feat: initial draft of dataset script generator

ui_refexp.py

@@ -0,0 +1,150 @@
+# coding=utf-8
+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import glob
+import os
+
+import datasets
+
+import numpy as np
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@misc{bai2021uibert,
+      title={UIBert: Learning Generic Multimodal Representations for UI Understanding},
+      author={Chongyang Bai and Xiaoxue Zang and Ying Xu and Srinivas Sunkara and Abhinav Rastogi and Jindong Chen and Blaise Aguera y Arcas},
+      year={2021},
+      eprint={2107.13731},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This dataset is intended for UI understanding, referring expression and action automation model training. It's based on the UIBert RefExp dataset from Google Research, which is based on the RICO dataset.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/google-research-datasets/uibert"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC BY 4.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_DATA_URLs = {
+    "ui_refexp": "https://huggingface.co/datasets/ncoop57/rico_captions/resolve/main/captions_hierarchies_images.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class UIRefExp(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(
+    #         name="ui_refexp",
+    #         version=VERSION,
+    #         description="Contains 66k+ unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model.",
+    #     ),
+    #     # datasets.BuilderConfig(
+    #     #     name="screenshots_captions_filtered",
+    #     #     version=VERSION,
+    #     #     description="Contains 25k unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model. Filtering was done as discussed in this paper: https://aclanthology.org/2020.acl-main.729.pdf",
+    #     # ),
+    # ]
+
+    # DEFAULT_CONFIG_NAME = "screenshots_captions_filtered"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "screenshot_path": datasets.Value("string"),
+                "caption": datasets.Value("string"),
+                # This is a JSON obj, but will be coded as a string
+                "hierarchy": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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
+        my_urls = _DATA_URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "root_dir": data_dir,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self,
+        root_dir,
+        split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        screen_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.jpg")))
+        hierarchy_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.json")))
+        caption_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.txt")))
+        for idx, (screen_filepath, hierarchy_filepath, caption_filepath) in enumerate(
+            zip(screen_glob, hierarchy_glob, caption_glob)
+        ):
+            with open(hierarchy_filepath, "r", encoding="utf-8") as f:
+                hierarchy = f.read()
+            with open(caption_filepath, "r", encoding="utf-8") as f:
+                caption = f.read()
+
+            yield idx, {"screenshot_path": screen_filepath, "hierarchy": hierarchy, "caption": caption}