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# 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 pytorch_lightning import Trainer, LightningModule | |
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.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 run_test(): | |
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, | |
gpus=2, | |
accelerator="ddp", | |
auto_select_gpus=True, | |
) | |
trainer.fit(model, train_data, val_data) | |
trainer.test(test_dataloaders=test_data) | |
if __name__ == '__main__': | |
run_test() |
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