diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md index 3918f3d1c1..bf69d4d3c6 100644 --- a/model_zoo/official/cv/deeplabv3/README.md +++ b/model_zoo/official/cv/deeplabv3/README.md @@ -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）
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）
480 ms/step（8pcs, s16）
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:
s16: 77.37
s8: 78.84%
s8_multiscale: 79.70%
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.