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!5243 1、add hub config for resnet 2、fix readme of deeplabv3 and inceptionv3 3、Extend hccl time out of resnext50 

Merge pull request !5243 from zhouyaqiang0/master
This commit is contained in:
mindspore-ci-bot 2020-08-28 14:25:24 +08:00 committed by Gitee
parent ac9b69ba4b 3277185684
commit eced8b32e9
12 changed files with 138 additions and 95 deletions
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65
model_zoo/official/cv/deeplabv3/README.md
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@ -46,8 +46,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
# [Environment Requirements](#contents) # [Environment Requirements](#contents)
- Hardware（Ascend/GPU） - Hardware（Ascend）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. - Prepare hardware environment with Ascend. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Framework - Framework
- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
- For more information, please check the resources below： - For more information, please check the resources below：
@ -60,32 +60,32 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
```shell ```shell
. .
└─DeeplabV3 └─deeplabv3
│ README.md ├──README.md
│ eval.py ├──eval.py
│ train.py ├──train.py
├─scripts ├──scripts
│ run_distribute_train.sh # launch distributed training with ascend platform(8p) │ ├──run_distribute_train.sh # launch distributed training with ascend platform(8p)
│ run_eval.sh # launch evaluating with ascend platform │ ├──run_eval.sh # launch evaluating with ascend platform
│ run_standalone_train.sh # launch standalone training with ascend platform(1p) │ ├──run_standalone_train.sh # launch standalone training with ascend platform(1p)
└─src ├──src
│ config.py # parameter configuration │ ├──config.py # parameter configuration
│ deeplabv3.py # network definition │ ├──deeplabv3.py # network definition
│ ei_dataset.py # data preprocessing for EI │ ├──ei_dataset.py # data preprocessing for EI
│ losses.py # customized loss function │ ├──losses.py # customized loss function
│ md_dataset.py # data preprocessing │ ├──md_dataset.py # data preprocessing
│ miou_precision.py # miou metrics │ ├──miou_precision.py # miou metrics
│ __init__.py │ ├──__init__.py
│ │ │
├─backbone │ ├──backbone
│ resnet_deeplab.py # backbone network definition │ │ ├──resnet_deeplab.py # backbone network definition
│ __init__.py │ │ ├──__init__.py
│ │ │
└─utils │ └──utils
adapter.py # adapter of dataset │ ├──adapter.py # adapter of dataset
custom_transforms.py # random process dataset │ ├──custom_transforms.py # random process dataset
file_io.py # file operation module │ ├──file_io.py # file operation module
__init__.py │ ├──__init__.py
``` ```
## [Script Parameters](#contents) ## [Script Parameters](#contents)
@ -107,7 +107,7 @@ Major parameters in train.py and config.py are:
to refine segmentation results, default is None. to refine segmentation results, default is None.
image_pyramid Input scales for multi-scale feature extraction, default is None. image_pyramid Input scales for multi-scale feature extraction, default is None.
epoch_size Epoch size, default is 6. epoch_size Epoch size, default is 6.
batch_size batch size of input dataset: N, default is 2. batch_size Batch size of input dataset: N, default is 2.
enable_save_ckpt Enable save checkpoint, default is true. enable_save_ckpt Enable save checkpoint, default is true.
save_checkpoint_steps Save checkpoint steps, default is 1000. save_checkpoint_steps Save checkpoint steps, default is 1000.
save_checkpoint_num Save checkpoint numbers, default is 1. save_checkpoint_num Save checkpoint numbers, default is 1.
@ -123,7 +123,7 @@ You can start training using python or shell scripts. The usage of shell scripts
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH) sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
> Notes: > Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got in /etc/hccn.conf in ascend server. RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
### Launch ### Launch
@ -133,7 +133,7 @@ sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH)
python train.py --dataset_url DATA_PATH python train.py --dataset_url DATA_PATH
shell: shell:
sh scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH (CKPT_PATH) sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH (CKPT_PATH)
``` ```
> Notes: > Notes:
If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file. If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
@ -171,7 +171,7 @@ sh scripts/run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH
### Result ### Result
Evaluation result will be stored in the example path, you can find result like the followings in `log.txt`. Evaluation result will be stored in the example path, you can find result like the followings in `eval.log`.
``` ```
mIoU = 0.65049 mIoU = 0.65049
@ -197,6 +197,7 @@ mIoU = 0.65049
| Total time | 5mins | | Total time | 5mins |
| Params (M) | 94M | | Params (M) | 94M |
| Checkpoint for Fine tuning | 100M | | Checkpoint for Fine tuning | 100M |
| Scripts | [deeplabv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) | [deeplabv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) |
#### Inference Performance #### Inference Performance

8
model_zoo/official/cv/deeplabv3/scripts/run_distribute_train.sh
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@ -49,10 +49,10 @@ do
end=`expr $start \+ $core_gap` end=`expr $start \+ $core_gap`
cmdopt=$start"-"$end cmdopt=$start"-"$end
rm -rf LOG$i rm -rf train_parallel$i
mkdir ./LOG$i mkdir ./train_parallel$i
cp *.py ./LOG$i cp *.py ./train_parallel$i
cd ./LOG$i || exit cd ./train_parallel$i || exit
echo "start training for rank $i, device $DEVICE_ID" echo "start training for rank $i, device $DEVICE_ID"
mkdir -p ms_log mkdir -p ms_log
CUR_DIR=`pwd` CUR_DIR=`pwd`

2
model_zoo/official/cv/deeplabv3/scripts/run_eval.sh
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@ -31,4 +31,4 @@ export GLOG_logtostderr=0
python eval.py \ python eval.py \
--device_id=$DEVICE_ID \ --device_id=$DEVICE_ID \
--checkpoint_url=$PATH_CHECKPOINT \ --checkpoint_url=$PATH_CHECKPOINT \
--data_url=$DATA_DIR > log.txt 2>&1 & --data_url=$DATA_DIR > eval.log 2>&1 &

119
model_zoo/official/cv/inceptionv3/README.md
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@ -20,7 +20,7 @@
# [InceptionV3 Description](#contents) # [InceptionV3 Description](#contents)
InceptionV3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures. InceptionV3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures. Inception v3 mainly focuses on burning less computational power by modifying the previous Inception architectures. This idea was proposed in the paper Rethinking the Inception Architecture for Computer Vision, published in 2015.
[Paper](https://arxiv.org/pdf/1512.00567.pdf) Min Sun, Ali Farhadi, Steve Seitz. Ranking Domain-Specific Highlights by Analyzing Edited Videos[J]. 2014. [Paper](https://arxiv.org/pdf/1512.00567.pdf) Min Sun, Ali Farhadi, Steve Seitz. Ranking Domain-Specific Highlights by Analyzing Edited Videos[J]. 2014.
@ -36,8 +36,8 @@ The overall network architecture of InceptionV3 is show below:
Dataset used can refer to paper. Dataset used can refer to paper.
- Dataset size: ~125G, 1.2W colorful images in 1000 classes - Dataset size: ~125G, 1.2W colorful images in 1000 classes
- Train: 120G, 1.2W images - Train: 120G, 1200k images
- Test: 5G, 50000 images - Test: 5G, 50k images
- Data format: RGB images. - Data format: RGB images.
- Note: Data will be processed in src/dataset.py - Note: Data will be processed in src/dataset.py
@ -68,11 +68,11 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
├─README.md ├─README.md
├─scripts ├─scripts
├─run_standalone_train.sh # launch standalone training with ascend platform(1p) ├─run_standalone_train.sh # launch standalone training with ascend platform(1p)
├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p) ├─run_standalone_train_gpu.sh # launch standalone training with gpu platform(1p)
├─run_distribute_train.sh # launch distributed training with ascend platform(8p) ├─run_distribute_train.sh # launch distributed training with ascend platform(8p)
├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p) ├─run_distribute_train_gpu.sh # launch distributed training with gpu platform(8p)
├─run_eval.sh # launch evaluating with ascend platform ├─run_eval.sh # launch evaluating with ascend platform
└─run_eval_for_gpu.sh # launch evaluating with gpu platform └─run_eval_gpu.sh # launch evaluating with gpu platform
├─src ├─src
├─config.py # parameter configuration ├─config.py # parameter configuration
├─dataset.py # data preprocessing ├─dataset.py # data preprocessing
@ -88,27 +88,32 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
```python ```python
Major parameters in train.py and config.py are: Major parameters in train.py and config.py are:
'random_seed': 1, # fix random seed 'random_seed' # fix random seed
'rank': 0, # local rank of distributed 'rank' # local rank of distributed
'group_size': 1, # world size of distributed 'group_size' # world size of distributed
'work_nums': 8, # number of workers to read the data 'work_nums' # number of workers to read the data
'decay_method': 'cosine', # learning rate scheduler mode 'decay_method' # learning rate scheduler mode
"loss_scale": 1, # loss scale "loss_scale" # loss scale
'batch_size': 128, # input batchsize 'batch_size' # input batchsize
'epoch_size': 250, # total epoch numbers 'epoch_size' # total epoch numbers
'num_classes': 1000, # dataset class numbers 'num_classes' # dataset class numbers
'smooth_factor': 0.1, # label smoothing factor 'smooth_factor' # label smoothing factor
'aux_factor': 0.2, # loss factor of aux logit 'aux_factor' # loss factor of aux logit
'lr_init': 0.00004, # initiate learning rate 'lr_init' # initiate learning rate
'lr_max': 0.4, # max bound of learning rate 'lr_max' # max bound of learning rate
'lr_end': 0.000004, # min bound of learning rate 'lr_end' # min bound of learning rate
'warmup_epochs': 1, # warmup epoch numbers 'warmup_epochs' # warmup epoch numbers
'weight_decay': 0.00004, # weight decay 'weight_decay' # weight decay
'momentum': 0.9, # momentum 'momentum' # momentum
'opt_eps': 1.0, # epsilon 'opt_eps' # epsilon
'keep_checkpoint_max': 100, # max numbers to keep checkpoints 'keep_checkpoint_max' # max numbers to keep checkpoints
'ckpt_path': './checkpoint/', # save checkpoint path 'ckpt_path' # save checkpoint path
'is_save_on_master': 1 # save checkpoint on rank0, distributed parameters 'is_save_on_master' # save checkpoint on rank0, distributed parameters
'dropout_keep_prob' # the keep rate, between 0 and 1, e.g. keep_prob = 0.9, means dropping out 10% of input units
'has_bias' # specifies whether the layer uses a bias vector.
'amp_level' # option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
# precision training. Supports [O0, O2, O3].
``` ```
## [Training process](#contents) ## [Training process](#contents)
@ -125,13 +130,15 @@ sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
# standalone training # standalone training
sh run_standalone_train.sh DEVICE_ID DATA_PATH sh run_standalone_train.sh DEVICE_ID DATA_PATH
``` ```
> Notes:
RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training_ascend.html) , and the device_ip can be got as https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
- GPU: - GPU:
``` ```
# distribute training example(8p) # distribute training example(8p)
sh run_distribute_train_for_gpu.sh DATA_DIR sh run_distribute_train_gpu.sh DATA_DIR
# standalone training # standalone training
sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR sh run_standalone_train_gpu.sh DEVICE_ID DATA_DIR
``` ```
### Launch ### Launch
@ -143,10 +150,16 @@ sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR
GPU: python train.py --dataset_path /dataset/train --platform GPU GPU: python train.py --dataset_path /dataset/train --platform GPU
shell: shell:
# distributed training example(8p) for GPU Ascend:
sh scripts/run_distribute_train_for_gpu.sh /dataset/train # distribute training example(8p)
# standalone training example for GPU sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train # standalone training
sh run_standalone_train.sh DEVICE_ID DATA_PATH
GPU:
# distributed training example(8p)
sh scripts/run_distribute_train_gpu.sh /dataset/train
# standalone training example
sh scripts/run_standalone_train_gpu.sh 0 /dataset/train
``` ```
### Result ### Result
@ -166,7 +179,7 @@ Epoch time: 160917.911, per step time: 128.631
You can start training using python or shell scripts. The usage of shell scripts as follows: You can start training using python or shell scripts. The usage of shell scripts as follows:
- Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT - Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
- GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT - GPU: sh run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
### Launch ### Launch
@ -178,14 +191,14 @@ You can start training using python or shell scripts. The usage of shell scripts
shell: shell:
Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT GPU: sh run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
``` ```
> checkpoint can be produced in training process. > checkpoint can be produced in training process.
### Result ### Result
Evaluation result will be stored in the example path, you can find result like the followings in `log.txt`. Evaluation result will be stored in the example path, you can find result like the followings in `eval.log`.
``` ```
metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942} metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
@ -197,21 +210,23 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
### Training Performance ### Training Performance
| Parameters | InceptionV3 | | | Parameters | InceptionV3 | |
| -------------------------- | ---------------------------------------------------------- | ------------------------- | | -------------------------- | ---------------------------------------------- | ------------------------- |
| Model Version | | | | Model Version | | |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G | | Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMI V100-16G(PCIE),cpu:2.10GHz 96cores, memory:250G |
| uploaded Date | 08/21/2020 | 08/21/2020 | | uploaded Date | 08/21/2020 | 08/21/2020 |
| MindSpore Version | 0.6.0-beta | 0.6.0-beta | | MindSpore Version | 0.6.0-beta | 0.6.0-beta |
| Training Parameters | src/config.py | src/config.py | | Training Parameters | src/config.py | src/config.py |
| Optimizer | RMSProp | RMSProp | | Optimizer | RMSProp | RMSProp |
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | | Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
| outputs | probability | probability | | outputs | probability | probability |
| Loss | 1.98 | 1.98 | | Loss | 1.98 | 1.98 |
| Accuracy | ACC1[78.8%] ACC5[94.2%] | ACC1[78.7%] ACC5[94.1%] | | Accuracy (8p) | ACC1[78.8%] ACC5[94.2%] | ACC1[78.7%] ACC5[94.1%] |
| Total time | 11h | 72h | | Total time (8p) | 11h | 72h |
| Params (M) | 103M | 103M | | Params (M) | 103M | 103M |
| Checkpoint for Fine tuning | 313M | 312.41M | | Checkpoint for Fine tuning | 313M | 312M |
| Scripts | [inceptionv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3) | [inceptionv3 script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/inceptionv3) |
#### Inference Performance #### Inference Performance
@ -221,7 +236,7 @@ metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}
| Resource | Ascend 910 | | Resource | Ascend 910 |
| Uploaded Date | 08/22/2020 (month/day/year) | | Uploaded Date | 08/22/2020 (month/day/year) |
| MindSpore Version | 0.6.0-beta | | MindSpore Version | 0.6.0-beta |
| Dataset | 50,000 images | | Dataset | 50k images |
| batch_size | 128 | | batch_size | 128 |
| outputs | probability | | outputs | probability |
| Accuracy | ACC1[78.8%] ACC5[94.2%] | | Accuracy | ACC1[78.8%] ACC5[94.2%] |

8
model_zoo/official/cv/inceptionv3/scripts/run_distribute_train.sh
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@ -35,10 +35,10 @@ do
end=`expr $start \+ $core_gap` end=`expr $start \+ $core_gap`
cmdopt=$start"-"$end cmdopt=$start"-"$end
rm -rf LOG$i rm -rf train_parallel$i
mkdir ./LOG$i mkdir ./train_parallel$i
cp *.py ./LOG$i cp *.py ./train_parallel$i
cd ./LOG$i || exit cd ./train_parallel$i || exit
echo "start training for rank $i, device $DEVICE_ID" echo "start training for rank $i, device $DEVICE_ID"
env > env.log env > env.log

0
model_zoo/official/cv/inceptionv3/scripts/run_distribute_train_for_gpu.sh → model_zoo/official/cv/inceptionv3/scripts/run_distribute_train_gpu.sh
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2
model_zoo/official/cv/inceptionv3/scripts/run_eval.sh
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@ -21,4 +21,4 @@ PATH_CHECKPOINT=$3
python eval.py \ python eval.py \
--platform=Ascend \ --platform=Ascend \
--checkpoint=$PATH_CHECKPOINT \ --checkpoint=$PATH_CHECKPOINT \
--dataset_path=$DATA_DIR > log.txt 2>&1 & --dataset_path=$DATA_DIR > eval.log 2>&1 &

0
model_zoo/official/cv/inceptionv3/scripts/run_eval_for_gpu.sh → model_zoo/official/cv/inceptionv3/scripts/run_eval_gpu.sh
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0
model_zoo/official/cv/inceptionv3/scripts/run_standalone_train_for_gpu.sh → model_zoo/official/cv/inceptionv3/scripts/run_standalone_train_gpu.sh
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2
model_zoo/official/cv/inceptionv3/src/config.py
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@ -37,7 +37,7 @@ config_gpu = edict({
'weight_decay': 0.00004, 'weight_decay': 0.00004,
'momentum': 0.9, 'momentum': 0.9,
'opt_eps': 1.0, 'opt_eps': 1.0,
'keep_checkpoint_max': 100, 'keep_checkpoint_max': 10,
'ckpt_path': './checkpoint/', 'ckpt_path': './checkpoint/',
'is_save_on_master': 0, 'is_save_on_master': 0,
'dropout_keep_prob': 0.5, 'dropout_keep_prob': 0.5,

25
model_zoo/official/cv/resnet/mindspore_hub_conf.py Normal file
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@ -0,0 +1,25 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""hub config."""
from src.resnet import resnet50, resnet101, se_resnet50
def create_network(name, **kwargs):
if name == 'resnet50':
return resnet50(*args, **kwargs)
if name == 'resnet101':
return resnet101(*args, **kwargs)
if name == 'se_resnet50':
return se_resnet50(*args, **kwargs)
raise NotImplementedError(f"{name} is not implemented in the repo")

2
model_zoo/official/cv/resnext50/scripts/run_distribute_train.sh
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@ -17,6 +17,8 @@
DATA_DIR=$2 DATA_DIR=$2
export RANK_TABLE_FILE=$1 export RANK_TABLE_FILE=$1
export RANK_SIZE=8 export RANK_SIZE=8
export HCCL_CONNECT_TIMEOUT=600
echo "hccl connect time out has changed to 600 second"
PATH_CHECKPOINT="" PATH_CHECKPOINT=""
if [ $# == 3 ] if [ $# == 3 ]
then then