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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2021-10-11 22:09:40 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2021-10-11 22:09:40 +0200
commit2c377b6f7e2d4ba8a7c424c748053cc8d560599a (patch)
tree2707f3cbf8e22b821880cdcbada17da189759655 /text_recognizer
parent3118c227c229f87321dd02c3bcafea1d7a8bc1a9 (diff)
Refactor barlow twins lit model
Diffstat (limited to 'text_recognizer')
-rw-r--r--text_recognizer/models/barlow_twins.py40
1 files changed, 7 insertions, 33 deletions
diff --git a/text_recognizer/models/barlow_twins.py b/text_recognizer/models/barlow_twins.py
index 36638e7..6e2719d 100644
--- a/text_recognizer/models/barlow_twins.py
+++ b/text_recognizer/models/barlow_twins.py
@@ -1,54 +1,28 @@
"""PyTorch Lightning Barlow Twins model."""
from typing import Tuple, Type
import attr
-import torch
from torch import nn
from torch import Tensor
-import torch.nn.functional as F
-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()
+from text_recognizer.criterions.barlow_twins import BarlowTwinsLoss
@attr.s(auto_attribs=True, eq=False)
class BarlowTwinsLitModel(BaseLitModel):
"""Barlow Twins training proceduer."""
- projector: Projector = attr.ib()
- lambda_: float = attr.ib()
- augment: Type[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
+ network: Type[nn.Module] = attr.ib()
+ loss_fn: BarlowTwinsLoss = attr.ib()
def forward(self, data: Tensor) -> Tensor:
"""Encodes image to projector latent."""
- z = self.network(data)
- z_e = F.adaptive_avg_pool2d(z, (1, 1)).flatten(start_dim=1)
- z_p = self.projector(z_e)
- return z_p
+ return self.network(data)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, _ = batch
- x1, x2 = self.augment(data), self.augment(data)
+ x1, x2 = data
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("train/loss", loss)
@@ -57,7 +31,7 @@ class BarlowTwinsLitModel(BaseLitModel):
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, _ = batch
- x1, x2 = self.augment(data), self.augment(data)
+ x1, x2 = data
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("val/loss", loss, prog_bar=True)
@@ -65,7 +39,7 @@ class BarlowTwinsLitModel(BaseLitModel):
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, _ = batch
- x1, x2 = self.augment(data), self.augment(data)
+ x1, x2 = data
z1, z2 = self(x1), self(x2)
loss = self.loss_fn(z1, z2)
self.log("test/loss", loss, prog_bar=True)