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!7516 Amend deeplabv3 readme in r1.0

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Merge pull request !7516 from jiangzhenguang/amend_deeplabv3_readme_in_r1.0
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mindspore-ci-bot 5 years ago committed by Gitee
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model_zoo/official/cv/deeplabv3/README.md
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@ -85,7 +85,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
# [Quick Start](#contents) # [Quick Start](#contents)
After installing MindSpore via the official website, you can start training and evaluation as follows: After installing MindSpore via the official website, you can start training and evaluation as follows:
- Runing on Ascend - Running on Ascend
Based on original DeepLabV3 paper, we reproduce two training experiments on vocaug (also as trainaug) dataset and evaluate on voc val dataset. Based on original DeepLabV3 paper, we reproduce two training experiments on vocaug (also as trainaug) dataset and evaluate on voc val dataset.
@ -130,7 +130,7 @@ run_eval_s8_multiscale_flip.sh
. .
└──deeplabv3 └──deeplabv3
├── README.md ├── README.md
├── script ├── scripts
├── build_data.sh # convert raw data to mindrecord dataset ├── build_data.sh # convert raw data to mindrecord dataset
├── run_distribute_train_s16_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s16 structure ├── run_distribute_train_s16_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s16 structure
├── run_distribute_train_s8_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s8 structure ├── run_distribute_train_s8_r1.sh # launch ascend distributed training(8 pcs) with vocaug dataset in s8 structure
@ -161,7 +161,7 @@ run_eval_s8_multiscale_flip.sh
## [Script Parameters](#contents) ## [Script Parameters](#contents)
Default Configuration Default configuration
``` ```
"data_file":"/PATH/TO/MINDRECORD_NAME" # dataset path "data_file":"/PATH/TO/MINDRECORD_NAME" # dataset path
"train_epochs":300 # total epochs "train_epochs":300 # total epochs
@ -177,7 +177,6 @@ Default Configuration
"ckpt_pre_trained":"/PATH/TO/PRETRAIN_MODEL" # path to load pretrain checkpoint "ckpt_pre_trained":"/PATH/TO/PRETRAIN_MODEL" # path to load pretrain checkpoint
"is_distributed": # distributed training, it will be True if the parameter is set "is_distributed": # distributed training, it will be True if the parameter is set
"save_steps":410 # steps interval for saving "save_steps":410 # steps interval for saving
"freeze_bn": # freeze_bn, it will be True if the parameter is set
"keep_checkpoint_max":200 # max checkpoint for saving "keep_checkpoint_max":200 # max checkpoint for saving
``` ```
@ -214,11 +213,11 @@ For 8 devices training, training steps are as follows:
# run_distribute_train_s16_r1.sh # run_distribute_train_s16_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++)); for((i=0;i<=$RANK_SIZE-1;i++));
do do
export RANK_ID=$i export RANK_ID=${i}
export DEVICE_ID=`expr $i + $RANK_START_ID` export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='$i', device id='$DEVICE_ID'...' echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device$DEVICE_ID mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device$DEVICE_ID cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \ python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \ --data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \ --train_epochs=300 \
@ -242,11 +241,11 @@ done
# run_distribute_train_s8_r1.sh # run_distribute_train_s8_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++)); for((i=0;i<=$RANK_SIZE-1;i++));
do do
export RANK_ID=$i export RANK_ID=${i}
export DEVICE_ID=`expr $i + $RANK_START_ID` export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='$i', device id='$DEVICE_ID'...' echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device$DEVICE_ID mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device$DEVICE_ID cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \ python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \ --data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=800 \ --train_epochs=800 \
@ -271,11 +270,11 @@ done
# run_distribute_train_s8_r2.sh # run_distribute_train_s8_r2.sh
for((i=0;i<=$RANK_SIZE-1;i++)); for((i=0;i<=$RANK_SIZE-1;i++));
do do
export RANK_ID=$i export RANK_ID=${i}
export DEVICE_ID=`expr $i + $RANK_START_ID` export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='$i', device id='$DEVICE_ID'...' echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device$DEVICE_ID mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device$DEVICE_ID cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \ python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \ --data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \ --train_epochs=300 \
@ -353,7 +352,7 @@ Epoch time: 5962.164, per step time: 542.015
## [Evaluation Process](#contents) ## [Evaluation Process](#contents)
### Usage ### Usage
#### Running on Ascend #### Running on Ascend
Config checkpoint with --ckpt_path, run script, mIOU with print in eval_path/eval_log. Configure checkpoint with --ckpt_path and dataset path. Then run script, mIOU will be printed in eval_path/eval_log.
``` ```
./run_eval_s16.sh # test s16 ./run_eval_s16.sh # test s16
./run_eval_s8.sh # test s8 ./run_eval_s8.sh # test s8
@ -409,9 +408,27 @@ Note: There OS is output stride, and MS is multiscale.
| Loss Function | Softmax Cross Entropy | | Loss Function | Softmax Cross Entropy |
| Outputs | probability | | Outputs | probability |
| Loss | 0.0065883575 | | Loss | 0.0065883575 |
| Speed | 31ms/step（1pc, s8）<br> 234ms/step（8pcs, s8） | | Speed | 60 ms/step（1pc, s16）<br> 480 ms/step（8pcs, s16） <br> 244 ms/step (8pcs, s8) |
| Total time | 8pcs: 706 mins |
| Parameters (M) | 58.2 |
| Checkpoint for Fine tuning | 443M (.ckpt file) | | Checkpoint for Fine tuning | 443M (.ckpt file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) | | Model for inference | 223M (.air file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/deeplabv3) |
### Inference Performance
| Parameters | Ascend |
| ------------------- | --------------------------- |
| Model Version | DeepLabV3 V1 |
| Resource | Ascend 910 |
| Uploaded Date | 09/04/2020 (month/day/year) |
| MindSpore Version | 0.7.0-alpha |
| Dataset | VOC datasets |
| batch_size | 32 (s16); 16 (s8) |
| outputs | probability |
| Accuracy | 8pcs: <br> s16: 77.37 <br> s8: 78.84% <br> s8_multiscale: 79.70% <br> s8_Flip: 79.89% |
| Model for inference | 443M (.ckpt file) |
# [Description of Random Situation](#contents) # [Description of Random Situation](#contents)
In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py. In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py.

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