add gpu resnext50
This commit is contained in:
parent
ca6da6751f
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20e5f7196e
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@ -90,10 +90,15 @@ sh run_standalone_train.sh DEVICE_ID DATA_PATH
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#### Launch
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```bash
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# distributed training example(8p)
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# distributed training example(8p) for Ascend
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sh scripts/run_distribute_train.sh MINDSPORE_HCCL_CONFIG_PATH /dataset/train
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# standalone training example
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# standalone training example for Ascend
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sh scripts/run_standalone_train.sh 0 /dataset/train
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# distributed training example(8p) for GPU
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sh scripts/run_distribute_train_for_gpu.sh /dataset/train
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# standalone training example for GPU
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sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train
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```
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#### Result
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@ -106,14 +111,15 @@ You can find checkpoint file together with result in log.
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```
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# Evaluation
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sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
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sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM
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```
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PLATFORM is Ascend or GPU, default is Ascend.
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#### Launch
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```bash
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# Evaluation with checkpoint
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt Ascend
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```
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> checkpoint can be produced in training process.
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@ -29,15 +29,11 @@ from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from src.utils.logging import get_logger
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from src.utils.auto_mixed_precision import auto_mixed_precision
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from src.image_classification import get_network
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from src.dataset import classification_dataset
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from src.config import config
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target="Ascend", save_graphs=False, device_id=devid)
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class ParameterReduce(nn.Cell):
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"""ParameterReduce"""
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@ -56,6 +52,7 @@ class ParameterReduce(nn.Cell):
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def parse_args(cloud_args=None):
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"""parse_args"""
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parser = argparse.ArgumentParser('mindspore classification test')
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parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='/opt/npu/datasets/classification/val', help='eval data dir')
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@ -108,12 +105,25 @@ def merge_args(args, cloud_args):
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def test(cloud_args=None):
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"""test"""
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args = parse_args(cloud_args)
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target=args.platform, save_graphs=False)
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if os.getenv('DEVICE_ID', "not_set").isdigit():
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context.set_context(device_id=int(os.getenv('DEVICE_ID')))
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# init distributed
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if args.is_distributed:
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if args.platform == "Ascend":
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init()
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elif args.platform == "GPU":
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init("nccl")
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args.rank = get_rank()
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args.group_size = get_group_size()
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parallel_mode = ParallelMode.DATA_PARALLEL
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
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parameter_broadcast=True, mirror_mean=True)
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else:
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args.rank = 0
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args.group_size = 1
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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@ -140,7 +150,7 @@ def test(cloud_args=None):
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max_epoch=1, rank=args.rank, group_size=args.group_size,
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mode='eval')
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eval_dataloader = de_dataset.create_tuple_iterator()
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network = get_network(args.backbone, args.num_classes)
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network = get_network(args.backbone, args.num_classes, platform=args.platform)
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if network is None:
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raise NotImplementedError('not implement {}'.format(args.backbone))
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@ -157,12 +167,13 @@ def test(cloud_args=None):
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load_param_into_net(network, param_dict_new)
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args.logger.info('load model {} success'.format(model))
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# must add
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network.add_flags_recursive(fp16=True)
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img_tot = 0
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top1_correct = 0
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top5_correct = 0
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if args.platform == "Ascend":
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network.to_float(mstype.float16)
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else:
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auto_mixed_precision(network)
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network.set_train(False)
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t_end = time.time()
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it = 0
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@ -0,0 +1,30 @@
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#!/bin/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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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 $RANK_SIZE \
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python train.py \
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--is_distribute=1 \
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--platform="GPU" \
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--pretrained=$PATH_CHECKPOINT \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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@ -14,11 +14,16 @@
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# limitations under the License.
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# ============================================================================
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DEVICE_ID=$1
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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=$4
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fi
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python eval.py \
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--device_id=$DEVICE_ID \
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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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@ -14,7 +14,7 @@
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# limitations under the License.
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# ============================================================================
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DEVICE_ID=$1
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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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@ -0,0 +1,30 @@
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#!/bin/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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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=$3
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fi
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python train.py \
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--is_distribute=0 \
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--pretrained=$PATH_CHECKPOINT \
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--platform="GPU" \
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--data_dir=$DATA_DIR > log.txt 2>&1 &
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@ -87,7 +87,8 @@ class BasicBlock(nn.Cell):
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"""
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expansion = 1
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def __init__(self, in_channels, out_channels, stride=1, down_sample=None, use_se=False, **kwargs):
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def __init__(self, in_channels, out_channels, stride=1, down_sample=None, use_se=False,
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platform="Ascend", **kwargs):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(in_channels, out_channels, stride=stride)
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self.bn1 = nn.BatchNorm2d(out_channels)
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@ -142,7 +143,7 @@ class Bottleneck(nn.Cell):
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expansion = 4
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def __init__(self, in_channels, out_channels, stride=1, down_sample=None,
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base_width=64, groups=1, use_se=False, **kwargs):
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base_width=64, groups=1, use_se=False, platform="Ascend", **kwargs):
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super(Bottleneck, self).__init__()
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width = int(out_channels * (base_width / 64.0)) * groups
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@ -153,7 +154,11 @@ class Bottleneck(nn.Cell):
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self.conv3x3s = nn.CellList()
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if platform == "GPU":
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self.conv2 = nn.Conv2d(width, width, 3, stride, pad_mode='pad', padding=1, group=groups)
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else:
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self.conv2 = GroupConv(width, width, 3, stride, pad=1, groups=groups)
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self.op_split = Split(axis=1, output_num=self.groups)
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self.op_concat = Concat(axis=1)
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@ -211,7 +216,7 @@ class ResNet(nn.Cell):
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Examples:
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>>>ResNet()
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"""
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def __init__(self, block, layers, width_per_group=64, groups=1, use_se=False):
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def __init__(self, block, layers, width_per_group=64, groups=1, use_se=False, platform="Ascend"):
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super(ResNet, self).__init__()
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self.in_channels = 64
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self.groups = groups
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@ -222,10 +227,10 @@ class ResNet(nn.Cell):
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self.relu = P.ReLU()
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
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self.layer1 = self._make_layer(block, 64, layers[0], use_se=use_se)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, use_se=use_se)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2, use_se=use_se)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2, use_se=use_se)
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self.layer1 = self._make_layer(block, 64, layers[0], use_se=use_se, platform=platform)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, use_se=use_se, platform=platform)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2, use_se=use_se, platform=platform)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2, use_se=use_se, platform=platform)
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self.out_channels = 512 * block.expansion
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self.cast = P.Cast()
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@ -242,7 +247,7 @@ class ResNet(nn.Cell):
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return x
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def _make_layer(self, block, out_channels, blocks_num, stride=1, use_se=False):
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def _make_layer(self, block, out_channels, blocks_num, stride=1, use_se=False, platform="Ascend"):
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"""_make_layer"""
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down_sample = None
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if stride != 1 or self.in_channels != out_channels * block.expansion:
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@ -257,11 +262,12 @@ class ResNet(nn.Cell):
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down_sample=down_sample,
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base_width=self.base_width,
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groups=self.groups,
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use_se=use_se))
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use_se=use_se,
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platform=platform))
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self.in_channels = out_channels * block.expansion
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for _ in range(1, blocks_num):
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layers.append(block(self.in_channels, out_channels,
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base_width=self.base_width, groups=self.groups, use_se=use_se))
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layers.append(block(self.in_channels, out_channels, base_width=self.base_width,
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groups=self.groups, use_se=use_se, platform=platform))
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return nn.SequentialCell(layers)
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@ -269,5 +275,5 @@ class ResNet(nn.Cell):
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return self.out_channels
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def resnext50():
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return ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32)
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def resnext50(platform="Ascend"):
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return ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32, platform=platform)
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@ -36,7 +36,8 @@ config = ed({
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"label_smooth": 1,
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"label_smooth_factor": 0.1,
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"ckpt_interval": 1250,
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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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@ -143,8 +143,10 @@ def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank
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de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
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de_dataset.set_dataset_size(len(sampler))
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de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
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de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
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de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=num_parallel_workers,
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operations=transform_img)
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de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=num_parallel_workers,
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operations=transform_label)
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columns_to_project = ["image", "label"]
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de_dataset = de_dataset.project(columns=columns_to_project)
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@ -50,9 +50,9 @@ class Resnet(ImageClassificationNetwork):
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Returns:
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Resnet.
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"""
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def __init__(self, backbone_name, num_classes):
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def __init__(self, backbone_name, num_classes, platform="Ascend"):
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self.backbone_name = backbone_name
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backbone = backbones.__dict__[self.backbone_name]()
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backbone = backbones.__dict__[self.backbone_name](platform=platform)
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out_channels = backbone.get_out_channels()
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head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels)
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super(Resnet, self).__init__(backbone, head)
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@ -79,7 +79,7 @@ class Resnet(ImageClassificationNetwork):
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def get_network(backbone_name, num_classes):
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def get_network(backbone_name, num_classes, platform="Ascend"):
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if backbone_name in ['resnext50']:
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return Resnet(backbone_name, num_classes)
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return Resnet(backbone_name, num_classes, platform)
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return None
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@ -0,0 +1,56 @@
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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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"""Auto mixed precision."""
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import mindspore.nn as nn
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from mindspore.ops import functional as F
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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class OutputTo(nn.Cell):
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"Cast cell output back to float16 or float32"
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def __init__(self, op, to_type=mstype.float16):
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super(OutputTo, self).__init__(auto_prefix=False)
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self._op = op
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validator.check_type_name('to_type', to_type, [mstype.float16, mstype.float32], None)
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self.to_type = to_type
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def construct(self, x):
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return F.cast(self._op(x), self.to_type)
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def auto_mixed_precision(network):
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"""Do keep batchnorm fp32."""
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cells = network.name_cells()
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change = False
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network.to_float(mstype.float16)
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for name in cells:
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subcell = cells[name]
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if subcell == network:
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continue
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elif name == 'fc':
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network.insert_child_to_cell(name, OutputTo(subcell, mstype.float32))
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change = True
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elif name == 'conv2':
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subcell.to_float(mstype.float32)
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change = True
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elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)):
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network.insert_child_to_cell(name, OutputTo(subcell.to_float(mstype.float32), mstype.float16))
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change = True
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else:
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auto_mixed_precision(subcell)
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if isinstance(network, nn.SequentialCell) and change:
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network.cell_list = list(network.cells())
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@ -29,14 +29,10 @@ class GlobalAvgPooling(nn.Cell):
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"""
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def __init__(self):
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super(GlobalAvgPooling, self).__init__()
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self.mean = P.ReduceMean(True)
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self.shape = P.Shape()
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self.reshape = P.Reshape()
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self.mean = P.ReduceMean(False)
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def construct(self, x):
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x = self.mean(x, (2, 3))
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b, c, _, _ = self.shape(x)
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x = self.reshape(x, (b, c))
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return x
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@ -36,11 +36,9 @@ from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
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from src.utils.logging import get_logger
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from src.utils.optimizers__init__ import get_param_groups
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from src.image_classification import get_network
|
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from src.utils.auto_mixed_precision import auto_mixed_precision
|
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from src.config import config
|
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
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device_target="Ascend", save_graphs=False, device_id=devid)
|
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|
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class BuildTrainNetwork(nn.Cell):
|
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"""build training network"""
|
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|
|
@ -109,6 +107,7 @@ class ProgressMonitor(Callback):
|
|||
def parse_args(cloud_args=None):
|
||||
"""parameters"""
|
||||
parser = argparse.ArgumentParser('mindspore classification training')
|
||||
parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU'), help='run platform')
|
||||
|
||||
# dataset related
|
||||
parser.add_argument('--data_dir', type=str, default='', help='train data dir')
|
||||
|
|
@ -141,6 +140,7 @@ def parse_args(cloud_args=None):
|
|||
args.label_smooth = config.label_smooth
|
||||
args.label_smooth_factor = config.label_smooth_factor
|
||||
args.ckpt_interval = config.ckpt_interval
|
||||
args.ckpt_save_max = config.ckpt_save_max
|
||||
args.ckpt_path = config.ckpt_path
|
||||
args.is_save_on_master = config.is_save_on_master
|
||||
args.rank = config.rank
|
||||
|
|
@ -166,12 +166,25 @@ def merge_args(args, cloud_args):
|
|||
def train(cloud_args=None):
|
||||
"""training process"""
|
||||
args = parse_args(cloud_args)
|
||||
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
||||
device_target=args.platform, save_graphs=False)
|
||||
if os.getenv('DEVICE_ID', "not_set").isdigit():
|
||||
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
|
||||
|
||||
# init distributed
|
||||
if args.is_distributed:
|
||||
if args.platform == "Ascend":
|
||||
init()
|
||||
else:
|
||||
init("nccl")
|
||||
args.rank = get_rank()
|
||||
args.group_size = get_group_size()
|
||||
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
else:
|
||||
args.rank = 0
|
||||
args.group_size = 1
|
||||
|
||||
if args.is_dynamic_loss_scale == 1:
|
||||
args.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
|
||||
|
|
@ -192,7 +205,7 @@ def train(cloud_args=None):
|
|||
# dataloader
|
||||
de_dataset = classification_dataset(args.data_dir, args.image_size,
|
||||
args.per_batch_size, 1,
|
||||
args.rank, args.group_size)
|
||||
args.rank, args.group_size, num_parallel_workers=8)
|
||||
de_dataset.map_model = 4 # !!!important
|
||||
args.steps_per_epoch = de_dataset.get_dataset_size()
|
||||
|
||||
|
|
@ -201,15 +214,9 @@ def train(cloud_args=None):
|
|||
# network
|
||||
args.logger.important_info('start create network')
|
||||
# get network and init
|
||||
network = get_network(args.backbone, args.num_classes)
|
||||
network = get_network(args.backbone, args.num_classes, platform=args.platform)
|
||||
if network is None:
|
||||
raise NotImplementedError('not implement {}'.format(args.backbone))
|
||||
network.add_flags_recursive(fp16=True)
|
||||
# loss
|
||||
if not args.label_smooth:
|
||||
args.label_smooth_factor = 0.0
|
||||
criterion = CrossEntropy(smooth_factor=args.label_smooth_factor,
|
||||
num_classes=args.num_classes)
|
||||
|
||||
# load pretrain model
|
||||
if os.path.isfile(args.pretrained):
|
||||
|
|
@ -252,31 +259,29 @@ def train(cloud_args=None):
|
|||
loss_scale=args.loss_scale)
|
||||
|
||||
|
||||
criterion.add_flags_recursive(fp32=True)
|
||||
# loss
|
||||
if not args.label_smooth:
|
||||
args.label_smooth_factor = 0.0
|
||||
loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
|
||||
|
||||
# package training process, adjust lr + forward + backward + optimizer
|
||||
train_net = BuildTrainNetwork(network, criterion)
|
||||
if args.is_distributed:
|
||||
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||
else:
|
||||
parallel_mode = ParallelMode.STAND_ALONE
|
||||
if args.is_dynamic_loss_scale == 1:
|
||||
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
|
||||
else:
|
||||
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
|
||||
|
||||
# Model api changed since TR5_branch 2020/03/09
|
||||
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
model = Model(train_net, optimizer=opt, metrics=None, loss_scale_manager=loss_scale_manager)
|
||||
if args.platform == "Ascend":
|
||||
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager,
|
||||
metrics={'acc'}, amp_level="O3")
|
||||
else:
|
||||
auto_mixed_precision(network)
|
||||
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, metrics={'acc'})
|
||||
|
||||
# checkpoint save
|
||||
progress_cb = ProgressMonitor(args)
|
||||
callbacks = [progress_cb,]
|
||||
if args.rank_save_ckpt_flag:
|
||||
ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval
|
||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
|
||||
keep_checkpoint_max=ckpt_max_num)
|
||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
|
||||
keep_checkpoint_max=args.ckpt_save_max)
|
||||
ckpt_cb = ModelCheckpoint(config=ckpt_config,
|
||||
directory=args.outputs_dir,
|
||||
prefix='{}'.format(args.rank))
|
||||
|
|
|
|||
Loading…
Reference in New Issue