mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
Rename variables (#124)
- data_batch → batch - batch_i → batch_idx - dataloader_i → dataloader_idx - tng → training - training_dataloader → train_dataloader - add_log_row_interval → row_log_interval - gradient_clip → gradient_clip_val - prog → progress - tqdm_dic → tqdm_dict
This commit is contained in:
Alok Singh
committed by
William Falcon
William Falcon
parent
3d16a686b3
commit
b0a0a47a0b
@@ -10,7 +10,7 @@ Otherwise, to Define a Lightning Module, implement the following methods:
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**Required**:
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- [training_step](RequiredTrainerInterface.md#training_step)
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- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
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- [train_dataloader](RequiredTrainerInterface.md#train_dataloader)
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- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
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**Optional**:
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@@ -23,7 +23,7 @@ Otherwise, to Define a Lightning Module, implement the following methods:
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- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)
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- [on_save_checkpoint](RequiredTrainerInterface.md#on_save_checkpoint)
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- [on_load_checkpoint](RequiredTrainerInterface.md#on_load_checkpoint)
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- [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)
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- [update_training_log_metrics](RequiredTrainerInterface.md#update_training_log_metrics)
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- [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)
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---
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@@ -81,7 +81,7 @@ class CoolModel(pl.LightningModule):
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return [torch.optim.Adam(self.parameters(), lr=0.02)]
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@pl.data_loader
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def tng_dataloader(self):
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def train_dataloader(self):
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return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
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@pl.data_loader
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@@ -111,7 +111,7 @@ The LightningModule interface is on the right. Each method corresponds to a part
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### training_step
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``` {.python}
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def training_step(self, data_batch, batch_nb)
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def training_step(self, batch, batch_nb)
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```
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In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something specific to your model.
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@@ -120,7 +120,7 @@ In this step you'd normally do the forward pass and calculate the loss for a bat
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| Param | description |
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| data_batch | The output of your dataloader. A tensor, tuple or list |
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| batch | The output of your dataloader. A tensor, tuple or list |
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| batch_nb | Integer displaying which batch this is |
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**Return**
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@@ -130,14 +130,14 @@ Dictionary or OrderedDict
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| key | value | is required |
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| loss | tensor scalar | Y |
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| prog | Dict for progress bar display. Must have only tensors | N |
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| progress | Dict for progress bar display. Must have only tensors | N |
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**Example**
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``` {.python}
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def training_step(self, data_batch, batch_nb):
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x, y, z = data_batch
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def training_step(self, batch, batch_nb):
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x, y, z = batch
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# implement your own
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out = self.forward(x)
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@@ -145,7 +145,7 @@ def training_step(self, data_batch, batch_nb):
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output = {
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'loss': loss, # required
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'prog': {'tng_loss': loss, 'batch_nb': batch_nb} # optional
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'progress': {'training_loss': loss, 'batch_nb': batch_nb} # optional
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}
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# return a dict
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@@ -155,7 +155,7 @@ def training_step(self, data_batch, batch_nb):
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If you define multiple optimizers, this step will also be called with an additional ```optimizer_idx``` param.
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``` {.python}
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# Multiple optimizers (ie: GANs)
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def training_step(self, data_batch, batch_nb, optimizer_idx):
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def training_step(self, batch, batch_nb, optimizer_idx):
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if optimizer_idx == 0:
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# do training_step with encoder
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if optimizer_idx == 1:
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@@ -163,11 +163,11 @@ def training_step(self, data_batch, batch_nb, optimizer_idx):
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```
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---
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### tng_dataloader
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### train_dataloader
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``` {.python}
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@pl.data_loader
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def tng_dataloader(self)
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def train_dataloader(self)
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```
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Called by lightning during training loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.
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@@ -178,7 +178,7 @@ PyTorch DataLoader
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``` {.python}
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@pl.data_loader
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def tng_dataloader(self):
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def train_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
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loader = torch.utils.data.DataLoader(
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@@ -240,10 +240,10 @@ the [optimizer_step](https://williamfalcon.github.io/pytorch-lightning/Trainer/h
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``` {.python}
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# if you have one val dataloader:
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def validation_step(self, data_batch, batch_nb)
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def validation_step(self, batch, batch_nb)
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# if you have multiple val dataloaders:
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def validation_step(self, data_batch, batch_nb, dataloader_idx)
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def validation_step(self, batch, batch_nb, dataloader_idxdx)
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```
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**OPTIONAL**
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If you don't need to validate you don't need to implement this method. In this step you'd normally generate examples or calculate anything of interest such as accuracy.
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@@ -256,9 +256,9 @@ The dict you return here will be available in the `validation_end` method.
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| Param | description |
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|---|---|
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| data_batch | The output of your dataloader. A tensor, tuple or list |
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| batch | The output of your dataloader. A tensor, tuple or list |
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| batch_nb | Integer displaying which batch this is |
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| dataloader_i | Integer displaying which dataloader this is (only if multiple val datasets used) |
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| dataloader_idx | Integer displaying which dataloader this is (only if multiple val datasets used) |
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**Return**
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@@ -270,8 +270,8 @@ The dict you return here will be available in the `validation_end` method.
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``` {.python}
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# CASE 1: A single validation dataset
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def validation_step(self, data_batch, batch_nb):
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x, y = data_batch
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def validation_step(self, batch, batch_nb):
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x, y = batch
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# implement your own
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out = self.forward(x)
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@@ -302,7 +302,7 @@ If you pass in multiple validation datasets, validation_step will have an additi
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```python
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# CASE 2: multiple validation datasets
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def validation_step(self, data_batch, batch_nb, dataset_idx):
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def validation_step(self, batch, batch_nb, dataset_idx):
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# dataset_idx tells you which dataset this is.
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```
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@@ -351,8 +351,8 @@ def validation_end(self, outputs):
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val_loss_mean /= len(outputs)
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val_acc_mean /= len(outputs)
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tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dic
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tqdm_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dict
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```
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With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
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@@ -377,18 +377,18 @@ def validation_end(self, outputs):
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val_loss_mean /= i
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val_acc_mean /= i
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tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dic
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tqdm_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dict
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```
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### test_step
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``` {.python}
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# if you have one test dataloader:
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def test_step(self, data_batch, batch_nb)
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def test_step(self, batch, batch_nb)
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# if you have multiple test dataloaders:
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def test_step(self, data_batch, batch_nb, dataloader_idx)
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def test_step(self, batch, batch_nb, dataloader_idxdx)
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```
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**OPTIONAL**
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If you don't need to test you don't need to implement this method. In this step you'd normally generate examples or calculate anything of interest such as accuracy.
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@@ -403,9 +403,9 @@ This function is used when you execute `trainer.test()`.
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| Param | description |
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| data_batch | The output of your dataloader. A tensor, tuple or list |
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| batch | The output of your dataloader. A tensor, tuple or list |
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| batch_nb | Integer displaying which batch this is |
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| dataloader_i | Integer displaying which dataloader this is (only if multiple test datasets used) |
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| dataloader_idx | Integer displaying which dataloader this is (only if multiple test datasets used) |
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**Return**
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@@ -417,8 +417,8 @@ This function is used when you execute `trainer.test()`.
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``` {.python}
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# CASE 1: A single test dataset
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def test_step(self, data_batch, batch_nb):
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x, y = data_batch
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def test_step(self, batch, batch_nb):
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x, y = batch
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# implement your own
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out = self.forward(x)
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@@ -443,7 +443,7 @@ If you pass in multiple test datasets, test_step will have an additional argumen
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```python
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# CASE 2: multiple test datasets
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def test_step(self, data_batch, batch_nb, dataset_idx):
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def test_step(self, batch, batch_nb, dataset_idx):
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# dataset_idx tells you which dataset this is.
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```
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@@ -490,8 +490,8 @@ def test_end(self, outputs):
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test_loss_mean /= len(outputs)
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test_acc_mean /= len(outputs)
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tqdm_dic = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
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return tqdm_dic
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tqdm_dict = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
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return tqdm_dict
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```
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With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
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@@ -516,8 +516,8 @@ def test_end(self, outputs):
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test_loss_mean /= i
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test_acc_mean /= i
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tqdm_dic = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
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return tqdm_dic
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tqdm_dict = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
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return tqdm_dict
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```
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---
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@@ -633,10 +633,10 @@ def test_dataloader(self):
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```
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---
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### update_tng_log_metrics
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### update_training_log_metrics
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``` {.python}
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def update_tng_log_metrics(self, logs)
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def update_training_log_metrics(self, logs)
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```
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Called by lightning right before it logs metrics for this batch.
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This is a chance to amend or add to the metrics about to be logged.
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@@ -647,7 +647,7 @@ Dict
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**Example**
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``` {.python}
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def update_tng_log_metrics(self, logs):
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def update_training_log_metrics(self, logs):
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# modify or add to logs
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return logs
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```
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@@ -674,7 +674,7 @@ def add_model_specific_args(parent_parser, root_dir):
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parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
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# param overwrites
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# parser.set_defaults(gradient_clip=5.0)
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# parser.set_defaults(gradient_clip_val=5.0)
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# network params
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parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
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@@ -22,7 +22,7 @@ self.experiment.add_scalars(...)
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Total training batches seen across all epochs
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---
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#### gradient_clip
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#### gradient_clip_val
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The current gradient clip value
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---
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