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!6817 mobilenetv2 modify readme 

Merge pull request !6817 from yepei6/master_mobilenetv2
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model_zoo/official/cv/mobilenetv2/README.md
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@ -86,45 +86,45 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
You can start training using python or shell scripts. The usage of shell scripts as follows:
- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH]
- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH]
- CPU: sh run_trian.sh CPU [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH]
- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER]
- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER]
- CPU: sh run_trian.sh CPU [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER]
### Launch
```shell
# training example
python:
Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH] --train_method train
GPU: python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH] --train_method train
CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] --train_method train
Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH]
GPU: python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH]
CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH]
shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH] train
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] train
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] train
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH]
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH]
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH]
# fine tune example
# fine tune whole network example
python:
Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH] --train_method fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
GPU: python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH] --train_method fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] --train_method fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer none
GPU: python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer none
CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer none
shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH] fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH] [CKPT_PATH] none
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] [CKPT_PATH] none
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] [CKPT_PATH] none
# incremental learn example
# fine tune full connected layers example
python:
Ascend: python --platform Ascend train.py --dataset_path [TRAIN_DATASET_PATH] --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
GPU: python --platform GPU train.py --dataset_path [TRAIN_DATASET_PATH] --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
CPU: python --platform CPU train.py --dataset_path [TRAIN_DATASET_PATH] --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
Ascend: python --platform Ascend train.py --dataset_path [TRAIN_DATASET_PATH]--pretrain_ckpt [CKPT_PATH] --freeze_layer backbone
GPU: python --platform GPU train.py --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer backbone
CPU: python --platform CPU train.py --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer backbone
shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH] incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [TRAIN_DATASET_PATH] [CKPT_PATH] backbone
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] [CKPT_PATH] backbone
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] [CKPT_PATH] backbone
```
### Result
@ -144,23 +144,23 @@ epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
You can start training using python or shell scripts.If the train method is train or fine tune, should not input the `[CHECKPOINT_PATH]` The usage of shell scripts as follows:
- Ascend: sh run_eval.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH] [HEAD_CKPT_PATH]
- GPU: sh run_eval.sh GPU [DATASET_PATH] [CHECKPOINT_PATH] [HEAD_CKPT_PATH]
- CPU: sh run_eval.sh CPU [DATASET_PATH] [BACKBONE_CKPT_PATH] [HEAD_CKPT_PATH]
- Ascend: sh run_eval.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
- GPU: sh run_eval.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
- CPU: sh run_eval.sh CPU [DATASET_PATH] [BACKBONE_CKPT_PATH]
### Launch
```shell
# eval example
python:
Ascend: python eval.py --platform Ascend --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./pretrain_ckpt/mobilenetv2.ckpt --head_ckpt ./checkpoint/mobilenetv2_head_15.ckpt
GPU: python eval.py --platform GPU --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./pretrain_ckpt/mobilenetv2.ckpt --head_ckpt ./checkpoint/mobilenetv2_head_15.ckpt
CPU: python eval.py --platform CPU --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./pretrain_ckpt/mobilenetv2.ckpt --head_ckpt ./checkpoint/mobilenetv2_head_15.ckpt
Ascend: python eval.py --platform Ascend --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./ckpt_0/mobilenetv2_15.ckpt
GPU: python eval.py --platform GPU --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./ckpt_0/mobilenetv2_15.ckpt
CPU: python eval.py --platform CPU --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./ckpt_0/mobilenetv2_15.ckpt
shell:
Ascend: sh run_eval.sh Ascend [VAL_DATASET_PATH] ./pretrain_ckpt/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
GPU: sh run_eval.sh GPU [VAL_DATASET_PATH] ./pretrain_ckpt/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
CPU: sh run_eval.sh CPU [VAL_DATASET_PATH] ./pretrain_ckpt/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt
Ascend: sh run_eval.sh Ascend [VAL_DATASET_PATH] ./checkpoint/mobilenetv2_head_15.ckpt
GPU: sh run_eval.sh GPU [VAL_DATASET_PATH] ./checkpoint/mobilenetv2_head_15.ckpt
CPU: sh run_eval.sh CPU [VAL_DATASET_PATH] ./checkpoint/mobilenetv2_head_15.ckpt
```
> checkpoint can be produced in training process.
@ -170,7 +170,7 @@ You can start training using python or shell scripts.If the train method is trai
Inference result will be stored in the example path, you can find result like the followings in `eval.log`.
```shell
result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
result: {'acc': 0.71976314102564111} ckpt=./ckpt_0/mobilenet-200_625.ckpt
```
# [Model description](#contents)