import from S3

mnist-text-small.py

@@ -0,0 +1,174 @@
+
+"""Compressed 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.
+
+*Modified images to be ~1/4 the original area.*
+Done by taking a max pool.
+
+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: (16 x 14 tokens, 224 tokens total): Textual representation of MNIST digit, for example:
+```
+00 down ! ! ! ! ! ! ! ! ! ! ! ! ! !
+01 down ! ! ! ! ! ! ! ! ! ! ! ! ! !
+02 down ! ! ! ! ! ! % % C L a ^ ! !
+03 down ! ! ! - ` ` ` ` ` Y ` Q ! !
+04 down ! ! ! % ` ` ` R ^ ! ! ! ! !
+05 down ! ! ! ! $ G ` ! ! ! ! ! ! !
+06 down ! ! ! ! ! # ` Y < ! ! ! ! !
+07 down ! ! ! ! ! ! 5 ` ` F ! ! ! !
+08 down ! ! ! ! ! ! ! % ` ` 1 ! ! !
+09 down ! ! ! ! ! ! F ` ` ` ! ! ! !
+10 down ! ! ! ! 1 ` ` ` ` 4 ! ! ! !
+11 down ! ! L ` ` ` ` 5 ! ! ! ! ! !
+12 down ! ! ` ` V B ! ! ! ! ! ! ! !
+13 down ! ! ! ! ! ! ! ! ! ! ! ! ! !
+```
+- label: Just a number with the texts matching label.
+
+"""
+
+_CITATION = """\
+@dataset{dataset,
+    author = {Fraser Greenlee},
+    year = {2021},
+    month = {1},
+    pages = {},
+    title = {MNIST small text dataset.},
+    doi = {}
+}
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-small/train.json.zip"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-small/test.json"
+
+LABELS = list(range(10))
+CUSTOM_METHODS = ['array_to_text', 'text_to_array']
+IMG_SIZE = (16, 14)
+
+
+class MnistTextSmall(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.split('\n')
+        pixels = np.zeros((IMG_SIZE[1], IMG_SIZE[0] - 2))
+
+        tokens = None
+        for y, line in enumerate(lines):
+            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] = (ord(token) - 33) / 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