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"""IAM original and sythetic dataset class."""
from typing import Callable, Optional
from torch.utils.data import ConcatDataset
import text_recognizer.metadata.iam_paragraphs as metadata
from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
from text_recognizer.data.iam_paragraphs import IAMParagraphs
from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs
from text_recognizer.data.tokenizer import Tokenizer
from text_recognizer.data.transforms.pad import Pad
from text_recognizer.data.transforms.paragraph import ParagraphStem
class IAMExtendedParagraphs(BaseDataModule):
"""A dataset with synthetic and real handwritten paragraph."""
def __init__(
self,
tokenizer: Tokenizer,
transform: Optional[Callable] = None,
test_transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
train_fraction: float = 0.8,
batch_size: int = 16,
num_workers: int = 0,
pin_memory: bool = True,
) -> None:
super().__init__(
tokenizer,
transform,
test_transform,
target_transform,
train_fraction,
batch_size,
num_workers,
pin_memory,
)
self.iam_paragraphs = IAMParagraphs(
tokenizer=self.tokenizer,
batch_size=self.batch_size,
num_workers=self.num_workers,
train_fraction=self.train_fraction,
transform=self.transform,
test_transform=self.test_transform,
target_transform=self.target_transform,
)
self.iam_synthetic_paragraphs = IAMSyntheticParagraphs(
tokenizer=self.tokenizer,
batch_size=self.batch_size,
num_workers=self.num_workers,
train_fraction=self.train_fraction,
transform=self.transform,
test_transform=self.test_transform,
target_transform=self.target_transform,
)
self.dims = self.iam_paragraphs.dims
self.output_dims = self.iam_paragraphs.output_dims
def prepare_data(self) -> None:
"""Prepares the paragraphs data."""
self.iam_paragraphs.prepare_data()
self.iam_synthetic_paragraphs.prepare_data()
def setup(self, stage: str = None) -> None:
"""Loads data for training/testing."""
self.iam_paragraphs.setup(stage)
self.iam_synthetic_paragraphs.setup(stage)
self.data_train = ConcatDataset(
[self.iam_paragraphs.data_train, self.iam_synthetic_paragraphs.data_train]
)
self.data_val = self.iam_paragraphs.data_val
self.data_test = self.iam_paragraphs.data_test
def __repr__(self) -> str:
"""Returns info about the dataset."""
basic = (
"IAM Original and Synthetic Paragraphs Dataset\n" # pylint: disable=no-member
f"Num classes: {len(self.tokenizer)}\n"
f"Dims: {self.dims}\n"
f"Output dims: {self.output_dims}\n"
)
if self.data_train is None and self.data_val is None and self.data_test is None:
return basic
x, y = next(iter(self.train_dataloader()))
xt, yt = next(iter(self.test_dataloader()))
x = x[0] if isinstance(x, list) else x
xt = xt[0] if isinstance(xt, list) else xt
data = (
"Train/val/test sizes: "
f"{len(self.data_train)}, "
f"{len(self.data_val)}, "
f"{len(self.data_test)}\n"
"Train Batch x stats: "
f"{(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
f"Train Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
f"Test Batch x stats: "
f"{(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
f"Test Batch y stats: {(yt.shape, yt.dtype, yt.min(), yt.max())}\n"
)
return basic + data
def show_dataset_info() -> None:
"""Displays Iam extended dataset information."""
transform = ParagraphStem(augment=False)
test_transform = ParagraphStem(augment=False)
target_transform = Pad(metadata.MAX_LABEL_LENGTH, 3)
load_and_print_info(
IAMExtendedParagraphs(
transform=transform,
test_transform=test_transform,
target_transform=target_transform,
)
)
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