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"""EMNIST dataset: downloads it from FSDL aws url if not present."""
import json
import os
from pathlib import Path
import shutil
from typing import Callable, Dict, List, Optional, Sequence, Tuple
import zipfile
import attr
import h5py
from loguru import logger
import numpy as np
import toml
import torchvision.transforms as T
from text_recognizer.data.base_data_module import (
BaseDataModule,
load_and_print_info,
)
from text_recognizer.data.base_dataset import BaseDataset, split_dataset
from text_recognizer.data.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"
@attr.s(auto_attribs=True)
class EMNIST(BaseDataModule):
"""Lightning DataModule class for loading EMNIST dataset.
'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.
"""
train_fraction: float = attr.ib()
transform: Callable = attr.ib(init=False, default=T.Compose([T.ToTensor()]))
def __attrs_post_init__(self) -> None:
self.mapping, self.inverse_mapping, input_shape = emnist_mapping()
self.dims = (1, *input_shape)
def prepare_data(self) -> None:
"""Downloads dataset if not present."""
if not PROCESSED_DATA_FILENAME.exists():
download_and_process_emnist()
def setup(self, stage: str = None) -> None:
"""Loads the dataset specified by the stage."""
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
)
self.data_train, self.data_val = split_dataset(
dataset_train, fraction=self.train_fraction, seed=SEED
)
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:
"""Returns string with info about the dataset."""
basic = (
"EMNIST Dataset\n"
f"Num classes: {len(self.mapping)}\n"
f"Mapping: {self.mapping}\n"
f"Dims: {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 = (
"Train/val/test sizes: "
f"{len(self.data_train)}, {len(self.data_val)}, {len(self.data_test)}\n"
"Batch x stats: "
f"{(datum.shape, datum.dtype, datum.min())}"
f"{(datum.mean(), datum.std(), datum.max())}\n"
f"Batch y stats: "
f"{(target.shape, target.dtype, target.min(), target.max())}\n"
)
return basic + data
def emnist_mapping(
extra_symbols: Optional[Sequence[str]] = None,
) -> Tuple[List, Dict[str, int], List[int]]:
"""Return the EMNIST mapping."""
if not ESSENTIALS_FILENAME.exists():
download_and_process_emnist()
with ESSENTIALS_FILENAME.open() as f:
essentials = json.load(f)
mapping = list(essentials["characters"])
if extra_symbols is not None:
mapping += extra_symbols
inverse_mapping = {v: k for k, v in enumerate(mapping)}
input_shape = essentials["input_shape"]
return mapping, inverse_mapping, input_shape
def download_and_process_emnist() -> None:
"""Downloads and preprocesses EMNIST dataset."""
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:
"""Processes the raw EMNIST dataset."""
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>", "<e>", "<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)
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