lightning/tests/debug.py

from pytorch_lightning import Trainer
from examples import LightningTemplateModel
from pytorch_lightning.testing import LightningTestModel
from argparse import Namespace
from test_tube import Experiment
from pytorch_lightning.callbacks import ModelCheckpoint
import os
import shutil

import pytorch_lightning as pl
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
import numpy as np
import pdb
# 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_nb):
        x, y = batch
        y_hat = self.forward(x)
        return {'training_loss': self.my_loss(y_hat, y)}

    def validation_step(self, batch, batch_nb):
        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)

#
# def main():
#     reset_seed()
#     set_random_master_port()
#
#     hparams = get_hparams()
#     model = LightningTestModel(hparams)
#
#     save_dir = init_save_dir()
#
#     # exp file to get meta
#     logger = get_test_tube_logger(False)
#
#     print(logger.debug)
#
#     # exp file to get weights
#     checkpoint = init_checkpoint_callback(logger)
#
#     trainer_options = dict(
#         show_progress_bar=False,
#         max_nb_epochs=1,
#         train_percent_check=0.4,
#         val_percent_check=0.2,
#         checkpoint_callback=checkpoint,
#         logger=logger,
#         gpus=[0, 1],
#         distributed_backend='ddp'
#     )
#
#     # fit model
#     trainer = Trainer(**trainer_options)
#     result = trainer.fit(model)
#
#     exp = logger.experiment
#     print(os.listdir(exp.get_data_path(exp.name, exp.version)))
#
#     # correct result and ok accuracy
#     assert result == 1, 'training failed to complete'
#     pretrained_model = load_model(logger.experiment, save_dir,
#                                   module_class=LightningTestModel)
#
#     # run test set
#     new_trainer = Trainer(**trainer_options)
#     new_trainer.test(pretrained_model)
#
#     # test we have good test accuracy
#     clear_save_dir()
#
# if __name__ == '__main__':
#     main()