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
|
"""Lightning model for base Perceiver."""
from typing import Optional, Tuple, Type
from omegaconf import DictConfig
import torch
from torch import nn, Tensor
from text_recognizer.data.mappings import EmnistMapping
from text_recognizer.models.base import LitBase
from text_recognizer.models.metrics import CharacterErrorRate
class LitPerceiver(LitBase):
"""A PyTorch Lightning model for transformer networks."""
def __init__(
self,
network: Type[nn.Module],
loss_fn: Type[nn.Module],
optimizer_config: DictConfig,
lr_scheduler_config: Optional[DictConfig],
mapping: EmnistMapping,
max_output_len: int = 682,
start_token: str = "<s>",
end_token: str = "<e>",
pad_token: str = "<p>",
) -> None:
super().__init__(
network, loss_fn, optimizer_config, lr_scheduler_config, mapping
)
self.max_output_len = max_output_len
self.start_token = start_token
self.end_token = end_token
self.pad_token = pad_token
self.start_index = int(self.mapping.get_index(self.start_token))
self.end_index = int(self.mapping.get_index(self.end_token))
self.pad_index = int(self.mapping.get_index(self.pad_token))
self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
self.val_cer = CharacterErrorRate(self.ignore_indices)
self.test_cer = CharacterErrorRate(self.ignore_indices)
def forward(self, data: Tensor) -> Tensor:
"""Forward pass with the transformer network."""
return self.predict(data)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self.network(data)
loss = self.loss_fn(logits, targets)
self.log("train/loss", loss)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
preds = self.predict(data)
self.val_acc(preds, targets)
self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
self.val_cer(preds, 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
# Compute the text prediction.
pred = self(data)
self.test_cer(pred, targets)
self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.test_acc(pred, targets)
self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor:
return self.network(x).argmax(dim=1)
|
