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
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
|
"""PyTorch Lightning model for base Transformers."""
from typing import Dict, List, Optional, Union, Tuple, Type
import attr
from omegaconf import DictConfig
from torch import nn, Tensor
from text_recognizer.data.emnist import emnist_mapping
from text_recognizer.data.mappings import AbstractMapping
from text_recognizer.models.metrics import CharacterErrorRate
from text_recognizer.models.base import LitBaseModel
@attr.s
class TransformerLitModel(LitBaseModel):
"""A PyTorch Lightning model for transformer networks."""
network: Type[nn.Module] = attr.ib()
criterion_config: DictConfig = attr.ib(converter=DictConfig)
optimizer_config: DictConfig = attr.ib(converter=DictConfig)
lr_scheduler_config: DictConfig = attr.ib(converter=DictConfig)
monitor: str = attr.ib()
mapping: Type[AbstractMapping] = attr.ib()
def __attrs_post_init__(self) -> None:
super().__init__(
network=self.network,
optimizer_config=self.optimizer_config,
lr_scheduler_config=self.lr_scheduler_config,
criterion_config=self.criterion_config,
monitor=self.monitor,
)
self.mapping, ignore_tokens = self.configure_mapping(mapping)
self.val_cer = CharacterErrorRate(ignore_tokens)
self.test_cer = CharacterErrorRate(ignore_tokens)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.network.predict(data)
@staticmethod
def configure_mapping(mapping: Optional[List[str]]) -> Tuple[List[str], List[int]]:
"""Configure mapping."""
# TODO: Fix me!!!
mapping, inverse_mapping, _ = emnist_mapping(["\n"])
start_index = inverse_mapping["<s>"]
end_index = inverse_mapping["<e>"]
pad_index = inverse_mapping["<p>"]
ignore_tokens = [start_index, end_index, pad_index]
# TODO: add case for sentence pieces
return mapping, ignore_tokens
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self.network(data, targets[:, :-1])
loss = self.loss_fn(logits, targets[:, 1:])
self.log("train/loss", loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
logits = self.network(data, targets[:-1])
loss = self.loss_fn(logits, targets[1:])
self.log("val/loss", loss, prog_bar=True)
pred = self.network.predict(data)
self.val_cer(pred, targets)
self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, targets = batch
pred = self.network.predict(data)
self.test_cer(pred, targets)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
|
