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"""Base PyTorch Lightning model."""
from typing import Any, Dict, List, Union, Tuple, Type
import madgrad
from omegaconf import DictConfig, OmegaConf
import pytorch_lightning as pl
import torch
from torch import nn
from torch import Tensor
import torchmetrics
class LitBaseModel(pl.LightningModule):
"""Abstract PyTorch Lightning class."""
def __init__(
self,
network: Type[nn.Module],
optimizer: Union[DictConfig, Dict],
lr_scheduler: Union[DictConfig, Dict],
criterion: Union[DictConfig, Dict],
monitor: str = "val_loss",
) -> None:
super().__init__()
self.monitor = monitor
self.network = network
self._optimizer = OmegaConf.create(optimizer)
self._lr_scheduler = OmegaConf.create(lr_scheduler)
self.loss_fn = self.configure_criterion(criterion)
# Accuracy metric
self.train_acc = torchmetrics.Accuracy()
self.val_acc = torchmetrics.Accuracy()
self.test_acc = torchmetrics.Accuracy()
@staticmethod
def configure_criterion(criterion: Union[DictConfig, Dict]) -> Type[nn.Module]:
"""Returns a loss functions."""
criterion = OmegaConf.create(criterion)
args = {} or criterion.args
return getattr(nn, criterion.type)(**args)
def _configure_optimizer(self) -> Type[torch.optim.Optimizer]:
"""Configures the optimizer."""
args = {} or self._optimizer.args
if self._optimizer.type == "MADGRAD":
optimizer_class = madgrad.MADGRAD
else:
optimizer_class = getattr(torch.optim, self._optimizer.type)
return optimizer_class(params=self.parameters(), **args)
def _configure_lr_scheduler(
self, optimizer: Type[torch.optim.Optimizer]
) -> Dict[str, Any]:
"""Configures the lr scheduler."""
scheduler = {"monitor": self.monitor}
args = {} or self._lr_scheduler.args
if "interval" in args:
scheduler["interval"] = args.pop("interval")
scheduler["scheduler"] = getattr(
torch.optim.lr_scheduler, self._lr_scheduler.type
)(optimizer, **args)
return scheduler
def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
"""Configures optimizer and lr scheduler."""
optimizer = self._configure_optimizer()
scheduler = self._configure_lr_scheduler(optimizer)
return [optimizer], [scheduler]
def forward(self, data: Tensor) -> Tensor:
"""Feedforward pass."""
return self.network(data)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self(data)
loss = self.loss_fn(logits, targets)
self.log("train_loss", loss)
self.train_acc(logits, targets)
self.log("train_acc", self.train_acc, on_step=False, on_epoch=True)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
logits = self(data)
loss = self.loss_fn(logits, targets)
self.log("val_loss", loss, prog_bar=True)
self.val_acc(logits, targets)
self.log("val_acc", self.val_acc, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, targets = batch
logits = self(data)
self.test_acc(logits, targets)
self.log("test_acc", self.test_acc, on_step=False, on_epoch=True)
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