prune Results usage in notebooks (#3911)

* notebooks

* notebooks

notebooks/01-mnist-hello-world.ipynb

@@ -123,7 +123,7 @@
  " def training_step(self, batch, batch_nb):\n",
  " x, y = batch\n",
  " loss = F.cross_entropy(self(x), y)\n",
- " return pl.TrainResult(loss)\n",
+ " return loss\n",
  "\n",
  " def configure_optimizers(self):\n",
  " return torch.optim.Adam(self.parameters(), lr=0.02)"
@@ -250,20 +250,19 @@
  " x, y = batch\n",
  " logits = self(x)\n",
  " loss = F.nll_loss(logits, y)\n",
- " return pl.TrainResult(loss)\n",
+ " return loss\n",
  "\n",
  " def validation_step(self, batch, batch_idx):\n",
  " x, y = batch\n",
  " logits = self(x)\n",
  " loss = F.nll_loss(logits, y)\n",
  " preds = torch.argmax(logits, dim=1)\n",
  " acc = accuracy(preds, y)\n",
- " result = pl.EvalResult(checkpoint_on=loss)\n",
  "\n",
- " # Calling result.log will surface up scalars for you in TensorBoard\n",
- " result.log('val_loss', loss, prog_bar=True)\n",
- " result.log('val_acc', acc, prog_bar=True)\n",
- " return result\n",
+ " # Calling self.log will surface up scalars for you in TensorBoard\n",
+ " self.log('val_loss', loss, prog_bar=True)\n",
+ " self.log('val_acc', acc, prog_bar=True)\n",
+ " return loss\n",
  "\n",
  " def test_step(self, batch, batch_idx):\n",
  " # Here we just reuse the validation_step for testing\n",

notebooks/02-datamodules.ipynb

@@ -169,18 +169,17 @@
  " x, y = batch\n",
  " logits = self(x)\n",
  " loss = F.nll_loss(logits, y)\n",
- " return pl.TrainResult(loss)\n",
+ " return loss\n",
  "\n",
  " def validation_step(self, batch, batch_idx):\n",
  " x, y = batch\n",
  " logits = self(x)\n",
  " loss = F.nll_loss(logits, y)\n",
  " preds = torch.argmax(logits, dim=1)\n",
  " acc = accuracy(preds, y)\n",
- " result = pl.EvalResult(checkpoint_on=loss)\n",
- " result.log('val_loss', loss, prog_bar=True)\n",
- " result.log('val_acc', acc, prog_bar=True)\n",
- " return result\n",
+ " self.log('val_loss', loss, prog_bar=True)\n",
+ " self.log('val_acc', acc, prog_bar=True)\n",
+ " return loss\n",
  "\n",
  " def configure_optimizers(self):\n",
  " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
@@ -394,7 +393,7 @@
  " x, y = batch\n",
  " logits = self(x)\n",
  " loss = F.nll_loss(logits, y)\n",
- " return pl.TrainResult(loss)\n",
+ " return loss\n",
  "\n",
  " def validation_step(self, batch, batch_idx):\n",
  "\n",
@@ -403,10 +402,9 @@
  " loss = F.nll_loss(logits, y)\n",
  " preds = torch.argmax(logits, dim=1)\n",
  " acc = accuracy(preds, y)\n",
- " result = pl.EvalResult(checkpoint_on=loss)\n",
- " result.log('val_loss', loss, prog_bar=True)\n",
- " result.log('val_acc', acc, prog_bar=True)\n",
- " return result\n",
+ " self.log('val_loss', loss, prog_bar=True)\n",
+ " self.log('val_acc', acc, prog_bar=True)\n",
+ " return loss\n",
  "\n",
  " def configure_optimizers(self):\n",
  " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",

notebooks/04-transformers-text-classification.ipynb

@@ -299,7 +299,7 @@
  " def training_step(self, batch, batch_idx):\n",
  " outputs = self(**batch)\n",
  " loss = outputs[0]\n",
- " return pl.TrainResult(loss)\n",
+ " return loss\n",
  "\n",
  " def validation_step(self, batch, batch_idx, dataloader_idx=0):\n",
  " outputs = self(**batch)\n",
@@ -322,20 +322,17 @@
  " preds = torch.cat([x['preds'] for x in output]).detach().cpu().numpy()\n",
  " labels = torch.cat([x['labels'] for x in output]).detach().cpu().numpy()\n",
  " loss = torch.stack([x['loss'] for x in output]).mean()\n",
- " if i == 0:\n",
- " result = pl.EvalResult(checkpoint_on=loss)\n",
- " result.log(f'val_loss_{split}', loss, prog_bar=True)\n",
+ " self.log(f'val_loss_{split}', loss, prog_bar=True)\n",
  " split_metrics = {f\"{k}_{split}\": v for k, v in self.metric.compute(predictions=preds, references=labels).items()}\n",
- " result.log_dict(split_metrics, prog_bar=True)\n",
- " return result\n",
+ " self.log_dict(split_metrics, prog_bar=True)\n",
+ " return loss\n",
  "\n",
  " preds = torch.cat([x['preds'] for x in outputs]).detach().cpu().numpy()\n",
  " labels = torch.cat([x['labels'] for x in outputs]).detach().cpu().numpy()\n",
  " loss = torch.stack([x['loss'] for x in outputs]).mean()\n",
- " result = pl.EvalResult(checkpoint_on=loss)\n",
- " result.log('val_loss', loss, prog_bar=True)\n",
- " result.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True)\n",
- " return result\n",
+ " self.log('val_loss', loss, prog_bar=True)\n",
+ " self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True)\n",
+ " return loss\n",
  "\n",
  " def setup(self, stage):\n",
  " if stage == 'fit':\n",