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123 lines
3.9 KiB
123 lines
3.9 KiB
# ResNet-50_quant Example
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## Description
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This is an example of training ResNet-50_quant 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 include train and eval dataset as follows:
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> ```
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> .
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> ├── ilsvrc # train dataset
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> └── ilsvrc_eval # infer dataset: images should be classified into 1000 directories firstly, just like train images
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> ```
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## Example structure
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```shell
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.
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├── Resnet50_quant
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├── Readme.md
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├── scripts
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│ ├──run_train.sh
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│ ├──run_eval.sh
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├── src
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│ ├──config.py
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│ ├──crossentropy.py
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│ ├──dataset.py
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│ ├──luanch.py
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│ ├──lr_generator.py
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│ ├──utils.py
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├── models
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│ ├──resnet_quant.py
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├── train.py
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├── eval.py
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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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"class_num": 1001, # dataset class number
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"batch_size": 32, # batch size of input tensor
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"loss_scale": 1024, # loss scale
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"momentum": 0.9, # momentum optimizer
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"weight_decay": 1e-4, # weight decay
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"epoch_size": 120, # only valid for taining, which is always 1 for inference
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"pretrained_epoch_size": 90, # epoch size that model has been trained before load pretrained checkpoint
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"buffer_size": 1000, # number of queue size in data preprocessing
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"image_height": 224, # image height
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"image_width": 224, # image width
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"save_checkpoint": True, # whether save checkpoint or not
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"save_checkpoint_epochs": 1, # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
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"keep_checkpoint_max": 50, # only keep the last keep_checkpoint_max checkpoint
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"save_checkpoint_path": "./", # path to save checkpoint relative to the executed path
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"warmup_epochs": 0, # number of warmup epoch
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"lr_decay_mode": "cosine", # decay mode for generating learning rate
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"label_smooth": True, # label smooth
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"label_smooth_factor": 0.1, # label smooth factor
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"lr_init": 0, # initial learning rate
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"lr_max": 0.005, # maximum learning rate
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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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- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
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### Launch
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```
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# training example
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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
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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: 1 step: 5004, loss is 4.8995576
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epoch: 2 step: 5004, loss is 3.9235563
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epoch: 3 step: 5004, loss is 3.833077
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epoch: 4 step: 5004, loss is 3.2795618
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epoch: 5 step: 5004, loss is 3.1978393
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```
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## Eval process
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### Usage
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- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
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### Launch
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```
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# infer example
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Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/checkpoint/resnet50-110_5004.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, whose folder name is "infer". Under this, you can find result like the followings in log.
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```
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result: {'acc': 0.75.252054737516005} ckpt=train_parallel0/resnet-110_5004.ckpt
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```
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