129 lines
3.9 KiB
Python
129 lines
3.9 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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import torch
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import pytorch_lightning as pl
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from torch.nn import functional as F
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from torch.utils.data import DataLoader, random_split
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try:
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from torchvision.datasets.mnist import MNIST
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from torchvision import transforms
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except Exception as e:
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from tests.base.datasets import MNIST
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class Backbone(torch.nn.Module):
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def __init__(self, hidden_dim=128):
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super().__init__()
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self.l1 = torch.nn.Linear(28 * 28, hidden_dim)
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self.l2 = torch.nn.Linear(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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class LitClassifier(pl.LightningModule):
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def __init__(self, backbone, learning_rate=1e-3):
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super().__init__()
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self.save_hyperparameters()
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self.backbone = backbone
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def forward(self, x):
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# use forward for inference/predictions
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embedding = self.backbone(x)
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return embedding
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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.backbone(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.backbone(x)
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loss = F.cross_entropy(y_hat, y)
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result = pl.EvalResult(checkpoint_on=loss)
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result.log('valid_loss', loss)
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return result
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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.backbone(x)
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loss = F.cross_entropy(y_hat, y)
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result = pl.EvalResult(checkpoint_on=loss)
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result.log('test_loss', loss)
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return result
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def configure_optimizers(self):
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# self.hparams available because we called self.save_hyperparameters()
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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('--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.add_argument('--batch_size', default=32, type=int)
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parser.add_argument('--hidden_dim', type=int, default=128)
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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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args = parser.parse_args()
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# ------------
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# data
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# ------------
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dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
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mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
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mnist_train, mnist_val = random_split(dataset, [55000, 5000])
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train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
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val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
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test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
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# ------------
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# model
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# ------------
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model = LitClassifier(Backbone(hidden_dim=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, train_loader, val_loader)
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# ------------
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# testing
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# ------------
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trainer.test(test_dataloaders=test_loader)
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if __name__ == '__main__':
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cli_main()
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