import from S3

mnist-text-no-spaces.py

@@ -0,0 +1,156 @@
+
+"""MNIST text dataset with no spaces."""
+
+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.
+
+Removed spaces to get better BPE compression on sequences.
+**Should only be used with a trained tokenizer.**
+
+Data format:
+- text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example:
+```
+00down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+01down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+02down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+03down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+04down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+05down!!!!!!!!!!!!!%%%@CL'Ja^@!!!!
+06down!!!!!!!!(*8GK`````YL`]Q1!!!!
+07down!!!!!!!-\\````````_855/*!!!!!
+08down!!!!!!!%W`````RN^]!!!!!!!!!!
+09down!!!!!!!!5H;``T#!+G!!!!!!!!!!
+10down!!!!!!!!!$!G`7!!!!!!!!!!!!!!
+11down!!!!!!!!!!!C`P!!!!!!!!!!!!!!
+12down!!!!!!!!!!!#P`2!!!!!!!!!!!!!
+13down!!!!!!!!!!!!)]YI<!!!!!!!!!!!
+14down!!!!!!!!!!!!!5]``>'!!!!!!!!!
+15down!!!!!!!!!!!!!!,O``F'!!!!!!!!
+16down!!!!!!!!!!!!!!!%8``O!!!!!!!!
+17down!!!!!!!!!!!!!!!!!_`_1!!!!!!!
+18down!!!!!!!!!!!!!!,AN``T!!!!!!!!
+19down!!!!!!!!!!!!*FZ```_N!!!!!!!!
+20down!!!!!!!!!!'=X````S4!!!!!!!!!
+21down!!!!!!!!&1V````R5!!!!!!!!!!!
+22down!!!!!!%KW````Q5#!!!!!!!!!!!!
+23down!!!!.LY````^B#!!!!!!!!!!!!!!
+24down!!!!C```VBB%!!!!!!!!!!!!!!!!
+25down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+26down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+27down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+```
+- label: Just a number with the texts matching label.
+
+"""
+
+_CITATION = """\
+@dataset{dataset,
+    author = {Fraser Greenlee},
+    year = {2021},
+    month = {2},
+    pages = {},
+    title = {MNIST text dataset (no spaces).},
+    doi = {}
+}
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-no-spaces/train.json.zip"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/mnist-text-no-spaces/test.json"
+
+LABELS = list(range(10))
+
+
+class MnistText(datasets.GeneratorBasedBuilder):
+    """MNIST represented by text."""
+    def array_to_text(pixels: np.array):
+        '''
+            Takes a 2D array of pixel brightness, converts to text using 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)]
+
+    def text_to_array(text: str):
+        lines = text.split('\n')
+        pixels = np.zeros((len(lines), len(lines[0].split(' ')) - 2))
+
+        for y, line in enumerate(lines):
+            tokens = line.split(' ')
+            assert(tokens[1] == 'down')
+            pixel_tokens = tokens[2:]
+            for x, token in enumerate(pixel_tokens):
+                pixels[y, x] = (ord(token) - 33) / 64
+
+        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