simple_image_classifier.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MNIST simple image classifier example.

To run:
python simple_image_classifier.py --trainer.max_epochs=50
"""

import torch
from torch.nn import functional as F

import pytorch_lightning as pl
from pl_examples import cli_lightning_logo
from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
from pytorch_lightning.utilities.cli import LightningCLI, SaveConfigCallback


class LitClassifier(pl.LightningModule):
    """
    >>> LitClassifier()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
    LitClassifier(
      (l1): Linear(...)
      (l2): Linear(...)
    )
    """

    def __init__(
        self,
        l1: torch.nn.Linear,
        l2: torch.nn.Linear,
        learning_rate: float = 0.0001,
    ):
        super().__init__()
        self.save_hyperparameters()

        self.l1 = l1
        self.l2 = l2 

        # self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
        # self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)

    def forward(self, x):
        x = x.view(x.size(0), -1)
        x = torch.relu(self.l1(x))
        x = torch.relu(self.l2(x))
        return x

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('valid_loss', loss)

    def test_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('test_loss', loss)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)


class WandBandSafeConfigCallBackFixCLI(LightningCLI):
    def add_arguments_to_parser(self, parser):
        parser.add_argument('save_config_callback_filename', default='',
                help='Change config filename in order to avoid clashes with wandb.'
                    'Discussed already in https://github.com/PyTorchLightning/pytorch-lightning/pull/7675')

    def before_fit(self):
        save_config_cb = [c for c in self.trainer.callbacks if isinstance(c, SaveConfigCallback)]
        if save_config_cb:
            config_filename = self.config.get('save_config_callback_filename', '')
            if config_filename:
                save_config_cb[0].config_filename = config_filename


def cli_main():
    # cli = LightningCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234)
    cli = WandBandSafeConfigCallBackFixCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234)
    cli.trainer.test(cli.model, datamodule=cli.datamodule)


if __name__ == '__main__':
    cli_lightning_logo()
    cli_main()