lightning/legacy/zero_training.py

94 lines
2.7 KiB
Python

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from torch.utils.data import Dataset
import pytorch_lightning as pl
PATH_LEGACY = os.path.dirname(__file__)
class RandomDataset(Dataset):
def __init__(self, size, length: int = 100):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class DummyModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(32, 2)
def forward(self, x):
return self.layer(x)
def _loss(self, batch, prediction):
# An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
def _step(self, batch, batch_idx):
output = self.layer(batch)
loss = self._loss(batch, output)
# return {'loss': loss} # used for PL<1.0
return loss # used for PL >= 1.0
def training_step(self, batch, batch_idx):
return self._step(batch, batch_idx)
def validation_step(self, batch, batch_idx):
self._step(batch, batch_idx)
def test_step(self, batch, batch_idx):
self._step(batch, batch_idx)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return torch.utils.data.DataLoader(RandomDataset(32, 64))
def val_dataloader(self):
return torch.utils.data.DataLoader(RandomDataset(32, 64))
def test_dataloader(self):
return torch.utils.data.DataLoader(RandomDataset(32, 64))
def main_train(dir_path, max_epochs: int = 5):
trainer = pl.Trainer(
default_root_dir=dir_path,
checkpoint_callback=True,
max_epochs=max_epochs,
)
model = DummyModel()
trainer.fit(model)
if __name__ == '__main__':
path_dir = os.path.join(PATH_LEGACY, 'checkpoints', str(pl.__version__))
main_train(path_dir)