renamed datasets to data, added iam refactor

14 files changed, 0 insertions, 2017 deletions

diff --git a/text_recognizer/datasets/__init__.py b/text_recognizer/datasets/__init__.py
deleted file mode 100644
index 2727b20..0000000
--- a/text_recognizer/datasets/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Dataset modules."""
diff --git a/text_recognizer/datasets/base_data_module.py b/text_recognizer/datasets/base_data_module.py
deleted file mode 100644
index f5e7300..0000000
--- a/text_recognizer/datasets/base_data_module.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""Base lightning DataModule class."""
-from pathlib import Path
-from typing import Dict
-
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-
-
-def load_and_print_info(data_module_class: type) -> None:
-    """Load EMNISTLines and prints info."""
-    dataset = data_module_class()
-    dataset.prepare_data()
-    dataset.setup()
-    print(dataset)
-
-
-class BaseDataModule(pl.LightningDataModule):
-    """Base PyTorch Lightning DataModule."""
-
-    def __init__(self, batch_size: int = 128, num_workers: int = 0) -> None:
-        super().__init__()
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-        # Placeholders for subclasses.
-        self.dims = None
-        self.output_dims = None
-        self.mapping = None
-
-    @classmethod
-    def data_dirname(cls) -> Path:
-        """Return the path to the base data directory."""
-        return Path(__file__).resolve().parents[2] / "data"
-
-    def config(self) -> Dict:
-        """Return important settings of the dataset."""
-        return {
-            "input_dim": self.dims,
-            "output_dims": self.output_dims,
-            "mapping": self.mapping,
-        }
-
-    def prepare_data(self) -> None:
-        """Prepare data for training."""
-        pass
-
-    def setup(self, stage: str = None) -> None:
-        """Split into train, val, test, and set dims.
-
-        Should assign `torch Dataset` objects to self.data_train, self.data_val, and
-            optionally self.data_test.
-
-        Args:
-            stage (Any): Variable to set splits.
-
-        """
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
-
-    def train_dataloader(self) -> DataLoader:
-        """Retun DataLoader for train data."""
-        return DataLoader(
-            self.data_train,
-            shuffle=True,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )
-
-    def val_dataloader(self) -> DataLoader:
-        """Return DataLoader for val data."""
-        return DataLoader(
-            self.data_val,
-            shuffle=False,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )
-
-    def test_dataloader(self) -> DataLoader:
-        """Return DataLoader for val data."""
-        return DataLoader(
-            self.data_test,
-            shuffle=False,
-            batch_size=self.batch_size,
-            num_workers=self.num_workers,
-            pin_memory=True,
-        )
diff --git a/text_recognizer/datasets/base_dataset.py b/text_recognizer/datasets/base_dataset.py
deleted file mode 100644
index a9e9c24..0000000
--- a/text_recognizer/datasets/base_dataset.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Base PyTorch Dataset class."""
-from typing import Any, Callable, Dict, Sequence, Tuple, Union
-
-import torch
-from torch import Tensor
-from torch.utils.data import Dataset
-
-
-class BaseDataset(Dataset):
-    """
-    Base Dataset class that processes data and targets through optional transfroms.
-
-    Args:
-        data (Union[Sequence, Tensor]): Torch tensors, numpy arrays, or PIL images.
-        targets (Union[Sequence, Tensor]): Torch tensors or numpy arrays.
-        tranform (Callable): Function that takes a datum and applies transforms.
-        target_transform (Callable): Fucntion that takes a target and applies
-            target transforms.
-    """
-
-    def __init__(
-        self,
-        data: Union[Sequence, Tensor],
-        targets: Union[Sequence, Tensor],
-        transform: Callable = None,
-        target_transform: Callable = None,
-    ) -> None:
-        if len(data) != len(targets):
-            raise ValueError("Data and targets must be of equal length.")
-        self.data = data
-        self.targets = targets
-        self.transform = transform
-        self.target_transform = target_transform
-
-    def __len__(self) -> int:
-        """Return the length of the dataset."""
-        return len(self.data)
-
-    def __getitem__(self, index: int) -> Tuple[Any, Any]:
-        """Return a datum and its target, after processing by transforms.
-
-        Args:
-            index (int): Index of a datum in the dataset.
-
-        Returns:
-            Tuple[Any, Any]: Datum and target pair.
-
-        """
-        datum, target = self.data[index], self.targets[index]
-
-        if self.transform is not None:
-            datum = self.transform(datum)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return datum, target
-
-
-def convert_strings_to_labels(
-    strings: Sequence[str], mapping: Dict[str, int], length: int
-) -> Tensor:
-    """
-    Convert a sequence of N strings to (N, length) ndarray, with each string wrapped with <s> and </s> tokens,
-    and padded wiht the <p> token.
-    """
-    labels = torch.ones((len(strings), length), dtype=torch.long) * mapping["<p>"]
-    for i, string in enumerate(strings):
-        tokens = list(string)
-        tokens = ["<s>", *tokens, "</s>"]
-        for j, token in enumerate(tokens):
-            labels[i, j] = mapping[token]
-    return labels
diff --git a/text_recognizer/datasets/download_utils.py b/text_recognizer/datasets/download_utils.py
deleted file mode 100644
index e3dc68c..0000000
--- a/text_recognizer/datasets/download_utils.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Util functions for downloading datasets."""
-import hashlib
-from pathlib import Path
-from typing import Dict, List, Optional
-from urllib.request import urlretrieve
-
-from loguru import logger
-from tqdm import tqdm
-
-
-def _compute_sha256(filename: Path) -> str:
-    """Returns the SHA256 checksum of a file."""
-    with filename.open(mode="rb") as f:
-        return hashlib.sha256(f.read()).hexdigest()
-
-
-class TqdmUpTo(tqdm):
-    """TQDM progress bar when downloading files.
-
-    From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
-
-    """
-
-    def update_to(
-        self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None
-    ) -> None:
-        """Updates the progress bar.
-
-        Args:
-            blocks (int): Number of blocks transferred so far. Defaults to 1.
-            block_size (int): Size of each block, in tqdm units. Defaults to 1.
-            total_size (Optional[int]): Total size in tqdm units. Defaults to None.
-        """
-        if total_size is not None:
-            self.total = total_size  # pylint: disable=attribute-defined-outside-init
-        self.update(blocks * block_size - self.n)
-
-
-def _download_url(url: str, filename: str) -> None:
-    """Downloads a file from url to filename, with a progress bar."""
-    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
-        urlretrieve(url, filename, reporthook=t.update_to, data=None)  # nosec
-
-
-def download_dataset(metadata: Dict, dl_dir: Path) -> Optional[Path]:
-    """Downloads dataset using a metadata file.
-
-    Args:
-        metadata (Dict): A metadata file of the dataset.
-        dl_dir (Path): Download directory for the dataset.
-
-    Returns:
-        Optional[Path]: Returns filename if dataset is downloaded, None if it already
-            exists.
-
-    Raises:
-        ValueError: If the SHA-256 value is not the same between the dataset and
-            the metadata file.
-
-    """
-    dl_dir.mkdir(parents=True, exist_ok=True)
-    filename = dl_dir / metadata["filename"]
-    if filename.exists():
-        return
-    logger.info(f"Downloading raw dataset from {metadata['url']} to {filename}...")
-    _download_url(metadata["url"], filename)
-    logger.info("Computing the SHA-256...")
-    sha256 = _compute_sha256(filename)
-    if sha256 != metadata["sha256"]:
-        raise ValueError(
-            "Downloaded data file SHA-256 does not match that listed in metadata document."
-        )
-    return filename
diff --git a/text_recognizer/datasets/emnist.py b/text_recognizer/datasets/emnist.py
deleted file mode 100644
index 66101b5..0000000
--- a/text_recognizer/datasets/emnist.py
+++ /dev/null
@@ -1,210 +0,0 @@
-"""EMNIST dataset: downloads it from FSDL aws url if not present."""
-from pathlib import Path
-from typing import Sequence, Tuple
-import json
-import os
-import shutil
-import zipfile
-
-import h5py
-import numpy as np
-from loguru import logger
-import toml
-import torch
-from torch.utils.data import random_split
-from torchvision import transforms
-
-from text_recognizer.datasets.base_dataset import BaseDataset
-from text_recognizer.datasets.base_data_module import (
-    BaseDataModule,
-    load_and_print_info,
-)
-from text_recognizer.datasets.download_utils import download_dataset
-
-
-SEED = 4711
-NUM_SPECIAL_TOKENS = 4
-SAMPLE_TO_BALANCE = True
-
-RAW_DATA_DIRNAME = BaseDataModule.data_dirname() / "raw" / "emnist"
-METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
-DL_DATA_DIRNAME = BaseDataModule.data_dirname() / "downloaded" / "emnist"
-PROCESSED_DATA_DIRNAME = BaseDataModule.data_dirname() / "processed" / "emnist"
-PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "byclass.h5"
-ESSENTIALS_FILENAME = Path(__file__).parents[0].resolve() / "emnist_essentials.json"
-
-
-class EMNIST(BaseDataModule):
-    """
-    "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19
-    and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset."
-    From https://www.nist.gov/itl/iad/image-group/emnist-dataset
-
-    The data split we will use is
-    EMNIST ByClass: 814,255 characters. 62 unbalanced classes.
-    """
-
-    def __init__(
-        self, batch_size: int = 128, num_workers: int = 0, train_fraction: float = 0.8
-    ) -> None:
-        super().__init__(batch_size, num_workers)
-        if not ESSENTIALS_FILENAME.exists():
-            _download_and_process_emnist()
-        with ESSENTIALS_FILENAME.open() as f:
-            essentials = json.load(f)
-        self.train_fraction = train_fraction
-        self.mapping = list(essentials["characters"])
-        self.inverse_mapping = {v: k for k, v in enumerate(self.mapping)}
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
-        self.transform = transforms.Compose([transforms.ToTensor()])
-        self.dims = (1, *essentials["input_shape"])
-        self.output_dims = (1,)
-
-    def prepare_data(self) -> None:
-        if not PROCESSED_DATA_FILENAME.exists():
-            _download_and_process_emnist()
-
-    def setup(self, stage: str = None) -> None:
-        if stage == "fit" or stage is None:
-            with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-                self.x_train = f["x_train"][:]
-                self.y_train = f["y_train"][:].squeeze().astype(int)
-
-            dataset_train = BaseDataset(
-                self.x_train, self.y_train, transform=self.transform
-            )
-            train_size = int(self.train_fraction * len(dataset_train))
-            val_size = len(dataset_train) - train_size
-            self.data_train, self.data_val = random_split(
-                dataset_train, [train_size, val_size], generator=torch.Generator()
-            )
-
-        if stage == "test" or stage is None:
-            with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-                self.x_test = f["x_test"][:]
-                self.y_test = f["y_test"][:].squeeze().astype(int)
-            self.data_test = BaseDataset(
-                self.x_test, self.y_test, transform=self.transform
-            )
-
-    def __repr__(self) -> str:
-        basic = f"EMNIST Dataset\nNum classes: {len(self.mapping)}\nMapping: {self.mapping}\nDims: {self.dims}\n"
-        if not any([self.data_train, self.data_val, self.data_test]):
-            return basic
-
-        datum, target = next(iter(self.train_dataloader()))
-        data = (
-            f"Train/val/test sizes: {len(self.data_train)}, {len(self.data_val)}, {len(self.data_test)}\n"
-            f"Batch x stats: {(datum.shape, datum.dtype, datum.min(), datum.mean(), datum.std(), datum.max())}\n"
-            f"Batch y stats: {(target.shape, target.dtype, target.min(), target.max())}\n"
-        )
-
-        return basic + data
-
-
-def _download_and_process_emnist() -> None:
-    metadata = toml.load(METADATA_FILENAME)
-    download_dataset(metadata, DL_DATA_DIRNAME)
-    _process_raw_dataset(metadata["filename"], DL_DATA_DIRNAME)
-
-
-def _process_raw_dataset(filename: str, dirname: Path) -> None:
-    logger.info("Unzipping EMNIST...")
-    curdir = os.getcwd()
-    os.chdir(dirname)
-    content = zipfile.ZipFile(filename, "r")
-    content.extract("matlab/emnist-byclass.mat")
-
-    from scipy.io import loadmat
-
-    logger.info("Loading training data from .mat file")
-    data = loadmat("matlab/emnist-byclass.mat")
-    x_train = (
-        data["dataset"]["train"][0, 0]["images"][0, 0]
-        .reshape(-1, 28, 28)
-        .swapaxes(1, 2)
-    )
-    y_train = data["dataset"]["train"][0, 0]["labels"][0, 0] + NUM_SPECIAL_TOKENS
-    x_test = (
-        data["dataset"]["test"][0, 0]["images"][0, 0].reshape(-1, 28, 28).swapaxes(1, 2)
-    )
-    y_test = data["dataset"]["test"][0, 0]["labels"][0, 0] + NUM_SPECIAL_TOKENS
-
-    if SAMPLE_TO_BALANCE:
-        logger.info("Balancing classes to reduce amount of data")
-        x_train, y_train = _sample_to_balance(x_train, y_train)
-        x_test, y_test = _sample_to_balance(x_test, y_test)
-
-    logger.info("Saving to HDF5 in a compressed format...")
-    PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-    with h5py.File(PROCESSED_DATA_FILENAME, "w") as f:
-        f.create_dataset("x_train", data=x_train, dtype="u1", compression="lzf")
-        f.create_dataset("y_train", data=y_train, dtype="u1", compression="lzf")
-        f.create_dataset("x_test", data=x_test, dtype="u1", compression="lzf")
-        f.create_dataset("y_test", data=y_test, dtype="u1", compression="lzf")
-
-    logger.info("Saving essential dataset parameters to text_recognizer/datasets...")
-    mapping = {int(k): chr(v) for k, v in data["dataset"]["mapping"][0, 0]}
-    characters = _augment_emnist_characters(mapping.values())
-    essentials = {"characters": characters, "input_shape": list(x_train.shape[1:])}
-
-    with ESSENTIALS_FILENAME.open(mode="w") as f:
-        json.dump(essentials, f)
-
-    logger.info("Cleaning up...")
-    shutil.rmtree("matlab")
-    os.chdir(curdir)
-
-
-def _sample_to_balance(x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    """Balances the dataset by taking the mean number of instances per class."""
-    np.random.seed(SEED)
-    num_to_sample = int(np.bincount(y.flatten()).mean())
-    all_sampled_indices = []
-    for label in np.unique(y.flatten()):
-        indices = np.where(y == label)[0]
-        sampled_indices = np.unique(np.random.choice(indices, num_to_sample))
-        all_sampled_indices.append(sampled_indices)
-    indices = np.concatenate(all_sampled_indices)
-    x_sampled = x[indices]
-    y_sampled = y[indices]
-    return x_sampled, y_sampled
-
-
-def _augment_emnist_characters(characters: Sequence[str]) -> Sequence[str]:
-    """Augment the mapping with extra symbols."""
-    # Extra characters from the IAM dataset.
-    iam_characters = [
-        " ",
-        "!",
-        '"',
-        "#",
-        "&",
-        "'",
-        "(",
-        ")",
-        "*",
-        "+",
-        ",",
-        "-",
-        ".",
-        "/",
-        ":",
-        ";",
-        "?",
-    ]
-
-    # Also add special tokens for:
-    # - CTC blank token at index 0
-    # - Start token at index 1
-    # - End token at index 2
-    # - Padding token at index 3
-    # Note: Do not forget to update NUM_SPECIAL_TOKENS if changing this!
-    return ["<b>", "<s>", "</s>", "<p>", *characters, *iam_characters]
-
-
-def download_emnist() -> None:
-    """Download dataset from internet, if it does not exists, and displays info."""
-    load_and_print_info(EMNIST)
diff --git a/text_recognizer/datasets/emnist_essentials.json b/text_recognizer/datasets/emnist_essentials.json
deleted file mode 100644
index 3f46a73..0000000
--- a/text_recognizer/datasets/emnist_essentials.json
+++ /dev/null
@@ -1 +0,0 @@
-{"characters": ["<b>", "<s>", "</s>", "<p>", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", " ", "!", "\"", "#", "&", "'", "(", ")", "*", "+", ",", "-", ".", "/", ":", ";", "?"], "input_shape": [28, 28]}
diff --git a/text_recognizer/datasets/emnist_lines.py b/text_recognizer/datasets/emnist_lines.py
deleted file mode 100644
index 9ebad22..0000000
--- a/text_recognizer/datasets/emnist_lines.py
+++ /dev/null
@@ -1,280 +0,0 @@
-"""Dataset of generated text from EMNIST characters."""
-from collections import defaultdict
-from pathlib import Path
-from typing import Callable, Dict, Tuple, Sequence
-
-import h5py
-from loguru import logger
-import numpy as np
-from PIL import Image
-import torch
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-
-from text_recognizer.datasets.base_dataset import BaseDataset, convert_strings_to_labels
-from text_recognizer.datasets.base_data_module import (
-    BaseDataModule,
-    load_and_print_info,
-)
-from text_recognizer.datasets.emnist import EMNIST
-from text_recognizer.datasets.sentence_generator import SentenceGenerator
-
-
-DATA_DIRNAME = BaseDataModule.data_dirname() / "processed" / "emnist_lines"
-ESSENTIALS_FILENAME = (
-    Path(__file__).parents[0].resolve() / "emnist_lines_essentials.json"
-)
-
-SEED = 4711
-IMAGE_HEIGHT = 56
-IMAGE_WIDTH = 1024
-IMAGE_X_PADDING = 28
-MAX_OUTPUT_LENGTH = 89  # Same as IAMLines
-
-
-class EMNISTLines(BaseDataModule):
-    """EMNIST Lines dataset: synthetic handwritten lines dataset made from EMNIST,"""
-
-    def __init__(
-        self,
-        augment: bool = True,
-        batch_size: int = 128,
-        num_workers: int = 0,
-        max_length: int = 32,
-        min_overlap: float = 0.0,
-        max_overlap: float = 0.33,
-        num_train: int = 10_000,
-        num_val: int = 2_000,
-        num_test: int = 2_000,
-    ) -> None:
-        super().__init__(batch_size, num_workers)
-
-        self.augment = augment
-        self.max_length = max_length
-        self.min_overlap = min_overlap
-        self.max_overlap = max_overlap
-        self.num_train = num_train
-        self.num_val = num_val
-        self.num_test = num_test
-
-        self.emnist = EMNIST()
-        self.mapping = self.emnist.mapping
-        max_width = (
-            int(self.emnist.dims[2] * (self.max_length + 1) * (1 - self.min_overlap))
-            + IMAGE_X_PADDING
-        )
-
-        if max_width >= IMAGE_WIDTH:
-            raise ValueError(
-                    f"max_width {max_width} greater than IMAGE_WIDTH {IMAGE_WIDTH}"
-                    )
-
-        self.dims = (
-            self.emnist.dims[0],
-            IMAGE_HEIGHT,
-            IMAGE_WIDTH
-        )
-
-        if self.max_length >= MAX_OUTPUT_LENGTH:
-            raise ValueError("max_length greater than MAX_OUTPUT_LENGTH")
-
-        self.output_dims = (MAX_OUTPUT_LENGTH, 1)
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
-
-    @property
-    def data_filename(self) -> Path:
-        """Return name of dataset."""
-        return (
-            DATA_DIRNAME / (f"ml_{self.max_length}_"
-            f"o{self.min_overlap:f}_{self.max_overlap:f}_"
-            f"ntr{self.num_train}_"
-            f"ntv{self.num_val}_"
-            f"nte{self.num_test}.h5")
-        )
-
-    def prepare_data(self) -> None:
-        if self.data_filename.exists():
-            return
-        np.random.seed(SEED)
-        self._generate_data("train")
-        self._generate_data("val")
-        self._generate_data("test")
-
-    def setup(self, stage: str = None) -> None:
-        logger.info("EMNISTLinesDataset loading data from HDF5...")
-        if stage == "fit" or stage is None:
-            print(self.data_filename)
-            with h5py.File(self.data_filename, "r") as f:
-                x_train = f["x_train"][:]
-                y_train = torch.LongTensor(f["y_train"][:])
-                x_val = f["x_val"][:]
-                y_val = torch.LongTensor(f["y_val"][:])
-
-            self.data_train = BaseDataset(
-                x_train, y_train, transform=_get_transform(augment=self.augment)
-            )
-            self.data_val = BaseDataset(
-                x_val, y_val, transform=_get_transform(augment=self.augment)
-            )
-
-        if stage == "test" or stage is None:
-            with h5py.File(self.data_filename, "r") as f:
-                x_test = f["x_test"][:]
-                y_test = torch.LongTensor(f["y_test"][:])
-
-            self.data_test = BaseDataset(
-                x_test, y_test, transform=_get_transform(augment=False)
-            )
-
-    def __repr__(self) -> str:
-        """Return str about dataset."""
-        basic = (
-            "EMNISTLines2 Dataset\n"  # pylint: disable=no-member
-            f"Min overlap: {self.min_overlap}\n"
-            f"Max overlap: {self.max_overlap}\n"
-            f"Num classes: {len(self.mapping)}\n"
-            f"Dims: {self.dims}\n"
-            f"Output dims: {self.output_dims}\n"
-        )
-
-        if not any([self.data_train, self.data_val, self.data_test]):
-            return basic
-
-        x, y = next(iter(self.train_dataloader()))
-        data = (
-            f"Train/val/test sizes: {len(self.data_train)}, {len(self.data_val)}, {len(self.data_test)}\n"
-            f"Batch x stats: {(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
-            f"Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
-        )
-        return basic + data
-
-    def _generate_data(self, split: str) -> None:
-        logger.info(f"EMNISTLines generating data for {split}...")
-        sentence_generator = SentenceGenerator(
-            self.max_length - 2
-        )  # Subtract by 2 because start/end token
-
-        emnist = self.emnist
-        emnist.prepare_data()
-        emnist.setup()
-
-        if split == "train":
-            samples_by_char = _get_samples_by_char(
-                emnist.x_train, emnist.y_train, emnist.mapping
-            )
-            num = self.num_train
-        elif split == "val":
-            samples_by_char = _get_samples_by_char(
-                emnist.x_train, emnist.y_train, emnist.mapping
-            )
-            num = self.num_val
-        else:
-            samples_by_char = _get_samples_by_char(
-                emnist.x_test, emnist.y_test, emnist.mapping
-            )
-            num = self.num_test
-
-        DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-        with h5py.File(self.data_filename, "a") as f:
-            x, y = _create_dataset_of_images(
-                num,
-                samples_by_char,
-                sentence_generator,
-                self.min_overlap,
-                self.max_overlap,
-                self.dims,
-            )
-            y = convert_strings_to_labels(
-                y, emnist.inverse_mapping, length=MAX_OUTPUT_LENGTH
-            )
-            f.create_dataset(f"x_{split}", data=x, dtype="u1", compression="lzf")
-            f.create_dataset(f"y_{split}", data=y, dtype="u1", compression="lzf")
-
-
-def _get_samples_by_char(
-    samples: np.ndarray, labels: np.ndarray, mapping: Dict
-) -> defaultdict:
-    samples_by_char = defaultdict(list)
-    for sample, label in zip(samples, labels):
-        samples_by_char[mapping[label]].append(sample)
-    return samples_by_char
-
-
-def _select_letter_samples_for_string(string: str, samples_by_char: defaultdict):
-    null_image = torch.zeros((28, 28), dtype=torch.uint8)
-    sample_image_by_char = {}
-    for char in string:
-        if char in sample_image_by_char:
-            continue
-        samples = samples_by_char[char]
-        sample = samples[np.random.choice(len(samples))] if samples else null_image
-        sample_image_by_char[char] = sample.reshape(28, 28)
-    return [sample_image_by_char[char] for char in string]
-
-
-def _construct_image_from_string(
-    string: str,
-    samples_by_char: defaultdict,
-    min_overlap: float,
-    max_overlap: float,
-    width: int,
-) -> torch.Tensor:
-    overlap = np.random.uniform(min_overlap, max_overlap)
-    sampled_images = _select_letter_samples_for_string(string, samples_by_char)
-    N = len(sampled_images)
-    H, W = sampled_images[0].shape
-    next_overlap_width = W - int(overlap * W)
-    concatenated_image = torch.zeros((H, width), dtype=torch.uint8)
-    x = IMAGE_X_PADDING
-    for image in sampled_images:
-        concatenated_image[:, x : (x + W)] += image
-        x += next_overlap_width
-    return torch.minimum(torch.Tensor([255]), concatenated_image)
-
-
-def _create_dataset_of_images(
-    num_samples: int,
-    samples_by_char: defaultdict,
-    sentence_generator: SentenceGenerator,
-    min_overlap: float,
-    max_overlap: float,
-    dims: Tuple,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    images = torch.zeros((num_samples, IMAGE_HEIGHT, dims[2]))
-    labels = []
-    for n in range(num_samples):
-        label = sentence_generator.generate()
-        crop = _construct_image_from_string(
-            label, samples_by_char, min_overlap, max_overlap, dims[-1]
-        )
-        height = crop.shape[0]
-        y = (IMAGE_HEIGHT - height) // 2
-        images[n, y : (y + height), :] = crop
-        labels.append(label)
-    return images, labels
-
-
-def _get_transform(augment: bool = False) -> Callable:
-    if not augment:
-        return transforms.Compose([transforms.ToTensor()])
-    return transforms.Compose(
-        [
-            transforms.ToTensor(),
-            transforms.ColorJitter(brightness=(0.5, 1.0)),
-            transforms.RandomAffine(
-                degrees=3,
-                translate=(0.0, 0.05),
-                scale=(0.4, 1.1),
-                shear=(-40, 50),
-                interpolation=InterpolationMode.BILINEAR,
-                fill=0,
-            ),
-        ]
-    )
-
-
-def generate_emnist_lines() -> None:
-    """Generates a synthetic handwritten dataset and displays info,"""
-    load_and_print_info(EMNISTLines)
diff --git a/text_recognizer/datasets/iam_dataset.py b/text_recognizer/datasets/iam_dataset.py
deleted file mode 100644
index a8998b9..0000000
--- a/text_recognizer/datasets/iam_dataset.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities."""
-import os
-from typing import Any, Dict, List
-import zipfile
-
-from boltons.cacheutils import cachedproperty
-import defusedxml.ElementTree as ET
-from loguru import logger
-import toml
-
-from text_recognizer.datasets.util import _download_raw_dataset, DATA_DIRNAME
-
-RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam"
-METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
-EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb"
-RAW_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-
-DOWNSAMPLE_FACTOR = 2  # If images were downsampled, the regions must also be.
-LINE_REGION_PADDING = 0  # Add this many pixels around the exact coordinates.
-
-
-class IamDataset:
-    """IAM dataset.
-
-    "The IAM Lines dataset, first published at the ICDAR 1999, contains forms of unconstrained handwritten text,
-    which were scanned at a resolution of 300dpi and saved as PNG images with 256 gray levels."
-    From http://www.fki.inf.unibe.ch/databases/iam-handwriting-database
-
-    The data split we will use is
-    IAM lines Large Writer Independent Text Line Recognition Task (lwitlrt): 9,862 text lines.
-        The validation set has been merged into the train set.
-        The train set has 7,101 lines from 326 writers.
-        The test set has 1,861 lines from 128 writers.
-        The text lines of all data sets are mutually exclusive, thus each writer has contributed to one set only.
-
-    """
-
-    def __init__(self) -> None:
-        self.metadata = toml.load(METADATA_FILENAME)
-
-    def load_or_generate_data(self) -> None:
-        """Downloads IAM dataset if xml files does not exist."""
-        if not self.xml_filenames:
-            self._download_iam()
-
-    @property
-    def xml_filenames(self) -> List:
-        """List of xml filenames."""
-        return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml"))
-
-    @property
-    def form_filenames(self) -> List:
-        """List of forms filenames."""
-        return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg"))
-
-    def _download_iam(self) -> None:
-        curdir = os.getcwd()
-        os.chdir(RAW_DATA_DIRNAME)
-        _download_raw_dataset(self.metadata)
-        _extract_raw_dataset(self.metadata)
-        os.chdir(curdir)
-
-    @property
-    def form_filenames_by_id(self) -> Dict:
-        """Creates a dictionary with filenames as keys and forms as values."""
-        return {filename.stem: filename for filename in self.form_filenames}
-
-    @cachedproperty
-    def line_strings_by_id(self) -> Dict:
-        """Return a dict from name of IAM form to a list of line texts in it."""
-        return {
-            filename.stem: _get_line_strings_from_xml_file(filename)
-            for filename in self.xml_filenames
-        }
-
-    @cachedproperty
-    def line_regions_by_id(self) -> Dict:
-        """Return a dict from name of IAM form to a list of (x1, x2, y1, y2) coordinates of all lines in it."""
-        return {
-            filename.stem: _get_line_regions_from_xml_file(filename)
-            for filename in self.xml_filenames
-        }
-
-    def __repr__(self) -> str:
-        """Print info about dataset."""
-        return "IAM Dataset\n" f"Number of forms: {len(self.xml_filenames)}\n"
-
-
-def _extract_raw_dataset(metadata: Dict) -> None:
-    logger.info("Extracting IAM data.")
-    with zipfile.ZipFile(metadata["filename"], "r") as zip_file:
-        zip_file.extractall()
-
-
-def _get_line_strings_from_xml_file(filename: str) -> List[str]:
-    """Get the text content of each line. Note that we replace &quot; with "."""
-    xml_root_element = ET.parse(filename).getroot()  # nosec
-    xml_line_elements = xml_root_element.findall("handwritten-part/line")
-    return [el.attrib["text"].replace("&quot;", '"') for el in xml_line_elements]
-
-
-def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:
-    """Get the line region dict for each line."""
-    xml_root_element = ET.parse(filename).getroot()  # nosec
-    xml_line_elements = xml_root_element.findall("handwritten-part/line")
-    return [_get_line_region_from_xml_element(el) for el in xml_line_elements]
-
-
-def _get_line_region_from_xml_element(xml_line: Any) -> Dict[str, int]:
-    """Extracts coordinates for each line of text."""
-    # TODO: fix input!
-    word_elements = xml_line.findall("word/cmp")
-    x1s = [int(el.attrib["x"]) for el in word_elements]
-    y1s = [int(el.attrib["y"]) for el in word_elements]
-    x2s = [int(el.attrib["x"]) + int(el.attrib["width"]) for el in word_elements]
-    y2s = [int(el.attrib["y"]) + int(el.attrib["height"]) for el in word_elements]
-    return {
-        "x1": min(x1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
-        "y1": min(y1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
-        "x2": max(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
-        "y2": max(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
-    }
-
-
-def main() -> None:
-    """Initializes the dataset and print info about the dataset."""
-    dataset = IamDataset()
-    dataset.load_or_generate_data()
-    print(dataset)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/text_recognizer/datasets/iam_lines_dataset.py b/text_recognizer/datasets/iam_lines_dataset.py
deleted file mode 100644
index 1cb84bd..0000000
--- a/text_recognizer/datasets/iam_lines_dataset.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""IamLinesDataset class."""
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import h5py
-from loguru import logger
-import torch
-from torch import Tensor
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.util import (
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-)
-
-
-PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines"
-PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "iam_lines.h5"
-PROCESSED_DATA_URL = (
-    "https://s3-us-west-2.amazonaws.com/fsdl-public-assets/iam_lines.h5"
-)
-
-
-class IamLinesDataset(Dataset):
-    """IAM lines datasets for handwritten text lines."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        init_token: Optional[str] = None,
-        pad_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        self.pad_token = "_" if pad_token is None else pad_token
-
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-            init_token=init_token,
-            pad_token=pad_token,
-            eos_token=eos_token,
-            lower=lower,
-        )
-
-    @property
-    def input_shape(self) -> Tuple:
-        """Input shape of the data."""
-        return self.data.shape[1:] if self.data is not None else None
-
-    @property
-    def output_shape(self) -> Tuple:
-        """Output shape of the data."""
-        return (
-            self.targets.shape[1:] + (self.num_classes,)
-            if self.targets is not None
-            else None
-        )
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        if not PROCESSED_DATA_FILENAME.exists():
-            PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-            logger.info("Downloading IAM lines...")
-            download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME)
-        with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-            self._data = f[f"x_{self.split}"][:]
-            self._targets = f[f"y_{self.split}"][:]
-        self._subsample()
-
-    def __repr__(self) -> str:
-        """Print info about the dataset."""
-        return (
-            "IAM Lines Dataset\n"  # pylint: disable=no-member
-            f"Number classes: {self.num_classes}\n"
-            f"Mapping: {self.mapper.mapping}\n"
-            f"Data: {self.data.shape}\n"
-            f"Targets: {self.targets.shape}\n"
-        )
-
-    def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        if self.transform:
-            data = self.transform(data)
-
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets
diff --git a/text_recognizer/datasets/iam_paragraphs_dataset.py b/text_recognizer/datasets/iam_paragraphs_dataset.py
deleted file mode 100644
index 8ba5142..0000000
--- a/text_recognizer/datasets/iam_paragraphs_dataset.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""IamParagraphsDataset class and functions for data processing."""
-import random
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import click
-import cv2
-import h5py
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-from torchvision.transforms import ToTensor
-
-from text_recognizer import util
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.iam_dataset import IamDataset
-from text_recognizer.datasets.util import (
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-)
-
-INTERIM_DATA_DIRNAME = DATA_DIRNAME / "interim" / "iam_paragraphs"
-DEBUG_CROPS_DIRNAME = INTERIM_DATA_DIRNAME / "debug_crops"
-PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_paragraphs"
-CROPS_DIRNAME = PROCESSED_DATA_DIRNAME / "crops"
-GT_DIRNAME = PROCESSED_DATA_DIRNAME / "gt"
-
-PARAGRAPH_BUFFER = 50  # Pixels in the IAM form images to leave around the lines.
-TEST_FRACTION = 0.2
-SEED = 4711
-
-
-class IamParagraphsDataset(Dataset):
-    """IAM Paragraphs dataset for paragraphs of handwritten text."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-    ) -> None:
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-        )
-        # Load Iam dataset.
-        self.iam_dataset = IamDataset()
-
-        self.num_classes = 3
-        self._input_shape = (256, 256)
-        self._output_shape = self._input_shape + (self.num_classes,)
-        self._ids = None
-
-    def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        seed = np.random.randint(SEED)
-        random.seed(seed)  # apply this seed to target tranfsorms
-        torch.manual_seed(seed)  # needed for torchvision 0.7
-        if self.transform:
-            data = self.transform(data)
-
-        random.seed(seed)  # apply this seed to target tranfsorms
-        torch.manual_seed(seed)  # needed for torchvision 0.7
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets.long()
-
-    @property
-    def ids(self) -> Tensor:
-        """Ids of the dataset."""
-        return self._ids
-
-    def get_data_and_target_from_id(self, id_: str) -> Tuple[Tensor, Tensor]:
-        """Get data target pair from id."""
-        ind = self.ids.index(id_)
-        return self.data[ind], self.targets[ind]
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        num_actual = len(list(CROPS_DIRNAME.glob("*.jpg")))
-        num_targets = len(self.iam_dataset.line_regions_by_id)
-
-        if num_actual < num_targets - 2:
-            self._process_iam_paragraphs()
-
-        self._data, self._targets, self._ids = _load_iam_paragraphs()
-        self._get_random_split()
-        self._subsample()
-
-    def _get_random_split(self) -> None:
-        np.random.seed(SEED)
-        num_train = int((1 - TEST_FRACTION) * self.data.shape[0])
-        indices = np.random.permutation(self.data.shape[0])
-        train_indices, test_indices = indices[:num_train], indices[num_train:]
-        if self.train:
-            self._data = self.data[train_indices]
-            self._targets = self.targets[train_indices]
-        else:
-            self._data = self.data[test_indices]
-            self._targets = self.targets[test_indices]
-
-    def _process_iam_paragraphs(self) -> None:
-        """Crop the part with the text.
-
-        For each page, crop out the part of it that correspond to the paragraph of text, and make sure all crops are
-        self.input_shape. The ground truth data is the same size, with a one-hot vector at each pixel
-        corresponding to labels 0=background, 1=odd-numbered line, 2=even-numbered line
-        """
-        crop_dims = self._decide_on_crop_dims()
-        CROPS_DIRNAME.mkdir(parents=True, exist_ok=True)
-        DEBUG_CROPS_DIRNAME.mkdir(parents=True, exist_ok=True)
-        GT_DIRNAME.mkdir(parents=True, exist_ok=True)
-        logger.info(
-            f"Cropping paragraphs, generating ground truth, and saving debugging images to {DEBUG_CROPS_DIRNAME}"
-        )
-        for filename in self.iam_dataset.form_filenames:
-            id_ = filename.stem
-            line_region = self.iam_dataset.line_regions_by_id[id_]
-            _crop_paragraph_image(filename, line_region, crop_dims, self.input_shape)
-
-    def _decide_on_crop_dims(self) -> Tuple[int, int]:
-        """Decide on the dimensions to crop out of the form image.
-
-        Since image width is larger than a comfortable crop around the longest paragraph,
-        we will make the crop a square form factor.
-        And since the found dimensions 610x610 are pretty close to 512x512,
-        we might as well resize crops and make it exactly that, which lets us
-        do all kinds of power-of-2 pooling and upsampling should we choose to.
-
-        Returns:
-            Tuple[int, int]: A tuple of crop dimensions.
-
-        Raises:
-            RuntimeError: When max crop height is larger than max crop width.
-
-        """
-
-        sample_form_filename = self.iam_dataset.form_filenames[0]
-        sample_image = util.read_image(sample_form_filename, grayscale=True)
-        max_crop_width = sample_image.shape[1]
-        max_crop_height = _get_max_paragraph_crop_height(
-            self.iam_dataset.line_regions_by_id
-        )
-        if not max_crop_height <= max_crop_width:
-            raise RuntimeError(
-                f"Max crop height is larger then max crop width: {max_crop_height} >= {max_crop_width}"
-            )
-
-        crop_dims = (max_crop_width, max_crop_width)
-        logger.info(
-            f"Max crop width and height were found to be {max_crop_width}x{max_crop_height}."
-        )
-        logger.info(f"Setting them to {max_crop_width}x{max_crop_width}")
-        return crop_dims
-
-    def __repr__(self) -> str:
-        """Return info about the dataset."""
-        return (
-            "IAM Paragraph Dataset\n"  # pylint: disable=no-member
-            f"Num classes: {self.num_classes}\n"
-            f"Data: {self.data.shape}\n"
-            f"Targets: {self.targets.shape}\n"
-        )
-
-
-def _get_max_paragraph_crop_height(line_regions_by_id: Dict) -> int:
-    heights = []
-    for regions in line_regions_by_id.values():
-        min_y1 = min(region["y1"] for region in regions) - PARAGRAPH_BUFFER
-        max_y2 = max(region["y2"] for region in regions) + PARAGRAPH_BUFFER
-        height = max_y2 - min_y1
-        heights.append(height)
-    return max(heights)
-
-
-def _crop_paragraph_image(
-    filename: str, line_regions: Dict, crop_dims: Tuple[int, int], final_dims: Tuple
-) -> None:
-    image = util.read_image(filename, grayscale=True)
-
-    min_y1 = min(region["y1"] for region in line_regions) - PARAGRAPH_BUFFER
-    max_y2 = max(region["y2"] for region in line_regions) + PARAGRAPH_BUFFER
-    height = max_y2 - min_y1
-    crop_height = crop_dims[0]
-    buffer = (crop_height - height) // 2
-
-    # Generate image crop.
-    image_crop = 255 * np.ones(crop_dims, dtype=np.uint8)
-    try:
-        image_crop[buffer : buffer + height] = image[min_y1:max_y2]
-    except Exception as e:  # pylint: disable=broad-except
-        logger.error(f"Rescued {filename}: {e}")
-        return
-
-    # Generate ground truth.
-    gt_image = np.zeros_like(image_crop, dtype=np.uint8)
-    for index, region in enumerate(line_regions):
-        gt_image[
-            (region["y1"] - min_y1 + buffer) : (region["y2"] - min_y1 + buffer),
-            region["x1"] : region["x2"],
-        ] = (index % 2 + 1)
-
-    # Generate image for debugging.
-    import matplotlib.pyplot as plt
-
-    cmap = plt.get_cmap("Set1")
-    image_crop_for_debug = np.dstack([image_crop, image_crop, image_crop])
-    for index, region in enumerate(line_regions):
-        color = [255 * _ for _ in cmap(index)[:-1]]
-        cv2.rectangle(
-            image_crop_for_debug,
-            (region["x1"], region["y1"] - min_y1 + buffer),
-            (region["x2"], region["y2"] - min_y1 + buffer),
-            color,
-            3,
-        )
-    image_crop_for_debug = cv2.resize(
-        image_crop_for_debug, final_dims, interpolation=cv2.INTER_AREA
-    )
-    util.write_image(image_crop_for_debug, DEBUG_CROPS_DIRNAME / f"{filename.stem}.jpg")
-
-    image_crop = cv2.resize(image_crop, final_dims, interpolation=cv2.INTER_AREA)
-    util.write_image(image_crop, CROPS_DIRNAME / f"{filename.stem}.jpg")
-
-    gt_image = cv2.resize(gt_image, final_dims, interpolation=cv2.INTER_NEAREST)
-    util.write_image(gt_image, GT_DIRNAME / f"{filename.stem}.png")
-
-
-def _load_iam_paragraphs() -> None:
-    logger.info("Loading IAM paragraph crops and ground truth from image files...")
-    images = []
-    gt_images = []
-    ids = []
-    for filename in CROPS_DIRNAME.glob("*.jpg"):
-        id_ = filename.stem
-        image = util.read_image(filename, grayscale=True)
-        image = 1.0 - image / 255
-
-        gt_filename = GT_DIRNAME / f"{id_}.png"
-        gt_image = util.read_image(gt_filename, grayscale=True)
-
-        images.append(image)
-        gt_images.append(gt_image)
-        ids.append(id_)
-    images = np.array(images).astype(np.float32)
-    gt_images = np.array(gt_images).astype(np.uint8)
-    ids = np.array(ids)
-    return images, gt_images, ids
-
-
-@click.command()
-@click.option(
-    "--subsample_fraction",
-    type=float,
-    default=None,
-    help="The subsampling factor of the dataset.",
-)
-def main(subsample_fraction: float) -> None:
-    """Load dataset and print info."""
-    logger.info("Creating train set...")
-    dataset = IamParagraphsDataset(train=True, subsample_fraction=subsample_fraction)
-    dataset.load_or_generate_data()
-    print(dataset)
-    logger.info("Creating test set...")
-    dataset = IamParagraphsDataset(subsample_fraction=subsample_fraction)
-    dataset.load_or_generate_data()
-    print(dataset)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/text_recognizer/datasets/iam_preprocessor.py b/text_recognizer/datasets/iam_preprocessor.py
deleted file mode 100644
index a93eb00..0000000
--- a/text_recognizer/datasets/iam_preprocessor.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""Preprocessor for extracting word letters from the IAM dataset.
-
-The code is mostly stolen from:
-
-    https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py
-
-"""
-
-import collections
-import itertools
-from pathlib import Path
-import re
-from typing import List, Optional, Union
-
-import click
-from loguru import logger
-import torch
-
-
-def load_metadata(
-    data_dir: Path, wordsep: str, use_words: bool = False
-) -> collections.defaultdict:
-    """Loads IAM metadata and returns it as a dictionary."""
-    forms = collections.defaultdict(list)
-    filename = "words.txt" if use_words else "lines.txt"
-
-    with open(data_dir / "ascii" / filename, "r") as f:
-        lines = (line.strip().split() for line in f if line[0] != "#")
-        for line in lines:
-            # Skip word segmentation errors.
-            if use_words and line[1] == "err":
-                continue
-            text = " ".join(line[8:])
-
-            # Remove garbage tokens:
-            text = text.replace("#", "")
-
-            # Swap word sep form | to wordsep
-            text = re.sub(r"\|+|\s", wordsep, text).strip(wordsep)
-            form_key = "-".join(line[0].split("-")[:2])
-            line_key = "-".join(line[0].split("-")[:3])
-            box_idx = 4 - use_words
-            box = tuple(int(val) for val in line[box_idx : box_idx + 4])
-            forms[form_key].append({"key": line_key, "box": box, "text": text})
-    return forms
-
-
-class Preprocessor:
-    """A preprocessor for the IAM dataset."""
-
-    # TODO: add lower case only to when generating...
-
-    def __init__(
-        self,
-        data_dir: Union[str, Path],
-        num_features: int,
-        tokens_path: Optional[Union[str, Path]] = None,
-        lexicon_path: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        self.wordsep = "▁"
-        self._use_word = use_words
-        self._prepend_wordsep = prepend_wordsep
-
-        self.data_dir = Path(data_dir)
-
-        self.forms = load_metadata(self.data_dir, self.wordsep, use_words=use_words)
-
-        # Load the set of graphemes:
-        graphemes = set()
-        for _, form in self.forms.items():
-            for line in form:
-                graphemes.update(line["text"].lower())
-        self.graphemes = sorted(graphemes)
-
-        # Build the token-to-index and index-to-token maps.
-        if tokens_path is not None:
-            with open(tokens_path, "r") as f:
-                self.tokens = [line.strip() for line in f]
-        else:
-            self.tokens = self.graphemes
-
-        if lexicon_path is not None:
-            with open(lexicon_path, "r") as f:
-                lexicon = (line.strip().split() for line in f)
-                lexicon = {line[0]: line[1:] for line in lexicon}
-                self.lexicon = lexicon
-        else:
-            self.lexicon = None
-
-        self.graphemes_to_index = {t: i for i, t in enumerate(self.graphemes)}
-        self.tokens_to_index = {t: i for i, t in enumerate(self.tokens)}
-        self.num_features = num_features
-        self.text = []
-
-    @property
-    def num_tokens(self) -> int:
-        """Returns the number or tokens."""
-        return len(self.tokens)
-
-    @property
-    def use_words(self) -> bool:
-        """If words are used."""
-        return self._use_word
-
-    def extract_train_text(self) -> None:
-        """Extracts training text."""
-        keys = []
-        with open(self.data_dir / "task" / "trainset.txt") as f:
-            keys.extend((line.strip() for line in f))
-
-        for _, examples in self.forms.items():
-            for example in examples:
-                if example["key"] not in keys:
-                    continue
-                self.text.append(example["text"].lower())
-
-    def to_index(self, line: str) -> torch.LongTensor:
-        """Converts text to a tensor of indices."""
-        token_to_index = self.graphemes_to_index
-        if self.lexicon is not None:
-            if len(line) > 0:
-                # If the word is not found in the lexicon, fall back to letters.
-                line = [
-                    t
-                    for w in line.split(self.wordsep)
-                    for t in self.lexicon.get(w, self.wordsep + w)
-                ]
-            token_to_index = self.tokens_to_index
-        if self._prepend_wordsep:
-            line = itertools.chain([self.wordsep], line)
-        return torch.LongTensor([token_to_index[t] for t in line])
-
-    def to_text(self, indices: List[int]) -> str:
-        """Converts indices to text."""
-        # Roughly the inverse of `to_index`
-        encoding = self.graphemes
-        if self.lexicon is not None:
-            encoding = self.tokens
-        return self._post_process(encoding[i] for i in indices)
-
-    def tokens_to_text(self, indices: List[int]) -> str:
-        """Converts tokens to text."""
-        return self._post_process(self.tokens[i] for i in indices)
-
-    def _post_process(self, indices: List[int]) -> str:
-        """A list join."""
-        return "".join(indices).strip(self.wordsep)
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to iam dataset")
-@click.option(
-    "--use_words", is_flag=True, help="Load word segmented dataset instead of lines"
-)
-@click.option(
-    "--save_text", type=str, default=None, help="Path to save parsed train text"
-)
-@click.option("--save_tokens", type=str, default=None, help="Path to save tokens")
-def cli(
-    data_dir: Optional[str],
-    use_words: bool,
-    save_text: Optional[str],
-    save_tokens: Optional[str],
-) -> None:
-    """CLI for extracting text data from the iam dataset."""
-    if data_dir is None:
-        data_dir = (
-            Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb"
-        )
-        logger.debug(f"Using data dir: {data_dir}")
-        if not data_dir.exists():
-            raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-    else:
-        data_dir = Path(data_dir)
-
-    preprocessor = Preprocessor(data_dir, 64, use_words=use_words)
-    preprocessor.extract_train_text()
-
-    processed_dir = data_dir.parents[2] / "processed" / "iam_lines"
-    logger.debug(f"Saving processed files at: {processed_dir}")
-
-    if save_text is not None:
-        logger.info("Saving training text")
-        with open(processed_dir / save_text, "w") as f:
-            f.write("\n".join(t for t in preprocessor.text))
-
-    if save_tokens is not None:
-        logger.info("Saving tokens")
-        with open(processed_dir / save_tokens, "w") as f:
-            f.write("\n".join(preprocessor.tokens))
-
-
-if __name__ == "__main__":
-    cli()
diff --git a/text_recognizer/datasets/sentence_generator.py b/text_recognizer/datasets/sentence_generator.py
deleted file mode 100644
index 53b781c..0000000
--- a/text_recognizer/datasets/sentence_generator.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""Downloading the Brown corpus with NLTK for sentence generating."""
-
-import itertools
-import re
-import string
-from typing import Optional
-
-import nltk
-from nltk.corpus.reader.util import ConcatenatedCorpusView
-import numpy as np
-
-from text_recognizer.datasets.util import DATA_DIRNAME
-
-NLTK_DATA_DIRNAME = DATA_DIRNAME / "downloaded" / "nltk"
-
-
-class SentenceGenerator:
-    """Generates text sentences using the Brown corpus."""
-
-    def __init__(self, max_length: Optional[int] = None) -> None:
-        """Loads the corpus and sets word start indices."""
-        self.corpus = brown_corpus()
-        self.word_start_indices = [0] + [
-            _.start(0) + 1 for _ in re.finditer(" ", self.corpus)
-        ]
-        self.max_length = max_length
-
-    def generate(self, max_length: Optional[int] = None) -> str:
-        """Generates a word or sentences from the Brown corpus.
-
-        Sample a string from the Brown corpus of length at least one word and at most max_length, padding to
-        max_length with the '_' characters if sentence is shorter.
-
-        Args:
-            max_length (Optional[int]): The maximum number of characters in the sentence. Defaults to None.
-
-        Returns:
-            str: A sentence from the Brown corpus.
-
-        Raises:
-            ValueError: If max_length was not specified at initialization and not given as an argument.
-
-        """
-        if max_length is None:
-            max_length = self.max_length
-        if max_length is None:
-            raise ValueError(
-                "Must provide max_length to this method or when making this object."
-            )
-        
-        for _ in range(10):
-            try:
-                index = np.random.randint(0, len(self.word_start_indices) - 1)
-                start_index = self.word_start_indices[index]
-                end_index_candidates = []
-                for index in range(index + 1, len(self.word_start_indices)):
-                    if self.word_start_indices[index] - start_index > max_length:
-                        break
-                    end_index_candidates.append(self.word_start_indices[index])
-                end_index = np.random.choice(end_index_candidates)
-                sampled_text = self.corpus[start_index:end_index].strip()
-                return sampled_text
-            except Exception:
-                pass
-        raise RuntimeError("Was not able to generate a valid string")
-
-
-def brown_corpus() -> str:
-    """Returns a single string with the Brown corpus with all punctuations stripped."""
-    sentences = load_nltk_brown_corpus()
-    corpus = " ".join(itertools.chain.from_iterable(sentences))
-    corpus = corpus.translate({ord(c): None for c in string.punctuation})
-    corpus = re.sub(" +", " ", corpus)
-    return corpus
-
-
-def load_nltk_brown_corpus() -> ConcatenatedCorpusView:
-    """Load the Brown corpus using the NLTK library."""
-    nltk.data.path.append(NLTK_DATA_DIRNAME)
-    try:
-        nltk.corpus.brown.sents()
-    except LookupError:
-        NLTK_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-        nltk.download("brown", download_dir=NLTK_DATA_DIRNAME)
-    return nltk.corpus.brown.sents()
diff --git a/text_recognizer/datasets/transforms.py b/text_recognizer/datasets/transforms.py
deleted file mode 100644
index b6a48f5..0000000
--- a/text_recognizer/datasets/transforms.py
+++ /dev/null
@@ -1,266 +0,0 @@
-"""Transforms for PyTorch datasets."""
-from abc import abstractmethod
-from pathlib import Path
-import random
-from typing import Any, Optional, Union
-
-from loguru import logger
-import numpy as np
-from PIL import Image
-import torch
-from torch import Tensor
-import torch.nn.functional as F
-from torchvision import transforms
-from torchvision.transforms import (
-    ColorJitter,
-    Compose,
-    Normalize,
-    RandomAffine,
-    RandomHorizontalFlip,
-    RandomRotation,
-    ToPILImage,
-    ToTensor,
-)
-
-from text_recognizer.datasets.iam_preprocessor import Preprocessor
-from text_recognizer.datasets.util import EmnistMapper
-
-
-class RandomResizeCrop:
-    """Image transform with random resize and crop applied.
-
-    Stolen from
-
-    https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py
-
-    """
-
-    def __init__(self, jitter: int = 10, ratio: float = 0.5) -> None:
-        self.jitter = jitter
-        self.ratio = ratio
-
-    def __call__(self, img: np.ndarray) -> np.ndarray:
-        """Applies random crop and rotation to an image."""
-        w, h = img.size
-
-        # pad with white:
-        img = transforms.functional.pad(img, self.jitter, fill=255)
-
-        # crop at random (x, y):
-        x = self.jitter + random.randint(-self.jitter, self.jitter)
-        y = self.jitter + random.randint(-self.jitter, self.jitter)
-
-        # randomize aspect ratio:
-        size_w = w * random.uniform(1 - self.ratio, 1 + self.ratio)
-        size = (h, int(size_w))
-        img = transforms.functional.resized_crop(img, y, x, h, w, size)
-        return img
-
-
-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
-
-
-class ApplyContrast:
-    """Sets everything below a threshold to zero, i.e. increase contrast."""
-
-    def __init__(self, low: float = 0.0, high: float = 0.25) -> None:
-        self.low = low
-        self.high = high
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Apply mask binary mask to input tensor."""
-        mask = x > np.random.RandomState().uniform(low=self.low, high=self.high)
-        return x * mask
-
-
-class Unsqueeze:
-    """Add a dimension to the tensor."""
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Adds dim."""
-        return x.unsqueeze(0)
-
-
-class Squeeze:
-    """Removes the first dimension of a tensor."""
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Removes first dim."""
-        return x.squeeze(0)
-
-
-class ToLower:
-    """Converts target to lower case."""
-
-    def __call__(self, target: Tensor) -> Tensor:
-        """Corrects index value in target tensor."""
-        device = target.device
-        return torch.stack([x - 26 if x > 35 else x for x in target]).to(device)
-
-
-class ToCharcters:
-    """Converts integers to characters."""
-
-    def __init__(
-        self, pad_token: str, eos_token: str, init_token: str = None, lower: bool = True
-    ) -> 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,
-                lower=lower,
-            )
-        else:
-            self.emnist_mapper = EmnistMapper(
-                pad_token=self.pad_token, eos_token=self.eos_token, lower=lower
-            )
-
-    def __call__(self, y: Tensor) -> str:
-        """Converts a Tensor to a str."""
-        return (
-            "".join([self.emnist_mapper(int(i)) for i in y])
-            .strip("_")
-            .replace(" ", "▁")
-        )
-
-
-class WordPieces:
-    """Abstract transform for word pieces."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        if data_dir is None:
-            data_dir = (
-                Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb"
-            )
-            logger.debug(f"Using data dir: {data_dir}")
-            if not data_dir.exists():
-                raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-        else:
-            data_dir = Path(data_dir)
-        processed_path = (
-            Path(__file__).resolve().parents[3] / "data" / "processed" / "iam_lines"
-        )
-        tokens_path = processed_path / tokens
-        lexicon_path = processed_path / lexicon
-
-        self.preprocessor = Preprocessor(
-            data_dir,
-            num_features,
-            tokens_path,
-            lexicon_path,
-            use_words,
-            prepend_wordsep,
-        )
-
-    @abstractmethod
-    def __call__(self, *args, **kwargs) -> Any:
-        """Transforms input."""
-        ...
-
-
-class ToWordPieces(WordPieces):
-    """Transforms str to word pieces."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        super().__init__(
-            num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
-        )
-
-    def __call__(self, line: str) -> Tensor:
-        """Transforms str to word pieces."""
-        return self.preprocessor.to_index(line)
-
-
-class ToText(WordPieces):
-    """Takes word pieces and converts them to text."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        super().__init__(
-            num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
-        )
-
-    def __call__(self, x: Tensor) -> str:
-        """Converts tensor to text."""
-        return self.preprocessor.to_text(x.tolist())
diff --git a/text_recognizer/datasets/util.py b/text_recognizer/datasets/util.py
deleted file mode 100644
index da87756..0000000
--- a/text_recognizer/datasets/util.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""Util functions for datasets."""
-import hashlib
-import json
-import os
-from pathlib import Path
-import string
-from typing import Dict, List, Optional, Union
-from urllib.request import urlretrieve
-
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-from torchvision.datasets import EMNIST
-from tqdm import tqdm
-
-DATA_DIRNAME = Path(__file__).resolve().parents[3] / "data"
-ESSENTIALS_FILENAME = Path(__file__).resolve().parents[0] / "emnist_essentials.json"
-
-
-def save_emnist_essentials(emnsit_dataset: EMNIST = EMNIST) -> None:
-    """Extract and saves EMNIST essentials."""
-    labels = emnsit_dataset.classes
-    labels.sort()
-    mapping = [(i, str(label)) for i, label in enumerate(labels)]
-    essentials = {
-        "mapping": mapping,
-        "input_shape": tuple(np.array(emnsit_dataset[0][0]).shape[:]),
-    }
-    logger.info("Saving emnist essentials...")
-    with open(ESSENTIALS_FILENAME, "w") as f:
-        json.dump(essentials, f)
-
-
-def download_emnist() -> None:
-    """Download the EMNIST dataset via the PyTorch class."""
-    logger.info(f"Data directory is: {DATA_DIRNAME}")
-    dataset = EMNIST(root=DATA_DIRNAME, split="byclass", download=True)
-    save_emnist_essentials(dataset)
-
-
-class EmnistMapper:
-    """Mapper between network output to Emnist character."""
-
-    def __init__(
-        self,
-        pad_token: str,
-        init_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        """Loads the emnist essentials file with the mapping and input shape."""
-        self.init_token = init_token
-        self.pad_token = pad_token
-        self.eos_token = eos_token
-        self.lower = lower
-
-        self.essentials = self._load_emnist_essentials()
-        # Load dataset information.
-        self._mapping = dict(self.essentials["mapping"])
-        self._augment_emnist_mapping()
-        self._inverse_mapping = {v: k for k, v in self.mapping.items()}
-        self._num_classes = len(self.mapping)
-        self._input_shape = self.essentials["input_shape"]
-
-    def __call__(self, token: Union[str, int, np.uint8, Tensor]) -> Union[str, int]:
-        """Maps the token to emnist character or character index.
-
-        If the token is an integer (index), the method will return the Emnist character corresponding to that index.
-        If the token is a str (Emnist character), the method will return the corresponding index for that character.
-
-        Args:
-            token (Union[str, int, np.uint8, Tensor]): Either a string or index (integer).
-
-        Returns:
-            Union[str, int]: The mapping result.
-
-        Raises:
-            KeyError: If the index or string does not exist in the mapping.
-
-        """
-        if (
-            (isinstance(token, np.uint8) or isinstance(token, int))
-            or torch.is_tensor(token)
-            and int(token) in self.mapping
-        ):
-            return self.mapping[int(token)]
-        elif isinstance(token, str) and token in self._inverse_mapping:
-            return self._inverse_mapping[token]
-        else:
-            raise KeyError(f"Token {token} does not exist in the mappings.")
-
-    @property
-    def mapping(self) -> Dict:
-        """Returns the mapping between index and character."""
-        return self._mapping
-
-    @property
-    def inverse_mapping(self) -> Dict:
-        """Returns the mapping between character and index."""
-        return self._inverse_mapping
-
-    @property
-    def num_classes(self) -> int:
-        """Returns the number of classes in the dataset."""
-        return self._num_classes
-
-    @property
-    def input_shape(self) -> List[int]:
-        """Returns the input shape of the Emnist characters."""
-        return self._input_shape
-
-    def _load_emnist_essentials(self) -> Dict:
-        """Load the EMNIST mapping."""
-        with open(str(ESSENTIALS_FILENAME)) as f:
-            essentials = json.load(f)
-        return essentials
-
-    def _augment_emnist_mapping(self) -> None:
-        """Augment the mapping with extra symbols."""
-        # Extra symbols in IAM dataset
-        if self.lower:
-            self._mapping = {
-                k: str(v)
-                for k, v in enumerate(list(range(10)) + list(string.ascii_lowercase))
-            }
-
-        extra_symbols = [
-            " ",
-            "!",
-            '"',
-            "#",
-            "&",
-            "'",
-            "(",
-            ")",
-            "*",
-            "+",
-            ",",
-            "-",
-            ".",
-            "/",
-            ":",
-            ";",
-            "?",
-        ]
-
-        # padding symbol, and acts as blank symbol as well.
-        extra_symbols.append(self.pad_token)
-
-        if self.init_token is not None:
-            extra_symbols.append(self.init_token)
-
-        if self.eos_token is not None:
-            extra_symbols.append(self.eos_token)
-
-        max_key = max(self.mapping.keys())
-        extra_mapping = {}
-        for i, symbol in enumerate(extra_symbols):
-            extra_mapping[max_key + 1 + i] = symbol
-
-        self._mapping = {**self.mapping, **extra_mapping}
-
-
-def compute_sha256(filename: Union[Path, str]) -> str:
-    """Returns the SHA256 checksum of a file."""
-    with open(filename, "rb") as f:
-        return hashlib.sha256(f.read()).hexdigest()
-
-
-class TqdmUpTo(tqdm):
-    """TQDM progress bar when downloading files.
-
-    From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
-
-    """
-
-    def update_to(
-        self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None
-    ) -> None:
-        """Updates the progress bar.
-
-        Args:
-            blocks (int): Number of blocks transferred so far. Defaults to 1.
-            block_size (int): Size of each block, in tqdm units. Defaults to 1.
-            total_size (Optional[int]): Total size in tqdm units. Defaults to None.
-        """
-        if total_size is not None:
-            self.total = total_size  # pylint: disable=attribute-defined-outside-init
-        self.update(blocks * block_size - self.n)
-
-
-def download_url(url: str, filename: str) -> None:
-    """Downloads a file from url to filename, with a progress bar."""
-    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
-        urlretrieve(url, filename, reporthook=t.update_to, data=None)  # nosec
-
-
-def _download_raw_dataset(metadata: Dict) -> None:
-    if os.path.exists(metadata["filename"]):
-        return
-    logger.info(f"Downloading raw dataset from {metadata['url']}...")
-    download_url(metadata["url"], metadata["filename"])
-    logger.info("Computing SHA-256...")
-    sha256 = compute_sha256(metadata["filename"])
-    if sha256 != metadata["sha256"]:
-        raise ValueError(
-            "Downloaded data file SHA-256 does not match that listed in metadata document."
-        )