1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
|
"""PyTorch Lightning model for base Transformers."""
from typing import Tuple
import attr
from torch import Tensor
from text_recognizer.models.base import BaseLitModel
@attr.s(auto_attribs=True, eq=False)
class VQVAELitModel(BaseLitModel):
"""A PyTorch Lightning model for transformer networks."""
latent_loss_weight: float = attr.ib(default=0.25)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network(data)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, _ = batch
reconstructions, vq_loss = self(data)
loss = self.loss_fn(reconstructions, data)
loss = loss + self.latent_loss_weight * vq_loss
self.log("train/vq_loss", vq_loss)
self.log("train/loss", loss)
# self.train_acc(reconstructions, data)
# 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, _ = batch
reconstructions, vq_loss = self(data)
loss = self.loss_fn(reconstructions, data)
loss = loss + self.latent_loss_weight * vq_loss
self.log("val/vq_loss", vq_loss)
self.log("val/loss", loss, prog_bar=True)
# self.val_acc(reconstructions, data)
# 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, _ = batch
reconstructions, vq_loss = self(data)
loss = self.loss_fn(reconstructions, data)
loss = loss + self.latent_loss_weight * vq_loss
self.log("test/vq_loss", vq_loss)
self.log("test/loss", loss)
# self.test_acc(reconstructions, data)
# self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
|
