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"""Compressed MNIST text dataset.""" |
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from __future__ import absolute_import, division, print_function |
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import json |
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import os |
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import math |
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import numpy as np |
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import datasets |
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_DESCRIPTION = """\ |
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MNIST dataset adapted to a text-based representation. |
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*Modified images to be ~1/4 the original area.* |
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Done by taking a max pool. |
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This allows testing interpolation quality for Transformer-VAEs. |
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System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM |
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Works by quantising each MNIST pixel into one of 64 characters. |
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Every sample has an up & down version to encourage the model to learn rotation invarient features. |
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Use `.array_to_text(` and `.text_to_array(` methods to test your generated data. |
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Data format: |
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- text: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example: |
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``` |
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00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! |
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01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! |
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02 down ! ! ! ! ! ! % % C L a ^ ! ! |
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03 down ! ! ! - ` ` ` ` ` Y ` Q ! ! |
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04 down ! ! ! % ` ` ` R ^ ! ! ! ! ! |
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05 down ! ! ! ! $ G ` ! ! ! ! ! ! ! |
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06 down ! ! ! ! ! # ` Y < ! ! ! ! ! |
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07 down ! ! ! ! ! ! 5 ` ` F ! ! ! ! |
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08 down ! ! ! ! ! ! ! % ` ` 1 ! ! ! |
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09 down ! ! ! ! ! ! F ` ` ` ! ! ! ! |
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10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! ! |
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11 down ! ! L ` ` ` ` 5 ! ! ! ! ! ! |
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12 down ! ! ` ` V B ! ! ! ! ! ! ! ! |
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13 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! |
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``` |
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- label: Just a number with the texts matching label. |
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""" |
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_CITATION = """\ |
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@dataset{dataset, |
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author = {Fraser Greenlee}, |
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year = {2021}, |
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month = {1}, |
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pages = {}, |
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title = {MNIST small text dataset.}, |
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doi = {} |
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} |
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""" |
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_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-small/train.json.zip" |
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_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-small/test.json" |
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LABELS = list(range(10)) |
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CUSTOM_METHODS = ['array_to_text', 'text_to_array'] |
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IMG_SIZE = (16, 14) |
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class MnistTextSmall(datasets.GeneratorBasedBuilder): |
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"""MNIST represented by text.""" |
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def as_dataset(self, *args, **kwargs): |
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f""" |
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Return a Dataset for the specified split. |
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Modified to add custom methods {CUSTOM_METHODS} to the dataset. |
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This allows rendering the text as images & vice versa. |
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""" |
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a_dataset = super().as_dataset(*args, **kwargs) |
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for method in CUSTOM_METHODS: |
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setattr(a_dataset, f'custom_{method}', getattr(self, method)) |
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return a_dataset |
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@staticmethod |
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def array_to_text(pixels: np.array): |
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''' |
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Takes a 2D array of pixel brightnesses and converts them to text. |
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Uses 64 tokens to represent all brightness values. |
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''' |
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width = pixels.shape[0] |
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height = pixels.shape[1] |
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lines = [] |
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for y in range(height): |
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split = ['%02d down' % y] |
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for x in range(width): |
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brightness = pixels[y, x] |
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mBrightness = math.floor(brightness * 64) |
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s = chr(mBrightness + 33) |
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split.append(s) |
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lines.append(' '.join(split)) |
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reversed = [] |
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for line in lines: |
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reversed.insert(0, (line.replace(' down ', ' up ', 1))) |
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return ['\n'.join(lines), '\n'.join(reversed)] |
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@staticmethod |
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def text_to_array(text: str): |
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''' |
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Takes a text sequences and tries to convert it into a 2D numpy array of brightnesses. |
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If parts of the text don't match the format they will be skipped. |
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''' |
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lines = text.split('\n') |
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pixels = np.zeros((IMG_SIZE[1], IMG_SIZE[0] - 2)) |
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tokens = None |
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for y, line in enumerate(lines): |
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tokens = line.split(' ') |
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for i in range(2, min(IMG_SIZE[0], len(tokens))): |
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token = tokens[i] |
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if len(token) == 1: |
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tkn_v = (ord(token) - 33) |
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if tkn_v >= 0 and tkn_v <= 64: |
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pixels[y, i - 2] = (ord(token) - 33) / 64 |
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if not lines: |
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return pixels |
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if tokens and len(tokens) > 1 and tokens[1] == 'up': |
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pixels = pixels[::-1] |
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return pixels |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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'label': datasets.features.ClassLabel(names=LABELS), |
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'text': datasets.Value("string"), |
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} |
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), |
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homepage="https://github.com/Fraser-Greenlee/my-huggingface-datasets", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) |
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test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": os.path.join(train_path, 'train.json')} |
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), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate examples.""" |
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with open(filepath, encoding="utf-8") as json_lines_file: |
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data = [] |
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for line in json_lines_file: |
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data.append(json.loads(line)) |
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for id_, row in enumerate(data): |
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yield id_, row |
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