"""MNIST text dataset.""" from __future__ import absolute_import, division, print_function import json import os import math import numpy as np import datasets _DESCRIPTION = """\ MNIST dataset adapted to a text-based representation. This allows testing interpolation quality for Transformer-VAEs. System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM Works by quantising each MNIST pixel into one of 64 characters. Every sample has an up & down version to encourage the model to learn rotation invarient features. Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. Data format: - text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example: ``` 00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 02 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 03 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 04 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 05 down ! ! ! ! ! ! ! ! ! ! ! ! ! % % % @ C L ' J a ^ @ ! ! ! ! 06 down ! ! ! ! ! ! ! ! ( * 8 G K ` ` ` ` ` Y L ` ] Q 1 ! ! ! ! 07 down ! ! ! ! ! ! ! - \ ` ` ` ` ` ` ` ` _ 8 5 5 / * ! ! ! ! ! 08 down ! ! ! ! ! ! ! % W ` ` ` ` ` R N ^ ] ! ! ! ! ! ! ! ! ! ! 09 down ! ! ! ! ! ! ! ! 5 H ; ` ` T # ! + G ! ! ! ! ! ! ! ! ! ! 10 down ! ! ! ! ! ! ! ! ! $ ! G ` 7 ! ! ! ! ! ! ! ! ! ! ! ! ! ! 11 down ! ! ! ! ! ! ! ! ! ! ! C ` P ! ! ! ! ! ! ! ! ! ! ! ! ! ! 12 down ! ! ! ! ! ! ! ! ! ! ! # P ` 2 ! ! ! ! ! ! ! ! ! ! ! ! ! 13 down ! ! ! ! ! ! ! ! ! ! ! ! ) ] Y I < ! ! ! ! ! ! ! ! ! ! ! 14 down ! ! ! ! ! ! ! ! ! ! ! ! ! 5 ] ` ` > ' ! ! ! ! ! ! ! ! ! 15 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , O ` ` F ' ! ! ! ! ! ! ! ! 16 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! % 8 ` ` O ! ! ! ! ! ! ! ! 17 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! _ ` _ 1 ! ! ! ! ! ! ! 18 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , A N ` ` T ! ! ! ! ! ! ! ! 19 down ! ! ! ! ! ! ! ! ! ! ! ! * F Z ` ` ` _ N ! ! ! ! ! ! ! ! 20 down ! ! ! ! ! ! ! ! ! ! ' = X ` ` ` ` S 4 ! ! ! ! ! ! ! ! ! 21 down ! ! ! ! ! ! ! ! & 1 V ` ` ` ` R 5 ! ! ! ! ! ! ! ! ! ! ! 22 down ! ! ! ! ! ! % K W ` ` ` ` Q 5 # ! ! ! ! ! ! ! ! ! ! ! ! 23 down ! ! ! ! . L Y ` ` ` ` ^ B # ! ! ! ! ! ! ! ! ! ! ! ! ! ! 24 down ! ! ! ! C ` ` ` V B B % ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 25 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 26 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 27 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ``` - label: Just a number with the texts matching label. """ _CITATION = """\ @dataset{dataset, author = {Fraser Greenlee}, year = {2021}, month = {1}, pages = {}, title = {MNIST text dataset.}, doi = {} } """ _TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text/train.json.zip" _TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text/test.json" LABELS = list(range(10)) CUSTOM_METHODS = ['array_to_text', 'text_to_array'] IMG_SIZE = (30, 28) class MnistText(datasets.GeneratorBasedBuilder): """MNIST represented by text.""" def as_dataset(self, *args, **kwargs): f""" Return a Dataset for the specified split. Modified to add custom methods {CUSTOM_METHODS} to the dataset. This allows rendering the text as images & vice versa. """ a_dataset = super().as_dataset(*args, **kwargs) for method in CUSTOM_METHODS: setattr(a_dataset, f'custom_{method}', getattr(self, method)) return a_dataset @staticmethod def array_to_text(pixels: np.array): ''' Takes a 2D array of pixel brightnesses and converts them to text. Uses 64 tokens to represent all brightness values. ''' width = pixels.shape[0] height = pixels.shape[1] lines = [] for y in range(height): split = ['%02d down' % y] for x in range(width): brightness = pixels[y, x] mBrightness = math.floor(brightness * 64) s = chr(mBrightness + 33) split.append(s) lines.append(' '.join(split)) reversed = [] for line in lines: reversed.insert(0, (line.replace(' down ', ' up ', 1))) return ['\n'.join(lines), '\n'.join(reversed)] @staticmethod def text_to_array(text: str): ''' Takes a text sequences and tries to convert it into a 2D numpy array of brightnesses. If parts of the text don't match the format they will be skipped. ''' lines = text.strip().split('\n') pixels = np.zeros((IMG_SIZE[1], IMG_SIZE[0] - 2)) tokens = None for y in range(min(IMG_SIZE[1], len(lines))): line = lines[y].strip() tokens = line.split(' ') for i in range(2, min(IMG_SIZE[0], len(tokens))): token = tokens[i] if len(token) == 1: tkn_v = (ord(token) - 33) if tkn_v >= 0 and tkn_v <= 64: pixels[y, i - 2] = tkn_v / 64 if not lines: return pixels if tokens and len(tokens) > 1 and tokens[1] == 'up': pixels = pixels[::-1] return pixels def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { 'label': datasets.features.ClassLabel(names=LABELS), 'text': datasets.Value("string"), } ), homepage="https://github.com/Fraser-Greenlee/my-huggingface-datasets", citation=_CITATION, ) def _split_generators(self, dl_manager): train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(train_path, 'train.json')} ), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), ] def _generate_examples(self, filepath): """Generate examples.""" with open(filepath, encoding="utf-8") as json_lines_file: data = [] for line in json_lines_file: data.append(json.loads(line)) for id_, row in enumerate(data): yield id_, row