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"""Base PyTorch Lightning model."""
from typing import Any, Dict, List, Optional, Tuple, Type
import attr
import hydra
from loguru import logger as log
from omegaconf import DictConfig
from pytorch_lightning import LightningModule
import torch
from torch import nn
from torch import Tensor
import torchmetrics
from text_recognizer.data.base_mapping import AbstractMapping
@attr.s(eq=False)
class BaseLitModel(LightningModule):
"""Abstract PyTorch Lightning class."""
def __attrs_pre_init__(self) -> None:
super().__init__()
network: Type[nn.Module] = attr.ib()
mapping: Type[AbstractMapping] = attr.ib()
loss_fn: Type[nn.Module] = attr.ib()
optimizer_configs: DictConfig = attr.ib()
lr_scheduler_configs: Optional[DictConfig] = attr.ib()
train_acc: torchmetrics.Accuracy = attr.ib(
init=False, default=torchmetrics.Accuracy()
)
val_acc: torchmetrics.Accuracy = attr.ib(
init=False, default=torchmetrics.Accuracy()
)
test_acc: torchmetrics.Accuracy = attr.ib(
init=False, default=torchmetrics.Accuracy()
)
def optimizer_zero_grad(
self,
epoch: int,
batch_idx: int,
optimizer: Type[torch.optim.Optimizer],
optimizer_idx: int,
) -> None:
optimizer.zero_grad(set_to_none=True)
def _configure_optimizer(self) -> List[Type[torch.optim.Optimizer]]:
"""Configures the optimizer."""
optimizers = []
for optimizer_config in self.optimizer_configs.values():
module = self
for m in str(optimizer_config.parameters).split("."):
module = getattr(module, m)
del optimizer_config.parameters
log.info(f"Instantiating optimizer <{optimizer_config._target_}>")
optimizers.append(
hydra.utils.instantiate(optimizer_config, params=module.parameters())
)
return optimizers
def _configure_lr_schedulers(
self, optimizers: List[Type[torch.optim.Optimizer]]
) -> List[Dict[str, Any]]:
"""Configures the lr scheduler."""
if self.lr_scheduler_configs is None:
return []
schedulers = []
for optimizer, lr_scheduler_config in zip(
optimizers, self.lr_scheduler_configs.values()
):
# Extract non-class arguments.
monitor = lr_scheduler_config.monitor
interval = lr_scheduler_config.interval
del lr_scheduler_config.monitor
del lr_scheduler_config.interval
log.info(
f"Instantiating learning rate scheduler <{lr_scheduler_config._target_}>"
)
scheduler = {
"monitor": monitor,
"interval": interval,
"scheduler": hydra.utils.instantiate(
lr_scheduler_config, optimizer=optimizer
),
}
schedulers.append(scheduler)
return schedulers
def configure_optimizers(
self,
) -> Tuple[List[Type[torch.optim.Optimizer]], List[Dict[str, Any]]]:
"""Configures optimizer and lr scheduler."""
optimizers = self._configure_optimizer()
schedulers = self._configure_lr_schedulers(optimizers)
return optimizers, schedulers
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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