117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from argparse import ArgumentParser
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from pprint import pprint
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import torch
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from torch.nn import functional as F
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import pytorch_lightning as pl
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from pl_examples import cli_lightning_logo
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from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
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class LitClassifier(pl.LightningModule):
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"""
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>>> LitClassifier() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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LitClassifier(
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(l1): Linear(...)
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(l2): Linear(...)
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)
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"""
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def __init__(self, hidden_dim=128, learning_rate=1e-3):
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super().__init__()
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self.save_hyperparameters()
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self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
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self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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x = torch.relu(self.l1(x))
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x = torch.relu(self.l2(x))
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return x
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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return loss
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def validation_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('valid_loss', loss)
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def test_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('test_loss', loss)
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = ArgumentParser(parents=[parent_parser], add_help=False)
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parser.add_argument('--hidden_dim', type=int, default=128)
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parser.add_argument('--learning_rate', type=float, default=0.0001)
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return parser
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def cli_main():
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pl.seed_everything(1234)
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# ------------
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# args
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# ------------
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parser = ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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parser = LitClassifier.add_model_specific_args(parser)
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parser = MNISTDataModule.add_argparse_args(parser)
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args = parser.parse_args()
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# ------------
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# data
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# ------------
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dm = MNISTDataModule.from_argparse_args(args)
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# ------------
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# model
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# ------------
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model = LitClassifier(args.hidden_dim, args.learning_rate)
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# ------------
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# training
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# ------------
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trainer = pl.Trainer.from_argparse_args(args)
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trainer.fit(model, datamodule=dm)
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# ------------
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# testing
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# ------------
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# todo: without passing model it fails for missing best weights
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# MisconfigurationException, 'ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.'
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result = trainer.test(model, datamodule=dm)
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pprint(result)
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if __name__ == '__main__':
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cli_lightning_logo()
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cli_main()
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