140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
# Copyright The Lightning AI 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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"""MNIST backbone image classifier example.
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To run: python backbone_image_classifier.py --trainer.max_epochs=50
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"""
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from os import path
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from typing import Optional
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import torch
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from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
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from lightning.pytorch.cli import LightningCLI
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from lightning.pytorch.demos.mnist_datamodule import MNIST
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from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
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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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if _TORCHVISION_AVAILABLE:
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from torchvision import transforms
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DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
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class Backbone(torch.nn.Module):
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"""
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>>> Backbone() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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Backbone(
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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):
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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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return torch.relu(self.l2(x))
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class LitClassifier(LightningModule):
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"""
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>>> LitClassifier(Backbone()) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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LitClassifier(
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(backbone): ...
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)
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"""
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def __init__(self, backbone: Optional[Backbone] = None, learning_rate: float = 0.0001):
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super().__init__()
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self.save_hyperparameters(ignore=["backbone"])
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if backbone is None:
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backbone = Backbone()
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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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return self.backbone(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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self.log("train_loss", loss, on_epoch=True)
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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, on_step=True)
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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 predict_step(self, batch, batch_idx, dataloader_idx=None):
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x, _ = batch
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return self(x)
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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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class MyDataModule(LightningDataModule):
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def __init__(self, batch_size: int = 32):
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super().__init__()
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dataset = MNIST(DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
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self.mnist_test = MNIST(DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
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self.mnist_train, self.mnist_val = random_split(
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dataset, [55000, 5000], generator=torch.Generator().manual_seed(42)
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)
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self.batch_size = batch_size
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def train_dataloader(self):
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return DataLoader(self.mnist_train, batch_size=self.batch_size)
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def val_dataloader(self):
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return DataLoader(self.mnist_val, batch_size=self.batch_size)
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def test_dataloader(self):
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return DataLoader(self.mnist_test, batch_size=self.batch_size)
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def predict_dataloader(self):
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return DataLoader(self.mnist_test, batch_size=self.batch_size)
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def cli_main():
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cli = LightningCLI(
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LitClassifier, MyDataModule, seed_everything_default=1234, save_config_kwargs={"overwrite": True}, run=False
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)
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cli.trainer.fit(cli.model, datamodule=cli.datamodule)
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cli.trainer.test(ckpt_path="best", datamodule=cli.datamodule)
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predictions = cli.trainer.predict(ckpt_path="best", datamodule=cli.datamodule)
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print(predictions[0])
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if __name__ == "__main__":
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cli_lightning_logo()
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cli_main()
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