lightning/tests/helpers/boring_model.py

169 lines
4.8 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.
from typing import Optional
import torch
from torch.utils.data import DataLoader, Dataset, Subset
from pytorch_lightning import LightningDataModule, LightningModule
class RandomDictDataset(Dataset):
def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
a = self.data[index]
b = a + 2
return {'a': a, 'b': b}
def __len__(self):
return self.len
class RandomDictStringDataset(Dataset):
def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
return {"id": str(index), "x": self.data[index]}
def __len__(self):
return self.len
class RandomDataset(Dataset):
def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class BoringModel(LightningModule):
def __init__(self):
"""
Testing PL Module
Use as follows:
- subclass
- modify the behavior for what you want
class TestModel(BaseTestModel):
def training_step(...):
# do your own thing
or:
model = BaseTestModel()
model.training_epoch_end = None
"""
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, x):
x = self(x)
out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
return out
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def training_step_end(self, training_step_outputs):
return training_step_outputs
def training_epoch_end(self, outputs) -> None:
torch.stack([x["loss"] for x in outputs]).mean()
def validation_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"x": loss}
def validation_epoch_end(self, outputs) -> None:
torch.stack([x['x'] for x in outputs]).mean()
def test_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"y": loss}
def test_epoch_end(self, outputs) -> None:
torch.stack([x["y"] for x in outputs]).mean()
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 DataLoader(RandomDataset(32, 64))
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64))
class BoringDataModule(LightningDataModule):
def __init__(self, data_dir: str = './'):
super().__init__()
self.data_dir = data_dir
self.non_picklable = None
self.checkpoint_state: Optional[str] = None
def prepare_data(self):
self.random_full = RandomDataset(32, 192)
def setup(self, stage: Optional[str] = None):
if stage == "fit" or stage is None:
self.random_train = Subset(self.random_full, indices=range(64))
self.random_val = Subset(self.random_full, indices=range(64, 128))
self.dims = self.random_train[0].shape
if stage == "test" or stage is None:
self.random_test = Subset(self.random_full, indices=range(128, 192))
self.dims = getattr(self, "dims", self.random_test[0].shape)
def train_dataloader(self):
return DataLoader(self.random_train)
def val_dataloader(self):
return DataLoader(self.random_val)
def test_dataloader(self):
return DataLoader(self.random_test)