+
+
+ Template model definition
+In 99% of cases you want to just copy this template to start a new lightningModule and change the core of what your model is actually trying to do.
+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(self.parameters(), 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
+
+