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"""Base PyTorch Lightning model."""
from typing import Any, Dict, List, Tuple, Type
import madgrad
import pytorch_lightning as pl
import torch
from torch import nn
from torch import Tensor
import torchmetrics
from text_recognizer import networks
class LitBaseModel(pl.LightningModule):
"""Abstract PyTorch Lightning class."""
def __init__(
self,
network_args: Dict,
optimizer_args: Dict,
lr_scheduler_args: Dict,
criterion_args: Dict,
monitor: str = "val_loss",
) -> None:
super().__init__()
self.monitor = monitor
self.network = getattr(networks, network_args["type"])(**network_args["args"])
self.optimizer_args = optimizer_args
self.lr_scheduler_args = lr_scheduler_args
self.loss_fn = self.configure_criterion(criterion_args)
# Accuracy metric
self.train_acc = torchmetrics.Accuracy()
self.val_acc = torchmetrics.Accuracy()
self.test_acc = torchmetrics.Accuracy()
@staticmethod
def configure_criterion(criterion_args: Dict) -> Type[nn.Module]:
"""Returns a loss functions."""
args = {} or criterion_args["args"]
return getattr(nn, criterion_args["type"])(**args)
def configure_optimizer(self) -> Tuple[List[type], List[Dict[str, Any]]]:
"""Configures optimizer and lr scheduler."""
args = {} or self.optimizer_args["args"]
if self.optimizer_args["type"] == "MADGRAD":
optimizer = getattr(madgrad, self.optimizer_args["type"])(**args)
else:
optimizer = getattr(torch.optim, self.optimizer_args["type"])(**args)
scheduler = {"monitor": self.monitor}
args = {} or self.lr_scheduler_args["args"]
if "interval" in args:
scheduler["interval"] = args.pop("interval")
scheduler["scheduler"] = getattr(
torch.optim.lr_scheduler, self.lr_scheduler_args["type"]
)(**args)
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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