diff --git a/.gitignore b/.gitignore
index c7e1583db610bb00fd67605c59f040a5b395d6aa..73c615f495e411a8b0f83d29460d4279dce974d0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -120,3 +120,8 @@ venv.bak/
# IDEs
.idea
+
+# seed project
+lightning_logs/
+MNIST
+.DS_Store
diff --git a/README.md b/README.md
index f0466bbd2ba11dc92c8bc880fd3f6b0c004a46ca..fe7bf4977dd18264db595b49789a05aae058e406 100644
--- a/README.md
+++ b/README.md
@@ -47,19 +47,20 @@ pip install -r requirements.txt
Next, navigate to [Your Main Contribution (MNIST here)] and run it.
```bash
# module folder
-cd research_seed/mnist/
+cd src/
# run module (example: mnist as your main contribution)
-python mnist_trainer.py
+python simplest_mnist.py
```
## Main Contribution
List your modules here. Each module contains all code for a full system including how to run instructions.
-- [MNIST](https://github.com/williamFalcon/pytorch-lightning-conference-seed/tree/master/research_seed/mnist)
+- [Production MNIST](https://github.com/PyTorchLightning/pytorch-lightning-conference-seed/tree/master/src/produtcion_mnist)
+- [Research MNIST](https://github.com/PyTorchLightning/pytorch-lightning-conference-seed/tree/master/src/research_mnist)
## Baselines
List your baselines here.
-- [MNIST_baseline](https://github.com/williamFalcon/pytorch-lightning-conference-seed/tree/master/research_seed/baselines/mnist_baseline)
+- [Research MNIST](https://github.com/PyTorchLightning/pytorch-lightning-conference-seed/tree/master/src/research_mnist)
### Citation
```
diff --git a/src/baselines/README.md b/src/baselines/README.md
deleted file mode 100644
index 89a1e9a4049edf23f49415f83f9c94baf0254981..0000000000000000000000000000000000000000
--- a/src/baselines/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Baselines
-Set up a folder for each baseline. Each baseline needs a trainer and LightningModule
-
diff --git a/src/baselines/__init__.py b/src/baselines/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/src/baselines/mnist_baseline/README.md b/src/baselines/mnist_baseline/README.md
deleted file mode 100644
index 3945ca712e2957ca82258411870c0133e7407616..0000000000000000000000000000000000000000
--- a/src/baselines/mnist_baseline/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-## MNIST Baseline
-In this readme, give instructions on how to run your code.
-
-#### CPU
-```bash
-python mnist_baseline_trainer.py
-```
-
-#### Multiple-GPUs
-```bash
-python mnist_baseline_trainer.py --gpus '0,1,2,3'
-```
-
-#### On multiple nodes
-```bash
-python mnist_baseline_trainer.py --gpus '0,1,2,3' --nodes 4
-```
diff --git a/src/baselines/mnist_baseline/__init__.py b/src/baselines/mnist_baseline/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/src/baselines/mnist_baseline/mnist_baseline.py b/src/baselines/mnist_baseline/mnist_baseline.py
deleted file mode 100644
index e1d54bda640669e01b026046b3b8f00fbf12a0d9..0000000000000000000000000000000000000000
--- a/src/baselines/mnist_baseline/mnist_baseline.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-This file defines the core research contribution
-"""
-import os
-import torch
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-from argparse import ArgumentParser
-
-import pytorch_lightning as pl
-
-
-class CoolSystem(pl.LightningModule):
-
- def __init__(self, hparams):
- super(CoolSystem, self).__init__()
- # not the best model...
- self.hparams = hparams
- self.l1 = torch.nn.Linear(28 * 28, 10)
-
- def forward(self, x):
- return torch.relu(self.l1(x.view(x.size(0), -1)))
-
- def training_step(self, batch, batch_idx):
- # REQUIRED
- x, y = batch
- y_hat = self.forward(x)
- return {'loss': F.cross_entropy(y_hat, y)}
-
- def validation_step(self, batch, batch_idx):
- # OPTIONAL
- x, y = batch
- y_hat = self.forward(x)
- return {'val_loss': F.cross_entropy(y_hat, y)}
-
- def validation_end(self, outputs):
- # OPTIONAL
- avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
- return {'avg_val_loss': avg_loss}
-
- def configure_optimizers(self):
- # REQUIRED
- # can return multiple optimizers and learning_rate schedulers
- return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
- def train_dataloader(self):
- # REQUIRED
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- def val_dataloader(self):
- # OPTIONAL
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- def test_dataloader(self):
- # OPTIONAL
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- @staticmethod
- def add_model_specific_args(parent_parser):
- """
- Specify the hyperparams for this LightningModule
- """
- # MODEL specific
- parser = ArgumentParser(parents=[parent_parser])
- parser.add_argument('--learning_rate', default=0.02, type=float)
- parser.add_argument('--batch_size', default=32, type=int)
-
- # training specific (for this model)
- parser.add_argument('--max_nb_epochs', default=2, type=int)
-
- return parser
-
diff --git a/src/baselines/mnist_baseline/mnist_baseline_trainer.py b/src/baselines/mnist_baseline/mnist_baseline_trainer.py
deleted file mode 100644
index 152ae4d1caaf6c7bee5fc6e496baa17917518290..0000000000000000000000000000000000000000
--- a/src/baselines/mnist_baseline/mnist_baseline_trainer.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""
-This file runs the main training/val loop, etc... using Lightning Trainer
-"""
-from pytorch_lightning import Trainer
-from argparse import ArgumentParser
-from research_seed.mnist.mnist import CoolSystem
-
-
-def main(hparams):
- # init module
- model = CoolSystem(hparams)
-
- # most basic trainer, uses good defaults
- trainer = Trainer(
- max_nb_epochs=hparams.max_nb_epochs,
- gpus=hparams.gpus,
- nb_gpu_nodes=hparams.nodes,
- )
- trainer.fit(model)
-
-
-if __name__ == '__main__':
- parser = ArgumentParser(add_help=False)
- parser.add_argument('--gpus', type=str, default=None)
- parser.add_argument('--nodes', type=int, default=1)
-
- # give the module a chance to add own params
- # good practice to define LightningModule speficic params in the module
- parser = CoolSystem.add_model_specific_args(parser)
-
- # parse params
- hparams = parser.parse_args()
-
- main(hparams)
diff --git a/src/research_mnist/mnist.py b/src/research_mnist/mnist.py
index 00ac111aae204b64dbb7d04800c0afa6b55e69e2..d441101d3af99b6910c5965ae2f8abd1faadba9c 100644
--- a/src/research_mnist/mnist.py
+++ b/src/research_mnist/mnist.py
@@ -46,6 +46,19 @@ class CoolSystem(pl.LightningModule):
tensorboard_logs = {'avg_val_loss': avg_loss}
return {'val_loss': avg_loss, 'log': tensorboard_logs}
+ def test_step(self, batch, batch_idx):
+ # OPTIONAL
+ x, y = batch
+ y_hat = self.forward(x)
+ return {'test_loss': F.cross_entropy(y_hat, y)}
+
+ def test_epoch_end(self, outputs):
+ # OPTIONAL
+ avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
+
+ tensorboard_logs = {'test_val_loss': avg_loss}
+ return {'test_loss': avg_loss, 'log': tensorboard_logs}
+
def configure_optimizers(self):
# REQUIRED
# can return multiple optimizers and learning_rate schedulers
diff --git a/src/research_mnist/mnist_trainer.py b/src/research_mnist/mnist_trainer.py
index ac2719b140fb105711162d6a7371df03b365a2c7..74396b93af5ba4ca8f3d0c0b2ccc14c6420ddaa8 100644
--- a/src/research_mnist/mnist_trainer.py
+++ b/src/research_mnist/mnist_trainer.py
@@ -3,7 +3,7 @@ This file runs the main training/val loop, etc... using Lightning Trainer
"""
from pytorch_lightning import Trainer, seed_everything
from argparse import ArgumentParser
-from srv.mnist.mnist import CoolSystem
+from src.research_mnist.mnist import CoolSystem
# sets seeds for numpy, torch, etc...
# must do for DDP to work well
@@ -17,6 +17,8 @@ def main(args):
trainer = Trainer.from_argparse_args(args)
trainer.fit(model)
+ trainer.test()
+
if __name__ == '__main__':
parser = ArgumentParser(add_help=False)
diff --git a/src/simplest_mnist.py b/src/simplest_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..34becdf2e4cb9fbe87b24d3a9e5cd5552b564bbf
--- /dev/null
+++ b/src/simplest_mnist.py
@@ -0,0 +1,58 @@
+"""
+This file defines the core research contribution
+"""
+import os
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
+from argparse import ArgumentParser
+import pytorch_lightning as pl
+
+pl.seed_everything(123)
+
+
+class CoolSystem(pl.LightningModule):
+
+ def __init__(self, hparams):
+ super(CoolSystem, self).__init__()
+ self.hparams = hparams
+ self.l1 = torch.nn.Linear(28 * 28, 10)
+
+ def forward(self, x):
+ return torch.relu(self.l1(x.view(x.size(0), -1)))
+
+ def training_step(self, batch, batch_idx):
+ x, y = batch
+ y_hat = self(x)
+ loss = F.cross_entropy(y_hat, y)
+ tensorboard_logs = {'train_loss': loss}
+ return {'loss': loss, 'log': tensorboard_logs}
+
+ def configure_optimizers(self):
+ # REQUIRED
+ # can return multiple optimizers and learning_rate schedulers
+ return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
+
+
+if __name__ == '__main__':
+ train_data = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+
+ parser = ArgumentParser(add_help=False)
+ parser.add_argument('--learning_rate', default=0.02, type=float)
+
+ # add args from trainer
+ parser = pl.Trainer.add_argparse_args(parser)
+
+ # parse params
+ args = parser.parse_args()
+
+ # init module
+ model = CoolSystem(hparams=args)
+
+ # most basic trainer, uses good defaults
+ trainer = pl.Trainer.from_argparse_args(args)
+ trainer.fit(model, train_data)
+
+
diff --git a/src/single_file_mnist/mnist.py b/src/single_file_mnist/mnist.py
deleted file mode 100644
index 07d77f2660508ec8ee156ecceb6276af12e22df9..0000000000000000000000000000000000000000
--- a/src/single_file_mnist/mnist.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""
-This file defines the core research contribution
-"""
-import os
-import torch
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-from argparse import ArgumentParser
-
-import pytorch_lightning as pl
-
-pl.seed_everything(123)
-
-
-class CoolSystem(pl.LightningModule):
-
- def __init__(self, hparams):
- super(CoolSystem, self).__init__()
- # not the best model...
- self.hparams = hparams
- self.l1 = torch.nn.Linear(28 * 28, 10)
-
- def forward(self, x):
- return torch.relu(self.l1(x.view(x.size(0), -1)))
-
- def training_step(self, batch, batch_idx):
- # REQUIRED
- x, y = batch
- y_hat = self.forward(x)
- loss = F.cross_entropy(y_hat, y)
-
- tensorboard_logs = {'train_loss': loss}
-
- return {'loss': loss, 'log': tensorboard_logs}
-
- def validation_step(self, batch, batch_idx):
- # OPTIONAL
- x, y = batch
- y_hat = self.forward(x)
- return {'val_loss': F.cross_entropy(y_hat, y)}
-
- def validation_epoch_end(self, outputs):
- # OPTIONAL
- avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-
- tensorboard_logs = {'avg_val_loss': avg_loss}
- return {'val_loss': avg_loss, 'log': tensorboard_logs}
-
- def configure_optimizers(self):
- # REQUIRED
- # can return multiple optimizers and learning_rate schedulers
- return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
- def train_dataloader(self):
- # REQUIRED
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- def val_dataloader(self):
- # OPTIONAL
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- def test_dataloader(self):
- # OPTIONAL
- return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=self.hparams.batch_size)
-
- @staticmethod
- def add_model_specific_args(parent_parser):
- """
- Specify the hyperparams for this LightningModule
- """
- # MODEL specific
- parser = ArgumentParser(parents=[parent_parser], add_help=False)
- parser.add_argument('--learning_rate', default=0.02, type=float)
- parser.add_argument('--batch_size', default=32, type=int)
-
- # training specific (for this model)
- parser.add_argument('--max_nb_epochs', default=2, type=int)
-
- return parser
-
-
-if __name__ == '__main__':
- parser = ArgumentParser(add_help=False)
-
- # add args from trainer
- parser = Trainer.add_argparse_args(parser)
-
- # give the module a chance to add own params
- # good practice to define LightningModule speficic params in the module
- parser = CoolSystem.add_model_specific_args(parser)
-
- # parse params
- args = parser.parse_args()
-
- # init module
- model = CoolSystem(hparams=args)
-
- # most basic trainer, uses good defaults
- trainer = pl.Trainer.from_argparse_args(args)
- trainer.fit(model)
-
-
diff --git a/src/single_file_mnist/mnist_trainer.py b/src/single_file_mnist/simplest_mnist.py
similarity index 100%
rename from src/single_file_mnist/mnist_trainer.py
rename to src/single_file_mnist/simplest_mnist.py