cleared spaces

pytorch_lightning/core/__init__.py

@@ -94,157 +94,6 @@ Check out this `COLAB <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-Le
 for a live demo.
 
 """
-# """
-# Lightning Module interface
-# ==========================
-#
-# A lightning module is a strict superclass of nn.Module, it provides a standard interface
-#  for the trainer to interact with the model.
-#
-# The easiest thing to do is copy the minimal example below and modify accordingly.
-#
-# Otherwise, to Define a Lightning Module, implement the following methods:
-#
-#
-# Minimal example
-# ---------------
-#
-# .. code-block:: python
-#
-#     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
-#
-#     import pytorch_lightning as pl
-#
-#     class CoolModel(pl.LightningModule):
-#
-#         def __init__(self):
-#             super(CoolModel, self).__init__()
-#             # not the best model...
-#             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
-#             val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean()
-#             return {'val_loss': val_loss_mean}
-#
-#         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_end(self, outputs):
-#             # OPTIONAL
-#             test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
-#             return {'test_loss': test_loss_mean}
-#
-#         def configure_optimizers(self):
-#             # REQUIRED
-#             return torch.optim.Adam(self.parameters(), lr=0.02)
-#
-#         @pl.data_loader
-#         def train_dataloader(self):
-#             return DataLoader(MNIST(os.getcwd(), train=True, download=True,
-#                               transform=transforms.ToTensor()), batch_size=32)
-#
-#         @pl.data_loader
-#         def val_dataloader(self):
-#             # OPTIONAL
-#             # can also return a list of val dataloaders
-#             return DataLoader(MNIST(os.getcwd(), train=True, download=True,
-#                               transform=transforms.ToTensor()), batch_size=32)
-#
-#         @pl.data_loader
-#         def test_dataloader(self):
-#             # OPTIONAL
-#             # can also return a list of test dataloaders
-#             return DataLoader(MNIST(os.getcwd(), train=False, download=True,
-#                               transform=transforms.ToTensor()), batch_size=32)
-#
-#
-# How do these methods fit into the broader training?
-# ---------------------------------------------------
-#
-# The LightningModule interface is on the right. Each method corresponds
-#  to a part of a research project. Lightning automates everything not in blue.
-#
-# .. figure::  docs/source/_static/images/overview_flat.jpg
-#    :align:   center
-#
-#    Overview.
-#
-#
-# Optional Methods
-# ----------------
-#
-# **add_model_specific_args**
-#
-# .. code-block:: python
-#
-#     @staticmethod
-#     def add_model_specific_args(parent_parser, root_dir)
-#
-# Lightning has a list of default argparse commands.
-#  This method is your chance to add or modify commands specific to your model.
-#  The `hyperparameter argument parser
-#   <https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser>`_
-#  is available anywhere in your model by calling self.hparams.
-#
-# **Return**
-# An argument parser
-#
-# **Example**
-#
-# .. code-block:: python
-#
-#     @staticmethod
-#     def add_model_specific_args(parent_parser, root_dir):
-#         parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-#
-#         # param overwrites
-#         # parser.set_defaults(gradient_clip_val=5.0)
-#
-#         # network params
-#         parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-#         parser.add_argument('--in_features', default=28*28)
-#         parser.add_argument('--out_features', default=10)
-#         # use 500 for CPU, 50000 for GPU to see speed difference
-#         parser.add_argument('--hidden_dim', default=50000)
-#
-#         # data
-#         parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
-#
-#         # training params (opt)
-#         parser.opt_list('--learning_rate', default=0.001, type=float,
-#                         options=[0.0001, 0.0005, 0.001, 0.005], tunable=False)
-#         parser.opt_list('--batch_size', default=256, type=int,
-#                         options=[32, 64, 128, 256], tunable=False)
-#         parser.opt_list('--optimizer_name', default='adam', type=str,
-#                         options=['adam'], tunable=False)
-#         return parser
-#
-# """
-
 from .lightning import LightningModule
 
 __all__ = ['LightningModule']