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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
|
"""EMNIST dataset: downloads it from FSDL aws url if not present."""
import json
import os
import shutil
import zipfile
from pathlib import Path
from typing import Optional, Sequence, Tuple
import h5py
import numpy as np
import toml
from loguru import logger as log
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.utils.download_utils import download_dataset
import text_recognizer.metadata.emnist as metadata
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.
"""
def __init__(self) -> None:
super().__init__()
self.dims = (1, *self.tokenizer.input_size)
def prepare_data(self) -> None:
"""Downloads dataset if not present."""
if not metadata.PROCESSED_DATA_FILENAME.exists():
download_and_process_emnist()
def setup(self, stage: Optional[str] = None) -> None:
"""Loads the dataset specified by the stage."""
if stage == "fit" or stage is None:
with h5py.File(metadata.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=metadata.SEED
)
if stage == "test" or stage is None:
with h5py.File(metadata.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.tokenizer)}\n"
f"Mapping: {self.tokenizer}\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 download_and_process_emnist() -> None:
"""Downloads and preprocesses EMNIST dataset."""
metadata_ = toml.load(metadata.METADATA_FILENAME)
download_dataset(metadata_, metadata.DL_DATA_DIRNAME)
_process_raw_dataset(metadata_["filename"], metadata.DL_DATA_DIRNAME)
def _process_raw_dataset(filename: str, dirname: Path) -> None:
"""Processes the raw EMNIST dataset."""
log.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
log.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] + metadata.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] + metadata.NUM_SPECIAL_TOKENS
if metadata.SAMPLE_TO_BALANCE:
log.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)
log.info("Saving to HDF5 in a compressed format...")
metadata.PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
with h5py.File(metadata.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")
log.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 metadata.ESSENTIALS_FILENAME.open(mode="w") as f:
json.dump(essentials, f)
log.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(metadata.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())
|
