added bug report model

pl_examples/bug_report_model.py

@@ -0,0 +1,133 @@
+# 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]
+
+
+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,
+    )
+    trainer.fit(model, train_data, val_data)
+    trainer.test(test_dataloaders=test_data)
+
+
+if __name__ == '__main__':
+    run_test()