Add Barlow Twins network and training proceduer

3 files changed, 108 insertions, 0 deletions

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)
diff --git a/text_recognizer/networks/barlow_twins/__init__.py b/text_recognizer/networks/barlow_twins/__init__.py
new file mode 100644
index 0000000..0b74818
--- /dev/null
+++ b/text_recognizer/networks/barlow_twins/__init__.py
@@ -0,0 +1 @@
+"""Module for projector network in Barlow Twins."""
diff --git a/text_recognizer/networks/barlow_twins/projector.py b/text_recognizer/networks/barlow_twins/projector.py
new file mode 100644
index 0000000..05d5e2e
--- /dev/null
+++ b/text_recognizer/networks/barlow_twins/projector.py
@@ -0,0 +1,36 @@
+"""Projector network in Barlow Twins."""
+
+from typing import List
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class Projector(nn.Module):
+    """MLP network."""
+
+    def __init__(self, dims: List[int]) -> None:
+        super().__init__()
+        self.dims = dims
+        self.network = self._build()
+
+    def _build(self) -> nn.Sequential:
+        """Builds projector network."""
+        layers = [
+            nn.Sequential(
+                nn.Linear(
+                    in_features=self.dims[i], out_features=self.dims[i + 1], bias=False
+                ),
+                nn.BatchNorm1d(self.dims[i + 1]),
+                nn.ReLU(inplace=True),
+            )
+            for i in range(len(self.dims) - 2)
+        ]
+        layers.append(
+            nn.Linear(in_features=self.dims[-2], out_features=self.dims[-1], bias=False)
+        )
+        return nn.Sequential(*layers)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Project latent to higher dimesion."""
+        return self.network(x)