lightning/tests/models/debug.py

import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST

import pytorch_lightning as pl


# from test_models import assert_ok_test_acc, load_model, \
#     clear_save_dir, get_test_tube_logger, get_hparams, init_save_dir, \
#     init_checkpoint_callback, reset_seed, set_random_master_port


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))

    def my_loss(self, y_hat, y):
        return F.cross_entropy(y_hat, y)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.forward(x)
        return {'training_loss': self.my_loss(y_hat, y)}

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.forward(x)
        return {'val_loss': self.my_loss(y_hat, y)}

    def validation_end(self, outputs):
        avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
        return avg_loss

    def configure_optimizers(self):
        return [torch.optim.Adam(self.parameters(), lr=0.02)]

    @pl.data_loader
    def train_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)

    @pl.data_loader
    def val_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)

    @pl.data_loader
    def test_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)