adding docs

docs/Pytorch-Lightning/lightning_module_template.py

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+import os
+from collections import OrderedDict
+import torch.nn as nn
+from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
+import torch
+import torch.nn.functional as F
+from test_tube import HyperOptArgumentParser
+from torch import optim
+
+from pytorch_lightning.root_module.root_module import LightningModule
+
+
+class LightningTemplateModel(LightningModule):
+    """
+    Sample model to show how to define a template
+    """
+
+    def __init__(self, hparams):
+        """
+        Pass in parsed HyperOptArgumentParser to the model
+        :param hparams:
+        """
+        # init superclass
+        super(LightningTemplateModel, self).__init__(hparams)
+
+        self.batch_size = hparams.batch_size
+
+        # build model
+        self.__build_model()
+
+    # ---------------------
+    # MODEL SETUP
+    # ---------------------
+    def __build_model(self):
+        """
+        Layout model
+        :return:
+        """
+        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
+        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
+        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
+
+        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
+
+    # ---------------------
+    # TRAINING
+    # ---------------------
+    def forward(self, x):
+        """
+        No special modification required for lightning, define as you normally would
+        :param x:
+        :return:
+        """
+
+        x = self.c_d1(x)
+        x = torch.tanh(x)
+        x = self.c_d1_bn(x)
+        x = self.c_d1_drop(x)
+
+        x = self.c_d2(x)
+        logits = F.log_softmax(x, dim=1)
+
+        return logits
+
+    def loss(self, labels, logits):
+        nll = F.nll_loss(logits, labels)
+        return nll
+
+    def training_step(self, data_batch, batch_i):
+        """
+        Lightning calls this inside the training loop
+        :param data_batch:
+        :return:
+        """
+        # forward pass
+        x, y = data_batch
+        x = x.view(x.size(0), -1)
+        y_hat = self.forward(x)
+
+        # calculate loss
+        loss_val = self.loss(y, y_hat)
+
+        output = OrderedDict({
+            'loss': loss_val,
+            'tqdm_metrics': {}
+        })
+        return output
+
+    def validation_step(self, data_batch, batch_i):
+        """
+        Lightning calls this inside the validation loop
+        :param data_batch:
+        :return:
+        """
+        x, y = data_batch
+        x = x.view(x.size(0), -1)
+        y_hat = self.forward(x)
+
+        loss_val = self.loss(y, y_hat)
+
+        # acc
+        labels_hat = torch.argmax(y_hat, dim=1)
+        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
+
+        output = OrderedDict({
+            'val_loss': loss_val,
+            'val_acc': torch.tensor(val_acc),
+        })
+        return output
+
+    def validation_end(self, outputs):
+        """
+        Called at the end of validation to aggregate outputs
+        :param outputs: list of individual outputs of each validation step
+        :return:
+        """
+        val_loss_mean = 0
+        val_acc_mean = 0
+        for output in outputs:
+            val_loss_mean += output['val_loss']
+            val_acc_mean += output['val_acc']
+
+        val_loss_mean /= len(outputs)
+        val_acc_mean /= len(outputs)
+        tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
+        return tqdm_dic
+
+    def update_tng_log_metrics(self, logs):
+        return logs
+
+    # ---------------------
+    # MODEL SAVING
+    # ---------------------
+    def get_save_dict(self):
+        checkpoint = {'state_dict': self.state_dict()}
+        return checkpoint
+
+    def load_model_specific(self, checkpoint):
+        self.load_state_dict(checkpoint['state_dict'])
+        pass
+
+    # ---------------------
+    # TRAINING SETUP
+    # ---------------------
+    def configure_optimizers(self):
+        """
+        return whatever optimizers we want here
+        :return: list of optimizers
+        """
+        optimizer = optim.Adam(lr=self.hparams.learning_rate)
+        return [optimizer]
+
+    def __dataloader(self, train):
+        # init data generators
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+
+        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
+
+        loader = torch.utils.data.DataLoader(
+            dataset=dataset,
+            batch_size=self.hparams.batch_size,
+            shuffle=True
+        )
+
+        return loader
+
+    @property
+    def tng_dataloader(self):
+        if self._tng_dataloader is None:
+            try:
+                self._tng_dataloader = self.__dataloader(train=True)
+            except Exception as e:
+                print(e)
+                raise e
+        return self._tng_dataloader
+
+    @property
+    def val_dataloader(self):
+        if self._val_dataloader is None:
+            try:
+                self._val_dataloader = self.__dataloader(train=False)
+            except Exception as e:
+                print(e)
+                raise e
+        return self._val_dataloader
+
+    @property
+    def test_dataloader(self):
+        if self._test_dataloader is None:
+            try:
+                self._test_dataloader = self.__dataloader(train=False)
+            except Exception as e:
+                print(e)
+                raise e
+        return self._test_dataloader
+
+    @staticmethod
+    def add_model_specific_args(parent_parser, root_dir):
+        """
+        Parameters you define here will be available to your model through self.hparams
+        :param parent_parser:
+        :param root_dir:
+        :return:
+        """
+        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
+
+        # param overwrites
+        # parser.set_defaults(gradient_clip=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)
+        parser.add_argument('--hidden_dim', default=50000) # use 500 for CPU, 50000 for GPU to see speed difference
+
+        # 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

pytorch_lightning/callbacks/__init__.py

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+from .pt_callbacks import EarlyStopping, ModelCheckpoint