Add 10k

README.md

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+---
+dataset_info:
+- config_name: mixed
+  features:
+  - name: svg
+    dtype: string
+  - name: png
+    dtype: image
+  - name: layer_mask
+    dtype: image
+  - name: object_mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 44979830
+    num_examples: 32000
+  - name: validation
+    num_bytes: 5629432
+    num_examples: 4000
+  - name: test
+    num_bytes: 4389077
+    num_examples: 4000
+  download_size: 510689903
+  dataset_size: 54998339
+- config_name: circles
+  features:
+  - name: svg
+    dtype: string
+  - name: png
+    dtype: image
+  - name: layer_mask
+    dtype: image
+  - name: object_mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 10970618
+    num_examples: 8000
+  - name: validation
+    num_bytes: 1373482
+    num_examples: 1000
+  - name: test
+    num_bytes: 1070432
+    num_examples: 1000
+  download_size: 220171148
+  dataset_size: 13414532
+- config_name: squares
+  features:
+  - name: svg
+    dtype: string
+  - name: png
+    dtype: image
+  - name: layer_mask
+    dtype: image
+  - name: object_mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 11349095
+    num_examples: 8000
+  - name: validation
+    num_bytes: 1424342
+    num_examples: 1000
+  - name: test
+    num_bytes: 1115270
+    num_examples: 1000
+  download_size: 31759345
+  dataset_size: 13888707
+- config_name: squares_and_circles
+  features:
+  - name: svg
+    dtype: string
+  - name: png
+    dtype: image
+  - name: layer_mask
+    dtype: image
+  - name: object_mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 11459654
+    num_examples: 8000
+  - name: validation
+    num_bytes: 1431454
+    num_examples: 1000
+  - name: test
+    num_bytes: 1096945
+    num_examples: 1000
+  download_size: 128738219
+  dataset_size: 13988053
+- config_name: scer
+  features:
+  - name: svg
+    dtype: string
+  - name: png
+    dtype: image
+  - name: layer_mask
+    dtype: image
+  - name: object_mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 11200463
+    num_examples: 8000
+  - name: validation
+    num_bytes: 1400154
+    num_examples: 1000
+  - name: test
+    num_bytes: 1106430
+    num_examples: 1000
+  download_size: 130021191
+  dataset_size: 13707047
+---
+
+# Dataset Card for BasicShapes10K
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://eezy.com
+
+
+### Dataset Summary
+
+This is a synthetic dataset containing randomly-generated SVGs with various shapes
+### Supported Tasks and Leaderboards
+
+NA
+
+### Languages
+
+NA
+
+## Dataset Structure
+
+The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
+* `circles` - only circles
+* `squares` - only squares
+* `squares_and_circles` - circles and squares present in the same svg
+* `scer` - squares, circles, ellipses, and rectangles present in the same svg
+* `mixed` - an aggregation of all of the above
+
+### Data Instances
+
+There's stuff there
+
+### Data Fields
+
+Each example has 4 fields:
+
+* `svg` - the raw svg as a string
+* `png` - a raster rendering of the svg with a white background
+* `object_mask` - a black/white mask that defines the outlines of the svg objects
+* `layer_mask` - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
+
+### Data Splits
+
+Train & validation include the layer and object masks, test does not
+
+## Dataset Creation
+
+Generated by randomly inserting objects into an SVG.
+
+### Curation Rationale
+
+Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle
+
+### Source Data
+
+`/dev/urandom`
+
+#### Initial Data Collection and Normalization
+
+NA
+
+#### Who are the source language producers?
+
+NA
+
+### Annotations
+
+see [Data Fields](#data-fields)
+
+#### Annotation process
+
+see [Data Fields](#data-fields)
+
+#### Who are the annotators?
+
+Imagemagick/pysvg
+
+### Personal and Sensitive Information
+
+Unlikely
+
+## Considerations for Using the Data
+
+Please do not use for world domination.
+
+### Social Impact of Dataset
+
+NA
+
+### Discussion of Biases
+
+Dataset is highly biased against triangles and concave shapes
+
+### Other Known Limitations
+
+Color selection is pretty limited.
+
+## Additional Information
+
+### Dataset Curators
+
+[Aleks Clark](https://github.com/aleksclark)
+
+### Licensing Information
+
+CC-BY
+
+### Citation Information
+
+Link it I guess?
+
+### Contributions
+
+Thanks to [@aleksclark](https://github.com/aleksclark) for adding this dataset.

basic_shapes_10k.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""A dataset consisting of svgs, their png representations, and various masks"""
+
+
+import csv
+import json
+import os
+from math import floor
+
+import datasets
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A dataset for understanding vector graphics},
+author={eezy, Inc.
+},
+year={2023}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to provide a corpus for training machine vision tasks on the understanding of basic vector graphics
+"""
+
+_HOMEPAGE = "https://eezy.com"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC-BY"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "circles": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/circles.tgz",
+    "squares": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/squares.tgz",
+    "squares_and_circles": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/squares_and_circles.tgz",
+    "scer": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/scer.tgz"
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class BasicShapes10K(datasets.GeneratorBasedBuilder):
+    """A dataset consisting of simple vector shapes and various kinds of masks"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    SPLIT_COUNTS = {
+        'train': (0, 8000),
+        'dev': (8000, 9000),
+        'test': (9000, 10000)
+    }
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="mixed", version=VERSION, description="These images are a mixture of all the other datasets"),
+        datasets.BuilderConfig(name="circles", version=VERSION, description="These images only contain circles"),
+        datasets.BuilderConfig(name="squares", version=VERSION, description="These images only contain squares"),
+        datasets.BuilderConfig(name="squares_and_circles", version=VERSION, description="These images contain circles and squares"),
+        datasets.BuilderConfig(name="scer", version=VERSION, description="These images contain circles, squares, rectangles, and ellipses"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mixed"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "svg": datasets.Value("string"),
+                "png": datasets.Image(),
+                "layer_mask": datasets.Image(),
+                "object_mask": datasets.Image(),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            supervised_keys=("png", "layer_mask"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        if self.config.name == 'mixed':
+            urls = _URLS
+        else:
+            urls = {self.config.name: _URLS[self.config.name]}
+
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, data_dir, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        start, stop = self.SPLIT_COUNTS[split]
+        domains = [self.config.name]
+
+        if self.config.name == 'mixed':
+            start = start * 4
+            stop = stop * 4
+            domains = [
+                'circles',
+                'squares',
+                'squares_and_circles',
+                'scer'
+            ]
+
+        divisions = len(domains)
+
+        for key in range(start, stop):
+            domain = domains[key % divisions]
+            idx = floor(key / divisions)
+            yield f'{self.config.name}_{str(key).zfill(6)}', \
+                  self._example_for_domain(data_dir, domain, idx, split)
+
+
+    def _example_for_domain(self, data_dir, domain, idx, split):
+        data = {}
+        svg_path = os.path.join(data_dir[domain], domain, 'svg', str(idx).zfill(6) + '.svg')
+        with open(svg_path, 'r') as file:
+            data['svg'] = file.read()
+
+        png_path = os.path.join(data_dir[domain], domain, 'png', str(idx).zfill(6) + '.png')
+        with open(png_path, 'rb') as file:
+            data['png'] = {"path": png_path, "bytes": file.read()}
+
+        if split != "test":
+            layer_mask_path = os.path.join(data_dir[domain], domain, 'layer_mask', str(idx).zfill(6) + '.png')
+            with open(layer_mask_path, 'rb') as file:
+                data['layer_mask'] = {"path": layer_mask_path, "bytes": file.read()}
+            object_mask_path = os.path.join(data_dir[domain], domain, 'obj_mask', str(idx).zfill(6) + '.png')
+            with open(object_mask_path, 'rb') as file:
+                data['object_mask'] = {"path": object_mask_path, "bytes": file.read()}
+        else:
+            data['layer_mask'] = ''
+            data['object_mask'] = ''
+        return data