157 lines
4.4 KiB
Python
157 lines
4.4 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.
|
|
|
|
# --------------------------------------------
|
|
# --------------------------------------------
|
|
# --------------------------------------------
|
|
# USE THIS MODEL TO REPRODUCE A BUG YOU REPORT
|
|
# --------------------------------------------
|
|
# --------------------------------------------
|
|
# --------------------------------------------
|
|
import os
|
|
|
|
import torch
|
|
from torch.utils.data import Dataset
|
|
|
|
from pl_examples import cli_lightning_logo
|
|
from pytorch_lightning import LightningModule, Trainer
|
|
|
|
|
|
class RandomDataset(Dataset):
|
|
"""
|
|
>>> RandomDataset(size=10, length=20) # doctest: +ELLIPSIS
|
|
<...bug_report_model.RandomDataset object at ...>
|
|
"""
|
|
|
|
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):
|
|
"""
|
|
>>> BoringModel() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
|
|
BoringModel(
|
|
(layer): Linear(...)
|
|
)
|
|
"""
|
|
|
|
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.layer(x)
|
|
out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
|
|
return out
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
output = self.layer(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.layer(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.layer(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]
|
|
|
|
|
|
# NOTE: If you are using a cmd line to run your script,
|
|
# provide the cmd line as below.
|
|
# opt = "--max_epochs 1 --limit_train_batches 1".split(" ")
|
|
# parser = ArgumentParser()
|
|
# args = parser.parse_args(opt)
|
|
|
|
|
|
def test_run():
|
|
|
|
class TestModel(BoringModel):
|
|
|
|
def on_train_epoch_start(self) -> None:
|
|
print('override any method to prove your bug')
|
|
|
|
# fake data
|
|
train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
|
|
val_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
|
|
test_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
|
|
|
|
# model
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=os.getcwd(),
|
|
limit_train_batches=1,
|
|
limit_val_batches=1,
|
|
max_epochs=1,
|
|
weights_summary=None,
|
|
)
|
|
trainer.fit(model, train_data, val_data)
|
|
trainer.test(test_dataloaders=test_data)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
cli_lightning_logo()
|
|
test_run()
|