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Merge pull request #52 from alok/ptl-pl

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Rename `ptl` to `pl`
This commit is contained in:
William Falcon
2019-08-07 09:09:15 -04:00
committed by GitHub
parent b4c14a4b77 8b9f021ee6
commit 549d0f66df
4 changed files with 29 additions and 29 deletions
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README.md
+6 -6
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@@ -65,9 +65,9 @@ from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
import torchvision.transforms as transforms
import pytorch_lightning as ptl
import pytorch_lightning as pl
class CoolModel(ptl.LightningModule):
class CoolModel(pl.LightningModule):
def __init__(self):
super(CoolModel, self).__init__()
@@ -97,15 +97,15 @@ class CoolModel(ptl.LightningModule):
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters(), lr=0.02)]
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
@pl.data_loader
def val_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
@pl.data_loader
def test_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
```
@@ -352,7 +352,7 @@ Nope! We use pure Pytorch everywhere and don't add unecessary abstractions!
Nope.
## Contributing
Welcome to the PTL community! We're building the most advanced research platform on the planet to implement the latest, best practices that the amazing PyTorch team rolls out!
Welcome to the PyTorch Lightning community! We're building the most advanced research platform on the planet to implement the latest, best practices that the amazing PyTorch team rolls out!
## Lightning Design Principles
We encourage all sorts of contributions you're interested in adding! When coding for lightning, please follow these principles.
docs/LightningModule/RequiredTrainerInterface.md
+14 -14
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@@ -36,9 +36,9 @@ from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
import torchvision.transforms as transforms
import pytorch_lightning as ptl
import pytorch_lightning as pl
class CoolModel(ptl.LightningModule):
class CoolModel(pl.LightningModule):
def __init__(self):
super(CoolModel, self).__init__()
@@ -68,15 +68,15 @@ class CoolModel(ptl.LightningModule):
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters(), lr=0.02)]
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
@pl.data_loader
def val_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
@ptl.data_loader
@pl.data_loader
def test_dataloader(self):
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
```
@@ -303,10 +303,10 @@ def on_load_checkpoint(self, checkpoint):
### tng_dataloader
``` {.python}
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self)
```
Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
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.
##### Return
PyTorch DataLoader
@@ -314,7 +314,7 @@ PyTorch DataLoader
**Example**
``` {.python}
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
@@ -330,10 +330,10 @@ def tng_dataloader(self):
### val_dataloader
``` {.python}
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self)
```
Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
Called by lightning during validation loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.
##### Return
PyTorch DataLoader
@@ -341,7 +341,7 @@ PyTorch DataLoader
**Example**
``` {.python}
@ptl.data_loader
@pl.data_loader
def val_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -358,10 +358,10 @@ def val_dataloader(self):
### test_dataloader
``` {.python}
@ptl.data_loader
@pl.data_loader
def test_dataloader(self)
```
Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
Called by lightning during test loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.
##### Return
PyTorch DataLoader
@@ -369,7 +369,7 @@ PyTorch DataLoader
**Example**
``` {.python}
@ptl.data_loader
@pl.data_loader
def test_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
examples/new_project_templates/lightning_module_template.py
+4 -4
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@@ -13,7 +13,7 @@ from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import pytorch_lightning as ptl
import pytorch_lightning as pl
from pytorch_lightning.root_module.root_module import LightningModule
@@ -199,17 +199,17 @@ class LightningTemplateModel(LightningModule):
return loader
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self):
print('tng data loader called')
return self.__dataloader(train=True)
@ptl.data_loader
@pl.data_loader
def val_dataloader(self):
print('val data loader called')
return self.__dataloader(train=False)
@ptl.data_loader
@pl.data_loader
def test_dataloader(self):
print('test data loader called')
return self.__dataloader(train=False)
tests/debug.py
+5 -5
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@@ -6,14 +6,14 @@ from pytorch_lightning.callbacks import ModelCheckpoint
import os
import shutil
import pytorch_lightning as ptl
import pytorch_lightning as pl
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
class CoolModel(ptl.LightningModule):
class CoolModel(pl.LightningModule):
def __init(self):
super(CoolModel, self).__init__()
@@ -43,15 +43,15 @@ class CoolModel(ptl.LightningModule):
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters(), lr=0.02)]
@ptl.data_loader
@pl.data_loader
def tng_dataloader(self):
return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
@ptl.data_loader
@pl.data_loader
def val_dataloader(self):
return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
@ptl.data_loader
@pl.data_loader
def test_dataloader(self):
return DataLoader(MNIST('path/to/save', train=False), batch_size=32)