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"""PyTorch Lightning Barlow Twins model."""
from typing import Type
import attr
import pytorch_lightning as pl
import torch
from torch import nn
from torch import Tensor
import torchvision.transforms as T
from text_recognizer.models.base import BaseLitModel
from text_recognizer.networks.barlow_twins.projector import Projector
def off_diagonal(x: Tensor) -> Tensor:
n, m = x.shape
assert n == m
return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
@attr.s(auto_attribs=True, eq=False)
class BarlowTwinsLitModel(BaseLitModel):
"""Barlow Twins training proceduer."""
encoder: Type[nn.Module] = attr.ib()
projector: Projector = attr.ib()
lambda_: float = attr.ib()
augment: T.Compose = attr.ib()
def __attrs_post_init__(self) -> None:
"""Post init configuration."""
self.bn = nn.BatchNorm1d(self.projector.dims[-1], affine=False)
def loss_fn(self, z1: Tensor, z2: Tensor) -> Tensor:
"""Calculates the Barlow Twin loss."""
c = torch.mm(self.bn(z1), self.bn(z2))
c.div_(z1.shape[0])
on_diag = torch.diagonal(c).add_(-1).pow_(2).sum()
off_diag = off_diagonal(c).pow_(2).sum()
return on_diag + self.lambda_ * off_diag
def forward(self, data: Tensor) -> Tensor:
"""Encodes image to projector latent."""
z_e = self.encoder(data).flatten(start_dim=1)
z_p = self.projector(z_e)
return z_p
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, _ = batch
x1, x2 = self.augment(data), self.augment(data)
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("train/loss", loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, _ = batch
x1, x2 = self.augment(data), self.augment(data)
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("val/loss", loss, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, _ = batch
x1, x2 = self.augment(data), self.augment(data)
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("test/loss", loss, prog_bar=True)
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