Add dataset loading file

rico_captions.py

@@ -0,0 +1,141 @@
+# 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
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+    title = {A great new dataset},
+    author={huggingface, Inc.
+    },
+    year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "http://interactionmining.org/rico"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# 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 = {
+    "screenshots_captions": "https://huggingface.co/datasets/ncoop57/rico_captions/resolve/main/captions_images.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class RicoDataset(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="screenshots_captions",
+            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.",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "screenshots_captions"
+
+    def _info(self):
+        if self.config.name == "screenshots_captions":
+            features = datasets.Features(
+                {
+                    "screenshot_path": datasets.Value("string"),
+                    # This is a JSON obj, but will be coded as a string
+                    "caption": 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")))
+        caption_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.txt")))
+        for idx, (screen_filepath, caption_filepath) in enumerate(
+            zip(screen_glob, caption_glob)
+        ):
+            with open(caption_filepath, "r", encoding="utf-8") as f:
+                caption = f.read()
+
+            yield idx, {"screenshot_path": screen_filepath, "caption": caption}