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https://github.com/davinnovation/pytorch-boilerplate
The OTHER pytorch boilerplate
https://github.com/davinnovation/pytorch-boilerplate
boilerplate nni omegaconf pytorch pytorch-lightning
Last synced: about 2 months ago
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The OTHER pytorch boilerplate
- Host: GitHub
- URL: https://github.com/davinnovation/pytorch-boilerplate
- Owner: davinnovation
- Created: 2020-05-20T06:36:58.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-25T03:17:09.000Z (over 3 years ago)
- Last Synced: 2024-04-17T00:19:23.588Z (5 months ago)
- Topics: boilerplate, nni, omegaconf, pytorch, pytorch-lightning
- Language: Python
- Homepage:
- Size: 43 KB
- Stars: 41
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# pytorch-boilerplate : flashlight
The OTHER pytorch boilerplate.
- [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`
2. after experiment... `tensorboard --logdir Logs`
### Run Experiments with NNI
0. Prepare environment
1. `nnictl create --config nni_config.yml`
2. localhost:8080
## Diving into Code
- 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`