args.lr,
|
||||
args.lr_epochs,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
gamma=args.lr_gamma,
|
||||
)
|
||||
elif args.lr_scheduler == 'cosine_annealing':
|
||||
lr = warmup_cosine_annealing_lr(args.lr,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
args.T_max,
|
||||
args.eta_min)
|
||||
else:
|
||||
raise NotImplementedError(args.lr_scheduler)
|
||||
return lr
|
||||
@ -0,0 +1,53 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""Auto mixed precision."""
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.common import dtype as mstype
|
||||
|
||||
|
||||
class OutputTo(nn.Cell):
|
||||
"Cast cell output back to float16 or float32"
|
||||
|
||||
def __init__(self, op, to_type=mstype.float16):
|
||||
super(OutputTo, self).__init__(auto_prefix=False)
|
||||
self._op = op
|
||||
validator.check_type_name('to_type', to_type, [mstype.float16, mstype.float32], None)
|
||||
self.to_type = to_type
|
||||
|
||||
def construct(self, x):
|
||||
return F.cast(self._op(x), self.to_type)
|
||||
|
||||
|
||||
def auto_mixed_precision(network):
|
||||
"""Do keep batchnorm fp32."""
|
||||
cells = network.name_cells()
|
||||
change = False
|
||||
network.to_float(mstype.float16)
|
||||
for name in cells:
|
||||
subcell = cells[name]
|
||||
if subcell == network:
|
||||
continue
|
||||
elif name == 'fc':
|
||||
network.insert_child_to_cell(name, OutputTo(subcell, mstype.float32))
|
||||
change = True
|
||||
elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||
network.insert_child_to_cell(name, OutputTo(subcell.to_float(mstype.float32), mstype.float16))
|
||||
change = True
|
||||
else:
|
||||
auto_mixed_precision(subcell)
|
||||
if isinstance(network, nn.SequentialCell) and change:
|
||||
network.cell_list = list(network.cells())
|
||||
@ -0,0 +1,104 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
network operations
|
||||
"""
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.common import dtype as mstype
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
global average pooling feature map.
|
||||
|
||||
Args:
|
||||
mean (tuple): means for each channel.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(False)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class SEBlock(nn.Cell):
|
||||
"""
|
||||
squeeze and excitation block.
|
||||
|
||||
Args:
|
||||
channel (int): number of feature maps.
|
||||
reduction (int): weight.
|
||||
"""
|
||||
def __init__(self, channel, reduction=16):
|
||||
super(SEBlock, self).__init__()
|
||||
|
||||
self.avg_pool = GlobalAvgPooling()
|
||||
self.fc1 = nn.Dense(channel, channel // reduction)
|
||||
self.relu = P.ReLU()
|
||||
self.fc2 = nn.Dense(channel // reduction, channel)
|
||||
self.sigmoid = P.Sigmoid()
|
||||
self.reshape = P.Reshape()
|
||||
self.shape = P.Shape()
|
||||
self.sum = P.Sum()
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, x):
|
||||
b, c = self.shape(x)
|
||||
y = self.avg_pool(x)
|
||||
|
||||
y = self.reshape(y, (b, c))
|
||||
y = self.fc1(y)
|
||||
y = self.relu(y)
|
||||
y = self.fc2(y)
|
||||
y = self.sigmoid(y)
|
||||
y = self.reshape(y, (b, c, 1, 1))
|
||||
return x * y
|
||||
|
||||
class GroupConv(nn.Cell):
|
||||
"""
|
||||
group convolution operation.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channels of feature map.
|
||||
out_channels (int): Output channels of feature map.
|
||||
kernel_size (int): Size of convolution kernel.
|
||||
stride (int): Stride size for the group convolution layer.
|
||||
|
||||
Returns:
|
||||
tensor, output tensor.
|
||||
"""
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
|
||||
super(GroupConv, self).__init__()
|
||||
assert in_channels % groups == 0 and out_channels % groups == 0
|
||||
self.groups = groups
|
||||
self.convs = nn.CellList()
|
||||
self.op_split = P.Split(axis=1, output_num=self.groups)
|
||||
self.op_concat = P.Concat(axis=1)
|
||||
self.cast = P.Cast()
|
||||
for _ in range(groups):
|
||||
self.convs.append(nn.Conv2d(in_channels//groups, out_channels//groups,
|
||||
kernel_size=kernel_size, stride=stride, has_bias=has_bias,
|
||||
padding=pad, pad_mode=pad_mode, group=1))
|
||||
|
||||
def construct(self, x):
|
||||
features = self.op_split(x)
|
||||
outputs = ()
|
||||
for i in range(self.groups):
|
||||
outputs = outputs + (self.convs[i](self.cast(features[i], mstype.float32)),)
|
||||
out = self.op_concat(outputs)
|
||||
return out
|
||||
@ -0,0 +1,82 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
get logger.
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
class LOGGER(logging.Logger):
|
||||
"""
|
||||
set up logging file.
|
||||
|
||||
Args:
|
||||
logger_name (string): logger name.
|
||||
log_dir (string): path of logger.
|
||||
|
||||
Returns:
|
||||
string, logger path
|
||||
"""
|
||||
def __init__(self, logger_name, rank=0):
|
||||
super(LOGGER, self).__init__(logger_name)
|
||||
if rank % 8 == 0:
|
||||
console = logging.StreamHandler(sys.stdout)
|
||||
console.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
console.setFormatter(formatter)
|
||||
self.addHandler(console)
|
||||
|
||||
def setup_logging_file(self, log_dir, rank=0):
|
||||
"""set up log file"""
|
||||
self.rank = rank
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
|
||||
self.log_fn = os.path.join(log_dir, log_name)
|
||||
fh = logging.FileHandler(self.log_fn)
|
||||
fh.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
fh.setFormatter(formatter)
|
||||
self.addHandler(fh)
|
||||
|
||||
def info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO):
|
||||
self._log(logging.INFO, msg, args, **kwargs)
|
||||
|
||||
def save_args(self, args):
|
||||
self.info('Args:')
|
||||
args_dict = vars(args)
|
||||
for key in args_dict.keys():
|
||||
self.info('--> %s: %s', key, args_dict[key])
|
||||
self.info('')
|
||||
|
||||
def important_info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO) and self.rank == 0:
|
||||
line_width = 2
|
||||
important_msg = '\n'
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += '*'*line_width + ' '*8 + msg + '\n'
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
self.info(important_msg, *args, **kwargs)
|
||||
|
||||
|
||||
def get_logger(path, rank):
|
||||
logger = LOGGER("mindversion", rank)
|
||||
logger.setup_logging_file(path, rank)
|
||||
return logger
|
||||
@ -0,0 +1,36 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
optimizer parameters.
|
||||
"""
|
||||
def get_param_groups(network):
|
||||
"""get param groups"""
|
||||
decay_params = []
|
||||
no_decay_params = []
|
||||
for x in network.trainable_params():
|
||||
parameter_name = x.name
|
||||
if parameter_name.endswith('.bias'):
|
||||
# all bias not using weight decay
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.gamma'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.beta'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
no_decay_params.append(x)
|
||||
else:
|
||||
decay_params.append(x)
|
||||
|
||||
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
|
||||
@ -0,0 +1,53 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
choose samples from the dataset
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
class DistributedSampler():
|
||||
"""
|
||||
sampling the dataset.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
num_samples, number of samples.
|
||||
"""
|
||||
def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
|
||||
self.dataset = dataset
|
||||
self.rank = rank
|
||||
self.group_size = group_size
|
||||
self.dataset_length = len(self.dataset)
|
||||
self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
|
||||
self.total_size = self.num_samples * self.group_size
|
||||
self.shuffle = shuffle
|
||||
self.seed = seed
|
||||
|
||||
def __iter__(self):
|
||||
if self.shuffle:
|
||||
self.seed = (self.seed + 1) & 0xffffffff
|
||||
np.random.seed(self.seed)
|
||||
indices = np.random.permutation(self.dataset_length).tolist()
|
||||
else:
|
||||
indices = list(range(len(self.dataset_length)))
|
||||
|
||||
indices += indices[:(self.total_size - len(indices))]
|
||||
indices = indices[self.rank::self.group_size]
|
||||
return iter(indices)
|
||||
|
||||
def __len__(self):
|
||||
return self.num_samples
|
||||
|
||||
@ -0,0 +1,228 @@
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
Initialize.
|
||||
"""
|
||||
import os
|
||||
import math
|
||||
from functools import reduce
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common import initializer as init
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
|
||||
def _calculate_gain(nonlinearity, param=None):
|
||||
r"""
|
||||
Return the recommended gain value for the given nonlinearity function.
|
||||
|
||||
The values are as follows:
|
||||
================= ====================================================
|
||||
nonlinearity gain
|
||||
================= ====================================================
|
||||
Linear / Identity :math:`1`
|
||||
Conv{1,2,3}D :math:`1`
|
||||
Sigmoid :math:`1`
|
||||
Tanh :math:`\frac{5}{3}`
|
||||
ReLU :math:`\sqrt{2}`
|
||||
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
|
||||
================= ====================================================
|
||||
|
||||
Args:
|
||||
nonlinearity: the non-linear function
|
||||
param: optional parameter for the non-linear function
|
||||
|
||||
Examples:
|
||||
>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
|
||||
"""
|
||||
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
|
||||
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
|
||||
return 1
|
||||
if nonlinearity == 'tanh':
|
||||
return 5.0 / 3
|
||||
if nonlinearity == 'relu':
|
||||
return math.sqrt(2.0)
|
||||
if nonlinearity == 'leaky_relu':
|
||||
if param is None:
|
||||
negative_slope = 0.01
|
||||
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
|
||||
negative_slope = param
|
||||
else:
|
||||
raise ValueError("negative_slope {} not a valid number".format(param))
|
||||
return math.sqrt(2.0 / (1 + negative_slope ** 2))
|
||||
|
||||
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
|
||||
|
||||
def _assignment(arr, num):
|
||||
"""Assign the value of `num` to `arr`."""
|
||||
if arr.shape == ():
|
||||
arr = arr.reshape((1))
|
||||
arr[:] = num
|
||||
arr = arr.reshape(())
|
||||
else:
|
||||
if isinstance(num, np.ndarray):
|
||||
arr[:] = num[:]
|
||||
else:
|
||||
arr[:] = num
|
||||
return arr
|
||||
|
||||
def _calculate_in_and_out(arr):
|
||||
"""
|
||||
Calculate n_in and n_out.
|
||||
|
||||
Args:
|
||||
arr (Array): Input array.
|
||||
|
||||
Returns:
|
||||
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
|
||||
"""
|
||||
dim = len(arr.shape)
|
||||
if dim < 2:
|
||||
raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
|
||||
|
||||
n_in = arr.shape[1]
|
||||
n_out = arr.shape[0]
|
||||
|
||||
if dim > 2:
|
||||
counter = reduce(lambda x, y: x * y, arr.shape[2:])
|
||||
n_in *= counter
|
||||
n_out *= counter
|
||||
return n_in, n_out
|
||||
|
||||
def _select_fan(array, mode):
|
||||
mode = mode.lower()
|
||||
valid_modes = ['fan_in', 'fan_out']
|
||||
if mode not in valid_modes:
|
||||
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
|
||||
|
||||
fan_in, fan_out = _calculate_in_and_out(array)
|
||||
return fan_in if mode == 'fan_in' else fan_out
|
||||
|
||||
class KaimingInit(init.Initializer):
|
||||
r"""
|
||||
Base Class. Initialize the array with He kaiming algorithm.
|
||||
|
||||
Args:
|
||||
a: the negative slope of the rectifier used after this layer (only
|
||||
used with ``'leaky_relu'``)
|
||||
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
||||
preserves the magnitude of the variance of the weights in the
|
||||
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
||||
backwards pass.
|
||||
nonlinearity: the non-linear function, recommended to use only with
|
||||
``'relu'`` or ``'leaky_relu'`` (default).
|
||||
"""
|
||||
def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
|
||||
super(KaimingInit, self).__init__()
|
||||
self.mode = mode
|
||||
self.gain = _calculate_gain(nonlinearity, a)
|
||||
def _initialize(self, arr):
|
||||
pass
|
||||
|
||||
|
||||
class KaimingUniform(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming uniform algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
|
||||
|
||||
.. math::
|
||||
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
|
||||
data = np.random.uniform(-bound, bound, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
class KaimingNormal(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming normal algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
|
||||
|
||||
.. math::
|
||||
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
std = self.gain / math.sqrt(fan)
|
||||
data = np.random.normal(0, std, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
def default_recurisive_init(custom_cell):
|
||||
"""default_recurisive_init"""
|
||||
for _, cell in custom_cell.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.shape,
|
||||
cell.weight.dtype))
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight)
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
cell.bias.set_data(init.initializer(init.Uniform(bound),
|
||||
cell.bias.shape,
|
||||
cell.bias.dtype))
|
||||
elif isinstance(cell, nn.Dense):
|
||||
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.shape,
|
||||
cell.weight.dtype))
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight)
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
cell.bias.set_data(init.initializer(init.Uniform(bound),
|
||||
cell.bias.shape,
|
||||
cell.bias.dtype))
|
||||
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||
pass
|
||||
|
||||
|
||||
def load_pretrain_model(ckpt_file, network, args):
|
||||
"""load pretrain model."""
|
||||
if os.path.isfile(ckpt_file):
|
||||
param_dict = load_checkpoint(ckpt_file)
|
||||
param_dict_new = {}
|
||||
for key, values in param_dict.items():
|
||||
if key.startswith('moments.'):
|
||||
continue
|
||||
elif key.startswith('network.'):
|
||||
param_dict_new[key[8:]] = values
|
||||
else:
|
||||
param_dict_new[key] = values
|
||||
load_param_into_net(network, param_dict_new)
|
||||
args.logger.info('load model {} success'.format(ckpt_file))
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in new issue