106 lines
2.9 KiB
Python
106 lines
2.9 KiB
Python
import torch
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torchvision.datasets import MNIST
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import pytorch_lightning as pl
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# from test_models import assert_ok_test_acc, load_model, \
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# clear_save_dir, get_test_tube_logger, get_hparams, init_save_dir, \
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# init_checkpoint_callback, reset_seed, set_random_master_port
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class CoolModel(pl.LightningModule):
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def __init(self):
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super(CoolModel, self).__init__()
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# not the best model...
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x))
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def my_loss(self, y_hat, y):
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return F.cross_entropy(y_hat, y)
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def training_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self.forward(x)
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return {'training_loss': self.my_loss(y_hat, y)}
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def validation_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self.forward(x)
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return {'val_loss': self.my_loss(y_hat, y)}
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def validation_end(self, outputs):
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avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
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return avg_loss
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def configure_optimizers(self):
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return [torch.optim.Adam(self.parameters(), lr=0.02)]
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@pl.data_loader
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def train_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
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@pl.data_loader
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def val_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
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@pl.data_loader
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def test_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
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#
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# def main():
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# reset_seed()
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# set_random_master_port()
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#
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# hparams = get_hparams()
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# model = LightningTestModel(hparams)
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#
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# save_dir = init_save_dir()
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#
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# # exp file to get meta
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# logger = get_test_tube_logger(False)
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#
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# print(logger.debug)
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#
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# # exp file to get weights
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# checkpoint = init_checkpoint_callback(logger)
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#
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# trainer_options = dict(
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# show_progress_bar=False,
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# max_nb_epochs=1,
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# train_percent_check=0.4,
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# val_percent_check=0.2,
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# checkpoint_callback=checkpoint,
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# logger=logger,
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# gpus=[0, 1],
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# distributed_backend='ddp'
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# )
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#
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# # fit model
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# trainer = Trainer(**trainer_options)
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# result = trainer.fit(model)
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#
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# exp = logger.experiment
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# print(os.listdir(exp.get_data_path(exp.name, exp.version)))
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#
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# # correct result and ok accuracy
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# assert result == 1, 'training failed to complete'
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# pretrained_model = load_model(logger.experiment, save_dir,
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# module_class=LightningTestModel)
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#
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# # run test set
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# new_trainer = Trainer(**trainer_options)
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# new_trainer.test(pretrained_model)
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#
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# # test we have good test accuracy
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# clear_save_dir()
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#
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# if __name__ == '__main__':
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# main()
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