169 lines
4.8 KiB
Python
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)
|