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102 lines
3.1 KiB
102 lines
3.1 KiB
# MobileNetV2 Example
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## Description
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This is an example of training MobileNetV2 with ImageNet2012 dataset in MindSpore.
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## Requirements
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* Install [MindSpore](https://www.mindspore.cn/install/en).
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* Download the dataset [ImageNet2012].
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> Unzip the ImageNet2012 dataset to any path you want and the folder structure should be as follows:
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> ```
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> .
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> ├── train # train dataset
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> └── val # infer dataset
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> ```
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## Example structure
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``` shell
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.
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├── config.py # parameter configuration
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├── dataset.py # data preprocessing
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├── eval.py # infer script
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├── launch.py # launcher for distributed training
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├── lr_generator.py # generate learning rate for each step
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├── run_infer.sh # launch infering
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├── run_train.sh # launch training
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└── train.py # train script
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```
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## Parameter configuration
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Parameters for both training and inference can be set in 'config.py'.
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```
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"num_classes": 1000, # dataset class num
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"image_height": 224, # image height
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"image_width": 224, # image width
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"batch_size": 256, # training or infering batch size
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"epoch_size": 200, # total training epochs, including warmup_epochs
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"warmup_epochs": 4, # warmup epochs
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"lr": 0.4, # base learning rate
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"momentum": 0.9, # momentum
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"weight_decay": 4e-5, # weight decay
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"loss_scale": 1024, # loss scale
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"save_checkpoint": True, # whether save checkpoint
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"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints
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"keep_checkpoint_max": 200, # only keep the last keep_checkpoint_max checkpoint
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"save_checkpoint_path": "./checkpoint" # path to save checkpoint
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```
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## Running the example
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### Train
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#### Usage
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Usage: sh run_train.sh [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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#### Launch
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```
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# training example
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sh run_train.sh 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet
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```
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#### Result
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Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
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```
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epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
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epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
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epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
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epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
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```
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### Infer
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#### Usage
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Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
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#### Launch
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```
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# infer example
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sh run_infer.sh ~/imagenet ~/train/mobilenet-200_625.ckpt
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```
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> checkpoint can be produced in training process.
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#### Result
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Inference result will be stored in the example path, you can find result like the followings in `val.log`.
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```
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result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
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```
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