From 484dc2b09c87729b4e777e94efdd2e7583651df9 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Thu, 7 Oct 2021 08:56:40 +0200
Subject: Add Barlow Twins network and training proceduer
---
text_recognizer/models/barlow_twins.py | 71 ++++++++++++++++++++++++++++++++++
1 file changed, 71 insertions(+)
create mode 100644 text_recognizer/models/barlow_twins.py
(limited to 'text_recognizer/models')
diff --git a/text_recognizer/models/barlow_twins.py b/text_recognizer/models/barlow_twins.py
new file mode 100644
index 0000000..d044ba2
--- /dev/null
+++ b/text_recognizer/models/barlow_twins.py
@@ -0,0 +1,71 @@
+"""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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