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Payne 2020-09-24 10:48:50 +08:00
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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: 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] - 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] [TRAIN_METHOD] [CKPT_PATH] - 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] [TRAIN_METHOD] [CKPT_PATH] - CPU: sh run_trian.sh CPU [DATASET_PATH] [CKPT_PATH] [FREEZE_LAYER]
### Launch ### Launch
```shell ```shell
# training example # training example
python: python:
Ascend: python train.py --platform Ascend --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] --train_method train GPU: python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH]
CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] --train_method train CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH]
shell: shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json [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] train 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] train CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH]
# fine tune example # fine tune whole network example
python: python:
Ascend: python train.py --platform Ascend --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] --train_method fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt 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] --train_method fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt CPU: python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer none
shell: 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 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] fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt 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] fine_tune ./pretrain_checkpoint/mobilenetv2.ckpt CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] [CKPT_PATH] none
# incremental learn example # fine tune full connected layers example
python: 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 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] --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt 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] --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2.ckpt ./checkpoint/mobilenetv2_head_15.ckpt CPU: python --platform CPU train.py --dataset_path [TRAIN_DATASET_PATH] --pretrain_ckpt [CKPT_PATH] --freeze_layer backbone
shell: 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 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] 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] [CKPT_PATH] backbone
CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt ./checkpoint/mobilenetv2_head_15.ckpt CPU: sh run_train.sh CPU [TRAIN_DATASET_PATH] [CKPT_PATH] backbone
``` ```
### Result ### 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: 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] - Ascend: sh run_eval.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
- GPU: sh run_eval.sh GPU [DATASET_PATH] [CHECKPOINT_PATH] [HEAD_CKPT_PATH] - GPU: sh run_eval.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
- CPU: sh run_eval.sh CPU [DATASET_PATH] [BACKBONE_CKPT_PATH] [HEAD_CKPT_PATH] - CPU: sh run_eval.sh CPU [DATASET_PATH] [BACKBONE_CKPT_PATH]
### Launch ### Launch
```shell ```shell
# eval example # eval example
python: 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 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 ./pretrain_ckpt/mobilenetv2.ckpt --head_ckpt ./checkpoint/mobilenetv2_head_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 ./pretrain_ckpt/mobilenetv2.ckpt --head_ckpt ./checkpoint/mobilenetv2_head_15.ckpt CPU: python eval.py --platform CPU --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt ./ckpt_0/mobilenetv2_15.ckpt
shell: shell:
Ascend: sh run_eval.sh Ascend [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] ./pretrain_ckpt/mobilenetv2.ckpt ./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] ./pretrain_ckpt/mobilenetv2.ckpt ./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. > 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`. Inference result will be stored in the example path, you can find result like the followings in `eval.log`.
```shell ```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) # [Model description](#contents)