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Amend deeplabv3 readme in r1.0.

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jzg 2020-10-20 17:26:00 +08:00
parent 093653628b
commit e9ea7f73a0
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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)
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.
@ -130,7 +130,7 @@ run_eval_s8_multiscale_flip.sh
.
└──deeplabv3
├── README.md
├── script
├── scripts
├── 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_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)
Default Configuration
Default configuration
```
"data_file":"/PATH/TO/MINDRECORD_NAME" # dataset path
"train_epochs":300 # total epochs
@ -177,7 +177,6 @@ Default Configuration
"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
"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
```
@ -214,11 +213,11 @@ For 8 devices training, training steps are as follows:
# run_distribute_train_s16_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=$i
export DEVICE_ID=`expr $i + $RANK_START_ID`
echo 'start rank='$i', device id='$DEVICE_ID'...'
mkdir ${train_path}/device$DEVICE_ID
cd ${train_path}/device$DEVICE_ID
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \
@ -242,11 +241,11 @@ done
# run_distribute_train_s8_r1.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=$i
export DEVICE_ID=`expr $i + $RANK_START_ID`
echo 'start rank='$i', device id='$DEVICE_ID'...'
mkdir ${train_path}/device$DEVICE_ID
cd ${train_path}/device$DEVICE_ID
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=800 \
@ -271,11 +270,11 @@ done
# run_distribute_train_s8_r2.sh
for((i=0;i<=$RANK_SIZE-1;i++));
do
export RANK_ID=$i
export DEVICE_ID=`expr $i + $RANK_START_ID`
echo 'start rank='$i', device id='$DEVICE_ID'...'
mkdir ${train_path}/device$DEVICE_ID
cd ${train_path}/device$DEVICE_ID
export RANK_ID=${i}
export DEVICE_ID=$((i + RANK_START_ID))
echo 'start rank='${i}', device id='${DEVICE_ID}'...'
mkdir ${train_path}/device${DEVICE_ID}
cd ${train_path}/device${DEVICE_ID} || exit
python ${train_code_path}/train.py --train_dir=${train_path}/ckpt \
--data_file=/PATH/TO/MINDRECORD_NAME \
--train_epochs=300 \
@ -353,7 +352,7 @@ Epoch time: 5962.164, per step time: 542.015
## [Evaluation Process](#contents)
### Usage
#### 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_s8.sh # test s8
@ -409,9 +408,27 @@ Note: There OS is output stride, and MS is multiscale.
| Loss Function | Softmax Cross Entropy |
| Outputs | probability |
| Loss | 0.0065883575 |
| Speed | 31ms/step（1pc, s8）<br> 234ms/step（8pcs, s8） |
| Checkpoint for Fine tuning | 443M (.ckpt file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeplabv3) |
| 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) |
| 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)
In dataset.py, we set the seed inside "create_dataset" function. We also use random seed in train.py.