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
|
"""Transforms for PyTorch datasets."""
import numpy as np
from PIL import Image
import torch
from torch import Tensor
import torch.nn.functional as F
from torchvision.transforms import Compose, ToPILImage, ToTensor
from text_recognizer.datasets.util import EmnistMapper
class Transpose:
"""Transposes the EMNIST image to the correct orientation."""
def __call__(self, image: Image) -> np.ndarray:
"""Swaps axis."""
return np.array(image).swapaxes(0, 1)
class Resize:
"""Resizes a tensor to a specified width."""
def __init__(self, width: int = 952) -> None:
# The default is 952 because of the IAM dataset.
self.width = width
def __call__(self, image: Tensor) -> Tensor:
"""Resize tensor in the last dimension."""
return F.interpolate(image, size=self.width, mode="nearest")
class AddTokens:
"""Adds start of sequence and end of sequence tokens to target tensor."""
def __init__(self, pad_token: str, eos_token: str, init_token: str = None) -> None:
self.init_token = init_token
self.pad_token = pad_token
self.eos_token = eos_token
if self.init_token is not None:
self.emnist_mapper = EmnistMapper(
init_token=self.init_token,
pad_token=self.pad_token,
eos_token=self.eos_token,
)
else:
self.emnist_mapper = EmnistMapper(
pad_token=self.pad_token, eos_token=self.eos_token,
)
self.pad_value = self.emnist_mapper(self.pad_token)
self.eos_value = self.emnist_mapper(self.eos_token)
def __call__(self, target: Tensor) -> Tensor:
"""Adds a sos token to the begining and a eos token to the end of a target sequence."""
dtype, device = target.dtype, target.device
# Find the where padding starts.
pad_index = torch.nonzero(target == self.pad_value, as_tuple=False)[0].item()
target[pad_index] = self.eos_value
if self.init_token is not None:
self.sos_value = self.emnist_mapper(self.init_token)
sos = torch.tensor([self.sos_value], dtype=dtype, device=device)
target = torch.cat([sos, target], dim=0)
return target
|
