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pytorch-boilerplate

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The OTHER pytorch boilerplate

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# pytorch-boilerplate : flashlight

The OTHER pytorch boilerplate.

![Untitled](https://user-images.githubusercontent.com/3917185/84566705-4a425f80-adae-11ea-92f5-6290aff0478a.png)

- [x] LightningModule [flashlight/runner/pl.py]
- [x] Trainer [flashlight/runner/main_pl.py]

- [ ] Accelerators 
- [ ] Callback
- [x] Logging [flashlight/runner/pl.py]
- [ ] Metrics
- [ ] Plugins

## Pre-requirements for local [*PRTM!*]

- python 3.5 >
- pytorch 1.5.0, torchvision 0.6.0 for your OS/CUDA match version
- ... and install requirements.txt packages `pip install -r requirements.txt`

## Getting Started

`master` branch runs MNIST classification (torchvision dataset) with squeezenet (torchvision model)
for detail, check `config/config.py`

### Run Single Experiment without NNI

0. Prepare enviroment : gpu docker, local python env... whatever

- if docker : `docker pull davinnovation/pytorch-boilerplate:alpha`

1. `python run.py` or `python -W ignore run.py`

![image](https://user-images.githubusercontent.com/3917185/84721592-fc9b4200-afbb-11ea-9602-c41dc58f8b8a.png)

2. after experiment... `tensorboard --logdir Logs`

![image](https://user-images.githubusercontent.com/3917185/84721667-26ecff80-afbc-11ea-8152-4025cbaeda90.png)

### Run Experiments with NNI

0. Prepare environment

1. `nnictl create --config nni_config.yml`

2. localhost:8080

![image](https://user-images.githubusercontent.com/3917185/84721734-484deb80-afbc-11ea-8585-60f1752dd1d8.png)

## Diving into Code

![image](https://user-images.githubusercontent.com/3917185/84723043-ac25e380-afbf-11ea-9116-fbabd47b5cc0.png)

- Adding Network

`flashlight.network.__init__.py`

```python
"""Network Define"""
# Add {"Network Name" : and nn.Module without initalize}
def _get_squeezenet(num_classes, version:str="1_0", pretrained=False, progress=True):
    VERSION = {
        "1_0" : torchvision.models.squeezenet1_0,
        "1_1" : torchvision.models.squeezenet1_1
    }

return VERSION[version](pretrained=pretrained, progress=progress, num_classes=num_classes)

NETWORK_DICT = {
    "squeezenet": _get_squeezenet
}
```

- Adding Dataset

`flashlight.dataloader.__init__.py`

```python
""" Dataset """
# Add {Dataset Name : torch.utils.data.Dataset}
DATA_DICT = {"MNIST": torchvision.datasets.MNIST}

""" Dataset Transform """

transform = torchvision.transforms.Compose(
    [torchvision.transforms.Grayscale(num_output_channels=3), torchvision.transforms.ToTensor()]
)

def get_datalaoder(data, root="../datasets/", split="train"):
    if data in ["MNIST"]:  # if torchvision
        if split == "val":
            print(f"{data} dataset dosen't support validation set. val replaced by train")
        if split in ["train", "val"]:
            return DATA_DICT[data](root=root, train=True, download=True, transform=transform)
        else:
            return DATA_DICT[data](root=root, train=False, download=True, transform=transform)
```

- Change Loss, forward/backward... [Research Code]

`flashlight.runner.pl.py`

- Change Logger, hw options... [Engineering Code]

`flashlight.runner.main_pl.py`

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